blob_id
stringlengths 40
40
| directory_id
stringlengths 40
40
| path
stringlengths 2
327
| content_id
stringlengths 40
40
| detected_licenses
listlengths 0
91
| license_type
stringclasses 2
values | repo_name
stringlengths 5
134
| snapshot_id
stringlengths 40
40
| revision_id
stringlengths 40
40
| branch_name
stringclasses 46
values | visit_date
timestamp[us]date 2016-08-02 22:44:29
2023-09-06 08:39:28
| revision_date
timestamp[us]date 1977-08-08 00:00:00
2023-09-05 12:13:49
| committer_date
timestamp[us]date 1977-08-08 00:00:00
2023-09-05 12:13:49
| github_id
int64 19.4k
671M
⌀ | star_events_count
int64 0
40k
| fork_events_count
int64 0
32.4k
| gha_license_id
stringclasses 14
values | gha_event_created_at
timestamp[us]date 2012-06-21 16:39:19
2023-09-14 21:52:42
⌀ | gha_created_at
timestamp[us]date 2008-05-25 01:21:32
2023-06-28 13:19:12
⌀ | gha_language
stringclasses 60
values | src_encoding
stringclasses 24
values | language
stringclasses 1
value | is_vendor
bool 2
classes | is_generated
bool 2
classes | length_bytes
int64 7
9.18M
| extension
stringclasses 20
values | filename
stringlengths 1
141
| content
stringlengths 7
9.18M
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9697577ba0e9b7a5a39dd855e72fc526738c2e57
|
b4dce79daf932919f53b64a88db5cd90ae1e71a3
|
/cachematrix.R
|
86e705afd665e8bb3a29edb93228c0ae2e2168d7
|
[] |
no_license
|
roccap/ProgrammingAssignment2
|
160919cd4babaaee910b4a7c08a72df17ce6e951
|
39db545da99d0e854ab7864a61b042cfa413cf5e
|
refs/heads/master
| 2021-01-18T08:29:22.301802
| 2016-02-28T23:40:50
| 2016-02-28T23:40:50
| 52,475,844
| 0
| 0
| null | 2016-02-24T21:31:44
| 2016-02-24T21:31:43
| null |
UTF-8
|
R
| false
| false
| 1,483
|
r
|
cachematrix.R
|
## Matrix inversion is a costly computation and there may be some benefit
## to caching the inverse of a matrix rather than compute it repeatedly
## This module contains a pair of functions that cache the inverse of a matrix.
## This function creates a matrix object, thats is really a list containing a
## number of functions which allow you to set or get the value of the vector
## and set or get the value of the inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setInverse <- function(solve) m <<- solve
getInverse <- function() m
list(set = set, get = get,
setInverse = setInverse,
getInverse = getInverse)
}
## This function calculates the inverse of the cachedMatrix created with the makeCacheMatrix function.
## Before doing the calc, it first checks to see if the inverse has already been calculated.
## If so, it gets the inverse from the cache and skips the computation.
## Otherwise, it calculates the inverse of the data using the solve library function and sets the
## value of the inverse of the matrix in the cache via the setInverse function
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
m <- x$getInverse()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data)
x$setInverse(m)
m
}
|
9420cd43586cfff83403452632993efbc5abcaab
|
fd0ab0f09d3c07f03e0af82bf93875524c44a0e9
|
/tmp-tests/test-auto-postp.R
|
3cf2a5ab112c29d0d38e1ff9f84481e58b50ef93
|
[] |
no_license
|
privefl/bigsnpr
|
b05f9e36bcab6d8cc86fb186c37fe94a6425960a
|
83db98f974b68132a9a3f3ee7ca388159a4c12b5
|
refs/heads/master
| 2023-08-02T13:31:18.508294
| 2023-06-30T12:15:55
| 2023-06-30T12:15:55
| 62,644,144
| 162
| 47
| null | 2022-10-12T16:46:15
| 2016-07-05T14:36:34
|
R
|
UTF-8
|
R
| false
| false
| 2,086
|
r
|
test-auto-postp.R
|
library(bigsnpr)
# celiac <- snp_attach("../Dubois2010_data/FinnuncorrNLITUK1UK3hap300_QC_norel.rds")
# G <- celiac$genotypes$copy(code = c(0, 1, 2, 0, rep(NA, 252)))
# CHR <- celiac$map$chromosome
# POS <- celiac$map$physical.pos
#
# obj.svd <- snp_autoSVD(G, CHR, POS, ncores = 4, thr.r2 = 0.1)
# plot(obj.svd)
#
# dist <- bigutilsr::dist_ogk(obj.svd$u)
# hist(log(dist))
#
# snp_subset(celiac, ind.row = which(log(dist) < 4), ind.col = which(CHR == 6),
# backingfile = "../Dubois2010_data/celiac_chr6")
chr6 <- snp_attach("../Dubois2010_data/celiac_chr6.rds")
G <- chr6$genotypes$copy(code = c(0, 1, 2, 0, rep(NA, 252)))
dim(G)
big_counts(G, ind.col = 1:10)
CHR <- chr6$map$chromosome
POS <- chr6$map$physical.pos
POS2 <- snp_asGeneticPos(CHR, POS, dir = "tmp-data/")
plot(POS, POS2, pch = 20)
corr <- snp_cor(chr6$genotypes, infos.pos = POS2, size = 3 / 1000,
ncores = 6, alpha = 1)
# system.time(test <- Matrix::Cholesky(corr))
object.size(corr) / 1024**2 # 0.3 -> 51 Mb / 0.9 -> 79 / 1 -> 84
str(corr)
median(Matrix::colSums(corr != 0))
ld <- Matrix::colSums(corr ** 2)
plot(ld, pch = 20)
plot(POS, bigutilsr::rollmean(ld, 100))
hist(S <- bigutilsr::rollmean(ld, 100), "FD")
abline(v = (q <- bigutilsr::tukey_mc_up(S)), col = "red")
# Simu phenotype
ind.HLA <- snp_indLRLDR(CHR, POS, LD.wiki34[12, ])
set.seed(1)
# y2 <- snp_simuPheno(G, h2 = 0.5, M = 1000, ind.possible = ind.HLA)$pheno
y2 <- snp_simuPheno(G, h2 = 0.2, M = 1000)$pheno
# GWAS
ind.gwas <- sample(nrow(G), 8e3)
gwas <- big_univLinReg(G, y2[ind.gwas], ind.train = ind.gwas)
plot(gwas, type = "Manhattan")
ind.val <- setdiff(rows_along(G), ind.gwas)
# LDSc reg
df_beta <- data.frame(beta = gwas$estim, beta_se = gwas$std.err, n_eff = length(ind.gwas))
(ldsc <- snp_ldsc2(corr, df_beta))
h2_est <- ldsc[["h2"]]
corr2 <- bigsparser::as_SFBM(as(corr, "dgCMatrix"))
# LDpred-auto
auto <- snp_ldpred2_auto(corr2, df_beta, h2_init = h2_est,
burn_in = 200, num_iter = 200, verbose = TRUE)
auto[[1]]$p_est * ncol(corr2)
auto[[1]]$p_est^0.68 * ncol(corr2)
|
e7b71edb9ec7f4289087d54a2027337426a64bb6
|
9a2686d7e4c0830a9d20f26de1570f6c5fc5da9d
|
/Rcode/kaggleXgbScript.R
|
374251c4adb715acd340f48c0992fc9cbc1a1968
|
[] |
no_license
|
FourDoctors/Rossmann
|
9deb3dc5b5d4532114944ddd41de262441da611b
|
f4554ca469412952324223b5f7ba6bdccc33e471
|
refs/heads/master
| 2016-08-12T22:59:34.102855
| 2015-12-11T07:28:06
| 2015-12-11T07:28:06
| 44,999,916
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,714
|
r
|
kaggleXgbScript.R
|
## ----loaddata-------------
training <- read.csv("../data/train.csv")
testing <- read.csv("../data/test.csv")
store <- read.csv("../data/store.csv")
training$Date <- ymd(training$Date)
testing$Date <- ymd(testing$Date)
## ----useonlyopenstores---------------------------------------------------
testing$Open[is.na(testing$Open)] <- 1
training <- subset(training, Open != 0)
training <- subset(training, Sales > 0)
##----add remove cols------
testing$Sales <- NA
testing$Customers <- NA
training$Id <- 1:nrow(training)
##----combine test train
training$tr.ts <- "tr"
testing$tr.ts <- "ts"
trts <- rbind(training, testing)
## ----merge store---------------------------------------------------------
trts <- merge(trts, store, by="Store")
## ----featuredirect-------------------------------------------------------
preds <- c('Store', 'CompetitionDistance',
'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear',
'Promo', 'Promo2',
'Promo2SinceWeek', 'Promo2SinceYear')
## ----processedFeatures---------------------------------------------------
trts$SchoolHoliday <- as.numeric(trts$SchoolHoliday)
preds <- c(preds, "SchoolHoliday")
trts$StateHoliday <- as.numeric(trts$StateHoliday)
preds <- c(preds, "StateHoliday")
trts <- cbind(trts,
data.frame(
Year = year(trts$Date),
Month = month(trts$Date),
DayOfWeek = wday(trts$Date),
DayOfMonth = mday(trts$Date))
)
preds <- c(preds, "Year", "Month", "DayOfWeek", "DayOfMonth")
storeType.ic <- independentCategories( "StoreType", trts)
trts <- cbind(trts, storeType.ic)
preds <- c(preds, names(storeType.ic))
assortmnent.ic <- independentCategories("Assortment", trts)
trts <- cbind(trts, assortmnent.ic)
preds <- c(preds, names(assortmnent.ic))
##---- extract training and testing out of trts
trts$LogSales <- log(trts$Sales)
trts.p <- trts[, c("Id", "tr.ts", preds)]
trts.o <- trts$LogSales
trts.p[is.na(trts.p)] <- 0
trts <- trts.p
trts$LogSales <- trts.o
trp <- subset(trts, tr.ts=="tr")
trp$DeviationLogSales <- with(trp, LogSales - mean(LogSales))
tsp <- subset(trts, tr.ts=="ts")
## ----heldout----------------
set.seed(210)
idxho <- sample(1:nrow(trp), 0.0125*nrow(trp))
trp.ho <- trp[idxho, ]
trp.tr <- trp[-idxho, ]
##------train---------
source("../Rcode/trainAndPredictXGB.R")
bst <- train.xgb(training = trp.tr,
testing = trp.ho,
preds = preds,
outcome = "DeviationLogSales",
nrounds = 300,
max.depth = 10,
eta = 0.3,
subsample = 0.7,
colsample_bytree = 0.7,
num.threads = 4
)
prho <- exp( mean(trp.tr$LogSales) +
predict(bst, data.matrix(trp.ho[, preds])))
print("held out rms error")
trp.ho$Sales <- exp(trp.ho$LogSales)
print( mean(sqrt((prho/trp.ho$Sales - 1)^2)))
prtst <- data.frame(Id = tsp$Id,
Sales = exp(mean(trp.tr$LogSales) +
predict(bst, data.matrix(tsp[, preds]))
)
)
prtst <- prtst[order(prtst$Id),]
dn <- "../data/predictions/prediction_xgb"
params <- c(nrounds = 300,
maxDepth = 10,
eta = 0.3)
outcome = "DeviationLogSales"
fndf <- data.frame(
name = c(names(params), "outcome"),
value = as.character(c(params, outcome)),
stringsAsFactors = FALSE
)
fn <- paste(dn,
paste(paste(fndf$name, fndf$value, sep="_"),
collapse="."),
"csv",
sep=".")
write.csv(prtst, file = fn, row.names=FALSE)
|
5d7c4ff8c5360782e053b514ca20d555d14da659
|
490416145916e35130588701194a086e41682048
|
/run_analysis.R
|
b0558128e5a1f83f3bea4b1878d978b79013f7c4
|
[] |
no_license
|
sergiped/gettingandcleaningdata
|
402e1f51c4ad41e48ba598ec431c3eb7887275ff
|
a114930b4e96ab5ae53557e92cab60e1b6618838
|
refs/heads/master
| 2016-08-05T23:47:12.016148
| 2015-05-24T21:25:14
| 2015-05-24T21:25:14
| 36,149,104
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,638
|
r
|
run_analysis.R
|
## Getting and Cleaning Data Class Project
## Author: sergiped
## Git Repository: https://github.com/sergiped/gettingandcleaningdata
## Define function to that will download and unzip files that we will use
downloadUCIFiles <- function() {
library("RCurl")
zipfile <- "UCI%20HAR%20Dataset.zip"
projectdir <- "projectfiles"
## If directory already exists, this function should just throw a warning
dir.create(projectdir)
## Check to see if we have already downloaded our data file before downloading.
## If we have already downloaded it, don't download it again. Unzip files.
if(!file.exists(file.path(projectdir,zipfile))) {
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
filehandle <- CFILE(file.path(projectdir, zipfile), mode="wb")
curlPerform(url=url, writedata = filehandle@ref, ssl.verifypeer=FALSE)
close(filehandle)
unzip(file.path(projectdir,zipfile), exdir = projectdir)
}
}
createTidyData <- function() {
## Initialize variables that describe the file structure and where data will be stored
projectdir <- "projectfiles"
testdatapath <- file.path(projectdir, "UCI HAR Dataset", "test", "X_test.txt")
testsubjectpath <- file.path(projectdir, "UCI HAR Dataset", "test", "subject_test.txt")
testactivitypath <- file.path(projectdir, "UCI HAR Dataset", "test", "y_test.txt")
traindatapath <- file.path(projectdir, "UCI HAR Dataset", "train", "X_train.txt")
trainsubjectpath <- file.path(projectdir, "UCI HAR Dataset", "train", "subject_train.txt")
trainactivitypath <- file.path(projectdir, "UCI HAR Dataset", "train", "y_train.txt")
featurepath <- file.path(projectdir, "UCI HAR Dataset", "features.txt")
activitylabelspath <- file.path(projectdir, "UCI HAR Dataset", "activity_labels.txt")
loadLabels <- function() {
library("plyr")
## Load file with activity lables
activitylabels <<- read.delim(activitylabelspath, sep=" ", header=FALSE)
## Load file with column headings
features <<- read.delim(featurepath, sep=" ", header=FALSE)
## Load file with row labels identifying study participant subject #s
testsubjects <<- read.delim(testsubjectpath, header=FALSE)
trainsubjects <<- read.delim(trainsubjectpath, header=FALSE)
## Load file with row labels identifying activities and merge with activity labels based on activity code
testactivities <<- merge(read.delim(testactivitypath, header=FALSE), activitylabels)
trainactivities <<- merge(read.delim(trainactivitypath, header=FALSE), activitylabels)
}
loadMainData <- function() {
## Load file with study data on various measures
testrawobs <<- read.fwf(testdatapath, widths=rep(16, 561), buffersize = 500, strip.white=TRUE)
trainrawobs <<- read.fwf(traindatapath, widths=rep(16, 561), buffersize = 500, strip.white=TRUE)
}
assignColumnLabels <- function() {
## Assign column labels
colnames(testrawobs) <<- features[,2]
colnames(trainrawobs) <<- features[,2]
}
filterColumnLabels <- function() {
## Filter column labels so we only include mean and std dev measures
testrawobs <<- testrawobs[,grepl("mean()", colnames(testrawobs), fixed=TRUE) | grepl("std()", colnames(testrawobs), fixed=TRUE)]
trainrawobs <<- trainrawobs[,grepl("mean()", colnames(trainrawobs), fixed=TRUE) | grepl("std()", colnames(trainrawobs), fixed=TRUE)]
}
addSubjectActivityLabels <- function() {
## Bind columns to identify subject and activity labels
testrawobs <<- cbind(subject = testsubjects[,1], activity = testactivities[,2], testrawobs)
trainrawobs <<- cbind(subject = trainsubjects[,1], activity = trainactivities[,2], trainrawobs)
}
combineDatasets <- function() {
## Create combined dataset with both test and training observations
combobs <<- rbind(testrawobs, trainrawobs)
}
calculateColumnMeans <- function() {
## Calculate column means by subject
subjectmeans <- ddply(combobs[,-2], .(subject), function(x) colMeans(x[,-1]))
colnames(subjectmeans)[1] <- "observation"
## Calculate column means by activity
activitymeans <- ddply(combobs[,-1], .(activity), function(x) colMeans(x[,-1]))
colnames(activitymeans)[1] <- "observation"
}
loadLabels()
loadMainData()
assignColumnLabels()
filterColumnLabels()
addSubjectActivityLabels()
combineDatasets()
calculateColumnMeans()
## Combine to form our tidy dataset
tidydata <- rbind(subjectmeans, activitymeans)
## Write tidy data to table
write.table(tidydata, "tidydata.txt", row.names=FALSE)
}
|
2e732ea0c1839fe7a18980f25d180d2747870f31
|
4f2743db548d08f57ec5c441011d94c28aa0ccac
|
/man/as.model.character.Rd
|
4ea3a72000b5489b2a802923e97e48be5e3a1e23
|
[] |
no_license
|
bergsmat/nonmemica
|
85cdf26fa83c0fcccc89112c5843958669373a2a
|
8eddf25fdd603a5aca719a665c5b9475013c55b3
|
refs/heads/master
| 2023-09-04T06:10:48.651153
| 2023-08-28T13:23:18
| 2023-08-28T13:23:18
| 78,268,029
| 4
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,028
|
rd
|
as.model.character.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model.R
\name{as.model.character}
\alias{as.model.character}
\title{Coerce character to model}
\usage{
\method{as.model}{character}(
x,
pattern = "^\\\\s*\\\\$(\\\\S+)(\\\\s.*)?$",
head = "\\\\1",
tail = "\\\\2",
parse = TRUE,
...
)
}
\arguments{
\item{x}{character}
\item{pattern}{pattern to identify record declarations}
\item{head}{subpattern to identify declaration type}
\item{tail}{subpattern remaining}
\item{parse}{whether to convert thetas omegas and sigmas to inits, tables to items, and runrecords to fields}
\item{...}{ignored}
}
\value{
list
}
\description{
Coerces chacter to model.
}
\examples{
library(magrittr)
options(project = system.file('project/model',package='nonmemica'))
1001 \%>\% as.model
}
\seealso{
Other as.model:
\code{\link{[.model}()},
\code{\link{[[.model}()},
\code{\link{as.model.numeric}()},
\code{\link{as.model}()},
\code{\link{read.model}()},
\code{\link{write.model}()}
}
\concept{as.model}
|
795b0269bfac17c84ad301509e014c2da33f3067
|
5f684a2c4d0360faf50fe055c1147af80527c6cb
|
/2023/2023-week_16/founder_crops.R
|
2382f9edbf1f01202db1b19b3e91cf4ee96eb605
|
[
"MIT"
] |
permissive
|
gkaramanis/tidytuesday
|
5e553f895e0a038e4ab4d484ee4ea0505eebd6d5
|
dbdada3c6cf022243f2c3058363e0ef3394bd618
|
refs/heads/master
| 2023-08-03T12:16:30.875503
| 2023-08-02T18:18:21
| 2023-08-02T18:18:21
| 174,157,655
| 630
| 117
|
MIT
| 2020-12-27T21:41:00
| 2019-03-06T14:11:15
|
R
|
UTF-8
|
R
| false
| false
| 3,253
|
r
|
founder_crops.R
|
library(tidyverse)
library(sf)
library(patchwork)
library(camcorder)
gg_record(dir = "tidytuesday-temp", device = "png", width = 10.5, height = 8.5, units = "in", dpi = 320)
founder_crops <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-04-18/founder_crops.csv')
world <- rgeoboundaries::gb_adm0()
long_min <- min(founder_crops$longitude)
long_max <- max(founder_crops$longitude)
lat_min <- min(founder_crops$latitude)
lat_max <- max(founder_crops$latitude)
world_cropped <- world %>%
st_make_valid() %>%
st_crop(xmin = long_min - 2,
xmax = long_max + 2,
ymin = lat_min - 1,
ymax = lat_max + 1)
world_ortho <- world %>%
st_transform(crs = "+proj=ortho +lon_0=45 +lat_0=40")
rect <- st_as_sf(data.frame(lat = 35, long = 36), coords = c("lat", "long"), crs = 4326)
ocean <- st_point(x = c(0,0)) %>%
st_buffer(dist = 6371000) %>%
st_sfc(crs = "+proj=ortho +lon_0=45 +lat_0=40")
rect <- tibble(lon = c(long_min - 1, long_max + 1), lat = c(lat_min - 1, lat_max + 1)) %>%
st_as_sf(coords = c("lon", "lat"), crs = 4326) %>%
st_bbox() %>%
st_as_sfc()
f1 <- "Domine"
pal <- rev(MetBrewer::met.brewer("Homer2"))
p <- ggplot(founder_crops %>% filter(!is.na(category))) +
geom_sf(data = world_cropped, fill = "grey97", color = "grey80") +
ggpointdensity::geom_pointdensity(aes(longitude, latitude, size = n), shape = 21) +
scale_size_continuous(range = c(0.5, 10), guide = guide_legend(label.position = "bottom", title.position = "top"), breaks = c(0, 25e6, 50e6, 75e6, 125e6), labels = c("< 1 M", "25 M", "50 M", "75 M", "125 M")) +
scale_color_stepsn(colors = pal, guide = guide_colorsteps(title.position = "top")) +
coord_sf(xlim = c(long_min, long_max), ylim = c(lat_min, lat_max)) +
facet_wrap(vars(category)) +
labs(
title = "Revisiting the concept of the ‘Neolithic Founder Crops’ in southwest Asia",
subtitle = "Sites and samples by category",
caption = 'Source: The "Neolithic Founder Crops" in Southwest Asia: Research Compendium · Graphic: Georgios Karamanis',
color = "Number of samples in the area",
size = "Number of individuals in the sample"
) +
theme_void(base_family = f1) +
theme(
legend.position = "top",
legend.key.width = unit(2.5, "lines"),
legend.key.height = unit(0.6, "lines"),
legend.margin = margin(0, 10, 10, 10),
plot.background = element_rect(fill = "grey99", color = NA),
strip.text = element_text(margin = margin(0, 0, 5, 0), face = "bold"),
plot.title = element_text(face = "bold"),
plot.subtitle = element_text(margin = margin(5, 0, 30, 0)),
plot.caption = element_text(hjust = 0, margin = margin(10, 0, 0, 0))
)
g <- ggplot(world_ortho) +
geom_sf(data = ocean, color = NA, fill = "#EEF2F6") +
geom_sf(fill = "grey20", color = "grey99", linewidth = 0.1) +
geom_sf(data = rect, fill = NA, color = "red2", linewidth = 0.5) +
theme_void() +
theme(
plot.background = element_rect(fill = NA, color = NA)
)
p +
inset_element(g, 0.75, 1.05, 1.05, 1.35) +
plot_annotation(
theme = theme(
plot.background = element_rect(fill = "grey99", color = NA),
plot.margin = margin(10, 10, 10, 10)
)
)
|
9d817c3374f9d6f851137d2d09cfb1abb8310bfd
|
6acae9f3d4a7e19cead8d78e360f9d3ef7e1d4dc
|
/leaflet/clustered_map.R
|
624bb0edf2912602e8136d5f25b45d9f16a40be0
|
[] |
no_license
|
shelbybachman/developing-data-products-course
|
dc316088737f5e9577967cfec81f691671ec7db1
|
c1ec9c8ffec6d61261b6d3d9679a91bfb379e29a
|
refs/heads/master
| 2022-03-02T20:11:34.563571
| 2022-01-12T05:04:20
| 2022-01-12T05:04:20
| 197,085,059
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 606
|
r
|
clustered_map.R
|
# this script creates a clustered map
library(leaflet)
# create dataframe with points
df <- data.frame(lat = runif(500, min = 39.25, max = 39.35),
lng = runif(500, min = -76.65, max = -76.55))
# create map with points clustered
df %>%
leaflet() %>%
addTiles() %>%
addMarkers(clusterOptions = markerClusterOptions())
# instead of adding clusters or standard popups,
# we can add standard circles
df <- data.frame(lat = runif(20, min = 39.25, max = 39.35),
lng = runif(20, min = -76.65, max = -76.55))
df %>%
leaflet() %>%
addTiles() %>%
addCircleMarkers()
|
f532a25a8a89e56c4a3bbdbeef99b712b50ba985
|
ede0e16eb9ac6dae510f1779514be99aa82c1fe9
|
/Rassignment1.R
|
ff1756b433f98cbf379d013f76bf6f8e4f0bcf5b
|
[] |
no_license
|
shubhangiBelhekar/R-Assignment
|
f92407359414075179c46d4441d2928e9d45c150
|
464a294e8df36b769d22e861cd7dd21e9645b4fb
|
refs/heads/main
| 2023-02-25T04:55:09.905537
| 2021-02-05T17:22:21
| 2021-02-05T17:22:21
| 336,340,238
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,180
|
r
|
Rassignment1.R
|
#{1].Try to write a code for printing sequence of numbers from 1 to 50 with the differences of 3, 5, 10
a <- seq(1,50)
#[2]. What are the different data objects in R? and write syntax and example for each and every object
#R consists of a number of data objects to perform various functions
# Six type of data objects
#1.vectors
#2.list
#3.matrix
#4.array
#5.factors
#6.data frame
#(1)Vectors:-Vectors are one of the basic R programming data objects.
#six types of atomic vectors
#1.logical
#2.integer
#3.character
#4.raw
#5.double
#6.complex
#(2)Lists:-Lists are data objects of R.
#Types of elements
#1.strings
#2.numbers
#3.vectors
#4.nested list
#(3)Matrix:- Matrices used to arrange elements in the two-dimensional layout.
#They contain elements of the same data type.
#They contain numeric values in order to perform mathematical operations.
#(4)Array:- array is used to store data in more than just 2 dimensions.
#It is used to store multidimensional data in the required format.
#array created with the help of an array() function.
#(5)Factors:-factors are used in order to categorize and store data as levels.
#They can be strings or integers.
# factors created using factor() function.
#(6)Data frame:-data frame is a two-dimensional data structure.
#Each column consists of the value of one variable and each row consists of a value set from each column.
#[3]. Create Data frame with 3 columns and 5 rows and write a code to fetch and delete row and a column using index and add new column and row to the existed data frame
df <- data.frame(name=c('Jonny','Rocky','Rita','Tom','Sunny'),age=c(23,25,23,21,22),sub=c('SQL','PHP','HTML','PYTHON','PANDAS'))
df
df1 <- df[-3,-2]
df2 <- df[,-2]
df2
x <- c(89,98,70,67,97)
df['marks']=x
#(4).Write nested if else statements to print whether the given number is negative, positive or Zero
x <- 0
if (x == 0) {
print('Zero')
} else if (x > 0) {
print('Positive number')
} else print('Negative Number')
#(5).write a program to input any value and check whether it is character, numeric or special character
x <- '@'
if(x>='a' && x<='z'){
print('charater')
}else if(x>='0' && x<='9'){
print('numeric')
}else print('special character')
#(6).write difference between break and next also write examples for both
#Break statement:-
# A break statement is used inside a loop to stop the iterations and flow the control outside of the loop.
#break statement used inside that loop repeat, for, while.
#The break statement can also be used inside the else branch of if...else statement.
#Example:
x <-1:7
for (value in x) {
if (value == 5){
break
}
print(value)
}
#Next statement:-
#A next statement is useful when we want to skip the current iteration of a loop without terminating it.
#On encountering next, the R parser skips further evaluation and starts next iteration of the loop.
x <- 1:8
for (value in x){
if(value==5){
next
}
print(value)
}
#(7).write a program to print a given vector in reverse format
x= c(1,5.6,3,10,3.5,5)
x <- c(1,5,6,3,10,3,5,5)
r <- rev(x)
r
#(8).write a program to get the mode value of the given vector
#('a','b','c','t','a','c','r','a','c','t','z','r','v','t','u','e','t')
x <- c('a','b','c','t','a','c','r','a','c','t','z','r','v','t','u','e','t')
m <- table(x)
m
y <- names(table(x))[table(x)==max(table(x))]
y
#OR
z <- names(m)[m==max(m)]
z
#(9).Write a function to filter only data belongs to 'setosa' in species of Iris data set.( using dplyr package)
iris <- read.csv('C:/Users/Satish1/Desktop/SQL/iris.csv')
iris
View(iris)
install.packages('dplyr')
library(dplyr)
filter(iris, Species=='setosa')
#(10).Create a new column for iris data set with the name of Means_of_obs, which contains mean value of each row.( using dplyr package)
Mean_of_obs <- mean(iris$Sepal.Length)
df <- cbind(iris,Mean_of_obs)
head(df)
#(11).Filter data for the "versicolor" and get only 'sepel_length' and Sepel _width' columns.( using dplyr package)
select=select(iris,Sepal.Length,Sepal.Width,Species)
select
fe <- filter(select(iris,Sepal.Length,Sepal.Width,Species),Species=='versicolor')
head(fe)
#(12).create below plots for the mtcars also write your inferences for each and every plot (use ggplot package) Use Different ( Size , Colour )
install.packages('ggplot2')
library('ggplot2')
install.packages("GGally")
library(GGally)
data(mtcars)
View(mtcars)
#scatter plot
ggplot(mtcars, aes(x=disp,y=hp))+geom_point()
ggplot(mtcars, aes(x=disp,y=hp))+geom_point(size=3,color="blue")
#box plot
boxplot(mtcars$disp,col='brown',border='blue',horizontal = TRUE,notch = TRUE)
#histogram
hist(mtcars$disp,col='gray',border='red')
#line graph
plot(mtcars$disp,type='o',col='black')
#bar graph
barplot(mtcars$disp,col='brown',border = 'blue')
|
05f7ac03342b28852810ba7551991e6f3f3b4192
|
5c1e2c6d5f10a0dbf01d2ac39e722a9977840f3b
|
/sandbox-impute.R
|
5ec2f51fa28ba8f144514eda769de7e4820e639d
|
[] |
no_license
|
sarikayamehmet/pumpprediction
|
740756cd45961aa3d22146a41815b11944447a3b
|
6b2aca076f758bae8b61cd39a3d08492ac982253
|
refs/heads/master
| 2021-01-18T23:33:00.618008
| 2016-08-17T09:10:31
| 2016-08-17T09:10:31
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,848
|
r
|
sandbox-impute.R
|
trainsetImpute.hex <- h2o.uploadFile(path = '~/projects/pumpprediction/training-NA-imp5.csv', sep = ',', header = TRUE)
# trainsetImpute.hex <- as.h2o(trainset.impute.values[,-1])
trainsetImputeFull.hex <- h2o.merge(trainsetImpute.hex[,-1], labels.hex)
trainsetImputeFull.hex$age <- 2015 - trainsetImputeFull.hex$construction_year
trainsetImputeFull.hex$functional <- ifelse(trainsetImputeFull.hex$status_group == 'functional', '1', '0')
trainsetImputeFull.hex$nonfunctional <- ifelse(trainsetImputeFull.hex$status_group == 'non functional', '1', '0')
trainsetImputeFull.hex$needrepair <- ifelse(trainsetImputeFull.hex$status_group == 'functional needs repair', '1', '0')
allVariables <- colnames(trainsetImputeFull.hex)
predictors <- colnames(trainsetImputeFull.hex)[!(allVariables
%in% c('id', 'wpt_name', 'subvillage', 'scheme_name', 'funder',
'status_group', 'functional', 'nonfunctional', 'needrepair', 'lga', 'recorded_by', 'quantity'))]
trainsetImputeFull.split <- h2o.splitFrame(trainsetImputeFull.hex, 0.8, seed = 123)
ovaModels <- list()
ovaModels['needrepair'] <- h2o.deeplearning(predictors, outcome, trainsetImputeFull.split[[1]], balance_classes = TRUE, hidden = c(20, 20, 20))
for(outcome in c('functional', 'nonfunctional')) {
#model <- h2o.deeplearning(predictors, outcome, trainsetImputeFull.split[[1]], balance_classes = FALSE, hidden = c(20, 20, 20))
model <- h2o.randomForest(predictors, outcome, trainsetImputeFull.split[[1]], balance_classes = TRUE)
# gbmModel <- h2o.gbm(predictors, outcome, trainsetImputeFull.split[[1]], balance_classes = FALSE)
# glmModel <- h2o.glm(predictors, outcome, trainsetImputeFull.hex, family = 'binomial')
ovaModels[outcome] <- model
}
# Append OVA predictors
ovaPreds <- sapply(names(ovaModels), function(x) {
as.vector(h2o.predict(ovaModels[[x]], trainsetImputeFull.split[[1]])$p1)
})
colnames(ovaPreds) <- gsub('^', 'pred_', colnames(ovaPreds))
ovaPredsDf <- cbind(as.data.frame(trainsetImputeFull.split[[1]][, c('id', 'status_group')]), as.data.frame(ovaPreds))
trainsetImputeFullWithPreds.hex <- as.h2o(ovaPredsDf)
rfModelWithOVAPreds <- h2o.gbm(c('pred_functional', 'pred_nonfunctional', 'pred_needrepair'), 'status_group', trainsetImputeFullWithPreds.hex)
# Append OVA pred to test set
lapply(ovaModels, h2o.confusionMatrix)
lapply(ovaModels, function(x) { h2o.performance(x, trainsetImputeFull.split[[2]]) })
ovaPreds <- sapply(names(ovaModels), function(x) {
as.vector(h2o.predict(ovaModels[[x]], trainsetImputeFull.split[[2]])$p1)
})
colnames(ovaPreds) <- gsub('^', 'pred_', colnames(ovaPreds))
validationImputeFullWithPreds.hex <- as.h2o(cbind(as.data.frame(trainsetImputeFull.split[[2]][, c('id', 'status_group')]), as.data.frame(ovaPreds)))
h2o.performance(rfModelWithOVAPreds, validationImputeFullWithPreds.hex)
ovaPredsDf$vote <- factor(apply(ovaPredsDf[, c('pred_functional', 'pred_nonfunctional', 'pred_needrepair')], 1, which.max),
levels = c(1, 3, 2), labels = c('functional', 'functional needs repair', 'non functional'))
table(ovaPredsDf$status_group, ovaPredsDf$vote)
score <- function (actual, preds) {
t <- table(actual, preds)
result <- list()
result[['table']] <- t
result[['scores']] <- diag(t) / rowSums(t)
result[['errors']] <- 1 - diag(t) / rowSums(t)
result[['total']] <- sum(diag(t)) / sum(t)
result
}
rfModel3classes <- h2o.randomForest(predictors, 'status_group', trainsetImputeFull.hex, balance_classes = TRUE)
model <- rfModel
h2o.confusionMatrix(model)
needRepair <- h2o.predict(rfModel, trainsetImputeFull.hex)
trainsetImputeFull.hex$needRepair <- needRepair$p1
allVariables <- colnames(trainsetImputeFull.hex)
predictors <- colnames(trainsetImputeFull.hex)[!(allVariables
%in% c('id', 'wpt_name', 'subvillage', 'scheme_name', 'funder',
'status_group', 'functional', 'nonfunctional', 'needrepair', 'lga', 'recorded_by', 'quantity'))]
rfModel3classes <- h2o.randomForest(predictors, 'status_group', trainsetImputeFull.hex)
model <- rfModel3classes
h2o.confusionMatrix(model)
validation.hex <- h2o.uploadFile(path = '~/projects/pumpprediction/testset_values.csv', destination_frame = 'validation.hex', sep = ',', header = TRUE)
needRepair <- h2o.predict(rfModel, validation.hex)
validation.hex$needRepair <- needRepair$p1
preds <- h2o.predict(model, validation.hex)
submission <- data.frame(as.data.frame(validation.hex$id), as.data.frame(preds$predict))
colnames(submission) <- c('id', 'status_group')
write.table(submission,
paste0('rfH2oSubmission', format(Sys.time(), "%Y%m%d_%H%M%S"), '.csv'),
row.names = FALSE,
sep=',', quote = FALSE)
|
210f551966a33b1cdf234783e4c561ec5c2db5e7
|
8c74729a6a4b16837901d4dbb0402132350c9925
|
/R/loglik_function.R
|
a19ee15df5f9368a4fe498e292a2cc8721d99824
|
[] |
no_license
|
fwijayanto/autoRasch
|
893a123fb51acdec4ee4fa6f1fe8546434e1e4c2
|
c2509f84c54c48089ab0730d25ae203cd189b5a6
|
refs/heads/master
| 2022-11-28T13:23:57.463424
| 2022-11-06T15:30:16
| 2022-11-06T15:30:16
| 243,410,248
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 34,904
|
r
|
loglik_function.R
|
##### The general implementation of the log-likelihood and gradient functions of the moodels ######
##### Estimated parameters extractor #####
#
# Mapping the estimated parameters to each variable, i.e., theta, beta, gamma, and delta.
#
# @param nlmPar A vector of the estimated parameters values.
# @param estPar_arr A vector of the types of the estimated parameters.
# @param estLength_array A vector of the lengths of each types of the estimated parameters.
# @param fixed_par A vector of the types of the fixed parameters.
# @param fixLength_arr A vector of the lengths of each types of the fixed parameters.
# @param fixValue A vector of the fixed parameters values.
#
# @return output A list of the extracted estimated parameter values.
#
##########################################
par_map <- function(nlmPar, estPar_arr, estLength_array, fixValue, fixed_par, fixLength_arr){
output <- list()
checkdata <- c("delta","gamma","beta","theta")
for(cdat in checkdata){
if(!identical(grep(cdat,estPar_arr), integer(0))){
parNo <- grep(cdat,estPar_arr)
if(parNo == 1){
output[[cdat]] <- nlmPar[c((1):(sum(estLength_array[1])))]
} else {
output[[cdat]] <- nlmPar[c((sum(estLength_array[1:(parNo-1)])+1):(sum(estLength_array[1:parNo])))]
}
} else {
parNo <- grep(cdat,fixed_par)
if(parNo == 1){
output[[cdat]] <- fixValue[c((1):(sum(fixLength_arr[1])))]
} else {
output[[cdat]] <- fixValue[c((sum(fixLength_arr[1:(parNo-1)])+1):(sum(fixLength_arr[1:parNo])))]
}
}
}
return(output)
}
##########################################################################################
##### THE LOG-LIKELIHOOD FUNCTION #####
#
# nlmPar : the estimated parameters
# dset : the dataset
# lamda_theta : the penalty coefficient on the theta parameters.
# lambda_in : the penalty coefficient on the included set
# lambda_out : the penalty coefficient on the excluded set
# lambda delta : the penalty coefficiant on the delta parameters
# estPar_arr : A vector of the types of the estimated parameters.
# estLength_array : A vector of the lengths of each types of the estimated parameters.
# fixed_par : A vector of the types of the fixed parameters.
# fixLength_arr : A vector of the lengths of each types of the fixed parameters.
# fixValue : A vector of the fixed parameters values.
# groups_map : Binary matrix. Respondents membership to DIF groups; rows represent individuals, column represent group partitions.
# mt_vek : A vector of the number of thresholds
# mt_idx : A vector of indexes to map the categories in each items.
# dimResp : A vector of the dimension of the response. Columns represent items and rows represent subjects.
# allcat : The number of beta parameters/thresholds for all items
# n_th : the number of beta for each items (the assumption for now is that every item has the same number of thresholds)
# XN : matrix for mapping x_vi (true response of person v to item i)
# XNA : matrix for mapping the NA responses
# eps : Small constant value as a workaround to solve the lasso penalty.
# isPenalized_theta : It is a logical parameter whether, in the estimation procedure, theta is penalized or not.
# isPenalized_gamma : It is a logical parameter whether, in the estimation procedure, gamma is penalized or not.
# isPenalized_delta : It is a logical parameter whether, in the estimation procedure, delta is penalized or not.
#
##########################################################################################
loglik_fun <- function(nlmPar, dset, opts, dataPrep,
estPar_arr, fixed_par, fixValue, fixLength_arr, estLength_array){
# map the nlmPar
map_nlmPar <- par_map(nlmPar, estPar_arr = estPar_arr, estLength_array = estLength_array,
fixValue = fixValue, fixed_par = fixed_par, fixLength_arr = fixLength_arr)
theta <- map_nlmPar$theta
beta <- map_nlmPar$beta
gamma <- map_nlmPar$gamma
delta <- map_nlmPar$delta
deltagamma <- map_nlmPar$deltagamma
# get the value of alpha, i.e. exp(gamma)
exp_gamma <- exp(gamma)
exp_gamma <- rep.int(exp_gamma, dataPrep$mt_vek)
# groups_map should be formed as matrix, size is V x G
groups_map <- as.matrix(dataPrep$groups_map)
# takes the total of DIF effect for all group for beta
# here we iterate over V, delta is a vector of length I X G, where the rows are stacked
# dset is actually data.frame(X) and has dimension V X I, so ncol(dset) == I
# delta_tot is written as an I x V matrix, as the outer product of delta (I x G) and k / groups_map (G x V)
# !alternatively delta_tot <- delta %*% t(groups_map)
delta_tot <- 0
for(i in seq_len(ncol(groups_map))) {
delta_tot <- delta_tot + outer(delta[(((i-1)*ncol(dset))+1):(i*ncol(dset))],groups_map[,i],"*")
}
# the total delta which has been replicated to all categories
# - theta, size V, where each element is repeated I x J times
# - vec(beta), size I x J, is repeated V times, for which the columns of beta (length J) are stacked ?!
# # !! stacked on J first and then on I
# # idx = I * J * (v - 1) + J * (i - 1) + (j - 1) + 1
delta_tot_rep <- rep.int((delta_tot), rep.int(dataPrep$mt_vek,nrow(groups_map)))
# compute the theta - (beta+delta)
t_diff <- rep(theta,each = dataPrep$allcat) - rep.int(beta,length(theta))
t_diff <- t_diff - delta_tot_rep
# multiplied by exp(gamma)
disc_diff <- t_diff * exp_gamma
# map the corresponding NA value of the dataset to the matrix
disc_diff <- dataPrep$XNA * disc_diff
disc_diff <- matrix(disc_diff, nrow = dataPrep$allcat)
# compute the first part of the log-likelihood (simple addition part)
l1 <- sum((dataPrep$XN * disc_diff), na.rm = TRUE)
### compute the second part of the log-likelohood (with log)
### begin
length.mt_idx <- table(dataPrep$mt_idx)
temp_prob_split <- split(disc_diff,dataPrep$mt_idx)
temp_l2 <- c()
for(j in seq_along(temp_prob_split)){
if(length.mt_idx[j] > 1){
temp_l2 <- rbind(temp_l2,colSums(exp(apply(matrix(temp_prob_split[[j]], nrow = length.mt_idx[j]),2,cumsum)), na.rm = TRUE))
} else {
temp_l2 <- rbind(temp_l2,exp(temp_prob_split[[j]]))
}
}
l2 <- sum(log(temp_l2+1), na.rm = TRUE)
### end
st1st2 <- c(l1,l2)
lnL <- st1st2[1] - st1st2[2]
if(opts$isPenalized_theta){
lnL <- lnL - (opts$lambda_theta*(sum(theta^2)))
}
if(opts$isPenalized_gamma & opts$isPenalized_theta){
lnL <- lnL - (opts$lambda_in*(sum(gamma^2)))
} else if(opts$isPenalized_gamma){
lnL <- lnL - (opts$lambda_out*(sum(gamma^2)))
}
if(opts$isPenalized_delta){
lnL <- lnL - (opts$lambda_delta*(sum(abs(delta)^(1+opts$eps))))
}
return(-lnL)
}
# Different way of implementing the log-likelihood function
loglik_fun_novel <- function(nlmPar, dset, lambda_theta, lambda_in, lambda_out, lambda_delta,
estPar_arr, fixed_par, fixValue, fixLength_arr, estLength_array,
groups_map, mt_vek, mt_idx, dimResp, allcat, n_th, XN, XNA, eps = 0,
isPenalized_gamma, isPenalized_theta, isPenalized_delta) {
# TODO: incorporate the XNA part
# map the nlmPar
map_nlmPar <- par_map(nlmPar, estPar_arr = estPar_arr, estLength_array = estLength_array,
fixValue = fixValue, fixed_par = fixed_par, fixLength_arr = fixLength_arr)
theta <- map_nlmPar$theta
beta <- map_nlmPar$beta
gamma <- map_nlmPar$gamma
delta <- map_nlmPar$delta
# deltagamma <- map_nlmPar$deltagamma
exp_gamma <- exp(gamma)
X <- as.matrix(dset)
I <- ncol(dset)
V <- nrow(dset)
J <- max(mt_vek + 1) # max(m_i)
# this needs to be fixed, beta needs to be passed differently
beta_mat <- matrix(beta, nrow = I, byrow = TRUE)
# delta_group <- groups_map %*% t(delta)
delta_group <- groups_map %*% matrix(delta, nrow = ncol(groups_map), byrow = TRUE)
Psi <- array(0, dim = c(V, I, J))
# most efficient way is to iterate first by category
for (i in 1:I) {
J <- mt_vek[i] + 1
for (v in 1:V) {
for (j in 1:(J-1)) {
Psi[v, i, j+1] <- Psi[v, i, j] + (theta[v] - beta_mat[i, j] - delta_group[v, i]) * exp_gamma[i]
}
}
}
l1 <- 0
for (v in 1:V) {
for (i in 1:I) {
if(!is.na(X[v, i])){
l1 <- l1 + Psi[v, i, X[v, i] + 1]
}
}
}
### computed likelihood denominator (second part)
exp_Psi <- exp(Psi)
cumul_Psi <- matrix(0, V, I)
# TODO: is m_i then the maximum number of categories (mt_vek?)
for (i in 1:I) {
J <- mt_vek[i] + 1
for (v in 1:V) {
if(!is.na(X[v,i])){
for (j in 1:J) {
cumul_Psi[v, i] <- cumul_Psi[v, i] + exp_Psi[v, i, j]
}
} else {
cumul_Psi[v, i] <- NA
}
}
}
l2 <- sum(log(cumul_Psi), na.rm = TRUE)
# Sidenote: If it were possible to compute everything with matrices
# ## Compute first term
# l1 <- t(theta) %*% X %*% exp_gamma
#
# # Compute the second term
# # for (i in 1:I) {
# # beta[(i-1) * ]
# # }
# ones <- rep(1, length(theta))
# XB <- matrix(colSums(matrix(XN * rep(beta, length(theta)), nrow = 4)), byrow = TRUE, ncol = ncol(dset))
# l1 <- l1 - t(ones) %*% XB %*% exp_gamma
#
# # Compute the third term
# # groups_map is V X G, t(delta) is G x I (hopefully)
# delta_group <- groups_map %*% t(delta) # size V x I
# XDG <- X * delta_group
# l1 <- l1 - t(ones) %*% (XDG) %*% exp_gamma
lnL <- l1 - l2
if(isPenalized_theta){
lnL <- lnL - lambda_theta * sum(theta^2)
}
if(isPenalized_gamma & isPenalized_theta){
lnL <- lnL - lambda_in * sum(gamma^2)
} else if(isPenalized_gamma){
lnL <- lnL - lambda_out * sum(gamma^2)
}
if(isPenalized_delta){
lnL <- lnL - lambda_delta * sum(abs(delta)^(1+eps))
}
return (-lnL)
}
# Wrapper for the Rcpp implementation of the log-likelihood function
loglik_fun_fast <- function(nlmPar, dset, estPar_arr, fixed_par, fixValue, fixLength_arr, estLength_array,
opts, dataPrep) {
# TODO: implement XNA part
map_nlmPar <- par_map(nlmPar, estPar_arr = estPar_arr, estLength_array = estLength_array,
fixValue = fixValue, fixed_par = fixed_par, fixLength_arr = fixLength_arr)
theta <- map_nlmPar$theta
beta_mat <- matrix(map_nlmPar$beta, ncol(dset), dataPrep$n_th, byrow = TRUE)
gamma <- map_nlmPar$gamma
if(opts$mode == "DIF"){
delta_mat <- matrix(map_nlmPar$delta, ncol(dset), ncol(dataPrep$groups_map))#, byrow = TRUE)
mode <- 1
} else {
delta_mat <- matrix(map_nlmPar$delta, length(beta_mat), ncol(dataPrep$groups_map))#, byrow = TRUE)
mode <- 2
}
ll_cpp(theta, gamma, delta_mat, dataPrep$groups_map, beta_mat, dataPrep$mt_vek, as.matrix(dset),
opts$isPenalized_gamma, opts$isPenalized_delta, opts$isPenalized_theta,
opts$lambda_in, opts$lambda_out, opts$lambda_delta, opts$lambda_theta, opts$eps,mode)
}
# loglik_fun_fast <- function(nlmPar, dset, lambda_theta, lambda_in, lambda_out, lambda_delta,
# estPar_arr, fixed_par, fixValue, fixLength_arr, estLength_array,
# groups_map, mt_vek, mt_idx, dimResp, allcat, n_th, XN, XNA, eps = 0,
# isPenalized_gamma, isPenalized_theta, isPenalized_delta) {
#
# # TODO: implement XNA part
#
# map_nlmPar <- par_map(nlmPar, estPar_arr = estPar_arr, estLength_array = estLength_array,
# fixValue = fixValue, fixed_par = fixed_par, fixLength_arr = fixLength_arr)
# theta <- map_nlmPar$theta
# beta_mat <- matrix(map_nlmPar$beta, ncol(dset), n_th, byrow = TRUE)
# gamma <- map_nlmPar$gamma
# delta_mat <- matrix(map_nlmPar$delta, ncol(dset), ncol(groups_map))#, byrow = TRUE)
#
# ll_cpp(theta, gamma, delta_mat, groups_map, beta_mat, mt_vek, as.matrix(dset),
# isPenalized_gamma, isPenalized_delta, isPenalized_theta,
# lambda_in, lambda_out, lambda_delta, lambda_theta, eps)
# }
############################################################################
#
#THE GRADIENT FUNCTION
#
############################################################################
grad_fun <- function(nlmPar, dset, opts, dataPrep,
estPar_arr, fixed_par, fixValue, fixLength_arr, estLength_array){
lambda_theta <- opts$lambda_theta*2
lambda_in <- opts$lambda_in*2
lambda_out <- opts$lambda_out*2
map_nlmPar <- par_map(nlmPar, estPar_arr = estPar_arr, estLength_array = estLength_array,
fixValue = fixValue, fixed_par = fixed_par, fixLength_arr = fixLength_arr)
theta <- map_nlmPar$theta
beta <- map_nlmPar$beta
gamma <- map_nlmPar$gamma
delta <- map_nlmPar$delta
deltagamma <- map_nlmPar$deltagamma
exp_gamma <- exp(gamma)
exp_gamma <- rep.int(exp_gamma, dataPrep$mt_vek)
t_exp_gamma_mat <- rep.int(1,(length(dataPrep$XN)))*exp_gamma
groups_map <- as.matrix(dataPrep$groups_map)
delta_tot <- 0
for(i in seq_len(ncol(groups_map))) {
delta_tot <- delta_tot + outer(delta[(((i-1)*ncol(dset))+1):(i*ncol(dset))],groups_map[,i],"*")
}
delta_tot_rep <- rep.int((delta_tot), rep.int(dataPrep$mt_vek,nrow(groups_map)))
total_alpha_mat <- rep(exp_gamma,length(theta))
t_diff <- rep(theta,each = dataPrep$allcat) - rep.int(beta,length(theta))
t_diff <- t_diff - delta_tot_rep #delta.tot.rep is total delta which has been replicated to every categoory
disc_diff <- t_diff * total_alpha_mat
disc_diff <- dataPrep$XNA * disc_diff
disc_diff <- matrix(disc_diff, nrow = dataPrep$allcat)
##### first part of gradient ###
## theta ##
t1_theta_mat <- (dataPrep$XN) * total_alpha_mat
t1_theta_mat <- matrix(t1_theta_mat, nrow = dataPrep$allcat)
t1_theta <- colSums(t1_theta_mat, na.rm = TRUE)
## beta ##
t1_beta_mat <- t1_theta_mat
t1_beta_mat <- matrix(t1_beta_mat, nrow = dataPrep$allcat)
t1_beta <- rowSums(t1_beta_mat, na.rm = TRUE)
## gamma ##
t1_gamma_mat <- t_diff * t1_beta_mat
t1_gamma_mat <- matrix(t1_gamma_mat, nrow = dataPrep$allcat)
t1_gamma <- rowSums(t1_gamma_mat, na.rm = TRUE)
t1_gamma <- tapply(t1_gamma, dataPrep$mt_idx, sum, na.rm = TRUE)
t1_delta <- c()
for(i in seq_len(ncol(groups_map))) {
## delta ##
t1_delta_mat <- t(t(t1_theta_mat) * groups_map[,i])
t1_delta_temp <- rowSums(t1_delta_mat, na.rm = TRUE)
t1_delta_temp <- tapply(t1_delta_temp, dataPrep$mt_idx, sum, na.rm = TRUE)
t1_delta <- c(t1_delta, t1_delta_temp)
}
##### first part of gradient end ###
# make them as a vector
total_alpha_mat <- matrix(total_alpha_mat, nrow = dataPrep$allcat)
temp_denom <- c()
temp_gamma <- c()
expTempProb <- c()
temp_theta_nom <- c()
temp_gamma_nom <- c()
length.mt_idx <- table(dataPrep$mt_idx)
temp_prob_split <- split(disc_diff,dataPrep$mt_idx)
# print(length(temp_prob_split))
temp_gamma_split <- split(total_alpha_mat, dataPrep$mt_idx)
temp_denom_part <- c()
temp_gamma_part <- c()
expTempProb_part <- c()
temp_beta_part <- c()
temp_tot <- c()
temp_theta_nom_part <- c()
temp_gamma_nom_part <- c()
temp_delta_peritem <- list()
for(j in seq_along(temp_prob_split)){
if(length.mt_idx[j] > 1){
temp_prob_part <- matrix(temp_prob_split[[j]], nrow = length.mt_idx[j])
temp_prob_part <- apply(temp_prob_part,2,cumsum)
expTempProb_temp <- exp(temp_prob_part)
temp_denom_part <- rbind(temp_denom_part,colSums(expTempProb_temp, na.rm = TRUE))
temp_gamma_temp <- matrix(temp_gamma_split[[j]], nrow = length.mt_idx[j])
temp_gamma_temp <- apply(temp_gamma_temp,2,cumsum)
temp_theta_nom_mat_part <- expTempProb_temp*temp_gamma_temp
temp_theta_nom_part <- rbind(temp_theta_nom_part,colSums(temp_theta_nom_mat_part,na.rm = TRUE))
temp_gamma_nom_mat_part <- expTempProb_temp*temp_prob_part
temp_gamma_nom_part <- rbind(temp_gamma_nom_part,colSums(temp_gamma_nom_mat_part,na.rm = TRUE))
temp_delta_peritem_test <- c()
for(k in seq_len(ncol(groups_map))) {
temp_delta_temp_peritem <- t(t(temp_theta_nom_mat_part) * groups_map[,k])
temp_delta_peritem_test <- rbind(temp_delta_peritem_test,colSums(temp_delta_temp_peritem,na.rm = TRUE))
}
temp_delta_peritem[[j]] <- temp_delta_peritem_test
temp_tot_temp <- apply(as.matrix(expTempProb_temp[length.mt_idx[j]:1,]),2,cumsum)
temp_tot <- rbind(temp_tot,as.matrix(temp_tot_temp[length.mt_idx[j]:1,]))
temp_gamma_part <- rbind(temp_gamma_part,temp_gamma_temp)
expTempProb_part <- rbind(expTempProb_part,expTempProb_temp)
} else {
temp_prob_part <- temp_prob_split[[j]]
expTempProb_temp <- exp(temp_prob_part)
temp_denom_part <- rbind(temp_denom_part,expTempProb_temp)
temp_gamma_temp <- temp_gamma_split[[j]]
temp_theta_nom_mat_part <- expTempProb_temp*temp_gamma_temp
temp_theta_nom_part <- rbind(temp_theta_nom_part,temp_theta_nom_mat_part)
temp_gamma_nom_mat_part <- expTempProb_temp*temp_prob_part
temp_gamma_nom_part <- rbind(temp_gamma_nom_part,temp_gamma_nom_mat_part)
temp_delta_peritem_test <- c()
for(k in seq_len(ncol(groups_map))) {
temp_delta_temp_peritem <- temp_theta_nom_mat_part * groups_map[,k]
temp_delta_peritem_test <- rbind(temp_delta_peritem_test,temp_delta_temp_peritem)
}
temp_delta_peritem[[j]] <- temp_delta_peritem_test
temp_tot_temp <- expTempProb_temp
temp_tot <- rbind(temp_tot,temp_tot_temp)
temp_gamma_part <- rbind(temp_gamma_part,temp_gamma_temp)
expTempProb_part <- rbind(expTempProb_part,expTempProb_temp)
}
}
temp_delta_nom_part <- list()
for(k in seq_len(ncol(groups_map))) {
temp <- c()
for(i in seq_along(temp_delta_peritem)){
temp <- rbind(temp, temp_delta_peritem[[i]][k,])
}
temp_delta_nom_part[[k]] <- temp
}
# print(temp_delta_nom_part[[1]][1:3, 1:3])
temp_gamma <- temp_gamma_part
expTempProb <- expTempProb_part
temp_beta_nom <- temp_tot * total_alpha_mat
Nom_theta <- matrix(temp_theta_nom_part,nrow = ncol(dset))
Nom_beta <- matrix(temp_beta_nom,nrow = dataPrep$allcat)
Nom_gamma <- matrix(temp_gamma_nom_part,nrow = ncol(dset))
Denom <- matrix(temp_denom_part,nrow = ncol(dset))+1
Denom_mat <- matrix(rep(Denom, rep(dataPrep$mt_vek,dataPrep$dimResp[1])), nrow = dataPrep$allcat)
t2_theta_mat <- (Nom_theta/Denom)
t2_theta <- colSums(t2_theta_mat, na.rm = TRUE)
t2_beta_mat <- (Nom_beta/Denom_mat)
t2_beta <- rowSums(t2_beta_mat, na.rm = TRUE)
# print(paste("Nom theta", Nom_theta[1, 1]))
# print(paste("Nom gamma", Nom_gamma[1, 1]))
# print(paste("Denom", Denom[1, 1]))
t2_gamma_mat <- (Nom_gamma/Denom)
t2_gamma <- rowSums(t2_gamma_mat, na.rm = TRUE)
t2_delta <- c()
for(k in seq_len(ncol(groups_map))) {
Nom_delta <- matrix(temp_delta_nom_part[[k]],nrow = ncol(dset))
t2_delta_mat <- (Nom_delta/Denom)
# print(Denom)
# print(t2_delta_mat)
#print(dim(t2_delta_mat))
t2_delta <- c(t2_delta,rowSums(t2_delta_mat, na.rm = TRUE))
}
# print(t1_theta - t2_theta)
#print (t1_beta - t2_beta)
# print(paste("theta:", length(t1_theta), length(t2_theta)))
# print(t1_theta - t2_theta)
# print(paste("beta:", length(t1_beta), length(t2_beta)))
# print(- t1_beta + t2_beta)
# print(paste("gamma:", length(t1_gamma), length(t2_gamma)))
# print(t1_gamma - t2_gamma)
#print(paste("delta:", length(t1_delta), length(t2_delta)))
# print(- t1_delta + t2_delta)
# print(t1_delta)
#print(t2_delta)
if(opts$isPenalized_theta){
# grad_theta <- t1_theta - t2_theta - (0.1*theta)
grad_theta <- t1_theta - t2_theta - (lambda_theta*theta)
} else {
grad_theta <- t1_theta - t2_theta
}
# NOTE: this doesn't really do anything, no if statement necessary
if(opts$isPenalized_theta){
grad_beta <- (-t1_beta) + t2_beta
} else {
grad_beta <- (-t1_beta) + t2_beta
}
if(opts$isPenalized_gamma & opts$isPenalized_theta){
grad_gamma <- t1_gamma - t2_gamma - (lambda_in*(gamma))
}else if(opts$isPenalized_gamma){
grad_gamma <- t1_gamma - t2_gamma - (lambda_out*(gamma))
}else {
grad_gamma <- t1_gamma - t2_gamma
}
if(opts$isPenalized_delta){
grad_delta <- (-t1_delta) + t2_delta - (opts$lambda_delta*(1+opts$eps)*sign(delta)*(abs(delta)^opts$eps))
} else {
grad_delta <- (-t1_delta) + t2_delta
}
output <- c()
if(!identical(grep("delta",estPar_arr), integer(0))){
output <- c(grad_delta,output)
}
if(!identical(grep("^gamma",estPar_arr), integer(0))){
output <- c(grad_gamma,output)
}
if(!identical(grep("^beta",estPar_arr), integer(0))){
output <- c(grad_beta,output)
}
if(!identical(grep("theta",estPar_arr), integer(0))){
output <- c(grad_theta,output)
}
grad_tot <- output
return(-grad_tot)
}
# Different way of implementing the gradient function
grad_fun_novel <- function(nlmPar, dset, lambda_theta, lambda_in, lambda_out, lambda_delta,
estPar_arr, fixed_par, fixValue, fixLength_arr, estLength_array,
groups_map, mt_vek, mt_idx, dimResp, allcat, n_th, XN, XNA, eps = 0,
isPenalized_gamma, isPenalized_theta, isPenalized_delta){
# TODO: incorporate the XNA part
lambda_theta <- lambda_theta*2
lambda_in <- lambda_in*2
lambda_out <- lambda_out*2
map_nlmPar <- par_map(nlmPar, estPar_arr = estPar_arr, estLength_array = estLength_array,
fixValue = fixValue, fixed_par = fixed_par, fixLength_arr = fixLength_arr)
theta <- map_nlmPar$theta
beta <- map_nlmPar$beta
gamma <- map_nlmPar$gamma
delta <- map_nlmPar$delta
exp_gamma <- exp(gamma)
X <- as.matrix(dset)
I <- ncol(dset)
V <- nrow(dset)
J <- max(mt_vek + 1) # max(m_i)
# this needs to be fixed, beta needs to be passed differently
beta_mat <- matrix(beta, nrow = I, byrow = TRUE)
# size V x G
groups_mat <- as.matrix(groups_map)
G <- ncol(groups_mat)
# size I X G
delta_mat <- matrix(delta, I, G)
# delta_group <- groups_mat %*% t(delta_mat)
delta_group <- groups_mat %*% matrix(delta, nrow = ncol(groups_map), byrow = TRUE)
Psi <- array(0, dim = c(V, I, J))
# most efficient way is to iterate first by category
# NOTE: this could be potentially shared with loglik function
for (i in 1:I) {
J <- mt_vek[i] + 1
for (v in 1:V) {
for (j in 1:(J-1)) {
Psi[v, i, j+1] <- Psi[v, i, j] + (theta[v] - beta_mat[i, j] - delta_group[v, i]) * exp_gamma[i]
}
}
}
### computed likelihood denominator (second part)
exp_Psi <- exp(Psi)
cumul_Psi <- matrix(0, V, I)
theta_Psi <- matrix(0, V, I)
gamma_Psi <- matrix(0, V, I)
# TODO: is m_i then the maximum number of categories (mt_vek?)
for (i in 1:I) {
J <- mt_vek[i] + 1
for (v in 1:V) {
if(!is.na(X[v,i])){
for (j in 2:J) {
cumul_Psi[v, i] <- cumul_Psi[v, i] + exp_Psi[v, i, j]
theta_Psi[v, i] <- theta_Psi[v, i] + (j - 1) * exp_gamma[i] * exp_Psi[v, i, j]
gamma_Psi[v, i] <- gamma_Psi[v, i] + Psi[v, i, j] * exp_Psi[v, i, j]
}
} else {
cumul_Psi[v, i] <- NA
theta_Psi[v, i] <- NA
gamma_Psi[v, i] <- NA
}
}
}
beta_Psi <- array(0, c(V, I, J))
delta_Psi <- array(0, c(V, I, G))
# NOTE: cumul needs to be fully computed by this point
for (i in 1:I) {
J <- mt_vek[i] + 1
for (v in 1:V) {
for (j in 2:J) {
delta_Psi[v, i, ] <- delta_Psi[v, i, ] + (j - 1) * exp_gamma[i] * exp_Psi[v, i, j] * groups_mat[v, ] / (1 + cumul_Psi[v, i])
}
for (j in J:2) {
beta_Psi[v, i, j-1] <- beta_Psi[v, i, j] + exp_gamma[i] * exp_Psi[v, i, j] / (1 + cumul_Psi[v, i])
}
}
}
grad_theta <- X %*% exp_gamma - rowSums(theta_Psi / (1 + cumul_Psi))
grad_beta <- matrix(0, I, J-1)
for (j in 1:(J-1)) {
grad_beta[,j] <- colSums(X >= j, na.rm = TRUE) * exp_gamma
}
grad_beta <- (-grad_beta) + apply(beta_Psi, 2:3, sum, na.rm=TRUE)[, -J]
grad_gamma <- numeric(I)
for (i in 1:I) {
for (v in 1:V) {
if(!is.na(X[v,i])){
grad_gamma[i] <- grad_gamma[i] + Psi[v, i, X[v, i] + 1]
}
}
}
grad_gamma <- grad_gamma - colSums((gamma_Psi / (1 + cumul_Psi)),na.rm = TRUE)
X_wo_NA <- X
X_wo_NA[is.na(X_wo_NA)] <- 0
grad_delta <- - diag(exp_gamma) %*% t(X_wo_NA) %*% groups_mat + apply(delta_Psi, 2:3, sum, na.rm = TRUE)
if(isPenalized_theta){
# grad_theta <- t1_theta - t2_theta - (0.1*theta)
grad_theta <- grad_theta - (lambda_theta*theta)
}
if(isPenalized_gamma && isPenalized_theta){
grad_gamma <- grad_gamma - (lambda_in*(gamma))
} else if(isPenalized_gamma){
grad_gamma <- grad_gamma - (lambda_out*(gamma))
}
if(isPenalized_delta){
grad_delta <- grad_delta - (lambda_delta*(1+eps)*sign(delta)*(abs(delta)^eps))
}
output <- c()
if(!identical(grep("delta",estPar_arr), integer(0))){
output <- c(t(grad_delta),output)
}
if(!identical(grep("^gamma",estPar_arr), integer(0))){
output <- c(grad_gamma,output)
}
if(!identical(grep("^beta",estPar_arr), integer(0))){
output <- c(t(grad_beta),output)
}
if(!identical(grep("theta",estPar_arr), integer(0))){
output <- c(grad_theta,output)
}
grad_tot <- output
return(-grad_tot)
}
# grad_fun_fast(nlmPar, dset = dataPrep$dset,
# lambda_theta = opts$lambda_theta, lambda_in = opts$lambda_in,lambda_out = opts$lambda_out, eps = opts$eps,
# lambda_delta = opts$lambda_delta, estPar_arr = estPar_arr, estLength_array = estLength_array,
# fixLength_arr = fixLength_arr, allcat = dataPrep$allcat, dimResp = dataPrep$dimResp, n_th = dataPrep$n_th, XN = dataPrep$XN, XNA = dataPrep$XNA, #XREAL = dataPrep$XREAL,
# groups_map = dataPrep$groups_map, mt_vek = dataPrep$mt_vek, mt_idx = dataPrep$mt_idx, fixed_par = opts$fixed_par, fixValue = fixValue,
# isPenalized_gamma = opts$isPenalized_gamma, isPenalized_theta = opts$isPenalized_theta,
# isPenalized_delta = opts$isPenalized_delta)
# Wrapper for the Rcpp implementation of the gradient function
grad_fun_fast <- function(nlmPar, dset, estPar_arr, fixed_par, fixValue, fixLength_arr, estLength_array,
opts, dataPrep) {
# TODO: implement XNA part
map_nlmPar <- par_map(nlmPar, estPar_arr = estPar_arr, estLength_array = estLength_array,
fixValue = fixValue, fixed_par = fixed_par, fixLength_arr = fixLength_arr)
theta <- map_nlmPar$theta
beta_mat <- matrix(map_nlmPar$beta, ncol(dset), dataPrep$n_th, byrow = TRUE)
gamma <- map_nlmPar$gamma
# # delta_mat <- matrix(map_nlmPar$delta, ncol(dset), ncol(dataPrep$groups_map))#, byrow = TRUE)
# delta_mat <- matrix(map_nlmPar$delta, length(beta_mat), ncol(dataPrep$groups_map))#, byrow = TRUE)
if(opts$mode == "DIF"){
delta_mat <- matrix(map_nlmPar$delta, ncol(dset), ncol(dataPrep$groups_map))#, byrow = TRUE)
mode <- 1
} else {
delta_mat <- matrix(map_nlmPar$delta, length(beta_mat), ncol(dataPrep$groups_map))#, byrow = TRUE)
mode <- 2
}
result <- grad_cpp(theta, gamma, delta_mat, dataPrep$groups_map, beta_mat, dataPrep$mt_vek, as.matrix(dset),
opts$isPenalized_gamma, opts$isPenalized_delta, opts$isPenalized_theta,
opts$lambda_in, opts$lambda_out, opts$lambda_delta, opts$lambda_theta, opts$eps, mode)
# print(result)
output <- c()
if(!identical(grep("delta",estPar_arr), integer(0))){
output <- c(result$grad_delta,output)
}
if(!identical(grep("^gamma",estPar_arr), integer(0))){
output <- c(result$grad_gamma,output)
}
if(!identical(grep("^beta",estPar_arr), integer(0))){
output <- c(result$grad_beta,output)
}
if(!identical(grep("theta",estPar_arr), integer(0))){
output <- c(result$grad_theta,output)
}
grad_tot <- output
return(-grad_tot)
}
# grad_fun_fast <- function(nlmPar, dset, lambda_theta, lambda_in, lambda_out, lambda_delta,
# estPar_arr, fixed_par, fixValue, fixLength_arr, estLength_array,
# groups_map, mt_vek, mt_idx, dimResp, allcat, n_th, XN, XNA, eps = 0,
# isPenalized_gamma, isPenalized_theta, isPenalized_delta) {
#
# # TODO: implement XNA part
#
# map_nlmPar <- par_map(nlmPar, estPar_arr = estPar_arr, estLength_array = estLength_array,
# fixValue = fixValue, fixed_par = fixed_par, fixLength_arr = fixLength_arr)
# theta <- map_nlmPar$theta
# beta_mat <- matrix(map_nlmPar$beta, ncol(dset), n_th, byrow = TRUE)
# gamma <- map_nlmPar$gamma
# delta_mat <- matrix(map_nlmPar$delta, ncol(dset), ncol(groups_map))#, byrow = TRUE)
#
# result <- grad_cpp(theta, gamma, delta_mat, groups_map, beta_mat, mt_vek, as.matrix(dset),
# isPenalized_gamma, isPenalized_delta, isPenalized_theta,
# lambda_in, lambda_out, lambda_delta,lambda_theta, eps)
#
# # print(result)
#
# output <- c()
#
# if(!identical(grep("delta",estPar_arr), integer(0))){
# output <- c(result$grad_delta,output)
# }
# if(!identical(grep("^gamma",estPar_arr), integer(0))){
# output <- c(result$grad_gamma,output)
# }
# if(!identical(grep("^beta",estPar_arr), integer(0))){
# output <- c(result$grad_beta,output)
# }
# if(!identical(grep("theta",estPar_arr), integer(0))){
# output <- c(result$grad_theta,output)
# }
#
# grad_tot <- output
#
# return(-grad_tot)
# }
GPCMDIF_LL <- function(dset = c(), fixValue.theta, fixValue.beta, fixValue.gamma, fixValue.delta, lambda_theta = 0.05, lambda_in = 50, lambda_out = 5e-3, lambda_delta = 17, lambda_deltagamma = 100000, eps = 0, resp.info = c(), resp.th = c(1,1), isPenalized_gamma = TRUE, isPenalized_theta = TRUE, isPenalized_delta = TRUE, mt_vek_init = c()){
if(is.null(dim(dset))){
dset <- matrix(dset, ncol = 1)
}
print(dim(dset))
lambda_theta <- lambda_theta
lambda_in <- lambda_in
lambda_out <- lambda_out
lambda_delta <- lambda_delta
lambda_deltagamma <- lambda_deltagamma
eps <- eps
groups_map <- as.matrix(resp.info)
print(dim(groups_map))
if(!is.null(mt_vek_init)){
mt_vek <- mt_vek_init
} else {
mt_vek <- apply(dset, 2L, max, na.rm = TRUE) ### create vector of max categories for each item
}
# mt_vek <- apply(dset, 2L, max, na.rm = TRUE) #number of categories - 1 for each item
mt_vek <- rep(max(mt_vek),length(mt_vek))
print(mt_vek)
allcat <- sum(mt_vek) #number of items * categories (assumption : item has the same number of categories)
n_th <- max(mt_vek)
xn.mat <- matrix(0,nrow = nrow(dset), ncol = allcat) ## response position
xna.mat <- matrix(1,nrow = nrow(dset), ncol = allcat)## NA position
for(i in 1:n_th){
idx <- which(dset==i)
new.idx <- (nrow(dset)*n_th*(ceiling(idx/nrow(dset))-1))+(idx%%nrow(dset))+(ifelse(idx%%nrow(dset) == 0,1,0)*nrow(dset))
full.idx <- c()
for(j in 1:i){
next.idx <- new.idx+(nrow(dset)*(j-1))
full.idx <- c(full.idx,next.idx)
}
xn.mat[full.idx] <- 1
}
idx <- which(is.na(dset))
new.idx <- (nrow(dset)*n_th*(ceiling(idx/nrow(dset))-1))+(idx%%nrow(dset))+(ifelse(idx%%nrow(dset) == 0,1,0)*nrow(dset))
full.idx <- c()
for(j in 1:mt_vek[1]){
next.idx <- new.idx+(nrow(dset)*(j-1))
full.idx <- c(full.idx,next.idx)
}
xn.mat[full.idx] <- NA
xna.mat[full.idx] <- NA
# xn.mat[full.idx] <- 9
XN <- t(xn.mat) #need to be transposed from the original dataset form
XN <- as.vector(XN)
XNA <- t(xna.mat)
XNA <- as.vector(XNA)
theta <- fixValue.theta
beta <- fixValue.beta
gamma <- fixValue.gamma
delta <- fixValue.delta
deltagamma <- rep(0,length(delta))
### get the value of alpha
exp_gamma <- exp(gamma)
exp_gamma <- rep.int(exp_gamma, mt_vek)
groups_map <- as.matrix(groups_map) #groups_map should be formed as matrix
### take the total of DIF effect for every group for beta or gamma
delta_tot <- 0
deltagamma_tot <- 0
for(i in seq_len(ncol(groups_map))) {
delta_tot <- delta_tot + outer(delta[(((i-1)*ncol(dset))+1):(i*ncol(dset))],groups_map[,i],"*")
deltagamma_tot <- deltagamma_tot + outer(deltagamma[(((i-1)*ncol(dset))+1):(i*ncol(dset))],groups_map[,i],"*")
}
delta_tot_rep <- rep.int((delta_tot), rep.int(mt_vek,nrow(groups_map))) #delta_tot_rep is total delta which has been replicated to every categoory
exp_deltagamma_tot_rep <- rep.int((exp(deltagamma_tot)), rep.int(mt_vek,nrow(groups_map))) #deltagamma_tot.rep is total deltagamma which has been replicated to every categoory
### compute the theta - (beta+delta)
t_diff <- rep(theta,each = allcat) - rep.int(beta,length(theta))
t_diff <- t_diff - delta_tot_rep
### multiplied by (exp(gamma+deltagamma))
disc_diff <- t_diff * exp_gamma
disc_diff <- disc_diff * exp_deltagamma_tot_rep
### map the corresponding NA value of the dataset to the matrix
disc_diff <- XNA * disc_diff
### compute the first part of the log-likelihood (simple addition part)
l1 <- sum((XN * disc_diff), na.rm = TRUE)
### compute the second part of the log-likelohood (with log)
### begin
per_cat_list <- matrix(disc_diff, nrow = n_th)
temp_prob <- as.matrix(per_cat_list[1,])
temp_l2 <- exp(temp_prob)
if(n_th > 1){
for(i in 2:n_th){
temp_prob <- cbind(temp_prob,(temp_prob[,i-1]+per_cat_list[i,]))
temp_l2 <- temp_l2 + (exp(temp_prob[,i]))
}
}
l2 <- sum(log(temp_l2+1), na.rm = TRUE)
st1st2 <- c(l1,l2)
lnL <- st1st2[1] - st1st2[2]
if(isPenalized_theta){
lnL <- lnL - (lambda_theta*(sum(theta^2)))
}
if(isPenalized_theta & isPenalized_gamma){
lnL <- lnL - (lambda_in*(sum(gamma^2)))
} else if(isPenalized_gamma){
lnL <- lnL - (lambda_out*(sum(gamma^2)))
}
if(isPenalized_delta){
lnL <- lnL - (lambda_delta*(sum(abs(delta)^(1+eps))))
}
return(lnL)
}
|
5bed62e506a82389751125f10b50ac25cb73ee1d
|
b69ea4c85c60f4a3c59d302eea64c620270cfaae
|
/BRT-code-salvage/helper-funs/jaccard_grid_et_al.R
|
7e4bbaa16e7de9a77b4faa9c8116e1ccdf557e06
|
[] |
no_license
|
rvanmazijk/Hons-thesis-code-salvage
|
52c1dbef9c136afabe385550d0258262c59d2773
|
b3492c5f5c212d75631557c8f280c898f91718a7
|
refs/heads/master
| 2020-03-20T01:30:39.817374
| 2018-08-30T14:30:26
| 2018-08-30T14:30:26
| 137,078,280
| 0
| 0
| null | 2018-08-30T14:30:27
| 2018-06-12T13:47:58
|
HTML
|
UTF-8
|
R
| false
| false
| 7,290
|
r
|
jaccard_grid_et_al.R
|
get_neighbouring_cells <- function(raster, focal_cell_name,
directions = 8) {
adjacency <- raster %>%
adjacent(
cells = focal_cell_name,
directions = directions,
include = FALSE # Don't return the focal cell
)
neighbours <- adjacency[, 2]
return(neighbours)
}
jaccard_focal_vs_neighbours <- function(communities_by_cell,
focal_cell_name,
neighbour_cell_names) {
# Get all 9 cells in the window (= focal + 8x NNs)
focal_cell <- communities_by_cell[
rownames(communities_by_cell) == focal_cell_name,
]
neighbour_cells <- communities_by_cell[
rownames(communities_by_cell) %in% neighbour_cell_names,
]
# Calculate
jaccard_dists <-
vegdist(
x = rbind(
focal_cell,
neighbour_cells
),
method = "jaccard"
) %>%
as.matrix()
# Only return mean dissim. to neighbours
focal_mean_jaccard_dist <- mean(jaccard_dists[-1, 1])
return(focal_mean_jaccard_dist)
}
jaccard_focal_vs_neighbours_quick <- function(community_dict,
focal_cell_name,
neighbour_cell_names,
return_raw_dist_matrix = FALSE) {
# Get all 9 cells in the window (= focal + 8x NNs) -------------------------
focal_cell <-
community_dict[
names(community_dict) == focal_cell_name
] %>%
map(as.character) %>%
unlist() %>%
as.vector()
neighbour_cells <-
community_dict[
names(community_dict) %in% neighbour_cell_names
] %>%
map(as.character) %>%
map(as.vector)
# Pair-wise mini community matrices ----------------------------------------
# Between a focal cell and one of its neighbours each time
jaccard_dists <- vector("list")
focal_cell <- community_dict[
names(community_dict) == focal_cell_name
] %>%
map(as.character) %>%
unlist() %>%
as.vector()
neighbour_cells <- community_dict[
names(community_dict) %in% neighbour_cell_names
]
for (i in seq_along(neighbour_cells)) {
mini_community <- focal_cell %and% neighbour_cells[[i]]
mini_community_matrix <- matrix(
nrow = 2,
ncol = length(mini_community),
data = NA
)
colnames(mini_community_matrix) <- mini_community
rownames(mini_community_matrix) <- c("focal_cell", "neighbour_cell")
# Fill focal cell row in mini community matrix
for (sp in 1:ncol(mini_community_matrix)) {
is_member <-
colnames(mini_community_matrix)[sp] %in% focal_cell
mini_community_matrix[1, sp] <- if (is_member) TRUE
else FALSE
}
# Fill neighbour cell row in mini community matrix
for (sp in 1:ncol(mini_community_matrix)) {
is_member <-
colnames(mini_community_matrix)[sp] %in% neighbour_cells[[i]]
mini_community_matrix[2, sp] <- if (is_member) TRUE
else FALSE
}
# Calculate Jaccard's distance between these two communities
jaccard_dists[[i]] <- designdist(
mini_community_matrix,
method = "(A + B - 2*J) / (A + B - J)"
)
names(jaccard_dists)[i] <- names(neighbour_cells)[i]
}
if (return_raw_dist_matrix) {
return(jaccard_dists)
} else {
focal_mean_jaccard_dist <- mean(unlist(jaccard_dists), na.rm = TRUE)
return(focal_mean_jaccard_dist)
}
}
jaccard_focal_vs_neighbours_quick2 <- function(mini_community_matrix) {
# Calculate Jaccard's distance between these two communities
jaccard_dists <- designdist(
mini_community_matrix,
method = "(A + B - 2*J) / (A + B - J)"
)
# Mean the dists between focal cell each neighbour cell
focal_mean_jaccard_dist <- mean(as.matrix(jaccard_dists)[-1, 1])
return(focal_mean_jaccard_dist)
}
jaccard_grid2 <- function(raster, community_dict) {
jaccard_raster <- raster
jaccard_raster[] <- NA
for (i in seq_along(community_dict)) {
focal_cell_name <- as.numeric(names(community_dict)[i])
neighbour_cell_names <- get_neighbouring_cells(
raster,
focal_cell_name
)
mini_community_matrix <- make_mini_community_matrix(
focal_cell = community_dict[as.character(focal_cell_name)],
neighbour_cells = community_dict[as.character(neighbour_cell_names)]
)
avg_jaccard_for_a_focal_cell <- jaccard_focal_vs_neighbours_quick2(
mini_community_matrix
)
jaccard_raster[focal_cell_name] <- avg_jaccard_for_a_focal_cell
message(glue("
Cell no. {i} of {length(community_dict)} completed
"))
}
return(jaccard_raster)
}
jaccard_grid <- function(raster, quickly = FALSE,
communities_by_cell = NULL, community_dict = NULL) {
if (is.null(communities_by_cell) && is.null(community_dict)) {
stop("
Please supply either a community matrix, row-wise by raster cell,
or a community dictionary (sensu `make_community_dictionary()`)
")
}
if (quickly) {
# Quicker implem with `jaccard_focal_vs_neighbours_quick()` ------------
jaccard_raster <- raster
jaccard_raster[] <- NA
for (i in seq_along(community_dict)) {
focal_cell_name <- as.numeric(names(community_dict)[i])
neighbour_cell_names <- get_neighbouring_cells(
raster,
focal_cell_name
)
focal_mean_jaccard_dist <- jaccard_focal_vs_neighbours_quick(
community_dict,
focal_cell_name,
neighbour_cell_names
)
jaccard_raster[focal_cell_name] <- focal_mean_jaccard_dist
message(glue("
Cell no. {i} of {length(community_dict)} ceomplete
"))
}
return(jaccard_raster)
} else {
# Slower, earlier implem with `jaccard_focal_vs_neighbours()` ----------
# Implem `get_neighbouring_cells()`, `jaccard_focal_vs_neighbours()`
# across a raster and the communities labelled by
# cell no. from that raster
jaccard_raster <- raster
jaccard_raster[] <- NA
for (i in 1:nrow(communities_by_cell)) {
focal_cell_name <- as.numeric(rownames(communities_by_cell)[i])
neighbour_cell_names <- get_neighbouring_cells(
raster,
focal_cell_name
)
focal_mean_jaccard_dist <- jaccard_focal_vs_neighbours(
communities_by_cell,
focal_cell_name,
neighbour_cell_names
)
jaccard_raster[focal_cell_name] <- focal_mean_jaccard_dist
}
return(jaccard_raster)
}
}
|
b74bc04d5d90073d11d93f6360605fbbacb50ef7
|
9ebb664ae2a778876d05e78ad8985816721e2706
|
/Rscripts/DB10_cancer_related/DB10_cancer_blood_comparison.R
|
1d337675bb1ccfed3d493bb762b65885aef6357c
|
[] |
no_license
|
seongyeol-park/Human_Lineage_Tracing
|
dc35818b629a3960e93a485e60eee26b764ede30
|
5012529fee63fa3f64dc80b18ff0c9968a25b078
|
refs/heads/main
| 2023-06-03T20:25:41.662730
| 2021-06-22T07:57:37
| 2021-06-22T07:57:37
| 314,397,015
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,286
|
r
|
DB10_cancer_blood_comparison.R
|
library(tidyverse)
ch_dt <- read_tsv('/home/users/sypark/00_Project/06_LineageTracing/merged_data/clonal_hematopoiesis/CH_final_list.txt.anv.readc')
ch_dt <- ch_dt %>% filter(case_list == "10_Blood_3s_merged")
ch_dt <- ch_dt %>% mutate(vaf = var_readN/(var_readN + ref_readN))
ch_dt <- ch_dt %>% mutate(var_id = paste(`#CHROM`, POS, REF, ALT, sep='_'))
ch_dt$mt_loca='blood'
colnames(ch_dt)[(ncol(ch_dt)-10):ncol(ch_dt)]
ch_dt <- ch_dt %>% separate("10_Breastcancer_WGS_ref;10_Breastcancer_WGS_var;10_Breastcancer_WGS_ukn;10_Breastcancer_WGS_vafpct", c("tumor_ref","tumor_var","tumor_ukn","tumor_vafpct"), sep=';', convert=T)
nrow(ch_dt)
ggplot(ch_dt)+
geom_histogram(aes(x=tumor_vafpct))+
ggtitle('Tumor VAFs of blood mutations (n=255)')
#cancer vcf
dt <- read_tsv('/home/users/sypark/00_Project/06_LineageTracing/db10_cancer_WGS/somatic_call_with_blood/04_mutect2_strelka2_union/tmp.txt.anv.readc.seqzcn.ccf',
col_types=cols(`#CHROM`='c'))
colnames(dt)
dt <- dt %>% mutate(vaf= (var_readN/(var_readN + ref_readN))) %>%
separate("10_Blood_3s_merged_ref;10_Blood_3s_merged_var;10_Blood_3s_merged_ukn;10_Blood_3s_merged_vafpct", c("blood_ref","blood_var","blood_ukn","blood_vaf"), sep=';', convert=T)
dt$blood_vaf %>% summary()
dt <- dt %>% mutate(var_id = paste(`#CHROM`,POS,REF,ALT,sep='_'))
dt <- left_join(dt, ch_dt %>% select(var_id, mt_loca))
dt$mt_loca[is.na(dt$mt_loca)==T] <- 'tumor_only'
dt$mt_loca %>% table()
tmp1 <- dt %>% select(`#CHROM`, POS, vaf, ref_readN, var_readN, blood_vaf, mt_loca)
tmp2 <- ch_dt %>% mutate(blood_vaf=vaf*100, vaf=tumor_vafpct*0.01, ref_readN = tumor_ref, var_readN = tumor_var) %>% select(`#CHROM`, POS, ref_readN, var_readN,vaf, blood_vaf, mt_loca)
tmp <- bind_rows(tmp1, tmp2) %>% unique()
tmp$vaf %>% summary()
tmp$blood_vaf %>% summary()
tmp %>% write_tsv('/home/users/sypark/00_Project/06_LineageTracing/db10_cancer_WGS/somatic_call_with_blood/04_mutect2_strelka2_union/intersection_plus_blooddetected.txt')
#cancer + blood vcf
merge_dt <- read_tsv('/home/users/sypark/00_Project/06_LineageTracing/db10_cancer_WGS/somatic_call_with_blood/04_mutect2_strelka2_union/intersection_plus_blooddetected.txt.seqzcn.ccf',
col_types =cols(`#CHROM`='c', CCF='n'))
merge_dt <- merge_dt %>% mutate(group = ifelse(blood_vaf >=1, 'pos','neg'))
t_chr='1'
plot_list=list()
n=0
for(t_chr in c(1:22,'X')){
n=n+1
plot_list[[n]] <- ggplot(subset(merge_dt, `#CHROM`==t_chr), aes(x=POS, y=mutCN))+
geom_point(alpha=0.6, aes(color=group))+
geom_segment(data = subset(seg_dt, chromosome == t_chr), aes(x=start.pos, xend=end.pos, y=A, yend=A), color="red")+
geom_segment(data = subset(seg_dt, chromosome == t_chr), aes(x=start.pos, xend=end.pos, y=B, yend=B), color="blue")+
scale_y_continuous(limits=c(0,10), breaks=seq(0,10,1))+
scale_color_manual(values=c('pos'='red', 'neg'='gray'))+
#scale_color_manual(values=c('blood'='red','tumor_only'='gray'))+
#scale_color_gradient2(low='gray', mid='gray',high='red', midpoint=3)+
theme(legend.position = 'none')+
xlab(t_chr)
}
library(cowplot)
plot_grid(plotlist = plot_list[1:6], nrow=1, align="h", axis="tb")
merge_dt %>% filter(`#CHROM`=='4' & mutCN >5 & blood_vaf < 5)
ggplot(merge_dt, aes())
##########################
ggplot(dt, aes(x=vaf, y=blood_vaf))+
geom_point()
#
seg_dt <- read_tsv('/home/users/sypark/00_Project/06_LineageTracing/db10_cancer_WGS/somatic_call_with_blood/01_sequenza/10_Breastcancer_WGS_sequenza.seqz.v3/10_Breastcancer_WGS_sequenza_Sol1_c0.48_p3_gF/10_Breastcancer_WGS_sequenza_Sol1_c0.48_p3_gF_segments.txt',
col_types=cols(chromosome='c'))
t_chr='1'
ggplot(subset(dt, `#CHROM`==t_chr), aes(x=POS, y=vaf))+
geom_point(alpha=0.8)
geom_point(data=subset(dt, `#CHROM`==t_chr), aes(x=POS, y=vaf*10+10, color=mt_loca), alpha=0.3)+
geom_segment(data = subset(seg_dt, chromosome == t_chr), aes(x=start.pos, xend=end.pos, y=CNt, yend=CNt), color="red")+
scale_y_continuous(limits=c(0,20), breaks=seq(0,20,2))+
xlab(t_chr)+theme(axis.title.y=element_blank())
plot_list=list()
n=0
for(t_chr in c(1:22,'X')){
n=n+1
plot_list[[n]] <- ggplot(subset(ch_dt, `#CHROM`==t_chr), aes(x=POS, y=vaf*10))+
geom_point(alpha=0.8)+
geom_point(data=subset(dt, `#CHROM`==t_chr), aes(x=POS, y=vaf*10+10, color=mt_loca), alpha=0.3)+
geom_segment(data = subset(seg_dt, chromosome == t_chr), aes(x=start.pos, xend=end.pos, y=CNt, yend=CNt), color="red")+
scale_y_continuous(limits=c(0,20), breaks=seq(0,20,2))+
xlab(t_chr)+theme(axis.title.y=element_blank())+theme(legend.position='none')
}
library(cowplot)
plot_grid(plotlist = plot_list[1:6], nrow=1, align="h", axis="tb")
plot_list=list()
n=0
for(t_chr in c(1:22,'X')){
n=n+1
plot_list[[n]] <- ggplot(subset(ch_dt, `#CHROM`==t_chr), aes(x=POS, y=vaf*10))+
geom_point(alpha=0.8)+
geom_point(data=subset(dt, `#CHROM`==t_chr), aes(x=POS, y=vaf*10+10, color=mt_loca), alpha=0.3)+
geom_segment(data = subset(seg_dt, chromosome == t_chr), aes(x=start.pos, xend=end.pos, y=CNt, yend=CNt), color="red")+
scale_y_continuous(limits=c(0,20), breaks=seq(0,20,2))+
xlab(t_chr)+theme(axis.title.y=element_blank())
}
library(cowplot)
plot_grid(plotlist = plot_list[21:23], nrow=1, align="h", axis="tb")
|
a1179e4b2dbd7cb150c0894a860eb20cf8d3b6e9
|
63d0c2310c9a8ed88eb08439bb6d697940d21b66
|
/Rcompile
|
3ab647f3937ddafca5d8f980c591a7e63837f893
|
[] |
no_license
|
stnava/eigenanatomy1
|
c716ad7f617f6e50e31241688110764e1169429e
|
ef8c8ac379a89c9c3bc427cc02733fb6efc16b99
|
refs/heads/master
| 2021-01-19T20:16:21.460038
| 2014-03-10T15:22:11
| 2014-03-10T15:22:11
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 246
|
Rcompile
|
#!/usr/bin/env Rscript
library(knitr)
knit('Eigenanatomy1.Rnw',tangle=TRUE)
knit('Eigenanatomy1.Rnw',tangle=FALSE)
for ( i in c(1,2,3) ) {
system('pdflatex Eigenanatomy1 ')
system('bibtex Eigenanatomy1 ')
}
#system('rm *log *aux *blg *tex')
|
|
321d815caf4a8fc9a751b9633dda751e5f3d758c
|
4427956ba82fd35ce8ba8c0c61214b63bd607a58
|
/man/ifftshift.Rd
|
15400ad071e6cfefd4320c75fd867e69652e79be
|
[] |
no_license
|
romanflury/mrbsizeR
|
b971e69ca01bc7b4db8780a627957dbde641231a
|
936edff8a3f265d3dae8cf65b11d733eb2d8f7a7
|
refs/heads/master
| 2020-03-16T04:08:44.884321
| 2019-12-10T12:37:18
| 2019-12-10T12:37:18
| 132,504,550
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,224
|
rd
|
ifftshift.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/HelpFunctions.R
\name{ifftshift}
\alias{ifftshift}
\title{Inverse FFT shift of a 2d matrix.}
\usage{
ifftshift(inputMatrix, dimension = -1)
}
\arguments{
\item{inputMatrix}{Matrix to be swapped.}
\item{dimension}{Which swap should be performed?
\itemize{
\item \code{1}: swap halves along the rows.
\item \code{2}: swap halves along the columns.
\item \code{-1}: swap first quadrant with third and second quadrant with fourth.
}}
}
\value{
Swapped matrix.
}
\description{
\code{ifftshift} is an R equivalent to the Matlab function \code{ifftshift}
applied on matrices. For more information about \code{ifftshift} see
the Matlab documentation.
}
\details{
\code{ifftshift} is the inverse function to \code{\link{fftshift}}. For more
information see the details of \code{\link{fftshift}}
}
\examples{
set.seed(987)
sampleMat <- matrix(sample(1:10, size = 25, replace = TRUE), nrow = 5)
# Swap halves along the rows:
ifftshift(sampleMat, dimension = 1)
# Swap halves along the columns:
ifftshift(sampleMat, dimension = 2)
# Swap first quadrant with third and second quadrant with fourth:
ifftshift(sampleMat, dimension = -1)
}
|
01ead16d0b4e67b992a3f85b87157344b8fcb4b3
|
b90cf0bd5c664fce3f6f195c5a49636afe4e5dfd
|
/man/coerce_each.Rd
|
65d6d06b2622f992bdb486e4f9074c4bc3399930
|
[] |
no_license
|
decisionpatterns/coercion
|
77314d6013e6e499cd4282b47bc3a16723aca24b
|
fae10fb0cb15be112894a91c4bf6ba9796d5b7dc
|
refs/heads/master
| 2020-08-12T18:50:29.078815
| 2019-10-13T13:16:46
| 2019-10-13T13:16:46
| 214,823,025
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 870
|
rd
|
coerce_each.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/coerce_each.R
\name{coerce_each}
\alias{coerce_each}
\alias{coerce_each.data.frame}
\alias{coerce_each.data.table}
\title{Coerce each elements of a container class from one class to a different
class}
\usage{
coerce_each(x, from, to)
\method{coerce_each}{data.frame}(x, from, to)
\method{coerce_each}{data.table}(x, from, to)
}
\arguments{
\item{x}{A container object such as a \code{list}, \code{data.frame} or
\code{data.table}}
\item{from}{A character vector containing the classes to coerce from}
\item{to}{A character vector containing}
}
\value{
An object with the same class as \code{x}
\code{data.table}
\code{data.table}
}
\description{
Coerce each elements of a container class from one class to a different
class
}
\examples{
coerce_each( iris, "factor", "character" )
}
|
96e11460a8ddc670f43a1ed30a3f86cf06fb3ce9
|
c7c6a02920d4e66df2f8358198989d6cc641e52b
|
/R code/4_Process TPC model output_zinf.R
|
6ee1f7f67e5dc5cf4bb65d5818d97e5939cb2e7d
|
[] |
no_license
|
rwoolive/Cardinalis_TPC_evolution
|
f2d1ef51071ba33c7d7a6d2ccc43fe3d8413c2c3
|
937b401687d6977dfd3a58b3d4276b3a173db8e2
|
refs/heads/master
| 2022-07-17T15:32:10.381269
| 2020-05-19T17:36:43
| 2020-05-19T17:36:43
| 234,155,803
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,044
|
r
|
4_Process TPC model output_zinf.R
|
#### PROJECT: Mimulus cardinalis TPC project
#### PURPOSE: Process TPC model output,
#### export model summary (Table S3)
#### DATE LAST MODIFIED: 2020-05-18 by rcw
#### WARNING: MODEL OUTPUT FILES ARE LARGE AND WILL TAKE TIME TO READ IN.
#### SOME CALCULATIONS (E.G. PERFORMANCE MAXIMUM, P-VALUES, AND B50)
#### ARE COMPUTATIONALLY INTENSIVE AND WILL TAKE TIME TO RUN.
#load performr
devtools::install_github("silastittes/performr", local = FALSE,
ref="zin", force=FALSE)
#load other packages
library(dplyr)
library(devtools)
library(performr)
library(tidyverse)
library(ggridges)
library(cowplot)
library(ggpubr)
library(agricolae)
library(GGally)
# Other settings
theme_set(theme_cowplot())
options(mc.cores = parallel::detectCores())
extract <- rstan::extract
############
# load model fits
model_fits_groups_av <- rstan::read_stan_csv(c("Analysis output/model/stan_example_groups_av_zinf.samples_1.csv",
"Analysis output/model/stan_example_groups_av_zinf.samples_2.csv",
"Analysis output/model/stan_example_groups_av_zinf.samples_3.csv",
"Analysis output/model/stan_example_groups_av_zinf.samples_4.csv"))
############
# read in overall average rgr and temperature
meansTot_avDat <- read.csv("Processed data/meansTot_avDat.csv")
############
# read in family-averaged data
avDat <- read.csv("Processed data/avDat.csv")
############
# The output is the same as any Stan model.
# n_eff should be very large and Rhat should be close to 1
sumtab <- round(rstan::summary(model_fits_groups_av)$summary, digits=2)
write.csv(sumtab, file="Analysis output/Table S3_zinf.csv")
############
# We can access the posterior draws using rstan::extract(), which produces a list
# containing draws for each parameter of the model.
draws_groups_av <- rstan::extract(model_fits_groups_av)
ndraws_groups_av <- length(draws_groups_av$lp__)
############
# Generate a tidy data frame with all the parameters, plus some
# valuable derived parameters, like the optimum, area, and breadth for each group
# We will use this data frame for other tasks below as well.
head(
tidy_perf_groups_av <-
performr::perform_df(
model_fits_groups_av,
species_order = c(1:12)
)
)
# tidy_perf_groups_av is the dataframe that we can use for pairwise
# comparisons and plotting.
# shape1: First of the two parameters that modifies curve asymmetry; when shape1
# is larger than shape2 , the curve will skew right
# shape2: Second parameter that modifies curve asymmetry; when shape2
# is larger than shape1 , the curve will skew left
# stretch: Dictates the maximum expected value of the response trait (maximum RGR)
# min_max: Performance breadth
# nu: Variance?
# x_min: Location along the environmental axis left of the optimum where the
# response trait falls to 0 (low temperature threshold)
# x_max: Location along the environmental axis right of the optimum where the
# response trait falls to 0 (high temperature threshold)
############
# Calculate performance maximum for each iteration of the model.
# To get the actual RGR max value, it should just be the value of the Kumaraswamy-ish
# function at the optimum. The values and scale will differ, but it should be very
# correlated with the value of stretch. If you want to calculate it, the easiest
# would be to use performr::performance_mu().
# This mutate() function seems to work to add in the variable for max_RGR in-place.
# Sadly, kinda slow, but do() adds a performance bar, so that's fun!
tidy_perf_groups_av %<>%
ungroup() %>%
mutate(idx = 1:n()) %>%
group_by(idx) %>%
do({
mutate(.data = ., max_RGR = performance_mu(xs = .$maxima, .$shape1, .$shape2, .$stretch, .$x_min, .$x_max))
})
############
# Bayesian p-value:
# A Bayesian p-value of 0.5 suggests the model generates data that look like the true data.
# the function:
bayes_p <- function(stan_df, raw_df, raw_group, raw_treatment, raw_response, ndraw = nrow(stan_df)){
1:ndraw %>%
map_df(~{
draw_i <- stan_df[.x, ]
tidy_spp <- stan_df$species[.x]
df_i <- raw_df[raw_df[[raw_group]] == tidy_spp,]
mus <- performance_mu(xs = df_i[[raw_treatment]],
shape1 = draw_i$shape1,
shape2 = draw_i$shape2,
stretch = draw_i$stretch,
x_min = draw_i$x_min,
x_max = draw_i$x_max
)
pseudo <- posterior_predict(x = df_i[[raw_treatment]], draw_i)
data.frame(
ssq_obs = sum((mus - df_i[[raw_response]])^2),
ssq_pseudo = sum((mus - pseudo$trait)^2),
species = tidy_spp
)
})
}
#example of function use
# stan_df -- the tidy stan data frame
# raw_df -- the data frame used as input to performr
# raw_group -- the column name that contains the groups the model was fit with *in quotes*
# raw_treatment -- the column name that contains the treatment variable the model was fit with *in quotes*
# raw_response -- the column name that contains the response variable the model was fit with *in quotes*
avDat$Group.ord <- as.character(avDat$Group.ord)
ssq_df <- bayes_p(
stan_df = tidy_perf_groups_av,
raw_df = avDat,
raw_treatment = "daytimeTemp",
raw_response = "RGR",
raw_group = "Group.ord")
write.csv(ssq_df, "Analysis output/model/ssq_df_av_zinf.csv")
ssq_df <- read.csv("Analysis output/model/ssq_df_av_zinf.csv")
View(ssq_df)
#compute bayesian p value
ssq_df %>%
summarise(b_pval=mean(ssq_obs > ssq_pseudo))
# overall bayesian p value = 0.8019292, that's good!
#compute bayesian p value for each group
ssq_group <- ssq_df %>%
dplyr::group_by(species) %>%
summarise(b_pval=mean(ssq_obs > ssq_pseudo))
ssq_group <- as.data.frame(ssq_group)
#create a column for our groups in the overall ssq dataset
# Group 1 is N1 2010, group 2 is N1 2017, etc.
ssq_df$speciesF <- ssq_df$species
ssq_df$speciesF[which(ssq_df$species==1)] <- "N1 2010"
ssq_df$speciesF[which(ssq_df$species==2)] <- "N1 2017"
ssq_df$speciesF[which(ssq_df$species==3)] <- "N2 2010"
ssq_df$speciesF[which(ssq_df$species==4)] <- "N2 2017"
ssq_df$speciesF[which(ssq_df$species==5)] <- "C1 2010"
ssq_df$speciesF[which(ssq_df$species==6)] <- "C1 2017"
ssq_df$speciesF[which(ssq_df$species==7)] <- "C2 2010"
ssq_df$speciesF[which(ssq_df$species==8)] <- "C2 2017"
ssq_df$speciesF[which(ssq_df$species==9)] <- "S1 2010"
ssq_df$speciesF[which(ssq_df$species==10)] <- "S1 2017"
ssq_df$speciesF[which(ssq_df$species==11)] <- "S2 2010"
ssq_df$speciesF[which(ssq_df$species==12)] <- "S2 2017"
ssq_df$speciesF <- as.factor(ssq_df$speciesF)
ssq_df$speciesF <- factor(ssq_df$speciesF, levels(ssq_df$speciesF)[c(5:8,1:4,9:12)])
#create a column for groups and merge with their p values in the group p value dataset
ssq_group$ps <- round(ssq_group$b_pval,2)
ssq_group$group <- c("N1 2010", "N1 2017","N2 2010","N2 2017",
"C1 2010", "C1 2017","C2 2010","C2 2017",
"S1 2010", "S1 2017","S2 2010","S2 2017")
ssq_group$group <- paste(ssq_group$group," (",ssq_group$ps,")", sep="")
# then write the file so we have it saved
write.csv(ssq_group, "Analysis output/p-values-group_zinf.csv")
# in the overall ssq dataset, create a column of groups with their associated p value
# REMEMBER that group 1 is N1 2010, group 2 is N2 2010, etc
ssq_df$group <- as.character(ssq_df$species)
ssq_df <- ssq_df %>%
mutate(group = fct_recode(group,
"N1 2010 (0.91)" = "1",
"N1 2017 (0.91)" = "2",
"N2 2010 (0.94)" = "3",
"N2 2017 (0.85)" = "4",
"C1 2010 (0.61)" = "5",
"C1 2017 (0.70)" = "6",
"C2 2010 (0.95)" = "7",
"C2 2017 (0.71)" = "8",
"S1 2010 (0.72)" = "9",
"S1 2017 (0.71)" = "10",
"S2 2010 (0.77)" = "11",
"S2 2017 (0.86)" = "12"))
# then reorder to N1 2010, N1 2017, etc.
ssq_df$group <- factor(ssq_df$group, levels(ssq_df$group)[c(1,5:12,2:4)])
# subsample to make a figure
subsamp <- ssq_df%>%
group_by(group)%>%
sample_frac(size = 0.1, replace = F)
# figure of predicted vs. observed ssq
ssq_plot <- ggplot(subsamp, aes(y=log(ssq_pseudo), x=log(ssq_obs), color=group)) +
geom_point(alpha=0.1) +
geom_abline(intercept=0,slope=1,linetype="dashed") +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
xlab("Log observed ssq") +
ylab("Log posterior predictive ssq") +
theme_bw() +
theme(axis.text=element_text(size=16), axis.title=element_text(size=18),
#legend.title=element_blank(), legend.text = element_text(size = 11), legend.background = element_blank(), legend.position = c(20, 0.5),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
strip.background = element_rect(colour=NA, fill=NA), strip.text.x = element_text(size=18, face="bold"),
panel.grid.major = element_blank(), panel.grid.minor = element_blank())
ssq_plot
pdf("Figures/Figure S2_zinf.pdf", height=5, width=6.5)
ssq_plot
dev.off()
############
############
# back-transform posterior draws
tidy_perf_groups_av$maximaBT <- tidy_perf_groups_av$maxima + meansTot_avDat$Temp
tidy_perf_groups_av$x_minBT <- tidy_perf_groups_av$x_min + meansTot_avDat$Temp
tidy_perf_groups_av$x_maxBT <- tidy_perf_groups_av$x_max + meansTot_avDat$Temp
tidy_perf_groups_av$breadthBT <- tidy_perf_groups_av$x_maxBT-tidy_perf_groups_av$x_minBT
tidy_perf_groups_av$max_RGR <- tidy_perf_groups_av$max_RGR * meansTot_avDat$RGR
############
# CALCULATE B50, LOWER LIMITS, UPPER LIMITS
############
# THE FUNCTION #
# The higher prop_max, the narrower the output breadth will be as the interval is
# moving nearer to the optimum. The function reports the x axis values the breadth
# is calculated from: 'opt_breadth_low' & 'opt_breadth_high'. 'opt_breadth' is the
# difference between low and high, i.e., the breadth.
optimum_breadth <-
function(par_df, prop_max = 0.5, n_grid = 100){
1:nrow(par_df) %>% map_df(function(i){
xs = seq(par_df$x_min[i],
par_df$x_max[i],
length.out = n_grid)
sim_mu <- performr::performance_mu(
xs = xs,
shape1 = par_df$shape1[i],
shape2 = par_df$shape2[i],
stretch = par_df$stretch[i],
x_min = par_df$x_min[i],
x_max = par_df$x_max[i]
)
max_y <- which.max(sim_mu)
prop_max_y <- sim_mu[max_y] * prop_max
idx_max_low <- which.min((sim_mu[1:(max_y-1)] - prop_max_y)^2)
idx_max_high <- which.min((sim_mu[max_y:length(sim_mu)] - prop_max_y)^2) + max_y
tibble("opt_breadth_low" = xs[idx_max_low],
"opt_breadth_high" = xs[idx_max_high],
"opt_breadth" = xs[idx_max_high] - xs[idx_max_low])
})
}
#########
# USAGE #
#########
#gotta do ungroup for now, unfortunately
#increase n_grid for more precise approximation
tidy_perf_groups_av %<>%
ungroup() %>%
bind_cols(
optimum_breadth(tidy_perf_groups_av, prop_max = 0.5, n_grid = 100)
)
# opt_breadth_low: lower limit for B50
# opt_breadth_high: upper limit for B50
# opt_breadth: difference between high and low
# Back-transorm
tidy_perf_groups_av$B50_low <- tidy_perf_groups_av$opt_breadth_low + meansTot_avDat$Temp
tidy_perf_groups_av$B50_high <- tidy_perf_groups_av$opt_breadth_high + meansTot_avDat$Temp
tidy_perf_groups_av$B50 <- tidy_perf_groups_av$B50_high - tidy_perf_groups_av$B50_low
############
# Now calculate B80
############
tidy_perf_groups_av %<>%
ungroup() %>%
bind_cols(
optimum_breadth(tidy_perf_groups_av, prop_max = 0.8, n_grid = 100)
)
# opt_breadth_low: lower limit for B80
# opt_breadth_high: upper limit for B80
# opt_breadth: difference between high and low
# Back-transorm
tidy_perf_groups_av$B80_low <- tidy_perf_groups_av$opt_breadth_low1 + meansTot_avDat$Temp
tidy_perf_groups_av$B80_high <- tidy_perf_groups_av$opt_breadth_high1 + meansTot_avDat$Temp
tidy_perf_groups_av$B80 <- tidy_perf_groups_av$B80_high - tidy_perf_groups_av$B80_low
############
############
# get mean values of each parameter
mean_df <- tidy_perf_groups_av %>%
group_by(species) %>%
summarise_if(is.numeric, .funs = c(mean))
mean_df$species <- as.integer(mean_df$species)
mean_df <- mean_df[order(mean_df$species),]
mean_df$species <- as.factor(mean_df$species)
mean_df_ci <- tidy_perf_groups_av %>%
group_by(species) %>%
summarise(maximaBT_lci = quantile(maximaBT, probs=c(0.025)),
maximaBT_uci = quantile(maximaBT, probs=c(0.975)),
B50_lci = quantile(B50, probs=c(0.025)),
B50_uci = quantile(B50, probs=c(0.975)),
B80_lci = quantile(B80, probs=c(0.025)),
B80_uci = quantile(B80, probs=c(0.975)),
breadthBT_lci = quantile(breadthBT, probs=c(0.025)),
breadthBT_uci = quantile(breadthBT, probs=c(0.975)),
x_minBT_lci = quantile(x_minBT, probs=c(0.025)),
x_minBT_uci = quantile(x_minBT, probs=c(0.975)),
x_maxBT_lci = quantile(x_maxBT, probs=c(0.025)),
x_maxBT_uci = quantile(x_maxBT, probs=c(0.975)),
max_RGR_lci = quantile(max_RGR, probs=c(0.025)),
max_RGR_uci = quantile(max_RGR, probs=c(0.975)),
stretch_lci = quantile(stretch, probs=c(0.025)),
stretch_uci = quantile(stretch, probs=c(0.975)),
area_lci = quantile(area, probs=c(0.025)),
area_uci = quantile(area, probs=c(0.975)))
mean_df_ci$species <- as.integer(mean_df_ci$species)
mean_df_ci <- mean_df_ci[order(mean_df_ci$species),]
mean_df_ci$species <- as.factor(mean_df_ci$species)
############
# The next step is to generate prediction intervals using the predict_interval()
# function. The function generates posterior quantiles for each set of posterior
# draws specified (x_draws), and averages over the quantiles. The argument p can
# takes a vector of credible levels, which can be modified to consider other and/or
# additional levels.
############
# set up sequence from smallest to largest x
x_seq_groups_av = seq(min(draws_groups_av$x_min),
max(draws_groups_av$x_max),
length.out = 100)
############
# sub-sample draws randomly
poly_draws_groups_av <- sample(1:ndraws_groups_av, 100)
predict_interval <- function (x, spp, par_df, x_draws, p){
if (missing(x)) {
x <- seq(min(par_df$x_min), max(par_df$x_max), length.out = 100)
}
sub_df <- par_df %>% filter(draw %in% x_draws)
p %>% map_df(~{
posterior_quantile(x = x, par_df = sub_df, p = .x) %>%
group_by(species, x) %>%
summarise_all(.funs = mean) %>%
mutate(level = .x) %>%
arrange(x) %>%
dplyr::select(-draw) %>%
mutate(level = round(.x * 100, 0))
}) %>%
arrange(species, x)
}
head(
creds_groups_av <-
predict_interval(
x = x_seq_groups_av,
spp = species,
par_df = tidy_perf_groups_av,
x_draws = poly_draws_groups_av,
p = c(0.95, 0.5))
)
# Notice the output of predict_interval() also produces a “mu” column, which
# contains the mean preduction of the curve for each input species.
############
# Write the model output/data to csv files
############
write.csv(creds_groups_av, "Analysis output/model/creds_groups_av_zinf.csv")
write.csv(mean_df, "Analysis output/mean_df_groups_av_zinf.csv")
write.csv(mean_df_ci, "Analysis output/mean_df_ci_groups_av_zinf.csv")
write.csv(avDat, "Analysis output/avDat_zinf.csv")
write.csv(tidy_perf_groups_av, "Analysis output/model/tidy_perf_groups_av_zinf.csv")
|
b4c36f83421ec9adfb536b00d7c52d2956d9e349
|
75d958dbb30e634995a732f1fd3682933bd36cef
|
/plot4.R
|
588995e0889d90211fd9d3b25bf79ed4daf33581
|
[] |
no_license
|
wpmcdonald2000/ExData_Plotting1
|
6e48e4487348d6e3932d3188a405a95f52c81505
|
0bc045f063b51fa08d8d80748916b26b452416be
|
refs/heads/master
| 2020-12-24T21:36:26.572630
| 2014-06-07T05:41:39
| 2014-06-07T05:41:39
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,531
|
r
|
plot4.R
|
# download data from local text file
ElecPower <- read.table(file="household_power_consumption.txt", header=TRUE, sep=";", na.strings="?")
# subset data for selected dates
ElecPower <- subset(ElecPower, ElecPower$Date == "1/2/2007" | ElecPower$Date == "2/2/2007")
# open png file device, name file, initialize size
png(file="plot4.png",width=480,height=480)
# Set row parameter for 2 X 2 graph
par(mfrow= c(2,2))
# Draw first plot upper left (Global active power)
plot(ElecPower$Global_active_power, type = 'l',xaxt = 'n' ,
xlab = "", ylab="Global Active Power")
axis(1,at = c(0,1440,2880), labels = c('Thu', 'Fri', 'Sat'))
# Draw second plot upper right (Voltage)
plot(ElecPower$Voltage, type = 'l',xaxt = 'n' ,xlab = "datetime",
ylab="Voltage")
axis(1,at = c(0,1440,2880), labels = c('Thu', 'Fri', 'Sat'))
# Draw third plot lower left (submetering)
plot(ElecPower$Sub_metering_1,type="l", xaxt= "n",
xlab = "", ylab="Energy sub metering")
axis(1,c(0,1440,2880), labels = c('Thu', 'Fri', 'Sat'))
lines(ElecPower$Sub_metering_3, col = 'Blue')
lines(ElecPower$Sub_metering_2, col = 'Red')
legend("topright",lty = 1, col = c("black","blue", "red"),
legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),
bty = "n")
# Draw fourth plot lower right (global reactive power)
plot(ElecPower$Global_reactive_power, type = 'l',xaxt = 'n' ,
xlab = "datetime",ylab="Global_Reactive_Power")
axis(1,at = c(0,1440,2880), labels = c('Thu', 'Fri', 'Sat'))
# turn off png file device
dev.off()
|
9d5fd35c93cb724645670f38cfbd109fe6b0ce3e
|
27f53c5a9aa2d0962b5cd74efd373d5e9d9e0a99
|
/R/FeatSelControlRandom.R
|
aa4cf98b76c8cb07670712f91ee02be2e6b23d65
|
[] |
no_license
|
dickoa/mlr
|
aaa2c27e20ae9fd95a0b63fc5215ee373fa88420
|
4e3db7eb3f60c15ce2dfa43098abc0ed84767b2d
|
refs/heads/master
| 2020-12-24T13:44:59.269011
| 2015-04-18T19:57:42
| 2015-04-18T19:57:42
| 31,710,800
| 2
| 0
| null | 2015-04-18T19:57:43
| 2015-03-05T11:29:18
|
R
|
UTF-8
|
R
| false
| false
| 544
|
r
|
FeatSelControlRandom.R
|
#' @export
#' @rdname FeatSelControl
makeFeatSelControlRandom = function(same.resampling.instance = TRUE,
maxit = 100L, max.features = NA_integer_, prob = 0.5, tune.threshold = FALSE,
tune.threshold.args = list(), log.fun = NULL) {
maxit = asCount(maxit, positive = TRUE)
makeFeatSelControl(same.resampling.instance = same.resampling.instance,
maxit = maxit, max.features = max.features, prob = prob, tune.threshold = tune.threshold,
tune.threshold.args = tune.threshold.args, log.fun = log.fun, cl = "FeatSelControlRandom")
}
|
c8c8017a5ab28e617a1b8a81c09a4001f0c31d1a
|
fb6f4bf4f515ed17cb6027a288f60a53e07abbc9
|
/GRS_post_annotation.R
|
d4238379168ba94d95f6714dbbf83bc7e8997d20
|
[] |
no_license
|
AdrianS85/GRS_gene_name_meta-analysis
|
8226e6381efb74f8331f549b8cacdf99d5351ed0
|
e14dbce716e74a692623db7f0b7d169904c8f14d
|
refs/heads/master
| 2021-10-27T17:19:42.051094
| 2021-10-20T07:15:56
| 2021-10-20T07:15:56
| 207,287,225
| 0
| 0
| null | 2019-11-05T11:47:54
| 2019-09-09T10:50:01
|
R
|
UTF-8
|
R
| false
| false
| 10,128
|
r
|
GRS_post_annotation.R
|
source('functions_for_genename_conversions.R')
source('https://raw.githubusercontent.com/AdrianS85/helper_R_functions/master/little_helpers.R')
opts_ann_1_n = 'Probe_ID_left_from_first_ncbi_annotation_stage'
opts_ann_1 = 'Probe_ID_left_from_first_annotating_stage'
opts_ann_2_n = 'Probe_ID_left_from_second_ncbi_annotation_stage'
opts_ann_2 = 'Probe_ID_left_from_second_annotating_stage'
opts_ann_3_n = 'Probe_ID_left_from_third_ncbi_annotation_stage'
opts_ann_3 = 'Probe_ID_left_from_third_annotating_stage'
opts_ann_4_n = 'Probe_ID_left_from_fourth_ncbi_annotation_stage'
opts_ann_4 = 'Probe_ID_left_from_fourth_annotating_stage'
opts_nb_of_analysis_stages = 4
for (current_stage in seq(opts_nb_of_analysis_stages)) {
if (current_stage == 1) {
load('finalized')
input_bad_gene_symbol <- dplyr::select(read.csv(file = 'checked_input_1_stage/input_bad_gene_symbol.tsv', sep = '\t'), Probe_ID, dplyr::everything())
} else if (current_stage != 1) {
load(paste0('finalized_', current_stage))
input_bad_gene_symbol <- read.csv(file = paste0('checked_input_', current_stage,'_stage/input_bad_gene_symbol.tsv'), sep = '\t')
}
if (current_stage == opts_nb_of_analysis_stages) {
load(paste0('leftovers_', opts_nb_of_analysis_stages)) # Load undone leftovers
}
finalized <- uniformize_finalized_colnames()
input_bad_gene_symbol <- uniformize_bad_gene_symbol_colnames()
leftovers <- uniformize_leftovers_colnames()
assign(x = paste0('finalized_', current_stage), value = finalized)
assign(x = paste0('bad_gene_symbol_', current_stage), value = input_bad_gene_symbol)
rm(finalized, input_bad_gene_symbol)
}
list_all_dataobjects_colnames <-
lapply(
ls(pattern = '^bad_gene_symbol.*|^finalized_*|^leftovers'),
FUN = function(x) {
colnames(eval(parse(text = x), parent.frame()))
}
)
if(are_vectors_the_same(chr_vec_list = list_all_dataobjects_colnames)){
bads <- rlist::list.rbind(lapply(
ls(pattern = '^bad_gene_symbol.*|^leftovers'),
FUN = function(x) {
eval(parse(text = x), parent.frame())
}
))
}
bads2 <- get_usable_bad_ids(bads_ = bads)
if(are_vectors_the_same(chr_vec_list = list_all_dataobjects_colnames)){
final_good_dataset <- rlist::list.rbind(lapply(
ls(pattern = '^bads2|^finalized.*'),
FUN = function(x) {
eval(parse(text = x), parent.frame())
}
))
final_good_dataset <- final_good_dataset[order(final_good_dataset$Paper),]
# Here we collapse multiple genenames to single gene name
final_good_dataset$final_gene_name <- as.character(lapply(
X = final_good_dataset$external_gene_name,
FUN = function(x) {
return(select_best_geneName_wrapper_for_single_string(x)) ### !!! Gm's cna have 5 digits... damn ### !!! Ive changed \\d{5} to \\d{5,}, because the latter one matches exactly 5 and not 5 and more!
}
))
# readr::write_tsv(x = final_merged_dataset, path = 'final_merged_dataset.tsv')
# Uniformarize gene names by making them all all-lower case
final_good_dataset$lower_final_gene_name <- tolower(final_good_dataset$final_gene_name)
# save(final_good_dataset, file = 'final_good_dataset')
# load('final_good_dataset')
# readr::write_tsv(x = final_good_dataset, path = 'final_good_dataset.tsv')
}
# Medianing values for the same gene within Experiment
medianed_final_good_dataset <- final_good_dataset %>%
dplyr::group_by(Experiment, lower_final_gene_name) %>%
dplyr::summarize(logFC_median = median(logFC))
save(medianed_final_good_dataset, file = 'medianed_final_good_dataset')
### GET FULL DATASET ###
# This is a very sparse matrix. Perhaps try working with it as such
spread_medianed_final_good_dataset <- tidyr::spread(data = medianed_final_good_dataset, key = Experiment, value = logFC_median)
# save(spread_medianed_final_good_dataset, file = 'spread_medianed_final_good_dataset')
# load('spread_medianed_final_good_dataset')
# readr::write_tsv(x = spread_medianed_final_good_dataset, path = 'spread_medianed_final_good_dataset.tsv')
bool_entries_with_3_or_more_values <- get_subset_vector_for_entries_with_3_or_more_values_per_paper(final_good_dataset_ = final_good_dataset)
at_least_in_3_papers_spread_med_fin_g_ds <- subset(x = spread_medianed_final_good_dataset, subset = bool_entries_with_3_or_more_values)
# save(at_least_in_3_papers_spread_med_fin_g_ds, file = 'at_least_in_3_papers_spread_med_fin_g_ds')
### GET FULL DATASET ###
####### PREPARING FINAL TABLE #######
####### PREPARING SUBSET TABLES FOR SUBSTRUCTURES #######
descriptions <- readr::read_tsv("descriptions.txt", col_types = "nccccccccccccccccccccccc")
search_for <- list()
search_for[[1]] = stringr::str_detect(string = tolower(descriptions$Brain_part), pattern = '.*hipp*')
search_for[[2]] = stringr::str_detect(string = tolower(descriptions$Brain_part), pattern = '.*cumbens*')
search_for[[3]] = stringr::str_detect(string = tolower(descriptions$Brain_part), pattern = '.*amyg*')
search_for[[4]] = stringr::str_detect(string = tolower(descriptions$Brain_part), pattern = '.*fro*')
names <- list()
names[[1]] <- 'hippocampus'
names[[2]] <- 'nucleus_accumbens'
names[[3]] <- 'amygdala'
names[[4]] <- 'prefrontal_cortex'
experiments_to_subset <- lapply(X = search_for, function(x){
descriptions %>%
dplyr::select(Group_ID, Brain_part) %>%
dplyr::filter(x)
})
experiments_to_include <-
purrr::map2(
.x = experiments_to_subset,
.y = names,
.f = function(x, y) {
incluse_these_exps <- x$Group_ID
temp_ <- create_subset_of_exps_with_at_least_3_papers_wrapper(experiments_to_include_ = incluse_these_exps, save_as_chr = y)
temp_[is.na(temp_)] <- 0
readr::write_tsv(x = temp_, path = , paste0('for_clustering_', y, '.tsv'))
assign(x = y, value = temp_, envir = globalenv())
})
####### PREPARING SUBSET TABLES FOR SUBSTRUCTURES #######
####### GET NON-ANNOTATED DATA IN FINAL TABLE #######
# input_bad_gene_symbol - manual
# logFCs for the same gene within single Experiment are avaraged using median. No other filtering is used
# It is critical to use this workflow only after producing actual, tested proper merged dataset - we need to add all the data and check if the lenght of this set is equal to lenght of input dataset.
# final_merged_dataset <- gather_all_datasets_into_single_df(regex_pattern_to_find_datasets_with = '^annotations.*')
#
# noNAs_final_merged_dataset <- final_merged_dataset %>%
# subset(subset = !(is.na(final_merged_dataset$external_gene_name) | final_merged_dataset$external_gene_name == ''))
#
# not_annotated_data_2 <- final_merged_dataset %>%
# subset(subset = (is.na(final_merged_dataset$external_gene_name) | final_merged_dataset$external_gene_name == ''))
#
# check_was_the_spliting_of_df_by_filtering_ok(str_what_was_splited = 'final_merged_dataset', df_original = final_merged_dataset, list_df_splited = list(noNAs_final_merged_dataset, not_annotated_data_2))
#
# not_annotated_data <- rbind(not_annotated_data_1, not_annotated_data_2)
####### GET NON-ANNOTATED DATA IN FINAL TABLE #######
####### ANALYZE NON-ANNOTATED DATA #######
# There are some entries that have p above 0.05
# not_annotated_data$composite_id <- paste(not_annotated_data$Experiment, not_annotated_data$adj_p, not_annotated_data$p, not_annotated_data$logFC, sep = "__")
#
# whole_dataset <- read_preformated_data(str_filename = 'data_whole.tsv', col_types_ = 'nccccccccccccc', int_numbers_are_from = 11, int_numbers_are_to = 13)
#
# whole_dataset$composite_id <- paste(whole_dataset$Experiment, whole_dataset$adj_p, whole_dataset$p, whole_dataset$logFC, sep = "__")
#
# whole_dataset$all_names <- as.character(apply(X = whole_dataset, MARGIN = 1, FUN = function(x) { paste(x, collapse = '__') } ))
#
# whole_and_not_annotated <- merge(x = not_annotated_data, y = whole_dataset, by = 'composite_id', all.x = T)
# test_short <- whole_and_not_annotated[1:1000,]
# is_the_ProbeID_from_notAnnotated_in_the_wholeDataset <-
# purrr::map2_lgl(
# .x = whole_and_not_annotated$Probe_ID.x,
# .y = whole_and_not_annotated$all_names,
# .f = function(x, y) {
#
# pattern_ <- paste0('(.*)\\Q', x, '\\E(.*)')
#
# stringr::str_detect(string = y,
# pattern = pattern_)
# }
# )
#
# proper_whole_and_not_annotated <- subset(x = whole_and_not_annotated, subset = is_the_ProbeID_from_notAnnotated_in_the_wholeDataset)
####### ANALYZE NON-ANNOTATED DATA #######
####### PREPARING FINAL TABLE #######
# Here we collapse multiple genenames to single gene name
# noNAs_final_merged_dataset$final_gene_name <- as.character(lapply(
# X = noNAs_final_merged_dataset$external_gene_name,
# FUN = function(x) {
# return(select_best_geneName_wrapper_for_single_string(x)) ### !!! Gm's cna have 5 digits... damn ### !!! Ive changed \\d{5} to \\d{5,}, because the latter one matches exactly 5 and not 5 and more!
# }
# ))
#
# readr::write_tsv(x = final_merged_dataset, path = 'final_merged_dataset.tsv')
#
# # Uniformarize gene names by making them all all-lower case
# noNAs_final_merged_dataset$lower_final_gene_name <- tolower(noNAs_final_merged_dataset$final_gene_name)
#
# # Medianing values for the same gene within Experiment
# medianed_noNAs_final_merged_dataset <- noNAs_final_merged_dataset %>%
# dplyr::group_by(Experiment, lower_final_gene_name) %>%
# dplyr::summarize(logFC_median = median(logFC))
#
# # This is a very sparse matrix. Perhaps try working with it as such
# spread_lowercase_final_merged_dataset <- tidyr::spread(data = medianed_final_merged_dataset, key = Experiment, value = logFC_median)
#
# readr::write_tsv(x = spread_lowercase_final_merged_dataset, path = 'final_merged_dataset_for_clustering.tsv')
####### PREPARING FINAL TABLE #######
#######################################################
####### PREPARING FINAL TABLE #######
####### CHECK IF INPUT DATA IS GOOD #######
####### PREPARING NEW, IMPROVED DATASET #######
###### POST-PREPARATION OF ANNOTATED PROBE_ID TABLES ######
|
9a3096c7eb79b0a55c923f4f029757dbb645af21
|
65c440894fe681e80b52c9c7fa988ea15c4cb733
|
/linear_regression.R
|
97f808002b4cfbb7ce8436c3f6c956c654d315ec
|
[] |
no_license
|
limxuanyu127/Risk-analysis-of-mutual-funds
|
c645c22b073d04026f11ae37a085d6fceb126dc4
|
f2b493aa2280e1ec8f39bfa5d87d4830748dc5be
|
refs/heads/main
| 2023-03-30T04:30:35.957685
| 2021-04-02T17:21:35
| 2021-04-02T17:21:35
| 353,627,447
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,947
|
r
|
linear_regression.R
|
library(caret)
library(mlbench)
# ----------------- SET UP------------------------------
setwd("C:/Users/limxu/Documents/NTU/Year4/RE6013 Bus Ana/project code")
full.df <- read.csv("EDA.csv")
set.seed(5)
predictors <- c("portfolio_cash", "portfolio_stocks", "portfolio_bonds", "portfolio_others", "portfolio_preferred", "portfolio_convertable" )
predictors.columns <- full.df[, predictors]
# calculate correlation matrix
correlationMatrix <- cor(predictors.columns)
# summarize the correlation matrix
print(correlationMatrix)
relevant.columns <- append(predictors,c("alpha_3y", "beta_3y"))
#funds.df <- funds.df[funds.df$investment==growth,]
funds.df <- full.df[, relevant.columns]
trainIndex <- createDataPartition(y = funds.df$alpha_3y, p = .7,
list = FALSE,
times = 1)
train.data = funds.df[trainIndex, ]
test.data <- funds.df[-trainIndex, ]
dim(train.data)
dim(test.data)
x_train <- train.data[, predictors]
y_train <- train.data[, c("alpha_3y")]
#---------- PHASE 1: RFE ----------------------
# Define the control using a random forest selection function
control <- rfeControl(functions = lmFuncs, # random forest
method = "repeatedcv", # repeated cv
repeats = 5, # number of repeats
number = 10) # number of folds
result_rfe1 <- rfe(x = x_train,
y = y_train,
sizes = c(1:6),
rfeControl = control)
# Print the selected features
new.predictors <- predictors(result_rfe1)
print(result_rfe1)
# Print the results visually
ggplot(data = result_rfe1, metric = "RMSE") + theme_bw()
#-----------------PHASE 2: EVALUATE ON TEST SET, FOR COMPARISON WITH OTHER MODELS---------------
#new_x_train <- train.data[, new.predictors]
# ---using RFE variables
lm.fit=lm(alpha_3y ~ portfolio_preferred + portfolio_bonds + portfolio_others + portfolio_cash + portfolio_convertable, data = train.data)
summary(lm.fit)
test.prediction <- predict(lm.fit, test.data)
error <- (test.prediction - test.data$alpha_3y)
RMSE<- sqrt(mean(error^2))
print(RMSE)
# --- using all variables
lm.fit =lm(alpha_3y ~ portfolio_preferred + portfolio_bonds + portfolio_others + portfolio_cash + portfolio_convertable + portfolio_stocks, data = train.data)
summary(lm.fit)
test.prediction <- predict(lm.fit, test.data)
error <- (test.prediction - test.data$alpha_3y)
RMSE<- sqrt(mean(error^2))
print(RMSE)
#-------------------- PHASE 3: RETRAIN MODEL WITH ALL DATA-----------------------------
lm.fit.full =lm(alpha_3y ~ portfolio_preferred + portfolio_bonds + portfolio_others + portfolio_cash + portfolio_convertable + portfolio_stocks, data = funds.df)
#lm.fit.full =lm(alpha_3y ~ portfolio_preferred + portfolio_bonds + portfolio_others + portfolio_cash + portfolio_convertable, data = funds.df)
summary(lm.fit.full)
par(mfrow = c(2,2))
plot(lm.fit.full)
|
0d97b34d06327cb886056de5b9e9bf33509dd15b
|
08a5f60f07a07b1be41d9656b58643204d37d221
|
/man/view_neighbor.Rd
|
7440d4eac644f4a5096d4e2a5307b13766a5f5ca
|
[
"MIT"
] |
permissive
|
denis-or/neighbor
|
6028cc117983f1aff9323c27696276dd0b5711f8
|
e973adb0c847249cba87d159adfa6c4449315c32
|
refs/heads/master
| 2023-08-09T12:24:57.525411
| 2021-09-12T21:35:30
| 2021-09-12T21:35:30
| 405,495,900
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 633
|
rd
|
view_neighbor.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/view_neighbor.R
\name{view_neighbor}
\alias{view_neighbor}
\title{View the neighborhood}
\usage{
view_neighbor(nbr)
}
\arguments{
\item{nbr}{A neighbor object create by \code{get_neighbor()} function.}
}
\value{
An object \code{ggplot}
}
\description{
\code{{view_neighbor()}} get neighbors from an neighbor object and returns a ggplot object.
}
\examples{
\dontrun{
# Import
nc <- sf::read_sf(system.file("gpkg/nc.gpkg", package = "sf"))
# Get the neighbor
l_nb <- get_neighbor(nc, "Queen")
# Visualize
view_connections(l_nb)
view_neighbor(l_nb)
}
}
|
df6166a642ca8fb87c348c3fc4f750c040a61ccf
|
8c95c48d48a5a6a351de57d90b56eb6e2642914c
|
/TGVsAll_new.R
|
c3befcf41a71faee07132b54ce29fb087034b3fb
|
[
"curl",
"Apache-2.0"
] |
permissive
|
janaobsteter/Genotype_CODES
|
e24dafcf00476a9e0cc989b3c822bd13a391f76f
|
8adf70660ebff4dd106c666db02cdba8b8ce4f97
|
refs/heads/master
| 2021-08-09T14:50:50.721598
| 2021-01-27T14:23:16
| 2021-01-27T14:23:16
| 63,061,123
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,226
|
r
|
TGVsAll_new.R
|
################
#Funkcija summarySE
#####################
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
######################################################################
args = commandArgs(trailingOnly=TRUE)
strategy = args[1]
date = args[2]
print(args)
print(strategy)
print(args)
TGVsAll <- data.frame()
for (scenario in c("LargePop", "SmallPop")) {
TGVs <- data.frame(Generation=0:60)
#ped <- read.table(paste0('~/PedOCS.txt'), header=TRUE)
ped <- read.table(paste0('~/Ped', scenario, ".txt"), header=TRUE)
#to standardise onto the generation 40 - which is the generation of comparison
ped <- ped[ped$Generation %in% 0:40,]
#obtain mean and sd of genetic values
TGV <- summarySE(ped, measurevar = "gvNormUnres1", groupvars = "Generation")[,c(1,3,4)]
#variance of genetic values
TGV$var <- (TGV$sd)^2
colnames(TGV)[1] <- c("Generation")
#standardise genetic standard devistion
TGV$SDSt <- TGV$sd / TGV$sd[1]
#standardise genetic values with genetic standard deviation
TGV$zMean <- (TGV$gvNormUnres1 - TGV$gvNormUnres1[1]) / TGV$sd[1]
TGVs <- merge(TGVs, TGV, by="Generation")
#read in genic variance
#Var <- read.table(paste0('~/VarOCS.txt'), header=T)
Var <- read.table(paste0('~/Var', scenario, '.txt'), header=T)
#Qtn model 1 is unrestricted
Var <- Var[Var$QtnModel==1,c(1,3)]
TGVs <- merge(TGVs, Var, by="Generation")
#obtain genic standard deviation
TGVs$SDGenic <- (sqrt(TGVs$AdditGenicVar1))
#standarise genic standard devistion
TGVs$SDGenicSt <- TGVs$SDGenic / TGVs$SDGenic[1]
#standardise genetic values with genic standard devistion
TGVs$zMeanGenic <- (TGVs$gvNormUnres1 - TGVs$gvNormUnres1[1]) / TGVs$SDGenic[1]
#reciprocated genic standard deviation
TGVs$SDGenicStNeg <- 1 - (TGVs$SDGenic / TGVs$SDGenic[1])
#genic variance standardised onto genetic variance
koef <- TGVs$var[1] / TGVs$AdditGenicVar1[1]
TGVs$Genic_Genetic_VAR <- TGVs$AdditGenicVar1 * koef
TGVs$Genic_Genetic_SD <- sqrt(TGVs$Genic_Genetic_VAR)
#standardise genic_genetic standard deviation
TGVs$Genic_Genetic_SDSt <- TGVs$Genic_Genetic_SD / TGVs$Genic_Genetic_SD[1]
#standarise genetic values with genic_genetic standard deviation
TGVs$zMeanGenic_Genetic <- (TGVs$gvNormUnres1 - TGVs$gvNormUnres1[1]) / TGVs$Genic_Genetic_SD[1]
#TGVsAll$zSdGenic <- (sqrt(TGVsAll$AdditGenicVar1) - sqrt(TGVsAll$))
TGVs$scenario <- scenario
TGVs$Rep <- 0
#TGVs$Strategy <- "SU55"
#colnames(TGVs) < c("Generation", paste0("TGV_mean", scenario), paste0("TGV_sd", scenario), paste0("zMean_", scenario), paste0("GenicVar_", scenario), paste0("zMeanGenic_", scenario))
TGVsAll <- rbind(TGVsAll, TGVs)
}
write.table(TGVsAll, paste0("TGVsAll_10KRef_", strategy, "_", date, ".csv"), quote=FALSE, row.names=FALSE)
library(ggplot2)
ggplot(data=TGVsAll, aes(x=Generation, y=zMean, colour=scenario)) + geom_line()
pedOCS <- read.table("~/PedOCS.txt", header=TRUE)
pedOCS40 <- ped[ped$Generation %in% 40:60,]
aggregate(pedOCS40$Father ~ pedOCS40$Generation, FUN = function (x) {length(unique(x))})
|
017922349b41a6ba70126920dff273acaf60ec60
|
e0a32598fc787df6bd105b7d2ac7c5476b0d274a
|
/R/New_Simu_Gen_1.R
|
c5a3362ac790f50a8d606cc238da6c78d61a873f
|
[] |
no_license
|
cran/DDPGPSurv
|
378369d773bc1eae4b715221b487558bf5716f97
|
789cd0c1322bd120dc5346e2d7f3f7eef37fbaa6
|
refs/heads/master
| 2020-03-21T10:58:40.885918
| 2018-06-24T11:50:51
| 2018-06-24T11:50:51
| 138,481,875
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,717
|
r
|
New_Simu_Gen_1.R
|
#Npat is the number of patient
#Ncov is the number of covariates here, which is fixed at 4 for now(including AUC^2)
#Comp_prob_0 and Comp_prob_1 are the probability for different CR
#CR is complete remission status
#mus and sds are the means and sds for AUC
#sigma is the sd for the survival time
#beta is the linear coefficients for lifetime mean
#censormean is the "mean" for censor time
#prob is the probability used when simulate life time
#We have four covaraites for generating the truth: Age, CR, AUC and AUC^2
#method=1: two mixture of lognormals
#method=2: two mixture of weibulls
#mixture equals to 1 means 2 component
#mixture equals to 0 means only one component, i.e. no mixture
#' Simulate data
#' @param Npat A positive integer indicating the number of patients to be simulated.
#'
#' @param method A binary value (either 0 or 1). If method is set to 0, the survival times are simulated from a Weibull distribution.
#' If method is set to 1, the survival times are simulated from a Lognormal distribution. Default value is 1.
#'
#' @param mixture A binary value (either 0 or 1). If method is set to 0, the survival times are simulated from a mixture of two
#' distributions. If method is set to 1, the survival times are simulated from single distribution. Default value is 1.
#'
#' @param censor A binary value (either 0 or 1). If censor is set to 1, survival times of patients will be censored (at roughly 25%).
#' If censor is set to 0, the survival times of patients will not be censored.
#' Default value is 0.
#'
#' @return Returns a list composed of the patients' generated attributes. The covariates are Age, AUC, and CR.
#'
#' @examples
#' #Simulate Data for 10 patients
#' Npat<- 10
#' data<-simulate_data(Npat)
#'
#' @export
simulate_data <- function(Npat, method=1, mixture=1, censor=0)
{
Comp_prob=c(0.55,0.35,0.1)
mus=c(4.5,5.5,7.25);sds=sqrt(c(1,1,1)); sigma = 0.4;
shape = 1.5; censcale = 650 #parameters used for weibull distribution
censormean = log(650)
if(mixture) prob = 0.4 else
prob = 0
Data <- NULL
Data$Npat <- Npat
#Simulate Age from discrete uniform
# Age <- Data$Age <- sample(c(16:65), Npat, replace=TRUE)
#Simulate CR
CR <- Data$CR <- rbinom(Npat,1,0.5)
#Simulate AUC
# AUC <- numeric(Npat)
# for (i in 1:Npat)
# {
# components <- sample(1:length(mus),prob=Comp_prob,size=Npat,replace=TRUE)
# AUC[i]=rnorm(1,mean=mus[components],sd=sds[components])
# }
# Data$AUC <- AUC
True_Age<-c(59, 46 ,52 ,56 ,37 ,54 ,53 ,27 ,39 ,60 ,49 ,58 ,57 ,35 ,48 ,46 ,50 ,36 ,49 ,36 ,65 ,16 ,53 ,50 ,27 ,55 ,37 ,55 ,13 ,57 ,51,
36 ,34 ,59 ,40 ,50 ,56 ,53 ,53 ,51 ,31 ,33 ,44 ,61 ,51 ,38 ,57 ,64 ,55 ,50 ,36 ,52 ,42 ,28 ,24 ,53 ,56 ,60 ,58 ,55 ,42 ,61,
43 ,54 ,36 ,46 ,61 ,52 ,23 ,55 ,53 ,55 ,61 ,24 ,59 ,62 ,42 ,52 ,32 ,62 ,37 ,58 ,22 ,44 ,55 ,63 ,56 ,55 ,50 ,48 ,32 ,52 ,65,
38 ,13 ,28 ,48 ,35 ,53 ,52 ,24 ,32 ,65 ,51 ,31 ,50 ,47 ,44 ,39 ,21 ,20 ,48 ,27 ,45 ,22 ,51 ,54 ,62 ,49 ,23 ,35 ,57 ,42 ,45,
54 ,44 ,58 ,58 ,44 ,53 ,38 ,43 ,61 ,35 ,45 ,43 ,26 ,44 ,32 ,59 ,60 ,41 ,56 ,57 ,36 ,50 ,53 ,31 ,27 ,27 ,29)
remove<-c(13,16)
True_Age<-True_Age[! True_Age %in% remove]
add<-seq(25,35)
True_Age<-c(True_Age,add,add)
True_AUC<-c(4.218909 , 2.621726 ,4.274692 ,4.600881 ,5.114714 ,5.506069 ,5.232825 ,5.309927 ,6.875603 ,5.417502
,4.669992 ,4.845335 ,5.816077 ,7.507137 ,6.371383 ,5.777296 ,4.390200 ,5.258493 ,7.324009 ,5.463360
,5.626651 ,4.588791 ,5.902899 ,5.548625 ,4.630019 ,5.959815 ,4.310302 ,5.466724 ,6.012101 ,5.240228
,5.327913 ,6.161089 ,5.204301 ,3.914655 ,4.885487 ,5.923654 ,5.158748 ,4.760329 ,4.412944 ,6.405455
,4.118528 ,5.240039 ,3.556275 ,5.763631 ,7.208160 ,4.344777 ,4.269055 ,4.392512 ,3.609402 ,7.228775
,4.449121 ,5.367043 ,5.869129 ,5.898398 ,4.231518 ,4.743104 ,5.169906 ,6.275440 ,7.359592 ,6.656494
,5.595894 ,5.889592 ,3.812498 ,4.158808 ,5.286502 ,4.547545 ,4.040851 ,6.152578 ,5.703770 ,4.567299
,5.949434 ,5.927006 ,6.182740 ,4.504007 ,3.541518 ,4.355357 ,5.444196 ,5.474398 ,5.804139 ,4.888809
,5.412979 ,5.409267 ,4.373623 ,4.640287 ,6.705499 ,5.671171 ,4.638472 ,4.277929 ,4.535522 ,4.714513
,5.485711 ,4.285346 ,6.070886 ,7.058000 ,6.770000 ,6.909000 ,5.369000 ,4.266000 ,4.995000 ,5.290000
,4.668000 ,3.449000 ,4.694000 ,3.976000 ,3.435000 ,3.535000 ,4.908000 ,3.936000 ,3.881000 ,3.995000
,4.122000 ,4.472000 ,5.830000 ,5.594000 ,4.956000 ,5.797000 ,4.151000 ,4.127000 ,4.013000 ,5.744000
,4.158000 ,5.615000 ,4.943000 ,5.092000 ,4.181000 ,4.613000 ,3.912000 ,5.077000 ,6.111000 ,5.733000
,3.598000 ,5.764000 ,4.906000 ,3.928000 ,6.550000 ,4.599000 ,5.043000 ,7.151000 ,3.385000 ,4.416000
,5.340000 ,3.621000 ,4.615000 ,5.255000 ,4.966000 ,5.219000 ,4.168000 ,4.045000 ,5.169000 ,8.259000
,2.931000)
Age <- Data$Age <- sample(True_Age,Npat,replace=TRUE)
AUC<- Data$AUC <-sample(True_AUC,Npat,replace=TRUE)
#Simulate
lifetimes<-numeric(Npat)
Ncov = 7
dpat <- matrix(0, Npat, Ncov)
dpat[,1]=scale(Age)
dpat[,2] = AUC
dpat[,3] = CR
dpat[,4] = AUC^2
dpat[,5] = AUC*scale(Age)
dpat[,6] = AUC*CR
# dpat[,7] = log(1+exp(50+(AUC*(scale(Age)-0.3)^3*2.1*abs((0.5-CR)))))
dpat[,7] = AUC*scale(Age)*abs(CR)
if(method ==1)
{
beta1<-numeric(Ncov+1)
#For log exp as 7th covariate
# beta2<-c(4.9,-0.10, 0.7, 0.1, -0.084,-0.07, 0.27,-0.05)
# beta2<-c(5.5,-0.1, 0.7, 0.1, -0.09,-0.07, 0.15,-0.05)
#Low impact 3 way inter
# beta2<-c(3.5,-0.10, 0.7, 0.1, -0.075,-0.07, 0.06,-0.1)
#3 way inter
# beta2<-c(5,-0.10, 0.7, 0.1, -0.075,-0.07, 0.06,-0.3)
#3 way inter a
# beta2<-c(3.5,-0.10, 0.7, 0.1, -0.075,-0.07, 0.5,-0.3)
beta2<-c(4,-0.10, 0.7, 0.3, -0.1,-0.068, 0.2,-0.18)
beta1<-beta2
} else
{
beta2<-c(5, -0.05, 0.05, 0.15, -0.15)
beta1<-c(3.1, -0.5,0.45,0.4, -0.2)
}
lifetimes <- rep(0, Npat)
if(method==1){
Data$truth='lognorm'
censtimes<-numeric(Npat)
for (i in 1:Npat){
pat_cov<- c(1,dpat[i,])
if (runif(1)<prob)
{
meanlog<- sum(beta1*pat_cov)
lifetimes[i]<-rlnorm(1,meanlog=meanlog,sdlog=sigma)
}
else{
meanlog<- sum(beta2*pat_cov)
lifetimes[i]<-rlnorm(1,meanlog=meanlog,sdlog=sigma)
}
censtimes[i]<-rlnorm(1,meanlog=meanlog+0.41,sdlog=sigma)
}
# censtimes <-rlnorm(Npat,meanlog=censormean,sdlog=sigma)
}else{#method==2
Data$truth='weib'
for (i in 1:Npat){
pat_cov<- c(1,dpat[i,])
u<-runif(1)
if (u<prob){
scale<- exp(sum(beta1*pat_cov))
lifetimes[i]<-rweibull(1,shape=shape,scale=scale)
}
else{scale<- exp(sum(beta2*pat_cov))
lifetimes[i]<-rweibull(1,shape=shape,scale=scale)
}
}
censtimes <-rweibull(Npat,shape=shape,scale=censcale)
}
if (censor==0)
{
life_cens = lifetimes
death = rep(1, Npat)
} else
{
life_cens<-pmin(lifetimes,censtimes)
death <- (lifetimes < censtimes) #death = 1 means observe death
}
Data$OS <- life_cens
Data$death <- death
Data$beta1<-beta1
Data$beta2<-beta2
Data$prob<-prob
Data$sigma<-sigma
Data$dpat<-dpat
# plot(density(log(Data$OS)))
# sum(Data$death)
return(Data)
}
# bad<-which(Data$OS==min(Data$OS))
# c(1,dpat[bad,])*beta2
|
bbc3db57707263f54ba20b10d80a44fd0cf38b35
|
780587ca52127daebdf7346458f45db74aac5825
|
/R Programming Week 4/rankall.R
|
31728b4ae4949aa493403add413af3293a22af7d
|
[] |
no_license
|
blazyy/assignment_submissions
|
9bd3c4ad36352996aedd431e2653f0bc74bfc28d
|
a32a971e7c3e3542280722fe6eb33a8052dc26e7
|
refs/heads/master
| 2021-01-26T12:19:30.257896
| 2020-03-24T17:28:59
| 2020-03-24T17:28:59
| 243,433,287
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,164
|
r
|
rankall.R
|
rankall <- function(outcome, num = "best") {
## Read outcome data
outcome_df <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
## Check that outcome is valid
if(!outcome %in% c("heart attack", "pneumonia", "heart failure"))
stop('invalid outcome')
# Assign appropriate column names deopending on specified outcome
if(outcome == "heart attack")
col_name = "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
else if(outcome == "heart failure")
col_name = "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
else
col_name = "Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
# Convert outcome column to numeric and remove rows with NAs in this column
outcome_df[, col_name] <- as.numeric(outcome_df[, col_name])
outcome_df <- outcome_df[!is.na(outcome_df[, col_name]), ]
# Creating empty dataframe which we will fill and return later
return_df <- data.frame('hospital' = character(0), 'state' = character(0), stringsAsFactors = FALSE)
# Looping through each state
for(name in names(table(outcome_df$State))){
# Getting rows pertaining to each state
all_hospitals_in_state <- outcome_df[outcome_df['State'] == name, ]
# Ordering rows by col_name first, then by alphabetical order
ordered_hospitals_in_state <- all_hospitals_in_state[order(all_hospitals_in_state[col_name], all_hospitals_in_state['Hospital.Name']), ]
# Depending on rank, index different parts of the dataframe
if(num == "worst")
hospital <- tail(ordered_hospitals_in_state[, 'Hospital.Name'], 1)
else if(num == "best")
hospital <- head(ordered_hospitals_in_state[, 'Hospital.Name'], 1)
else if(num > nrow(all_hospitals_in_state))
hospital <- NA
else
hospital <- ordered_hospitals_in_state[num, 'Hospital.Name']
# Return dataframe appended with states and their ranked hospitals
return_df[nrow(return_df) + 1, ] <- c(hospital, name)
}
return_df
}
|
f8965a68906ffd40bf4e941b925c18da84bb775c
|
d38ce2d2427bd2127e8286d6e0fd72c8dfe6f2ae
|
/pop_dens_plot.R
|
ac9fb42556d0a932380f299013d369bca089117c
|
[
"CC0-1.0"
] |
permissive
|
giacfalk/GFTS_african_cities
|
292b1391d75af42b19194e530212222bc1221e5d
|
1905acc6a329a37b28eab3e33cef59ff5ca0b17f
|
refs/heads/main
| 2023-03-03T09:41:35.259673
| 2021-02-18T15:23:14
| 2021-02-18T15:23:14
| 330,647,986
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,227
|
r
|
pop_dens_plot.R
|
# https://git.digitaltransport4africa.org/data/africa
# https://www.overleaf.com/project/5fb920c2bf03cb0c6e3f8cf3
library(sf)
library(raster)
library(tidyverse)
library(gdistance)
library(GADMTools)
library(ggsn)
# minutes per meter assuming walking speed of 5 km / h
minperm <- 0.012
setwd("D:/OneDrive - FONDAZIONE ENI ENRICO MATTEI/Current papers/Accessibility public transit")
city_boundaries <- read_sf("GHS_FUA_UCDB2015_GLOBE_R2019A_54009_1K_V1_0/GHS_FUA_UCDB2015_GLOBE_R2019A_54009_1K_V1_0.gpkg")
city_boundaries$geometry = city_boundaries$geom
city_boundaries$geom=NULL
city_boundaries <- st_as_sf(city_boundaries)
city_boundaries <- st_transform(city_boundaries, 4326)
#
wgs84.tif.list <- lapply(list.files(pattern = "population_AF"), raster)
nairobi <- filter(city_boundaries, eFUA_name=="Nairobi")
a <- list()
for (i in 1:28){
a[i] <- exactextractr::exact_extract(wgs84.tif.list[[i]], nairobi, fun="sum")
}
# load gridded population of kenya
pop_kenya <- wgs84.tif.list[[which.max(do.call(rbind, a)>0)]]
pop_kenya <- crop(pop_kenya, extent(nairobi))
pop_kenya <- rgis::fast_mask(pop_kenya, nairobi)
pop_kenya <- aggregate(pop_kenya, fact=30, fun="sum")
pop_kenya <- st_as_sf(rasterToPolygons(pop_kenya))
pop_kenya$area <- st_transform(pop_kenya, 3395) %>% st_area() / 1e6
pop_kenya$area <- as.numeric(pop_kenya$area)
pop_kenya$layer <- pop_kenya$layer / pop_kenya$area
nairobi_plot <- ggplot(pop_kenya)+
geom_sf(aes(fill=layer), colour = NA)+
ggtitle("")+
scale_fill_binned(trans="log", breaks=c(500, 1000, 2500, 5000, 10000, 25000, 50000), type = "viridis", name="")+
theme(legend.direction = "horizontal", legend.position = "bottom", legend.key.width = unit(1.5, "cm"), legend.key.height = unit(0.1, "cm"))+
ggtitle("Nairobi")+
theme(axis.text.x = element_text(angle = 90))
#
Freetown <- filter(city_boundaries, eFUA_name=="Freetown")
a <- list()
for (i in 1:28){
a[i] <- exactextractr::exact_extract(wgs84.tif.list[[i]], Freetown, fun="sum")
}
# load gridded population of kenya
pop_kenya <- wgs84.tif.list[[which.max(do.call(rbind, a)>0)]]
pop_kenya <- crop(pop_kenya, extent(Freetown))
pop_kenya <- rgis::fast_mask(pop_kenya, Freetown)
pop_kenya <- aggregate(pop_kenya, fact=30, fun="sum")
pop_kenya <- st_as_sf(rasterToPolygons(pop_kenya))
pop_kenya$area <- st_transform(pop_kenya, 3395) %>% st_area() / 1e6
pop_kenya$area <- as.numeric(pop_kenya$area)
pop_kenya$layer <- pop_kenya$layer / pop_kenya$area
freetown_plot <- ggplot(pop_kenya)+
geom_sf(aes(fill=layer), colour = NA)+
ggtitle("")+
scale_fill_binned(trans="log", breaks=c(500, 1000, 2500, 5000, 10000, 25000, 50000), type = "viridis", name="")+
theme(legend.direction = "horizontal", legend.position = "bottom", legend.key.width = unit(1.5, "cm"), legend.key.height = unit(0.1, "cm"))+
ggtitle("Freetown")+
theme(axis.text.x = element_text(angle = 90))
#
Addis <- filter(city_boundaries, eFUA_name=="Addis Ababa")
# load gridded population of kenya
pop_kenya <- raster("population_eth_2018-10-01.tif")
pop_kenya <- crop(pop_kenya, extent(Addis))
pop_kenya <- rgis::fast_mask(pop_kenya, Addis)
pop_kenya <- aggregate(pop_kenya, fact=30, fun="sum")
pop_kenya <- st_as_sf(rasterToPolygons(pop_kenya))
pop_kenya$area <- st_transform(pop_kenya, 3395) %>% st_area() / 1e6
pop_kenya$area <- as.numeric(pop_kenya$area)
pop_kenya$layer <- pop_kenya$layer / pop_kenya$area
addis_plot <- ggplot(pop_kenya)+
geom_sf(aes(fill=layer), colour = NA)+
ggtitle("")+
scale_fill_binned(trans="log", breaks=c(500, 1000, 2500, 5000, 10000, 25000, 50000), type = "viridis", name="")+
theme(legend.direction = "horizontal", legend.position = "bottom", legend.key.width = unit(1.5, "cm"), legend.key.height = unit(0.1, "cm"))+
ggtitle("Addis Ababa")+
theme(axis.text.x = element_text(angle = 90))
#
Kampala <- filter(city_boundaries, eFUA_name=="Kampala")
a <- list()
for (i in 1:28){
a[i] <- exactextractr::exact_extract(wgs84.tif.list[[i]], Kampala, fun="sum")
}
# load gridded population of kenya
pop_kenya <- wgs84.tif.list[[which.max(do.call(rbind, a)>0)]]
pop_kenya <- crop(pop_kenya, extent(Kampala))
pop_kenya <- rgis::fast_mask(pop_kenya, Kampala)
pop_kenya <- aggregate(pop_kenya, fact=30, fun="sum")
pop_kenya <- st_as_sf(rasterToPolygons(pop_kenya))
pop_kenya$area <- st_transform(pop_kenya, 3395) %>% st_area() / 1e6
pop_kenya$area <- as.numeric(pop_kenya$area)
pop_kenya$layer <- pop_kenya$layer / pop_kenya$area
kampala_plot <- ggplot(pop_kenya)+
geom_sf(aes(fill=layer), colour = NA)+
ggtitle("")+
scale_fill_binned(trans="log", breaks=c(500, 1000, 2500, 5000, 10000, 25000, 50000), type = "viridis", name="")+
theme(legend.direction = "horizontal", legend.position = "bottom", legend.key.width = unit(1.5, "cm"), legend.key.height = unit(0.1, "cm"))+
ggtitle("Kampala")+
theme(axis.text.x = element_text(angle = 90))
#
Harare <- filter(city_boundaries, eFUA_name=="Harare")
a <- list()
for (i in 1:28){
a[i] <- exactextractr::exact_extract(wgs84.tif.list[[i]], Harare, fun="sum")
}
# load gridded population of kenya
pop_kenya <- wgs84.tif.list[[which.max(do.call(rbind, a)>0)]]
pop_kenya <- crop(pop_kenya, extent(Harare))
pop_kenya <- rgis::fast_mask(pop_kenya, Harare)
pop_kenya <- aggregate(pop_kenya, fact=30, fun="sum")
pop_kenya <- st_as_sf(rasterToPolygons(pop_kenya))
pop_kenya$area <- st_transform(pop_kenya, 3395) %>% st_area() / 1e6
pop_kenya$area <- as.numeric(pop_kenya$area)
pop_kenya$layer <- pop_kenya$layer / pop_kenya$area
harare_plot <- ggplot(pop_kenya)+
geom_sf(aes(fill=layer), colour = NA)+
ggtitle("")+
scale_fill_binned(trans="log", breaks=c(500, 1000, 2500, 5000, 10000, 25000, 50000), type = "viridis", name="")+
theme(legend.direction = "horizontal", legend.position = "bottom", legend.key.width = unit(1.5, "cm"), legend.key.height = unit(0.1, "cm"))+
ggtitle("Harare")+
theme(axis.text.x = element_text(angle = 90))
#
Abidjan <- filter(city_boundaries, eFUA_name=="Abidjan")
a <- list()
for (i in 1:28){
a[i] <- exactextractr::exact_extract(wgs84.tif.list[[i]], Abidjan, fun="sum")
}
# load gridded population of kenya
pop_kenya <- wgs84.tif.list[[which.max(do.call(rbind, a)>0)]]
pop_kenya <- crop(pop_kenya, extent(Abidjan))
pop_kenya <- rgis::fast_mask(pop_kenya, Abidjan)
pop_kenya <- aggregate(pop_kenya, fact=30, fun="sum")
pop_kenya <- st_as_sf(rasterToPolygons(pop_kenya))
pop_kenya$area <- st_transform(pop_kenya, 3395) %>% st_area() / 1e6
pop_kenya$area <- as.numeric(pop_kenya$area)
pop_kenya$layer <- pop_kenya$layer / pop_kenya$area
abidjan_plot <- ggplot(pop_kenya)+
geom_sf(aes(fill=layer), colour = NA)+
ggtitle("")+
scale_fill_binned(trans="log", breaks=c(500, 1000, 2500, 5000, 10000, 25000, 50000), type = "viridis", name="")+
theme(legend.direction = "horizontal", legend.position = "bottom", legend.key.width = unit(1.5, "cm"), legend.key.height = unit(0.1, "cm"))+
ggtitle("Abidjan")+
theme(axis.text.x = element_text(angle = 90))
#
Accra <- filter(city_boundaries, eFUA_name=="Accra")
a <- list()
for (i in 1:28){
a[i] <- exactextractr::exact_extract(wgs84.tif.list[[i]], Accra, fun="sum")
}
# load gridded population of kenya
pop_kenya <- wgs84.tif.list[[which.max(do.call(rbind, a)>0)]]
pop_kenya <- crop(pop_kenya, extent(Accra))
pop_kenya <- rgis::fast_mask(pop_kenya, Accra)
pop_kenya <- aggregate(pop_kenya, fact=30, fun="sum")
pop_kenya <- st_as_sf(rasterToPolygons(pop_kenya))
pop_kenya$area <- st_transform(pop_kenya, 3395) %>% st_area() / 1e6
pop_kenya$area <- as.numeric(pop_kenya$area)
pop_kenya$layer <- pop_kenya$layer / pop_kenya$area
accra_plot <- ggplot(pop_kenya)+
geom_sf(aes(fill=layer), colour = NA)+
ggtitle("")+
scale_fill_binned(trans="log", breaks=c(500, 1000, 2500, 5000, 10000, 25000, 50000), type = "viridis", name="")+
theme(legend.direction = "horizontal", legend.position = "bottom", legend.key.width = unit(1.5, "cm"), legend.key.height = unit(0.1, "cm"))+
ggtitle("Accra")+
theme(axis.text.x = element_text(angle = 90))
maps <- cowplot::plot_grid(abidjan_plot, accra_plot, addis_plot, freetown_plot, harare_plot, kampala_plot, nairobi_plot, ncol = 2)
ggsave("maps_pop.png", maps, scale = 2, height = 5)
|
bc77aa245ec6579ff971e23422ef80ed384f7e3d
|
401cee8191e50cfb14faa52cb35ccc0a439a238d
|
/data_science_image_clustering/code.R
|
14891722ecd01e58f1eaa4dec94d897f0283c0a3
|
[] |
no_license
|
ppojun/data_science_image_clustering
|
2a61be287201d3f2fa0a9e4e06145875f2b03883
|
85052307c23393a59bacd99d898ce77f2a8531d7
|
refs/heads/master
| 2022-01-23T03:04:43.309387
| 2019-07-23T08:24:35
| 2019-07-23T08:24:35
| 198,378,845
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,841
|
r
|
code.R
|
install.packages(\"jpeg\")
#(Installing a library for loading images)
library(jpeg)
#(load library)
image = readJPEG(\'c:/house.jpeg\')
#(Bring in an image and put it in a variable.)
dim(image)
#(You can see that Dimension is X by X 3 (rgb).)
plot(1:2, type=\"n\", xlab=\'\', ylab=\'\')
#(I made an empty plot.)
rasterImage(image, 1, 1, 2, 2)
#(Load the image to the desired size.)
image\[1,1,\]
#(Check the rgb value of the pixel(1,1).)
image.dataframe = data.frame(r=as.vector(image\[,,1\]),
g=as.vector(image\[,,2\]), b=as.vector(image\[,,3\]))
# (Now the data has been transformed to apply K-means clustering.
#
# Since the concept of horizontal or vertical in segmentation was
# meaningless anyway, it was transformed into a one-dimensional vector
# and transformed into a three-dimensional data type of (r, g, b).)
dim(image.dataframe)
# (Verifies that the exact number of data samples is equal to the number
# of pixels)
head(image.dataframe)
# (Check only the first part using the head function)
kmeans.fit = kmeans(image.dataframe, centers=6, nstart=5)
# (These pixels were separated by a cluster of random numbers (K=6).)
kmeans.fit\$centers
#(Then, check what these segmented pixels look like.
# To do so, the data is restored to the original array format.)
kmeansCompressed.dataframe =
kmeans.fit\$centers\[kmeans.fit\$cluster,\]
# (each pixel\'s data is filled with the data from the pentroid.)
dim(kmeansCompressed.dataframe)
kmeansCompressed = array(kmeansCompressed.dataframe, dim(image))
# (Move the dataframe type back to the array format.)
dim(kmeansCompressed)
# (Confirmation that it is again in the form of (street, vertical, 3))
plot(1:2, type=\"n\", xlab=\'\', ylab=\'\')
rasterImage(kmeansCompressed, 1, 1, 2, 2)
# (get the result picture)
#
|
f75c817f48554a11a8ebd3f378c0fddf780fc031
|
9528c7e85fe5f70193100f2e00c6616e07d270a4
|
/man/ppdb_query.Rd
|
102b8d349ea47beb3312aa04ba7b2ff7ef1e2084
|
[
"MIT"
] |
permissive
|
cdr6934/webchem
|
2630c8ff8d180163d094134d2ebb55f3067ed8d8
|
585fa5d5a91122837fbb924289a3ff77a90861a1
|
refs/heads/master
| 2020-12-03T05:33:20.504210
| 2015-09-26T12:02:29
| 2015-09-26T12:02:29
| 43,830,232
| 0
| 0
| null | 2015-10-07T16:45:35
| 2015-10-07T16:45:35
| null |
UTF-8
|
R
| false
| false
| 926
|
rd
|
ppdb_query.Rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/ppdb.R
\name{ppdb_query}
\alias{ppdb_query}
\title{Query the ppdb for information}
\usage{
ppdb_query(cas, verbose = TRUE)
}
\arguments{
\item{cas}{character; CAS number to query.}
\item{verbose}{logical; print message during processing to console?}
}
\value{
A list of 10 data.frames : ec_regulation, approved_in, general, parents, fate,
deg, soil, metab, etox and names.
See also \url{http://sitem.herts.ac.uk/aeru/iupac/docs/Background_and_Support.pdf} for more information on the data
}
\description{
This function queries the PPDB \url{http://sitem.herts.ac.uk/aeru/iupac/search.htm} for information.
}
\examples{
\dontrun{
# might fail if Server is not available
gly <- ppdb_query('1071-83-6')
gly$approved_in
# handle multiple CAS
cas <- c('1071-83-6', '50-00-0')
}
}
\author{
Eduard Szoecs, \email{eduardszoecs@gmail.com}
}
|
7fb520505ca761de28a0cdec81c1f15a7f9477d8
|
a6f4c8c91414d62fad5f8f7f53b1dee9c9d099ee
|
/R-Portable-Mac/library/iterators/unitTests/basicTest.R
|
eb166af4db006ac02a9716d303a8504888d2ee0f
|
[
"GPL-2.0-only",
"LicenseRef-scancode-unknown-license-reference",
"CC0-1.0"
] |
permissive
|
sdownin/sequencer
|
6a2d70777fbd8109e26f126229b5ee10348cf4e7
|
045d0580e673cba6a3bd8ed1a12ff19494bf36fa
|
refs/heads/master
| 2023-08-04T08:06:02.891739
| 2023-08-03T04:07:36
| 2023-08-03T04:07:36
| 221,256,941
| 2
| 1
|
CC0-1.0
| 2023-02-04T15:06:14
| 2019-11-12T16:00:50
|
C++
|
UTF-8
|
R
| false
| false
| 3,541
|
r
|
basicTest.R
|
library(iterators)
test00 <- function() {}
# test vector iterator creation
test01 <- function() {
x <- iter(1:10)
}
# test hasNext, nextElem
test02 <- function() {
x <- iter(1:10)
checkEquals(nextElem(x), 1)
for(i in 1:9) nextElem(x)
checkException(nextElem(x))
}
# check checkFunc
test03 <- function() {
x <- iter(1:100, checkFunc=function(i) i%%10==0)
checkEquals(nextElem(x), 10)
for(i in 1:9) nextElem(x)
checkException(nextElem(x))
}
# test matrix iterator creation
test04 <- function() {
x <- matrix(1:10,ncol=2)
}
# test hasNext, nextElem
test05 <- function() {
x <- matrix(1:10,ncol=2)
# by cell
y <- iter(x,by='cell')
checkEquals(nextElem(y), 1)
for(i in 1:9) nextElem(y)
checkException(nextElem(y))
# by col
y <- iter(x,by='column')
checkEquals(nextElem(y), matrix(1:5, ncol=1))
nextElem(y)
checkException(nextElem(y))
# by row
y <- iter(x,by='row')
checkEquals(nextElem(y), matrix(c(1,6),nrow=1))
for(i in 1:4) nextElem(y)
checkException(nextElem(y))
}
# test checkFunc
test06 <- function() {
# create a larger matrix
x <- matrix(1:100, ncol=20)
# by cell
y <- iter(x, by='cell', checkFunc=function(i) i%%10==0)
checkEquals(nextElem(y), 10)
for(i in 1:9) nextElem(y)
checkException(nextElem(y))
# by col
y <- iter(x, by='column', checkFunc=function(i) i[5]%%10==0)
checkEquals(nextElem(y), as.matrix(x[,2]))
for(i in 1:9) nextElem(y)
checkException(nextElem(y))
# by row
# create an easier matrix to deal with
x <- matrix(1:100, nrow=20, byrow=TRUE)
y <- iter(x, by='row', checkFunc=function(i) i[5]%%10==0)
checkEquals(as.vector(nextElem(y)), x[2,])
for(i in 1:9) nextElem(y)
checkException(nextElem(y))
}
# test data frame iterator creation
test07 <- function() {
x <- data.frame(1:10, 11:20)
y <- iter(x)
}
# test hasNext, nextElem
test08 <- function() {
x <- data.frame(1:10, 11:20)
# by row
y <- iter(x, by='row')
checkEquals(nextElem(y), x[1,])
for(i in 1:9) nextElem(y)
checkException(nextElem(y))
# by col
y <- iter(x, by='column')
checkEquals(nextElem(y), x[,1])
nextElem(y)
checkException(nextElem(y))
}
# test checkFunc
test09 <- function() {
x <- data.frame(1:10, 11:20)
# by row
y <- iter(x, by='row', checkFunc=function(i) i[[1]][1]%%2==0)
checkEquals(nextElem(y),x[2,])
for(i in 1:4) nextElem(y)
checkException(nextElem(y))
# by col
y <- iter(x, by='column', checkFunc=function(i) i[[1]][1]%%11==0)
checkEquals(nextElem(y), x[,2])
checkException(nextElem(y))
}
# test function iterator creation
# we need to test a function that takes no arguement as
# well as one that takes the index
test10 <- function() {
noArgFunc <- function() 1
needArgFunc <- function(i)
if(i>100)
stop('too high')
else
i
}
# test hasNext, nextElem
test11 <- function() {
noArgFunc <- function() 1
needArgFunc <- function(i) if(i>100) stop('too high') else i
y <- iter(noArgFunc)
checkEquals(nextElem(y), 1)
nextElem(y)
y <- iter(needArgFunc)
checkEquals(nextElem(y), 1)
for (i in 1:99) nextElem(y)
checkException(nextElem(y))
}
# test checkFunc
test12 <- function() {
noArgFunc <- function() 1
needArgFunc <- function(i)
if(i>100)
stop('too high')
else
i
y <- iter(noArgFunc, checkFunc=function(i) i==1)
checkEquals(nextElem(y), 1)
nextElem(y)
y <- iter(needArgFunc, checkFunc=function(i) i%%10==0)
checkEquals(nextElem(y), 10)
for(i in 1:9) nextElem(y)
checkException(nextElem(y))
}
|
6c53ce0d5f28245e2ccbb361f874f0ca1e29db32
|
fc18ec80388622ade08d1ed6af6b64cd2b01b687
|
/R/do.hex.R
|
d762ab01776b6603851d9a664af274b6cca3ab2c
|
[] |
no_license
|
yannabraham/Radviz
|
2942d96e8a2fbe337250c10a592aa17b0e1fe514
|
c9c0d92c6e0acb7b6df6d913f3835156e0acbe68
|
refs/heads/master
| 2022-05-15T06:33:44.126045
| 2022-03-25T09:00:31
| 2022-03-25T09:00:31
| 46,946,711
| 9
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 190
|
r
|
do.hex.R
|
#' @rdname Radviz-deprecated
#' @export
do.hex <- function(x,...) {
.Defunct(new='hexplot',
msg='the do.hex function is not required anymore, use hexplot directly')
return(x)
}
|
a4430294921af44b04943ace77f3c4c1f188e47f
|
fd7a2effcd87a6c20f020d5ede4b9aed62659cef
|
/man/species_int_mat.Rd
|
4995307c0147bfb60ab07e20b28b702747f70d16
|
[] |
no_license
|
plthompson/mcomsimr
|
a563c22bb1ab1f2a6ca791c49bb8d17bb71cb496
|
50f206140d86a0a59f9cd82603bf22ce2e801798
|
refs/heads/master
| 2023-02-06T11:43:12.547929
| 2023-02-03T01:03:36
| 2023-02-03T01:03:36
| 244,210,441
| 15
| 2
| null | 2022-01-19T05:14:40
| 2020-03-01T19:28:45
|
R
|
UTF-8
|
R
| false
| true
| 1,014
|
rd
|
species_int_mat.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sim_setup.R
\name{species_int_mat}
\alias{species_int_mat}
\title{Generate Species Interaction Matrix}
\usage{
species_int_mat(
species,
intra = 1,
min_inter = 0,
max_inter = 1.5,
int_mat,
comp_scaler = 0.05,
plot = TRUE
)
}
\arguments{
\item{species}{number of species to simulate}
\item{intra}{intraspecific competition coefficient, single value or vector of length species}
\item{min_inter}{min interspecific comp. coefficient}
\item{max_inter}{max interspecific comp. coefficient}
\item{int_mat}{option to supply externally generated competition matrix}
\item{comp_scaler}{value to multiply all competition coefficients by}
\item{plot}{option to show plot of competition coefficients}
}
\value{
species interaction matrix
}
\description{
Generates density dependent matrix of per capita competition
}
\examples{
env_traits(species = 10)
}
\author{
Patrick L. Thompson, \email{patrick.thompson@zoology.ubc.ca}
}
|
6fa74a9c9dcb47039d7c8df3ed9360093bd16a20
|
fa9b6c058462ed0ab36a8776326ad549c5c411bb
|
/man/CVEC.Rd
|
b0597b989e3584e6a1d3acfb3743dea3a653e3c7
|
[] |
no_license
|
cran/EleChemr
|
5a93eea2abe305e024a623c587b87af20dd1dcda
|
aa0935ebef6e9338693345b24c97d52680897e19
|
refs/heads/master
| 2021-06-19T09:00:44.990030
| 2021-02-09T13:20:03
| 2021-02-09T13:20:03
| 176,323,063
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,739
|
rd
|
CVEC.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EleChemr.R
\name{CVEC}
\alias{CVEC}
\title{EC behaviour cyclic voltammetry simulator}
\usage{
CVEC(
Co = 0.001,
Dx = 1e-05,
Eo = 0,
Dm = 0.45,
Vi = 0.3,
Vf = -0.3,
Vs = 0.001,
ko = 0.01,
kc = 0.001,
l = 100,
alpha = 0.5,
Temp = 298.15,
n = 1,
Area = 1,
DerApprox = 2,
errCheck = FALSE,
Method = "Euler"
)
}
\arguments{
\item{Co}{bulk concentration expressed in Molar}
\item{Dx}{diffusion coefficient expressed in cm^2/s}
\item{Eo}{reduction potential of the species expressed in Volt}
\item{Dm}{simulation parameter, maximum 0.5 for explicit methods}
\item{Vi}{initial potential of the sweep expressed in Volt}
\item{Vf}{final potential of the sweep expressed in Volt}
\item{Vs}{potential scan rate of the simulation expressed in V/s}
\item{ko}{heterogeneous electron transfer rate constant expressed in m/s}
\item{kc}{rate constant of the reaction Red -> C expressed in s^-1}
\item{l}{number of time steps of the simulation}
\item{alpha}{charge transfer coefficient}
\item{Temp}{temperature in kelvin}
\item{n}{number of electrons involved in the process}
\item{Area}{area of the electrode expressed in cm^2}
\item{DerApprox}{number of point for the approximation of the first derivative}
\item{errCheck}{if true the function returns a list with parameters for CottrCheck function}
\item{Method}{method to be used for the simulation = "Euler" "BI" "RK4" "CN "BDF"}
}
\value{
if errCheck == F a graph I vs E, if errCheck == T a list
}
\description{
Return a graph I vs E of the electrochemical process
}
\examples{
CVEC(Co = 0.001, DerApprox = 2, Dm = 0.45, kc = 0.00001, errCheck = FALSE, Method = "Euler")
}
|
fc2c7290d5658952991f8ca2b1cb6ab9fe8e9127
|
32fe2ee054042792dae03c82a1dd6f172ea3617f
|
/Figure2A_PromoterMeth.r
|
2bd38e1cc2809f302f3c06d2b3208b0533d175b6
|
[] |
no_license
|
emmabell42/Bell-et-al-2018
|
0bc03e03b7f1988cccae81be0f6c9333bb9a7900
|
5a73730953ff3b5bb4f2e82c403eec6df9dd52df
|
refs/heads/master
| 2020-03-11T15:42:54.710151
| 2018-05-03T13:15:31
| 2018-05-03T13:15:31
| 130,093,692
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 851
|
r
|
Figure2A_PromoterMeth.r
|
prom <- read.table("session_20180305 - interacting_genes - promoter info.txt",head=T,sep="\t",stringsAsFactors=F,comment.char="",quote="")
se <- read.table("20161115_corrected_ES_super_enhancer_with_methylation.txt",head=T,sep="\t",stringsAsFactors=F,comment.char="",quote="")
library(vioplot)
names <- c("2i","Serum","EpiSC")
png("Figure2_promoterMethylation.png",h=6,w=6,res=300,unit="in")
vioplot(na.omit(prom[,6]),na.omit(prom[,8]),na.omit(prom[,10]),col="lightgrey",names=names)
title(main="Promoters",ylab="mCpG/CpG")
axis(1, at=c(1.5),labels=c("ESC"), padj=1.5,tick=F)
dev.off()
png("Figure2_enhancerMethylation.png",h=6,w=6,res=300,unit="in")
vioplot(na.omit(se[,9]),na.omit(se[,15]),na.omit(se[,27]),col="lightgrey",names=names)
title(main="Super Enhancers",ylab="mCpG/CpG")
axis(1, at=c(1.5),labels=c("ESC"), padj=1.5,tick=F)
dev.off()
|
7f709600091013446112a27c0b80b44cc879fa66
|
bb5da0afd1e157afb3374c078dbf021e0152ea1b
|
/man/toc.Rd
|
19daab54c42bea3c065cea682d054cf16bb3e88c
|
[
"Apache-2.0"
] |
permissive
|
anu-bioinfo/tumorcomparer
|
0edaa7ad85fe85961d6919dee382fcb303c68147
|
8027da9bfca5323c21107c63974cbee3d916bae1
|
refs/heads/master
| 2023-06-04T07:19:23.390093
| 2021-06-25T13:45:44
| 2021-06-25T13:45:44
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 269
|
rd
|
toc.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tic_toc.R
\name{toc}
\alias{toc}
\title{Stops a stopwatch timer to measure performance}
\usage{
toc()
}
\description{
Stops a stopwatch timer to measure performance
}
\concept{tumorcomparer}
|
33c10338ea4906dfa86c53a0798b0db320921fa3
|
75a6df07e2a2f6c32e021320622287850d6a3329
|
/eda/map.R
|
f007ec0544db9b5ba6a06a1078a3a7df40ffbf40
|
[
"MIT"
] |
permissive
|
ss251/info478-project
|
42b0e96ff8d036350e24aa40d0225352ffe7a808
|
3ba1f697ee2e82a5eb7a360561a1583c85134d85
|
refs/heads/master
| 2022-12-30T05:56:08.098524
| 2020-10-12T19:21:12
| 2020-10-12T19:21:12
| 259,414,110
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,740
|
r
|
map.R
|
library("gdata")
library(tidyverse)
library(maps)
library("rworldmap")
library("classInt")
water_data <- read.csv("water2.csv", stringsAsFactors = FALSE)
water_data_map <- data.frame(water_data$Geographic.Area, water_data$X2017_Proportion.of.population.using.unimproved.drinking.water.sources)
water_data_map <-water_data_map %>%
rename(
Country = water_data.Geographic.Area,
`Proportion Using Unimproved Sources of Drinking Water` = water_data.X2017_Proportion.of.population.using.unimproved.drinking.water.sources
)
water_data_map <- remove_missing(water_data_map)
water_data_map_joined <- joinCountryData2Map(water_data_map
, joinCode = "NAME"
, nameJoinColumn = "Country")
#getting class intervals using a ✬jenks✬ classification in classInt package
classInt <- classInt::classIntervals( water_data_map_joined[["Proportion Using Unimproved Sources of Drinking Water"]], n=5, style="jenks")
catMethod = classInt[["brks"]]
#getting a colour scheme from the RColorBrewer package
colourPalette <- RColorBrewer::brewer.pal(5,'BuGn')
#calling mapCountryData with the parameters from classInt and RColorBrewer
mapParams <- mapCountryData( water_data_map_joined
, nameColumnToPlot="Proportion Using Unimproved Sources of Drinking Water"
, addLegend=FALSE
, catMethod = catMethod
, colourPalette = colourPalette )
do.call( addMapLegend , c( mapParams
, legendLabels="all"
, legendWidth=0.5
, legendIntervals="data" , legendMar = 2 ) )
|
6aefff6efd07844ecd3848486ceb143fbe681760
|
3abacd5c249dccb2979277a26be0168597ab1e1b
|
/man/protectLinkedTables.Rd
|
2e78ed1122817f2ee68b0c06654cc05d53e55c09
|
[] |
no_license
|
mattdowle/sdcTable
|
0c7e367f4c2dac9c5ef425c6f49ba3cf040f29e4
|
68978ca587af61b2018f8f5cd6efafbf776cfaad
|
refs/heads/master
| 2020-04-22T14:17:26.036357
| 2019-02-08T13:34:58
| 2019-02-08T13:34:58
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 5,616
|
rd
|
protectLinkedTables.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/export_functions.r
\name{protectLinkedTables}
\alias{protectLinkedTables}
\title{protect two \code{\link{sdcProblem-class}} objects that have common cells}
\usage{
protectLinkedTables(objectA, objectB, commonCells, method, ...)
}
\arguments{
\item{objectA}{a \code{\link{sdcProblem-class}} object}
\item{objectB}{a \code{\link{sdcProblem-class}} object}
\item{commonCells}{a list object defining common cells in \code{objectA} and \code{objectB}. For each variable that has one or more common codes in both tables, a list element needs to be specified.
\itemize{
\item List-elements of length 3: Variable has exact same levels and structure in both tables
\itemize{
\item \code{first element}: character vector of length 1 specifying the variable name in argument \code{objectA}
\item \code{second element}: character vector of length 1 specifying the variable name in argument \code{objectB}
\item \code{third element}: character vector of length 1 being with keyword \code{ALL} }
\item List-elements of length 4: Variable has different codes and levels in tables \code{objectA} and \code{objectB}
\itemize{
\item \code{first element}: character vector of length 1 specifying the variable name in argument \code{objectA}
\item \code{second element}: character vector of length 1 specifying the variable name in argument \code{objectB}
\item \code{third element}: character vector defining codes within \code{objectA}
\item \code{fourth element}: character vector with length that equals the length of the third list-element. The vector defines codes of the variable in \code{objectB} that match the codes given in the third list-element for \code{objectA}.
}
}}
\item{method}{a character vector of length 1 specifying the algorithm that should be used to protect the primary sensitive table cells. Allowed values are:
\itemize{
\item \code{HITAS}:
\item \code{SIMPLEHEURISTIC}:
\item \code{OPT}: }}
\item{...}{additional arguments to control the secondary cell suppression algorithm. For details, see \code{\link{protectTable}}.}
}
\value{
a list of length 2 with each list-element being an \code{\link{safeObj-class}} object
}
\description{
\code{\link{protectLinkedTables}} can be used to protect tables, that have
common cells. It is of course required that after the anonymization process
has finished, all common cells have the same anonymization state in both
tables.
}
\examples{
\dontrun{
# load micro data for further processing
sp <- searchpaths()
fn <- paste(sp[grep("sdcTable", sp)], "/data/microData2.RData", sep="")
microData <- get(load(fn))
# table1: defined by variables 'gender' and 'ecoOld'
microData1 <- microData[,c(2,3,5)]
# table2: defined by variables 'region', 'gender' and 'ecoNew'
microData2 <- microData[,c(1,2,4,5)]
# we need to create information on the hierarchies
# variable 'region': exists only in microDat2
dim.region <- data.frame(h=c('@','@@','@@'), l=c('Tot', 'R1','R2'))
# variable 'gender': exists in both datasets
dim.gender <- data.frame(h=c('@','@@','@@'), l=c('Tot', 'm','f'))
# variable 'ecoOld': exists only in microDat1
dim.ecoOld <- data.frame(
h=c('@','@@','@@@','@@@','@@','@@@','@@@'),
l=c('Tot','A','Aa','Ab','B','Ba','Bb'))
# variable 'ecoNew': exists only in microDat2
dim.ecoNew <- data.frame(
h=c('@','@@','@@@','@@@','@@@','@@','@@@','@@@','@@@'),
l=c('Tot','C','Ca','Cb','Cc','D','Da','Db','Dc'))
# creating objects holding information on dimensions
dimList1 <- list(gender=dim.gender, ecoOld=dim.ecoOld)
dimList2 <- list(region=dim.region, gender=dim.gender, ecoNew=dim.ecoNew)
# creating input objects for further processing. For details have a look at
# \\code{\\link{makeProblem}}.
problem1 <- makeProblem(data=microData1, dimList=dimList1, dimVarInd=c(1,2),
numVarInd=3)
problem2 <- makeProblem(data=microData2, dimList=dimList2, dimVarInd=c(1,2,3),
numVarInd=4)
# the cell specified by gender=='Tot' and ecoOld=='A'
# is one of the common cells! -> we mark it as primary suppression
problem1 <- changeCellStatus(problem1, characteristics=c('Tot', 'A'),
varNames=c('gender','ecoOld'), rule='u', verbose=FALSE)
# the cell specified by region=='Tot' and gender=='f' and ecoNew=='C'
# is one of the common cells! -> we mark it as primary suppression
problem2 <- changeCellStatus(problem2, characteristics=c('Tot', 'f', 'C'),
varNames=c('region','gender', 'ecoNew'), rule='u', verbose=FALSE)
# specifying input to define common cells
commonCells <- list()
# variable "gender"
commonCells$v.gender <- list()
commonCells$v.gender[[1]] <- 'gender' # variable name in 'problem1'
commonCells$v.gender[[2]] <- 'gender' # variable name in 'problem2'
# 'gender' has equal characteristics on both datasets -> keyword 'ALL'
commonCells$v.gender[[3]] <- 'ALL'
# variable: ecoOld and ecoNew
commonCells$v.eco <- list()
commonCells$v.eco[[1]] <- 'ecoOld' # variable name in 'problem1'
commonCells$v.eco[[2]] <- 'ecoNew' # variable name in 'problem2'
# vector of common characteristics: A and B in variable 'ecoOld' in 'problem1'
commonCells$v.eco[[3]] <- c("A","B")
# correspond to characteristics 'C' and 'D' in variable 'ecoNew' in 'problem2'
commonCells$v.eco[[4]] <- c("C","D")
# protect the linked data
result <- protectLinkedTables(problem1, problem2, commonCells, method='HITAS', verbose=TRUE)
# having a look at the results
result.tab1 <- result[[1]]
result.tab2 <- result[[2]]
summary(result.tab1)
summary(result.tab2)
}
}
\seealso{
\code{\link{protectTable}}
}
\author{
Bernhard Meindl \email{bernhard.meindl@statistik.gv.at}
}
|
fee8dea3262c4f0f50ede4df040cc85d3cfcd69e
|
f5bacb9e62521a7445ac7bbcdeb36e862d2d622c
|
/man/permutation_power.Rd
|
7b264089345fa352712dc44d75f0766238b8edd3
|
[] |
no_license
|
astamm/fdahotelling
|
7c4932d2ad02c27c87640c88aefeef34c21022a4
|
2f82b2240817573d74f1bb20d14c6c7867e57122
|
refs/heads/master
| 2021-01-21T06:24:08.051996
| 2018-06-25T10:01:37
| 2018-06-25T10:01:37
| 83,222,803
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 4,262
|
rd
|
permutation_power.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulations.R
\name{permutation_power}
\alias{permutation_power}
\title{Power calculation for the permutation test}
\usage{
permutation_power(mu1 = 0, Sigma = diag(length(mu1)), n1 = 10L,
n2 = NULL, MC = 1000L, alpha = 0.05, paired = FALSE, step_size = 0,
B = 1000L, statistic = "Hotelling", mc.cores = 1L)
}
\arguments{
\item{mu1}{True mean function or difference between mean functions
(default: 0).}
\item{Sigma}{True covariance matrix \eqn{\Sigma} (default: 1).}
\item{n1}{Sample size of data pertaining to the 1st population (default: 10).}
\item{n2}{Sample size of data pertaining to the 2nd population (default:
\code{NULL}).}
\item{MC}{Number of Monte-Carlo runs to estimate statistical power (default:
1000).}
\item{alpha}{Significance level (default: 0.05).}
\item{paired}{Is the input data paired? (default: \code{FALSE}).}
\item{step_size}{The step size used to perform integral approximation via the
method of rectangles (default: \code{0}). When set to \code{0}, it assumes
that we are dealing with multivariate data rather than functional data and
thus no integration is necessary.}
\item{B}{Number of bootstrap permutations (default: 1000).}
\item{statistic}{Statistic to be used within the permutation framework.
Choices are Hotelling (default), L1, L2, Linf, StandardizedL1,
StandardizedL2, StandardizedLinf and All.}
\item{mc.cores}{Number of cores to run the estimation on (default: 1).}
}
\value{
An estimate of the statistical power of the test.
}
\description{
\code{permutation_power} computes an estimate of the statistical power of
permutation-based test on the mean function (or on the difference between
mean functions) using Monte-Carlo simulations.
}
\details{
This function computes the statistical power of permutation-based tests using
a set of user-specified statistics. The power calculation relies on a
specific generative model. It is assumed that data is generated from a
Gaussian distribution. The user-defined inputs are
\itemize{
\item \code{mu1}: a numeric vector containing the actual mean function of the
distribution (or difference between mean functions) evaluated in a pointwise
fashion on a uniform grid.
\item \code{Sigma}: a numeric matrix containing the actual covariance kernel
of the distribution (or pooled covariance kernel) evaluated in a pointwise
fashion on a uniform grid.
}
}
\examples{
# Set the sample sizes for a two-sample test
n1 <- 10
n2 <- 10
# Set the dimensionality for curve approximation
p <- 100
# Set the actual covariance kernel
Sigma <- diag(1, p)
#----------------------------------------------------------------------------
# The following lines of code computes the actual significance level of the
# test and runs in about 1 minute on a single core. Computation time is
# linear in the number of cores and does not depend on the statistic or the
# number of statistics used in the testing procedure.
mu1 <- rep(0, p)
permutation_power(mu1, Sigma, n1, n2, MC = 100, B = 250)
#----------------------------------------------------------------------------
# Usually, it is recommended to set MC = 1000 and B = 1000, which would take
# about 40 minutes. However, this function is parallelized and can be run on
# multiple cores by setting the optional argument mc.cores to a suitable
# integer. On a computer with 4 physical cores, the following lines of code
# runs in about 10 minutes:
\dontrun{
mu1 <- rep(0, p)
permutation_power(mu1, Sigma, n1, n2, MC = 1000, B = 1000, mc.cores = 4)
}
#----------------------------------------------------------------------------
# We can use more complex covariance matrices and compute an estimate of the
# statistical power of the test for a given non-null mean difference.
\dontrun{
mu1 <- rep(4, p)
s <- seq(-1, 1, length.out = 100)
Sigma <- outer(s, s, function(t, s) exp(-abs(t - s)))
permutation_power(mu1, Sigma, n1, n2, MC = 1000, B = 1000, mc.cores = 4)
}
}
\seealso{
The underlying statistical test is described in details in the
technical report by Pini, A., Stamm, A., & Vantini, S. (2015).
\emph{Hotelling \eqn{T^2} in functional Hilbert spaces}, available online
at \url{https://mox.polimi.it/publication-results/?id=524&tipo=add_qmox}.
}
|
2f31a499dd882623e59d1319b12eba3220de397b
|
89046f847ef46501aa53fec57e74846a1eac8505
|
/run_analysis.R
|
5bd1fe05ac88e3eb60a954c348663757ab292240
|
[] |
no_license
|
kerkrouchick/Getting-and-cleaning-Data-Project
|
eb479759b03f7e8e537108985fb62d0d28adc1a9
|
bb79061c85d044509b6121062e070e452622fb38
|
refs/heads/master
| 2022-10-11T07:56:43.171481
| 2020-06-12T20:04:11
| 2020-06-12T20:04:11
| 271,877,839
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,628
|
r
|
run_analysis.R
|
library(dplyr)
library(plyr)
library(reshape2)
library(stringr)
library(tidyr)
download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip", destfile = "Run_zip.zip")
Run_zip <- "Run_zip.zip"
Files <- as.character(unzip(Run_zip, list = TRUE)$Name)
subject_test <- read.table(unz(Run_zip, "UCI HAR Dataset/test/subject_test.txt"))
X_test <- read.table(unz(Run_zip, "UCI HAR Dataset/test/X_test.txt"))
y_test <- read.table(unz(Run_zip, "UCI HAR Dataset/test/y_test.txt"))
subject_train <- read.table(unz(Run_zip, "UCI HAR Dataset/train/subject_train.txt"))
X_train <- read.table(unz(Run_zip, "UCI HAR Dataset/train/X_train.txt"))
y_train <- read.table(unz(Run_zip, "UCI HAR Dataset/train/y_train.txt"))
Features <- read.table(unz(Run_zip, "UCI HAR Dataset/features.txt"))
activity_labels <- read.table(unz(Run_zip, "UCI HAR Dataset/activity_labels.txt"))
Test <- cbind(subject_test, y_test, X_test)
Train <- cbind(subject_train, y_train, X_train)
Merged_data <- rbind(Test, Train)
Features_Names <- as.character(Features$V2)
names(Merged_data)[3:563] <- Features_Names
names(Merged_data)[1] <- "Subject"
names(Merged_data)[2] <- "Activity"
columns <- grep(pattern = "[a-zA-Z]+\\-+(mean|std)\\()+\\-*[a-zA-Z]*", names(Merged_data), value = TRUE)
Merged_data %>%
select(Subject, Activity, all_of(columns)) %>%
group_by(Subject, Activity) %>%
summarise_at(vars(columns), mean, na.rm=TRUE) -> Mean_by_Subject_Activity
Mean_by_Subject_Activity$Activity <- activity_labels[match(Mean_by_Subject_Activity$Activity, activity_labels$V1),2]
View(Mean_by_Subject_Activity)
|
f40968d0a6d6e83fa4f9d6510abcc7a92a7927f5
|
9e8ea97853a1313a659c300765414cb63037cb0a
|
/r/science.R
|
75b6dafbbbe0cbbd332b587722b5eae9d6ae1d3b
|
[
"MIT"
] |
permissive
|
ygeunkim/ecg-experiment
|
d14ef744757dd39666d56d131f9b2f869e6ffa5a
|
470943d8e16d1c32775f06820cda5fe9b5aa5b0e
|
refs/heads/master
| 2023-05-15T12:26:37.469729
| 2021-05-31T02:44:37
| 2021-05-31T02:44:37
| 356,441,103
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,630
|
r
|
science.R
|
# packages------------------------
library(dplyr)
library(readr)
library(tidyr)
# experiment data------------------
ecg <- read_csv("data/raw/ecg.csv")
# diff-----------------------------
ecg_diff <-
ecg %>%
mutate(diff = post - pre) %>%
select(-pre, -post)
# pre-process----------------------
science_ecg <-
ecg_diff %>%
select(-date) %>%
mutate(
trt = case_when(
coffee == 1 ~ "HB",
coffee == 2 ~ "S",
coffee == 3 ~ "De",
coffee == 4 ~ "W"
)
) %>%
pivot_wider(names_from = trt, values_from = diff)
# sharp null of no effect----------
sharp_null <-
science_ecg %>%
mutate(
HB = ecg_diff$diff,
S = ecg_diff$diff,
De = ecg_diff$diff,
W = ecg_diff$diff
)
# write----------------------------
write_csv(ecg_diff, "data/processed/ecg-diff.csv")
write_csv(science_ecg, "data/processed/science.csv")
write_csv(sharp_null, "data/processed/science-sharp.csv")
# log return-----------------------
ecg_log <-
ecg %>%
mutate_at(vars(pre, post), ~log(.)) %>%
mutate(diff = post - pre) %>%
select(-pre, -post)
# sharp null-----------------------
sharp_log <-
ecg_log %>%
select(-date) %>%
mutate(
trt = case_when(
coffee == 1 ~ "HB",
coffee == 2 ~ "S",
coffee == 3 ~ "De",
coffee == 4 ~ "W"
)
) %>%
pivot_wider(names_from = trt, values_from = diff) %>%
mutate(
HB = ecg_log$diff,
S = ecg_log$diff,
De = ecg_log$diff,
W = ecg_log$diff
)
# write----------------------------
write_csv(ecg_log, "data/processed/ecg-logreturn.csv")
write_csv(sharp_log, "data/processed/science-sharp-log.csv")
|
d2c964c4154f5982995e21c455ff7b00f7e4a4b0
|
b5b43e544d901665cc2a2ff2518fff5b44d882a0
|
/man/cleanup_synonyme_table.Rd
|
a95b556837b16aa37bce8921385830b92017067f
|
[] |
no_license
|
jamielatham15/bibliographica
|
14b894fb16403a22051903598fd2b443709fdb6d
|
62bb9feb231c1d6b93960ced52404bcfb496252e
|
refs/heads/master
| 2021-01-23T01:51:17.343584
| 2017-03-03T11:14:56
| 2017-03-03T11:14:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 743
|
rd
|
cleanup_synonyme_table.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cleanup_table.R
\name{cleanup_synonyme_table}
\alias{cleanup_synonyme_table}
\title{Clean synonyme table}
\usage{
cleanup_synonyme_table(f, sep = ";", write = FALSE)
}
\arguments{
\item{f}{File name}
\item{sep}{Separator}
\item{write}{Write back to file after cleaning up ? Logical.}
}
\value{
Polished synonyme table.
}
\description{
Clean up the synonyme table.
}
\details{
Adds, lowercase version of each name; removes duplicates;
writes back to file (optional)
}
\examples{
# tmp <- cleanup_synonyme_table(f, sep = ";", write = FALSE)
}
\author{
Leo Lahti \email{leo.lahti@iki.fi}
}
\references{
See citation("bibliographica")
}
\keyword{utilities}
|
ca689762645ce2a5ab1bdaaed40b3fabd11f4762
|
9aab092e74d3287fffff729c47a3f063072f4d5a
|
/filter.R
|
d5d332c22eaf5e1e8cefb6e6390dfef6156cfe42
|
[] |
no_license
|
pcairns-rgu/Vessels
|
87b6625c4a6471d45f023bf75c4a75d48029ea3b
|
085903e4ab039d58b9e7b577ac1a1742e62c1093
|
refs/heads/master
| 2020-07-11T11:34:28.609799
| 2019-08-26T17:45:37
| 2019-08-26T17:45:37
| 204,528,706
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,154
|
r
|
filter.R
|
#Author: Pauline Cairns Date: 1 August 2019
#Create dataframes from database connection and
#then filters out records from dataframes
#set up of working directory
setwd("H:/Vessels")
#to allow queries to be run on the database
source('connect.R')
#import packages
#all sourced from the packages install facility within RStudio
#in turn these are sourced from the cran repository
#https://cran.r-project.org/
library(dplyr)
library(tidyr)
library(stringr)
library(leaflet)
library(lubridate)
#Once this script has been run once, the next 5 connect commands
#below can be hashed out to speed up app load
#Obtain all vessel details from database
#vessels_download<- connect_vessels$find('{}')
#Obtain vessels in extended area from database
#Guidance provided by https://jeroen.github.io/mongolite/query-data.html
#montrose_extended_vessels<- connect_positions$find('{"Latitude": {"$gt":56.651 , "$lt":56.758}, "Longitude": {"$gt": -2.476 , "$lt":-2.367}} ')
#peterhead_extended_vessels<- connect_positions$find('{"Latitude": {"$gt":57.442 , "$lt":57.542}, "Longitude": {"$gt": -1.795 , "$lt":-1.695}} ')
#aberdeen_extended_vessels<- connect_positions$find('{"Latitude": {"$gt":57.089 , "$lt":57.189}, "Longitude": {"$gt": -2.096 , "$lt":-1.947}} ')
#all_vessels_collected<- connect_positions$find('{"Latitude": {"$gt":56.45 , "$lt":58}, "Longitude": {"$gt": -2.9 , "$lt":-1.4}, "RecvTime": {"$gte":{"$date":"2017-06-01T00:00:20.000Z"}, "$lte":{"$date":"2017-06-14T00:00:20.000Z"}}}')
#inspect summary contents
#summary(vessels_download)
#remove columns not required
#CallSign, Dimension_A, Dimension_B,Dimension_C, Dimension_D,
#pslow, count, IMO, PositionFixingDevice, MaxDraught, DTE,
#AtoNType, VirtualAtoN, AssignedMode, AISVersion
vessels<- vessels_download[, c(3:4, 16, 21)]
vessels<- vessels%>% replace_na(list(Name="Unknown"))
vessels<- vessels%>% replace_na(list(description="Unknown"))
#Create harbour area from extended area
montrose_vessels<- montrose_extended_vessels %>% filter(between(Latitude, 56.701, 56.708), between(Longitude, -2.476, -2.413))
peterhead_vessels<- peterhead_extended_vessels %>% filter(between(Latitude, 57.472, 57.512), between(Longitude, -1.795, -1.735))
aberdeen_vessels<- aberdeen_extended_vessels %>% filter(between(Latitude,57.101,57.176), between(Longitude, -2.096, -1.978))
#join vessels dataframe with positions dataframe to show names
montrose<- montrose_vessels %>% left_join(vessels, by=c('MMSI'= 'MMSI'))
aberdeen<- aberdeen_vessels %>% left_join(vessels, by=c('MMSI'= 'MMSI'))
peterhead<- peterhead_vessels %>% left_join(vessels, by=c('MMSI'= 'MMSI'))
montrose_extended<- montrose_extended_vessels %>% left_join(vessels, by=c('MMSI'= 'MMSI'))
aberdeen_extended<- aberdeen_extended_vessels %>% left_join(vessels, by=c('MMSI'= 'MMSI'))
peterhead_extended<- peterhead_extended_vessels %>% left_join(vessels, by=c('MMSI'= 'MMSI'))
all_vessels<- all_vessels_collected %>% left_join(vessels, by=c('MMSI'= 'MMSI'))
#ShipType pulling through as a number - change to character
montrose$ShipType<- as.character(montrose$ShipType)
aberdeen$ShipType<- as.character(aberdeen$ShipType)
peterhead$ShipType<- as.character(peterhead$ShipType)
montrose_extended$ShipType<- as.character(montrose_extended$ShipType)
aberdeen_extended$ShipType<- as.character(aberdeen_extended$ShipType)
peterhead_extended$ShipType<- as.character(peterhead_extended$ShipType)
all_vessels$ShipType<- as.character(all_vessels$ShipType)
#check contents
#look at first 6 rows of dataset
#head(all_vessels)
#look at fivenum summary
#summary(all_vessels)
#look at type of each feature
#str(all_vessels)
#Remove columns not used in MessageID, PositionAccuracy, Second, RAIM, OffPostion, Altitude
montrose<-montrose[, -c(2,6,11:14, 16)]
peterhead<- peterhead[, -c(2,6,11:14,16)]
aberdeen<- aberdeen[, -c(2,6,11:14,16) ]
montrose_extended<-montrose_extended[, -c(2,6,11:14, 16)]
peterhead_extended<- peterhead_extended[, -c(2,6,11:14,16)]
aberdeen_extended<- aberdeen_extended[, -c(2,6,11:14,16) ]
all_vessels<-all_vessels[, -c(2,6,11:14,16)]
|
19892b64cbe06f5354d983e7e9e06b3646751c12
|
45587d79f947427f5283fde67c0b02b501eae614
|
/model_comparison/03_compare_models.R
|
7b5f06e92aa48c2405fa95d7a17c01138ca3cdf6
|
[
"MIT",
"CC-BY-4.0"
] |
permissive
|
vankesteren/f1model
|
82c8cbafc0bf41ee31803377050d933175f71721
|
bc1117450d818e63a2e4f69e6c2b9dca022ca24e
|
refs/heads/main
| 2023-07-09T17:21:00.803991
| 2023-06-26T11:02:16
| 2023-06-26T11:02:16
| 352,695,980
| 4
| 2
|
MIT
| 2023-02-02T09:30:11
| 2021-03-29T15:40:57
|
R
|
UTF-8
|
R
| false
| false
| 14,071
|
r
|
03_compare_models.R
|
# Code accompanying the manuscript "Bayesian Analysis of Formula One Race Results"
# Last edited 2022-12-14 by @vankesteren
# Contents: visually comparing models for formula one race results
library(tidyverse)
library(ggridges)
library(cmdstanr)
library(firatheme)
library(patchwork)
library(loo)
# Load data ----
f1_dat <- read_rds("dat/f1_dat.rds") |> filter(finished)
drivers_focus <- c("hamilton", "bottas", "norris", "sainz", "leclerc", "max_verstappen", "perez", "alonso",
"raikkonen", "giovinazzi", "vettel", "gasly")
teams_focus <- c("mercedes", "red_bull", "ferrari", "williams", "mclaren", "toro_rosso")
recode_constructors <- c(
"Ferrari" = "ferrari",
"McLaren" = "mclaren",
"Mercedes" = "mercedes",
"Red Bull" = "red_bull",
"Toro Rosso" = "toro_rosso",
"Williams" = "williams"
)
beta_fit <- read_rds("model_comparison/fits/beta_fit.rds")
rank_fit <- read_rds("model_comparison/fits/rank_fit.rds")
ar_fit <- read_rds("model_comparison/fits/ar_fit.rds")
slope_fit <- read_rds("model_comparison/fits/slope_fit.rds")
# Comparing beta likelihood model to rank ordered logit likelihood ----
## Variance components ----
beta_sd_driver <- beta_fit$draws("tau_driver")
beta_sd_driver_season <- beta_fit$draws("tau_driver_season")
beta_sd_team <- beta_fit$draws("tau_team")
beta_sd_team_season <- beta_fit$draws("tau_team_season")
beta_var_driver <- beta_sd_driver^2 + beta_sd_driver_season^2
beta_var_team <- beta_sd_team^2 + beta_sd_team_season^2
beta_prop_var <- beta_var_team / (beta_var_team + beta_var_driver)
rank_sd_driver <- rank_fit$draws("tau_driver")
rank_sd_driver_season <- rank_fit$draws("tau_driver_season")
rank_sd_team <- rank_fit$draws("tau_team")
rank_sd_team_season <- rank_fit$draws("tau_team_season")
rank_var_driver <- rank_sd_driver^2 + rank_sd_driver_season^2
rank_var_team <- rank_sd_team^2 + rank_sd_team_season^2
rank_prop_var <- rank_var_team / (rank_var_team + rank_var_driver)
tibble(
prop = c(beta_prop_var, rank_prop_var),
model = as_factor(rep(c("Beta Model", "Rank-Ordered Logit Model"), each = 8000))
) |>
ggplot(aes(x = prop, fill = model)) +
geom_density(alpha = 0.6) +
theme_fira() +
labs(
title = "Variance components",
x = "Proportion of variance explained by constructor",
fill = "", y = "Posterior density"
) +
scale_fill_fira() +
theme(legend.position = "top")
ggsave("model_comparison/img/variance.png", bg = "white", width = 9, height = 5)
# Driver plots ----
# beta model
beta_driver_skill <-
beta_fit$draws("theta_driver", format = "draws_df") |>
pivot_longer(
starts_with("theta_driver"),
names_to = "driver_id",
names_pattern = "theta_driver\\[(\\d+)]",
names_transform = as.integer
)
beta_driver_form <-
beta_fit$draws("theta_driver_season", format = "draws_df") |>
pivot_longer(
starts_with("theta_driver"),
names_to = c("driver_id", "season_num"),
names_pattern = "theta_driver_season\\[(\\d+),(\\d+)]",
names_transform = as.integer
) |>
mutate(driver_name = levels(f1_dat |> pull(driver))[driver_id])
beta_driver <-
left_join(beta_driver_form, beta_driver_skill, by = c(".chain", ".iteration", ".draw", "driver_id")) |>
mutate(value = value.x + value.y) |>
select(-value.x, -value.y) |>
group_by(driver_name, season_num) |>
summarize(y = mean(value), ymin = quantile(value, 0.055), ymax = quantile(value, 0.945))
beta_driver |>
filter(driver_name %in% drivers_focus) |>
ungroup() |>
mutate(
driver_name = fct_recode(driver_name, verstappen = "max_verstappen"),
driver_name = fct_relabel(driver_name, str_to_title)
) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = driver_name, fill = driver_name)) +
geom_ribbon(alpha = .5, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(driver_name)) +
labs(title = "Beta model driver skills", colour = "Driver", fill = "Driver")
ggsave("model_comparison/img/beta_driver.png", bg = "white", width = 12, height = 8)
# rank model
rank_driver_skill <-
rank_fit$draws("theta_driver", format = "draws_df") |>
pivot_longer(
starts_with("theta_driver"),
names_to = "driver_id",
names_pattern = "theta_driver\\[(\\d+)]",
names_transform = as.integer
)
rank_driver_form <-
rank_fit$draws("theta_driver_season", format = "draws_df") |>
pivot_longer(
starts_with("theta_driver"),
names_to = c("driver_id", "season_num"),
names_pattern = "theta_driver_season\\[(\\d+),(\\d+)]",
names_transform = as.integer
) |>
mutate(driver_name = levels(f1_dat |> pull(driver))[driver_id])
rank_driver <-
left_join(rank_driver_form, rank_driver_skill, by = c(".chain", ".iteration", ".draw", "driver_id")) |>
mutate(value = value.x + value.y) |>
select(-value.x, -value.y) |>
group_by(driver_name, season_num) |>
summarize(y = mean(value), ymin = quantile(value, 0.055), ymax = quantile(value, 0.945))
rank_driver |>
filter(driver_name %in% drivers_focus) |>
ungroup() |>
mutate(
driver_name = fct_recode(driver_name, verstappen = "max_verstappen"),
driver_name = fct_relabel(driver_name, str_to_title)
) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = driver_name, fill = driver_name)) +
geom_ribbon(alpha = .5, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(driver_name)) +
labs(title = "Rank model driver skills", colour = "Driver", fill = "Driver")
ggsave("model_comparison/img/rank_driver.png", bg = "white", width = 12, height = 8)
# direct comparison
bind_rows(
rank_driver |> mutate(model = "rank"),
beta_driver |> mutate(model = "beta")
) |>
filter(driver_name %in% drivers_focus) |>
ungroup() |>
mutate(
driver_name = fct_recode(driver_name, verstappen = "max_verstappen"),
driver_name = fct_relabel(driver_name, str_to_title)
) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = model, fill = model)) +
geom_ribbon(alpha = .35, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(driver_name)) +
labs(title = "Driver skills comparison")
ggsave("model_comparison/img/driver_comparison.png", bg = "white", width = 12, height = 8)
# Team plots ----
# beta model
beta_team_skill <-
beta_fit$draws("theta_team", format = "draws_df") |>
pivot_longer(
starts_with("theta_team"),
names_to = "team_id",
names_pattern = "theta_team\\[(\\d+)]",
names_transform = as.integer
)
beta_team_form <-
beta_fit$draws("theta_team_season", format = "draws_df") |>
pivot_longer(
starts_with("theta_team"),
names_to = c("team_id", "season_num"),
names_pattern = "theta_team_season\\[(\\d+),(\\d+)]",
names_transform = as.integer
) |>
mutate(team_name = levels(f1_dat |> pull(constructor))[team_id])
beta_team <-
left_join(beta_team_form, beta_team_skill, by = c(".chain", ".iteration", ".draw", "team_id")) |>
mutate(value = value.x + value.y) |>
select(-value.x, -value.y) |>
group_by(team_name, season_num) |>
summarize(y = mean(value), ymin = quantile(value, 0.055), ymax = quantile(value, 0.945))
beta_team |>
filter(team_name %in% teams_focus) |>
ungroup() |>
mutate(team_name = fct_recode(team_name, !!!recode_constructors)) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = team_name, fill = team_name)) +
geom_ribbon(alpha = .5, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(team_name)) +
labs(title = "Beta model team advantage", fill = "Constructor", colour = "Constructor")
ggsave("model_comparison/img/beta_team.png", bg = "white", width = 12, height = 8)
# rank model
rank_team_skill <-
rank_fit$draws("theta_team", format = "draws_df") |>
pivot_longer(
starts_with("theta_team"),
names_to = "team_id",
names_pattern = "theta_team\\[(\\d+)]",
names_transform = as.integer
)
rank_team_form <-
rank_fit$draws("theta_team_season", format = "draws_df") |>
pivot_longer(
starts_with("theta_team"),
names_to = c("team_id", "season_num"),
names_pattern = "theta_team_season\\[(\\d+),(\\d+)]",
names_transform = as.integer
) |>
mutate(team_name = levels(f1_dat |> pull(constructor))[team_id])
rank_team <-
left_join(rank_team_form, rank_team_skill, by = c(".chain", ".iteration", ".draw", "team_id")) |>
mutate(value = value.x + value.y) |>
select(-value.x, -value.y) |>
group_by(team_name, season_num) |>
summarize(y = mean(value), ymin = quantile(value, 0.055), ymax = quantile(value, 0.945))
rank_team |>
filter(team_name %in% teams_focus) |>
ungroup() |>
mutate(team_name = fct_recode(team_name, !!!recode_constructors)) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = team_name, fill = team_name)) +
geom_ribbon(alpha = .5, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(team_name)) +
labs(title = "Rank model team advantage", fill = "Constructor", colour = "Constructor")
ggsave("model_comparison/img/rank_team.png", bg = "white", width = 12, height = 8)
# direct comparison
bind_rows(
rank_team |> mutate(model = "rank"),
beta_team |> mutate(model = "beta")
) |>
filter(team_name %in% teams_focus) |>
ungroup() |>
mutate(team_name = fct_recode(team_name, !!!recode_constructors)) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = model, fill = model)) +
geom_ribbon(alpha = .35, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(team_name)) +
labs(title = "Team advantage comparison")
ggsave("model_comparison/img/team_comparison.png", bg = "white", width = 12, height = 8)
# Compare AR and slope to rank model ----
# first, use LOO
loo_rank <- rank_fit$loo(cores = 10)
loo_ar <- ar_fit$loo(cores = 10)
loo_slope <- slope_fit$loo(cores = 10)
loo_compare(list(rank = loo_rank, ar = loo_ar, slope = loo_slope))
# the slope model is clearly worse, the AR model is similar to the rank model
# compare AR model & rank model for drivers
ar_driver <-
ar_fit$draws("driver_skill", format = "draws_df") |>
pivot_longer(
starts_with("driver_skill"),
names_to = c("driver_id", "season_num"),
names_pattern = "driver_skill\\[(\\d+),(\\d+)]",
names_transform = as.integer
) |>
mutate(driver_name = levels(f1_dat |> pull(driver))[driver_id]) |>
group_by(driver_name, season_num) |>
summarize(y = mean(value), ymin = quantile(value, 0.055), ymax = quantile(value, 0.945))
bind_rows(
rank_driver |> mutate(model = "rank"),
ar_driver |> mutate(model = "auto")
) |>
filter(driver_name %in% drivers_focus) |>
ungroup() |>
mutate(
driver_name = fct_recode(driver_name, verstappen = "max_verstappen"),
driver_name = fct_relabel(driver_name, str_to_title)
) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = model, fill = model)) +
geom_ribbon(alpha = .35, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(driver_name)) +
labs(title = "Driver skills comparison")
ggsave("model_comparison/img/ar_rank_driver_comparison.png", bg = "white", width = 12, height = 8)
# compare AR model & rank model for teams
ar_team <-
ar_fit$draws("team_contribution", format = "draws_df") |>
pivot_longer(
starts_with("team_contribution"),
names_to = c("team_id", "season_num"),
names_pattern = "team_contribution\\[(\\d+),(\\d+)]",
names_transform = as.integer
) |>
mutate(team_name = levels(f1_dat |> pull(constructor))[team_id]) |>
group_by(team_name, season_num) |>
summarize(y = mean(value), ymin = quantile(value, 0.055), ymax = quantile(value, 0.945))
bind_rows(
rank_team |> mutate(model = "rank"),
ar_team |> mutate(model = "auto")
) |>
filter(team_name %in% teams_focus) |>
ungroup() |>
mutate(team_name = fct_recode(team_name, !!!recode_constructors)) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = model, fill = model)) +
geom_ribbon(alpha = .35, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(team_name)) +
labs(title = "Team advantage comparison")
ggsave("model_comparison/img/ar_rank_team_comparison.png", bg = "white", width = 12, height = 8)
# Bonus: show why the slope model is bad:
# Slope model
slope_driver_intercepts <-
slope_fit$draws("theta_driver_intercept", format = "draws_df") |>
pivot_longer(
starts_with("theta_drive"),
names_to = "driver_id",
names_pattern = "theta_driver_intercept\\[(\\d+)]",
names_transform = as.integer
)
slope_driver_slopes <-
slope_fit$draws("theta_driver_slope", format = "draws_df") |>
pivot_longer(
starts_with("theta_drive"),
names_to = "driver_id",
names_pattern = "theta_driver_slope\\[(\\d+)]",
names_transform = as.integer
)
slope_driver <-
left_join(slope_driver_intercepts, slope_driver_slopes, by = c(".chain", ".iteration", ".draw", "driver_id")) |>
mutate(season_num = list(1:8)) |>
unnest_longer(season_num) |>
mutate(value = value.x + (season_num - 5) * value.y) |>
mutate(driver_name = levels(f1_dat |> pull(driver))[driver_id]) |>
group_by(driver_name, season_num) |>
summarize(y = mean(value), ymin = quantile(value, 0.055), ymax = quantile(value, 0.945))
slope_driver |>
filter(driver_name %in% drivers_focus) |>
ungroup() |>
mutate(
driver_name = fct_recode(driver_name, verstappen = "max_verstappen"),
driver_name = fct_relabel(driver_name, str_to_title)
) |>
ggplot(aes(x = season_num, y = y, ymin = ymin, ymax = ymax, colour = driver_name, fill = driver_name)) +
geom_ribbon(alpha = .5, colour = NA) +
geom_line() +
geom_point() +
theme_minimal() +
facet_wrap(~as_factor(driver_name)) +
labs(title = "Slope model driver skills", colour = "Driver", fill = "Driver")
ggsave("model_comparison/img/slope_driver.png", bg = "white", width = 12, height = 8)
# giovinazzi is suddenly the best?
|
018a456d50b4507dfaf14171902683bee7d0f420
|
b33392b0ad688f07613804f5707b9ce22e787027
|
/R/ip_internal.r
|
ad56e0702dd6db131c7e98c6d165271af462ed31
|
[
"BSD-2-Clause"
] |
permissive
|
wrathematics/getip
|
3f0a11b46271a6fb64bfcedcbfaaad134b774210
|
eaea192ad020d19e51e11c5dddc9bfeb3b46cdd6
|
refs/heads/master
| 2023-01-28T18:36:41.561075
| 2023-01-23T23:01:03
| 2023-01-23T23:01:03
| 62,585,431
| 6
| 1
| null | 2017-02-20T16:36:50
| 2016-07-04T20:05:44
|
C
|
UTF-8
|
R
| false
| false
| 86
|
r
|
ip_internal.r
|
#' @useDynLib getip R_ip_internal
ip_internal = function()
{
.Call(R_ip_internal)
}
|
a40cd69ac50a2813f5e75a189b026b6ff91c9b2e
|
09ce315ccd153a2ca74867ef0017e7c0443a39f3
|
/man/hello.name.Rd
|
555eb8f88a1d2f945b3c32821e6307baff6ada7d
|
[] |
no_license
|
MW89/testpckg1337
|
eb1269efef52214a5ec583f7d18e1060590e24a0
|
1a9eddb26cb8cc6f9c283039e79d5908c557beae
|
refs/heads/master
| 2021-08-11T07:59:04.156803
| 2017-11-13T11:14:00
| 2017-11-13T11:14:00
| 110,232,594
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 540
|
rd
|
hello.name.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hello.R
\name{hello.name}
\alias{hello.name}
\title{Say hello to anybody}
\usage{
hello.name(name)
}
\arguments{
\item{name}{A character specifying who you want to greet.}
}
\value{
Character.
}
\description{
This function returns the string "Hello, *name*!" where name is the input of
type character that the user needs to input.
}
\note{
An error is thrown if the input name is not a character.
}
\examples{
hello.name("Ann")
x <- hello.name("Christian")
x
}
|
f1c6983cd368e9676f64445322819364fc873517
|
542ea44d056fa800a67f80d403c3e92a7f73747d
|
/drop/template/Scripts/AberrantSplicing/Overview.R
|
707f56029d7f29f993a0673e0ec802119ca12e8e
|
[
"MIT"
] |
permissive
|
gagneurlab/drop
|
01dfff87721253559f2c6d4dceb57c194f6b859f
|
594d7daaff872604d65ae1537a0fe59f463de6b3
|
refs/heads/master
| 2023-07-27T18:28:02.999705
| 2023-04-14T09:50:33
| 2023-04-14T09:50:33
| 213,693,892
| 102
| 44
|
MIT
| 2023-07-07T09:41:58
| 2019-10-08T16:21:43
|
Python
|
UTF-8
|
R
| false
| false
| 3,626
|
r
|
Overview.R
|
#'---
#' title: Aberrant Splicing
#' author:
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "Overview.Rds")`'
#' params:
#' - annotations: '`sm cfg.genome.getGeneVersions()`'
#' - datasets: '`sm cfg.AS.groups`'
#' - htmlDir: '`sm config["htmlOutputPath"] + "/AberrantSplicing"`'
#' input:
#' - functions: '`sm cfg.workDir / "Scripts/html_functions.R"`'
#' - fds_files: '`sm expand(cfg.getProcessedResultsDir() +
#' "/aberrant_splicing/datasets/savedObjects/{dataset}--{annotation}/" +
#' "fds-object.RDS", dataset=cfg.AS.groups, annotation=cfg.genome.getGeneVersions())`'
#' - result_tables: '`sm expand(cfg.getProcessedResultsDir() +
#' "/aberrant_splicing/results/{annotation}/fraser/{dataset}/results_per_junction.tsv",
#' dataset=cfg.AS.groups, annotation=cfg.genome.getGeneVersions())`'
#' output:
#' html_document:
#' code_folding: show
#' code_download: TRUE
#'---
#+ include=FALSE
saveRDS(snakemake, snakemake@log$snakemake)
source(snakemake@input$functions)
#+ eval=TRUE, echo=FALSE
# get parameters
datasets <- sort(snakemake@params$datasets)
annotations <- snakemake@params$annotations
htmlDir <- snakemake@params$htmlDir
count_links <- build_link_list(
file_paths = file.path(htmlDir, paste0(datasets, '_countSummary.html')),
captions = datasets
)
results_links <- sapply(
annotations, function(x) build_link_list(
file_paths = file.path(htmlDir, paste0(datasets, '--', x, '_summary.html')),
captions = datasets
)
)
fds_links <- build_link_list(snakemake@input$fds_files)
results_tables <- build_link_list(snakemake@input$result_tables)
## start html
#'
#' **Datasets:** `r paste(datasets, collapse = ', ')`
#'
#' **Gene annotations:** `r paste(annotations, collapse = ', ')`
#'
#' ## Summaries
#' ### Counts summary
#' `r display_text(links = count_links)`
#'
#' ### FRASER summary
#' `r display_text(caption = 'Gene annotation version ', links = results_links)`
#'
#' ## Files
#' `r display_text(caption = 'FRASER datasets (fds)', links = fds_links)`
#' `r display_text(caption = 'Results tables', links = results_tables)`
#'
#+ echo=FALSE
library(FRASER)
library(magrittr)
#' ## Analyze individual results
# Read the first fds object and results table
fds <- loadFraserDataSet(file = snakemake@input$fds_files[[1]])
res <- fread(snakemake@input$result_tables[[1]])
#' Display the results table of the first dataset
#+ echo=FALSE
if(nrow(res) > 0){
DT::datatable(head(res, 100), caption = 'FRASER results (up to 100 rows shown)',
options=list(scrollX=TRUE), filter = 'top')
} else print("no significant results")
#' Get a splice site and sample of interest. Outliers are in red.
#+ echo=TRUE
sample <- ifelse(nrow(res)>1, res[1, sampleID], colnames(fds)[1])
siteIndex <- 4
#' Get asplice metric of interest. Choose any of the ones that have been fitted
#' Here we use the splice metric of the first outlier in the results table
splice_metric <- res[1, type]
#' ### Volcano plot
# set basePlot to FALSE to create an interactive plot
FRASER::plotVolcano(fds, sample, type = splice_metric, basePlot = TRUE,
deltaPsiCutoff = snakemake@config$aberrantSplicing$deltaPsiCutoff,
padjCutoff = snakemake@config$aberrantSplicing$padjCutoff)
#' ### Expression plot
FRASER::plotExpression(fds, type = splice_metric, idx = siteIndex, basePlot = TRUE)
#' ### Expected vs observed PSI (or theta)
FRASER::plotExpectedVsObservedPsi(fds, type = splice_metric,
idx = siteIndex, basePlot = TRUE)
|
2d2918e92c71093628e1c3388eb7a0784ec9cf15
|
8f2987326acb2b2394e776d8a3feb20151e4625d
|
/msg-harvester.R
|
dbfeaa92a64c4973a0565c2e6f910221f7b842ff
|
[] |
no_license
|
MarineGEO/okr-harvester
|
c0e29e2eeddbb08c8f7e52eea849553313acff1e
|
dfec18649afdc1c2beb38412c731bee96ef46865
|
refs/heads/master
| 2020-03-28T14:50:33.219335
| 2018-09-12T19:00:23
| 2018-09-12T19:00:23
| 148,527,951
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,435
|
r
|
msg-harvester.R
|
# harvest messages in a channel through querying slack's API
# Load packages
library(tidyverse)
library(purrr)
# load the config file that contains the API token
source('CONFIG')
# query API and save all messages to a data frame
url_groupHistory <- 'https://slack.com/api/groups.history'
msgs <- content(GET(str_c(url_groupHistory, "?", "token=", token, "&", "channel=", channel, "&pretty=1")))$messages
# function to turn each nested list from the API response into a tibble
nest2df <- function(x){
tribble(
~user, ~text, ~timestamp, ~reaction,
x$user, x$text, x$ts, x$reaction
)
}
# Apply function to each message in list to turn into a single data frame
msgs_df <- map_df(msgs, nest2df)
# Function that queries the API for user's actual name
getUserRealName <- function(userid, api_token=token){
# construct the API URL
userInfoURL <- str_c('https://slack.com/api/users.info?token=', api_token, '&user=', userid, '&pretty=1')
# pull the user's real name from the API response
real_name <- content(GET(userInfoURL))$user$real_name
return(real_name)
}
# get unique user names from ID's as a data frame
realNames <- msgs_df %>%
select(user) %>%
unique() %>% # select unique IDs so don't have query API for each message
rowwise() %>%
mutate(real_name=getUserRealName(user))
# join real names to the message dataframe
msgs_df_names <- left_join(msgs_df, realNames, by='user')
msgs_df_names
|
539deaa65a7cb3746cfe5e84bb51e77e9b8bc3ff
|
e888224cc0d84041cedcb6ee9968dcb3e86e8ede
|
/06_normalized_clustering_DH.R
|
3ae5f86110a599e0f4516e991b98b4172eef547c
|
[] |
no_license
|
Driesheylen1/introductiondecember
|
75ac883078ba83c0b905977f712c627532d4bd5d
|
8d2b042dfc26ded4551070990b3c090c6da5a6a2
|
refs/heads/main
| 2023-02-24T01:02:07.194685
| 2021-01-27T09:56:09
| 2021-01-27T09:56:09
| 320,223,422
| 0
| 0
| null | 2020-12-10T13:17:32
| 2020-12-10T09:33:12
|
R
|
UTF-8
|
R
| false
| false
| 8,411
|
r
|
06_normalized_clustering_DH.R
|
library(data.table)
library(ggplot2)
library(GGally)
library(ggrepel)
library(randomcoloR)
library("readxl")
library("reshape2")
library(dplyr)
iam = Sepsis_data_normalized_outliers_out
iam = melt(iam, id.vars = c("ID", "Batch", "Sex", "Age", "Diagnosis", "In_Hospital_Mortality",
"ICU_admission", "severity_groups", "Diag_cat_number", "CCI", "Gram_stain", "SOFA_score", "WBC", "CRP"),
variable.name = "assay", value.name = "npx")
iam = as.data.table(iam)
### Standardize the data
# Note that value_n is the result of batch normalization
iam[, value_ns := scale(npx), by = assay] # scale (mean zero, sd 1) #important to set as dataframe
x = as.data.frame(dcast.data.table(iam, ID + Batch + Sex + Age + Diagnosis + ICU_admission + severity_groups
+ In_Hospital_Mortality + CCI + Gram_stain + SOFA_score ~ assay,
value.var = "value_ns"))
#sum(is.na(x_mat))
#summary(is.na(x))
#x = x[-393,]
#x2 = x[-1,] # x 12 times; as first twelve lines (controls) as well as line 393 do not contain metadata
# remove first twelve lines
x2 <- x[ -c(1:11) ]
x_mat = as.matrix(x2)
row.names(x_mat) = as.character((x$ID)) #difficulty because of duplicate bridge samples rownames reason for weir rows
t = t(x2)
t = t[-1,]
colnames(t) = x$ID
x_matr = as.matrix(t)
x_dist_L2 = dist(x_mat, method = "euclidean")# compute distances between samples, needed for clustering
x_dist_L2r = dist(x_matr, method = "euclidean") # compute distances between samples, needed for clustering
x_dist_L1 = dist(x_mat, method = "manhattan") # compute distances between samples, needed for clustering
#dim(as.matrix(x_dist_L1))
#dim(as.matrix(x_dist_L2))
#x_dist_L1[1:5, 1:5]
clust_methods = c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid")
kmea
clust_list_L1 = list()
clust_list_L2 = list()
for (i in 1:length(clust_methods)) {
clust_list_L1[[clust_methods[i]]] = hclust(x_dist_L1, method = clust_methods[i])
clust_list_L2[[clust_methods[i]]] = hclust(x_dist_L2, method = clust_methods[i])
}
x_clust = hclust(x_dist_L2)
x_clustr = hclust(x_dist_L2r, method = 'ward.D')
class(clust_list_L1[[1]])
#summary(clust_list_L1[[1]])#to summarize clustering with ward D method
#x_clust
#summary(x_clust)
plot(x_clustr) #use toch check matrix
# Look at using pheatmap (or aheatmap)
# refs
# https://slowkow.com/notes/pheatmap-tutorial/
library(pheatmap)
# pheatmap(mat = as.matrix(x_dist), show_colnames = FALSE) simplified no options code
my_annot = data.frame(gender = x$Sex, Batch = x$Batch,
age = x$Age, Comorbidities = x$CCI, GRAM_stain = x$Gram_stain, Diagnosis = x$Diagnosis,
person = x$ID, SOFA = x$SOFA_score, Severity_group = x$severity_groups)
#dim(my_annot)
#rownames(my_annot) <- colnames(x[10:101])
#head(my_annot)
rownames(my_annot) = colnames(x_matr) #CRUCIAL ONE
pal6 = distinctColorPalette(20)
tail(my_annot)
#mycolors = list(period = c("M"))
get_colors = function(X) {
res = list()
for (v in names(X)) {
lev = levels(X[,v])
p = distinctColorPalette(length(lev))
names(p) = lev
res[[v]] = p
}
return(res)
}
my_colors = get_colors(my_annot[,c("gender")])
my_colors[['batch']] = c('batch1'='#fc8d59', 'batch2'='#ffffbf')
my_colors[['gender']] = c('1'='#FF69B4', '0'='#00BFFF')
my_colors[['Diagnosis']] = c('other'='#333131', 'Pneumonia'='#DA5353', 'Influenza' = '#1ECD41', 'BSI & Endocarditis' = '#3B77F0')
my_colors[['Gram_stain']] = c('0'='#35A513', '1'='#25A2D8', '2' = '#A621C7')
my_colors[['Severity_group']] = c('1'='#F6F6F6', '2'='#170606')
#made for sepsis
#mat = as.matrix(x_dist_L2)
#colnames(mat) = colnames(t) #was this important or not?
# now use hclust results, rather than pheatmap default, which is k-means
pheatmap(mat = as.matrix(x_dist_L2),
annotation_col = my_annot,
fontsize = 6,
cluster_cols = x_clust,
cluster_rows = x_clustr,
annotation_colors = my_colors,
main = "Hierarchical clustering (complete)")
# to files:
for (i in 1:length(clust_methods)) {
clust_list_L1[[clust_methods[i]]] = hclust(x_dist_L1, method = clust_methods[i])
clust_list_L2[[clust_methods[i]]] = hclust(x_dist_L2, method = clust_methods[i])
fnL1 = paste("hclust_L1_", clust_methods[i], ".pdf", sep = "")
fnL2 = paste("hclust_L2_", clust_methods[i], ".pdf", sep = "")
pheatmap(mat = as.matrix(x_dist_L1),
show_colnames = TRUE,
show_rownames = TRUE,
annotation_col = my_annot,
fontsize = 5,
cluster_cols = clust_list_L1[[i]],
cluster_rows = x_clustr,
annotation_colors = my_colors,
main = paste("Hierarchical clustering (L1, ", clust_methods[i],")", sep = ""),
filename = fnL1,
width = 8.3,
height = 11.7)
flush.console()
while (!is.null(dev.list())) dev.off()
flush.console()
pheatmap(mat = as.matrix(x_dist_L2),
show_colnames = TRUE,
show_rownames = TRUE,
labels_row = rownames(t),
annotation_col = my_annot,
labels_col = colnames(t),
fontsize = 2,
cluster_cols = clust_list_L2[[i]],
cluster_rows = x_clustr,
annotation_colors = my_colors,
main = paste("Hierarchical clustering (L2, ", clust_methods[i], ")", sep = ""),
filename = fnL2,
width = 12.3,
height = 11.7)
flush.console()
while (!is.null(dev.list())) dev.off()
flush.console()
}
#for sepsis project best clustering choice will also be based on this ID-Protein heatmap
### Conclusion, we proceed with L2, Ward.D: best clustering
the_clust = clust_list_L2[["ward.D"]]
class(the_clust)
the_clust$order
# if samples cluster by person, the we can use the 6x6 submatrices in x_dist_L2
# compute means by person, not relevant for sepsis
dim(x)
OIDcols = names(x)[grepl(pattern = "OID", x = names(x))]
length(OIDcols)
x_dt = data.table(x)
person_means = x_dt[,sapply(.SD, function(x) list(mean = mean(x))), .SDcols = OIDcols, by = person_id]
dim(person_means)
# person_means[1:5,1:8]
# x_dt[1:5, 1:8]
names(person_means) = sub(pattern = ".mean", replacement = "", x= names(person_means))
person_means_df = as.data.frame(person_means)
class(person_means_df)
x$dist_to_clust_mean = -1
for (i in 1:nrow(x)) {
pers_i = x$person_id[i]
mean_i = unlist(person_means_df[person_means_df$person_id == pers_i, OIDcols])
this_i = unlist(x[i, OIDcols])
x$dist_to_clust_mean[i] = sqrt(sum((mean_i - this_i)^2))
}
sample_list_clust = x[,c("sample_name","person_id", "batch", "sampling_period", "dist_to_clust_mean")] # this data frame to keep as result
head(sample_list_clust)
save(sample_list_clust, file = "samples_clust_dist_to_mean.RData")
################################################################################
## now k-means
dim(x_mat)
# NOTE THERE IS RANDOMNESS THAT AFFECTS THE RESULT,
# Therefore choose centers to start from manually, eg. the first sample of each person
row.names(x_matr)
start_centers = x_mat[seq(from=1, to=180, by=6),]
start_centers = x_mat[seq(from=1, to=2),]
# x_kmeans = kmeans(x_mat, centers = start_centers, iter.max = 100) not for sepsis centers?
x_kmeans = kmeans(x_mat, centers = 2, iter.max = 50)
dim(x_kmeans)
x_kmeans$iter
summary(x_kmeans$cluster)
all.equal(names(x_kmeans$cluster), x$ID) #to check whether both objects take same samples into account
x$cluster = x_kmeans$cluster
mypal30 = distinctColorPalette(4)
ggplot(x, aes(x = ID, y = cluster)) +
geom_point(aes(colour = Diagnosis)) +
scale_color_manual(values = mypal30)
x$cluster = as.factor(x$cluster)
summary(x$severity_groups)
summary(x$cluster)
ggscatter(as.data.frame(x), x = "ID", y = "severity_groups",
color = 'cluster',
size = 1, repel = FALSE)
scale_color_continuous(type = 'viridis'))
sum(x$cluster)
x$cluster = as.factor(x$cluster)
ggplot(x, aes(x = sample_name, y = cluster)) +
geom_point(aes(colour = person_id), size=3) +
scale_color_manual(values = mypal30) +
facet_wrap(~ person_id)
a = as.data.frame(xtabs( ~ person_id + cluster, x))
head(a)
a = subset(a, Freq > 0)
a
a[order(a$person_id),]
#
|
b866075ae011f2428c90596f317c839619927467
|
03f57d4d510b0c36a6aae6a680c276dd15fa2f40
|
/paper/civicmine/fig_prcurves.R
|
14053c3372d2c9733ecb6998a94182e8fed5fe68
|
[
"MIT"
] |
permissive
|
jakelever/civicmine
|
d2dc7f7306f82c6623dcf5af465db1fc77eb2193
|
8b8ab1c196c961799dd75eb20510487504eae595
|
refs/heads/master
| 2023-04-15T02:02:59.253498
| 2023-03-31T16:49:58
| 2023-03-31T16:49:58
| 114,714,074
| 18
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,152
|
r
|
fig_prcurves.R
|
source('civicmine/dependencies.R')
AssociatedVariant <- read.table('civicmine/prCurves/AssociatedVariant.txt',header=T)
colnames(AssociatedVariant) <- c('threshold','precision','recall')
AssociatedVariant$reltype <- 'Associated Variant'
Diagnostic <- read.table('civicmine/prCurves/Diagnostic.txt',header=T)
colnames(Diagnostic) <- c('threshold','precision','recall')
Diagnostic$reltype <- 'Diagnostic'
Predictive <- read.table('civicmine/prCurves/Predictive.txt',header=T)
colnames(Predictive) <- c('threshold','precision','recall')
Predictive$reltype <- 'Predictive'
Predisposing <- read.table('civicmine/prCurves/Predisposing.txt',header=T)
colnames(Predisposing) <- c('threshold','precision','recall')
Predisposing$reltype <- 'Predisposing'
Prognostic <- read.table('civicmine/prCurves/Prognostic.txt',header=T)
colnames(Prognostic) <- c('threshold','precision','recall')
Prognostic$reltype <- 'Prognostic'
data <- rbind(AssociatedVariant,Diagnostic,Predictive,Predisposing,Prognostic)
data <- data[order(data$precision,decreasing=T),]
data <- data[order(data$recall),]
data$reltype <- factor(as.character(data$reltype),levels=c("Predisposing","Prognostic","Associated Variant","Diagnostic","Predictive"))
prcurvesPlot <- xyplot(precision ~ recall | reltype,
xlab="Recall", ylab="Precision",
#xlim=c(0,1),ylim=c(0,1),
data,
lwd=3,
type="l")
#prcurvesPlot <- arrangeGrob(prcurvesPlot,top="(a)")
data <- data[order(data$threshold),]
myColours <- c(brewer.pal(3,"Dark2"),"#000000")
my.settings <- list(
superpose.polygon=list(col=myColours),
#strip.background=list(col=myColours),
superpose.line=list(col=myColours),
strip.border=list(col="black")
)
thresholdPlot <- xyplot(precision + recall ~ threshold | reltype,
xlab="Threshold", ylab="Precision / Recall",
#auto.key=T,
par.settings = my.settings,
auto.key=list(space="top", columns=2,
points=FALSE, rectangles=TRUE),
data, type="l")
#thresholdPlot <- arrangeGrob(thresholdPlot,top="(b)")
fig_prcurves <- arrangeGrob(prcurvesPlot,thresholdPlot,ncol=1)
grid.arrange(fig_prcurves)
targetMatching <- data
targetMatching$target <- 0.9
targetMatching[targetMatching$reltype=='Associated Variant','target'] <- 0.94
targetMatching$closeToTarget <- (targetMatching$precision-targetMatching$target)^2
targetMatching <- targetMatching[order(targetMatching$closeToTarget),]
thresholdChoice <- targetMatching[!duplicated(targetMatching$reltype),]
thresholdChoice <- thresholdChoice[order(as.character(thresholdChoice$reltype)),]
thresholdChoice$precision <- round(thresholdChoice$precision,3)
thresholdChoice$recall <- round(thresholdChoice$recall,3)
paper.performanceTable <- thresholdChoice[,c('reltype','threshold','precision','recall')]
#fig_performance <- tableGrob(thresholdChoice[,c('reltype','threshold','precision','recall')],
# rows=NULL,
# cols=c("Extracted Relation","Threshold","Precision","Recall"))
#grid.arrange(fig_performance)
|
54d3c9e290bbed0912dc5da3209c41aef2fafce4
|
92972c7a4bbdc0498bb080af75bd36345ab32ea1
|
/man/disc_camera_compass_angle.Rd
|
1c2b1109d0cfba857d5c1c1f61327d253a31f88d
|
[] |
no_license
|
beatrixparis/DISC
|
1f30ad4f2451350a721586c36e6f05d1860d3aa5
|
fc9e384721fdd5b1d11695f7f542af86d4ccf35f
|
refs/heads/master
| 2016-08-11T22:01:10.436084
| 2015-10-18T04:21:10
| 2015-10-18T04:21:10
| 44,465,142
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,451
|
rd
|
disc_camera_compass_angle.Rd
|
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/disc_camera_compass_angle.R
\name{disc_camera_compass_angle}
\alias{disc_camera_compass_angle}
\title{Measure angle between camera and compass}
\usage{
disc_camera_compass_angle(dir, sub = NULL, verbose = FALSE, ...)
}
\arguments{
\item{dir}{deployment directory}
\item{sub}{subsampling interval, in s}
\item{verbose}{output messages on the console when TRUE}
\item{...}{passthrough argument}
}
\description{
Measure angle of analog compasses on images, use that as a reference, compare it to the digital compass record and deduce the angle between the camera and the digital compass. This is mandatory to use the digital compass to correct the tracks.
}
\examples{
# get example deployments included with the package
deploys <- system.file("extdata", "deployments", package = "discr")
# copy them to a writable, temporary directory
temp <- tempdir()
file.copy(deploys, temp, recursive=TRUE)
dd <- paste0(temp, "/deployments/")
deploy1 <- paste0(dd, "1")
# run the action
disc_conf(deploy.dir=dd)
\donttest{disc_camera_compass_angle(dir=deploy1, sub=10, verbose=TRUE)}
# the angle is also stored in a file "angle_camera_compass.txt"
list.files(deploy1)
}
\seealso{
Other action.functions: \code{\link{disc_calibrate}};
\code{\link{disc_correct}}; \code{\link{disc_stats}};
\code{\link{disc_track_compass}};
\code{\link{disc_track}}; \code{\link{disc}}
}
|
45b4d19a885e9b99f9aef9fea4e17ac05634cd9b
|
c88845e9dea484df85380f4129b0e00a2d35191e
|
/cachematrix.R
|
de7ada1bcdf1256ae8019bc1df8c5a5392a2bcf1
|
[] |
no_license
|
whiteRetriever/ProgrammingAssignment2
|
1afd4ce93fd4eb3b7b9862860de6ba4f051e11e3
|
2266b9232c71c8bb200caed82a2c79ec96cb4287
|
refs/heads/master
| 2021-01-14T11:30:29.104908
| 2015-01-25T20:05:14
| 2015-01-25T20:05:14
| 29,827,580
| 0
| 0
| null | 2015-01-25T19:34:08
| 2015-01-25T19:34:06
| null |
UTF-8
|
R
| false
| false
| 1,664
|
r
|
cachematrix.R
|
## Matrix inversion
## following functions is used to cache the inverse of a matrix
## makeCacheMatrix creates a list containing a function to
## 1. set the value of the matrix
## 2. get the value of the matrix
## 3. set the value of inverse of the matrix
## 4. get the value of inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
inverse <- NULL
set <- function(y) {
x <<- y
inverse <<- NULL
}
get <- function() x
setInverseMatrix <- function(inverseMatrix) inverse <<- inverseMatrix
getInverseMatrix <- function() inverse
list(set=set, get=get, setInverseMatrix=setInverseMatrix, getInverseMatrix=getInverseMatrix)
}
## The following function returns the inverse matrix of the given matrix.
## Function in first step checks if the inverse matrix has already been computed.
## If it is TRUE, it gets the result and skips the computation.
## In second step, function also checks if whether there is any changes
## in a given matrix. If not, it computes the inverse, sets the value in the
## cache via setInverseMatrix function.
## The assumption of this function is that given matrix is always invertible.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inverse <- x$getInverseMatrix()
a<- x
b<-m$get()
if(!is.null(inverse)) {
if(identical(a, b)){ ##Compare two matrices (TRUE or FALSE)
message("getting cached data.")
return(inverse)
}
}
data <- x$get()
inverse <- solve(data)
x$setInverseMatrix(inverse)
inverse
}
}
|
ff93bfb0eaf4c54ca2e7c20c712929752bd76562
|
0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb
|
/cran/paws.management/man/appregistry_list_associated_attribute_groups.Rd
|
a52b5a0f5a4c570cf051c5120da595a59f8ce900
|
[
"Apache-2.0"
] |
permissive
|
paws-r/paws
|
196d42a2b9aca0e551a51ea5e6f34daca739591b
|
a689da2aee079391e100060524f6b973130f4e40
|
refs/heads/main
| 2023-08-18T00:33:48.538539
| 2023-08-09T09:31:24
| 2023-08-09T09:31:24
| 154,419,943
| 293
| 45
|
NOASSERTION
| 2023-09-14T15:31:32
| 2018-10-24T01:28:47
|
R
|
UTF-8
|
R
| false
| true
| 988
|
rd
|
appregistry_list_associated_attribute_groups.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/appregistry_operations.R
\name{appregistry_list_associated_attribute_groups}
\alias{appregistry_list_associated_attribute_groups}
\title{Lists all attribute groups that are associated with specified
application}
\usage{
appregistry_list_associated_attribute_groups(
application,
nextToken = NULL,
maxResults = NULL
)
}
\arguments{
\item{application}{[required] The name or ID of the application.}
\item{nextToken}{The token to use to get the next page of results after a previous API
call.}
\item{maxResults}{The upper bound of the number of results to return (cannot exceed 25).
If this parameter is omitted, it defaults to 25. This value is optional.}
}
\description{
Lists all attribute groups that are associated with specified application. Results are paginated.
See \url{https://www.paws-r-sdk.com/docs/appregistry_list_associated_attribute_groups/} for full documentation.
}
\keyword{internal}
|
46acdf419e768e2efbeb72cb1f2de2ee12a43dbb
|
8a66a7adca1bef28ea2ef434df194956ca5a3a02
|
/gimme/R/print.varLabels.R
|
cd231642e80f66d1b8a9ac07370dc8ece4820236
|
[] |
no_license
|
kaduffy/gimme
|
87bd0780ee17e7d50a5b5c898197234f82b6688a
|
017bf0444a30eac9711e74f7d5e4888fb9cc99d7
|
refs/heads/master
| 2023-03-16T23:24:42.145497
| 2022-08-23T15:11:34
| 2022-08-23T15:11:34
| 116,872,716
| 0
| 0
| null | 2019-03-20T22:17:45
| 2018-01-09T21:28:16
|
R
|
UTF-8
|
R
| false
| false
| 919
|
r
|
print.varLabels.R
|
#' Print method for varLabels object.
#' @keywords internal
print.varLabels <- function(x){
cat(
" Original Variable Names \n ", paste0(x$orig, collapse = ", "), "\n\n",
"User-Specified Exogenous Variable Names \n ", paste0(x$uexo, collapse = ", "), "\n\n",
"Lagged Variable Names \n ", paste0(x$lagg, collapse = ", "), "\n\n",
"Convolved Variable Names \n ", paste0(x$conv, collapse = ", "), "\n\n",
"Multiplied Variable Names \n ", paste0(x$mult, collapse = ", "), "\n\n",
"Exogenous Variable Names \n ", paste0(x$exog, collapse = ", "), "\n\n",
"Endogenous Variable Names \n ", paste0(x$endo, collapse = ", "), "\n\n",
"Categorical Variable Names \n ", paste0(x$catg, collapse = ", "), "\n\n",
"Standardized Variable Names \n ", paste0(x$stnd, collapse = ", "), "\n\n",
"All Variable Names (column order) \n ", paste0(x$coln, collapse = ", "), "\n\n"
)
}
|
6a5d42645d84d7d66afd3e943e91102d37180aa1
|
12078d8b1524a9028c2cf6540f66f05508fd0e11
|
/grades.R
|
7ab22fc2ff4070f2a074f8616a8eae492525a505
|
[] |
no_license
|
teejas/GradeVisualization
|
a18d0f5989f4565be4c89423957f3b2c5adf63cb
|
f95f0a0d4fce40bbc76edac948e70bd1c181d119
|
refs/heads/master
| 2021-01-20T13:56:02.482400
| 2017-02-21T20:27:50
| 2017-02-21T20:27:50
| 82,723,101
| 6
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,094
|
r
|
grades.R
|
library(ggplot2)
data <- read.csv("dspgrades.csv", header=T, sep=",")
df <- data.frame(data)
cum_gpa <- df$Cumulative.GPA
term_gpa <- df$Term.GPA
names <- df$Name
term_hrs <- df$Term.hours
living_in <- df$Living.in
norm_cum <- (cum_gpa - mean(cum_gpa)) / sd(cum_gpa)
norm_term <- (term_gpa - mean(term_gpa)) / sd(term_gpa)
norm_hrs <- (term_hrs - mean(term_hrs)) / sd(term_hrs)
plot(term_hrs, cum_gpa, main="Plot of Cumulative GPA per term hours", ylab="Cumulative GPA", xlab="Term hours")
plot(term_hrs, term_gpa, main="Plot of term grades per term hours", ylab="Term GPA", xlab="Term hours")
boxplot(cum_gpa~living_in, data=df, main="Cumulative GPA Data", xlab="Living In", ylab="Cumulative GPA")
boxplot(term_gpa~living_in, data=df, main="Term GPA Data", xlab="Living In", ylab="Term GPA")
mean_cum <- mean(cum_gpa)
mean_term <- mean(term_gpa)
attach(data)
sorteddata <- data[order(cum_gpa), ]
detach(data)
print(sorteddata)
paste("Mean cumulative GPA: ", mean_cum)
paste("Mean term GPA: ", mean_term)
paste("Std Dev cum GPA: ", sd(cum_gpa))
paste("Std Dev term GPA: ", sd(term_gpa))
|
57331bb73d8518cee9e9ce9ec14697d3aa980264
|
d56cf62cc3e2ac72fe4fa351471e4d7542db6c88
|
/NYC_BB.R
|
d485e2437fa570eef8178e91e3825d6887f8cdfa
|
[] |
no_license
|
agreen4/bedbugs
|
855a008bcaccae41b07793acc244e240525b3dff
|
f6a23618269ebd8811d871daeac5468bdb17173e
|
refs/heads/master
| 2020-03-23T22:27:47.270689
| 2018-07-27T12:58:42
| 2018-07-27T12:58:42
| 142,177,638
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,707
|
r
|
NYC_BB.R
|
library(data.table)
NYC_311 <- fread("/nfs/bedbugs-data/City Housing Complaint Databases 2-28-18/NYC_Original_Data_Feb_28_2018/311_Call_Center_Inquiry.csv")
NYC_com <- fread("/nfs/bedbugs-data/City Housing Complaint Databases 2-28-18/NYC_Original_Data_Feb_28_2018/Housing_Maintenance_Code_Complaints.csv")
NYC_vio <- fread("/nfs/bedbugs-data/City Housing Complaint Databases 2-28-18/NYC_Original_Data_Feb_28_2018/Housing_Maintenance_Code_Violations.csv")
library(dplyr)
NYC_BB <- subset(NYC_com, Code == "BEDBUGS" | Code == "BED BUGS")
#another option
NYC_BB <- NYC_com %>% filter(Code %in% c("BEDBUGS", "BED BUGS"))
#Time Series Analysis
library(here)
library(tidyverse)
library(tidytext)
library(lubridate)
#setting up date
NYC_BB <- NYC_BB %>% mutate(C_Date = lubridate::mdy(`StatusDate`))
NYC_BB <- NYC_BB %>% mutate(C_Year = lubridate::year(C_Date))
NYC_BB <- NYC_BB %>% mutate(C_Month = lubridate::month(C_Date))
NYC_BB <- NYC_BB %>% mutate(C_Month_Date = paste(C_Month, C_Year, sep="-"))
NYC_BB <- NYC_BB %>% mutate(Violation = recode(`Code`, "BEDBUGS" = "Bed Bugs", "BED BUGS" = "Bed Bugs"))
BB_month<-NYC_BB %>% group_by(C_Year, C_Month, Violation) %>% summarise(count=n())
BB_month<-BB_month %>% mutate(C_Month_Year = as_date(paste(C_Year,C_Month, "01", sep="-")))
library(ggplot2)
ggplot(BB_month, aes(C_Month_Year, count)) + geom_line()
#hmm something wrong here -> going to go from 311
##violations for NYC-dataset
library(stringr)
bb_terms <- c("BED BUGS", "BEDBUGS", "BED BUG", "BEDBUG")
#attempting on smaller dataset
NYC_s <- NYC_vio[1:10000,]
NYC_s$BB <- sapply(NYC_s$NOVDescription, function(x) any(str_detect(x, pattern = bb_terms)))
#full dataset
NYC_vio$BB <- sapply(NYC_vio$NOVDescription, function(x) any(str_detect(x, pattern = bb_terms)))
#Number of Bed bug violations
## FALSE TRUE
#4510312 24070
NYC_BB <- subset(NYC_vio, BB == "TRUE")
NYC_BB <- NYC_BB %>% mutate(C_Date = lubridate::mdy(`InspectionDate`))
NYC_BB <- NYC_BB %>% mutate(C_Year = lubridate::year(C_Date))
NYC_BB <- NYC_BB %>% mutate(C_Month = lubridate::month(C_Date))
NYC_BB <- NYC_BB %>% mutate(C_Month_Date = paste(C_Month, C_Year, sep="-"))
BB_month<-NYC_BB %>% group_by(C_Year, C_Month, BB) %>% summarise(count=n())
BB_month<-BB_month %>% mutate(C_Month_Year = as_date(paste(C_Year,C_Month, "01", sep="-")))
library(ggplot2)
ggplot(BB_month, aes(C_Month_Year, count)) + geom_line() +
scale_x_date(date_breaks = "2 years", date_labels = "%b-%Y") +
xlab("") + ylab("Number of Bed Bug Violations") +
ggtitle("Bed Bug Code Violations for New York City 2002 - Present")
#subsetting to study period for 2010 - present
#hmm something wrong here -> going to go from 311
|
de8f6e31191315cbe5abfeb4a0e2b196fbaa7751
|
dc513cb8c80a9d8e4ec45bc511e77de8759a72b7
|
/simulation.R
|
269ff856de218019d70b7aa4f20c9723bab31b37
|
[] |
no_license
|
TheoVerhelst/Causality-in-Churn
|
7632209f99ba175b42cf91ef1f43a1c02c5e58e9
|
efb60cb105c61d0ffde5ff25142534ce6fb8a683
|
refs/heads/master
| 2023-03-20T03:37:35.523722
| 2021-03-16T12:16:04
| 2021-03-16T12:16:04
| 347,121,382
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,532
|
r
|
simulation.R
|
# Derives the parameters of the bivariate beta distribution such that
# the resulting population respects the proportions beta, gamma and delta.
derive_bivariate_beta_parameters <- function(beta, gamma, delta, A = 1) {
alpha <- 1 - beta - gamma - delta
v <- beta * gamma - delta * alpha
a_00 <- -v + A * alpha
a_01 <- v + A * gamma
a_10 <- v + A * beta
a_11 <- -v + A * delta
cov_S0_S1 <- (a_11*a_00 - a_10*a_01) / (A * (A + 1))
E_S0 <- (a_11 + a_10) / A
E_S1 <- (a_11 + a_01) / A
return(list(
a = c(a_00, a_01, a_10, a_11),
cov_S0_S1 = cov_S0_S1,
E_S0 = E_S0,
E_S1 = E_S1
))
}
# Create N samples from a Dirichlet distribution with parameter vector alpha.
sample_dirichlet <- function(N, alpha) {
stopifnot(all(alpha > 0))
gamma_sample <- sapply(alpha, function(alpha_i)
rgamma(N, shape = alpha_i, rate = 1)
)
# Transpose the result so we have N rows and length(alpha) columns
return(aperm(sapply(1:N, function(j) gamma_sample[j,] / sum(gamma_sample[j,]))))
}
# Creates N samples from a bivariate beta distribution described in
# "Olkin, Ingram, and Thomas A. Trikalinos. "Constructions for a bivariate
# beta distribution." Statistics & Probability Letters 96 (2015): 54-60."
# The treatment and target variables are then sampled from Bernoulli
# distributions. Beta, gamma and delta dictate the distribution of Y_0 and Y_1.
# noise_S_0 and noise_S_1 are (std dev of) Gaussian noise added to S_0 and S_1.
# A controls the spread of the beta marginals.
# use_churn_convention determines which value of Y_0, Y_1 is considered
# persuadable.
sample_bivariate_beta <- function(beta, gamma, delta,
N = 20000, proba_treatment = 0.65, A = 1,
noise_S_0 = 0.01, noise_S_1 = 0.01,
use_churn_convention = TRUE) {
parameters <- derive_bivariate_beta_parameters(beta, gamma, delta, A = A)
U <- sample_dirichlet(N=N, alpha=parameters$a)
data <- data.frame(
S_0 = U[,4] + U[,3],
S_1 = U[,4] + U[,2]
)
if (use_churn_convention) {
data$uplift <- data$S_0 - data$S_1
} else {
data$uplift <- data$S_1 - data$S_0
}
data$Y_0 <- as.factor(as.numeric(runif(N) <= data$S_0))
data$Y_1 <- as.factor(as.numeric(runif(N) <= data$S_1))
data$T_ <- as.factor(as.numeric(runif(N) <= proba_treatment))
data$Y <- as.factor(ifelse(
data$T_ == "0",
as.integer(as.character(data$Y_0)),
as.integer(as.character(data$Y_1))
))
# Add random noise (increase variance) of the scores
data$S_0_hat <- data$S_0 + rnorm(n = N, sd = noise_S_0)
data$S_1_hat <- data$S_1 + rnorm(n = N, sd = noise_S_1)
# Compute the estimated uplift
if (use_churn_convention) {
data$uplift_hat <- data$S_0_hat - data$S_1_hat
} else {
data$uplift_hat <- data$S_1_hat - data$S_0_hat
}
return(data)
}
# Creates N samples from a unit simplex of dimension k
# Source: Smith, Noah A., and Roy W. Tromble. "Sampling uniformly from
# the unit simplex." Johns Hopkins University, Tech. Rep 29 (2004).
sample_simplex <- function(N, k) {
aperm(sapply(1:N, function(i) {
# Sample k - 1 reals from [0, 1] without replacement
numbers <- unique(runif(n = k - 1))
remaining <- k - 1 - length(numbers)
while (remaining > 0) {
numbers <- unique(c(numbers, runif(n = remaining)))
remaining <- k - 1 - length(numbers)
}
numbers <- c(0, sort(numbers), 1)
return(sapply(1:k, function(i) numbers[i + 1] - numbers[i]))
}))
}
|
ef4ef16e603c0e77b01c61dd9aeaef3325f65f69
|
2d310fd545505bb0fb7396011cd0c4859e8f0927
|
/inst/analysis/optimal_comorbid/make_cmb.R
|
b09555d5f5350ba61a46d25feef36644d49024ac
|
[
"MIT"
] |
permissive
|
olladapunaresh/pRs
|
b871d5ccec03b074c2cdc97eacad8c7d14c549dc
|
e0e81c76c87897930086cbd0fd4a477d481d1f7d
|
refs/heads/master
| 2023-03-18T18:53:56.621307
| 2018-12-10T13:40:08
| 2018-12-10T13:40:08
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 497
|
r
|
make_cmb.R
|
library(pRs)
bfile = "/scratch/hpc2862/OH/grs/plink/OHGS_B2_ALL_imp_b_s1"
assoc = c("cad_weights_hdl.assoc",
"cad_weights_ldl.assoc",
"cad_weights_tg.assoc")
p = seq(0, 0.5, by = 0.01)
n_i = c(180000,
180000,
180000)
r_i = c(-0.252,
0.25,
0.318)
h_i = c(0.1572,
0.1347,
0.1161)
h_1 = 0.30
cmb <- make_optimal_comborbid_prs(
bfile = bfile,
assoc = assoc,
p = p,
n_i = n_i,
r_i = r_i,
h_i =h_i,
h_1 = h_1
)
save.image("cmb.Rdata")
|
c9a8db96c81c851c4f6a8d8cc54464913db413c2
|
9c9328e812b8d3edcd9c97af0494110ea3c64054
|
/man/NBSpliceRes-plotGeneResults.Rd
|
dd0497674231e731e116906e57806e7ce71de095
|
[] |
no_license
|
gamerino/NBSplice
|
90fb5ad934f9a118aa8e6feb1215c2764af6bf52
|
ba4d4ee1c23a467eed3d0f5ad3ecc5acb000a5ec
|
refs/heads/master
| 2021-09-08T11:30:16.736683
| 2021-09-03T14:24:59
| 2021-09-03T14:24:59
| 124,399,153
| 3
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,432
|
rd
|
NBSpliceRes-plotGeneResults.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NBSpliceRes-plotGeneResults.R
\docType{methods}
\name{plotGeneResults}
\alias{plotGeneResults}
\alias{plotGeneResults-methods}
\alias{plotGeneResults}
\alias{plotGeneResults,NBSpliceRes-method}
\title{Method to obtain isoform's relative expression barplot for an specific
gene.}
\usage{
plotGeneResults(myNBRes, gene, filterLowExpIso = TRUE,
filterNotSignificant = TRUE, adjusted = TRUE, p.value = 0.05,
group = TRUE)
\S4method{plotGeneResults}{NBSpliceRes}(myNBRes, gene, filterLowExpIso = TRUE,
filterNotSignificant = TRUE, adjusted = TRUE, p.value = 0.05,
group = TRUE)
}
\arguments{
\item{myNBRes}{NBSpliceRes class object.}
\item{gene}{Character indicating the gene name.}
\item{filterLowExpIso}{Logical indicating if lower-expression isoforms should
be filtered out.}
\item{filterNotSignificant}{Logical indicating if not significant
isoforms should be filtered out.}
\item{adjusted}{Logical indicating if adjusted p values should be used.}
\item{p.value}{Numeric value between 0 and 1 giving the required family-wise
error rate or false discovery rate.}
\item{group}{Logical indicating if isoforms bars should be stacked or not}
}
\value{
A ggplot object.
}
\description{
\code{plotGeneResults} returns ggplot object which illustrates the isoform's
relative expression in the two contrasted conditions.
}
\note{
see full example in \code{\link{NBSpliceRes-class}}
}
\examples{
data(myDSResults, package="NBSplice")
gene<-results(myDSResults)[,"gene"][1]
##Plot gene results filtering low expressed isoforms
g<-plotGeneResults(myDSResults, gene)
if(interactive()){
g
}
##Plot gene results keeping low expressed isoforms
g<-plotGeneResults(myDSResults, gene, filterLowExpIso=FALSE)
if(interactive()){
g
}
##Plot isoform bar plots keeping low expressed isoforms
g<-plotGeneResults(myDSResults, gene, filterLowExpIso=FALSE, group=FALSE)
if(interactive()){
g
}
}
\seealso{
\code{\link{NBSpliceRes}}
Other NBSpliceRes: \code{\link{GetDSGenes}},
\code{\link{GetDSResults}}, \code{\link{GetGeneResults}},
\code{\link{NBSpliceRes-class}},
\code{\link{NBSpliceRes-initialize}},
\code{\link{NBSpliceRes}}, \code{\link{myDSResults}},
\code{\link{plotRatiosDisp}}, \code{\link{plotVolcano}}
}
\author{
Gabriela A. Merino \email{merino.gabriela33@gmail.com} and Elmer A.
Fernandez \email{efernandez@bdmg.com.ar}
}
|
4e2394a4e27a7674e1688a306d3dca3138195d0d
|
b3618d19fb644bd8fbae8a091cd179a80eaa4da3
|
/E323 - Cell identification.R
|
73dcf84932007020aa04f517a9d5719c59649797
|
[] |
no_license
|
andrewrgross/E323---COVID-pancreas
|
1935f166abb62bbe650ceaaf9d9ead075ba942de
|
f5f81054e4e02f1a68a2cba996cd6adc10cd21af
|
refs/heads/master
| 2022-12-22T09:31:39.668881
| 2020-09-23T17:10:48
| 2020-09-23T17:10:48
| 298,040,370
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,760
|
r
|
E323 - Cell identification.R
|
### E323 Cell Identification
### Header
library(QICFIT)
### Functions
### Import Data
query.df.full <- read.csv("C:/Users/grossar/Box/Sareen Lab Shared/Data/Vicky/E283-COVID Infection/Round 2 RNAseq/VW-10228--08--04--2020_FPKM.csv", row.names = 1)
ref.df <- read.csv("C:/Users/grossar/Box/Sareen Lab Shared/Data/Andrew/reference_data/gtex/GTEx_median_expression_scores_by_tissue.csv", row.names = 1)
### Format Data
query.df.full <- convert.ids(query.df.full)
query.df <- query.df.full[1]
### Edit Ensembl IDs and reorder
test <- head(ref.df[1:10])
test[names]
input.df <- ref.df
for(row.num in 1:nrow(input.df)){
id = ref.df$Name[row.num]
new.id = strsplit(id,"\\.")[[1]][1]
input.df$Name[row.num] <- new.id
}
row.names(ref.df) <- input.df$Name
ref.df <- ref.df[3:55]
ref.df <- ref.df[order(row.names(ref.df)),]
### Run Spearman Correlation
spearman.results <- spearman.corr.for.single.sample(query.df = query.df[1], ref.df = ref.df)
spearman.results.ordered <- spearman.results[order(spearman.results[1], decreasing = TRUE),, drop=FALSE]
### Comparing to Pituitary
which(names(ref.df) == "Pancreas")
ref.df.panc <- ref.df[41]#[1:20,, drop = FALSE]
### Generate list of top 100 Genes
ref.panc.1000 <- ref.df.panc[order(ref.df.panc, decreasing = TRUE),,drop=FALSE][1:1000,, drop = FALSE]
query.1000 <- query.df2[order(query.df2, decreasing = TRUE),, drop=FALSE][1:1000,, drop=FALSE]
matches <- match(row.names(ref.panc.100), row.names(query.1000))
head(ref.panc.1000)
head(query.1000)
head(matches)
ref.panc.1000[15,,drop=FALSE]
query.1000[17,,drop = FALSE]
### Find the spearman score of the top 1000?
spearman.results.1000 <- spearman.corr.for.single.sample(query.df = query.1000, ref.df = ref.df)
spearman.results.1000.ordered <- spearman.results.1000[order(spearman.results.1000[1], decreasing = TRUE),, drop=FALSE]
### Find the top 1000 from each sample and compare to the top 1000 of the query
### For each sample, find the top 1000
spearman.corr.for.single.sample.10000 <- function(query.df, ref.df) { # Calculate the spearman correlation between the query.df and the references
spearman.results <- data.frame(rep(0,ncol(ref.df))) # Generate empty results table
row.names(spearman.results) <- names(ref.df) # Name empty results table
names(spearman.results) <- names(query.df)
for(ref.tissue.num in 1:ncol(ref.df)) { # Loop through each reference
ref.tissue.data <- ref.df[ref.tissue.num] # Call the current tissue from the references
tissue <- names(ref.tissue.data)
### Subset to the top 10000 genes
ref.tissue.data <- ref.tissue.data[order(ref.tissue.data, decreasing = TRUE),,drop=FALSE][1:10000,, drop=FALSE]
### Generate a data frame containing the two transcriptomes being compared
ref.tissue.data <- ref.tissue.data[which(ref.tissue.data[,1]>0),,drop=FALSE] # Filter out missing values from tissue
genes.present.in.ref <- row.names(ref.tissue.data) # Declare the genes present in the reference
genes.missing.in.query <- setdiff(genes.present.in.ref, row.names(query.df)) # Declare genes in reference missing from query.df
rows.to.add <- data.frame(rep(0,length(genes.missing.in.query))) # Generate a zero data frame the size of the missing rows
row.names(rows.to.add) <- genes.missing.in.query # Name rows after missing rows
names(rows.to.add) <- names(query.df) # Name column the same as the query
query.df2 <- rbind(query.df,rows.to.add) # Use rbind to make a full data frame containing exactly the genes in the reference
query.df2 <- query.df2[genes.present.in.ref, , drop = FALSE] # Reorder query.df to match reference
spearman.input <- cbind(query.df2, ref.tissue.data) # Bind query.df and reference
result <- rcorr(as.matrix(spearman.input), type = 'spearman')[[1]] # Perform spearman calculation using rcorr
result <- round(result[2], 5) # Round result
spearman.results[tissue,] <- result # Add to results table
}
return(spearman.results)
}
query.10000 <- query.df2[order(query.df2, decreasing = TRUE),, drop=FALSE][1:10000,, drop=FALSE]
spearman.results.10000 <- spearman.corr.for.single.sample(query.df = query.10000, ref.df = ref.df)
spearman.results.10000.ordered <- spearman.results.10000[order(spearman.results.10000[1], decreasing = TRUE),, drop=FALSE]
spearman.results.1000 <- spearman.corr.for.single.sample(query.df = query.1000, ref.df = ref.df)
(spearman.results.1000.ordered <- spearman.results.1000[order(spearman.results.1000[1], decreasing = TRUE),, drop=FALSE])
spearman.results.1000 <- spearman.corr.for.single.sample.1000(query.df = query.1000, ref.df = ref.df)
(spearman.results.1000.ordered <- spearman.results.1000[order(spearman.results.1000[1], decreasing = TRUE),, drop=FALSE])
spearman.manual.results <- spearman.manual.for.single.sample(query.df,ref.df,weighted = TRUE)
(spearman.manual.results.o <- spearman.manual.results[order(spearman.manual.results[1], decreasing = TRUE),, drop=FALSE])
spearman.genes <- spearman.gene.assessor.single.sample(query.df, ref.df, weighted = FALSE)
spearman.gene.assessor.single.sample <- function(query.df, ref.df, weighted = FALSE) { # Calculate the spearman correlation between the query.df and the references
spearman.results <- data.frame(rep(0,ncol(ref.df))) # Generate empty results table
row.names(spearman.results) <- names(ref.df) # Name empty results table
names(spearman.results) <- names(query.df)
for(ref.tissue.num in 1:ncol(ref.df)) { # Loop through each reference
ref.tissue.data <- ref.df[ref.tissue.num] # Call the current tissue from the references
tissue <- names(ref.tissue.data)
### Generate a data frame containing the two transcriptomes being compared
ref.tissue.data <- ref.tissue.data[which(ref.tissue.data[,1]>0),,drop=FALSE] # Filter out missing values from tissue
genes.present.in.ref <- row.names(ref.tissue.data) # Declare the genes present in the reference
genes.missing.in.query <- setdiff(genes.present.in.ref, row.names(query.df)) # Declare genes in reference missing from query.df
rows.to.add <- data.frame(rep(0,length(genes.missing.in.query))) # Generate a zero data frame the size of the missing rows
row.names(rows.to.add) <- genes.missing.in.query # Name rows after missing rows
names(rows.to.add) <- names(query.df) # Name column the same as the query
query.df2 <- rbind(query.df,rows.to.add) # Use rbind to make a full data frame containing exactly the genes in the reference
query.df2 <- query.df2[genes.present.in.ref, , drop = FALSE] # Reorder query.df to match reference
spearman.input <- cbind(query.df2, ref.tissue.data) # Bind query.df and reference
#result <- rcorr(as.matrix(spearman.input), type = 'spearman')[[1]] # Perform spearman calculation using rcorr
#result <- round(result[2], 5) # Round result
#spearman.results[tissue,] <- result # Add to results table
#spearman.input.o <- spearman.input[order(spearman.input[1], decreasing = TRUE),, drop=FALSE]
### Assign rank and delta rank
spearman.input$sample_rank <- rank(-spearman.input[1])
spearman.input$ref_rank <- rank(-spearman.input[2])
spearman.input$d <- spearman.input$sample_rank - spearman.input$ref_rank
spearman.input$d2 <- (spearman.input$d)^2
if(weighted == TRUE) {
### Calculate weighted delta rank
spearman.input$d2 <- spearman.input$d2 * ((-2*spearman.input$ref_rank/max(spearman.input$ref_rank))+2)
}
gene.report <- spearman.input[order(spearman.input$d2, decreasing = TRUE),]
# Add to results table
}
return(gene.report)
}
spearman.genes <- spearman.gene.assessor.single.sample(query.1000, ref.df[41], weighted = FALSE)
spearman.genes.g <- addGene(spearman.genes[1:100,])
spearman.genes.g2 <- addGene(tail(spearman.genes,100))
spearman.genes.g2 <- spearman.genes.g2[order(spearman.genes.g2$sample_rank),]
spearman.genes.g2 <- add.description(spearman.genes.g2)
|
551a62f639e2c7df3dfee72ec5b5d28a1d1f86df
|
ed8dfa1bf03d10630e22c0b3d9f27227de941752
|
/51-60/54.Get_hand.R
|
fe7ed0bc00cbbdf8f217e950bb00aa00927c273d
|
[] |
no_license
|
shahronak47/Project-Euler
|
3bcd1001ed5a7dfe288e20b1dbb5718aaaa673e5
|
30ba8fa5250bb4a4c755ece09020e0c31f4adac8
|
refs/heads/master
| 2021-12-24T04:43:35.244694
| 2021-12-18T06:39:04
| 2021-12-18T06:39:04
| 77,676,595
| 2
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,880
|
r
|
54.Get_hand.R
|
source("/Users/Ronak Shah/Google Drive/Git-Project-Euler/51-60/54.Poker_card_supporting_functions.R")
get_hand <- function(hands_1, hands_2) {
value1 <- substr(hands_1, 1, 1)
suit1 <- substr(hands_1, 2, 2)
value2 <- substr(hands_2, 1, 1)
suit2 <- substr(hands_2, 2, 2)
output1 = is_royal_flush(value1, suit1)
output2 = is_royal_flush(value2, suit2)
if(sum(output1, output2) == 1) {
if (output1) return("Player 1") else return("Player 2")
}
output1 = is_straight_flush(value1, suit1)
output2 = is_straight_flush(value2, suit2)
if(sum(output1, output2) == 1){
if (output1) return("Player 1") else return("Player 2")
}
if(sum(output1, output2) == 2) {
return(break_ties(value1, value2))
}
output1 = is_four_of_a_kind(value1)
output2 = is_four_of_a_kind(value2)
if(sum(output1, output2) == 1){
if (output1) return("Player 1") else return("Player 2")
}
if(sum(output1, output2) == 2) {
return(break_ties(value1, value2))
}
output1 = is_full_house(value1)
output2 = is_full_house(value2)
if(sum(output1, output2) == 1){
if (output1) return("Player 1") else return("Player 2")
}
if(sum(output1, output2) == 2) {
return(break_ties(value1, value2))
}
output1 = is_flush(suit1)
output2 = is_flush(suit2)
if(sum(output1, output2) == 1){
if (output1) return("Player 1") else return("Player 2")
}
if(sum(output1, output2) == 2) {
return(break_ties(value1, value2))
}
output1 = is_straight(value1)
output2 = is_straight(value2)
if(sum(output1, output2) == 1){
if (output1) return("Player 1") else return("Player 2")
}
if(sum(output1, output2) == 2) {
return(break_ties(value1, value2))
}
output1 = is_three_of_a_kind(value1)
output2 = is_three_of_a_kind(value2)
if(sum(output1, output2) == 1){
if (output1) return("Player 1") else return("Player 2")
}
if(sum(output1, output2) == 2) {
return(break_ties(value1, value2))
}
output1 = is_two_pair(value1)
output2 = is_two_pair(value2)
if(sum(output1, output2) == 1){
if (output1) return("Player 1") else return("Player 2")
}
if(sum(output1, output2) == 2) {
return(break_ties(value1, value2))
}
output1 = is_a_pair(value1)
output2 = is_a_pair(value2)
if(sum(output1, output2) == 1){
if (output1) return("Player 1") else return("Player 2")
}
if(sum(output1, output2) == 2) {
return(break_ties(value1, value2))
}
output1 = high_card(value1)
output2 = high_card(value2)
if (output1 > output2) return("Player 1") else return("Player 2")
}
break_ties <- function(value1, value2) {
out1 = tie_breaker(value1)
out2 = tie_breaker(value2)
if (out1 == out2) {
output1 = high_card(value1)
output2 = high_card(value2)
if (output1 > output2) return("Player 1") else return("Player 2")
}
if(out1 > out2) return("Player 1") else return("Player 2")
}
|
4f8de0cc3be0739934744a7275fca2118ae4d9ed
|
0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb
|
/cran/paws.business.applications/man/chime_describe_channel_membership_for_app_instance_user.Rd
|
7cc0840e82910f6402f0583986f1d50fb31424f6
|
[
"Apache-2.0"
] |
permissive
|
paws-r/paws
|
196d42a2b9aca0e551a51ea5e6f34daca739591b
|
a689da2aee079391e100060524f6b973130f4e40
|
refs/heads/main
| 2023-08-18T00:33:48.538539
| 2023-08-09T09:31:24
| 2023-08-09T09:31:24
| 154,419,943
| 293
| 45
|
NOASSERTION
| 2023-09-14T15:31:32
| 2018-10-24T01:28:47
|
R
|
UTF-8
|
R
| false
| true
| 1,394
|
rd
|
chime_describe_channel_membership_for_app_instance_user.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chime_operations.R
\name{chime_describe_channel_membership_for_app_instance_user}
\alias{chime_describe_channel_membership_for_app_instance_user}
\title{Returns the details of a channel based on the membership of the
AppInstanceUser specified}
\usage{
chime_describe_channel_membership_for_app_instance_user(ChannelArn,
AppInstanceUserArn)
}
\arguments{
\item{ChannelArn}{[required] The ARN of the channel to which the user belongs.}
\item{AppInstanceUserArn}{[required] The ARN of the user in a channel.}
}
\value{
A list with the following syntax:\preformatted{list(
ChannelMembership = list(
ChannelSummary = list(
Name = "string",
ChannelArn = "string",
Mode = "UNRESTRICTED"|"RESTRICTED",
Privacy = "PUBLIC"|"PRIVATE",
Metadata = "string",
LastMessageTimestamp = as.POSIXct(
"2015-01-01"
)
),
AppInstanceUserMembershipSummary = list(
Type = "DEFAULT"|"HIDDEN",
ReadMarkerTimestamp = as.POSIXct(
"2015-01-01"
)
)
)
)
}
}
\description{
Returns the details of a channel based on the membership of the
\code{AppInstanceUser} specified.
}
\section{Request syntax}{
\preformatted{svc$describe_channel_membership_for_app_instance_user(
ChannelArn = "string",
AppInstanceUserArn = "string"
)
}
}
\keyword{internal}
|
0f009087d669de66d6f90fcc1bd43d7ddc3b606d
|
49fc9dad8aa96993c49e8f09c668fcc3ae112629
|
/R/legendre.R
|
34f6c78109e786370fe8958a7b0e78747c8c1fa9
|
[] |
no_license
|
NathanWycoff/GenInterpConv
|
daf2faa3f69505bcee667105c762262263fd77d1
|
c798a74ba8acda4b7c7c99931d4b5676d973b2c4
|
refs/heads/master
| 2020-05-03T11:42:35.620224
| 2019-03-30T20:56:45
| 2019-03-30T20:56:45
| 178,606,974
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,479
|
r
|
legendre.R
|
#!/usr/bin/Rscript
# R/legendre.R Author "Nathan Wycoff <nathanbrwycoff@gmail.com>" Date 01.19.2019
#' Calculate a degree P Legendre polynomial evaluated at x
leg_poly <- function(P, x) {
if (x > 1 | x < -1) {
stop("Legendre Polynomial defined on [-1,1]")
}
n <- P
ret <- 0
for (m in 0:floor(P/2)) {
if (m %% 2 == 0) {
sgn <- 1
} else {
sgn <- -1
}
num <- factorial(2*(n-m))
denom <- 2^n * factorial(m) * factorial(n-m) * factorial(n-2*m)
ret <- ret + sgn*num/denom*x^(n-2*m)
}
return(ret)
}
#' Polynomial Basis Conversion
#'
#' Convert from a Legendre polynomial representation to a canonical/monomial one.
#'
#' Translation of Mathematica code form J.M here: https://math.stackexchange.com/questions/86298/the-relationship-between-legendre-polynomials-and-monomial-basis-polynomials
#' Implements Clenshaw's algorithm.
#'
#' @param lc The Legendre coefficients.
#' @return A vector of length length(lc) giving the monomial basis of the input polynomial.
leg_to_mon <- function(lc) {
n <- length(lc)-1#Poly degree
pc <- c(lc[length(lc)], rep(0, n))
z <- rep(0, n+1)
v <- w <- 0
for (j in n:1) {
w <- pc[1]
pc[1] <- lc[j] - v * z[1]
z[1] = w
for (i in 2:(n-j+2)) {
w <- pc[i]
pc[i] <- (2 * j - 1) * z[i-1] / j - v * z[i]
z[i] <- w
}
v <- (j-1) / j
}
return(pc)
}
#' Polynomial Basis Conversion
#'
#' Convert from a polynomial in monomial / canonical form to one in Legendre form.
#'
#' Translation of Mathematica code form J.M here: https://math.stackexchange.com/questions/86298/the-relationship-between-legendre-polynomials-and-monomial-basis-polynomials
#' Implements Clenshaw's algorithm.
#'
#' @param pc The monomial coefficients.
#' @return A vector of length length(lc) giving the Legendre basis of the input polynomial.
mon_to_leg <- function(pc) {
n <- length(pc) - 1
q <- lc <- rep(0, n+1)
lc[1] <- pc[n]
lc[2] <- pc[length(pc)]
for (k in 2:n) {
q[1] <- lc[1]
lc[1] <- pc[n-k+1] + lc[2] / 3
if (k-1 >= 2) {
for (j in 2:(k-1)) {
q[j] <- lc[j]
lc[j] <- j * lc[j+1] / (2*j+1) + (j-1) * q[j-1]/(2*j-3)
}
}
q[k] <- lc[k]
lc[k] <- (k-1) * q[k-1] / (2*k-3)
lc[k+1] <- k * q[k] / (2*k-1)
}
return(lc)
}
|
e8eafb7f50f08439f3bde8b9a6381aeed885ab7a
|
cb453e22a4d3ab7ba7b7a96972887a0d5babb4d6
|
/plot4.R
|
2b0505eb015454a7c8a3553a40426519dde3f9f2
|
[] |
no_license
|
heikalm/ExData_Plotting1
|
e4b0c40b5f382daa48850f0c35a0810f4aa50beb
|
0e0e8ae87c2defd71d4482a3cd6bb7c085ac8adc
|
refs/heads/master
| 2020-04-30T01:17:50.972769
| 2019-03-19T19:06:46
| 2019-03-19T19:06:46
| 176,525,709
| 0
| 0
| null | 2019-03-19T14:08:02
| 2019-03-19T14:08:01
| null |
UTF-8
|
R
| false
| false
| 1,551
|
r
|
plot4.R
|
library(data.table)
filename <- "data/EDAweek1.zip"
if (!dir.exists("data")) {
dir.create("data")
}
# Check for file and unzip
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileURL, filename, method="curl")
unzip(filename, exdir="data")
}
#read data, format date and time, and get relevant date subset
dat <- read.table("data/household_power_consumption.txt", header=TRUE, na.strings="?", sep=";")
dat$Date <- as.Date(dat$Date, "%d/%m/%Y")
my_dat <- subset(dat, Date>="2007-2-1" & Date<="2007-2-2")
my_dat$DateTime <- as.POSIXct(paste(my_dat$Date, my_dat$Time))
#draw plot
par(mfrow=c(2,2), mar=c(4,4,2,1), oma=c(0,0,2,0))
with(my_dat, {
plot(Global_active_power~DateTime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
plot(Voltage~DateTime, type="l",
ylab="Voltage (volt)", xlab="")
plot(Sub_metering_1~DateTime, type="l",
ylab="Global Active Power (kilowatts)", xlab="")
lines(Sub_metering_2~DateTime,col='Red')
lines(Sub_metering_3~DateTime,col='Blue')
legend("topright", col=c("black", "red", "blue"), lty=1, lwd=2, bty="n",
legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"))
plot(Global_reactive_power~DateTime, type="l",
ylab="Global Reactive Power (kilowatts)",xlab="")
})
# export to png
dev.copy(png, file="plot4.png", height=480, width=480)
dev.off()
|
97a4161c1da775e436d1940c8dacfd7a86284515
|
f71c9ac51e3dc6d3d255b8548e7a16e208be72bf
|
/inst/scripts/make-data-human-DLPFC.R
|
17c8bc2e6a6f8b40a82ec614c35d23c35ecb4ea3
|
[
"MIT"
] |
permissive
|
drighelli/STdata
|
1e22a7bbfe1ba31418262796f7bd76b9a5932276
|
39ad07313846916237fe329c567665e77fcbbe3b
|
refs/heads/main
| 2023-02-01T14:37:52.379079
| 2020-12-18T09:18:53
| 2020-12-18T09:18:53
| 321,612,112
| 0
| 0
| null | 2020-12-15T09:08:44
| 2020-12-15T09:08:43
| null |
UTF-8
|
R
| false
| false
| 4,183
|
r
|
make-data-human-DLPFC.R
|
########################################################
# Script to create human DLPFC data object from raw data
# Lukas Weber, December 2020
########################################################
# for more details on this dataset see:
# http://spatial.libd.org/spatialLIBD/
# data can be downloaded as a SingleCellExperiment object through spatialLIBD
# package from Bioconductor (http://bioconductor.org/packages/spatialLIBD) -
# this contains all 12 samples, so here we subset sample 151673
# raw data files are also available from:
# http://spatial.libd.org/spatialLIBD/
# https://github.com/LieberInstitute/HumanPilot/tree/master/10X/151673
library(ExperimentHub)
library(spatialLIBD)
library(rjson)
library(SingleCellExperiment)
library(SpatialExperiment)
# ---------
# Load data
# ---------
# load data for all 12 samples from spatialLIBD package
ehub <- ExperimentHub()
# download and load full dataset (12 samples)
sce <- fetch_data(type = "sce", eh = ehub)
# subset to keep sample 151673 only
sce_sub <- sce[, sce$sample_name == "151673"]
# ----------------
# Load image files
# ----------------
# download image files separately from links at:
# http://spatial.libd.org/spatialLIBD/
# and:
# https://github.com/LieberInstitute/HumanPilot/tree/master/10X/151673
# saved locally in the following location
dir_local <- "~/data/HumanPilot/10X/151673"
# image file paths
# note: not including "aligned_fiducials.jpg" or "detected_tissue_image.jpg" from Loupe
img_paths <- c(
tissue_hires_image = file.path(dir_local, "tissue_hires_image.png"),
tissue_lowres_image = file.path(dir_local, "tissue_lowres_image.png")
)
# spatial scale factors
file_scale_factors <- file.path(dir_local, "scalefactors_json.json")
scale_factors <- fromJSON(file = file_scale_factors)
# ------------------------
# Create SpatialExperiment
# ------------------------
# reformat SingleCellExperiment into SpatialExperiment, keeping only minimal
# columns from rowData and colData
# counts
counts <- assays(sce_sub)[["counts"]]
# row data
row_data <- rowData(sce_sub)[, c("gene_id", "gene_name", "type", "gene_source",
"gene_version", "gene_biotype")]
rownames(row_data) <- rowData(sce_sub)$gene_id
# column data
col_data <- colData(sce_sub)[, c("barcode", "row", "col", "imagerow", "imagecol",
"height", "width", "cell_count")]
# ground truth labels with NAs included as factor level for easier plotting
ground_truth <- as.character(colData(sce_sub)[, "layer_guess_reordered"])
ground_truth[ground_truth == "WM"] <- "White_matter"
ground_truth[is.na(ground_truth)] <- "NA"
ground_truth <- factor(ground_truth, levels = c("Layer1", "Layer2", "Layer3", "Layer4", "Layer5", "Layer6", "White_matter", "NA"))
col_data$ground_truth <- ground_truth
# add custom sample ID
# note: currently not working with custom sample ID
#col_data$sample_id <- "sample_01"
colnames(col_data)[1] <- "barcode_id"
rownames(col_data) <- colData(sce_sub)$barcode
# spatial coordinates
# add custom "x_coord" and "y_coord" with flipped/reversed coordinates for Visium platform
spatial_coords <- colData(sce_sub)[, c("barcode", "tissue")]
colnames(spatial_coords) <- c("barcode_id", "in_tissue")
spatial_coords$x_coord <- colData(sce_sub)[, "imagecol"]
spatial_coords$y_coord <- -1 * colData(sce_sub)[, "imagerow"] + max(colData(sce_sub)[, "imagerow"]) + 1
# note: column "in_tissue" must be logical
spatial_coords$in_tissue <- as.logical(as.numeric(spatial_coords$in_tissue))
rownames(spatial_coords) <- colData(sce_sub)$barcode
# image data
# both low and high resolution images from Space Ranger
img_data <- readImgData(
path = dir_local,
sample_id = "Sample01",
imageSources = c(img_paths["tissue_lowres_image"], img_paths["tissue_hires_image"]),
scaleFactors = file_scale_factors,
load = TRUE
)
# create SpatialExperiment
spe <- SpatialExperiment(
assays = list(counts = counts),
rowData = row_data,
colData = col_data,
spatialCoords = spatial_coords,
imgData = img_data
)
spe
# ----------------
# Save data object
# ----------------
save(spe, file = "~/Dropbox/STdata/human_DLPFC.RData")
|
da3fad99e213c569d60f25660d03e90334ce3044
|
a0e469d016fa3d8f26cf53c9c332aa2b159120aa
|
/Riboseq_Normalization_and_limma.R
|
2328db4f12072cfc015bebb3f6358ae16c37c360
|
[] |
no_license
|
Ornela88/Arabidopsis_Riboseq
|
c1509bb084ee9b6852bef5ac3b3c59ed07040579
|
6b918f0600eee932e2cb12c9edc81e01449ec548
|
refs/heads/main
| 2023-02-24T04:35:10.175682
| 2021-02-01T09:15:30
| 2021-02-01T09:15:30
| 313,895,025
| 0
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,547
|
r
|
Riboseq_Normalization_and_limma.R
|
#library
options(stringsAsFactors = F)
#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("TxDb.Athaliana.BioMart.plantsmart28")
#BiocManager::install("Rsamtools")
library(Rsamtools)
library(RiboProfiling)
library(TxDb.Athaliana.BioMart.plantsmart28)
library(org.At.tair.db)
library("GO.db")
library("GOstats")
library(GenomicAlignments)
library(xlsx)
library(BSgenome)
library(BSgenome.Athaliana.TAIR.TAIR9)
library(Biostrings)
library(GenomeInfoDb)
library(BSgenome)
library('GenomicFeatures')
library(GenomeInfoDb)
library(GenomicRanges)
library(Biostrings)
library(GenomicFeatures)
library(ggplot2)
library(grid)
library(systemPipeR)
library(S4Vectors)
library(GGally)
library(RiboseQC)
library(limma)
library(Rsubread)
library(edgeR)
library(biomaRt)
library(RColorBrewer)
library(DESeq2, quietly = TRUE)
library(ape, warn.conflicts = FALSE)
library(gplots)
######Read multiple BAM files together
WORKDIR="/usr/data/bgfs1/maloku/riboseq_data_Ornela/bam_files/";
dir.create(WORKDIR)
setwd(WORKDIR)
WORK_RiboProfiling ="/usr/home/maloku/RiboProfiling_test/";
dir.create(WORK_RiboProfiling_Result)
BAI <- list.files(WORKDIR, pattern = "bam.bai$",full.names = T)
SAMPLES_bai <- gsub(".bai","",list.files("/usr/data/bgfs1/maloku/riboseq_data_Ornela/bam_files/", pattern = ".bai$"))
WORK_RiboProfiling_Result <-paste0(WORK_RiboProfiling, "Results/")
BAMS <- list.files(WORKDIR, pattern = ".bam$",full.names = T)
################### FeatureCounts to quantifying read counts for each gene
fc <-featureCounts(files=BAMS, annot.ext="/usr/home/maloku/RiboProfiling_test/Arabidopsis_thaliana.TAIR10.45.gtf",
useMetaFeatures = F, isPairedEnd = F, isGTFAnnotationFile = T, nthreads = 20, annot.inbuilt="TAIR10")
names(fc)
###Normalization step
# Convert counts to dge object
d0 <- DGEList(fc$counts)
d0$genes <- fc$annotation[,c( "GeneID","Length"), drop=FALSE]
d0 <-calcNormFactors(d0)
##### or
isexpr <- rowSums(cpm(d0) > 2) >= 3 ## Create filter genes that have less than 2 counts per million in at least three libraries
d0<- d0[isexpr,]
d0.rpkm <- rpkm(d0,d0$genes$Length) # create object with rpkm values estimated from the counts
write.csv(file = '/usr/home/maloku/RiboProfiling_test/CountValues.csv', d0$counts) # Save raw count data
write.csv(file = '/usr/home/maloku/RiboProfiling_test/RPKMValues.csv', d0.rpkm) # Save RPKM values
## definition of a model matrix. This design matrix simply links each group to the samples that belong to it.
targets <-readTargets("/usr/home/maloku/RiboProfiling_test/samples.csv",sep=",")
time <-targets$time
mm <- model.matrix(~0+time)
colnames(time) <- levels(time)
mm
##############Differential expression using voom ###################
# use this when the library sizes are quite variable between samples, then the voom approach is theoretically more powerful than limma-trend
y <- voom(d0, mm, plot=T, normalize ="quantile")
names(y)
cor(y$E)
#correlations among samples and graphically show similarity among samples with a MDS plot.
write.csv(cor(y$E), file="/usr/home/maloku/RiboProfiling_test/d0_correlation_yE.csv")
plotMDS(y)
par(mfrow=c(1,2))
boxplot(y$E, xlab="", ylab="Log2 counts per million",las=2,main="Voom transformed logCPM")
#blue horizontal line that corresponds to the median logCPM
abline(h=median(y$E),col="blue")
#### fitting linear models
fitV <-lmFit(y,mm)
names(fitV)
head(coef(fitV))
write.csv(fitV, file="/usr/home/maloku/RiboProfiling_test/fitV_model_all.csv")
fitV <- eBayes(fitV)
#Summarize the number of significant genes, p < 0.05 after BH-correction
top_table_all <-topTable(fitV, coef=ncol(mm), adjust.method="BH",sort.by="none")
#TestResults matrix. This is numeric matrix of 0's, 1's and -1's indicating significance of a test or membership of a set.
results <- decideTests(fitV)
#write.csv(vennCounts(results, include="both"), file="/usr/home/maloku/RiboProfiling_test/fitV_model_up_and_down_genes.csv")
# Venn diagram that shows the sets of sign. genes that are common among the contrasts
vennCounts(results)
vennDiagram(results[,4:7], circle.col=c("red","green","blue","yellow"), main = 'All significant below adjusted P-value 0.05')
vennDiagram(results[,4:7], include = 'down', circle.col=c("red","green","blue","yellow"), main = 'Down-regulated below adjusted P-value 0.05')
vennDiagram(results[,4:7], include = 'up', circle.col=c("red","green","blue","yellow"), main = 'Up-regulated below adjusted P-value 0.05')
##comparison between groups and contrast estimation
contr <-makeContrasts(T1vsT3=timeT1-timeT3,T5vsT7=timeT5-timeT7,T9vsT11=timeT9-timeT11,T13vsT15=timeT13-timeT15,
T17vsT19=timeT17-timeT19,T21vsT23=timeT21-timeT23,
levels=colnames(coef(fitV)))
tmp <- contrasts.fit(fitV,contr)
tmp <-eBayes(tmp)
summa.fit <- decideTests(tmp)
summary(summa.fit)
top.table <-topTable(tmp,sort.by="none", adjust.method="BH")
head(top.table,5)
write.table(top.table, file ="/usr/home/maloku/RiboProfiling_test/top_table_limma.txt",
row.names=F, sep="\t", quote=F)
#### Plot P value and adj P value
pdf(paste0(WORK_RiboProfiling_Result, "P_VAl_adj_P_val.pdf"), width = 15, height = 15)
layout(matrix(1:2, ncol=1))
tab1<-cumsum(table(cut(top.table$P.Value, breaks=c(0,1e-7,1e-6,1e-5,1e-4,1e-3,1e-2,0.05, 1e-1,1))))
barplot(tab1, cex.names=0.7, col="darkolivegreen2", space=0, main="Cumulative number of genes attaining defined significance - unadjusted P values Correlation Analyses", xlab="Gene significance", ylab="Gene number")
for(i in 1:9)text(x=i-0.5, y=1, tab1[i], pos=3)
tab4<-cumsum(table(cut(top.table$adj.P.Val, breaks=c(0,1e-7,1e-6,1e-5,1e-4,1e-3,1e-2,0.05, 1e-1,1))))
barplot(tab1, cex.names=0.7, col="darkolivegreen2", space=0, main="Cumulative number of genes attaining defined significance - adjusted P values Correlation Analyses", xlab="Gene significance", ylab="Gene number")
for(i in 1:9)text(x=i-0.5, y=1, tab4[i], pos=3)
dev.off()
################comparison between groups and contrast estimation for T1 vs T3
contrT1_T3 <-makeContrasts(T1vsT3=timeT1-timeT3,
levels=colnames(coef(fitV)))
tmp1 <- contrasts.fit(fitV,contrT1_T3)
tmp1 <-eBayes(tmp1)
top.table1 <-topTable(tmp1,sort.by="none",adjust.method="BH")
write.csv(top.table1, file ="/usr/home/maloku/RiboProfiling_test/topT1_T3_limma.csv")
#### Plot P value and adj P value for T1 vs T3
pdf(paste0(WORK_RiboProfiling_Result, "P_VAl_adj_P_val_T1_T3.pdf"), width = 15, height = 15)
layout(matrix(1:2, ncol=1))
tab1<-cumsum(table(cut(top.table1$P.Value, breaks=c(0,1e-7,1e-6,1e-5,1e-4,1e-3,1e-2,0.05, 1e-1,1))))
barplot(tab1, cex.names=0.7, col="darkolivegreen2", space=0, main="Cumulative number of genes attaining defined significance - unadjusted P values Correlation Analyses", xlab="Gene significance", ylab="Gene number")
for(i in 1:9)text(x=i-0.5, y=1, tab1[i], pos=3)
tab4<-cumsum(table(cut(top.table1$adj.P.Val, breaks=c(0,1e-7,1e-6,1e-5,1e-4,1e-3,1e-2,0.05, 1e-1,1))))
barplot(tab1, cex.names=0.7, col="darkolivegreen2", space=0, main="Cumulative number of genes attaining defined significance - adjusted P values Correlation Analyses", xlab="Gene significance", ylab="Gene number")
for(i in 1:9)text(x=i-0.5, y=1, tab4[i], pos=3)
dev.off()
######scatterplot of T1 effect vs T3 effect
T1<- topTable(fitV[,1], number = Inf, sort.by = "t", coef = grep("timeT1",
colnames(coef(fitV[,1]))))
T3 <- topTable(fitV, number = Inf, sort.by = "t", coef = grep("timeT3",
colnames(coef(fitV))))
names(T1)
names(T3)
smoothScatter(T1$t ~ T3$t, xlim = c(-20, 20), ylim = c(-20, 20), xlab = "t-statistic for effect of T1",
ylab = "t-statistic for effect of T3")
abline(a = 0, b = 1, col = "orange")
##################heatmap plot
png("/usr/home/maloku/RiboProfiling_test/heatmaps_in_r", width=700, height=700)
logCPM <- cpm(d0, log=TRUE, prior.count=3)
var_genes <- apply(logCPM, 1, var)
select_var <- names(sort(var_genes, decreasing=TRUE))[1:20]
my_palette <- colorRampPalette(c("red", "yellow", "green"))
highly_variable_lcpm <- logCPM[select_var,]
heatmap.2(highly_variable_lcpm,col=rev(my_palette(70)),trace="none", main="Top 300 most variable genes across samples",scale="row")
###or use pheatmap
pheatmap(highly_variable_lcpm, scale = "row", clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation")
dev.off()
|
51b24d4441b976cd0255c20f1852b97618ef56ba
|
5ed2eda1f12a981732f42a363a3cce46c72c4e67
|
/man/signatures.Rd
|
37af8565542b89d3818572d32340cea87742468e
|
[] |
no_license
|
mgaldino/r_we_the_people
|
8f7e0029a3af243bb1cb06a3e1bd461e93987921
|
eab1e11d26de81b9aa1955824744bd88a23437f0
|
refs/heads/master
| 2021-01-15T17:02:18.669278
| 2013-06-24T13:42:58
| 2013-06-24T13:43:31
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 261
|
rd
|
signatures.Rd
|
\docType{data}
\name{signatures}
\alias{signatures}
\title{Signatures loaded Feb 19, 2013}
\description{
Sample signatures loaded from the API on February 19,
2013.
}
\author{
Yoni Ben-Meshulam \email{yoni@opower.com}
}
\keyword{data}
\keyword{datasets}
|
d873925a2a7afe5248ccaffab1818b663a8b5587
|
163ac854249950435047e5c3ba968f86f4a4521b
|
/plot1.R
|
349b9edb4bbb13440818d6c181b70c9d1b1b1507
|
[] |
no_license
|
resoliwan/rGraph_p1
|
08634f2b8ea9f151069547fe01f0ac2412abc2ed
|
d76860771d7d17ca4045348a98c0d986f8742c0b
|
refs/heads/master
| 2020-05-30T13:09:57.695610
| 2015-02-07T08:31:47
| 2015-02-07T08:31:47
| 30,416,756
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 315
|
r
|
plot1.R
|
setwd('/Users/lee/machine/rGraph_p1')
source("common.R")
t <- loadData()
#quartz()
png("plot1.png", width=480, height=480,bg="white")
with(t,
hist(Global_active_power
, col = "red"
, main = "Global Actice Power"
, xlab = "Global Active Power (kilowatts)"
)
)
dev.off()
|
de1108cd0f00e3c44eeebcddc938e85c38353e58
|
197a57e980d2ccff2914c3a66eb56641961719c5
|
/analysis/raw_scripts/correlations_analysis.R
|
5635134d44528469ca903db3f4a4e25be349edf6
|
[] |
no_license
|
artwr/srs_work
|
2f5b6e6869842e48c43bd11dc1a7f7f4945dd32b
|
6fc3fa51248cbf7589889013c057addd78552d4f
|
refs/heads/master
| 2016-09-06T15:11:31.562540
| 2014-05-17T02:56:01
| 2014-05-17T02:56:01
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,107
|
r
|
correlations_analysis.R
|
#correlations_analysis
# required libraries
require(Hmisc)
require(spatstat)
require(gclus)
require(plyr)
#require(dplyr)
#
source("analysis/raw_scripts/functions_correl.R")
corr.clean <- readRDS(file = "srs_data/processed/corrclean.rdata")
datavals <- corr.clean[, 16:100]
# names(corr.clean[, 16:100])
# rcorr(as.matrix(corr.clean[, c("TRITIUM", "NONVOLATILE.BETA", "GROSS.ALPHA", "NITRATE.NITRITE.AS.NITROGEN", "SPECIFIC.CONDUCTANCE", "IODINE.129", "URANIUM.235", "URANIUM.238", "CESIUM.137", "TECHNETIUM.99", "STRONTIUM.90", "TOTAL.ACTIVITY")]))
cormatrixlist <- rcorr(as.matrix(datavals))
cormatrix <- cormatrixlist$r
numpairs <- cormatrixlist$n
pvals <- cormatrixlist$P
# Correlogram plot
plot(im(cormatrix[nrow(cormatrix):1,]))
numpairsmat1 <- numpairs
diag(numpairsmat1) <- NA
numpairsmat1[upper.tri(numpairsmat1)] <- NA
pairnumdf <- as.data.frame(as.table(numpairsmat1, useNA = "no"))
names(pairnumdf) <- c("First.Variable", "Second.Variable", "n")
head(pairnumdf[order(pairnumdf$n),], n = 20L)
hist(pairnumdf$n, breaks = 25)
# pairnumdf[order(abs(pairnumdf$n),decreasing=T),]
#try dissimilarity
dissimilarity <- 1 - cormatrix
dissim2 <- .5 * (1 - cormatrix)
dissim3 <- 1 - abs(cormatrix)
distance <- as.dist(dissimilarity)
distance2 <- as.dist(dissim2)
distance3 <- as.dist(dissim3)
varclusterpearson <- varclus(as.matrix(datavals) , similarity="pearson", na.action=na.retain)
# varclusterspearman <- varclus(datavals, similarity="spearman", method="complete", na.action=na.retain)
plot(hclust(distance), main="Dissimilarity = 1 - Correlation", xlab="")
mosthighlycorrelated(datavals, 20)
correlationsdf(datavals)
cormatrix1 <- cormatrix
diag(cormatrix1) <- NA
cormatrix1[lower.tri(cormatrix1)] <- NA
cordf <-as.data.frame(as.table(cormatrix1, useNA = "always"))
names(cordf) <- c("First.Variable", "Second.Variable","Correlation")
head(cordf[!is.na(cordf$Correlation),])
head(cordf[order(abs(cordf$Correlation),decreasing=T),], n = 20)
head(as.vector(numpairs))
cordfclean <- cordf[!is.na(cordf$Correlation),]
is.numeric(cordfclean$Correlation)
abs(cordfclean$Correlation)
|
27e69e0c8fda427169435c5b0acf0ff7f4e0abb3
|
7cd2f8c8139a7f39d33f8943f428ffa3c05b9d13
|
/tests/testthat/test__model.interface__get.offset.name.r
|
09f14beca8d27d095dbe30807501164542be9f4c
|
[
"MIT"
] |
permissive
|
Marchen/model.adapter
|
18abecd24b7bbe7339f654a43e725a887afcc55c
|
bf499fff40a120b6a73ed924de255ff1586d4695
|
refs/heads/master
| 2023-06-07T23:54:31.818713
| 2023-06-04T11:37:35
| 2023-06-04T11:37:35
| 64,151,231
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,842
|
r
|
test__model.interface__get.offset.name.r
|
#==============================================================================
# Test for has.offset method in model.interface.
#==============================================================================
library(testthat)
library(model.adapter)
#------------------------------------------------------------------------------
# Create test data.
#------------------------------------------------------------------------------
test.data <- list(
# Test formula with no offset.
call.no.offset = list(
x = substitute(glm(Petal.Length ~ ., data = iris)),
expected = character()
),
# Test formula with two offset without and with log.
call.formula.1 = list(
x = substitute(
glm(
Petal.Length ~ . + offset(iris$Sepal.Width)
+ offset(log(iris$Petal.Width)),
data = iris
)
),
expected = c("iris$Sepal.Width", "iris$Petal.Width")
),
# Test formula without '$'.
call.formula.2 = list(
x = substitute(
glm(Petal.Length ~ . + offset(Sepal.Width), data = iris)
),
expected = c("Sepal.Width")
),
# Test argument with '$'
call.argument.1 = list(
x = substitute(
glm(Petal.Length ~ ., offset = iris$Sepal.Width, data = iris)
),
expected = c("iris$Sepal.Width")
),
# Test argument with log
call.argument.2 = list(
x = substitute(
glm(Petal.Length ~ ., offset = log(Sepal.Width), data = iris)
),
expected = c("Sepal.Width")
),
# Test argument with log and '$'
call.argument.3 = list(
x = substitute(
glm(Petal.Length ~ ., offset = log(iris$Sepal.Width), data = iris)
),
expected = c("iris$Sepal.Width")
),
# Test argument and formula together.
call.both = list(
x = substitute(
glm(
Petal.Length ~ . + offset(log(Petal.Width)),
offset = Sepal.Width, data = iris
)
),
expected = c("Petal.Width", "Sepal.Width")
)
)
for (i in names(test.data)) {
test.data[[gsub("^call", "object", i)]] <- test.data[[i]]
test.data[[i]]$x <- eval(test.data[[i]]$x)
}
#------------------------------------------------------------------------------
# Run tests.
#------------------------------------------------------------------------------
test_that(
"Testing model.interface.derault$has.offset()",
{
for (i in names(test.data)) {
expect_equal(
model.adapter:::model.interface.default()$get.offset.names(
test.data[[i]]$x, .GlobalEnv, "stats"
),
test.data[[i]]$expected, info = sprintf("While testing %s", i)
)
}
}
)
|
9a04bee9f4d7ee34aaf46f3c586809f37eb0eb77
|
f99d825d23a3b84f961c231a0a2fce9a5eb25c2a
|
/r_sql/sql.R
|
7dec6554e7bbdfff7ab06c36e5093de59a513790
|
[] |
no_license
|
EaindrayKhin/st2195_assignment_3
|
e4c371bd03c1bcaeaf1d3572d4acb80874c8b18e
|
3a87f81c82bcb6a27a7817d440de9828d115ba0b
|
refs/heads/master
| 2023-08-25T19:24:26.035985
| 2021-10-28T13:35:32
| 2021-10-28T13:35:32
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,134
|
r
|
sql.R
|
#install.packages("RSQLite")
library(DBI)
library(dplyr)
conn <- dbConnect(RSQLite::SQLite(), "airline2.db")
airports <- read.csv("~/st2195_assignment_3/r_sql/airports.csv", header = TRUE)
dbWriteTable(conn, "airports", airports)
carriers <- read.csv("~/st2195_assignment_3/r_sql/carriers.csv", header = TRUE)
dbWriteTable(conn, "carriers", carriers)
planes <- read.csv("~/st2195_assignment_3/r_sql/plane-data.csv", header = TRUE)
dbWriteTable(conn, "planes", planes)
ontime_2000 <- read.csv("~/st2195_assignment_3/r_sql/2000.csv", header = TRUE)
ontime_2001 <- read.csv("~/st2195_assignment_3/r_sql/2001.csv", header = TRUE)
ontime_2002 <- read.csv("~/st2195_assignment_3/r_sql/2002.csv", header = TRUE)
ontime_2003 <- read.csv("~/st2195_assignment_3/r_sql/2003.csv", header = TRUE)
ontime_2004 <- read.csv("~/st2195_assignment_3/r_sql/2004.csv", header = TRUE)
ontime_2005 <- read.csv("~/st2195_assignment_3/r_sql/2005.csv", header = TRUE)
#combine all the ontime tables
ontime = rbind(ontime_2000, ontime_2001, ontime_2002, ontime_2003, ontime_2004, ontime_2005)
dbWriteTable(conn, "ontime", ontime)
#remove temporary dataframes
rm(ontime_2000, ontime_2001, ontime_2002, ontime_2003, ontime_2004, ontime_2005)
#garbage collection
gc()
dbListTables(conn)
dbListFields(conn, "ontime")
#which plane model has the lowest associated average depature delay
#excluding cancelled and diverted flights
q1 <- dbGetQuery(conn,
"SELECT model AS model, AVG(DepDelay) AS Avg_Delay
FROM Planes
JOIN Ontime
USING (tailnum)
WHERE Cancelled = 0
AND Diverted = 0
AND DepDelay > 0
GROUP BY model
ORDER BY Avg_Delay")
print(paste(q1[1, "model"], "has the lowest associated average depature delay,
excluding cancelled and diverted flights"))
write.csv(as.data.frame(q1), "sql_q1_output.csv", row.names = FALSE)
#which city has the highest number of inbound flights
#excluded cancelled flights
q2 <- dbGetQuery(conn,
"SELECT Airports.city AS city, COUNT(*) AS total
FROM Airports
JOIN Ontime
On Ontime.Dest = Airports.iata
WHERE Cancelled = 0
GROUP BY airports.city
ORDER BY total")
print(paste(q2[1, "city"], "has the highest number of inbounf flights,
excluding cancelled flights"))
write.csv(as.data.frame(q2), "sql_q2_output.csv", row.names = FALSE)
#which carrier has the highest number of cancelled flights
q3 <- dbGetQuery(conn,
"SELECT Carriers.Description AS carrier, COUNT(*) AS total
FROM Carriers
JOIN Ontime
On Ontime.UniqueCarrier = Carriers.Code
WHERE Ontime.Cancelled = 1
GROUP BY Carriers.Description
ORDER BY total")
print(paste(q3[1, "carrier"], "has the highest number of cancelled flights"))
write.csv(as.data.frame(q3), "sql_q3_output.csv", row.names = FALSE)
#which carrier has the highest number of cancelled flights
#relative to their number of total flights
q4 <- dbGetQuery(conn,
"SELECT a1.Carrier AS carrier,
(CAST(a1.numerator AS FLOAT)/CAST(a2.denominator AS FLOAT)) AS ratio
FROM
(SELECT Carriers.Description AS carrier, COUNT(*) AS numerator
FROM Carriers
JOIN Ontime
On Ontime.UniqueCarrier = Carriers.Code
WHERE Ontime.Cancelled = 1
GROUP BY Carriers.Description)
AS a1 JOIN
(SELECT Carriers.Description AS carrier, COUNT(*) AS denominator
FROM Carriers
JOIN ontime
On Ontime.UniqueCarrier = Carriers.Code
GROUP BY Carriers.Description)
AS a2 USING (carrier)
ORDER BY ratio")
print(paste(q4[1, "carrier"], "has the highest number of cancelled flights,
relative to their number of total flights"))
write.csv(as.data.frame(q4), "sql_q4_output.csv", row.names = FALSE)
|
62fe6d0530c053eecb1f11ba3292d75c94eda10a
|
4951e7c534f334c22d498bbc7035c5e93c5b928d
|
/statistics/circular/13-ext-reg.R
|
c54cdee90c9a64c81ab39c86a746852fead9cbb2
|
[] |
no_license
|
Derek-Jones/ESEUR-code-data
|
140f9cf41b2bcc512bbb2e04bcd81b5f82eef3e1
|
2f42f3fb6e46d273a3803db21e7e70eed2c8c09c
|
refs/heads/master
| 2023-04-04T21:32:13.160607
| 2023-03-20T19:19:51
| 2023-03-20T19:19:51
| 49,327,508
| 420
| 50
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,929
|
r
|
13-ext-reg.R
|
#
# 13-ext-reg.R, 23 Apr 20
#
# Data from:
# Correlations between Bugginess and Time-Based Commit Characteristics
# Jon Eyolfson and Lin Tan and Patrick Lam
#
# Example from:
# Evidence-based Software Engineering: based on the publicly available data
# Derek M. Jones
#
# TAG commit_time-of-day fault_time-of-day
source("ESEUR_config.r")
library("circular")
library("plyr")
pal_col=rainbow(4)
sum_commits=function(df)
{
t=count(df$hour)
return(data.frame(hour=t$x, freq=t$freq))
}
day=0:23
# id repository_id raw_author_id sha1 merge utc_time local_time
commits=read.csv(paste0(ESEUR_dir, "time-series/commits/scc_commitbasicinformation.tsv.xz"), sep="\t", as.is=TRUE)
commits$is_introducing= (commits$is_introducing == "t")
commits$is_fixing= (commits$is_fixing == "t")
fault_commits=subset(commits, is_introducing)
basic_commits=subset(commits, !is_introducing)
fault_total=ddply(fault_commits, .(week_day), sum_commits)
basic_total=ddply(basic_commits, .(week_day), sum_commits)
week_fault=subset(fault_total, week_day < 5)
week_basic=subset(basic_total, week_day < 5)
plot(week_basic$hour, week_basic$freq, col=pal_col[2],
xaxs="i", yaxs="i",
ylim=c(0, 3500),
xlab="Hour", ylab="Commits\n")
legend(x="topleft", legend=c("non-fault commits", "fault commits"), bty="n", fill=c(pal_col[2], pal_col[4]), cex=1.2)
basic_mod = nls(freq ~ gam0+gam1*cos(omega*hour-phi+nu*cos(omega*hour-phi)),
start = list(gam0 = 800, gam1 = 700, omega=0.3, phi = 1, nu = 0),
data=week_basic)
pred=predict(basic_mod, newdata=data.frame(hour=day))
lines(day, pred, col=pal_col[1])
points(week_fault$hour, week_fault$freq, col=pal_col[4])
fault_mod = nls(freq ~ gam0+gam1*cos(omega*hour-phi+nu*cos(omega*hour-phi)),
start = list(gam0 = 800, gam1 = 700, omega=0.3, phi = 1, nu = 0),
data=week_fault)
pred=predict(fault_mod, newdata=data.frame(hour=day))
lines(day, pred, col=pal_col[3])
|
657a598a59733bd7c8534a5144412c840582f80d
|
ab8949a88efbcd69bf595c3df168b639ea420125
|
/class_1/class_1.R
|
5a09003a288f38e49f45c9e82a895547242a5fa3
|
[] |
no_license
|
abdu95/web_scraping_2020
|
402a061a5f84b459bcdf3270166075bad4503d72
|
a0b8556c8febe778130dcd9183c8d68ebbb3bc8e
|
refs/heads/main
| 2023-01-19T13:40:55.244326
| 2020-12-01T18:39:36
| 2020-12-01T18:39:36
| 314,953,382
| 0
| 0
| null | 2020-12-01T16:36:23
| 2020-11-22T03:43:51
|
HTML
|
UTF-8
|
R
| false
| false
| 1,836
|
r
|
class_1.R
|
# lapply ------------------------------------------------------------------
my_list <- 1:10
my_list^2
my_numbers <- NULL
for (variable in my_list) {
my_numbers <- c(my_numbers, variable^2)
}
lapply(1:10, function(x){
return(x^2)
})
#1st element is a vector
#2nd element is a function. function will be applied to each element of vector
my_square <- function(x){
return(x^2)
}
lapply(1:10, my_square)
#Exercise
to_upper <- function(x){
return(paste0("#", toupper(x), "#"))
}
lapply(letters, to_upper)
result_list <- lapply(letters, to_upper)
result_vector <- sapply(letters, to_upper)
# lapply - returns list
# sapply - returns vector
#3rd element
result_list[[3]]
str(result_list[[3]])
#making sapply of lapply
result_unlist <- unlist(
lapply(letters, function(x){
return(paste0("#", toupper(x), "#"))
})
)
#sapply - Named chr [1:26] - named character vector
#unlist - chr [1:26] - just vector
# rvest -------------------------------------------------------------------
library(rvest)
t <- read_html('https://www.wired.com/search/?q=big%20data&page=1&sort=score')
write_html(t, 't.html')
#10
titles <-
t %>%
html_nodes('.archive-item-component__title') %>%
html_text
#9
times <-
t %>%
html_nodes('time') %>%
html_text()
#9
authors <-
t %>%
html_nodes('.byline-component__link') %>%
html_text()
#10
text_summary <-
t %>%
html_nodes('.archive-item-component__desc') %>%
html_text()
#duplicates
#20
#10
my_link <-
unique(
t %>%
html_nodes('.archive-item-component__link') %>%
html_attr('href'))
df <- data.frame('title' = titles, 'links' = my_link, 'summary' = text_summary, 'date' = times, fill = TRUE)
cbind('title' = titles, 'links' = my_link, 'summary' = text_summary, 'date' = times, fill = TRUE)
#list of dataframes
library(data.table)
|
6eb4e86b7225f43e1ac24556159772f6c425dfd4
|
05e7e0c255573d9f0195f7b8f60eca643b7b2d13
|
/R/Yeild.R
|
1f0cfabdb1991d1e63b60907b3f701843018573d
|
[] |
no_license
|
cran/RSSOP
|
8d19c483fb6ce74962988c817d7771f8f9a11209
|
5869ab9a3ceb411c3b3bf6fc3e8aef8d7dd53674
|
refs/heads/master
| 2020-09-13T09:41:02.279965
| 2016-08-30T07:49:10
| 2016-08-30T07:49:10
| 66,818,037
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,032
|
r
|
Yeild.R
|
Yeild <-
function(object,s.const=0.95)
{
R<-object$operation$Release
D<-object$operation$Demand
Base<-function(Re,De)
{
Criaterian<-rep(0,3)
names(Criaterian)<-c("Vulnerability","Reliability","Resiliency")
if(sum(De==0)>0)
{
De[which(De==0)]<-0.001
cat("some demands were equal to zero, they were set to 0.001 !!","\n")
}
T<-length(Re)
failure<-rep(NA,T)
# Vulnerability
Criaterian[1]<-sum((De-Re)/De)
# Reliability
Criaterian[2]<-1-sum(Re<s.const*De)/T
# Resiliency
failure[which(Re<s.const*De)]<-0
failure[which(Re>s.const*De)]<-1
f<-sum(diff(failure)==1)
F<-sum(failure==0)
Criaterian[3]<-f/F
return (Criaterian)
}
C<-matrix(NA,3,ncol(R))
rownames(C)<-c("Vulnerability","Reliability","Resiliency")
colnames(C)<-colnames(R)
for(i in 1:ncol(R))
{
C[,i]<-Base(Re=R[,i],De=D[,i])
}
return(C)
}
|
820ee482067dcf7d29a2dd303694b1c07f13fc95
|
00cc1b888f8c361ff3e744b1f59e020903725581
|
/sqrt/sqrt.r
|
b5a90e696372c672fbf01d1261604066323109aa
|
[
"MIT"
] |
permissive
|
madilraza/hacktoberfest
|
0f7efa0d65b5c0fa518342b8baa38038b86829e5
|
d0124689eb3ea84d14d75f6c456caef6757912fd
|
refs/heads/master
| 2020-12-14T23:46:35.360019
| 2020-01-20T05:04:55
| 2020-01-20T05:04:55
| 234,914,286
| 1
| 0
|
MIT
| 2020-01-20T05:04:56
| 2020-01-19T14:35:44
|
C#
|
UTF-8
|
R
| false
| false
| 359
|
r
|
sqrt.r
|
# SQRT in R example
# Square Root of Positive values
sqrt(64)
sqrt(25.659)
# Square Root of Negative values
sqrt(-10.0897)
# Absolute function will convert the Negative value to Positive
# Next, sqrt will find the square root of 35.659
sqrt(abs(-35.659))
# Square Root on vectors
num <- c(-25.526, 256.32, -36.5, -81 , -525.796)
sqrt(num)
sqrt(abs(num))
|
6ca8b1456c0a2490630a401e81bbb65f79cc983f
|
e9c728d8c249396ae1d920e543b069b83f511f9e
|
/plot1.R
|
3c580db544e80f14e4f3f66c156daa801c7385f3
|
[
"Apache-2.0"
] |
permissive
|
ankurgiri200/Study-on-PM2.5-Emissions-Data-USA
|
b97662bd40334d71829eaad9562e02f18ce44fa7
|
0af4c686db587894b6f9c8612c91a4f0bbf5e227
|
refs/heads/master
| 2022-12-11T07:18:18.450160
| 2020-09-07T19:58:39
| 2020-09-07T19:58:39
| 293,154,783
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 544
|
r
|
plot1.R
|
source <- readRDS("Source_Classification_Code.rds")
pm <- readRDS("summarySCC_PM25.rds")
totalbyyear <- aggregate(Emissions~year,pm,sum)
barplot(height=totalbyyear$Emissions, names.arg=totalbyyear$year, xlab="years", ylab=expression('total PM'[2.5]*' emission'),main=expression('Total PM'[2.5]*' emissions at various years'))
png("plot1.png")
barplot(height=totalbyyear$Emissions, names.arg=totalbyyear$year, xlab="years", ylab=expression('total PM'[2.5]*' emission'),main=expression('Total PM'[2.5]*' emissions at various years'))
dev.off()
|
e28b41ede98348a048f7f9d99692581a64ab9838
|
b4c1bc98a83dc8f4ad492911faca4c709c146288
|
/man/population_attributable_fraction.Rd
|
0208cb48ed3052dab3fbc2d309d8e2875c51b2c1
|
[] |
no_license
|
danielgils/ITHIM-R
|
facd3e74e3b4cdea279245372e3f2ec3bcb35cc0
|
7e306f0aea3e6ea21521104206a0281e5882af13
|
refs/heads/master
| 2023-09-05T05:33:00.779268
| 2021-11-10T10:07:42
| 2021-11-10T10:07:42
| 277,875,917
| 0
| 0
| null | 2020-07-07T17:04:21
| 2020-07-07T17:04:20
| null |
UTF-8
|
R
| false
| true
| 480
|
rd
|
population_attributable_fraction.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/population_attributable_fraction.R
\name{population_attributable_fraction}
\alias{population_attributable_fraction}
\title{Calculate population attributable fraction}
\usage{
population_attributable_fraction(pop, cn, mat)
}
\arguments{
\item{pop}{}
\item{cn}{}
\item{mat}{}
}
\value{
population attributable fractions by demographic group
}
\description{
Calculate population attributable fraction
}
|
0fe90ffcc59c79d57f040a672e9a58f86f7a8b72
|
135725be7153ff6ec6cc79b8db29cdea80ecc875
|
/R/1_bedinfo.R
|
c7334af9f2d4c78903acb07beaed77ee0f8f7423
|
[] |
no_license
|
cran/CollapsABEL
|
f2969755a8fe831eeab9c4971ddd1488a1e39b81
|
b3be3f831ba5e9500d016aca0ca768a367e79c84
|
refs/heads/master
| 2021-01-10T13:18:22.127650
| 2016-12-11T19:35:07
| 2016-12-11T19:35:07
| 55,400,560
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,504
|
r
|
1_bedinfo.R
|
plinkTrio <- function(bedstem, must_exist = FALSE) {
ext_trio = c("bed", "bim", "fam")
plink_trio = paste(bedstem, ext_trio, sep = ".")
if(must_exist) {
filePath(plink_trio)@path
} else {
plink_trio
}
}
#' An S4 class representing info about plink files
#'
#' Info about plink files, including the root directory,
#' paths of plink .bed, .bim, .fam and .frq files, ff backing
#' directories for .bim, .fam and .frq files, etc.
#'
#' @slot main_dir Root directory where .bed, .bim and .fam files sit.
#' @slot plink_stem character. Path to the .bed file sans the extension name
#' @slot plink_trio character of length 3. Paths to .bed, .bim and .fam files (in that order).
#' @slot plink_trio_base character. Basenames of \code{plink_trio}.
#' @slot plink_frq character. Path to .frq file.
#'
#' @export
.PlInfoC = setClass("PlInfoC", representation(
main_dir = "character",
plink_stem = "character",
plink_trio = "character",
plink_trio_base = "character",
plink_frq = "character"
),
prototype(
main_dir = "",
plink_stem = "",
plink_trio = rep("", 3),
plink_trio_base = rep("", 3),
plink_frq = ""
), validity = function(object) {
obj_slots = list(
object@main_dir,
object@plink_stem ,
object@plink_trio ,
object@plink_trio_base ,
object@plink_frq
)
names(obj_slots) = c(
"main_dir",
"plink_stem",
"plink_trio",
"plink_trio_base",
"plink_frq"
)
msg = lenCheck(
obj_slots,
c(1, 1, 3, 3, 1))
if(msg != TRUE) {
return(msg)
}
ext_trio = c("bed", "bim", "fam")
if(!all(tools::file_ext(object@plink_trio)
== ext_trio)) {
return(paste("Extensions should be: ", strConcat(ext_trio)))
}
miss_files = nonExistentFiles(object@plink_trio)
if(length(miss_files) > 0) {
return(miss_files)
} else {
return(TRUE)
}
})
#' Constructor for PlInfoC class
#'
#' Populates an PlInfoC object from a given plink bed filename stem (i.e. exclude extension name)
#'
#' @param pl_info a PlInfoC object, possibly empty.
#' @param bedstem path of bed file excluding extension name
#' @param db_setup logical. Whether to setup SQLite database for .bim, .fam and .frq files.
#' @return a PlInfoC object
#' @examples
#' \dontrun{
#' pl_info = plInfo(.PlInfoC(), "mmp13", db_setup = TRUE)
#' isSetup(pl_info)
#' bim_ff = getQuery(sqliteFilePl(pl_info), "select * from bim")
#' fam_ff = getQuery(sqliteFilePl(pl_info), "select * from fam")
#' frq_ff = getQuery(sqliteFilePl(pl_info), "select * from frq")
#' }
#' @author Kaiyin Zhong, Fan Liu
#' @export
setGeneric("plInfo",
function(pl_info, bedstem, db_setup) {
standardGeneric("plInfo")
})
#' @rdname plInfo
#' @export
setMethod("plInfo",
signature(pl_info = "PlInfoC", bedstem = "character", db_setup = "logical"),
function(pl_info, bedstem, db_setup) {
# plink trio
ext_trio = c("bed", "bim", "fam")
plink_trio = normalizePath(
plinkTrio(bedstem = bedstem, must_exist = FALSE)
)
plink_trio_base = basename(plink_trio)
names(plink_trio) = names(plink_trio_base) = ext_trio
plink_stem = tools::file_path_sans_ext(plink_trio["bed"])
names(plink_stem) = NULL
# main dir where plink files sit
main_dir = dirname(plink_trio[1])
# frq file
# TODO: autogen frq files using plinkr
plink_frq = paste(bedstem, ".frq", sep="")
# return a PlInfoC obj
pl_info@main_dir = main_dir
pl_info@plink_stem = plink_stem
pl_info@plink_trio = plink_trio
pl_info@plink_trio_base = plink_trio_base
pl_info@plink_frq = plink_frq
methods::validObject(pl_info)
if(db_setup) {
setup(pl_info)
}
pl_info
})
#' @rdname plInfo
#' @export
setMethod("plInfo",
signature(pl_info = "PlInfoC", bedstem = "character", db_setup = "missing"),
function(pl_info, bedstem, db_setup) {
plInfo(pl_info, bedstem, FALSE)
})
#' @rdname plInfo
#' @export
setMethod("plInfo",
signature(pl_info = "missing", bedstem = "character", db_setup = "logical"),
function(pl_info, bedstem, db_setup) {
plInfo(.PlInfoC(), bedstem, db_setup)
})
#' @rdname plInfo
#' @export
setMethod("plInfo",
signature(pl_info = "missing", bedstem = "character", db_setup = "missing"),
function(pl_info, bedstem, db_setup) {
plInfo(.PlInfoC(), bedstem, FALSE)
})
#' SQLite file of a PlInfoC object
#'
#' @param x PlInfoC or PlGwasC object
#' @return character. Path to SQLite database file.
#'
#' @author Kaiyin Zhong, Fan Liu
#' @export
sqliteFilePl = function(x) {
if(isS4Class(x, "PlInfoC")) {
filename = sprintf("%s.sqlite", x@plink_stem)
} else if(isS4Class(x, "PlGwasC")) {
filename = sprintf("%s.sqlite", x@pl_info@plink_stem)
}
filename
}
#' Check if a directory containing .bed .fam and .bim files is properly setup
#'
#' @param pl_info PlInfoC object
#' @return TRUE or FALSE
#'
#' @author Kaiyin Zhong, Fan Liu
#' @export
isSetup = function(pl_info) {
stopifnot(isS4Class(pl_info, "PlInfoC"))
sql_file = sqliteFilePl(pl_info)
isSQLite3(sql_file) && dbUpToDate(sql_file)
}
#' Setup up a directory containing plink files
#'
#' @param pl_info PlInfoC object
#'
#' @author Kaiyin Zhong, Fan Liu
#' @export
setup = function(pl_info) {
stopifnot(isS4Class(pl_info, "PlInfoC"))
if(isSetup(pl_info)) {
TRUE
} else {
sqlite_file = sqliteFilePl(pl_info)
if(file.exists(sqlite_file)) {
file.remove(sqlite_file)
}
if(!file.exists(pl_info@plink_frq) || !frqUpToDate(pl_info@plink_frq)) {
plinkr(bfile = pl_info@plink_stem,
freq = "",
out = pl_info@plink_stem,
wait = TRUE)
}
frq = read.table(pl_info@plink_frq, header = TRUE, stringsAsFactors = FALSE)
bim = readBim(pl_info@plink_trio["bim"])
fam = readFam(pl_info@plink_trio["fam"])
fam = setNames(fam, c("FID", "IID", "PID", "MID", "SEX", "PHE"))
tryCatch({
file.create2(sqlite_file)
db = RSQLite::dbConnect(RSQLite::SQLite(), sqlite_file)
RSQLite::dbWriteTable(db, "bim", bim)
RSQLite::dbWriteTable(db, "fam", fam)
RSQLite::dbWriteTable(db, "frq", frq)
}, finally = {
RSQLite::dbDisconnect(db)
})
}
}
#' Get number of individuals
#'
#' @param pl_info PlInfoC object
#' @export
nIndivPl = function(pl_info) {
stopifnot(isS4Class(pl_info, "PlInfoC"))
getQuery(sqliteFilePl(pl_info), "select count(iid) from fam")[1, 1]
}
#' Get number of SNPs.
#'
#' @param pl_info PlInfoC object
#' @export
nSnpPl = function(pl_info) {
stopifnot(isS4Class(pl_info, "PlInfoC"))
getQuery(sqliteFilePl(pl_info), "select count(snp) from bim")[1, 1]
}
#' Get number of bytes used by each SNP.
#'
#' @param pl_info PlInfoC object
#' @export
bytesSnp = function(pl_info) {
stopifnot(isS4Class(pl_info, "PlInfoC"))
as.numeric(ceiling(nIndivPl(pl_info) / 4))
}
#' Get apparent number of individuals
#'
#' @param pl_info PlInfoC object
#' @export
nIndivApprPl = function(pl_info) {
stopifnot(isS4Class(pl_info, "PlInfoC"))
as.numeric(bytesSnp(pl_info) * 4)
}
#' FID and IID columns from fam file
#'
#' @param pl_info PlInfoC object
#' @return data.frame of two columns "FID" and "IID"
#'
#' @examples
#' \dontrun{
#' pl_info = plInfo(bedstem = "mmp13", db_setup = TRUE)
#' fidiid = fidIid(pl_info)
#' fam = readFam("mmp13.fam", c("FID", "IID"))
#' all(fam == fidiid)
#' }
#'
#' @author Kaiyin Zhong, Fan Liu
#' @export
fidIid = function(pl_info) {
setup(pl_info)
getQuery(sqliteFilePl(pl_info), "select fid, iid from fam order by rowid")
}
|
2336c80289e0287db91726d5436d843d92b2e105
|
7ea306ad26dd56de3cc3f586448f9b680bad2696
|
/tests/testthat.R
|
d16f7600d6acac63da416c9960736f8d7f93725c
|
[] |
no_license
|
Darwinita/FARSpackageLCP
|
b39ea9e83e9cb44e9134361bb24c04adba565623
|
5dbe4528fd35ed87603cfb35ba871840e9f81586
|
refs/heads/master
| 2021-06-27T12:04:13.138880
| 2017-09-14T13:08:38
| 2017-09-14T13:08:38
| 103,516,571
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 72
|
r
|
testthat.R
|
library(testthat)
library(FARSpackageLCP)
test_check("FARSpackageLCP")
|
628fa74e3a345fe316fdcb53d942dbe72c127ca1
|
8f2d33ce811c0667ad82056f70a372ead18478f6
|
/R/mapai.R
|
b6ff761d72f832bc300b69785bf183d04de24942
|
[] |
no_license
|
Tomas19840823/transportas_v2
|
6806fd39d1bc0eedf9f9a65bab355d08de574724
|
5fdbe20538cdde54750d21fba457749ad5349a9a
|
refs/heads/master
| 2021-01-19T03:49:08.649313
| 2017-04-24T15:25:39
| 2017-04-24T15:25:39
| 87,336,063
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 501,720
|
r
|
mapai.R
|
#'
#'
#' This function
#' @keywords mapai
#' @export 1
#' @examples 1
#' @export
mapai <- list(
m1 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:1], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m2 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[1], group = autopav[2])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:2], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m3 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:3], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m4 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:4], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m5 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:5], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m6 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:6], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m7 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:7], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m8 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:8], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m9 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:9], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m10 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:10], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m11 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:11], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m12 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:12], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m13 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:13], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m14 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:14], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m15 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:15], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m16 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:16], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m17 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:17], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m18 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:18], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m19 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:19], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m20 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:20], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m21 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:21], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m22 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:22], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m23 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:23], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m24 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:24], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m25 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:25], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m26 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:26], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m27 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:27], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m28 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:28], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m29 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:29], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m30 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:30], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m31 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:31], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m32 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:32], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m33 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:33], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m34 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:34], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m35 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:35], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m36 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:36], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m37 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:37], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m38 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:38], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m39 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:39], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m40 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:40], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m41 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:41], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m42 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:42], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m43 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:43], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m44 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addPopups(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]), popup = as.character(unlist(c1[[44]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[44])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addCircleMarkers(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]),radius = 10, color = spalva[44], group = autopav[44])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:44], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m45 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addPopups(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]), popup = as.character(unlist(c1[[44]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[44])%>%
addPopups(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]), popup = as.character(unlist(c1[[45]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[45])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addCircleMarkers(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]),radius = 10, color = spalva[44], group = autopav[44])%>%
addCircleMarkers(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]),radius = 10, color = spalva[45], group = autopav[45])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:45], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m46 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addPopups(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]), popup = as.character(unlist(c1[[44]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[44])%>%
addPopups(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]), popup = as.character(unlist(c1[[45]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[45])%>%
addPopups(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]), popup = as.character(unlist(c1[[46]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[46])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addCircleMarkers(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]),radius = 10, color = spalva[44], group = autopav[44])%>%
addCircleMarkers(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]),radius = 10, color = spalva[45], group = autopav[45])%>%
addCircleMarkers(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]),radius = 10, color = spalva[46], group = autopav[46])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:46], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m47 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addPopups(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]), popup = as.character(unlist(c1[[44]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[44])%>%
addPopups(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]), popup = as.character(unlist(c1[[45]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[45])%>%
addPopups(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]), popup = as.character(unlist(c1[[46]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[46])%>%
addPopups(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]), popup = as.character(unlist(c1[[47]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[47])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addCircleMarkers(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]),radius = 10, color = spalva[44], group = autopav[44])%>%
addCircleMarkers(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]),radius = 10, color = spalva[45], group = autopav[45])%>%
addCircleMarkers(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]),radius = 10, color = spalva[46], group = autopav[46])%>%
addCircleMarkers(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]),radius = 10, color = spalva[47], group = autopav[47])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:47], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m48 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addPopups(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]), popup = as.character(unlist(c1[[44]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[44])%>%
addPopups(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]), popup = as.character(unlist(c1[[45]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[45])%>%
addPopups(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]), popup = as.character(unlist(c1[[46]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[46])%>%
addPopups(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]), popup = as.character(unlist(c1[[47]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[47])%>%
addPopups(m, lat = unlist(lat[[48]][1]), lng = unlist(lng[[48]][1]), popup = as.character(unlist(c1[[48]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[48])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addCircleMarkers(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]),radius = 10, color = spalva[44], group = autopav[44])%>%
addCircleMarkers(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]),radius = 10, color = spalva[45], group = autopav[45])%>%
addCircleMarkers(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]),radius = 10, color = spalva[46], group = autopav[46])%>%
addCircleMarkers(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]),radius = 10, color = spalva[47], group = autopav[47])%>%
addCircleMarkers(m, lat = unlist(lat[[48]][1]), lng = unlist(lng[[48]][1]),radius = 10, color = spalva[48], group = autopav[48])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:48], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m49 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addPopups(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]), popup = as.character(unlist(c1[[44]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[44])%>%
addPopups(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]), popup = as.character(unlist(c1[[45]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[45])%>%
addPopups(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]), popup = as.character(unlist(c1[[46]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[46])%>%
addPopups(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]), popup = as.character(unlist(c1[[47]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[47])%>%
addPopups(m, lat = unlist(lat[[48]][1]), lng = unlist(lng[[48]][1]), popup = as.character(unlist(c1[[48]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[48])%>%
addPopups(m, lat = unlist(lat[[49]][1]), lng = unlist(lng[[49]][1]), popup = as.character(unlist(c1[[49]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[49])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addCircleMarkers(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]),radius = 10, color = spalva[44], group = autopav[44])%>%
addCircleMarkers(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]),radius = 10, color = spalva[45], group = autopav[45])%>%
addCircleMarkers(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]),radius = 10, color = spalva[46], group = autopav[46])%>%
addCircleMarkers(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]),radius = 10, color = spalva[47], group = autopav[47])%>%
addCircleMarkers(m, lat = unlist(lat[[48]][1]), lng = unlist(lng[[48]][1]),radius = 10, color = spalva[48], group = autopav[48])%>%
addCircleMarkers(m, lat = unlist(lat[[49]][1]), lng = unlist(lng[[49]][1]),radius = 10, color = spalva[49], group = autopav[49])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:49], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m50 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addPopups(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]), popup = as.character(unlist(c1[[44]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[44])%>%
addPopups(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]), popup = as.character(unlist(c1[[45]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[45])%>%
addPopups(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]), popup = as.character(unlist(c1[[46]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[46])%>%
addPopups(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]), popup = as.character(unlist(c1[[47]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[47])%>%
addPopups(m, lat = unlist(lat[[48]][1]), lng = unlist(lng[[48]][1]), popup = as.character(unlist(c1[[48]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[48])%>%
addPopups(m, lat = unlist(lat[[49]][1]), lng = unlist(lng[[49]][1]), popup = as.character(unlist(c1[[49]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[49])%>%
addPopups(m, lat = unlist(lat[[50]][1]), lng = unlist(lng[[50]][1]), popup = as.character(unlist(c1[[50]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[50])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addCircleMarkers(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]),radius = 10, color = spalva[44], group = autopav[44])%>%
addCircleMarkers(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]),radius = 10, color = spalva[45], group = autopav[45])%>%
addCircleMarkers(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]),radius = 10, color = spalva[46], group = autopav[46])%>%
addCircleMarkers(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]),radius = 10, color = spalva[47], group = autopav[47])%>%
addCircleMarkers(m, lat = unlist(lat[[48]][1]), lng = unlist(lng[[48]][1]),radius = 10, color = spalva[48], group = autopav[48])%>%
addCircleMarkers(m, lat = unlist(lat[[49]][1]), lng = unlist(lng[[49]][1]),radius = 10, color = spalva[49], group = autopav[49])%>%
addCircleMarkers(m, lat = unlist(lat[[50]][1]), lng = unlist(lng[[50]][1]),radius = 10, color = spalva[50], group = autopav[50])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:50], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)},
m51 = function(lng,lat,c1,autopav1,spalva,autolegend,autopav) {
(m <- leaflet() %>% addTiles())
(z <- m %>% setView(lng = mean(unlist(lng)), lat = mean(unlist(lat)), zoom = 10) %>% addTiles(group = "OpenStreetMap") %>% addProviderTiles("Stamen.Toner", group = "Balta / Juoda") %>%
addPopups(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]), popup = as.character(unlist(c1[[1]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[1])%>%
addPopups(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]), popup = as.character(unlist(c1[[2]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[2])%>%
addPopups(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]), popup = as.character(unlist(c1[[3]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[3])%>%
addPopups(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]), popup = as.character(unlist(c1[[4]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[4])%>%
addPopups(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]), popup = as.character(unlist(c1[[5]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[5])%>%
addPopups(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]), popup = as.character(unlist(c1[[6]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[6])%>%
addPopups(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]), popup = as.character(unlist(c1[[7]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[7])%>%
addPopups(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]), popup = as.character(unlist(c1[[8]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[8])%>%
addPopups(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]), popup = as.character(unlist(c1[[9]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[9])%>%
addPopups(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]), popup = as.character(unlist(c1[[10]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[10])%>%
addPopups(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]), popup = as.character(unlist(c1[[11]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[11])%>%
addPopups(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]), popup = as.character(unlist(c1[[12]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[12])%>%
addPopups(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]), popup = as.character(unlist(c1[[13]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[13])%>%
addPopups(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]), popup = as.character(unlist(c1[[14]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[14])%>%
addPopups(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]), popup = as.character(unlist(c1[[15]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[15])%>%
addPopups(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]), popup = as.character(unlist(c1[[16]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[16])%>%
addPopups(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]), popup = as.character(unlist(c1[[17]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[17])%>%
addPopups(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]), popup = as.character(unlist(c1[[18]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[18])%>%
addPopups(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]), popup = as.character(unlist(c1[[19]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[19])%>%
addPopups(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]), popup = as.character(unlist(c1[[20]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[20])%>%
addPopups(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]), popup = as.character(unlist(c1[[21]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[21])%>%
addPopups(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]), popup = as.character(unlist(c1[[22]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[22])%>%
addPopups(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]), popup = as.character(unlist(c1[[23]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[23])%>%
addPopups(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]), popup = as.character(unlist(c1[[24]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[24])%>%
addPopups(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]), popup = as.character(unlist(c1[[25]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[25])%>%
addPopups(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]), popup = as.character(unlist(c1[[26]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[26])%>%
addPopups(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]), popup = as.character(unlist(c1[[27]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[27])%>%
addPopups(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]), popup = as.character(unlist(c1[[28]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[28])%>%
addPopups(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]), popup = as.character(unlist(c1[[29]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[29])%>%
addPopups(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]), popup = as.character(unlist(c1[[30]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[30])%>%
addPopups(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]), popup = as.character(unlist(c1[[31]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[31])%>%
addPopups(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]), popup = as.character(unlist(c1[[32]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[32])%>%
addPopups(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]), popup = as.character(unlist(c1[[33]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[33])%>%
addPopups(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]), popup = as.character(unlist(c1[[34]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[34])%>%
addPopups(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]), popup = as.character(unlist(c1[[35]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[35])%>%
addPopups(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]), popup = as.character(unlist(c1[[36]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[36])%>%
addPopups(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]), popup = as.character(unlist(c1[[37]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[37])%>%
addPopups(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]), popup = as.character(unlist(c1[[38]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[38])%>%
addPopups(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]), popup = as.character(unlist(c1[[39]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[39])%>%
addPopups(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]), popup = as.character(unlist(c1[[40]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[40])%>%
addPopups(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]), popup = as.character(unlist(c1[[41]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[41])%>%
addPopups(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]), popup = as.character(unlist(c1[[42]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[42])%>%
addPopups(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]), popup = as.character(unlist(c1[[43]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[43])%>%
addPopups(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]), popup = as.character(unlist(c1[[44]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[44])%>%
addPopups(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]), popup = as.character(unlist(c1[[45]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[45])%>%
addPopups(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]), popup = as.character(unlist(c1[[46]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[46])%>%
addPopups(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]), popup = as.character(unlist(c1[[47]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[47])%>%
addPopups(m, lat = unlist(lat[[48]][1]), lng = unlist(lng[[48]][1]), popup = as.character(unlist(c1[[48]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[48])%>%
addPopups(m, lat = unlist(lat[[49]][1]), lng = unlist(lng[[49]][1]), popup = as.character(unlist(c1[[49]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[49])%>%
addPopups(m, lat = unlist(lat[[50]][1]), lng = unlist(lng[[50]][1]), popup = as.character(unlist(c1[[50]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[50])%>%
addPopups(m, lat = unlist(lat[[51]][1]), lng = unlist(lng[[51]][1]), popup = as.character(unlist(c1[[51]])),options = popupOptions(closeOnClick = FALSE, closeButton = FALSE,maxHeight = 20), group = autopav1[51])%>%
addCircleMarkers(m, lat = unlist(lat[[1]][1]), lng = unlist(lng[[1]][1]),radius = 10, color = spalva[1], group = autopav[1])%>%
addCircleMarkers(m, lat = unlist(lat[[2]][1]), lng = unlist(lng[[2]][1]),radius = 10, color = spalva[2], group = autopav[2])%>%
addCircleMarkers(m, lat = unlist(lat[[3]][1]), lng = unlist(lng[[3]][1]),radius = 10, color = spalva[3], group = autopav[3])%>%
addCircleMarkers(m, lat = unlist(lat[[4]][1]), lng = unlist(lng[[4]][1]),radius = 10, color = spalva[4], group = autopav[4])%>%
addCircleMarkers(m, lat = unlist(lat[[5]][1]), lng = unlist(lng[[5]][1]),radius = 10, color = spalva[5], group = autopav[5])%>%
addCircleMarkers(m, lat = unlist(lat[[6]][1]), lng = unlist(lng[[6]][1]),radius = 10, color = spalva[6], group = autopav[6])%>%
addCircleMarkers(m, lat = unlist(lat[[7]][1]), lng = unlist(lng[[7]][1]),radius = 10, color = spalva[7], group = autopav[7])%>%
addCircleMarkers(m, lat = unlist(lat[[8]][1]), lng = unlist(lng[[8]][1]),radius = 10, color = spalva[8], group = autopav[8])%>%
addCircleMarkers(m, lat = unlist(lat[[9]][1]), lng = unlist(lng[[9]][1]),radius = 10, color = spalva[9], group = autopav[9])%>%
addCircleMarkers(m, lat = unlist(lat[[10]][1]), lng = unlist(lng[[10]][1]),radius = 10, color = spalva[10], group = autopav[10])%>%
addCircleMarkers(m, lat = unlist(lat[[11]][1]), lng = unlist(lng[[11]][1]),radius = 10, color = spalva[11], group = autopav[11])%>%
addCircleMarkers(m, lat = unlist(lat[[12]][1]), lng = unlist(lng[[12]][1]),radius = 10, color = spalva[12], group = autopav[12])%>%
addCircleMarkers(m, lat = unlist(lat[[13]][1]), lng = unlist(lng[[13]][1]),radius = 10, color = spalva[13], group = autopav[13])%>%
addCircleMarkers(m, lat = unlist(lat[[14]][1]), lng = unlist(lng[[14]][1]),radius = 10, color = spalva[14], group = autopav[14])%>%
addCircleMarkers(m, lat = unlist(lat[[15]][1]), lng = unlist(lng[[15]][1]),radius = 10, color = spalva[15], group = autopav[15])%>%
addCircleMarkers(m, lat = unlist(lat[[16]][1]), lng = unlist(lng[[16]][1]),radius = 10, color = spalva[16], group = autopav[16])%>%
addCircleMarkers(m, lat = unlist(lat[[17]][1]), lng = unlist(lng[[17]][1]),radius = 10, color = spalva[17], group = autopav[17])%>%
addCircleMarkers(m, lat = unlist(lat[[18]][1]), lng = unlist(lng[[18]][1]),radius = 10, color = spalva[18], group = autopav[18])%>%
addCircleMarkers(m, lat = unlist(lat[[19]][1]), lng = unlist(lng[[19]][1]),radius = 10, color = spalva[19], group = autopav[19])%>%
addCircleMarkers(m, lat = unlist(lat[[20]][1]), lng = unlist(lng[[20]][1]),radius = 10, color = spalva[20], group = autopav[20])%>%
addCircleMarkers(m, lat = unlist(lat[[21]][1]), lng = unlist(lng[[21]][1]),radius = 10, color = spalva[21], group = autopav[21])%>%
addCircleMarkers(m, lat = unlist(lat[[22]][1]), lng = unlist(lng[[22]][1]),radius = 10, color = spalva[22], group = autopav[22])%>%
addCircleMarkers(m, lat = unlist(lat[[23]][1]), lng = unlist(lng[[23]][1]),radius = 10, color = spalva[23], group = autopav[23])%>%
addCircleMarkers(m, lat = unlist(lat[[24]][1]), lng = unlist(lng[[24]][1]),radius = 10, color = spalva[24], group = autopav[24])%>%
addCircleMarkers(m, lat = unlist(lat[[25]][1]), lng = unlist(lng[[25]][1]),radius = 10, color = spalva[25], group = autopav[25])%>%
addCircleMarkers(m, lat = unlist(lat[[26]][1]), lng = unlist(lng[[26]][1]),radius = 10, color = spalva[26], group = autopav[26])%>%
addCircleMarkers(m, lat = unlist(lat[[27]][1]), lng = unlist(lng[[27]][1]),radius = 10, color = spalva[27], group = autopav[27])%>%
addCircleMarkers(m, lat = unlist(lat[[28]][1]), lng = unlist(lng[[28]][1]),radius = 10, color = spalva[28], group = autopav[28])%>%
addCircleMarkers(m, lat = unlist(lat[[29]][1]), lng = unlist(lng[[29]][1]),radius = 10, color = spalva[29], group = autopav[29])%>%
addCircleMarkers(m, lat = unlist(lat[[30]][1]), lng = unlist(lng[[30]][1]),radius = 10, color = spalva[30], group = autopav[30])%>%
addCircleMarkers(m, lat = unlist(lat[[31]][1]), lng = unlist(lng[[31]][1]),radius = 10, color = spalva[31], group = autopav[31])%>%
addCircleMarkers(m, lat = unlist(lat[[32]][1]), lng = unlist(lng[[32]][1]),radius = 10, color = spalva[32], group = autopav[32])%>%
addCircleMarkers(m, lat = unlist(lat[[33]][1]), lng = unlist(lng[[33]][1]),radius = 10, color = spalva[33], group = autopav[33])%>%
addCircleMarkers(m, lat = unlist(lat[[34]][1]), lng = unlist(lng[[34]][1]),radius = 10, color = spalva[34], group = autopav[34])%>%
addCircleMarkers(m, lat = unlist(lat[[35]][1]), lng = unlist(lng[[35]][1]),radius = 10, color = spalva[35], group = autopav[35])%>%
addCircleMarkers(m, lat = unlist(lat[[36]][1]), lng = unlist(lng[[36]][1]),radius = 10, color = spalva[36], group = autopav[36])%>%
addCircleMarkers(m, lat = unlist(lat[[37]][1]), lng = unlist(lng[[37]][1]),radius = 10, color = spalva[37], group = autopav[37])%>%
addCircleMarkers(m, lat = unlist(lat[[38]][1]), lng = unlist(lng[[38]][1]),radius = 10, color = spalva[38], group = autopav[38])%>%
addCircleMarkers(m, lat = unlist(lat[[39]][1]), lng = unlist(lng[[39]][1]),radius = 10, color = spalva[39], group = autopav[39])%>%
addCircleMarkers(m, lat = unlist(lat[[40]][1]), lng = unlist(lng[[40]][1]),radius = 10, color = spalva[40], group = autopav[40])%>%
addCircleMarkers(m, lat = unlist(lat[[41]][1]), lng = unlist(lng[[41]][1]),radius = 10, color = spalva[41], group = autopav[41])%>%
addCircleMarkers(m, lat = unlist(lat[[42]][1]), lng = unlist(lng[[42]][1]),radius = 10, color = spalva[42], group = autopav[42])%>%
addCircleMarkers(m, lat = unlist(lat[[43]][1]), lng = unlist(lng[[43]][1]),radius = 10, color = spalva[43], group = autopav[43])%>%
addCircleMarkers(m, lat = unlist(lat[[44]][1]), lng = unlist(lng[[44]][1]),radius = 10, color = spalva[44], group = autopav[44])%>%
addCircleMarkers(m, lat = unlist(lat[[45]][1]), lng = unlist(lng[[45]][1]),radius = 10, color = spalva[45], group = autopav[45])%>%
addCircleMarkers(m, lat = unlist(lat[[46]][1]), lng = unlist(lng[[46]][1]),radius = 10, color = spalva[46], group = autopav[46])%>%
addCircleMarkers(m, lat = unlist(lat[[47]][1]), lng = unlist(lng[[47]][1]),radius = 10, color = spalva[47], group = autopav[47])%>%
addCircleMarkers(m, lat = unlist(lat[[48]][1]), lng = unlist(lng[[48]][1]),radius = 10, color = spalva[48], group = autopav[48])%>%
addCircleMarkers(m, lat = unlist(lat[[49]][1]), lng = unlist(lng[[49]][1]),radius = 10, color = spalva[49], group = autopav[49])%>%
addCircleMarkers(m, lat = unlist(lat[[50]][1]), lng = unlist(lng[[50]][1]),radius = 10, color = spalva[50], group = autopav[50])%>%
addCircleMarkers(m, lat = unlist(lat[[51]][1]), lng = unlist(lng[[51]][1]),radius = 10, color = spalva[51], group = autopav[51])%>%
addLegend(position = c("bottomleft"),values = as.character(autopav), colors = spalva[1:51], labels = as.character(autolegend))%>%
addLayersControl(baseGroups = c("Gatvi\u0173 \u017Eem\u0117lapis","Balta / Juoda"), overlayGroups = c(autopav, autopav1)))
return(z)}
)
|
18728319d9e72269b39e5010dbe893eb5ebfd04b
|
bf0584087680817f28a4be6656af2f28d307e6a0
|
/Scripts/Creating_Texture.R
|
c60d45d019521345fb1b5175366063b19de1390a
|
[] |
no_license
|
everydayduffy/Wales_Seagrass_Example_Code
|
a7db2e97da9fc00f46047bc9e1c28d1141dbc444
|
79bc19f2e86c8bec9b4bb97f50a6a21c19c7302e
|
refs/heads/master
| 2021-05-07T13:54:29.627055
| 2017-12-22T17:35:09
| 2017-12-22T17:35:09
| 109,719,670
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 944
|
r
|
Creating_Texture.R
|
##Creating texture layers from the green orthomosaic band
##Read in data
in_rast <- "Angle_Ricoh_BNG_PS2_Ortho_Crop.tif" # RGB orthomosaic
r <- raster::stack(paste0("Data/",in_rast))
r <- raster::dropLayer(r, 4) # drop alpha band
r_g <- raster::raster(paste0("Data/",in_rast),band=2) # green band only
##Calculate texture
r_glcm_g <- glcm::glcm(r_g) # takes a long time
##Write out as individual layers
#raster::writeRaster(r_glcm_g, paste0("Data/Angle_Ricoh_BNG_PS2_Ortho_green_",
# names(r.glcm.g)), bylayer = TRUE,
# format = "GTiff")
##Write out as one stack
#raster::writeRaster(r_glcm_g, "Data/Angle_Ricoh_BNG_PS2_Ortho_green_tex",
# format = "GTiff")
##Write out as one stack (combined RGB + texture bands)
r_combo <- raster::stack(r,r_glcm_g)
raster::writeRaster(r_combo, "Data/Angle_Ricoh_BNG_PS2_Ortho_Tex_All",
format = "GTiff")
|
8ba6d316a6a4e1510d513e41ba364bee80297a70
|
63836efbb930c37f39fa02d2ee3ab77db580229e
|
/man/unlist_as_char.Rd
|
3c7022faeab67742713bb3467dbde372ac0ec70d
|
[] |
no_license
|
srhoads/srhoads
|
f31a9649de550b114ff8b3386fac8532cc8375ea
|
27f86e82b3503222180fe071868e69d094eb3265
|
refs/heads/master
| 2023-08-23T22:47:05.572527
| 2023-07-21T18:43:25
| 2023-07-21T18:43:25
| 164,003,749
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 259
|
rd
|
unlist_as_char.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{unlist_as_char}
\alias{unlist_as_char}
\title{A function}
\usage{
unlist_as_char(df)
}
\description{
This function allows you to
}
\examples{
unlist_as_char(df)
}
|
6165aca6bffb8fd1ba5099cc4a4d051175124a0e
|
4fff451a5028ada8e08289cc0d07a11f003bb615
|
/Plot4.R
|
b99af89c37f60b63de2099f9b0b12bcd4dc4958c
|
[] |
no_license
|
lsnyder6/Project-2-Exploratory-Data-Analysis
|
b527d2c2ed94a6bb2a6b421d85dbb41c0f6e6b48
|
bd4cf02c0e988923b81c0fa3d0190b70561f948e
|
refs/heads/master
| 2020-04-14T03:39:28.905745
| 2019-01-03T21:20:14
| 2019-01-03T21:20:14
| 163,613,362
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,227
|
r
|
Plot4.R
|
## Set wd where unzipped files are stored
## This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
## Use dir() to ensure you have the files. Bring up NEI and SCC in View() to aid in analysis.
## Question 4
## Across the United States, how have emissions from coal combustion-related sources changed from 1999-2008?
## Start by making sure ggplot2 is available (library).
library(ggplot2)
## SCC is the name of the source as indicated by a digit string, is unique, and allows merging of NEI and SCC data.
Both <- merge(NEI, SCC, by="SCC")
# ID records with coal in the name.
coalID <- grepl("coal", Both$Short.Name, ignore.case=TRUE)
coalBoth <- Both[coalID, ]
ByYear <- aggregate(Emissions ~ year, coalBoth, sum)
png("plot4.png", width=840, height=480)
g <- ggplot(ByYear, aes(factor(year), Emissions))
g <- g + geom_bar(stat="identity") +
xlab("Year") +
ylab("PM 2.5 Level, tons") +
ggtitle('Total Emissions from coal sources')
print(g)
dev.off()
## CONCLUSION: the plot suggests that emissions from coal has decreased steadily and slowly across the US.
## Data is taken 4 times, in the years between 1999 and 2008.
|
92d27896292a550a3a9327ae0b43e14c014e89be
|
c13ce1d62b066f4180b0a4b5c4db6a068eae079f
|
/man/parse_named_map.Rd
|
88bfe4d21dcb2b99fb278e0949e04b5abb154f44
|
[] |
no_license
|
muschellij2/cifti
|
9aa5c0ef0edeafd1a2688166dfc99a8b0e9f661e
|
84b7947310dd5657dd22b809ca838e876f03673b
|
refs/heads/master
| 2020-12-24T11:53:10.701313
| 2020-08-10T16:06:53
| 2020-08-10T16:06:53
| 73,105,792
| 4
| 7
| null | 2020-07-20T12:35:01
| 2016-11-07T17:57:04
|
R
|
UTF-8
|
R
| false
| true
| 706
|
rd
|
parse_named_map.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parse_named_map.R
\name{parse_named_map}
\alias{parse_named_map}
\alias{get_named_map}
\title{Parse Named Map from CIFTI}
\usage{
parse_named_map(nodeset)
get_named_map(fname, verbose = TRUE)
}
\arguments{
\item{nodeset}{Set of XML nodes corresponding to \code{NamedMap}}
\item{fname}{filename of CIFTI file}
\item{verbose}{print diagnostic messages}
}
\value{
List of values
}
\description{
Extracts information about Named Maps from CIFTI file
}
\examples{
\dontrun{
doc = cifti_xml(fname)
nodes = xml_find_all(doc, "/CIFTI/Matrix/MatrixIndicesMap")
nodeset = xml_find_all(nodes, "./NamedMap")
parse_named_map(nodeset)
}
}
|
ad78247ade88711b2fc935814b5041e3bcffd441
|
b5429fcf87899da0705f0bb162b352b0d1977502
|
/man/fars_map_state.Rd
|
52d5a5b24475aa87dd1737175da0b8f035da2d4b
|
[] |
no_license
|
danielfsilva88/ex4fars
|
5bea358d32f014ce0c6c9464a0433832b64c1799
|
698f403149cbd875e00e6def2a4838494f5490ee
|
refs/heads/master
| 2021-05-17T13:17:36.347331
| 2020-03-29T14:00:32
| 2020-03-29T14:00:32
| 250,794,273
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 577
|
rd
|
fars_map_state.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fars_functions.R
\name{fars_map_state}
\alias{fars_map_state}
\title{Print fars data in a plot}
\usage{
fars_map_state(state.num, year)
}
\arguments{
\item{state.num}{String containing a number of USA state}
\item{year}{String of a year}
}
\value{
This function plots fars data in specified state
}
\description{
This function gets two strings representing state number and a year
check if state number is valid and plot fars data in map of state
}
\examples{
\dontrun{fars_map_state(52, 2014)}
}
|
4a90f5fa77d35b11bc6d54c31fea1a2029abbeb7
|
c5aea334627b71f1f29544009d6615a0de956d4f
|
/man/get_dummy_df.Rd
|
19861de58650dfe00b2fe7915b06f7c6302d2bd6
|
[] |
no_license
|
zsigmas/rtsimpack
|
0157c711a0fd41debeeef807a178d83de3d9db88
|
cd9c61dbf9c8d53d30b25f873aa4f361dc6b133c
|
refs/heads/master
| 2022-11-04T10:58:34.568600
| 2020-06-25T09:08:44
| 2020-06-25T09:08:44
| 274,867,178
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 252
|
rd
|
get_dummy_df.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{get_dummy_df}
\alias{get_dummy_df}
\title{Create dummy data frame}
\usage{
get_dummy_df(ni, np, ntc, random_seed = 1)
}
\description{
Create dummy data frame
}
|
97f49a220b16253a700d6a0a9f3b72cd4602cc45
|
b8f952fed76921aa5666d8d49402aa36d2faf207
|
/R/categoricalfreq.R
|
f1e827c1d5750a6e2bb3c3f151456e6618303882
|
[] |
no_license
|
datamaneuver/categoricalfreq
|
537c42a10545d64b820cb9a904d3b07408420cc6
|
1f15fe8d25099a1a75da7660cb5bf32efb98897b
|
refs/heads/master
| 2020-06-22T10:27:36.309558
| 2019-07-19T18:21:55
| 2019-07-19T18:21:55
| 197,698,800
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 901
|
r
|
categoricalfreq.R
|
categoricalfreq1 <- function(data, target_var)
{
d0 <- data[data[target_var] == 0, ]
d1 <- data[data[target_var] == 1, ]
categorical_columns = c()
for (i in 1:ncol(data))
{
if (is.factor(data[,i]))
{
values = c()
values = append(values, c(names(data[i])))
categorical_columns = append(categorical_columns, values, after = length(categorical_columns))
}
}
for (i in 1:length(categorical_columns))
{
j = data.frame()
k = data.frame()
l = data.frame()
j = as.data.frame(table(d0[categorical_columns[i]]))
k = as.data.frame(table(d1[categorical_columns[i]]))
m = k[,2]
l = cbind(j,m)
colnames(l) <- c('category','distribution of non events', 'distribution of events')
write.xlsx(l, file="categorical_variables.xlsx", sheetName=categorical_columns[i],append=TRUE, row.names=FALSE)
}
}
|
7bf149e569059c88b8c562b61701c302d22e09a3
|
fbca0cb26d06e18dd5ff84233b6378633a4340fd
|
/man/my_company_cols.Rd
|
1fc242f1aa8e40af47b4ca24cad09d4a123db68b
|
[] |
no_license
|
bvancil/paletti
|
894381b0d90b869e7278f905d61aca44ff3c4ae8
|
a557e7684e908135c1cc9b711164ce4a9c41afb6
|
refs/heads/master
| 2022-11-03T06:34:57.247058
| 2020-06-23T14:17:44
| 2020-06-23T14:17:44
| 274,422,324
| 0
| 0
| null | 2020-06-23T14:07:25
| 2020-06-23T14:07:24
| null |
UTF-8
|
R
| false
| true
| 783
|
rd
|
my_company_cols.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/my_company_cols.R
\docType{data}
\name{my_company_cols}
\alias{my_company_cols}
\title{Made up character vector of company colours}
\format{An object of class \code{character} of length 3.}
\usage{
my_company_cols
}
\description{
Made up character vector of company colours
}
\examples{
viz_pallette(my_company_cols)
my_company_hex <- get_hex(my_company_cols)
ggplot(mtcars \%>\% dplyr::mutate(cyl = as.character(cyl)), aes(cyl)) +
geom_bar(aes(fill = cyl)) +
scale_fill_manual(values = my_company_hex(red, yellow, blue))
my_comp_pal <- get_pal(my_company_cols)
my_comp_scale_col <- get_scale_color(my_company_cols)
my_comp_scale_fill <- get_scale_fill(my_company_cols)
}
\keyword{datasets}
|
23a5f86bc2220a4177259881ba8a1a04567e84f0
|
20fb140c414c9d20b12643f074f336f6d22d1432
|
/man/NISTkgPerCubMeterTOpoundPerGallonImperial.Rd
|
05970cf976288c5165a8dfa32a0b18a4998a43ea
|
[] |
no_license
|
cran/NISTunits
|
cb9dda97bafb8a1a6a198f41016eb36a30dda046
|
4a4f4fa5b39546f5af5dd123c09377d3053d27cf
|
refs/heads/master
| 2021-03-13T00:01:12.221467
| 2016-08-11T13:47:23
| 2016-08-11T13:47:23
| 27,615,133
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,023
|
rd
|
NISTkgPerCubMeterTOpoundPerGallonImperial.Rd
|
\name{NISTkgPerCubMeterTOpoundPerGallonImperial}
\alias{NISTkgPerCubMeterTOpoundPerGallonImperial}
\title{Convert kilogram per cubic meter to pound per gallon }
\usage{NISTkgPerCubMeterTOpoundPerGallonImperial(kgPerCubMeter)}
\description{\code{NISTkgPerCubMeterTOpoundPerGallonImperial} converts from kilogram per cubic meter (kg/m3) to pound per gallon [Canadian and U.K. (Imperial)] (lb/gal) }
\arguments{
\item{kgPerCubMeter}{kilogram per cubic meter (kg/m3) }
}
\value{pound per gallon [Canadian and U.K. (Imperial)] (lb/gal) }
\source{
National Institute of Standards and Technology (NIST), 2014
NIST Guide to SI Units
B.8 Factors for Units Listed Alphabetically
\url{http://physics.nist.gov/Pubs/SP811/appenB8.html}
}
\references{
National Institute of Standards and Technology (NIST), 2014
NIST Guide to SI Units
B.8 Factors for Units Listed Alphabetically
\url{http://physics.nist.gov/Pubs/SP811/appenB8.html}
}
\author{Jose Gama}
\examples{
NISTkgPerCubMeterTOpoundPerGallonImperial(10)
}
\keyword{programming}
|
9d71fb9459b359f2611eb48f0d4b029c2e55d11d
|
188de98cb4489613e3c8903388756557f6bb2b2b
|
/inst/doc_env/test_suite/testthat/test-cluster-wealth.R
|
fbdbfb40181a35f1e3319563f0487642a6b27a23
|
[] |
no_license
|
regisoc/kibior
|
639fdbd895357fe8f5044c6263d67b6fab8fe708
|
4c09013b81280d9d28b7ea267a3333f3de5aaf77
|
refs/heads/master
| 2023-07-09T03:09:33.266523
| 2021-08-10T14:47:30
| 2021-08-10T14:47:30
| 254,393,785
| 2
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,680
|
r
|
test-cluster-wealth.R
|
context("Cluster wealth")
testthat::setup({
# remove indices if they exist
remove_all_indices()
})
testthat::teardown({
# remove indices if they exist
remove_all_indices()
})
# start infos ----
test_that("kibior::infos, nominal use, no arg", {
s <- kc$infos()
expect_equal(s$status, kc$cluster_status)
expect_equal(s$cluster_name, kc$cluster_name)
})
test_that("kibior::infos, version check", {
remove_all_indices()
expect_true(paste0(kc$version, collapse = ".") %in% kc$infos()$nodes$versions)
})
test_that("kibior::infos, nominal use, constant", {
remove_all_indices()
expect_equal(kc$infos()$indices$count, 0)
res <- kc$create(single_index_name)
res %>% unlist(use.names = FALSE) %>% expect_true()
expect_equal(kc$infos()$indices$count, length(single_index_name))
remove_all_indices()
expect_equal(kc$infos()$indices$count, 0)
res <- kc$create(multiple_indice_names)
res %>% unlist(use.names = FALSE) %>% all() %>% expect_true()
expect_equal(kc$infos()$indices$count, length(multiple_indice_names))
})
test_that("kibior::infos, list vs", {
remove_all_indices()
expect_null(kc$list())
expect_true(kc$infos()$indices$count == 0)
res <- kc$create(single_index_name)
res %>% unlist(use.names = FALSE) %>% expect_true()
expect_equal(length(kc$list()), kc$infos()$indices$count)
})
# end infos
# start ping ----
test_that("kibior::ping, nominal use", {
p <- kc$ping()
expect_equal(p$version$number, paste0(kc$version, collapse = "."))
expect_equal(p$cluster_name, kc$cluster_name)
expect_equal(p$tagline, "You Know, for Search")
})
# end ping
|
5aacad2822a9f788dbdb873481ad199b3ef75398
|
8b9a07af06ac1186780260a197ac45713fcda514
|
/week1quiz.R
|
f0677396fba31b72de1d797a0fec9a8d39da7e32
|
[] |
no_license
|
dmitrik-git/gettingandclearingdata
|
b90a919d7fbbfaf5efb8744bcc8d08e64ce210f4
|
b12a17525941085b684eab05dc3cab79afc13b78
|
refs/heads/master
| 2023-01-29T14:44:41.405261
| 2020-12-10T16:47:19
| 2020-12-10T16:47:19
| 316,709,215
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,295
|
r
|
week1quiz.R
|
# Question 1
# code book https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FPUMSDataDict06.pdf
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
if (!file.exists("data")) {
dir.create("data")
}
download.file(fileURL, destfile = "./data/2006survey.csv")
dateDownloaded <- date()
data <- read.csv("./data/2006survey.csv")
sum(data$VAL == 24, na.rm = TRUE)
# Question 3
fileXLS = "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FDATA.gov_NGAP.xlsx"
# NB! It is important to include mode = 'wb' when downloading xlsx files. Otherwise R won't be able to read the file
download.file(fileXLS, destfile = "./data/ngap.xlsx", mode = 'wb')
# xlsx package must be installed and loaded first. NB! It required Java installation, too.
download.packages("xlsx", getwd())
install.packages("xlsx")
library(xlsx)
rows <- 18:23
cols <- 7:15
data_xls = read.xlsx("./data/ngap.xlsx", sheetIndex = 1, rowIndex = rows, colIndex = cols, header = TRUE)
dat <- data_xls
sum(dat$Zip*dat$Ext,na.rm=T)
# Question 4
fileXML = "http://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Frestaurants.xml"
download.packages("XML",getwd())
install.packages("XML")
library(XML)
data_xml <- xmlTreeParse(fileXML, useInternal = TRUE)
topNode <- xmlRoot(data_xml)
zipNode <- getNodeSet(topNode, "//zipcode")
zipValues <- xpathSApply(topNode, "//zipcode",xmlValue)
sum(zipValues == 21231)
# Question 5
# It's not important to run the commands. The correct answers is actually in the lesson.
# Table package is faster when it comes to subsetting and using by= operator.
# So one must look for the answer closest to this.
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06pid.csv"
if (!file.exists("data")) {
dir.create("data")
}
download.file(fileURL, destfile = "./data/communities.csv")
dateDownloaded <- date()
install.packages("data.table")
library("data.table")
DT <- fread ("./data/communities.csv")
system.time(mean(DT[DT$SEX==1,]$pwgtp15))
system.time(mean(DT[DT$SEX==2,]$pwgtp15))
system.time (DT[, mean(pwgtp15), by=SEX])
system.time (mean(DT$pwgtp15, by=DT$SEX))
system.time (tapply(DT$pwgtp15, DT$SEX, mean))
system.time (sapply(split(DT$pwgtp15, DT$SEX), mean))
system.time (rowMeans(DT)[DT$SEX==1])
system.time (rowMeans(DT)[DT$SEX==2])
|
63c9a968171a9ac5d79b354b7ee27d72525836b6
|
944706304ea9f1ef7a3cb370fbe54894bf2ab074
|
/run_analysis.R
|
fb353e88000b9f4eba0eb07475d87aa0c1aa5935
|
[] |
no_license
|
Luckyeva/Getting-and-Cleaning-Data
|
142d0958d8096f54814f78e10b6605b34d58f6a4
|
ee4e37e806487655499eb14c008d8e68c53f03fa
|
refs/heads/master
| 2021-05-30T18:19:34.700479
| 2016-03-17T15:28:45
| 2016-03-17T15:28:45
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,007
|
r
|
run_analysis.R
|
#########################################
# R Script for Getting and CLeaning data
#########################################
# Load data from downloaded text files
filepath <- "/Users/lucky1eva/Downloads/UCI HAR Dataset/train/X_train.txt"
rawdata_train <- read.table(filepath)
filepath <- "/Users/lucky1eva/Downloads/UCI HAR Dataset/train/y_train.txt"
rawdata_train_y <- read.table(filepath)
filepath <- "/Users/lucky1eva/Downloads/UCI HAR Dataset/test/X_test.txt"
rawdata_test <- read.table(filepath)
filepath <- "/Users/lucky1eva/Downloads/UCI HAR Dataset/test/y_test.txt"
rawdata_test_y <- read.table(filepath)
filepath <- "/Users/lucky1eva/Downloads/UCI HAR Dataset/train/subject_train.txt"
subject_train <- read.table(filepath)
filepath <- "/Users/lucky1eva/Downloads/UCI HAR Dataset/test/subject_test.txt"
subject_test <- read.table(filepath)
filepath = "/Users/lucky1eva/Downloads/UCI HAR Dataset/features.txt"
labels <- read.table(filepath)
## Get the variable names from the lables
colnames(rawdata_test) <- lables[, 2]
colnames(rawdata_train) <- lables[, 2]
## Create 2 columns for subject and activity ID numbers with descrptive names
newnames <- c("User_ID", "Activity_ID")
test_id <- cbind(subject_test, rawdata_test_y)
train_id <- cbind(subject_train, rawdata_train_y)
fullset_id <- rbind(train_id, test_id)
names(fullset_id) <- newnames
## Merge datasets into one table
fullset <- rbind(rawdata_train, rawdata_test)
fullset <- cbind(fullset_id, fullset)
## Extracts only the measurements on the mean and standard deviation for each measurement.
sub_id <- grep (("mean|std") , names(fullset)) # select variable with either "mean" or "std" in the names
fullset_sub <- fullset[, sub_id]
## bind ID table with data table
fullset2 <- cbind(fullset_id, fullset_sub)
## To calculate mean for each subject by each activity, use aggregate()
## function and a for_loop to compute the mean value for each variable.
tb <- aggregate(fullset2[, 3] ~ User_ID + Activity_ID, data = fullset2, FUN = "mean" )
names(tb)[3] <- colnames(fullset2)[3]
for (i in 4:length(fullset2)) {
tb.x <- aggregate(fullset2[, i] ~ User_ID + Activity_ID, data = fullset2, FUN = "mean" )
tb <- cbind(tb, tb.x[, 3])
names(tb)[length(tb)] <- colnames(fullset2)[i]
}
## Create new variable to store Activity_Name based on the Activity_ID
attach(tb)
tb$Activity_Name <- ifelse(Activity_ID == "1", "WALKING",
ifelse(Activity_ID == "2", "WALKING_UPSTAIRS",
ifelse(Activity_ID == "3", "WALKING_DOWNSTAIRS",
ifelse(Activity_ID == "4", "SITTING",
ifelse(Activity_ID == "5", "STANDING", "LAYING")))) )
detach(tb)
## Rearrange the order for Activity_Name column
tb <- tb[, c(1,2, length(tb), 3:(length(tb)-1) )]
## Write the table into a text file
setwd("/Users/lucky1eva/Downloads/")
write.table(tb, file = "tidydata.txt", row.names = FALSE)
|
79fa4bac11be5fefb1904700a6e9c58cd6297384
|
382eb7b2f50f68282d73bc5d5df2e294168408bd
|
/R/predict.R
|
20fe11bd59189a2d4a12d556a900186c4ec36e24
|
[] |
no_license
|
AgrDataSci/PlackettLuce
|
b4dbbcdaae4c19077c56403f6e437964b03e163a
|
da6bca1b0ad06eac630b44a7927f358a253a95f0
|
refs/heads/master
| 2023-06-05T12:14:57.498923
| 2021-05-05T15:23:27
| 2021-05-05T15:23:27
| 342,913,202
| 0
| 0
| null | 2021-05-07T08:07:29
| 2021-02-27T17:15:10
|
HTML
|
UTF-8
|
R
| false
| false
| 1,504
|
r
|
predict.R
|
#' @method predict PLADMM
#' @importFrom stats as.formula model.matrix
#' @export
predict.PLADMM <- function(object, newdata = NULL,
type = c("lp", "itempar"),
se.fit = FALSE,
...){ #na.action?
type <- match.arg(type)
# if newdata, create new X matrix
if (!is.null(newdata)){
if (se.fit) object$vcov <- vcov(object) # vcov based on original X matrix
# allow for missing factor levels (avoid terms etc for now)
X <- matrix(0, nrow = nrow(newdata), ncol = ncol(object$x),
dimnames = list(seq(nrow(newdata)), colnames(object$x)))
X1 <- model.matrix(as.formula(object$call$formula), newdata)
X[, colnames(X1)] <- X1
object$x <- X
}
# if itempar return constrained item parameters
if (type == "itempar"){
res <- itempar(object, vcov = se.fit, ...)
if (!se.fit) return(c(res))
return(list(fit = c(res),
se.fit = sqrt(diag(attr(res, "vcov")))))
}
# else return linear predictor (same location as original fit)
res <- drop(object$x %*% object$coefficients)
if (!se.fit) return(res)
V <- object$x[, -1, drop = FALSE] %*% vcov(object) %*%
t(object$x[, -1, drop = FALSE])
return(list(fit = res,
se.fit = sqrt(diag(V))))
}
#' @method fitted PLADMM
#' @importFrom stats predict
#' @export
fitted.PLADMM <- function(object, ...){
predict(object)
}
|
2905d1bf73722fa0d9995b516619170378a585d6
|
8f5ff57feb3468a375ebb36d5392259843f627e8
|
/R/facebook.R
|
d134c5601187e60cfcc36124d77cd8e66c14ef6c
|
[] |
no_license
|
addixvietnam/gftShinyAuthR
|
6a0214b78ca4447c9d37213f18e0cfa2c1c09e91
|
0dd645f03961b429e93ef54e9e86a8cf0f308ce7
|
refs/heads/master
| 2020-08-26T13:13:50.503290
| 2019-10-23T09:54:26
| 2019-10-23T09:54:26
| 217,022,340
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,293
|
r
|
facebook.R
|
#' Facebook APIを呼び出す
#'
#' @param path 呼び出したいAPIエンドポイントのパスからバージョン部分を抜いたもの。例: "/me?fields=id,name"
#' @param token アクセストークンをTokenオブジェクトまたは文字列で渡す
#' @param api_version APIバージョン。例: "v3.2"
#' @return APIレスポンスのJSONをリストオブジェクトとしてパースしたもの
#' @examples
#' callFacebookAPI("/me?fields=id,name")
#' @export
callFacebookAPI <- function(path, token = NULL, api_version = "v3.2") {
url <- httr::parse_url(path)
url$scheme <- "https"
url$hostname <- "graph.facebook.com"
if (!stringr::str_detect(url$path, "^/?v[\\d\\.]+/")) {
url$path <- stringr::str_c("/", api_version, url$path)
}
if (is.null(token)) {
token <- FacebookAuthentication$public_fields$token
}
if ("Token" %in% class(token)) {
token <- token$credentials$access_token
}
if ("access_token" %in% names(url$query)) {
token <- url$query$access_token
}
if (!is.character(token)) {
stop("token should be a Token object or a character vector")
}
url$query$access_token <- token
url <- httr::build_url(url)
response <- httr::GET(url)
content <- rawToChar(response$content)
jsonlite::fromJSON(content)
}
|
546a12d5696c558ed452c676f42663a86282d982
|
3e4b53d0f420e8beebefbc2f5c4e9526d9e27198
|
/r/variantkey/man/FindAllRvVariantKeyByRsid.Rd
|
de4c8a3e43db877f10e523693ebfc1d5710c9fa0
|
[
"MIT"
] |
permissive
|
yzharold/variantkey
|
5f3fc96e874b5dd607c6901d5f331b80f6667b62
|
37f5096e21e181b4fba27a73071a733b455cc75a
|
refs/heads/master
| 2020-03-27T18:24:11.039072
| 2018-08-28T08:36:36
| 2018-08-28T08:36:36
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 831
|
rd
|
FindAllRvVariantKeyByRsid.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/variantkey.R
\name{FindAllRvVariantKeyByRsid}
\alias{FindAllRvVariantKeyByRsid}
\title{Search for the specified rsID and returns all the associated VariantKeys in the RV file.
NOTE that the output is limited to maximum 10 results.}
\usage{
FindAllRvVariantKeyByRsid(src, first, last, rsid)
}
\arguments{
\item{src}{Address of the memory mapped binary file containing the rsID to VariantKey lookup table (rsvk.bin).}
\item{first}{First element of the range to search (min value = 0).}
\item{last}{Last element of the range to search (max value = nitems - 1).}
\item{rsid}{rsID to search.}
}
\description{
Search for the specified rsID and returns all the associated VariantKeys in the RV file.
NOTE that the output is limited to maximum 10 results.
}
|
e6921faff5e5b1badba56b1204054ba6ea6c37fa
|
ef1d6fa0df37fa552c4c4625e6e9cb974e8482f0
|
/man/sigOvcAngiogenic.Rd
|
d77aad67bff132d57366fdd9587a3e932a1ea66c
|
[] |
no_license
|
bhklab/genefu
|
301dd37ef91867de8a759982eb9046d3057723af
|
08aec9994d5ccb46383bedff0cbfde04267d9c9a
|
refs/heads/master
| 2022-11-28T09:22:02.713737
| 2022-05-30T15:35:53
| 2022-05-30T15:35:53
| 1,321,876
| 17
| 15
| null | 2022-11-07T11:52:05
| 2011-02-02T21:06:25
|
R
|
UTF-8
|
R
| false
| true
| 636
|
rd
|
sigOvcAngiogenic.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{sigOvcAngiogenic}
\alias{sigOvcAngiogenic}
\title{sigOvcAngiogenic dataset}
\source{
\url{http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1}
}
\description{
sigOvcAngiogenic dataset
}
\references{
Bentink S, Haibe-Kains B, Risch T, Fan J-B, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane AC, Drapkin R, Quackenbush JF, Matulonis UA (2012) "Angiogenic mRNA and microRNA Gene Expression Signature Predicts a Novel Subtype of Serous Ovarian Cancer", PloS one, 7(2):e30269
}
\keyword{data}
|
4c643e655b86797670693f563f733331292aa20f
|
0d0d0a8baa83af3ad38ea2e419544db094d4b9fd
|
/man/goosefish.Rd
|
f295ced2b03732d4f27e73dd57da27031facc594
|
[] |
no_license
|
cran/fishmethods
|
81162cf5bf35201c7ce85dd6e9815c4bca6b7646
|
ac49e77d9f2b5ee892eb5eae1807e802cddd4ac8
|
refs/heads/master
| 2023-05-17T13:53:37.128033
| 2023-04-27T07:33:01
| 2023-04-27T07:33:01
| 17,696,062
| 5
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 849
|
rd
|
goosefish.Rd
|
\name{goosefish}
\alias{goosefish}
\docType{data}
\title{Mean Length and Numbers of Lengths for Northern Goosefish, 1963-2002}
\description{
The \code{goosefish} data frame has 40 rows and 3 columns.
The mean lengths (mlen) by year and number (ss) of observations for length>=smallest length at first capture (Lc)
for northern goosefish used in Gedamke and Hoenig (2006)
}
\format{
This data frame contains the following columns:
\describe{
\item{year}{year code}
\item{mlen}{mean length of goosefish, total length (cm)}
\item{ss}{number of samples used to calculate mean length}
}
}
\source{Gedamke, T. and J. M. Hoenig. 2006. Estimating mortality from mean length
data in nonequilibrium situations, with application to the assessment of goosefish. Trans. Am. Fish. Soc. 135:476-487}
\usage{
goosefish
}
\keyword{datasets}
|
dadab36222e36b54e5cb1e3c3d0b051a2b425ba4
|
75db022357f0aaff30d419c13eafb9dddfce885a
|
/R/indicatorAttributesMatrixPlot.r
|
df08ffae9229897921f53454e0949866cb6d8a04
|
[] |
no_license
|
LobsterScience/bio.lobster
|
d4c553f0f55f561bb9f9cd4fac52c585e9cd16f8
|
b2af955291cb70c2d994e58fd99d68c6d7907181
|
refs/heads/master
| 2023-09-01T00:12:23.064363
| 2023-08-23T16:34:12
| 2023-08-23T16:34:12
| 60,636,005
| 11
| 5
| null | 2017-01-20T14:35:09
| 2016-06-07T18:18:28
|
R
|
UTF-8
|
R
| false
| false
| 2,772
|
r
|
indicatorAttributesMatrixPlot.r
|
#' @export
indicatorAttributesMatrixPlot <- function(x=nn,groupings='default',attribute=c('ecosystemIndicators')) {
syms = c()
if(attribute=='ecosystemIndicators'){
if(groupings=='default'){
options(stringsAsFactors=F)
groupings = data.frame(nam = c('ResourcePotential','EcosystemStrFunc','EcosystemStabRes','Biodiversity','FishingPressure'),
sym = c(intToUtf8(9698), intToUtf8(9673), intToUtf8(9651),intToUtf8(10055),intToUtf8(9645)))#c(intToUtf8(9924),intToUtf8(9962),intToUtf8(9982),intToUtf8(9752)))
}
for(i in 1:length(x)){
if(x[i] %in% c('MeanTrophicLevel.L')){
syms = c(syms,paste(groupings$sym[which(groupings$nam == 'ResourcePotential')],groupings$sym[which(groupings$nam == 'FishingPressure')]))
}
if(x[i] %in% c('BiomassSkates','BiomassFlatfish')) {
syms = c(syms,paste(groupings$sym[which(groupings$nam == 'ResourcePotential')],groupings$sym[which(groupings$nam == 'EcosystemStrFunc')]))
}
if(x[i] %in% c('BiomassInvertebrates')) {
syms = c(syms,paste(groupings$sym[which(groupings$nam == 'ResourcePotential')],groupings$sym[which(groupings$nam == 'EcosystemStrFunc')],groupings$sym[which(groupings$nam == 'FishingPressure')]))
}
if(x[i] %in% c('MeanLifespan','InverseCVBiomass','BiomassTL2','Intrinsicvulnerabilityindex.L')) {
syms = c(syms,groupings$sym[which(groupings$nam == 'EcosystemStabRes')])
}
if(x[i] %in% c('BiomassGadoids')) {
syms = c(syms,paste(groupings$sym[which(groupings$nam == 'ResourcePotential')],groupings$sym[which(groupings$nam == 'EcosystemStabRes')]))
}
if(x[i] %in% c('Heips','MargalefRichness')) {
syms = c(syms,groupings$sym[which(groupings$nam == 'Biodiversity')])
}
if(x[i] %in% c('CommunityCondition','BTGPiscivore','MeanLengthAbundance','CCPiscivore','CCZoopiscivore','CCMediumBenthivore','CCLargeBenthivore','MeanTrophicLevel','BTGZoopiscivore')) {
syms = c(syms,groupings$sym[which(groupings$nam == 'EcosystemStrFunc')])
}
if(x[i] %in% c('LargeFishIndicator')) {
syms = c(syms,paste(groupings$sym[which(groupings$nam == 'EcosystemStrFunc')],groupings$sym[which(groupings$nam == 'EcosystemStabRes')]))
}
if(x[i] %in% c('Biomass')) {
syms = c(syms,paste(groupings$sym[which(groupings$nam == 'EcosystemStrFunc')],groupings$sym[which(groupings$nam == 'EcosystemStabRes')],groupings$sym[which(groupings$nam == 'ResourcePotential')]))
}
if(x[i] %in% c('DiversityTargetSpp.L','FPClupeids.L','LLargePelagic.L','FishingPressure.L','LSkates.L','MarineTrophicIndex.L','Landings.L','LFlatfish.L')) {
syms = c(syms,groupings$sym[which(groupings$nam == 'FishingPressure')])
}
}
}
return(syms)
}
|
d3f79d1513873a13e55021320868ce1242403538
|
ce6dc57e224dca921921be9f2fbfe4ec55ec38d5
|
/man/genNextStateTopicModel.Rd
|
ef39e36b48e387a71fa9542f7dbe1e6bf768929d
|
[] |
no_license
|
cran/McParre
|
30ac4cd65b568c9929e6038c0c568d9233f435d2
|
7429554529d5e33546182b76708e1b147903a8be
|
refs/heads/master
| 2021-01-25T05:22:17.702378
| 2011-09-10T00:00:00
| 2011-09-10T00:00:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,331
|
rd
|
genNextStateTopicModel.Rd
|
\name{genNextStateTopicModel}
\alias{genNextStateTopicModel}
\title{
One-step generation function for Topic Models Gibbs Sampler
}
\description{
For the Latent Dirichlet Allocation model defined below in the reference,
this function generates one iteration of the Gibbs sampler to sample
from the posterior distribution.
}
\usage{
genNextStateTopicModel(x, wid, did, K, numDocs, V, totalWords, alpha, betaPrior)
}
\arguments{
\item{x}{A vector of length totalWords + K*numDocs + K*V, which is divided
as follows. The first totalWords elements are categorical, and they
represent the latent assignment of words to topics.}
\item{wid}{A vector representing the words, as indexed by 1:V - the words in
the vocabulary.}
\item{did}{A vector demarcating the documents that the words belong to.}
\item{K}{The number of topics.}
\item{numDocs}{The number of documents in the corpus.}
\item{V}{The number of words in the vocabulary.}
\item{totalWords}{The total number of words in the corpus.}
\item{alpha}{The prior parameter on the mixture across topics. A vector of
length K.}
\item{betaPrior}{The prior parameter for the Dirichlet distribution on the
vocabulary. A vector of length V.}
}
\details{
Topic models are used to describe the structure of a corpus, by attempting to
classify each of the documents as a mixture of topics. The full data
generating hierarchy can be found in the paper below.
}
\value{
A vector that represents the next state of the Gibbs sampler,
when started at state x.
}
\references{
Blei, D.M. and Ng, A.Y. and Jordan, M.I. (2003) Latent dirichlet allocation.
_Journal of Machine Learning Research_, *3*, 993-1022.
}
\author{
Vik Gopal \email{viknesh@stat.ufl.edu}
}
\examples{
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
#function(x, wid, did, K, numDocs, V, totalWords,
# alpha, betaPrior) {
# y <- rep(0, length(x))
#
# .C("genNextStateTopicModelInC", as.double(x), as.double(y), as.integer(wid),
# as.integer(did), as.integer(K), as.integer(numDocs), as.integer(V),
# as.integer(totalWords), as.double(alpha), as.double(betaPrior),
# PACKAGE="McParre")[[2]]
# }
}
\keyword{ htest }
|
cf7a50ef054d7ceb45efc9f00e9405bebfa4fcf6
|
af77cc9ccadb9cf4d451831fdd07abe13503a879
|
/yelp/wekafiles/packages/RPlugin/mlr/mlr/man/train.learner.Rd
|
32aef78bde0f9b14484f22222dfc5ab8cf729949
|
[] |
no_license
|
tummykung/yelp-dataset-challenge
|
7eed6a4d38b6c9c90011fd09317c5fa40f9bc75c
|
84f12682cba75fa4f10b5b3484ce9f6b6c8dad4a
|
refs/heads/master
| 2021-01-18T14:10:55.722349
| 2013-05-21T09:30:37
| 2013-05-21T09:30:37
| 9,527,545
| 4
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,204
|
rd
|
train.learner.Rd
|
\name{train.learner}
\alias{train.learner,classif.ada,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\title{train.learner,classif.ada,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner,classif.adaboost.M1,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,base.wrapper,character,data.frame,data.desc,task.desc,numeric,ANY-method}
\alias{train.learner}
\alias{train.learner,classif.blackboost,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.blackboost,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.ctree,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.earth,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,filter.wrapper,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.gbm,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.gbm,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.glmboost,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.grplasso,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.J48,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.JRip,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.kknn,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.kknn,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,regr.km,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.ksvm,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.ksvm,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,regr.lasso,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.lda,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.lm,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.loclda,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.logreg,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.lssvm,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.lvq1,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.mars,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.mda,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,multiclass.wrapper,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.multinom,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.naiveBayes,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.nnet,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.nnet,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.OneR,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,opt.wrapper,character,data.frame,data.desc,task.desc,numeric,ANY-method}
\alias{train.learner}
\alias{train.learner,classif.PART,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.pcr,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.lpsvm,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,preproc.wrapper,character,data.frame,data.desc,task.desc,numeric,ANY-method}
\alias{train.learner}
\alias{train.learner,classif.qda,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.randomForest,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.randomForest,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.rda,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.ridge,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.rpart,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,regr.rpart,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,regr.rsm,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,regr.rvm,character,data.frame,data.desc,task.desc,numeric,missing-method}
\alias{train.learner}
\alias{train.learner,classif.sda,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner,classif.svm,character,data.frame,data.desc,task.desc,numeric,matrix-method}
\alias{train.learner}
\alias{train.learner-methods}
\alias{train.learner}
\description{Mainly for internal use. Trains a wrapped learner on a given training set,
w.r.t. some hyperparameters, case weights and costs.
You have to implement this method if you want to add another learner to this package.}
\value{\code{train.learner-methods}: Model of the underlying learner.
}
\arguments{\item{.learner}{[\\code{\\linkS4class{learner}}] \cr
Wrapped learner from this package.}
\item{.targetvar}{[\code{\link{character}}] \cr
Name of the target variable.}
\item{.data}{[\code{\link{data.frame}}] \cr
Complete training set.}
\item{weights}{[\code{\link{numeric}}] \cr
Case weights, default is 1, which means every case is assigned equal weight.
If your learner does not support this, simply ignore this argument.}
\item{costs}{[\code{\link{matrix}}] \cr
Misclassification costs, which should be used during training.
If your learner does not support this, simply ignore this argument.}
\item{...}{[any] \cr
Additional parameters, which need to be passed to the underlying train function.}
}
|
7aeaa2e4ea50026822ec39d8f3d47d7f6ebdf8a5
|
75991cdb65fae651004424d8081e66d30db7b799
|
/scripts/buildMapping.r
|
6ca98a00dd0dc1bf039f12fdb566eb8cf7a676ac
|
[] |
no_license
|
lachmann12/alignmentbenchmark
|
5dc11259098859d61313cfa1a8fc9fa4c4a93ef2
|
5e5cdc064fce7171626587de112853cec3add0a1
|
refs/heads/master
| 2020-06-18T10:05:57.229715
| 2019-07-10T19:43:03
| 2019-07-10T19:43:03
| 195,828,801
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 708
|
r
|
buildMapping.r
|
library("plyr")
library("stringr")
getMapping <- function(species){
file = paste0("reference/",species,"_cdna_96.fa")
ll = readLines(file)
ll = ll[grep("^>", ll)]
gsym = str_extract(ll, "gene_symbol:.+?\\s|gene_symbol:.+?$")
gsym = gsub("gene_symbol:|\\s", "", gsym)
gene = str_extract(ll, "gene:.+?\\s|gene:.+?$")
gene = gsub("gene:|\\s|\\.[0-9]\\s$", "", gene)
transcript = str_extract(ll, "^>.+?\\s")
transcript = gsub("^>|\\s|\\.[0-9]\\s$", "", transcript)
return(do.call(cbind, list(transcript, gsym, gene)))
}
human_map = getMapping("human")
mouse_map = getMapping("mouse")
save(human_map, mouse_map, file="supportfiles/gene_mapping.rda")
|
0b373cd4933b1375c0ac8a4678e8c1228f458365
|
d0ea25c8ccf382f6e412a7bb34dbb0587601c000
|
/R/floridaCountyMarital.R
|
80d221e6cbd0d065845534fdee6994c859b2dac8
|
[] |
no_license
|
lizhongliu1996/marital
|
9775af26601f9df191b2edfa2a80e14a42b7e7b2
|
512c63867da313ae5bab100228a388570483d89d
|
refs/heads/master
| 2023-09-05T08:29:25.201590
| 2023-08-18T17:24:36
| 2023-08-18T17:24:36
| 136,665,983
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,380
|
r
|
floridaCountyMarital.R
|
#' Large SpatialPolygonDataFrame with race data on all the counties in Florida
#'
#' An acs object It can be downloaded using the ACS package. After getting an
#' API key run this line \code{
#' acs.fetch(endyear = 2016,
#' geography = geo.make(state = "FL", county = "*"),
#' variable = acs.lookup(endyear = 2015,table.number = "B12002",
#' dataset = "acs"),
#' dataset = "acs",
#' col.names = "pretty")}.
#'
#' @usage data(floridaCountyMarital)
#'
#' @format Large acs:
#' \describe{
#' \item{@@endyear}{2016}
#' \item{@@span}{5}
#' \item{@@geography}{data.frame with 67 observations:}
#' \itemize{
#' \item{NAME: }{}
#' \item{state: }{12}
#' \item{county: }{five character place code}
#' }
#' \item{@@acs.colnames}{}
#' \item{@@modified}{}
#' \item{@@acs.units}{}
#' \item{@@currency.year}{}
#' \item{@@estimate}{list with two items:}
#' \itemize{
#' \item{1}{string holding place names}
#' \item{2}{three strings holding variable names}
#' }
#' \item{@@standard.error}{Full Full Geocode for tracts}
#' \itemize{
#' \item{1}{string holding place names}
#' \item{2}{three strings holding variable names}
#' }
#' }
#'
#' @source {Data downloaded ACS package}
"floridaCountyMarital"
|
f04091b77be55f033f83ddbd54f6f247406880f5
|
731e5e45513f97d973f81c6b035eef0a9f03c385
|
/man/raw_data_pci.Rd
|
8a70f9647a8178adae18714d5bc9c7f6d630e799
|
[
"MIT"
] |
permissive
|
iqtigorg/iqtigprm
|
570493c0d3f12516ff5472f053c291664ca9e56b
|
084953ff38e72e0b8fdb3afe82ffae9352e751d6
|
refs/heads/main
| 2023-08-14T05:33:27.451868
| 2021-09-20T14:21:26
| 2021-09-20T14:21:26
| 408,469,185
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,934
|
rd
|
raw_data_pci.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{raw_data_pci}
\alias{raw_data_pci}
\title{PCI patient survey data}
\format{
A \code{data.frame} with 109 rows and 21 variables, which include 14 survey items:
\describe{
\item{ID}{ID of data entry}
\item{ID_LE}{ID of the patient's care provider}
\item{Fragebogen}{Questionnaire type}
\item{FB_Sent}{Date of questionnaire submission}
\item{Gebdatum}{Date of birth}
\item{Geschlecht}{Sex}
\item{BMI}{Body mass index}
\item{ARermutigtn}{Survey item: "Doctors encouraged me to ask questions during a consultation."}
\item{ARernstn}{Survey item: "My concerns were taken seriously."}
\item{ARrespektn}{Survey item: "Doctors treated me with respect."}
\item{ARgelegenheitn}{Survey item: "I was given the opportunity to talk to a doctor, if I had any question."}
\item{ARInfverstn}{Survey item: "The information I was given by doctors were comprehensible.}
\item{ARfachwortn}{Survey item: "When talking to me doctors used medical terms that I did not comprehend."}
\item{ARdeutschn}{Survey item: "Sometimes I cound not communicate with doctors, because they did not speak german."}
\item{ARgesprochenn}{Survey item: "In my presence doctors talked about me as if I was not there."}
\item{PAvoranginan}{Survey item: "Did you have any angina pectoris related problems or shortness of breath in advance of the procedure?"}
\item{Anginaruhen}{Survey item: "Angina pectoris related problems: while resting."}
\item{Anginaleichtn}{Survey item: "Angina pectoris related problems: during light everyday activities."}
\item{PAvorbeeintrn}{Survey item: "How much was your everyday life affected by angina pectoris related problems or shortness of breath in advance of the procedure?"}
\item{Anginaschwern}{Survey item: "Angina pectoris related problems: during heavy everyday activities."}
\item{Anginaaussergn}{Survey item: "Angina pectoris related problems: during extraordinary physical efforts."}
}
}
\usage{
raw_data_pci
}
\description{
An artificial dataset containing patient characteristics and raw survey answers for the quality assurance domain PCI (percutaneous coronary intervention and coronary angiography).
Each data entry refers to one patient. Patient responses to survey items can take prespecified values between 0 and 100, where 0 generally refers to
"complete disagreement" or "not at all" and 100 refers to "full agreement" or "completely", depending on the item. All survey items
allow one or more special response categories for cases, in which the patient cannot or does not want to answer the survey question. These special
categories are "-99: no statement", "-98: I do not know or cannot remember", "-97: does not apply due to erroneous questionnaire filter" and "-96: question does not apply for me",
"-90: I could not do that, because of other reasons".
}
\keyword{datasets}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.