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
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cdb9c0fe2922832106b99617747ccf897e0d95c4
|
1e7503a32f037d8e4826d66911f7a96950841690
|
/R/emigration_map.R
|
0f60afc46efe8d962c9e752d590b9629e9a6dcd0
|
[
"Apache-2.0"
] |
permissive
|
willettk/nobel
|
bd7dcf6c1bdaee9839cafe684c5cc97147dacee7
|
df7dd5f48735e84425e5eb2c62aac0d54636e9a9
|
refs/heads/master
| 2021-01-19T18:17:32.077865
| 2018-08-16T01:54:07
| 2018-08-16T01:54:07
| 68,550,062
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,433
|
r
|
emigration_map.R
|
# Function for drawing arrows on paths
arrowLine <- function(x, y, color,N=2){
lengths <- c(0, sqrt(diff(x)^2 + diff(y)^2))
l <- cumsum(lengths)
tl <- l[length(l)]
el <- seq(0, to=tl, length=N+1)[-1]
for(ii in el){
int <- findInterval(ii, l)
xx <- x[int:(int+1)]
yy <- y[int:(int+1)]
## points(xx,yy, col="grey", cex=0.5)
dx <- diff(xx)
dy <- diff(yy)
new.length <- ii - l[int]
segment.length <- lengths[int+1]
ratio <- new.length / segment.length
xend <- x[int] + ratio * dx
yend <- y[int] + ratio * dy
#points(xend,yend, col="white", pch=19)
arrows(x[int], y[int], xend, yend, length=0.1,col=color)
}
}
# Limits for certain zooms on continents
xlim_europe <- c(-25, 45)
ylim_europe <- c(35, 71)
# Create palette for paths based on counts
pal <- colorRampPalette(c("#f2f2f2","green"))
colors <- pal(100)
maxcnt <- max(nobel$cnt)
# Generate map
map('world',col='#787878',fill=TRUE,bg='black',lwd=0.20,xlim=xlim_europe,ylim=ylim_europe)
# Sort by count so most common paths are on top
nobel <- nobel[order(nobel$cnt),]
# Loop over unique paths
for (j in 1:length(nobel$lon1)) {
# Compute great circle
inter<-gcIntermediate(c(nobel$lon1[j],nobel$lat1[j]),c(nobel$lon2[j],nobel$lat2[j]),n=500,addStartEnd=TRUE,breakAtDateLine=TRUE)
colindex <- round( (nobel$cnt[j] / maxcnt) * length(colors) )
# Break line if it crosses International Date Line; draw in two pieces
if(length(inter)==2){
lines(inter[[1]],col=colors[colindex],lwd=1.2)
lines(inter[[2]],col=colors[colindex],lwd=1.2)
}
# Draw single line if it doesn't cross IDL
else{
lines(inter,col=colors[colindex],lwd=1.2)
arrowLine(inter[,1],inter[,2],colors[colindex])
}
}
# Laureates who died in their birth country or are not dead
# load file
nobel_points <- read.table('../data/points_r.csv',header=TRUE)
nobel_points <- nobel_points[order(nobel_points$cnt),]
# Loop over unique points
for (j in 1:length(nobel_points$lon1)) {
size_points <- log10(nobel_points$cnt[j]) + 1
points(x=nobel_points$lon1[j],y=nobel_points$lat1[j],pch=21,col="orange",cex=size_points)
}
# title
title(main='Nobel laureate emigration patterns',col.main="white",sub="1901-2013",col.sub="white")
# legend
legend(60, -15, c("1", "10", "100"), col = "orange", text.col = "black", pch = 21, y.intersp=1.3,cex = 0.8, pt.cex=c(1,2,3), bg = "gray90")
|
b9102d6ccb093f91da0feacb54c695969551f67b
|
11e7058fe6c009ff63b7977c6d5fd3ae426c817d
|
/f20-IBEVAR/figure20.R
|
49cb13fc72c9a24a92407cee0b275fe46e1336d6
|
[] |
no_license
|
energy-modelling-toolkit/figures-JRC-report-power-system-and-climate-variability
|
77d37a835761e28d6d59002c164ee88d67793e26
|
ef4e431d2de5dc3d31c5e82af4edec3c1f9b1e41
|
refs/heads/master
| 2022-05-27T15:29:33.119267
| 2020-04-30T10:57:22
| 2020-04-30T10:57:22
| 259,654,008
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,089
|
r
|
figure20.R
|
# This code has been used to generate the figures in the following report:
# De Felice, M., Busch, S., Kanellopoulos, K., Kavvadias, K. and Hidalgo Gonzalez, I.,
# Power system flexibility in a variable climate,
# ISBN 978-92-76-18183-5 (online), doi:10.2760/75312 (online)
# This code is released under Creative Commons Attribution 4.0 International (CC BY 4.0) licence
# (https://creativecommons.org/licenses/by/4.0/).
library(tidyverse)
library(ggrepel)
toplot <- read_csv("data_figure20.csv")
g <- ggplot(toplot, aes(x = `Hydro-power`, y = `Solar & Wind`)) +
geom_point(aes(fill = intensity, size = cost_per_mwh),
pch = 21
) +
geom_text_repel(
aes(label = label),
point.padding = unit(1, "lines"),
box.padding = unit(0.3, "lines"),
segment.size = 0.5,
force = 1,
segment.color = "grey50",
nudge_y = ifelse(toplot$id == 1996, -3, 5),
size = 2.5
) +
theme_light() +
scale_size_continuous(name = "EUR/MWh") +
scale_fill_viridis_c(name = expression(gCO[2] / kWh))
ggsave(filename = "figure20.png", width = 6 * 1.3, height = 3.2 * 1.3)
|
f2e1f9ad8357419e58a8810ff35f7613d891f891
|
1b7d9b59fbc41a83f204a7678ae1e9837a1aea22
|
/scripts/XX_centre_of_gravity_plots.R
|
610108438aff03e49bfc1e5c88a3cc93ca9c9d87
|
[] |
no_license
|
HelloMckenna/WKFISHDISH
|
3ea6b1c5bdf5f112a4d7337d617330b8569748d2
|
d170b6848814e5c3617b8a658f22c42088e5a577
|
refs/heads/master
| 2021-01-19T06:20:10.065618
| 2016-12-01T09:30:52
| 2016-12-01T09:30:52
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,791
|
r
|
XX_centre_of_gravity_plots.R
|
# -------------------------------------
#
# Fit density surface for each species and survey
#
# -------------------------------------
source("scripts/header.R")
# read in spatial datasets
load("input/spatial_model_data.rData")
if (!dir.exists("figures")) dir.create("figures")
plot.report <- function(df) {
if (nrow(df$data[[1]]) == 0) return(NULL)
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE), heights = c(1.5,1))
plot(df$statrec[[1]], main = paste0(df$species, " ",df$Survey, " Q", df$Quarter), border = grey(0.5, alpha=0.5))
#plot(area, border = grey(0.7, alpha=0.5), add = TRUE)
pdat <- df %>% unnest(data) %>% unnest(cg, cg_ci)
lines(pdat$y, pdat$x)
years <- pdat$Year - min(pdat$Year) + 1
nyears <- max(years)
cols <- colorRampPalette(c("cyan", "magenta"))(nyears)
points(pdat$y, pdat$x, col = cols[years], pch = 16)
plot(pdat$Year, pdat$x, type = "l", ylim = range(pdat$x, pdat$x.ciu, pdat$x.cil),
axes = FALSE, ylab = "Latitude", xlab = "Year")
points(pdat$Year, pdat$x)
lines(pdat$Year, pdat$x.cil, lty = 2)
lines(pdat$Year, pdat$x.ciu, lty = 2)
axis(1); axis(2, las = 1); box(bty="l")
plot(pdat$Year, pdat$y, type = "l", ylim = range(pdat$y, pdat$y.ciu, pdat$y.cil),
axes = FALSE, ylab = "Longitude", xlab = "Year")
points(pdat$Year, pdat$y)
lines(pdat$Year, pdat$y.cil, lty = 2)
lines(pdat$Year, pdat$y.ciu, lty = 2)
axis(1); axis(2, las = 1); box(bty="l")
}
selected.species <- "Norway Pout"
for (selected.species in unique(getControlTable()$Species)) {
load(paste0("output/", selected.species, "_centre_gravity.rData"))
# plot
pdf(paste0("figures/", selected.species, "_centre_gravity.pdf"), onefile = TRUE, paper = "a4")
tmp <- sapply(1:nrow(data), function(i) plot.report(data[i,]))
dev.off()
}
|
187305c19ff8a3cdca0dc24db0dabd72c428e3a8
|
bb48af2d119db733039cacc40b5c2ad75565edbb
|
/01Data/ETL_dominant_race.R
|
c94f349ffdd663166c7c01b62a25903ecd425fdf
|
[] |
no_license
|
CannataUTDV/s17dvfinalproject-dvproject5-wilczek-lopez-perez-pant
|
5253f9df17d21c833e78cabd4e23147792b57c3e
|
f44c70a977eab274e22769c7c595cc9a32c4583c
|
refs/heads/master
| 2021-01-20T04:50:36.757034
| 2017-05-02T17:17:58
| 2017-05-02T17:17:58
| 89,740,812
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,772
|
r
|
ETL_dominant_race.R
|
require(readr)
require(plyr)
# Set the Working Directory to the 00 Doc folder
file_path = "../01Data/dominant_race.csv"
df <- readr::read_csv(file_path)
print(head(df))
measures <- c()
dimensions <- setdiff(names(df), measures)
# Get rid of special characters in each column.
for(n in names(df)) {
df[n] <- data.frame(lapply(df[n], gsub, pattern="[^ -~]",replacement= ""))
}
na2emptyString <- function (x) {
x[is.na(x)] <- ""
return(x)
}
if( length(dimensions) > 0) {
for(d in dimensions) {
# Get rid of " and ' in dimensions.
df[d] <- data.frame(lapply(df[d], gsub, pattern="[\"']",replacement= ""))
# Change & to and in dimensions.
#put spaces between lowercase and upercase eg. NorthAmerica -> North America
df[d] <- data.frame(lapply(df[d], gsub, pattern="([a-z])([A-Z])",replacement= "\\1 \\2"))
#Korea,Republicof -> Republic of Korea
df[d] <- data.frame(lapply(df[d], gsub, pattern="Korea,Republicof",replacement= "Republic of Korea"))
# Bosniaand Herzegovina -> Bosnia and Herzegovina
df[d] <- data.frame(lapply(df[d], gsub, pattern="and ",replacement= " and "))
}
}
na2zero <- function (x) {
x[is.na(x)] <- 0
return(x)
}
# Get rid of all characters in measures except for numbers, the - sign, and period.dimensions, and change NA to 0.
if( length(measures) > 1) {
for(m in measures) {
df[m] <- data.frame(lapply(df[m], gsub, pattern="[^--.0-9]",replacement= ""))
#df[m] <- data.frame(lapply(df[m], na2zero))
df[m] <- data.frame(lapply(df[m], function(x) as.numeric(as.character(x)))) # This is needed to turn measures back to numeric because gsub turns them into strings.
}
}
write.csv(df, file="../01Data/dominant_race.csv", row.names=FALSE, na = "NA")
print(head(df))
|
3844f3218339aec6015a12baa5ac3e60740f5faa
|
bc2f20832f789add3a32d76c0df09ce0ee6158e2
|
/R/help.R
|
ad90ab75e2b80eed1d1eac2933e851174cf2db70
|
[] |
no_license
|
ehsanx/matchingWeight
|
c8f35b7a2545d7dfd67ec04e5847fe9bb7f98cb7
|
d6fa1db9fa69dd8eba66d509d7c14ba12cda6dc3
|
refs/heads/master
| 2021-01-03T19:20:16.214997
| 2014-08-26T18:39:10
| 2014-08-26T18:39:10
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 479
|
r
|
help.R
|
#####################################################################################################
# Package documentation
#####################################################################################################
#' @title ggrfsrc package for smooth plotting of \code{\link{randomForestSRC}} objects.
#'
#' @description ggrfsrc is an add on package for \code{\link{randomForestSRC}}.
#'
#'
#' @docType package
#' @name ggrfsrc-package
#'
################
NULL
|
d316e4204a505b19820f1149aa05580840995444
|
681241d52b64bbe04ac45dc474aea2e07c4a399b
|
/run_analysis.R
|
4aad581dfd94e61abe093fb3c26fe4845b3f8933
|
[] |
no_license
|
dirkpadfield/getting_and_cleaning_data
|
6b53c817284bc49787e823e79e3493e8bc9e382e
|
195ec85f1f57df33fd82a67a6c6ad816cd4dbe7f
|
refs/heads/master
| 2020-06-04T15:03:23.527765
| 2015-08-23T08:41:14
| 2015-08-23T08:41:14
| 41,239,385
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,751
|
r
|
run_analysis.R
|
library(dplyr)
# This code reads a set of data and labels that are stored in multiple files and joins them together into a coherent dataframe.
# It also summarizes a subset of the columns to generate a tidy dataset.
# Merge the training and the test sets to create one data set.
# Read the train data
X_train <- read.csv("UCI HAR Dataset/train/X_train.txt",header=FALSE,sep="")
y_train <- read.csv("UCI HAR Dataset/train/y_train.txt",header=FALSE)
# Read the test data
X_test <- read.csv("UCI HAR Dataset/test/X_test.txt",header=FALSE,sep="")
y_test <- read.csv("UCI HAR Dataset/test/y_test.txt",header=FALSE)
# Merge the datasets together by rows
data <- rbind(X_train,X_test)
# Merge the activities by rows
activities <- rbind(y_train,y_test)
activities <- activities[[1]]
# Read the column names
feature_names <- read.csv("UCI HAR Dataset/features.txt",header=FALSE,sep="")
feature_names <- as.character(feature_names$V2)
# Extract only the measurements on the mean and standard deviation for each measurement.
# Get the columns that have the word "mean"
mean_columns <- grep("mean",feature_names)
# Get the columns that have the word "std"
std_columns <- grep("std",feature_names)
# We sort the column numbers so that the order is the same as in the original data
col_numbers = sort(c(mean_columns,std_columns))
feature_names = feature_names[col_numbers]
data <- data[,col_numbers]
# Appropriately label the data set with descriptive variable names.
# We use the feature_names as the labels for the variable names.
colnames(data) <- feature_names
# Use descriptive activity names to name the activities in the data set
# Read the activity labels
activity_labels <- read.csv("UCI HAR Dataset/activity_labels.txt",header=FALSE,sep="")
activity_labels <- as.character(activity_labels$V2)
# Add the activities to the dataframe
data <- mutate(data,activities = factor(activities,labels = activity_labels))
# From the data set in step 4, create a second, independent tidy data set with the average of each variable for each activity and each subject.
# Read the subject data.
subject_train <- read.csv("UCI HAR Dataset/train/subject_train.txt",header=FALSE)
subject_test <- read.csv("UCI HAR Dataset/test/subject_test.txt",header=FALSE)
subjects <- rbind(subject_train,subject_test)
subjects <- subjects[[1]]
# Add the subjects to the dataframe
data <- mutate(data,subjects = factor(subjects))
# Create the tidy_data using the summarize_each function from dplyr
tidy_data = data %>% group_by(activities,subjects) %>% summarise_each(funs(mean))
# Write out the table
write.table(tidy_data,file="tidy_data.txt",row.name=FALSE)
# Read the table back in to view the result
tidy_data <- read.table("tidy_data.txt", header = TRUE)
View(tidy_data)
|
42d0f987ba5fb2ca2ba9f168663c87b1126b6b64
|
b5a1fdc3a50b2a87fd0cf26042b108727730afbf
|
/PlotVacantVsTotalUnitsV2.R
|
0427825126903c1477c2c3ea146963860550a626
|
[] |
no_license
|
drjanieforbes/VacanciesVisualization
|
0000c7a17ddf555e67787963e7539b10ba7380f9
|
0fa15df6b44ad764e34c777e358f7c9747ffbff1
|
refs/heads/master
| 2020-05-25T15:14:53.642111
| 2017-03-30T19:19:05
| 2017-03-30T19:19:05
| 84,943,112
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 20,706
|
r
|
PlotVacantVsTotalUnitsV2.R
|
# ##################################################################
# Author: Dolores Jane Forbes
#
# Date: 03/10/2017
#
# Email: dolores.j.forbes@census.gov
#
# File: PlotVacantVsTotalUnitsV2.R Version 2
#
# Branch: Geographic Research & Innovation Staff/Geography
#
# ##################################################################
#
# Using R version 3.2.2 (2015-08-14) -- "Fire Safety"
#
# This file reads summary files generated from a Python script:
# ProcessHUDfilesForVizWithFnV2.py
#
# The summary files consist of residential vacancy statistics
# at multiple scales: national, state, county, and census tract.
#
# This script opens and reads each of these files, and generates
# tables of statistics based on percentages. Each of the scales
# is then visualized as a time series of percentage statistics.
#
# ##################################################################
#
# Modules:
#
# 1) Checks/sets the working directory
# 2) Opens each of the separate scale summary files:
# national.csv
# state.csv
# county.csv
# tract.csv
#
# ##################################################################
# ##################################################################
# load libraries
# ##################################################################
library(foreign)
library(plotly)
# ##################################################################
# environment settings
# ##################################################################
options(scipen=999)
# ##################################################################
# constants
# ##################################################################
NUMTRACTS = 73767
# ##################################################################
# check to see if working directory has already been set
# ##################################################################
# version for on site work
#if(!getwd() == "T:/$$JSL/Janie/Private/VacantHouses") {
# oldwd = getwd()
# setwd("T:/$$JSL/Janie/Private/VacantHouses")
#}
# version for telework site (home)
if(!getwd() == "C:/CensusProjs/HUDData/VacantHouses") {
oldwd = getwd()
setwd("C:/CensusProjs/HUDData/VacantHouses")
}
# ##################################################################
# process the national level file
# ##################################################################
national.data <- read.csv(file="./HUD/national.csv",
header=TRUE,
sep=",")
# create empty list
my.vector = list()
# create empty data frame
my.df <- data.frame(matrix(ncol = ncol(national.data)-1,
nrow = nrow(national.data)))
colnames(my.df) <- c("Month.Year",
"VAC_3_RESpc",
"VAC_3_6_RESpc",
"VAC_6_12_RESpc",
"VAC_12_24_RESpc",
"VAC_24_36_RESpc",
"VAC_36_RESpc",
"AVG_DAYS_VAC",
"RES_VACpc",
"AMS_RES")
for (i in 1:nrow(national.data)) {
my.vector <- as.character(national.data$Month.Year[i])
if (national.data$totalAllRES_VAC[i] != 0) {
my.vector <- c(my.vector,
(national.data$totalAllVAC_3_RES[i] / national.data$totalAllRES_VAC[i]),
(national.data$totalAllVAC_3_6_R[i] / national.data$totalAllRES_VAC[i]),
(national.data$totalAllVAC_6_12R[i] / national.data$totalAllRES_VAC[i]),
(national.data$totalAllVAC_12_24R[i] / national.data$totalAllRES_VAC[i]),
(national.data$totalAllVAC_24_36R[i] / national.data$totalAllRES_VAC[i]),
(national.data$totalAllVAC_36_RES[i] / national.data$totalAllRES_VAC[i]),
(national.data$totalAllAVG_VAC_R[i]), # calculated in Python
# using a constant!!!!!
# (sum(national.data$totalAllAVG_VAC_R[i])/NUMTRACTS),
(national.data$totalAllRES_VAC[i] / national.data$totalAllAMS_RES[i]),
(national.data$totalAllAMS_RES[i]))
} else {
my.vector <- c(my.vector,0,0,0,0,0,0,
#sum(national.data$totalAllAVG_VAC_R[i])/73767,0,0)
national.data$totalAllAVG_VAC_R[i],0,0)
}
# append to the data frame
my.df[i,] <- my.vector
# reset the vector to empty
my.vector = list()
}
head(my.df)
# ##################################################################
# plot the national level file
# ##################################################################
trace1 <- list(
x = as.numeric(my.df$VAC_3_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(17, 78, 166)"),
name = "0-3 mo",
orientation = "h",
type = "bar",
uid = "063b98",
xsrc = "Dreamshot:4231:b631ec",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace2 <- list(
x = as.numeric(my.df$VAC_3_6_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(41, 128, 171)"),
name = "3-6 mo",
orientation = "h",
type = "bar",
uid = "d2ea67",
xsrc = "Dreamshot:4231:9a1926",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace3 <- list(
x = as.numeric(my.df$VAC_6_12_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(104, 157, 46)"),
name = "6-12 mo",
orientation = "h",
type = "bar",
uid = "5e63a2",
xsrc = "Dreamshot:4231:2ec534",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace4 <- list(
x = as.numeric(my.df$VAC_12_24_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(36, 118, 23)"),
name = "12-24 mo",
orientation = "h",
type = "bar",
uid = "24f079",
xsrc = "Dreamshot:4231:c7663a",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace5 <- list(
x = as.numeric(my.df$VAC_24_36_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(169, 140, 31)"),
name = "24-36 mo",
orientation = "h",
type = "bar",
uid = "ae6448",
xsrc = "Dreamshot:4231:8f7c41",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace6 <- list(
x = as.numeric(my.df$VAC_36_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(178, 81, 28)"),
name = "36+ mo",
orientation = "h",
type = "bar",
uid = "173fcb",
xsrc = "Dreamshot:4231:a324f1",
ysrc = "Dreamshot:4231:b4bc0c"
)
data <- list(trace1, trace2, trace3, trace4, trace5, trace6)
layout <- list(
# changed autosize to TRUE here
autosize = TRUE,
bargap = 0.05,
bargroupgap = 0.15,
barmode = "stack",
boxgap = 0.3,
boxgroupgap = 0.3,
boxmode = "overlay",
dragmode = "zoom",
font = list(
color = "rgb(255, 255, 255)",
family = "'Open sans', verdana, arial, sans-serif",
size = 12
),
height = 700,
hidesources = FALSE,
hovermode = "x",
legend = list(
x = 1.11153846154,
y = 1.01538461538,
bgcolor = "rgba(255, 255, 255, 0)",
bordercolor = "rgba(0, 0, 0, 0)",
borderwidth = 1,
font = list(
color = "",
family = "",
size = 0
),
traceorder = "normal",
xanchor = "auto",
yanchor = "auto"
),
margin = list(
r = 80,
t = 100,
autoexpand = TRUE,
b = 80,
l = 100,
pad = 0
),
paper_bgcolor = "rgb(67, 67, 67)",
plot_bgcolor = "rgb(67, 67, 67)",
separators = ".,",
showlegend = TRUE,
smith = FALSE,
title = "National Level (United States)<br>Percent Units Vacant by Length of Time",
titlefont = list(
color = "rgb(255, 255, 255)",
family = "",
size = 0
),
width = 700,
xaxis = list(
anchor = "y",
autorange = TRUE,
autotick = TRUE,
domain = c(0, 1),
dtick = 20,
exponentformat = "e",
gridcolor = "#ddd",
gridwidth = 1,
linecolor = "#000",
linewidth = 1,
mirror = FALSE,
nticks = 0,
overlaying = FALSE,
position = 0,
range = c(0, 105.368421053),
rangemode = "normal",
showexponent = "all",
showgrid = FALSE,
showline = FALSE,
showticklabels = TRUE,
tick0 = 0,
tickangle = "auto",
tickcolor = "#000",
tickfont = list(
color = "",
family = "",
size = 0
),
ticklen = 5,
ticks = "",
tickwidth = 1,
title = "<br><i>Data Source: Housing & Urban Development</i>",
titlefont = list(
color = "",
family = "",
size = 0
),
type = "linear",
zeroline = FALSE,
zerolinecolor = "#000",
zerolinewidth = 1
),
yaxis = list(
anchor = "x",
autorange = TRUE,
autotick = TRUE,
# added this so that the order is preserved on the output
categoryorder = "trace",
domain = c(0, 1),
dtick = 1,
exponentformat = "e",
gridcolor = "#ddd",
gridwidth = 1,
linecolor = "#000",
linewidth = 1,
mirror = FALSE,
nticks = 0,
overlaying = FALSE,
position = 0,
range = c(-0.5, 23.5),
rangemode = "normal",
showexponent = "all",
showgrid = FALSE,
showline = FALSE,
showticklabels = TRUE,
tick0 = 0,
tickangle = "auto",
tickcolor = "#000",
tickfont = list(
color = "",
family = "",
size = 0
),
ticklen = 5,
ticks = "",
tickwidth = 1,
title = "",
titlefont = list(
color = "",
family = "",
size = 0
),
type = "category",
zeroline = FALSE,
zerolinecolor = "#000",
zerolinewidth = 1
)
)
p <- plot_ly(width=layout$width,height=layout$height)
p <- add_trace(p, x=trace1$x, y=trace1$y, marker=trace1$marker, name=trace1$name, orientation=trace1$orientation, type=trace1$type, uid=trace1$uid, xsrc=trace1$xsrc, ysrc=trace1$ysrc)
p <- add_trace(p, x=trace2$x, y=trace2$y, marker=trace2$marker, name=trace2$name, orientation=trace2$orientation, type=trace2$type, uid=trace2$uid, xsrc=trace2$xsrc, ysrc=trace2$ysrc)
p <- add_trace(p, x=trace3$x, y=trace3$y, marker=trace3$marker, name=trace3$name, orientation=trace3$orientation, type=trace3$type, uid=trace3$uid, xsrc=trace3$xsrc, ysrc=trace3$ysrc)
p <- add_trace(p, x=trace4$x, y=trace4$y, marker=trace4$marker, name=trace4$name, orientation=trace4$orientation, type=trace4$type, uid=trace4$uid, xsrc=trace4$xsrc, ysrc=trace4$ysrc)
p <- add_trace(p, x=trace5$x, y=trace5$y, marker=trace5$marker, name=trace5$name, orientation=trace5$orientation, type=trace5$type, uid=trace5$uid, xsrc=trace5$xsrc, ysrc=trace5$ysrc)
p <- add_trace(p, x=trace6$x, y=trace6$y, marker=trace6$marker, name=trace6$name, orientation=trace6$orientation, type=trace6$type, uid=trace6$uid, xsrc=trace6$xsrc, ysrc=trace6$ysrc)
#p <- add_trace(p, x=trace7$x, y=trace7$y, marker=trace7$marker, name=trace7$name, orientation=trace7$orientation, type=trace7$type, uid=trace7$uid, xsrc=trace7$xsrc, ysrc=trace7$ysrc)
# removed 'bargroupgap', 'boxgap', 'boxgroupgap', 'boxmode' (deprecated?)
p <- layout(p, autosize=layout$autosize, bargap=layout$bargap, barmode=layout$barmode, dragmode=layout$dragmode, font=layout$font, hidesources=layout$hidesources, hovermode=layout$hovermode, legend=layout$legend, margin=layout$margin, paper_bgcolor=layout$paper_bgcolor, plot_bgcolor=layout$plot_bgcolor, separators=layout$separators, showlegend=layout$showlegend, smith=layout$smith, title=layout$title, titlefont=layout$titlefont, xaxis=layout$xaxis, yaxis=layout$yaxis)
p
# ##################################################################
# process the state level file
# ##################################################################
state.data <- read.csv(file="./HUD/state.csv",
header=TRUE,
quote = "\"",
sep=",")
# create empty list
my.vector = list()
# create empty data frame
my.df <- data.frame(matrix(ncol = ncol(state.data),
nrow = nrow(state.data)))
colnames(my.df) <- c("Month.Year",
"GEOID",
"VAC_3_RESpc",
"VAC_3_6_RESpc",
"VAC_6_12_RESpc",
"VAC_12_24_RESpc",
"VAC_24_36_RESpc",
"VAC_36_RESpc",
"AVG_DAYS_VAC",
"RES_VACpc",
"AMS_RES")
for (state.data$GEOID %in% as.factor(state.data$GEOID)):
print(i)
my.vector <- as.character(state.data$Month.Year[i])
if (state.data$totalAllRES_VAC[i] != 0) {
my.vector <- c(my.vector,
(state.data$totalAllVAC_3_RES[i] / state.data$totalAllRES_VAC[i]),
(state.data$totalAllVAC_3_6_R[i] / state.data$totalAllRES_VAC[i]),
(state.data$totalAllVAC_6_12R[i] / state.data$totalAllRES_VAC[i]),
(state.data$totalAllVAC_12_24R[i] / state.data$totalAllRES_VAC[i]),
(state.data$totalAllVAC_24_36R[i] / state.data$totalAllRES_VAC[i]),
(state.data$totalAllVAC_36_RES[i] / state.data$totalAllRES_VAC[i]),
(state.data$totalAllAVG_VAC_R[i]), # calculated in Python
# using a constant!!!!!
# (sum(state.data$totalAllAVG_VAC_R[i])/NUMTRACTS),
(state.data$totalAllRES_VAC[i] / state.data$totalAllAMS_RES[i]),
(state.data$totalAllAMS_RES[i]))
} else {
my.vector <- c(my.vector,0,0,0,0,0,0,
#sum(state.data$totalAllAVG_VAC_R[i])/73767,0,0)
state.data$totalAllAVG_VAC_R[i],0,0)
}
# append to the data frame
my.df[i,] <- my.vector
# reset the vector to empty
my.vector = list()
}
head(my.df)
# ##################################################################
# plot the state level file
# ##################################################################
trace1 <- list(
x = as.numeric(my.df$VAC_3_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(17, 78, 166)"),
name = "0-3 mo",
orientation = "h",
type = "bar",
uid = "063b98",
xsrc = "Dreamshot:4231:b631ec",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace2 <- list(
x = as.numeric(my.df$VAC_3_6_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(41, 128, 171)"),
name = "3-6 mo",
orientation = "h",
type = "bar",
uid = "d2ea67",
xsrc = "Dreamshot:4231:9a1926",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace3 <- list(
x = as.numeric(my.df$VAC_6_12_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(104, 157, 46)"),
name = "6-12 mo",
orientation = "h",
type = "bar",
uid = "5e63a2",
xsrc = "Dreamshot:4231:2ec534",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace4 <- list(
x = as.numeric(my.df$VAC_12_24_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(36, 118, 23)"),
name = "12-24 mo",
orientation = "h",
type = "bar",
uid = "24f079",
xsrc = "Dreamshot:4231:c7663a",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace5 <- list(
x = as.numeric(my.df$VAC_24_36_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(169, 140, 31)"),
name = "24-36 mo",
orientation = "h",
type = "bar",
uid = "ae6448",
xsrc = "Dreamshot:4231:8f7c41",
ysrc = "Dreamshot:4231:b4bc0c"
)
trace6 <- list(
x = as.numeric(my.df$VAC_36_RESpc)*100,
y = my.df$Month.Year,
marker = list(color = "rgb(178, 81, 28)"),
name = "36+ mo",
orientation = "h",
type = "bar",
uid = "173fcb",
xsrc = "Dreamshot:4231:a324f1",
ysrc = "Dreamshot:4231:b4bc0c"
)
data <- list(trace1, trace2, trace3, trace4, trace5, trace6)
layout <- list(
# changed autosize to TRUE here
autosize = TRUE,
bargap = 0.05,
bargroupgap = 0.15,
barmode = "stack",
boxgap = 0.3,
boxgroupgap = 0.3,
boxmode = "overlay",
dragmode = "zoom",
font = list(
color = "rgb(255, 255, 255)",
family = "'Open sans', verdana, arial, sans-serif",
size = 12
),
height = 700,
hidesources = FALSE,
hovermode = "x",
legend = list(
x = 1.11153846154,
y = 1.01538461538,
bgcolor = "rgba(255, 255, 255, 0)",
bordercolor = "rgba(0, 0, 0, 0)",
borderwidth = 1,
font = list(
color = "",
family = "",
size = 0
),
traceorder = "normal",
xanchor = "auto",
yanchor = "auto"
),
margin = list(
r = 80,
t = 100,
autoexpand = TRUE,
b = 80,
l = 100,
pad = 0
),
paper_bgcolor = "rgb(67, 67, 67)",
plot_bgcolor = "rgb(67, 67, 67)",
separators = ".,",
showlegend = TRUE,
smith = FALSE,
title = "National Level (United States)<br>Percent Units Vacant by Length of Time",
titlefont = list(
color = "rgb(255, 255, 255)",
family = "",
size = 0
),
width = 700,
xaxis = list(
anchor = "y",
autorange = TRUE,
autotick = TRUE,
domain = c(0, 1),
dtick = 20,
exponentformat = "e",
gridcolor = "#ddd",
gridwidth = 1,
linecolor = "#000",
linewidth = 1,
mirror = FALSE,
nticks = 0,
overlaying = FALSE,
position = 0,
range = c(0, 105.368421053),
rangemode = "normal",
showexponent = "all",
showgrid = FALSE,
showline = FALSE,
showticklabels = TRUE,
tick0 = 0,
tickangle = "auto",
tickcolor = "#000",
tickfont = list(
color = "",
family = "",
size = 0
),
ticklen = 5,
ticks = "",
tickwidth = 1,
title = "<br><i>Data Source: Housing & Urban Development</i>",
titlefont = list(
color = "",
family = "",
size = 0
),
type = "linear",
zeroline = FALSE,
zerolinecolor = "#000",
zerolinewidth = 1
),
yaxis = list(
anchor = "x",
autorange = TRUE,
autotick = TRUE,
# added this so that the order is preserved on the output
categoryorder = "trace",
domain = c(0, 1),
dtick = 1,
exponentformat = "e",
gridcolor = "#ddd",
gridwidth = 1,
linecolor = "#000",
linewidth = 1,
mirror = FALSE,
nticks = 0,
overlaying = FALSE,
position = 0,
range = c(-0.5, 23.5),
rangemode = "normal",
showexponent = "all",
showgrid = FALSE,
showline = FALSE,
showticklabels = TRUE,
tick0 = 0,
tickangle = "auto",
tickcolor = "#000",
tickfont = list(
color = "",
family = "",
size = 0
),
ticklen = 5,
ticks = "",
tickwidth = 1,
title = "",
titlefont = list(
color = "",
family = "",
size = 0
),
type = "category",
zeroline = FALSE,
zerolinecolor = "#000",
zerolinewidth = 1
)
)
p <- plot_ly(width=layout$width,height=layout$height)
p <- add_trace(p, x=trace1$x, y=trace1$y, marker=trace1$marker, name=trace1$name, orientation=trace1$orientation, type=trace1$type, uid=trace1$uid, xsrc=trace1$xsrc, ysrc=trace1$ysrc)
p <- add_trace(p, x=trace2$x, y=trace2$y, marker=trace2$marker, name=trace2$name, orientation=trace2$orientation, type=trace2$type, uid=trace2$uid, xsrc=trace2$xsrc, ysrc=trace2$ysrc)
p <- add_trace(p, x=trace3$x, y=trace3$y, marker=trace3$marker, name=trace3$name, orientation=trace3$orientation, type=trace3$type, uid=trace3$uid, xsrc=trace3$xsrc, ysrc=trace3$ysrc)
p <- add_trace(p, x=trace4$x, y=trace4$y, marker=trace4$marker, name=trace4$name, orientation=trace4$orientation, type=trace4$type, uid=trace4$uid, xsrc=trace4$xsrc, ysrc=trace4$ysrc)
p <- add_trace(p, x=trace5$x, y=trace5$y, marker=trace5$marker, name=trace5$name, orientation=trace5$orientation, type=trace5$type, uid=trace5$uid, xsrc=trace5$xsrc, ysrc=trace5$ysrc)
p <- add_trace(p, x=trace6$x, y=trace6$y, marker=trace6$marker, name=trace6$name, orientation=trace6$orientation, type=trace6$type, uid=trace6$uid, xsrc=trace6$xsrc, ysrc=trace6$ysrc)
#p <- add_trace(p, x=trace7$x, y=trace7$y, marker=trace7$marker, name=trace7$name, orientation=trace7$orientation, type=trace7$type, uid=trace7$uid, xsrc=trace7$xsrc, ysrc=trace7$ysrc)
# removed 'bargroupgap', 'boxgap', 'boxgroupgap', 'boxmode' (deprecated?)
p <- layout(p, autosize=layout$autosize, bargap=layout$bargap, barmode=layout$barmode, dragmode=layout$dragmode, font=layout$font, hidesources=layout$hidesources, hovermode=layout$hovermode, legend=layout$legend, margin=layout$margin, paper_bgcolor=layout$paper_bgcolor, plot_bgcolor=layout$plot_bgcolor, separators=layout$separators, showlegend=layout$showlegend, smith=layout$smith, title=layout$title, titlefont=layout$titlefont, xaxis=layout$xaxis, yaxis=layout$yaxis)
p
|
5e0451b3340bd721e9209efbecac7ba3a302b3b8
|
2f4ac698e45acd2509a7a2cfb6444ed44f07129a
|
/code/search.r
|
9f12c7d4941512b9998e94fd824fd10366bd9379
|
[] |
no_license
|
lebebr01/software_pop
|
63d506c5fda2aad64bf4dce4689c8e40c6ddd110
|
d3b75c9778c27c43a9e7a2c63f1f4979f6fae1ce
|
refs/heads/main
| 2021-07-30T04:37:05.625566
| 2021-07-20T18:31:26
| 2021-07-20T18:31:26
| 218,545,723
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,138
|
r
|
search.r
|
library(pdfsearch)
# -------------------------
# Setup
soft_keywords <- c('SPSS Statistics', ' R ', 'SAS', 'STATA', 'MATLAB',
'Statistica ', 'Statsoft', 'Java', 'Hadoop',
'Python', 'Minitab', 'Systat', 'JMP', 'SPSS Modeler',
'Tableau', 'Scala', 'Julia', 'Azure Machine',
'Mplus', 'LISREL', 'AMOS', 'BILOG', 'BILOG-MG',
'R-project', 'R project', 'Multilog', 'PARSCALE', 'IRT PRO',
'HLM[0-9]', 'HLM [0-9]', 'SAS Institute', 'SPSS',
'CRAN', 'R software', 'R core team',
'M-Plus', 'RStudio')
soft_ignore <- c(FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, TRUE, FALSE,
FALSE, TRUE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, FALSE, TRUE,
FALSE, FALSE, TRUE, TRUE, TRUE, TRUE)
# -------------------------
# AERJ
keyword_results_aerj <- keyword_directory(directory = 'C:/Users/bleb/Documents/AERJ.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_aerj$journal <- 'AERJ'
save(keyword_results_aerj, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_aerj_v2.rda')
# -------------------------
# EEPA
keyword_results_eepa <- keyword_directory(directory = 'C:/Users/bleb/Documents/EEPA.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
split_pdf = TRUE,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_eepa$journal <- 'EEPA'
save(keyword_results_eepa, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_eepa_v2.rda')
# -------------------------
# JEE
keyword_results_jee <- keyword_directory(directory = 'C:/Users/bleb/Documents/JEE.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_jee$journal <- 'JEE'
save(keyword_results_jee, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_jee_v2.rda')
# -------------------------
# am_j_pol_sci
keyword_results_am_j_pol_sci <- keyword_directory(directory = 'C:/Users/bleb/Documents/am_j_pol_sci.Data/PDF',
keyword = soft_keywords,
split_pdf = TRUE,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_am_j_pol_sci$journal <- 'am_j_pol_sci'
save(keyword_results_am_j_pol_sci, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_am_j_pol_sci_v2.rda')
# -------------------------
# economic journal
keyword_results_ej <- keyword_directory(directory = 'C:/Users/bleb/Documents/ej.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_ej$journal <- 'ej'
save(keyword_results_ej, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_ej_v2.rda')
# -------------------------
# educational researcher
keyword_results_er <- keyword_directory(directory = 'C:/Users/bleb/Documents/ER.Data/PDF',
keyword = soft_keywords,
split_pdf = TRUE,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_er$journal <- 'er'
save(keyword_results_er, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_er_v2.rda')
# -------------------------
# HE
keyword_results_he <- keyword_directory(directory = 'C:/Users/bleb/Documents/HE.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_he$journal <- 'he'
save(keyword_results_he, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_he_v2.rda')
# -------------------------
# pol_sci_quar
keyword_results_pol_sci_quar <- keyword_directory(directory = 'C:/Users/bleb/Documents/pol_sci_quar.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_pol_sci_quar$journal <- 'pol_sci_quar'
save(keyword_results_pol_sci_quar, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_pol_sci_quar_v2.rda')
# -------------------------
# pub_policy_admin
keyword_results_pub_policy_admin <- keyword_directory(directory = 'C:/Users/bleb/Documents/pub_policy_admin.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_pub_policy_admin$journal <- 'pub_policy_admin'
save(keyword_results_pub_policy_admin, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_pub_policy_admin_v2.rda')
# -------------------------
# SE
keyword_results_SE <- keyword_directory(directory = 'C:/Users/bleb/Documents/SE.Data/PDF',
keyword = soft_keywords,
split_pdf = TRUE,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_SE$journal <- 'SE'
save(keyword_results_SE, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_SE_v2.rda')
# -------------------------
# public_policy
keyword_results_public_policy <- keyword_directory(directory = 'C:/Users/bleb/Documents/public_policy.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_public_policy$journal <- 'public_policy'
save(keyword_results_public_policy, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_public_policy_v2.rda')
# -------------------------
# American Economic Journal
keyword_results_aej_ae <- keyword_directory(directory = 'C:/Users/bleb/Documents/aej_ae.Data/PDF',
keyword = soft_keywords,
surround_lines = FALSE,
ignore_case = soft_ignore,
full_names = TRUE,
recursive = TRUE,
max_search = NULL)
keyword_results_aej_ae$journal <- 'aej_ae'
save(keyword_results_aej_ae, file = 'C:/Users/bleb/OneDrive - University of Iowa/JournalArticlesInProgress/software_pop/data/keyword_aej_ae_v2.rda')
|
2d6260e50cdae9e98d922d9070219de8992d5ce2
|
cedc620c85b5f34eb2ea5c1aa4b79354e7a57d21
|
/man/plotPart.Rd
|
24837c0e587b1f39214ad5f2782e38963fa3b990
|
[] |
no_license
|
AntonGagin/nanop
|
35ba062587376eb351c63dcf6a94f5bcb2a4ff96
|
56211a00fecac9d95cb620189bfb72934ee4b750
|
refs/heads/master
| 2021-01-15T22:57:55.806166
| 2015-09-25T17:23:10
| 2015-09-25T17:23:10
| 33,210,645
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,590
|
rd
|
plotPart.Rd
|
\name{plotPart}
\Rdversion{1.1}
\alias{plotPart}
\title{Draws a three-dimensional scatterplot}
\description{Function to visualize a nanoparticle using \href{http://CRAN.R-project.org/package=rgl}{rgl} package.}
\usage{
plotPart(nanop, radius=0.4, legend=TRUE, col=NA, box=FALSE,
play=FALSE, atoms=NA, miller=NA, lattice=c(4.08))
}
\arguments{
\item{nanop}{numeric matrix in which each row gives the coordinates of an atomic position in the nanoparticle. If nanop is not an object returned by \code{\link{simPart}} or \code{\link{displacePart}} attributes \code{nAtomTypes}, \code{atomType}, \code{r}, \code{sym}, and \code{symShell} must be set manually; see \code{\link{simPart}}.
}
\item{radius}{numeric vector or single value. Each atom on a plot is represented by a sphere. \code{radius} defines the sphere radius (radii).}
\item{legend}{logical indicating whether to print plot legend.}
\item{col}{numeric vector defining colours to be used for plotted items. If vector \code{col} length does not correspond to number of atom types within the particle then standard colouring scheme is used.
}
\item{box}{logical indicating whether to draw box and axes.}
\item{play}{logical. If \code{TRUE} animation with constantly rotating particle is played.}
\item{atoms}{character. If not \code{NA} specifies atoms to be displayed, see details.}
\item{miller}{numeric vector, specifies Miller indices. If not \code{NA} only the plane in a particle core described by given indices is displayed. Should be given in a form \code{c(h, k, l)} for the non-hexagonal symmetry and \code{c(h, k, i, l)} for the hexagonal symmetry. Should be specified together with \code{lattice} parameter.}
\item{lattice}{numeric vector indicating particle core lattice parameters. Should be given in the same form as in \code{\link{simPart}}.}
}
\value{a vector of object IDs.}
\details{
If only core (shell) atoms of a specific type to be plotted \code{atoms} should be set up to \code{"core X"} or \code{"shell X"}, respectively. Character describing atom type \code{"X"} can be taken from attributes(part)$atomsCore or attributes(part)$atomsShell.
}
\examples{
## rgl library demands graphical terminal to be available
## uncoment all plotPart() calls for 3D visualization
## simulate particle
Au <- createAtom("Cu")
Au$name <- "Au"
Pd <- createAtom("Cu")
Pd$name <- "Pd"
part <- simPart(atoms=list(Au), atomsShell=list(Pd), rcore=8)
## 3d scatter plot
#plotPart(part, col=c(2,4))
## increase number of atom types within the particle:
Zn <- createAtom("Zn")
S <- createAtom("S")
part <- simPart(atoms=list(Zn ,S), atomsShell=list(Au), r=14,
rcore=12, sym="hcp", symShell="fcc", latticep=c(4.3, 7.04),
latticepShell=4.08)
## 3d scatter plot
#plotPart(part, col=c(2,4,3))
## play animation:
#plotPart(part, col=c(2,4,3), play=TRUE)
## plot only shell particles
#plotPart(part, col=c(2,4,3), atoms="shell Au", play=TRUE)
part <- simPart(atoms=list(Zn ,S),r=20, sym="hcp",
latticep=c(4.3, 7.04))
## display plane normal to z-axis:
#plotPart(part, miller=c(0, 0, 0 ,1), lattice=c(4.3, 7.04))
##S atoms:
#plotPart(part, miller=c(0, 0, 0 ,1), lattice=c(4.3, 7.04),
# atoms = "core S")
## save picture in a file using rgl function:
#rgl.snapshot( filename = "plane0001 S atoms.png")
Na <- createAtom("Na")
Cl <- createAtom("Cl")
part <- simPart(atoms=list(Na,Cl), sym="fcc")
#plotPart(part, miller=c(1,0,1), box=TRUE, lattice=c(4.08))
## plot only Na atoms:
#plotPart(part, miller=c(1,0,1), box=TRUE, lattice=c(4.08),
# atoms = "core Na")
}
\keyword{visualization}
|
2498684b3c4bee68abb050012c3f24d829499f7f
|
e869c00a1e32dd8bc304ceb9e9843e87db91752c
|
/R/conformal_mapping_code_circlepack.R
|
493c186c580f28ce0e52e73e539fca2f7c6ff3b1
|
[
"MIT"
] |
permissive
|
rdinnager/Australia_as_a_circle
|
68246e7a078181182fa545423dfeb2ff3073157a
|
5a96a48d5ffab35c98a05975f35bd643d7981dab
|
refs/heads/master
| 2021-07-12T19:25:59.362236
| 2020-10-08T11:15:44
| 2020-10-08T11:15:44
| 212,463,175
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,271
|
r
|
conformal_mapping_code_circlepack.R
|
library(rnaturalearth)
library(sf)
library(maps)
library(rmapshaper)
library(tidyverse)
library(ggplot2)
library(ggrepel)
library(ggforce)
library(distances)
library(spdep)
library(packcircles)
Oz_cities <- world.cities %>%
dplyr::filter(country.etc == "Australia",
pop > 1000000 | name == "Darwin" | name == "Canberra") %>%
sf::st_as_sf(coords = c("long", "lat"), crs = 4326) %>%
sf::st_transform(3112) ## Geoscience Australia Lambert
Oz_coast <- rnaturalearth::ne_states("Australia", returnclass = "sf") %>%
dplyr::filter(code_hasc %in% c("AU.NT",
"AU.WA",
"AU.CT",
"AU.NS",
"AU.SA",
"AU.VI",
"AU.QL")) %>%
rmapshaper::ms_filter_islands(1e+14) %>% ## get only mainland Australia
rmapshaper::ms_simplify(0.05) %>% ## aggressive simplification to reduce number if 'insets'
sf::st_union() %>%
sf::st_transform(3112) ## Geoscience Australia Lambert
oz <- ggplot(Oz_coast ) +
geom_sf(fill = "grey20") +
geom_sf(data = Oz_cities, colour = "red", size = 3) +
theme_minimal()
oz +
ggrepel::geom_label_repel(
data = Oz_cities,
aes(label = name, geometry = geometry),
stat = "sf_coordinates",
min.segment.length = 0,
segment.color = "red",
force = 3,
direction = "both",
max.iter = 50000,
nudge_x = 10000,
nudge_y = -10000
)
#geom_sf_label(data = Oz_cities, aes(label = name)) +
## We start our circle packing by generating a hexagonal grid on our polygon
cellsize <- 100000
hexes <- Oz_coast %>%
sf::st_make_grid(cellsize = cellsize, square = FALSE)
oz +
geom_sf(data = hexes, colour = "white", alpha = 0.6, fill = NA)
## Now we generate circles at each hexagon vertice and centre
hex_vert <- Oz_coast %>%
sf::st_make_grid(cellsize = cellsize, square = FALSE, what = "polygons") %>%
sf::st_cast("POINT") %>%
lwgeom::st_snap_to_grid(25)
hex_cents <- Oz_coast %>%
sf::st_make_grid(cellsize = cellsize, square = FALSE, what = "centers") %>%
sf::st_centroid() %>%
lwgeom::st_snap_to_grid(25)
circ_cents <- c(hex_vert, hex_cents)
circ_coords <- circ_cents %>%
sf::st_coordinates() %>%
dplyr::as_tibble() %>%
dplyr::distinct()
rad <- (hexes[[1]] %>% sf::st_cast("LINESTRING") %>% sf::st_length()) / 12
oz +
ggforce::geom_circle(data = circ_coords,
aes(x0 = X, y0 = Y, r = rad), colour = "white")
## Now remove circles with centres outside coast
circle_centres <- circ_cents %>%
sf::st_as_sf() %>%
dplyr::distinct() %>%
sf::st_intersection(Oz_coast)
ggplot(circle_centres %>% sf::st_coordinates() %>% dplyr::as_tibble(),
aes(x0 = X, y0 = Y, r = rad)) +
geom_circle() +
coord_equal() +
theme_minimal()
## Now make a network of tangent circles, discard any not tangent to at least 3 others
circle_dists <- circle_centres %>%
sf::st_coordinates() %>%
distances::distances()
closest <- circle_dists %>%
distances::nearest_neighbor_search(8)
# index <- closest[ , 1]
find_tangent_indices <- function(index, rad) {
dists <- distances::distance_columns(circle_dists, index, closest[ , index])
rownames(dists)[dists < (2.1 * rad)] %>% as.integer()
}
tangents <- purrr::map(seq_len(ncol(closest)), ~find_tangent_indices(.x, rad))
class(tangents) <- "nb"
tangent_mat <- spdep::nb2mat(tangents, style = "B")
nnum <- sapply(tangents, length) - 1
sum(nnum > 6)
sum(nnum < 3)
## remove circles with less than 3 tangent circles
num_less <- sum(nnum < 3)
while(num_less != 0) {
circle_centres <- circle_centres[!nnum < 3, ]
circle_dists <- circle_centres %>%
sf::st_coordinates() %>%
distances::distances()
closest <- circle_dists %>%
distances::nearest_neighbor_search(8)
tangents <- purrr::map(seq_len(ncol(closest)), ~find_tangent_indices(.x, rad))
nnum <- sapply(tangents, length) - 1
num_less <- sum(nnum < 3)
}
sum(nnum < 3)
sum(nnum > 6)
ggplot(circle_centres %>% sf::st_coordinates() %>% dplyr::as_tibble(),
aes(x0 = X, y0 = Y, r = rad)) +
geom_circle() +
coord_equal() +
theme_minimal()
nnet <- lapply(seq_along(tangents), function(x) tangents[[x]][tangents[[x]] != x])
pnts <- circle_centres[nnet[[1]], ] %>%
sf::st_coordinates()
order_points <- function(pnts, clockwise = FALSE) {
cent <- apply(pnts, 2, mean)
pnts2 <- t(t(pnts) - cent)
angles <- atan2(pnts2[ , 2], pnts2[ , 1])
if(clockwise) {
indices <- order(angles, decreasing = TRUE)
} else {
indices <- order(angles)
}
}
coords <- circle_centres %>%
sf::st_coordinates()
all_pnts <- lapply(nnet, function(x) coords[x, ])
nnet <- lapply(seq_along(all_pnts), function(x) nnet[[x]][order_points(all_pnts[[x]])])
nnet <- lapply(seq_along(nnet), function(x) c(x, nnet[[x]]))
internal <- nnet[sapply(nnet, length) == 7]
external <- dplyr::tibble(id = which(sapply(nnet, length) < 7),
radius = 10000)
test <- circleGraphLayout(internal, external)
ggplot(test[test$radius != 10000, ],
aes(x0 = x, y0 = y, r = radius)) + geom_circle() +
theme_minimal()
ggplot(test,
aes(x0 = x, y0 = y, r = radius)) + geom_circle() +
theme_minimal()
|
e5d32990a22c82a1f5c15542f43099a1048710ec
|
905ff318e8e26ab8121cef57034e08e459ff041f
|
/man/sq_numbers_cpp_tbb.Rd
|
b0d536558ce04323e399f6e25fb2c07e56a4546d
|
[] |
no_license
|
thijsjanzen/enviDiv
|
1442568cf490ba3bdd55bafe85c6f89f0f417e47
|
a67953d35b08eaa305896d4dd799c3b911bb0f5c
|
refs/heads/master
| 2021-06-28T13:40:22.413730
| 2020-09-09T06:29:45
| 2020-09-09T06:29:45
| 171,687,841
| 0
| 0
| null | 2019-09-05T11:30:48
| 2019-02-20T14:26:57
|
R
|
UTF-8
|
R
| false
| true
| 380
|
rd
|
sq_numbers_cpp_tbb.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{sq_numbers_cpp_tbb}
\alias{sq_numbers_cpp_tbb}
\title{test multithreaded works!}
\usage{
sq_numbers_cpp_tbb(n, num_threads)
}
\arguments{
\item{n}{size of vector}
\item{num_threads}{number of threads}
}
\value{
vector of squared numbers
}
\description{
test multithreaded works!
}
|
2e0fac7256d1a1b863a47ee3519e39952ececbdc
|
bfd0a3a3e84a6e8cb4f2806ddaf1dd771529cf31
|
/R/gradethis.R
|
86a397fded41ec3ce462251b6cfa2271a03a1fd8
|
[] |
no_license
|
DataScienceProjectsJapan/tutorialR4DS
|
e4964d1fae6b3f5d211640336da5f91d1efccbe7
|
6306130615f91add472afd73a1eff72a8f9145e9
|
refs/heads/main
| 2023-07-16T05:58:27.700536
| 2021-08-31T04:28:27
| 2021-08-31T04:28:27
| 394,162,367
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 238
|
r
|
gradethis.R
|
```{r delay-check}
gradethis::grade_this_code(correct = "Good work! Shortly we will learn an alternative way to construct a new variable with the mutate() function", incorrect = "Not quite. {code_feedback()} {random_encouragement()}")
```
|
dfec3ea47ea8fe5263729d0f4981ef5accbd4857
|
032d5b16e6d9afa5d08cae5e4121b4f2e5e161f6
|
/map_event.R
|
0200b0e26c186e0f86e11a7b0da45e3da4d20678
|
[] |
no_license
|
tysonwepprich/photovoltinism
|
93d3dca661f97a3bb559c791b3bb7a90670eef67
|
7c951f4983570bbf9f7c894f887ce57ea2e096a1
|
refs/heads/master
| 2022-11-21T21:26:12.152840
| 2022-11-03T21:54:03
| 2022-11-03T21:54:03
| 100,308,351
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,724
|
r
|
map_event.R
|
# Make pest event maps
# given a degree-day value, plot date it occurs by raster
# could also extract distribution of dates by shapefile polygons
library(sp)
library(rgdal)
library(raster)
library(ggplot2)
library(viridis)
library(mapdata)
library(dplyr)
library(tidyr)
library(geofacet)
theme_set(theme_bw(base_size = 16))
# library(gridExtra)
# library(grid)
gdd_cutoff <- 100
source('CDL_funcs.R')
region_param <- "EAST"
years <- c(2014:2017)
gdd_files <- paste0("dailygdd_", years, "_", region_param, ".grd")
REGION <- assign_extent(region_param = region_param)
states <- map_data("state", xlim = c(REGION@xmin, REGION@xmax),
ylim = c(REGION@ymin, REGION@ymax), lforce = "e")
names(states)[1:2] <- c("x", "y")
GDD <- brick(gdd_files[1])
template <- GDD[[1]]
template[!is.na(template)] <- 0
template <- crop(template, REGION)
dflist <- list()
for (yr in years){
filename <- gdd_files[grep(pattern = yr, x = gdd_files, fixed = TRUE)]
res <- brick(filename)
# fix NA problem, just assigned as zero
res <- res + template # template is all zeros and NA
accum <- calc(res, fun = cumsum)
accum <- calc(accum, fun = function(x) {min(which(x >= gdd_cutoff))})
df <- as.data.frame(accum, xy=TRUE)
names(df)[3] <- "doy"
df$doy[-which(is.finite(df$doy))] <- NA
df$year <- yr
dflist[[length(dflist)+1]] <- df
}
resdf <- dplyr::bind_rows(dflist)
resdf <- resdf %>%
group_by(year) %>%
mutate(date = format(as.Date(doy, origin=as.Date(paste0((year[1] - 1), "-12-31"))),
format = "%m-%d"),
week = lubridate::week(as.Date(doy, origin=as.Date(paste0((year[1] - 1), "-12-31")))))
plt <- ggplot(resdf, aes(x, y)) +
geom_raster(aes(fill = week)) +
scale_fill_viridis(na.value = "white") +
geom_polygon(data = states, aes(group = group), fill = NA, color = "black", size = .1) +
facet_wrap(~year, nrow = 2) +
coord_fixed(1.3)
plt
# extract by sites
sites <- data.frame(ID = c("Corvallis, OR", "Richland, WA", "JB Lewis-McChord, WA", "Palermo, CA",
"Ephrata, WA", "Yakima Training Center, WA", "Camp Rilea, OR",
"Ft Drum, NY", "West Point, NY", "Kellogg LTER, MI",
"The Wilds, OH", "Duluth, MN", "Coeburn, VA", "Mountain Home AFB, ID",
"Quantico MCB, VA", "Hanscom AFB, MA", "Ft Bragg, NC",
"Ogden, UT", "Buckley AFB, CO", "S Portland, OR",
"Sutherlin, OR", "Bellingham, WA", "Wentzville, MO"),
x = c(-123.263, -119.283, -122.53, -121.625360, -119.555424, -120.461073,
-123.934759, -75.763566, -73.962210, -85.402260, -81.733314,
-92.158597, -82.466417, -115.865101, -77.311254, -71.276231,
-79.083248, -112.052908, -104.752266, -122.658887,
-123.315854, -122.479482, -90.852),
y = c(44.564, 46.275, 47.112, 39.426829, 47.318546, 46.680138,
46.122867, 44.055684, 41.388456, 42.404749, 39.829447,
46.728247, 36.943103, 43.044083, 38.513995, 42.457068,
35.173401, 41.252509, 39.704018, 45.470532,
43.387721, 48.756105, 38.816))
dflist <- list()
for (yr in years){
filename <- gdd_files[grep(pattern = yr, x = gdd_files, fixed = TRUE)]
res <- brick(filename)
# fix NA problem, just assigned as zero
res <- res + template # template is all zeros and NA
accum <- calc(res, fun = cumsum)
accum <- calc(accum, fun = function(x) {min(which(x >= gdd_cutoff))})
e <- raster::extract(accum, sites[, c(2:3)], buffer=1000, fun = mean, na.rm = TRUE)
df <- sites
df$doy <- e
df$year <- yr
dflist[[length(dflist)+1]] <- df
}
resdf <- dplyr::bind_rows(dflist)
resdf <- resdf %>%
filter(complete.cases(.)) %>%
group_by(year) %>%
mutate(date = as.Date(doy, origin=as.Date("2000-12-31")))
plt <- ggplot(resdf, aes(x = reorder(ID, date), y = date, color = as.factor(year))) +
geom_jitter(size = 3, width = .15) +
scale_y_date(date_breaks = "1 week", date_labels = "%b-%d") +
coord_flip() +
ggtitle("Date when 100 degree-days recorded for Galerucella")
plt
ggsave(filename = "Galerucella_100DD_EAST.png", plot = plt, device = "png", width = 15, height = 8, units = "in")
# Map range of GDD at different sites, 100-150DD dates
cutoffday <- 55 # when PRISM observations switch to forecasts
# Add new predictions for current year
preddf <- gdddf %>%
filter(complete.cases(.),
year != 2018) %>%
mutate(yday = as.numeric(yday)) %>%
arrange(yday) %>%
group_by(ID, yday) %>%
summarise(DD = mean(DD)) %>%
filter(yday > cutoffday) %>%
mutate(year = "2018+5yrmean")
# replace predictions after cutoff day
obs <- gdddf %>%
select(-x, -y) %>%
mutate(yday = as.numeric(yday)) %>%
filter(complete.cases(.),
year == 2018,
yday <= cutoffday) %>%
mutate(year = "2018+5yrmean")
preds <- bind_rows(preddf, obs)
gdddf$year <- as.factor(as.character(gdddf$year))
levels(gdddf$year)[6] <- "2018+10yrmean"
resdf <- gdddf %>%
select(-x, -y) %>%
filter(complete.cases(.)) %>%
mutate(yday = as.numeric(yday)) %>%
bind_rows(preds) %>%
arrange(yday) %>%
group_by(year, ID) %>%
mutate(AccumDD = cumsum(DD)) %>%
filter(AccumDD >= 100) %>%
filter(AccumDD <= 150) %>%
filter(row_number() == 1L | row_number() == n()) %>%
mutate(date = as.Date(yday, origin=as.Date("2000-12-31")),
bound = c("start", "end")) %>%
dplyr::select(ID, year, date, bound) %>%
tidyr::spread(bound, date) %>%
# filter(ID %in% levels(gdddf$ID)[c(1, 3, 4, 5, 10, 12, 15, 16, 17, 21, 22, 25, 26, 28, 30)]) %>% # East and West
filter(ID %in% levels(gdddf$ID)[c(1, 3, 5, 10, 15, 17, 21, 22, 25, 26, 30)]) %>% # Just NW sites
group_by(ID) %>%
mutate(meanstart = mean(start))
theme_set(theme_bw(base_size = 16))
plt <- ggplot(resdf, aes(x = reorder(ID, meanstart), ymin = start, ymax = end, group = as.factor(year), color = as.factor(year))) +
# geom_jitter(size = 3, width = .15) +
geom_linerange(position = position_dodge(width = .6), size = 2) +
scale_color_viridis(discrete = TRUE, name = "Year", option = "D") +
scale_y_date(date_breaks = "1 week", date_labels = "%b-%d") +
coord_flip() +
ggtitle("Date range for Galerucella overwintering adult emergence (100-150 degree-days)") +
xlab("Sites ordered by\nmean start of emergence") +
guides(color = guide_legend(reverse=TRUE))
plt
ggsave(filename = "Galerucella_daterange.png", plot = plt, device = "png", width = 15, height = 8, units = "in")
|
789c1ba001b444e9e7f78ee96e6b232fee4a5f40
|
a04b9f8edf6be22aa7ee7c6222bbb32cc6443b40
|
/lib/helpers.R
|
a634b448fb17e7214ecdeb2d6bbaf4581ae19676
|
[] |
no_license
|
whasew/rstanPK
|
560d8d74e3478135ef89782ae1c3c2e6fc2f9902
|
063b619c6565ac7846dbbbc402ef6f572b2c5d0d
|
refs/heads/master
| 2021-01-01T20:42:33.982641
| 2015-04-11T08:14:45
| 2015-04-11T08:14:45
| 33,406,646
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 943
|
r
|
helpers.R
|
f2n<-function(f) as.numeric(levels(f))[f]
geomSeries <- function(base, max) {
base^(0:floor(log(max, base)))
}
cumulationFactor<-function(tstar,k,n,tau) (1-exp(-n*k*tau))/(1-exp(-k*tau))
bateman<-function(Dose,time,fm,ka,CL,V,n=1,tau=24){
K<-CL/V
tstar<-time-(n-1)*tau
cumulationFactor(tstar,k,n,tau)
fm*ka*Dose/(ka-K)/V*(exp(-K*time)-exp(-ka*time))
}
xyLogscale <- function (lims = c(1e-04, 1000),labAdd=NULL)
{
labels <- c(1:9) * rep(10^c(-4:5), rep(9, 10))
labels <- labels[labels >= lims[1] & labels <= lims[2]]
if (!is.null(labAdd)) labels<-sort(c(labels,labAdd))
at <- log10(labels)
sel10 <- round(at - round(at), 6)==0
if(!is.null(labAdd)) {
att<-(at-log10(labAdd))
selAdd <- round(att - round(att), 6)==0
} else selAdd <-rep(FALSE,length(labels))
sel<-labels%in%c(labels[sel10],labels[selAdd])
if (sum(sel) > 1)
labels[!sel] <- ""
list(log = TRUE, at = 10^at, labels = labels)
}
|
fcbdfc0aee8df06c82d4269ff38209d985649526
|
f4563b000f2db75daae46b5653d6afc0a432907f
|
/life-exp-across-canada.R
|
e8c484a384b46c178ce10e3325d399a5fb13bddc
|
[] |
no_license
|
emily-gong/hackseq18_project8
|
22e529ef9923f783a41cb4a9bb6405113078a2b2
|
851d35c14a83cdc31bfc91c37f5eeb89076aa50d
|
refs/heads/master
| 2020-04-01T06:19:45.419001
| 2018-10-14T21:38:32
| 2018-10-14T21:38:32
| 152,943,150
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 894
|
r
|
life-exp-across-canada.R
|
library(data.table)
library(ggplot2)
library(plotly)
data <- fread("data/life_data_sep_avg.csv", sep = ",")
lifeExp <- data[data$Element == "Life expectancy (in years) at age x (ex)" & data$Sex == "Both" & data$GEO == "Canada",]
Age <- lifeExp[,lifeExp$Age_group]
LifeExp <- lifeExp[,lifeExp$AVG_VALUE]
Year <- lifeExp[,lifeExp$YEAR]
p <- plot_ly(
x = Age,
y = LifeExp,
frame = Year,
type = 'scatter',
mode = 'lines'
) %>%
layout(
title = "Life Expectancy in Canada (1980-2016)",
xaxis = list(
type = "linear",
title = "Age"
),
yaxis = list(
title = "Life Expectancy"
)
)
p <- p %>%
animation_opts(
1000, easing = "elastic", redraw = FALSE
)
p <- p %>%
animation_button(
x = 1, xanchor = "right", y = 0, yanchor = "bottom"
)
p
chart_link = api_create(p, filename="Life-Expectancy-In-Canada")
chart_link
|
4209d9220848a959d72f6c3091bc01e80a8a8af3
|
3da145d8919a361f83ea04184d80c21c253ac981
|
/LogisticRegression.R
|
26d558f327f78984a353db5ac02a90e32fa64cc3
|
[] |
no_license
|
AJaafer/ML-Benchmark
|
5c0b66416d96f8c722d3a2c3ec2f0cc94cd35455
|
457364f39737589ef54337c5a3c73b9abdf18754
|
refs/heads/master
| 2020-03-07T09:44:49.666028
| 2018-03-30T10:30:01
| 2018-03-30T10:30:01
| 127,415,225
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 779
|
r
|
LogisticRegression.R
|
train=german_credit_dataset[-1,-1]
train$Good<- factor(train$Good, levels = c("1", "2"), labels = c("0", "1"))
install.packages('caTools')
library(caTools)
set.seed(88)
split <- sample.split(train$Good, SplitRatio = 0.75)
#get training and test data
dresstrain <- subset(train, split == TRUE)
dresstest <- subset(train, split == FALSE)
#logistic regression model
View(dresstrain)
model <- glm (Good ~ ., data = dresstrain, family=binomial(link="logit"))
summary(model)
predict <- predict(model, type = 'response')
#confusion matrix
table(dresstrain$Good, predict > 0.5)
#ROCR Curve
install.packages("ROCR")
library(ROCR)
ROCRpred <- prediction(predict, dresstrain$Good)
ROCRperf <- performance(ROCRpred, 'tpr','fpr')
plot(ROCRperf, colorize = TRUE, text.adj = c(-0.2,1.7))
|
a0928912fff2e6659098ab27e06e1148e9405ca4
|
c77734e81fec57e93f2b264e0367a90d3dba288f
|
/first.R
|
b2a231f037ab619f14c1997bc52da7349c515745
|
[] |
no_license
|
jfxu/Rcode
|
c6c01d44b15c7ae1959c36b0166c5918dbe2b60f
|
2ebe98cdde2ccd82ca2876604237d49d50ae976d
|
refs/heads/master
| 2020-04-08T10:30:05.754569
| 2015-04-13T21:42:44
| 2015-04-13T21:42:44
| 14,380,981
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 44
|
r
|
first.R
|
### A simple test for GitHub
set.seed(1942)
|
93e6762e0926a247620e0a85acc55897a0329418
|
c5c1c9c8ebfc5a50de7550b67fad878666540336
|
/jpg_convert.R
|
b1f99feb3fa28346cfb5fbb0f8bf8660bd440417
|
[] |
no_license
|
IngridLimaS/RGB
|
a2efbf18bebbc6e38a368e4c8b15c7805867dcc4
|
9f5090c2fe7cf97b5a958dc8b09d09e0a6a2b1ac
|
refs/heads/main
| 2023-06-20T11:11:45.290975
| 2021-07-19T16:15:26
| 2021-07-19T16:15:26
| 377,680,618
| 0
| 2
| null | 2021-06-19T18:29:40
| 2021-06-17T02:19:22
|
R
|
ISO-8859-1
|
R
| false
| false
| 842
|
r
|
jpg_convert.R
|
################################################
# D: Converter jpg em tiff
# Autor: Victor Leandro-silva, Ingrid Lima
# Criação: 19-06-2021
# Última edição: 21-06-2021
################################################
#dir
setwd("C:/Users/ingri/Documents/GitHub/RGB/imagem_para_cor")
getwd()
dir()
#package
library(imager)
library(ggplot2)
# Lista imagens dentro da pasta
ti <- dir(pattern = ".jpg")
ti
i <- 1
#Conversão
while (i < 328) {
setwd("C:/Users/ingri/Documents/GitHub/RGB/imagem_para_cor") #diretório com imagens JPEG
d <- load.image(ti[i])
plot(d)
thmb <- resize(d)
setwd("C:/Users/ingri/Documents/imagens_tif") #Lugar onde vai salvar as imagens
p <- raster::plot(thmb)
tiff(p, filename = ti[i]) #cria um tiff vazia
dev.off()
i <- i + 1 # pasta para salvar os tiff
}
|
07bcb82fbc5213b5c432800e73d6daa3e3958d3c
|
0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb
|
/cran/paws.machine.learning/man/sagemaker_create_transform_job.Rd
|
904959c131d54b8af8f2b1477b86a3185ce91eed
|
[
"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
| 5,292
|
rd
|
sagemaker_create_transform_job.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sagemaker_operations.R
\name{sagemaker_create_transform_job}
\alias{sagemaker_create_transform_job}
\title{Starts a transform job}
\usage{
sagemaker_create_transform_job(
TransformJobName,
ModelName,
MaxConcurrentTransforms = NULL,
ModelClientConfig = NULL,
MaxPayloadInMB = NULL,
BatchStrategy = NULL,
Environment = NULL,
TransformInput,
TransformOutput,
DataCaptureConfig = NULL,
TransformResources,
DataProcessing = NULL,
Tags = NULL,
ExperimentConfig = NULL
)
}
\arguments{
\item{TransformJobName}{[required] The name of the transform job. The name must be unique within an Amazon
Web Services Region in an Amazon Web Services account.}
\item{ModelName}{[required] The name of the model that you want to use for the transform job.
\code{ModelName} must be the name of an existing Amazon SageMaker model
within an Amazon Web Services Region in an Amazon Web Services account.}
\item{MaxConcurrentTransforms}{The maximum number of parallel requests that can be sent to each
instance in a transform job. If \code{MaxConcurrentTransforms} is set to \code{0}
or left unset, Amazon SageMaker checks the optional execution-parameters
to determine the settings for your chosen algorithm. If the
execution-parameters endpoint is not enabled, the default value is \code{1}.
For more information on execution-parameters, see \href{https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests}{How Containers Serve Requests}.
For built-in algorithms, you don't need to set a value for
\code{MaxConcurrentTransforms}.}
\item{ModelClientConfig}{Configures the timeout and maximum number of retries for processing a
transform job invocation.}
\item{MaxPayloadInMB}{The maximum allowed size of the payload, in MB. A \emph{payload} is the data
portion of a record (without metadata). The value in \code{MaxPayloadInMB}
must be greater than, or equal to, the size of a single record. To
estimate the size of a record in MB, divide the size of your dataset by
the number of records. To ensure that the records fit within the maximum
payload size, we recommend using a slightly larger value. The default
value is \code{6} MB.
The value of \code{MaxPayloadInMB} cannot be greater than 100 MB. If you
specify the \code{MaxConcurrentTransforms} parameter, the value of
\code{(MaxConcurrentTransforms * MaxPayloadInMB)} also cannot exceed 100 MB.
For cases where the payload might be arbitrarily large and is
transmitted using HTTP chunked encoding, set the value to \code{0}. This
feature works only in supported algorithms. Currently, Amazon SageMaker
built-in algorithms do not support HTTP chunked encoding.}
\item{BatchStrategy}{Specifies the number of records to include in a mini-batch for an HTTP
inference request. A \emph{record} is a single unit of input data that
inference can be made on. For example, a single line in a CSV file is a
record.
To enable the batch strategy, you must set the \code{SplitType} property to
\code{Line}, \code{RecordIO}, or \code{TFRecord}.
To use only one record when making an HTTP invocation request to a
container, set \code{BatchStrategy} to \code{SingleRecord} and \code{SplitType} to
\code{Line}.
To fit as many records in a mini-batch as can fit within the
\code{MaxPayloadInMB} limit, set \code{BatchStrategy} to \code{MultiRecord} and
\code{SplitType} to \code{Line}.}
\item{Environment}{The environment variables to set in the Docker container. We support up
to 16 key and values entries in the map.}
\item{TransformInput}{[required] Describes the input source and the way the transform job consumes it.}
\item{TransformOutput}{[required] Describes the results of the transform job.}
\item{DataCaptureConfig}{Configuration to control how SageMaker captures inference data.}
\item{TransformResources}{[required] Describes the resources, including ML instance types and ML instance
count, to use for the transform job.}
\item{DataProcessing}{The data structure used to specify the data to be used for inference in
a batch transform job and to associate the data that is relevant to the
prediction results in the output. The input filter provided allows you
to exclude input data that is not needed for inference in a batch
transform job. The output filter provided allows you to include input
data relevant to interpreting the predictions in the output from the
job. For more information, see \href{https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html}{Associate Prediction Results with their Corresponding Input Records}.}
\item{Tags}{(Optional) An array of key-value pairs. For more information, see \href{https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what}{Using Cost Allocation Tags}
in the \emph{Amazon Web Services Billing and Cost Management User Guide}.}
\item{ExperimentConfig}{}
}
\description{
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.
See \url{https://www.paws-r-sdk.com/docs/sagemaker_create_transform_job/} for full documentation.
}
\keyword{internal}
|
1ee3142089ef3966e9375faaaa980b709005056d
|
6929d12941949f290d98379913048ee93143e491
|
/man/string_to_numeric.Rd
|
38ed353c9671f7038a1b778ae36571b7f04f3d20
|
[
"MIT"
] |
permissive
|
srmatth/value.investing
|
9aeaaa476e7731a6cf5a6012f4c0ae38f016dc42
|
720dbd7ba4f38c2ca896515d72cb2bfa4009582d
|
refs/heads/master
| 2023-07-19T04:06:13.679639
| 2021-09-18T16:46:33
| 2021-09-18T16:46:33
| 275,306,180
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 482
|
rd
|
string_to_numeric.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fct_webscrape_utils.R
\name{string_to_numeric}
\alias{string_to_numeric}
\title{String to Dollar}
\usage{
string_to_numeric(x)
}
\arguments{
\item{x}{a vector of character strings that represent abbreviated
dollar amounts}
}
\value{
a double vector of the same length as the input vector
}
\description{
Converts a numeric string (such as '53.5B') to its corresponding
numeric value (eg. 53500000000)
}
|
32e13db96a956fa7a97129b2215c76bb0c22dd65
|
0b1b048bc2c5efd33c7eadfd72862f758f9e2447
|
/scripts/build_sqlit.R
|
bedc87454201b846f4acca91e1a28764aa374e45
|
[] |
no_license
|
biopig/chip-seq.snakemake
|
1b7a634683f87a42823f4611fe67edc2cc852b77
|
c2a9871266fea18561a216a24ce333c70ea69575
|
refs/heads/master
| 2020-04-28T17:01:32.922982
| 2019-11-24T12:38:25
| 2019-11-24T12:38:25
| 175,431,386
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 960
|
r
|
build_sqlit.R
|
#使用genomicfeatures来构建txdb对象，首先就是安装对应的包
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GenomicFeatures", version = "3.8")
BiocManager::install("biomaRt", version = "3.8")
library("GenomicFeatures")
library("biomaRt")
library("httr") #in china will get error for the internel
library("curl") #in china will get error for the internel
#列出可用的数据库的
listMarts()
listMarts(host = "http://plants.ensembl.org")
#查找数据库中所有的植物的物种
mart<-useMart(biomart = "plants_mart",host = "http://plants.ensembl.org")
datasets <- listDatasets(mart)
datasets$dataset
#下载数据
maize_txdb<-makeTxDbFromBiomart(biomart = "plants_mart",dataset = "zmays_eg_gene",host = "http://plants.ensembl.org")
#保存数据
saveDb(maize_txdb, file="maize_v4_2018_11.sqlite")
#载入
maize_txdb <- loadDb("maize_v4_2018_11.sqlite")
|
933e0de847e7a3f8e7fa4d9f3eeb256ac53143a1
|
c843f06528d8512f85cdd8a29337c567125604c9
|
/supplement_paper/Rscripts/IMG_GenBank_Datasets/IMG_tvt_selection.R
|
557644f22d305978212e945efa4a79c3aa382df0
|
[
"MIT"
] |
permissive
|
JakubBartoszewicz/DeePaC
|
0f036f8e729135cf2d06e1efbac6dca4dfd08ab2
|
eab1be09df6bc0bdc7a015c48a5d9636ed9516e5
|
refs/heads/master
| 2023-08-05T02:24:22.441187
| 2022-12-21T10:48:45
| 2022-12-21T10:48:45
| 199,677,945
| 3
| 2
|
MIT
| 2023-07-06T23:35:37
| 2019-07-30T15:23:09
|
Jupyter Notebook
|
UTF-8
|
R
| false
| false
| 9,795
|
r
|
IMG_tvt_selection.R
|
set.seed(0)
library(stringr)
date.postfix.img <- "_fungi"
random.best <- FALSE
ambiguous.species <- FALSE
# no of folds to generate
k <- 9
validation_percent <- 0.1
test_percent <- 0.1
fold_names <- paste0("fold", 1:k)
download_format = ".fna"
# no of folds to download
k_download <- 1
# generate urls
urls <- TRUE
IMG.all <- readRDS(paste0("IMG_assemblies",date.postfix.img,".rds"))
if (!ambiguous.species) {
IMG.all$Ambiguous <- FALSE
}
IMG.all$assembly_level <- factor(IMG.all$assembly_level,levels(IMG.all$assembly_level), ordered = TRUE)
IMG.all$assembly_accession <- as.character(IMG.all$assembly_accession)
Species_HP <- as.character(unique(IMG.all$Species[which(IMG.all$Pathogenic == TRUE & IMG.all$Ambiguous == FALSE)]))
Species_NP <- as.character(unique(IMG.all$Species[which(IMG.all$Pathogenic == FALSE & IMG.all$Ambiguous == FALSE)]))
Species <- c(Species_HP, Species_NP)
### tvt
IMG.all[,fold_names] <- ""
IMG.all$subset <- ""
Species_HP_test_manual <- c("Candida auris", "Aspergillus fumigatus")
Species_NP_test_manual <- c("Pyricularia oryzae", "Batrachochytrium dendrobatidis")
Species_HP_test <- sample(Species_HP, ceiling(test_percent*length(Species_HP))-length(Species_HP_test_manual))
Species_HP_test <- c(Species_HP_test_manual, Species_HP_test)
Species_NP_test <- sample(Species_NP, ceiling(test_percent*length(Species_NP))-length(Species_NP_test_manual))
Species_NP_test <- c(Species_NP_test_manual, Species_NP_test)
IMG.all[IMG.all$Species %in% Species_HP_test,fold_names] <- "test"
IMG.all[IMG.all$Species %in% Species_NP_test,fold_names] <- "test"
Species_test <- c(Species_HP_test,Species_NP_test)
Species_HP_done <- Species_HP_test
Species_NP_done <- Species_NP_test
SampleStrains <- function(SpeciesList, LabelsList, IMGdata, Prefix, fold=1){
# get strains
Accessions <- lapply(SpeciesList, function(QuerySpecies){
IMGdata[IMGdata$Species == QuerySpecies,"assembly_accession"]
})
names(Accessions) <- SpeciesList
RandomStrain.Accession <- sapply(Accessions, function(Species) {
Species[sample(1:length(Species),1)]
})
IMGdata$subset[match(RandomStrain.Accession,IMGdata$assembly_accession)] <- "selected"
if (fold<2){
saveRDS(RandomStrain.Accession,file.path(paste(Prefix,"Strains.rds", sep="") ))
} else {
saveRDS(RandomStrain.Accession,file.path(paste(Prefix,"Strains_", fold, ".rds", sep="") ))
}
# save label information
names(LabelsList) <- RandomStrain.Accession
if (fold<2){
saveRDS(LabelsList, file.path(paste(Prefix,"Labels.rds", sep="") ))
} else {
saveRDS(LabelsList, file.path(paste(Prefix,"Labels_", fold, ".rds", sep="") ))
}
return(IMGdata)
}
SampleBestStrains <- function(){
### sample strains
assembly_level <- sapply(Species, function(s){min(IMG.all$assembly_level[IMG.all$Species == s])})
names(assembly_level) <- Species
IMG.all$subset[as.character(IMG.all$assembly_level) == as.character(assembly_level[IMG.all$Species])] <- "candidate"
IMG.all$subset[!(as.character(IMG.all$assembly_level) == as.character(assembly_level[IMG.all$Species]))] <- "other"
selected_assemblies <- sapply(Species, function(s){candidate_assemblies <- IMG.all[IMG.all$Species == s & IMG.all$subset == "candidate", "assembly_accession"]; return(candidate_assemblies[sample(1:length(candidate_assemblies),1)]) })
IMG.all$subset[IMG.all$assembly_accession %in% selected_assemblies] <- "selected"
}
if(!random.best & !ambiguous.species){
Labels_test <- c(rep(T,length(Species_HP_test)),rep(F,length(Species_NP_test)))
IMG.all <- SampleStrains(Species_test, Labels_test, IMG.all, "Test")
}
Species_HP_trainval <- setdiff(Species_HP,Species_HP_done)
Species_NP_trainval <- setdiff(Species_NP,Species_NP_done)
fold_val_sizes_HP <- sapply(1:k, function(i) {floor(length(Species_HP_trainval)/k)})
if(length(Species_HP_trainval) %% k > 0){
fold_val_sizes_HP[1:(length(Species_HP_trainval) %% k)] <- fold_val_sizes_HP[1:(length(Species_HP_trainval) %% k)] + 1
}
fold_val_sizes_NP <- sapply(1:k, function(i) {floor(length(Species_NP_trainval)/k)})
if(length(Species_NP_trainval) %% k > 0){
fold_val_sizes_NP[1:(length(Species_NP_trainval) %% k)] <- fold_val_sizes_NP[1:(length(Species_NP_trainval) %% k)] + 1
}
for (i in 1:k) {
Species_HP_trainval <- setdiff(Species_HP,Species_HP_done)
Species_NP_trainval <- setdiff(Species_NP,Species_NP_done)
Species_HP_training <- sample(Species_HP_trainval, length(Species_HP_trainval) - fold_val_sizes_HP[i])
Species_NP_training <- sample(Species_NP_trainval, length(Species_NP_trainval) - fold_val_sizes_NP[i])
Species_HP_validation <- setdiff(Species_HP_trainval,Species_HP_training)
Species_NP_validation <- setdiff(Species_NP_trainval,Species_NP_training)
IMG.all[IMG.all$Species %in% Species_HP_training,fold_names[i]] <- "train"
IMG.all[IMG.all$Species %in% Species_NP_training,fold_names[i]] <- "train"
IMG.all[IMG.all$Species %in% Species_HP_validation,fold_names[i]] <- "val"
IMG.all[IMG.all$Species %in% Species_NP_validation,fold_names[i]] <- "val"
if (i<k){
# if validation here, training in other folds
IMG.all[IMG.all$Species %in% Species_HP_validation,fold_names[(i+1):k]] <- "train"
IMG.all[IMG.all$Species %in% Species_NP_validation,fold_names[(i+1):k]] <- "train"
}
Species_HP_done <- c(Species_HP_done, Species_HP_validation)
Species_NP_done <- c(Species_NP_done, Species_NP_validation)
if(!random.best & !ambiguous.species & i==1){
Species_training <- c(Species_HP_training,Species_NP_training)
Labels_training <- c(rep(T,length(Species_HP_training)),rep(F,length(Species_NP_training)))
Species_validation <- c(Species_HP_validation,Species_NP_validation)
Labels_validation <- c(rep(T,length(Species_HP_validation)),rep(F,length(Species_NP_validation)))
IMG.all <- SampleStrains(Species_training, Labels_training, IMG.all, "Training")
IMG.all <- SampleStrains(Species_validation, Labels_validation, IMG.all, "Validation")
}
}
### sample strains
if(random.best){
SampleBestStrains()
}
if (ambiguous.species) {
IMG.all$subset <- "selected"
}
IMG.all[, grep(pattern = "\\.orig", x = colnames(IMG.all))] <- NULL
selected <- IMG.all[IMG.all$subset == "selected",]
# only folds to download
if(k>1 & k_download<k){
for (fold_name in fold_names[(1+k_download):length(fold_names)]) {
selected[,fold_name] <- NULL
}
}
# Save data for backup
if (!ambiguous.species) {
IMG.all$Ambiguous <- NULL
}
saveRDS(IMG.all, paste0("IMG_all_folds", date.postfix.img, ".rds"))
saveRDS(selected, paste0("IMG_", k_download, "_folds", date.postfix.img, ".rds"))
if(urls){
# Save urls for downloading
urls.test.HP <- sapply(as.character(selected$ftp_path[selected$fold1=="test" & selected$Pathogenic]), function(f){name <- unlist(strsplit(as.character(f), split = "/")); name <- name[length(name)]; return(paste0(f, "/", name, "_genomic", download_format, ".gz"))})
writeLines(urls.test.HP, con = paste0("urls.test.HP", download_format, ".txt"))
urls.test.NP <- sapply(as.character(selected$ftp_path[selected$fold1=="test" & !selected$Pathogenic]), function(f){name <- unlist(strsplit(as.character(f), split = "/")); name <- name[length(name)]; return(paste0(f, "/", name, "_genomic", download_format, ".gz"))})
writeLines(urls.test.NP, con = paste0("urls.test.NP", download_format, ".txt"))
for (i in 1:k_download) {
urls.train.HP <- sapply(as.character(selected$ftp_path[selected[,paste0("fold", i)]=="train" & selected$Pathogenic]), function(f){name <- unlist(strsplit(as.character(f), split = "/")); name <- name[length(name)]; return(paste0(f, "/", name, "_genomic", download_format, ".gz"))})
urls.val.HP <- sapply(as.character(selected$ftp_path[selected[,paste0("fold", i)]=="val" & selected$Pathogenic]), function(f){name <- unlist(strsplit(as.character(f), split = "/")); name <- name[length(name)]; return(paste0(f, "/", name, "_genomic", download_format, ".gz"))})
writeLines(urls.train.HP, con = paste0("urls.train.HP.", fold_names[i], download_format, ".txt"))
writeLines(urls.val.HP, con = paste0("urls.val.HP.", fold_names[i], download_format, ".txt"))
urls.train.NP <- sapply(as.character(selected$ftp_path[selected[,paste0("fold", i)]=="train" & !selected$Pathogenic]), function(f){name <- unlist(strsplit(as.character(f), split = "/")); name <- name[length(name)]; return(paste0(f, "/", name, "_genomic", download_format, ".gz"))})
urls.val.NP <- sapply(as.character(selected$ftp_path[selected[,paste0("fold", i)]=="val" & !selected$Pathogenic]), function(f){name <- unlist(strsplit(as.character(f), split = "/")); name <- name[length(name)]; return(paste0(f, "/", name, "_genomic", download_format, ".gz"))})
writeLines(urls.train.NP, con = paste0("urls.train.NP.", fold_names[i], download_format, ".txt"))
writeLines(urls.val.NP, con = paste0("urls.val.NP.", fold_names[i], download_format, ".txt"))
}
# Save urls for downloading ALL TRAINING STRAINS
for (i in 1:k_download) {
urls.all.train.HP <- sapply(as.character(IMG.all$ftp_path[IMG.all[,paste0("fold", i)]=="train" & IMG.all$Pathogenic]), function(f){name <- unlist(strsplit(as.character(f), split = "/")); name <- name[length(name)]; return(paste0(f, "/", name, "_genomic", download_format, ".gz"))})
writeLines(urls.all.train.HP, con = paste0("urls.all.train.HP.", fold_names[i], download_format, ".txt"))
urls.all.train.NP <- sapply(as.character(IMG.all$ftp_path[IMG.all[,paste0("fold", i)]=="train" & !IMG.all$Pathogenic]), function(f){name <- unlist(strsplit(as.character(f), split = "/")); name <- name[length(name)]; return(paste0(f, "/", name, "_genomic", download_format, ".gz"))})
writeLines(urls.all.train.NP, con = paste0("urls.all.train.NP.", fold_names[i], download_format, ".txt"))
}
}
|
c95bd768365e1eb211872663e2e9d64014c00403
|
7b1c749b93cde9572c32af65a34d86f708dac114
|
/TP2/acp.R
|
37ce87c232e503277b9a951c7dd10f0894e835fb
|
[] |
no_license
|
slebastard/PISA_Results_Analysis
|
5dfb996ac897c2c8e9fbf208515684ca8fe02807
|
11afe131bd44effb1caecd7cdab9c743399b051f
|
refs/heads/master
| 2021-01-10T04:13:06.763735
| 2016-03-11T12:10:18
| 2016-03-11T12:10:18
| 51,708,109
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,445
|
r
|
acp.R
|
rm(list = ls())
graphics.off()
Voitures <- read.table("emissions.csv", sep = ";", row.names = 1, header = TRUE)
Voitures <- na.omit(Voitures)
quant = Voitures[,1:9]
qual = Voitures[,10:11]
print(Voitures)
pairs(quant)
acp = princomp(quant, cor = T, scores = T)
summary(acp)
valp = acp $ sdev^2
dev.new()
plot(valp, type = "b")
scores = acp $ scores
print(scores)
dev.new()
plot(scores[, 1], scores[, 2], type = "n")
text(scores[,1], scores[,2], labels = row.names(quant), cex = 0.5)
loadings = acp $ loadings
print(loadings)
corr1 = loadings[, 1]*sqrt(valp[1])
corr2 = loadings[, 2]*sqrt(valp[2])
dev.new()
plot(corr1, corr2, xlim = c(-1, 1), ylim = c(-1, 1), asp = 1, type = "n")
text(corr1, corr2, labels = colnames(quant), cex = 0.5)
symbols(0, 0, circles = 1, inches = F, add = T)
#Retracer les voitures dans le plan u1, u2 en mettant les noms des modèles d'une couleur qui caractérise leur carburant.
dev.new()
plot(scores[, 1], scores[, 2], type = "n")
text(scores[,1], scores[,2], labels = row.names(quant), cex = 0.5, col = ifelse(Voitures[, 11] == "EH", "green", ifelse(Voitures[, 11] == "ES", "black", "blue")))
#Placer le symbole LUXE au barycentre des positions des véhicules de type luxe.
posx = 0
posy = 0
num = 0
for (i in 1:length(Voitures[, 10])) {
if (Voitures[i, 10] == "LUXE") {
num = num + 1
posx = posx + scores[i, 1]
posy = posy + scores[i, 2]
}
}
text(posx/num, posy/num, labels = "LUXE", cex = 1)
|
a91f3c734ff461f953e11487fdcf9c3d5d04284a
|
2b41bcab679bc4569261f22f0024a5e727e4f59f
|
/rnbl3_prophetX_Hols_clean.R
|
8895b073211da99f46487c413463e9f89119789e
|
[] |
no_license
|
akuppam/timeS
|
f86b5afe7d4f64be2d502322f36870e1bafba6af
|
9299f1cedb0039a01e4afe4392f88ffdb40f2af1
|
refs/heads/master
| 2021-09-08T14:38:07.123880
| 2021-09-02T20:57:30
| 2021-09-02T20:57:30
| 147,609,389
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,866
|
r
|
rnbl3_prophetX_Hols_clean.R
|
# ----------------------
# Prophet Model w/ regressors
# propherX
# ----------------------
library(corrplot)
library(plotly)
library(prophet)
library(tidyverse)
library(bsts)
library(prophet)
library(dplyr)
library(ggplot2)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
setwd("/users/akuppam/documents/Data/RoverData/")
DF <- read.csv("rnbl2agg.csv")
#DF <- read.csv("UK-paid.csv")
DF <- mutate(DF, ds = as.Date(date))
# adding regressors
library(dplyr)
colnames(DF) <- c("date","region","marketing","visits","br","inq","gb","cb","y","ss","ts","listings","ds")
pdat <- data.frame(ds=DF$ds, y=DF$y, visits=DF$visits, br=DF$br, listings=DF$listings, inq=DF$inq)
pfdat <- data.frame(ds=max(DF$ds) + 1:365)
pvisits <- DF %>% dplyr::select(ds,y=visits) %>% prophet() %>% predict(pfdat)
pbr <- DF %>% dplyr::select(ds,y=br) %>% prophet() %>% predict(pfdat)
plistings <- DF %>% dplyr::select(ds,y=listings) %>% prophet() %>% predict(pfdat)
pinq <- DF %>% dplyr::select(ds,y=inq) %>% prophet() %>% predict(pfdat)
fdat <- data.frame(ds=pfdat$ds, visits=pvisits$yhat, br=pbr$yhat, listings=plistings$yhat, inq=pinq$yhat)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
library(dplyr)
major <- data_frame(
holiday = 'majorH',
ds = as.Date(c('2017-01-01', '2017-05-29', '2017-07-04',
'2017-09-04', '2017-11-23', '2017-12-25',
'2018-01-01', '2018-05-28', '2018-07-04',
'2018-09-03', '2018-11-22', '2018-12-25')),
lower_window = 0,
upper_window = 1
)
minor <- data_frame(
holiday = 'minorH',
ds = as.Date(c('2017-01-16', '2017-02-20', '2017-10-09',
'2018-01-15', '2018-02-19', '2018-10-08')),
lower_window = 0,
upper_window = 1
)
holidays <- bind_rows(major, minor)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
fit6 <- prophet(holidays = holidays) %>%
fit.prophet(pdat)
forecast <- predict(fit6, fdat)
fpred <- predict(fit6)
fpred$ds <- as.Date(fpred$ds)
fpred <- pdat %>% left_join(fpred,by="ds")
fpred$resid <- fpred$y - fpred$yhat
dfprophet <- c(fpred$yhat, forecast$yhat)
mape_prophet <- mean(abs((pdat$y - fpred$yhat)/pdat$y))
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
fit6 <- prophet(holidays = holidays) %>%
add_regressor('visits') %>%
fit.prophet(pdat)
forecast <- predict(fit6, fdat)
fpred <- predict(fit6)
fpred$ds <- as.Date(fpred$ds)
fpred <- pdat %>% left_join(fpred,by="ds")
fpred$resid <- fpred$y - fpred$yhat
dfprophet_visits <- c(fpred$yhat, forecast$yhat)
mape_prophet_visits <- mean(abs((pdat$y - fpred$yhat)/pdat$y))
# -------------------
# -------------------
fit6 <- prophet(holidays = holidays) %>%
add_regressor('br') %>%
fit.prophet(pdat)
forecast <- predict(fit6, fdat)
fpred <- predict(fit6)
fpred$ds <- as.Date(fpred$ds)
fpred <- pdat %>% left_join(fpred,by="ds")
fpred$resid <- fpred$y - fpred$yhat
dfprophet_br <- c(fpred$yhat, forecast$yhat)
mape_prophet_br <- mean(abs((pdat$y - fpred$yhat)/pdat$y))
# -------------------
# -------------------
fit6 <- prophet(holidays = holidays) %>%
add_regressor('visits') %>%
add_regressor('br') %>%
fit.prophet(pdat)
forecast <- predict(fit6, fdat)
fpred <- predict(fit6)
dfprophet_vibr <- c(fpred$yhat, forecast$yhat)
mape_prophet_vibr <- mean(abs((pdat$y - fpred$yhat)/pdat$y))
# -------------------
# -------------------
fit6 <- prophet(holidays = holidays) %>%
add_regressor('visits') %>%
add_regressor('br') %>%
add_regressor('listings') %>%
fit.prophet(pdat)
forecast <- predict(fit6, fdat)
fpred <- predict(fit6)
dfprophet_vibrli <- c(fpred$yhat, forecast$yhat)
mape_prophet_vibrli <- mean(abs((pdat$y - fpred$yhat)/pdat$y))
# -------------------
fit6 <- prophet(holidays = holidays) %>%
add_regressor('br') %>%
add_regressor('listings') %>%
fit.prophet(pdat)
forecast <- predict(fit6, fdat)
fpred <- predict(fit6)
dfprophet_brli <- c(fpred$yhat, forecast$yhat)
mape_prophet_brli <- mean(abs((pdat$y - fpred$yhat)/pdat$y))
# -------------------
prophet_plot_components(fit6, forecast)
plot_forecast_component(fit6, forecast, 'majorH')
plot_forecast_component(fit6, forecast, 'minorH')
# just checking the contribution of majorH, minorH on bookings
forecast %>%
dplyr::select(ds, majorH, minorH) %>%
filter(abs(majorH + minorH) > 0) %>%
tail(10)
# ----------------------------------------------------
# ----------------------------------------------------
# ----------------------------------------------------
# ##############################
# -----------------------------
# SES, ARIMA, HW
# -----------------------------
# ##############################
library(forecast)
library(ggplot2)
library(dplyr)
library(tidyr)
# -------------------------------------------
# Make the 'y' variables to a ts object
rnb <- ts(DF$y, start=2016,freq=365)
str(rnb)
# -------------------------------------------
# Exponential Smoothing using state space approach
## STLF Exponential smoothing
st_ets <- stlf(rnb, method="ets", h=365)$mean
write.csv(st_ets, "1_st_ets.csv")
# -------------------------------------------
fit_ets <- ets(rnb)$fitted
fit_ets_fc <- forecast(fit_ets, h=365)
write.csv(fit_ets_fc, "1_fit_ets.csv")
# Note - there are slight differences between stlf/ets and ets()
# -------------------------------------------
# -------------------------------------------
# HoltWinters
rnbAlphaBetaGamma <- HoltWinters(rnb)
fit_hw_fc <- forecast(rnbAlphaBetaGamma, h=365)
write.csv(fit_hw_fc, "11_fit_hw.csv")
# -------------------------------------------
# -------------------------------------------
## Auto.Arima
rnb_arima <- auto.arima(DF[,9]) # ALWAYS INPUT RAW DATA THAT IS 'NOT' A ts() object
arimaorder(rnb_arima)
## STLM - apply Auto.Arima model to data
fit <- stlm(rnb, modelfunction=Arima, order=arimaorder(rnb_arima))
fit_arima_fc <- forecast(fit, h=365)
write.csv(fit_arima_fc, "6_rnb_arima_pred_fc.csv")
# -------------------------------------------
# -------------------------------------------
# Compute MAPES
df_mape <- data.frame(rnb, fit_arima_fc$fitted, fit_hw_fc$fitted)
mape_arima <- mean(abs((df_mape$rnb - df_mape$fit_arima_fc.fitted)/df_mape$rnb))
df_mape_hw <- df_mape[366:nrow(df_mape),]
mape_hw <- mean(abs((df_mape_hw$rnb - df_mape_hw$fit_hw_fc.fitted)/df_mape_hw$rnb))
# Output MAPES by Model
library(dplyr)
mapes_by_model_Hols <- data_frame(
ts_model = c('prophet','prophet_br','prophet_brli','prophet_vibr',
'prophet_vibrli','prophet_visits',
'hw','arima'),
mape = c(mape_prophet,mape_prophet_br,mape_prophet_brli,mape_prophet_vibr,
mape_prophet_vibrli,mape_prophet_visits,
mape_hw,mape_arima)
)
# -------------------------------------------
# -------------------------------------------
# Plot forecasts from different methods
df_rnb <- c(rnb, rep(NA,365))
df1 <- c(rnb, st_ets)
df6 <- c(fit_arima_fc$fitted, fit_arima_fc$mean) # Using 'forecast()' function: mean = forecasts (n=120); fitted = backcasts (n=998)
df11 <- c(fit_hw_fc$fitted, fit_hw_fc$mean)
dfreal <- c(DF$y, rep(NA,365))
df_all <- data.frame(dfreal,dfprophet,dfprophet_visits,dfprophet_br,dfprophet_vibr,dfprophet_vibrli,df1,df6,df11)
names(df_all) <- c("real","prophet","prophet_visits","prophet_br","prophet_vibr","prophet_vibrli","ets","arima","hw")
write.csv(df_all, "df_all_agg.csv")
# adding date as index
df_all1 <- data.frame(dates=seq(from=(as.POSIXct(strftime("2016-01-01"))),
length.out = nrow(df_all),
by="days"),
data = df_all)
dfplot <- df_all1 %>% gather(key, value, -dates)
jpeg("real vs all models.jpg", height=4.25, width=5.5, res=200, units = "in")
ggplot(dfplot, mapping = aes(x = dates, y = value, color = key) ) + geom_line() +
ggtitle("real vs all models")
dev.off()
# ----- forecasts ONLY -----------
df_all_f <- df_all[999:nrow(df_all),]
# adding date as index
df_all1 <- data.frame(dates=seq(from=(as.POSIXct(strftime("2018-09-25"))),
length.out = nrow(df_all_f),
by="days"),
data = df_all_f)
dfplot <- df_all1 %>% gather(key, value, -dates)
jpeg("real vs all models - forecasts ONLY.jpg", height=4.25, width=5.5, res=200, units = "in")
ggplot(dfplot, mapping = aes(x = dates, y = value, color = key) ) + geom_line() +
ggtitle("real vs all models - forecasts ONLY")
dev.off()
|
0319039bd1e3865fc7164ec8009a483161a1187c
|
ea4fed8707dc916aa765137de316ce428ba29a06
|
/h.R
|
c473a628050d18a13874a7eff426cfbf5a236d20
|
[] |
no_license
|
troywu666/R
|
475fb38ec8bff948dc3117fddda2466a2f9a3982
|
62fdc26feddd71ae5b19f12a9772a09693ccc0d1
|
refs/heads/master
| 2020-07-19T00:30:55.700466
| 2019-09-12T14:07:06
| 2019-09-12T14:07:06
| 206,342,799
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 76
|
r
|
h.R
|
x <- 0
x[1] <- 1
i <- 1
while (x[1]<100) {i=i+1;x[i]= x[i-1]+2}
print(x)
|
b2cc09c8cfc2a10eac76cccf8a1d32a8f27bad50
|
837369fea4392c7429f3fe2ab8d2119b1b7013e2
|
/plot3.R
|
d021972d9a08b6a67ff6cfcb985e2f127a655175
|
[] |
no_license
|
shadowmoon1988/ExData_Plotting1
|
7d0c1a8ac543bf81ff74dbb48aba602d41b0670c
|
c9a99f30c4a414f216756fcb50ce046ea409caaf
|
refs/heads/master
| 2021-01-15T17:20:51.337941
| 2016-02-04T08:50:20
| 2016-02-04T08:50:20
| 51,030,906
| 0
| 0
| null | 2016-02-03T21:11:57
| 2016-02-03T21:11:57
| null |
UTF-8
|
R
| false
| false
| 613
|
r
|
plot3.R
|
library(data.table)
data <- fread("household_power_consumption.txt",header=TRUE,na.strings="?")
data[,Date:=as.Date(Date,format="%d/%m/%Y")]
data <-data[data$Date>=as.Date("2007-02-01")&data$Date<=as.Date("2007-02-02"),]
t <- strptime(paste(data$Date,data$Time),format="%Y-%m-%d %H:%M:%S")
png(file="plot3.png",width=480,height=480)
plot(t,data$Sub_metering_1,type="l",xlab="",ylab="Energy sub metering")
lines(t,data$Sub_metering_2,col="red")
lines(t,data$Sub_metering_3,col="blue")
legend("topright", col=c("black","red","blue"), legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),lty=1)
dev.off()
|
b82a6780389e7c3d5c48fe04d3c839e6222c7f76
|
7c8b9c6b91d344acb7f3dd8212d3a8ec23488a9f
|
/App/HTMLR/input.R
|
2624c7a33d7b84f6ee8f004a9230992a2b5fc662
|
[
"MIT"
] |
permissive
|
dnordgren/Finance-O-Tron
|
d69cd014a6e8a2e541191f5323f0aebeed0128ed
|
22a98a1a4f39ad67d9a87fef73ac8bf760e1b10e
|
refs/heads/master
| 2021-05-30T01:20:28.192056
| 2014-12-11T05:33:06
| 2014-12-11T05:33:06
| 26,923,922
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 918
|
r
|
input.R
|
actionButtonHalf <- function(inputId, value){
tags$button(id = inputId, type="button", style="margin-top:10px", class = "btn btn-primary action-button shiny-bound-input", value)
}
actionButtonRow <- function(inputId, value){
div(tags$button(id = inputId, type="button", class = "btn btn-primary action-button shiny-bound-input", value))
}
disableInputSmall <- function(session){
session$sendCustomMessage(type="jsCode", list(code= "$('.input-small').attr('disabled',true)"))
}
enableInputSmall <- function(session){
session$sendCustomMessage(type="jsCode", list(code= "$('.input-small').attr('disabled',false)"))
}
disableUIElement <- function(id, session){
session$sendCustomMessage(type="jsCode", list(code=paste0("$('#",id,"').attr('disabled',true)")))
}
enableUIElement <- function(id, session){
session$sendCustomMessage(type="jsCode", list(code=paste0("$('#",id,"').attr('disabled',false)")))
}
|
40934dbb3f6e26060a7bea6546c14c757311970c
|
0ad1708efc25df24d2243f75d0fa54b920b2b1bb
|
/man/bootstrapThresholds.Rd
|
8db07e25c6d6bfb0296753e0f34751d110f155c4
|
[] |
no_license
|
pearcedom/survivALL
|
6cdc6112394b02904170e71c5ce49dd21ec3881a
|
efcbf6ba09cc90b8b14ee7e7491d5963b7298241
|
refs/heads/master
| 2021-01-15T23:22:20.393168
| 2018-05-24T14:52:04
| 2018-05-24T14:52:04
| 99,936,011
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,013
|
rd
|
bootstrapThresholds.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bootstrapThresholds.R
\name{bootstrapThresholds}
\alias{bootstrapThresholds}
\title{Calculate per-separation point hazard ratio thresholds}
\usage{
bootstrapThresholds(bs_dfr, n_sd = 1.96)
}
\arguments{
\item{bs_dfr}{A matrix of bootstrapped hazard ratio computations as ordered
by a random measurement vector. Typically consisting of 5-10,000 repeat
samplings}
\item{n_sd}{The number of standard deviations used to define threshold width.
95% of random hazard ratio values fall within the thresholds with a standard
deviation of 1.96}
}
\value{
A dataframe of per-separation point mean, upper and lower thresholds
}
\description{
Calculate per-separation point hazard ratio thresholds
}
\examples{
data(nki_subset)
library(Biobase)
library(magrittr)
library(ggplot2)
#simulate example HR bootstrapped data
bs_dfr <- matrix(rnorm(150000), ncol = 1000, nrow = 150)
#calculate thresholds
thresholds <- bootstrapThresholds(bs_dfr)
}
|
1e9a98eaa4a067f6b3f2ac0b1f251e268f3220f6
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/BiocInstaller/examples/packageGroups.Rd.R
|
fc9a18aa55e9b0b525116804661b1d5ecea92e06
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 692
|
r
|
packageGroups.Rd.R
|
library(BiocInstaller)
### Name: Package Groups
### Title: Convenience functions to return package names associated with
### Bioconductor publications.
### Aliases: biocases_group RBioinf_group monograph_group all_group
### Keywords: environment
### ** Examples
## Get the names of packages used in the book
## "Bioconductor Case Studies":
biocases_group()
## Get the names of packages used in the book
## "R Programming for Bioinformatics":
RBioinf_group()
## Get the names of packages used in the monograph
## "Bioinformatics and Computational Biology Solutions
## Using R and Bioconductor":
monograph_group()
## Get the names of all Bioconductor software packages
all_group()
|
709f3f60829b13de181eab81a91f1a21d82a513f
|
3b62ffa02efef29b8bbaa9041d74a1ee72b4807a
|
/man/rhrIsopleths.Rd
|
7faea9f927249663631cd174e4f3dccdd6fb1fad
|
[] |
no_license
|
jmsigner/rhr
|
52bdb94af6a02c7b10408a1dce549aff4d100709
|
7b8d1b2dbf984082aa543fe54b1fef31a7853995
|
refs/heads/master
| 2021-01-17T09:42:32.243262
| 2020-06-22T14:24:00
| 2020-06-22T14:24:00
| 24,332,931
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 562
|
rd
|
rhrIsopleths.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rhrIsopleths.R
\name{rhrIsopleths}
\alias{rhrIsopleths}
\title{Isopleths of Home Range Estimate}
\usage{
rhrIsopleths(x, ...)
}
\arguments{
\item{x}{Instance of \code{RhrEst}.}
\item{...}{see details.}
}
\value{
\code{SpatialPolygonsDataFrame}
}
\description{
Function to retrieve isopleths of a home range estimate.
}
\details{
Probabilistic estimators take (i.e. kernel density estimates) take
an additional argument, \code{levels}, that determines which isopleth are
returned.
}
|
feaf9e264bb125dc958d46dd50e04ff2bea51db2
|
6519538b5837012573465ce9471b053622fe0830
|
/ZOL851/Elise_Zipkin/notes/ggplot2_demo.R
|
ebcb2a12a6c02107da316fd754dcd6c5fb8ac955
|
[] |
no_license
|
pvaelli/Advanced_Statistics
|
926e4229918d3a50ceebeefdf4469e2173c08c95
|
27b628b0355f9c4d8f2e1648a33aa1ec21b230c5
|
refs/heads/master
| 2021-08-29T20:18:27.970826
| 2017-12-14T22:54:20
| 2017-12-14T22:54:20
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,848
|
r
|
ggplot2_demo.R
|
install.packages("ggplot2")
library("ggplot2")
diamonds # sample dataset present in ggplot2 library
summary(diamonds)
help(diamonds) # can only do this because it's a sample dataset
ggplot(diamonds, aes(x=carat, y=price)) + geom_point() #(dataset, aesthetics, ) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price, color=clarity)) + geom_point() #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point() #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price, color=clarity, size=cut)) + geom_point() #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price, color=clarity, size=cut)) + geom_point(alpha=0.3) #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price)) + geom_point() + geom_smooth() #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price)) + geom_point() + geom_smooth(se=FALSE) #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price, color=clarity)) + geom_point() + geom_smooth(se=FALSE) #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price, color=clarity)) + geom_smooth(se=FALSE) #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price, color=clarity)) + geom_point() + geom_smooth(se=FALSE) + ggtitle("Colors so many !!") #(dataset, aesthetics, adding color coding) + layers for plot()
ggplot(diamonds, aes(x=carat, y=price, color=clarity)) + geom_point() + geom_smooth(se=FALSE) + ggtitle("Colors so many !!") + xlab("Weight (carats)") + ylab("Price (dollars)")#(dataset, aesthetics, adding color coding) + layers for plot()
# these lines are getting really long. here's a trick:
plot <- ggplot(diamonds, aes(x=carat, y=price, color=cut))
plot <- plot + geom_point()
plot <- plot + ggtitle("Colors so many !!!")
plot <- plot + xlab("Weight (carats)") + ylab("Price (dollars)")
plot <- plot + xlim(0,2) + ylim(0, 10000)
plot <- plot + scale_y_log10()
plot
# Histograms
ggplot(diamonds, aes(x=price)) + geom_histogram() # by default each bin is 30
ggplot(diamonds, aes(x=price)) + geom_histogram(binwidth=2000)
ggplot(diamonds, aes(x=price)) + geom_histogram(binwidth=200)
ggplot(diamonds, aes(x=price)) + geom_histogram(binwidth=200) +facet_wrap(~clarity)
ggplot(diamonds, aes(x=price)) + geom_histogram(binwidth=200) +facet_wrap(~clarity, scale="free_y") # can add this last part to change the y scaling. Good for looking at shape of distribution; otherwise all y axes are identical across plots
ggplot(diamonds, aes(x=price, fill=cut)) + geom_histogram() # this could be useful for microbial abundance bar plots!
# Density plots
ggplot(diamonds, aes(x=price)) + geom_density()
ggplot(diamonds, aes(x=price, color=cut)) + geom_density()
ggplot(diamonds, aes(x=price, fill=cut)) + geom_density()
ggplot(diamonds, aes(x=price, fill=cut)) + geom_density(alpha=0.3)
# Boxplots
ggplot(diamonds, aes(x=color, y=price)) + geom_boxplot()
ggplot(diamonds, aes(x=color, y=price)) + geom_boxplot() + scale_y_log10()
# Violin plots
ggplot(diamonds, aes(x=color, y=price)) + geom_violin()
ggplot(diamonds, aes(x=color, y=price)) + geom_violin() + scale_y_log10()
# bar plots
ggplot(diamonds, aes(x=cut)) + geom_bar()
plot <- ggplot(diamonds, aes(x=color, fill=cut)) + geom_bar() # stacked vs adjacent (next code)
ggplot(diamonds, aes(x=color, fill=cut)) + geom_bar(position = "dodge")
x <- 0:5
y <- x * 3000
model <- data.frame(x,y)
ggplot(diamonds, aes(x=carat, y=price)) + geom_point() + geom_line(data=model, x=x, y=y)
ggsave(filename="pretty.pdf", plot) # can do .png or .jpeg etc.
|
2523f67aaa5b137bdfb806ebd6d00516ade1fe3a
|
e8dc4d4a4988126f8beedd9b8e920c8c0468df7a
|
/INPUT/script_gene.r
|
9e166255410674f973d442cc2c0d8ebf9537f373
|
[] |
no_license
|
marta-coronado/master-thesis
|
ca5ddc36a1637487e4b0ad7872b1f65ce84cd466
|
a0a1c19c3bd4b9a21f00731af7c7996d921cc13c
|
refs/heads/master
| 2021-01-01T18:34:43.007019
| 2014-05-30T14:50:35
| 2014-05-30T14:50:35
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,872
|
r
|
script_gene.r
|
library(car)
library(lattice)
library(ppcor)
library(pls)
rm(list = ls())
#data<-read.table(file="GENE",header=TRUE,sep="\t")
#data<-read.table(file="GENE.SHORTINTRON.65",header=TRUE,sep="\t")
data<-read.table(file="GENE.SHORTINTRON.65_8-30",header=TRUE,sep="\t")
data <- na.omit(data)
data <- subset(data,comeron_100kb > 0)
nrow(data)
data <- subset(data,chr == "2L")
data <- subset(data,chr == "2R")
data <- subset(data,chr == "3L")
data <- subset(data,chr == "3R")
data <- subset(data,chr == "X")
data <- subset(data,chr != "X")
m <- data[["mdmel_0f"]]+data[["mdmel_4f"]]+data[["mdmel_2f"]]
k4f <- (data[["div_4f"]])/(data[["mdyak_4f"]])
k0f <- (data[["div_0f"]])/(data[["mdyak_0f"]])
k0f <- (data[["div_0f"]]+1)/(data[["mdyak_0f"]])
kins <- (data[["div_ins"]])/(data[["mdyak_ins"]])
### ASSESSIN COEVOLUTION ###
gene <- data.frame(
Transcripts = data[["num_transcripts"]],
Exons = data[["num_exons"]],
m = m,
Distance = data[["distance"]],
Breadth = data[["bias_dev"]],
Expression = data[["max_dev"]],
Ki = kins,
AAsubstitutions = k0f,
Mutation = k4f,
#Chr_state = data[["state"]],
#Chr_domain = data[["domain"]],
#Chromosome = data[["chr"]],
Recombination=(data[["comeron_100kb"]]),
mess_com = (data[["num_transcripts"]]/data[["num_exons"]])
)
gene <- na.omit(gene)
nrow(gene)
spcor(gene,method=c("spearman"))
Transcripts = data[["num_transcripts"]]
Exons = data[["num_exons"]]
Distance = data[["distance"]]
Breadth = data[["bias_dev"]]
Expression = data[["max_dev"]]
AAsubstitutions = k0f
Mutation = k4f
Chr_state = data[["state"]]
Chr_domain = data[["domain"]]
Chromosome = data[["chr"]]
Recombination=(data[["comeron_100kb"]])
mess_com = (data[["num_transcripts"]]/data[["num_exons"]])
### Distance ###
summary(data[["distance"]])
quantile(data[["distance"]],(0:3)/3)
density = cut(data[["distance"]],c(0,554,1979,140000))
with(data,tapply(m,density,sum))
summary(density)
boxplot(k0f~density,outline=F,xlab="Distance",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,data[["distance"]],method="spearman")
cor.test(data[["distance"]],m,method="spearman")
cor.test(k0f,m,method="spearman")
cor.test(k0f,data[["distance"]]/(m/data[["num_exons"]]),method="spearman")
kruskal.test(k0f~density)
### Size ###
summary(m)
quantile(m,(0:3)/3)
m_factor = cut(m,c(0,888,1743,55353))
summary(m_factor)
with(data,tapply(m,m_factor,sum))
boxplot(k0f~m_factor,outline=F,xlab="Gene Size",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,m,method="spearman")
kruskal.test(k0f~m_factor)
### Transcripts ###
quantile(data$num_transcripts,(0:3)/3)
hist(data$num_transcripts,breaks=100,xlim=c(0,20))
summary(data[["num_transcripts"]])
with(data,tapply(m,data[["num_transcripts"]],sum))
num_transcripts_factor = cut(data[["num_transcripts"]],c(0,1,2,75))
summary(num_transcripts_factor)
with(data,tapply(m,num_transcripts_factor,sum))
boxplot(k0f~num_transcripts_factor,outline=F,xlab="Number of Transcripts/Gene",ylab="Ka")
boxplot(k0f~data$num_transcripts,outline=F,xlab="Number of Transcripts/Gene",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,data$num_transcripts,method="spearman")
kruskal.test(k0f~num_transcripts_factor)
### Exons ###
quantile(data$num_exons,(0:3)/3)
hist(data$num_exons,breaks=100,xlim=c(0,20))
summary(data[["num_exons"]])
with(data,tapply(m,data[["num_exons"]],sum))
num_exons_factor = cut(data[["num_exons"]],c(0,3,8,114))
summary(num_exons_factor)
with(data,tapply(m,num_exons_factor,sum))
boxplot(k0f~num_exons_factor,outline=F,xlab="Number of Exons/Gene",ylab="Ka")
boxplot(k0f~data$num_exons,outline=F,xlab="Number of Exons/Gene",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,data$num_exons,method="spearman")
kruskal.test(k0f~num_exons_factor)
### Messenger Complexity ###
plot(data[["num_transcripts"]],data[["num_exons"]])
quantile(data[["num_transcripts"]]/data[["num_exons"]],(0:3)/3)
summary(data[["num_transcripts"]]/data[["num_exons"]])
hist(data[["num_transcripts"]]/data[["num_exons"]])
mess_com = cut(data[["num_transcripts"]]/data[["num_exons"]],c(0,0.33,0.66,8))
tapply(m,mess_com,sum)
summary(mess_com)
boxplot(k0f~mess_com,outline=F,xlab="Messenger Complexity",ylab="Ka")
abline(h=median(k0f),col="black")
boxplot(data[["num_exons"]]~mess_com,outline=F,xlab="Messenger Complexity",ylab="#Exons")
boxplot(data[["num_transcripts"]]~mess_com,outline=F,xlab="Messenger Complexity",ylab="#mRNAs")
cor.test(k0f,data[["num_transcripts"]]/data[["num_exons"]],method="spearman")
cor.test(data[["num_transcripts"]],data[["num_exons"]],method="spearman")
kruskal.test(k0f~mess_com)
### Expression ###
plot(data[["bias_dev"]],data[["max_dev"]])
summary(data[["max_dev"]])
quantile(data[["max_dev"]],(0:3)/3)
expression_factor = cut(data[["max_dev"]],c(0,1.477,1.87,4.4))
summary(expression_factor)
tapply(m,expression_factor,sum)
boxplot(k0f~expression_factor,outline=F,xlab="Expression",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,data[["max_dev"]],method="spearman")
kruskal.test(k0f~expression_factor)
### Breadth ###
summary(data[["bias_dev"]])
quantile(data[["bias_dev"]],(0:3)/3)
breadth_factor = cut(data[["bias_dev"]],c(0,0.27,0.54,1))
summary(breadth_factor)
tapply(m,breadth_factor,sum)
boxplot(k0f~breadth_factor,outline=F,xlab="Expression Breadth",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,data[["bias_dev"]],method="spearman")
kruskal.test(k0f~breadth_factor)
### Chromosome ###
tapply(m,data[["chr"]],sum)
summary(data[["chr"]])
boxplot(k0f~data[["chr"]],outline=F,xlab="Chromosome",ylab="Ka")
### Chromatin State and Chromatin Domain ###
tapply(m,data[["state"]],sum)
tapply(m,data[["domain"]],sum)
summary(data[["state"]])
summary(data[["domain"]])
boxplot(k0f~data[["state"]],outline=F,xlab="Chromatin State",ylab="Ka")
boxplot(k0f~data[["domain"]],outline=F,xlab="Chromatin Domain",ylab="Ka")
### Recombination ###
summary(data[["comeron_100kb"]])
quantile(data[["comeron_100kb"]],(0:3)/3)
recombination_factor = cut(data[["comeron_100kb"]],c(0,1.44,2.89,15))
summary(recombination_factor)
tapply(m,recombination_factor,sum)
boxplot(k0f~recombination_factor,outline=F,xlab="Recombination Rate",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,data[["comeron_100kb"]],method="spearman")
kruskal.test(k0f~recombination_factor)
### mutation 4f ###
summary(k4f)
quantile(k4f,(0:3)/3)
mutation_factor = cut(k4f,c(0,0.16,0.20,0.75))
summary(mutation_factor)
tapply(m,mutation_factor,sum)
boxplot(k0f~mutation_factor,outline=F,xlab="Mutation Rate",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,k4f,method="spearman")
kruskal.test(k0f~mutation_factor)
### mutation ins ###
summary(kins)
quantile(kins,(0:3)/3)
mutation_factor = cut(kins,c(0,0.18,0.25,0.7))
summary(mutation_factor)
tapply(m,mutation_factor,sum)
boxplot(k0f~mutation_factor,outline=F,xlab="Mutation Rate",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,kins,method="spearman")
kruskal.test(k0f~mutation_factor)
##### THETA #####
var = 1
cal = 0
dom = 0
lines = 128
while (var < lines)
{
cal = 1/var
dom = dom + cal
var = var + 1
}
dom #5.4, media harmónica
K_watterson <- (data[["seg_ins"]])/(data[["mdmel_ins"]])
watterson = K_watterson/dom
head(watterson)
summary(watterson)
quantile(watterson ,(0:3)/3)
watterson_factor = cut(watterson,c(0,0.005,0.013,0.087))
summary(watterson_factor)
tapply(m,watterson_factor,sum)
boxplot(k0f~watterson_factor,outline=F,xlab="Watterson estimator",ylab="Ka")
abline(h=median(k0f),col="black")
cor.test(k0f,watterson,method="spearman")
kruskal.test(k0f~watterson_factor)
qplot(Chr_domain,Recombination, geom=("boxplot"),fill=Chromosome, binwidth=3) + xlab("Chromatin domain") + ylab("Recombination") + scale_fill_brewer(type="seq", palette=3) + theme(axis.title=element_text(face="bold",size="12", color="black"), legend.position="right") + ggtitle("R")
|
93a05d081ad0c132c736464c565ed6fd6a5f02a5
|
3ee266f78c11d751c94a0d8ca3d77bc216e4f621
|
/code/2-prep_names.R
|
6006b5e0d8f9d9e6651e2fb207c54285a4325c1e
|
[] |
no_license
|
sakho3600/names_eval
|
c59a3a2b40e5d1947d38a83c981b56286a3e701b
|
41f81ee5e9015db27489b49515a52e1125f89471
|
refs/heads/master
| 2020-05-09T22:54:35.115245
| 2017-09-14T15:31:59
| 2017-09-14T15:31:59
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,769
|
r
|
2-prep_names.R
|
rm(list = ls())
gc()
# Cole Tanigawa-Lau
# Sun Jul 16 10:21:19 2017
# Description: Prepare name strings from 2007 and late-2012 voter registers.
library(data.table)
library(stringr)
library(dplyr)
library(multidplyr)
library(parallel)
library(babynames)
source("code/functions.R")
# Clean up 2012 name strings with various methods ----
vr12 <- readRDS("data/vr12.rds")
# Concatenate common US baby names to remove when cleaning
# (these names won't be informative)
# Retain common Muslim, which will be helpful in guessing ethnicity
comm_muslim <- c(agrep("mohammed", x = babynames$name, ignore.case = TRUE, value = TRUE),
agrep("abdul", x = babynames$name, ignore.case = TRUE, value = TRUE),
agrep("hussein", x = babynames$name, ignore.case = TRUE, value = TRUE),
agrep("fatima", x = babynames$name, ignore.case = TRUE, value = TRUE),
agrep("sumaiya", x = babynames$name, ignore.case = TRUE, value = TRUE),
"Ayesha", "Aisha", "Farah", "Omar", "Issa", "Hamza", "Ali", "Ibrahim") %>% unique()
# Retains roughly 1.7% of unique names, about 78% of the original amount of occurences
bnames <- filter(babynames,
prop > 6e-4,
! name %in% comm_muslim
)$name %>%
unique() %>% toupper()
# Intialize cluster for parallel processing, split across polling station IDs ----
vr12p <- partition(vr12, psid12)
cluster_copy(vr12p, clean_func)
cluster_copy(vr12p, bnames)
cluster_library(vr12p, "dplyr")
# Clean first, middle, and last names first, then paste together later
vr12p2 <- group_by(vr12p, psid12) %>%
do(first = clean_func(.$FIRST_NAME) %>% str_split("\\s") %>% unlist(),
middle = clean_func(.$MIDDLE_NAME) %>% str_split("\\s") %>% unlist(),
last = clean_func(.$SURNAME) %>% str_split("\\s") %>% unlist()
)
# Build vector of (split) names according to polling station
vr12p3 <- group_by(vr12p2, psid12) %>%
do(.,
first = unlist(.$first),
middle = unlist(.$middle),
last = unlist(.$last),
fullname = unlist(c(.$first, .$middle, .$last)),
name = unlist(c(.$middle, .$last))
)
# Remove baby names
vr12p4 <- group_by(vr12p3, psid12) %>%
do(.,
first = unlist(.$first),
middle = unlist(.$middle),
last = unlist(.$last),
fullname = unlist(.$fullname),
name = unlist(.$name),
rm_bnames = unlist(.$fullname)[which(! unlist(.$fullname) %in% bnames)]
)
# Collect all 2012 names in a single tibble and save -----
vr12_names <- data.table(collect(vr12p4))
# Apply clean_func() once more to all columns, just in case
for(jj in 2:ncol(vr12_names)) set(vr12_names, j = jj, value = mclapply(vr12_names[[jj]], clean_func, mc.cores = 6))
saveRDS(vr12_names, "data/vr12_names.rds")
# vr12_names <- readRDS("data/vr12_names.rds")
# Clear up some memory
rm(vr12, vr12p, vr12p2, vr12p3, vr12p4)
gc()
# Clean 2007 name strings -----
vr07 <- readRDS("data/VR07dt.Rdata")
vr07p <- partition(vr07, id07)
cluster_copy(vr07p, clean_func)
cluster_copy(vr07p, bnames)
cluster_library(vr07p, "dplyr")
cluster_library(vr07p, "stringr")
# Clean first, middle, and last names first, then paste together later
vr07p2 <- group_by(vr07p, id07) %>%
do(first = str_extract(clean_func(.$oth), "^[A-Z]+") %>% unlist(),
middle = gsub(clean_func(.$oth), patt = "^[A-Z]+ ",
repl = "", perl = TRUE
) %>%
str_split("\\s") %>% unlist(),
last = clean_func(.$sur) %>% str_split("\\s") %>% unlist()
)
# Build vector of (split) names according to polling station
vr07p3 <- group_by(vr07p2, id07) %>%
do(.,
first = unlist(.$first),
middle = unlist(.$middle),
last = unlist(.$last),
fullname = unlist(c(.$first, .$middle, .$last)),
name = unlist(c(.$middle, .$last)
)
)
# Remove baby names
vr07p4 <- group_by(vr07p3, id07) %>%
do(.,
first = unlist(.$first),
middle = unlist(.$middle),
last = unlist(.$last),
fullname = unlist(.$fullname),
name = unlist(.$name),
rm_bnames = unlist(.$fullname)[which(! unlist(.$fullname) %in% bnames)]
)
# Collect names and save
vr07_names <- collect(vr07p4)
# Apply clean_func() once more to all columns, just in case
system.time(for(jj in 2:ncol(vr07_names)) set(vr07_names, j = jj, value = mclapply(vr07_names[[jj]], clean_func, mc.cores = 6)))
saveRDS(vr07_names, "data/vr07_names.rds")
|
6262fbc021a023a846913384a83d44f48ee54179
|
aba4026c593dc205b2b12735ec51427d0a1d1bd1
|
/LFR_Data_Genration_File.R
|
daa527c258709884fb064cc518fd05bf63d6f109
|
[] |
no_license
|
Adnanbukhari123/SMA
|
023256d2ad59ae7c9de8188a0947133df298c187
|
a4a77795b2053aee370f272f2245597292b46bed
|
refs/heads/main
| 2023-08-22T00:46:46.737724
| 2021-11-01T13:38:56
| 2021-11-01T13:38:56
| 423,462,535
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,243
|
r
|
LFR_Data_Genration_File.R
|
library(ggplot2)
library(igraph)
library(writexl)
library(readxl)
config_df = as.data.frame(read_excel("SMA_Project/Configurations.xlsx"))
col_names <- c( "SNO","FileNO","n","pt", "n0", "mu","multilevel_community_modularity","walktrap_community_modularity", "infomap_community_modularity", "louvain_community_modularity", "label_pop_community_modularity", "fast_community_modularity",
"multilevel_community_size","walktrap_community_size", "infomap_community_size", "louvain_community_size", "label_pop_community_size", "fast_community_size",
"clustering_coefficient", "average_path_length","number_of_nodes")
get_modularity_LFR<-function(){
for(folder_name in c("LFR")){
full_path = paste("/home/adnan/Documents/",folder_name,'/',sep="")
df = data.frame(matrix(nrow = 0, ncol = length(col_names)))
colnames(df) = col_names
for(file_number in 1:375){
print(paste("File number", file_number))
graph <- read.graph(paste(full_path,file_number,'.gml', sep=""), format=c('gml'))
wc <- multilevel.community(graph)
walktrap_community <- cluster_walktrap(graph)
infomap_community <- cluster_infomap(graph)
louvain_community <- cluster_louvain(graph)
label_pop_community <-cluster_label_prop(graph)
fast_greedy_community<-cluster_fast_greedy(graph)
df[nrow(df) + 1,] <- c(file_number, paste(file_number,'.gml',sep=""), 0,0,0,0,modularity(wc),
modularity(walktrap_community), modularity(infomap_community), modularity(louvain_community), modularity(label_pop_community),
modularity(fast_greedy_community),length(wc), length(walktrap_community), length(infomap_community), length(louvain_community), length(label_pop_community),
length(fast_greedy_community),
transitivity(graph),average.path.length(graph),vcount(graph))
}
df['n']= config_df['n']
df['pt']= config_df['pt']
df['n0']=config_df['n0']
df['mu']=config_df['mu']
write_xlsx(df, paste("/home/adnan/Documents/",folder_name,'.xlsx', sep=''))
}
return (df)
}
df_LFR = get_modularity_LFR()
|
324030ecf95d22a10732a183e8b8a8d06c37f240
|
595a4ead5c1d7761c429d9dde8f3c84e5a6e99ca
|
/R/power_ftest.R
|
ca940231072dc598753a3d6138fc4497feb4852d
|
[
"MIT"
] |
permissive
|
arcaldwell49/Superpower
|
163804ae7682be43c3f7241671948bc73f1706ea
|
ed624538f6b28d9243720994b3428edfe80b8bfa
|
refs/heads/master
| 2023-02-16T19:24:16.312351
| 2023-02-11T00:20:47
| 2023-02-11T00:20:47
| 206,843,269
| 60
| 17
|
NOASSERTION
| 2023-02-10T23:41:36
| 2019-09-06T17:28:44
|
HTML
|
UTF-8
|
R
| false
| false
| 5,309
|
r
|
power_ftest.R
|
#' Power Calculations for an F-test
#'
#' Compute power of test or determine parameters to obtain target power. Inspired by the pwr.f2.test function in the pwr package, but allows for varying noncentrality parameter estimates for a more liberal (default in pwr.f2.test) or conservative (default in this function) estimates (see Aberson, Chapter 5, pg 72).
#'
#' @param num_df degrees of freedom for numerator
#' @param den_df degrees of freedom for denominator
#' @param cohen_f Cohen's f effect size. Note: this is the sqrt(f2) if you are used to using pwr.f2.test
#' @param alpha_level Alpha level used to determine statistical significance.
#' @param beta_level Type II error probability (power/100-1)
#' @param liberal_lambda Logical indicator of whether to use the liberal (cohen_f^2\*(num_df+den_df)) or conservative (cohen_f^2\*den_df) calculation of the noncentrality (lambda) parameter estimate. Default is FALSE.
#'
#' @return
#' num_df = degrees of freedom for numerator,
#' den_df = degrees of freedom for denominator,
#' cohen_f = Cohen's f effect size,
#' alpha_level = Type 1 error probability,
#' beta_level = Type 2 error probability,
#' power = Power of test (1-beta_level\*100%),
#' lambda = Noncentrality parameter estimate (default = cohen_f^2\*den_df, liberal = cohen_f^2\*(num_df+den_df))
#'
#' @examples
#' design_result <- ANOVA_design(design = "2b",
#' n = 65,
#' mu = c(0,.5),
#' sd = 1,
#' plot = FALSE)
#' x1 = ANOVA_exact2(design_result, verbose = FALSE)
#' ex = power.ftest(num_df = x1$anova_table$num_df,
#' den_df = x1$anova_table$den_df,
#' cohen_f = x1$main_result$cohen_f,
#' alpha_level = 0.05,
#' liberal_lambda = FALSE)
#' @section References:
#' Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.
#' Aberson, C. (2019). Applied Power Analysis for the Behavioral Sciences (2nd ed.). New York,NY: Routledge.
#' @importFrom stats uniroot optimize
#' @export
#'
power.ftest <- function(num_df = NULL, den_df = NULL,
cohen_f = NULL,
alpha_level = Superpower_options("alpha_level"),
beta_level = NULL,
liberal_lambda = Superpower_options("liberal_lambda"))
{
#if (sum(sapply(list(num_df, den_df, cohen_f, beta_level, alpha_level), is.null)) !=
# 1) {
# stop("exactly one of num_df, den_df, cohen_f, beta_level, and alpha_level must be NULL")
#}
if (!is.null(cohen_f)) {
if (any(cohen_f < 0)) {
stop("cohen_f must be positive")
}
}
if (!is.null(num_df) && any(num_df < 1)) {
stop("degree of freedom num_df for numerator must be at least 1")
}
if (!is.null(den_df) && any(den_df < 1)) {
stop("degree of freedom den_df for denominator must be at least 1")
}
if (!is.null(alpha_level) && !is.numeric(alpha_level) || any(0 >
alpha_level | alpha_level > 1)) {
stop(sQuote("alpha_level"), " must be numeric in [0, 1]")
}
if (!is.null(beta_level) && !is.numeric(beta_level) || any(0 > beta_level |
beta_level > 1)) {
stop(sQuote("beta_level"), " must be numeric in [0, 1].")
}
if (liberal_lambda == TRUE) {
p.body <- quote({
pf(qf(alpha_level, num_df, den_df, lower.tail = FALSE), num_df, den_df, cohen_f^2 * (num_df+den_df+1),
lower.tail = FALSE)
})
} else {
p.body <- quote({
pf(qf(alpha_level, num_df, den_df, lower.tail = FALSE), num_df, den_df, cohen_f^2 * (den_df),
lower.tail = FALSE)
})
}
if (!is.null(beta_level)){
pow = 1 - beta_level
}
if (is.null(beta_level)){
pow <- eval(p.body)
} else if (is.null(num_df)) {
p.body2 = p.body[2]
p.body2 = gsub("alpha_level",
alpha_level,
p.body2)
p.body2 = gsub("den_df",
den_df,
p.body2)
p.body2 = gsub("cohen_f",
cohen_f,
p.body2)
num_df = optimize(f = function(num_df) {
abs(eval(parse(text=paste(p.body2)))-pow)
}, c(0,1000))$min
#num_df <- uniroot(function(num_df) eval(p.body) - pow, c(1, 100))$root
}
else if (is.null(den_df)) {
den_df <- uniroot(function(den_df) eval(p.body) - pow, c(1 +
1e-10, 1e+09))$root
}
else if (is.null(cohen_f)) {
cohen_f <- uniroot(function(cohen_f) eval(p.body) - pow, c(1e-07,
1e+07))$root
}
else if (is.null(alpha_level)) {
alpha_level <- uniroot(function(alpha_level) eval(p.body) -
pow, c(1e-10, 1 - 1e-10))$root
}
else {
stop("internal error: exactly one of num_df, den_df, cohen_f, beta_level, and alpha_level must be NULL")
}
power_final = pow * 100
beta_level = 1 - pow
METHOD <- "Power Calculation for F-test"
structure(list(num_df = num_df,
den_df = den_df,
cohen_f = cohen_f,
alpha_level = alpha_level,
beta_level = beta_level,
power = power_final,
method = METHOD), class = "power.htest")
}
|
bb6235a4ef8c6afb10ac8116835842fbe8904340
|
642d338221f44aad742b39230cffcbc297f48306
|
/R/getANTsRData.R
|
51a3ef9f194471840fb2b7de34269e8858d304b0
|
[] |
no_license
|
stnava/itkImageR
|
3577ccb5785893c510f6e0fabd6a68d5d6c095a2
|
8b218e81d8e3cc642c8891a4eb4c43dc941cf870
|
refs/heads/master
| 2016-09-05T14:29:26.851337
| 2013-11-25T22:53:34
| 2013-11-25T22:53:34
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,260
|
r
|
getANTsRData.R
|
getANTsRData <- function(fileid, usefixedlocation = FALSE) {
library(tools)
myusage <- "usage: getANTsRData(fileid = whatever , usefixedlocation = TRUE )"
if (missing(fileid)) {
print(myusage)
return(NULL)
}
# ch2b = brodmann ch2a = aal mnib = brodmann mnia = all mnit = tracts
myurl <- switch(fileid, r16 = "http://placid.nlm.nih.gov/download?items=10764", r64 = "http://placid.nlm.nih.gov/download?items=10765",
KK = "http://placid.nlm.nih.gov/download?items=10766", ADNI = "http://placid.nlm.nih.gov/download?folders=238",
K1 = "http://www.nitrc.org/frs/downloadlink.php/2201", BT = "http://placid.nlm.nih.gov/download?items=10767",
AB = "http://placid.nlm.nih.gov/download?items=10753", ch2 = "http://placid.nlm.nih.gov/download?items=10778",
ch2b = "http://placid.nlm.nih.gov/download?items=10780", ch2a = "http://placid.nlm.nih.gov/download?items=10784",
mni = "http://placid.nlm.nih.gov/download?items=10785", mnib = "http://placid.nlm.nih.gov/download?items=10787",
mnia = "http://placid.nlm.nih.gov/download?items=10786", mnit = "http://placid.nlm.nih.gov/download?items=11660")
myext <- ".nii.gz"
if (fileid == "ADNI" | fileid == "K1")
myext <- ".zip"
tdir <- tempdir() # for temporary storage
tfn <- tempfile(pattern = "antsr", tmpdir = tdir, fileext = myext) # for temporary storage
if (usefixedlocation == TRUE) {
tdir <- system.file(package = "ANTsR") # for a fixed location
tfn <- paste(tdir, "/html/", fileid, myext, sep = "") # for a fixed location
}
if (!file.exists(tfn))
download.file(myurl, tfn)
if (fileid == "ADNI" | fileid == "K1") {
unzip(tfn)
return(tfn)
}
# could use md5sum
mymd5 <- switch(fileid, r16 = "37aaa33029410941bf4affff0479fa18", r64 = "8a629ee7ea32013c76af5b05f880b5c6",
KK = "397a773658558812e91c03bbb29334bb", BT = "eb1f8ee2bba81fb80fed77fb459600f0", AB = "d38b04c445772db6e4ef3d2f34787d67",
ch2 = "501c45361cf92dadd007bee55f02e053", ch2b = "5db6c10eb8aeabc663d10e010860465f", ch2a = "caf2d979a7d9c86f515a5bc447856e7c",
mnit = "dab456335a4bfa2b3bc31e9882699ee9")
if (!is.null(mymd5))
if (md5sum(tfn) != mymd5) {
print("checksum failure")
return(NULL)
}
return(tfn)
}
|
3430d7113a927cbb3161afbecf972cb4e5d2451a
|
21d326b9b3f7acc825ee422e58a20ab1bfc4ab57
|
/ui.R
|
51205d1927973bdb5222f3ffed5c4ebeafcd1d83
|
[] |
no_license
|
jiayi9/diagrammer
|
8a9c0d01886bb0e5fb642f6757f4fd8af61ca485
|
549935c04e23b2d356b13d7783406e379f1ccf87
|
refs/heads/master
| 2020-03-21T07:15:47.304190
| 2018-06-22T07:34:45
| 2018-06-22T07:34:45
| 138,270,521
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 182
|
r
|
ui.R
|
library(shiny)
shinyUI(fluidPage(
navbarPage("Analytics",
tabPanel("Example",
DiagrammeR::grVizOutput("CHART",height = 700)
)
)
))
|
7717f7bd52965fc0e76407c14506b2e2b982c3cf
|
6aed7b75c2bb33695908366e0faa14dfa42e5b35
|
/R/code/total_entropy_utility.R
|
646154d4c116cbeb323f3087f3eae573028886fd
|
[] |
no_license
|
ebonilla/sequential_design_for_predator_prey_experiments
|
e026f1e876302d2f37f6c4443a5fb1cef4271522
|
adc76644a203b922b6a586b0e0dafd393934ce6d
|
refs/heads/master
| 2022-07-11T11:46:56.192525
| 2020-05-13T04:07:33
| 2020-05-13T04:07:33
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,679
|
r
|
total_entropy_utility.R
|
#-----------------------------------------------------------------------------------------------#
# Script Name: total_entropy_utility #
# Author: Hayden Moffat #
# email: hayden.moffat@hdr.qut.edu.au #
# #
# This R script determines the next design point from the discrete design space which maximises #
# the total entropy utility. #
# # #
#-----------------------------------------------------------------------------------------------#
Nt <- Nmin:Nmax # possible designs
utility <- matrix(0, length(Nt), 1) # initialise utility values
for (j in 1:length(Nt)){
log_wsum = matrix(0, K, Nt[j]+1)
B <- matrix(0, K, Nt[j]+1)
for (mod in 1:K){
# Compute the log likelihood for each of the possible y values using the set of particles from model mod
parameters <- find_parameters(theta[,,mod], Nt[j], time, models[mod])
y <- 0:Nt[j]
llh <- log_lik(y, Nt[j], parameters[,1], parameters[,2], models[mod]) # Calculate the log likelihood of observing the datapoint [Nt(j), y]
log_w_hat <- log(W[,mod]) + llh # updated log unnormalised weights of particles if y was the new observation
log_wsum[mod,] = logsumexp(log_w_hat,1)
log_W_hat = sweep(log_w_hat, 2, log_wsum[mod,]) # updated log normalised weights
b = llh * exp(log_W_hat) # multiply log likelihood by normalised weights
b[is.nan(b)] <- 0
B[mod,] = colSums(b)
rm(parameters, y, llh, log_w_hat, log_W_hat)
}
log_Z_n = log_Z - logsumexp(log_Z,0) # normalised evidence
log_p_y = logsumexp(sweep(t(log_wsum), 2, log_Z_n, FUN = '+'), 2) # posterior predictive probabilities
log_p_y = as.matrix(log_p_y - logsumexp(log_p_y,0), ncol = 1) # normalised posterior predictive probabilities
# Determine the expected utility
utility[j] = exp(log_Z_n)%*% matrix(rowSums(exp(log_wsum) * B), ncol = 1) - (t(log_p_y) %*% exp(log_p_y))
rm(log_wsum, B, b, log_p_y, log_Z_n)
}
# Display the optimal design point
idx = which(utility == max(utility))
cat(paste0('Optimal design point at ', Nt[idx]))
data[i,1] <- Nt[idx]
|
80a8fd787dfd93ac3a41f33c7772cad69a765b8a
|
10c2c5d15f68e6b01aea824e8515083faee9d77c
|
/R/plot.PAM.R
|
9dca37d96373f6299118660a79ddf39d10a7d2b0
|
[] |
no_license
|
cran/PAMhm
|
0d285b078cb745a32e6a09355bba0af6ff9f8a56
|
af3755892dc8e20d5a98d41b653cd061495ee479
|
refs/heads/master
| 2023-07-27T13:12:33.167946
| 2021-09-06T06:50:02
| 2021-09-06T06:50:02
| 403,674,607
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,735
|
r
|
plot.PAM.R
|
#' @noRd
plot.PAM <-
function (clust, what, res.folder = ".", cols = "bwr", trim = NULL,
winsorize.mat = TRUE, autoadj = TRUE, pdf.width = 13,
pdf.height = 10, labelwidth = 0.6, labelheight = 0.25,
reorder = c(TRUE, TRUE), r.cex = 0.5, c.cex = 1, PDF = TRUE,
PNG = FALSE, main = NULL, file = main, shiny = FALSE)
{
if (autoadj) {
adj.l <- plotAdjust(clust$dat)
} else {
adj.l <- list(pdf.width = pdf.width, pdf.height = pdf.height, labelwidth = labelwidth,
labelheight = labelheight, r.cex = r.cex, c.cex = c.cex)
}
if (is.null(file)) {
filename.pam <- paste("PAM clustering of", what)
} else {
filename.pam <- file
}
if (shiny) {
make_heatmap(clust, what, cols = cols, trim = trim,
winsorize.mat = winsorize.mat, pdf.width = adj.l$pdf.width,
pdf.height = adj.l$pdf.height, labelwidth=adj.l$labelwidth,
labelheight=adj.l$labelheight, reorder=reorder,
r.cex=adj.l$r.cex, c.cex=adj.l$c.cex,
project.folder = res.folder, main=main)
} else {
if (PDF) {
pdf.name <- file.path(res.folder, paste(filename.pam, ".pdf", sep=""))
pdf(pdf.name, width=adj.l$pdf.width, height=adj.l$pdf.height)
invisible(make_heatmap(clust, what, cols = cols, trim = trim,
winsorize.mat = winsorize.mat,
labelwidth=adj.l$labelwidth,
labelheight=adj.l$labelheight, reorder=reorder,
r.cex=adj.l$r.cex, c.cex=adj.l$c.cex, main=main))
dev.off()
}
if (PNG) {
png.name <- file.path(res.folder, paste(filename.pam, ".png", sep=""))
png(png.name, width=adj.l$pdf.width, height=adj.l$pdf.height, units="in")
make_heatmap(clust, what, cols = cols, trim = trim,
winsorize.mat = winsorize.mat, pdf.width = adj.l$pdf.width,
pdf.height = adj.l$pdf.height, labelwidth=adj.l$labelwidth,
labelheight=adj.l$labelheight, reorder=reorder,
r.cex=adj.l$r.cex, c.cex=adj.l$c.cex,
project.folder = res.folder, PNG = TRUE, main=main)
if (all(unlist(plyr::llply(png.name, is.null)))) { png.name <- NULL }
if (!PDF) {
return(png.name)
}
}
if (!PDF && !PNG) {
make_heatmap(clust, what, cols = cols, trim = trim,
winsorize.mat = winsorize.mat, pdf.width = adj.l$pdf.width,
pdf.height = adj.l$pdf.height, labelwidth=adj.l$labelwidth,
labelheight=adj.l$labelheight, reorder=reorder,
r.cex=adj.l$r.cex, c.cex=adj.l$c.cex,
project.folder = res.folder, main=main)
return()
}
if (PDF) {
return(pdf.name)
} else {
return(NULL)
}
}
}
|
546068f9b11295f72bd23c5ccbabbbed6a78b617
|
91ae8c92263dacf2d0072d434e64171e6a77e143
|
/chapter 14.4.4.1 exercise.r
|
3f9b13de3721d208dbe6c11f1bdd9d9911f9bd87
|
[] |
no_license
|
lawrencekurniawan/r4ds
|
da41599a37a7fa5a1646aec8a0298d220faefaaa
|
bdcd898302c69ea774886ef5cf533763b2d419e2
|
refs/heads/master
| 2020-05-23T23:06:12.466441
| 2017-03-14T16:34:10
| 2017-03-14T16:34:10
| 84,798,748
| 0
| 1
| null | 2017-03-13T08:39:06
| 2017-03-13T07:53:58
| null |
UTF-8
|
R
| false
| false
| 750
|
r
|
chapter 14.4.4.1 exercise.r
|
library(tidyverse)
library(stringr)
#q1
regex <- "(the) ([A-z]+)" #replace (the) with (number) to find that there is 0 match
has_number <- sentences %>%
str_subset(regex) %>%
head(10)
str_view_all(has_number, regex)
#below is alternative with tibble to generate both the sentence and the match in the same tibble
tibble(sentence = sentences) %>%
tidyr::extract(sentence, c("word", "number"), regex, remove = FALSE)
#q2
regex2 <- "([A-z]+)\\'([A-z]*)"
tibble(sentence = sentences) %>%
tidyr::extract(
sentence, c("before", "after"), regex2, remove = FALSE
)
#the str_subset way
has_apostrophe <- str_subset(sentences, regex2)
str_view_all(has_apostrophe, regex2)
|
0715582457c5bb228d458bbb7c0cfc1724c5decc
|
21327fc1d5030fc4bb69e6505b66b471290fae5e
|
/overrideTestValue.R
|
e3b3c81658b3aecf493fae7c94e2cd72237418e7
|
[] |
no_license
|
sportebois/Rbash
|
995f6c6aac874cde109c7ae661d76cc49d005421
|
de809e643055f4856c5175bb44e71bdb4f4d53f3
|
refs/heads/master
| 2021-01-10T03:42:28.372645
| 2016-03-14T01:23:53
| 2016-03-14T01:23:53
| 53,816,427
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 105
|
r
|
overrideTestValue.R
|
#overrideTestValue.R
print("sourcing 'overideTest2Value.R'")
options(test2="defaultTest2InOverrideFile")
|
2011e6901bbabf2271f98a787171e3b7ca092f57
|
a0971d0f9a66d32318e2f1eb98ea2b4766288839
|
/man/projMat.Rd
|
43da5adfc86ed2de438577945a31e3370649241f
|
[] |
no_license
|
cran/infoDecompuTE
|
cd4ed6500b6fc3353cb87af1c63f3f400aca3e33
|
be77a310872650b7c2eba1bfb811d6f261b65d82
|
refs/heads/master
| 2020-06-06T21:24:21.145140
| 2020-03-28T08:10:02
| 2020-03-28T08:10:02
| 17,696,778
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 429
|
rd
|
projMat.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/projMat.R
\name{projMat}
\alias{projMat}
\title{Construct a Projection Matrix}
\usage{
projMat(X)
}
\arguments{
\item{X}{a square matrix.}
}
\value{
A square matrix.
}
\description{
Compute the projection matrix from a square matrix.
}
\examples{
m = matrix(1, nrow = 10, ncol = 3)
projMat(m)
}
\author{
Kevin Chang
}
|
9b69690039461a2f9c69877991f60a002fabba8f
|
7e06448831ee45fef970b6f705e08b1de01911e1
|
/Basic R_1/GraficosTarea.R
|
a6d35fd9f4a58163c34a56c8749a0cb3d31f3fc7
|
[] |
no_license
|
ejgarcia1991/UniversityProjects
|
e9a3e5cfbcbdfe06a5ff11ba4eb5f54daa7faeca
|
334aa97058c6f17a183ad48ab289e1bb22488060
|
refs/heads/main
| 2023-02-18T06:27:01.277050
| 2021-01-19T16:09:05
| 2021-01-19T16:09:05
| 330,946,626
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,260
|
r
|
GraficosTarea.R
|
#Ejercicio 1
#Mostrar graficamente la informacion correspondiente a summary(iris[1:4]).
#Pista: uso de boxplot
boxplot(iris[1:4])
#Ejercicio 2
#Rellenar una matriz nrow = 200, ncol = 4, con numero aleatorios cada
#columna, supongamos que son las tasas de exito en clasificacion
#correspondiente a 4 algoritmos distintos. Pintar una grafica con las curvas de cada,
#identificando cada uno de los algoritmos con su leyenda.
m<- matrix(sample(1:10000,800),200,4)
opar <- par(no.readonly = TRUE)
plot(m[,1],type="l",col="red")
lines(m[,2],col="blue")
lines(m[,3],col="brown")
lines(m[,4])
legend(x=160, y=10000,legend=c("alg1","alg2","alg3","alg4"), lty=c(1,2,3,4), col=c("red", "blue","brown","black"))
#Ejercicio 3
#Ejecuta las siguientes instrucciones: library(MASS); str(quine); xtabs(~ Age,data=quine);
#prop.table(xtabs(~ Age,data=quine)) Haz un grafico compuesto, con
#dos graficas de barras correspondientes a xtabs y prop.table, la frecuencia
#absoluta y frecuencia relativa de las edades.
library(MASS)
str(quine)
d1<-xtabs(~ Age,data=quine)
d2<-prop.table(xtabs(~ Age,data=quine))
opar<-par(no.readonly=TRUE)
par(mfrow = c(1,2))
barplot(xtabs(~ Age,data=quine),main="Frecuencia absoluta")
barplot(prop.table(xtabs(~ Age,data=quine)), main="Frecuencia relativa")
par(opar)
#Ejercicio 4
#Representa la misma informacion anterior mediante graficas tipo pie y
#dotchart con tıtulo. En pie, fija colores y sentido horario.
library(MASS)
str(quine)
d1<-xtabs(~ Age,data=quine)
d2<-prop.table(xtabs(~ Age,data=quine))
opar<-par(no.readonly=TRUE)
par(mfrow = c(2,2))
dotchart(d1,main="Frecuencia absoluta")
dotchart(d2, main="Frecuencia relativa")
pie(d1,clockwise = TRUE,col = c("red","blue","brown","black"),main="Frecuencia absoluta")
pie(d2,clockwise = TRUE,col = c("red","blue","brown","black"), main="Frecuencia relativa")
par(opar)
#Ejercicio 5
#Sea un dataset cars, representar los puntos dist vs speed, esto es, el
#atributo dist en ordenadas. Sea m, m = lm(speed~dist, data=cars) el
#resultado de aplicar un ajuste mediante regresion lineal. El valor resultado
#es una recta en forma pinta la lınea de ajuste del modelo m, en rojo. Pista: abline.
library(CARS)
m<-lm(speed~dist,data=cars)
plot(cars$dist,cars$speed)
abline(m,col="red")
read_
|
14a40675214aa9e86b11ede969d57ec20a3606cc
|
9ad4b4acb8bd2b54fd7b82526df75c595bc614f7
|
/Plot/PlotEloRD_PBMC_Umap.R
|
9ae7d567452dae904210d767b9f3ae3bbc111480
|
[] |
no_license
|
sylvia-science/Ghobrial_EloRD
|
f27d2ff20bb5bbb90aa6c3a1d789c625540fbc42
|
041da78479433ab73335b09ed69bfdf6982e7acc
|
refs/heads/master
| 2023-03-31T14:46:27.999296
| 2021-04-02T15:09:49
| 2021-04-02T15:09:49
| 301,811,184
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 8,632
|
r
|
PlotEloRD_PBMC_Umap.R
|
library(wesanderson)
library(RColorBrewer)
library(scico)
library(pals)
library(nord)
library(palettetown)
# Plot EloRD_PBMC_noNPBMC Umaps
folder = paste0('/home/sujwary/Desktop/scRNA/Output/Harmony/Batch_Sample_Kit/Cluster/PCA30/res3/Plots/Paper/EloRD_PBMC/')
data_harmony_run_label_remove = data_harmony_run_label_remove[, !(Idents(data_harmony_run_label_remove) == 'T-cell' &
data_harmony_run_label_remove@reductions[["umap"]]@cell.embeddings[,2] < 2.5)]
data_harmony_run_label_remove = data_harmony_run_label_remove[, !(Idents(data_harmony_run_label_remove) == 'CD8+ T-cell' &
data_harmony_run_label_remove@reductions[["umap"]]@cell.embeddings[,2] < 2.5)]
data_harmony_run_label_remove = data_harmony_run_label_remove[, !(Idents(data_harmony_run_label_remove) == 'Plasma cell' &
data_harmony_run_label_remove@reductions[["umap"]]@cell.embeddings[,1] > -5)]
plot = DimPlot(data_harmony_run_label_remove,pt.size = 0.7, reduction = "umap",label = TRUE,label.size = 3)
print(plot)
num_cluster = length(unique(Idents(data_harmony_run_label_remove)))
values = colorRampPalette(brewer.pal(12, "Accent"))(num_cluster)
values = colorRampPalette(wes_palette("Zissou1"))(num_cluster)
values = scico(num_cluster, palette = 'roma')
values = as.vector(ocean.delta(num_cluster))
values = rainbow(num_cluster) # Bright but not horrible
values = colorRampPalette(brewer.pal(11, "Paired"))(num_cluster) # Nice pastel feel
values = colorRampPalette(brewer.pal(8, "Dark2"))(num_cluster) # Too spooky
str = '_nolabel'
str = ''
labelTF = T
values = hsv(seq(0, 1 - 1/num_cluster,length.out = num_cluster), .8, .85)
folder = paste0('/home/sujwary/Desktop/scRNA/Output/Harmony/Batch_Sample_Kit/Cluster/PCA30/res3/Plots/Paper/EloRD_PBMC/')
dir.create(folder, recursive = T)
pathName <- paste0(folder,'ClusterUmapAll_PBMC_','hsv',str,'.pdf')
pdf(file=pathName, width = 8,height = 6)
fontSize = 12
plot = DimPlot(data_harmony_run_label_remove,pt.size = 0.7, reduction = "umap",label = labelTF,
cols =values, shape.by = NULL)
plot = plot + theme(
legend.title = element_text( size = fontSize),
legend.text = element_text( size = fontSize))
plot = plot +theme(axis.text=element_text(size=fontSize),
axis.title=element_text(size=fontSize,face="bold"))
print(plot)
dev.off()
values = colorRampPalette(brewer.pal(11, "Paired"))(num_cluster) # Nice pastel feel
pathName <- paste0(folder,'ClusterUmapAll_PBMC_','Paired',str,'.pdf')
pdf(file=pathName, width = 8,height = 6)
fontSize = 12
plot = DimPlot(data_harmony_run_label_remove,pt.size = 0.7, reduction = "umap",label = labelTF,
cols =values,shape.by = NULL, raster = F)
plot = plot + theme(
legend.title = element_text( size = fontSize),
legend.text = element_text( size = fontSize))
plot = plot +theme(axis.text=element_text(size=fontSize),
axis.title=element_text(size=fontSize,face="bold"))
print(plot)
dev.off()
###############
# T Cell
###############
str = '_nolabel'
str = ''
labelTF = T
for (i in 1:40){
pokedex(10*i, 10)
}
Ident_order = c('Naive CD8+ T-cell', 'Naive CD4+ T-cell','IFN+ CD4+ T-cell','TSCM','Stim Naive CD4+ T-cell',
'cTreg','eTreg','CD4+ TCM','TRM','Th2','Th17','aTh17','CCL5+ CD4+ T-cell',
'CD8+ TCM','GZMK+ CD8+ T-cell','GZMK+ CCL3+ CCL4+ CD8+ T-cell','GZMH+ GZMB+ CD8+ T-cell','TEMRA')
all(Ident_order %in% unique(Idents(data_run_subset_label) ))
data_idents = unique(Idents(data_run_subset_label))
Ident_order[!(Ident_order %in%data_idents )]
data_idents[!(data_idents %in%Ident_order )]
data_run_subset_label_remove = data_run_subset_label[,Idents(data_run_subset_label) %in% Ident_order]
plot = DimPlot(data_run_subset_label_remove,pt.size = 0.7, reduction = "umap",label = TRUE,label.size = 3)
print(plot)
Idents(data_run_subset_label_remove) = factor(as.character(Idents(data_run_subset_label_remove)),Ident_order)
num_cluster = length(unique(Idents(data_run_subset_label_remove)))
color1 = 'kingdra'
values1 = colorRampPalette(ichooseyou(pokemon = color1, spread = NULL))(6)
color2 = 'mewtwo'
values2 = colorRampPalette(ichooseyou(pokemon = color2, spread = NULL))(6)
values3 = colorRampPalette(ichooseyou(pokemon = 'magcargo', spread = NULL))(5)
values = c(values1,values2,values3)
pathName <- paste0(folder,'ClusterUmap_Tcell_',color1,'_',color2,str,'.pdf')
pdf(file=pathName, width = 8,height = 8)
fontSize = 8
plot = DimPlot(data_run_subset_label_remove,pt.size = 0.5, reduction = "umap",label = labelTF,
cols =values)
plot = plot + theme(
legend.title = element_text( size = fontSize),
legend.text = element_text( size = fontSize))
plot = plot +theme(axis.text=element_text(size=fontSize),
axis.title=element_text(size=fontSize,face="bold"))
print(plot)
dev.off()
################################
### Monocytes
#################################
str = '_nolabel'
str = ''
labelTF = T
Ident_order = c('cDC1','cDC2', 'sDC',
'SELL+ CD14+ Mono','sMono','TGFb1+ CD14+ Mono',
'IFN+ Mono','CD14+ CD16+ Mono','CD16+ Mono')
all(Ident_order %in% unique(Idents(data_run_subset_label) ))
data_idents = unique(Idents(data_run_subset_label))
Ident_order[!(Ident_order %in%data_idents )]
data_idents[!(data_idents %in%Ident_order )]
Ident_order[!(Ident_order %in% unique(Idents(data_run_subset_label_remove) ))]
data_run_subset_label_remove = data_run_subset_label[,Idents(data_run_subset_label) %in% Ident_order]
plot = DimPlot(data_run_subset_label_remove,pt.size = 0.7, reduction = "umap",label = TRUE,label.size = 3)
print(plot)
#tmp = unique(Idents(data_run_subset_label_remove))[3]
Idents(data_run_subset_label_remove) = factor(as.character(Idents(data_run_subset_label_remove)),Ident_order)
num_cluster = length(unique(Idents(data_run_subset_label_remove)))
color1 = 'venonat'
values1 = colorRampPalette(ichooseyou(pokemon = color1, spread = NULL))(3)
color2 = 'girafarig'
values2 = colorRampPalette(ichooseyou(pokemon = color2, spread = NULL))(6)
#values2 = values2[!(values2 %in% c('#F8F8F8'))]
values = c(values1,values2)
#color1 = 'magcargo'
#values1 = colorRampPalette(ichooseyou(pokemon = color1, spread = NULL))(num_cluster)
#values = values1
pathName <- paste0(folder,'ClusterUmap_Mono_',color1,'_',color2,str,'.pdf')
pdf(file=pathName, width = 8,height = 6)
fontSize = 12
plot = DimPlot(data_run_subset_label_remove,pt.size = 0.5, reduction = "umap",label = labelTF,
cols =values)
plot = plot + theme(
legend.title = element_text( size = fontSize),
legend.text = element_text( size = fontSize))
plot = plot +theme(axis.text=element_text(size=fontSize),
axis.title=element_text(size=fontSize,face="bold"))
print(plot)
dev.off()
#########################
## NK
#########################
str = '_EloRD_PBMC'
labelTF = T
Ident_order = c('CD56bright','aCCL3+ CD56dim','NFkB-high','cCD56dim','Tgd')
all(Ident_order %in% unique(Idents(data_run_subset_label) ))
data_idents = unique(Idents(data_run_subset_label))
Ident_order[!(Ident_order %in%data_idents )]
data_idents[!(data_idents %in%Ident_order )]
Ident_order[!(Ident_order %in% unique(Idents(data_run_subset_label_remove) ))]
data_run_subset_label_remove = data_run_subset_label[,Idents(data_run_subset_label) %in% Ident_order]
plot = DimPlot(data_run_subset_label_remove,pt.size = 0.7, reduction = "umap",label = TRUE,label.size = 3)
print(plot)
#tmp = unique(Idents(data_run_subset_label_remove))[3]
Idents(data_run_subset_label_remove) = factor(as.character(Idents(data_run_subset_label_remove)),Ident_order)
num_cluster = length(unique(Idents(data_run_subset_label_remove)))
color1 = 'magmar'
values1 = colorRampPalette(ichooseyou(pokemon = color1, spread = NULL))(5)
values = c(values1)
#color1 = 'magcargo'
#values1 = colorRampPalette(ichooseyou(pokemon = color1, spread = NULL))(num_cluster)
#values = values1
pathName <- paste0(folder,'ClusterUmap_NK_',color1,str,'.pdf')
pdf(file=pathName, width = 8,height = 6)
fontSize = 12
plot = DimPlot(data_run_subset_label_remove,pt.size = 0.5, reduction = "umap",label = labelTF,
cols =values)
plot = plot + theme(
legend.title = element_text( size = fontSize),
legend.text = element_text( size = fontSize))
plot = plot +theme(axis.text=element_text(size=fontSize),
axis.title=element_text(size=fontSize,face="bold"))
print(plot)
dev.off()
|
746cd6094d78acef6684c00019f79bdc80982b88
|
ebfa2d15c93474e0118f8bec9987d135c45bf42d
|
/format_process_data.R
|
a3d03d6f6af9ed5e3cf22b0d10df8441ec145834
|
[] |
no_license
|
daviinada/dashboard_precipitacao
|
983ce5aa48203fd5b3d746e6f041e62626750097
|
bc76e174e0fb13efd1698550a51a18e11ea08461
|
refs/heads/master
| 2020-03-16T04:00:13.074074
| 2018-05-08T12:59:22
| 2018-05-08T12:59:22
| 132,500,676
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,686
|
r
|
format_process_data.R
|
library(ggplot2)
library(dplyr)
library(tidyr)
library(readxl)
library(lubridate)
library(plotly)
library(heatmaply)
library(qgraph)
library(highcharter)
library(dygraphs)
library(d3heatmap)
setwd('/home/toshi/Desktop/work_butanta/dashboard_precipitacao/')
raw_data <- read_excel(path = 'tabela_precipitacao_tome_acu.xlsx')
raw_data <- as.data.frame(raw_data)
head(raw_data)
firstup <- function(x) {
substr(x, 1, 1) <- toupper(substr(x, 1, 1))
x
}
raw_data_gather <- raw_data %>%
gather(key= month, value= precipitation_mm, -day, -year) %>%
mutate(month_numeric = match(firstup(month), month.abb),
date = ymd(paste(year, month_numeric, day, sep = '-')))
str(raw_data_gather)
# Cleaning miss values
raw_data_gather$precipitation_mm[ which(raw_data_gather$precipitation_mm == '0') ] <- NA
raw_data_gather$precipitation_mm[ which(raw_data_gather$precipitation_mm == '-') ] <- NA
raw_data_gather$precipitation_mm <- as.numeric(raw_data_gather$precipitation_mm)
# Formating data
raw_data_gather$day <- as.factor(raw_data_gather$day)
raw_data_gather$month <- as.factor(raw_data_gather$month)
raw_data_gather$year <- as.factor(raw_data_gather$year)
head(raw_data_gather)
order_month <- unique(as.character(raw_data_gather$month))
# Remover linhas com NA na data (nao existem por conta do mes)
raw_data_gather <- raw_data_gather[ -which(is.na(raw_data_gather$date)), ]
# Mean precipitation per months per rain days
preciptation_per_month_per_rain_days <- raw_data_gather %>%
group_by(year, month) %>%
summarise(days_rain_month = sum(!is.na(precipitation_mm)),
mean_precip_in_rain_days = sum(precipitation_mm, na.rm = TRUE)/ sum(!is.na(precipitation_mm)))
# Dias de chuva por mes
preciptation_per_month_per_rain_days %>%
ggplot(aes(x=factor(month, levels = order_month), y=days_rain_month, col=year, group=year )) +
geom_line() +
geom_point()
preciptation_per_month_per_rain_days %>%
ggplot(aes(x=factor(month, levels = order_month), y=days_rain_month, col=year, group=year )) +
geom_line() +
geom_point() +
facet_wrap(~year)
# Precipitacao media por mes
raw_data_gather %>%
group_by(year, month) %>%
summarise(mean_precip = mean(precipitation_mm, na.rm = TRUE)) %>%
ggplot(aes(x=factor(month, levels = order_month), y=mean_precip, col=year, group=year )) +
geom_line() +
geom_point()
# Precipitacao toal por mes
raw_data_gather %>%
group_by(year, month) %>%
summarise(total_precip = sum(precipitation_mm, na.rm = TRUE)) %>%
ggplot(aes(x=factor(month, levels = order_month), y=total_precip, col=year, group=year )) +
geom_line() +
geom_point()
# Precipitacao toal por mes
raw_data_gather %>%
group_by(year, month) %>%
summarise(total_precip = sum(precipitation_mm, na.rm = TRUE)) %>%
ggplot(aes(x=factor(month, levels = order_month), y=total_precip, col=year, group=year )) +
geom_line() +
geom_point() +
facet_wrap(~year)
# boxplot precipitacao mensao ao logo de todos os anos
raw_data_gather %>%
ggplot(aes(x=factor(month, levels = order_month), y=precipitation_mm, fill=year)) +
geom_boxplot()
# boxplot precipitacao mensal geral
raw_data_gather %>%
ggplot(aes(x=factor(month, levels = order_month), y=precipitation_mm, fill=month)) +
geom_boxplot()
hclust_df <- preciptation_per_month_per_rain_days %>%
mutate(var = paste(year, month, sep = '_')) %>%
ungroup() %>%
select(days_rain_month, mean_precip_in_rain_days, var) %>%
as.data.frame()
rownames(hclust_df) <- hclust_df$var
hclust_df <- hclust_df[ ,c(1,2)]
boxplot(hclust_df[ ,1])
boxplot(hclust_df[ ,2]) # Remover o outlier, esta causando problema!
hclust_df <- subset(hclust_df, hclust_df[,2] < max(hclust_df[,2]))
# scale data to mean=0, sd=1 and convert to matrix
hclust_df_scaled <- as.matrix(scale(hclust_df))
heatmaply(t(hclust_df_scaled), k_col = 10 )
d3heatmap(hclust_df, scale="column", colors="Blues")
library(dygraphs)
library(xts)
df_dygraph <- preciptation_per_month_per_rain_days %>%
ungroup() %>%
select(year, month, mean_precip_in_rain_days) %>%
spread(year, mean_precip_in_rain_days)
# df_dygraph <- as.data.frame(df_dygraph)
# df_dygraph <- df_dygraph[ ,-1]
library("viridisLite")
cols <- viridis(3)
cols <- substr(cols, 0, 7)
m_order <- c(unique(as.character(raw_data_gather$month)))
# sorting month in correct order
df_dygraph <- df_dygraph %>%
mutate(month = factor(month, levels = m_order)) %>%
arrange(factor(month, levels = m_order) )
hc <- highchart() %>%
hc_xAxis(categories = df_dygraph$month) %>%
hc_add_series(name = "2001", data = df_dygraph$`2001`) %>%
hc_add_series(name = "2002", data = df_dygraph$`2002`) %>%
hc_add_series(name = "2003", data = df_dygraph$`2003`) %>%
hc_add_series(name = "2004", data = df_dygraph$`2004`) %>%
hc_add_series(name = "2005", data = df_dygraph$`2005`) %>%
hc_add_series(name = "2006", data = df_dygraph$`2006`) %>%
hc_add_series(name = "2007", data = df_dygraph$`2007`) %>%
hc_add_series(name = "2008", data = df_dygraph$`2008`) %>%
hc_add_series(name = "2009", data = df_dygraph$`2009`) %>%
hc_add_series(name = "2010", data = df_dygraph$`2010`) %>%
hc_add_series(name = "2011", data = df_dygraph$`2011`) %>%
hc_add_series(name = "2012", data = df_dygraph$`2012`) %>%
hc_add_series(name = "2013", data = df_dygraph$`2013`) %>%
hc_add_series(name = "2014", data = df_dygraph$`2014`) %>%
hc_add_series(name = "2015", data = df_dygraph$`2015`) %>%
hc_add_series(name = "2016", data = df_dygraph$`2016`)
hc
str(citytemp)
|
bbd8507ac796c5acaf468b76c7965a86c54fe35f
|
8062b05ae60f135948d94eb8576845cfd49872a4
|
/man/fuzzyBHexact.Rd
|
acafa2ced76b768f0c8e23122e17f959615fbdee
|
[] |
no_license
|
cran/fuzzyFDR
|
4c40bf07e4085af2e4ca348f7658186138bc8768
|
20eea638d935915d825bd6245dc2514de950c546
|
refs/heads/master
| 2016-09-11T04:03:10.368045
| 2007-10-16T00:00:00
| 2007-10-16T00:00:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,474
|
rd
|
fuzzyBHexact.Rd
|
\name{fuzzyBHexact}
\alias{fuzzyBHexact}
\title{Exact calculation of fuzzy decision rules (Benjamini and Hochberg
FDR)}
\description{
Exact calculation of fuzzy decision rules for multiple
testing. Controls the FDR (false discovery rate) using the
Benjamini and Hochberg method.
}
\usage{
fuzzyBHexact(pvals, pprev, alpha = 0.05, tol = 1e-05, q.myuni = T, dp = 20)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{pvals}{ observed discrete p-values}
\item{pprev}{ previously attainable p-values under the null distribution}
\item{alpha}{ significance level of the FDR procedure}
\item{tol}{ tolerance for my.match and my.unique}
\item{q.myuni}{ logical. Use my.match instead of match?}
\item{dp}{ no. decimal places to round p-values to}
}
\details{
my.match and my.unique may be used instead of match and unique
if there is a problem with calculating the unique set of p-values
(sometimes a problem with very small p-values)
}
\value{
Data frame containing the p-values and previously attainable p-values
input to the function, and the tau (fuzzy decision rule) output. Also
contains the minimum and maximum ranks over allocations for each p-value.
}
\references{ Kulinsakaya and Lewin (2007).}
\author{ Alex Lewin }
\examples{
data(example1)
names(example1)
fuzzyBHexact(example1$pvals,example1$pprev,alpha=0.05)
data(example2)
names(example2)
fuzzyBHexact(example2$pvals,example2$pprev,alpha=0.05)
}
\keyword{ htest }
|
8ce35dc5be702f7dba6d17b2eec7b9c42425e08e
|
4bd7d7f4ea836ea8ff61f6ca66df766cb9ac831d
|
/Derby Horse Sentiment Analysis.R
|
bb690ddf6ddf934c6c8c5f67d50200d613df145e
|
[] |
no_license
|
nick-holt/kentuckyderby144
|
7c26e6728eff9b5e42196f1e1fb57e5b5d45fd10
|
53389279b7c02328720c0f0cb28f530f3e6817f8
|
refs/heads/master
| 2020-03-15T12:29:26.910759
| 2018-05-04T13:54:42
| 2018-05-04T13:54:42
| 132,145,309
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,428
|
r
|
Derby Horse Sentiment Analysis.R
|
# 2018 Kentucky Derby (144th) - Social Listening Analytics Visualization Demo
# libraries
library(tidyverse)
# read in data
derby <- read_csv("derby_book_2018.csv")
colnames(derby) <- tolower(str_replace_all(colnames(derby), " ", "_"))
colnames(derby) <- str_replace_all(colnames(derby), "_tweets", "")
# reorder horse factor by post position
derby$horse <- factor(derby$horse, levels = c(derby$horse))
# gather tweet volume data into long format and capitalize sentiment tags
derby_clean <- derby %>%
gather(positive, negative, neutral, key = "sentiment", value = "tweet_count") %>%
mutate(sentiment = str_to_title(sentiment))
# x axis helper functions from: https://jcarroll.com.au/2016/06/03/images-as-x-axis-labels-updated/
library(cowplot)
# convert spaces in horse names into line breaks
addline_format <- function(x,...){
gsub('\\s','\n',x)
}
# plot tweet volume and sentiment by horse (arranged by post position) with coord flip (tall plot)
plot <- ggplot(arrange(derby_clean), aes(x = horse, y = tweet_count, fill = factor(sentiment, levels = c("Positive", "Neutral", "Negative")))) +
geom_bar(stat = "identity", width=0.9) +
scale_fill_manual(values = c("#4D7B18", "#C5B4A0", "#B6121B")) +
theme_classic() +
coord_flip()+ # tall plot
xlab("Tweet Volume\n") +
guides(fill = guide_legend(title = "Sentiment")) +
theme(axis.text.y = element_text(angle = 0, size = 8, face = "bold"),
legend.position = c(.93, .8),
axis.line=element_blank(),
axis.ticks = element_blank(),
axis.text.x=element_blank(),
axis.title = element_blank()) +
geom_text(aes(horse, tweets_in_last_week, label = current_odds, fill = NULL, face = "bold"), data = derby, position = position_dodge(width = .9), hjust = -.5) +
ylab("\nHorse") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 3250)) #+
#scale_x_discrete(breaks=unique(derby$horse),
#labels=addline_format(unique(derby$horse)))
# read in images for x axis
library(magick)
pimage <- axis_canvas(plot, axis = 'y') +
draw_image("Firenze Fire.jpg", y = 0, x = 0.5, scale = 1) +
draw_image("Free Drop Billy.jpg", y = 1.5, scale = 1) +
draw_image("Promises Fulfilled.jpg", y = 2.5, scale = 1) +
draw_image("Flameaway.jpg", y = 3.5, scale = 1) +
draw_image("Audible.jpg", y = 4.5, scale = 1) +
draw_image("Good Magic.jpg", y = 5.5, scale = 1) +
draw_image("Justify.jpg", y = 6.5, scale = 1) +
draw_image("Lone Sailor.jpg", y = 7.5, scale = 1) +
draw_image("Hofburg.jpg", y = 8.5, scale = 1) +
draw_image("My Boy Jack.jpg", y = 9.5, scale = 1) +
draw_image("Bolt d'oro.jpg", y = 10.5, scale = 1) +
draw_image("Enticed.jpg", y = 11.5, scale = 1) +
draw_image("Bravazo.jpg", y = 12.5, scale = 1) +
draw_image("Mendelssohn.jpg", y = 13.5, scale = 1) +
draw_image("Instilled Regard.jpg", y = 14.5, scale = 1) +
draw_image("Magnum Moon.jpg", y = 15.5, scale = 1) +
draw_image("Solomini.jpg", y = 16.5, scale = 1) +
draw_image("Vino Rosso.jpg", y = 17.5, scale = 1) +
draw_image("Noble Indy.jpg", y = 18.5, scale = 1) +
draw_image("Combatant.jpg", y = 19.5, scale = 1) +
draw_image("Blended Citizen.jpg", y = 20.5, scale = 1)
# overlay jockey silk images
ggdraw(insert_yaxis_grob(plot, pimage, position = "left"))
# wide plot
plot <- ggplot(arrange(derby_clean), aes(x = horse, y = tweet_count, fill = factor(sentiment, levels = c("Positive", "Neutral", "Negative")))) +
geom_bar(stat = "identity", width=0.9) +
scale_fill_manual(values = c("#4D7B18", "#C5B4A0", "#B6121B")) +
theme_classic() +
ylab("Tweet Volume\n") +
guides(fill = guide_legend(title = "Tweet\nSentiment")) +
theme(axis.text.x = element_text(angle = 0, size = 6, face = "bold"),
legend.position = c(.93, .8),
legend.text = element_text(size = 8),
legend.title = element_text(size = 8),
axis.title.y = element_text(size = 8),
axis.line=element_blank(),
axis.ticks = element_blank(),
axis.text.y=element_blank(),
axis.title.x = element_blank()) +
geom_text(aes(horse, tweets_in_last_week, label = current_odds, fill = NULL), data = derby, position = position_dodge(width = .9), vjust = -.5) +
xlab("\nHorse") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 3250)) +
scale_x_discrete(breaks=unique(derby$horse),
labels=addline_format(unique(derby$horse)))
pimage <- axis_canvas(plot, axis = 'x') +
draw_image("Firenze Fire.jpg", x = 0.5, y = 0, scale = 0.9) +
draw_image("Free Drop Billy.jpg", x = 1.5, scale = 0.9) +
draw_image("Promises Fulfilled.jpg", x = 2.5, scale = 0.9) +
draw_image("Flameaway.jpg", x = 3.5, scale = 0.9) +
draw_image("Audible.jpg", x = 4.5, scale = 0.9) +
draw_image("Good Magic.jpg", x = 5.5, scale = 0.9) +
draw_image("Justify.jpg", x = 6.5, scale = 0.9) +
draw_image("Lone Sailor.jpg", x = 7.5, scale = 0.9) +
draw_image("Hofburg.jpg", x = 8.5, scale = 0.9) +
draw_image("My Boy Jack.jpg", x = 9.5, scale = 0.9) +
draw_image("Bolt d'oro.jpg", x = 10.5, scale = 0.9) +
draw_image("Enticed.jpg", x = 11.5, scale = 0.9) +
draw_image("Bravazo.jpg", x = 12.5, scale = 0.9) +
draw_image("Mendelssohn.jpg", x = 13.5, scale = 0.9) +
draw_image("Instilled Regard.jpg", x = 14.5, scale = 0.9) +
draw_image("Magnum Moon.jpg", x = 15.5, scale = 0.9) +
draw_image("Solomini.jpg", x = 16.5, scale = 0.9) +
draw_image("Vino Rosso.jpg", x = 17.5, scale = 0.9) +
draw_image("Noble Indy.jpg", x = 18.5, scale = 0.9) +
draw_image("Combatant.jpg", x = 19.5, scale = 0.9) +
draw_image("Blended Citizen.jpg", x = 20.5, scale = 0.9)
png(file="HorsePlot.png",width=3600,height=1200,res=300)
ggdraw(insert_xaxis_grob(plot, pimage, position = "bottom"))
dev.off()
|
e86eb252fe5c4851fa4106e088f5061728e415c1
|
6bd5b97104280c2f2d80fd233923dcd8cb4b2304
|
/run_intcode.R
|
c21d9fa6efb60865e9856baee3d897bf4480b3a8
|
[] |
no_license
|
cettt/Advent_of_Code2019
|
0982fce90118faed23d212beae9dfe304cfde6e0
|
2d467a0120624fc37b721581286a8c3486c9ab51
|
refs/heads/master
| 2023-02-15T12:42:13.475154
| 2021-01-03T14:46:44
| 2021-01-03T20:20:09
| 326,495,655
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,474
|
r
|
run_intcode.R
|
#this is the base function for running intcode and can be usesd starting on Day 9
# z is the intcode programm (usually the problem input)
# input_fun is a function which generates an based on previous outputs
run_intcode <- function(z, input_fun = function(output) 1, o_fun = NULL, j = 1L,
base = 0L, ...) {
cls <- function(x) if (!is.na(x)) x else 0L# own coalesce function
find_para_val <- function(para_id, j) { #para is either 1 or 2.
.inst <- floor(z[j] / 100) #this is the instruction without the opcode
mode <- floor((.inst %% 10^(para_id)) / 10^(para_id - 1))
.base <- if (mode == 2) base else 0
if (mode == 1) return(cls(z[j + para_id])) #immediate mode
else return(cls(z[cls(z[j + para_id]) + 1L + .base])) #postion mode resp. relative mode
}
output <- NULL
run <- TRUE
while (run) {
opcode <- z[j] %% 100L #first two digits contain the opcode
if (!opcode %in% c(3L, 99L)) {
para1 <- find_para_val(1L, j)
if (!opcode %in% c(4L, 9L)) para2 <- find_para_val(2L, j)
}
dummy_base <- if (z[j] >= 2e4) base else 0L #check if output is if in relative mode
if (opcode == 1L) { #addition, multiplication
z[z[j + 3L] + 1L + dummy_base] <- para1 + para2
j <- j + 4L
}
else if (opcode == 2L) { #addition, multiplication
z[z[j + 3L] + 1L + dummy_base] <- para1 * para2
j <- j + 4L
}
else if (opcode == 3L) { #input
dummy_base <- if (z[j] %% 1e3L >= 200L) base else 0L
inp <- input_fun(output, ...)
if (!is.null(inp)) {
z[z[j + 1L] + 1L + dummy_base] <- inp
j <- j + 2L
} else {
return(list(intcode = z, j = j, base = base, output = output))
}
}
else if (opcode == 4L) { #output
output <- c(output, para1)
j <- j + 2L
}
else if (opcode == 5L) { #jump if true/false
j <- if (para1 != 0L) para2 + 1L else j + 3L
}
else if (opcode == 6L) { #jump if true/false
j <- if (para1 == 0L) para2 + 1L else j + 3L
}
else if (opcode == 7L) { #less than
z[z[j + 3L] + 1L + dummy_base] <- (para1 < para2)
j <- j + 4L
}
else if (opcode == 8L) { #less than
z[z[j + 3L] + 1L + dummy_base] <- (para1 == para2)
j <- j + 4L
}
else if (opcode == 9L) { #rebase
base <- base + para1
j <- j + 2L
}
else if (opcode == 99L) break # stop
}
if (is.null(o_fun)) output else o_fun(output)
}
|
7acff1dd2235cca33c15cd50a06829df15102bcc
|
2bec5a52ce1fb3266e72f8fbeb5226b025584a16
|
/lsbclust/man/summary.int.lsbclust.Rd
|
8cfdfa49bfce8ea574dbaa58f5be4cb8425c8de8
|
[] |
no_license
|
akhikolla/InformationHouse
|
4e45b11df18dee47519e917fcf0a869a77661fce
|
c0daab1e3f2827fd08aa5c31127fadae3f001948
|
refs/heads/master
| 2023-02-12T19:00:20.752555
| 2020-12-31T20:59:23
| 2020-12-31T20:59:23
| 325,589,503
| 9
| 2
| null | null | null | null |
UTF-8
|
R
| false
| true
| 511
|
rd
|
summary.int.lsbclust.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary.int.lsbclust.R
\name{summary.int.lsbclust}
\alias{summary.int.lsbclust}
\title{Summary Method for Class "int.lsbclust"}
\usage{
\method{summary}{int.lsbclust}(object, digits = 3, ...)
}
\arguments{
\item{object}{An object of class 'int.lsbclust'.}
\item{digits}{The number of digits in the printed output.}
\item{\dots}{Unimplemented.}
}
\description{
Some goodness-of-fit diagnostics are provided for all three margins.
}
|
0b5c8275ddfbc9452d0b6391f769dfe9323ab493
|
3e5abd06979afc7b8da873831279ea6739b37961
|
/man/report_get.Rd
|
7a65e1bd57b51941a1089803c9f0bfe1408774bf
|
[] |
no_license
|
isabella232/crowdflower
|
f0f704669cc29d7713ee94687b260b1f88ec04c5
|
6d545e8d2106fdaf3b1c44ac5bfcfc0f9ec89a1b
|
refs/heads/master
| 2022-04-11T02:24:55.272712
| 2017-08-28T16:12:25
| 2017-08-28T16:12:25
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,537
|
rd
|
report_get.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/report_get.R
\name{report_get}
\alias{report_get}
\alias{report_regenerate}
\title{Generate and retrieve job results}
\usage{
report_get(id, report_type = c("full", "aggregated", "json", "gold_report",
"workset", "source"), csv_args = list(stringsAsFactors = FALSE, check.names
= FALSE), verbose = TRUE, ...)
report_regenerate(id, report_type = c("full", "aggregated", "json",
"gold_report", "workset", "source"), verbose = TRUE, ...)
}
\arguments{
\item{id}{A character string containing an ID for job.}
\item{report_type}{Type of report}
\item{csv_args}{A list of arguments passed to \code{\link[utils]{read.csv}} when \code{report_type = 'source'}.}
\item{verbose}{A logical indicating whether to print additional information about the request.}
\item{...}{Additional arguments passed to \code{\link{cf_query}}.}
}
\value{
If \code{report_type = 'json'}, a list. Otherwise a data.frame.
}
\description{
Results
}
\details{
\code{report_get} regenerates one of six types of reports within a job. Here is how they are described by Crowdflower:
\itemize{
\item \code{full}: Returns the Full report containing every judgment.
\item \code{aggregated}: Returns the Aggregated report containing the aggregated response for each row.
\item \code{json}: Returns the JSON report containing the aggregated response, as well as the individual judgments, for the first 50 rows.
\item \code{gold_report}: Returns the Test Question report.
\item \code{workset}: Returns the Contributor report.
\item \code{source}: Returns a CSV of the source data uploaded to the job.
}
Where possible, the package tries to return a data.frame of the results.
}
\examples{
\dontrun{
# create new job
f1 <- system.file("templates/instructions1.html", package = "crowdflower")
f2 <- system.file("templates/cml1.xml", package = "crowdflower")
j <- job_create(title = "Job Title",
instructions = readChar(f1, nchars = 1e8L),
cml = readChar(f2, nchars = 1e8L))
# add data
d <- data.frame(variable = 1:3)
job_add_data(id = j, data = d)
# launch job
job_launch(id = j)
# get results for job
report_regenerate(id = j, report_type = "full")
report_get(id = j, report_type = "full")
# delete job
job_delete(j)
}
}
\references{
\href{https://success.crowdflower.com/hc/en-us/articles/202703425-CrowdFlower-API-Requests-Guide#report_get}{Crowdflower API documentation}
}
\seealso{
\code{\link{cf_account}}
}
\keyword{data}
\keyword{jobs}
|
3c36df9ac1a4c9918fc0f39156dae71d0437edd8
|
302d026524486f0ad386599fac8dd4f57278ba38
|
/man/modelSetPredictors.Rd
|
5aadc8b6f601c7d9f133d1ab0905078ab7c43927
|
[
"CC0-1.0",
"LicenseRef-scancode-public-domain",
"LicenseRef-scancode-warranty-disclaimer"
] |
permissive
|
cwhitman/GenEst
|
96d72e50eafe5e71c25a230c8046f80e152b1963
|
7c84c887b3f671fa8786eee8077512b8d80b7883
|
refs/heads/master
| 2020-03-30T18:03:28.168191
| 2018-10-11T07:04:03
| 2018-10-11T07:04:03
| 151,481,672
| 0
| 0
|
NOASSERTION
| 2018-10-03T21:17:44
| 2018-10-03T21:17:44
| null |
UTF-8
|
R
| false
| true
| 424
|
rd
|
modelSetPredictors.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model_utility_functions.R
\name{modelSetPredictors}
\alias{modelSetPredictors}
\title{Determine the predictors for a whole model set}
\usage{
modelSetPredictors(modelSet)
}
\arguments{
\item{modelSet}{model set}
}
\value{
vector of the predictors from a model set
}
\description{
Determine the predictors for a whole model set
}
|
333c1f107c270f305d3fa001f731e2acaa586f18
|
f3dbe1612c0e80886df0e2fd02a444b5f7d5efa9
|
/man/LVMMCOR.Rd
|
b7066053c1749de1a59c92e0c2e70aad27647d58
|
[] |
no_license
|
cran/LVMMCOR
|
072778f2546f565290c4187ad3c22dda48b36d94
|
e6958dbf94bd6807c418efb20c03578236e59131
|
refs/heads/master
| 2021-01-13T02:11:53.987968
| 2013-05-10T00:00:00
| 2013-05-10T00:00:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,172
|
rd
|
LVMMCOR.Rd
|
\name{LVMMCOR}
\alias{LVMMCOR}
\title{
A Latent Variable Model for Mixed Continuous and Ordinal Responses.
}
\description{
A model for mixed ordinal and continuous responses is presented where the heteroscedasticity of the variance of the continuous response is also modeled. In this model ordinal response can be dependent on the continuous response. The aim is to use an approach similar to that of Heckman (1978) for the joint modelling of the ordinal and continuous responses. With this model, the dependence between responses can be taken into account by the correlation between errors in the models for continuous and ordinal responses.
}
\usage{
LVMMCOR(ini = NA, X, y, z, p, q, ...)
}
\arguments{
\item{ini}{Initial values}
\item{X}{Design matrix}
\item{z}{Continuous responses}
\item{y}{Ordinal responses with three levels}
\item{p}{Order of dimension of continuous responses}
\item{q}{Order of dimension of ordinal responses}
\item{\dots}{Other arguments}}
\details{
Models for LVMMCOR are specified symbolically. A typical model has the form response1 ~ terms and response2 ~ terms where response1and response2 are the (numeric) ordinal and
continuous responses vector and terms is a series of terms which specifies a linear predictor for responses. A terms specification of the form first + second indicates all
the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained
by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.
}
\value{
\item{Continuous Response}{Coefficient of continuous response}
\item{Variance of Continuous Response}{Variance of continuous response}
\item{Ordinal Response}{Coefficient of ordinal response}
\item{Cut points}{Cut points for ordinal response}
\item{Correlation}{Coefficient of continuous response}
\item{Hessian}{Hessian matrix}
\item{convergence}{An integer code. 0 indicates successful convergence}
}
\references{
Bahrami Samani, E., Ganjali, M. and Khodaddadi, A. (2008). A Latent Variable Model for Mixed Continuous and Ordinal Responses. Journal of Statistical Theory and Applications. 7(3):337-349.
}
\author{
Bahrami Samani and Nourallah Tazikeh Miyandarreh
}
\note{
Supportted by Shahid Beheshti University
}
\seealso{
\code{\link{nlminb}},\code{\link{fdHess}}
}
\examples{
data("Bahrami")
gender<-Bahrami$ GENDER
age<-Bahrami$AGE
duration <-Bahrami$ DURATION
y<-Bahrami$ STEATOS
z<-Bahrami$ BMI
sbp<-Bahrami$ SBP
X=cbind(gender,age,duration ,sbp)
P<-lm(z~X)[[1]]
names(P)<-paste("Con_",names(P),sep="")
Q<-polr(factor(y)~X)[[1]]
names(Q)<-paste("Ord_",names(Q),sep="")
W=c(cor(y,z),polr(factor(y)~X)[[2]],var(z))
names(W)=c("Corr","cut_point1","cut_point2","Variance of Continous Response")
ini=c(P,Q,W)
p=5;
q=4;
LVMMCOR(ini,X=X,y=y,z=z,p=p,q=q)
## The function is currently defined as
structure(function (x, ...)
UseMethod("LVMMCOR"), class = "LVMMCOR")
}
\keyword{regression}
|
cf4a3b37191dfb287c84b5aef4561a3056782e20
|
dfb18d1b4608c4404cc291a5232692abe04ca230
|
/R/phase.zg.R
|
dae84e40c2e2da00ab99a9d775a80495ae116f11
|
[] |
no_license
|
sugam72-os/dendRoAnalyst-1
|
0d2fb30336cc7fa287eb428472347a711bd6aaf9
|
9b009f909dddbbba0837f6e2aa18b97f0b2e17c8
|
refs/heads/master
| 2022-11-13T16:15:10.604682
| 2020-07-03T18:00:07
| 2020-07-03T18:00:07
| 285,296,186
| 1
| 0
| null | 2020-08-05T13:28:24
| 2020-08-05T13:28:23
| null |
UTF-8
|
R
| false
| false
| 14,954
|
r
|
phase.zg.R
|
#' @title Application of the zero-growth approach to calculate different phases, their duration and to plot them.
#'
#' @description This function analyses data using the zero-growth approach. Initially, it divides the data in two categories: 1) Tree water deficiency (TWD), i.e. the reversible shrinkage and expansion of the tree stem when the current reading is below the previous maximum and, 2) Increment (GRO), the irreversible expansion of the stem when the current reading is above the previous maximum. Then it calculates the TWD for each data point as the difference between the modelled "growth line" and the observed measurement. See \href{https://doi.org/10.1111/nph.13995}{Zweifel et. al.,(2016) } for details.
#'
#' @references Zweifel R, Haeni M, Buchmann N, Eugster W (2016) Are trees able to grow in periods of stem shrinkage? New Phytol 211:839–849. https://doi.org/10.1111/nph.13995
#'
#' @param df dataframe with first column containing date and time in the format \code{yyyy-mm-dd HH:MM:SS}. It should contain data with constant temporal resolution for best results.
#'
#' @param TreeNum numerical value indicating the tree to be analysed. E.g. '1' refers to the first dendrometer data column in \emph{df}.
#'
#' @param outputplot logical, to \code{plot} the phase diagram.
#'
#' @param days array with initial and final day for plotting. E.g. \emph{c(a,b)}, where a = initial date and b = final date.
#'
#' @param linearCol color for the modelled curve.
#'
#' @param twdCol color for the TWD curve.
#'
#' @param twdFill filling method for the area under the TWD curve. Equivalent to \code{density} argument of the \code{\link[graphics:polygon]{polygon}} function in the \pkg{graphics} package of R. Default value is \code{NULL}.
#'
#' @param twdFillCol color to fill the area under the TWD curve.
#'
#' @param xlab string, x label of the \code{plot}.
#'
#' @param ylab1 string, y label of the upper \code{plot}.
#'
#' @param ylab2 string, y label of the lower \code{plot}.
#'
#' @param twdYlim numeric, to define the limit of the y-axis of the lower plot. Default is \code{NULL}, which automatically adjusts the y-axis limit.
#'
#' @param cex.lab numeric, for the size of the axis labels. Default is \code{NULL}.
#'
#' @param cex.axis numeric, for the size of the axis tick labels. Default is \code{NULL}.
#'
#' @param font.lab numeric, for the font type of the axis labels. Default is \code{NULL}.
#'
#' @param font.axis numeric, for the font type of the axis tick labels. Default is \code{NULL}.
#'
#' @param col.axis color names, for the color of the axis tick labels. Default is \code{NULL}.
#'
#' @param col.lab color names, for the color of the axis labels. Default is \code{NULL}.
#'
#'
#' @return A list of two dataframes. The first dataframe \emph{ZG_cycle} contains the cyclic phases along with various statistics and the second dataframe \emph{ZG_phase} with assigned phases for each data point.The contents of \emph{ZG_cycle} are:
#' \tabular{llll}{
#' \strong{Columns}\tab\tab \strong{Description}\cr
#' \code{DOY}\tab\tab Day of year for the corresponding phase.\cr
#' \code{Phase}\tab\tab TWD for tree water deficit and GRO for irreversible expansion.\cr
#' \code{start}\tab\tab Time when the corresponding phase starts.\cr
#' \code{end}\tab\tab Time when the corresponding phase ends.\cr
#' \code{Duration_h}\tab\tab Duration of the corresponding phase in hours.\cr
#' \code{Magnitude}\tab\tab Radial/circumferential change in millimeters.\cr
#' \code{rate}\tab\tab Rate of Radial/circumferential change in micrometers per hour.\cr
#' \code{Max.twd}\tab\tab Maximum TWD recorded for the corresponding TWD phase.\cr
#' \code{Max.twd.time}\tab\tab Time of occurrence of maximum TWD value for each TWD phase.\cr
#' \code{Avg.twd}\tab\tab Average of TWD values for each TWD phase.\cr
#' \code{STD.twd}\tab\tab Standard deviation of TWD values for each TWD phase.
#' }
#'
#' @examples library(dendRoAnalyst)
#' data(gf_nepa17)
#' zg.phase<-phase.zg(df=gf_nepa17, TreeNum=1, outputplot=TRUE, days=c(150,160))
#' head(zg.phase[[1]],10)
#' head(zg.phase[[2]],10)
#'
#' @importFrom grDevices rgb
#'
#' @importFrom graphics abline axis axis.POSIXct box legend lines mtext par points polygon rect text plot
#'
#' @importFrom stats approx median na.exclude na.omit sd
#'
#' @export
phase.zg<-function(df, TreeNum, outputplot, days, linearCol='#2c7fb8',twdCol='#636363',twdFill=NULL,twdFillCol='#f03b20',xlab='DOY',ylab1='Stem size variation [mm]',ylab2='TWD [mm]',twdYlim=NULL, cex.axis=NULL, cex.lab=NULL, font.lab=NULL, col.lab=NULL, font.axis=NULL, col.axis=NULL){
temp13<-df
dm<-TreeNum+1
data<-temp13[,c(1,dm)]
dm<-2
a<-NULL
b<-NULL
temp<-as.POSIXct(strptime(data[,1], "%Y-%m-%d %H:%M:%S"), tz='UTC')
if(is.na(as.POSIXct(temp[1], format = '%Y-%m-%d %H:%M:%S'))){
stop('Date not in the right format')
}
data$doy<-as.integer(format(strptime(temp, format = '%Y-%m-%d %H:%M:%S'), '%j'))
data$yr<-as.integer(format(strptime(temp, format = '%Y-%m-%d %H:%M:%S'), '%y'))
y1<-unique(data$yr)
if(length(y1)>1){
data$doy2<-data$doy
d<-max(which(data$doy==ifelse(y1[1]%%4==0,366,365)))
data$doy2[(d+1):nrow(data)]<-data$doy2[(d+1):nrow(data)]+data$doy2[d]
}
#require(pspline)
############################
reso_den<-function(input_time){
time1<-input_time
reference<-time1[1]
time_min<-as.integer(difftime(time1,reference, units = "mins"))
diff_time<-diff(time_min)
diff2_time<-unique(diff_time)
reso<-mean(diff2_time)
if(isTRUE(length(diff2_time>1))==T){
print(diff2_time)
warning('Warning: The temporal resolution of dendrometer data is not consistent, For better result, please use dendrometer data with consistent resolution. There may be NA values in the dataset.')
#cat(paste('Mean temporal resolution is :', round(reso),' minutes.'))
}else{
reso<-mean(diff2_time)
#cat(paste('Temporal resolution is :', round(reso),' minutes.'))
return(round(reso))
}
}
#####################################################
#########################
r.denro<-reso_den(temp)
############################
y<-c()
for(i in 1:nrow(data)){
if(isTRUE(data[,dm][i+1]-data[,dm][i]>0)==TRUE & isTRUE(max(data[,dm][1:i], na.rm = T)<data[,dm][i+1])==TRUE){
y[i+1]<-3
}else{
if(isTRUE(data[,dm][i+1]-data[,dm][i]>0)==TRUE){
y[i+1]<-2
}else{
y[i+1]<-1
}
}
}
data$y<-y[1:length(y)-1]
##########################################################
data$phs<-data$y
zg_ph<-data$y
zg_ph[zg_ph==2|zg_ph==1]<-'TWD'
zg_ph[zg_ph==3]<-'GRO'
data$y<-zg_ph
##########################################
x<-which(data$y=='GRO')
dp.3.time<-data[,dm][x]
y.3<-na.exclude(data[,dm][x])
ap.all<-approx(c(1,x[1]),c(y.3[1],y.3[1]),xout =1:(x[1]-1))$y
for(i in 2:length(x)){
ap<-approx(c(x[(i-1)],x[i]),c(y.3[(i-1)],y.3[i]),xout =x[(i-1)]:(x[i]-1))
#lines(x[i]:(x[i+1]-1), ap$y)
ap.all<-c(ap.all,ap$y[1:length(ap$y)])
}
if(x[length(x)]!=nrow(data)){
ap.all2<-approx(c(x[length(x)],nrow(data)),c(y.3[length(y.3)],y.3[length(y.3)]),xout =x[length(x)]:nrow(data))$y
}else{
#ap.all2=approx(c(x[length(x)-1],nrow(data)),c(y.3[length(y.3)-1],y.3[length(y.3)]),xout =x[length(x)-1]:nrow(data))$y
n<-which(zg_ph=='TWD')
n1<-n[length(n)]
ap.all2<-data[,dm][(n1+1):nrow(data)]
}
ap.all<-c(ap.all, ap.all2)
data$twd<-data[,dm]-ap.all
data$strght.line<-ap.all
#Calculating different phases in ZG method
gr.ph<-c()
doy<-c()
magn<-c()
for(i in 1:nrow(data)){
if(isTRUE(data$y[i]==data$y[i+1])==TRUE & isTRUE((i+1)-i==1)==TRUE){
next
}
else{
doy<-c(doy, i)
gr.ph<-c(gr.ph,data$y[i])
magn<-c(magn,data[,dm][i])
}
}
mx.twd1<-c()
avg.twd1<-c()
s.twd1<-c()
md.twd1<-c()
mn.twd1<-c()
d.min1<-c()
d.max1<-c()
for(i in 2:length(x)){
df<-data[x[i-1]:x[(i)],]
mx.twd1<-c(mx.twd1, max(df$twd,na.rm = T))
mn.twd1<-c(mn.twd1, min(df$twd,na.rm = T))
avg.twd1<-c(avg.twd1, mean(df$twd, na.rm = T))
s.twd1<-c(s.twd1, sd(df$twd, na.rm = T))
md.twd1<-c(md.twd1, median(df$twd, na.rm = T))
t.mn<-subset(df,df$twd==min(df$twd, na.rm = T))
d.min1<-c(d.min1, strftime(t.mn[1,1], format = '%Y-%m-%d %H:%M:%S'))
t.mx<-subset(df,df$twd==max(df$twd, na.rm = T))
d.max1<-c(d.max1, strftime(t.mx[1,1], format = '%Y-%m-%d %H:%M:%S'))
}
df2<-data[(x[length(x)]+1):nrow(data),]
mx.twd<-c(mx.twd1, max(df2$twd,na.rm = T))
mn.twd<-c(mn.twd1, min(df2$twd,na.rm = T))
avg.twd<-c(avg.twd1, mean(df2$twd, na.rm = T))
s.twd<-c(s.twd1, sd(df2$twd, na.rm = T))
md.twd<-c(md.twd1, median(df2$twd, na.rm = T))
t.mn<-subset(df2,df2$twd==min(df2$twd, na.rm = T))
d.min<-c(d.min1, strftime(t.mn[1,1], format = '%Y-%m-%d %H:%M:%S'))
t.mx<-subset(df2,df2$twd==max(df2$twd, na.rm = T))
d.max<-c(d.max1, strftime(t.mx[1,1], format = '%Y-%m-%d %H:%M:%S'))
xyz<-data.frame(d.max,mx.twd, d.min, mn.twd, avg.twd, s.twd, md.twd)
s<-subset(xyz, xyz$mn.twd!=0)
abc<-data.frame(doy)
strt_time<-c()
for (i in 1:nrow(abc)){
strt_time[i]<-strftime(data[,1][abc$doy[i]], format = '%Y-%m-%d %H:%M:%S')
}
abc$doy<-NULL
abc$DOY<-as.integer(format(strptime(strt_time, format = '%Y-%m-%d %H:%M:%S'), '%j'))
abc$Phases<-gr.ph
abc<-na.omit(abc)
abc$start<-strt_time[1:length(strt_time)-1]
abc$end<-strt_time[2:(length(strt_time))]
abc$Duration_h<-round(as.numeric(difftime(strptime(abc$end, format = '%Y-%m-%d %H:%M:%S'), strptime(abc$start, format = '%Y-%m-%d %H:%M:%S'), units = 'hours')),1)
abc$magnitude<-as.numeric(round(diff(magn),8))
abc$rate<-(abc$magnitude/abc$Duration_h)*1000
########################################################
data$tm=data[,1]
twd12=data$twd
tp<-abc$start[abc$Phases=='TWD']
ep<-abc$end[abc$Phases=='TWD']
twd.loc=which(abc$Phases=='TWD')
max.t=c()
max.tm=c()
mn.t=c()
sd.t=c()
for(q in 1:length(tp)){
r= as.numeric(which(data$tm==tp[q]))
t= as.numeric(which(data$tm==ep[q]))
f=data[r:t,]
max.t=c(max.t, min(f$twd))
d=which(f$twd==min(f$twd))
max.tm=c(max.tm,strftime(f[,1][d[length(d)]], format = '%Y-%m-%d %H:%M:%S') )
mn.t=c(mn.t,mean(f$twd, na.rm=T))
sd.t=c(sd.t,sd(f$twd, na.rm=T))
}
#print(f)
abc$Max.twd<-NA
abc$Max.twd[twd.loc]<- -max.t
abc$Max.twd.time<-NA
abc$Max.twd.time[twd.loc]<-strftime(max.tm, format = '%Y-%m-%d %H:%M:%S')
abc$Avg.twd<-NA
abc$Avg.twd[twd.loc]<- -mn.t
abc$STD.twd<-NA
abc$STD.twd[twd.loc]<- sd.t
#######################################################
#abc2<-abc[order(abc$Phases, decreasing = T),]
#abc2$Max.twd<-NA
#abc2$Max.twd[1:length(s$mx.twd)]<- -s$mn.twd
#abc2$Max.twd.time<-NA
#abc2$Max.twd.time[1:length(s$mx.twd)]<-strftime(s$d.min, format = '%Y-%m-%d %H:%M:%S')
#abc2$Avg.twd<-NA
#abc2$Avg.twd[1:length(s$mx.twd)]<- -s$avg.twd
#abc2$STD.twd<-NA
#abc2$STD.twd[1:length(s$mx.twd)]<- s$s.twd
#abc<-abc2[order(as.numeric(row.names(abc2)), decreasing = F),]
abc$rate[abc$Phases=='TWD']<-NA
abc$magnitude[abc$Phases=='TWD']<-NA
###################################################################
################Plotting#########################################
if(outputplot==TRUE){
if(days[2]>days[1]){
a1<-as.numeric(which(data$doy==days[1]&data$yr==y1[1]))
b1<-as.numeric(which(data$doy==days[2]&data$yr==y1[1]))
}else{
if(length(y1)<=1|length(y1)>=3){
warning('WARNING: days[1] > days[2] not valid in this case. The plot is not possible.')
}else{
a1<-as.numeric(which(data$doy==days[1]&data$yr==y1[1]))############
b1<-as.numeric(which(data$doy2==(days[2]+data$doy[d])&data$yr==y1[2]))
}
}
#a1<-which(data$doy==days[1])
#b1<-which(data$doy==days[2])
a1_mn<-min(a1)
b1_mx<-max(b1)
c1<-days[2]-days[1]
data2<-data[a1_mn:b1_mx,]
xloc<-seq(a1_mn,b1_mx,(1440/r.denro))
xloc2<-c()
xloc4<-c()
for(i in 1:length(xloc)){
xloc2<-c(xloc2,data$doy[xloc[i]])
xloc4<-c(xloc4,data$yr[xloc[i]])
}
tw.mn<-ifelse(is.null(twdYlim),min(data2$twd, na.rm = T),twdYlim)
opar <- par(no.readonly =TRUE)
on.exit(par(opar))
par(mfrow=c(2,1))
par(mar=c(0, 4.1, 5, 4.1), xpd=F)
plot(x=row.names(data2), y=data2[,dm], type='l', col='grey25', xlab = '', ylab = ylab1, xaxt='none',cex.lab=ifelse(is.null(cex.lab),1,cex.lab), cex.axis=ifelse(is.null(cex.axis),1,cex.axis),font.axis=ifelse(is.null(font.axis),1,font.axis), col.axis=ifelse(is.null(col.axis),'black',col.axis), font.lab=ifelse(is.null(font.lab),1,font.lab), col.lab=ifelse(is.null(col.lab),'black',col.lab))
#abline(v=xloc, col='lightgray',lty=2, lwd=0.5)
lines(row.names(data2), data2$strght.line, col=linearCol, lwd=1.25)
axis(3, at = xloc, xloc2, las=3, cex.axis=ifelse(is.null(cex.axis),1,cex.axis),font.axis=ifelse(is.null(font.axis),1,font.axis), col.axis=ifelse(is.null(col.axis),'black',col.axis))
par(mar=c(5.1, 4.1, 0, 4.1))
plot(x=row.names(data2), y=data2$twd, col='red', type='n', ylab = '', xlab = xlab,yaxt='n',xaxt='n', ylim = c(tw.mn,0),cex.lab=ifelse(is.null(cex.lab),1,cex.lab), cex.axis=ifelse(is.null(cex.axis),1,cex.axis),font.axis=ifelse(is.null(font.axis),1,font.axis), col.axis=ifelse(is.null(col.axis),'black',col.axis), font.lab=ifelse(is.null(font.lab),1,font.lab), col.lab=ifelse(is.null(col.lab),'black',col.lab))
polygon(c(row.names(data2)[1], row.names(data2), row.names(data2)[length(row.names(data2))]), c(0, data2$twd, 0), col=twdFillCol, border = twdCol, density = twdFill)
lines(row.names(data2), data2$twd)
#lines()
abline(h=0, col='blue', lwd=1)
axis(4, at = seq(0,tw.mn,(tw.mn*0.25)), round(seq(0,-tw.mn,-tw.mn*0.25),2), las=3, cex.axis=ifelse(is.null(cex.axis),1,cex.axis),font.axis=ifelse(is.null(font.axis),1,font.axis), col.axis=ifelse(is.null(col.axis),'black',col.axis))
axis(1, at = xloc, xloc2, las=3, cex.axis=ifelse(is.null(cex.axis),1,cex.axis),font.axis=ifelse(is.null(font.axis),1,font.axis), col.axis=ifelse(is.null(col.axis),'black',col.axis))
mtext(ylab2, side = 4, line = 2.5,cex=ifelse(is.null(cex.lab),1,cex.lab),font=ifelse(is.null(font.lab),1,font.lab), col=ifelse(is.null(col.lab),'black',col.lab))
box()
#legend('top',inset=c(0,-0.1), legend = c('Shrinkage','Expansion','Increment'), col = c('red','blue','green'), pch = 16, ncol = 3, box.lty = 0)
}
################################################################
data3<-data.frame('Time'=data[,1],'Phases'=data$y,'TWD'=-data$twd)
return(list(ZG_cycle=abc, ZG_phase=data3))
}
|
0043ba3fa23a9645aaed2ce2b789b2f38df43c6a
|
21d829fe665a3177c03b2020c581399a51a1ba6c
|
/scripts/20181027_sfs_generation/new_sfs.R
|
cd4103e383082ff736bd703bfd088a983e5afa74
|
[
"MIT"
] |
permissive
|
AndersenLab/sfs-dfe
|
1cd88ca511d4cc5bcf8fa5fc3f1c2bb64387817a
|
1c466e0fb8c96c8823238af90cac0909d3b8011c
|
refs/heads/master
| 2021-10-08T05:04:44.974223
| 2018-12-07T23:04:17
| 2018-12-07T23:04:17
| 105,313,149
| 0
| 1
| null | 2017-10-02T13:04:26
| 2017-09-29T20:14:25
|
Python
|
UTF-8
|
R
| false
| false
| 3,271
|
r
|
new_sfs.R
|
#!/usr/bin/env Rscript
library(tidyverse)
library(glue)
multi_dfe_out <- function(df, fname) {
writeLines(
c(
nrow(df) - 1,
paste(df$Selected, collapse =" "),
paste(df$Neutral, collapse =" ")
),
con = glue::glue(fname),
sep = "\n"
)
}
# writing function
process_sfs <- function(neut, sel, n_strains) {
# c_regions <- gsub(".tsv", "", strsplit(neut, split = "_")[[1]][4])
n_deets <- strsplit(neut, split = "_")[[1]][3]
s_deets <- gsub(".tsv", "",strsplit(sel, split = "_SELECTED_")[[1]][2])
save_name <- glue::glue("{n_deets}_{s_deets}")
af_df <- data.frame(DERIVED_AF = round(seq(0,1,by = 1/n_strains), digits = 5))
neutral_sites <- data.table::fread(neut, col.names = c("CHROM","POS","AA","GT","COUNT","AA_CLASS")) %>%
dplyr::group_by(CHROM,POS) %>%
dplyr::mutate(allele_counts = sum(COUNT)) %>%
dplyr::rowwise() %>%
dplyr::mutate(ALLELE_AF = COUNT/allele_counts) %>%
dplyr::mutate(DERIVED_AF = ifelse(AA_CLASS == "ANCESTOR" && ALLELE_AF == 1, 0,
ifelse(AA_CLASS == "DERIVED", round(ALLELE_AF,digits = 5), NA))) %>%
na.omit() %>%
dplyr::group_by(DERIVED_AF) %>%
dplyr::summarise(Neutral = n()) %>%
dplyr::left_join(af_df, ., by = "DERIVED_AF")
selected_sites <- data.table::fread(sel, col.names = c("CHROM","POS","AA","GT","COUNT","AA_CLASS")) %>%
dplyr::group_by(CHROM,POS) %>%
dplyr::mutate(allele_counts = sum(COUNT)) %>%
dplyr::rowwise() %>%
dplyr::mutate(ALLELE_AF = COUNT/allele_counts) %>%
dplyr::mutate(DERIVED_AF = ifelse(AA_CLASS == "ANCESTOR" && ALLELE_AF == 1, 0,
ifelse(AA_CLASS == "DERIVED", round(ALLELE_AF,digits = 5), NA))) %>%
na.omit() %>%
dplyr::group_by(DERIVED_AF) %>%
dplyr::summarise(Selected = n()) %>%
dplyr::left_join(af_df, ., by = "DERIVED_AF")
sfs_df <- dplyr::left_join(neutral_sites, selected_sites , by = "DERIVED_AF")
# Replace NAs with 0
sfs_df[is.na(sfs_df)] <- 0
# save file
multi_dfe_out(df = sfs_df,
fname = glue::glue("{save_name}.sfs"))
# plot
sfs_plot <- sfs_df%>%
tidyr::gather(SITES, COUNTS, -DERIVED_AF) %>%
dplyr::group_by(SITES) %>%
dplyr::mutate(frq = COUNTS/sum(COUNTS)) %>%
ggplot()+
aes(x = DERIVED_AF, y = frq, color = SITES)+
geom_line()+
theme_bw(15)+
labs(x = "Derived AF", y = "Frequency")
ggsave(sfs_plot, filename = glue::glue("{save_name}.pdf"), height = 6, width = 8)
}
# prep file names
neutral <- grep("NEUTRAL", list.files(), value = T)
neutral_arms <- grep("ARMS", neutral, value = T)
neutral_centers <- grep("CENTERS", neutral, value = T)
neutral_genome <- grep("GENOME", neutral, value = T)
selected <- grep("SELECTED", list.files(), value = T)
selected_arms <- grep("ARMS", selected, value = T)
selected_centers <- grep("CENTERS", selected, value = T)
selected_genome <- grep("GENOME", selected, value = T)
for(nt_f in 1:length(neutral_genome)) {
for(st_f in 1:length(selected_genome)) {
process_sfs(neutral_genome[nt_f], selected_genome[st_f], 239)
process_sfs(neutral_arms[nt_f], selected_arms[st_f], 239)
process_sfs(neutral_centers[nt_f], selected_centers[st_f], 239)
}
}
|
ae7d21104a57d6de91faaeb308e18d7b260011f9
|
439322912321e742e7f92374ecb76c76743984cb
|
/R/generics.R
|
0f8c2074053df7a02ae67bcbb021801731d3275d
|
[
"MIT"
] |
permissive
|
vbaliga/univariateML
|
62d2c00017b93f2e1ed8357dd5d9062cf66744cd
|
0410647e2528dc61fa7b7b7cce95273777059baf
|
refs/heads/master
| 2020-09-11T19:04:44.904257
| 2019-11-24T19:12:20
| 2019-11-24T19:12:20
| 222,160,994
| 0
| 0
|
NOASSERTION
| 2019-11-16T21:23:07
| 2019-11-16T21:23:07
| null |
UTF-8
|
R
| false
| false
| 5,529
|
r
|
generics.R
|
#' Wrangles arguments for use in the plot, lines and points functions.
#'
#' @param x The input data.
#' @param range Range of the data.
#' @param points Boolean; should points be plotted by default?
#' @keywords internal
plot_wrangler = function(x, range, points = FALSE, ...) {
if(is.null(range)) {
if(abs(attr(x, "support")[1]) + abs(attr(x, "support")[2]) < Inf) {
limits = attr(x, "support")
} else if (abs(attr(x, "support")[1]) == 0 & abs(attr(x, "support")[2]) == Inf) {
limits = c(0, qml(0.99, x))
} else {
limits = qml(c(0.01, 0.99), x)
}
range = seq(limits[1], limits[2], length.out = 1000)
}
defaults = list(type = if(points) "p" else "l",
main = paste0(attr(x, "model"), " model"),
ylab = "Density",
xlab = "x",
lwd = 1)
args = listmerge(x = defaults,
y = list(...))
args$x = range
args$y = dml(args$x, x)
args
}
#' Plot, Lines and Points Methods for Maximum Likelihood Estimates
#'
#' The \code{plot}, \code{lines}, and \code{points} methods for \code{univariateML} objects.
#'
#' @export
#' @param x a \code{univariateML} object.
#' @param range range of \code{x} values to plot, i.e. \code{c(lower, upper)}.
#' @param ... parameters passed to \code{plot}, \code{lines}, or \code{points}.
#' @return An invisible copy of \code{x}.
#' @examples
#' plot(mlweibull(datasets::precip), main = "Annual Precipitation in US Cities")
#' lines(mlgamma(datasets::precip), lty = 2)
#' rug(datasets::precip)
#' @export
#'
plot.univariateML = function(x, range = NULL, ...) {
args = plot_wrangler(x, range, points = FALSE, ...)
do.call(graphics::plot, args)
invisible(x)
}
#' @export
#' @rdname plot.univariateML
lines.univariateML = function(x, range = NULL, ...) {
args = plot_wrangler(x, range, points = FALSE, ...)
do.call(graphics::lines, args)
invisible(x)
}
#' @export
#' @rdname plot.univariateML
points.univariateML = function(x, range = NULL, ...) {
args = plot_wrangler(x, range, points = TRUE, ...)
do.call(graphics::points, args)
invisible(x)
}
#' @export
logLik.univariateML = function(object, ...) {
val = attr(object, "logLik")
attr(val, "nobs") = attr(object, "n")
attr(val, "df") = length(object)
class(val) = "logLik"
val
}
#' @export
coef.univariateML = function(object, ...) {
stats::setNames(as.numeric(object), names(object))
}
#' @export
summary.univariateML = function(object, ...) {
data.name = deparse(as.list(attr(object, "call"))$x)
digits = list(...)$digits
cat("\nMaximum likelihood for the", attr(object, "model"), "model \n",
"\nCall: ", deparse(attr(object, "call")), "\n\nEstimates: \n")
print.default(format(object, digits = digits), print.gap = 2L, quote = FALSE)
cat("\nData: ", data.name, " (", attr(object, "n"), " obs.)\n",
"Support: (", attr(object, "support")[1], ", ", attr(object, "support")[2], ")\n",
"Density: ", attr(object, "density"), "\n",
"Log-likelihood: ", attr(object, "logLik"), "\n",
sep = "")
invisible(object)
}
#' @export
print.univariateML = function(x, ...) {
digits = list(...)$digits
if(is.null(digits)) digits = 4
cat("Maximum likelihood estimates for the", attr(x, "model"), "model \n")
print.default(format(x, digits = digits), print.gap = 2L, quote = FALSE)
invisible(x)
}
#' Confidence Intervals for Maximum Likelihood Estimates
#'
#' Computes a confidence interval for one or more parameters in a \code{unvariateML}
#' object.
#'
#' \code{confint.univariateML} is a wrapper for \code{\link{bootstrapml}} that computes
#' confidence intervals for the main parameters of \code{object}. The main parameters of \code{object} are
#' the members of \code{names(object)}. For instance,the main parameters of an object obtained from \code{mlnorm} are \code{mean} and \code{sd}.
#' The confidence intervals are parametric bootstrap percentile intervals with limits \code{(1-level)/2} and \code{1 - (1-level)}.
#'
#' @param object An object of class \code{univariateML}.
#' @param parm Vector of strings; the parameters to calculate a confidence
#' interval for. Each parameter must be a member of \code{names(object)}.
#' @param level The confidence level.
#' @param Nreps Number of bootstrap iterations. Passed to \code{\link{bootstrapml}}.
#' @param ... Additional arguments passed to \code{\link{bootstrapml}}.
#' @return A matrix or vector with columns giving lower and upper confidence
#' limits for each parameter in \code{parm}.
#' @seealso \code{\link[stats]{confint}} for the generic function and \code{\link{bootstrapml}} for the
#' function used to calculate the confidence intervals.
#' @export
#' @examples
#' object = mlinvgauss(airquality$Wind)
#' confint(object) # 95% confidence interval for mean and shape
#' confint(object, "mean") # 95% confidence interval for the mean parameter
#' # confint(object, "variance") # Fails since 'variance isn't a main parameter.
confint.univariateML = function(object, parm = NULL, level = 0.95, Nreps = 1000, ...) {
if(is.null(parm)) parm = names(object)
assertthat::assert_that(all(parm %in% names(object)), msg =
"'parm' must contain valid parameter names or be NULL")
indices = which(names(object) %in% parm)
map = function(x) x[indices]
probs = c((1 - level)/2, 1 - (1 - level)/2)
bootstrapml(object, map = map, probs = probs, Nreps = Nreps, ...)
}
|
387489689298a4d8d3c426c2a857a3d4ae3119dd
|
6c71e2f04573abc8498a6ba1303413c088bbaec8
|
/Evaluacion.R
|
deb98bb034248a455cd492d4ccc1d3b3d336d742
|
[] |
no_license
|
Jgallo-R/TareaClase05
|
f8c86375a11086c5d20d96fed7529661ffc3e72a
|
51d243b84c18056983cdd9b999ab9862af98d4c1
|
refs/heads/master
| 2022-11-10T22:35:18.667881
| 2020-06-21T01:45:15
| 2020-06-21T01:45:15
| 273,814,931
| 0
| 0
| null | null | null | null |
ISO-8859-1
|
R
| false
| false
| 17,237
|
r
|
Evaluacion.R
|
############################################# EVALUACION ##########################################
# Describir modelos AR(2), graficarlos para valores diferentes
# de los argumentos (ar=c(p1,p2))
#AR(2)
AR2 <- arima.sim(model=list(order=c(2,0,0), ar=c(0.1,0.2)),
n=100, sd=0.1)
#Probar varias combinaciones de p1 y p2, graficas las series de tiempo
# simularlas y sus correspondientes funciones de autocorrelacion simple
# y funciones de autocorrelacion parcial
# repetir lo mismo con los procesos MA
############################################## SOLUCION ######################################
### Cargamos las librerias necesarias
library(ggplot2)
library(tseries)
library(forecast)
library(quantmod)
######################################## Para N=100 (N GRANDE)###########################################
##1.-Simulando Modelo AR(2)
#Para que el proceso sea estacionario se debe cumplir:
# -1 < phi(2) < 1 ó phi(1) + phi(2)< 1 ó phi(2)-phi(1)< 1
# Con este criterio generemos algunos coeficientes phi(1)y phi(2)
#definimos la semilla
set.seed(999)
##Generamos los modelos con diferentes valores de phi1 y phi2 con valores altos, bajos y negativos
AR2.1 <- arima.sim(n = 100 , model = list(order = c(2,0,0) , ar=c(0.1,0.2) , sd=0.1))
AR2.2 <- arima.sim(n = 100 , model = list(order = c(2,0,0) , ar=c(-0.1,0.2) , sd=0.1))
AR2.3 <- arima.sim(n = 100 , model = list(order = c(2,0,0) , ar=c(0.1,-0.2) , sd=0.1))
AR2.4 <- arima.sim(n = 100 , model = list(order = c(2,0,0) , ar=c(-0.1,-0.2) , sd=0.1))
AR2.5 <- arima.sim(n = 100, model = list(order = c(2,0,0) , ar=c(0.1,0.8) , sd=0.1))
AR2.6 <- arima.sim(n = 100,model = list(order = c(2,0,0) , ar=c(-0.1,0.8) , sd=0.1))
AR2.7 <- arima.sim(n = 100, model = list(order = c(2,0,0) , ar=c(0.1,-0.8) , sd=0.1))
AR2.8 <- arima.sim(n = 100, model = list(order = c(2,0,0) , ar=c(-0.1,-0.8) , sd=0.1))
AR2.9 <- arima.sim(n = 100, model = list(order = c(2,0,0) , ar=c(0.8,0.1) , sd=0.1))
AR2.10 <- arima.sim(n = 100, model = list(order = c(2,0,0) , ar=c(-0.8,0.1) , sd=0.1))
AR2.11 <- arima.sim(n = 100, model = list(order = c(2,0,0) , ar=c(0.8,-0.1) , sd=0.1))
AR2.12 <- arima.sim(n = 100, model = list(order = c(2,0,0) , ar=c(-0.8,-0.1) , sd=0.1))
# grafiquemos estas series de tiempo simuladas
graphics.off()
par(mfrow = c(3,4))
ylm <- c(min(AR2.1, AR2.2, AR2.3, AR2.4, AR2.5, AR2.6, AR2.7, AR2.8, AR2.9, AR2.10, AR2.11, AR2.12) ,
max(AR2.1, AR2.2, AR2.3, AR2.4, AR2.5, AR2.6, AR2.7, AR2.8, AR2.9, AR2.10, AR2.11, AR2.12))
plot.ts(AR2.1, ylim = ylm, main = "AR(2) phi[1] = 0.1 y phi[2] = 0.2")
plot.ts(AR2.2, ylim = ylm, main = "AR(2) phi[1] = -0.1 y phi[2] = 0.2")
plot.ts(AR2.3, ylim = ylm, main = "AR(2) phi[1] = 0.1 y phi[2] = -0.2")
plot.ts(AR2.4, ylim = ylm, main = "AR(2) phi[1] = -0.1 y phi[2] = -0.2")
plot.ts(AR2.5, ylim = ylm, main = "AR(2) phi[1] = 0.1 y phi[2] = 0.8")
plot.ts(AR2.6, ylim = ylm, main = "AR(2) phi[1] = -0.1 y phi[2] = 0.8")
plot.ts(AR2.7, ylim = ylm, main = "AR(2) phi[1] = 0.1 y phi[2] = -0.8")
plot.ts(AR2.8, ylim = ylm, main = "AR(2) phi[1] = -0.1 y phi[2] = -0.8")
plot.ts(AR2.9, ylim = ylm, main = "AR(2) phi[1] = 0.8 y phi[2] = 0.1")
plot.ts(AR2.10, ylim = ylm, main = "AR(2) phi[1] = -0.8 y phi[2] = 0.1")
plot.ts(AR2.11, ylim = ylm, main = "AR(2) phi[1] = 0.8 y phi[2] = -0.1")
plot.ts(AR2.12, ylim = ylm, main = "AR(2) phi[1] = -0.8 y phi[2] = -0.1")
##Graficamos las AFC (Funciones de autocorrelacion simples) para el AR(2)
graphics.off()
par(mfrow = c(3,4))
acf(AR2.1,main="AR(2) phi[1] = 0.1 y phi[2] = 0.2")
acf(AR2.2,main="AR(2) phi[1] = -0.1 y phi[2] = 0.2")
acf(AR2.3,main="AR(2) phi[1] = 0.1 y phi[2] = -0.2")
acf(AR2.4,main="AR(2) phi[1] = -0.1 y phi[2] = -0.2")
acf(AR2.5,main="AR(2) phi[1] = 0.1 y phi[2] = 0.8")
acf(AR2.6,main="AR(2) phi[1] = -0.1 y phi[2] = 0.8")
acf(AR2.7,main="AR(2) phi[1] = 0.1 y phi[2] = -0.8")
acf(AR2.8,main="AR(2) phi[1] = -0.1 y phi[2] = -0.8")
acf(AR2.9,main="AR(2) phi[1] = 0.8 y phi[2] = 0.1")
acf(AR2.10,main="AR(2) phi[1] = -0.8 y phi[2] = 0.1")
acf(AR2.11,main="AR(2) phi[1] = 0.8 y phi[2] = -0.1")
acf(AR2.12,main="AR(2) phi[1] = -0.8 y phi[2] = -0.1")
##Graficamos las PAFC (Funciones de autocorrelacion simples) para el AR(2)
graphics.off()
par(mfrow = c(3,4))
pacf(AR2.1,main="phi[1] = 0.1 y phi[2] = 0.2")
pacf(AR2.2,main="phi[1] = -0.1 y phi[2] = 0.2")
pacf(AR2.3,main="phi[1] = 0.1 y phi[2] = -0.2")
pacf(AR2.4,main="phi[1] = -0.1 y phi[2] = -0.2")
pacf(AR2.5,main="phi[1] = 0.1 y phi[2] = 0.8")
pacf(AR2.6,main="phi[1] = -0.1 y phi[2] = 0.8")
pacf(AR2.7,main="phi[1] = 0.1 y phi[2] = -0.8")
pacf(AR2.8,main="phi[1] = -0.1 y phi[2] = -0.8")
pacf(AR2.9,main="phi[1] = 0.8 y phi[2] = 0.1")
pacf(AR2.10,main="phi[1] = -0.8 y phi[2] = 0.1")
pacf(AR2.11,main="phi[1] = 0.8 y phi[2] = -0.1")
pacf(AR2.12,main="phi[1] = -0.8 y phi[2] = -0.1")
##1.-Simulando Modelo MA(2)
# Los Modelos MA siempre son estacionarios
#definimos la semilla
set.seed(999)
##Generamos los modelos con diferentes valores de phi1 y phi2 con valores altos, bajos y negativos
MA2.1 <- arima.sim(n = 100 , model = list(order = c(0,0,2) , ma=c(0.1,0.2) , sd=0.1))
MA2.2 <- arima.sim(n = 100 , model = list(order = c(0,0,2) , ma=c(-0.1,0.2) , sd=0.1))
MA2.3 <- arima.sim(n = 100 , model = list(order = c(0,0,2) , ma=c(0.1,-0.2) , sd=0.1))
MA2.4 <- arima.sim(n = 100 , model = list(order = c(0,0,2) , ma=c(-0.1,-0.2) , sd=0.1))
MA2.5 <- arima.sim(n = 100, model = list(order = c(0,0,2) , ma=c(0.1,0.8) , sd=0.1))
MA2.6 <- arima.sim(n = 100,model = list(order = c(0,0,2) , ma=c(-0.1,0.8) , sd=0.1))
MA2.7 <- arima.sim(n = 100, model = list(order = c(0,0,2) , ma=c(0.1,-0.8) , sd=0.1))
MA2.8 <- arima.sim(n = 100, model = list(order = c(0,0,2) , ma=c(-0.1,-0.8) , sd=0.1))
MA2.9 <- arima.sim(n = 100, model = list(order = c(0,0,2) , ma=c(0.8,0.1) , sd=0.1))
MA2.10 <- arima.sim(n = 100, model = list(order = c(0,0,2) , ma=c(-0.8,0.1) , sd=0.1))
MA2.11 <- arima.sim(n = 100, model = list(order = c(0,0,2) , ma=c(0.8,-0.1) , sd=0.1))
MA2.12 <- arima.sim(n = 100, model = list(order = c(0,0,2) , ma=c(-0.8,-0.1) , sd=0.1))
# grafiquemos estas series de tiempo simuladas
grathetacs.off()
par(mfrow = c(3,4))
ylm <- c(min(MA2.1, MA2.2, MA2.3, MA2.4, MA2.5, MA2.6, MA2.7, MA2.8, MA2.9, MA2.10, MA2.11, MA2.12) ,
max(MA2.1, MA2.2, MA2.3, MA2.4, MA2.5, MA2.6, MA2.7, MA2.8, MA2.9, MA2.10, MA2.11, MA2.12))
plot.ts(MA2.1, ylim = ylm, main = "MA(2) theta[1] = 0.1 y theta[2] = 0.2")
plot.ts(MA2.2, ylim = ylm, main = "MA(2) theta[1] = -0.1 y theta[2] = 0.2")
plot.ts(MA2.3, ylim = ylm, main = "MA(2) theta[1] = 0.1 y theta[2] = -0.2")
plot.ts(MA2.4, ylim = ylm, main = "MA(2) theta[1] = -0.1 y theta[2] = -0.2")
plot.ts(MA2.5, ylim = ylm, main = "MA(2) theta[1] = 0.1 y theta[2] = 0.8")
plot.ts(MA2.6, ylim = ylm, main = "MA(2) theta[1] = -0.1 y theta[2] = 0.8")
plot.ts(MA2.7, ylim = ylm, main = "MA(2) theta[1] = 0.1 y theta[2] = -0.8")
plot.ts(MA2.8, ylim = ylm, main = "MA(2) theta[1] = -0.1 y theta[2] = -0.8")
plot.ts(MA2.9, ylim = ylm, main = "MA(2) theta[1] = 0.8 y theta[2] = 0.1")
plot.ts(MA2.10, ylim = ylm, main = "MA(2) theta[1] = -0.8 y theta[2] = 0.1")
plot.ts(MA2.11, ylim = ylm, main = "MA(2) theta[1] = 0.8 y theta[2] = -0.1")
plot.ts(MA2.12, ylim = ylm, main = "MA(2) theta[1] = -0.8 y theta[2] = -0.1")
##Graficamos las AFC (Funciones de autocorrelacion simples)
grathetacs.off()
par(mfrow = c(3,4))
acf(MA2.1,main="ACF MA(2) theta[1] = 0.1 y theta[2] = 0.2")
acf(MA2.2,main="ACF MA(2) theta[1] = -0.1 y theta[2] = 0.2")
acf(MA2.3,main="ACF MA(2) theta[1] = 0.1 y theta[2] = -0.2")
acf(MA2.4,main="ACF MA(2) theta[1] = -0.1 y theta[2] = -0.2")
acf(MA2.5,main="ACF MA(2) theta[1] = 0.1 y theta[2] = 0.8")
acf(MA2.6,main="ACF MA(2) theta[1] = -0.1 y theta[2] = 0.8")
acf(MA2.7,main="ACF MA(2) theta[1] = 0.1 y theta[2] = -0.8")
acf(MA2.8,main="ACF MA(2) theta[1] = -0.1 y theta[2] = -0.8")
acf(MA2.9,main="ACF MA(2) theta[1] = 0.8 y theta[2] = 0.1")
acf(MA2.10,main="ACF MA(2) theta[1] = -0.8 y theta[2] = 0.1")
acf(MA2.11,main="ACF MA(2) theta[1] = 0.8 y theta[2] = -0.1")
acf(MA2.12,main="ACF MA(2) theta[1] = -0.8 y theta[2] = -0.1")
##Graficamos las PAFC (Funciones de autocorrelacion simples)
grathetacs.off()
par(mfrow = c(3,4))
pacf(MA2.1,main="PACF theta[1] = 0.1 y theta[2] = 0.2")
pacf(MA2.2,main="PACF theta[1] = -0.1 y theta[2] = 0.2")
pacf(MA2.3,main="PACF theta[1] = 0.1 y theta[2] = -0.2")
pacf(MA2.4,main="PACF theta[1] = -0.1 y theta[2] = -0.2")
pacf(MA2.5,main="PACF theta[1] = 0.1 y theta[2] = 0.8")
pacf(MA2.6,main="PACF theta[1] = -0.1 y theta[2] = 0.8")
pacf(MA2.7,main="PACF theta[1] = 0.1 y theta[2] = -0.8")
pacf(MA2.8,main="PACF theta[1] = -0.1 y theta[2] = -0.8")
pacf(MA2.9,main="PACF theta[1] = 0.8 y theta[2] = 0.1")
pacf(MA2.10,main="PACF theta[1] = -0.8 y theta[2] = 0.1")
pacf(MA2.11,main="PACF theta[1] = 0.8 y theta[2] = -0.1")
pacf(MA2.12,main="PACF theta[1] = -0.8 y theta[2] = -0.1")
######################################## Para N=30 (N PEQUEÑO)###########################################
##1.-Simulando Modelo AR(2)
#Para que el proceso sea estacionario se debe cumplir:
# -1 < phi(2) < 1 ó phi(1) + phi(2)< 1 ó phi(2)-phi(1)< 1
# Con este criterio generemos algunos coeficientes phi(1)y phi(2)
#definimos la semilla
set.seed(999)
##Generamos los modelos con diferentes valores de phi1 y phi2 con valores altos, bajos y negativos
AR2.1 <- arima.sim(n = 30 , model = list(order = c(2,0,0) , ar=c(0.1,0.2) , sd=0.1))
AR2.2 <- arima.sim(n = 30 , model = list(order = c(2,0,0) , ar=c(-0.1,0.2) , sd=0.1))
AR2.3 <- arima.sim(n = 30 , model = list(order = c(2,0,0) , ar=c(0.1,-0.2) , sd=0.1))
AR2.4 <- arima.sim(n = 30 , model = list(order = c(2,0,0) , ar=c(-0.1,-0.2) , sd=0.1))
AR2.5 <- arima.sim(n = 30, model = list(order = c(2,0,0) , ar=c(0.1,0.8) , sd=0.1))
AR2.6 <- arima.sim(n = 30,model = list(order = c(2,0,0) , ar=c(-0.1,0.8) , sd=0.1))
AR2.7 <- arima.sim(n = 30, model = list(order = c(2,0,0) , ar=c(0.1,-0.8) , sd=0.1))
AR2.8 <- arima.sim(n = 30, model = list(order = c(2,0,0) , ar=c(-0.1,-0.8) , sd=0.1))
AR2.9 <- arima.sim(n = 30, model = list(order = c(2,0,0) , ar=c(0.8,0.1) , sd=0.1))
AR2.10 <- arima.sim(n = 30, model = list(order = c(2,0,0) , ar=c(-0.8,0.1) , sd=0.1))
AR2.11 <- arima.sim(n = 30, model = list(order = c(2,0,0) , ar=c(0.8,-0.1) , sd=0.1))
AR2.12 <- arima.sim(n = 30, model = list(order = c(2,0,0) , ar=c(-0.8,-0.1) , sd=0.1))
# grafiquemos estas series de tiempo simuladas
graphics.off()
par(mfrow = c(3,4))
ylm <- c(min(AR2.1, AR2.2, AR2.3, AR2.4, AR2.5, AR2.6, AR2.7, AR2.8, AR2.9, AR2.10, AR2.11, AR2.12) ,
max(AR2.1, AR2.2, AR2.3, AR2.4, AR2.5, AR2.6, AR2.7, AR2.8, AR2.9, AR2.10, AR2.11, AR2.12))
plot.ts(AR2.1, ylim = ylm, main = "AR(2) phi[1] = 0.1 y phi[2] = 0.2")
plot.ts(AR2.2, ylim = ylm, main = "AR(2) phi[1] = -0.1 y phi[2] = 0.2")
plot.ts(AR2.3, ylim = ylm, main = "AR(2) phi[1] = 0.1 y phi[2] = -0.2")
plot.ts(AR2.4, ylim = ylm, main = "AR(2) phi[1] = -0.1 y phi[2] = -0.2")
plot.ts(AR2.5, ylim = ylm, main = "AR(2) phi[1] = 0.1 y phi[2] = 0.8")
plot.ts(AR2.6, ylim = ylm, main = "AR(2) phi[1] = -0.1 y phi[2] = 0.8")
plot.ts(AR2.7, ylim = ylm, main = "AR(2) phi[1] = 0.1 y phi[2] = -0.8")
plot.ts(AR2.8, ylim = ylm, main = "AR(2) phi[1] = -0.1 y phi[2] = -0.8")
plot.ts(AR2.9, ylim = ylm, main = "AR(2) phi[1] = 0.8 y phi[2] = 0.1")
plot.ts(AR2.10, ylim = ylm, main = "AR(2) phi[1] = -0.8 y phi[2] = 0.1")
plot.ts(AR2.11, ylim = ylm, main = "AR(2) phi[1] = 0.8 y phi[2] = -0.1")
plot.ts(AR2.12, ylim = ylm, main = "AR(2) phi[1] = -0.8 y phi[2] = -0.1")
##Graficamos las AFC (Funciones de autocorrelacion simples) para el AR(2)
graphics.off()
par(mfrow = c(3,4))
acf(AR2.1,main="AR(2) phi[1] = 0.1 y phi[2] = 0.2")
acf(AR2.2,main="AR(2) phi[1] = -0.1 y phi[2] = 0.2")
acf(AR2.3,main="AR(2) phi[1] = 0.1 y phi[2] = -0.2")
acf(AR2.4,main="AR(2) phi[1] = -0.1 y phi[2] = -0.2")
acf(AR2.5,main="AR(2) phi[1] = 0.1 y phi[2] = 0.8")
acf(AR2.6,main="AR(2) phi[1] = -0.1 y phi[2] = 0.8")
acf(AR2.7,main="AR(2) phi[1] = 0.1 y phi[2] = -0.8")
acf(AR2.8,main="AR(2) phi[1] = -0.1 y phi[2] = -0.8")
acf(AR2.9,main="AR(2) phi[1] = 0.8 y phi[2] = 0.1")
acf(AR2.10,main="AR(2) phi[1] = -0.8 y phi[2] = 0.1")
acf(AR2.11,main="AR(2) phi[1] = 0.8 y phi[2] = -0.1")
acf(AR2.12,main="AR(2) phi[1] = -0.8 y phi[2] = -0.1")
##Graficamos las PAFC (Funciones de autocorrelacion simples) para el AR(2)
graphics.off()
par(mfrow = c(3,4))
pacf(AR2.1,main="phi[1] = 0.1 y phi[2] = 0.2")
pacf(AR2.2,main="phi[1] = -0.1 y phi[2] = 0.2")
pacf(AR2.3,main="phi[1] = 0.1 y phi[2] = -0.2")
pacf(AR2.4,main="phi[1] = -0.1 y phi[2] = -0.2")
pacf(AR2.5,main="phi[1] = 0.1 y phi[2] = 0.8")
pacf(AR2.6,main="phi[1] = -0.1 y phi[2] = 0.8")
pacf(AR2.7,main="phi[1] = 0.1 y phi[2] = -0.8")
pacf(AR2.8,main="phi[1] = -0.1 y phi[2] = -0.8")
pacf(AR2.9,main="phi[1] = 0.8 y phi[2] = 0.1")
pacf(AR2.10,main="phi[1] = -0.8 y phi[2] = 0.1")
pacf(AR2.11,main="phi[1] = 0.8 y phi[2] = -0.1")
pacf(AR2.12,main="phi[1] = -0.8 y phi[2] = -0.1")
##1.-Simulando Modelo MA(2)
# Los Modelos MA siempre son estacionarios
#definimos la semilla
set.seed(999)
##Generamos los modelos con diferentes valores de phi1 y phi2 con valores altos, bajos y negativos
MA2.1 <- arima.sim(n = 30 , model = list(order = c(0,0,2) , ma=c(0.1,0.2) , sd=0.1))
MA2.2 <- arima.sim(n = 30 , model = list(order = c(0,0,2) , ma=c(-0.1,0.2) , sd=0.1))
MA2.3 <- arima.sim(n = 30 , model = list(order = c(0,0,2) , ma=c(0.1,-0.2) , sd=0.1))
MA2.4 <- arima.sim(n = 30 , model = list(order = c(0,0,2) , ma=c(-0.1,-0.2) , sd=0.1))
MA2.5 <- arima.sim(n = 30, model = list(order = c(0,0,2) , ma=c(0.1,0.8) , sd=0.1))
MA2.6 <- arima.sim(n = 30,model = list(order = c(0,0,2) , ma=c(-0.1,0.8) , sd=0.1))
MA2.7 <- arima.sim(n = 30, model = list(order = c(0,0,2) , ma=c(0.1,-0.8) , sd=0.1))
MA2.8 <- arima.sim(n = 30, model = list(order = c(0,0,2) , ma=c(-0.1,-0.8) , sd=0.1))
MA2.9 <- arima.sim(n = 30, model = list(order = c(0,0,2) , ma=c(0.8,0.1) , sd=0.1))
MA2.10 <- arima.sim(n = 30, model = list(order = c(0,0,2) , ma=c(-0.8,0.1) , sd=0.1))
MA2.11 <- arima.sim(n = 30, model = list(order = c(0,0,2) , ma=c(0.8,-0.1) , sd=0.1))
MA2.12 <- arima.sim(n = 30, model = list(order = c(0,0,2) , ma=c(-0.8,-0.1) , sd=0.1))
# grafiquemos estas series de tiempo simuladas
grathetacs.off()
par(mfrow = c(3,4))
ylm <- c(min(MA2.1, MA2.2, MA2.3, MA2.4, MA2.5, MA2.6, MA2.7, MA2.8, MA2.9, MA2.10, MA2.11, MA2.12) ,
max(MA2.1, MA2.2, MA2.3, MA2.4, MA2.5, MA2.6, MA2.7, MA2.8, MA2.9, MA2.10, MA2.11, MA2.12))
plot.ts(MA2.1, ylim = ylm, main = "MA(2) theta[1] = 0.1 y theta[2] = 0.2")
plot.ts(MA2.2, ylim = ylm, main = "MA(2) theta[1] = -0.1 y theta[2] = 0.2")
plot.ts(MA2.3, ylim = ylm, main = "MA(2) theta[1] = 0.1 y theta[2] = -0.2")
plot.ts(MA2.4, ylim = ylm, main = "MA(2) theta[1] = -0.1 y theta[2] = -0.2")
plot.ts(MA2.5, ylim = ylm, main = "MA(2) theta[1] = 0.1 y theta[2] = 0.8")
plot.ts(MA2.6, ylim = ylm, main = "MA(2) theta[1] = -0.1 y theta[2] = 0.8")
plot.ts(MA2.7, ylim = ylm, main = "MA(2) theta[1] = 0.1 y theta[2] = -0.8")
plot.ts(MA2.8, ylim = ylm, main = "MA(2) theta[1] = -0.1 y theta[2] = -0.8")
plot.ts(MA2.9, ylim = ylm, main = "MA(2) theta[1] = 0.8 y theta[2] = 0.1")
plot.ts(MA2.10, ylim = ylm, main = "MA(2) theta[1] = -0.8 y theta[2] = 0.1")
plot.ts(MA2.11, ylim = ylm, main = "MA(2) theta[1] = 0.8 y theta[2] = -0.1")
plot.ts(MA2.12, ylim = ylm, main = "MA(2) theta[1] = -0.8 y theta[2] = -0.1")
##Graficamos las AFC (Funciones de autocorrelacion simples)
grathetacs.off()
par(mfrow = c(3,4))
acf(MA2.1,main="ACF MA(2) theta[1] = 0.1 y theta[2] = 0.2")
acf(MA2.2,main="ACF MA(2) theta[1] = -0.1 y theta[2] = 0.2")
acf(MA2.3,main="ACF MA(2) theta[1] = 0.1 y theta[2] = -0.2")
acf(MA2.4,main="ACF MA(2) theta[1] = -0.1 y theta[2] = -0.2")
acf(MA2.5,main="ACF MA(2) theta[1] = 0.1 y theta[2] = 0.8")
acf(MA2.6,main="ACF MA(2) theta[1] = -0.1 y theta[2] = 0.8")
acf(MA2.7,main="ACF MA(2) theta[1] = 0.1 y theta[2] = -0.8")
acf(MA2.8,main="ACF MA(2) theta[1] = -0.1 y theta[2] = -0.8")
acf(MA2.9,main="ACF MA(2) theta[1] = 0.8 y theta[2] = 0.1")
acf(MA2.10,main="ACF MA(2) theta[1] = -0.8 y theta[2] = 0.1")
acf(MA2.11,main="ACF MA(2) theta[1] = 0.8 y theta[2] = -0.1")
acf(MA2.12,main="ACF MA(2) theta[1] = -0.8 y theta[2] = -0.1")
##Graficamos las PAFC (Funciones de autocorrelacion simples)
grathetacs.off()
par(mfrow = c(3,4))
pacf(MA2.1,main="PACF theta[1] = 0.1 y theta[2] = 0.2")
pacf(MA2.2,main="PACF theta[1] = -0.1 y theta[2] = 0.2")
pacf(MA2.3,main="PACF theta[1] = 0.1 y theta[2] = -0.2")
pacf(MA2.4,main="PACF theta[1] = -0.1 y theta[2] = -0.2")
pacf(MA2.5,main="PACF theta[1] = 0.1 y theta[2] = 0.8")
pacf(MA2.6,main="PACF theta[1] = -0.1 y theta[2] = 0.8")
pacf(MA2.7,main="PACF theta[1] = 0.1 y theta[2] = -0.8")
pacf(MA2.8,main="PACF theta[1] = -0.1 y theta[2] = -0.8")
pacf(MA2.9,main="PACF theta[1] = 0.8 y theta[2] = 0.1")
pacf(MA2.10,main="PACF theta[1] = -0.8 y theta[2] = 0.1")
pacf(MA2.11,main="PACF theta[1] = 0.8 y theta[2] = -0.1")
pacf(MA2.12,main="PACF theta[1] = -0.8 y theta[2] = -0.1")
|
7b8202045d93426ffd7b40a2dd36d0f7525f25e4
|
e4b5596f5dc9dd0b1408ee2a72d5274eb97fc0a5
|
/Course2/week3/w3.R
|
4b6b2bc55651f0c14d06ce1bbd7c686c84be3a16
|
[] |
no_license
|
luuduytung/statistics-with-r-coursera
|
145cd0541fb1b4e0de24526cc9e40ed0df261708
|
bee573560badb61500cbb713f2a131c7250bfda8
|
refs/heads/master
| 2020-03-31T13:43:06.435476
| 2018-10-09T23:15:55
| 2018-10-09T23:15:55
| 152,266,738
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 701
|
r
|
w3.R
|
library(statsr)
library(dplyr)
library(ggplot2)
data(nc)
str(nc)
summary(nc$gained)
ggplot(data = nc,aes(x=habit,y=weight))+geom_boxplot()
nc %>% group_by(habit) %>% summarise(x_bar = mean(weight))
nc %>% group_by(habit) %>% summarise(nb = n())
inference(y = weight, x = habit, data = nc, statistic = "mean", type = "ht", null = 0,
alternative = "twosided", method = "theoretical")
inference(y=weight, x=habit,data=nc,statistic="mean",type="ci",method="theoretical")
inference(y = weight, x = habit, data = nc, statistic = "mean", type = "ci",
method = "theoretical", order = c("smoker","nonsmoker"))
nc %>% group_by(mature) %>% summarise(x_max = max(mage),x_min=min(mage))
|
5bf7b4c19cf2a235720371db10bfa3cd8a6fafba
|
5e2726e2ea5bff209473d757bce76ac20e4bd17a
|
/fishy_hz.R
|
b755ed47382b3540f834f67f7b658a1d52735569
|
[] |
no_license
|
julievdh/FHL
|
f8170d01300897b2d4a0359883ca7830ec24d917
|
ad68e1d2615b8f09a84090a1d3de9de6cc04679b
|
refs/heads/master
| 2020-04-24T17:48:22.110459
| 2018-05-27T16:04:29
| 2018-05-27T16:04:29
| 38,321,517
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,801
|
r
|
fishy_hz.R
|
# look at fin beat stuff....
# David L Miller 5 December 2017
hz <- read.csv("FishVO2Hz.csv")
# make codes for the speeds
hz$speedcode <- "H"
hz$speedcode[hz$speed == 0.5] <- "L"
hz$speedcode[hz$speed == 1.5] <- "M"
# speeds set at 0.5, 1.5 and then the max that got data for both turbulent
# and laminar flows. (Measured in bodylengths/s of course)
library(ggplot2)
# quick visualisation
p <- ggplot(hz) +
#geom_point(aes(x=speed, y=VO2minBac, colour=cond)) +
geom_point(aes(x=PecHz, y=VO2minBac, colour=cond)) +
theme_minimal() +
facet_wrap(~Fish)
print(p)
# let's fit a GAM!
library(mgcv)
# this is fitting a model that says:
# O2 varies as a function of pectoral hz, but we should estimate a different
# curve for each condition. We also think that fish acts like a random effect
b_pec <- gam(VO2minBac~s(PecHz, cond, bs="fs") + s(Fish, bs="re"),
data=hz, method="REML")
# check this model
gam.check(b_pec)
# looks like there might be a minor issue with heteroskedasticity? probably not
# worth worrying about (top right). Looks like the model predicts well (bottom
# right). Residuals look normal enough (left side). Text output shows that we let
# our model be wiggly enough
summary(b_pec)
# v. high % deviance explained, maybe the fish random effect is not super-relevant
# but we can include anyway as we think there is between-fish variability
# now to make predictions, make a grid of values with all the pectoral hz we need
# plus values for the condition and fish
preddat <- expand.grid(PecHz = seq(0.5, 3.5, by=0.1),
cond = c("T", "L"),
Fish = unique(hz$Fish))
# make predictions, also predict the standard error
pr <- predict(b_pec, preddat, type="response", se=TRUE)
preddat$VO2minBac <- pr$fit
# generate the CIs
preddat$upper <- pr$fit + 2*pr$se.fit
preddat$lower <- pr$fit - 2*pr$se.fit
# what does that look like?
p <- ggplot(hz) +
geom_line(aes(x=PecHz, y=VO2minBac, colour=cond, group=cond), data=preddat) +
geom_line(aes(x=PecHz, y=upper, colour=cond, group=cond), linetype=2, data=preddat) +
geom_line(aes(x=PecHz, y=lower, colour=cond, group=cond), linetype=2, data=preddat) +
geom_text(aes(x=PecHz, y=VO2minBac, colour=cond, label=speedcode)) +
scale_colour_brewer(type="qual") +
theme_minimal() +
labs(x="Pectoral Hz", colour="Condition") +
facet_wrap(~Fish, nrow=2)
print(p)
# ignore fish and predict excluding the fish random effect
preddat <- expand.grid(PecHz = seq(0.5, 3.5, by=0.1),
cond = c("T", "L"), Fish="F5")
pr <- predict(b_pec, preddat, type="response", se=TRUE, exclude="s(Fish)")
preddat$VO2minBac <- pr$fit
preddat$upper <- pr$fit + 2*pr$se.fit
preddat$lower <- pr$fit - 2*pr$se.fit
# what does that look like?
p <- ggplot(hz) +
geom_line(aes(x=PecHz, y=VO2minBac, colour=cond, group=cond), data=preddat) +
geom_line(aes(x=PecHz, y=upper, colour=cond, group=cond), linetype=2, data=preddat) +
geom_line(aes(x=PecHz, y=lower, colour=cond, group=cond), linetype=2, data=preddat) +
geom_text(aes(x=PecHz, y=VO2minBac, colour=cond, label=speedcode)) +
scale_colour_brewer(type="qual") +
theme_minimal() +
labs(x="Pectoral Hz", colour="Condition")
print(p)
# what about caudal fin?
# quick visualisation
p <- ggplot(hz) +
geom_point(aes(x=CaudHz, y=VO2minBac, colour=cond)) +
theme_minimal() +
facet_wrap(~Fish)
print(p)
# try the same kind of model as above for the caudal?
# need to reduce k (smooth complexity) as we don't have many unique values
b_caud <- gam(VO2minBac~s(CaudHz, cond, bs="fs", k=5) + s(Fish, bs="re"),
data=hz, method="REML")
# model check
summary(b_caud)
# less deviance explained than before and less wiggly effects?
plot(b_caud)
# do we believe that O2 is n-shaped in caudal hz?
preddat <- expand.grid(CaudHz = seq(0, 5, by=0.1),
cond = c("T", "L"),
Fish = unique(hz$Fish))
preddat$VO2minBac <- predict(b_caud, preddat, type="response")
p <- ggplot(hz) +
geom_line(aes(x=CaudHz, y=VO2minBac, colour=cond, group=cond), data=preddat) +
geom_text(aes(x=CaudHz, y=VO2minBac, colour=cond, label=speedcode)) +
scale_colour_brewer(type="qual") +
theme_minimal() +
labs(x="Caudal Hz", colour="Condition")# +
# facet_wrap(~Fish, nrow=2)
print(p)
per <- read.csv("FHL_periodicity.csv")
per$condition <- as.factor(per$condition)
# try the same kind of model as above for the caudal?
# need to reduce k (smooth complexity) as we don't have many unique values
b_pery <- gam(yfreq~s(speed, condition, bs="fs", k=5) + s(fish, bs="re"),
data=per, method="REML")
# model check
summary(b_caud)
# less deviance explained than before and less wiggly effects?
plot(b_caud)
# do we believe that O2 is n-shaped in caudal hz?
preddat <- expand.grid(CaudHz = seq(0, 5, by=0.1),
cond = c("T", "L"),
Fish = unique(hz$Fish))
preddat$VO2minBac <- predict(b_caud, preddat, type="response")
p <- ggplot(hz) +
geom_line(aes(x=CaudHz, y=VO2minBac, colour=cond, group=cond), data=preddat) +
geom_text(aes(x=CaudHz, y=VO2minBac, colour=cond, label=speedcode)) +
scale_colour_brewer(type="qual") +
theme_minimal() +
labs(x="Pectoral Hz", colour="Condition") +
facet_wrap(~Fish, nrow=2)
print(p)
per <- read.csv("FHL_periodicity.csv")
# add condition code
per$Flow <- "L"
per$Flow[per$condition == 1] <- "L"
per$Flow[per$condition == 0] <- "T"
per$Flow <- as.factor(per$Flow)
p <- ggplot(per) +
geom_point(aes(x=speed, y=zfreq,colour=condition), data=per) +
scale_colour_manual(values=c("red", "blue")) +
scale_shape_manual(values=c(17, 16))+
theme_minimal() +
labs(y="z-frequency", x = "Speed BL/s", colour="Condition")
print(p)
modr <- nlme(zfreq~a+b*speed,
fixed = a+b~Flow,
random = a~1|fish,
start=c(0, 0.5),
data=per)
# FANCY MODELS
#
#b_use <- gam(VO2minBac~s(PecHz, CaudHz, cond,
# bs="fs", k=10, xt="tp") +
# s(Fish, bs="re"),
# data=hz, method="REML")
#
#
#
#par(mfrow=c(1,2))
#hist(hz$PecHz)
#hist(hz$CaudHz)
#
#
#
#preddat <- expand.grid(PecHz = seq(0, 3.5, len=100),
# CaudHz = seq(0, 5, len=100),
# cond = c("T", "L"),
# Fish = unique(hz$Fish))
#
#preddat$VO2minBac <- predict(b_use, preddat, type="response", exclude="s(Fish)")
#
#
## YIKES plotting
#
#pred_yikes <- preddat
#
#
#library(viridis)
#
#p <- ggplot(pred_yikes) +
# geom_tile(aes(x=PecHz, y=CaudHz, fill=VO2minBac)) +
# geom_point(aes(x=PecHz, y=CaudHz), data=hz) +
# theme_minimal() +
# scale_fill_viridis() +
# coord_equal() +
# facet_wrap(~cond)
#
#print(p)
#
#
#
|
7a6df44812890c492ab702c67fa9c78695e5e0eb
|
2704ea941fbbd3f3e0a9ac7cf03b03111429b46c
|
/man/WRRnnls.Rd
|
6a7732d93db041ce972266dc2c2740f12a032d07
|
[
"Apache-2.0"
] |
permissive
|
olangsrud/experimentalRpackage
|
6b43daf72e56069161ec36605c548f4a628f72f9
|
a897f1f608af62c8fafb2a51d8f06a0764902a85
|
refs/heads/master
| 2021-09-24T23:05:52.640787
| 2021-09-16T13:54:19
| 2021-09-16T13:54:19
| 215,278,537
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 2,503
|
rd
|
WRRnnls.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/WeightedRidgeRegression.R
\name{WRRnnls}
\alias{WRRnnls}
\alias{WRRginv}
\alias{WRRglmnet}
\title{Special weighted and ridge penalized regression}
\usage{
WRRnnls(x, y, xExact = NULL, yExact = NULL, wExact = 1000, lambda = 1e-04^2)
WRRginv(x, y, xExact = NULL, yExact = NULL, wExact = 1000, lambda = 1e-04^2)
WRRglmnet(
x,
y,
xExact = NULL,
yExact = NULL,
wExact = 1000,
lambda = exp(((2 * 12):(-17 * 2))/2),
intercept = FALSE,
standardize = FALSE,
thresh = 1e-10,
lower.limits = 0,
...
)
}
\arguments{
\item{x}{Input matrix, each row is an ordinary observation}
\item{y}{Ordinary response observation (vector or matrix)}
\item{xExact}{Input matrix, each row is a highly weighted observation}
\item{yExact}{Highly weighted response observation (vector or matrix)}
\item{wExact}{Weight for highly weighted observations}
\item{lambda}{Ridge regression penalty parameter (sequence when glmnet)}
\item{intercept}{glmnet parameter}
\item{standardize}{glmnet parameter}
\item{thresh}{glmnet parameter}
\item{lower.limits}{glmnet parameter}
\item{...}{Further glmnet parameters}
}
\value{
Output from \code{\link{nnls}}, \code{\link{glmnet}} or coefficient estimate calculated using \code{\link{ginv}}
}
\description{
By using \code{\link{nnls}}, \code{\link{ginv}} or \code{\link{glmnet}}
}
\examples{
x <- cbind(1:11, -11:-1, c(1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 2))
y <- matrix(5 - sin(2 * (1:11)), dimnames = list(NULL, "y"))
x1 <- x[1:9, ]
x2 <- x[10:11, ]
y1 <- y[1:9, , drop = FALSE]
y2 <- y[10:11, , drop = FALSE]
# Generalized inverse estimation
ginvCoef <- WRRginv(x1, y1, x2, y2)
# Non-negative estimation by nnls
# Coefficients in output element x
nnlsCoef <- WRRnnls(x1, y1, x2, y2)$x
# Non-negative estimation by glmnet
# Take out best fit from matrix of coefficients
gn <- WRRglmnet(x1, y1, x2, y2)
glmnetCoef <- coef(gn)[-1, which.max(gn$dev.ratio), drop = FALSE]
# Another estimation by glmnet (not non-negative)
# Take out best fit from matrix of coefficients
gnInf <- WRRglmnet(x1, y1, x2, y2, lower.limits = -Inf)
glmnetCoefInf <- coef(gnInf)[-1, which.max(gn$dev.ratio), drop = FALSE]
# All coefficients
coef4 <- as.matrix(cbind(ginvCoef, nnlsCoef, glmnetCoef, glmnetCoefInf))
colnames(coef4) <- c("ginv", "nnls", "glmnet", "glmnetInf")
print(coef4)
# Original y and fitted values. Close fit for last two observation.
cbind(y, x \%*\% coef4)
}
\author{
Øyvind Langsrud
}
|
62fb70eec3304d108affe82f1b87bd16941285a4
|
94e33777e055bf71f867bd158d25f76d75e01bcc
|
/R/read_SegmentDirectory.R
|
e23be11eb673f46a524dd8dbc1c1fc001d812a48
|
[] |
no_license
|
norgardp/psd4
|
305bd8094815dca6cf55f276e5b6d37f29ecb82c
|
5b8a291c6b4e072d463876b4e88d771256e98783
|
refs/heads/master
| 2022-09-24T15:13:36.762092
| 2020-06-03T20:25:59
| 2020-06-03T20:25:59
| 269,189,048
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 703
|
r
|
read_SegmentDirectory.R
|
read_SegmentDirectory = function(dname, dets=NA, type)
{
initial_directory = getwd();
setwd(dname);
# Determine if some or all detectors are to be included in the return list
detectors = dir(pattern = "det[0-9]{2}.raw");
if( (length(dets) > 1) & !any(is.na(dets)) )
{
desired_detectors = sapply(1:length(dets), function(i) sprintf("det%02d.raw", dets[i]));
detectors = detectors[sapply(1:length(desired_detectors), function(i) grep(detectors, pattern=desired_detectors[i]))];
}
retval = list();
retval[['segment']] = dname;
retval = do.call(c, list(retval, lapply(1:length(detectors), function(i) read_DetectorData(detectors[i], type) )));
setwd(initial_directory);
return(retval);
}
|
a6801b1c5bd2f6afb10f8c1affb9f35582eb44ed
|
74fa8fe5f0d5568fcde60e569463469a8d172190
|
/man/get_mentioned_gkg_gcams.Rd
|
5904086f05252f99f459b4f89ef9b2a423ac9e59
|
[] |
no_license
|
raj0926/gdeltr2
|
d3d792affdef8230a4cf4a86cbc15aecad07b670
|
398a97a64bebac6a1662da2fcb5d5b65f33c2f2b
|
refs/heads/master
| 2020-12-25T11:21:05.914927
| 2016-06-05T18:30:59
| 2016-06-05T18:30:59
| 60,638,860
| 2
| 0
| null | 2016-06-07T19:02:54
| 2016-06-07T19:02:52
|
R
|
UTF-8
|
R
| false
| true
| 403
|
rd
|
get_mentioned_gkg_gcams.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gdelt_event_gkg.R
\name{get_mentioned_gkg_gcams}
\alias{get_mentioned_gkg_gcams}
\title{Returns GCAM codes from a gkg data frame}
\usage{
get_mentioned_gkg_gcams(gdelt_data, merge_gcam_codes = F, filter_na = T,
return_wide = F)
}
\arguments{
\item{return_wide}{}
}
\description{
Returns GCAM codes from a gkg data frame
}
|
64370aeb15a3cf2c0f9bb62c71d1d16df2bc1210
|
96de9c558793e8a9d88406d4848112d28b9f3a8b
|
/server.R
|
7b914930651d81002e5883b05fdf6a68af9258c8
|
[] |
no_license
|
xujz4f/Shiny-Application-and-Reproducible-Pitch
|
7226f126636b25bbe2269fe98a3108051311f824
|
493d4ee739ce0ea0886087037a2b891a6fd66664
|
refs/heads/master
| 2021-01-21T06:39:34.742869
| 2017-02-27T04:30:07
| 2017-02-27T04:30:07
| 83,268,320
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 221
|
r
|
server.R
|
library(datasets)
function(input, output) {
output$phonePlot <- renderPlot({
barplot(WorldPhones[input$year,],
main=input$year,
ylab="Number of Telephones",
xlab="region")
})
}
|
513f54438ae412c24c3a455eeee4d036f96a04db
|
df84e16f31db27af9cb69d95cd802ef7e4b4a065
|
/R/get_functions.R
|
76c61bc8de05eddd1b54e34d658578a3fc8f3f1e
|
[] |
no_license
|
banskt/ebmr.alpha
|
375d1cace6dce2c59e5ddf0095c3eec36d30d80b
|
53c025f09e46f72e20f7240839408c79fbcfc807
|
refs/heads/main
| 2023-03-22T00:52:12.301317
| 2021-03-15T18:25:00
| 2021-03-15T18:25:00
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 110
|
r
|
get_functions.R
|
ebmr_get_elbodiff = function(fit){
niter = length(fit$elbo)
return(fit$elbo[niter] - fit$elbo[niter-1])
}
|
96653fe856ed2cb6cc62744fb482e10721c3d87d
|
1c0975d66acdc096f06f7827618f69a6792a3e77
|
/man/match_font.Rd
|
7e741ab85d1be697e0262dc635f72bd0de9a7647
|
[
"MIT"
] |
permissive
|
r-lib/systemfonts
|
2ad74f06f42e54e0bc48d5dd88c8e9cbc3442818
|
4ccb03c9e5d6f64b63fcd5e133830f906dab405f
|
refs/heads/main
| 2023-05-10T23:41:40.440662
| 2023-05-08T14:04:11
| 2023-05-08T14:04:11
| 190,163,549
| 58
| 16
|
NOASSERTION
| 2023-04-20T01:16:32
| 2019-06-04T08:45:46
|
C++
|
UTF-8
|
R
| false
| true
| 1,021
|
rd
|
match_font.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/match_font.R
\name{match_font}
\alias{match_font}
\title{Find a system font by name and style}
\usage{
match_font(family, italic = FALSE, bold = FALSE)
}
\arguments{
\item{family}{The name of the font family}
\item{italic, bold}{logicals indicating the font style}
}
\value{
A list containing the path locating the font file and the 0-based
index of the font in the file.
}
\description{
This function locates the font file (and index) best matching a name and
optional style (italic/bold). A font file will be returned even if a match
isn't found, but it is not necessarily similar to the requested family and
it should not be relied on for font substitution. The aliases \code{"sans"},
\code{"serif"}, and \code{"mono"} match to the system default sans-serif, serif, and
mono fonts respectively (\code{""} is equivalent to \code{"sans"}).
}
\examples{
# Get the system default sans-serif font in italic
match_font('sans', italic = TRUE)
}
|
673bd9d16c4b7be0b033c2b14cde51361da57869
|
d5e85cab536f569fb662d942ebe1d51315234ab5
|
/man/HiddenF.Rd
|
a99c6e640a0608783966de33897bef9f277beef7
|
[] |
no_license
|
cran/hiddenf
|
60017a965a7200d172da4ded5b42197327c33f34
|
2e26480358c38caa1da8fc08da4c0c320d8ca6a7
|
refs/heads/master
| 2020-12-24T15:49:52.621097
| 2016-01-05T22:29:08
| 2016-01-05T22:29:08
| 20,746,855
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,452
|
rd
|
HiddenF.Rd
|
\name{HiddenF}
\alias{HiddenF}
\title{Hidden F function for matrix data
}
\description{
Fits linear model to ymtx, a matrix of responses of dimension r-by-c. Constructs all possible configurations of rows into two non-empty groups, then, for each configuration, fits full factorial effects models with three factors for group, group-by-column, row and row nested within column. The maximum F-ratio for group-by-column interaction is reported along with Bonferroni-adjusted p-value.
}
\usage{
HiddenF(ymtx)
}
\arguments{
\item{ymtx}{
A matrix of responses, with rows corresponding to levels of one factor, and columns the levels of a second factor
}
}
\value{List-object of class `HiddenF' with components
\item{adjpvalue}{(Bonferroni-adjusted) pvalue from configuration with maximal hidden additivity}
\item{config.vector}{Vector of group indicators for configuration with maximal hidden additivity}
\item{tall}{A list with components y, row, col}
\item{cc}{Number of possible configurations} }
\references{
Franck CT, Nielsen, DM and Osborne, JA. (2013) A Method for Detecting Hidden
Additivity in two-factor Unreplicated Experiments, Computational Statistics
and Data Analysis, 67:95-104.}
\author{
Jason A. Osborne \email{jaosborn@ncsu.edu}, Christopher T. Franck and
Bongseog Choi }
\seealso{\code{\link{summary.HiddenF}}}
\examples{
library(hiddenf)
data(cjejuni.mtx)
cjejuni.out <- HiddenF(cjejuni.mtx)
summary(cjejuni.out) }
\keyword{anova}
|
cd6f91e2d1d0f786da3fd70139221dea46f37362
|
46c1cdb91955b383ea6176d108f2d67fd0cd5a8d
|
/ISCAM2/R/iscamaddlnorm.R
|
58fe85faf9a55f4c56a68179bbe8ad15b9d87c91
|
[] |
no_license
|
shannonpileggi/SP--Pablo--RProgramming
|
0f1a6621847d013e0c445059894e5bad5a940086
|
ad60dc509b077dd9657f7e97f587809251e19c18
|
refs/heads/master
| 2018-10-22T19:21:27.375766
| 2018-07-21T07:01:43
| 2018-07-21T07:01:43
| 114,397,446
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 452
|
r
|
iscamaddlnorm.R
|
#' iscamaddlnorm Function
#'
#' This function creates a histogram of the inputted variable and overlays a log normal density function.
#' @param x a vector of numeric values.
#' @keywords lognormal
#' @export
#' @examples
#' iscamaddlnorm(x)
iscamaddlnorm <- function(x){
hist(x, freq=FALSE, xlab = deparse(substitute(x)), ylim=c(0,1))
min = 0
max = max(x)
myseq = seq(min, max, .001)
lines(myseq, dlnorm(myseq, mean(log(x)), sd(log(x))))
}
|
a7be725e7d5b3e7e4dcea16a1da5a883ffba26cc
|
8951569f9b40c540e8606867db71c40a27539422
|
/2019_R_Files/Partials_Header_Treatment.R
|
2f62aa7627fdf5869151752418339c58a071300f
|
[
"CC-BY-4.0",
"MIT"
] |
permissive
|
HannesOberreiter/coloss_honey_bee_colony_losses_austria
|
3be5c198d8e253d3e6c810025b71bf63b7b933e4
|
f1bdb47e2c5e647788a080925a82c21e51e0199e
|
refs/heads/master
| 2021-06-15T13:09:58.171620
| 2021-04-02T12:33:22
| 2021-04-02T12:33:22
| 181,077,729
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,899
|
r
|
Partials_Header_Treatment.R
|
##############################
### Survey Bee Hive Losses ###
# (c) 2019 Hannes Oberreiter #
##############################
##############################
# List of Factors we want in our Plot
treatmentList = list(
c("T_drone_", "T_drone_total", "Drone brood removal"),
c("T_hyperthermia_", "T_hyperthermia_total", "Hyperthermia"),
c("T_biotechnical_", "T_biotechnical_total", "Other biotechnical method"),
c("T_formic_short_", "T_formic_short_total", "Formic acid - short term"),
c("T_formic_long_", "T_formic_long_total", "Formic acid - long term"),
c("T_lactic_", "T_lactic_total", "Lactic acid"),
#c("T_oxalic_trickle_pure_", "T_oxalic_trickle_pure_total", "Oxalic acid - trickling pure"),
c("T_oxalic_vapo_", "T_oxalic_vapo_total", "Oxalic acid - sublimation"),
#c("T_oxalic_trickle_mix_", "T_oxalic_trickle_mix_total", "Oxalic acid mixture"),
c("T_oxalic_trickle_", "T_oxalic_trickle_total", "Oxalic acid - trickling"),
c("T_thymol_", "T_thymol_total", "Thymol"),
#c("T_synthetic_apistan_", "T_apistan_total", "Tau-fluvalinat"),
#c("T_synthetic_flumethrin_", "T_flumethrin_total", "Flumethrin"),
#c("T_synthetic_amitraz_strips_", "T_amitraz_strips_total", "Amitraz - Strips"),
#c("T_synthetic_amitraz_vapo_", "T_amitraz_vapo_total", "Amitraz - Vaporize"),
#c("T_synthetic_coumaphos_p_", "T_coumaphos_p_total", "Coumaphos - Perizin"),
#c("T_synthetic_coumaphos_c_", "T_coumaphos_c_total", "Coumaphos - Checkmite+"),
#c("T_synthetic_synother_", "T_chemical_total", "Other Synthetic"),
c("T_synthetic_", "T_synthetic_total", "Synthetic methods"),
c("T_other_", "T_other_total", "Another method")
)
# Second List were Mix is not combined with trickle
treatmentListwMix = list(
c("T_drone_", "T_drone_total", "Drone brood removal"),
c("T_hyperthermia_", "T_hyperthermia_total", "Hyperthermia"),
c("T_biotechnical_", "T_biotechnical_total", "Other biotechnical method"),
c("T_formic_short_", "T_formic_short_total", "Formic acid - short term"),
c("T_formic_long_", "T_formic_long_total", "Formic acid - long term"),
c("T_lactic_", "T_lactic_total", "Lactic acid"),
#c("T_oxalic_trickle_pure_", "T_oxalic_trickle_pure_total", "Oxalic acid - trickling pure"),
c("T_oxalic_vapo_", "T_oxalic_vapo_total", "Oxalic acid - sublimation"),
c("T_oxalic_trickle_mix_", "T_oxalic_trickle_mix_total", "Oxalic acid mixture"),
c("T_oxalic_trickle_", "T_oxalic_trickle_total", "Oxalic acid - trickling"),
c("T_thymol_", "T_thymol_total", "Thymol"),
#c("T_synthetic_apistan_", "T_apistan_total", "Tau-fluvalinat"),
#c("T_synthetic_flumethrin_", "T_flumethrin_total", "Flumethrin"),
#c("T_synthetic_amitraz_strips_", "T_amitraz_strips_total", "Amitraz - Strips"),
#c("T_synthetic_amitraz_vapo_", "T_amitraz_vapo_total", "Amitraz - Vaporize"),
#c("T_synthetic_coumaphos_p_", "T_coumaphos_p_total", "Coumaphos - Perizin"),
#c("T_synthetic_coumaphos_c_", "T_coumaphos_c_total", "Coumaphos - Checkmite+"),
#c("T_synthetic_synother_", "T_chemical_total", "Other Synthetic"),
c("T_synthetic_", "T_synthetic_total", "Synthetic methods"),
c("T_other_", "T_other_total", "Another method")
)
fulltreatmentList = list(
c("T_drone_", "T_drone_total", "Drone brood removal"),
c("T_hyperthermia_", "T_hyperthermia_total", "Hyperthermia"),
c("T_biotechnical_", "T_biotechnical_total", "Other biotechnical method"),
c("T_formic_short_", "T_formic_short_total", "Formic acid - short term"),
c("T_formic_long_", "T_formic_long_total", "Formic acid - long term"),
c("T_lactic_", "T_lactic_total", "Lactic acid"),
c("T_oxalic_trickle_pure_", "T_oxalic_trickle_pure_total", "Oxalic acid - trickling"),
c("T_oxalic_vapo_", "T_oxalic_vapo_total", "Oxalic acid - sublimation"),
c("T_oxalic_trickle_mix_", "T_oxalic_trickle_mix_total", "Oxalic acid mixture"),
#c("T_oxalic_trickle_all_", "T_oxalic_trickle_all_total", "Oxalic acid - trickling all methods"),
c("T_thymol_", "T_thymol_total", "Thymol"),
c("T_synthetic_apistan_", "T_apistan_total", "Tau-fluvalinat"),
c("T_synthetic_flumethrin_", "T_flumethrin_total", "Flumethrin"),
c("T_synthetic_amitraz_strips_", "T_amitraz_strips_total", "Amitraz - Strips"),
c("T_synthetic_amitraz_vapo_", "T_amitraz_vapo_total", "Amitraz - Vaporize"),
c("T_synthetic_coumaphos_p_", "T_coumaphos_p_total", "Coumaphos - Perizin"),
c("T_synthetic_coumaphos_c_", "T_coumaphos_c_total", "Coumaphos - Checkmite+"),
c("T_synthetic_synother_", "T_chemical_total", "Other Synthetic"),
#c("T_synthetic_", "T_synthetic_total", "Synthetic methods"),
c("T_other_", "T_other_total", "Another method")
)
#### SPRING Treatment Values ####
# Dummy List
D.CACHE <- list()
# Loop through our Treatment Types
for(i in treatmentList){
# Get Columns which are starting with List value
treatmentexp <- paste("(", i[1], ")\\S*0[1-2]", sep = "")
x <- grepl(treatmentexp, colnames(D.FULL), fixed = FALSE, perl = TRUE)
# sum the row values (means 1 = for 1 month, 2 = 2 months etc.)
D.CACHE[[paste(i[2], "_spring", sep = "")]] <- rowSums(D.FULL[, x], na.rm = TRUE)
# create a yes no list too
xn <- paste( i[2], "yn_spring", sep = "")
D.CACHE[[xn]] <- ifelse((rowSums(D.FULL[, x], na.rm = TRUE)) > 0, 1, 0)
}
# Convert List to Dataframe
D.CACHE <- data.frame(D.CACHE)
D.FULL <- cbind(D.FULL, D.CACHE)
#### SUMMER Treatment Values ####
# Dummy List
D.CACHE <- list()
# Loop through our Treatment Types
for(i in treatmentList){
# Get Columns which are starting with List value
treatmentexp <- paste("(", i[1], ")\\S*0[3-7]", sep = "")
x <- grepl(treatmentexp, colnames(D.FULL), fixed = FALSE, perl = TRUE)
# sum the row values (means 1 = for 1 month, 2 = 2 months etc.)
D.CACHE[[paste(i[2], "_summer", sep = "")]] <- rowSums(D.FULL[, x], na.rm = TRUE)
# create a yes no list too
xn <- paste( i[2], "yn_summer", sep = "")
D.CACHE[[xn]] <- ifelse((rowSums(D.FULL[, x], na.rm = TRUE)) > 0, 1, 0)
}
# Convert List to Dataframe
D.CACHE <- data.frame(D.CACHE)
D.FULL <- cbind(D.FULL, D.CACHE)
#### WINTER Treatment Values ####
# Dummy List
D.CACHE <- list()
# Loop through our Treatment Types
for(i in treatmentList){
# Get Columns which are starting with List value
treatmentexp <- paste("(", i[1], ")\\S*0[8-9]|(", i[1], ")\\S*1[0]", sep = "")
x <- grepl(treatmentexp, colnames(D.FULL), fixed = FALSE, perl = TRUE)
# sum the row values (means 1 = for 1 month, 2 = 2 months etc.)
D.CACHE[[paste(i[2], "_winter", sep = "")]] <- rowSums(D.FULL[, x], na.rm = TRUE)
# create a yes no list too
xn <- paste( i[2], "yn_winter", sep = "")
D.CACHE[[xn]] <- ifelse((rowSums(D.FULL[, x], na.rm = TRUE)) > 0, 1, 0)
}
# Convert List to Dataframe
D.CACHE <- data.frame(D.CACHE)
D.FULL <- cbind(D.FULL, D.CACHE)
#### TOTAL Treatment Values ####
# Dummy List
D.CACHE <- list()
# Loop through our Treatment Types
for(i in treatmentList){
# Get Columns which are starting with List value
# double blackslash otherwise R wont escape the backslash
treatmentexp <- paste("(", i[1], ")\\S*0[1-9]|(", i[1], ")\\S*1[0]", sep = "")
x <- grepl(treatmentexp, colnames(D.FULL), fixed = FALSE)
# sum the row values (means 1 = for 1 month, 2 = 2 months etc.)
D.CACHE[[i[2]]] <- rowSums(D.FULL[, x], na.rm = TRUE)
# create a yes (1) no (2) list too
xn <- paste( i[2], "_yn", sep = "")
D.CACHE[[xn]] <- ifelse((rowSums(D.FULL[, x], na.rm = TRUE)) > 0, 1, 0)
}
# sum rows for total different methods and seasons
# sum rows by yn column, that way we get amount of different treatments used
x <- grep("(yn_)", colnames(D.FULL), fixed = FALSE)
D.FULL$T_amount_total <- rowSums(D.FULL[, x], na.rm = TRUE)
# Convert List to Dataframe
D.CACHE <- data.frame(D.CACHE)
# sum rows by yn column, that way we get amount of different treatments used
x <- grep("_yn", colnames(D.CACHE), fixed = TRUE)
D.CACHE$T_amount <- rowSums(D.CACHE[, x], na.rm = TRUE)
D.FULL <- cbind(D.FULL, D.CACHE)
|
57d0084c7e0f061119df7ec1ad13be305b0d0d51
|
c365e70f7489e674a1785db128f7aab931322333
|
/fig/plot2.R
|
4348ab15dba2bac293886e9d3f9d54877e90ab6a
|
[] |
no_license
|
varghesearunct/ExData_Plotting1
|
042c47eaf407f3b782c225647e5a1d93ccbdc63c
|
ae975c13f3ffef3f1cedd7cd6a86b7434adf5ca5
|
refs/heads/master
| 2020-12-24T12:34:35.352239
| 2016-11-06T08:15:38
| 2016-11-06T08:15:38
| 72,976,288
| 0
| 0
| null | 2016-11-06T07:30:05
| 2016-11-06T07:30:04
| null |
UTF-8
|
R
| false
| false
| 786
|
r
|
plot2.R
|
#opening file "household_power_consumption.txt"
f<-file("household_power_consumption.txt")
#reading only the lines starting with 1/2/2007 or 2/2/2007
val<-read.csv(text = grep("^[1,2]/2/2007", readLines(f), value = T), sep = ";",stringsAsFactors=F,header=F)
#naming the column variables
names(val)<-c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
#converting the date and time class to POSIXct class
val$tim<-paste(val$Date,val$Time,sep='_')
val$tim<-as.POSIXct(strptime(val$tim,"%d/%m/%Y_%H:%M:%S"))
#opening the file device
png(file="plot2.png")
#plotting the graph
with(val,plot(Global_active_power~tim,ylab="Global Active Power (kilowatts)",type='l',xlab=""))
dev.off()
|
297fc9abf1c3747e9a6fd2687aa08fb596d2754d
|
ecc6602d6be09b1e24160da680dc0068710660d7
|
/man/power.RatioF.Rd
|
43f6b6e3671c76d44fb5feff251f9de8a9a8736e
|
[] |
no_license
|
ShuguangSun/PowerTOST
|
a60e97794d00301d048a7cba2b84454be3493b59
|
b94655713782cab9ba09ac9ecb2042731ce9d272
|
refs/heads/master
| 2020-08-03T07:46:11.927805
| 2019-09-25T08:41:58
| 2019-09-25T08:41:58
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,902
|
rd
|
power.RatioF.Rd
|
\encoding{utf-8}
\name{power.RatioF}
\alias{power.RatioF}
\title{
Power for equivalence of the ratio of two means with normality on original scale
}
\description{
Calculates the power of the test of equivalence of the ratio of two means
with normality on original scale.\cr
This test is based on Fieller’s confidence (\sQuote{fiducial}) interval and Sasabuchi’s
test (a TOST procedure as well).
}
\usage{
power.RatioF(alpha = 0.025, theta1 = 0.8, theta2, theta0 = 0.95,
CV, CVb, n, design = "2x2", setseed=TRUE)
}
\arguments{
\item{alpha}{
Type I error probability, aka significance level.\cr
Defaults here to 0.025 because this function is intended for studies
with clinical endpoints.
}
\item{theta1}{
Lower bioequivalence limit. Typically 0.8 (default).
}
\item{theta2}{
Upper bioequivalence limit. Typically 1.25.\cr
Is set to \code{1/theta1} if missing.
}
\item{theta0}{
\sQuote{True} or assumed T/R ratio. Typically set to 0.95 for planning.
}
\item{CV}{
Coefficient of variation as ratio. In case of \code{design="parallel"} this is
the CV of the total variability, in case of \code{design="2x2"} the
intra-subject CV (CVw in the reference).
}
\item{CVb}{
CV of the between-subject variability. Only necessary for \code{design="2x2"}.
}
\item{n}{
Number of subjects to be planned.\cr
\code{n} is for both designs implemented the \bold{total} number of subjects.\cr
}
\item{design}{
A character string describing the study design.\cr
\code{design="parallel"} or \code{design="2x2"} allowed for a two-parallel
group design or a classical TR|RT crossover design.
}
\item{setseed}{
If set to \code{TRUE} the dependence of the power from the state of the random number
generator is avoided. With \code{setseed = FALSE} you may see the dependence
from the state of the random number generator.
}
}
\details{
The power is calculated exact using the bivariate non-central \emph{t}-distribution
via function \code{\link[mvtnorm]{pmvt}} of the package \code{mvtnorm}.\cr
Due to the calculation method of the used package mvtnorm -- randomized
Quasi-Monte-Carlo -- these probabilities are dependent from the state of the
random number generator within the precision of the power.
See argument \code{setseed}.
}
\value{
Value of power according to the input.
}
\references{
Hauschke D, Kieser M, Diletti E, Burke M. \emph{Sample size determination for proving equivalence based on the ratio of two means for normally distributed data.} Stat Med. 1999;18(1):93--105.\cr
doi: \href{https://dx.doi.org/10.1002/(SICI)1097-0258(19990115)18\%3A1\%3C93\%3A\%3AAID-SIM992\%3E3.0.CO\%3B2-8}{10.1002/(SICI)1097-0258(19990115)18:1<93::AID-SIM992>3.0.CO;2-8}.
Hauschke D, Steinijans V, Pigeot I. \emph{Bioequivalence Studies in Drug Development.} Chichester: Wiley; 2007. Chapter 10.
European Agency for the Evaluation of Medicinal Products, CPMP. \emph{Points to Consider on Switching between Superiority and Non-Inferiority.} London, 27 July 2000. \href{https://www.ema.europa.eu/en/documents/scientific-guideline/points-consider-switching-between-superiority-non-inferiority_en.pdf}{CPMP/EWP/482/99}
}
\author{
D. Labes
}
\note{
This function is intended for studies with clinical endpoints where the 95\% confidence intervals are usually used for equivalence testing.\cr
Therefore, alpha defaults here to 0.025 (see EMEA 2000).\cr\cr
The formulas given in the references rely on the assumption of equal variances
in the two treatment groups for the parallel group design or on assuming equal
within-subject and between-subject variabilities for the 2×2 crossover design.
}
\seealso{
\code{\link{sampleN.RatioF}}
}
\examples{
# power for alpha=0.025, ratio0=0.95, theta1=0.8, theta2=1/theta1=1.25
# within-subject CV=0.2, between-subject CV=0.4
# 2x2 crossover study, n=24
# using all the defaults:
power.RatioF(CV = 0.2, CVb = 0.4, n = 24)
# gives [1] 0.7315357
}
|
698a3f125302960076debf9d66b0240d0284162e
|
639b8ca98fe73eb7732322ea2260031286f4aedc
|
/Dropbox_Conflicts/main (Ben Gibson's conflicted copy 2015-01-17).R
|
09cad27a44a49b6cc19ca87726cf9fa51da36d33
|
[] |
no_license
|
cbengibson/QCArevision2
|
373d443b390597c10561b1ef1fdb700bc80db4bb
|
2b50bad8e79cc194af50490cf357bcbb6b54f785
|
refs/heads/master
| 2020-06-16T08:27:20.749639
| 2017-03-01T23:02:28
| 2017-03-01T23:02:28
| 75,122,791
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 323
|
r
|
main (Ben Gibson's conflicted copy 2015-01-17).R
|
laQCA<-function(mod, ncut="", sim=100){
source("sim.ltQCA.R")
source("configuration.table.R")
library("QCA")
#
#if (type=="crisp"){
s.data<-sim.ltQCA(mod, ncut=ncut, sim=sim)
#}
#if (type=="fuzzy"){
#s.data<-sim.fsQCA(qca.data, ncut=ncut, sim=sim)}
results<-conf.table(s.data[[1]], ncut=s.data[[2]])
return(results)
}
|
1029eec634aea3829d376cdd6870e17e33413ee3
|
26523018c9b3da0b3e2ff0f8d644920208ed7bc3
|
/man/interaction.fit.Rd
|
b04d7407e7bdf50ec74f56d2ef0c5c20d3831661
|
[
"MIT"
] |
permissive
|
jlevy44/InteractionTransformer
|
5dfa7aa1bdbee8ffdb6c2f6b509fdfc30b92a887
|
e92473889cca25a292baa4a54e84490bf7699063
|
refs/heads/master
| 2023-02-17T06:01:43.658463
| 2021-01-18T15:12:56
| 2021-01-18T15:12:56
| 208,927,856
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,276
|
rd
|
interaction.fit.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interaction_transformer.R
\name{interaction.fit}
\alias{interaction.fit}
\title{Generate design matrix acquired from using SHAP on tree model.}
\usage{
interaction.fit(
X.train,
y.train,
untrained_model = "default",
max_train_test_samples = 100,
mode_interaction_extract = "knee",
include_self_interactions = F,
cv_splits = 5L
)
}
\arguments{
\item{X.train}{Predictors in the form of a dataframe.}
\item{y.train}{One column dataframe containing outcomes.}
\item{untrained_model}{Scikit-learn tree-based estimator that has not been fit to the data.}
\item{max_train_test_samples}{Number of samples to train SHAP model off of.}
\item{mode_interaction_extract}{: Options for choosing number of interactions are 'sqrt' for square root number features, 'knee' for experimental knee method based on interaction scores, and any integer number of interactions.}
\item{include_self_interactions}{Whether to include self-interactions / quadratic terms.}
\item{cv_splits}{Number of CV splits for finding top interactions.}
}
\value{
Interaction transformer object that can be applied to the design matrix.
}
\description{
Returns transformer object. Y must be a single column dataframe.
}
|
e9abfa5a8e137a0ae210e8e115384ce2239ac2c5
|
a7cf32a5dcd89906f697cd10537ecbbe83794fd5
|
/exam-exx/GenAlgoForTSP/results/graphs/graphs42/time_plotter.r
|
8ab45b2e1635bd58372e8d95798ff198067f2330
|
[] |
no_license
|
Cyofanni/MeMoCo-exercises
|
acc57bedad0dea4e920242afa574c973d6441e87
|
c41d0c4942b0231e15af8a49cac7303599646937
|
refs/heads/master
| 2021-09-09T15:24:55.379843
| 2018-03-17T12:48:55
| 2018-03-17T12:48:55
| 110,260,599
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 653
|
r
|
time_plotter.r
|
#run 'png(time.png)' from R shell
generations <- c(100,200,300)
time1 <- c(8.08,9.22,9.18) #no simulated annealing
time2 <- c(5.01,5.18,5.56) #simulated annealing
plot(generations, time1, main="Running time against number of generations for 800-500 indiv.", sub="", xlab="generations", ylab="time (in seconds)",
xlim=c(100, 300), ylim=c(0, 10), lty=1)
lines(generations,time1,col="red",lty=1)
points(generations, time2, col="blue", pch="*")
lines(generations, time2, col="blue",lty=2)
legend(101, 3, legend=c("Without S.A.,800 indiv.", "With S.A.,500 indiv."),
col=c("red", "blue"), lty=1:2, cex=0.8)
#run 'dev.off()' from R shell
|
f7590eacdd9a8017729a13eaa5fe59aea5bb3b43
|
7aeaed0b07d51f91529c758ad4230df5faaf9fb6
|
/tests/testthat/test-doctest-onetime_mark_as_done.R
|
39911c1f315809a8264fe96ff40f621d2e75e149
|
[
"MIT"
] |
permissive
|
hughjonesd/onetime
|
e7adaf5d0bfdcef36701f407e910989a0ddffb96
|
e463d43a4410c0aa9228d9d6191a9a5951d25b17
|
refs/heads/master
| 2023-06-08T08:17:28.297841
| 2023-05-29T11:24:52
| 2023-05-29T11:24:52
| 234,599,989
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 449
|
r
|
test-doctest-onetime_mark_as_done.R
|
# Generated by doctest: do not edit by hand
# Please edit file in R/utils.R
test_that("Doctest: onetime_mark_as_done", {
# Created from @doctest for `onetime_mark_as_done`
# Source file: R/utils.R
# Source line: 60
oo <- options(onetime.dir = tempdir(check = TRUE))
id <- sample(10000L, 1)
expect_true(onetime_mark_as_done(id = id))
expect_silent(onetime_message("Won't be shown", id = id))
onetime_reset(id = id)
options(oo)
})
|
a8ea96d03a7c2546c679d844ca080a2676d32599
|
5b7324f2e2b119bddf9e5519aae63bb42f881a09
|
/man/MCA.Rd
|
f7678b3e855c447f6b592b6f75031d30da18f9de
|
[] |
no_license
|
guenardg/codep
|
58a3fcc743ebe89bdb6712f4789e5d65db631ed1
|
aa2ed70755a1c54645f444c531c8a82015f7e9d4
|
refs/heads/master
| 2023-04-06T06:08:19.620970
| 2023-04-01T16:43:41
| 2023-04-01T16:43:41
| 155,072,033
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 9,927
|
rd
|
MCA.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MCA.R
\name{MCA}
\alias{MCA}
\alias{test.cdp}
\alias{permute.cdp}
\alias{parPermute.cdp}
\title{Multiple-descriptors, Multiscale Codependence Analysis}
\usage{
MCA(Y, X, emobj)
test.cdp(object, alpha = 0.05, max.step, response.tests = TRUE)
permute.cdp(object, permute, alpha = 0.05, max.step, response.tests = TRUE)
parPermute.cdp(
object,
permute,
alpha = 0.05,
max.step,
response.tests = TRUE,
nnode,
seeds,
verbose = TRUE,
...
)
}
\arguments{
\item{Y}{A numeric matrix or vector containing the response variable(s).}
\item{X}{A numeric matrix or vector containing the explanatory variable(s).}
\item{emobj}{A \link{eigenmap-class} object.}
\item{object}{A \link{cdp-class} object.}
\item{alpha}{The type I (alpha) error threshold used by the
testing procedure.}
\item{max.step}{The maximum number of steps to perform when testing for
statistical significance.}
\item{response.tests}{A boolean specifying whether to test individual
response variables.}
\item{permute}{The number of random permutations used for testing. When
omitted, the number of permutations is calculated using function
\code{\link{minpermute}}.}
\item{nnode}{The number of parallel computation nodes.}
\item{seeds}{Seeds for computation nodes' random number generators when using
parallel computation during the permutation test.}
\item{verbose}{Whether to return user notifications.}
\item{...}{Parameters to be passed to function \code{parallel::makeCluster}}
}
\value{
A \link{cdp-class} object.
}
\description{
Class, Functions, and methods to perform Multiscale Codependence Analysis
(MCA)
}
\details{
Multiscale Codependence Analysis (MCA) allows to calculate
correlation-like (i.e.codependence) coefficients between two variables with
respect to structuring variables (Moran's eigenvector maps). The purpose of
this function is limited to parameter fitting.
Test procedures are handled through \code{test.cdp} (parametric testing) or
\code{permute.cdp} (permutation testing). Moreover, methods are provided for
printing (\code{print.cdp}), displaying a summary of the tests
(\code{summary.cdp}), plotting results (\code{plot.cdp}), calculating
fitted (\code{fitted.cdp}) and residuals values (\code{redisuals.cdp}), and
making predictions (\code{predict.cdp}).
It is noteworthy that the test procedure used by \code{MCA} deviates from the
standard R workflow since intermediate testing functions (\code{test.cdp} and
\code{permute.cdp}) need first to be called before any testing be performed.
Function \code{parPermute.cdp} allows the user to spread the number of
permutation on many computation nodes. It relies on package parallel.
Omitting argument \code{nnode} lets function \code{parallel::detectCores}
specify the number of node. Similarly, omitting parameter \code{seeds} lets
the function define the seeds as a set of values drawn from a uniform random
distribution between with minimum value \code{-.Machine$integer.max} and
maximum value \code{.Machine$integer.max}.
}
\section{Functions}{
\itemize{
\item \code{MCA()}: Main function to compute the multiscale codependence analysis
\item \code{test.cdp()}: Parametric statistical testing for multiscale codependence analysis
\item \code{permute.cdp()}: Permutation testing for multiscale codependence analysis.
\item \code{parPermute.cdp()}: Permutation testing for multiscale codependence analysis using parallel
processing.
}}
\examples{
### Example 1: St. Marguerite River Salmon Transect
data(salmon)
## Converting the data from data frames to to matrices:
Abundance <- log1p(as.matrix(salmon[,"Abundance",drop = FALSE]))
Environ <- as.matrix(salmon[,3L:5])
## Creating a spatial eigenvector map:
map1 <- eigenmap(x = salmon[,"Position"], weighting = wf.binary,
boundaries = c(0,20))
## Case of a single descriptor:
mca1 <- MCA(Y = Abundance, X = Environ[,"Substrate",drop = FALSE],
emobj = map1)
mca1
mca1_partest <- test.cdp(mca1)
mca1_partest
summary(mca1_partest)
par(mar = c(6,4,2,4))
plot(mca1_partest, las = 3, lwd=2)
mca1_pertest <- permute.cdp(mca1)
\dontrun{
## or:
mca1_pertest <- parPermute.cdp(mca1, permute = 999999)
}
mca1_pertest
summary(mca1_pertest)
plot(mca1_pertest, las = 3)
mca1_pertest$UpYXcb$C # Array containing the codependence coefficients
## With all descriptors at once:
mca2 <- MCA(Y = log1p(as.matrix(salmon[,"Abundance",drop = FALSE])),
X = as.matrix(salmon[,3L:5]), emobj = map1)
mca2
mca2_partest <- test.cdp(mca2)
mca2_partest
summary(mca2_partest)
par(mar = c(6,4,2,4))
plot(mca2_partest, las = 3, lwd=2)
mca2_pertest <- permute.cdp(mca2)
\dontrun{
## or:
mca2_pertest <- parPermute.cdp(mca2, permute = 999999)
}
mca2_pertest
summary(mca2_pertest)
plot(mca2_pertest, las = 3, lwd=2)
mca2_pertest$UpYXcb$C # Array containing the codependence coefficients
mca2_pertest$UpYXcb$C[,1L,] # now turned into a matrix.
### Example 2: Doubs Fish Community Transect
data(Doubs)
## Sites with no fish observed are excluded:
excl <- which(rowSums(Doubs.fish) == 0)
## Creating a spatial eigenvector map:
map2 <- eigenmap(x = Doubs.geo[-excl,"DFS"])
## The eigenvalues are in map2$lambda, the MEM eigenvectors in matrix map2$U
## MCA with multivariate response data analyzed on the basis of the Hellinger
## distance:
Y <- LGTransforms(Doubs.fish[-excl,],"Hellinger")
mca3 <- MCA(Y = Y, X=Doubs.env[-excl,], emobj = map2)
mca3_pertest <- permute.cdp(mca3)
\dontrun{
## or:
mca3_pertest <- parPermute.cdp(mca3, permute = 999999)
}
mca3_pertest
summary(mca3_pertest)
par(mar = c(6,4,2,4))
plot(mca3_pertest, las = 2, lwd=2)
## Array containing all the codependence coefficients:
mca3_pertest$UpYXcb$C
## Display the results along the transect
spmeans <- colMeans(Y)
pca1 <- svd(Y - rep(spmeans, each=nrow(Y)))
par(mar = c(5,5,2,5) + 0.1)
plot(y = pca1$u[,1L], x = Doubs.geo[-excl,"DFS"], pch = 21L, bg = "red",
ylab = "PCA1 loadings", xlab = "Distance from river source (km)")
## A regular transect of sites from 0 to 450 (km) spaced by 1 km intervals
## (451 sites in total). It is used for plotting spatially-explicit
## predictions.
x <- seq(0,450,1)
newdists <- matrix(NA, length(x), nrow(Doubs.geo[-excl,]))
for(i in 1L:nrow(newdists))
newdists[i,] <- abs(Doubs.geo[-excl,"DFS"] - x[i])
## Calculating predictions for the regular transect under the same set of
## environmental conditions from which the codependence model was built.
prd1 <- predict(mca3_pertest,
newdata = list(target = eigenmap.score(map2, newdists)))
## Projection of the predicted species abundance on pca1:
Uprd1 <-
(prd1 - rep(spmeans, each = nrow(prd1))) \%*\%
pca1$v \%*\% diag(pca1$d^-1)
lines(y = Uprd1[,1L], x = x, col=2, lty = 1)
## Projection of the predicted species abundance on pca2:
plot(y = pca1$u[,2L], x = Doubs.geo[-excl,"DFS"], pch = 21L, bg = "red",
ylab = "PCA2 loadings", xlab = "Distance from river source (km)")
lines(y = Uprd1[,2L], x = x, col = 2, lty = 1)
## Displaying only the observed and predicted abundance for Brown Trout.
par(new = TRUE)
plot(y = Y[,"TRU"], Doubs.geo[-excl,"DFS"], pch = 21L,
bg = "green", ylab = "", xlab = "", new = FALSE, axes = FALSE)
axis(4)
lines(y = prd1[,"TRU"], x = x, col = 3)
mtext(side = 4, "sqrt(Brown trout rel. abundance)", line = 2.5)
### Example 3: Borcard et al. Oribatid Mite
## Testing the (2-dimensional) spatial codependence between the Oribatid Mite
## community structure and environmental variables, while displaying the
## total effects of the significant variables on the community structure
## (i.e., its first principal component).
data(mite)
map3 <- eigenmap(x = mite.geo)
Y <- LGTransforms(mite.species, "Hellinger")
## Organize the environmental variables
mca4 <- MCA(Y = Y, X = mite.env, emobj = map3)
mca4_partest <- test.cdp(mca4, response.tests = FALSE)
summary(mca4_partest)
plot(mca4_partest, las = 2, lwd = 2)
plot(mca4_partest, col = rainbow(1200)[1L:1000], las = 3, lwd = 4,
main = "Codependence diagram", col.signif = "white")
## Making a regular point grid for plotting the spatially-explicit
## predictions:
rng <- list(
x = seq(min(mite.geo[,"x"]) - 0.1, max(mite.geo[,"x"]) + 0.1, 0.05),
y = seq(min(mite.geo[,"y"]) - 0.1, max(mite.geo[,"y"]) + 0.1, 0.05))
grid <- cbind(x = rep(rng[["x"]], length(rng[["y"]])),
y = rep(rng[["y"]], each = length(rng[["x"]])))
newdists <- matrix(NA, nrow(grid), nrow(mite.geo))
for(i in 1L:nrow(grid)) {
newdists[i,] <- ((mite.geo[,"x"] - grid[i,"x"])^2 +
(mite.geo[,"y"] - grid[i,"y"])^2)^0.5
}
spmeans <- colMeans(Y)
pca2 <- svd(Y - rep(spmeans, each = nrow(Y)))
prd2 <- predict(mca4_partest,
newdata = list(target = eigenmap.score(map3, newdists)))
Uprd2 <-
(prd2 - rep(spmeans, each = nrow(prd2))) \%*\%
pca2$v \%*\% diag(pca2$d^-1)
## Printing the response variable (first principal component of the mite
## community structure).
prmat <- Uprd2[,1L]
dim(prmat) <- c(length(rng$x), length(rng$y))
zlim <- c(min(min(prmat), min(pca2$u[,1L])), max(max(prmat),
max(pca2$u[,1L])))
image(z = prmat, x = rng$x, y = rng$y, asp = 1, zlim = zlim,
col = rainbow(1200L)[1L:1000], ylab = "y", xlab = "x")
points(
x=mite.geo[,"x"], y=mite.geo[,"y"], pch=21L,
bg = rainbow(1200L)[round(1+(999*(pca2$u[,1L]-zlim[1L])/(zlim[2L]-zlim[1L])),0)])
}
\references{
Guénard, G., Legendre, P., Boisclair, D., and Bilodeau, M. 2010. Multiscale
codependence analysis: an integrated approach to analyse relationships across
scales. Ecology 91: 2952-2964
Guénard, G. Legendre, P. 2018. Bringing multivariate support to multiscale
codependence analysis: Assessing the drivers of community structure across
spatial scales. Meth. Ecol. Evol. 9: 292-304
}
\author{
\packageAuthor{codep}
Maintainer: \packageMaintainer{codep}
}
|
1678ab62695ff836149374ebbe816ce56e215a94
|
bb897377948a02b7ab68df22ab5aa2acbc73215a
|
/R/SummaryTableExcel.R
|
bbb4b1948e01df8679cc6257dc6f972d470f119a
|
[] |
no_license
|
ShunHasegawa/WTC_Extractable
|
8ed0c9c1d27b883c9f76fe3b75cff80491ee7597
|
43922caa9acf819337a188838fbab2b9414998e6
|
refs/heads/master
| 2021-01-10T22:05:33.631756
| 2015-07-26T08:07:45
| 2015-07-26T08:07:45
| 20,788,814
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,136
|
r
|
SummaryTableExcel.R
|
# melt data frame
extrMlt <- melt(extr, id = c("time", "date", "chamber", "location", "side", "id", "temp"))
# chamber summary table & mean
ChSmmryTbl <- dlply(extrMlt, .(variable), function(x) CreateTable(x, fac = "chamber"))
ChMean <- ddply(extrMlt, .(time, date, temp, chamber, variable), summarise, value = mean(value, na.rm = TRUE))
# treat summary table $ mean
TrtSmmryTbl <- dlply(ChMean, .(variable), function(x) CreateTable(x, fac = "temp"))
## create xcel workbook ##
wb <- createWorkbook()
# worksheet for rawdata
sheet <- createSheet(wb,sheetName="raw_data")
addDataFrame(extr, sheet, showNA=TRUE, row.names=FALSE, characterNA="NA")
# worksheets for chamber summary
shnames <- paste("Chamber_mean.",c("Nitrate", "Ammonium","Phosphate", sep=""))
l_ply(1:3, function(x) crSheet(sheetname = shnames[x], dataset = ChSmmryTbl[[x]]))
# worksheets for temp trt summary
shnames <- paste("Temp_mean.", c("Nitrate", "Ammonium","Phosphate"), sep = "")
l_ply(1:3, function(x) crSheet(sheetname = shnames[x], dataset = TrtSmmryTbl[[x]]))
#save file
saveWorkbook(wb,"Output/Table/WTC_Extractable.xlsx")
|
ef0e38c4e9402d66fda445a48f138fe81e9c275c
|
49645c4e57889635638399e88cb490a49b79607d
|
/R_scripts/EBSEQ_Multi.R
|
9de524679d702d956708abbe9c245816643b7360
|
[] |
no_license
|
pbpayal/Bioinformatics-Documents
|
f9440c5efb735c5f9ac705f15832d4eb163248cc
|
3c79fc8c9afc87b962c7297edde8cbf5dffe12b0
|
refs/heads/master
| 2023-05-21T02:04:12.057441
| 2021-06-14T19:55:09
| 2021-06-14T19:55:09
| 170,368,307
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,678
|
r
|
EBSEQ_Multi.R
|
setwd("/Users/pbanerjee/Desktop/Bioinfo Unit_RNA seq/AB/")
load("Forbrain.RData")
save.image("Forbrain.RData")
library(EBSeq)
#write.csv(MultiPP_Forebrain_heatmap_datamat, "MultiPP_Forebrain_heatmap_datamat1.csv") ##incase to write the data out to a csv
#Forebrain - EBSEQ
Forbrain_data = read.table("all.genes.results.fb.txt", stringsAsFactors=F, row.names=1, header=T)
Forbrain_datamat = data.matrix(Forbrain_data)
str(Forbrain_datamat)
MultiSize <- MedianNorm(Forbrain_datamat)
Conditions=c("untreated","untreated","untreated","stress","stress","stress","stress_drug","stress_drug","stress_drug","drug","drug","drug")
PosParti=GetPatterns(Conditions)
EBOut_Forbrain <- EBMultiTest(Forbrain_datamat,NgVector = NULL,Conditions = Conditions, AllParti = PosParti, sizeFactors = MultiSize, maxround = 1)
MultiFC_Forbrain <- GetMultiFC(EBOut_Forbrain, SmallNum = 0.01)
dfMultiFC_Forebrain <- data.frame(MultiFC_Forbrain)
MultiPP_Forbrain <- GetMultiPP(EBOut_Forbrain)
dfMultiPP_Forebrain <- data.frame(MultiPP_Forbrain)
#Fold change Heatmaps
##Stress_drug over Untreated
Stress_drugoverUntreated <- dfMultiFC_Forebrain[,c(1,12)]
Stress_drugoverUntreated <- Stress_drugoverUntreated[-c(1)]
Stress_drugoverUntreated_heatmap_datamat <- data.matrix(Stress_drugoverUntreated)
#All fold change
Foldchange <- dfMultiFC_Forebrain[,c(7:12)]
Foldchange_heatmap_datamat <- data.matrix(Foldchange)
heatmap.2(Foldchange_heatmap_datamat, Rowv = FALSE, Colv = FALSE, hclustfun = hclust, scale = "column", trace = "none")
#Forebrain - HEATMAP
dfMultiPP_Forebrain_heatmap <- dfMultiPP_Forebrain[,c(0,2:15)]
MultiPP_Forebrain_heatmap_datamat <- data.matrix(dfMultiPP_Forebrain_heatmap)
na.omit(MultiPP_Forebrain_heatmap_datamat) #this is because while runing Heatmap, it gave errors. na.omit helps to find out if NA is there in data matrix
MultiPP_Forebrain_heatmap_datamat_NArmovd <- MultiPP_Forebrain_heatmap_datamat[-c(21863),]
heatmap(MultiPP_Forebrain_heatmap_datamat_NArmovd)
#Hindbrain
library(gplots)
hbdata=read.table("all.genes.results.hb.txt", stringsAsFactors=F, row.names=1, header=T)
hbdatamat = data.matrix(hbdata)
str(hbdatamat)
MultiSize=MedianNorm(hbdatamat)
Conditions=c("C1","C1","C1","C2","C2","C2","C3","C3","C3","C4","C4","C4")
PosParti=GetPatterns(Conditions)
EBOut_HB <- EBMultiTest(hbdatamat,NgVector = NULL,Conditions = Conditions, AllParti = PosParti, sizeFactors = MultiSize, maxround = 5)
HBMultiFC <- GetMultiFC(EBOut_HB, SmallNum = 0.01)
dfHBMultiFC <- data.frame(HBMultiFC)
HBMultiPP <- GetMultiPP(EBOut_HB)
dfHBMultiPP <- data.frame(HBMultiPP)
write.csv(dfFBMultiFC, "FBMultiFC.csv")
save.image(file='myEnvironment.RData')
load('myEnvironment.RData')
|
573b92c90095c84b969966e7ab84ea0cd6593e02
|
cd606a7cb762c786f92b7fe7ba974b49a899c732
|
/scripts/update-plots.R
|
9e73f90c7661c262e9ecd3e1ed07475959b95026
|
[
"CC0-1.0"
] |
permissive
|
seabbs/CovidInterventionReview
|
31e8c92645f11c47c16f6977fc17cedcbd38a4f8
|
7397cc48559d10056c111e990648f50acd079cb2
|
refs/heads/master
| 2022-04-18T18:50:24.828751
| 2020-04-05T11:02:57
| 2020-04-05T11:02:57
| 245,412,109
| 3
| 0
|
CC0-1.0
| 2020-04-05T11:02:59
| 2020-03-06T12:18:45
|
R
|
UTF-8
|
R
| false
| false
| 2,178
|
r
|
update-plots.R
|
# Packages ----------------------------------------------------------------
## Get required packages - managed using pacman
if (!require(pacman)) install.packages("pacman"); library(pacman)
p_load("dplyr")
p_load("readr")
p_load("lubridate")
p_load("ggplot2")
p_load("cowplot")
# Functions ---------------------------------------------------------------
source("functions/plot_interventions.R")
# Load data ---------------------------------------------------------------
cases <- readr::read_csv("output-data/counts.csv")
interventions <- readr::read_csv("output-data/interventions.csv")
first_cases <- readr::read_csv("output-data/first-cases.csv")
cases <- cases %>%
dplyr::mutate(country = country %>%
factor(levels = first_cases$Country))
interventions <- interventions %>%
dplyr::mutate(country = country %>%
factor(levels = first_cases$Country))
# Make plots --------------------------------------------------------------
## Social distancing interventions
social_plot <- plot_interventions(cases, interventions, "yes",
linetype = "Scale", scales = "free_y")
ggsave("figures/social-plot.png", social_plot, width = 12, height = 12, dpi = 330)
## Social distancing interventions
non_social_plot <- plot_interventions(cases, interventions, "no",
linetype = "Scale", scales = "free_y")
ggsave("figures/non-social-plot.png", non_social_plot, width = 12, height = 12, dpi = 330)
# Make enforced plots -----------------------------------------------------
## Social distancing interventions
enforced_social_plot <- plot_interventions(cases, interventions, "yes",
linetype = "Enforced", scales = "free_y")
ggsave("figures/enforced-non-social-plot.png", enforced_social_plot, width = 12, height = 12, dpi = 330)
## Social distancing interventions
enforced_non_social_plot <- plot_interventions(cases, interventions, "no",
linetype = "Enforced", scales = "free_y")
ggsave("figures/enforced-non-social-plot.png", enforced_non_social_plot, width = 12, height = 12, dpi = 330)
|
946aa3ef00982a247d2c765901eb8c35551fa9b7
|
0960dbdf02e232f2a8d6bd5f116a36876a57183a
|
/man/segmentPMDs.Rd
|
924a22c9abfadc8c799da8b84f76ac343d4e508d
|
[] |
no_license
|
LukasBurger/MethylSeekR
|
fb9003cbeff6276be77f2e3fab4f0c3b045131f3
|
efb31e260fd2be0a2e1310c552e07d91a048c4a4
|
refs/heads/master
| 2023-06-06T09:22:17.932335
| 2021-06-21T06:13:09
| 2021-06-21T06:13:09
| 327,341,839
| 4
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,901
|
rd
|
segmentPMDs.Rd
|
\name{segmentPMDs}
\alias{segmentPMDs}
\title{
PMD segmenter
}
\description{
This function trains a Hidden Markov Model (HMM) to detect partially methylated domains (PMDs) in Bis-seq data.
}
\usage{
segmentPMDs(m, chr.sel, pdfFilename = NULL, seqLengths, num.cores = 1, nCGbin = 101)
}
\arguments{
\item{m}{
GRanges object containing the methylation data.
}
\item{chr.sel}{
Chromosome on which HMM should be trained. Must be one of the
sequence levels of m.
}
\item{pdfFilename}{
Name of the pdf file in which the figure is saved. If no name is
provided (default), the figure is printed to the screen.
}
\item{seqLengths}{
A named vector indicating the chromosome lengths of
the genome used.
}
\item{num.cores}{
The number of cores used for the calculations (default 1).
}
\item{nCGbin}{
The number of CpGs in each sliding window used to calculate
alpha (default 101). The default is highly recommended.
}
}
\value{
A GRanges object containing segments that partition the genome into
PMDs and regions outside of PMDs. The object contains two metadata
columns indicating the type of region (PMD/notPMD) and the number of
covered (by at least 5 reads) CpGs (nCG) in the region. The function
also creates a figure showing the inferred emission distributions of
the HMM that is either printed to the screen (default) or saved as a
pdf if a filename is provided.
} \author{ Lukas Burger lukas.burger@fmi.ch }
\examples{
library(MethylSeekR)
# get chromosome lengths
library("BSgenome.Hsapiens.UCSC.hg18")
sLengths=seqlengths(Hsapiens)
# read methylation data
methFname <- system.file("extdata", "Lister2009_imr90_hg18_chr22.tab",
package="MethylSeekR")
meth.gr <- readMethylome(FileName=methFname, seqLengths=sLengths)
#segment PMDs
PMDsegments.gr <- segmentPMDs(m=meth.gr, chr.sel="chr22",
seqLengths=sLengths)
}
|
0b74fe201bfeba8df4f6318c141f7b5a44392272
|
7a93571135c17e7ea4d3d3ed2e52b5ada1f8483f
|
/test-cassandra-part.r
|
6047f1424466630bdd34562827d7731a2b53349b
|
[] |
no_license
|
kaneplusplus/cnidaria
|
cf5b7cd751f5dd67890884a764e03bd9847a0c98
|
d33f812641df51739ce31e6558ddcac4b6a9a137
|
refs/heads/master
| 2021-01-19T22:29:37.014301
| 2018-01-25T14:31:00
| 2018-01-25T14:31:00
| 16,467,882
| 8
| 0
| null | 2016-09-22T13:38:00
| 2014-02-03T01:54:14
|
R
|
UTF-8
|
R
| false
| false
| 215
|
r
|
test-cassandra-part.r
|
source("cassandra-part.r")
# Initialize disk parting.
init_cassandra_part()
# Create a new part from a matrix.
a <- as_part(matrix(1:10, ncol=2))
# Get rows 1, 3, 4.
get_values(a, c(1, 3, 4))
get_attributes(a)
|
3fa94a7c7d5d326185640ee1fe1e479f34350025
|
2be9db22fd29bd6925944b19cbc880bbe465957b
|
/05-LogisticRegression/CaseStudy-02/Script.R
|
c48a1b4bc8a78de4c6cd7f1445f0862c702d4561
|
[] |
no_license
|
wilberlinhares/Statistic_R
|
760df5dfd458cedd368b7330dd91fcce53533726
|
ddd6036fda9b9eb8e234585d2ea4d32d09ba9fd3
|
refs/heads/main
| 2023-07-08T23:10:53.944595
| 2021-08-11T13:27:39
| 2021-08-11T13:27:39
| 394,981,017
| 0
| 0
| null | null | null | null |
ISO-8859-1
|
R
| false
| false
| 16,103
|
r
|
Script.R
|
#**********************************************************
# ESTUDO DE CASO - MODELO PREDITIVO DE CHURN
#**********************************************************
setwd("/Users/wilbe/OneDrive/Documentos/GoogleDrive/Estudos/FIA/Estatística Aplicada/Aula 17 e 18")
getwd()
# **********************************************************
# Leitura da base de telefonia
telefonia <- read.table("Telefonia.txt", header = TRUE, sep = "\t", dec = ".")
# **********************************************************
# Verificar estrutura e informações sobre a base de telefonia
names(telefonia)
nrow(telefonia)
# **********************************************************
# (a) Faça a análise exploratória univariada dos dados, avalie a consistência das informações e missing values.
# Medidas resumo das variável, menos a primeira coluna
summary(telefonia[,-1])
# Tratamento da variável 'Idade'
telefonia$Idade <- ifelse(is.na(telefonia$Idade), 999, telefonia$Idade)
#Idades inconsistentes
idade_17 <- ifelse(telefonia$Idade<18, 1, 0)
sum(idade_17)
idade_100 <- ifelse(telefonia$Idade>100, 1, 0)
sum(idade_100)
#Atribuindo idades incosistentes para missing
telefonia$Idade <- ifelse(telefonia$Idade<18|telefonia$Idade>100, 999, telefonia$Idade)
summary(telefonia$Idade)
# Tratamento da variável 'Minutos realizados'
telefonia$Minutos_realizados_T0 <- ifelse(is.na(telefonia$Minutos_realizados_T0), 0, telefonia$Minutos_realizados_T0)
summary(telefonia$Minutos_realizados_T0)
# Tabela de frequência para a variável 'resposta'
(resposta_a <- table(telefonia$resposta))
(resposta_p <- prop.table(resposta_a) * 100) # 0,8% de cancelamento voluntário
# Qtde de rentenção
# Apesar de ser uma variável quantitativa, por se tratar de uma variável quantitativa discreta também é interessante também avaliar a frequência de cada valor
(freq_Retencao <- table(telefonia$Qtd_retencao_6meses))
(pct_Retencao <- prop.table(table(telefonia$Qtd_retencao_6meses))) # utilizando a função prop.table, temos como output a tabela em percentual
round(pct_Retencao * 100, 2) #Percentual de cada categoria
# Qtde de produtos
# Apesar de ser uma variável quantitativa, por se tratar de uma variável quantitativa discreta também é interessante também avaliar a frequência de cada valor
(freq_Prod <- table(telefonia$Qtd_prod))
pct_Prod <- prop.table(table(telefonia$Qtd_prod)) # utilizando a função prop.table, temos como output a tabela em percentual
round(pct_Prod * 100, 2) # percentual de cada categoria
# **********************************************************
# (b) Faça a análise descritiva bivariada covariável x resposta e identifique as
# covariáveis que tem mais relação com a resposta.
# Pacote com a função 'quantcut'
library(gtools)
telefonia$Idade_q_aux <- quantcut(telefonia$Idade, 4)
telefonia$Idade_q <- ifelse(telefonia$Idade == 999, "Missing", telefonia$Idade_q_aux)
telefonia$Minutos_realizados_T0_q <- quantcut(telefonia$Minutos_realizados_T0, 4)
telefonia$Tempo_safra_q <- quantcut(telefonia$Tempo_safra, 4)
telefonia$Tempo_casa_q <- quantcut(telefonia$Tempo_casa, 4)
telefonia$Qtd_retencao_6meses_q <- quantcut(telefonia$Qtd_retencao_6meses, 4)
telefonia$Qtd_prod_q <- quantcut(telefonia$Qtd_prod, 4)
# Tabela bidimensional: covariável x resposta
Idade_table_q <- table(telefonia$Idade_q, telefonia$resposta)
Minutos_table_q <- table(telefonia$Minutos_realizados_T0_q, telefonia$resposta)
Tempo_safra_table_q <- table(telefonia$Tempo_safra_q, telefonia$resposta)
Tempo_casa_table_q <- table(telefonia$Tempo_casa_q, telefonia$resposta)
Qtd_retencao_table_q <- table(telefonia$Qtd_retencao_6meses_q, telefonia$resposta)
Qtd_prod_table_q <- table(telefonia$Qtd_prod_q, telefonia$resposta)
# Multiplicando por 100 para virar porcentagem e arredondamento para 2 casas decimais
round(prop.table(Idade_table_q, 1) * 100, 2) # parâmetro 1 dentro de prop.table indica que é a proporção da linha
table(telefonia$Idade_q,telefonia$Idade_q_aux)#Ver as categorias
round(prop.table(Minutos_table_q, 1) * 100, 2)
round(prop.table(Tempo_safra_table_q, 1) * 100, 2)
round(prop.table(Tempo_casa_table_q, 1) * 100, 2)
round(prop.table(Qtd_retencao_table_q, 1) * 100, 2)
round(prop.table(Qtd_prod_table_q, 1) * 100, 2)
# **********************************************************
# (c) Faça a análise de multicolinearidade entre as covariáveis.
# Biblioteca para o cálculo da estatÃstica de Cramers'V
library(lsr)
# Idade com as demais covariáveis
cramersV(table(telefonia$Idade_q, telefonia$Minutos_realizados_T0_q))
cramersV(table(telefonia$Idade_q, telefonia$Tempo_safra_q))
cramersV(table(telefonia$Idade_q, telefonia$Tempo_casa_q))
cramersV(table(telefonia$Idade_q, telefonia$Qtd_retencao_6meses_q))
cramersV(table(telefonia$Idade_q, telefonia$Qtd_prod_q))
# Minutos realizados com as demais covariáveis
cramersV(table(telefonia$Minutos_realizados_T0_q, telefonia$Tempo_safra_q))
cramersV(table(telefonia$Minutos_realizados_T0_q, telefonia$Tempo_casa_q))
cramersV(table(telefonia$Minutos_realizados_T0_q, telefonia$Qtd_retencao_6meses_q))
cramersV(table(telefonia$Minutos_realizados_T0_q, telefonia$Qtd_prod_q))
# Tempo_safra com as demais covariáveis
cramersV(table(telefonia$Tempo_safra_q, telefonia$Tempo_casa_q))
cramersV(table(telefonia$Tempo_safra_q, telefonia$Qtd_retencao_6meses_q))
cramersV(table(telefonia$Tempo_safra_q, telefonia$Qtd_prod_q))
# Tempo_casa com as demais covariáveis
cramersV(table(telefonia$Tempo_casa_q, telefonia$Qtd_retencao_6meses_q))
cramersV(table(telefonia$Tempo_casa_q, telefonia$Qtd_prod_q))
# Qtde de retenção com Qtd de produtos
cramersV(table(telefonia$Qtd_retencao_6meses_q, telefonia$Qtd_prod_q))
# **********************************************************
# (d) Rode a regressão Logística considerando as covariáveis categorizadas. Identifique quais variáveis foram selecionadas pelo modelo, interprete-as, e avalie o desempenho do modelo pela Tabela de Classificação.
# Modelo completo
modelo_full <- glm(resposta ~ Idade_q +
Minutos_realizados_T0_q +
Tempo_safra_q +
Tempo_casa_q +
Qtd_retencao_6meses_q +
Qtd_prod_q,
family = binomial(link = "logit"),
data = telefonia)
summary(modelo_full)
# Para o modelo logístico, com a função 'predict', tendo como parâmetro type = 'response' conseguimos obter as probabilidades do modelo para a classificação '1'
telefonia$reg_log_p1 <- predict(modelo_full, newdata = telefonia, type = "response")
summary(telefonia$reg_log_p1)
# Cria variável resposta predita com base na probabilidade predita pelo modelo
telefonia$resp_bin1 <- as.factor(ifelse(telefonia$reg_log_p1 >= 0.008685467, 1, 0)) # transforma a probabilidade em variável binária
# Mostra a tabela de desempenho: Predito x Resposta observada
(tabela_desempenho <- table(telefonia$resposta, telefonia$resp_bin1))
# Calcula as medidas de desempenho: Sensibilidade, Especificidade e Acurácia
(sensibilidade <- tabela_desempenho[2, 2] / sum(tabela_desempenho[2, ]))
(especificidade <- tabela_desempenho[1, 1] / sum(tabela_desempenho[1, ]))
(n <- nrow(telefonia))
(acuracia <- sum(tabela_desempenho[1, 1] + tabela_desempenho[2, 2]) / n)
(ks <- abs(sensibilidade - (1 - especificidade)))
# Modelo reduzido: sem tempo safra
modelo_red1 <- glm(resposta ~ Idade_q + Minutos_realizados_T0_q +
Tempo_casa_q +
Qtd_retencao_6meses_q +
Qtd_prod_q,
family = binomial(link = "logit"),
data = telefonia)
summary(modelo_red1)
telefonia$reg_log_p1 <- predict(modelo_red1, newdata = telefonia, type = "response")
summary(telefonia$reg_log_p1)
# Transforma a probabilidade em variável binária
telefonia$resp_bin1 <- as.factor(ifelse(telefonia$reg_log_p1 >= 0.008685467, 1, 0))
(tabela_desempenho <- table(telefonia$resposta, telefonia$resp_bin1))
(sensibilidade <- tabela_desempenho[2, 2] / sum(tabela_desempenho[2, ]))
(especificidade <- tabela_desempenho[1, 1] / sum(tabela_desempenho[1, ]))
(n <- nrow(telefonia))
(acuracia <- sum(tabela_desempenho[1, 1] + tabela_desempenho[2, 2]) / n)
(ks <- abs(sensibilidade - (1 - especificidade)))
# Modelo reduzido: sem tempo casa
modelo_red2 <- glm(resposta ~ Idade_q +
Minutos_realizados_T0_q +
Tempo_safra_q +
Qtd_retencao_6meses_q +
Qtd_prod_q,
family = binomial(link = "logit"),
data = telefonia)
summary(modelo_red2)
telefonia$reg_log_p1 <- predict(modelo_red2, newdata = telefonia, type = "response")
summary(telefonia$reg_log_p1)
telefonia$resp_bin1 <- as.factor(ifelse(telefonia$reg_log_p1 >= 0.008685467, 1, 0)) # transforma a probabilidade em variável binária
(tabela_desempenho <- table(telefonia$resposta, telefonia$resp_bin1))
(sensibilidade <- tabela_desempenho[2, 2] / sum(tabela_desempenho[2, ]))
(especificidade <- tabela_desempenho[1, 1] / sum(tabela_desempenho[1, ]))
(n <- nrow(telefonia))
(acuracia <- sum(tabela_desempenho[1, 1] + tabela_desempenho[2, 2]) / n)
(ks <- abs(sensibilidade - (1 - especificidade)))
# Modelo reduzido: sem tempo casa e Idade (tem categoria missing)
modelo_red3 <- glm(resposta ~ Minutos_realizados_T0_q +
Tempo_safra_q +
Qtd_retencao_6meses_q +
Qtd_prod_q,
family = binomial(link = "logit"),
data = telefonia)
summary(modelo_red3)
telefonia$reg_log_p1 <- predict(modelo_red3, newdata = telefonia, type = "response")
summary(telefonia$reg_log_p1)
telefonia$resp_bin1 <- as.factor(ifelse(telefonia$reg_log_p1 >= 0.008685467, 1, 0)) # transforma a probabilidade em variável binária
(tabela_desempenho <- table(telefonia$resposta, telefonia$resp_bin1 ))
(sensibilidade <- tabela_desempenho[2, 2] / sum(tabela_desempenho[2, ]))
(especificidade <- tabela_desempenho[1, 1] / sum(tabela_desempenho[1, ]))
(n <- nrow(telefonia))
(acuracia <- sum(tabela_desempenho[1, 1] + tabela_desempenho[2, 2]) / n)
(ks <- abs(sensibilidade - (1 - especificidade)))
# **********************************************************
# (e) Rode árvore de decisão usando o método CHAID com 3 níveis. Identifique quais variáveis foram selecionadas pelo modelo, interprete-as, e avalie o desempenho do modelo pela Tabela de Classificação.
# Função 'chaid' nos permite criar uma árvore de decisão de acordo com o algoritmo CHAID
library(partykit) # pacote precisa ser instalado previamente para usar o CHAID
# install.packages("CHAID", repos="http://R-Forge.R-project.org")
library(CHAID) # pacote com a função 'chaid'
# Todas as variáveis como um fator (não como numérico para ser input da Árvore)
telefonia$resposta <- as.factor(telefonia$resposta)
telefonia$Idade_q <- as.factor(telefonia$Idade_q)
# Para a árvore não ficar muito grande, a três níveis
controle <- chaid_control(maxheight = 3)
# Função 'chaid' nos permite criar uma árvore de decisão de acordo com o algoritmo CHAID
(arvore_full <- chaid(resposta ~
Idade_q +
Minutos_realizados_T0_q +
Tempo_safra_q +
Tempo_casa_q +
Qtd_retencao_6meses_q +
Qtd_prod_q,
data = telefonia,
control = controle)) # indicando em qual base o modelo deve ser estimado
# Incluir na base de dados a probabilidade predita pela Árvore de Decisão
probs<- as.data.frame(predict(arvore_full, newdata = telefonia, type = "p")) # "p" salva o valor predito da probabilidade
names(probs) <- c("P_0", "P_1")
telefonia <- cbind(telefonia, probs) # insere 2 colunas na base com as probabilidades preditas de 0 e 1
# Cria variável resposta predita com base na probabilidade predita pela Árvore de Decisão
telefonia$predict_AD <- as.factor(ifelse(telefonia$P_1 >= 0.008685467, 1, 0)) # transforma a probabilidade em variável binária
# Mostra a tabela de desempenho: Predito x Resposta observada
(tabela_desempenho <- table(telefonia$resposta, telefonia$predict_AD))
# Calcula as medidas de desempenho: Sensibilidade, Especificidade e Acurácia
(sensibilidade <- tabela_desempenho[2, 2] / sum(tabela_desempenho[2, ]))
(especificidade <- tabela_desempenho[1, 1] / sum(tabela_desempenho[1, ]))
(n <- nrow(telefonia))
(acuracia <- sum(tabela_desempenho[1, 1] + tabela_desempenho[2, 2])/n)
(ks <- abs(sensibilidade - (1 - especificidade)))
# Função 'chaid' sem Idade
(arvore_red1 <- chaid(resposta ~
Minutos_realizados_T0_q +
Tempo_safra_q +
Tempo_casa_q +
Qtd_retencao_6meses_q +
Qtd_prod_q,
data = telefonia,
control = controle)) # indicando em qual base o modelo deve ser estimado
probs <- as.data.frame(predict(arvore_red1, newdata = telefonia, type = "p")) # "p" salva o valor predito da probabilidade
names(probs) <- c("P_0_AD", "P_1_AD")
summary(telefonia)
telefonia <- cbind(telefonia, probs) # insere 2 colunas na base com as probabilidades preditas de 0 e 1
telefonia$predict_AD <- as.factor(ifelse(telefonia$P_1 >= 0.008685467, 1, 0)) # transforma a probabilidade em variável binária
(tabela_desempenho <- table(telefonia$resposta, telefonia$predict_AD))
(sensibilidade <- tabela_desempenho[2, 2] / sum(tabela_desempenho[2, ]))
(especificidade <- tabela_desempenho[1, 1] / sum(tabela_desempenho[1, ]))
(n <- nrow(telefonia))
(acuracia <- sum(tabela_desempenho[1, 1] + tabela_desempenho[2, 2]) / n)
(ks <- abs(sensibilidade - (1 - especificidade)))
# **********************************************************
# (g) Calcule a área abaixo da curva ROC, e avalie seu desempenho.
library(pROC)
# Área abaixo da curva ROC: Regressão Logística
roc(telefonia$resposta,
telefonia$reg_log_p1,
plot = TRUE,
legacy.axes = TRUE,
print.auc = TRUE,
main = "Regressão Logística")
# Área abaixo da curva ROC: Árvore de Decisão
roc(telefonia$resposta,
telefonia$P_1_AD,
plot = TRUE,
legacy.axes = TRUE,
print.auc = TRUE,
main = "Árvore de Decisão")
# **********************************************************
# (i) Construa a tabela de probabilidade preditas x resposta observada em VINTIS para Regressão Logística,
# e obtenha de forma análoga a tabela de probabilidades por nó x resposta para Árvore de Decisão.
# Use a planilha do Excel 'An_Desempenho_Exercicio'.
# Tabela de Desempenho: Regressão Logística
# (Use a planilha do Excel 'An_Desempenho_Exercicio')
# Calcular as faixas de vintil
telefonia$fx_reg_log <- quantcut(telefonia$reg_log_p1, 20)
# Distribuição da resposta por faixa de probabilidade
(table(telefonia$fx_reg_log, telefonia$resposta))
# Propensão dos nós finais: Árvore de Decisão
telefonia$node <- predict(arvore_red1, type = "node")
# Tabela de Desempenho: Árvore de Decisão
(tabela_AD <- (table(telefonia$node, telefonia$resposta)))
# Agrega a base para pegar propensão do associado ao nó
attach(telefonia)
aggdata <- aggregate(telefonia, by = list(node), FUN = mean)
(DE_PARA <- cbind(aggdata$node, round(aggdata$P_1_AD, 4)))
detach(telefonia)
# Copiar as duas tabelas do excel, juntá-las e ordenar em ordem crescente de probabilidade
|
d5c666c2795aad1d49227f00f0838cfd9a1c1958
|
bffaec26f4f82430912725f9d83006aa63b8a4ab
|
/man/arima_multistep.Rd
|
52ad97de718a1b075ca84effe2dae7068fadd621
|
[] |
no_license
|
vcerqueira/vest
|
37b0ce7d61796ad3474cd8a0a96ed519933c5509
|
a19aac8d7b9c2ada96f9afc4d8fcfe033ad75c4b
|
refs/heads/master
| 2022-12-17T17:49:21.603487
| 2021-02-08T21:31:26
| 2021-02-08T21:31:26
| 239,560,166
| 17
| 4
| null | 2022-12-08T11:51:49
| 2020-02-10T16:37:16
|
R
|
UTF-8
|
R
| false
| true
| 357
|
rd
|
arima_multistep.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/arima-expsmooth.r
\name{arima_multistep}
\alias{arima_multistep}
\title{ARIMA MODEL}
\usage{
arima_multistep(train, test, freq, h)
}
\arguments{
\item{train}{train series, ts}
\item{test}{test series, ts}
\item{freq}{frequency}
\item{h}{horizon}
}
\description{
ARIMA MODEL
}
|
c7035901ed3db4deb175726051f4b37280bafc11
|
abe567a26b3b20081ecf6a3952f16b90035f7ecf
|
/man/conways_game_of_life.Rd
|
34c80210eda1f5ad057d6d14d5f627fddee589a7
|
[] |
no_license
|
KIT-IfGG/pets
|
4af61de028f29fa0495ecb149f3d774f4d3ef332
|
3cee6fe620354d682b5cef917b44a89f640d3748
|
refs/heads/master
| 2021-01-18T11:24:45.130165
| 2018-11-21T11:48:22
| 2018-11-21T11:48:22
| 100,360,310
| 2
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,999
|
rd
|
conways_game_of_life.Rd
|
\name{conways_game_of_life}
\alias{conways_game_of_life}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Conways game of life
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
}
\usage{
conways_game_of_life(x)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
%% ~~Describe \code{x} here~~
}
}
\details{
%% ~~ If necessary, more details than the description above ~~
}
\value{
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
\references{
%% ~put references to the literature/web site here ~
}
\author{
%% ~~who you are~~
}
\note{
%% ~~further notes~~
}
%% ~Make other sections like Warning with \section{Warning }{....} ~
\seealso{
%% ~~objects to See Also as \code{\link{help}}, ~~~
}
\examples{
### Simulate
mygrid <- Conway_game_of_life(size = 50, num_reps = 200, prob = c(0.01, 0.99), plot=TRUE)
### Plot the result
x11()
for(i in 1:length(mygrid)) {
image(mygrid[[i]], main=i, col=c("white", "green"))
Sys.sleep(0.1)
}
### Plot population sizes over time.
pop <- sapply(mygrid, sum)/length(mygrid[[1]])
x11()
par(mar=c(5,5,1,1))
plot(pop ~ seq(1,length(pop), by=1), xlab="Time", ylab="Population size", cex.axis=1.5, cex.lab=1.5, type="l", col="darkred", lwd=1.5, ylim=c(0,0.5))
### Compare several runs with the same parameters!
reps <- replicate(10, Conway_game_of_life(size = 10, num_reps = 30, prob = c(0.5, 0.5), plot=FALSE), simplify = FALSE)
pops <- sapply(reps, function(x) sapply(x, sum)/length(x[[1]]))
x11()
matplot(seq(1,nrow(pops), by=1), pops, xlab="Time", ylab="Population size", cex.axis=1.5, cex.lab=1.5, type="l", col=rainbow(ncol(pops)), lwd=1.5, ylim=c(0,0.6))
### Create a gif
saveGIF(Conway_game_of_life(size = 50, num_reps = 100, prob = c(0.01, 0.99), plot=TRUE), movie.name = "animation.gif", img.name = "Rplot", convert = "convert", interval = 0.6)
}
|
f2bbb2292ebfeed4af9ef082f2dcebc7522b9aa1
|
54e6946ed588b5b2814b818249163e53ab14e9ce
|
/man/hdg_norm.Rd
|
f3eb7ebbab22246fdebdf2d9561be9e9be5cbbd6
|
[
"MIT"
] |
permissive
|
paleolimbot/headings
|
1b640a96d6fa4420c1616fe80440d9fbcf4f5b3b
|
e7489d8f44c3d4ffd8c88044a3b6943196abba00
|
refs/heads/master
| 2023-04-01T20:03:06.835779
| 2021-04-07T15:19:35
| 2021-04-07T15:19:35
| 341,327,046
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 956
|
rd
|
hdg_norm.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/heading.R
\name{hdg_norm}
\alias{hdg_norm}
\alias{uv}
\alias{uv_norm}
\alias{uv_from_hdg}
\alias{hdg_from_uv}
\alias{rad_from_hdg}
\alias{hdg_from_rad}
\title{Create and export headings}
\usage{
hdg_norm(hdg)
uv(u, v)
uv_norm(uv)
uv_from_hdg(hdg)
hdg_from_uv(uv)
rad_from_hdg(hdg)
hdg_from_rad(rad)
}
\arguments{
\item{hdg}{A heading in degrees, where 0 is north,
90 is east, 180 is south, and 270 is west. Values
outside the range [0-360) are coerced to this range
using \code{\link[=hdg_norm]{hdg_norm()}}.}
\item{uv, u, v}{A data.frame with columns \code{u} (magnitude east)
and \code{v} (magnitude north).}
\item{rad}{An angle in radians such that a \code{hdg} of 90 is
zero radians and a \code{hdg} of 0 is \code{pi / 2} radians.}
}
\description{
Create and export headings
}
\examples{
hdg_norm(-10:10)
hdg_from_uv(uv(1, 0))
uv_from_hdg(5:10)
uv_norm(uv(2, 0))
}
|
8aafe079fe98dbc98fc4ad64ac3023c4ab1ac8d9
|
6d0999bafd5986933e13719034254d9bfab4d47c
|
/Code/R/megacategory_assign.R
|
2c1176bef86f8ebf9657c645f25c9517032198eb
|
[] |
no_license
|
emilio-berti/rewiring-rewilding
|
63199f62d1b403c5f59b2e38d4942f448da3416d
|
864ac86669162238154da89a974b0ef66c95a9a7
|
refs/heads/master
| 2022-12-22T00:18:43.366985
| 2020-09-24T06:43:23
| 2020-09-24T06:43:23
| 290,488,904
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 575
|
r
|
megacategory_assign.R
|
megacategory <- function(species){
mass <- subset(phy, Binomial.1.2 == species)$Mass.g
family <- subset(phy, Binomial.1.2 == species)$Family.1.2
if(family %in% c("Ursidae", "Felidae")){
candidates <- subset(phy, Family.1.2 == family)[c("Binomial.1.2", "Mass.g", "IUCN.Status.1.2")]
candidates <- subset(candidates, Mass.g >= 100000)
} else{
candidates <- NULL
}
if(!is.null(candidates)){
candidates <- subset(candidates, !IUCN.Status.1.2 %in% c("EX", "EW", "EP"))
return(as.vector(candidates$Binomial.1.2))
} else{
return(NULL)
}
}
|
06331e8db041003e35bca1b8051af964cf26e353
|
dc7d3873fd7896fd4a81329a7aa24d4704a8bd90
|
/scripts/BcBOTnet/09_BOAforMEGA.R
|
6ae903cc97fd68d804c950937e1e52bbbf96a4aa
|
[] |
no_license
|
nicolise/BcAt_RNAGWAS
|
4cd4cf169c06f46057e10ab1773779c8eaf77ab1
|
64f15ad85186718295c6a44146befa3ca57b7efc
|
refs/heads/master
| 2021-01-12T11:40:59.400854
| 2019-10-21T19:54:53
| 2019-10-21T19:54:53
| 72,249,016
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,763
|
r
|
09_BOAforMEGA.R
|
#Nicole E Soltis
#--------------------------------------------------
#need FASTA file input for MEGA
#want: beginning of C1 (5133) to 97414
#BcBoa17 goes up to 69449. 3 genes downstream is gene:Bcin01g00190. end position is 97414.
#or, if looking within deletion ONLY:
#last base of deletion = 82614
#first base of deletion = first base of C1 = 4029
#approach 2: read in BOA data, convert to fasta format in R
#have been using binary PED -- lost SNP state info.
#need to use:
install.packages("seqRFLP")
library("seqRFLP")
#Your sequences need to be in a data frame with sequence headers in column 1 and sequences in column 2 [doesn't matter if it's nucleotide or amino acid]
names <- c("seq1","seq2","seq3","seq4")
sequences<-c("EPTFYQNPQFSVTLDKR","SLLEDPCYIGLR","YEVLESVQNYDTGVAK","VLGALDLGDNYR")
df <- data.frame(names,sequences)
#Then convert the data frame to .fasta format using the function: 'dataframe2fas'
df.fasta = dataframe2fas(df, file="df.fasta")
#---------------------------------------------------------------------
#failed approach 1: read in Chr 1 fasta, try to extract nucleotides of interest
#BUT this does not include SNP state info for individual isolates. Dropping this approach.
setwd("~/Projects/BcGenome/data/ensembl/B05.10/fasta/singleChr")
library("Biostrings")
library("seqinr")
coi.fa <- read.fasta(file = textConnection("Botrytis_cinerea.ASM83294v1.dna.chromosome.1.fa/gpfs/nobackup/ensembl/amonida/rel91/eg38/fungi/fasta/botrytis_cinerea/dna/Botrytis_cinerea.ASM83294v1.dna.chromosome.1.fa"), as.string = T)
myFA <- read.fasta(file = "Botrytis_cinerea.ASM83294v1.dna.chromosome.1.fa/gpfs/nobackup/ensembl/amonida/rel91/eg38/fungi/fasta/botrytis_cinerea/dna/Botrytis_cinerea.ASM83294v1.dna.chromosome.1.fa", as.string=FALSE, seqonly=FALSE)
myFAdf <- as.data.frame(myFA)
myFA2 = readDNAStringSet("Botrytis_cinerea.ASM83294v1.dna.chromosome.1.fa/gpfs/nobackup/ensembl/amonida/rel91/eg38/fungi/fasta/botrytis_cinerea/dna/Botrytis_cinerea.ASM83294v1.dna.chromosome.1.fa")
seq_name = names(myFA2)
sequence = paste(myFA2)
myFAdf <- data.frame(seq_name, sequence)
library("Biostrings")
fasta2dataframe=function(fastaFile){
s = readDNAStringSet(fastaFile)
RefSeqID = names(s)
RefSeqID = sub(" .*", "", RefSeqID)
#erase all characters after the first space: regular expression matches a space followed by any sequence of characters and sub replaces that with a string having zero characters
for (i in 1:length(s)){
seq[i]=toString(s[i])
}
RefSeqID_seq=data.frame(RefSeqID,seq)
return(RefSeqID_seq)
}
mydf2 = fasta2dataframe("Botrytis_cinerea.ASM83294v1.dna.chromosome.1.fa/gpfs/nobackup/ensembl/amonida/rel91/eg38/fungi/fasta/botrytis_cinerea/dna/Botrytis_cinerea.ASM83294v1.dna.chromosome.1.fa")
|
6bffb21b87f5c60c695e46ab7976037bdac61903
|
31bb7850a8f76259887e142611bab3f3e45ff981
|
/NCAR/code/stl2/stl.tw617.td2.sw103.sd1.i10.o0/tmax.100stations.stl2.R
|
8ad266bc18bc8d6700cbfe1145c008dbfce2589b
|
[] |
no_license
|
XiaosuTong/Spatial
|
4bb6951697f69477612fcef6c987a3a2a72b0143
|
068d1a00a2b5f08404da23049f7ddd3c76d03c96
|
refs/heads/master
| 2020-12-29T01:54:00.658233
| 2016-06-16T18:30:46
| 2016-06-16T18:30:46
| 20,975,405
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 22,809
|
r
|
tmax.100stations.stl2.R
|
############################################################################
##Max temperature, 100 stations, seasonal+trend with raw data by stl2
##########################################################################
#load the data
library(lattice)
library(plyr)
library(stl2)
library(maps)
outputdir <- "~/Projects/Spatial/NCAR/output/"
datadir <- "~/Projects/Spatial/NCAR/RData/"
load(paste(datadir,"USmonthlyMet.RData", sep=""))
load(paste(datadir,"stations.RData", sep=""))
#find the 100 stations with largest observation number for max temperature
stations <- stations.tmax
tmp <- UStemp[UStemp[,1] %in% stations,]
tmp <- tmp[!(is.na(tmp$tmax)),]
month <- tmp$month
levels(month) <- c(1:12)
month <- as.numeric(factor(month, levels=c(1:12)))
date <- paste(tmp$year, month, "01", sep="-")
tmp$date <- as.POSIXct(strptime(date, format = "%Y-%m-%d"), format='%Y%m%d', tz="")
tmp <- tmp[order(tmp$date),]
tmp <- tmp[order(tmp[,1]),-8]
tmp$factor <- factor(rep(rep(paste("Period", 1:9), c(rep(144,8),84)), times=100), levels=paste("Period", c(9:1)))
tmp$time <- c(rep(0:143,8), 0:83)
lattice.theme <- trellis.par.get()
col <- lattice.theme$superpose.symbol$col
dr <- ddply(tmp, "station.id", function(r){stl2(r$tmax, r$date, n.p=12, s.window=103, s.degree=1, t.window=617, t.degree=2, inner = 10, outer = 0)$data})
tmp <- cbind(tmp, dr)
tmp <- tmp[order(tmp$station.id,tmp$date),]
##################################
##trend+seasonal time series plot
##################################
tmp1 <- tmp[,1:11]
names(tmp1)[8] <- "response"
tmp1$group <- rep("raw", 123600)
tmp1 <- tmp1[,c(1:7,9,10,11,8,12)]
tmp2 <- tmp[,c(1:7,9,10,11,14,15)]
tmp2$response <- tmp2$seasonal + tmp2$trend
tmp2 <- tmp2[,-c(11,12)]
tmp2$group <- rep("seasonal+trend", 123600)
rst <- rbind(tmp1, tmp2)
#start <- as.POSIXct(strptime(paste(head(tmp$year,1), "01", "01", sep="-"), format = "%Y-%m-%d"), format='%Y%m%d', tz="")
#end <- as.POSIXct(strptime(paste("2000", "01", "01", sep="-"), format = "%Y-%m-%d"), format='%Y%m%d', tz="")
trellis.device(postscript, file = paste(outputdir, "scatterplot_of_tmax_with_stl2_trend+seasonal_for_100_stations",".ps", sep = ""), color=TRUE, paper="legal")
for(i in stations){
b <- xyplot( response ~ time | factor,
data = subset(rst, station.id==i),
groups = group,
xlab = list(label = "Month", cex = 1.2),
ylab = list(label = "Maximum Temperature (degrees centigrade)", cex = 1.2),
main = list(label = paste("Station ", i, sep=""), cex=1.5),
type = c("p","l"),
distribute.type= TRUE,
layout = c(1,9),
pch = 16,
cex = 0.5,
aspect= 0.06,
strip = FALSE,
# strip.left = TRUE,
grib = TRUE,
xlim = c(0, 143),
# scales = list(y = list(relation = 'free', cex=1.5), x=list(relation= 'free',format = "%b %Y", tick.number=10), cex=1.2),
scales = list(y = list(relation = 'same', alternating=TRUE), x=list(at=seq(0, 143, by=12), relation='same')),
panel = function(...) {
panel.abline(v=seq(0,145, by=12), color="lightgrey", lty=3, lwd=0.5)
panel.xyplot(...)
}
)
print(b)
}
dev.off()
##################################################################################
#QQ plot and time series plot of Pooled remainder of max temperature for 100 stations
##################################################################################
#Create the QQ plot of temperature for one station
trellis.device(postscript, file = paste(outputdir, "QQ_plot_of_stl2_remainder_tmax_of_100_stations", ".ps", sep = ""), color=TRUE, paper="legal")
a <- qqmath(~ remainder | station.id,
data = tmp,
distribution = qnorm,
aspect = 1,
pch = 16,
cex = 0.5,
layout = c(6,3),
# main = list(label= paste("Station ", i, sep=""), cex=2),
xlab = list(label="Unit normal quantile", cex=1.2),
ylab = list(label="Maximum Temperature(degrees centigrade)", cex=1.2),
# scales = list(x = list(cex=1.5), y = list(cex=1.5)),
prepanel = prepanel.qqmathline,
panel = function(x, y,...) {
panel.grid(lty=3, lwd=0.5, col="black",...)
panel.qqmathline(x, y=x)
panel.qqmath(x, y,...)
}
)
print(a)
dev.off()
tmp.remainder <- tmp
tmp.remainder$time1 <- rep(0:1235,100)
tmp.remainder$factor <- factor(tmp$factor, levels=paste("Period", c(3,2,1,6,5,4,9,8,7)))
trellis.device(postscript, file = paste(outputdir, "scatterplot_of_tmax_with_stl2_remainder_for_100_stations",".ps", sep = ""), color=TRUE, paper="legal")
for(i in stations){
b <- xyplot( remainder ~ time | factor,
data = subset(tmp.remainder, station.id==i),
xlab = list(label = "Month", cex = 1.2),
ylab = list(label = paste("Station", i, "Maximum Temperature (degrees centigrade)"), cex = 1.2),
# main = list(label = paste("Station ", i, sep=""), cex=1.5),
type = "p",
layout = c(1,3),
pch = 16,
cex = 0.5,
# aspect= "xy",
xlim = c(0, 143),
strip = FALSE,
# strip.left = TRUE,
grib = TRUE,
# xlim = c(start, end),
# scales = list(y = list(relation = 'free', cex=1.5), x=list(relation= 'free',format = "%b %Y", tick.number=10), cex=1.2),
scales = list(y = list(relation = 'same', alternating=TRUE), x=list(at=seq(0, 143, by=12), relation='same')),
panel = function(x,y,...) {
panel.abline(h=0, v=seq(0,143, by=12), color="lightgrey", lty=3, lwd=0.5)
panel.xyplot(x,y,...)
panel.loess(x,y,degree=2,span=1/4, col=col[2], ...)
}
)
print(b)
}
dev.off()
trellis.device(postscript, file = paste(outputdir, "scatterplot_of_tmax_with_stl2_remainder2_for_100_stations",".ps", sep = ""), color=TRUE, paper="legal")
for(i in stations){
b <- xyplot( remainder ~ time1,
data = subset(tmp.remainder, station.id==i),
xlab = list(label = "Month", cex = 1.2),
ylab = list(label = paste("Station", i, "Maximum Temperature (degrees centigrade)"), cex = 1.2),
# main = list(label = paste("Station ", i, sep=""), cex=1.5),
type = "p",
pch = 16,
cex = 0.5,
xlim = c(0, 1235),
key=list(type="l", text=list(label=c("remainder","degree=2,span=0.15","degree=1,span=0.35")), lines=list(lwd=1.5, col=col[1:3]), columns=3),
# scales = list(y = list(relation = 'free', cex=1.5), x=list(relation= 'free',format = "%b %Y", tick.number=10), cex=1.2),
scales = list(y = list(relation = 'same', alternating=TRUE), x=list(at=seq(0, 1235, by=120), relation='same')),
panel = function(x,y,...) {
panel.abline(h=0)
panel.xyplot(x,y,...)
panel.loess(x,y,degree=2,span=0.15, col=col[2])
panel.loess(x,y,degree=1,span=0.35, col=col[3])
}
)
print(b)
}
dev.off()
#################################################
##Auto correlation ACF for the remainder
#################################################
ACF <- ddply(.data=tmp.remainder,
.variables="station.id",
.fun= summarise,
correlation = c(acf(remainder, plot=FALSE)$acf),
lag = c(acf(remainder, plot=FALSE)$lag)
)
trellis.device(postscript, file = paste(outputdir, "acf_of_tmax_with_stl2_remainder_for_100_stations",".ps", sep = ""), color=TRUE, paper="legal")
for(i in stations){
b <- xyplot( correlation ~ lag,
data = subset(ACF, station.id==i & lag!=0),
xlab = list(label = "Lag", cex = 1.2),
ylab = list(label = paste("Station", i, "ACF"), cex = 1.2),
# main = list(label = paste("Station ", i, sep=""), cex=1.5),
type = "h",
panel = function(x,y,...) {
panel.abline(h=0)
panel.xyplot(x,y,...)
}
)
print(b)
}
dev.off()
##################################################
##time series plot of seaosnal+remainder from stl2
##################################################
#tmp$sr <- tmp$seasonal + tmp$remainder
#
#trellis.device(postscript, file = paste("scatterplot_of_tmax_with_stl2_seasonal+remainder_for_100_stations",".ps", sep = ""), color=TRUE, paper="legal")
# for(i in stations){
# b <- xyplot( sr ~ time | factor,
# data = subset(tmp, station.id==i),
# xlab = list(label = "Month", cex = 1.5),
# ylab = list(label = "Maximum Temperature (degrees centigrade)", cex = 1.5),
# main = list(label = paste("Station ", i, sep=""), cex=1.5),
# type = "p",
# layout = c(1,9),
# pch = 16,
# cex = 0.5,
# aspect= 0.06,
# strip = FALSE,
# strip.left = TRUE,
# grib = TRUE,
## xlim = c(start, end),
## scales = list(y = list(relation = 'free', cex=1.5), x=list(relation= 'free',format = "%b %Y", tick.number=10), cex=1.2),
# scales = list(y = list(relation = 'same', cex=1, alternating=TRUE), x=list(tick.number=10, cex=1.2, relation='same')),
# panel = function(...) {
# panel.abline(h=seq(-10,10,by=5), v=seq(0,140, by=12), color="lightgrey", lty=3, lwd=0.5)
# panel.xyplot(...)
#
# }
# )
# print(b)
# }
#dev.off()
##########################################################################
##time series plot of trend component and yearly average of raw from stl2
##########################################################################
tmp.trend <- tmp[,c(1:9,15)]
dr <- ddply(.data = tmp.trend,
.variables = c("station.id","year"),
.fun = summarise,
mean = mean(tmax)
)
mm <- dr[rep(row.names(dr), each=12),]
tmp.trend <- cbind(tmp.trend, mean= mm$mean)
order <- ddply(.data = tmp.trend,
.variables = "station.id",
.fun = summarise,
mean = mean(tmax)
)
order.st <- as.character(order[order(order$mean, decreasing=TRUE), ]$station.id)
tmp.trend$station.id <- factor(tmp.trend$station.id, levels=order.st)
tmp1 <- tmp.trend[,1:10]
tmp1$time <- rep(0:1235,100)
tmp2 <- tmp.trend[,c(1:9,11)]
tmp2$time <- rep(0:1235,100)
names(tmp1)[10] <- names(tmp2)[10] <- "response"
tmp.trend <- rbind(tmp1, tmp2)
tmp.trend$group <- rep(c("trend","yearly mean"), each = 123600)
#Attend to add a small map in the corner of the trend loess plot
#us.map <- map('state', plot = FALSE, fill = TRUE)
trellis.device(postscript, file = paste(outputdir, "scatterplot_of_tmax_with_stl2_trend_for_100_stations",".ps", sep = ""), color=TRUE, paper="legal")
for(i in levels(tmp.trend$station.id)){
b <- xyplot( response ~ time,
data = subset(tmp.trend, station.id==i & group == "trend"),
# main = list(label= paste("Station ", i, sep=""), cex=1.5),
xlab = list(label = "Month", cex = 1.2),
ylab = list(label = paste("Station", i, "Maximum Temperature (degrees centigrade)"), cex = 1.2),
# groups = group,
# distribute.type = TRUE,
# type = c("l"),
# strip = strip.custom(par.strip.text= list(cex = 1.5)),
# par.settings = list(layout.heights = list(strip = 1.5)),
xlim = c(0, 1235),
ylim = c(min(subset(tmp.trend, station.id==i)$response),max(subset(tmp.trend, station.id==i)$response)),
pch = 16,
aspect="xy",
key=list(type="l", text=list(label=c("trend component","yearly mean")), lines=list(lwd=1.5, col=col[1:2]), columns=2),
cex = 0.3,
# scales = list(y = list(relation = 'free', cex=1.5), x=list(relation= 'same',format = "%b %Y", tick.number=8), cex=1.2),
scales = list(y = list(relation = 'free'), x=list(at=seq(0, 1236, by=120), relation = 'same')),
panel = function(x,y,...) {
panel.abline(h=seq(10,40,by=1), v=seq(0,1236, by=120), color="lightgrey", lty=3, lwd=0.5)
panel.xyplot(x,y,type="l",...)
v <- subset(tmp.trend, station.id==i&group=="yearly mean")
panel.xyplot(v$time, type="p", v$response, col=col[2], ...)
}
)
print(b)
# a <- xyplot(lat ~ lon,
# data = subset(tmp.trend, station.id==i),
# xlab = NULL,
# ylab = NULL,
# pch = 16,
# cex = 0.4,
# xlim = c(-127,-65),
# ylim = c(23,50),
# col = "red",
## scales = list(x = list(draw=FALSE), y = list(draw=FALSE)),
## strip = strip.custom(par.strip.text= list(cex = 1.5)),
## par.settings = list(layout.heights = list(strip = 1.5)),
# panel = function(x,y,...) {
# panel.polygon(us.map$x,us.map$y,lwd=0.2)
# panel.xyplot(x,y,...)
# }
# )
# plot.new()
# title(paste("Station ", i, sep=""), cex=1.5)
# print(b, pos=c(0.4,0,1,0.95), newpage=FALSE, more=TRUE)
# print(a, pos=c(0,0.55,0.4,1), more=FALSE)
}
dev.off()
#####################################################
##Time series plot of trend+remainder against time
#####################################################
#tmp1 <- tmp[,c(1:9,15, 16)]
#tmp1$tr <- tmp1$trend + tmp1$remainder
#tmp2 <- tmp[, c(1:9, 15)]
#names(tmp2)[10] <- names(tmp1)[12] <- "response"
#tmp1$group <- rep("trend+remainder", 123600)
#tmp2$group <- rep("trend", 123600)
#tmp2$time <- rep(0:1235,100)
#tmp1$time <- rep(0:1235,100)
#tmp.tr <- do.call("rbind", list(tmp1[,-c(10:11)], tmp2))
#
#trellis.device(postscript, file = paste(outputdir, "scatterplot_of_tmax_with_stl2_trend+remainder_for_100_stations",".ps", sep = ""), color=TRUE, paper="legal")
# for(i in stations){
# b <- xyplot( response ~ time,
# data = subset(tmp.tr, station.id==i),
# main = list(label = paste("Station ", i, sep=""), cex=1.5),
# xlab = list(label = "Month", cex = 1.2),
# ylab = list(label = "Maximum Temperature (degrees centigrade)", cex = 1.2),
# groups = group,
# distribute.type = TRUE,
# type = c("l","p"),
## strip = strip.custom(par.strip.text= list(cex = 1.5)),
## par.settings = list(layout.heights = list(strip = 1.5)),
# xlim = c(0, 1235),
# pch = 16,
# aspect= "xy",
# key=list(type="l", text=list(label=c("Trend","Trend+remainder", "loess")), lines=list(lwd=1.5, col=col[1:3]), columns=3),
# cex = 0.3,
## scales = list(y = list(relation = 'free', cex=1.5), x=list(relation= 'same',format = "%b %Y", tick.number=8), cex=1.2),
# scales = list(y = list(relation = 'free'), x=list(at=seq(0, 1236, by=120), relation = 'same')),
# panel = function(x,y,...) {
# panel.abline(h=seq(0,40,by=2), v=seq(0,1236, by=120), color="lightgrey", lty=3, lwd=0.5)
# panel.xyplot(x,y,...)
# panel.loess(x,y,span=1/3, degree=2, col = col[3], ...)
#
# }
# )
# print(b)
# }
#dev.off()
#####################################################
##time series plot of trend component loess from stl2
#####################################################
tmp.trend <- tmp[,c(1:9,15)]
tmp.trend$time <- rep(0:1235,100)
order <- ddply(.data = tmp.trend,
.variables = "station.id",
.fun = summarise,
mean = mean(tmax)
)
order.st <- as.character(order[order(order$mean, decreasing=TRUE), ]$station.id)
tmp.trend$station.id <- factor(tmp.trend$station.id, levels=order.st)
#Attend to add a small map in the corner of the trend loess plot
#us.map <- map('state', plot = FALSE, fill = TRUE)
trellis.device(postscript, file = paste(outputdir, "scatterplot_of_tmax_with_stl2_trend_loess_for_100_stations",".ps", sep = ""), color=TRUE, paper="legal")
for(i in levels(tmp.trend$station.id)){
b <- xyplot( trend ~ time,
data = subset(tmp.trend[order(tmp.trend$time),], station.id==i),
# main = list(label= paste("Station ", i, sep=""), cex=1.5),
xlab = list(label = "Month", cex = 1.2),
ylab = list(label = paste("Station", i, "Maximum Temperature (degrees centigrade)"), cex = 1.2),
# strip = strip.custom(par.strip.text= list(cex = 1.5)),
# par.settings = list(layout.heights = list(strip = 1.5)),
pch = 16,
cex = 0.3,
aspect="xy",
xlim = c(0, 1235),
key=list(type="l", text=list(label=c("loess smoothing","trend component")), lines=list(lwd=1.5, col=col[1:2]), columns=2),
# scales = list(y = list(relation = 'free', cex=1.5), x=list(relation= 'same',format = "%b %Y", tick.number=8), cex=1.2),
scales = list(y = list(relation = 'free'), x=list(at=seq(0, 1236, by=120), relation = 'same')),
prepanel = function(x,y,...) prepanel.loess(x,y,span=3/4, degree=1, ...),
panel = function(x,y,...) {
panel.abline(h=seq(10,40,by=0.5), v=seq(0,1236, by=120), color="lightgrey", lty=3, lwd=0.5)
panel.loess(x,y,degree=1, span=3/4, col=col[1],...)
panel.xyplot(x,y,col=col[2],...)
}
)
print(b)
# a <- xyplot(lat ~ lon,
# data = subset(tmp.trend, station.id==i),
# xlab = NULL,
# ylab = NULL,
# pch = 16,
# cex = 0.4,
# xlim = c(-127,-65),
# ylim = c(23,50),
# col = "red",
## scales = list(x = list(draw=FALSE), y = list(draw=FALSE)),
## strip = strip.custom(par.strip.text= list(cex = 1.5)),
## par.settings = list(layout.heights = list(strip = 1.5)),
# panel = function(x,y,...) {
# panel.polygon(us.map$x,us.map$y,lwd=0.2)
# panel.xyplot(x,y,...)
# }
# )
# plot.new()
# title(paste("Station ", i, sep=""), cex=1.5)
# print(b, pos=c(0.4,0,1,0.95), newpage=FALSE, more=TRUE)
# print(a, pos=c(0,0.55,0.4,1), more=FALSE)
}
dev.off()
##########################################################################################
##QQ plot of the slopes of loess of trend component, ordered stations by average of slopes
##########################################################################################
#Get the loess estimate of trend component
#tmp.trend <- tmp[,c(1:9,15)]
#tmp.trend$time <- rep(0:1235,100)
#loess.trend <- ddply(.data = tmp.trend,
# .variables = "station.id",
# .fun = summarise,
# elev = elev,
# lon = lon,
# lat = lat,
# time = time,
# loess = loess(trend ~ time, span=0.4, degree=2)$fitted
#)
#
##Since the x-axis are time with 1 unit,
##the difference between two loess estimate is the approximate of the slope.
##Each station will have 1235 slope approximations.
#slope.trend <- ddply(.data = loess.trend,
# .variables = "station.id",
# .fun = summarise,
# elev = elev[1:1235],
# lon = lon[1:1235],
# lat = lat[1:1235],
# slope = diff(loess)
#)
#
##Calculate the mean of slopes for each station, and then order the stations by mean slope.
#mean.slope <- ddply(.data = slope.trend,
# .variables = "station.id",
# .fun = summarise,
# elev = unique(elev),
# lon = unique(lon),
# lat = unique(lat),
# mean = mean(slope)
#)
#mean.slope <- mean.slope[order(mean.slope$mean),]
#station.or <- as.character(mean.slope$station.id)
#
##Attend to add a small map in the corner of the trend loess plot
#us.map <- map('state', plot = FALSE, fill = TRUE)
#
#trellis.device(postscript, file = paste(outputdir, "QQ_plot_of_tmax_slope_of_trend_loess",".ps", sep = ""), color=TRUE, paper="legal")
# for(i in station.or){
# a <- qqmath(~ slope,
# data = subset(slope.trend, station.id == i),
# distribution = qunif,
# aspect = 1,
# pch = 16,
# cex = 0.5,
## main = list(label= paste("Station ", i, sep=""), cex=2),
# xlab = list(label="f-value", cex=1.2),
# ylab = list(label="Maximum temperature(degrees centigrade)", cex=1.2),
## scales = list(x = list(cex=1.5), y = list(cex=1.5)),
# prepanel = prepanel.qqmathline,
# panel = function(x, y,...) {
# #panel.grid(lty=3, lwd=0.5, col="black",...)
# panel.qqmath(x, y,...)
# }
## )
# b <- xyplot(lat ~ lon,
# data = subset(tmp.trend, station.id==i),
# xlab = NULL,
# ylab = NULL,
# pch = 16,
# cex = 0.4,
# xlim = c(-127,-65),
# ylim = c(23,50),
# col = "red",
## scales = list(x = list(draw=FALSE), y = list(draw=FALSE)),
## strip = strip.custom(par.strip.text= list(cex = 1.5)),
## par.settings = list(layout.heights = list(strip = 1.5)),
# panel = function(x,y,...) {
# panel.polygon(us.map$x,us.map$y,lwd=0.2)
# panel.xyplot(x,y,...)
# }
# )
# plot.new()
# title(paste("Station ", i, sep=""), cex=1.5)
# print(a, pos=c(0.4,0,1,1), newpage=FALSE, more=TRUE)
# print(b, pos=c(0,0.5,0.4,1), more=FALSE)
# }
#dev.off()
#
##scatter plot of average slops vs spatial factor
#trellis.device(postscript, file = paste(outputdir, "scatter_plot_of_average_slopes_tmax",".ps", sep = ""), color=TRUE, paper="legal")
# c <- xyplot(mean ~ log2(elev),
# data = mean.slope,
# xlab = list(label = "Elevation (meter)", cex=1.2),
# ylab = list(label = "Average slope of trend", cex=1.2),
# pch = 16,
# cex = 0.7,
## scales = list(x = list(draw=FALSE), y = list(draw=FALSE)),
## strip = strip.custom(par.strip.text= list(cex = 1.5)),
## par.settings = list(layout.heights = list(strip = 1.5)),
# panel = function(x,y,...) {
# panel.xyplot(x,y,...)
# }
# )
# print(c)
#dev.off()
|
1f62a6b7063441d7d4cf5397ccb7ef1607a29a42
|
b5ae747457d833c6543319332f47bd937a4d446e
|
/man/cvPSYmc.Rd
|
579541cc294d1ac5db30f46ed0febc71554d1dab
|
[] |
no_license
|
cran/psymonitor
|
278f6d1e53a4c1b7a5f9441e6e39440dd6903968
|
84029dc78224d495a3e880d26300403998f01a15
|
refs/heads/master
| 2020-04-07T00:21:08.173656
| 2019-03-20T06:00:03
| 2019-03-20T06:00:03
| 157,900,046
| 0
| 2
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,988
|
rd
|
cvPSYmc.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cvPSYmc.R
\name{cvPSYmc}
\alias{cvPSYmc}
\title{Simulate the finite sample critical values for the PSY test.}
\usage{
cvPSYmc(obs, swindow0, IC = 0, adflag = 0, nrep = 199,
multiplicity = TRUE, Tb, useParallel = TRUE, nCores)
}
\arguments{
\item{obs}{A positive integer. The number of observations.}
\item{swindow0}{A positive integer. Minimum window size (default = \eqn{T
(0.01 + 1.8/\sqrt{T})}, where \eqn{T} denotes the sample size)}
\item{IC}{An integer. 0 for fixed lag order (default), 1 for AIC and 2 for
BIC (default = 0).}
\item{adflag}{An integer, lag order when IC=0; maximum number of
lags when IC>0 (default = 0).}
\item{nrep}{A positive integer. Number of replications (default = 199).}
\item{multiplicity}{Logical. If \code{multiplicity=TRUE}, use family-wise
size control in the recursive testing algorithms.}
\item{Tb}{A positive integer. The simulated sample size (swindow0+
controlling). Ignored if \code{multiplicity=FALSE}.}
\item{useParallel}{Logical. If \code{useParallel=TRUE}, use multi core
computation.}
\item{nCores}{A positive integer. Optional. If \code{useParallel=TRUE}, the
number of cores defaults to all but one.}
}
\value{
A matrix. BSADF bootstrap critical value sequence at the 90, 95 and
99 percent level.
}
\description{
\code{cvPSYmc} implements the real time bubble detection
procedure of Phillips, Shi and Yu (2015a,b)
}
\examples{
cv <- cvPSYmc(80, IC = 0, adflag = 1, Tb = 30, nrep = 99, nCores = 1)
}
\references{
Phillips, P. C. B., Shi, S., & Yu, J. (2015a). Testing for
multiple bubbles: Historical episodes of exuberance and collapse in the S&P
500. \emph{International Economic Review}, 56(4), 1034--1078.
Phillips, P. C. B., Shi, S., & Yu, J. (2015b). Testing for
multiple bubbles: Limit Theory for Real-Time Detectors. \emph{International
Economic Review}, 56(4), 1079--1134.
}
|
c9bf9952577317033b8fc2a2cf2870425de3bf2d
|
d3c24352a2959a31ec0d452ca94fde270594e0c8
|
/R/fars_read.R
|
0a06cf89444ec3c73126fd78116b0a16a83d9dcd
|
[
"MIT"
] |
permissive
|
tikizu/famove
|
9f80d92aa63b3c1673357b0b86a3d08de802fa0b
|
7cc44171079c148c2eb21a93499a5bc41661d6b2
|
refs/heads/master
| 2023-02-25T11:16:23.524967
| 2021-02-05T16:34:33
| 2021-02-05T16:34:33
| 330,194,970
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 419
|
r
|
fars_read.R
|
#' Read in a CSV file
#'
#' Create a tibble from CSV file
#'
#' @param filename as a character
#'
#' @return tibble
#'
#' @importFrom readr read_csv
#' @importFrom dplyr as_tibble
#'
#'
#' @export
fars_read <- function(filename) {
if(!file.exists(filename))
stop("file '", filename, "' does not exist")
data <- suppressMessages({
readr::read_csv(filename, progress = FALSE)
})
dplyr::as_tibble(data)
}
|
0d82055558396388b16f8da1c9fc8f9db2be510c
|
72261129cc58ac0368b012eebfad8b9cb9b9a94b
|
/Data Cleaning for sales data in R.R
|
f07b2643504f7e1f9902a6a9df5b9fbe9b0bfe0e
|
[] |
no_license
|
AifenW/Project-for-R
|
4192849996085489c40c75292cbd38286c8005ec
|
13c3bc02c6ef1f567d64b7ab0d4c19419249e395
|
refs/heads/master
| 2020-08-09T05:56:54.020905
| 2020-01-02T18:37:25
| 2020-01-02T18:37:25
| 214,014,510
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,859
|
r
|
Data Cleaning for sales data in R.R
|
# Import data
install.packages("readxl")
library(readxl)
salesdata<-read_excel("~/Data Cleaning/salesdata.xlsx")
# glimpse salesdata
library(tidyverse)
glimpse(salesdata)
# subset salesdata which contain only 5 attributes
install.packages("sqldf")
library(sqldf)
subset_salesdata<-sqldf("
select Customer, `Sales Order #`, Amount, Date, Email
from salesdata
")
nrow(subset_salesdata) #248996
length(unique(subset_salesdata$Customer)) #12172
# create new_salesdata1 which Amount>0 and not from O
nointer_sd<-subset_salesdata %>%
filter(!grepl("@o",Email))%>%
filter(Amount>0)
head(nointer_sd)
nrow(nointer_sd) #235877 (Amount>0) 248331 (!@o) 235877 (both)
length(unique(nointer_sd$Email)) #9814
length(unique(nointer_sd$Customer)) #12006
# split Customer into Account_ID and Customer_ID
install.packages("stringi")
library(stringi)
bftrans_sd<-nointer_sd %>%
mutate(Account_ID=lapply(strsplit(Customer, " "),function(n)n[[1]][1]),
Customer_ID=lapply(lapply(strsplit(Customer, " "), function(x)x[-1]),paste, collapse=" "),
Customer_ID=stri_trim(tolower(Customer_ID))
) %>%
rename(SalesOrder='Sales Order #') %>%
select(-Customer)
bftrans_sd<-bftrans_sd[,c("Account_ID","Customer_ID","SalesOrder","Amount", "Date", "Email")]
glimpse(bftrans_sd)
head(bftrans_sd)
nrow(bftrans_sd) #235877
length(unique(bftrans_sd$Customer_ID)) #11535
# check bftrans_sd
check<-bftrans_sd %>%
filter(str_detect(Customer_ID,"univ"))
check<-bftrans_sd %>%
filter(str_detect(Customer_ID,":"))
check<-bftrans_sd %>%
filter(str_detect(Customer_ID,"-"))
head(check)
tail(check)
# first cleaning of bftrans_sd which convert "university" in Customer_ID to "univ"
install.packages("stringr")
library(stringr)
fclean_bftrans_sd<- bftrans_sd %>%
mutate(
Customer_ID=lapply(str_split(Customer_ID, "[:]"),function(n)n[[1]][1]),
Customer_ID=str_trim(sub("university","univ",x=Customer_ID)))
nrow(fclean_bftrans_sd) #235877
length(unique(fclean_bftrans_sd$Customer_ID)) #10879
head(fclean_bftrans_sd)
class(fclean_bftrans_sd$Email)
# Second time to clean fclean_bftrans_sd
# produce a list of near matches for Customer_ID
# create empty dataframe
library(stringdist)
df<-data.frame(Account_ID=character(),Salesorder=character(),Amount=double(),Date=as.Date(character()),Email=character(),LikelyGroup=integer(),Group_Customer_ID=character())
for (i in 0:floor(n/10000))
{
n<-nrow(fclean_bftrans_sd) #235877
start=i*10000+1
end= (i+1)*10000
if(n>end)
{
lclean_bftrans_sd<-fclean_bftrans_sd[start:end,]
}else{
lclean_bftrans_sd<-fclean_bftrans_sd[start:n,]
}
dist_Customer_ID <- stringdistmatrix(lclean_bftrans_sd$Customer_ID,lclean_bftrans_sd$Customer_ID,useNames="strings",method="jw",p=0.1)
row.names(dist_Customer_ID)<-lclean_bftrans_sd$Customer_ID
names(dist2_Customer_ID)<-lclean_bftrans_sd$Customer_ID
dist_Customer_ID<-as.dist(dist_Customer_ID)
#Hierarchical clustering to find closest
clusts<-hclust(dist_Customer_ID,method="ward.D2")
#Cut into appropriate clusters based upon height in the dendrogram
lclean_bftrans_sd$LikelyGroup<-cutree(clusts,h=0.1)
#Define "mode" function which only selects one mode even in bimodal cases.
Mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
#Select modal name for each group
lclean_bftrans_sd<-lclean_bftrans_sd%>%
group_by(LikelyGroup)%>%
mutate(Group_Customer_ID=Mode(Customer_ID))
df=rbind(df,as.data.frame(lclean_bftrans_sd))
}
nrow(df) #235877
length(unique(df$Customer_ID)) #10879
length(unique(df$Group_Customer_ID)) #10716
head(df)
check<-df %>%
filter(Customer_ID != Group_Customer_ID)
head(check)
tail(check)
nrow(check) # 1336
length(unique(check$Customer_ID)) #534
length(unique(check$Group_Customer_ID)) #500
# final cleaned data ...........will be used for RFM analysis
final_tran_sd<-df
nrow(df)
nrow(final_tran_sd)
class(df)
class(final_tran_sd)
# data subset for Customer_ID is different from Group_Customer_ID
diff_CustomerID<-final_tran_sd %>%
filter(Customer_ID != Group_Customer_ID)
nrow(diff_CustomerID) #1336
# export fntrans_sd for RFM analysis
library(openxlsx)
final_tran_sd<-openxlsx::write.xlsx(final_tran_sd, file="C:/Users/awang/Documents/2018-1-22/SAS_R/R/RLearning/RProject/Project 6 Origene sales data analysis/Data Cleaning/final_tran_sd.xlsx")
diff_CustomerID<-openxlsx::write.xlsx(diff_CustomerID, file="C:/Users/awang/Documents/2018-1-22/SAS_R/R/RLearning/RProject/Project 6 Origene sales data analysis/Data Cleaning/diff_CustomerID.xlsx")
install.packages("xlsx")
library(xlsx)
write.xlsx(df, file="C:/Users/awang/Documents/2018-1-22/SAS_R/R/RLearning/RProject/Project 6 Origene sales data analysis/Data Cleaning/final_tran_sd.xlsx")
|
5f60c8fd8cfa710a67ca60a3b984768c95c6cb3f
|
45ace96c2914c90eadca3f5b9e0c381b414a480b
|
/data-code/_BuildFinalData.R
|
a50ffec05a09a0959b235e91ff491f9dc1785f19
|
[] |
no_license
|
subeniwal/Physician-Shared-Patients
|
3b855e633b6f8a02fe8c01a261d0398ac673bfd1
|
71644ae7e9682b71610347379d1cd5ddb17a6b29
|
refs/heads/master
| 2023-07-17T14:20:04.617460
| 2021-08-26T21:22:56
| 2021-08-26T21:22:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,538
|
r
|
_BuildFinalData.R
|
# Meta --------------------------------------------------------------------
## Title: Physician Shared Patients Data
## Author: Ian McCarthy
## Date Created: 10/10/2019
## Date Edited: 9/15/2020
# Preliminaries -----------------------------------------------------------
if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, ggplot2, dplyr, lubridate, stringr,
igraph, network, sna, ggraph, visNetwork, threejs,
networkD3, ndtv)
## set paths
source("data-code/paths.R")
## Run initial code files
#source("data-code/SharedPatientData.R")
#source("data-code/PhysicianCompare.R")
source("data-code/SharedPatientData_2010.R")
source("data-code/PhysicianCompare_2013.R")
# Networks ----------------------------------------------------------------
node.data <- nodes.2010 %>%
filter(str_to_lower(city_1)=="atlanta" |
str_to_lower(city_2)=="atlanta" |
str_to_lower(city_3)=="atlanta" |
str_to_lower(city_4)=="atlanta" |
str_to_lower(city_5)=="atlanta") %>%
left_join(PSPD.final.2010 %>%
distinct(npi1) %>%
mutate(pcp=1,
npi1=as.numeric(npi1)), by=c("npi"="npi1")) %>%
mutate(pcp=replace_na(pcp,0))
edge.data <- PSPD.final.2010 %>%
mutate(npi1=as.numeric(npi1),
npi2=as.numeric(npi2)) %>%
filter(str_detect(desc_tax2,"Ortho")) %>%
inner_join(node.data %>% distinct(npi), by=c("npi1"="npi")) %>%
inner_join(node.data %>% distinct(npi), by=c("npi2"="npi")) %>%
select(from=npi1, to=npi2, weight=paircount)
# Small Networks ----------------------------------------------------------
large.pcps <- edge.data %>%
group_by(from) %>%
mutate(pcp_count=sum(weight)) %>%
ungroup() %>%
distinct(from, pcp_count) %>%
arrange(-pcp_count)
large.pcps <- head(large.pcps, 20)
edge.small <- edge.data %>%
inner_join(large.pcps %>% distinct(from), by="from")
small.from <- edge.small %>%
distinct(from) %>%
mutate(npi=from)
small.to <- edge.small %>%
distinct(to) %>%
mutate(npi=to)
npi.small <- bind_rows(small.from, small.to)
node.small <- node.data %>%
inner_join(npi.small %>% distinct(npi), by="npi")
# Save Data ---------------------------------------------------------------
saveRDS(node.data, file="data/node_data.RData")
saveRDS(edge.data, file="data/edge_data.RData")
saveRDS(node.small, file="data/node_small.RData")
saveRDS(edge.small, file="data/edge_small.RData")
saveRDS(PSPD.final.2010, file="data/pspd_data.RData")
|
6d5f5dea55d6e8104b040499d75e4dd99ccd9dc3
|
18893292dc638efa94c92bb826a7aaa772fa7a52
|
/man/bnets.Rd
|
5a91c9cb550cc2ef745f555defaa2b76d1d5ba40
|
[] |
no_license
|
rithwik/bnets
|
1da4218d50c999cb4ad321abd99dee6b431537cb
|
e4a747493ac0e81a72f259fca2671ec2519ecaec
|
refs/heads/master
| 2021-01-10T05:09:57.730354
| 2016-02-25T07:43:20
| 2016-02-25T07:43:20
| 52,260,662
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 332
|
rd
|
bnets.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bnets.R
\docType{package}
\name{bnets}
\alias{bnets}
\alias{bnets-package}
\title{bnets: A package to play with Bayesian Networks}
\description{
bnets combines functionalities from bnlearn, visNetwork, and shiny to make interactive bayesian networks.
}
|
67d27d1d1a504b869c2b729f2630fb22af4ebcc9
|
d1d1d4d7cf34ffac0c5c421c0f6fb3846ae97939
|
/Plot1.R
|
b0f70eb72c35ac73d4b3a2676207a7aa9fef3d55
|
[] |
no_license
|
vallvi/ExData_Plotting1
|
afed4f4d500d51c6bb5a92ead71068e9e02e8f72
|
2b887b11cc6b21e7a82349e71d41cd646d2bc8d7
|
refs/heads/master
| 2021-01-17T19:57:01.605608
| 2014-12-07T01:14:03
| 2014-12-07T01:14:03
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 613
|
r
|
Plot1.R
|
file <- read.table("household_power_consumption.txt",sep=";",header=T,colClasses=c("character","character","numeric","numeric","numeric","numeric","numeric","numeric","numeric"),na.strings="?")
file1 <- file[file$Date=="1/2/2007",]
file2 <- file[file$Date=="2/2/2007",]
powdata <- rbind(file1,file2)
powdata$DateTime <- strptime(paste(powdata$Date,powdata$Time),format="%d/%m/%Y %H:%M:%S")
powdata$Date <- as.Date(powdata$Date,format="%d/%m/%Y")
hist(powdata[,3],col="red",main="Global Active Power",xlab="Global Active Power (kilowatts)",ylab="Frequency",ylim=c(0,1200))
dev.copy(png,file="Plot1.png")
dev.off()
|
6788d9ac3ea2eb14d153f6c051c967e29c90137a
|
677962a3141a34254a91e6c7ea7855695340e89e
|
/Bo-Chen-Code/3-1.R
|
c2f2944f8f43b7a5dbdd0192f1564aa3b99dbf8f
|
[] |
no_license
|
YuCheng21/nkust-data-science
|
2f70f9eb007dac1b6ee9c5d556922afc4fbbbaba
|
c10812007fdec27e8cccf03b26f868964ebda1ea
|
refs/heads/master
| 2023-06-02T01:36:35.474870
| 2021-06-24T19:05:06
| 2021-06-24T19:05:06
| 346,954,244
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 124
|
r
|
3-1.R
|
a <- 1:9
b <- 1:9
for(i in a){
for(j in b){
x <- sprintf("%d * %d = %d",i,j,i*j)
print(x)
}
}
|
58f9437c8babe12a97949dd1275726f8146bcab1
|
52b818cfc1d707cbc08e3d54b5db9bce828b83d2
|
/script.R
|
30c4dfc2025d1859d8475696c8ee4c067c2e1199
|
[] |
no_license
|
julianpsd/SICSS2020
|
283a8ab1eff2d8ac157ad7e14c70062f4d3d501f
|
bea4d3b569779fe8bd3dc71b80a9c926dfcb763b
|
refs/heads/master
| 2023-07-18T22:24:27.284806
| 2021-09-18T19:35:37
| 2021-09-18T19:35:37
| 276,173,588
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 20,867
|
r
|
script.R
|
## COVID 19 Sentiment analysis
#packages
library(dplyr)
library(ggplot2)
library(tidyr)
library(sjmisc)
library(psych)
library(MASS)
library(reshape2)
library(reshape)
library(ggpubr)
library(topicmodels)
library(tm)
library(tidytext)
library(dplyr)
library(SnowballC)
library(lubridate)
library(wordcloud2)
library(wordcloud)
library(syuzhet)
library(scales)
SICSS_Montreal <- read.csv("rehydrated_COVID_TweetIDs_MarchAprilMay_1Perc.csv",stringsAsFactors=FALSE)
#Clean dataset: untoken the text
SICSS_Mont <- SICSS_Montreal %>%
filter(lang=="en", grepl('Canada', user_location))%>%
unnest_tokens(word,text)%>%
anti_join(stop_words)%>%
filter(!(word=="https"|
word=="rt"|
word=="t.co"|
word=="amp" |
word== "3"|
word== "19"|
word=="2"|
word=="1"|
word== "coronavirus"|
word=="covid"|
word=="covid19"|
word=="it’s"|
word=="i'm"))
SICSS_Mont %>%
count(word, sort = TRUE) %>%
filter(n > 100) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n)) +
geom_col() +
xlab(NULL) +
coord_flip()
```
## Topic Modelling
```{r warning=FALSE}
#Version 1
# Load data
Tweets_topic <- read.csv("rehydrated_COVID_TweetIDs_MarchAprilMay_1Perc.csv",stringsAsFactors=FALSE)
# Pull the tweet text
Tweets_sec <- Tweets_topic %>% filter(lang=="en", grepl('Canada', user_location)) %>%
pull(text)
# Create a corpus of tweets for a Document-Term Matrix
corpus <- Corpus(VectorSource(Tweets_sec))
corpus <- tm_map(corpus, tolower)
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, removeWords, c("rt", "https", "t.co", stopwords("english")))
corpus <- tm_map(corpus, stemDocument)
# Create the Document Term Matrix
DTM <- DocumentTermMatrix(corpus)
# OPTIONAL: we can delete the less frequent terms, for this, change "DTM" below for "sparse_DTM"
#frequent_ge_20 <- findFreqTerms(DTM, lowfreq = 20)
#sparse_DTM <- removeSparseTerms(DTM, 0.995)
# Group the terms by topic
# OPTIONAL: we can change the "k" variable velow to set the number of topics. For now it's set at 6
tweet_lda <- LDA(DTM, k = 4, control = list(seed = 1234))
# Extract the "per topic per word" probabilities of a topic ("beta")
tweet_topics <- tidy(tweet_lda, matrix = "beta")
# Select the top terms per topic
tweet_top_terms <- tweet_topics %>%
group_by(topic) %>%
top_n(10, beta) %>%
ungroup() %>%
arrange(topic, -beta)
# Plot the different topics and the top words
tweet_top_terms %>%
mutate(term = reorder_within(term, beta, topic)) %>%
ggplot(aes(term, beta, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
coord_flip() +
scale_x_reordered()
#Version 2
tidy_dtm <- SICSS_Mont %>%
filter(!(word=="https"|
word=="rt"|
word=="t.co"|
word=="amp")) %>%
count(word) %>%
cast_dtm(word, word, n)
tweet_topic_model<-LDA(tidy_dtm , k=4, control = list(seed = 321))
topics <- tidy(tweet_topic_model, matrix = "beta")
top_terms1 <- topics %>%
group_by(topic) %>%
top_n(10, beta) %>%
ungroup() %>%
arrange(topic, -beta)
top_terms1 %>%
mutate(term = reorder_within(term, beta, topic)) %>%
ggplot(aes(term, beta, fill = factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap(~ topic, scales = "free") +
coord_flip() +
scale_x_reordered()
#Sentiment analysis (Matt’s version)
Tweets_topic <- read.csv("rehydrated_COVID_TweetIDs_MarchAprilMay_1Perc.csv",stringsAsFactors=FALSE)
################
## Filter the tweets
################
library(plyr)
## Select only the English Canadian tweets
Tweets_filtered<- Tweets_topic %>%
filter(lang=="en", grepl("canada",
ignore.case = TRUE,
user_location))
## Select only tweets that mention Canada
Tweets_canada<- Tweets_filtered %>%
filter(grepl("canad.*",
ignore.case = TRUE,
text))
## Select only tweets that mention Trudeau
Tweets_trudeau<- Tweets_filtered %>%
filter(grepl("trudeau",
ignore.case = TRUE,
text))
## Join the datasets
Tweets_about_canada <- full_join(Tweets_canada,
Tweets_trudeau)
## Rename dataset back to filtered (to work with legacy code below)
Tweets_filtered <- Tweets_about_canada
## Select only tweets that mention America, US, Trump
################
## Clean the filtered tweets
################
## Create a new dataset based on the filtered tweets
Tweets_tidy<-Tweets_filtered %>%
dplyr::select(created_at,text)%>%
unnest_tokens("word", text)
#removing stop words
data("stop_words")
Tweets_tidy_rmstop <-Tweets_tidy %>%
anti_join(stop_words)
## Remove numbers
Tweets_tidy_rmstop<-Tweets_tidy_rmstop[-grep("\\b\\d+\\b", Tweets_tidy_rmstop$word),]
## Remove white spaces
Tweets_tidy_rmstop$word <- gsub("\\s+","",Tweets_tidy_rmstop$word)
#Stemming
Tweets_tidy_rmstop %>%
mutate_at("word", funs(wordStem((.), language = "en")))
#removal of twitter-specific language
detach(package:plyr)
top_words<-
Tweets_tidy_rmstop %>%
#anti_join(stop_words) %>%
filter(!(word=="https"|
word=="rt"|
word=="t.co"|
word=="amp")) %>%
count(word) %>%
arrange(desc(n))
# top_words %>%
# ggplot(aes(x=reorder(word, -n), y=n, fill=word))+
# geom_bar(stat="identity")+
# theme_minimal()+
# theme(axis.text.x =
# element_text(angle = 60, hjust = 1, size=13))+
# theme(plot.title =
# element_text(hjust = 0.5, size=18))+
# ylab("Frequency")+
# xlab("")+
# ggtitle("Most Frequent Words in Tweets")+
# guides(fill=FALSE)+
# coord_flip()
#sentiment analysis using dictionary
Tweet_sentiment <- top_words %>%
filter(!(word=="https"|
word=="rt"|
word=="t.co"|
word=="amp")) %>%
inner_join(get_sentiments("bing")) %>%
count(sentiment)
## Plot by date
## Wrangle the date format
# Tweets_tidy_rmstop$date<-as.Date(Tweets_tidy_rmstop$created_at,
# format="%b %d %x %x %Y")
Tweets_tidy_rmstop$createdClean<-paste(substr(Tweets_tidy_rmstop$created_at,5,10),"2020")
## Create new column with standard date format
## https://www.r-bloggers.com/date-formats-in-r/
Tweets_tidy_rmstop$date<-as.Date(Tweets_tidy_rmstop$createdClean,
format="%b %d %Y")
## Plot negative sentiment by date
# Tweet_sentiment_plot <-
# Tweets_tidy_rmstop %>%
# inner_join(get_sentiments("bing")) %>%
# filter(sentiment=="negative") %>%
# count(date, sentiment)
## Get a count of how many tweets per day
words_perDay <-
Tweets_tidy_rmstop %>%
dplyr::count(date)
words_perDay <-
words_perDay %>%
dplyr::rename("n_words" = n)
## Aggregate positive and negative sentiment by tweet
# Aggregate by negative sentiment
sentiment_negative <-
Tweets_tidy_rmstop %>%
inner_join(get_sentiments("bing")) %>%
filter(sentiment=="negative") %>%
count(date, sentiment)
# Rename the new column
sentiment_negative <-
sentiment_negative %>%
dplyr::rename(n_negative = n)
## Aggregate by positive sentiment
## Aggregate positive and negative sentiment by tweet
sentiment_positive <-
Tweets_tidy_rmstop %>%
inner_join(get_sentiments("bing")) %>%
filter(sentiment=="positive") %>%
count(date, sentiment)
sentiment_positive <-
sentiment_positive %>%
dplyr::rename(n_positive = n)
## Join the two datasets to get both positive and negative sentiment, and words per day
sentiment_both <- full_join(sentiment_negative, sentiment_positive, by="date")
sentiment_both <- full_join(sentiment_both, words_perDay, by="date")
## Replace missing values with 0
sentiment_both$n_negative <- ifelse(is.na(sentiment_both$n_negative),0,sentiment_both$n_negative)
sentiment_both$n_positive <- ifelse(is.na(sentiment_both$n_positive),0,sentiment_both$n_positive)
## Create some derived variables
sentiment_both$ratioPosNeg = sentiment_both$n_positive/sentiment_both$n_negative
sentiment_both$ratioPosPerWord = sentiment_both$n_positive/sentiment_both$n_words
sentiment_both$ratioNegPerWord = sentiment_both$n_negative/sentiment_both$n_words
## Create a new variable that keeps track of the week
sentiment_both$week <- week(sentiment_both$date)
## Summarize the tweets by week
sentiment_byWeek <- sentiment_both %>%
group_by(week) %>%
summarize(n_positive = sum(n_positive),
n_negative = sum(n_negative),
n_words = sum(n_words))
## Create some derived variables
sentiment_byWeek$ratioPosNeg = sentiment_byWeek$n_positive/sentiment_byWeek$n_negative
sentiment_byWeek$ratioPosPerWord = sentiment_byWeek$n_positive/sentiment_byWeek$n_words
sentiment_byWeek$ratioNegPerWord = sentiment_byWeek$n_negative/sentiment_byWeek$n_words
##################
## Save the datasets
##################
# write.csv(sentiment_byWeek,
# "~/Documents/GitHub/SICSS_2020/sentiment_aboutCanada_byWeek.csv")
```
```{r warning=FALSE}
##################
## Plot the results
##################
sentiment_byWeek <- readr::read_csv("sentiment_aboutCanada_byWeek.csv")
## Number of words
ggplot(sentiment_both, aes(x=date)) +
geom_line(aes(y = n_negative), color = "red") +
geom_line(aes(y = n_positive), color="blue") +
# geom_line(aes(y = n_words), color="black") +
theme_minimal()+
theme(axis.text.x =
element_text(angle = 60, hjust = 1, size=13))+
theme(plot.title =
element_text(hjust = 0.5, size=18))+
ylab("Number of Words")+
xlab("")+
ggtitle("Sentiment in Tweets")+
theme(aspect.ratio=1/4)
## Plot the results
## Ratio of positive / negative sentiment per word
ggplot(sentiment_both, aes(x=date)) +
scale_x_date(date_breaks = "1 week", date_minor_breaks = "2 day") +
#scale_x_date(date_minor_breaks = "2 day") +
#geom_line(aes(y = ratioNegPerWord), color = "red") +
#geom_line(aes(y = ratioPosPerWord), color="blue") +
geom_line(aes(y = ratioPosNeg), color="black") +
theme_minimal()+
theme(axis.text.x =
element_text(angle = 60, hjust = 1, size=13))+
theme(plot.title =
element_text(hjust = 0.5, size=18))+
ylab("Ratio of Positive/Negative Sentiment per total Words")+
xlab("")+
ggtitle("Sentiment in Tweets")+
theme(aspect.ratio=1/4)
# ggplot(Tweet_sentiment_plot, aes(x=date, y=n))+
# geom_line(color="red", size=.5)+
# theme_minimal()+
# theme(axis.text.x =
# element_text(angle = 60, hjust = 1, size=13))+
# theme(plot.title =
# element_text(hjust = 0.5, size=18))+
# ylab("Number of Negative Words")+
# xlab("")+
# ggtitle("Negative Sentiment in Tweets")+
# theme(aspect.ratio=1/4)
survey_weekly <- readr::read_csv("SURVEY_LABOR_WEEKLY.csv")
## Convert the start date column to a date
survey_weekly$date <- as.Date(survey_weekly$Survey_Date_Start)
## Calculate the % satisfied with measures overall
survey_weekly$satMeas_overall <- survey_weekly$SATMEAS_SS + survey_weekly$SATMEAS_VS
## Calculate the % fearful overall
survey_weekly$fear_overall <- survey_weekly$FEAR_V + survey_weekly$FEAR_SW
## Note: $FEAR_AL = already contracted the virus
## Drop the first row
survey_weekly <- survey_weekly[-c(1), ]
# Add in the correct country data
survey_weekly$Country <- "Canada"
## Wrangle the dates into weeks
survey_weekly_tidy <- survey_weekly
survey_weekly_tidy$week <- week(survey_weekly_tidy$date)
##################
## Correlations
##################
## Join the sentiment and survey data by week
sentiment_with_survey <- full_join(sentiment_byWeek, survey_weekly_tidy, by="week")
## See if there is a correlation between sentiment and % satisfied
cor.test(sentiment_with_survey$ratioPosNeg,
sentiment_with_survey$satMeas_overall,
method = "spearman"
)
## See if there is a correlation between positive sentiment and sat overall
cor.test(sentiment_with_survey$ratioPosPerWord,
sentiment_with_survey$satMeas_overall,
method = "spearman"
)
## See if there is a correlation between negative sentiment and sat overall
cor.test(sentiment_with_survey$ratioNegPerWord,
sentiment_with_survey$satMeas_overall,
method = "spearman"
)
## See if there is a correlation between positive sentiment and % fearful
cor.test(sentiment_with_survey$ratioPosPerWord,
sentiment_with_survey$fear_overall,
method = "spearman"
)
library(readxl)
SURVEY_LABOR_CAN <- read_excel("SURVEY_LABOR_CAN.xlsx",
sheet = "Feuil1")
#View(SURVEY_LABOR_CAN)
sentiment_with_survey <- full_join(sentiment_byWeek, SURVEY_LABOR_CAN, by="week")
## See if there is a correlation tweets sentiment and Proportion of respondents that already exposed to covid
cor.test(sentiment_with_survey$ratioPosNeg,
sentiment_with_survey$FEAR_AL,
method = "spearman"
)
## See if there is a correlation tweets sentiment and Proportion that think we are in the worst of the pandemic now
cor.test(sentiment_with_survey$ratioPosNeg,
sentiment_with_survey$EVL_WN,
method = "spearman"
)
## See if there is a correlation tweets sentiment and Proportion that approve provincial measures
cor.test(sentiment_with_survey$ratioPosNeg,
sentiment_with_survey$SATMEAS_PR,
method = "spearman"
)
## See if there is a correlation tweets sentiment and Proportion that do not commit to measures
cor.test(sentiment_with_survey$ratioPosNeg,
sentiment_with_survey$NCOMM,
method = "spearman"
)
## See if there is a correlation tweets sentiment and Proportion that think the threat is blown out of proportion
cor.test(sentiment_with_survey$ratioPosNeg,
sentiment_with_survey$THR_OP,
method = "spearman"
)
## Plot the results
## By week
## Ratio of positive / negative sentiment per word
ggplot(sentiment_with_survey, aes(x=week)) +
#scale_x_date(date_breaks = "1 week", date_minor_breaks = "2 day") +
geom_line(aes(y = ratioPosNeg), color="black") +
geom_line(aes(y = SATMEAS_S), color="blue") +
geom_line(aes(y = FEAR_NS), color="red") +
#geom_line(aes(y = FEAR_AL), color="green")
#geom_line(aes(y = EVL_WN), color="green")
#geom_line(aes(y = SATMEAS_PR), color="green")
#geom_line(aes(y = NCOMM), color="green")
theme_minimal()+
theme(axis.text.x =
element_text(angle = 60, hjust = 1, size=13))+
theme(plot.title =
element_text(hjust = 0.5, size=18))+
ylab("Ratio of Positive/Negative Sentiment per total Words")+
xlab("")+
ggtitle("Sentiment in Tweets")+
theme(aspect.ratio=1/4)
#########
## Read the coronavirus data
########
library(readxl)
covid_canada <- read_excel("Public_COVID-19_Canada.xlsx", skip = 3)
######
## Optionally read the other datasets (if not already in memory)
######
#sentiment_both <- read_csv("~/Documents/GitHub/SICSS_2020/sentiment_aboutCanada_byDay.csv")
#sentiment_byWeek <- read_csv("~/Documents/GitHub/SICSS_2020/sentiment_aboutCanada_byWeek.csv")
#survey_weekly <- read_csv("~/Documents/GitHub/SICSS_2020/survey_weekly_tidy_canada")
##########
## Wrangle dates
#########
covid_canada$date <- as.Date(covid_canada$date_report)
covid_canada$week <- week(covid_canada$date)
## Create dataframe of cases per day
casesPerDay <-
covid_canada %>%
count(date)
casesPerDay <-
casesPerDay %>%
dplyr::rename(n_cases = n)
## Join the sentiment data per day with the casesPerDay dataframe
sentiment_both <- full_join(sentiment_both, casesPerDay, by="date")
## scale the cases per day such that it is between 0 and 1 (/max)
#sentiment_both$casesScaled <- sentiment_both$n_cases/max(sentiment_both$n_cases,na.rm = TRUE)
## Plot the result
## Number of words and cases
#ggplot(sentiment_both, aes(x=date)) +
# geom_line(aes(y = n_negative), color = "red") +
#geom_line(aes(y = n_positive), color="blue") +
# geom_line(aes(y = n_words), color="black")
# theme_minimal()+
#theme(axis.text.x =
# element_text(angle = 60, hjust = 1, size=13))+
#theme(plot.title =
# element_text(hjust = 0.5, size=18))+
#ylab("Number of Words")+
#xlab("")+
#ggtitle("Sentiment in Tweets")+
#theme(aspect.ratio=1/4)
## Ratio of positivity and scaled cases
ggplot(sentiment_both, aes(x=date)) +
# geom_line(aes(y = n_negative), color = "red") +
# geom_line(aes(y = n_positive), color="blue") +
geom_line(aes(y = ratioPosNeg), color="black") +
geom_line(aes(y = casesScaled), color = "green") +
theme_minimal()+
theme(axis.text.x =
element_text(angle = 60, hjust = 1, size=13))+
theme(plot.title =
element_text(hjust = 0.5, size=18))+
ylab("Number of Words")+
xlab("")+
ggtitle("Sentiment in Tweets")+
theme(aspect.ratio=1/4)
## See if there is a correlation between sickness and positivity
cor.test(sentiment_both$ratioPosNeg,sentiment_both$casesScaled, method = "spearman")
############
## Create dataframe of cases per week
############
casesPerWeek <-
covid_canada %>%
count(week)
casesPerWeek <-
casesPerWeek %>%
dplyr::rename(n_cases = n)
## Join the sentiment data per week with the casesPerWeek dataframe
sentiment_with_cases <- full_join(sentiment_byWeek, casesPerWeek, by="week")
## Join the survey data
#sentiment_with_cases_survey <- full_join(sentiment_with_cases, survey_weekly, by="week")
## Scale the cases between 0 and 1 (/max)
sentiment_with_cases_survey$casesScaled <- sentiment_with_cases_survey$n_cases /
max(sentiment_with_cases_survey$n_cases,na.rm = TRUE)
##############
### Save the dataset
#############
#write.csv(sentiment_with_cases_survey,
# "~/Documents/GitHub/SICSS_2020/sentiment_cases_survey_weekly_Canada.csv")
```
```{r warning=FALSE}
##############
## Final analyses (after all wrangling is done)
##############
## Load the dataset
library(readr)
sentiment_with_cases_survey <- read_csv("sentiment_cases_survey_weekly_Canada.csv")
###############
## Plot the results
###############
library(ggplot2)
## Ratio of positivity and scaled cases
ggplot(sentiment_with_cases_survey, aes(x=week)) +
geom_line(aes(y = fear_overall), color = "red") +
geom_line(aes(y = satMeas_overall), color="blue") +
geom_line(aes(y = ratioPosNeg), color="black") +
geom_line(aes(y = casesScaled), color = "green") +
theme_minimal()+
theme(axis.text.x =
element_text(angle = 60, hjust = 1, size=13))+
theme(plot.title =
element_text(hjust = 0.5, size=18))+
ylab("Number of Words")+
xlab("")+
ggtitle("Sentiment in Tweets")+
theme(aspect.ratio=1/4)
## See if there is a correlation between positivity and survey approval
cor.test(sentiment_with_cases_survey$ratioPosNeg,
sentiment_with_cases_survey$satMeas_overall,
method = "spearman",
na.action = "na.exclude")
## See if there is a correlation between cases and positive sentiment
cor.test(sentiment_with_cases_survey$ratioPosNeg,
sentiment_with_cases_survey$casesScaled,
method = "spearman",
na.action = "na.exclude")
## See if there is a correlation between cases and fear
cor.test(sentiment_with_cases_survey$fear_overall,
sentiment_with_cases_survey$casesScaled,
method = "spearman",
na.action = "na.exclude")
```
```{r warning=FALSE}
##############
## Final analyses (after all wrangling is done)
##############
## Load the dataset
library(readr)
sentiment_with_cases_survey <- read_csv("sentiment_cases_survey_weekly_Canada.csv")
###############
## Plot the results
###############
library(ggplot2)
## Ratio of positivity and scaled cases
ggplot(sentiment_with_cases_survey, aes(x=week)) +
geom_line(aes(y = fear_overall), color = "red") +
geom_line(aes(y = satMeas_overall), color="blue") +
geom_line(aes(y = ratioPosNeg), color="black") +
geom_line(aes(y = casesScaled), color = "green") +
theme_minimal()+
theme(axis.text.x =
element_text(angle = 60, hjust = 1, size=13))+
theme(plot.title =
element_text(hjust = 0.5, size=18))+
ylab("Number of Words")+
xlab("")+
ggtitle("Sentiment in Tweets")+
theme(aspect.ratio=1/4)
## See if there is a correlation between positivity and survey approval
cor.test(sentiment_with_cases_survey$ratioPosNeg,
sentiment_with_cases_survey$satMeas_overall,
method = "spearman",
na.action = "na.exclude")
## See if there is a correlation between cases and positive sentiment
cor.test(sentiment_with_cases_survey$ratioPosNeg,
sentiment_with_cases_survey$casesScaled,
method = "spearman",
na.action = "na.exclude")
## See if there is a correlation between cases and fear
cor.test(sentiment_with_cases_survey$fear_overall,
sentiment_with_cases_survey$casesScaled,
method = "spearman",
na.action = "na.exclude")
|
f49fb0f94b8faac9b3804c1d6872c2aad9835712
|
b57e31db43d7218962fd36d2f5281e0efd8aad24
|
/man/batch.Rd
|
42bd2644d62827a427fea8a4553bdea14b337db3
|
[] |
no_license
|
cran/cyclestreets
|
69e6bd64bb5b9e4dd72f649252c5f0637acb8ff9
|
3215e5321a9fe79cb8d7449f1dd884a44e4edac3
|
refs/heads/master
| 2023-09-01T05:50:11.581440
| 2023-08-15T07:20:14
| 2023-08-15T08:31:03
| 131,992,217
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 4,550
|
rd
|
batch.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/batch.R
\name{batch}
\alias{batch}
\title{Interface to CycleStreets Batch Routing API}
\usage{
batch(
desire_lines = NULL,
id = NULL,
directory = tempdir(),
wait = FALSE,
wait_time = NULL,
name = "Batch job",
serverId = 21,
strategies = "quietest",
bothDirections = 0,
minDistance = 50,
maxDistance = 5000,
filename = "test",
includeJsonOutput = 1,
emailOnCompletion = "you@example.com",
username = Sys.getenv("CYCLESTREETS_UN"),
password = Sys.getenv("CYCLESTREETS_PW"),
base_url = "https://api.cyclestreets.net/v2/batchroutes.createjob",
pat = Sys.getenv("CYCLESTREETS_BATCH"),
silent = TRUE,
delete_job = TRUE,
cols_to_keep = c("id", "name", "provisionName", "distances", "time", "quietness",
"gradient_smooth"),
segments = TRUE
)
}
\arguments{
\item{desire_lines}{Geographic desire lines representing origin-destination data}
\item{id}{int
Batch job ID, as returned from batchroutes.createjob.
action string (start|pause|continue|terminate)
Action to take. Available actions are:
start: Start (open) job
pause: Pause job
continue: Continue (re-open) job
terminate: Terminate job and delete data}
\item{directory}{Where to save the data? \code{tempdir()} by default}
\item{wait}{Should the process block your R session but return a route?
FALSE by default.}
\item{wait_time}{How long to wait before getting the data in seconds?
NULL by default, meaning it will be calculated by the private function
\code{wait_s()}.}
\item{name}{The name of the batch routing job for CycleStreets}
\item{serverId}{The server ID to use (21 by default)}
\item{strategies}{Route plan types, e.g. \code{"fastest"}}
\item{bothDirections}{int (1|0)
Whether to plan in both directions, i.e. A-B as well as B-A.
0, meaning only one way routes, is the default in the R default.}
\item{minDistance}{Min Euclidean distance of routes to be calculated}
\item{maxDistance}{Maximum Euclidean distance of routes to be calculated}
\item{filename}{Character string}
\item{includeJsonOutput}{int (1|0)
Whether to include a column in the resulting CSV data giving the full JSON output from the API, rather than just summary
information like distance and time.}
\item{emailOnCompletion}{Email on completion?}
\item{username}{string
Your CycleStreets account username. In due course this will be replaced with an OAuth token.}
\item{password}{string
Your CycleStreets account password. You can set it with
Sys.setenv(CYCLESTREETS_PW="xxxxxx")}
\item{base_url}{The base url from which to construct API requests
(with default set to main server)}
\item{pat}{The API key used. By default this uses \code{Sys.getenv("CYCLESTREETS")}.}
\item{silent}{Logical (default is FALSE). TRUE hides request sent.}
\item{delete_job}{Delete the job? TRUE by default to avoid clogged servers}
\item{cols_to_keep}{Columns to return in output sf object}
\item{segments}{logical, return segments TRUE/FALSE/"both"}
}
\description{
Note: set \code{CYCLESTREETS_BATCH}, \code{CYCLESTREETS_PW} and \code{CYCLESTREETS_PW}
environment variables, e.g. with \code{usethis::edit_r_environ()}
before trying this.
}
\details{
See https://www.cyclestreets.net/journey/batch/ for web UI.
Recommneded max batch size: 300k routes
}
\examples{
if(FALSE) {
library(sf)
desire_lines = od::od_to_sf(od::od_data_df, od::od_data_zones)[4:5, 1:3]
u = paste0("https://github.com/cyclestreets/cyclestreets-r/",
"releases/download/v0.5.3/od-longford-10-test.Rds")
desire_lines = readRDS(url(u))
routes_id = batch(desire_lines, username = "robinlovelace", wait = FALSE)
# Wait for some time, around a minute or 2
routes_wait = batch(id = routes_id, username = "robinlovelace", wait = TRUE, delete_job = FALSE)
names(routes_wait)
plot(routes_wait)
plot(desire_lines$geometry[4])
plot(routes_wait$geometry[routes_wait$route_number == "4"], add = TRUE)
head(routes_wait$route_number)
unique(routes_wait$route_number)
# Job is deleted after this command:
routes_attrib = batch(desire_lines, id = routes_id, username = "robinlovelace", wait = TRUE)
names(routes_attrib)
unique(routes_attrib$route_number)
desire_lines_huge = desire_lines[sample(nrow(desire_lines), 250000, replace = TRUE), ]
routes_id = batch(desire_lines_huge, username = "robinlovelace", wait = FALSE)
names(routes)
plot(routes$geometry)
plot(desire_lines$geometry, add = TRUE, col = "red")
routes = batch(desire_lines, username = "robinlovelace", wait_time = 5)
# profvis::profvis(batch_read("test-data.csv.gz"))
}
}
|
e8ea6829110c92a66dc5d188c42ab240bcb00306
|
fd570307c637f9101ab25a223356ec32dacbff0a
|
/src-local/specpr/src.specpr/fcn43-47/zfeat.r
|
2a09420502fc810514f642a9fa36353683f4d3e8
|
[] |
no_license
|
ns-bak/tetracorder-tutorial
|
3ab4dd14950eff0d63429291c648820fb14bb4cb
|
fd07c008100f6021c293ce3c1f69584cc35de98a
|
refs/heads/master
| 2022-07-30T06:04:07.138507
| 2021-01-03T22:19:09
| 2021-01-03T22:49:48
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,298
|
r
|
zfeat.r
|
subroutine zfeat(dataa,datab,datac,data,jdat,number,itnum,anadat,y)
implicit integer*4 (i-q)
#ccc name: zfeat
#ccc version date: June 30, 1987
#ccc author(s): Noel Gorelick
#ccc language: RATFOR
#ccc
#ccc short description: Finds continuum points for feature analysis
#ccc and calls zstuf to do the dirty work
#ccc
#ccc algorithm description:
#ccc system requirements:
#ccc subroutines called:
#ccc argument list description:
#ccc parameter description:
#ccc common description:
#ccc message files referenced:
#ccc internal variables:
#ccc file description:
#ccc user command lines:
#ccc update information:
#ccc NOTES:
#ccc
# RED Declared ilast to ensure integer*4 vs default of real*4 for undeclared
integer*4 ilast
integer*4 itnum,number,y,z,ilow
real*4 center,width,depth,asym,contum,fract,anadat(4864),low,error
real*4 dataa(number),datab(number),datac(number),
data(number),jdat(number),ewidth
#RED Initialize ilast to number
ilast=number
1 do i=1,number {
if (datac(i)==-1.23e34 | dataa(i)==-1.23e34) next
if (datac(i)==1.0) {
ilast=i
go to 2
}
}
2 if (ilast==number) return
if (y+9 > 4855) return # will overflow 4864 limit
do i=ilast+1,number {
if (datac(i)==-1.23e34 | dataa(i)==-1.23e34) next
if (datac(i)!=1.0) {
go to 3
} else {
ilast=i
go to 2
}
}
3 do j=i,number {
if (datac(j)==1.0) {
inext=j
go to 4
}
}
return
4 if (inext-ilast==2) {
error=(data(ilast)+data(inext))/2+data(ilast+1)
if (1.0-datac(ilast+1)>2*error) {
ilow=ilast+1
go to 10
} else {
ilast=inext
go to 2
}
}
low=1.0e25
do k=ilast,inext {
if (datac(k)==-1.23e34 | dataa(k)==-1.23e34) next
if (datac(k)<low) {
low=datac(k)
ilow=k
}
}
error=(data(ilast)+data(inext))/2+data(ilow)
if (1.0-datac(ilow)>error) {
go to 10
} else {
ilast=inext
go to 2
}
# --------
10 call zstuf(dataa(ilast),datab(ilast),datac(ilast),jdat(ilast),
inext-ilast+1,center,depth,width,asym,contum,fract,
ilow-ilast+1,ewidth)
# write (6,*) 'ewidth',ewidth
# -----------
# put in data
# -----------
anadat(y+1)=center
anadat(y+2)=width
anadat(y+3)=depth
anadat(y+4)=error/2
anadat(y+5)=asym
anadat(y+6)=contum
anadat(y+7)=itnum
anadat(y+8)=fract
anadat(y+9)=ewidth
y=y+9
ilast=inext
go to 2
end
|
ec1eea736982042c66fa9669254cf77363a3b622
|
573f8339b51f5df74e09bfbed6ef0ccec15cb7b0
|
/analyses/DGE.R
|
a4da0e6759f2eb59c053ed060a2f0cbf809e1135
|
[] |
no_license
|
eastmallingresearch/RNA-seq_pipeline
|
c40b0e62fbac468f823e0a86a7f83c7676fd5acf
|
edc4db511e17d7c7d364105138f071439cdbdfda
|
refs/heads/master
| 2022-08-09T22:34:49.319136
| 2022-08-03T14:03:24
| 2022-08-03T14:03:24
| 101,886,286
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 9,467
|
r
|
DGE.R
|
#===============================================================================
# Load libraries
#===============================================================================
library(DESeq2)
library("BiocParallel")
register(MulticoreParam(12))
library(ggplot2)
library(Biostrings)
library(data.table)
library(dplyr)
library(naturalsort)
library(tibble)
library(devtools)
install_github("eastmallingresearch/Metabarcoding_pipeline/scripts") # RUN ONCE
library(metafuncs) # contains some useful plotting function e.g. plotOrd
#===============================================================================
# Load data from fetureCounts
#===============================================================================
# load tables into a list of data tables - "." should point to counts directory, e.g. "counts/."
# depending on how your count files are named and stored (in subdirectories or not) the .*txt$ regex and recursive flag may need editing
# the example below assumes all count files have file names ending in .txt and are all in a single folder (featureCounts standard)
qq <- lapply(list.files(".",".*txt$",full.names=T,recursive=F),function(x) fread(x))
# rename the sample columns (7th column in a feature counts table, saved as the path to the BAM file)
# in the below I'm saving the 8th ([[1]][8]) path depth (which was the informative folder name containg the BAM file)
invisible(lapply(seq(1:length(qq)), function(i) colnames(qq[[i]])[7]<<-strsplit(colnames(qq[[i]])[7],"\\/")[[1]][8]))
# merge the list of data tables into a single data table
m <- Reduce(function(...) merge(..., all = T,by=c("Geneid","Chr","Start","End","Strand","Length")), qq)
# output "countData"
# write.table(m[,c(1,7:(ncol(m))),with=F],"countData",sep="\t",na="",quote=F,row.names=F)
# output gene details
write.table(m[,1:6,with=F],"genes.txt",sep="\t",quote=F,row.names=F)
# Get counts from m
countData <- data.frame(m[,c(7:length(m)),with=F],row.names=m$Geneid)
#===============================================================================
# Load data from SALMON quasi mapping
#===============================================================================
library(tximport)
library(rjson)
library(readr)
# import transcript to gene mapping info
tx2gene <- read.table("trans2gene.txt",header=T,sep="\t")
# import quantification files
# txi.reps <- tximport(list.files(".","quant.sf",full.names=T,recursive=T),type="salmon",tx2gene=tx2gene,txOut=T)
# ...change of plan (it's easier to use list.dirs) though the below is very cool so I've left it here
# mysamples <- rapply(strsplit(list.files(".","quant.sf",full.names=T,recursive=T),"\\/"), f=`[[`, ...=2, how="unlist")
# import quantification files - will not work if any additional directories are in the specified path
txi.reps <- tximport(paste(list.dirs(".",full.names=T,recursive=F),"/quant.sf",sep=""),type="salmon",tx2gene=tx2gene,txOut=T)
# get the sample names from the folders
mysamples <- list.dirs(".",full.names=F,recursive=F)
# summarise to gene level (this can be done in the tximport step, but is easier to understand in two steps)
txi.genes <- summarizeToGene(txi.reps,tx2gene)
# set the sample names for txi.genes
invisible(sapply(seq(1,3), function(i) colnames(txi.genes[[i]])<<-mysamples))
#==========================================================================================
# Read pre-prepared sample metadata and annotations
#=========================================================================================
# Read sample metadata
colData <- read.table("colData",header=T,sep="\t",row.names=1)
# reorder colData for salmon
colData <- colData[mysamples,]
# reorder colData for featureCounts
colData <- colData[colnames(countData),]
# get annotations (if you have any)
annotations <- read.table("annotations.txt", sep="\t",header=T)
#===============================================================================
# DESeq2 analysis
# Set alpha to the required significance level. This also effects how
# DESeq calculated FDR - setting to 0.05 and then extracting results with a
# significance below 0.01 will give slightly different results form setting
# alpha to 0.01
#================================================================================
# create DESeq object from featureCounts counts and sample metadata
dds <- DESeqDataSetFromMatrix(countData,colData,~1)
# or create from Salmon counts and sample metadata
dds <- DESeqDataSetFromTximport(txi.genes,colData,~1)
#### Technical replicates only ####
# add grouping factor to identify technical replicates
dds$groupby <- paste(dds$condition,dds$sample,sep="_")
# sum replicates (must use same library or library size correction will go wonky)
dds <- collapseReplicates(dds,groupby=dds$groupby)
#### end technical replicates ####
# featureCounts only - normalise counts for different library size (do after collapsing replicates)
# NOTE: need to work out what to do if there are technical replicates for salmon workflow
# probably take average of avgTxLength for the summed samples
sizeFactors(dds) <- sizeFactors(estimateSizeFactors(dds))
# define the DESeq 'GLM' model
design=~condition
# add design to DESeq object
design(dds) <- design
# Run the DESeq statistical model
dds <- DESeq(dds,parallel=T)
# set the significance level for BH adjustment
alpha <- 0.05
# calculate the differences - uses the "levels" of the condition factor as the third term for the contrast
# contrast=c("condition","S","H") etc - ?results for many more options
res <- results(dds,alpha=alpha)
# merge results with annotations
res.merged <- left_join(rownames_to_column(as.data.frame(res)),annotations,by=c("rowname"="query_id"))
# get significant results
sig.res <- subset(res.merge,padj<=alpha)
# sig.res <- res[which(res$padj<=alpha),] # use this is you don't have annotations
# write tables of results
write.table(res.merged,"results.txt",quote=F,na="",row.names=F,sep="\t")
# get sequences of significant transcripts - transcripts.fa is the file of transcripts
# this will only work if the fastas are over two lines, i.e. not split every 80 bases (and the names match)
# the below shell script is a method for converting to 2 line fasta
# awk '/^>/ {printf("\n%s\n",$0);next; } { printf("%s",$0);} END {printf("\n");}' split_transcripts.fa|sed '1d' > transcripts.fa
# seqs <- DNAStringSet(sapply(rownames(sig.res),function(s) {
# DNAString(system2("grep",c(paste0(s," "),"-A1", "transcripts.fa"),stdout=T)[[2]])
# }))
# alternatively just load the whole of the transcript file (split every 80 bases or not) into a Biostrings object and subset by rowname:
seqs <- readDNAStringSet("transcripts.fa")[rownames(sig.res)] # o.k this is probably a better method unless transcripts.fa is massive
#===============================================================================
# FPKM
#===============================================================================
# note this is pointless if using salmon or some other pseudo counting aligner
# set GRanges for each gene
# fast method - requires some editing
rowRanges(dds) <- GRanges(geneData$Chr,IRanges(geneData$Start,as.numeric(geneData$End)),geneData$Strand)
# slow method - doesn't require editing
rowRanges(dds) <- GRangesList(apply(m,1,function(x) GRanges(x[[1]],IRanges(1,as.numeric(x[[6]])),"+")))
# calculate FPKM values
myfpkm <- data.table(GeneID=m[,1],length=m[,6],fpkm(dds,robust=T))
# write FPKM values
write.table(myfpkm,"fpkm.txt",quote=F,na="",sep="\t")
#===============================================================================
# Heirachical clustering
#===============================================================================
# this is out of date - ward.D2 is the prefered method for clustering - actaully the whole function is a bit naff
clus <- function(X,clusters=10,m=1,name="hclust.pdf") {
if (m==1) {d <- dist(X, method = "manhattan")}
else if (m==2) {d <- dist(X, method = "euclidean")}
else if (m==3) {d <- dist(X, method = "maximum")}
else if (m==4) {d <- dist(X, method = "canberra")}
else if (m==5) {d <- dist(X, method = "binary")}
else d <- {dist(X, method = "minkowski")}
hc <- hclust(d, method="ward")
groups <- cutree(hc, k=clusters) # cut tree into n clusters
pdf(name,height=8,width=8)
plot(hc)
rect.hclust(hc,k=clusters)
dev.off()
return(list(hc,groups,d))
}
#===============================================================================
# Graphs
#===============================================================================
# PCA 1 vs 2 plot
vst <- varianceStabilizingTransformation(dds,blind=F,fitType="local")
# calculate PCs
mypca <- prcomp(t(assay(vst)))
# calculate variance for each PC
mypca$percentVar <- mypca$sdev^2/sum(mypca$sdev^2)
# create data frame of PCs x variance (sets PCA plot axes to same scale)
df <- t(data.frame(t(mypca$x)*mypca$percentVar))
# plotOrd is a PCA/ordination plotting function
ggsave("pca.pdf",plotOrd(df,vst@colData,design="condition",xlabel="PC1",ylabel="PC2", pointSize=3,textsize=14))
# MA plots
pdf("MA_plots.pdf")
# plot_ma is an MA plotting function
lapply(res.merged,function(obj) {
plot_ma(obj[,c(1:5,7]),xlim=c(-8,8))
})
dev.off()
|
869d1df6ad4a324e2c496c41673d2f5ee6547bf1
|
e51f969dbc5a0af54fa3a8f577140f6ac7199464
|
/01.Algorithms/10.Regression/01.RobustLenearRegression.R
|
28424eaa64157af633f392dc2ca2a7a9a8b57b1d
|
[] |
no_license
|
rmatam/DataScience
|
6dd02d81a24608a3f07df80f83c62d72732e5d36
|
ebd0f9f2b28404e66b481ec7179adb1fc8b090b7
|
refs/heads/master
| 2021-01-20T18:52:01.743018
| 2017-04-15T19:02:48
| 2017-04-15T19:02:48
| 64,743,868
| 0
| 0
| null | 2016-10-01T18:43:53
| 2016-08-02T09:34:02
|
R
|
UTF-8
|
R
| false
| false
| 1,020
|
r
|
01.RobustLenearRegression.R
|
library(MASS)
rlm_mod <- rlm(stack.loss ~ ., stackloss, psi = psi.bisquare) # robust reg model
summary(rlm_mod)
#> Call: rlm(formula = stack.loss ~ ., data = stackloss)
#> Residuals:
#> Min 1Q Median 3Q Max
#> -8.91753 -1.73127 0.06187 1.54306 6.50163
#>
#> Coefficients:
#> Value Std. Error t value
#> (Intercept) -41.0265 9.8073 -4.1832
#> Air.Flow 0.8294 0.1112 7.4597
#> Water.Temp 0.9261 0.3034 3.0524
#> Acid.Conc. -0.1278 0.1289 -0.9922
#>
#> Residual standard error: 2.441 on 17 degrees of freedom
lm_mod <- lm(stack.loss ~ ., stackloss) # lm reg model
# Errors from lm() model
install.packages("DMwR")
DMwR::regr.eval(stackloss$stack.loss, lm_mod$fitted.values)
#> mae mse rmse mape
#> 2.3666202 8.5157125 2.9181694 0.1458878
# Errors from rlm() model
DMwR::regr.eval(stackloss$stack.loss, rlm_mod$fitted.values)
#> mae mse rmse mape
#> 2.1952232 9.0735283 3.0122298 0.1317191
#
#
|
9ac3f032cce2b34f96b7cbb8c475e666c059b318
|
f18e1210ca120c9116e356a8549e89e04219dc75
|
/man/parse_mf.Rd
|
0f759d1bfec053573eec08e62955891a0560e864
|
[
"BSD-2-Clause"
] |
permissive
|
EMSL-Computing/ftmsRanalysis
|
46c73a727d7c5d5a5320bf97a07e9dac72abd281
|
dd3bc3afbf6d1250d1f86e22b936dcc154f4101d
|
refs/heads/master
| 2023-07-21T18:13:26.355313
| 2023-02-09T17:03:09
| 2023-02-09T17:03:09
| 122,233,846
| 14
| 10
|
NOASSERTION
| 2023-07-11T16:34:15
| 2018-02-20T17:52:18
|
R
|
UTF-8
|
R
| false
| true
| 615
|
rd
|
parse_mf.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parse_mf.R
\name{parse_mf}
\alias{parse_mf}
\title{Parse Molecular Formulae to Obtain Elemental Counts}
\usage{
parse_mf(ftmsObj)
}
\arguments{
\item{ftmsObj}{an object of class 'ftmsData', typically a result of \code{\link{as.peakData}}.}
}
\value{
an object of class 'ftmsData' with a column in \code{e\_meta} giving the molecular formula.
}
\description{
Construct elemental count columns based on provided molecular formulae
}
\details{
Parses molecular formulae for number of observed C, H, O, N, S, and P.
}
\author{
Lisa Bramer
}
|
9d754c8ad8b9c33f4efb8afa9e6b57dd1b79bf95
|
5b2487622b24806d9d83db12be83b5c5fef68316
|
/India_GIS_Analysis.R
|
d000f292ba39895677161cc23914c21ff3b70ef8
|
[] |
no_license
|
alexismenanieves/India_GIS
|
92ac8a8446d725b8a9d56c40ff5a30d3e24b5769
|
aafcc8352194611d713a9102c865bc4662719486
|
refs/heads/master
| 2020-08-10T02:56:07.982465
| 2019-10-11T18:46:42
| 2019-10-11T18:46:42
| 214,239,642
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,908
|
r
|
India_GIS_Analysis.R
|
# Load libraries
library(tidyverse)
library(sf)
library(sp)
library(GADMTools)
library(lwgeom)
# Load sf file
my_sf <- st_read("india_states_2014/india_states.shp")
india_wrapper <- gadm_sf.loadCountries("IND", level = 1, basefile = "./")
# See info structure as sp and sf files
my_spdf <- as(my_sf,"Spatial")
class(my_spdf)
str(my_spdf, max.level = 2)
glimpse(my_spdf@data)
ind_sf <- st_as_sf(my_spdf)
head(ind_sf, 3)
glimpse(ind_sf)
# Using sf as dataframe for dplyr
uts <- c("Delhi", "Andaman & Nicobar Islands", "Puducherry", "Lakshadweep",
"Dadra & Nagar Haveli", "Daman & Diu", "Chandigarh")
# sf can be used with dplyr, but sp can not
ind_sf <- ind_sf %>% select(name,abbr) %>%
mutate(type = ifelse(name %in% uts,"Union Territory", "State")) %>%
rename(abb = abbr, state_ut = name)
class(ind_sf)
# Loading attributes
attributes_df <- readRDS("attributes.rds")
ind_sf <- ind_sf %>% left_join(attributes_df, by = "state_ut") %>%
mutate(
per_capita_GDP_inr = nominal_gdp_inr / pop_2011,
per_capita_GDP_usd = nominal_gdp_usd / pop_2011)
head(ind_sf,3)
# Converting the units
library(units)
ind_sf <- ind_sf %>% mutate(my_area = st_area(.))
units(ind_sf$my_area) <- with(ud_units, km^2)
ind_sf <- ind_sf %>% mutate(GDP_density_usd_km2 = nominal_gdp_usd / my_area)
class(ind_sf$area_km2)
class(ind_sf$my_area)
ind_sf <- ind_sf %>% mutate(my_area = as.vector(my_area),
GDP_density_usd_km2 = as.vector(GDP_density_usd_km2))
original_geometry <- st_geometry(ind_sf)
library(rmapshaper)
simp_sf <- ms_simplify(ind_sf, keep = 0.01, keep_shapes = TRUE)
simple_geometry <- st_geometry(simp_sf)
par(mfrow = c(1,2))
plot(original_geometry, main = "Original geometry")
plot(simple_geometry, main = "Simplified geometry")
# Lets see the size of units
library(pryr)
object_size(original_geometry)
object_size(simple_geometry)
saveRDS(simp_sf,"simp_sf.rds")
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.