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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1db38aeb71bf2c5be032cfcb16c873a87601e03b | bec8a34363b8da5cefee3a98fe98d339a8ec8eab | /R/op_kill_cursors.r | ccf74e8f3bfbb8fba84b3552126d75db7e232371 | [] | no_license | strongh/mongor | 24fa4fb6fad577562db97c4356e403c7cd74568d | 7f1a95cb742e8163151f895f765be430a67dbe01 | refs/heads/master | 2016-09-05T09:16:14.667651 | 2010-11-15T01:42:36 | 2010-11-15T01:42:36 | 937,944 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 726 | r | op_kill_cursors.r | ##' OP_KILL_CURSORS
##'
##' One of the basic mongo messages.
##'
##' @param collection the name of the collection to query
##' @param cursor.ids a list of cursor ids
##' @return a raw vector encoding the query
op_kill_cursors <-
function(collection,
cursor.ids,
to_return = 10){
## header
fut_use <- numToRaw(0, nBytes = 4) # reserved for future use
full_name <- encode_cstring(collection) # full collection name
id.count <- encode_int32(length(cursor.ids))
cursor.id <- unlist(cursor.ids)
rawl <- c(fut_use, full_name, id.count, cursor.id)
header <- make_header(2007, length(rawl)) # make header last, so that it has the msgSize
return(c(header, rawl))
}
|
3dc1858bf8baad4ff38d2f74b127aa740505c316 | 7d53d36cdb86afd193e75301e0318abaf21c0b3f | /server.R | c4d05152075a7d2502d25f6b1d2b27047113858f | [] | no_license | ZivaXu/uw-club-journey | c6d07d865aa18fb58823348f112b46061edfb502 | d716a318b58e3903b287714ca2e02c79b0101b78 | refs/heads/master | 2022-12-02T10:37:56.488033 | 2020-08-16T18:49:21 | 2020-08-16T18:49:21 | 287,830,961 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,900 | r | server.R | library(shiny)
library(ggplot2)
library(dplyr)
library(R.utils)
library(lubridate)
library(scales)
library(plotly)
library(shinydashboard)
library(gapminder)
library(DT)
shinyServer(function(input, output) {
# Read task stamp
task_list <- read.csv("tasks/task-list.csv")
# Output stamp image
output$teststamp <- renderImage({
return(list(
src = "image/stamp/1.png",
filetype = "image/png",
width = "100%",
alt = "Rising Star"
))
}, deleteFile = FALSE)
output$teststamp2 <- renderImage({
return(list(
src = "image/stamp/3.png",
filetype = "image/png",
width = "100%",
alt = "Sharing is Caring"
))
}, deleteFile = FALSE)
output$teststamp3 <- renderImage({
return(list(
src = "image/stamp/5.png",
filetype = "image/png",
width = "100%",
alt = "Exploring"
))
}, deleteFile = FALSE)
output$teststamp4 <- renderImage({
return(list(
src = "image/stamp/9.png",
filetype = "image/png",
width = "100%",
alt = "Founder"
))
}, deleteFile = FALSE)
output$teststamp5 <- renderImage({
return(list(
src = "image/stamp/23.png",
filetype = "image/png",
width = "100%",
alt = "HuskyLink"
))
}, deleteFile = FALSE)
output$teststamp6 <- renderImage({
return(list(
src = "image/stamp/48.png",
filetype = "image/png",
width = "100%",
alt = "Insta Guru"
))
}, deleteFile = FALSE)
output$teststamp7 <- renderImage({
return(list(
src = "image/stamp/51.png",
filetype = "image/png",
width = "100%",
alt = "Recruiter"
))
}, deleteFile = FALSE)
output$teststamp8 <- renderImage({
return(list(
src = "image/stamp/67.png",
filetype = "image/png",
width = "100%",
alt = "Club Baby I"
))
}, deleteFile = FALSE)
output$teststamp9 <- renderImage({
return(list(
src = "image/stamp/84.png",
filetype = "image/png",
width = "100%",
alt = "Social Good"
))
}, deleteFile = FALSE)
output$teststamp10 <- renderImage({
return(list(
src = "image/stamp/92.png",
filetype = "image/png",
width = "100%",
alt = "WOAH!"
))
}, deleteFile = FALSE)
output$teststamp11 <- renderImage({
return(list(
src = "image/stamp/372.png",
filetype = "image/png",
width = "100%",
alt = "Virtual Club Fair"
))
}, deleteFile = FALSE)
output$teststamp12 <- renderImage({
return(list(
src = "image/stamp/258.png",
filetype = "image/png",
width = "100%",
alt = "Virtual Club Fair"
))
}, deleteFile = FALSE)
output$teststamp13 <- renderImage({
return(list(
src = "image/stamp/492.png",
filetype = "image/png",
width = "100%",
alt = "Virtual Club Fair"
))
}, deleteFile = FALSE)
#Tasks tab
output$rec_tasks <- DT::renderDataTable({
DT::datatable(rectask1, colnames=c("Task Name", "Description"), options = list(paging = FALSE))
})
output$my_tasks <- DT::renderDataTable({
DT::datatable(user_tasklist11, colnames=c("Task Name", "Description", "Completed?"), options = list(paging = FALSE))
})
#Club tab
output$all_clubs <- DT::renderDataTable({
DT::datatable(all_clubs, colnames=c("Club Name", "Contact Email"), options = list(paging = FALSE))
})
output$starred_clubs <- renderTable({
colnames(starred_clubs) <- c("Club Name", "Contact Email")
starred_clubs
})
#Top chart tab
output$top_20_club <- renderPlotly({
ggplotly(
ggplot(
data <- task_counts
) +
geom_col(mapping = aes(x = Club_Name, y = Task_Counts)) +
coord_flip() +
ggtitle(
"Top 20 Users"
) +
labs(x = "Club Name", y = "Completed Task Counts")
)
})
})
|
507b368a12edd6f729b409a8a9d38670d0b959ef | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spate/examples/propagate.spectral.Rd.R | 2b1d8cd47a8259ec26b513ea81ea4f0fc446f61b | [] | 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 | 780 | r | propagate.spectral.Rd.R | library(spate)
### Name: propagate.spectral
### Title: Function that propagates a state (spectral coefficients).
### Aliases: propagate.spectral
### ** Examples
n <- 50
spec <- matern.spec(wave=spate.init(n=n,T=1)$wave,n=n,rho0=0.05,sigma2=1,norm=TRUE)
alphat <- sqrt(spec)*rnorm(n*n)
##Propagate initial state
wave <- wave.numbers(n)
Gvec <- get.propagator.vec(wave=wave$wave,indCos=wave$indCos,zeta=0.1,rho1=0.02,gamma=2,
alpha=pi/4,muX=0.2,muY=0.2,dt=1,ns=4)
alphat1 <- propagate.spectral(alphat,n=n,Gvec=Gvec)
par(mfrow=c(1,2))
image(1:n,1:n,matrix(real.fft(alphat,n=n,inv=FALSE),nrow=n),main="Whittle
field",xlab="",ylab="",col=cols())
image(1:n,1:n,matrix(real.fft(alphat1,n=n,inv=FALSE),nrow=n),main="Propagated
field",xlab="",ylab="",col=cols())
|
1ad9678d9a39c0c9ec78763e4de9e2d6ff2bf59f | fc7c616bda497d9b193df08ec09241b1486bacac | /MEAN,MEDIAN,MODE.R | 1fc02eb6dd5c78f85094564108ef5c919e9d216e | [] | no_license | MinhajulAkib/R-Programming | 5c38306ce7486e283e3c51aa9ce81fb34fe6c850 | 7bbd654e4c6b8917bd21a380ffb120665915773a | refs/heads/main | 2023-02-02T02:39:48.166304 | 2020-12-11T11:26:12 | 2020-12-11T11:26:12 | 316,182,597 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 363 | r | MEAN,MEDIAN,MODE.R | A=c(1:20)
mean(A)
sum(A)
length(A)
median(A)
x<-c(8,3,4,5,2,1,3,5,6)
mean(x)
median(x)
sort(x)
sum(x)
sum(x)/length(x)
##
LungCapData<-read.table(file.choose(),header=T,sep = "\t")
attach(LungCapData)
names(LungCapData)
summary(LungCapData)
table(Smoke)
table(Smoke,Gender)
#standard deviation
sd(LungCap)
sd(LungCap)^2
sqrt(var(LungCap))
|
73b38b7bf69b592eb44bfa8003b32d3a59a3170a | 2cd54a4365c128d94c120a204aaccf68c3607b49 | /R/plsda.R | cc2d6fc12e94e44135d76f57cf0f82580564787d | [
"MIT"
] | permissive | tikunov/AlpsNMR | 952a9e47a93cbdc22d7f11b4cb1640edd736a5c7 | 748d140d94f65b93cb67fd34753cc1ef9e450445 | refs/heads/master | 2021-01-13T17:35:29.827357 | 2020-02-23T02:35:30 | 2020-02-23T02:35:30 | 242,443,517 | 0 | 0 | NOASSERTION | 2020-02-23T02:27:15 | 2020-02-23T02:27:15 | null | UTF-8 | R | false | false | 10,646 | r | plsda.R | #' Build a PLSDA model, optionally with multilevel
#' @param x the X training set
#' @param y the y training class to predict
#' @param identity the multilevel variable in [mixOmics::plsda]
#' @param ncomp The number of components of the model
#' @noRd
plsda_build <- function(x, y, identity, ncomp) {
plsda_model <- NULL
tryCatch({
suppressMessages(
utils::capture.output({
plsda_model <- mixOmics::plsda(
X = x, Y = y, ncomp = ncomp,
scale = TRUE, multilevel = identity
)
})
)
}, error = function(e) {
message("Error building PLSDA, continuing")
})
plsda_model
}
#' Compute the area under the ROC curve of a PLS-DA model on a test subset
#' @param plsda_model A mixOmics plsda model
#' @param x_test the x test set
#' @param y_test the y test class to predict
#' @param identity_test the multilevel variable in [mixOmics::plsda]
#' @return A list with two elements:
#' - `aucs`: A data frame with two columns: `ncomp` (the number of components) and
#' `auc` the area under roc curve for that number of components. For multiclass problems
#' the AUC returned is the mean of all the one-vs-other AUCs.
#' - `aucs_full`: A list of matrices, as returned by [mixOmics::auroc].
#' @noRd
plsda_auroc <- function(plsda_model, x_test, y_test, identity_test) {
aucs <- numeric(0L)
aucs_full <- list()
tryCatch({
suppressMessages(
utils::capture.output({
roc <- mixOmics::auroc(plsda_model, newdata = x_test,
outcome.test = y_test,
multilevel = identity_test,
plot = FALSE)
})
)
aucs <- purrr::map_dbl(roc, function(x) mean(x[, "AUC"]))
aucs_full <- roc
}, error = function(e) {
message("Error in auroc estimation, continuing")
})
ncomps <- as.integer(gsub(pattern = "Comp(.*)", replacement = "\\1", x = names(aucs)))
list(aucs = data.frame(ncomp = ncomps,
auc = aucs,
stringsAsFactors = FALSE),
aucs_full = aucs_full)
}
#' Compute the variable importance in the projection
#' @param plsda_model A mixOmics plsda model
#' @return A matrix with the variable importance in the projection
#' @noRd
plsda_vip <- function(plsda_model) {
vip <- NULL
tryCatch({
suppressMessages(
utils::capture.output({
vip <- mixOmics::vip(object = plsda_model)
})
)
}, error = function(e) {
message("Error in vip, continuing")
})
vip
}
#' Callback for building a PLSDA model, computing the AUROC and extract the VIP
#'
#' @param x_train Training data for x
#' @param y_train Training data for y
#' @param identity_train Training data for the identities
#' @param x_test Test data for x
#' @param y_test Test data for y
#' @param identity_test Test data for the identities
#' @param ncomp Number of components to use in the model
#' @param return_model A logical.
#' @param return_auroc A logical.
#' @param return_auroc_full A logical.
#' @param return_vip A logical.
#'
#'
#' For multiclass problems the AUC returned is the mean of all the one-vs-other AUCs.
#'
#' @return A list with the model, the area under the roc curve and the VIP items.
#' @noRd
callback_plsda_auroc_vip <- function(x_train, y_train, identity_train, x_test, y_test, identity_test,
ncomp, return_model = FALSE, return_auroc = TRUE,
return_auroc_full = FALSE, return_vip = FALSE) {
plsda_model <- plsda_build(x_train, y_train, identity_train, ncomp = max(ncomp))
out <- list(model = NULL, auroc = NULL, auroc_full = NULL, vip = NULL)
if (isTRUE(return_model)) {
out$model <- plsda_model
}
if (isTRUE(return_auroc) || isTRUE(return_auroc_full)) {
aurocs <- plsda_auroc(plsda_model, x_test, y_test, identity_test)
if (isTRUE(return_auroc)) {
out$auroc <- aurocs$aucs
}
if (isTRUE(return_auroc_full)) {
out$auroc_full <- aurocs$aucs_full
}
}
if (isTRUE(return_vip)) {
vip <- plsda_vip(plsda_model)
out$vip <- vip
}
out
}
#' Callback to choose the best number of latent variables based on the AUC threshold
#'
#' @param auc_threshold Threshold on the increment of AUC. Increasing the number of
#' latent variables must increase the AUC at least by this threshold.
#'
#' @return The actual function to compute the best number of latent variables according to a threshold on the increment of AUC
#' @noRd
fun_choose_best_ncomp_auc_threshold <- function(auc_threshold = 0.05) {
force(auc_threshold)
# Choose best number of latent variables based on a threshold on the auc increment.
#' @param inner_cv_results A list of elements returned by [callback_plsda_auroc_vip]
#' @return A list with:
#' - `train_evaluate_model_args`: A list wit one element named `ncomp` with the number of latent variables selected
#' for each outer cross-validation
#' - `num_latent_var`: A data frame with the number of latent variables chosen for each outer cross-validation
#' - `diagnostic_plot`: A plot showing the evolution of the AUC vs the number of latent variables for each iteration
#' - `model_performances`: A data frame with the AUC model performances
function(inner_cv_results) {
model_performances <- inner_cv_results %>%
purrr::map("auroc") %>%
purrr::map_dfr(~ ., .id = "outer_inner") %>%
tidyr::separate("outer_inner",
into = c("cv_outer_iteration", "cv_inner_iteration"),
convert = TRUE)
# There is a more elegant way to do this.
nlv <- model_performances %>%
dplyr::group_by(.data$cv_outer_iteration, .data$cv_inner_iteration) %>%
dplyr::arrange(.data$cv_outer_iteration, .data$cv_inner_iteration, .data$ncomp) %>%
dplyr::mutate(auc_diff = ifelse(is.na(dplyr::lag(.data$auc)), .data$auc, .data$auc - dplyr::lag(.data$auc))) %>%
dplyr::mutate(auc_limit_cumany = dplyr::cumall(.data$auc_diff > !!auc_threshold)) %>%
dplyr::mutate(auc_limit_cumanyd = .data$auc_limit_cumany == TRUE & dplyr::lead(.data$auc_limit_cumany) == FALSE) %>%
dplyr::filter(.data$auc_limit_cumanyd == TRUE) %>%
dplyr::select(-.data$auc_limit_cumany, -.data$auc_limit_cumanyd) %>%
dplyr::ungroup() %>%
dplyr::group_by(.data$cv_outer_iteration) %>%
dplyr::summarise(ncomp = round(stats::median(.data$ncomp)))
plot_to_choose_nlv <- ggplot2::ggplot(model_performances) +
ggplot2::geom_jitter(ggplot2::aes(x = .data$ncomp, y = .data$auc,
group = .data$ncomp, color = as.character(.data$cv_inner_iteration)),
width = 0.25, height = 0) +
ggplot2::geom_vline(data = nlv, mapping = ggplot2::aes(xintercept = .data$ncomp), color = "red") +
ggplot2::scale_x_continuous(name = "Number of latent variables", breaks = function(limits) {
seq(from = 1, to = max(limits))
}) +
ggplot2::scale_y_continuous(name = "Area Under ROC") +
ggplot2::facet_wrap(~cv_outer_iteration) +
ggplot2::guides(colour = "none")
list(train_evaluate_model_args = list(ncomp = nlv$ncomp),
num_latent_var = nlv,
diagnostic_plot = plot_to_choose_nlv,
model_performances = model_performances)
}
}
#################### Validation #######
#' Callback to digest the results of the outer cross validation
#' @noRd
callback_outer_cv_auroc_vip <- function(outer_cv_results) {
auroc <- outer_cv_results %>%
purrr::map("auroc") %>%
purrr::map_dfr(~ ., .id = "cv_outer_iteration") %>%
dplyr::mutate(cv_outer_iteration = as.integer(.data$cv_outer_iteration)) %>%
dplyr::group_by(.data$cv_outer_iteration) %>%
dplyr::filter(.data$ncomp == max(.data$ncomp)) %>%
dplyr::ungroup() %>%
dplyr::arrange(.data$cv_outer_iteration)
vip_vectors <- outer_cv_results %>%
purrr::map("vip") %>%
purrr::map2(auroc$ncomp, function(vip_matrix, selected_ncomp) {
vip_vec <- as.numeric(vip_matrix[, selected_ncomp, drop = TRUE])
names(vip_vec) <- rownames(vip_matrix)
vip_vec
})
vip_ranks <- do.call(cbind, purrr::map(vip_vectors, ~rank(-.)))
vip_rp <- apply(vip_ranks, 1, function(x) exp(mean(log(x)))) # geom mean (RankProducts)
list(auroc = auroc,
vip_vectors = vip_vectors,
vip_rankproducts = vip_rp)
}
#' Method for nmr_data_analysis (PLSDA model with AUROC and VIP outputs)
#' @param ncomp Max. number of latent variables to explore in the PLSDA analysis
#' @param auc_increment_threshold Choose the number of latent variables when the
#' AUC does not increment more than this threshold.
#'
#' Returns an object to be used with [nmr_data_analysis] to perform a (optionally
#' multilevel) PLS-DA model, using the area under the ROC curve as figure of
#' merit to determine the optimum number of latent variables.
#'
#'
#' @export
plsda_auroc_vip_method <- function(ncomp, auc_increment_threshold = 0.05) {
new_nmr_data_analysis_method(
train_evaluate_model = callback_plsda_auroc_vip,
train_evaluate_model_params_inner = list(ncomp = ncomp, return_model = FALSE,
return_auroc = TRUE, return_auroc_full = FALSE,
return_vip = FALSE),
choose_best_inner = fun_choose_best_ncomp_auc_threshold(auc_threshold = auc_increment_threshold),
train_evaluate_model_params_outer = list(return_model = TRUE, return_auroc = TRUE,
return_auroc_full = TRUE, return_vip = TRUE),
train_evaluate_model_digest_outer = callback_outer_cv_auroc_vip)
}
#' Compare PLSDA auroc VIP results
#'
#' @param ... Results of [nmr_data_analysis] to be combined. Give each result a name.
#'
#' @return A plot of the AUC for each method
#' @export
plsda_auroc_vip_compare <- function(...) {
dots <- list(...)
class_compare <- names(dots)
if (is.null(class_compare) || any(nchar(class_compare) == 0)) {
stop("All arguments should be named")
}
auroc_tables <- dots %>%
purrr::map("outer_cv_results_digested") %>%
purrr::map("auroc") %>%
purrr::map2(class_compare, function(auroc, group_name) {
auroc %>% dplyr::select(.data$auc) %>% dplyr::mutate(Group = !!group_name)
})
toplot <- do.call(rbind, c(auroc_tables, list(stringsAsFactors = FALSE)))
ggplot2::ggplot(toplot) +
ggplot2::geom_boxplot(ggplot2::aes(x = .data$Group, y = .data$auc, fill = .data$Group), show.legend = FALSE) +
ggplot2::scale_x_discrete(name = "Model") +
ggplot2::scale_y_continuous(name = "Area under ROC")
}
|
30cc4caeef11944775076a368af84a0b8eae10f2 | 237bcbdc6b09c57b251191471359eeefb8014410 | /dbSNP_to_annovar_NEW.r | 69e1169b41c4db74055a2b49fb4655588be1d82c | [] | no_license | achalneupane/rcodes | d2055b03ca70fcd687440e6262037507407ec7a5 | 98cbc1b65d85bbb6913eeffad62ad15ab9d2451a | refs/heads/master | 2022-10-02T20:35:18.444003 | 2022-09-09T20:53:03 | 2022-09-09T20:53:03 | 106,714,514 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,215 | r | dbSNP_to_annovar_NEW.r |
#answer = which.isMatchingAt("N", seqs, at=6:1, follow.index=TRUE)
code.dir<-"/media/UQCCG/Programming/VersionControl_GitRepository/UQCCG_Pipeline_Rscripts"
setwd(code.dir) # load in the UCSC tables these use the db file names and not their lable-names
source("annotate_SNPs_subroutines.r")
options("width"=250,"max.print"=1000)
vcf.files<-"00-All.vcf"
output.name<-"hg19_snp137"
vcf.files<-"clinvar_20120616_2.vcf"
output.name<-"hg19_snp137_clinical"
names(vcf.files)<-"snp"
snp.dir<-"/media/scratch2/dbSNP"
# .txt extension will be added
location<-snp.dir
unwanted.cols<-c("QUAL","FILTER")
########################BEGIN
setwd(location)
the.files<-dir(getwd())
if(paste(output.name,"_maf.txt",sep="") %in% the.files){print("WARNING output files exits they will be appended too!!")}
################ LARGE FILES ########
print(vcf.files)
######BELOW PROCESSING this for snp for indel varient types in vcf4.0 or vcf 3.0 format
rm(start.data)
start.data<-prepare.for.Vcf.file.read(vcf.files)
for(i in 1:length(start.data)){assign( names(start.data)[i],value=start.data[[i]])}
cbind(info.types,info.description)
con <- file(vcf.files, open="r") # close(con)
num.lines<-1 # so does while llop at least once
reads<-1000000 #1.3M lines in snp file 50000 goes out to 24Gb without QC cgeck
counter<- -1
while (num.lines >0 ){
counter<-counter+1
print(counter)
if(counter==0){
indels<-try(scan(con,what=character(num.vars),skip=(reads*counter)+skip.lines,nlines=reads,sep="\t",fill=TRUE,na.strings="",quote="\""))
}else{
indels<-try(scan(con,what=character(num.vars),nlines=reads,sep="\t",fill=TRUE,na.strings="",quote="\""))
}
## indels1 <- read.table(con,col.names=column.labels,sep="\t",skip=skip.lines,fill=TRUE,stringsAsFactors=FALSE,colClasses="character",nrows=reads,comment.char="",quote="")
num.lines<-length(indels)/(num.vars)
print(num.lines)
if(num.lines==0){next}
dim(indels)<-c(num.vars,num.lines)
indels<-t(indels)
colnames(indels)<-column.labels
#indels[1:5,]
##PM,Number=0,Type=Flag,Description="Variant is Precious(Clinical,Pubmed Cited)">
##INFO=<ID=TPA,Number=0,Type=Flag,Description="Provisional Third Party Annotation(TPA) (currently rs from PHARMGKB who will give phenotype data)">
##INFO=<ID=CDA,Number=0,Type=Flag,Description="Variation is interrogated in a clinical diagnostic assay">
####################################### FINISHED Read in data DO PROCESSIng below
###################################################################################################
alt.list<-strsplit(indels[,"ALT"],split=",")
has.g5.or.g5A<-grepl(";G5",indels[,"INFO"]) # catch G5 and G5A
## has.g5<-grepl(";G5;",indels[,"INFO"])
## has.g5a<-grepl(";G5A;",indels[,"INFO"])
has.gmaf<-grepl(";GMAF=",indels[,"INFO"])
#########extract A quatity from INFO GMAF DONE HERE
#indels[has.gmaf,"INFO"][1:20]
the.gmaf<-extract.value.from.info(indels[has.gmaf,"INFO"],"GMAF=")
###########################################
# make MAF
the.af<-rep(0,times=dim(indels)[1])
the.af[!has.gmaf & has.g5.or.g5A]<-0.5 ### maf > 0.5% so set to 0.5 so it is excluded entirely
the.af[has.gmaf]<-the.gmaf
################### fltten the data and previos tests
number.of.alleles<-unlist(lapply(alt.list,length))
flat.index<-rep(1:length(number.of.alleles),times=number.of.alleles)
indels<-indels[flat.index,]
the.af<-the.af[flat.index]
has.g5.or.g5A<-has.g5.or.g5A[flat.index]
indels[,"ALT"]<-unlist(alt.list) # they are unlisted in the same order as they appear
#################### FLAG interesting stuff
has.OM<-grepl(";OM",indels[,"INFO"])
has.GNO<-grepl(";GNO",indels[,"INFO"])
has.CLN<-grepl(";CLN",indels[,"INFO"]) # variant is clinical (but of that is benighn - taste)
has.CDA<-grepl(";CDA",indels[,"INFO"]) # "Variation is interrogated in a clinical diagnostic assay"
has.PM<-grepl(";PM",indels[,"INFO"]) # ""Variant is Precious(Clinical,Pubmed Cited)""
has.MUT<-grepl(";MUT",indels[,"INFO"]) # Is mutation (journal citation, explicit fact): a low frequency variation that is cited in journal and other reputable sources">
has.SCS<-grepl(";SCS",indels[,"INFO"]) # variant is clinical significance
#0 - unknown, 1 - untested, 2 - non-pathogenic, 3 - probable-non-pathogenic, 4 - probable-pathogenic, 5 - pathogenic, 6 - drug-response, 7 - histocompatibility, 255 - other">
## has.SCS.prob.patho<-grepl(";SCS=4;",indels[,"INFO"])
## has.SCS.patho<-grepl(";SCS=5;",indels[,"INFO"])
## has.SCS.drug<-grepl(";SCS=6;",indels[,"INFO"])
## has.SCS.histo<-grepl(";SCS=7;",indels[,"INFO"])
## has.bad.path<-(has.SCS.prob.patho | has.SCS.patho | has.SCS.drug | has.SCS.histo)
## to.test<- has.g5.or.g5A & has.bad.path
## if(sum(to.test)>0){print("common pathogenic allele");print(indels[to.test,])} # these appear to be GWAS hits
## pathological<-vector(mode="character",length=dim(indels)[1])
## clinical<-vector(mode="character",length=dim(indels)[1])
## pathological[has.SCS.patho]<-"pathogenic"
## pathological[has.SCS.prob.patho]<-"probable-pathogenic"
## pathological[has.SCS.drug]<-"drug-response"
## pathological[has.SCS.histo]<-"histocompatibility"
## clinical.event<-extract.value.from.info(indels[has.CLN,"INFO"],"CLN=")
## clinical[has.CLN]<-clinical.event
########################### PUT ALLELES IN ANNOVAR FORMAT from convert2annovar.pl line 1083########
ref.length<-nchar(as.character(indels[,"REF"]))
alt.length<-nchar(as.character(indels[,"ALT"]))
is.snp<-(ref.length==1 & alt.length==1)
POS.end<-as.numeric(indels[,"POS"])
del<-ref.length > alt.length
ins<-(ref.length <= alt.length) & !is.snp
#indels[del,][1:5,]
#POS.end[del][1:5]
### deletion or block substitution
head<-substr(as.character(indels[del,"REF"]),1,alt.length[del])
head.is.mut<-(head==as.character(indels[del,"ALT"]))
indels[del,"REF"][head.is.mut]<-substr(as.character(indels[del,"REF"][head.is.mut]),(alt.length[del][head.is.mut]+1),ref.length[del][head.is.mut])
indels[del,"ALT"][head.is.mut]<-"-"
indels[del,"POS"][head.is.mut]<-as.numeric(indels[del,"POS"][head.is.mut]) + nchar(as.character(head[head.is.mut]))
POS.end[del]<-POS.end[del]+ref.length[del]-1 # same for both head is mut and not head is mut
## indels
## POS.end
### insertion or block substitution
head<-substr(as.character(indels[ins,"ALT"]),1,ref.length[ins])
head.is.ref<-(head==as.character(indels[ins,"REF"]))
indels[ins,"ALT"][head.is.ref]<-substr(as.character(indels[ins,"ALT"][head.is.ref]),(ref.length[ins][head.is.ref]+1),alt.length[ins][head.is.ref])
indels[ins,"REF"][head.is.ref]<-"-"
indels[ins,"POS"][head.is.ref]<-as.numeric(indels[ins,"POS"][head.is.ref]) + ref.length[ins][head.is.ref]-1
POS.end[ins]<-POS.end[ins]+ref.length[ins]-1
########################################################################################################
#indels[ins,]
#POS.end[ins]
###wait here
indels<-cbind(indels[,c("chr","POS")],POS.end,indels[,c("REF","ALT")],the.af,indels[,c("ID","INFO")])
#indels[1:5,]
write.table(indels,file=paste(output.name,"_maf.txt",sep=""),col.names=FALSE,row.names=FALSE,sep="\t",quote=FALSE,append=TRUE)
## write.table(indels[has.bad.path,],file=paste(output.name,"_pathalogical_maf.txt",sep=""),col.names=FALSE,row.names=FALSE,sep="\t",quote=FALSE,append=TRUE)
write.table(indels[has.OM,],file=paste(output.name,"_omim_maf.txt",sep=""),col.names=FALSE,row.names=FALSE,sep="\t",quote=FALSE,append=TRUE)
write.table(indels[has.CDA,],file=paste(output.name,"_clinical_assay_maf.txt",sep=""),col.names=FALSE,row.names=FALSE,sep="\t",quote=FALSE,append=TRUE)
write.table(indels[has.MUT,],file=paste(output.name,"_mutation_maf.txt",sep=""),col.names=FALSE,row.names=FALSE,sep="\t",quote=FALSE,append=TRUE)
write.table(indels[has.PM,],file=paste(output.name,"_pubmed_maf.txt",sep=""),col.names=FALSE,row.names=FALSE,sep="\t",quote=FALSE,append=TRUE)
} ## loop over data chunks
## Error in indels[del, "REF"][head.is.mut] <- substr(as.character(indels[del, :
## NAs are not allowed in subscripted assignments
#This reads the vcf files stored as names in samples files an makes a corrending data object with the name provided
## > sample.files
## snp indel
## "SKDP-FAM-26_All_snps.raw.vcf" "SKDP-FAM-26_All_DINDEL.raw.vcf"
## http://www.ncbi.nlm.nih.gov/projects/SNP/docs/rs_attributes.html#gmaf
## Global minor allele frequency (MAF): dbSNP is reporting the minor allele frequency for each rs included in a default global population. Since this is being provided to distinguish common polymorphism from rare variants, the MAF is actually the second most frequent allele value. In other words, if there are 3 alleles, with frequencies of 0.50, 0.49, and 0.01, the MAF will be reported as 0.49. The current default global population is 1000Genome phase 1 genotype data from 1094 worldwide individuals, released in the May 2011 dataset.
## For example, refSNP page for rs222 reports: "MAF/MinorAlleleCount:G=0.262/330". This means that for rs222, minor allele is 'G' and has a frequency of 26.2% in the 1000Genome phase 1 population and that 'G' is observed 330 times in the sample population of 629 people (or 1258 chromosomes).
## ###################################to a read in a lrage file
|
01468fa815778ebf10a3569cc76ee4ced99b7802 | abc1bb92a1052e5dbc7d7a19b1205c2a20a93867 | /man/stirDistances.Rd | 5ea14eaf0eac43d7a6a05007a103bb32bd7d4b9d | [] | no_license | tactless2004/STIR | ed1ba363f3ce8d7d3ed6f0a998fcdb2c3374526b | b7043e659ae3563a7815fb0b45667df0fd24043f | refs/heads/master | 2020-04-13T13:22:38.841180 | 2018-12-26T18:50:51 | 2018-12-26T18:50:51 | 163,227,873 | 0 | 0 | null | 2018-12-27T00:14:36 | 2018-12-27T00:14:35 | null | UTF-8 | R | false | true | 601 | rd | stirDistances.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reSTIR.R
\name{stirDistances}
\alias{stirDistances}
\title{stirDistances}
\usage{
stirDistances(attr.mat, metric = "manhattan")
}
\arguments{
\item{attr.mat}{m x p matrix of m instances and p attributes}
\item{metric}{for distance matrix between instances (default: \code{"manhattan"}, \code{"euclidean"},
\code{"relief-scaled-manhattan"}, \code{"relief-scaled-euclidean"}, \code{"allele-sharing-manhattan"}).}
}
\description{
Note: Should probalby standardize data before manhattan and euclidean?
}
\examples{
Example
}
|
4d4dd18d63b173980e0d197e3b572509904355ca | 50a83dbdb80fae7fc386482d5720418ea892473e | /syntax/old/models_beat_longitudinal_010820.R | 2d4bcc86027f63ed3dc121826d30becd0952e60a | [] | no_license | clanfear/CampsPolicing | f2f9fedefabee89e9b0e4829f71d463f45355b92 | 0b0289bdcae84c1785c161235e39c31740e3c4ba | refs/heads/master | 2022-01-27T00:04:40.195375 | 2022-01-18T21:27:06 | 2022-01-18T21:27:06 | 234,187,988 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 645 | r | models_beat_longitudinal_010820.R | # What do I need here?
## Beat-level models of fear of crime, police efficacy, SPU complaints, and offenses.
## Can theoretically do beat-quarter level if they're evenly spaced?
# Fear / satisfaction covers 2007 through 2018 reliably
load("./data/derived/beats_all_pe_fe_aw.RData")
# Observations per beat per year
beats_all_pe_fe_aw %>% group_by(year) %>% summarize(mean_n = mean(n))
beats_all_pe_fe_aw %>% ggplot(aes(fill = police_efficacy)) + geom_sf() + facet_wrap(~year)
beats_all_pe_fe_aw %>% ggplot(aes(fill = fear_of_crime)) + geom_sf() + facet_wrap(~year)
beats_all_pe_fe_aw %>% ggplot(aes(fill = n)) + geom_sf() + facet_wrap(~year)
|
70e0ef6184535314936856b7666d068dbcd0ca7a | 9719ea69f693adfddc62b27eaf948fc7b16f6ad0 | /man/wastd_parse.Rd | 8a213c7a7d3c080bd5e5d1f7f0d13d5d2c531044 | [] | no_license | dbca-wa/wastdr | 49fe2fb1b8b1e518f6d38549ff12309de492a2ad | 5afb22d221d6d62f6482798d9108cca4c7736040 | refs/heads/master | 2022-11-18T01:00:41.039300 | 2022-11-16T08:32:12 | 2022-11-16T08:32:12 | 86,165,655 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,594 | rd | wastd_parse.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wastd_parse.R
\name{wastd_parse}
\alias{wastd_parse}
\title{Parse a \code{wastd_api_response} into a tibble}
\usage{
wastd_parse(wastd_api_response, payload = "data")
}
\arguments{
\item{wastd_api_response}{A \code{wastd_api_response} from WAStD}
\item{payload}{(chr) The name of the key containing the parsed
\code{httr::content()} from the WAStD API call}
}
\value{
A tibble with one row per record and columns corresponding to each
record's fields.
}
\description{
From a \code{wastd_api_response}, turn the key \code{payload}
(default: "features") into a \code{tibble:tibble}, and
\code{tidyr::unnest_wider} the tibble into columns equivalent to the fields
of the WAStD API serializer.
If GeoJSON is found, the keys \code{geometry} (including \code{coordinates}) will
remain unchanged, but the key \code{properties} will be unnested.
}
\details{
\lifecycle{stable}
}
\seealso{
Other api:
\code{\link{build_auth}()},
\code{\link{download_minimal_wastd_turtledata}()},
\code{\link{download_wastd_sites}()},
\code{\link{download_wastd_turtledata}()},
\code{\link{download_wastd_users}()},
\code{\link{export_wastd_turtledata}()},
\code{\link{filter_wastd_turtledata_area}()},
\code{\link{filter_wastd_turtledata_seasons}()},
\code{\link{filter_wastd_turtledata}()},
\code{\link{handle_http_status}()},
\code{\link{wastd_GET}()},
\code{\link{wastd_POST}()},
\code{\link{wastd_bulk_post}()},
\code{\link{wastd_chunk_post}()},
\code{\link{wastd_create_update_skip}()},
\code{\link{wastd_post_one}()}
}
\concept{api}
|
feaa22a2b7501da4eab33f95d462fb76d40acdf0 | 178fdf4459e4817988b60b69e93fbe079446b4a5 | /Load.R | 1575612b790a099249f1ab71d8faf46f8045b794 | [] | no_license | KaiqueS/Eletiva_Analise_De_Dados_UFPE_2021.1 | c4de6ba486db0e4bfd196a2bf4e984d384b45f40 | 258ddd3e06931a61fa0f1b201e7d00f4a16d187e | refs/heads/main | 2023-06-11T15:42:09.368480 | 2021-07-05T15:23:18 | 2021-07-05T15:23:18 | 357,022,682 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,415 | r | Load.R | install.packages( "microbenchmark" )
install.packages( "readxl" )
library( microbenchmark )
library( tidyverse )
library( plyr )
library( readxl )
setwd( "D:\\Trabalho\\Eletiva_Analise_De_Dados_UFPE_2021.1" )
sinistrosRecife2020Raw <- read.csv2('http://dados.recife.pe.gov.br/dataset/44087d2d-73b5-4ab3-9bd8-78da7436eed1/resource/fc1c8460-0406-4fff-b51a-e79205d1f1ab/download/acidentes_2020-novo.csv', sep = ';', encoding = 'UTF-8')
sinistrosRecife2021Raw <- read.csv2('http://dados.recife.pe.gov.br/dataset/44087d2d-73b5-4ab3-9bd8-78da7436eed1/resource/2caa8f41-ccd9-4ea5-906d-f66017d6e107/download/acidentes_2021-jan.csv', sep = ';', encoding = 'UTF-8')
sinistrosRecifeRaw <- rbind(sinistrosRecife2020Raw, sinistrosRecife2021Raw)
# exporta em formato nativo do R
saveRDS( sinistrosRecifeRaw, "sinistrosRecife.rds" )
# exporta em formato tabular (.csv) - padrão para interoperabilidade
write.csv2( sinistrosRecifeRaw, "sinistrosRecife.csv" )
# Exporta em formato excel csv2
write_excel_csv2( sinistrosRecifeRaw, "sinistrosRecife.xlsx" )
# compara os processos de exportação, usando a função microbenchmark
microbenchmark( a <- readRDS( 'sinistrosRecife.rds'), b <- read.csv2( 'sinistrosRecife.csv', sep = ';' ), times = 10L )
microbenchmark( a <- readRDS( 'sinistrosRecife.rds'), b <- readxl::read_xlsx( 'sinistrosRecife.xlsx' ), times = 10L )
teste <- readxl::read_xlsx( "sinistrosRecife.xlsx" )
|
a1cf612f3542daeba6fbf1d3d7754b976cf56447 | b8f69e2a1d3d706f2d9b767b99c0df95b23ad56f | /man/release_questions.Rd | fa153448c3936e5e014317fdb7116e61b6808d32 | [
"MIT"
] | permissive | cran/wilson | b03932a828d284a6b8b8b29411721727c6268ec0 | e2dec1181e01d212b545a6ebfb53beee6320cf2f | refs/heads/master | 2021-06-08T21:43:25.793829 | 2021-04-19T08:40:02 | 2021-04-19T08:40:02 | 145,903,340 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 364 | rd | release_questions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/release_questions.R
\name{release_questions}
\alias{release_questions}
\title{Defines additional questions asked before CRAN submission.
DO NOT EXPORT!}
\usage{
release_questions()
}
\description{
Defines additional questions asked before CRAN submission.
DO NOT EXPORT!
}
|
22d8cc5e6e7c5ccee95f3c36a590e871c280d3a2 | e8ec37267ed9441e229c4774ac20621806f69a81 | /Scripts/Annotate.R | ce14f4c96136aa1c74c7cf227e7f42193cee2a17 | [] | no_license | sohumgala/Single-Cell-RNA-Sequencing-Analysis | 150e23229ee546ac40eef4e32f011c8f71beaf72 | 712e917b121adc2f030442346d81bc482f37bbf4 | refs/heads/master | 2023-07-08T18:57:13.124369 | 2021-08-22T15:34:25 | 2021-08-22T15:34:25 | 398,834,735 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 600 | r | Annotate.R | # After cell types have been determined, it is easy to label the UMAP with appropriate cell types
# INPUT: cell ids based on previously specified annotation techniques
new.cluster.ids <- c("Naive CD4 T", "Memory CD4 T", "CD14+ Mono", "B", "CD8 T", "FCGR3A+ Mono",
"NK", "DC", "Platelet")
names(new.cluster.ids) <- levels(cells.filt)
cells.filt <- RenameIdents(cells.filt, new.cluster.ids)
DimPlot(cells.filt, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
# Further customization of the plot is possible
# save the plot by exporting from plots window
|
f8e1a240ba7257ce577a172c86930f6d65bc6471 | 70e015e71ce31e129c141ddfbcdbf5b200c52df4 | /Content/how_we_compare_mcmc_performance/how_we_compare_mcmc_performance.R | a29822c6072efe60f3afdb90c33b2e134bb65f89 | [] | no_license | lponisio/Vogelwarte_NIMBLE_workshop | ec258a845381621c303b779bb72c5b72924bbdd6 | 323a8ab63ba5b0199b2e3b368dfe2f51bfa17e1f | refs/heads/master | 2020-06-04T00:58:16.528340 | 2018-04-25T11:31:03 | 2018-04-25T11:31:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,543 | r | how_we_compare_mcmc_performance.R | ## ----setup, include=FALSE------------------------------------------------
library(methods) ## needed only when building documents outside of R
library(nimble)
library(mcmcplots)
if(!exists("nimble_course_dir")) ## set your own nimble_course_dir if needed
nimble_course_dir <- file.path(getwd(),'..')
cur_dir <- getwd()
setwd(file.path(nimble_course_dir,
'examples_code',
'CJS_dipper'))
source('dipper_basic.R')
setwd(cur_dir)
## ---- default-mcmc, eval = TRUE------------------------------------------
dipper_model <- nimbleModel(dipper_code,
constants = dipper_constants,
data = dipper_data,
inits = dipper_inits)
defaultMCMCconf <- configureMCMC(dipper_model)
defaultMCMC <- buildMCMC(defaultMCMCconf)
## We can compile both in one step
dipper_compiled <- compileNimble(dipper_model, defaultMCMC)
CdefaultMCMC <- dipper_compiled$defaultMCMC
## Illustration of running MCMC "directly"
CdefaultMCMC$run(5000)
defaultSamples <- as.matrix(CdefaultMCMC$mvSamples)
## Do burn-in manually when running in this mode
defaultSamples <- defaultSamples[1001:5000,]
dir.create('default_samples_plots', showWarnings = FALSE)
mcmcplot(defaultSamples, dir = 'default_samples_plots')
## ---- zoomed-trace-plot, eval = TRUE-------------------------------------
plot(defaultSamples[2051:2100, 'p'], type = 'b')
## ---- ess, eval = TRUE---------------------------------------------------
library(coda)
effectiveSize(defaultSamples)
|
282727bb4fdf62c606688af4f5be87adb43edc89 | 254937c1395588e176e61a81ee84bdccefc8ddfd | /man/getJobs.Rd | c4badac5a661c8dac73242e910b2fc89d81734dc | [] | no_license | cran/antaresEditObject | 3fd126458f8b9e98a18788f9cf212b5480f8d9ec | f13a7f09e8c32612f236f5b92b23cbb45d7b0e61 | refs/heads/master | 2023-04-17T19:17:10.925966 | 2023-04-06T10:00:06 | 2023-04-06T10:00:06 | 162,731,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 606 | rd | getJobs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/API.R
\name{getJobs}
\alias{getJobs}
\title{Retrieve API jobs}
\usage{
getJobs(job_id = NULL, opts = antaresRead::simOptions())
}
\arguments{
\item{job_id}{The job identifier, if \code{NULL} (default), retrieve all jobs.}
\item{opts}{List of simulation parameters returned by the function
\code{\link[antaresRead:setSimulationPath]{antaresRead::setSimulationPath()}}}
}
\value{
A \code{data.table} with information about jobs.
}
\description{
Retrieve API jobs
}
\examples{
\dontrun{
getJobs()
}
}
|
2164925dda50fdf46c4d6532f4613bffaefab168 | 698cd11b38d16eff0c6fd0429ea4106e5517fcc1 | /Chapter 03 (foreign)/03_05 foreign for SAS.R | d74aaa8d0b3c6d48ed1b109fbc8ff56280a124ae | [] | no_license | mnr/R-Data-Science-High-Variety | f0707f0eaad176f53716b45ef6f2c1778bd55c85 | 026bd6660545df6a0f17e2c35a325dfe8a628d6b | refs/heads/master | 2020-03-29T07:52:52.385823 | 2018-10-19T21:18:51 | 2018-10-19T21:18:51 | 149,683,442 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 893 | r | 03_05 foreign for SAS.R | # Copyright Mark Niemann-Ross, 2018
# Author: Mark Niemann-Ross. mark.niemannross@gmail.com
# LinkedIn: https://www.linkedin.com/in/markniemannross/
# Github: https://github.com/mnr
# More Learning: http://niemannross.com/link/mnratlil
# Description: R Programming in Data Science: High Variety Data
# use foreign to import and export sas
install.packages("foreign")
library(foreign)
# Some data to write to an excel range
Smalldf <- data.frame(thisThing = 1:5,
thatThing = 7:11,
AnotThing = LETTERS[1:5])
# read from SAS ----
read.ssd() # requires SAS. If no copy of SAS, use a utility to convert to csv
# additionally, look at the haven package for read_sas( ).
# write to SAS ----
write.foreign(Smalldf,
datafile = "sasData.csv",
codefile = "sasCode.sas",
package = "SAS")
|
99a2af4fe0a5b4ac07d916bd95c9bf0881beddf6 | 25dee6e43b0efbcb68856c11d0b2e39804eb4fb3 | /u5m_media_request.R | 572e8150c621e0c56185ec3197ea7158d19fc501 | [] | no_license | rburstein-IDM/scratch | 43e3f72fe37eaa17f12dcf23d3eb543f1af41141 | 6eaa6bfad15dd80cb0773f4e75d1a5761ac984f2 | refs/heads/master | 2020-05-02T23:33:09.047264 | 2019-12-08T22:00:36 | 2019-12-08T22:00:36 | 178,283,413 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 749 | r | u5m_media_request.R |
library(data.table)
library(sf)
library(ggplot2)
library(maps)
setwd('C:/Users/rburstein/Dropbox (IDM)/IHME/data')
# load and trim data
d <- fread('U5MR_LMICS_admin2_data_full.csv')
# Do we have Indonesia data comparable to the article which will be published ie: % of district/cities that have achieved SDG 3,2 targets and performance/ progress 2000 - 2017 in neonatal, infant and U5 mortality?
d1 <- d[year == 2017 & ADM0_NAME == 'Indonesia']
mean(d1$u5mr_mean<.025)
sum(d1$u5mr_mean<.025)
mean(d1$u5mr_upper<.025)
sum(d1$u5mr_upper<.025)
mean(d1$u5mr_lower<.025)
sum(d1$u5mr_lower<.025)
mean(d1$nnmr_mean<.012)
sum(d1$nnmr_mean<.012)
mean(d1$nnmr_upper<.012)
sum(d1$nnmr_upper<.012)
mean(d1$nnmr_lower<.012)
sum(d1$nnmr_lower<.012)
|
bea21dd0b9b56b387ecbda7da305cd576a01216f | 50066dae4216d17bd6f0dcb9a11d872e73246eb6 | /man/checkProvenance.Rd | 34e93b278c08937f19eef6bd53087f458ce52013 | [] | no_license | cran/PKNCA | 11de9db2cb98279c79d06022415b8772e7c1f5ea | 8f580da3e3c594e4e1be747cb2d8e35216784ed2 | refs/heads/master | 2023-05-10T16:54:19.131987 | 2023-04-29T18:30:02 | 2023-04-29T18:30:02 | 48,085,829 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 600 | rd | checkProvenance.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/provenance.R
\name{checkProvenance}
\alias{checkProvenance}
\title{Check the hash of an object to confirm its provenance.}
\usage{
checkProvenance(object)
}
\arguments{
\item{object}{The object to check provenance for}
}
\value{
\code{TRUE} if the provenance is confirmed to be consistent,
\code{FALSE} if the provenance is not consistent, or \code{NA} if
provenance is not present.
}
\description{
Check the hash of an object to confirm its provenance.
}
\seealso{
\code{\link{addProvenance}}
}
|
a1f8ebc203c3d7b03589b5aabb616751695435b6 | 1ed571c85cff621d21b04071af55d28ef1231c44 | /script_BIC.R | 7ae543f18bfd74abb308fcd8c06949d838f605dc | [] | no_license | amiaty/RFLLat | 33f3f62867bb5ee01ce61780e35db4da3d3d7e06 | 758d86937d85d3b7bed48555e92d27b65d0c0a0e | refs/heads/main | 2023-03-04T06:26:49.803295 | 2021-02-17T12:01:49 | 2021-02-17T12:01:49 | 327,432,774 | 0 | 1 | null | 2021-02-03T13:56:00 | 2021-01-06T21:25:58 | C++ | UTF-8 | R | false | false | 2,261 | r | script_BIC.R | ## BIC stands for Bayesian information criterion
## Load data
num_files <- length(list.files("./data/"))
files_name <- list.files(path = "./data/")
methods_name <- list("FLLat", "SFLLat", "RFLLat")
empty_vec <- vector(mode="character", length=num_files * 3)
opt_lam <- list(method = empty_vec, fname=empty_vec, lam0=numeric(num_files*3), lam1=numeric(num_files*3), lam2=numeric(num_files*3))
results <- list(bicinfo=numeric(num_files*3))
odd_val <- -3
temp <- 0
for (i in 1:num_files)
{
odd_val <- odd_val+2
fname = unlist(strsplit(files_name[i], split='.', fixed=TRUE))[1]
sim_data <- get(fname)
## Run FLLat.BIC to choose optimal hyper parameter for a specific J
for (k in 1:length(methods_name))
{
if (opt_feat$opt_feat_num[i+k+odd_val] != 0){
temp <- temp +1
if (methods_name[k] == "FLLat")
result.bic <- FLLat.BIC(sim_data, J=opt_feat$opt_feat_num[i+k+odd_val])
else if (methods_name[k] == "SFLLat")
result.bic <- FLLat.BIC(sim_data, J=opt_feat$opt_feat_num[i+k+odd_val])
else if (methods_name[k] == "RFLLat")
result.bic <- FLLat.BIC(sim_data, J=opt_feat$opt_feat_num[i+k+odd_val])
opt_lam$lam0[i+k+odd_val] = result.bic$lam0
opt_lam$lam1[i+k+odd_val] = result.bic$lam1
opt_lam$lam2[i+k+odd_val] = result.bic$lam2
opt_lam$fname[i+k+odd_val] = fname
opt_lam$method[i+k+odd_val] = methods_name[k]
results$bicinfo[i+k+odd_val] <- list(result.bic)
setEPS()
postscript(paste(paste(paste("./outputs/Bic/features_",fname), methods_name[k], sep="_"), "eps", sep = "."))
plot(result.bic$opt.FLLat)
## Plot a heatmap of the weights for the optimal FLLat model.
postscript(paste(paste(paste("./outputs/Bic/features_heatmap_",fname), methods_name[k], sep="_"), "eps", sep = "."))
plot(result.bic$opt.FLLat,type="weights")
#plot(result.bic$opt.FLLat,type="weights")
dev.off()
}
}
## Plot the features for the optimal FLLat model.
#png(paste(paste(paste("./outputs/features_ ",fname,sep="/"), methods_name[k], sep="_"), "png", sep='.'))
#plot(result.bic$opt.FLLat)
#dev.off()
}
save(opt_lam, file="./outputs/opt_lam.RData")
|
ce38ac3cb3f2a6de1b7ea615dcef2d1ab139fcf7 | d0d061329421401283a3db1f8e7aa016e61888d7 | /man/spider-package.Rd | 001c3cd9edfb109465baf855b9420961de9bcd1e | [
"MIT"
] | permissive | boopsboops/spider | 87885b53570a98aece6e7ca1ce600330d9b95d25 | e93c5b4bc7f50168b8a155a6dca7c87dfbdef134 | refs/heads/master | 2021-05-12T07:38:37.413486 | 2019-03-07T21:43:43 | 2019-03-07T21:43:43 | 117,250,046 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,166 | rd | spider-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/spider-package.R
\docType{package}
\name{spider-package}
\alias{spider-package}
\alias{spider}
\title{Species Identity and Evolution in R}
\description{
Spider: SPecies IDentity and Evolution in R, is an R package implementing a
number of useful analyses for DNA barcoding studies and associated research
into species delimitation and speciation. Included are functions for
generating summary statistics from DNA barcode data, assessing specimen
identification efficacy, and for testing and optimising divergence threshold
limits. In terms of investigating evolutionary and taxonomic questions,
techniques for sliding window, population aggregate, and nucleotide
diagnostic analyses are also provided.
}
\details{
The complete list of functions can be displayed with
\code{library(help=spider)}.
More information, including a tutorial on the use of spider can be found at
\code{http://spider.r-forge.r-project.org}.
\tabular{ll}{ Package: \tab spider\cr Type: \tab Package\cr Version: \tab
1.4-2\cr Date: \tab 2017-05-13\cr License: \tab GPL\cr LazyLoad: \tab yes\cr
}
A few of the key functions provided by spider:
DNA barcoding: \code{\link{bestCloseMatch}}, \code{\link{nearNeighbour}},
\code{\link{threshID}}, \code{\link{threshOpt}}, \code{\link{heatmapSpp}}.
Sliding window: \code{\link{slidingWindow}}, \code{\link{slideAnalyses}},
\code{\link{slideBoxplots}}.
Nucleotide diagnostics: \code{\link{nucDiag}}, \code{\link{rnucDiag}}.
Morphological techniques: \code{\link{paa}}.
}
\references{
Brown S. D. J., Collins R. A., Boyer S., Lefort M.-C.,
Malumbres-Olarte J., Vink C. J., & Cruickshank R. H. 2012. SPIDER: an R
package for the analysis of species identity and evolution, with particular
reference to DNA barcoding. _Molecular Ecology Resources_ 12:562-565. doi:
10.1111/j.1755-0998.2011.03108.x
}
\seealso{
\code{\link{ape-package}}, \code{\link{pegas-package}}.
}
\author{
Samuel Brown, Rupert Collins, Stephane Boyer, Marie-Caroline Lefort,
Jagoba Malumbres-Olarte, Cor Vink, Rob Cruickshank
Maintainer: Samuel Brown <s_d_j_brown@hotmail.com>
}
\keyword{package}
|
d5186e3abdef6eb5dffe0c98d700711469d76271 | 7e849b23af37f1b2921372e08bc896e085cf205a | /HW3/HW3_control_variate.r | 5c908d5dc750a14c2f29c1767d793b90dd5923c2 | [] | no_license | letsjdosth/statComputing2 | d4759073c282aeac8b1cf85e85922ebbc79b2230 | 623c7df13c6ad2e0a644270675f0eafb9b7d0e0d | refs/heads/master | 2020-07-15T08:48:45.192258 | 2019-12-23T17:37:34 | 2019-12-23T17:37:34 | 205,524,663 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 500 | r | HW3_control_variate.r | #example 2 (HW3)
m = 10000
# MC
u = runif(m)
mc.sample = (exp(-u)/(1+u^2))
mean(mc.sample)
var(mc.sample)
#control variate MC
con.var.sample = (exp(-0.5)/(1+u^2)) #같은 u 써야함
lambda = -cov(mc.sample, con.var.sample)/var(con.var.sample)
print(lambda) # -2.45가량
con.sample= mc.sample + lambda*(con.var.sample - exp(-0.5)*pi/4)
mean(con.sample)
var(con.sample)
#개선?
c(mean(mc.sample), mean(con.sample))
c(var(mc.sample), var(con.sample))
(var(mc.sample)-var(con.sample))/var(mc.sample) |
7cfb38fdbab282794801497ec7f4a30b3e942dbc | ecc3f86ed2f437c34c817761799e1179f8bee275 | /man/joinrate.Rd | 751c8ba81b9d2b9e186fd73f191275c795d53644 | [] | no_license | cran/relsurv | 8e6b4821d99fda97e58ab1ac284cbd12e3c9cde1 | 20447b8198707f9cebc678ec567a02c57ea4101b | refs/heads/master | 2023-01-10T22:20:36.163357 | 2022-12-22T12:30:02 | 2022-12-22T12:30:02 | 17,699,136 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,455 | rd | joinrate.Rd | \name{joinrate}
\alias{joinrate}
\title{Join ratetables}
\description{
The function joins two or more objects organized as \code{ratetable} by adding a new dimension.
}
\usage{
joinrate(tables,dim.name="country")
}
\arguments{
\item{tables}{
a list of ratetables. If names are given, they are included as \code{dimnames}.
}
\item{dim.name}{
the name of the added dimension. }
}
\details{
This function joins two or more \code{ratetable} objects by adding a new dimension. The cutpoints of all the
rate tables are compared and only the common intervals kept. If the intervals defined by the cutpoints are not of
the same length, a warning message is displayed. Each rate table must have 3 dimensions, i.e. age, sex and year
(the order is not important).
}
\value{An object of class \code{ratetable}.}
\references{
Package: Pohar M., Stare J. (2006) "Relative survival analysis in R." Computer Methods and Programs in Biomedicine, \bold{81}: 272-278.
Relative survival: Pohar, M., Stare, J. (2007) "Making relative survival analysis relatively easy." Computers in biology and medicine, \bold{37}: 1741-1749.
}
\examples{
#newpop <- joinrate(list(Arizona=survexp.az,Florida=survexp.fl,
# Minnesota=survexp.mn),dim.name="state")
}
\seealso{\code{\link{ratetable}}, \code{\link{transrate.hld}}, \code{\link{transrate.hmd}}, \code{\link{transrate}}.}
\keyword{survival}
|
6b257c36aa81e6e7f37f3f571b526a86cbc0e3e6 | 1714a940b25d785c13d53425e323ffdf5d306475 | /cachematrix.R | 900d2ed853c87064e8f1cf9c5b6a8f814cfceed9 | [] | no_license | robmill/ProgrammingAssignment2 | 84e2b2e445260d9a88d75a07ee4fb82294108064 | d21bdd83c7b7492cd0c7343f5a2b8eac2f25d69d | refs/heads/master | 2021-01-21T03:59:15.211191 | 2016-07-22T23:52:20 | 2016-07-22T23:52:20 | 63,987,964 | 0 | 0 | null | 2016-07-22T22:47:12 | 2016-07-22T22:47:11 | null | UTF-8 | R | false | false | 1,343 | r | cachematrix.R | ## Rob Miller
## Programming Assignment 2
## July 22, 2016
##
##
## cacheMatrix.R contains two functions:
## makeCacheMatrix() that creates a special matrix, and
## cachSolve() that computes the inverse of the special matrix.
## makeCacheMatrix creates a special matrix
## 1. set the value of the matrix.
## 2. get the value of the matrix.
## 3. set the value of the inverse.
## 4. get teh value of the inverse.
##
## the inverse is computed using solve()
makeCacheMatrix <- function(x = matrix()) {
s <- NULL
set <- function(y) {
x <<- y
s <<- NULL
}
get <- function() x
setinverse <- function(solve) s <<- solve
getinverse <- function() s
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## cacheSolve calculates the inverse of the matrix created
## by makeCacheMatrix().
## checks to see if the cached matrix has been inverted.
## if there is no inverse, then cacheSolve
## computes the inverse and returns the result as s.
cacheSolve <- function(x, ...) {
s <- x$getinverse()
if(!is.null(s)) {
message("getting cached matrix")
return(s)
}
matrix <- x$get()
s <- solve(matrix, ...)
x$setinverse(s)
s
}
|
599916ccf6ad2ac6fb816c1eb404386ba1ecc42e | 936a42930c9d9e4fa5911aa847510f56edcd3d5e | /Scalper-BH.R | bbc2fd468fc53debfc3f051c8d1d1aae839a4c64 | [] | no_license | maglavis138/DashboardDataUpdate | 46ccdbd2efd19436b6706026b73531123d20f8e9 | 3f61e70dea5ddf12b2c9b107698dc82a4a9d71cf | refs/heads/master | 2021-01-22T03:57:51.785779 | 2017-09-06T04:29:35 | 2017-09-06T04:29:35 | 102,261,023 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,906 | r | Scalper-BH.R |
library(curl)
library(jsonlite)
library(plyr)
since = as.Date("2017-08-21")
until = as.Date("2017-08-31")
post_type = 'all'
##############################################################################################
########################################## INSIGHTS ##########################################
##############################################################################################
GetPostIds = function(since, until){
base = "https://graph.facebook.com/v2.6/150424302108405/posts"
cross= paste("?fields=id,link,created_time,type,message,description&limit=10&since=", since, "T00:00:00-8:00&until=", until,"T00:00:00-8:00&access_token=", sep = "")
token= "EAAZAEuju9Pm4BAIi3rDJoAJPPw9Lmf37nnuitWyIrLQRBppTIk4fE3IaIjyIZBRA5CEdQredeKxt3TpD7rIuugS2H9QhMaC2SjrZCOSZCO3d5iGRV8h1xmBPNsUTlD3a6kUnZCUbPT73MVwZBCqOBpTaYxtqslKfwZD"
url = paste(base, cross, token, sep="")
id_data = fromJSON(url)
}
id_data = GetPostIds(since, until)
posts = id_data$data
has_next_page = TRUE
num_processed = 0
statuses = vector()
scrape_starttime = datetime.datetime.now()
while (has_next_page == TRUE ){
for (i in posts$id){
if (post_type == "all"){
statuses = append(statuses, i)
num_processed = num_processed + 1
if(num_processed %% 100 == 0){
print(paste(num_processed, 'Kekerino!!!'))
} ##Falta la fecha
}
else if(posts$type[match(i,posts$id)] == post_type)
{
statuses = append(statuses, i)
num_processed = num_processed + 1
if(num_processed %% 100 == 0){
print(paste(num_processed, 'Kekerino!!!'))
} ##Falta la fecha
}
}
if('paging' %in% names(id_data)){
id_data = fromJSON(id_data$paging$'next')
posts = id_data$data
}
else{
has_next_page = FALSE
}
}
print(paste('Severo perro...', num_processed, "Posts Procesados"))
GetPostData = function(status){
base = paste("https://graph.facebook.com/v2.6/", status, sep="")
cross= "?fields=id,created_time,message,link,name,type,comments.limit(1).summary(true),shares,reactions.limit(1).summary(true),full_picture&limit=100&access_token="
token= "EAAZAEuju9Pm4BAIi3rDJoAJPPw9Lmf37nnuitWyIrLQRBppTIk4fE3IaIjyIZBRA5CEdQredeKxt3TpD7rIuugS2H9QhMaC2SjrZCOSZCO3d5iGRV8h1xmBPNsUTlD3a6kUnZCUbPT73MVwZBCqOBpTaYxtqslKfwZD"
url = paste(base, cross, token, sep="")
post_data = fromJSON(url)
return(post_data)
}
getFacebookPageFeedData = function(status){
base = paste("https://graph.facebook.com/v2.6/", status, "/insights/", sep = '')
cross= "?limit=100&period=lifetime&access_token="
token= "EAAZAEuju9Pm4BAIi3rDJoAJPPw9Lmf37nnuitWyIrLQRBppTIk4fE3IaIjyIZBRA5CEdQredeKxt3TpD7rIuugS2H9QhMaC2SjrZCOSZCO3d5iGRV8h1xmBPNsUTlD3a6kUnZCUbPT73MVwZBCqOBpTaYxtqslKfwZD"
url = paste(base, cross, token, sep = '')
data = fromJSON(url)
return(data)
}
endate = since + 30
if(endate > until){
endate = until}
GetPageData = function(since, until){
base = "https://graph.facebook.com/v2.6/150424302108405/insights"
cross= paste("/page_fans,page_fan_adds?since=", since, "T00:00:00&until=", endate,"T00:00:00&access_token=", sep = "")
token= "EAAZAEuju9Pm4BAIi3rDJoAJPPw9Lmf37nnuitWyIrLQRBppTIk4fE3IaIjyIZBRA5CEdQredeKxt3TpD7rIuugS2H9QhMaC2SjrZCOSZCO3d5iGRV8h1xmBPNsUTlD3a6kUnZCUbPT73MVwZBCqOBpTaYxtqslKfwZD"
url = paste(base, cross, token, sep="")
id_data = fromJSON(url)
}
pi = data.frame()
finish = FALSE
while(finish == FALSE){
if(endate == until){
finish = TRUE}
pagedata = GetPageData(since, until)
pi = rbind(pi, data.frame('daily_new_likes' = pagedata$data$values[[2]]$value,
'total_likes' = pagedata$data$values[[1]]$value,
'date' = pagedata$data$values[[1]][2]))
since = endate
if(endate + 30 >= until){
endate = until}
else{endate = endate + 30}
}
pi$date = format(as.Date(pi$end_time), "%Y-%m-%d")
pi = within(pi, rm(end_time))
################
processFacebookPageFeedStatus = function(status){
post_data = GetPostData(status)
data = getFacebookPageFeedData(status)
status_id = post_data['id'][[1]]
permalink = paste('https://www.facebook.com/', post_data['id'][[1]], sep = '')
post_type = post_data['type'][[1]]
full_picture = post_data['full_picture'][[1]]
if(is.null(post_data['shares']$shares$count[[1]])){shares_on_post = 0} else {shares_on_post = post_data['shares']$shares$count[[1]]}
if(is.null(post_data['comments']$comments$summary$total_count[[1]])){comments_on_post = 0} else {comments_on_post = post_data['comments']$comments$summary$total_count[[1]]}
if(is.null(post_data['reactions']$reactions$summary$total_count[[1]])){likes_on_post = 0} else {likes_on_post = post_data['reactions']$reactions$summary$total_count[[1]]}
if(is.null(post_data['message'][[1]])){sharetext = ''} else {sharetext = post_data['message'][[1]]}
if(is.null(post_data['name'][[1]])){headline = ''} else {headline = post_data['name'][[1]]}
if(is.null(post_data['link'][[1]])){link = ''} else {link = post_data['link'][[1]]}
status_published = format(as.POSIXct(strptime(post_data['created_time'][[1]], "%Y-%m-%dT%H:%M:%OS", tz="UTC")), tz="America/Los_Angeles",usetz=TRUE)
status_published = as.POSIXct(status_published)
created_time = format(status_published, '%Y-%m-%d %H:%M:%S')
date = format(status_published, '%Y-%m-%d')
hour = format(status_published, "%H:%M")
post_consumptions_by_type = data$data$values[[match("post_consumptions_by_type", data$data$name)]][[1]]
colnames(post_consumptions_by_type) = gsub('\\s+', '_', colnames(post_consumptions_by_type))
post_story_adds_by_action_type = data$data$values[[match("post_story_adds_by_action_type", data$data$name)]][[1]]
colnames(post_story_adds_by_action_type) = gsub('\\s+', '_', colnames(post_story_adds_by_action_type))
if(is.null(data$data$values[[match('post_impressions',data$data$name)]][[1]])){post_impressions = 0} else {post_impressions =data$data$values[[match('post_impressions',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_impressions_unique',data$data$name)]][[1]])){post_reach = 0} else {post_reach =data$data$values[[match('post_impressions_unique',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_impressions_fan_unique',data$data$name)]][[1]])){post_reach_fan_unique = 0} else {post_reach_fan_unique =data$data$values[[match('post_impressions_fan_unique',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_impressions_viral_unique',data$data$name)]][[1]])){post_reach_viral_unique = 0} else {post_reach_viral_unique =data$data$values[[match('post_impressions_viral_unique',data$data$name)]][[1]]}
if(is.null(post_story_adds_by_action_type$comment)){total_comments = 0} else {total_comments = post_story_adds_by_action_type$comment}
if(is.null(post_story_adds_by_action_type$like)){total_likes = 0} else {total_likes = post_story_adds_by_action_type$like}
if(is.null(post_story_adds_by_action_type$share)){total_shares = 0} else {total_shares = post_story_adds_by_action_type$share}
likes_on_shares = total_likes - likes_on_post
comments_on_shares = total_comments - comments_on_post
shares_on_shares = total_shares - shares_on_post
if(likes_on_shares<0){likes_on_shares = 0}
if(comments_on_shares<0){comments_on_shares = 0}
if(shares_on_shares<0){shares_on_shares = 0}
if(is.null(post_consumptions_by_type$link_clicks)){link_clicks = 0} else {link_clicks = post_consumptions_by_type$link_clicks}
if(is.null(post_consumptions_by_type$photo_view)){photo_view = 0} else {photo_view = post_consumptions_by_type$photo_view}
if(is.null(post_consumptions_by_type$video_play)){video_play = 0} else {video_play = post_consumptions_by_type$video_play}
if(is.null(post_consumptions_by_type$other_clicks)){other_clicks = 0} else {other_clicks = post_consumptions_by_type$other_clicks}
if(is.null(data$data$values[[match('post_negative_feedback',data$data$name)]][[1]])){post_negative_feedback = 0} else {post_negative_feedback =data$data$values[[match('post_negative_feedback',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_story_adds',data$data$name)]][[1]])){post_story_adds = 0} else {post_story_adds =data$data$values[[match('post_story_adds',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_stories',data$data$name)]][[1]])){post_stories = 0} else {post_stories =data$data$values[[match('post_stories',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_storytellers',data$data$name)]][[1]])){post_storytellers = 0} else {post_storytellers =data$data$values[[match('post_storytellers',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_consumptions',data$data$name)]][[1]])){post_consumptions = 0} else {post_consumptions =data$data$values[[match('post_consumptions',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_engaged_users',data$data$name)]][[1]])){post_engaged_users = 0} else {post_engaged_users =data$data$values[[match('post_engaged_users',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_engaged_fan',data$data$name)]][[1]])){post_engaged_fan = 0} else {post_engaged_fan =data$data$values[[match('post_engaged_fan',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_complete_views_30s_autoplayed',data$data$name)]][[1]])){post_video_complete_views_30s_autoplayed = 0} else {post_video_complete_views_30s_autoplayed =data$data$values[[match('post_video_complete_views_30s_autoplayed',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_complete_views_30s_clicked_to_play',data$data$name)]][[1]])){post_video_complete_views_30s_clicked_to_play = 0} else {post_video_complete_views_30s_clicked_to_play =data$data$values[[match('post_video_complete_views_30s_clicked_to_play',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_complete_views_30s_organic',data$data$name)]][[1]])){post_video_complete_views_30s_organic = 0} else {post_video_complete_views_30s_organic =data$data$values[[match('post_video_complete_views_30s_organic',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_complete_views_30s_paid',data$data$name)]][[1]])){post_video_complete_views_30s_paid = 0} else {post_video_complete_views_30s_paid =data$data$values[[match('post_video_complete_views_30s_paid',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_complete_views_30s_unique',data$data$name)]][[1]])){post_video_complete_views_30s_unique = 0} else {post_video_complete_views_30s_unique =data$data$values[[match('post_video_complete_views_30s_unique',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_avg_time_watched',data$data$name)]][[1]])){post_video_avg_time_watched = 0} else {post_video_avg_time_watched =data$data$values[[match('post_video_avg_time_watched',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_complete_views_organic_unique',data$data$name)]][[1]])){post_video_complete_views_organic_unique = 0} else {post_video_complete_views_organic_unique =data$data$values[[match('post_video_complete_views_organic_unique',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_length',data$data$name)]][[1]])){post_video_length = 0} else {post_video_length =data$data$values[[match('post_video_length',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_views',data$data$name)]][[1]])){post_video_views = 0} else {post_video_views =data$data$values[[match('post_video_views',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_views_autoplayed',data$data$name)]][[1]])){post_video_views_autoplayed = 0} else {post_video_views_autoplayed =data$data$values[[match('post_video_views_autoplayed',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_views_clicked_to_play',data$data$name)]][[1]])){post_video_views_clicked_to_play = 0} else {post_video_views_clicked_to_play =data$data$values[[match('post_video_views_clicked_to_play',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_views_10s_unique',data$data$name)]][[1]])){post_video_views_10s_unique = 0} else {post_video_views_10s_unique =data$data$values[[match('post_video_views_10s_unique',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_views_10s_autoplayed',data$data$name)]][[1]])){post_video_views_10s_autoplayed = 0} else {post_video_views_10s_autoplayed =data$data$values[[match('post_video_views_10s_autoplayed',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_views_10s_clicked_to_play',data$data$name)]][[1]])){post_video_views_10s_clicked_to_play = 0} else {post_video_views_10s_clicked_to_play =data$data$values[[match('post_video_views_10s_clicked_to_play',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_views_10s_sound_on',data$data$name)]][[1]])){post_video_views_10s_sound_on = 0} else {post_video_views_10s_sound_on =data$data$values[[match('post_video_views_10s_sound_on',data$data$name)]][[1]]}
if(is.null(data$data$values[[match('post_video_views_sound_on',data$data$name)]][[1]])){post_video_views_sound_on = 0} else {post_video_views_sound_on =data$data$values[[match('post_video_views_sound_on',data$data$name)]][[1]]}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'0')){s0 = ''} else {s0 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'0'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'1')){s1 = ''} else {s1 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'1'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'2')){s2 = ''} else {s2 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'2'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'3')){s3 = ''} else {s3 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'3'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'4')){s4 = ''} else {s4 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'4'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'5')){s5 = ''} else {s5 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'5'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'6')){s6 = ''} else {s6 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'6'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'7')){s7 = ''} else {s7 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'7'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'8')){s8 = ''} else {s8 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'8'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'9')){s9 = ''} else {s9 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'9'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'10')){s10 = ''} else {s10 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'10'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'11')){s11 = ''} else {s11 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'11'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'12')){s12 = ''} else {s12 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'12'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'13')){s13 = ''} else {s13 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'13'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'14')){s14 = ''} else {s14 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'14'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'15')){s15 = ''} else {s15 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'15'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'16')){s16 = ''} else {s16 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'16'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'17')){s17 = ''} else {s17 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'17'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'18')){s18 = ''} else {s18 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'18'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'19')){s19 = ''} else {s19 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'19'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'20')){s20 = ''} else {s20 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'20'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'21')){s21 = ''} else {s21 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'21'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'22')){s22 = ''} else {s22 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'22'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'23')){s23 = ''} else {s23 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'23'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'24')){s24 = ''} else {s24 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'24'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'25')){s25 = ''} else {s25 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'25'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'26')){s26 = ''} else {s26 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'26'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'27')){s27 = ''} else {s27 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'27'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'28')){s28 = ''} else {s28 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'28'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'29')){s29 = ''} else {s29 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'29'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'30')){s30 = ''} else {s30 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'30'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'31')){s31 = ''} else {s31 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'31'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'32')){s32 = ''} else {s32 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'32'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'33')){s33 = ''} else {s33 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'33'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'34')){s34 = ''} else {s34 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'34'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'35')){s35 = ''} else {s35 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'35'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'36')){s36 = ''} else {s36 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'36'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'37')){s37 = ''} else {s37 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'37'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'38')){s38 = ''} else {s38 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'38'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'39')){s39 = ''} else {s39 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'39'}
if(is.null(data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'40')){s40 = ''} else {s40 = data$data$values[match('post_video_retention_graph',data$data$name)][[1]][,1]$'40'}
page_total_likes = pi$total_likes[match(date, pi$date)]
page_new_likes = pi$daily_new_likes[match(date, pi$date)]
result = as.data.frame(t(as.matrix(c(status_id,permalink,post_type,sharetext,headline,link,full_picture,created_time,date,hour,page_total_likes,page_new_likes,post_impressions,post_reach,post_reach_fan_unique,post_reach_viral_unique,comments_on_post,likes_on_post,shares_on_post,total_comments,total_likes,total_shares,comments_on_shares,likes_on_shares,shares_on_shares,link_clicks,photo_view,video_play,other_clicks,post_negative_feedback,post_story_adds,post_stories,post_storytellers,post_consumptions,post_engaged_users,post_engaged_fan,post_video_complete_views_30s_autoplayed,post_video_complete_views_30s_clicked_to_play,post_video_complete_views_30s_organic,post_video_complete_views_30s_paid,post_video_complete_views_30s_unique,post_video_avg_time_watched,post_video_complete_views_organic_unique,post_video_length,post_video_views,post_video_views_autoplayed,post_video_views_clicked_to_play,post_video_views_10s_unique,post_video_views_10s_autoplayed,post_video_views_10s_clicked_to_play,post_video_views_10s_sound_on,post_video_views_sound_on, s0, s1, s2, s3, s4, s5, s6, s7, s8, s9, s10, s11, s12, s13, s14, s15, s16, s17, s18, s19, s20, s21, s22, s23, s24, s25, s26, s27, s28, s29, s30, s31, s32, s33, s34, s35, s36, s37, s38, s39, s40))))
return(result)
}
# statuses = c('1405630409737397_1510785635888540','1405630409737397_1511436792490091','1405630409737397_1511436792490091','1405630409737397_1512136232420147','1405630409737397_1532264077074029','1405630409737397_1532392773727826','1405630409737397_1560033190963784','1405630409737397_1647037648930004','1405630409737397_1653348084965627','1405630409737397_1653363801630722','1405630409737397_1653349724965463','1405630409737397_1653346828299086','1405630409737397_1653375138296255','1405630409737397_1653423528291416','1405630409737397_1653337191633383','1405630409737397_1653411344959301','1405630409737397_1653338324966603','1405630409737397_1653295401637562','1405630409737397_1653423781624724','1405630409737397_1653339828299786','1405630409737397_1653285528305216','1405630409737397_1653425824957853','1405630409737397_1653715424928893','1405630409737397_1653298638303905','1405630409737397_1653754348258334','1405630409737397_1653786898255079','1405630409737397_1653773454923090','1405630409737397_1653837734916662','1405630409737397_1653864711580631','1405630409737397_1653826244917811','1405630409737397_1653823791584723','1405630409737397_1653860541581048','1405630409737397_1653841741582928','1405630409737397_1653883098245459','1405630409737397_1653829834917452','1405630409737397_1654051918228577','1405630409737397_1653345221632580','1405630409737397_1654052261561876','1405630409737397_1650206555279780','1405630409737397_1653850838248685','1405630409737397_1654052101561892','1405630409737397_1653253214975114','1405630409737397_1654294671537635','1405630409737397_1653851061581996','1405630409737397_1653773421589760','1405630409737397_1654295988204170','1405630409737397_1653304694969966','1405630409737397_1653302434970192','1405630409737397_1650666325233803','1405630409737397_1654435468190222','1405630409737397_1654435698190199','1405630409737397_1659396867694082','1405630409737397_1671326359834466','1405630409737397_1673861356247633','1405630409737397_1673848899582212','1405630409737397_1682996722000763')
np = 0
final = data.frame()
for(status in statuses){
final = rbind.fill(final, processFacebookPageFeedStatus(status))
np = np +1
if(np %% 20 == 0){
print(paste(np, 'Posts Procesados (Woooooot!!?!!!?)'))}
}
colnames(final) = c('status_id','permalink','post_type','sharetext','headline','link','full_picture','created_time','date','hour','page_total_likes','page_new_likes','post_impressions','post_reach','post_reach_fan_unique','post_reach_viral_unique','comments_on_post','likes_on_post','shares_on_post','total_comments','total_likes','total_shares','comments_on_shares','likes_on_shares','shares_on_shares','link_clicks','photo_view','video_play','other_clicks','post_negative_feedback','post_story_adds','post_stories','post_storytellers','post_consumptions','post_engaged_users','post_engaged_fan','post_video_complete_views_30s_autoplayed','post_video_complete_views_30s_clicked_to_play','post_video_complete_views_30s_organic','post_video_complete_views_30s_paid','post_video_complete_views_30s_unique','post_video_avg_time_watched','post_video_complete_views_organic_unique','post_video_length','post_video_views','post_video_views_autoplayed','post_video_views_clicked_to_play','post_video_views_10s_unique','post_video_views_10s_autoplayed','post_video_views_10s_clicked_to_play','post_video_views_10s_sound_on','post_video_views_sound_on','s0', 's1', 's2', 's3', 's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14', 's15', 's16', 's17', 's18', 's19', 's20', 's21', 's22', 's23', 's24', 's25', 's26', 's27', 's28', 's29', 's30', 's31', 's32', 's33', 's34', 's35', 's36', 's37', 's38', 's39', 's40')
write.csv(final, paste("Bad Hombres - ", as.character(Sys.Date()), ".csv"), row.names = FALSE)
|
b67a153efd99da5c1025fc1f55488056a3e447c1 | 818dd3954e873a4dcb8251d8f5f896591942ead7 | /Mouse/RNASequencing/SNPcalling/snps.R | c8abc041632f3997303dc2b5d9e4d3e19ee2e511 | [] | no_license | DannyArends/HU-Berlin | 92cefa16dcaa1fe16e58620b92e41805ebef11b5 | 16394f34583e3ef13a460d339c9543cd0e7223b1 | refs/heads/master | 2023-04-28T07:19:38.039132 | 2023-04-27T15:29:29 | 2023-04-27T15:29:29 | 20,514,898 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 245 | r | snps.R | # SNP calling using the population VCF
#
# copyright (c) 2014-2020 - Brockmann group - HU Berlin, Danny Arends
# last modified Dec, 2014
# first written Dec, 2014
setwd("E:/Mouse/RNA/Sequencing/Reciprocal Cross B6 BFMI by MPI/ReAnalysisSNPs")
|
b750b57961a192f95602feb7855bf687529d2a75 | da907c6cf0c26266ecf364a0fafb3f3a2615d58f | /day05 - slope/day05_slope.R | eae31ac9381e5598607564d5ad7b6f08e3aab467 | [] | no_license | MaiaPelletier/30DayChartChallenge | 08a1ff56b4aca5f7694025bbfd34e926e9aa1824 | 23d9c67a18a06e03fd5c0c21f71a230d28c5ff35 | refs/heads/main | 2023-04-10T07:09:01.669797 | 2021-04-19T00:53:15 | 2021-04-19T00:53:15 | 353,796,540 | 5 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,530 | r | day05_slope.R | # day 05 - slope ---------------------------------------------------
library(tidyverse)
library(here)
library(janitor)
library(jsonlite)
library(lubridate)
# data import -------------------------------------------------------------
# Function to clean the JSON files that my spotify data is stored in
read_my_streaming_data <- function(file) {
read_json(file, simplifyVector = TRUE) %>%
as_tibble() %>%
clean_names() %>%
mutate(
end_time = ymd_hm(end_time),
date = date(end_time)
)
}
# List of the streaming data files
my_streaming_files <- list.files(here("day05 - slope", "raw data"), full.names = TRUE)
# Map cleaning function to files and bind rows to a tibble
my_streaming_data <- map_dfr(my_streaming_files, read_my_streaming_data)
# data transformation -----------------------------------------------------
# Podcasts to filter out of data
podcasts <- c("Dungeons and Daddies", "You're Wrong About", "Revolutions", "The Daily Zeitgeist", "The Blasting Company")
# Data of my top artists of 2020
top_artists <-
my_streaming_data %>%
filter(!artist_name %in% podcasts) %>%
count(artist_name, sort = T) %>%
top_n(5)
# Data from the beginning of 2020 / end of 2020
data <-
my_streaming_data %>%
filter(date <= min(date) + 60 | date >= max(date) - 60) %>%
filter(artist_name %in% top_artists$artist_name) %>%
mutate(
time_period = ifelse(date <= min(date) + 30, "Beginning of 2020", "End of 2020")
) %>%
group_by(time_period) %>%
count(artist_name, sort = TRUE) %>%
mutate(time_period_num = ifelse(time_period == "Beginning of 2020", 0, 2))
# Write data to csv to link to in alt text on Twitter
write_csv(data, here("day05 - slope", "data", "my_top_artists.csv"))
# build plot --------------------------------------------------------------
# Load fonts
extrafont::loadfonts(device = "win")
# Colour scale palette
morbid_stuff_pal <- c(
"#ECF2CD",
"#DAE57B",
"#779169",
"#8DC4C1",
"#B2DDBF"
)
data %>%
mutate(
align = ifelse(time_period_num == 0, "right", "left"),
axis_nudge = ifelse(time_period_num == 0, -0.25, 2.25)
) %>%
ggplot(aes(time_period_num, n)) +
geom_segment(
aes(x = time_period_num, xend = time_period_num, y = 0, yend = 130),
color = "#3C4153"
) +
geom_line(
aes(color = artist_name, group = artist_name),
size = 2
) +
geom_point(
aes(color = artist_name),
size = 4
) +
geom_text(
aes(x = axis_nudge, label = artist_name, hjust = align),
size = 3.5,
family = "Montserrat SemiBold"
) +
geom_text(
data = data.frame(),
aes(x = -2.25, y = 70, label = "MY\nSPOTIFY\nTOP\nARTISTS\nOF 2020"),
hjust = "right",
size = 20,
color = "#352862",
family = "Compacta BT"
) +
geom_text(
data = data %>% distinct(time_period_num, time_period),
aes(x = time_period_num, y = 135, label = time_period),
size = 4,
color = "#352862",
family = "Montserrat ExtraBold"
) +
labs(
caption = "Viz - @MaiaPelletier"
) +
xlim(c(-4.5, 3.5)) +
ylim(c(-3, 140)) +
scale_color_manual(values = morbid_stuff_pal) +
theme_void(base_family = "Montserrat SemiBold") +
theme(
legend.position = "none",
plot.background = element_rect(fill = "#F7CDC1", color = NA),
plot.caption = element_text(hjust = 0.5, size = 6, color = "#352862"),
plot.margin = margin(10, 15, 10, 15)
) +
ggsave(here("day05 - slope", "day05_slope.png"), type = "cairo", dpi = 500, height = 6.5, width = 8)
|
9e91371f0b2a32ed8ecc89ccb32b686eb7004184 | 6b87119889f0e6645411d0029549ca296111c54c | /man/n_orgs_100k.Rd | 10a8ae9d6778919068f1881bc4b32f8850a9b4ae | [] | no_license | edson-github/newsatlasbr | 006a1d0cd50dc7a19be61141708827eb5bf2a5cd | 1f54bec2ba369102a79a12dffaad21a1cd29da18 | refs/heads/master | 2023-03-17T12:51:18.408828 | 2020-09-18T01:45:15 | 2020-09-18T01:45:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,779 | rd | n_orgs_100k.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/n_orgs_100k.R
\name{n_orgs_100k}
\alias{n_orgs_100k}
\title{Retrieve data on the number of news organizations per 100k/inhabitants}
\usage{
n_orgs_100k()
}
\value{
A \code{data.frame} with the following variables:
\itemize{ \item municipio: municipality name. \item uf: abbreviation of the
state's name. \item regiao: name of the country's region where the
municipality is located at. \item qtd_veiculos: number of news
organizations in the municipality. \item codmun: IBGE's (Brazilian
Institute of Geography and Statistics) code for the municipality (7-digit).
\item populacao: municipality's population. \item ano: year from IBGE's
population records (note that data on news organizations collected by
Atlas' team were last updated on Nov. 30th, 2019.) \item
veiculos_por_100k_hab: number of news organizations per 100,000
inhabitants. \item IDHM: Human Development Index for the municipality
(Census 2010). \item IDHM_R: Human Development Index - \emph{per capita}
income for the municipality (Census 2010). \item IDHM_E: Human Development
Index - education for the municipality (Census 2010).}
}
\description{
Retrieve data on the number of news organizations per 100,000
inhabitants in Brazilian municipalities.
}
\details{
\code{n_orgs_100k} returns a dataset containing information on
municipalities with at least one news outlet recorded by Atlas da Noticia's
research team up to November, 2019. It includes a variable on the number of
organizations per 100,000 inhabitants. The function returns only those
municipalities.
}
\examples{
# Extract data on all the municipalities with at least one news outlet.
municipalities_with_media <- n_orgs_100k()
}
|
815118002298abbb99ac1d6553836582d2ae87b5 | b06b16ab56edbd8d63384c849aa65a044029a3f5 | /script.R | 89ff7912118f38f6868d48391bbf5d0691cfb9d3 | [] | no_license | yobero/ter | 6472d6ea4d485a77c4bf1b32488906d6230e6de9 | 3ec36290587a0f8ead952dd379c3e15d2ed392f1 | refs/heads/master | 2021-04-12T04:41:00.552438 | 2018-05-20T21:28:34 | 2018-05-20T21:28:34 | 125,828,819 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 289 | r | script.R | g = read.table("result")
jpeg("plot.jpg")
plot(g$V1, g$V2, type = "n", ylim = range(c(g$V2, g$V3)), xlab = "Nombre de chiffre", ylab = "temps d'execution", main="Temps d'execution en fonction du nombre de chiffres")
lines(g$V1, g$V2, col = "blue")
lines(g$V1, g$V3, col = "red")
dev.off()
|
45131979670a6977ae0efafbd903f57ce1b3d8f5 | 0309d6e6296f4faccddfc6d4ecc5a9161564c1eb | /archive/kht/norsyss_purpose.R | e77356c7b3b334d99b0e37ad0ecafd8abe2c2370 | [] | no_license | ybkamaleri/shiny | 4bf864e9ea445d9ba6714c87ff2b98f977f5b542 | 1c185b6ffd069460260f570eeb341c18a20f62db | refs/heads/master | 2023-06-03T19:34:37.831626 | 2021-06-17T12:21:08 | 2021-06-17T12:21:08 | 255,894,191 | 0 | 0 | null | 2020-04-15T11:21:57 | 2020-04-15T11:21:56 | null | UTF-8 | R | false | false | 4,246 | r | norsyss_purpose.R |
norsyss_purpose_ui <- function(id, config) {
ns <- NS(id)
tagList(
fluidRow(
column(
width=2,
p("")
),
column(
width=8, align="left",
p(
"Vi får data til denne overvåkingen via Sykdomspulsen. ",
"Diagnosekoder som registreres hos lege eller legevakt sendes ",
"til Helsedirektoratet som en del av legenes refusjonskrav (KUHR-systemet). ",
"Folkehelseinstituttet mottar daglig oppdatert KUHR-data til Sykdomspulsen. ",
"Dataene er anonyme når vi mottar dem, uten pasientidentifikasjon, men med ",
"informasjon om kjønn, aldersgruppe, konsultasjonsdato og sted for konsultasjon.", br(), br(),
strong("Informasjon om dataene vi bruker i NorSySS:"), br(),
"- Både telefon og legekontakt er inkludert", br(),
"- Legekontor og legevakt er inkludert", br(),
"- Geografisk område basert på stedet for legekonsultasjon, ikke pasientens bosted", br(),
"- De kommunene som ikke har legevakt eller legekontor er ikke med i listen ",
"der man velger geografisk område da vi ikke har noe data om disse. ",
"Personene som bor i kommuner uten lege og legevakt benytter legekontor ",
"og legevakt i andre kommuner", br(),
"- Antallet konsultasjoner er vanligvis lavere i ferier og på helligdager. ",
"Dette er spesielt tydelig rundt jul/nyttår og påske, men også i ",
"sommerferieukene.", br(),
"- Det kan være 14 dager forsinkelse i dataene da de kommer fra KUHR ",
"systemet. Dersom det for noen datoer ikke er registrert noen ",
"konsultasjoner fra et geografisk område vil dette vises som røde ",
"stiplede linjer i grafene.", br(), br(),
strong("Fargekoder i grafene:"), br(),
"- Bakgrunnsfargen er laget ut fra beregninger fra de foregående 5 ",
"årene i samme geografiske område og samme sykdom/syndrom og ",
"aldersgruppe (for årene 2006-2010 er 5 fremtidige år brukt).", br(),
"- Blå bakgrunnsfarge viser at antallet konsultasjoner er som forventet", br(),
"- Gul bakgrunnsfarge viser at antallet konsultasjoner er høyere enn forventet", br(),
"- Rød bakgrunnsfarge viser at antall konsultasjoner er betydelig høyere enn forventet", br(),
"- I grafer der det er en svart strek viser det antallet faktiske konsultasjoner. ",
"Dersom denne streken er i det blå feltet er antallet konsultasjoner er som forventet, ",
"om den er i det gule feltet er antallet konsultasjoner høyere enn forventet og om ",
"den er i det røde feltet er antallet konsultasjoner betydelig høyere enn forventet ",
"for gitt tidsrom, alder og geografisk område.", br(), br(),
strong("Kommunereformen: "), "Kommuner som har blitt slått sammen og fått ",
"et nytt navn vil ikke finnes i oversiktene. Kommuner som har blitt slått ",
"sammen med en annen kommune men beholdt navnet vil vises i oversiktene, ",
"og beregningene tar hensyn til sammenslåingen. Det samme gjelder ",
"sammenslåtte kommuner som får nytt kommunenavn.", br(), br(),
strong("Små kommuner: "), "Kommuner med under 500 innbyggere vil ikke ",
"kunne se grafer for aldersgrupperinger, men bare 'totalt antall'. ",
"Dette er av hensyn til personvern.", br(), br(),
strong("Interkommunalt samarbeid om legekontor/legevakt: "),
"I Sykdomspulsen er geografisk område basert på stedet for legekonsultasjon, ",
"ikke pasientens bosted. Derfor vil legekontorets/legevaktens postadresse ",
"si hvilken kommune som vises i Sykdomspulsen. De andre kommunene som er ",
"med på det interkommunale samarbeidet vil ikke vises i Sykdomspulsen.", br(), br(),
strong("Ved tekniske feil, spørsmål eller tilbakemeldinger "),
"vennligst send en mail til sykdomspulsen@fhi.no"
),
br()
),
column(
width=2,
p("")
)
)
)
}
norsyss_purpose_server <- function(input, output, session, config) {
}
|
836e407b5de957a53f04f9be6fbe02be03de07cd | 784bbd690a54af0d941cf0891af957ccb5e6a44d | /tests/make_mi_histograms.R | 2524c79338bdbe522c15af916660b662cf29ec8d | [] | no_license | gditzler/MicrobiomeInformation | 4c15706ca1242c9d37234a444f2f3c1de329aa22 | e22dbdaa6975de338c871720525fc3287b5ec449 | refs/heads/master | 2020-06-05T23:46:43.452195 | 2015-06-03T19:09:09 | 2015-06-03T19:09:09 | 26,067,779 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,780 | r | make_mi_histograms.R | # generate heatmaps of the mutual and conditional mutual information
# after the features have been filtered.
# build the documentation and load the package into the environment
library("devtools")
library("ggplot2")
library("reshape2")
library("plyr")
library("fields")
#document()
# set up program constants
lvl = 0.75 # filter level for OTUs
nbins = 50 # number of bins for estimating the pdfs
bin_w = 0.0007
# set the paths of the biom & map files then load them
biom_fps <- c("~/Git/DataCollections/AmericanGut/AmericanGut-Gut-Diet.biom",
#"~/Git/DataCollections/AmericanGut/AmericanGut-Gut-Sex.biom",
"~/Git/DataCollections/Caporaso/caporaso-gut.biom"
)
map_fps <- c("~/Git/DataCollections/AmericanGut/AmericanGut-Gut-Diet-OV.txt",
#"~/Git/DataCollections/AmericanGut/AmericanGut-Gut-Sex.txt",
"~/Git/DataCollections/Caporaso/caporaso-gut.txt"
)
d_names <- c("ag-diet-ov",
#"ag-gut-sex",
"cap-gut-sex"
)
col_names <- c("DIET_TYPE",
#"SEX",
"SEX"
)
for (n in 1:length(biom_fps)) {
# get the latest files for plotting
biom_fp <- biom_fps[n]
map_fp <- map_fps[n]
d_name <- d_names[n]
col_name <- col_names[n]
print(paste("Running", d_name, col_name))
# load the biom & map files
biom_df <- load_biom_matrix(biom_fp)
map_df <- load_metadata(map_fp)
# scale the matrix
data <- scale_matrix(biom_df, rescale=FALSE)
biom_df$data_dense <- data
# extract the labels from the map file data struture
labels <- retrieve_labels(map_df, biom_df$sample_ids, col_name)
# filter the otus to remove the low ranking otus that are very low in terms of
# the abunance
lst <- filter_otus(biom_df, lvl=lvl)
data_filter <- lst$data
otus_filter <- lst$otu_names
# compute mi & cmi matrices
mi_vec_f <- measure_otu_mi(data_filter, labels, discrete=TRUE, disc="equalwidth", nbins=nbins, method="emp")
cmi_vec_f <- measure_otu_cmi(data_filter, labels, discrete=TRUE, disc="equalwidth", nbins=nbins, method="emp")
bin_w <- (max(mi_vec_f)-min(mi_vec_f))/25
ggplot(data.frame(x=1:length(mi_vec_f), mi=mi_vec_f), aes(x=mi))+
geom_histogram(aes(y=..density.., fill=..count..), colour='black', binwidth=bin_w)#+
#geom_line(stat="density", colour='blue',size=2)
ggsave(file=paste("data/plots/",d_name,"-density-mi-partial.pdf",sep=""))
bin_w <- (max(mi_vec_f)-min(mi_vec_f))/25
ggplot(data.frame(x=1:length(cmi_vec_f), cmi=cmi_vec_f), aes(x=cmi))+
geom_histogram(aes(y=..density.., fill=..count..), colour='black', binwidth=bin_w)#+
#geom_line(stat="density", colour='blue',size=2)
ggsave(file=paste("data/plots/",d_name,"-density-cmi-partial.pdf",sep=""))
}
|
9a5abb474159e6c8584e3c5b722395692f604bec | 12922577d8cbfed96add26bd2d5357e4e1806519 | /grimoirelibR/man/plotTimeSerieWeekN.Rd | 912094083731dd7a2e516256d7c523b3066b3caf | [] | no_license | VizGrimoire/VizGrimoireUtils | dd535efa2408109f3b71ef0c4f823b52def5b227 | 28ce8be6d01222ed86c8ebd2c48847e759a13aaa | refs/heads/master | 2020-12-21T22:58:32.034270 | 2017-05-09T10:26:20 | 2017-05-09T10:26:20 | 14,416,400 | 0 | 8 | null | 2016-05-25T12:09:10 | 2013-11-15T05:59:56 | Python | UTF-8 | R | false | false | 2,488 | rd | plotTimeSerieWeekN.Rd | \name{plotTimeSerieWeekN}
\alias{plotTimeSerieWeekN}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
%% ~~function to do ... ~~
Plot weekly time serie.
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
}
\usage{
plotTimeSerieWeekN(data, columns, filename, labels = columns)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{data}{
%% ~~Describe \code{data} here~~
}
\item{columns}{
%% ~~Describe \code{columns} here~~
}
\item{filename}{
%% ~~Describe \code{filename} here~~
}
\item{labels}{
%% ~~Describe \code{labels} 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{
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (data, columns, filename, labels = columns)
{
pdffilename <- paste(c(filename, ".pdf"), collapse = "")
pdffilenamediff <- paste(c(filename, "-diff.pdf"), collapse = "")
pdffilenamecum <- paste(c(filename, "-cumsum.pdf"), collapse = "")
label <- ""
for (col in 1:length(columns)) {
if (col != 1) {
label <- paste(c(label, " / "), collapse = "")
}
label = paste(c(label, labels[col], " (", colors[col],
")"), collapse = "")
}
pdf(file = pdffilename, height = 3.5, width = 5)
timeserie <- ts(data[columns[1]], start = c(data$year[1],
data$week[1]), frequency = 52)
ts.plot(timeserie, col = colors[1], ylab = label)
if (length(columns) > 1) {
for (col in 2:length(columns)) {
timeserie <- ts(data[columns[col]], start = c(data$year[1],
data$week[1]), frequency = 52)
lines(timeserie, col = colors[col])
}
}
dev.off()
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ ~kwd1 }
\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
|
86ef53e706c9006f2d7fb71705e5e3b91158c545 | ee4e6398420bb34c94c1ede5508064c0623f4e54 | /cachematrix.R | 6561a8b25e93b01f6fe0410097d69285bbc9204a | [] | no_license | jbgonzalez81/datasciencecoursera | 86626e25aaea8058a47844b32ed388947c851b5f | ad14751ea737de43fb200a5b56ebb591e00bd756 | refs/heads/master | 2020-04-29T09:17:52.772205 | 2015-04-01T20:47:55 | 2015-04-01T20:47:55 | 25,180,532 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 689 | r | cachematrix.R | ## This program attempts to cache a square matrix for future calculation.
## The functions with store and retrieve the matrix and its inverse when called.
## Store a square matrix
makeCacheMatrix <- function(x = matrix()) {
m<-NULL
set<-function(y){
x<<-y
m<<-NULL
}
get<-function() x
setmatrix<-function(solve) m<<- solve
getmatrix<-function() m
list(set=set, get=get,
setmatrix=setmatrix,
getmatrix=getmatrix)
}
## Solve the inverse of a matrix
cacheSolve <- function(x=matrix(), ...) {
m<-x$getmatrix()
if(!is.null(m)){
message("getting cached data")
return(m)
}
matrix<-x$get()
m<-solve(matrix, ...)
x$setmatrix(m)
m
}
|
25137f73a116c4340495fd8755f77dc028892d1f | 59b641615cc5e1cf60adb64de99e8ab2c609350d | /R/hauction.R | 1368ad75992d17031e3df7772de855df1628dc3a | [] | no_license | nullsatz/dauc | 1181778e0d781d72e3b71c76f40a1195997ee077 | 746f705835786555965867283f856ab309c40fa3 | refs/heads/master | 2016-09-06T01:29:31.959937 | 2013-02-19T16:36:15 | 2013-02-19T16:36:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 498 | r | hauction.R | hauction <- function(weights) {
ubidders <- sort(unique(weights$bidder))
nBidders <- length(ubidders)
uitems <- sort(unique(weights$item))
nItems <- length(uitems)
weights <- weights[order(weights$bidder, weights$item), ]
bidderItems <- .Call('host_auction', nBidders, nItems, weights$weight,
PACKAGE='dauc')
bidderItems <- ifelse(bidderItems == -1, NA, bidderItems)
bidderItems <- bidderItems + 1
result <- data.frame(bidder=ubidders, item=uitems[bidderItems])
return(result)
}
|
9b76730e0d7e21b561601247a52f170d64e9aeb7 | f75fdbd39e642ea2065ba4e5502e7e2aac44754d | /test/test_gg.r | e5adde53303b1cf7ec2a6deb8b613e1304a3805c | [
"Apache-2.0"
] | permissive | sinanshi/visotmed | 2b7787671e175d2ae7f715a8e06a8b8262ec5996 | cdea8bf0428145d310e77963d237343c554204d1 | refs/heads/master | 2016-09-05T19:53:53.075416 | 2014-10-30T16:56:47 | 2014-10-30T16:56:47 | 16,539,917 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,642 | r | test_gg.r | library(tikzDevice)
library(raster)
library(rgdal)
library(ggplot2)
library(RColorBrewer)
library("grid")
load("../data/clim.R")
# map<-cru_raster_10min_window$val[,,2]
# Longitude<<-cru_raster_10min_window$lon
# Latitude<<-cru_raster_10min_window$lat
# x<-vector()
# y<-vector()
# z<-vector()
# k<-1
# for(i in 1:length(Longitude)){
# for(j in 1: length(Latitude)){
# z[k]<-map[i,j]
# x[k]<-Longitude[i]
# y[k]<-Latitude[j]
# k<-k+1
# }
# }
# map<-data.frame(lon=x,lat=y,val=z)
# map<-map[which(!is.na(map$val)),]
# read shapefile
wmap <- readOGR(dsn="ne_110m_land", layer="ne_110m_land")
# convert to dataframe
wmap_df <- fortify(wmap)
# # create a blank ggplot theme
theme_opts <- list(theme(
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.background = element_rect(color = "black",fill=NA, size = 0.4, linetype = "solid"),
#panel.border=element_rect(color = "black",fill=NA, size = 0.4, linetype = "solid"),
plot.background = element_blank(),
#plot.background=element_blank(),
#panel.border = element_blank(),
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
panel.margin = unit(5,"lines"),
#axis.title.x = element_blank(),
#axis.title.y = element_blank(),
plot.title = element_text()))
#
# # plot map
a<-ggplot(wmap_df, aes(long,lat, group=group)) + geom_polygon() + labs(title="World map (longlat)") +
coord_equal() + theme_opts
ggsave("map1.png", width=12.5, height=8.25, dpi=72)
wmap_robin <- spTransform(wmap, CRS("+proj=robin"))
wmap_df_robin <- fortify(wmap_robin)
ggplot(wmap_df_robin, aes(long,lat, group=group)) +
geom_polygon() +
labs(title="World map (robinson)") +
coord_equal() +
theme_opts
ggsave("map2.png", width=12.5, height=8.25, dpi=72)
ggplot(wmap_df_robin, aes(long,lat, group=group, fill=hole)) +
geom_polygon() +
labs(title="World map (robin)") +
coord_equal() +
theme_opts
ggsave("map3.png", width=12.5, height=8.25, dpi=72)
#
# ggplot(wmap_df_robin, aes(long,lat, group=group, fill=hole)) +
# geom_polygon() +
# labs(title="World map (Robinson)") +
# coord_equal() +
# theme_opts +
# scale_fill_manual(values=c("#262626", "#e6e8ed"), guide="none") # change colors & remove legend
#
# ggsave("map4.png", width=12.5, height=8.25, dpi=72)
# #
# #
# #
# #
# #
# #
# #
grat <- readOGR("ne_110m_graticules_all", layer="ne_110m_graticules_15")
grat_df <- fortify(grat)
bbox <- readOGR("ne_110m_graticules_all", layer="ne_110m_wgs84_bounding_box")
bbox_df<- fortify(bbox)
#
# a<-ggplot(bbox_df, aes(long,lat, group=group)) +
# geom_polygon(fill="white") +
# geom_polygon(data=wmap_df, aes(long,lat, group=group, fill=hole)) +
# geom_path(data=grat_df, aes(long, lat, group=group, fill=NULL), linetype="dashed", color="grey50") +
# labs(title="World map + graticule (longlat)") +
# coord_equal() +
# theme_opts +
# scale_fill_manual(values=c("black", "white"), guide="none") # change colors & remove legend
#
# #ggsave("map5.png", width=12.5, height=8.25, dpi=72)
# #
# # graticule (Robin)
grat_robin <- spTransform(grat, CRS("+proj=robin")) # reproject graticule
grat_df_robin <- fortify(grat_robin)
bbox_robin <- spTransform(bbox, CRS("+proj=robin")) # reproject bounding box
bbox_robin_df <- fortify(bbox_robin)
#
# ggplot(bbox_robin_df, aes(long,lat, group=group)) +
# geom_polygon(fill="white") +
# geom_polygon(data=wmap_df_robin, aes(long,lat, group=group, fill=hole)) +
# geom_path(data=grat_df_robin, aes(long, lat, group=group, fill=NULL), linetype="dashed", color="grey50") +
# labs(title="World map (Robinson)") +
# coord_equal() +
# theme_opts +
# scale_fill_manual(values=c("black", "white"), guide="none") # change colors & remove legend
#
# ggsave("map6.png", width=12.5, height=8.25, dpi=72)
#
#
#
#
# # add country borders
# countries <- readOGR("ne_110m_admin_0_countries", layer="ne_110m_admin_0_countries")
# countries_robin <- spTransform(countries, CRS("+proj=robin"))
# countries_robin_df <- fortify(countries_robin)
#
# ggplot(bbox_robin_df, aes(long,lat, group=group)) +
# geom_polygon(fill="white") +
# theme_opts +
# geom_polygon(data=countries_robin_df, aes(long,lat, group=group, fill=hole)) +
# geom_path(data=countries_robin_df, aes(long,lat, group=group, fill=hole), color="white", size=0.3) +
# geom_path(data=grat_df_robin, aes(long, lat, group=group, fill=NULL), linetype="dashed", color="grey50") +
# labs(title="World map (Robinson)") +
# coord_equal() + xlim(-2e6,5e6) + ylim(2e6,7e6)+
# scale_fill_manual(values=c("black", "white"), guide="none") # change colors & remove legend
#
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab")
# #
# # zp1 <- ggplot(longData,
# # aes(x = Var2, y = Var1, fill = value))
# # zp1 <- zp1 + geom_tile()
# # zp1 <- zp1 + scale_fill_gradientn(colours = myPalette(100))
#===============
#scale bottom (a)
#===============
# plot.title = "General Circulation Models Climate Data Output"
# plot.subtitle = 'created by GCM runs'
#
# a<-ggplot(map,aes(lon,lat,fill=val))+
# geom_raster(hjust = 0, vjust = 0)+
# scale_fill_gradientn(colours = myPalette(100),breaks=c(-15,-10, -5,10,5, 0,5,15),
# guide= guide_colorbar(title=expression(Surface~Temperature~degree~C), title.position="top",
# barwidth = 25, barheight = 1,nbin=100,
# draw.ulim = FALSE, draw.llim = FALSE ))+
# #geom_path(data=wmap_df,aes(long,lat,group=group,fill=NULL))+
# #coord_cartesian()+coord_map()+
# geom_path(data=grat_df,aes(long,lat,group=group,fill=NULL),linetype="dashed", color="grey50")+
# geom_path(data=countries,aes(long,lat,goup=group,fill=NULL))+
# xlim(min(map$lon),max(map$lon))+ylim(min(map$lat),max(map$lat))+
# theme_opts+theme(legend.position="bottom", legend.background = element_rect(color = "black",
# fill = "grey90", size = 0.4, linetype = "solid"))+
# coord_equal()+
# ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) +
# labs( x = "", y="")
# # #labs(title="Mean Surface Temperature ",x="GCM")#+
# # #scale_x_discrete(grat@data$display)
# # #expression(Depth[mm])
# # ggsave("map10.png",width=12.5,height=6,dpi=72)
# #===============
# #scale right (b)
# #===============
# b<-ggplot(map,aes(lon,lat,fill=val))+
# geom_raster(hjust = 0, vjust = 0)+
# scale_fill_gradientn(colours = myPalette(100),breaks=c(-15,-10, -5,10,5, 0,5,15),
# guide= guide_colorbar(title=expression(degree~C), title.position="top",
# barwidth = 1, barheight = 15,#nbin=100,
# draw.ulim = FALSE, draw.llim = FALSE ))+
# #geom_path(data=wmap_df,aes(long,lat,group=group,fill=NULL))+
# #coord_cartesian()+coord_map()+
# geom_path(data=grat_df,aes(long,lat,group=group,fill=NULL),linetype="dashed", color="grey50")+
# geom_path(data=countries,aes(long,lat,goup=group,fill=NULL))+
# xlim(min(map$lon),max(map$lon))+ylim(min(map$lat),max(map$lat))+
# theme_opts+theme(legend.position="right", legend.background = element_rect(color = "black",
# fill = "white", size = 0.4, linetype = "solid"))+
# coord_equal()+
# ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) +
# labs( x = "", y="")
# #labs(title="Mean Surface Temperature ",x="GCM")#+
# #scale_x_discrete(grat@data$display)
# #expression(Depth[mm])
# # ggsave("map10.png",width=12.5,height=6,dpi=72)
# #===============
# #scale inside (c)
# #===============
# plot.title = "General Circulation Models Climate Data Output"
# plot.subtitle = 'created by GCM runs'
#
# c<-ggplot(map,aes(lon,lat,fill=val))+
# geom_raster(hjust = 0, vjust = 0)+
# scale_fill_gradientn(colours = myPalette(100),breaks=c(-15,-10, -5,10,5, 0,5,15),
# guide= guide_colorbar(title=expression(degree~C), title.position="top",
# # barwidth = 25, barheight = 1,nbin=100,
# draw.ulim = FALSE, draw.llim = FALSE ))+
# #geom_path(data=wmap_df,aes(long,lat,group=group,fill=NULL))+
# #coord_cartesian()+coord_map()+
# geom_path(data=grat_df,aes(long,lat,group=group,fill=NULL),linetype="dashed", color="grey50")+
# geom_path(data=countries,aes(long,lat,goup=group,fill=NULL))+
# xlim(min(map$lon),max(map$lon))+ylim(min(map$lat),max(map$lat))+
# theme_opts+theme(legend.position=c(0.9,0.3), legend.background = element_rect(color = "black",
# fill = "grey90", size = 0.4, linetype = "solid"))+
# coord_equal()+
# ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) +
# labs( x = "", y="")
#===============
#scale inside (d)
#===============
# plot.title = "GCM Surface Temperature"
# plot.subtitle = 'created by GCM runs'
# # plot.title = ""
# # plot.subtitle = ''
# # mr<-fortify(map_robin)
# d<-ggplot(map,aes(long,lat,fill=group))+
# geom_tile(hjust = 0, vjust = 0)+
# scale_fill_gradientn(colours= myPalette(10),breaks=c(15,5,0,-5,-15),
# guide= guide_legend(title=expression(degree~C), title.position="top",
# # barwidth = 25, barheight = 1,nbin=100,
# draw.ulim = FALSE, draw.llim = FALSE ))+
#
# #scale_color_manual(values=myPalette(1000))+
# # scale_fill_brewer(palette="Spectral")+
# #geom_path(data=wmap_df,aes(long,lat,group=group,fill=NULL))+
# #coord_cartesian()+coord_map()+
# #geom_path(data=grat_df,aes(long,lat,group=group,fill=NULL),linetype="dashed", color="grey50")+
# #geom_path(data=countries,aes(long,lat,goup=group,fill=NULL))+
# xlim(min(map$lon),max(map$lon))+ylim(min(map$lat),max(map$lat))+
# theme_opts+theme(legend.position=c(0.9,0.3), legend.background = element_rect(color = "black",
# fill = "grey90", size = 0.4, linetype = "solid"))+
# coord_equal()+
# ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) +
# labs( x = "", y="")
# ggsave("map10.png",width=12.5,height=6,dpi=72)
# names(map)<-c("long","lat","val")
# grid<-expand.grid(lon=unique(map$lon),lat=unique(map$lat))
# val<-array(NA,dim(grid)[1] )
# for(i in 1:dim(map)[1]){
# val[i]<-map$val[which(grid$lon==map$lon[i]&grid$lat==map$lat[i])]
# }
## coordinates (map)= ~long+lat
projection(map)<-CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
# k<-spTransform(map,CRS("+proj=robin"))
#
# cor<-as.data.frame(k@coords)
# pp<-data.frame("long"=cor$x,"lat"=cor$y,"val"=k@data)
#
# ggplot(pp,aes(long,lat,fill=z))+geom_tile()#+scale_fill_gradientn(colours= myPalette(10))
#
coordinates (map)= ~long+lat
projection(map)<-CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
pp_robin<-spTransform(pp,CRS("+proj=robin"))
a<-data.frame(pp_robin@coords,pp_robin@data)
map_pj_c$lat<-round(map_pj$lat/10000)
# s100 <- matrix(c(267573.9, 2633781, 213.29545, 262224.4, 2633781, 69.78261, 263742.7, 2633781, 51.21951, 259328.4, 2633781, 301.98413, 264109.8, 2633781, 141.72414, 255094.8, 2633781, 88.90244), ncol=3, byrow=TRUE)
# colnames(s100) <- c('X', 'Y', 'Z')
#
# library(raster)
# # set up an 'empty' raster, here via an extent object derived from your data
# e <- extent(s100[,1:2])
# e <- e + 1000 # add this as all y's are the same
#
# r <- raster(e, ncol=10, nrow=2)
# # or r <- raster(xmn=, xmx=, ...
#
# # you need to provide a function 'fun' for when there are multiple points per cell
# x <- rasterize(s100[, 1:2], r, s100[,3], fun=mean)
# plot(x) |
a21fad56139d9812565a1630a0c75229cd8914e4 | cd34ddfa6a89237c9ed41ff98f611c93abddb55f | /herring_rv_survey_analyses.r | bfe1b16c32f87f3e7b03a5f55aeaac3499718dfa | [] | no_license | danielgboyce/Herring_state_2018 | 02babecb67bd23640d921c1b7f950bac89e01662 | d7b10f55765d844d7fc547f7c404502c0258f6da | refs/heads/master | 2020-03-11T15:52:45.340441 | 2018-10-03T14:21:46 | 2018-10-03T14:21:46 | 130,098,781 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 104,749 | r | herring_rv_survey_analyses.r |
n<-number of sets
k<-outcome interested in testing
p<-probability of outcome (catchability)
d<-rbinom(n,k,prob=p)
hist(d,breaks=100)
p<-.02#probability of capture
1-dbinom(0,size=30,prob=p)#probability of detecting a herring if it is present
p<-0.02
1-dbinom(0,size=230,prob=p)#probability of detecting a herring if it is present
library(rgdal)
library(maptools)
library(segmented)
library(tidyr)
library(DataCombine)
library(RColorBrewer)
library(segmented)
library(splines)
library(strucchange)
library(data.table)
library(psych)
library(reshape2)
library(gplots)
library(forecast)
library(cluster)
library(vegan)
library(ggplot2)
library(hybridHclust)
library(raster)
library(fields)
library(gridExtra)
library(colorRamps)
library(mapdata)
library(scales)
library(MASS)
library(mgcv)
library(maps)
library(plyr)
library(plotrix)
library(lubridate)
library(fossil)
datadir1<-'N://cluster_2017//scratch//spera//data//stagingdat'
datadir<-'N://cluster_2017//scratch//spera//data//finaldat_v2'
figsdir<-'C://Users//copepod//Documents//aalldocuments//literature//research//active//SPERA//Figures'
figsdir<-'C://Users//sailfish//Documents//aalldocuments//literature//research//active//SPERA//Figures'
setwd('C:/Users/copepod/Documents/aalldocuments/literature/research/active/ESS_trophic_control/data')
setwd('C:/Users/sailfish/Documents/aalldocuments/literature/research/active/ESS_trophic_control/data')
plg<-readShapePoly('polygons_ecnasap.shp')#COMMAND TO READ BACK IN
plg<-subset(plg,region=='NS')
plot(plg)
text(plg,plg$stratum)
map('world',add=TRUE,col='gray',fill=TRUE)
mcrt<-"+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
coast<-readOGR('N://data/shapefiles/naturalearthdata_ne_10m_land_poly',layer='ne_10m_land')#works for Alfcoast<-fortify(coast)
coast.mc<-crop(coast,extent(-180,180,-90,90),proj4string = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
ggplot <- function(...) { ggplot2::ggplot(...) + theme_bw() }
theme_opts <- list(theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.background = element_blank(),
plot.background = element_blank(),
panel.border = element_blank(),
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position="right",
plot.title = element_text(size=16)))
##############################################################
##############################################################
setwd(datadir)
rvl<-read.csv("herring_lengths_RV_survey_spera_spawnar.csv",header=TRUE)
rvl$flen<-ifelse(rvl$mission=='NED2016016',rvl$flen/10,rvl$flen)
rvl$flen<-ifelse(rvl$mission!='NED2016016',(rvl$flen*1.0866)+0.95632,rvl$flen)
#plot(log10(rvl$flen),log10(rvl$fwt))
rvl<-subset(rvl,log10(rvl$flen)>=.5 & month %in% c(6,7,8))#REMOVE OUTLIERS
rvl$lonc<-round(rvl$lon,digits=0)
rvl$lonc<-ifelse(rvl$lon<=rvl$lonc,rvl$lonc-.25,rvl$lonc+.25)
rvl$latc<-round(rvl$lat,digits=0)
rvl$latc<-ifelse(rvl$lat>=rvl$latc,rvl$latc+.25,rvl$latc-.25)
rvl$cell<-gsub(' ','',paste(rvl$lonc,'_',rvl$latc))
plot(log10(rvl$flen),log10(rvl$fwt))
a<-rvl
a$lfwt<-log10(a$fwt)
a$lflen<-log10(a$flen)
mod<-lm(lfwt ~ lflen,data=a)
abline(mod,col='red')
rvl$fwtest<-10^predict(mod,newdata=data.frame(lflen=a$lflen))
rvl$fwtest<-ifelse(is.na(rvl$fwt)==TRUE,rvl$fwtest,rvl$fwt)
rvl2<-rvl
rvl2$jcat<-ifelse(rvl$flen<=24.75,'J','A')
rvl2$id<-gsub(' ','',paste(rvl2$mission,'_',rvl2$setno))
rvl2$id1<-gsub(' ','',paste(rvl2$mission,'_',rvl2$setno,'_',rvl2$fshno))
a<-subset(rvl2,jcat=='J')
b<-subset(rvl2,jcat=='A')
sum(a$fwtest,na.rm=TRUE)/sum(rvl2$fwtest)
sum(b$fwtest,na.rm=TRUE)/sum(rvl2$fwtest)
f<-function(d){
return(data.frame(year=unique(d$year),
lon=unique(d$lon),
lat=unique(d$lat),
tbin5=unique(d$tbin5),
strat=unique(d$strat),
time=unique(d$time),
no=sum(d$clen,na.rm=TRUE),
wt=sum(d$fwtest/1000,na.rm=TRUE)))
}
rvll<-ddply(rvl2,.(id,jcat),.fun=f,.progress='text')
#ADDS 0'S FOR EACH LENGTH CATEGORY
dt<-unique(subset(rvll,select=c('jcat')))
f<-function(d){
d2<-merge(dt,d,by=c('jcat'),all.x=TRUE,all.y=FALSE)
#d2<-rbind.fill(dt,d)
d2$id<-unique(d$id)
d2$tbin5<-unique(d$tbin5)
d2$year<-unique(d$year)
d2$lon<-unique(d$lon)
d2$lat<-unique(d$lat)
d2$strat<-unique(d$strat)
d2$time<-unique(d$time)
d2$no<-ifelse(is.na(d2$no)==TRUE,0,d2$no)
d2$wt<-ifelse(is.na(d2$wt)==TRUE,0,d2$wt)
return(d2)
}
rvll2<-ddply(rvll,.(id),.fun=f,.progress='text')
f<-function(d){
gblon<--66.3
gblat<-43.3
if(length(unique(d$year))>=5 & length(unique(d$lat))>10 & length(unique(d$time))>10 & length(unique(d$lat))>10){
#PREDICT ANNUAL AVERAGE VALUES
modw<-gam(wt ~ as.factor(year) + s(lon,lat,k=30) + s(time,bs='cc',k=5),data=d,family='nb'(link='log'))
modn<-gam(no ~ as.factor(year) + s(lon,lat,k=30) + s(time,bs='cc',k=5),data=d,family='nb'(link='log'))
#modw<-gam(wt ~ as.factor(year)+s(time,bs='cc',k=5),data=d,family='nb'(link='log'))
#modn<-gam(no ~ as.factor(year)+s(time,bs='cc',k=5),data=d,family='nb'(link='log'))
pdat<-data.frame(year=sort(unique(d$year)),
lon=gblon,
lat=gblat,
time=1200)
pdat$pn<-predict(modn,newdata=pdat,type='response')
pdat$pw<-predict(modw,newdata=pdat,type='response')
return(pdat)
} else NULL
}
ot<-ddply(rvll2,.(jcat),.fun=f,.progress='text')
a<-subset(ot,jcat=='A')
plot(a$year,a$pw,pch=15,las=1,xlab='Year',ylab='Predicted weight per tow [Adults]')
plot(a$year,a$pn,pch=15,las=1,xlab='Year',ylab='Predicted numbers per tow [Adults]')
b<-subset(ot,jcat=='J')
plot(b$year,b$pw,pch=15,las=1,xlab='Year',ylab='Predicted weight per tow [Juveniles]')
plot(b$year,b$pn,pch=15,las=1,xlab='Year',ylab='Predicted numbers per tow [Juveniles]')
plot(a$year,a$pw,pch=15,las=1,xlab='Year',ylab='Predicted weight per tow [Adults]')
par(new=TRUE)
plot(b$year,b$pw,pch=15,col='red',xlab='',ylab='',yaxt='n')
plot(a$pw,b$pw)
plot(log10(a$pw+1),log10(b$pw+1))
abline(a=0,b=1)
xx<-merge(a,b,by=c('year'))
ccf(as.ts(log10(xx$pn.x+1)),as.ts(log10(xx$pn.y+1)))
acf(as.ts(xx$pn.x))
xyplot(pw~year|as.factor(jcat),data=ot,pch=15)
xyplot(pn~year|as.factor(jcat),data=ot,pch=15)
a<-subset(ot,jcat=='J')
plot(a$year,a$pw,pch=15)
#############################################################
############ ESIMATE TRENDS IN HERRING BY INFERRED AGE
##################################################################
#LOOK AT LENGTH-AGE DATA TO PREDICT LENGTH OF JUVENILES V ADULTS
setwd(datadir)
adat<-read.csv("herring_assess_2016_len_wt_atage.csv",header=TRUE,na.strings=c('- ',' - '))
names(adat)<-tolower(names(adat))
names(adat)<-gsub('\\.','',names(adat))
adat<- adat %>% gather(age, y, age1:age11)
adat$agen<-as.numeric(gsub('age','',adat$age))
a<-unique(subset(adat,var=='no.x1000',select=c('age','agen','db','y')))
names(a)[4]<-c('no.x1000')
adat<-subset(adat,!(var=='no.x1000'))
adat<-merge(adat,a,by=c('db','age','agen'),all.x=TRUE,all.y=FALSE)
setwd(figsdir)
pdf('length_age_assess2016.pdf',height=7, width=8)
a<-subset(adat,var=='len.cm')
plot(a$agen,a$y,xlim=c(0,11),ylim=c(0,35),las=1,pch=15,col=alpha('black',.3),xaxt='n',xlab='Age',ylab='Length [cm]')
axis(1,seq(0,11,1))
mod <- nls(y ~ b0*(1-exp(-b1 * agen)), a, start=c(b0=1,b1=1),control=nls.control(maxiter=500),algorithm='port')
pdat<-data.frame(agen=seq(0,11,.01))
pdat$p<-predict(mod,newdata=pdat)
lines(pdat$agen,pdat$p)
pdat<-data.frame(agen=seq(0,11,.5))
pdat$p<-predict(mod,newdata=pdat)
aa<-subset(pdat,agen==3.5)
points(aa$agen,aa$p,pch=16,col='firebrick3',cex=2)
lines(c(-1,aa$agen),c(aa$p,aa$p),col='red',lty=2)
lines(c(aa$agen,aa$agen),c(-5,aa$p),col='red',lty=2)
#PREDICTED CUTOFF FOR JUVENILE/ADULT=24.8CM
agedat<-data.frame(agen=seq(0,11,.00001))
agedat$flen<-predict(mod,newdata=agedat)
a<-subset(adat,var=='len.cm')
plot(a$y,a$agen,las=1,pch=15,col=alpha('black',.3),ylab='Age',xlab='Length [cm]',ylim=c(0,11),xlim=c(0,40))
axis(1,seq(0,40,5))
md<-lm(agen~y,data=a)
mds<-summary(md)
k.strt<-mds$coef[2,1]
modage <- nls(agen ~ .1*exp(k * y), a, start=c(k=k.strt),control=nls.control(maxiter=500),algorithm='port')
pdat<-data.frame(y=seq(0,max(a$y,na.rm=TRUE),length.out=1000))
pdat$p<-predict(modage,newdata=pdat)
lines(pdat$y,pdat$p)
dev.off()
setwd(datadir)
#rvl<-read.csv("herring_lengths_RV_survey_spera_spawnar.csv",header=TRUE)
rvl<-read.csv("herring_lengths_RV_survey_spera_allar.csv",header=TRUE)
rvl$flen<-ifelse(rvl$mission=='NED2016016',rvl$flen/10,rvl$flen)
rvl$flen<-ifelse(rvl$mission!='NED2016016',(rvl$flen*1.0866)+0.95632,rvl$flen)
#plot(log10(rvl$flen),log10(rvl$fwt))
rvl<-subset(rvl,log10(rvl$flen)>=.5 & month %in% c(6,7,8) & lat<46)#REMOVE OUTLIERS
rvl$strat<-as.character(rvl$strat)
rvl<-subset(rvl,!(strat %in% c("5Z1","5Z2","5Z9",'','558','559','440','441','442','445','446','447','443','444','559','447','449','448','450','496','451')))
rvl$lonc<-round(rvl$lon,digits=0)
rvl$lonc<-ifelse(rvl$lon<=rvl$lonc,rvl$lonc-.25,rvl$lonc+.25)
rvl$latc<-round(rvl$lat,digits=0)
rvl$latc<-ifelse(rvl$lat>=rvl$latc,rvl$latc+.25,rvl$latc-.25)
rvl$cell<-gsub(' ','',paste(rvl$lonc,'_',rvl$latc))
rvl$flen<-round(rvl$flen,digits=2)
#plot(log10(rvl$flen),log10(rvl$fwt))
a<-rvl
a$lfwt<-log10(a$fwt)
a$lflen<-log10(a$flen)
mod<-lm(lfwt ~ lflen,data=a)
abline(mod,col='red')
rvl$fwtest<-10^predict(mod,newdata=data.frame(lflen=a$lflen))
rvl$fwtest<-ifelse(is.na(rvl$fwt)==TRUE,rvl$fwtest,rvl$fwt)
agedat$flen<-round(agedat$flen,digits=2)
agedat<-unique(agedat)
agedat<-data.frame(flen=sort(unique(agedat$flen)),
agen=tapply(agedat$agen,agedat$flen,mean))
rvl<-merge(rvl,agedat,by=c('flen'),all.x=TRUE,all.y=FALSE)
rvl$agen<-ifelse(is.na(rvl$agen)==TRUE,10,rvl$agen)
rvl$agen<-floor(rvl$agen)+1
rvl$agen<-ifelse(rvl$agen>=8,8,rvl$agen)
rvl2<-rvl
rvl2$id<-gsub(' ','',paste(rvl2$mission,'_',rvl2$setno))
rvl2$id1<-gsub(' ','',paste(rvl2$mission,'_',rvl2$setno,'_',rvl2$fshno))
f<-function(d){
return(data.frame(year=unique(d$year),
lon=unique(d$lon),
lat=unique(d$lat),
tbin5=unique(d$tbin5),
strat=unique(d$strat),
time=unique(d$time),
no=sum(d$clen,na.rm=TRUE),
wt=sum(d$fwtest/1000,na.rm=TRUE)))
}
rvll<-ddply(rvl2,.(id,agen),.fun=f,.progress='text')
#ADDS 0'S FOR EACH LENGTH CATEGORY
dt<-unique(subset(rvll,select=c('agen')))
f<-function(d){
d2<-merge(dt,d,by=c('agen'),all.x=TRUE,all.y=FALSE)
#d2<-rbind.fill(dt,d)
d2$id<-unique(d$id)
d2$tbin5<-unique(d$tbin5)
d2$year<-unique(d$year)
d2$lon<-unique(d$lon)
d2$lat<-unique(d$lat)
d2$strat<-unique(d$strat)
d2$time<-unique(d$time)
d2$no<-ifelse(is.na(d2$no)==TRUE,0,d2$no)
d2$wt<-ifelse(is.na(d2$wt)==TRUE,0,d2$wt)
return(d2)
}
rvll2<-ddply(rvll,.(id),.fun=f,.progress='text')
#sdata<-rvl2[,!c('flen','fwt')]
f<-function(d){
gblon<--66.3
gblat<-43.3
if(length(unique(d$year))>=5 & length(unique(d$lat))>10 & length(unique(d$time))>10 & length(unique(d$lat))>10){
#PREDICT ANNUAL AVERAGE VALUES
modw<-gam(wt ~ as.factor(year) + s(lon,lat,k=10) + s(time,bs='cc',k=5),data=d,family='nb'(link='log'))
modn<-gam(no ~ as.factor(year) + s(lon,lat,k=10) + s(time,bs='cc',k=5),data=d,family='nb'(link='log'))
pdat<-data.frame(year=sort(unique(d$year)),
lon=gblon,
lat=gblat,
time=1200)
pdat$pn<-predict(modn,newdata=pdat,type='response')
pdat$pw<-predict(modw,newdata=pdat,type='response')
pdat$lpn<-log10(pdat$pn+1)
pdat$lpw<-log10(pdat$pw+.01)
pdat$pnz<-(pdat$lpn-mean(pdat$lpn))/sd(pdat$lpn)
pdat$pwz<-(pdat$lpw-mean(pdat$lpw))/sd(pdat$lpw)
return(pdat)
} else NULL
}
#ot<-ddply(subset(rvll,lat<=44),.(lcat),.fun=f)#exclude smallest
#ot<-ddply(subset(rvll,lat<=44 & lon< -60),.(lcat),.fun=f)
ot<-ddply(rvll2,.(agen),.fun=f,.progress='text')
f<-function(d){
return(data.frame(totno=sum(d$no),
totwgt=sum(d$wt),
lon=mean(d$lon),
lat=mean(d$lat)))
}
dd<-ddply(rvll2,.(strat,agen),.fun=f)
a<-subset(dd,agen==1)
a<-subset(dd,agen==4)
plot(a$lon,a$lat,pch=16,cex=rescale(a$totno,newrange=c(.5,7)))
map('world',add=TRUE,col='gray',fill=TRUE)
d<-subset(rvll2,year==1970)
f<-function(d){
print(unique(d$year))
j<-subset(d,agen<=4)
a<-subset(d,agen>4)
if(dim(j)[1]>0){
return(data.frame(padult=mean(a$no)/mean(d$no),
pjuv=mean(j$no)/mean(d$no),
rt=mean(a$no)/mean(j$no)))
} else NULL
}
dd<-ddply(rvll2,.(year),.fun=f)
plot(dd$year,dd$padult)
plot(dd$year,dd$pjuv)
plot(dd$year,dd$rt,log='y',las=1,type='b')
plot(rvll2$lon,rvll2$lat,pch=16)
plot(plg,add=TRUE)
xyplot(pnz~year|as.factor(agen),data=ot,pch=15)
plot(ot$agen,ot$year,pch=16,cex=rescale(ot$lpn,newrange=c(.2,5)),col=alpha('darkred',.5),las=1)
pltfun<-function(ott,ttl){
names(ott)[1]<-'y'
return(ggplot()+
geom_tile(data=ott, aes(x=agen, y=year,fill=y),col='gray80',size=.0001)+
scale_fill_distiller(palette='Spectral')+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.1,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=sort(dt$agen),labels=sort(dt$agen),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(1970,2015,5),labels=as.character(seq(1970,2015,5)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(1969.5,2016.5),xlim=c(.5,8.5))+
xlab('')+
ylab('')+
labs(title = ttl,
subtitle = "",
caption = '')
)
}
pznum<-pltfun(subset(ot,select=c('pnz','agen','year')),'Log numbers')
pzwgt<-pltfun(subset(ot,select=c('pwz','agen','year')),'Log weight')
plnum<-pltfun(subset(ot,select=c('lpn','agen','year')),'Log numbers')
plwgt<-pltfun(subset(ot,select=c('lpw','agen','year')),'Log weight')
pnum<-pltfun(subset(ot,select=c('pn','agen','year')),'Numbers')
pwgt<-pltfun(subset(ot,select=c('pw','agen','year')),'Weight')
setwd(figsdir)
pdf('herring_rv_trends_bylength.pdf',height=8,width=10)
grid.arrange(plnum,plwgt,ncol=2)
grid.arrange(pnum,pwgt,ncol=2)
xyplot(lpn~year|as.factor(lcat),data=ot,type=c('p','spline'),pch=16,main='Log numbers')
xyplot(lpw~year|as.factor(lcat),data=ot,type=c('p','spline'),pch=16,main='Log weight')
f<-function(d){
gblon<--66.3
gblat<-43.3
if(length(unique(d$year))>=5 & length(unique(d$lat))>10 & length(unique(d$time))>10 & length(unique(d$lat))>10){
#PREDICT ANNUAL AVERAGE VALUES
mod<-gam(wt ~ year + s(lon,lat,k=10) + s(time,bs='cc',k=5),data=d,family='nb')
s<-summary(mod)
return(data.frame(b=s$p.table[2,1],
se=s$p.table[2,2]))
} else NULL
}
ot2<-ddply(rvll2,.(lcat),.fun=f,.progress='text')
ot2$lcat<-as.numeric(as.character(ot2$lcat))
f<-function(d){
return(data.frame(lcat=sort(unique(d$lcat)),
no=sum(d$no),
wt=sum(d$wt)))
}
o<-ddply(rvll2,.(lcat),.fun=f)
ot2<-merge(ot2,o,by=c('lcat'),all=TRUE)
par(mfrow=c(2,2),mar=c(4,4,1,1))
plot(ot2$lcat,ot2$b,las=1,pch=16,xlab='Length',ylab='Rate of change over time',col='white',ylim=c(-.15,.15),xaxt='n')
axis(1,at=seq(5,45,1))
abline(h=0,lty=2)
f<-function(d){lines(c(d$lcat,d$lcat),c(d$b+(1.96*d$se),d$b-(1.96*d$se)),col=alpha('dodgerblue3',.3),lwd=2) }
zz<-dlply(ot2,.(lcat),.fun=f)
points(ot2$lcat,ot2$b,las=1,pch=16,cex=rescale(ot2$wt,newrange=c(1,4)),col=alpha('darkblue',1))
points(ot2$lcat,ot2$b,las=1,pch=1,cex=rescale(ot2$wt,newrange=c(1,4)),col=alpha('lightgray',1),lwd=.5)
dev.off()
plot(ot2$wt,ot2$b,pch=15,las=1)
abline(h=0,lty=2)
f<-function(d){lines(c(d$wt,d$wt),c(d$b+(1.96*d$se),d$b-(1.96*d$se)))}
zz<-dlply(ot2,.(lcat),.fun=f)
o<-ddply(rvll2,.(lcat,tbin5),.fun=f)
o$lcat<-as.numeric(as.character(o$lcat))
xyplot(wt~lcat |as.factor(tbin5),data=o,type=c('p','spline'),pch=15)
xyplot(no~lcat |as.factor(tbin5),data=o,type=c('p','spline'),pch=15)
plot(o$lcat,o$no,pch=15)
plot(o$lcat,o$wt,pch=15)
plot(o$lcat,log10(o$wt),pch=15)
plot(rvl2$flen,rvl2$fwt)
mod<-gam(fwt ~ s(flen,k=4),data=rvl2)
pdat<-data.frame(flen=seq(min(rvl2$flen),max(rvl2$flen),length.out=100))
pdat$p<-predict(mod,newdata=pdat)
lines(pdat$flen,pdat$p,col='red')
rvll$lwgt<-predict(mod,newdata=data.frame(flen=as.numeric(as.character(rvll$lcat))))
d<-subset(rvll,lcat==7.5)
#CHECKS TO SEE IF GENERIC EARLY WARNINGS INDICATORS ARE RELEVANT
ewfun<-function{
library(earlywarnings)
mod<-gam(totwgt ~ as.factor(year) + s(lon,lat,k=50),data=rvw)
pdat<-data.frame(year=sort(unique(rvw$year)),
lon=gblon,
lat=gblat)
p<-predict(mod,newdata=pdat,se.fit=TRUE,type='response')
pdat$p<-10^p$fit
pdat$se<-10^p$se.fit
ew<-generic_ews(subset(pdat,select=c('year','p')),winsize=10,detrending='gaussian',interpolate=TRUE)
}
##############################################################
##ESTIMATE HERRING TRENDS FROM RV DATA AT STRATUM LEVEL
setwd(datadir)
#rvw<-read.csv("herring_weights_RV_survey_spera_spawnar.csv",header=TRUE)
rvw<-read.csv("herring_weights_RV_survey_spera_allar.csv",header=TRUE)
rvw<-subset(rvw,month %in% c(6,7,8) & is.na(dmin)==FALSE)
rvw$bank<-ifelse(rvw$strat %in% c(447,448),'banq','no')
rvw$bank<-ifelse(rvw$strat %in% c(443),'mis',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(458),'mid',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(455,456),'sab',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(464),'west',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(463),'em',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(473),'lh',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(474),'rw',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(475),'bac',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(480),'bn',rvw$bank)
rvw$no<-log10(rvw$totno+1)
rvw$wgt<-log10(rvw$totwgt+1)
rvw$sz<-rvw$totwgt/rvw$totno
rvw$sz<-ifelse(rvw$sz==Inf,rvw$totwgt,rvw$sz)
rvw$id<-gsub(' ','',paste(rvw$mission,'_',rvw$setno))
rvw$pres<-ifelse(rvw$totno>0,1,0)
rvw$tbin20<-ifelse(rvw$year<=1990,1980,2010)
rvw$lonc<-round(rvw$lon,digits=0)
rvw$lonc<-ifelse(rvw$lon<=rvw$lonc,rvw$lonc-.25,rvw$lonc+.25)
rvw$latc<-round(rvw$lat,digits=0)
rvw$latc<-ifelse(rvw$lat>=rvw$latc,rvw$latc+.25,rvw$latc-.25)
rvw$cell<-gsub(' ','',paste(rvw$lonc,'_',rvw$latc))
rvw$cell.1<-gsub(' ','',paste(round(rvw$lon,digits=1),'_',round(rvw$lat,digits=1)))
lonc.1<-seq(min(rvw$lon),max(rvw$lon),.1)
latc.1<-seq(min(rvw$lat),max(rvw$lat),.1)
crds<-expand.grid(lonc.1=lonc.1,latc.1=latc.1)
crds$cell.1<-gsub(' ','',paste(round(crds$lonc.1,digits=1),'_',round(crds$latc.1,digits=1)))
rvw<-merge(rvw,crds,by=c('cell.1'),all.x=TRUE,all.y=FALSE)
f<-function(d){
return(data.frame(mnno=mean(d$totno),
mdno=median(d$totno),
mnwt=mean(d$totwgt),
mdwt=median(d$totwgt),
lon=median(d$lonc),
lat=median(d$latc)))
}
dd<-ddply(rvw,.(strat),.fun=f)
dd<-ddply(subset(rvw,month==7 & year>=2000),.(cell),.fun=f)
dd<-ddply(subset(rvw,month==8 & year>=2000),.(cell),.fun=f)
plot(dd$lon,dd$lat,pch=16,cex=rescale(log10(dd$mdwt+1),newrange=c(.5,7)),col='dodgerblue')
map('world',add=TRUE,col='gray',fill=TRUE)
plot(plg,add=TRUE,fill=FALSE)
plot(plg,col=alpha('darkblue',.4))
points(rvw$lon,rvw$lat,pch=16,col=alpha('red3',.3),cex=.5)
map('world',add=TRUE,col='gray',fill=TRUE)
dm<-data.frame(year=sort(unique(rvw$year)),
n=tapply(rvw$totno,rvw$year,sum),
ntow=tapply(rvw$id,rvw$year,length),
ncell=tapply(rvw$cell.1,rvw$year,function(x) length(unique(x))),
ntime=tapply(rvw$time,rvw$year,function(x) length(unique(x))),
ndepth=tapply(round(rvw$dmax,digits=0),rvw$year,function(x) length(unique(x))),
nday=tapply(rvw$day,rvw$year,function(x) length(unique(x))),
dayrng=tapply(rvw$day,rvw$year,function(x) max(x)-min(x)))
a<-subset(dm,year<=1990)
b<-subset(dm,year>1990)
mean(a$ncell)
mean(b$ncell)
mean(a$dayrng)
mean(b$dayrng)
par(mfrow=c(2,2))
dm$n<-log10(dm$n)
plot(dm$year,dm$ncell)
plot(dm$year,dm$ntow)
plot(dm$year,dm$ntow)
plot(dm,pch=15)
setwd(figsdir)
pdf('rv_sample_effort_overtime.pdf',height=10,width=8)
par(mfrow=c(3,2),mar=c(4,4,1,1))
f<-function(d,lg){
nm<-names(d)[2]
names(d)<-c('year','y')
if(lg==TRUE){d$y<-log10(d$y)
} else NULL
plot(d$year,d$y,las=1,xlab='Year',ylab=nm,pch=16,col=alpha('gold3',1),cex=2,type='l',lwd=2)
points(d$year,d$y,pch=16,col=alpha('gold3',.3),cex=2)
points(d$year,d$y,pch=1,col='lightgray',lwd=.01,cex=2)
}
f(subset(dm,select=c('year','n')),TRUE)
f(subset(dm,select=c('year','n')),F)
f(subset(dm,select=c('year','ntow')),F)
f(subset(dm,select=c('year','ncell')),F)
f(subset(dm,select=c('year','nday')),F)
f(subset(dm,select=c('year','dayrng')),F)
dev.off()
d<-subset(rvw,bank=='sab')
fsm<-function(d){
d$y<-d$totwgt+.1
mod<-gam(y~as.factor(year) + s(time,bs='cc',k=5) + s(lon,lat,k=4),data=d,gamma=.5,family=Gamma('log'))
pdat<-data.frame(year=sort(unique(d$year)),
time=1200,
lon=median(d$lon),
lat=median(d$lat))
pdat$pwt<-predict(mod,newdata=pdat,type='response')
d$y<-ifelse(d$totwgt>0,1,0)
mod2<-gam(y~as.factor(year) + s(time,bs='cc',k=5) + s(lon,lat,k=4),data=d,gamma=.5,family=binomial)
pdat$pps<-predict(mod2,newdata=pdat,type='response')
pdat<-subset(pdat,select=c('year','pwt','pps'))
pdat$bank<-unique(d$bank)
pdat$lon<-mean(d$lon)
#pdat$p<-(pdat$p-mean(pdat$p))/sd(pdat$p)
#names(pdat)[2]<-unique(as.character(d$cell))
return(pdat)
}
qq<-ddply(rvw,.(bank),.fun=fsm)
xyplot(log(pwt)~year | bank,data=qq,type=c('p','l'),pch=15)
xyplot(pps~year | bank,data=qq,pch=15)
f<-function(d){
return(data.frame(no=mean(d$totno),
ntow=length(unique(d$id))))
}
ot<-ddply(rvw,.(bank),.fun=f)
ot<-ot[order(ot$no,decreasing=TRUE),]
ott<-subset(qq,bank!='no',select=c('pwt','bank','year','lon'))
ttl<-'Presence'
lg<-TRUE
pltfun<-function(ott,ttl,lg){
names(ott)[1]<-'y'
names(ott)[3]<-'year'
if(lg==TRUE){
lms<-c(max(ott$y)*-1,0)
} else { lms<-c(-1,0)}
ott$bank<-as.factor(ott$bank)
dm<-unique(subset(ott,select=c('lon','bank')))
dm<-dm[order(dm$lon),]
dm$id<-seq(1,dim(dm)[1],1)
ott<-merge(ott,dm,by=c('bank'),all.x=TRUE,all.y=FALSE)
ott$y<-ott$y*-1
return(ggplot()+
geom_tile(data=ott, aes(x=id, y=year,fill=y),col='gray',size=.0001) +
scale_fill_distiller(palette='YlOrRd',limits=lms) +
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.1,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(1,10,1),labels=dm$bank,limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(1970,2015,5),labels=as.character(seq(1970,2015,5)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(1969.5,2016.5),xlim=c(.5,10.5))+
xlab('')+
ylab('')+
labs(title = ttl,
subtitle = "",
caption = '')
)
}
p1<-pltfun(subset(qq,bank!='no',select=c('pps','bank','year','lon')),'Presence',FALSE)
p2<-pltfun(subset(qq,bank!='no',select=c('pwt','bank','year','lon')),'Presence',TRUE)
p2
p1
f<-function(d){return(data.frame(nyear=length(unique(d$year)),
myear=min(d$year)))
}
dm<-ddply(rvw,.(strat),.fun=f)
dmm<-dm[order(dm$nyear),]
dmm<-subset(dmm,nyear>=15 & myear<1990)
fsm<-function(d){
d$y<-d$totwgt+.1
mod<-gam(y~s(year) + s(time,bs='cc',k=5) + s(lon,lat,k=4),data=d,gamma=.5,family=Gamma('log'))
pdat<-data.frame(year=seq(min(d$year),max(d$year),1),
time=1200,
lon=median(d$lon),
lat=median(d$lat))
pdat$pwt<-predict(mod,newdata=pdat,type='response')
d$y<-ifelse(d$totwgt>0,1,0)
mod2<-gam(y~as.factor(year) + s(time,bs='cc',k=5) + s(lon,lat,k=4),data=d,gamma=.5,family=binomial)
pdat$pps<-predict(mod2,newdata=pdat,type='response')
pdat<-subset(pdat,select=c('year','pwt','pps'))
pdat$bank<-unique(d$bank)
pdat$lon<-mean(d$lon)
pdat$lat<-mean(d$lat)
return(pdat)
}
qt<-ddply(subset(rvw,strat %in% dmm$strat),.(strat),.fun=fsm)
#devtools::install_github('dgrtwo/gganimate',force=TRUE)
install.packages('magick')
library(magick)
library(gganimate)
qt2<-subset(qt,select=c('strat','year','pwt','pps','bank'))
qt2$id<-qt2$strat
a<-plg
a$id<-a$stratum
am<-fortify(a,region='id')
am<-subset(am,id%in% qt2$id)
mydat<-merge(am,qt2,by=c('id'))
mydat$lpwt<-log10(mydat$pwt+.1)
p<-ggplot(mydat,aes(x=long,y=lat,group = group, fill=lpwt,frame=year)) +
theme_opts +
coord_equal() +
geom_polygon(color = 'grey',size=.0001) +
# geom_polygon(aes(long,lat,group=group),fill=NA,colour='black',data=a) +
labs(title = "Herring presence between 1970 and 2015",
subtitle = "Dynamic Map",
caption = 'Data source: Statistics Canada') +
scale_fill_distiller(palette='Spectral')
setwd(figsdir)
gganimate(p)
gganimate(p,'output.gif')
gganimate(p,'output.log.gif')
gganimate(p,'output.mp4')
gganimate(p,'output.smooth.mp4')
###################################################################
#ESTIMATES SMOOTH TREND IN DIFFERENT QUANTITIES OVER TIME FOR CLUSTER
f<-function(d){ return(data.frame(nyear=length(unique(d$year)),
myear=min(d$year)))
}
dm<-ddply(rvw,.(cell),.fun=f)
dmm<-dm[order(dm$nyear),]
dmm<-subset(dmm,nyear>=15 & myear<1990)
dm2<-ddply(subset(rvw,is.na(sz)==FALSE),.(cell),.fun=f)
dmm2<-subset(dm2,nyear>=15 & myear<1990)
fsm<-function(d){
nm<-names(d)[1]
names(d)[1]<-'y'
if(nm=='sz'){
d<-subset(d,is.na(y)==FALSE)
mod<-gam(y~s(year) + s(lon,lat,k=4),data=d,gamma=.5,gamily='nb')
} else {
mod<-gam(y~s(year) + s(time,bs='cc',k=5) + s(lon,lat,k=4),data=d,gamma=.5,gamily='nb')
}
pdat<-data.frame(year=seq(min(d$year),max(d$year),.25),
time=1200,
lon=median(d$lon),
lat=median(d$lat))
pdat$p<-predict(mod,newdata=pdat,type='response')
pdat<-subset(pdat,select=c('year','p'))
pdat$p<-(pdat$p-mean(pdat$p))/sd(pdat$p)
names(pdat)[2]<-unique(as.character(d$cell))
return(pdat)
}
#TOTAL WEIGHT
qq<-dlply(subset(rvw,cell %in% dmm$cell,select=c('totwgt','strat','year','cell','time','lon','lat')),.(cell),.fun=fsm)
qsm<-Reduce(function(x, y) merge(x, y, by=c('year'),all=TRUE), qq)#COMBINE
qsm<-qsm[,colSums(is.na(qsm)) != nrow(qsm)]#REMOVES COLUMNS THAT ARE ALL MISSING
#TOTAL NUMBERS
qq<-dlply(subset(rvw,cell %in% dmm$cell,select=c('totno','cell','year','time','lon','lat')),.(cell),.fun=fsm)
qsm2<-Reduce(function(x, y) merge(x, y, by=c('year'),all=TRUE), qq)#COMBINE
qsm2<-qsm2[,colSums(is.na(qsm2)) != nrow(qsm2)]#REMOVES COLUMNS THAT ARE ALL MISSING
#AVERAGE SIZE
qq<-dlply(subset(rvw,sz!=Inf & is.na(sz)==FALSE & cell %in% dmm2$cell,select=c('sz','cell','year','time','lon','lat')),.(cell),.fun=fsm)
qsm3<-Reduce(function(x, y) merge(x, y, by=c('year'),all=TRUE), qq)#COMBINE
qsm3<-qsm3[,colSums(is.na(qsm3)) != nrow(qsm3)]#REMOVES COLUMNS THAT ARE ALL MISSING
q<-qsm
q2<- q %>% gather(cell, value, -year)
xyplot(value ~ year | strata,data=q2, pch=15, type=c('spline'),col='black')
xyplot(value ~ year | strata,data=q2, pch=15, type=c('p','spline'),col='black')
clfun<-function(df,k,lbl){
rownames(df)<-df$year
df<-df[,-1]
#k<-3#ER OF CLUSTERS
dmat<-1-cor(df,use='pairwise.complete.obs')
dst<-as.dist(dmat)
ff<-fanny(dst,k,maxit=5000,diss=T)
par(mfrow=c(3,1),mar=c(4,12,1,12))
dum<-c('red3','darkblue','gold3')
plot(silhouette(ff),col=dum[1:k],main='')#silhouette plot
dc.pcoa<-cmdscale(dst)
dc.scores<-scores(dc.pcoa,choices=c(1,2))
spefuz.g<-ff$clustering
a<-data.frame(cell=as.character(sort(unique(names(df)))),
clusters=ff$clustering)
aa<-data.frame(ff$membership)
aa$cell<-rownames(a)
plot(scores(dc.pcoa),asp=1,type='n',xlim=c(-1,1.5),ylim=c(-1,.5),las=1,axes=TRUE,xlab='',ylab='')
stars(ff$membership,location=scores(dc.pcoa),draw.segments=T,add=T,scale=F,len=.1,col.segments=alpha(c(dum[1:k]),.25),byt='n',labels=NULL,xlim=c(-1.1,1.4),ylim=c(-1,.5),lwd=.0001,xpd=TRUE,border=NULL,radius=FALSE,col.radius=alpha('white',.1))
for(i in 1:k){ cl<-dum[i]
gg<-dc.scores[spefuz.g==i,]
hpts<-chull(gg)
hpts<-c(hpts,hpts[1])
lines(gg[hpts,],col=cl,lwd=3,xlim=c(-1.1,1.4),ylim=c(-1,.5))
}
cx <- data.frame(cell=aa$cell,
cx=apply(aa[, 1:k], 1, max))
cx$cxx <- rescale(cx$cx,newrange=c(.05,.5))
#PLOTS THE TIMESERIES OF R FOR EACH CLUSTER
par(mfrow=c(3,1),mar=c(2,12,1,12),oma=c(1,1,1,1))
par(mar=c(4,8,4,8))
mb<-seq(1,k,1)
l<-list()
for(i in 1:length(mb)){
print(mb[i])
one<-subset(a,clusters==mb[i],select=c('cell'))# IN THIS CLUSTER
cx2<-subset(cx,cx>=0.5)#GETS INSTANCES WHERE CLUSTER PROBABILITY>0.5
data5<-subset(df,select=c(as.character(one$cell)))#TS FOR CLUSTER
cx2<-subset(cx2,cell %in% names(data5))
data5<-subset(df,select=c(as.character(cx2$cell)))#TS FOR CLUSTER
x<-rownames(data5)
t<-data.frame(year=as.numeric(x),mn=rowMeans(data5,na.rm=F))
t2<-data.frame(year=as.numeric(x),mn=rowMeans(data5,na.rm=T))
cl<-dum[i]
plot(0,0,pch=16,cex=.01,xlim=c(1970,2020),ylim=c(-2,3),main='',xaxt='n',las=1,axes=FALSE,xlab='',ylab='')
axis(side=2,at=seq(-2,3,1),las=1,lwd=.001,cex.axis=.75)
axis(side=1,at=seq(1970,2020,10),cex.axis=.75)
for(j in 1:length(data5[1,])){
try(dat<-data5[,j])
try(trnsp<-subset(cx,cell==as.character(names(data5[j])))$cxx)
try(lines(x,dat,cex=.5,ylim=c(0,1),lwd=2,col=alpha(cl,rescale(trnsp,newrange=c(.1,.5)))))
}
lines(as.numeric(as.character(t$year)),t$mn,col='black',cex=.7,lwd=4)
dm<-subset(t2,mn==max(t2$mn,na.rm=TRUE),select=c('year'))
dm$cluster=mb[i]
names(dm)<-c('clusterday','clusters')
l[[i]]<-dm
}
a2<-a
if(k==3){a2$cx<- apply(aa[,c('X1','X2','X3')], 1, function(x) max(x) )
} else {a2$cx<- apply(aa[,c('X1','X2')], 1, function(x) max(x) )
}
a2$id<-a2$cell
a2$cl<-ifelse(a2$clusters==1,dum[1],dum[3])
a2$cl<-ifelse(a2$clusters==2,dum[2],a2$cl)
crds<-unique(subset(rvw,select=c('cell','lonc','latc')))
a2<-merge(a2,crds,by=c('cell'),all.x=TRUE,all.y=FALSE)
dum<-unique(subset(a2,select=c('clusters','cl')))
return(ggplot()+
geom_tile(data=a2, aes(x=lonc, y=latc,fill=as.factor(clusters),alpha=cx),col='gray80',size=.0001)+
scale_fill_manual(breaks=as.character(dum$clusters),values=dum$cl,na.value="transparent",guide=guide_legend(title=''))+
scale_alpha(guide = 'none')+
geom_polygon(aes(long,lat, group=group), fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.9,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.1, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-56,1),labels=as.character(seq(-68,-56,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,48,1),labels=as.character(seq(41,48,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,48),xlim=c(-68,-56))+
xlab('')+
ylab('')+
labs(title = lbl,
subtitle = "",
caption = '')
)
}
setwd(figsdir)
pdf('herring_rvsurvey_cluster_all_grid_k3.pdf',height=10,width=6.5)
mp1<-clfun(qsm,3,"Total observed herring (weight)")
mp2<-clfun(qsm2,3,"Total observed herring (numb)")
mp3<-clfun(qsm3,3,"Average weight")
dev.off()
pdf('herring_rvsurvey_cluster_map_grid_k3.pdf',height=14,width=7)
grid.arrange(mp1,mp2,mp3,ncol=1)
dev.off()
#MAP OF TRENDS IN ABUNDANCE AND AVERAGE WEIGHT
#ESTIMATES SMOOTH TREND IN DIFFERENT QUANTITIES OVER TIME FOR CLUSTER
f<-function(d){ return(data.frame(nyear=length(unique(d$year)),
myear=min(d$year),
mx=max(d$totwgt)))
}
dm<-ddply(rvw,.(cell),.fun=f)
dmm<-dm[order(dm$nyear),]
dmm<-subset(dmm,nyear>=15 & myear<1990 & mx>0)
fsm<-function(d){
names(d)[1]<-'y'
#d$y<-(d$y-mean(d$y))/sd(d$y)
mod<-gam(y~s(year) + s(time,bs='cc',k=5) + s(lon,lat,k=4),data=d,gamma=.5,gamily='nb')
mod2<-gam(y~year + s(time,bs='cc',k=5) + s(lon,lat,k=4),data=d,gamma=.5,gamily='nb')
s<-summary(mod2)
pdat<-data.frame(year=seq(min(d$year),max(d$year),.25),
time=1200,
lon=median(d$lon),
lat=median(d$lat))
pdat$p<-predict(mod,newdata=pdat,type='response')
ot<-data.frame(pstart=pdat$p[1],
pend=pdat$p[dim(pdat)[1]],
lonc=unique(d$lonc),
latc=unique(d$latc),
span=max(pdat$year)-min(pdat$year),
year1=min(pdat$year),
beta=s$p.table[2,1],
pv=s$p.table[2,4])
ot$chng<-ot$pend-ot$pstart
d$y<-(d$y-mean(d$y))/sd(d$y)
mod<-gam(y~s(year,k=4) + s(lon,lat,k=4),data=d,gamma=.5,gamily='nb')
p<-predict(mod,newdata=pdat,type='response',se.fit=FALSE)
ot$chngz<-p[length(p)]-p[1]
return(ot)
}
#TOTAL WEIGHT
mdat<-ddply(subset(rvw,cell %in% dmm$cell,select=c('totwgt','strat','year','cell','time','lon','lat','lonc','latc')),.(cell),.fun=fsm)
###SAME BUT FOR AVERAGE WEIGHT
rvws<-subset(rvw,is.na(sz)==FALSE)
f<-function(d){ return(data.frame(nyear=length(unique(d$year)),
myear=min(d$year),
mx=max(d$totwgt)))
}
dm<-ddply(rvws,.(cell),.fun=f)
dmm<-dm[order(dm$nyear),]
dmm<-subset(dmm,nyear>=15 & myear<1990 & mx>0)
d<-subset(rvws,cell=="-58.25_45.75",select=c('sz','strat','year','cell','time','lon','lat','lonc','latc'))
fsm<-function(d){
print(unique(d$cell))
names(d)[1]<-'y'
mod<-gam(y~s(year,k=4) + s(lon,lat,k=4),data=d,gamma=.5,gamily='nb')
mod2<-gam(y~year + s(lon,lat,k=4),data=d,gamma=.5,gamily='nb')
s<-summary(mod2)
pdat<-data.frame(year=seq(min(d$year),max(d$year),.25),
lon=median(d$lon),
lat=median(d$lat))
pdat$p<-predict(mod,newdata=pdat,type='response')
ot<-data.frame(pstart=pdat$p[1],
pend=pdat$p[dim(pdat)[1]],
lonc=unique(d$lonc),
latc=unique(d$latc),
span=max(pdat$year)-min(pdat$year),
year1=min(pdat$year),
beta=s$p.table[2,1],
pv=s$p.table[2,4])
ot$chng<-ot$pend-ot$pstart
d$y<-(d$y-mean(d$y))/sd(d$y)
mod<-gam(y~s(year,k=4) + s(lon,lat,k=4),data=d,gamma=.5,gamily='nb')
p<-predict(mod,newdata=pdat,type='response',se.fit=FALSE)
ot$chngz<-p[length(p)]-p[1]
return(ot)
}
#AVERAGE WEIGHT
mdat2<-ddply(subset(rvws,cell %in% dmm$cell,select=c('sz','strat','year','cell','time','lon','lat','lonc','latc')),.(cell),.fun=fsm)
pfun<-function(a,mx,dg,lbl){
names(a)[1]<-'y'
a$y<-ifelse(a$y>mx,mx,a$y)
a$y<-ifelse(a$y< -mx,-mx,a$y)
aa<-data.frame(y=seq((-mx)-.001,max(abs(a$y)+.001,na.rm=TRUE),length.out=100))
a<-rbind.fill(a,aa)
n<-21
mxx<-max(abs(a$y))
brks<-seq((-mx)-0.001,mxx+.001,length.out=n)
brks2<-round(seq((-mx)-0.001,mxx+.001,length.out=n),digits=dg)
a$ycat<-cut(a$y,breaks=brks)
lbls<-sort(unique(a$ycat))
lbls2<-sort(unique(cut(a$y,breaks=brks2)))
cls<-matlab.like(length(lbls))
ggplot()+
geom_tile(data=a, aes(x=lonc, y=latc,fill=ycat),col='gray80',size=.0001)+
scale_fill_manual(breaks=as.character(lbls),values=cls,labels=lbls2,na.value="transparent")+
scale_alpha(guide = 'none')+
geom_polygon(aes(long,lat, group=group), fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.9,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.1, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-56,1),labels=as.character(seq(-68,-56,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,48,1),labels=as.character(seq(41,48,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,48),xlim=c(-68,-56))+
xlab('')+
ylab('')+
labs(title = lbl,
subtitle = "",
caption = '')
}
pfun(subset(mdat2,select=c('chngz','lonc','latc')),4,2,'Average size (Z)')
pfun(subset(mdat2,select=c('chng','lonc','latc')),.4,2,'Average size')
pfun(subset(mdat,select=c('chngz','lonc','latc')),1.5,2,'Biomass trend (Z)')
pfun(subset(mdat,select=c('chng','lonc','latc')),85,2,'Biomass trend')
#######################################################
#GETS CELLS WHERE AT LEAST ONE HERRING WAS CAPTURED DURING ALL YEARS OF SURVEY
zz<-data.frame(cell.1=sort(unique(rvw$cell.1)),
n=tapply(rvw$totno,rvw$cell.1,sum))
zzz<-subset(zz,n>0)
#GETS SUM OF ALL FISH CAPTURED BY CELL AND YEAR
f<-function(d){
return(data.frame(n=sum(unique(d$totno)),
lon=unique(d$lonc.1),
lat=unique(d$latc.1),
ntows=length(unique(d$id))))
}
dt<-ddply(rvw,.(cell.1,year),.fun=f,.progress='text')
f<-function(d){
pres<-subset(d,n>0)
return(data.frame(rng=(dim(pres)[1]/dim(d)[1])*100,
ncells=length(unique(d$cell.1)),
ntows=sum(d$ntows)))
}
oo<-ddply(subset(dt,cell.1 %in% zzz$cell.1),.(year),.fun=f)
plot(oo$year,oo$rng,pch=15)
plot(oo$year,oo$ncells,pch=15)
plot(oo$year,oo$ntows,pch=15)
plot(oo$year,oo$rng/oo$ncells,pch=15)
##########################################################
################ DIURNAL CHANGES
##########################################################
pltfun<-function(ott,ttl){
names(ott)[1]<-'y'
names(ott)[3]<-'year'
ott<-na.omit(ott)
return(ggplot()+
geom_tile(data=ott, aes(x=time, y=year,fill=y),size=.0001) +
scale_fill_distiller(palette='Spectral') +
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.1,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(0,2400,300),labels=seq(0,2400,300),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(1970,2015,5),labels=as.character(seq(1970,2015,5)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(1969.5,2016.5),xlim=c(0,2350))+
xlab('')+
ylab('')+
labs(title = ttl,
subtitle = "",
caption = '')
)
}
setwd(figsdir)
pdf('herring_rv_diurnal.pdf',height=8,width=10)
par(mfrow=c(2,2),mar=c(4,4,1,1))
f<-function(d){
if((max(d$time)-min(d$time))>15){
mod<-gam(totwgt~s(time,bs='cc',k=6)+s(lon,lat,k=20),data=d,gamma=1,family=nb)
s<-summary(mod)
pdat<-data.frame(time=seq(min(d$time),max(d$time),1),
lon=median(d$lon),
lat=median(d$lat))
pdat$p<-predict(mod,newdata=pdat,type='response')
pdat$pz<-(pdat$p-mean(pdat$p,na.rm=TRUE))/sd(pdat$p,na.rm=TRUE)
pdat$rng<-max(pdat$pz,na.rm=TRUE)-min(pdat$pz,na.rm=TRUE)
pdat$pmx<-subset(pdat,p==max(pdat$p)[1])$time[1]
pdat$pv<-s$s.table[1,4]
pdat$b<-s$s.table[1,1]
return(pdat)
} else NULL
}
ot<-ddply(rvw,.(year),.fun=f,.progress='text')
ot2<-acast(ot,year~time,value.var="pz")
image(x=sort(unique(ot$year)),y=sort(unique(ot$time)),ot2,col=palette(rich.colors(500)),xlab='Exploitation rate',ylab='Consumer trophic level',las=1,cex.axis=.8)
plot(ot$year,ot$rng,pch=15,las=1,xlab='Year',ylab='Daily range of catch')
plot(ot$year,ot$pmx,pch=15,las=1,xlab='Year',ylab='Time of max catch')
p1<-pltfun(subset(ot,select=c('pz','time','year')),'Z-score')
#SAME BUT BINNED TO 3 EYAR INTERVALS
f<-function(d){
mod<-gam(totwgt~s(time,bs='cc',k=6)+s(lon,lat,k=30) + as.factor(year),data=d,gamma=1,family=nb)
pdat<-data.frame(time=seq(min(d$time),max(d$time),1),
lon=median(d$lon),
lat=median(d$lat),
year=median(d$year))
pdat$p<-predict(mod,newdata=pdat,type='response')
pdat$pz<-(pdat$p-mean(pdat$p))/sd(pdat$p)
pdat$rng<-max(pdat$pz)-min(pdat$pz)
pdat$pmx<-subset(pdat,p==max(pdat$p)[1])$time
return(pdat)
}
ot<-ddply(rvw,.(tbin3),.fun=f,.progress='text')
ot2<-acast(ot,tbin3~time,value.var="pz")
image(x=sort(unique(ot$tbin3)),y=sort(unique(ot$time)),ot2,col=palette(rich.colors(500)),xlab='Exploitation rate',ylab='Consumer trophic level',las=1,cex.axis=.8)
plot(ot$tbin3,ot$rng,pch=15,las=1,xlab='Year',ylab='Daily range of catch')
plot(ot$tbin3,ot$pmx,pch=15,las=1,xlab='Year',ylab='Time of max catch')
p2<-pltfun(subset(ot,select=c('pz','time','tbin3')),'Z-score')
cls<-colorRampPalette(c('black','darkmagenta','hotpink','darkblue','lightskyblue','forestgreen','lawngreen','gold','orange','firebrick1','firebrick4'))
n<-length(unique(ot$tbin3))
dum3<-data.frame(tbin3=sort(unique(ot$tbin3)),
cls=cls(n+2)[3:(n+2)])
ot<-merge(ot,dum3,by=c('tbin3'),all.x=TRUE,all.y=FALSE)
f<-function(d){
lines(d$time,d$pz,col=alpha(as.character(unique(d$cl)),.5),lwd=3)
}
plot(0,0,xlim=c(0,2400),ylim=c(-1.5,2.5),las=1,xlab='Time',ylab='Herring',col='white')
zz<-dlply(ot,.(tbin3),.fun=f)
xyplot(pz~time | tbin3,data=ot)
grid.arrange(p1,p2,ncol=2)
dev.off()
dt$lcat<-as.numeric(as.character(dt$lcat))
sfun<-function(d){
return(d[sample(nrow(d),100,replace=FALSE),])
}
rvw2<-ddply(rvw,.(year),.fun=sfun,.progress='text')
a<-subset(rvw,id==sort(unique(rvw$id))[2])
par(mfrow=c(3,4),mar=c(1,1,1,1))
yrs<-seq(1975,2015,5)
for(i in 1:length(yrs)){
d<-subset(dt,year==yrs[i] & n>0)
plot(d$lon,d$lat,pch=16,col='red',las=1)
map('world',add=TRUE,fill=TRUE,col='lightgray')
}
oo$rng<-log10(oo$rng)
plot(oo,pch=16)
a<-subset(rvw,year<1990)
b<-subset(rvw,year>=1990)
plot(a$lonc.1,a$latc.1,pch=15)
plot(b$lonc.1,b$latc.1,pch=15)
par(mfrow=c(3,4),mar=c(1,1,1,1))
f<-function(d){
mod<-gam(totwgt~s(time,bs='cc',k=6) + s(lon,lat,k=10),data=d,family='nb',gamma=1)
pdat<-data.frame(time=seq(min(d$time),max(d$time),length.out=100),
lon=-55,
lat=42.5)
p<-predict(mod,newdata=pdat,type='response')
pdat$p<-p
# plot(pdat$time,pdat$p,type='l',main=unique(d$tbin10))
return(data.frame(time=subset(pdat,p==max(pdat$p))$time[1]))
}
zz<-ddply(rvw,.(year),.fun=f,.progress='text')
plot(zz$year,zz$time,pch=15,type='b')
plot(rvw$lon,rvw$lat,pch='.')
points(rvw$lonc,rvw$latc,pch=15,col='purple')
f<-function(d){ return(data.frame(n=length(unique(d$id)),
lon=unique(d$lonc.1),
lat=unique(d$latc.1),
totno=sum(d$totno)))}
dtt<-ddply(rvw,.(cell.1,year),.fun=f,.progress='text')
dtt$p<-1-dbinom(0,dtt$n,.02)
1-dbinom(0,1,.02)
1-dbinom(0,30,.02)
1-dbinom(0,1,.02)
1-dbinom(0,30,.02)
d<-subset(rvw,cell=="-57.25_44.25")
f<-function(d){
if(length(unique(d$year))>=5 & length(unique(d$time))>5){
mod<-gam(pres~as.factor(year),data=d,gamma=1.4,family='binomial')
s<-summary(mod)
pdat<-data.frame(year=sort(unique(d$year)),
time=1200,
ntows=tapply(d$id,d$year,function(x) length(unique(x))))
pdat$pabs<-1-dbinom(0,pdat$ntows,.02)#probability that 0 is real
length(c(1,s$p.table[,4]))
pdat$pprs<-s$p.table[,4]
pdat$pprs[1]<-1
p<-predict(mod,newdata=pdat,type='response',se.fit=TRUE)
pdat$p<-p$fit
return(pdat)
} else NULL
}
ot<-ddply(rvw,.(cell),.fun=f,.progress='text')
a<-subset(ot,p==0)
hist(ot$p,breaks=100,col='black')
plot(log(ot$p+.01),(ot$pb))
plot(log(ot$p+.01),log(ot$pb))
f<-function(d){ return(data.frame(n=length(unique(d$id)),
lon=unique(d$lonc.1),
lat=unique(d$latc.1),
totno=sum(d$totno)))}
dt<-ddply(rvw,.(cell.1),.fun=f,.progress='text')
dt$p<-dbinom(1,dt$n,.02)
#dt<-ddply(rvw,.(cell,tbin20),.fun=f,.progress='text')
plot(log10(dt$n),log10(dt$totno+1),pch=16)
cor(log10(dt$n),log10(dt$totno+1))
hist(dt$n,breaks=100,col='black')
adat<-subset(dt,select=c('p','lon','lat'))
adat<-subset(dt,select=c('n','lon','lat'))
ttl<-'dan'
ct<-30
dg<-2
nm<-names(adat)[1]
names(adat)[1]<-'y'
adat$y<-ifelse(adat$y>ct,ct,adat$y)
adat$y<-ifelse(adat$y< -ct,-ct,adat$y)
adat$y<-round(adat$y,digits=2)
a<-adat
aa<-data.frame(y=seq(0,max(abs(adat$y),na.rm=TRUE),length.out=100))
a<-rbind.fill(a,aa)
n<-21
mxx<-max(abs(adat$y))
brks<-seq(0,mxx+.01,length.out=n)
brks2<-round(seq(0,mxx+.01,length.out=n),digits=dg)
a$ycat<-cut(a$y,breaks=brks)
lbls<-sort(unique(a$ycat))
lbls2<-sort(unique(cut(a$y,breaks=brks2)))
cls<-matlab.like(length(lbls))
#cls<-colorRampPalette(c('magenta4','blue3','green','yellow','red3'))
#cls<-(cls(length(lbls)))
#cls<-colorRampPalette(c('dodgerblue4','white','firebrick4'))
#cls<-cls(length(lbls))
return(
ggplot()+
geom_tile(data=a, aes(x=lon, y=lat,fill=ycat),col='gray80',size=.0001) +
scale_fill_manual(breaks=as.character(lbls),values=cls,labels=lbls2,na.value="transparent",guide=guide_legend(title=paste(ttl)))+
scale_alpha(guide = 'none')+
geom_polygon(aes(long,lat, group=group), fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='right',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.1, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-70,-56,1),labels=as.character(seq(-70,-56,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,48,1),labels=as.character(seq(41,48,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(min(adat$lat)-.3,max(adat$lat)+.3),xlim=c(min(adat$lon)-.3,max(adat$lon)+.3))+
xlab('')+
ylab('')
)
pltfun<-function(adat,ttl,ct,dg){
nm<-names(adat)[1]
names(adat)[1]<-'y'
adat$y<-ifelse(adat$y>ct,ct,adat$y)
adat$y<-ifelse(adat$y< -ct,-ct,adat$y)
adat$y<-round(adat$y,digits=2)
a<-adat
aa<-data.frame(y=seq(-(max(abs(adat$y),na.rm=TRUE)),max(abs(adat$y),na.rm=TRUE),length.out=100))
a<-rbind.fill(a,aa)
n<-21
mxx<-max(abs(adat$y))
brks<-seq(-mxx-.01,mxx+.01,length.out=n)
brks2<-round(seq(-mxx-.01,mxx+.01,length.out=n),digits=dg)
a$ycat<-cut(a$y,breaks=brks)
lbls<-sort(unique(a$ycat))
lbls2<-sort(unique(cut(a$y,breaks=brks2)))
#cls<-matlab.like(length(lbls))
#cls<-colorRampPalette(c('magenta4','blue3','green','yellow','red3'))
#cls<-(cls(length(lbls)))
cls<-colorRampPalette(c('dodgerblue4','white','firebrick4'))
cls<-cls(length(lbls))
return(
ggplot()+
geom_tile(data=a, aes(x=lon, y=lat,fill=ycat),col='gray80',size=.0001) +
scale_fill_manual(breaks=as.character(lbls),values=cls,labels=lbls2,na.value="transparent",guide=guide_legend(title=paste(ttl)))+
scale_alpha(guide = 'none')+
geom_polygon(aes(long,lat, group=group), fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='right',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.1, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-70,-56,1),labels=as.character(seq(-70,-56,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,48,1),labels=as.character(seq(41,48,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(min(adat$lat)-.3,max(adat$lat)+.3),xlim=c(min(adat$lon)-.3,max(adat$lon)+.3))+
xlab('')+
ylab('')
)
}
p1<-pltfun(subset(phen2,select=c('delta.mxfall','lon','lat')),'mxfall',2.8,3)
return(ggplot()+
geom_polygon(aes(long,lat,group=group,fill=as.factor(clusters),alpha=cx),data=mydat,col='black',size=.0001) +
coord_equal()+
scale_fill_manual(values=c(dum[1],dum[2],dum[3]))+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='bottomright',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(42,47,1),labels=as.character(seq(42,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(42,47),xlim=c(-68,-57))+
labs(title = lbl,
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab(''))
}
setwd(figsdir)
pdf('herring_rvsurvey_cluster_all_k3.pdf',height=10,width=8)
mp1<-clfun(qsm,3,"Total observed herring (weight)")
summary(rvw$time)
sort(unique(rvw$time))
plot(rvw$time,log10(rvw$no+.1))
mod<-gam(totwgt ~ s(time,bs='cc'),data=rvw,family='nb')
pdat<-data.frame(time=seq(min(rvw$time),max(rvw$time),length.out=100))
p<-predict(mod,newdata=pdat,type='response')
pdat$p<-p
plot(pdat$time,pdat$p)
f<-function(d){ return(data.frame(nyear=length(unique(d$year)),
myear=min(d$year)))
}
dm<-ddply(rvw,.(strat),.fun=f)
dmm<-dm[order(dm$nyear),]
dmm<-subset(dmm,nyear>=20 & myear<1990)
f<-function(d){
names(d)[1]<-'y'
mod<-gam(y~as.factor(year),data=d,gamma=.5,gamily=negbin)
pdat<-data.frame(year=sort(unique(d$year)),
ntows=tapply(d$id,d$year,function(x) length(unique(x))))
pdat$p<-predict(mod,newdata=pdat,type='response')
return(pdat)
}
#TOTAL NUMBERS
qq<-ddply(subset(rvw,strat %in% dmm$strat,select=c('totwgt','strat','year','id')),.(strat),.fun=f)
par(mfrow=c(2,2))
plot(qq$ntows,qq$p,col=alpha('darkred',.4),pch=16,cex=2)
plot(log10(qq$ntows),log10(qq$p+1),col=alpha('darkred',.4),pch=16,cex=2)
cor(log10(qq$ntows),log10(qq$p+1))#.019
f<-function(d){
names(d)[1]<-'y'
mod<-gam(y~as.factor(year),data=d,gamma=.5,gamily=binomial)
pdat<-data.frame(year=sort(unique(d$year)),
ntows=tapply(d$id,d$year,function(x) length(unique(x))))
pdat$p<-predict(mod,newdata=pdat,type='response')
return(pdat)
}
#TOTAL NUMBERS
qq<-ddply(subset(rvw,strat %in% dmm$strat,select=c('pres','strat','year','id')),.(strat),.fun=f)
par(mfrow=c(2,2))
plot(qq$ntows,qq$p,col=alpha('darkred',.4),pch=16,cex=2)
plot(log10(qq$ntows),log10(qq$p+.1),col=alpha('darkred',.4),pch=16,cex=2)
cor(log10(qq$ntows),log10(qq$p+.1))#-.1
plot(qq$year,log10(qq$p+.1),col=alpha('darkred',.4),pch=16,cex=2)
xyplot(log10(p+1)~year|strat,data=qq,pch=15,col=alpha('darkred',.5))
#ESTIMATES SMOOTH TREND IN DIFFERENT QUANTITIES OVER TIME FOR CLUSTER
f<-function(d){
names(d)[1]<-'y'
#mod<-gam(y~as.factor(year),data=d,gamma=.5,gamily=Gamma('log'))
mod<-gam(y~as.factor(year),data=d,gamma=.5,family=nb)
pdat<-data.frame(year=sort(unique(d$year)),
ntows=tapply(d$id,d$year,function(x) length(unique(x))),
n=tapply(d$y,d$year,sum),
sz=tapply(d$sz,d$year,function(x) mean(x,na.rm=TRUE)))
pdat$p<-predict(mod,newdata=pdat,type='response')
#pdat$p<-(pdat$p-mean(pdat$p))/sd(pdat$p)
return(pdat)
}
#TOTAL NUMBERS
qq<-ddply(subset(rvw,strat %in% dmm$strat,select=c('totno','strat','year','id','sz')),.(strat),.fun=f)
plot(log10(qq$sz+.1),log10(qq$p+.01),pch=15)
cor(log10(qq$sz+.1),log10(qq$p+.01),use='pairwise.complete.obs')
plot(log10(qq$ntows),log10(qq$n+1),col=alpha('darkred',.3),pch=16,cex=2)
plot(log10(qq$ntows),log10(qq$p+.01),col=alpha('darkred',.3),pch=16,cex=2)
cor(log10(qq$ntows),log10(qq$p+.01))#.10
plot((qq$ntows),log10(qq$n+.01),col=alpha('darkred',.4),pch=16,cex=2)
plot(log10(qq$ntows),log10(qq$n+.01),col=alpha('darkred',.4),pch=16,cex=2)
cor(log10(qq$ntows),log10(qq$n+.01))#.40
f<-function(d){
return(data.frame(r=cor(log10(d$ntows),log10(d$p+.01),use='pairwise.complete.obs'),
r2=cor(log10(d$ntows),log10(d$n+.01),use='pairwise.complete.obs')))
}
dt<-ddply(qq,.(strat),.fun=f)
dt$dir<-ifelse(dt$r>0,1,-1)
dt$dir2<-ifelse(dt$r2>0,1,-1)
cor(log10(qq$ntows),log10(qq$p+1))
xyplot(log10(p+.1)~log10(ntows) |strat,data=qq,col=alpha('dodgerblue3',.7),pch=15, type=c('p','r'))
xyplot(log10(p+.01)~log10(ntows) |strat,data=qq,col=alpha('dodgerblue3',.7),pch=15, type=c('p','r'))
xyplot(log10(p+1)~log10(ntows) |strat,data=qq,col=alpha('dodgerblue3',.7),pch=15, type=c('p','r'))
#ESTIMATES SMOOTH TREND IN DIFFERENT QUANTITIES OVER TIME FOR CLUSTER
fsm<-function(d){
names(d)[1]<-'y'
mod<-gam(y~s(year),data=d,gamma=.5,gamily=Gamma('log'))
pdat<-data.frame(year=seq(min(d$year),max(d$year),.25))
pdat$p<-predict(mod,newdata=pdat,type='response')
pdat$p<-(pdat$p-mean(pdat$p))/sd(pdat$p)
names(pdat)[2]<-unique(as.character(d$strat))
return(pdat)
}
#TOTAL WEIGHT
qq<-dlply(subset(rvw,strat %in% dmm$strat,select=c('totwgt','strat','year')),.(strat),.fun=fsm)
qsm<-Reduce(function(x, y) merge(x, y, by=c('year'),all=TRUE), qq)#COMBINE
qsm<-qsm[,colSums(is.na(qsm)) != nrow(qsm)]#REMOVES COLUMNS THAT ARE ALL MISSING
#TOTAL NUMBERS
qq<-dlply(subset(rvw,strat %in% dmm$strat,select=c('totno','strat','year')),.(strat),.fun=fsm)
qsm2<-Reduce(function(x, y) merge(x, y, by=c('year'),all=TRUE), qq)#COMBINE
qsm2<-qsm2[,colSums(is.na(qsm2)) != nrow(qsm2)]#REMOVES COLUMNS THAT ARE ALL MISSING
#AVERAGE SIZE
qq<-dlply(subset(rvw,strat %in% dmm$strat,select=c('sz','strat','year')),.(strat),.fun=fsm)
qsm3<-Reduce(function(x, y) merge(x, y, by=c('year'),all=TRUE), qq)#COMBINE
qsm3<-qsm3[,colSums(is.na(qsm3)) != nrow(qsm3)]#REMOVES COLUMNS THAT ARE ALL MISSING
q<-qsm3
q2<- q %>% gather(strata, value, -year)
xyplot(value ~ year | strata,data=q2, pch=15, type=c('spline'),col='black')
xyplot(value ~ year | strata,data=q2, pch=15, type=c('p','spline'),col='black')
clfun<-function(df,k,lbl){
rownames(df)<-df$year
df<-df[,-1]
#k<-3#ER OF CLUSTERS
dmat<-1-cor(df,use='pairwise.complete.obs')
dst<-as.dist(dmat)
ff<-fanny(dst,k,maxit=5000,diss=T)
par(mfrow=c(2,1),mar=c(4,4,1,1))
dum<-c('red3','forestgreen','darkblue','cornflowerblue','darkblue')
plot(silhouette(ff),col=dum[1:k],main='')#silhouette plot
dc.pcoa<-cmdscale(dst)
dc.scores<-scores(dc.pcoa,choices=c(1,2))
spefuz.g<-ff$clustering
a<-data.frame(strat=as.character(sort(unique(names(df)))),
clusters=ff$clustering)
aa<-data.frame(ff$membership)
aa$strat<-rownames(a)
#par(mar=c(1,1,1,8),oma=c(1,1,1,1))
plot(scores(dc.pcoa),asp=1,type='n',xlim=c(-1.5,1),ylim=c(-1,1.2),las=1,axes=TRUE,xlab='',ylab='')
stars(ff$membership,location=scores(dc.pcoa),draw.segments=T,add=T,scale=F,len=.1,col.segments=alpha(c(dum[1:k]),.25),byt='n',labels=NULL,xlim=c(-1.1,1.4),ylim=c(-1,.5),lwd=.0001,xpd=TRUE,border=NULL,radius=FALSE,col.radius=alpha('white',.1))
for(i in 1:k){ cl<-dum[i]
gg<-dc.scores[spefuz.g==i,]
hpts<-chull(gg)
hpts<-c(hpts,hpts[1])
lines(gg[hpts,],col=cl,lwd=3,xlim=c(-1.1,1.4),ylim=c(-1,.5))
}
cx <- data.frame(strat=aa$strat,
cx=apply(aa[, 1:k], 1, max))
cx$cxx <- rescale(cx$cx,newrange=c(.05,.5))
#PLOTS THE TIMESERIES OF R FOR EACH CLUSTER
par(mfrow=c(3,1),mar=c(2,12,1,12),oma=c(1,1,1,1))
mb<-seq(1,k,1)
l<-list()
for(i in 1:length(mb)){
print(mb[i])
one<-subset(a,clusters==mb[i],select=c('strat'))# IN THIS CLUSTER
cx2<-subset(cx,cx>=0.5)#GETS INSTANCES WHERE CLUSTER PROBABILITY>0.5
data5<-subset(df,select=c(as.character(one$strat)))#TS FOR CLUSTER
cx2<-subset(cx2,strat %in% names(data5))
data5<-subset(df,select=c(as.character(cx2$strat)))#TS FOR CLUSTER
x<-rownames(data5)
t<-data.frame(year=as.numeric(x),mn=rowMeans(data5,na.rm=F))
t2<-data.frame(year=as.numeric(x),mn=rowMeans(data5,na.rm=T))
cl<-dum[i]
plot(0,0,pch=16,cex=.01,xlim=c(1970,2015),ylim=c(-2,3),main='',xaxt='n',las=1,axes=FALSE,xlab='',ylab='')
axis(side=2,at=seq(-2,3,1),las=1,lwd=.001,cex.axis=.75)
axis(side=1,at=seq(1970,2020,10),cex.axis=.75)
for(j in 1:length(data5[1,])){
try(dat<-data5[,j])
try(trnsp<-subset(cx,strat==as.character(names(data5[j])))$cxx)
try(lines(x,dat,cex=.5,ylim=c(0,1),lwd=2,col=alpha(cl,rescale(trnsp,newrange=c(.1,.5)))))
}
lines(as.numeric(as.character(t$year)),t$mn,col='gold3',cex=.7,lwd=3)
dm<-subset(t2,mn==max(t2$mn,na.rm=TRUE),select=c('year'))
dm$cluster=mb[i]
names(dm)<-c('clusterday','clusters')
l[[i]]<-dm
}
a2<-a
if(k==3){a2$cx<- apply(aa[,c('X1','X2','X3')], 1, function(x) max(x) )
} else {a2$cx<- apply(aa[,c('X1','X2')], 1, function(x) max(x) )
}
a2$id<-a2$strat
am<-fortify(plg,region='stratum')
am<-subset(am,id%in% a2$id)
mydat<-merge(am,a2,by=c('id'))
mydat$cx<-rescale(mydat$cx,newrange=c(.4,1))
par(mfrow=c(1,1),mar=c(3,3,3,3))
return(ggplot()+
geom_polygon(aes(long,lat,group=group,fill=as.factor(clusters),alpha=cx),data=mydat,col='black',size=.0001) +
coord_equal()+
scale_fill_manual(values=c(dum[1],dum[2],dum[3]))+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='bottomright',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(42,47,1),labels=as.character(seq(42,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(42,47),xlim=c(-68,-57))+
labs(title = lbl,
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab(''))
}
setwd(figsdir)
pdf('herring_rvsurvey_cluster_all_k3.pdf',height=10,width=8)
mp1<-clfun(qsm,3,"Total observed herring (weight)")
mp2<-clfun(qsm2,3,"Total observed herring (numb)")
mp3<-clfun(qsm3,3,"Average weight")
dev.off()
pdf('herring_rvsurvey_cluster_map_k3.pdf',height=14,width=8)
grid.arrange(mp1,mp2,mp3,ncol=1)
dev.off()
setwd(figsdir)
pdf('herring_rvsurvey_cluster_all_k2.pdf',height=10,width=8)
mp1<-clfun(qsm,2,"Total observed herring (weight)")
mp2<-clfun(qsm2,2,"Total observed herring (numb)")
mp3<-clfun(qsm3,2,"Average weight")
dev.off()
pdf('herring_rvsurvey_cluster_map_k2.pdf',height=14,width=8)
grid.arrange(mp1,mp2,mp3,ncol=1)
dev.off()
###################################################
#TOTAL NUMBER OF HERRING RECORDED FROM ALL TRAWLS ALL YEARS
f<-function(d){
return(data.frame(wt=mean(d$totwgt),
no=mean(d$totno),
dep=mean(d$depth,na.rm=TRUE),
sz=mean(d$sz)))
}
d1<-ddply(rvw,.(strat),.fun=f)
d1<-d1[order(d1$sz,decreasing=TRUE),]
cor(subset(d1,select=c('wt','no','dep','sz')),use='pairwise.complete.obs')
plot(subset(d1,select=c('wt','no','dep','sz')),pch=15)
a2<-d1
a2$id<-a2$strat
am<-fortify(plg,region='stratum')
am<-subset(am,id%in% a2$id)
mydat<-merge(am,a2,by=c('id'))
pwt<-ggplot()+
geom_polygon(aes(long,lat,group=group,fill=wt),data=mydat,col='black',size=.0001) +
coord_equal()+
scale_fill_distiller(palette='Spectral')+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='bottomright',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
labs(title = "Total observed herring (weight)",
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab('')
pno<-ggplot()+
geom_polygon(aes(long,lat,group=group,fill=no),data=mydat,col='black',size=.0001) +
coord_equal()+
scale_fill_distiller(palette='Spectral')+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='bottomright',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
labs(title = "Total observed herring (numbers)",
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab('')
psz<-ggplot()+
geom_polygon(aes(long,lat,group=group,fill=sz),data=mydat,col='black',size=.0001) +
coord_equal()+
scale_fill_distiller(palette='Spectral')+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='bottomright',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
labs(title = "Average weight of all observed herring",
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab('')
setwd(figsdir)
pdf('herring_rvsurvey_allobs_map2.pdf',height=14,width=8)
grid.arrange(pwt,pno,psz,ncol=1)
dev.off()
rvw$dayc<-cut(rvw$day,breaks=seq(170,230,10),labels=seq(175,225,10))
f<-function(d){
if(length(unique(d$dayc))>1){
mod<-gam(totwgt~as.factor(dayc),data=d,gamma=1)
pdat<-data.frame(dayc=sort(unique(d$dayc)))
pdat$p<-predict(mod,newdata=pdat)
return(pdat)
} else NULL
}
phdat<-ddply(rvw,.(strat),.fun=f)
a2<-phdat
a2$id<-a2$strat
am<-fortify(plg,region='stratum')
am<-subset(am,id%in% a2$id)
mydat<-merge(am,a2,by=c('id'))
mydat$p<-(mydat$p-mean(mydat$p))/sd(mydat$p)
dys<-sort(unique(phdat$dayc))
l<-list()
for(i in 1:length(dys)){
d<-subset(mydat,dayc==dys[i])
d$p<-log10(d$p+1)
print(dim(d))
l[[i]]<-ggplot()+
geom_polygon(aes(long,lat,group=group,fill=p),data=d,col='black',size=.0001) +
coord_equal()+
scale_fill_distiller(palette='Spectral')+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.8,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
labs(title = paste("Total observed herring (day=",dys[i]),
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab('')
}
setwd(figsdir)
pdf('herring_rvsurvey_seasonal_daysbin.pdf',height=14,width=10)
grid.arrange(l[[1]],l[[2]],l[[3]],l[[4]],l[[5]],l[[6]],ncol=2)
dev.off()
a<-subset(mydat,dayc==205)
hist(a$p,breaks=50)
hist(log(a$p),breaks=50)
d<-subset(rvw,strat==462)
a2<-a2[order(a2$wt,decreasing=TRUE),]
f<-function(d){
if(length(unique(d$month))>1){
mod<-gam(totwgt~as.factor(month),data=d,gamma=1)
pdat<-data.frame(month=sort(unique(d$month)))
pdat$p<-predict(mod,newdata=pdat)
return(pdat)
} else NULL
}
phdat<-ddply(rvw,.(strat),.fun=f)
plot(rvw$day,rvw$totno)
a2<-phdat
a2$id<-a2$strat
am<-fortify(plg,region='stratum')
am<-subset(am,id%in% a2$id)
mydat<-merge(am,a2,by=c('id'))
mydat1<-subset(mydat,month==6)
p1<-ggplot()+
geom_polygon(aes(long,lat,group=group,fill=p),data=mydat1,col='black',size=.0001) +
coord_equal()+
scale_fill_distiller(palette='Spectral')+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='bottomright',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
labs(title = "Total observed herring (June)",
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab('')
mydat2<-subset(mydat,month==7)
p2<-ggplot()+
geom_polygon(aes(long,lat,group=group,fill=p),data=mydat2,col='black',size=.0001) +
coord_equal()+
scale_fill_distiller(palette='Spectral')+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='bottomright',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
labs(title = "Total observed herring (July)",
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab('')
mydat3<-subset(mydat,month==8)
p3<-ggplot()+
geom_polygon(aes(long,lat,group=group,fill=p),data=mydat3,col='black',size=.0001) +
coord_equal()+
scale_fill_distiller(palette='Spectral')+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position='bottomright',plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.15, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
labs(title = "Total observed herring (August)",
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab('')
setwd(figsdir)
pdf('herring_rvsurvey_bymonth_map.pdf',height=14,width=8)
grid.arrange(p1,p2,p3,ncol=1)
dev.off()
rvw$yearc<-cut(rvw$year,breaks=seq(1970,2020,5),labels=seq(1972.5,2017.5,5),include.lowest=TRUE)
f<-function(d){
return(data.frame(no=median(d$totno),
wt=median(d$totwgt),
sz=median(d$sz)))
}
d1<-ddply(rvw,.(strat,yearc),.fun=f)
a2<-d1
a2$id<-a2$strat
am<-fortify(plg,region='stratum')
am<-subset(am,id%in% a2$id)
mydat<-merge(am,a2,by=c('id'))
#mydat$p<-(mydat$p-mean(mydat$p))/sd(mydat$p)
yrs<-sort(unique(mydat$yearc))
l<-list()
for(i in 1:length(yrs)){
d<-subset(mydat,yearc==yrs[i])
d$wt<-log10(d$wt)
print(dim(d))
l[[i]]<-ggplot()+
geom_polygon(aes(long,lat,group=group,fill=wt),data=d,col='black',size=.0001) +
coord_equal()+
scale_fill_distiller(palette='Spectral')+
geom_polygon(aes(long,lat,group=group),fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.8,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.11, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
labs(title = paste("Total observed herring (year=",yrs[i]),
subtitle = "",
caption = 'Data source: Summer RV survey') +
xlab('')+
ylab('')
}
setwd(figsdir)
pdf('herring_rvsurvey_wt_byyear.pdf',height=16,width=16)
grid.arrange(l[[1]],l[[2]],l[[3]],l[[4]],l[[5]],l[[6]],l[[7]],l[[8]],l[[9]],l[[10]],ncol=3)
dev.off()
f<-function(d){
if(length(unique(d$year))>=20){
print(unique(d$strat))
print(length(unique(d$year)))
mod<-gam(no~year,data=d,gamma=1)
s<-summary(mod)
no.pv<-s$p.table[2,4]
no.b<-(10^s$p.table[2,1])-1
mod<-gam(wgt~year,data=d,gamma=1)
s<-summary(mod)
wt.pv<-s$p.table[2,4]
wt.b<-(10^s$p.table[2,1])-1
d$depth<-(d$dmin+d$dmax)/2
d$totno<-ifelse(d$totno==0,1,d$totno)
d$M<-d$totwgt/d$totno
d$temperature<-(d$surface_temperature+d$bottom_temperature)/2
d<-subset(d,is.na(temperature)==FALSE)
d$kelvins<-d$temperature+273.15
k<-0.00008617#BOLTZMANN CONSTANT
d$metai<-(d$M^.25)*(exp(-1*(1/(k*d$kelvins))))
d$metai<-d$metai*10^18
mod<-gam(metai~as.factor(year),data=d,gamma=1)
s<-summary(mod)
met.pv<-s$p.table[2,4]
met.b<-(10^s$p.table[2,1])-1
return(data.frame(lon=mean(d$lon),
lat=mean(d$lat),
no.b=no.b,
no.pv=no.pv,
wt.b=wt.b,
wt.pv=wt.pv,
met.b=met.b,
met.pv=met.pv))
} else NULL
}
sdat1<-ddply(rvw,.(strat),.fun=f)
map('world',xlim=c(-70,-60),ylim=c(42,46),fill=TRUE,col='gray')
map.axes()
points(sdat1$lon,sdat1$lat,pch=16,cex=rescale(abs(sdat1$no.b),newrange=c(3,14)),col=ifelse(sdat1$no.b>0,'firebrick3','dodgerblue3'))
map('world',xlim=c(-70,-60),ylim=c(42,46),fill=TRUE,col='gray')
map.axes()
points(sdat1$lon,sdat1$lat,pch=16,cex=rescale(abs(sdat1$wt.b),newrange=c(3,14)),col=ifelse(sdat1$wt.b>0,'firebrick3','dodgerblue3'))
map('world',xlim=c(-70,-60),ylim=c(42,46),fill=TRUE,col='gray')
map.axes()
points(sdat1$lon,sdat1$lat,pch=16,cex=rescale(abs(sdat1$met.b),newrange=c(3,14)),col=ifelse(sdat1$met.b>0,'firebrick3','dodgerblue3'))
cor(sdat1$no.b,sdat1$wt.b)
cor(sdat1$no.b,sdat1$met.b)
plot(sdat1$met.b,sdat1$no.b,pch=15)
setwd(datadir)
rvl<-read.csv("herring_lengths_RV_survey_spera_spawnar.csv",header=TRUE)
rvl$flen<-ifelse(rvl$mission=='NED2016016',rvl$flen/10,rvl$flen)
rvl$flen<-ifelse(rvl$mission!='NED2016016',(rvl$flen*1.0866)+0.95632,rvl$flen)
#plot(log10(rvl$flen),log10(rvl$fwt))
rvl<-subset(rvl,log10(rvl$flen)>=.5 & month %in% c(6,7,8))#REMOVE OUTLIERS
rvl2<-rvl
rvl2$lcat<-cut(rvl$flen,breaks=seq(5,45,10),labels=seq(10,40,10))
rvl2$id<-gsub(' ','',paste(rvl2$mission,'_',rvl2$setno))
f<-function(d){
return(data.frame(year=unique(d$year),
lon=unique(d$lon),
lat=unique(d$lat),
strat=unique(d$strat),
no=sum(d$clen,na.rm=TRUE),
wt=sum(d$fwt/1000,na.rm=TRUE)))
}
rvll<-ddply(rvl2,.(id,lcat),.fun=f,.progress='text')
d<-subset(rvll,strat==sort(unique(rvll$strat))[1] & lcat==27.5)
f<-function(d){
if(length(unique(d$year))>=25){
print(unique(d$strat))
print(unique(d$lcat))
mod<-gam(no~year,data=d,gamma=1.4)
s<-summary(mod)
no.pv<-s$p.table[2,4]
no.b<-s$p.table[2,1]
return(data.frame(lon=mean(d$lon),
lat=mean(d$lat),
no.b=no.b,
no.pv=no.pv))
} else NULL
}
sdat2<-ddply(rvll,.(strat,lcat),.fun=f,.progress='text')
a<-subset(sdat2,lcat==20)
a<-subset(sdat2,lcat==30)
a<-subset(sdat2,lcat==40)
map('world',xlim=c(-70,-60),ylim=c(42,46),fill=TRUE,col='gray')
map.axes()
points(a$lon,a$lat,pch=16,cex=rescale(abs(a$no.b),newrange=c(3,14)),col=alpha(ifelse(a$no.b>0,'firebrick3','dodgerblue3'),.75))
###################################################################
################# PHENOLOGY
###################################################################
##################################################################
setwd(datadir)
rvw<-read.csv("herring_weights_RV_survey_spera_allar.csv",header=TRUE)
rvw$strat<-as.character(rvw$strat)
#rvw<-subset(rvw,!(strat %in% c('493','494','')))
rvw$bank<-ifelse(rvw$strat %in% c(447,448),'banq','no')
rvw$bank<-ifelse(rvw$strat %in% c(443),'mis',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(458),'mid',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(455,456),'sab',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(464),'west',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(463),'em',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(473),'lh',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(474),'rw',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(475),'bac',rvw$bank)
rvw$bank<-ifelse(rvw$strat %in% c(480),'bn',rvw$bank)
rvw$no<-log10(rvw$totno+1)
rvw$wgt<-log10(rvw$totwgt+1)
rvw$sz<-rvw$totwgt/rvw$totno
rvw$sz<-ifelse(rvw$sz==Inf,rvw$totwgt,rvw$sz)
rvw$id<-gsub(' ','',paste(rvw$mission,'_',rvw$setno))
rvw$pres<-ifelse(rvw$totno>0,1,0)
rvw$tbin20<-ifelse(rvw$year<=1990,1980,2010)
rvw$lonc<-round(rvw$lon,digits=0)
rvw$lonc<-ifelse(rvw$lon<=rvw$lonc,rvw$lonc-.25,rvw$lonc+.25)
rvw$latc<-round(rvw$lat,digits=0)
rvw$latc<-ifelse(rvw$lat>=rvw$latc,rvw$latc+.25,rvw$latc-.25)
rvw$cell<-gsub(' ','',paste(rvw$lonc,'_',rvw$latc))
rvw$cell.1<-gsub(' ','',paste(round(rvw$lon,digits=1),'_',round(rvw$lat,digits=1)))
lonc.1<-seq(min(rvw$lon),max(rvw$lon),.1)
latc.1<-seq(min(rvw$lat),max(rvw$lat),.1)
crds<-expand.grid(lonc.1=lonc.1,latc.1=latc.1)
crds$cell.1<-gsub(' ','',paste(round(crds$lonc.1,digits=1),'_',round(crds$latc.1,digits=1)))
rvw<-merge(rvw,crds,by=c('cell.1'),all.x=TRUE,all.y=FALSE)
#ESTIMATES SMOOTH TREND IN PHENOLOGY BY STRATA - SHORT FORMAT
rvw2<-subset(rvw,!(geardesc %in% c('Newston net','Campelen 1800 survey trawl')))
f<-function(d){
if(dim(subset(d,month %in% c(1,2,3)))[1]>10){wint<-1
} else { wint<-0 }
if(dim(subset(d,month %in% c(6,7,8)))[1]>10){summ<-1
} else { summ<-0 }
if(dim(subset(d,month %in% c(9,10,11)))[1]>10){fall<-1
} else { fall<-0 }
return(data.frame(nmo=length(unique(d$month)),
ndy=length(unique(d$day)),
nyr=length(unique(d$year)),
wint=wint,
summ=summ,
fall=fall,
tot=wint+summ+fall,
lon=mean(d$lon),
lat=mean(d$lat)))
}
dt<-ddply(rvw2,.(strat),.fun=f)
dt<-dt[order(dt$nmo,decreasing=TRUE),]
dmm<-subset(dt,nmo>= 4 & ndy>=30 & nyr>=3 & tot>=3)
f<-function(d){
if(dim(subset(d,month %in% c(1,2,3)))[1]>10){wint<-1
} else { wint<-0 }
if(dim(subset(d,month %in% c(6,7,8)))[1]>10){summ<-1
} else { summ<-0 }
if(dim(subset(d,month %in% c(9,10,11)))[1]>10){fall<-1
} else { fall<-0 }
return(data.frame(nmo=length(unique(d$month)),
ndy=length(unique(d$day)),
nyr=length(unique(d$year)),
wint=wint,
summ=summ,
fall=fall,
tot=wint+summ+fall,
lon=mean(d$lon),
lat=mean(d$lat)))
}
dt<-ddply(rvw2,.(cell),.fun=f)
dt<-dt[order(dt$nmo,decreasing=TRUE),]
dmm<-subset(dt,nmo>= 4 & ndy>=30 & nyr>=3 & tot>=3)
f1<-function(d0){
f<-function(d){
day<-unique(d$day)
d2<-subset(d0,select=c('day'))
d2$day2<-d2$day-(day-1)
d2$day2<-ifelse(d2$day2<=0,d2$day2+(365),d2$day2)
return(data.frame(day=day,
span=diff(range(min(d2$day2),max(d2$day2)))))
}
z<-ddply(d0,.(day),.fun=f)
z<-subset(z,span==max(z$span))
return(subset(z,day==min(z$day)))
}
#dayshift<-ddply(unique(subset(rvw2,strat %in% dmm$strat,select=c('strat','day'))),.(strat),.fun=f1,.progress='text')
dayshift<-ddply(unique(subset(rvw2,cell %in% dmm$cell,select=c('strat','day','cell'))),.(cell),.fun=f1,.progress='text')
##############################################
#ADJUSTS DAY VALUES TO MAXIMIZE PHENOLOGY SPAN
dayshiftfun<-function(d,shf){
d$day2<-d$day-(shf)
d$day2<-ifelse(d$day2<=0,d$day2+(365),d$day2)
return(d)
}
#BACK-CALCULATE ORIGINAL DAY OF THE YEAR
revdayshiftfun<-function(d,shf){
d$day3<-d$day2+shf
d$day3<-ifelse(d$day3>365,d$day3-365,d$day3)
return(d)
}
ff<-function(d){
shf<-subset(dayshift,cell==unique(d$cell))$day
print(shf)
names(d)[1]<-'y'
#SHIFT DAYS TO CENTER ON JULY AND MAKE CONTINUOUS
d<-dayshiftfun(d,shf)
mod<-gam(y~s(year) + s(time,k=4,bs='cc') + s(day2,k=4,bs='cc'),data=d,gamma=1.4,family='nb'(link='log'))
pdat<-data.frame(year=max(d$year),
time=1200,
day2=seq(min(d$day2),max(d$day2),1))
p<-predict(mod,newdata=pdat,type='response')
pdat$p<-p
pdat$pz<-(pdat$p-mean(pdat$p))/sd(pdat$p)
pdat<-revdayshiftfun(pdat,shf)
pdat<-subset(pdat,select=c('day3','p','pz'))
pdat$lon<-median(d$lon)
pdat$lat<-median(d$lat)
pdat$did<-ifelse(pdat$day %in% d$day,1,0)
names(pdat)[1]<-'day'
return(data.frame(pdat))
}
#TOTAL WEIGHT
#out<-ddply(subset(rvw2, strat %in% dmm$strat,select=c('totwgt','strat','year','day','time','lon','lat')),.(strat),.fun=ff,.progress='text')
out<-ddply(subset(rvw2, cell %in% dmm$cell,select=c('totwgt','strat','year','day','time','lon','lat','cell')),.(cell),.fun=ff,.progress='text')
xyplot(pz~day|strat,data=out,col=ifelse(out$did==1,'red','blue'))
xyplot(pcx~day|strat,data=out,col=ifelse(out$did==1,'red','blue'))
###########################################################
#ESTIMATE PHENOLOGY FOR EACH 1/4 GRID CELL AND ANIMATE
rvw2<-subset(rvw,!(geardesc %in% c('Newston net','Campelen 1800 survey trawl')))
f<-function(d){
if(dim(subset(d,month %in% c(1,2,3)))[1]>10){wint<-1
} else { wint<-0 }
if(dim(subset(d,month %in% c(6,7,8)))[1]>10){summ<-1
} else { summ<-0 }
if(dim(subset(d,month %in% c(9,10,11)))[1]>10){fall<-1
} else { fall<-0 }
return(data.frame(nmo=length(unique(d$month)),
ndy=length(unique(d$day)),
nyr=length(unique(d$year)),
wint=wint,
summ=summ,
fall=fall,
tot=wint+summ+fall,
lon=mean(d$lon),
lat=mean(d$lat)))
}
dt<-ddply(rvw2,.(cell),.fun=f)
dt<-dt[order(dt$nmo,decreasing=TRUE),]
dmm<-subset(dt,nmo>= 4 & ndy>=30 & nyr>=3 & tot>=3)
f1<-function(d0){
f<-function(d){
day<-unique(d$day)
d2<-subset(d0,select=c('day'))
d2$day2<-d2$day-(day-1)
d2$day2<-ifelse(d2$day2<=0,d2$day2+(365),d2$day2)
return(data.frame(day=day,
span=diff(range(min(d2$day2),max(d2$day2)))))
}
z<-ddply(d0,.(day),.fun=f)
z<-subset(z,span==max(z$span))
return(subset(z,day==min(z$day)))
}
#dayshift<-ddply(unique(subset(rvw2,strat %in% dmm$strat,select=c('strat','day'))),.(strat),.fun=f1,.progress='text')
dayshift<-ddply(unique(subset(rvw2,cell %in% dmm$cell,select=c('strat','day','cell'))),.(cell),.fun=f1,.progress='text')
##############################################
#ADJUSTS DAY VALUES TO MAXIMIZE PHENOLOGY SPAN
dayshiftfun<-function(d,shf){
d$day2<-d$day-(shf)
d$day2<-ifelse(d$day2<=0,d$day2+(365),d$day2)
return(d)
}
#BACK-CALCULATE ORIGINAL DAY OF THE YEAR
revdayshiftfun<-function(d,shf){
d$day3<-d$day2+shf
d$day3<-ifelse(d$day3>365,d$day3-365,d$day3)
return(d)
}
library(akima)
ff<-function(d){
shf<-subset(dayshift,cell==unique(d$cell))$day
print(shf)
names(d)[1]<-'y'
#SHIFT DAYS TO CENTER ON JULY AND MAKE CONTINUOUS
d<-dayshiftfun(d,shf)
mod<-gam(y~s(year) + s(time,k=4,bs='cc') + s(day2,k=4,bs='cc'),data=d,gamma=1.4,family='nb'(link='log'))
pdat0<-data.frame(year=2000,
time=1200,
day2=seq(min(d$day2),max(d$day2),1))
p<-predict(mod,newdata=pdat0,type='response')
pdat0$p<-p
pdat<-data.frame(day2=seq(1,365,1))
pdat$p<-pasp<-aspline(pdat0$day2,pdat0$p,xout=pdat$day2)$y
pdat$pz<-(pdat$p-mean(pdat$p))/sd(pdat$p)
pdat<-revdayshiftfun(pdat,shf)
pdat<-subset(pdat,select=c('day3','p','pz'))
pdat$lon<-unique(d$lonc)
pdat$lat<-unique(d$latc)
pdat$depth<-mean(d$dmax,na.rm=TRUE)
pdat$did<-ifelse(pdat$day %in% d$day,1,0)
names(pdat)[1]<-'day'
return(data.frame(pdat))
}
#TOTAL WEIGHT
out<-ddply(subset(rvw2, cell %in% dmm$cell,select=c('totwgt','strat','year','day','time','lon','lat','cell','lonc','latc','dmax')),.(cell),.fun=ff,.progress='text')
xyplot(pz~day|cell,data=out,col=ifelse(out$did==1,'red','blue'))
xyplot(log10(p+1)~day|cell,data=out,col=ifelse(out$did==1,'red','blue'))
xyplot(p~day|cell,data=out,col=ifelse(out$did==1,'red','blue'))
bnk<-subset(plg,stratum %in% c(443,458,455,456,464,463,473,474,475,480))
x1<--68
x2<--57
y<-41.5
out$dy<-rescale(out$day,newrange=c(x1,x2))
frames <- length(unique(out$day))
rename <- function(x){
if (x < 10) {
return(name <- paste('000',i,'plot.jpg',sep=''))
}
if (x < 100 && i >= 10) {
return(name <- paste('00',i,'plot.jpg', sep=''))
}
if (x >= 100) {
return(name <- paste('0', i,'plot.jpg', sep=''))
}
}
out$pcx<-rescale(out$pz,newrange=c(.2,7))
setwd('C:/Users/sailfish/Documents/aalldocuments/literature/research/active/SPERA/Figures/prac')
#loop through plots
for(i in 1:frames){
name <- rename(i)
d<-subset(out,day==i)
#saves the plot as a file in the working directory
jpeg(name,width=6,height=5,units='in',quality=100,res=300)
map('worldHires',fill=TRUE,col='gray',border=NA,xlim=c(x1,x2),ylim=c(y,46))
#plot(plg,add=TRUE,lwd=.01,border=alpha('lightgray',.5))
plot(bnk,add=TRUE,lwd=.01,border=NA,col=alpha('firebrick3',.2))
points(d$lon,d$lat,pch=16,cex=d$pcx,col=alpha('dodgerblue3',.3),xlim=c(x1,x2),ylim=c(y,46))
points(d$lon,d$lat,pch=1,lwd=.01,cex=d$pcx,col='dodgerblue3',xlim=c(-x1,x2),ylim=c(y,46))
legend('bottomright',paste('Day=',i),bty='n')
points(unique(d$dy),y,pch=17,col='red3',cex=2)
axis(1,at=seq(x1,x2,length.out=14),labels=c('','JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC',''),cex.axis=.7)
dev.off()
}
#################################################################
# SAME BUT FOR NON STANDARDIZED BIOMSSS
out$dy<-rescale(out$day,newrange=c(x1,x2))
out$pcx<-rescale(log10(out$p+1),newrange=c(.2,7))
setwd('C:/Users/sailfish/Documents/aalldocuments/literature/research/active/SPERA/Figures/prac')
#loop through plots
for(i in 1:frames){
name <- rename(i)
d<-subset(out,day==i)
#saves the plot as a file in the working directory
jpeg(name,width=6,height=5,units='in',quality=100,res=300)
map('worldHires',fill=TRUE,col='gray',border=NA,xlim=c(x1,x2),ylim=c(y,46))
#plot(plg,add=TRUE,lwd=.01,border=alpha('lightgray',.5))
plot(bnk,add=TRUE,lwd=.01,border=NA,col=alpha('firebrick3',.2))
points(d$lon,d$lat,pch=16,cex=d$pcx,col=alpha('dodgerblue3',.3),xlim=c(x1,x2),ylim=c(y,46))
points(d$lon,d$lat,pch=1,lwd=.01,cex=d$pcx,col='dodgerblue3',xlim=c(-x1,x2),ylim=c(y,46))
legend('bottomright',paste('Day=',i),bty='n')
points(unique(d$dy),y,pch=17,col='red3',cex=2)
#axis(1,at=seq(x1,x2,length.out=10),labels=round(seq(0,365,length.out=10),digits=0))
axis(1,at=seq(x1,x2,length.out=14),labels=c('','JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC',''),cex.axis=.7)
dev.off()
}
xyplot(p~day|cell,data=out,pch=16)
xyplot(log10(p+1)~day|cell,data=out,pch=16)
xyplot(pcx~day|cell,data=out,pch=16)
#FROM COMMAND LINE WITH IMAGEMAGIK INSTALLED RUN: CONVERT -DELAY 2 -QUALITY 100 *.JPG MOVIE.MP4
#run ImageMagick
#library(magick)
#my_command <- 'convert *.png -delay 2 -loop 0 animation.gif'
#system(my_command)
f<-function(d){
return(data.frame(lon=unique(d$lon),
lat=unique(d$lat),
depth=unique(d$depth),
day=subset(d,pz==max(d$pz))$day[1],
amp=max(d$pz)[1]-min(d$pz)[1]))
}
tm<-ddply(out,.(cell),.fun=f)
plot(subset(tm,select=c('lon','lat','depth','day','amp')),pch=15)
plot(tm$lon,tm$lat,pch=16,cex=rescale(tm$day,newrange=c(.2,6)),col='purple')
plot(tm$lon,tm$lat,pch=16,cex=rescale(tm$amp,newrange=c(.2,6)),col='purple')
###########################################################
#ESTIMATE PHENOLOGY OF HERRING SIZE AND ANIMATE
b<-subset(rvw2,(totwgt==0 & totno>0) | (totwgt>0 & totno==0))
rvw3<-subset(rvw2,!(id %in% b$id))
f<-function(d){
if(dim(subset(d,month %in% c(1,2,3)))[1]>10){wint<-1
} else { wint<-0 }
if(dim(subset(d,month %in% c(6,7,8)))[1]>10){summ<-1
} else { summ<-0 }
if(dim(subset(d,month %in% c(9,10,11)))[1]>10){fall<-1
} else { fall<-0 }
return(data.frame(nmo=length(unique(d$month)),
ndy=length(unique(d$day)),
nyr=length(unique(d$year)),
wint=wint,
summ=summ,
fall=fall,
tot=wint+summ+fall,
lon=mean(d$lon),
lat=mean(d$lat)))
}
dt<-ddply(rvw3,.(cell),.fun=f)
dt<-dt[order(dt$nmo,decreasing=TRUE),]
dmm<-subset(dt,nmo>= 4 & ndy>=30 & nyr>=3 & tot>=3)
f1<-function(d0){
f<-function(d){
day<-unique(d$day)
d2<-subset(d0,select=c('day'))
d2$day2<-d2$day-(day-1)
d2$day2<-ifelse(d2$day2<=0,d2$day2+(365),d2$day2)
return(data.frame(day=day,
span=diff(range(min(d2$day2),max(d2$day2)))))
}
z<-ddply(d0,.(day),.fun=f)
z<-subset(z,span==max(z$span))
return(subset(z,day==min(z$day)))
}
dayshift<-ddply(unique(subset(rvw3,cell %in% dmm$cell,select=c('strat','day','cell'))),.(cell),.fun=f1,.progress='text')
##############################################
#ADJUSTS DAY VALUES TO MAXIMIZE PHENOLOGY SPAN
dayshiftfun<-function(d,shf){
d$day2<-d$day-(shf)
d$day2<-ifelse(d$day2<=0,d$day2+(365),d$day2)
return(d)
}
#BACK-CALCULATE ORIGINAL DAY OF THE YEAR
revdayshiftfun<-function(d,shf){
d$day3<-d$day2+shf
d$day3<-ifelse(d$day3>365,d$day3-365,d$day3)
return(d)
}
library(akima)
ff<-function(d){
shf<-subset(dayshift,cell==unique(d$cell))$day
print(unique(d$cell))
print(length(unique(d$day)))
#SHIFT DAYS TO CENTER ON JULY AND MAKE CONTINUOUS
d<-dayshiftfun(d,shf)
modw<-gam(totwgt~s(year) + s(day2,k=4,bs='cc') + s(time,bs='cc',k=4),data=d,gamma=1.4,family='nb'(link='log'))
pdat0<-data.frame(year=sort(unique(d$year),decreasing=TRUE)[1],
time=1200,
day2=seq(min(d$day2),max(d$day2),1))
p<-predict(modw,newdata=pdat0,type='response')
pdat0$p<-p
pdat<-data.frame(day2=seq(1,365,1))
pdat$p<-pasp<-aspline(pdat0$day2,pdat0$p,xout=pdat$day2)$y
pdat<-revdayshiftfun(pdat,shf)
pdatw<-subset(pdat,select=c('day3','p'))
names(pdatw)<-c('day','pwt')
modn<-gam(totno~as.factor(year)+s(day2,k=4,bs='cc') + s(time,bs='cc',k=4),data=d,gamma=1.4,family='nb'(link='log'))
pdat0<-data.frame(year=sort(unique(d$year),decreasing=TRUE)[1],
time=1200,
day2=seq(min(d$day2),max(d$day2),1))
p<-predict(modn,newdata=pdat0,type='response')
pdat0$p<-p
pdat<-data.frame(day2=seq(1,365,1))
pdat$p<-pasp<-aspline(pdat0$day2,pdat0$p,xout=pdat$day2)$y
pdat<-revdayshiftfun(pdat,shf)
pdatn<-subset(pdat,select=c('day3','p'))
names(pdatn)<-c('day','pno')
pdat<-merge(pdatw,pdatn,by=c('day'),all=FALSE)
pdat$sz<-pdat$pwt/pdat$pno
pdat$szz<-(pdat$sz-mean(pdat$sz))/sd(pdat$sz)
pdat$lon<-unique(d$lonc)
pdat$lat<-unique(d$latc)
pdat$did<-ifelse(pdat$day %in% d$day,1,0)
return(data.frame(pdat))
}
#LENGTH
out<-ddply(subset(rvw3, cell %in% dmm$cell),.(cell),.fun=ff,.progress='text')
xyplot(log10(sz+1)~day|cell,data=out,col=ifelse(out$did==1,'red','blue'))
xyplot(sz~day|cell,data=out,col=ifelse(out$did==1,'red','blue'))
xyplot(szz~day|cell,data=out,col=ifelse(out$did==1,'red','blue'))
bnk<-subset(plg,stratum %in% c(443,458,455,456,464,463,473,474,475,480))
x1<--68
x2<--57
y<-41.5
out$dy<-rescale(out$day,newrange=c(x1,x2))
frames <- length(unique(out$day))
rename <- function(x){
if (x < 10) {
return(name <- paste('000',i,'plot.jpg',sep=''))
}
if (x < 100 && i >= 10) {
return(name <- paste('00',i,'plot.jpg', sep=''))
}
if (x >= 100) {
return(name <- paste('0', i,'plot.jpg', sep=''))
}
}
out$pcx<-rescale(log10(out$sz+1),newrange=c(.2,7))
setwd('C:/Users/sailfish/Documents/aalldocuments/literature/research/active/SPERA/Figures/prac')
#loop through plots
for(i in 1:frames){
name <- rename(i)
d<-subset(out,day==i)
#saves the plot as a file in the working directory
jpeg(name,width=6,height=5,units='in',quality=100,res=300)
map('worldHires',fill=TRUE,col='gray',border=NA,xlim=c(x1,x2),ylim=c(y,46))
#plot(plg,add=TRUE,lwd=.01,border=alpha('lightgray',.5))
plot(bnk,add=TRUE,lwd=.01,border=NA,col=alpha('firebrick3',.2))
points(d$lon,d$lat,pch=16,cex=d$pcx,col=alpha('dodgerblue3',.3),xlim=c(x1,x2),ylim=c(y,46))
points(d$lon,d$lat,pch=1,lwd=.01,cex=d$pcx,col='dodgerblue3',xlim=c(-x1,x2),ylim=c(y,46))
legend('bottomright',paste('Day=',i),bty='n')
points(unique(d$dy),y,pch=17,col='red3',cex=2)
axis(1,at=seq(x1,x2,length.out=14),labels=c('','JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC',''),cex.axis=.7)
dev.off()
}
out$pcx<-rescale(out$szz,newrange=c(.2,7))
setwd('C:/Users/sailfish/Documents/aalldocuments/literature/research/active/SPERA/Figures/prac')
#loop through plots
for(i in 1:frames){
name <- rename(i)
d<-subset(out,day==i)
#saves the plot as a file in the working directory
jpeg(name,width=6,height=5,units='in',quality=100,res=300)
map('worldHires',fill=TRUE,col='gray',border=NA,xlim=c(x1,x2),ylim=c(y,46))
#plot(plg,add=TRUE,lwd=.01,border=alpha('lightgray',.5))
plot(bnk,add=TRUE,lwd=.01,border=NA,col=alpha('firebrick3',.2))
points(d$lon,d$lat,pch=16,cex=d$pcx,col=alpha('dodgerblue3',.3),xlim=c(x1,x2),ylim=c(y,46))
points(d$lon,d$lat,pch=1,lwd=.01,cex=d$pcx,col='dodgerblue3',xlim=c(-x1,x2),ylim=c(y,46))
legend('bottomright',paste('Day=',i),bty='n')
points(unique(d$dy),y,pch=17,col='red3',cex=2)
axis(1,at=seq(x1,x2,length.out=14),labels=c('','JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC',''),cex.axis=.7)
dev.off()
}
a<-subset(rvw2,geardesc=='Campelen 1800 survey trawl')#2002 and 2005 during october
plot(a$lon,a$lat,pch=16,col='red')
setwd(figsdir)
pdf('campelen.survey.scotianshelf.pdf')
map('world',col='gray',fill=TRUE,xlim=c(-67,-58),ylim=c(43,45))
map.axes()
points(a$lon,a$lat,pch=16,col=alpha('red',.5))
dev.off()
#####################################################################
#####################################################################
#SMOOTH TREND IN PHENOLOGY BY 1/4 CELL FOR CLUSTER (LONG FORM)
rvw2<-subset(rvw,!(geardesc %in% c('Newston net','Campelen 1800 survey trawl')))
f<-function(d){
if(dim(subset(d,month %in% c(1,2,3)))[1]>10){wint<-1
} else { wint<-0 }
if(dim(subset(d,month %in% c(6,7,8)))[1]>10){summ<-1
} else { summ<-0 }
if(dim(subset(d,month %in% c(9,10,11)))[1]>10){fall<-1
} else { fall<-0 }
return(data.frame(nmo=length(unique(d$month)),
ndy=length(unique(d$day)),
nyr=length(unique(d$year)),
wint=wint,
summ=summ,
fall=fall,
tot=wint+summ+fall,
lon=mean(d$lon),
lat=mean(d$lat)))
}
dt<-ddply(rvw2,.(cell),.fun=f)
dt<-dt[order(dt$nmo,decreasing=TRUE),]
dmm<-subset(dt,nmo>= 4 & ndy>=30 & nyr>=3 & tot>=3)
f1<-function(d0){
f<-function(d){
day<-unique(d$day)
d2<-subset(d0,select=c('day'))
d2$day2<-d2$day-(day-1)
d2$day2<-ifelse(d2$day2<=0,d2$day2+(365),d2$day2)
return(data.frame(day=day,
span=diff(range(min(d2$day2),max(d2$day2)))))
}
z<-ddply(d0,.(day),.fun=f)
z<-subset(z,span==max(z$span))
return(subset(z,day==min(z$day)))
}
dayshift<-ddply(unique(subset(rvw2,cell %in% dmm$cell,select=c('strat','day','cell'))),.(cell),.fun=f1,.progress='text')
##############################################
#ADJUSTS DAY VALUES TO MAXIMIZE PHENOLOGY SPAN
dayshiftfun<-function(d,shf){
d$day2<-d$day-(shf)
d$day2<-ifelse(d$day2<=0,d$day2+(365),d$day2)
return(d)
}
#BACK-CALCULATE ORIGINAL DAY OF THE YEAR
revdayshiftfun<-function(d,shf){
d$day3<-d$day2+shf
d$day3<-ifelse(d$day3>365,d$day3-365,d$day3)
return(d)
}
d<-subset(rvw2,cell=="-57.75_44.25",select=c('totwgt','cell','year','time','lon','lat','day'))
fsm<-function(d){
shf<-subset(dayshift,cell==unique(d$cell))$day
names(d)[1]<-'y'
#SHIFT DAYS TO CENTER ON JULY AND MAKE CONTINUOUS
d<-dayshiftfun(d,shf)
mod<-gam(y~s(year) + s(time,k=4,bs='cc') + s(day2,k=4,bs='cc'),data=d,gamma=1.4,family='nb'(link='log'))
pdat0<-data.frame(year=2000,
time=1200,
day2=seq(min(d$day2),max(d$day2),1))
p<-predict(mod,newdata=pdat0,type='response')
pdat0$p<-p
pdat<-data.frame(day2=seq(1,365,1))
pdat$p<-aspline(pdat0$day2,pdat0$p,xout=pdat$day2)$y
pdat$pz<-(pdat$p-mean(pdat$p))/sd(pdat$p)
pdat<-revdayshiftfun(pdat,shf)
pdat<-subset(pdat,select=c('day3','pz'))
names(pdat)<-c('day',gsub(' ','',paste('X_',unique(d$cell))))
pdat<-pdat[order(pdat$day),]
return(data.frame(pdat))
}
#TOTAL WEIGHT
qq<-dlply(subset(rvw2,cell %in% dmm$cell,select=c('totwgt','cell','year','time','lon','lat','day')),.(cell),.fun=fsm)
qsm<-Reduce(function(x, y) merge(x, y, by=c('day'),all=TRUE), qq)
qsm<-qsm[,colSums(is.na(qsm)) != nrow(qsm)]
q<-qsm
q2<- q %>% gather(cell, value, -day)
xyplot(value ~ day | cell,data=q2, pch=15, type=c('spline'),col='black')
xyplot(value ~ year | strata,data=q2, pch=15, type=c('p','spline'),col='black')
clfun<-function(df,k,lbl){
rownames(df)<-df$year
df<-df[,-1]
#k<-3#ER OF CLUSTERS
dmat<-1-cor(df,use='pairwise.complete.obs')
dst<-as.dist(dmat)
ff<-fanny(dst,k,maxit=5000,diss=T)
par(mfrow=c(3,1),mar=c(4,12,1,12))
dum<-c('red3','darkblue','gold3')
plot(silhouette(ff),col=dum[1:k],main='')#silhouette plot
dc.pcoa<-cmdscale(dst)
dc.scores<-scores(dc.pcoa,choices=c(1,2))
spefuz.g<-ff$clustering
a<-data.frame(cell=as.character(sort(unique(names(df)))),
clusters=ff$clustering)
aa<-data.frame(ff$membership)
aa$cell<-rownames(a)
plot(scores(dc.pcoa),asp=1,type='n',xlim=c(-1,1.5),ylim=c(-1,1),las=1,axes=TRUE,xlab='',ylab='')
stars(ff$membership,location=scores(dc.pcoa),draw.segments=T,add=T,scale=F,len=.1,col.segments=alpha(c(dum[1:k]),.25),byt='n',labels=NULL,xlim=c(-1.1,1.4),ylim=c(-1,.5),lwd=.0001,xpd=TRUE,border=NULL,radius=FALSE,col.radius=alpha('white',.1))
for(i in 1:k){ cl<-dum[i]
gg<-dc.scores[spefuz.g==i,]
hpts<-chull(gg)
hpts<-c(hpts,hpts[1])
lines(gg[hpts,],col=cl,lwd=3,xlim=c(-1.1,1.4),ylim=c(-1,.5))
}
cx <- data.frame(cell=aa$cell,
cx=apply(aa[, 1:k], 1, max))
cx$cxx <- rescale(cx$cx,newrange=c(.05,.5))
#PLOTS THE TIMESERIES OF R FOR EACH CLUSTER
par(mfrow=c(3,1),mar=c(2,12,1,12),oma=c(1,1,1,1))
#par(mfrow=c(2,2),mar=c(2,12,1,12),oma=c(1,1,1,1))
mb<-seq(1,k,1)
l<-list()
for(i in 1:length(mb)){
print(mb[i])
one<-subset(a,clusters==mb[i],select=c('cell'))# IN THIS CLUSTER
cx2<-subset(cx,cx>=0.5)#GETS INSTANCES WHERE CLUSTER PROBABILITY>0.5
data5<-subset(df,select=c(as.character(one$cell)))#TS FOR CLUSTER
cx2<-subset(cx2,cell %in% names(data5))
data5<-subset(df,select=c(as.character(cx2$cell)))#TS FOR CLUSTER
x<-rownames(data5)
t<-data.frame(day=as.numeric(x),mn=rowMeans(data5,na.rm=F))
t2<-data.frame(day=as.numeric(x),mn=rowMeans(data5,na.rm=T))
cl<-dum[i]
plot(0,0,pch=16,cex=.01,xlim=c(1,365),ylim=c(-2,3),main='',xaxt='n',las=1,axes=FALSE,xlab='',ylab='')
axis(side=2,at=seq(-2,3,1),las=1,lwd=.001,cex.axis=.75)
axis(side=1,at=seq(1,365,10),cex.axis=.75)
for(j in 1:length(data5[1,])){
try(dat<-data5[,j])
try(trnsp<-subset(cx,cell==as.character(names(data5[j])))$cxx)
try(lines(x,dat,cex=.5,ylim=c(0,1),lwd=2,col=alpha(cl,rescale(trnsp,newrange=c(.1,.5)))))
}
lines(as.numeric(as.character(t$day)),t$mn,col='gold3',cex=.7,lwd=3)
dm<-subset(t2,mn==max(t2$mn,na.rm=TRUE),select=c('day'))
dm$cluster=mb[i]
names(dm)<-c('clusterday','clusters')
l[[i]]<-dm
}
a2<-a
a2$cell<-gsub('X_\\.','',a2$cell)
if(k==3){a2$cx<- apply(aa[,c('X1','X2','X3')], 1, function(x) max(x) )
} else {a2$cx<- apply(aa[,c('X1','X2')], 1, function(x) max(x) )
}
a2$id<-a2$cell
a2$cl<-ifelse(a2$clusters==1,dum[1],dum[3])
a2$cl<-ifelse(a2$clusters==2,dum[2],a2$cl)
crds<-unique(subset(rvw,select=c('cell','lonc','latc')))
crds$cell<-gsub('-','',crds$cell)
a2<-merge(a2,crds,by=c('cell'),all.x=TRUE,all.y=FALSE)
dum<-unique(subset(a2,select=c('clusters','cl')))
return(
ggplot()+
geom_polygon(aes(long,lat, group=group),fill='black', data=bnk,size=1)+
geom_tile(data=a2, aes(x=lonc, y=latc,fill=as.factor(clusters),alpha=cx),col='gray80',size=.0001)+
scale_fill_manual(breaks=as.character(dum$clusters),values=dum$cl,na.value="transparent",guide=guide_legend(title=''))+
scale_alpha(guide = 'none')+
geom_polygon(aes(long,lat, group=group), fill="grey65", data=coast.mc)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.9,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'), legend.key.size = unit(0.1, "in"),legend.text=element_text(size=6))+
scale_x_continuous(expand=c(0,0),breaks=seq(-68,-57,1),labels=as.character(seq(-68,-57,1)),limits=NA)+
scale_y_continuous(expand=c(0,0),breaks=seq(41,47,1),labels=as.character(seq(41,47,1)),limits=NA)+
coord_equal()+
coord_cartesian(ylim=c(41,47),xlim=c(-68,-57))+
xlab('')+
ylab('')+
labs(title = lbl,
subtitle = "",
caption = '')
)
}
setwd(figsdir)
pdf('herring_rvsurvey_cluster_phenology_grid_k3.pdf',height=10,width=6.5)
mp1<-clfun(qsm,3,"Average weight")
dev.off()
pdf('herring_rvsurvey_cluster_phenology_map_grid_k3.pdf',height=14,width=7)
grid.arrange(mp1,mp1,mp1,ncol=1)
dev.off()
#MAKES BARPLOT OF CLUSTER DISTRIBUTION BY LONGITUDE
setwd(figsdir)
pdf('herring_rvsurvey_cluster_phenology_extras.pdf',height=14,width=7)
f<-function(d){
f2<-function(d2){ return(data.frame(prp=dim(d2)[1]/dim(d)[1]))}
return(ddply(d,.(clusters),.fun=f2))
}
dd<-ddply(a2,.(lonc),.fun=f)
p2<-ggplot(dd, aes(x=lonc, fill=as.factor(clusters),y=prp))+
geom_bar(data=dd,stat='identity',aes(width=.7,order=clusters),size=.0001,col='gray')+
theme(legend.position=c(.8,.8),text = element_text(size=8),axis.text.x = element_text(angle=0, vjust=1), panel.grid.major=element_blank(), panel.grid.minor=element_blank(),panel.background = element_blank(), legend.key.size = unit(0.2, "in"),legend.text=element_text(size=6),axis.line = element_line(color="black", size = .1))+
scale_fill_manual(values=as.character(dum$cl),breaks=as.factor(dum$clusters),labels=as.character(dum$clusters),name='Cluster')+
scale_x_continuous(expand=c(0,0),breaks=seq(min(dd$lonc),max(dd$lonc),10),labels=round(seq(min(dd$lonc),max(dd$lonc),10),digits=0))+
expand_limits(x=c(min(dd$lonc),max(dd$lonc)))+
xlab('Longitude') +
ylab('Proportion of cells')
#MODEL PROBABILITY OF CLUSTER ASSIGNMENT BY LONGITUDE
f<-function(d,cl){
d$ps<-ifelse(d$clusters==cl,1,0)
mod<-gam(ps~s(lonc,k=6),data=d,family='binomial',weights=d$cx,gamma=1.5)
pdat<-data.frame(lonc=seq(min(d$lonc),max(d$lonc),length.out=100))
p<-predict(mod,newdata=pdat,se.fit=TRUE,type='response')
pdat$p<-p$fit
return(pdat)
}
c1<-f(a2,1)
c2<-f(a2,2)
c3<-f(a2,3)
par(mfrow=c(3,1),mar=c(4,4,1,1))
plot(c1$lonc,c1$p,ylim=c(0,1),col=dum$cl[1],las=1,pch=16,type='l',lwd=2,ylab='Proportion of all cells')
points(c2$lonc,c2$p,col=dum$cl[2],pch=16,type='l',lwd=2)
points(c3$lonc,c3$p,col=dum$cl[3],pch=16,type='l',lwd=2)
cx<-.3
plot(0,0,col='white',ylim=c(0,1),las=1,pch=16,type='l',lwd=2,ylab='Proportion of all cells',xlim=c(min(c1$lonc),max(c1$lonc)))
polygon(c(c2$lonc,c2$lonc[length(c2$lonc):1]),c(c2$p,rep(-5,dim(c2)[1])[length(c2$p):1]),col=alpha(dum$cl[2],cx),border=alpha(dum$cl[2],.8))
polygon(c(c1$lonc,c1$lonc[length(c1$lonc):1]),c(c1$p,rep(-5,dim(c1)[1])[length(c1$p):1]),col=alpha(dum$cl[1],cx),border=alpha(dum$cl[1],.8))
polygon(c(c3$lonc,c3$lonc[length(c3$lonc):1]),c(c3$p,rep(-5,dim(c3)[1])[length(c3$p):1]),col=alpha(dum$cl[3],cx),border=alpha(dum$cl[3],.8))
grid.arrange(p2,p2,p2,ncol=1)
dev.off()
#devtools::install_github('dgrtwo/gganimate',force=TRUE)
library(magick)
library(gganimate)
plot(coast.mc)
p<-bmap+
geom_point(aes(x=lon, y=lat,size=pz,frame=day),data=out,colour='purple',alpha=.5)+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position=c(.1,.2),plot.background=element_blank(),axis.line = element_line(color = 'black'))
out$strat<-as.character(out$strat)
o<-data.frame(strat=sort(unique(out$strat)),
n=tapply(out$day,out$strat,function(x) length(unique(x))))
library(ggthemes)
library(animation)
setwd(figsdir)
ani.options(interval = 0.2)
gganimate(p)
gganimate(p,'output.phen.gif')
gganimate(p,'output.phen.mp4')
gganimate(p,'output.smooth.mp4')
p
a<-subset(rvw2,geardesc=='Campelen 1800 survey trawl')#2002 and 2005 during october
plot(a$lon,a$lat,pch=16,col='red')
setwd(figsdir)
pdf('campelen.survey.scotianshelf.pdf')
map('world',col='gray',fill=TRUE,xlim=c(-67,-58),ylim=c(43,45))
map.axes()
points(a$lon,a$lat,pch=16,col=alpha('red',.5))
dev.off()
####################################################################
####################################################################
#ANIMATE: ESTIMATE TIME TREND IN AVERAGE BIOMASS AND ANIMATE
###########################################################
#ESTIMATE PHENOLOGY FOR EACH GRID CELL AND ANIMATE
rvw2<-subset(rvw,!(geardesc %in% c('Newston net','Campelen 1800 survey trawl')) & month %in% c(6,7,8))
f<-function(d){
return(data.frame(nmo=length(unique(d$month)),
ndy=length(unique(d$day)),
nyr=length(unique(d$year)),
myr=min(d$year),
nlon=length(unique(d$lon)),
nlat=length(unique(d$lat)),
ntm=length(unique(d$time)),
lon=unique(d$lonc),
lat=unique(d$latc)))
}
dt<-ddply(rvw2,.(cell),.fun=f)
dt<-dt[order(dt$nmo,decreasing=TRUE),]
dmm<-subset(dt,nyr>=10 & myr<1990)
library(akima)
ff<-function(d){
names(d)[1]<-'y'
#SHIFT DAYS TO CENTER ON JULY AND MAKE CONTINUOUS
mod<-gam(y~s(year,k=6) + s(time,k=4,bs='cc'),data=d,gamma=1.4,family='nb'(link='log'))
pdat<-data.frame(year=seq(min(d$year),max(d$year),.25),
time=1200,
lon=unique(d$lonc),
lat=unique(d$latc))
p<-predict(mod,newdata=pdat,type='response')
pdat$p<-p
pdat$pz<-(pdat$p-mean(pdat$p))/sd(pdat$p)
pdat<-subset(pdat,select=c('year','p','pz'))
pdat$lon<-unique(d$lonc)
pdat$lat<-unique(d$latc)
pdat$depth<-mean(d$dmax,na.rm=TRUE)
pdat$did<-ifelse(pdat$year %in% d$year,1,0)
return(data.frame(pdat))
}
#TOTAL WEIGHT
out<-ddply(subset(rvw2, cell %in% dmm$cell,select=c('totwgt','strat','year','day','time','lon','lat','cell','lonc','latc','dmax')),.(cell),.fun=ff,.progress='text')
xyplot(pz~year|cell,data=out,col=ifelse(out$did==1,'red','blue'))
xyplot(log10(p+1)~year|cell,data=out,col=ifelse(out$did==1,'red','blue'))
bnk<-subset(plg,stratum %in% c(443,458,455,456,464,463,473,474,475,480))
x1<--68
x2<--57
y<-41.5
out$dy<-rescale(out$year,newrange=c(x1,x2))
frames <- length(unique(out$year))
rename <- function(x){
if (x < 10) {
return(name <- paste('000',i,'plot.jpg',sep=''))
}
if (x < 100 && i >= 10) {
return(name <- paste('00',i,'plot.jpg', sep=''))
}
if (x >= 100) {
return(name <- paste('0', i,'plot.jpg', sep=''))
}
}
out$pcx<-rescale(log10(out$p+1),newrange=c(.2,7))
setwd('C:/Users/copepod/Documents/aalldocuments/literature/research/active/SPERA/Figures/prac')
#loop through plots
yr<-sort(unique(out$year))
for(i in 1:frames){
name <- rename(i)
d<-subset(out,year==yr[i])
#saves the plot as a file in the working directory
jpeg(name,width=6,height=5,units='in',quality=100,res=300)
map('worldHires',fill=TRUE,col='gray',border=NA,xlim=c(x1,x2),ylim=c(y,46))
#plot(plg,add=TRUE,lwd=.01,border=alpha('lightgray',.5))
plot(bnk,add=TRUE,lwd=.01,border=NA,col=alpha('firebrick3',.2))
points(d$lon,d$lat,pch=16,cex=d$pcx,col=alpha('dodgerblue3',.3),xlim=c(x1,x2),ylim=c(y,46))
points(d$lon,d$lat,pch=1,lwd=.01,cex=d$pcx,col='dodgerblue3',xlim=c(-x1,x2),ylim=c(y,46))
legend('bottomright',paste('Year=',round(yr[i],digits=0)),bty='n')
points(unique(d$dy),y,pch=17,col='red3',cex=2)
axis(1,at=seq(x1,x2,length.out=10),labels=seq(1970,2015,5),cex.axis=.7)
dev.off()
}
#FROM COMMAND LINE WITH IMAGEMAGIK INSTALLED RUN: CONVERT -DELAY 2 -QUALITY 100 *.JPG MOVIE.MP4
#run ImageMagick
#library(magick)
#my_command <- 'convert *.png -delay 2 -loop 0 animation.gif'
#system(my_command)
|
94542e13dc94707fad07a9fcf3adc3d96c3e16e0 | b330b067d8dfc8a4ee93b3cdab342a366b374b10 | /data/데이터전처리.R | ed0a46d4adbdd0ffa24ec27de772a9ce5cb1f020 | [] | no_license | statKim/Da_Vinci_SW_Hackathon | 84db39a8bfbc65ed1ce9b62b9606f3d9f22c4f0c | d2c8e786e297f2761a8926d03cd2667f3eb46346 | refs/heads/master | 2020-04-06T18:52:00.524845 | 2018-12-31T00:39:47 | 2018-12-31T00:39:47 | 157,715,878 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,241 | r | 데이터전처리.R | library(glue)
library(XML)
library(stringr)
api.key <- 'GJdKU2SKJ6oYQgTSsQrkT9BH2hIF%2FG6qtztAeyJHv9Zp31YlWhl%2FCKMmz0fKJnmxtPyQT9TY49AQqtpEeFCw9A%3D%3D'
url.format <-
'http://apis.data.go.kr/B090041/openapi/service/SpcdeInfoService/getRestDeInfo?ServiceKey={key}&solYear={year}&solMonth={month}'
holiday.request <-
function(key, year, month) glue(url.format)
# request and read data : year 2017
days<-c()
date<-c()
for(m in 1:12){
data <- xmlToList(holiday.request(api.key, 2018, str_pad(m, 2, pad=0)))
items <- data$body$items
for(item in items){
if(item$isHoliday == 'Y') days<-append(days, item$dateName); date<-append(date, item$locdate)
}
}
holi <- data.frame(days,date)
holi$date<- as.character(holi$date)
#====================================
library(dplyr)
peo_201803_ori <- read.csv("C:/Users/Jiwan/Downloads/LOCAL_PEOPLE_DONG_201803/LOCAL_PEOPLE_DONG_201803.csv",encoding="UTF-8", stringsAsFactors=FALSE,skip = 1,header = F)
peo_201804_ori <- read.csv("C:/Users/Jiwan/Downloads/LOCAL_PEOPLE_DONG_201804/LOCAL_PEOPLE_DONG_201804.csv",encoding="UTF-8", stringsAsFactors=FALSE,skip = 1,header = F)
peo_201805_ori <- read.csv("C:/Users/Jiwan/Downloads/LOCAL_PEOPLE_DONG_201805/LOCAL_PEOPLE_DONG_201805.csv",encoding="UTF-8", stringsAsFactors=FALSE,skip = 1,header = F)
peo_201806_ori <- read.csv("C:/Users/Jiwan/Downloads/LOCAL_PEOPLE_DONG_201806/LOCAL_PEOPLE_DONG_201806.csv",encoding="UTF-8", stringsAsFactors=FALSE,skip = 1,header = F)
peo_201807_ori <- read.csv("C:/Users/Jiwan/Downloads/LOCAL_PEOPLE_DONG_201807/LOCAL_PEOPLE_DONG_201807.csv",encoding="UTF-8", stringsAsFactors=FALSE,skip = 1,header = F)
peo_201808_ori <- read.csv("C:/Users/Jiwan/Downloads/LOCAL_PEOPLE_DONG_201808/LOCAL_PEOPLE_DONG_201808.csv",encoding="UTF-8", stringsAsFactors=FALSE,skip = 1,header = F)
peo_201809_ori <- read.csv("C:/Users/Jiwan/Downloads/LOCAL_PEOPLE_DONG_201809/LOCAL_PEOPLE_DONG_201809.csv",encoding="UTF-8", stringsAsFactors=FALSE,skip = 1,header = F)
peo_201810_ori <- read.csv("C:/Users/Jiwan/Downloads/LOCAL_PEOPLE_DONG_201810/LOCAL_PEOPLE_DONG_201810.csv",encoding="UTF-8", stringsAsFactors=FALSE,skip = 1,header = F)
aa <- c("03","04","05","06","07","08","09","10")
#################
park<-data.frame(NA,NA,NA,NA)
j<- 0
for (i in aa){
j <- j+1
assign(paste0("peo_2018",i),get(paste0("peo_2018",i,"_ori"))[1:4])
assign(paste0("peo_2018",i) , get(paste0("peo_2018",i)) %>% filter( V3 == 11215780 ) )
print(dim(get(paste0("peo_2018",i))))
if (j>1){
park <- rbind( park , get(paste0("peo_2018",i)))
}
else {
park <- get(paste0("peo_2018",i))
}
}
# 9월 18일~ 21일 데이터가 없음
dim(park)[1] == 744+720+744+720+744+720+624+744
head(park); tail(park)
#################
lotte<-data.frame(NA,NA,NA,NA)
j<- 0
for (i in aa){
j <- j+1
assign(paste0("peo_2018",i),get(paste0("peo_2018",i,"_ori"))[1:4])
assign(paste0("peo_2018",i) , get(paste0("peo_2018",i)) %>% filter( V3 == 11710680 ) )
print(dim(get(paste0("peo_2018",i))))
if (j>1){
lotte <- rbind( lotte , get(paste0("peo_2018",i)))
}
else {
lotte <- get(paste0("peo_2018",i))
}
}
# 9월 18일~ 21일 데이터가 없음
dim(lotte)[1] == 744+720+744+720+744+720+624+744
head(lotte); tail(lotte)
#################
nam<-data.frame(NA,NA,NA,NA)
j<- 0
for (i in aa){
j <- j+1
assign(paste0("peo_2018",i),get(paste0("peo_2018",i,"_ori"))[1:4])
assign(paste0("peo_2018",i) , get(paste0("peo_2018",i)) %>% filter( V3 == 11140570 ) )
print(dim(get(paste0("peo_2018",i))))
if (j>1){
nam <- rbind( nam , get(paste0("peo_2018",i)))
}
else {
nam <- get(paste0("peo_2018",i))
}
}
# 9월 18일~ 21일 데이터가 없음
dim(nam)[1] == 744+720+744+720+744+720+624+744
head(nam); tail(nam)
#################
gyeong<-data.frame(NA,NA,NA,NA)
j<- 0
for (i in aa){
j <- j+1
assign(paste0("peo_2018",i),get(paste0("peo_2018",i,"_ori"))[1:4])
assign(paste0("peo_2018",i) , get(paste0("peo_2018",i)) %>% filter( V3 == 11110515 ) )
print(dim(get(paste0("peo_2018",i))))
if (j>1){
gyeong <- rbind( gyeong , get(paste0("peo_2018",i)))
}
else {
gyeong <- get(paste0("peo_2018",i))
}
}
# 9월 18일~ 21일 데이터가 없음
dim(gyeong)[1] == 744+720+744+720+744+720+624+744
head(gyeong); tail(gyeong)
#################
duk<-data.frame(NA,NA,NA,NA)
j<- 0
for (i in aa){
j <- j+1
assign(paste0("peo_2018",i),get(paste0("peo_2018",i,"_ori"))[1:4])
assign(paste0("peo_2018",i) , get(paste0("peo_2018",i)) %>% filter( V3 == 11140520 ) )
print(dim(get(paste0("peo_2018",i))))
if (j>1){
duk <- rbind( duk , get(paste0("peo_2018",i)))
}
else {
duk <- get(paste0("peo_2018",i))
}
}
# 9월 18일~ 21일 데이터가 없음
dim(duk)[1] == 744+720+744+720+744+720+624+744
head(duk); tail(duk)
#################
buk<-data.frame(NA,NA,NA,NA)
j<- 0
for (i in aa){
j <- j+1
assign(paste0("peo_2018",i),get(paste0("peo_2018",i,"_ori"))[1:4])
assign(paste0("peo_2018",i) , get(paste0("peo_2018",i)) %>% filter( V3 == 11110600 ) )
print(dim(get(paste0("peo_2018",i))))
if (j>1){
buk <- rbind( buk , get(paste0("peo_2018",i)))
}
else {
buk <- get(paste0("peo_2018",i))
}
}
# 9월 18일~ 21일 데이터가 없음
dim(buk)[1] == 744+720+744+720+744+720+624+744
head(buk); tail(buk)
# park,lotte,nam,gyeong,duk,buk : 날씨 / 시간 / 장소코드 / 생활인구
# holi : 공휴일 데이터
################################################
### 롯데월드 생활인구 & 공휴일 & 요일 데이터 ###
################################################
# park,lotte,nam,gyeong,duk,buk : 날씨 / 시간 / 장소코드 / 생활인구
# holi : 공휴일 데이터
#
names(lotte)<-c("date","time","code","people")
lotte %>% filter(time==15) -> lotte
lotte$date<- as.character(lotte$date)
day_levels <- c("일요일", "월요일", "화요일", "수요일", "목요일", "금요일", "토요일")
# 일요일 1부터 토요일 7까지.
as.numeric(factor(weekdays(as.Date(lotte$date,"%Y%m%d")), levels=day_levels, ordered=TRUE)) -> lotte$weekday
lotte %>% mutate( holiday = as.numeric(date %in% holi$date) )
head(lotte)
xf <- factor(lotte$weekday, levels = 1:7)
d <- model.matrix( ~ xf - 1)
lotte %>%
mutate( holiday = as.numeric(date %in% holi$date) ) %>%
select(date,people,holiday) -> lotte
data.frame(lotte,as.data.frame(d)) -> lotte
for (i in 1:nrow(lotte)){
if (lotte[i,3] == 1){
lotte[i,4:10] = c(0,0,0,0,0,0,0)
}
}
################################################
### 롯데월드 트렌드 데이터 ###
################################################
library(xlsx)
trend_lotte <- read.xlsx2("C:/Users/Jiwan/Downloads/datalab_lotte.xlsx",sheetIndex=1,startRow=7,stringsAsFactors=F)
str(trend_lotte)
trend_lotte[,2]<-as.numeric(trend_lotte[,2])
names(trend_lotte) <- c("date","naver")
trend<-c()
for (i in 1:nrow(trend_lotte)){
trend<-append(trend, sum(trend_lotte[i:(i+6),2])/7 )
}
c(rep(NA,7),trend)->trend1
length(trend1) -> length_trend
data.frame( rbind(trend_lotte,NA) , trend=trend1[1:(length_trend-6)] ) -> trend_lotte1
str(trend_lotte1)
gsub("-", "", trend_lotte1$date) -> trend_lotte1$date
trend_lotte1[,-2]->trend_lotte1
################################################
### 어린이대공원 생활인구 & 공휴일 & 요일 데이터 ###
################################################
# park,lotte,nam,gyeong,duk,buk : 날씨 / 시간 / 장소코드 / 생활인구
# holi : 공휴일 데이터
#
library(dplyr)
names(park)<-c("date","time","code","people")
park %>% filter(time==15) -> park
park$date<- as.character(park$date)
day_levels <- c("일요일", "월요일", "화요일", "수요일", "목요일", "금요일", "토요일")
# 일요일 1부터 토요일 7까지.
as.numeric(factor(weekdays(as.Date(park$date,"%Y%m%d")), levels=day_levels, ordered=TRUE)) -> park$weekday
park %>% mutate( holiday = as.numeric(date %in% holi$date) )
head(park)
xf <- factor(park$weekday, levels = 1:7)
d <- model.matrix( ~ xf - 1)
park %>%
mutate( holiday = as.numeric(date %in% holi$date) ) %>%
select(date,people,holiday) -> park
data.frame(park,as.data.frame(d)) -> park
for (i in 1:nrow(park)){
if (park[i,3] == 1){
park[i,4:10] = c(0,0,0,0,0,0,0)
}
}
################################################
### 어린이대공원트렌드 데이터 ###
################################################
library(xlsx)
trend_park <- read.xlsx2("C:/Users/Jiwan/Downloads/datalab_park.xlsx",sheetIndex=1,startRow=7,stringsAsFactors=F)
str(trend_park)
trend_park[,2]<-as.numeric(trend_park[,2])
names(trend_park) <- c("date","naver")
trend<-c()
for (i in 1:nrow(trend_park)){
trend<-append(trend, sum(trend_park[i:(i+6),2])/7 )
}
c(rep(NA,7),trend)->trend1
length(trend1) -> length_trend
data.frame( rbind(trend_park,NA) , trend=trend1[1:(length_trend-6)] ) -> trend_park1
str(trend_park1)
gsub("-", "", trend_park1$date) -> trend_park1$date
trend_park1[,-2]->trend_park1
################################################
### 남산. 생활인구 & 공휴일 & 요일 데이터 ###
################################################
# park,lotte,nam,gyeong,duk,buk : 날씨 / 시간 / 장소코드 / 생활인구
# holi : 공휴일 데이터
#
library(dplyr)
names(nam)<-c("date","time","code","people")
nam %>% filter(time==15) -> nam
nam$date<- as.character(nam$date)
day_levels <- c("일요일", "월요일", "화요일", "수요일", "목요일", "금요일", "토요일")
# 일요일 1부터 토요일 7까지.
as.numeric(factor(weekdays(as.Date(nam$date,"%Y%m%d")), levels=day_levels, ordered=TRUE)) -> nam$weekday
nam %>% mutate( holiday = as.numeric(date %in% holi$date) )
head(nam)
xf <- factor(nam$weekday, levels = 1:7)
d <- model.matrix( ~ xf - 1)
nam %>%
mutate( holiday = as.numeric(date %in% holi$date) ) %>%
select(date,people,holiday) -> nam
data.frame(nam,as.data.frame(d)) -> nam
for (i in 1:nrow(nam)){
if (nam[i,3] == 1){
nam[i,4:10] = c(0,0,0,0,0,0,0)
}
}
################################################
### 남산 트렌드 데이터 ###
################################################
library(xlsx)
trend_nam <- read.xlsx2("C:/Users/Jiwan/Downloads/datalab_nam.xlsx",sheetIndex=1,startRow=7,stringsAsFactors=F)
str(trend_nam)
trend_nam[,2]<-as.numeric(trend_nam[,2])
names(trend_nam) <- c("date","naver")
trend<-c()
for (i in 1:nrow(trend_nam)){
trend<-append(trend, sum(trend_nam[i:(i+6),2])/7 )
}
c(rep(NA,7),trend)->trend1
length(trend1) -> length_trend
data.frame( rbind(trend_nam,NA) , trend=trend1[1:(length_trend-6)] ) -> trend_nam1
str(trend_nam1)
gsub("-", "", trend_nam1$date) -> trend_nam1$date
trend_nam1[,-2]->trend_nam1
################################################
### 경복궁 생활인구 & 공휴일 & 요일 데이터 ###
################################################
# park,lotte,nam,gyeong,duk,buk : 날씨 / 시간 / 장소코드 / 생활인구
# holi : 공휴일 데이터
#
library(dplyr)
names(gyeong)<-c("date","time","code","people")
gyeong %>% filter(time==15) -> gyeong
gyeong$date<- as.character(gyeong$date)
day_levels <- c("일요일", "월요일", "화요일", "수요일", "목요일", "금요일", "토요일")
# 일요일 1부터 토요일 7까지.
as.numeric(factor(weekdays(as.Date(gyeong$date,"%Y%m%d")), levels=day_levels, ordered=TRUE)) -> gyeong$weekday
gyeong %>% mutate( holiday = as.numeric(date %in% holi$date) )
head(gyeong)
xf <- factor(gyeong$weekday, levels = 1:7)
d <- model.matrix( ~ xf - 1)
gyeong %>%
mutate( holiday = as.numeric(date %in% holi$date) ) %>%
select(date,people,holiday) -> gyeong
data.frame(gyeong,as.data.frame(d)) -> gyeong
for (i in 1:nrow(gyeong)){
if (gyeong[i,3] == 1){
gyeong[i,4:10] = c(0,0,0,0,0,0,0)
}
}
################################################
### 경복궁 트렌드 데이터 ###
################################################
library(xlsx)
trend_gyeong <- read.xlsx2("C:/Users/Jiwan/Downloads/datalab_gyeong.xlsx",sheetIndex=1,startRow=7,stringsAsFactors=F)
str(trend_gyeong)
trend_gyeong[,2]<-as.numeric(trend_gyeong[,2])
names(trend_gyeong) <- c("date","naver")
trend<-c()
for (i in 1:nrow(trend_gyeong)){
trend<-append(trend, sum(trend_gyeong[i:(i+6),2])/7 )
}
c(rep(NA,7),trend)->trend1
length(trend1) -> length_trend
data.frame( rbind(trend_gyeong,NA) , trend=trend1[1:(length_trend-6)] ) -> trend_gyeong1
str(trend_gyeong1)
gsub("-", "", trend_gyeong1$date) -> trend_gyeong1$date
trend_gyeong1[,-2]->trend_gyeong1
################################################
### 덕수궁 생활인구 & 공휴일 & 요일 데이터 ###
################################################
# park,lotte,nam,gyeong,duk,buk : 날씨 / 시간 / 장소코드 / 생활인구
# holi : 공휴일 데이터
#
library(dplyr)
names(duk)<-c("date","time","code","people")
duk %>% filter(time==15) -> duk
duk$date<- as.character(duk$date)
day_levels <- c("일요일", "월요일", "화요일", "수요일", "목요일", "금요일", "토요일")
# 일요일 1부터 토요일 7까지.
as.numeric(factor(weekdays(as.Date(duk$date,"%Y%m%d")), levels=day_levels, ordered=TRUE)) -> duk$weekday
duk %>% mutate( holiday = as.numeric(date %in% holi$date) )
head(duk)
xf <- factor(duk$weekday, levels = 1:7)
d <- model.matrix( ~ xf - 1)
duk %>%
mutate( holiday = as.numeric(date %in% holi$date) ) %>%
select(date,people,holiday) -> duk
data.frame(duk,as.data.frame(d)) -> duk
for (i in 1:nrow(duk)){
if (duk[i,3] == 1){
duk[i,4:10] = c(0,0,0,0,0,0,0)
}
}
################################################
### 덕수궁 트렌드 데이터 ###
################################################
library(xlsx)
trend_duk <- read.xlsx2("C:/Users/Jiwan/Downloads/datalab_duk.xlsx",sheetIndex=1,startRow=7,stringsAsFactors=F)
str(trend_duk)
trend_duk[,2]<-as.numeric(trend_duk[,2])
names(trend_duk) <- c("date","naver")
trend<-c()
for (i in 1:nrow(trend_duk)){
trend<-append(trend, sum(trend_duk[i:(i+6),2])/7 )
}
c(rep(NA,7),trend)->trend1
length(trend1) -> length_trend
data.frame( rbind(trend_duk,NA) , trend=trend1[1:(length_trend-6)] ) -> trend_duk1
str(trend_duk1)
gsub("-", "", trend_duk1$date) -> trend_duk1$date
trend_duk1[,-2]->trend_duk1
################################################
### 북촌한옥마을 생활인구 & 공휴일 & 요일 데이터 ###
################################################
# park,lotte,nam,gyeong,duk,buk : 날씨 / 시간 / 장소코드 / 생활인구
# holi : 공휴일 데이터
#
library(dplyr)
names(buk)<-c("date","time","code","people")
buk %>% filter(time==15) -> buk
buk$date<- as.character(buk$date)
day_levels <- c("일요일", "월요일", "화요일", "수요일", "목요일", "금요일", "토요일")
# 일요일 1부터 토요일 7까지.
as.numeric(factor(weekdays(as.Date(buk$date,"%Y%m%d")), levels=day_levels, ordered=TRUE)) -> buk$weekday
buk %>% mutate( holiday = as.numeric(date %in% holi$date) )
head(buk)
xf <- factor(buk$weekday, levels = 1:7)
d <- model.matrix( ~ xf - 1)
buk %>%
mutate( holiday = as.numeric(date %in% holi$date) ) %>%
select(date,people,holiday) -> buk
data.frame(buk,as.data.frame(d)) -> buk
for (i in 1:nrow(buk)){
if (buk[i,3] == 1){
buk[i,4:10] = c(0,0,0,0,0,0,0)
}
}
################################################
### 북촌한옥마을 트렌드 데이터 ###
################################################
library(xlsx)
trend_buk <- read.xlsx2("C:/Users/Jiwan/Downloads/datalab_buk.xlsx",sheetIndex=1,startRow=7,stringsAsFactors=F)
str(trend_buk)
trend_buk[,2]<-as.numeric(trend_buk[,2])
names(trend_buk) <- c("date","naver")
trend<-c()
for (i in 1:nrow(trend_buk)){
trend<-append(trend, sum(trend_buk[i:(i+6),2])/7 )
}
c(rep(NA,7),trend)->trend1
length(trend1) -> length_trend
data.frame( rbind(trend_buk,NA) , trend=trend1[1:(length_trend-6)] ) -> trend_buk1
str(trend_buk1)
gsub("-", "", trend_buk1$date) -> trend_buk1$date
trend_buk1[,-2]->trend_buk1
################################################
### 기상변수 만들기 ### park,lotte,nam,gyeong,duk,buk
################################################
library(xlsx)
weather_data <- read.xlsx2("C:/Users/Jiwan/Downloads/data_weather.xlsx",sheetIndex=1,startRow=1,stringsAsFactors=F)
weather_data[,1:5]->weather_data
names(weather_data)<-c("date","sunny","cloudy","rainy","snowy")
################################################
### 데이터s Join ### park,lotte,nam,gyeong,duk,buk
################################################
dim(lotte)
merge(
merge(lotte, trend_lotte1, all.x=TRUE)
,weather_data, all.x=TRUE) -> lotte_final
merge(
merge(park, trend_park1, all.x=TRUE)
,weather_data, all.x=TRUE) -> park_final
merge(
merge(nam, trend_nam1, all.x=TRUE)
,weather_data, all.x=TRUE) -> nam_final
merge(
merge(gyeong, trend_gyeong1, all.x=TRUE)
,weather_data, all.x=TRUE) -> gyeong_final
merge(
merge(duk, trend_duk1, all.x=TRUE)
,weather_data, all.x=TRUE) -> duk_final
merge(
merge(buk, trend_buk1, all.x=TRUE)
,weather_data, all.x=TRUE) -> buk_final
write.csv(lotte_final,"lotte.csv")
write.csv(park_final,"park.csv")
write.csv(nam_final,"nam.csv")
write.csv(gyeong_final,"gyeong.csv")
write.csv(duk_final,"duk.csv")
write.csv(buk_final,"buk.csv")
summary(lotte_final$people)
|
e1701f4faf06fcc27195817ffe17b42d4d824df2 | 287add902a548b978254b03f571f5e127d325e88 | /man/lib_dist.Rd | 05dc39c7038feafcbcedadbfa57e5b332784fde9 | [] | no_license | Auburngrads/publicLibs | e36884552220fcf859d28ef5cc16d26baeb23f65 | 804efbb6bc80f5053712e375a09d2d46ce2f61a6 | refs/heads/master | 2021-01-17T02:44:58.943620 | 2020-07-20T00:32:03 | 2020-07-20T00:32:03 | 58,672,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,781 | rd | lib_dist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/libdistData.R
\docType{data}
\name{alts_Libs}
\alias{alts_Libs}
\alias{andr_Libs}
\alias{andw_Libs}
\alias{arnl_Libs}
\alias{bckl_Libs}
\alias{beal_Libs}
\alias{blln_Libs}
\alias{brkd_Libs}
\alias{brks_Libs}
\alias{chrl_Libs}
\alias{clmb_Libs}
\alias{cnnn_Libs}
\alias{crch_Libs}
\alias{dovr_Libs}
\alias{dvsm_Libs}
\alias{dyss_Libs}
\alias{edwr_Libs}
\alias{egln_Libs}
\alias{ells_Libs}
\alias{elmn_Libs}
\alias{elsn_Libs}
\alias{fewr_Libs}
\alias{frch_Libs}
\alias{gdfl_Libs}
\alias{gntr_Libs}
\alias{grnf_Libs}
\alias{hckm_Libs}
\alias{hill_Libs}
\alias{hllm_Libs}
\alias{hnsc_Libs}
\alias{hrlf_Libs}
\alias{krtl_Libs}
\alias{kslr_Libs}
\alias{lckl_Libs}
\alias{lghl_Libs}
\alias{lngl_Libs}
\alias{lsan_Libs}
\alias{lttr_Libs}
\alias{luke_Libs}
\alias{mcch_Libs}
\alias{mccn_Libs}
\alias{mcdl_Libs}
\alias{mcgr_Libs}
\alias{mint_Libs}
\alias{mlms_Libs}
\alias{mnth_Libs}
\alias{mody_Libs}
\alias{mxwl_Libs}
\alias{nlls_Libs}
\alias{offt_Libs}
\alias{otis_Libs}
\alias{pope_Libs}
\alias{ptrc_Libs}
\alias{ptrs_Libs}
\alias{rbns_Libs}
\alias{rndl_Libs}
\alias{schr_Libs}
\alias{sctt_Libs}
\alias{shaw_Libs}
\alias{shpp_Libs}
\alias{symj_Libs}
\alias{tnkr_Libs}
\alias{trvs_Libs}
\alias{tynd_Libs}
\alias{usaf_Libs}
\alias{vanc_Libs}
\alias{vndn_Libs}
\alias{whtm_Libs}
\alias{wrgp_Libs}
\title{Distances From Public Libraries to USAF Bases}
\format{A \code{data.table} with 4 variables:
\tabular{rlll}{
[, 1] \tab location \tab Character String of the Library name/address/city/state \tab \bold{Categoric}\cr
[, 2] \tab miles \tab Ordered list distances (in miles) from the base \tab \bold{Numeric}\cr
[, 3] \tab latitide \tab Latitude coordinate of the library \tab \bold{Numeric}\cr
[, 4] \tab longitude \tab Longitude coordinate of the library \tab \bold{Numeric}
}}
\source{
http://www.publiclibraries.com
Google Maps Geocode API
}
\usage{
alts_Libs
andr_Libs
andw_Libs
arnl_Libs
bckl_Libs
beal_Libs
blln_Libs
brkd_Libs
brks_Libs
chrl_Libs
clmb_Libs
cnnn_Libs
crch_Libs
dovr_Libs
dvsm_Libs
dyss_Libs
edwr_Libs
egln_Libs
ells_Libs
elmn_Libs
elsn_Libs
fewr_Libs
frch_Libs
gdfl_Libs
gntr_Libs
grnf_Libs
hckm_Libs
hill_Libs
hllm_Libs
hnsc_Libs
hrlf_Libs
krtl_Libs
kslr_Libs
lckl_Libs
lghl_Libs
lngl_Libs
lsan_Libs
lttr_Libs
luke_Libs
mcch_Libs
mccn_Libs
mcdl_Libs
mcgr_Libs
mint_Libs
mlms_Libs
mnth_Libs
mody_Libs
mxwl_Libs
nlls_Libs
offt_Libs
otis_Libs
pope_Libs
ptrc_Libs
ptrs_Libs
rbns_Libs
rndl_Libs
schr_Libs
sctt_Libs
shaw_Libs
shpp_Libs
symj_Libs
tnkr_Libs
trvs_Libs
tynd_Libs
usaf_Libs
vanc_Libs
vndn_Libs
whtm_Libs
wrgp_Libs
}
\description{
Information about public libraries in each state
}
\keyword{datasets}
|
db7d966e350429feda5397b8298df1b4caecb4f0 | 3b8fcf4e1fc1ed070e8e5b16f48c32f8e8ccec3a | /man/wtInfo.Rd | 12ed944b1e73d986585da6d08550435ac9160769 | [] | no_license | SwapanK/fracdet | 67379391e3722864eeb78129bfd686beee4840d4 | 69360a3431d7b323d6bc8947c20aa2f58af7cfa1 | refs/heads/master | 2020-04-30T22:26:37.277441 | 2018-06-16T08:01:03 | 2018-06-16T08:01:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 551 | rd | wtInfo.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/waveletVar.R
\name{wtInfo}
\alias{wtInfo}
\title{Get the family and the filter-number used in the wavelet transform asociated
with the \code{waveletVar} object.}
\usage{
wtInfo(x)
}
\arguments{
\item{x}{A \code{waveletVar} object.}
}
\value{
An R list containing the family (\code{family} field) and the
filter-number (\code{filter_number}).
}
\description{
Get the family and the filter-number used in the wavelet transform asociated
with the \code{waveletVar} object.
}
|
516b8a03f718cbd74815ff9e116fe18a3e7d21cc | 47ae73e4c6f69138bf7082636366f579dc975409 | /man/evaluate.DRWPClassGM.Rd | 52047f3374b49cbc01d0f4d8d983c6a807b04072 | [] | no_license | pikaqiu321/DRWPClass | ad0d4e4ad0df82dedae2a319cbf3e2bdff3705db | 2cb01c8ad8fb77f3113bedbc2344c1b6636d1725 | refs/heads/master | 2022-11-17T00:01:22.797764 | 2015-03-01T00:21:37 | 2015-03-01T00:21:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,336 | rd | evaluate.DRWPClassGM.Rd | \name{evaluate.DRWPClassGM}
\alias{evaluate.DRWPClassGM}
\title{
Evaluate predictions from a "DRWPClassGM" object.
}
\description{
This functions evaluates the classification performance of a fitted \code{"DRWPClassGM"} object.
}
\usage{
evaluate.DRWPClassGM(object, newx, newy.class1, newy.class2)
}
\arguments{
\item{object}{
Fitted \code{"DRWPClassGM"} model object.
}
\item{newx}{
A matrix with variables to predict.
}
\item{newy.class1}{
a integer vector comprising the indexes of class 1 samples in \code{newx}.
}
\item{newy.class2}{
a integer vector comprising the indexes of class 2 samples in \code{newx}.
}
}
\value{The classification performance of the predictions.}
\seealso{
\code{\link{predict.DRWPClassGM}}
}
\examples{
data(GProf8511)
data(GProf3325)
data(MProf)
data(pathSet)
data(dGMGraph)
fit <- fit.DRWPClassGM(xG=GProf8511$mRNA_matrix, yG.class1=GProf8511$normal,
yG.class2=GProf8511$PCA, xM=MProf$Meta_matrix, yM.class1=MProf$normal,
yM.class2=MProf$PCA, DEBUG=TRUE, pathSet=pathSet, globalGraph=dGMGraph,
testStatistic="t-test", classifier = "Logistic", normalize = TRUE,
nFolds = 5, numTops=50, iter = 1, Gamma=0.7, Alpha = 0.5)
evaluate.DRWPClassGM(object=fit, newx=GProf3325$mRNA_matrix, newy.class1=GProf3325$normal,
newy.class2=GProf3325$PCA)
} |
03141cfedc48dc1ece04e93c98a472b0eede40fe | f0c85baaaf0b9d2d2c725327c759fdb9ff58aad1 | /05e_Host_State_Variables_-_High_Unemployment.R | 9370730180f6f5b961b46da3767813b6e57e3ecc | [] | no_license | hrdii/post_conflict_refugee_returns | 4ead6d7ea2997cd8b88230fec76b8e4e9c3e0a9d | 757cbde10d49890901fad28db4f590dc6ae9635d | refs/heads/main | 2023-09-05T23:32:58.289994 | 2021-11-04T07:56:18 | 2021-11-04T07:56:18 | 424,493,243 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,420 | r | 05e_Host_State_Variables_-_High_Unemployment.R | ##########----------##########----------##########----------##########----------
##########---------- HEADER
##### meta-information
## Author: Hardika Dayalani(dayalani@rand.org)
## Creation: 2020-02-09 for Post-Conflict Refugee Returns Project
## Description: Adds Host state variables to conflict cases
## Opportunity for employment
## % refugees at the end of conflict that are in host countries with high
## unemployment rate. Global average unemployment rate has varied between
## 4.4 and 6% over 1991-2019. A host country is arbitrarily defined as having
## high unemployment if the rate exceeds 10%
##### environment set up
rm(list = ls())
## Load Libraries
library(data.table)
library(stringr)
## Load Functions
source(file = "00a_Custom_Functions.R")
## Load Conflict Cases
load("../Intermediate/Conflict case with refugee origin and host state information.RData")
## Load Refugee Information
load("../Intermediate/Refugee Population.RData")
## Load WB-UNHCR Country Name Lookup Dataframe
load("../Intermediate/WB-UNHCR Country Name Lookup.RData")
## Import Unemployment Rate Data
unemp_df <- fread("../Data/World Bank/Umemployment/API_SL.UEM.TOTL.ZS_DS2_en_csv_v2_672963.csv",
skip = 4, header = TRUE)
##### Clean Unemployment Rate Data
## Function to identify missing data
MissingIndex <- function(x, row = TRUE, n = 1){
# x is a matrix or dataframe
y <- is.na(x)
m <- ifelse(row, 2, 1)
n <- dim(x)[m] - n
m <- ifelse(row, 1, 2)
y <- apply(y, MARGIN = m, FUN = sum)
y <- y >= n
return(y)
}
## Drop completely empty rows
temp <- MissingIndex(unemp_df, row = TRUE, n = 4)
unemp_df <- unemp_df[!temp]
## Drop completely empty columns
temp <- MissingIndex(unemp_df, row = FALSE, n = 0)
unemp_df <- unemp_df[, .SD, .SDcols=-temp]
rm(temp)
## Drop other unnecessary columns
unemp_df <- unemp_df[, !c("Country Code", "Indicator Name", "Indicator Code")]
## Clean names
setnames(unemp_df, "Country Name", "country")
##### Harmonize Country Names
unemp_df$country <- NameCleaning(unemp_df$country)
## Replace Country names using Lookup Dataframe
unemp_df[lookup_df, on=.(country = wb_names), country := i.unhcr_names ]
rm(lookup_df)
##### Calculate % refugee
## Write a function to calculate % refugees
PropUnemp <- function(cas, r_df = refugee_df, econ_df = unemp_df, ref_threshold = 10){
## Source Country
s <- gsub('(.*) ([0-9]{4})','\\1',cas)
## Year0
y <- as.numeric(gsub('(.*) ([0-9]{4})','\\2',cas))
## Subset Refugee Data to Year0
temp_df <- r_df[year == y, ]
## Subset to sources that host are above the threshold
hosts <- SubsetHosts(country = s, df = temp_df)
## Subset Unemployment rate to year0
## we don't have unemployment data from before 1991
if(y < 1991){
y <- 1991:1993
econ_df <- econ_df[, .SD, .SDcols= c("country", as.character(y))]
econ_df <- econ_df[, unemp := rowSums(.SD, na.rm = TRUE)/3, .SDcols = as.character(y)]
econ_df <- econ_df[, .SD, .SDcols = c("country", "unemp")]
} else {
econ_df <- econ_df[, .SD, .SDcols= c("country", as.character(y))]
names(econ_df) <- c("country", "unemp")
}
## Add Unemployment rate for each host country
hosts <- merge(x = hosts,
y = econ_df,
by = "country",
all.x = TRUE)
## Hosts are considered to have high unemployment if the rate exceeds ref_threshold
hosts$high_unemp <- as.logical(
as.character(
cut(hosts$unemp,
breaks = c(-Inf, ref_threshold, Inf),
labels = c("FALSE", "TRUE"))))
## Calculate the proportion of source country refugees that live in host countries with high unemployment
hosts$high_unemp <- hosts$high_unemp * hosts$pop / sum(hosts$pop, na.rm = TRUE)
## Return proportion
return(sum(hosts$high_unemp, na.rm = TRUE))
}
## Calculate % refugee
agg_df$punemp_5 <- sapply(agg_df$case, FUN = PropUnemp, ref_threshold = 5)
summary(agg_df$punemp_5)
agg_df$punemp_10 <- sapply(agg_df$case, FUN = PropUnemp, ref_threshold = 10)
summary(agg_df$punemp_10)
agg_df$punemp_15 <- sapply(agg_df$case, FUN = PropUnemp, ref_threshold = 15)
summary(agg_df$punemp_15)
## Save File
save(agg_df,
file = "../Intermediate/Conflict case with refugee origin and host state information.RData")
print("05e")
|
849e51ea6f2a3c8b8319bf548c0bdc5858d81c14 | c78d6c58e47ff01700e082dde12bd08523352cc2 | /plot3.R | 609fc11ad01058a7f4fd2c8023438c48494de2c6 | [] | no_license | kaniapiatkowska/ExData_Plotting1 | e5786f12120af6a2bb7d888e0813805e1a7236bf | 4d705024285651831e7f605d01f17580496a5168 | refs/heads/master | 2020-12-28T19:34:02.917674 | 2019-11-04T11:22:47 | 2019-11-04T11:22:47 | 45,624,088 | 0 | 0 | null | 2015-11-05T16:30:07 | 2015-11-05T16:30:07 | null | UTF-8 | R | false | false | 1,095 | r | plot3.R | #Loading the data
setwd("C:/Dane/MOJE/Zuzka/Coursera/DataScientistToolbox/Rscripts/Course4")
file<-"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(file, destfile = "./exdata_data_household_power_consumption.zip")
unzip("./exdata_data_household_power_consumption.zip", overwrite = TRUE)
data<-subset(read.csv("./household_power_consumption.txt", header = TRUE,
sep = ";", na.strings = "?"), Date=="1/2/2007" | Date=="2/2/2007")
data$Date<-strptime(paste(data$Date, data$Time), "%e/%m/%Y %H:%M:%S")
library(dplyr)
data<-select(data, -Time)
#plot3
png(filename = "plot3.png", width = 480, height = 480, units = "px")
plot(data$Date, data$Sub_metering_1, type = "l", xlab = NA,
ylab = "Energy sub metering")
points(data$Date, data$Sub_metering_2, type = "l", col="red")
points(data$Date, data$Sub_metering_3, type = "l", col = "blue")
legend("topright", lty = c(1,1,1), col = c("black","red","blue"),
legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
dev.off() |
bfa3827d5f8e763dec4e8be3ed41b6d91f417ac3 | c5fac476b276f2d1c65547ec4f89292d3abf8ba8 | /man/confint.Rd | c286b68867edb01454680ec4508f1476fe3ee803 | [] | no_license | dakep/complmrob | 1f3090343de2cb6319b87fae289047ff60049b92 | c904ac453cb501417acc1bb7ec41a3c80ecc4015 | refs/heads/master | 2020-05-09T12:11:05.675243 | 2019-09-17T18:25:06 | 2019-09-17T18:25:06 | 181,104,338 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,765 | rd | confint.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/confint-methods.R
\name{confint.bccomplmrob}
\alias{confint.bccomplmrob}
\alias{confint.bclmrob}
\title{Calculate confidence intervals}
\usage{
\method{confint}{bccomplmrob}(object, parm, level = 0.95,
type = c("bca", "perc", "norm", "basic", "stud"), ...)
\method{confint}{bclmrob}(object, parm, level = 0.95, type = c("bca",
"perc", "norm", "basic", "stud"), ...)
}
\arguments{
\item{object}{an object returned from \code{\link{bootcoefs}}.}
\item{parm}{a specification of which parameters are to be given confidence intervals, either a vector
of numbers or a vector of names. If missing, all parameters are considered.}
\item{level}{the confidence level required.}
\item{type}{the type of interval required (see the type argument of \code{\link{boot.ci}}).}
\item{...}{currently ignored.}
}
\description{
Calculate confidence intervals for bootstrapped robust linear regression estimates with or without
compositional data
}
\section{Methods (by class)}{
\itemize{
\item \code{bccomplmrob}: for bootstrapped estimates of robust linear regression models for compositional data
\item \code{bclmrob}: for bootstrapped estimates of robust linear regression models
}}
\examples{
data <- data.frame(lifeExp = state.x77[, "Life Exp"], USArrests[ , -3])
mUSArr <- complmrob(lifeExp ~ ., data = data)
bc <- bootcoefs(mUSArr, R = 200) # the number of bootstrap replicates should
# normally be higher!
confint(bc, level = 0.95, type = "perc")
### For normal robust linear regression models ###
require(robustbase)
data(aircraft)
mod <- lmrob(Y ~ ., data = aircraft)
bootEst <- bootcoefs(mod, R = 200)
confint(bootEst, level = 0.95, type = "perc")
}
|
45e087a178fdb01523fbc8a01d411cf0684e8f8e | 0471999fce7bfcba220ae361a843b9fc69af53e7 | /tests/testthat/test-docker-client-networks.R | 37401523942f4c88fad43f557324286bdfaa6867 | [
"MIT"
] | permissive | karthik/stevedore | e25d0c1fb9073de4979a22c69e92acb54d7ab2d6 | 8d12d3a02a211557ff264780a17a4789604ee40e | refs/heads/master | 2020-03-19T10:06:51.132342 | 2018-06-06T15:03:29 | 2018-06-06T15:03:29 | 136,344,185 | 0 | 0 | null | 2018-06-06T14:54:38 | 2018-06-06T14:54:37 | null | UTF-8 | R | false | false | 2,186 | r | test-docker-client-networks.R | context("docker client: networks")
test_that("create", {
d <- test_docker_client()
nm <- rand_str(10, "stevedore_")
nw <- d$network$create(nm)
on.exit(try_silent(nw$remove()))
expect_is(nw, "docker_network")
expect_is(nw, "stevedore_object")
expect_equal(nw$name(), nm)
expect_equal(nw$inspect()$name, nm)
expect_identical(nw$reload(), nw)
expect_null(nw$remove())
e <- get_error(nw$inspect())
expect_is(e, "docker_error")
expect_equal(e$code, 404L)
})
test_that("get", {
d <- test_docker_client()
nm <- rand_str(10, "stevedore_")
nw1 <- d$network$create(nm)
on.exit(try_silent(nw1$remove()))
nw2 <- d$network$get(nm)
expect_identical(nw1$inspect(FALSE), nw2$inspect(FALSE))
d$network$remove(nm)
e <- get_error(d$network$get(nm))
expect_is(e, "docker_error")
expect_equal(e$code, 404L)
})
test_that("list", {
d <- test_docker_client()
nm <- rand_str(10, "stevedore_")
nw <- d$network$create(nm)
on.exit(nw$remove())
nwl <- d$network$list()
expect_is(nwl, "data.frame")
expect_true("name" %in% names(nwl))
expect_true(nm %in% nwl$name)
})
test_that("prune", {
d <- test_docker_client()
nm <- rand_str(10, "stevedore_")
nw <- d$network$create(nm)
ans <- d$network$prune()
expect_match(ans$networks_deleted, "^stevedore_", all = FALSE)
})
test_that("containers", {
d <- test_docker_client()
server <- rand_str(10, "stevedore_")
network <- rand_str(3, "stevedore_")
d <- test_docker_client()
nw <- d$network$create(network)
on.exit(nw$remove())
expect_identical(nw$containers(), list())
x <- d$container$create("nginx", name = server, network = network)
on.exit({
x$remove(force = TRUE)
nw$remove()
})
x$start()
res <- nw$containers()
expect_is(res, "list")
expect_equal(length(res), 1L)
expect_is(res[[1]], "docker_container")
expect_identical(res[[1]]$id(), x$id())
})
test_that("connect", {
skip("connect is untested")
})
test_that("disconnect", {
skip("disconnect is untested")
})
test_that("get (offline)", {
cl <- null_docker_client()
x <- cl$network$get(dummy_id())
expect_is(x, "docker_network")
expect_equal(x$id(), dummy_id())
})
|
8bd0d0f071e7b1405d567055b70e056bfa48d1ce | 7a903df21dcb2d80c726137438068f6335582aa4 | /R/tidydf.R | 3189429581249d45e6c455589d1a10496817c8e6 | [] | no_license | anujkhare/iregnet | 7cb9b739c64afe55d7a15f5ac1485258c6dd7758 | 89cc904894495511b801f71ff99cbfed6043dd97 | refs/heads/master | 2023-06-25T11:41:50.890845 | 2019-08-22T16:29:41 | 2019-08-22T16:29:41 | 59,438,433 | 7 | 16 | null | 2023-06-15T14:42:55 | 2016-05-22T23:04:32 | R | UTF-8 | R | false | false | 1,108 | r | tidydf.R | #' @title Return a tidy data.frame from iregnet fit
#' @export
#' @description
#' Returns a tidy \link{data.frame} from a fitted "iregnet" object.
#'
#' @param x The S3 object of type \code{iregnet} returned by the \code{iregnet}
#' method.
#'
#' @param ... Other parameters. Currently unused.
#'
#' @details
#' This function is used to obtain an intermediate \code{data.frame} used in
#' \link{plot.iregnet}.
#' It can be used for producing other plots using \code{ggplot2}.
#' NOTE: \code{Intercept} (if present) is \strong{not} included in the
#' \code{arclength} since it is never regularized.
#'
tidydf <- function(x, ...) {
stopifnot_error("Invalid / no fit object provided",
class(x) == "iregnet")
# Don't include intercept in norm (arclength) since it is never regularized.
start_index <- as.integer(x$intercept) + 1
n <- nrow(x$beta)
arclength <- apply(x$beta, 2, function(x) sum(abs(x[start_index: n])))
tidy.df <- with(x, data.frame(
weight=as.numeric(t(beta)),
lambda,
arclength,
variable=rownames(beta)[as.integer(col(t(beta)))]
))
tidy.df
}
|
2261cb28eb4d9e233a1dbc2557979f3ec93121a8 | f0ddc6dfc85a777561ad0e2a86ca4679cca8ca33 | /Final R Script.R | 9d1952ea8c3c05b4f744fdf2932b6ff01e20ddbf | [] | no_license | sagar-aps/movie-rating-predictor | f0e11141314f5e88ffa79f411382c38333d287df | 3444d868fd931fe779433ac9a4fc84ad09696d13 | refs/heads/master | 2023-06-15T17:57:55.805663 | 2021-07-08T19:38:05 | 2021-07-08T19:38:05 | 346,516,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,754 | r | Final R Script.R |
# Dependencies
packages_required <-c("dplyr", # Wrangling
"ggplot2", #for graphics
"stringr", #string operations
"tidyr", # Wrangling
"caret", #for CreateDataPartition and train()
"ggthemes", #for graphics themes
"lubridate", #for dealing with dates
"tidyverse", # Wrangling
"data.table", #for wrangling
"Matrix", #for SparseMatrix
"recosystem", #for Matrix Factorization model
"ggthemes", #for ggplot theme economist
"recommenderlab",#for UBCF,IBCF, POPULAR models
"knitr" #for RMD options
)
using<-function(...) {
libs<-unlist(list(...))
req<-unlist(lapply(libs,require,character.only=TRUE))
need<-libs[req==FALSE]
if(length(need)>0){
install.packages(need)
lapply(need,require,character.only=TRUE)
}
}
using(packages_required)
# Create Initial Dataframes
# MovieLens 10M dataset:
# https://grouplens.org/datasets/movielens/10m/
# http://files.grouplens.org/datasets/movielens/ml-10m.zip
dl <- tempfile()
download.file("http://files.grouplens.org/datasets/movielens/ml-10m.zip", dl)
ratings <- fread(text = gsub("::", "\t", readLines(unzip(dl, "ml-10M100K/ratings.dat"))),
col.names = c("userId", "movieId", "rating", "timestamp"))
movies <- str_split_fixed(readLines(unzip(dl, "ml-10M100K/movies.dat")), "\\::", 3)
colnames(movies) <- c("movieId", "title", "genres")
# if using R 3.6 or earlier:
#movies <- as.data.frame(movies) %>% mutate(movieId = as.numeric(levels(movieId))[movieId],
# title = as.character(title),
# genres = as.character(genres))
# if using R 4.0 or later:
movies <- as.data.frame(movies) %>% mutate(movieId = as.numeric(movieId),
title = as.character(title),
genres = as.character(genres))
movielens <- left_join(ratings, movies, by = "movieId")
# Validation set will be 10% of MovieLens data
set.seed(1, sample.kind="Rounding") # if using R 3.5 or earlier, use `set.seed(1)`
test_index <- createDataPartition(y = movielens$rating, times = 1, p = 0.1, list = FALSE)
edx <- movielens[-test_index,]
temp <- movielens[test_index,]
# Make sure userId and movieId in validation set are also in edx set
validation <- temp %>%
semi_join(edx, by = "movieId") %>%
semi_join(edx, by = "userId")
# Add rows removed from validation set back into edx set
removed <- anti_join(temp, validation)
edx <- rbind(edx, removed)
rm(dl, ratings, movies, temp, movielens, removed,test_index)
# Basic Exploration
paste("The dataset has " , nrow(edx) , "ratings")
paste("There are " , ncol(edx), "columns")
#Checking data validity
paste("There are " , edx %>% filter(rating==0) %>% nrow(), "ratings = 0")
paste("There are " , edx %>% filter(rating>5) %>% nrow(), "ratings > 5")
paste("There are " , edx %>% distinct(movieId) %>% count(), "distinct movies")
paste("There are " ,edx %>% distinct(userId) %>% count(), "distinct users")
summary(edx)
glimpse(edx)
# how many movies have rating X?
edx %>%
group_by(rating) %>%
summarize(count = n()) %>%
ggplot(aes(x = rating, y = count)) +
geom_line()+
theme_economist()
# how many movies do users rate ?
edx %>%
group_by(userId)%>%
summarise(n=n())%>%
arrange(desc(n))%>%
ggplot(aes(n))+
geom_histogram(bins=200)+
scale_x_continuous(limits = c(0,750))+
theme_economist()
# how many ratings do individual movies usually receive?
edx %>%
group_by(movieId)%>%
summarise(n=n())%>%
arrange(desc(n))%>%
ggplot(aes(n))+
geom_histogram(bins=200)+
scale_x_continuous(limits = c(0,750))+
theme_economist()
# What is the most common rating a movie receives?
mu <- mean(edx$rating)
movie_avgs <- edx %>%
group_by(movieId) %>%
summarize(b_i = mean(rating - mu))
qplot(movie_avgs$b_i,bins=10,color=I("red"))
#How do most users rate?
mu <- mean(edx$rating)
user_avgs <- edx %>%
group_by(userId) %>%
summarize(b_u = mean(rating - mu))
qplot(user_avgs$b_u,bins=10,color=I("red"))
# Which genres get the best rating? What is their spread?
#The following code is decodes the genre data by adding a 0 or an NA based on
#whether the name of the genre is present in the genre column
n_a=NA
genre_data <- edx %>% mutate(
Romance = ifelse(str_detect(genres,"Romance"),1*rating,n_a),
Comedy = ifelse(str_detect(genres,"Comedy"),1*rating,n_a),
Action = ifelse(str_detect(genres,"Action"),1*rating,n_a),
Crime = ifelse(str_detect(genres,"Crime"),1*rating,n_a),
Thriller = ifelse(str_detect(genres,"Thriller"),1*rating,n_a),
Drama = ifelse(str_detect(genres,"Drama"),1*rating,n_a),
Sci_Fi = ifelse(str_detect(genres,"Sci-Fi"),1*rating,n_a),
Adventure = ifelse(str_detect(genres,"Adventure"),1*rating,n_a),
Children = ifelse(str_detect(genres,"Children"),1*rating,n_a),
Fantasy = ifelse(str_detect(genres,"Fantasy"),1*rating,n_a),
War = ifelse(str_detect(genres,"War"),1*rating,n_a),
Animation = ifelse(str_detect(genres,"Animation"),1*rating,n_a),
Musical = ifelse(str_detect(genres,"Musical"),1*rating,n_a),
Western = ifelse(str_detect(genres,"Western"),1*rating,n_a),
Mystery = ifelse(str_detect(genres,"Mystery"),1*rating,n_a),
Film_Noir = ifelse(str_detect(genres,"Film-Noir"),1*rating,n_a),
Horror = ifelse(str_detect(genres,"Horror"),1*rating,n_a),
Documentary = ifelse(str_detect(genres,"Documentary"),1*rating,n_a),
IMAX = ifelse(str_detect(genres,"IMAX"),1*rating,n_a)
)
#In order to make a errorbar plot , we now need a table containing genre and rating
# we first gather all the columns into a single values column called rating and then
# we filter out all the na rows
# later we calculate the 2se for making the error plot
genre_data %>%
select(Romance:IMAX) %>%
gather(., key="Genre",value = "rating") %>%
filter(is.na(rating)==FALSE) %>%
group_by(Genre) %>%
summarise(n = n(), avg = mean(rating), se = sd(rating)/sqrt(n)) %>%
mutate(Genre = reorder(Genre, avg)) %>%
ggplot(aes(x = Genre, y = avg, ymin = avg - 2*se, ymax = avg + 2*se)) +
geom_errorbar() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Error bar plots by Genre" , caption = "Separated Genres from edx dataset")
# Does the mean rating change over time?
#Here, we use functions from the lubridate package to extract the week from the timestamp
edx %>%
mutate(week = round_date(as_datetime(timestamp), unit = "week")) %>%
group_by(week) %>%
summarize(rating = mean(rating)) %>%
ggplot(aes(week, rating)) +
geom_point() +
geom_smooth() +
ggtitle("Rating evolution by week")+
theme_economist()
# Is a rating given at night different than one given in the morning?
edx %>%
mutate(hour = hour(as_datetime(timestamp))) %>%
group_by(hour) %>%
summarize(rating = mean(rating)) %>%
ggplot(aes(hour, rating)) +
geom_point() +
geom_smooth() +
ggtitle("Rating evolution by hour")+
theme_economist()
# Add columns for each genre
#We choose 0 or 1 as the output since this will make it easier to make a linear model
data <- edx %>% mutate(Romance = ifelse(str_detect(genres,"Romance"),1,0),
Comedy = ifelse(str_detect(genres,"Comedy"),1,0),
Action = ifelse(str_detect(genres,"Action"),1,0),
Crime = ifelse(str_detect(genres,"Crime"),1,0),
Thriller = ifelse(str_detect(genres,"Thriller"),1,0),
Drama = ifelse(str_detect(genres,"Drama"),1,0),
Sci_Fi = ifelse(str_detect(genres,"Sci-Fi"),1,0),
Adventure = ifelse(str_detect(genres,"Adventure"),1,0),
Children = ifelse(str_detect(genres,"Children"),1,0),
Fantasy = ifelse(str_detect(genres,"Fantasy"),1,0),
War = ifelse(str_detect(genres,"War"),1,0),
Animation = ifelse(str_detect(genres,"Animation"),1,0),
Musical = ifelse(str_detect(genres,"Musical"),1,0),
Western = ifelse(str_detect(genres,"Western"),1,0),
Mystery = ifelse(str_detect(genres,"Mystery"),1,0),
Film_Noir = ifelse(str_detect(genres,"Film-Noir"),1,0),
Horror = ifelse(str_detect(genres,"Horror"),1,0),
Documentary = ifelse(str_detect(genres,"Documentary"),1,0),
IMAX = ifelse(str_detect(genres,"IMAX"),1,0)
)
glimpse (data)
# Ready for modeling! Create test and train
#We divide the data into training and test sets.
#Before this, we set a seed to ensure that the work is reproducible
#The index already made in the first code chunk was to separate
#the validation set so we need to remake the index
#We choose an 80/20 split, the standard in ML
test_index <- createDataPartition(y = data$rating, times = 1, p = 0.2, list = FALSE)
train_set <- data[-test_index,]
test_set <- data[test_index,]
#We don't want movies or users in the test set that don't appear in the training set
test_set <- test_set %>%
semi_join(train_set, by = "movieId") %>%
semi_join(train_set, by = "userId")
#Validation set was already created in the code chunk Initial dataframes
# Function to create sparse matrix. Somehow, recommenderlab models change the prior data to its handy to have a function to reproduce it
#For using recommenderlab models, we need to make a sparse matrix and partition it
#using the inbuilt partition functions
create_sparse_m <- function(){
sparse_m <- sparseMatrix(
i = as.numeric(as.factor(edx$userId)),
j = as.numeric(as.factor(edx$movieId)),
x = edx$rating,
dims = c(length(unique(edx$userId)),
length(unique(edx$movieId))),
dimnames = list(paste("u", 1:length(unique(edx$userId)), sep = ""),
paste("m", 1:length(unique(edx$movieId)), sep = "")))
sparse_m <- new("realRatingMatrix", data = sparse_m)
return (sparse_m)
}
# Data Reduction
#90 percentile data selection
sparse_m <- create_sparse_m()
y = 0.9
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
# Function to calculate RMSE
RMSE <- function(true_ratings, predicted_ratings){
sqrt(mean((true_ratings - predicted_ratings)^2))
}
# Basic Model: predict mean
mu <- mean(data$rating)
#We make a a dataframe as long as test_set with mu repeated as R Markdown
#interprets the difference in length between mu and test_set as an error.
naive_rmse <- RMSE(test_set$rating,rep(mu,nrow(test_set)))
naive_rmse
rmse_results <- tibble(method = "Just the average", RMSE = naive_rmse)
# User and Movie model, non regularized
movie_avgs <- train_set %>%
group_by(movieId) %>%
summarize(b_i = mean(rating - mu))
user_avgs <- train_set %>%
left_join(movie_avgs, by="movieId") %>%
group_by(userId) %>%
summarise(b_u= mean(rating-b_i-mu))
predicted_ratings <- test_set %>%
left_join(movie_avgs, by="movieId")%>%
left_join(user_avgs, by="userId") %>%
mutate(pred = mu + b_i +b_u ) %>%
pull(pred)
User_And_Movie_effect <- RMSE(test_set$rating,predicted_ratings)
User_And_Movie_effect
rmse_results <- add_row(rmse_results,method = "Movie Average and User Average",
RMSE = User_And_Movie_effect)
# Motivation for regularization
#Checking errors to see which are the one we got wrong
nmbr_ratings <- data %>% group_by(movieId) %>% summarise(n=n())
test_set %>%
left_join(movie_avgs, by='movieId') %>%
left_join(user_avgs, by="userId") %>%
left_join(nmbr_ratings, by = "movieId") %>%
mutate(residual = rating - (mu + b_i + b_u)) %>%
arrange(desc(abs(residual))) %>%
slice(1:10) %>%
select(title, residual, n) %>%
knitr::kable()
# Regularized user and movie model
lambdas <- seq(0, 10, 0.25)
rmses <- sapply(lambdas, function(l){
mu <- mean(train_set$rating)
b_i <- train_set %>%
group_by(movieId) %>%
summarize(b_i = sum(rating - mu)/(n()+l))
b_u <- train_set %>%
left_join(b_i, by="movieId") %>%
group_by(userId) %>%
summarize(b_u = sum(rating - b_i - mu)/(n()+l))
predicted_ratings <-
test_set %>%
left_join(b_i, by = "movieId") %>%
left_join(b_u, by = "userId") %>%
mutate(pred = mu + b_i + b_u) %>%
pull(pred)
return(RMSE(predicted_ratings, test_set$rating))
})
lambda <- lambdas[which.min(rmses)]
b_i <- train_set %>%
group_by(movieId) %>%
summarize(b_i = sum(rating - mu)/(n()+lambda))
b_u <- train_set %>%
left_join(b_i, by="movieId") %>%
group_by(userId) %>%
summarize(b_u = sum(rating - b_i - mu)/(n()+lambda))
gc()
qplot(lambdas,rmses)
rmse_results <- rmse_results %>% add_row(method ="Regularized user & Movie effects", RMSE =min(rmses))
rmse_results
# Regularized User Movie, Hour model
lambdas <- seq(0, 10, 0.25)
rmses <- sapply(lambdas, function(l){
mu <- mean(train_set$rating)
b_i <- train_set %>%
group_by(movieId) %>%
summarize(b_i = sum(rating - mu)/(n()+l))
b_u <- train_set %>%
left_join(b_i, by="movieId") %>%
group_by(userId) %>%
summarize(b_u = sum(rating - b_i - mu)/(n()+l))
b_t <- train_set %>%
left_join(b_i, by="movieId") %>%
left_join(b_u, by="userId") %>%
mutate(hour = hour(as_datetime(timestamp))) %>%
group_by(hour) %>%
summarize(b_t = sum(rating - mu - b_i - b_u)/(n()+l) )
predicted_ratings <-
test_set %>%
mutate(hour = hour(as_datetime(timestamp))) %>%
left_join(b_i, by = "movieId") %>%
left_join(b_u, by = "userId") %>%
left_join(b_t, by = "hour") %>%
mutate(pred = mu + b_i + b_u + b_t) %>%
pull(pred)
return(RMSE(predicted_ratings, test_set$rating))
})
lambda <- lambdas[which.min(rmses)]
b_i <- train_set %>%
group_by(movieId) %>%
summarize(b_i = sum(rating - mu)/(n()+lambda))
b_u <- train_set %>%
left_join(b_i, by="movieId") %>%
group_by(userId) %>%
summarize(b_u = sum(rating - mu -b_i)/(n()+lambda))
b_t <- train_set %>%
left_join(b_i, by="movieId") %>%
left_join(b_u, by="userId") %>%
mutate(hour = hour(as_datetime(timestamp))) %>%
group_by(hour) %>%
summarize(b_t = sum(rating - mu - b_i - b_u)/(n()+lambda) )
qplot(lambdas,rmses)
gc()#Garbage collection
rmse_results <- rmse_results %>% add_row(method ="Regularized user,movie,time effects",
RMSE =min(rmses))
# Regularized user movie, time model with lm genre
genre_train <- train_set %>%
mutate(hour = hour(as_datetime(timestamp))) %>%
left_join(b_i, by = "movieId") %>%
left_join(b_u, by = "userId") %>%
left_join(b_t, by = "hour") %>%
mutate(residual = rating - mu- b_i-b_u-b_t )
temp <- genre_train %>% select(c(Romance:IMAX,residual))
lm_fit <- lm(residual ~ ., data=temp)
temp2 <- test_set %>% select(c(Romance:IMAX))
pred <- predict.lm(lm_fit, newdata = temp2)
predicted_ratings <-
test_set %>%
mutate(hour = hour(as_datetime(timestamp))) %>%
left_join(b_i, by = "movieId") %>%
left_join(b_u, by = "userId")%>%
left_join(b_t, by = "hour") %>%
mutate(pred = mu + b_i + b_u + b_t + pred) %>%
pull(pred)
rmse_genre_effect <- RMSE(predicted_ratings, test_set$rating)
rmse_results <- rmse_results %>% add_row(method ="Regularized user,movie,time effects with Genre (lm)",
RMSE =min(rmse_genre_effect))
rm(temp,temp2)
rmse_results
# UBCF Model
y=0.9
sparse_m <- create_sparse_m()
nn=15
set.seed(1991)
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
sparse_m_limited <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user,
colCounts(sparse_m)>=min_ratings_per_movie]
e4 <- evaluationScheme(sparse_m_limited , method="split", train=0.9,
k=1, given=8)
r_UBCF <- Recommender(getData(e4, "train"), "UBCF",parameter=c(nn=nn))
p_UBCF <- predict(r_UBCF, getData(e4, "known"), type="ratings")
rmse_UBCF <- calcPredictionAccuracy(p_UBCF, getData(e4, "unknown"))[1]
rmse_results <- rmse_results %>% add_row(method ="UBCF using reccomenderlab",
RMSE =rmse_UBCF)
# IBCF Model
y=0.9
sparse_m <- create_sparse_m()
nn=15
set.seed(1991)
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
sparse_m_limited <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user,
colCounts(sparse_m)>=min_ratings_per_movie]
model_ibcf <- Recommender(sparse_m_limited, method="IBCF", param=list(normalize="center"))
#Testing prediction
#pred_pop <- predict(model_ibcf, sparse_m[1:10], type="ratings")
#as(pred_pop, "matrix")[,1:10]
#Finding RMSE
e6 <- evaluationScheme(sparse_m_limited , method="split", train=0.9,
k=1, given=8)
p_IBCF <- predict(model_ibcf, getData(e6, "known"), type="ratings")
rmse_IBCF <- calcPredictionAccuracy(p_IBCF, getData(e6, "unknown"))[1]
rmse_results <- rmse_results %>% add_row(method ="IBCF using reccomenderlab", RMSE =rmse_IBCF)
# Popular model
sparse_m <- create_sparse_m()
#Reduction parameter
y=0.9
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
sparse_m_limited <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user,
colCounts(sparse_m)>=min_ratings_per_movie]
#Making the model
model_popular <- Recommender(sparse_m_limited, method="POPULAR", param=list(normalize="center"))
#Testing prediction
#Finding RMSE
e5 <- evaluationScheme(sparse_m_limited , method="split", train=0.9,
k=1, given=8)
p_POPULAR <- predict(model_popular, getData(e5, "known"), type="ratings")
rmse_POP <- calcPredictionAccuracy(p_POPULAR, getData(e5, "unknown"))[1]
rmse_results <- rmse_results %>% add_row(method ="POPULAR using reccomenderlab", RMSE =rmse_POP)
# Recosystem Matrix factorization model
#we select only the userID, MovieID and rating in the next three statements
test_GD <- as.matrix (test_set [,1:3])
train_GD <- as.matrix(test_set [,1:3])
set.seed(1)
#We need to build data stream objects as these are accepted inputs for this algorithm
train_GD_2 <- data_memory(train_GD[,1],train_GD[,2],train_GD[,3])
test_GD_2 <- data_memory(test_GD[,1],test_GD[,2],test_GD[,3])
#Next step is to build Recommender object
r = Reco()
# Matrix Factorization : tuning training set
# lrate is the gradient descend step rate
# dim are the number of latent factors
# nthread is number of threads to use : REDUCE IF YOUR PROCESSOR DOESN'T SUPPORT 6 THREADS
opts = r$tune(train_GD_2, opts = list(dim = c(10, 20, 30), lrate = c(0.1, 0.2),
costp_l1 = 0, costq_l1 = 0,
nthread = 6, niter = 10))
r$train(train_GD_2, opts = c(opts$min, nthread = 6, niter = 20))
pred <- r$predict(test_GD_2, out_memory())
rmse_MFGD <- RMSE(pred,test_set$rating)
rmse_results <- rmse_results %>% add_row(method ="Matrix Factorization using recosystem",
RMSE =rmse_MFGD)
rmse_results
# Checking RMSE on validation set with MF model from recosystem
#Selecting only pertinent columns
valid_GD <-as.matrix(validation [,1:3])
valid_GD_2 <- data_memory(valid_GD[,1],valid_GD[,2],valid_GD[,3])
#selecting to output to variable
pred <- r$predict(valid_GD_2, out_memory())
rmse_MFGD_final <- RMSE(pred,validation$rating)
rmse_results <- rmse_results %>% add_row(method ="Matrix Factorization using recosystem : FINAL Validation RMSE ",
RMSE =rmse_MFGD_final)
options(pillar.sigfig = 10)
rmse_results
#------- END -----
# Testing of reccomenderlab models
#RecommenderLab Full Redo
install.packages("tictoc")
library(recommenderlab)
library(dplyr)
library(Matrix)
library(tictoc)
create_sparse_m <- function(){
sparse_m <- sparseMatrix(
i = as.numeric(as.factor(edx$userId)),
j = as.numeric(as.factor(edx$movieId)),
x = edx$rating,
dims = c(length(unique(edx$userId)),
length(unique(edx$movieId))),
dimnames = list(paste("u", 1:length(unique(edx$userId)), sep = ""),
paste("m", 1:length(unique(edx$movieId)), sep = "")))
sparse_m <- new("realRatingMatrix", data = sparse_m)
return (sparse_m)
}
identical(sparse_m,sparse_m2)
create_sparse_m()
as(sparse_m[1:10,1:10],"matrix")
as(sparse_m2[1:10,1:10],"matrix")
tic("Full r_UBCF_nn_25_given~15/36")
## create 90/10 split (known/unknown) for the first 500 users in Jester5k
given <- min(rowCounts(sparse_m))
e <- evaluationScheme(sparse_m, method="split", train=0.9,
k=1, given=4)
e
## create a user-based CF recommender using training data
r <- Recommender(getData(e, "train"), "UBCF", parameter= c(nn=15))
## create predictions for the test data using known ratings (see given above)
p <- predict(r, getData(e, "known"), type="ratings")
p
as(p[1:5,1:5],"matrix")
## compute error metrics averaged per user and then averaged over all
## recommendations
rmse_UBCF <-calcPredictionAccuracy(p, getData(e, "unknown"))
#Above statement executed to give an RMSE of 1.08 on the whole dataset with all standard parameters.
#The predict function took ~8 hours to execute
#head(calcPredictionAccuracy(p, getData(e, "unknown"), byUser=TRUE))
gc()
toc()
#seq(0,1000,by=100)
#sparse_m <- new("realRatingMatrix", data = sparse_m)
#hist(rowCounts(sparse_m)[rowCounts(sparse_m) >= 0 &rowCounts(sparse_m)< 1000], xlim=c(0,1000),breaks = seq(0,1000,by=25), main="Movie Rating Count")
#median(rowCounts(sparse_m))
#quantile(rowCounts(sparse_m),0.25)
#Making a model with the entire set of users and movies takes 8 hours
#the median user rates 51 movies
#Let us eliminate the bottom x percentiles.
#hist(colCounts(sparse_m)[colCounts(sparse_m)<2000], main="Ratings by movie Count", xlim = c(0,2000))
#quantile(colCounts(sparse_m))
x<-0.25
min_movies_by_user <- quantile(rowCounts(sparse_m),x)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),x)
sparse_m_limited <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user, colCounts(sparse_m)>=min_ratings_per_movie]
#tail(recommenderRegistry$get_entries(dataType = "realRatingMatrix"), 1)
tic("75 pc r_UBCF_nn_25_given_4")
#min(rowCounts(sparse_m_limited))
dim(sparse_m_limited) - dim(sparse_m)
## create 90/10 split (known/unknown) for the first 500 users in Jester5k
e2 <- evaluationScheme(sparse_m_limited , method="split", train=0.9,
k=1, given=4)
r_UBCF_nn_25 <- Recommender(getData(e2, "train"), "UBCF")
## create predictions for the test data using known ratings (see given above)
p_UBCF_nn_25 <- predict(r_UBCF_nn_25, getData(e2, "known"), type="ratings")
p_UBCF_nn_25
## compute error metrics averaged per user and then averaged over all
## recommendations
rmse_UBCF_limited_data <- calcPredictionAccuracy(p_UBCF_nn_25, getData(e2, "unknown"))
toc()
test_percentiles <- seq(0.95,0.25,-0.10)
#Checking time taken by percentile exclusion of data
time_and_rmse <- sapply(test_percentiles, function(y){
tic(y)
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
sparse_m_limited_t <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user, colCounts(sparse_m)>=min_ratings_per_movie]
e3 <- evaluationScheme(sparse_m_limited_t , method="split", train=0.9,
k=1, given=4)
r_UBCF_nn_25_t <- Recommender(getData(e3, "train"), "UBCF")
p_UBCF_nn_25_t <- predict(r_UBCF_nn_25_t, getData(e3, "known"), type="ratings")
rmse_ <- calcPredictionAccuracy(p_UBCF_nn_25_t, getData(e3, "unknown"))[1]
print(rmse_)
return(list(perc = y,time = toc(),rmse = rmse_))
})
#Checking response of rmse to change in nn keeping percentile reduction = 0.9
tic.clearlog()
test_nn <- seq(10,50,5)
time_and_rmse <- sapply(test_nn, function(nn){
y=0.9
tic(nn)
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
sparse_m_limited_nn <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user, colCounts(sparse_m)>=min_ratings_per_movie]
e4 <- evaluationScheme(sparse_m_limited_nn , method="split", train=0.9,
k=1, given=4)
r_UBCF_nn <- Recommender(getData(e4, "train"), "UBCF",parameter=c(nn=nn))
p_UBCF_nn <- predict(r_UBCF_nn, getData(e4, "known"), type="ratings")
rmse_ <- calcPredictionAccuracy(p_UBCF_nn, getData(e4, "unknown"))[1]
toc(log=TRUE,quiet = FALSE)
print(paste(nn," is nn. ", rmse_, " is RMSE" ))
return(list(perc = nn, rmse = rmse_))
})
#RMSE minimum at nn=25
#Checking response of rmse to change in given keeping percentile reduction = 0.2
min(rowCounts(sparse_m))
tic.clearlog()
test_given <- seq(3,8,1)
given_test <- sapply(test_given, function(g){
y=0.2
tic(g)
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
sparse_m_limited <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user, colCounts(sparse_m)>=min_ratings_per_movie]
e4 <- evaluationScheme(sparse_m_limited , method="split", train=0.9,
k=1, given=g)
r_UBCF <- Recommender(getData(e4, "train"), "UBCF",parameter=c(nn=25))
p_UBCF <- predict(r_UBCF, getData(e4, "known"), type="ratings")
rmse_ <- calcPredictionAccuracy(p_UBCF, getData(e4, "unknown"))[1]
toc(log=TRUE,quiet = FALSE)
print(paste(g," is given. ", rmse_, " is RMSE" ))
return(list(given = g, rmse = rmse_))
})
log_g1 <- tic.log(format = TRUE)
given_test
#increasing given reduced rmse until given was 7. Then reduced.
#Checking response of rmse to change in given with no reduction of data
min(rowCounts(sparse_m))
tic.clearlog()
test_given <- seq(3,8,1)
given_test_2 <- sapply(test_given, function(g){
y=0.2
tic(g)
min_movies_by_user <- 0
min_ratings_per_movie<- 0
sparse_m_limited <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user, colCounts(sparse_m)>=min_ratings_per_movie]
e4 <- evaluationScheme(sparse_m_limited , method="split", train=0.9,
k=1, given=g)
r_UBCF <- Recommender(getData(e4, "train"), "UBCF",parameter=c(nn=25))
p_UBCF <- predict(r_UBCF, getData(e4, "known"), type="ratings")
rmse_ <- calcPredictionAccuracy(p_UBCF, getData(e4, "unknown"))[1]
toc(log=TRUE,quiet = FALSE)
print(paste(g," is given. ", rmse_, " is RMSE" ))
return(list(given = g, rmse = rmse_))
})
log_g2 <- tic.log(format = TRUE)
#One iteration ran in 17.6 K secs and RMSE 2926 (bullshit)
#Given Test 3 : Adding sparse_m creation function and gc()
#Checking response of rmse to change in given keeping percentile reduction = 0.9
min(rowCounts(sparse_m))
tic.clearlog()
test_given <- seq(8,7,-1)
given_test_3 <- sapply(test_given, function(g){
y=0.9
tic(g)
sparse_m <- create_sparse_m()
print(identical(sparse_m,sparse_m2))
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
sparse_m_limited <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user, colCounts(sparse_m)>=min_ratings_per_movie]
e4 <- evaluationScheme(sparse_m_limited , method="split", train=0.9,
k=1, given=g)
r_UBCF <- Recommender(getData(e4, "train"), "UBCF",parameter=c(nn=25))
p_UBCF <- predict(r_UBCF, getData(e4, "known"), type="ratings")
rmse_ <- calcPredictionAccuracy(p_UBCF, getData(e4, "unknown"))[1]
toc(log=TRUE,quiet = FALSE)
print(paste(g," is given. ", rmse_, " is RMSE" ))
gc()
print(identical(sparse_m,sparse_m2))
return(list(given = g, rmse = rmse_))
})
log_g1 <- tic.log(format = TRUE)
given_test_3
#FINALLY!!! given 7 decreased rmse to 1.009 from 1.029
#Testing with different quantities of data from 0.9 - 0.5 and for given from 8-4 , nn from 15 to 55
test_perc_given_nn <- expand.grid(perc=seq(0.9,0.5,-0.1),given=seq(8,4,-1),nn=seq(15,55,10))
#min(rowCounts(sparse_m))
tic.clearlog()
#toString(test_perc_given_nn[1,],)
#test_perc_given_nn[1,1]
full_test_UBCF <- apply(test_perc_given_nn,1, function(t){
print( paste("Now running model :", t[1], " is perc ", t[2]," is given. ", t[3]," is nn. "))
tic(toString(t))
y=t[1]
sparse_m <- create_sparse_m()
nn=t[3]
print(identical(sparse_m,sparse_m2))
set.seed(1991)
min_movies_by_user <- quantile(rowCounts(sparse_m),y)
min_ratings_per_movie<- quantile(rowCounts(sparse_m),y)
sparse_m_limited <- sparse_m[rowCounts(sparse_m)>=min_movies_by_user, colCounts(sparse_m)>=min_ratings_per_movie]
e4 <- evaluationScheme(sparse_m_limited , method="split", train=0.9,
k=1, given=t[2])
r_UBCF <- Recommender(getData(e4, "train"), "UBCF",parameter=c(nn=nn))
p_UBCF <- predict(r_UBCF, getData(e4, "known"), type="ratings")
rmse_ <- calcPredictionAccuracy(p_UBCF, getData(e4, "unknown"))[1]
toc(log=TRUE,quiet = FALSE)
print(paste( rmse_, " is RMSE"))
gc()
print(identical(sparse_m,sparse_m2))
return(list(perc = t[1] , given = t[2], nn=t[3] , rmse = rmse_))
})
log_full_test <- tic.log(format = TRUE)
full_test_UBCF
log_full_test
df <- data.frame(matrix(unlist(full_test_UBCF), nrow=125, byrow=TRUE),stringsAsFactors=FALSE)
df
which.min(df$X4)
min(df$X4)
#Best parameters are : perc = 0.9. given = 8, nn=15
#somehow, 8 validation set items aren't a part of the test and validation set
# Probably because we didn't semijoin on genre
#With only 8 it wont make a difference to RMSE
na_index <- c(50641,118361,223470,487503,674188,688404,800929,830872)
8/nrow(validation)
val_set <- validation[-na_index]
pred <- predict.lm(lm_fit, newdata = temp2)
predicted_ratings_validation
val_set <- val_set %>%
mutate(Romance = ifelse(str_detect(genres,"Romance"),1,0),
Comedy = ifelse(str_detect(genres,"Comedy"),1,0),
Action = ifelse(str_detect(genres,"Action"),1,0),
Crime = ifelse(str_detect(genres,"Crime"),1,0),
Thriller = ifelse(str_detect(genres,"Thriller"),1,0),
Drama = ifelse(str_detect(genres,"Drama"),1,0),
Sci_Fi = ifelse(str_detect(genres,"Sci-Fi"),1,0),
Adventure = ifelse(str_detect(genres,"Adventure"),1,0),
Children = ifelse(str_detect(genres,"Children"),1,0),
Fantasy = ifelse(str_detect(genres,"Fantasy"),1,0),
War = ifelse(str_detect(genres,"War"),1,0),
Animation = ifelse(str_detect(genres,"Animation"),1,0),
Musical = ifelse(str_detect(genres,"Musical"),1,0),
Western = ifelse(str_detect(genres,"Western"),1,0),
Mystery = ifelse(str_detect(genres,"Mystery"),1,0),
Film_Noir = ifelse(str_detect(genres,"Film-Noir"),1,0),
Horror = ifelse(str_detect(genres,"Horror"),1,0),
Documentary = ifelse(str_detect(genres,"Documentary"),1,0),
IMAX = ifelse(str_detect(genres,"IMAX"),1,0)) %>%
mutate(hour = hour(as_datetime(timestamp))) %>%
left_join(b_i, by = "movieId") %>%
left_join(b_u, by = "userId")%>%
left_join(b_t, by = "hour")
genre_pred <- predict.lm(lm_fit, newdata = val_set)
predicted_ratings_Validation<-val_set%>%
cbind(genre_pred)%>%
mutate(pred = mu + b_i + b_u + b_t + genre_pred) %>%
pull(pred)
#na_index <-which(is.na(predicted_ratings_Validation))
RMSE(predicted_ratings_Validation,val_set$rating)
|
51666f0698e5ff9b2c788774011ffcc073ecf92a | 57b72c064f833f4a8ed2535ed4b05d90a002e83c | /R/DatabaseConnector-internal.R | c6c052931e9af02cbacc06efb1affabaf0175e2e | [] | no_license | writetoritu/DatabaseConnector | ebcfb34253c3b1cb42013dd7bae8886ab7ec46cd | c8deda5a051a89e75c2686a471e1fdadd979f7ef | refs/heads/master | 2021-01-22T20:39:04.312528 | 2014-12-29T19:42:22 | 2014-12-29T19:42:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,368 | r | DatabaseConnector-internal.R | .Random.seed <-
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|
d2107ae7168d6a3d83997e2ffa8ba8ddaf4f052b | 8b756c46656cef6637e8b21c79b0b180747453c6 | /projekt2.R | 29bc117898374ffea60938500accf5d935feeaba | [] | no_license | knycz/shiny_macro | b5680d0ce1fe620ef4688ff1ab9d8f5051d181f0 | 1938ad1a0c1719e778775af328d722665faaf162 | refs/heads/main | 2023-01-11T22:40:08.854617 | 2020-11-10T03:34:49 | 2020-11-10T03:34:49 | 311,534,711 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,255 | r | projekt2.R | #available on https://nomicten.shinyapps.io/WIZN/
library("shiny")
library("tidyr")
library("dplyr")
library("XML")
library("ggplot2")
library("grid")
library("plotly")
library("devtools")
library("WDI")
library("usethis")
### List of countries ###
WDI::WDI(extra = T) %>% filter(region != "NA") %>% select(iso2c, country) -> countries #NA instead of Aggregates
countries <- as.data.frame(cbind(unique(countries$iso2c), unique(countries$country)))
regions <- list("East Asia & Pacific", "Europe & Central Asia", "Latin America & Caribbean",
"Middle East & North Africa", "North America", "South Asia", "Sub-Saharan Africa")
### DATA ###
gdp_growth_an <- WDI(indicator ="NY.GDP.MKTP.KD.ZG", extra = T)
gdp_growth_an_world <- subset(gdp_growth_an, country == "World")
gdp_growth_an_world %>% select(NY.GDP.MKTP.KD.ZG, year) -> gdp_growth_an_world
gdp_growth_an <- subset(gdp_growth_an, region != "NA")
gdp_growth_pc <- WDI(indicator = "5.51.01.10.gdp", extra = T)
gdp_growth_pc <- subset(gdp_growth_pc, region != "NA")
gdp_level_2011 <- WDI(indicator = "NY.GDP.MKTP.KD", extra = T)
gdp_level_2010_world <- subset(gdp_level_2011, country == "World")
gdp_level_2011 <- subset(gdp_level_2011, region != "NA")
gdp_level_2011 %>% rename(gdp_lvl2010const = NY.GDP.MKTP.KD) %>% arrange(country, year) -> gdp_level_2011
gdp_level_2011 %>% group_by(country) %>% mutate(change=(gdp_lvl2010const-lag(gdp_lvl2010const,1))/lag(gdp_lvl2010const,1)*100) %>% as.data.frame -> gdp_level_2011
gdp_level_2010_world %>% rename(gdp_lvl2010const = NY.GDP.MKTP.KD) %>% arrange(country, year) -> gdp_level_2010_world
gdp_level_2010_world %>% group_by(country) %>% mutate(change=(gdp_lvl2010const-lag(gdp_lvl2010const,1))/lag(gdp_lvl2010const,1)*100) %>% as.data.frame -> gdp_level_2010_world
#Define UI for app that draws a histogram ----
ui <- navbarPage("Macro in a nutshell",
tabPanel("Global growth",
fluidPage(
titlePanel("World Development Indicators"),
sidebarLayout(
sidebarPanel(
sliderInput(inputId = "bins",
label = h3("Group countries into subgroups"),
min = 1,
max = 215,
value = 30),
sliderInput(inputId = "selectedyear", label = h3("Select year"),
min = min(gdp_growth_an$year)+1,
max = max(gdp_growth_an$year)-1,
value = max(gdp_growth_an$year)-1, step = 1),
sliderInput("growthrange", label = h3("Range of GDP growth"),
min = round(min(gdp_growth_an$NY.GDP.MKTP.KD.ZG, na.rm = T),-1),
max = round(max(gdp_growth_an$NY.GDP.MKTP.KD.ZG, na.rm = T),-1),
value = c(-20, 20))
),
mainPanel(
plotOutput(outputId = "distPlot")
)
)
)
),
tabPanel("Country profile",
fluidPage(
titlePanel("Demographics"),
sidebarLayout(
sidebarPanel(
selectInput("country", label = h3("Select country"),
choices = countries[,2],
selected = "Poland"),
sliderInput(inputId = "year", label = h3("Select year"),
min = 1960,
max = 2018,
value = 2018, step = 1, animate = T,
animationOptions(interval = 150,
loop = FALSE,
playButton = "play",
pauseButton = "pause"))
),
mainPanel(
plotlyOutput(outputId = "population_structure"),
plotlyOutput(outputId = "population_total"),
plotlyOutput(outputId = "TFR")
)
)
)
),
tabPanel("International comparison",
fluidPage(
titlePanel("Comparison"),
sidebarLayout(
sidebarPanel(
selectInput("indicator100", label = h3("Select indicator"),
choices = list(
# "Per capita GDP growth" = "5.51.01.10.gdp" ,
# "GDP (current $)" = "6.0.GDP_current" ,
# "GDP growth (annual %)" = "6.0.GDP_growth" ,
# "GDP (constant 2005 $)" = "6.0.GDP_usd" ,
# "GDP per capita, PPP (constant 2011 international $) " = "6.0.GDPpc_constant" ,
# "Trade in services (% of GDP)" = "BG.GSR.NFSV.GD.ZS" ,
# "Gross private capital flows (% of GDP, PPP)" = "BG.KAC.FNEI.GD.PP.ZS" ,
# "Gross private capital flows (% of GDP)" = "BG.KAC.FNEI.GD.ZS" ,
# "Gross foreign direct investment (% of GDP, PPP)" = "BG.KLT.DINV.GD.PP.ZS" ,
# "Gross foreign direct investment (% of GDP)" = "BG.KLT.DINV.GD.ZS" ,
# "Wage bill as a percentage of GDP" = "BI.WAG.TOTL.GD.ZS" ,
# "Merchandise imports (BOP): percentage of GDP (%)" = "BM.GSR.MRCH.ZS" ,
# "Foreign direct investment, net outflows (% of GDP)" = "BM.KLT.DINV.GD.ZS" ,
# "Foreign direct investment, net outflows (% of GDP)" = "BM.KLT.DINV.WD.GD.ZS" ,
# "Current account balance (% of GDP)" = "BN.CAB.XOKA.GD.ZS" ,
# "Current account balance (% of GDP)" = "BN.CAB.XOKA.GDP.ZS" ,
# "Curr. acc. bal. before official transf. (% of GDP)" = "BN.CAB.XOTR.ZS" ,
# "Current account balance excluding net official capital grants (% of GDP)" = "BN.CUR.GDPM.ZS" ,
# "Net income (% of GDP)" = "BN.GSR.FCTY.CD.ZS" ,
# "Foreign direct investment (% of GDP)" = "BN.KLT.DINV.CD.ZS" ,
"GDP per capita, PPP (constant 2011 international $)" = "NY.GDP.PCAP.PP.KD",
"Urban population (% of total)" = "SP.URB.TOTL.IN.ZS"),
selected = "NY.GDP.PCAP.PP.KD"
),
sliderInput(inputId = "year1", label = h3("Select year"),
min = 1990,
max = 2018,
value = 2018, step = 1, animate = F
),
sliderInput("slider2", label = h3("Position in the ranking"), min = 1,
max = 215, value = c(1, 10)
)
# ,
# checkboxGroupInput("region", label = h3("Choose area"),
# choices = regions)
),
mainPanel(
plotlyOutput(outputId = "international_comparison")
)
)
)
)
)
server <- function(input, output) {
### reactive expressions ###
country_code <- reactive({
countries %>% filter(V2 == input$country) -> tmp
as.character(tmp[,1])
})
population_data <- reactive({
piramida_ma <- c("SP.POP.0004.MA","SP.POP.0509.MA","SP.POP.1014.MA","SP.POP.1519.MA",
"SP.POP.2024.MA","SP.POP.2529.MA","SP.POP.3034.MA","SP.POP.3539.MA",
"SP.POP.4044.MA","SP.POP.4549.MA","SP.POP.5054.MA","SP.POP.5559.MA",
"SP.POP.6064.MA","SP.POP.6569.MA","SP.POP.7074.MA","SP.POP.7579.MA",
"SP.POP.80UP.MA")
datatmp_ma <- WDI(indicator = piramida_ma,
country = country_code(), extra = T)
datatmp_ma$id <- rownames(datatmp_ma)
datatmp_ma %>% select(id, country, year, SP.POP.0004.MA:SP.POP.80UP.MA) -> datatmp_ma
datatmp_ma_narrow <- gather(datatmp_ma, age, population, SP.POP.0004.MA:SP.POP.80UP.MA)
datatmp_ma_narrow %>% mutate(gender = "Male") -> datatmp_ma_narrow
datatmp_ma_narrow$age <- paste(substr(datatmp_ma_narrow$age,8,9), "-",
substr(datatmp_ma_narrow$age,10,11))
datatmp_ma_narrow$population <- -datatmp_ma_narrow$population
piramida_fe <- c("SP.POP.0004.FE","SP.POP.0509.FE","SP.POP.1014.FE","SP.POP.1519.FE",
"SP.POP.2024.FE","SP.POP.2529.FE","SP.POP.3034.FE","SP.POP.3539.FE",
"SP.POP.4044.FE","SP.POP.4549.FE","SP.POP.5054.FE","SP.POP.5559.FE",
"SP.POP.6064.FE","SP.POP.6569.FE","SP.POP.7074.FE","SP.POP.7579.FE",
"SP.POP.80UP.FE")
datatmp_fe <- WDI(indicator = piramida_fe,
country = country_code(), extra = T)
datatmp_fe$id <- rownames(datatmp_fe)
datatmp_fe %>% select(id, country, year, SP.POP.0004.FE:SP.POP.80UP.FE) -> datatmp_fe
datatmp_fe_narrow <- gather(datatmp_fe, age, population, SP.POP.0004.FE:SP.POP.80UP.FE)
datatmp_fe_narrow %>% mutate(gender = "Female") -> datatmp_fe_narrow
datatmp_fe_narrow$age <- paste(substr(datatmp_fe_narrow$age,8,9), "-",
substr(datatmp_fe_narrow$age,10,11))
datatmp <- rbind(datatmp_ma_narrow, datatmp_fe_narrow)
return(datatmp)
})
TFR_data <- reactive({
WDI(indicator = "SP.DYN.TFRT.IN", country = country_code(), extra = T) %>% rename(TFR = SP.DYN.TFRT.IN) -> datatmp
return(datatmp)
})
###
range <- reactive({
return(seq(input$slider2[1], input$slider2[2]))
})
data_indicator <- reactive({
WDI(indicator = as.character(input$indicator100), extra = T) -> temp
return(temp)
})
year_indicator <- reactive({
data_indicator() %>% filter(year == input$year1) -> temp
return(temp)
})
region_indicator <- reactive({
if(is.null(input$region)){
year_indicator() %>% filter(region != "NA") %>% arrange(-.[,3]) -> temp
} else {
year_indicator() %>% filter(region != "NA") %>% filter(region == input$region) %>% arrange(-.[,3]) -> temp
}
return(temp)
})
#############################################
output$distPlot <- renderPlot({
x <- gdp_growth_an %>% filter(year == input$selectedyear) %>% select(NY.GDP.MKTP.KD.ZG)
x$positive <- (x$NY.GDP.MKTP.KD.ZG >= 0)
bins <- seq(min(x$NY.GDP.MKTP.KD.ZG, na.rm = T), max(x$NY.GDP.MKTP.KD.ZG, na.rm = T), length.out = input$bins + 1)
p1 <- ggplot(x, aes(x=NY.GDP.MKTP.KD.ZG, fill = positive)) +
geom_histogram(bins = input$bins + 1, colour = "white") +
geom_vline(xintercept = gdp_growth_an_world %>% filter(year == input$selectedyear) %>% select(NY.GDP.MKTP.KD.ZG) %>% as.numeric -> temp, linetype = "dashed") +
geom_text(aes(x=temp, label=paste("\nworld avg: ", round(temp,1), "%"), y = 0, hjust=0), colour="black", angle=90) +
theme_minimal() +
scale_x_continuous(name = "GDP growth", limits = c(input$growthrange[1], input$growthrange[2]))+
ggtitle("Distribution of GDP growth - number of countries") +
theme(plot.title = element_text(hjust = 0.5))
x2 <- gdp_level_2011 %>% filter(year == input$selectedyear) %>% select(change, gdp_lvl2010const)
x2$positive <- (x2$change >= 0)
p2 <- ggplot(x2, aes(x=change, weight=gdp_lvl2010const/10^12, fill = positive)) +
geom_histogram(bins= input$bins + 1, colour = "white") + theme_minimal() +
geom_vline(xintercept = gdp_growth_an_world %>% filter(year == input$selectedyear) %>% select(NY.GDP.MKTP.KD.ZG) %>% as.numeric, linetype = "dashed")+
geom_text(aes(x=temp, label=paste("\nworld avg: ", round(temp,1), "%"), y = 0, hjust=0), colour="black", angle=90) +
scale_x_continuous(name = "GDP growth", limits = c(input$growthrange[1], input$growthrange[2]))+
scale_y_continuous(name = "GDP in trillion US dollars, 2010 prices")+
ggtitle("Distribution of GDP growth - size of GDP") +
theme(plot.title = element_text(hjust = 0.5))
grid.newpage()
grid.draw(rbind(ggplotGrob(p1), ggplotGrob(p2), size = "last"))
})
#####
output$population_total <- renderPlotly({
population_data() %>% mutate(population = abs(population)) %>% group_by(year) %>% summarise(population_total = sum(population)) -> temp
ggplot(temp, aes(x=year, y=population_total)) +
geom_line() + theme_classic() + geom_vline(xintercept = input$year, linetype = "dashed") +
# geom_text(aes(x = input$year+0.5,
# label = paste0(temp %>% filter(year == input$year) %>% select(population_total) %>% as.numeric %>% round(-5)/10^6, "m"),
# y = temp %>% filter(year == input$year) %>% select(population_total) %>% as.numeric()*1.08, hjust=0), colour="black", angle=90) +
scale_y_continuous(limits = c(0, 1.1*max(temp$population_total, na.rm = T)), name = "Population in total",
labels = f <- function(x){
return(paste0(abs(x)/10^6, "m"))
}) +
ggtitle(paste("Population of", input$country, "in total")) +
theme(plot.title = element_text(hjust = 0.5)) -> p3
p3 <- ggplotly(p3)
p3
})
output$population_structure <- renderPlotly({
validate(
need(!(population_data() %>% filter(year == input$year) %>% select(population) %>% anyNA), 'Data not complete')
)
pyramid <- ggplot(population_data() %>% filter(year == input$year) -> tmp, aes(x = age, y = population, fill = gender)) +
geom_bar(data = tmp1 <- subset(tmp, gender == "Female") , stat = "identity") +
geom_bar(data = tmp2 <- subset(tmp, gender == "Male") , stat = "identity") +
scale_y_continuous(limits = 1.05*c(-max(max(-population_data()$population, na.rm=T), max(population_data()$population, na.rm = T)),
max(max(-population_data()$population, na.rm=T), max(population_data()$population, na.rm = T))),
labels = f <- function(x){
return(paste0(abs(x)/10^6, "m"))
},
name = "number of people")+
theme_classic() + ggtitle(paste("The age structure of the population:", input$country)) +
theme(plot.title = element_text(hjust = 0.5)) + coord_flip()
pyramid <- ggplotly(pyramid)
pyramid
})
output$TFR <- renderPlotly({
ggplot(TFR_data() -> temp, aes(x=year, y=TFR)) +
geom_line() + theme_classic() + geom_vline(xintercept = input$year, linetype = "dashed") +
geom_hline(yintercept = 2.1, linetype = "dashed", colour = "red") +
# geom_text(aes(x = input$year+0.5,
# label = temp %>% filter(year == input$year) %>% select(TFR) %>% as.numeric,
# y = temp %>% filter(year == input$year) %>% select(TFR) %>% as.numeric()*1.5, hjust=0), colour="black", angle=90) +
scale_y_continuous(limits = c(0, 1.1*max(temp$TFR, na.rm = T))) +
ggtitle(paste("Total fertility rate of", input$country)) +
theme(plot.title = element_text(hjust = 0.5)) -> tmp
tmp <- ggplotly(tmp)
tmp
})
output$international_comparison <- renderPlotly({
temp <- region_indicator()[range(),]
countries_tmp <- temp[,1:2]
rownames(countries_tmp) <- countries_tmp[,1]
ggplot(temp , aes(x = reorder(iso2c, -temp[,3]), y = temp[,3],#)#)+
text = paste('country: ', countries_tmp[as.character(temp$iso2c),2],
'<br>value:', round(temp[,3])))) +
geom_bar(stat = "identity", aes(fill = "darkorange3")) + theme_classic() +
scale_y_continuous(name = tmp <- (WDIsearch(tmp <- colnames(temp)[3], field = "indicator") -> temp2) %>% is.vector %>% ifelse(., return(t(temp2)), return(temp2)) %>% as.data.frame %>% filter(indicator == tmp) %>% select(name) %>% .[1,] %>% as.character) +
scale_x_discrete(name = "country code") +
ggtitle("Comparison of countries") +
theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_text(angle = 90, size = 8, vjust =.5), axis.text.y = element_text(angle = 0, size = 8, vjust =.5), legend.position = "none") +
guides(fill=FALSE) -> tmp
tmp <- ggplotly(tmp, tooltip = c("text"))
tmp
})
}
shinyApp(ui = ui, server = server)
|
366c3645d7500a4f0cbd492a48524a2d17c0265f | 7b2aa85d3249b3c1ef4a0db5512b9bb2b152984c | /R/data.R | 3a74548e6faab3b61990cbf6f291ef29ce69342e | [] | no_license | ZW-xjtlu/RbashGEO | 5aed85a80e02665a614758590f4ddc99d621e0af | 50d77c6b2d03e7635434740070368b63ae282fb0 | refs/heads/master | 2021-09-12T13:17:04.946456 | 2018-04-17T05:28:23 | 2018-04-17T05:28:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 286 | r | data.R | #' The example Coldata data.frame
#'
#'
#'
#' @format A \code{data.frame}
#' \describe{
#' As long as the table contains a collumn of the file names, and indicating it is paired or single end library, then it should work fine.
#' }
#'
#' @usage Coldata_example
#'
"Coldata_example" |
2a674368e9e2abb5b0635696ad64010ae4504771 | 7aa60ad6e7aebc90dffbe6d6de2d9e431de30601 | /man/area_cal.Rd | 6c80bdd06f00839c7fb25934edd94363fcc62efb | [
"MIT"
] | permissive | zhujiedong/jiptest | f16a07730fdaf5183e0a6f4b5f8f9731b79ae4bb | 19eadc914c3481f218788b64c5f6d30e4c89b80e | refs/heads/main | 2022-05-21T20:39:16.406455 | 2022-05-09T02:00:57 | 2022-05-09T02:00:57 | 313,541,102 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 577 | rd | area_cal.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/area_cal.R
\name{area_cal}
\alias{area_cal}
\title{calculate the area under curve (BLUE > 1.2.2)}
\usage{
area_cal(df, use_PAM = FALSE)
}
\arguments{
\item{df}{data of a type dataframe.}
\item{use_PAM}{indicate to use PAM or continuous fluorescence signals}
}
\description{
use a method similar method like trapezium intergration
}
\details{
use Soto's answer as
https://stackoverflow.com/questions/4954507/calculate-the-area-under-a-curve
}
\examples{
\dontrun{
library(jiptest)
area_cal(df)
}
}
|
882e32d4ab94a0b551a68e20d0d31f2a9c0bfa16 | 57b6bc2896092a29cbd829199359f96be3da7572 | /2020/R/explore.R | c1fef85fa19cd2220ddcba4c1ec2824d6ab34ab1 | [] | no_license | azambranog/hash_code | 1a6b43c8af3388df1bba1e4750e092825eca6886 | 0e0787d72712d7084bb8c397e3e70c372ee1e3f0 | refs/heads/master | 2022-03-13T15:20:22.978505 | 2022-02-24T21:39:54 | 2022-02-24T21:39:54 | 239,148,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 894 | r | explore.R | library(data.table)
filesx <- list.files('data', full.names = T)
for (f in filesx) {
message(f)
data <- readLines(f)
D <- as.numeric(strsplit(data[1], ' ')[[1]][3])
books <- as.numeric(strsplit(data[2], ' ')[[1]])
books <- data.table(score = books)
books[, book := .I -1]
libs <- tail(data, -2)
L <- lapply(as.list(seq(1,length(libs), by = 2)), function(i) {
libinfo <- as.numeric(strsplit(libs[i], ' ')[[1]])
libbooks <- data.table(book = as.numeric(strsplit(libs[i+1], ' ')[[1]]))
libbooks[, lib := (i-1)/2]
libbooks[, sign := libinfo[2]]
libbooks[, bpd := libinfo[3]]
})
L <- rbindlist(L)
L <- merge(L, books, by = 'book')
data <- list(data = L, D = D)
ff <- gsub('^(.{1}).*$', '\\1', basename(f))
saveRDS(data, file.path('clean', paste0(ff, '.rds')))
message('DONE FILE')
}
|
8ae24fc96ea777ed52f9d852ae6f6ae64c78b2db | 0a906cf8b1b7da2aea87de958e3662870df49727 | /diffrprojects/inst/testfiles/dist_mat_absolute/libFuzzer_dist_mat_absolute/dist_mat_absolute_valgrind_files/1609960793-test.R | 48307a306425fedc8b210d7a4479beba60dfb850 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 784 | r | 1609960793-test.R | testlist <- list(x = c(-134225921L, NA, -256L, 0L, 16777215L, -1537L, -687865865L, NA, -1895825409L, -48897L, -702926875L, -1L, -1L, -702926849L, 673513472L, 15728639L, -1L, -42L, 439353343L, 620756992L, -268435712L, 16777215L, -687865857L, -2686977L, -134225921L, 405405516L, -162783703L, 692857129L, 1291845623L, -2107878L, 802619391L, 1277100031L, -10726L, 805306367L, -10497L), y = c(-1L, 692866262L, 858993459L, -10479057L, -24673L, -553638698L, 439353343L, -1L, -256L, 0L, 0L, 409104239L, 1814571619L, 1819243365L, 1466527309L, 1634752105L, 692854313L, -42L, 1377447756L, 704643071L, 405405516L, NA, 692857129L, 1289158614L, 438697942L, -2133452288L, NA, -2049L, -539616721L, -11788545L, -42L))
result <- do.call(diffrprojects:::dist_mat_absolute,testlist)
str(result) |
a6629e0d98ea9f91613769fae7b406f259da4f0a | 77eb1ef8d5984f3e2e925991d56faf4a24707850 | /rocketpackMM/R/yourncurve.R | 4ea18eba73b556e4825b01eb42ab0117877058d8 | [] | no_license | mm4753-12/rocketpackMM | 3d3b6837aae9a8a50fcf716da6bfbb9497b170dc | 220c0d2e8a3fa0de40d5f4fac8b3fa53dde22853 | refs/heads/main | 2023-01-19T15:38:39.594540 | 2020-11-19T05:35:38 | 2020-11-19T05:35:38 | 314,137,818 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 524 | r | yourncurve.R | #' YOURNCURVE
#'
#' @param A value for a variable distributed normally, a mean, and a standard deviation.
#'
#' @return A density curve, an area, and a probability.
#' @export
#'
#' @examples
#' \dontrun yourncurve(6,10,5)
yourncurve = function(Y,mu,sigma){
curve(dnorm(x,mean=mu,sd=sigma),xlim=c(mu-3*sigma,mu+3*sigma))
xcurve=seq(Y-10*sigma, Y, length=10000)
ycurve=dnorm(xcurve,mean=mu,sd=sigma)
polygon(c(Y-10*sigma,xcurve,Y),c(0,ycurve,0),col="Light Green")
list(round(pnorm(Y,mu,sigma),4))
}
|
3382f2621cefe0773595fa62bf522e0d3fb8f01f | b8deda0293025b126958530c589899fd43ea8197 | /arima_garch.r | fe86592c25c30a36b1752655f4ef817bcf0c40c2 | [] | no_license | Vikas1667/AnomalyDetectionOnRisk | 64c87d4ff6230a17bc1c7683bc5fcaff978eee76 | 7ed7fcce5aa22d308b76b1fb6bfe6a2c228f9510 | refs/heads/master | 2023-03-21T17:36:37.400139 | 2018-05-31T16:18:28 | 2018-05-31T16:18:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,943 | r | arima_garch.r | #"quantmod---------------------------------------------------------------------"
#install.packages("quantmod", repos='https://ftp.acc.umu.se/mirror/CRAN/')
#"lattice---------------------------------------------------------------------"
#install.packages("lattice", repos='https://ftp.acc.umu.se/mirror/CRAN/')
#"timeSeries---------------------------------------------------------------------"
#install.packages("timeSeries", repos='https://ftp.acc.umu.se/mirror/CRAN/')
#"rugarch---------------------------------------------------------------------"
#install.packages("rugarch", repos='https://ftp.acc.umu.se/mirror/CRAN/')
"Packages installed."
# defining a function
is.installed <- function(mypkg) is.element(mypkg, installed.packages()[,1])
checkNecessaryPackages <- function(){
"timeSeries:"
library("timeSeries")
is.installed('timeSeries')
"quantmod:"
library("quantmod")
is.installed('quantmod')
"lattice:"
library("lattice")
is.installed('lattice')
"nloptr:"
library("nloptr")
is.installed('nloptr')
"rugarch:"
library("rugarch")
is.installed('rugarch')
}
library("rugarch")
#print("RUGARCH IS INSTALLED:")
is.installed('rugarch')
args <- commandArgs(trailingOnly = TRUE)
datatype <- args[1]
size <- args[2]
nr_of_series <- args[3]
differentiation <- args[4]
#print(datatype)
#print(size)
file_name = paste("garch/", datatype, "_", size, ".csv", sep="")
#print(file_name)
#Convert strings to intergers
size_string = size
size = strtoi(size)
nr_of_series = strtoi(nr_of_series)
return_series = read.csv(file_name, header=FALSE)
tmp_serie = return_series[1,]
tmp_time_serie = as.ts(tmp_serie)
windowLength = as.integer(length(tmp_time_serie)*0.10)
forecasts_for_all_series=matrix(nrow=nr_of_series, ncol=size-windowLength, byrow = TRUE) # fill matrix by rows
sigmas_for_all_series =matrix(nrow=nr_of_series, ncol=size-windowLength, byrow = TRUE) # fill matrix by rows
##print(forecasts_for_all_series)
#Judge Order
#final.bic <- Inf
final.order <- c(2,0,2)
#first_serie = return_series[1,]
#first_serie_window = as.ts(first_serie[(1):(windowLength)])
#for (p in 0:5) for (q in 0:5) {
# if ( p == 0 && q == 0) {
# next
# }
#
# arimaFit = tryCatch( arima(first_serie_window, order=c(p, 0, q)),
# error=function( err ) FALSE,
# warning=function( err ) FALSE )
# ##print(arimaFit)
# if( !is.logical( arimaFit ) ) {
# ##print('actually managed to fit')
# ##print(p)
# ##print(q)
# current.bic <- BIC(arimaFit)
# if (current.bic < final.bic) {
# final.bic <- current.bic
# final.order <- c(p, 0, q)
# final.arima <- arima(first_serie_window, order=final.order)
# }
# } else {
# ##print('didnt managed to fit')
# next
# }
#}
print("Final order: ")
print(final.order)
#Start modellling
for (serie_nr in 1:nr_of_series) {
one_serie = return_series[serie_nr,]
one_time_serie = as.ts(one_serie)
if(differentiation == "Once") {
one_time_serie = c(0, diff(one_time_serie[1:length(one_time_serie)]))
}
foreLength = length(one_time_serie) - windowLength - 1
forecasts <- vector(mode="character", length=foreLength)
sigmas <- vector(mode="character", length=foreLength)
truths <- vector(mode="character", length=foreLength)
spec = ugarchspec(
variance.model=list(garchOrder=c(1,1)),
mean.model=list(armaOrder=final.order, include.mean=TRUE),
#mean.model=list(armaOrder=c(final.order[1], final.order[3]), include.mean=TRUE),
distribution.model="sged")
d = 0
max_roll = 100
while (d < foreLength) {
one_serie_window = as.ts(one_time_serie[(1+d):(windowLength+d)])
datapoints_left_in_serie = foreLength - d
nr_of_rolls = min(datapoints_left_in_serie, max_roll)
one_serie_window_future = as.ts(one_time_serie[(1+d):(windowLength+d+nr_of_rolls)])
#one_diffed_serie_window = one_serie_window #diff(one_serie_window)
#spec = ugarchspec(
# variance.model=list(garchOrder=c(1,1)),
# mean.model=list(armaOrder=c(1, 1), include.mean=TRUE),
# #mean.model=list(armaOrder=c(final.order[1], final.order[3]), include.mean=TRUE),
# distribution.model="sged")
model<-ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1), variance.targeting=TRUE),
mean.model = list(armaOrder = c(1, 1), include.mean = TRUE),
distribution.model = "norm")
modelfit<-ugarchfit(spec=model,data=one_serie_window, solver='hybrid')
#print(coef(modelfit))
if(is.null(coef(modelfit))){
print("FAILED to CONVERGE")
#Handles exception naively
forecasts[d+1] = 0
sigmas[d+1] = 1
d = d + 1
}
else{
spec = getspec(modelfit);
setfixed(spec) <- as.list(coef(modelfit))
fore = ugarchforecast(spec, n.ahead=1, n.roll = nr_of_rolls, data = one_serie_window_future, out.sample = nr_of_rolls)
for (index in 0:nr_of_rolls) {
forecasts[d+index+1] = fitted(fore)[index+1]
sigmas[d+index+1] = sigma(fore)[index+1]
}
d = d + nr_of_rolls
}
}
forecasts_for_all_series[serie_nr,] = forecasts
sigmas_for_all_series[serie_nr,] = sigmas
cat(".")
}
forecasts_for_all_series <- mapply(forecasts_for_all_series, FUN=as.numeric)
sigmas_for_all_series <- mapply(sigmas_for_all_series, FUN=as.numeric)
forecasts_for_all_series <- matrix(data=forecasts_for_all_series, ncol=foreLength+1, nrow=nr_of_series)
sigmas_for_all_series <- matrix(data=sigmas_for_all_series, ncol=foreLength+1, nrow=nr_of_series)
file_name_mean = paste("forecasts_mean_", datatype, "_", size, ".csv", sep="")
write.table(forecasts_for_all_series, row.names=FALSE, col.names=FALSE, file = file_name_mean)
file_name_var = paste("forecasts_variance_", datatype, "_", size, ".csv", sep="")
write.table(sigmas_for_all_series, row.names=FALSE, col.names=FALSE, file = file_name_var)
print("End of R Program") |
16356a8ddad1b80e1802dc631b3c3194d2434ebc | c1bf7397ddc833b7ba9617e22ee2bdc86e646ee4 | /rCode/aag_rank.R | 9a3417bae7018f6d5990932ea7db52d6696bb65e | [] | no_license | cxq914/Data-Science | 1b5f6f9ac089d098b8d6c91d722dbf70f75e5613 | 6f9648cf323f8cfbcd69aa6b97991f9162013d34 | refs/heads/master | 2021-01-19T18:55:56.696529 | 2016-02-18T00:34:11 | 2016-02-18T00:34:11 | 21,634,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,807 | r | aag_rank.R | library(data.table)
#########identify the best and worst restaurant###########
fileName2 <- paste('Applebee Data/Result/Data with Payment( 2015-11-23 - 2016-01-03 ).xlsx')
merged <- read.xlsx(fileName2,detectDates = TRUE)
aag.sub <- subset(merged,merged$Parent.Account=='Apple American Group, LLC (AAG)')
res.dat <- aag.sub[,c(3,4,12,19)]
res.dat$average.NetRevenue.perNBNTG <- round(res.dat$`199.Net.Sales`/res.dat$DineInNoBarStoolTickest,2)
res.dat$weekly <- as.Date(cut(res.dat$`Business.Day`,breaks = "week",start.on.monday = TRUE))
res.avg <- aggregate(res.dat$average.NetRevenue.perNBNTG,by=list(res.dat$`Restaurant.Number`,res.dat$weekly),data=res.dat,mean)
colnames(res.avg) <- c('Restaurant.Number','Week','Average.NetRevenue.per.NBNT')
res.avg$`Average.NetRevenue.per.NBNT` <- round(res.avg$`Average.NetRevenue.per.NBNT`,3)
period <- unique(res.avg$Week)
n <- length(period)
rank.dat <- as.data.frame(unique(res.dat$Restaurant.Number))
colnames(rank.dat) <- 'Restaurant.Number'
j=1
for (i in 1:n)
{
sub <- subset(res.avg,res.avg$Week==period[i])
sub$rank <- rank(-sub$`Average.NetRevenue.per.NBNT`,ties.method= "first")
sub<-sub[,c(1,3,4)]
rank.dat <- merge(rank.dat,sub,by="Restaurant.Number",all.x = TRUE)
colnames(rank.dat) <- c(colnames(rank.dat)[1:j],paste('NetRevenue',period[i]),paste('Rank',period[i]))
j = j+2
}
########################analysis##################
rank.dat[is.na(rank.dat)] <- 0
for (i in 1:6)
{
cut1 <- quantile(rank.dat[,(i*2)],probs=c(0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,0))
#a <- rbind(a,cut1)
level <- cut(rank.dat[,(i*2)],breaks=c(0.5,cut1),include.lowest = TRUE,right = TRUE)
levels(level) <- c(10,9,8,7,6,5,4,3,2,1)
rank.dat <- cbind(rank.dat,level)
}
colnames(rank.dat) <- c(colnames(rank.dat)[1:13],
'week1','week2','week3','week4','week5','week6')
################Transition Matrix#######
ranking.info <- createWorkbook()
week12.dat <- addWorksheet(wb=ranking.info,sheetName = 'Week1-Week2')
week13.dat <- addWorksheet(wb=ranking.info,sheetName = 'Week1-Week3')
week14.dat <- addWorksheet(wb=ranking.info,sheetName = 'Week1-Week4')
week15.dat <- addWorksheet(wb=ranking.info,sheetName = 'Week1-Week5')
week16.dat <- addWorksheet(wb=ranking.info,sheetName = 'Week1-Week6')
for (i in 16:20)
{
week <- as.matrix(table(rank.dat.na.rm[,15],rank.dat.na.rm[,i]))
week <- week[10:1,10:1]
week.per <- matrix(data = 0,nrow = 10,ncol = 10)
for (j in 1:10)
{
week.per[j,] <- percent(round(week[j,]/sum(week[j,]),2))
}
colnames(week.per) <- c('Level1','Level2','Level3','Level4','Level5','Level6','Level7',
'Level8','Level9','Level10')
writeData(ranking.info,i-15,week,rowNames = TRUE,colNames = TRUE)
writeData(ranking.info,i-15,week.per,startRow = 14,rowNames = TRUE,colNames = TRUE)
}
saveWorkbook(ranking.info,'RestaurantRank_Analysis_final.xlsx')
####################second method########################
ranking.info1 <- createWorkbook()
week12.dat <- addWorksheet(wb=ranking.info1,sheetName = 'Week1-Week2')
week23.dat <- addWorksheet(wb=ranking.info1,sheetName = 'Week2-Week3')
week34.dat <- addWorksheet(wb=ranking.info1,sheetName = 'Week3-Week4')
week45.dat <- addWorksheet(wb=ranking.info1,sheetName = 'Week4-Week5')
week56.dat <- addWorksheet(wb=ranking.info1,sheetName = 'Week5-Week6')
for (i in 15:19)
{
week <- as.matrix(table(rank.dat.na.rm[,i],rank.dat.na.rm[,i+1]))
week <- week[10:1,10:1]
week.per <- matrix(data = 0,nrow = 10,ncol = 10)
for (j in 1:10)
{
week.per[j,] <- percent(round(week[j,]/sum(week[j,]),2))
}
colnames(week.per) <- c('Level1','Level2','Level3','Level4','Level5','Level6','Level7',
'Level8','Level9','Level10')
writeData(ranking.info1,i-14,week,rowNames = TRUE,colNames = TRUE)
writeData(ranking.info1,i-14,week.per,startRow = 14,rowNames = TRUE,colNames = TRUE)
}
saveWorkbook(ranking.info1,'RestaurantRank_Analysis_method2.xlsx')
rank.dat.na.rm$general_level <- NULL
write.xlsx(rank.dat,'Applebee Data/Restaurant_Rank_Level_aag.xlsx')
#########
res.rank <- read.xlsx('Applebee Data/Restaurant_Rank_Level_aag.xlsx')
res.rank[,14] <- as.numeric(res.rank[,14])
res.rank[,15] <- as.numeric(res.rank[,15])
res.rank[,16] <- as.numeric(res.rank[,16])
res.rank[,17] <- as.numeric(res.rank[,17])
res.rank[,18] <- as.numeric(res.rank[,18])
res.rank[,19] <- as.numeric(res.rank[,19])
res.rank$rank_avg <- round((res.rank$week1+res.rank$week2+res.rank$week3
+res.rank$week4+res.rank$week5+res.rank$week6)/6,2)
res.rank$avg_netRevenue <- round((res.rank[,2]+res.rank[,4]+res.rank[,6]
+res.rank[,8]+res.rank[,10]+res.rank[,12])/6,2)
#res.rank$type <- ifelse(res.rank$rank_avg<2,'Top10%',ifelse(res.rank$rank_avg>=9,'Bottom10%','others'))
res.rank <- res.rank[order(res.rank$rank_avg),]
res.rank$type <- c(rep('Top10%',49),rep('10%-20%',48),rep('20%-30%',48),rep('30%-40%',48),
rep('40%-50%',48),rep('50%-60%',48),rep('60%-70%',48),rep('70%-80%',48),
rep('80%-90%',48),rep('Bottom10%',49))
#top <- res.rank[1:120,]
#bottom <- res.rank[1051:1170,]
#top$type <- 'Top10%'
#bottom$type <- 'Bottom10%'
#rest.list <- rbind(top,bottom)
#write.xlsx(rest.list,'TopBottomRestaurants.xlsx')
total.dat <- read.xlsx('Applebee Data/Result/Data with Payment( 2015-11-23 - 2016-01-03 ).xlsx',detectDates = TRUE)
#aggregated.dat <- aggregate(.~Restaurant.Number,data = rest.merged,mean)
res.rank.sub <- res.rank[,c(1,20,21,22)]
rest.rank.merged <- merge(total.dat,res.rank.sub,by='Restaurant.Number')
rest.rank.merged <- rest.rank.merged[,c(1,2,4,9:12,17:26,3,27,28:33,5,6,34:36)]
write.xlsx(rest.rank.merged,'Applebee Data/merged_restaurant_rank_aag.xlsx')
|
f01ae2cb9e1ad5a58359a32100761727b67ac152 | 2bb4abd3c6418f52aeb8591aa389d145f1fc4dc1 | /cachematrix.R | 9338efcd7e34c3f240da30e32d110cf813a59d77 | [] | no_license | reg401/ProgrammingAssignment2 | 1cdb676b27a1de1c857aae3c1ba3dd8515ce103e | 94f594ea4f7989d79b8364b99d433153f84b1d36 | refs/heads/master | 2021-01-15T11:42:38.712484 | 2015-09-25T05:36:46 | 2015-09-25T05:36:46 | 43,109,819 | 0 | 0 | null | 2015-09-25T04:34:00 | 2015-09-25T04:33:59 | null | UTF-8 | R | false | false | 3,547 | r | cachematrix.R | ## The following file contains 2 functions to address the requirments set in:R Programming - Programming Assignment 2 - Caching the Inverse of a Matrix
## The file also contains 2 test functions - one using the cache to retrieve the inverse matrix from the cache and the other doesn't.
##
## Please note that it is assumed that the matrix supplied to the functions is always invertible.
##
######################################################### F U N C T I O N S #########################################################
## This function creates a special "matrix" object that can cache its inverse.
## The following functions are available for the matrix object created:
## set - set the value of the matrix
## get - get the value of the matrix
## setInv - set the value of the inverse matrix
## getInv - get the value of the inverse matrix
makeCacheMatrix <- function(x = matrix()) {
## First, set the inverse matrix variable to NULL
im <- NULL
## This function applies the passed matrix (pm) to the special "matrix" object (x) and sets the inverse matrix variable (im) to NULL
set <- function(pm) {
x <<- pm
im <<- NULL
}
## This function returns the special "matrix" object
get <- function() x
## This function sets the inverse matrix of the original matrix in the special "matrix" object
setInv <- function(solve) im <<- solve
## This function gets the inverse matrix of the original matrix from the special "matrix" object
getInv <- function() im
## The list of available functions
list(set = set, get = get, setInv = setInv, getInv = getInv)
}
## This function computes (and returns) the inverse of the special "matrix" (x) returned by makeCacheMatrix function above.
## If the inverse has already been calculated then this function should retrieve the inverse matrix from the special "matrix" cache.
cacheSolve <- function(x, ...) {
## First, try to get the inverse matrix (im) from the passed matrix (x)
im <- x$getInv()
## Check if the im is null, if it's not, we are actually retrieving the im from the cache, return the im value and exist the function
if(!is.null(im)) {
message("getting inverse matrix cached data")
return(im)
}
## Too bad, no im value in the cache, let's get the original matrix data
data <- x$get()
## Let's create the inverse matrix (im)
im <- solve(data, ...)
## Set the inverse matrix in the special "matrix" object
x$setInv(im)
## Return the inverse matrix
## im variable can be used
## However, returning the inverse matrix stored in the special "matrix" (X) ensures that the set function is working properly
x$getInv()
}
######################################################### T E S T I N G #########################################################
##Test the above functions, not getting the cached inverse matrix
testNoCache <- function(){
## Create the test matrix
tm <- matrix(c(1, 2, 3, 4), nrow=2, ncol=2)
## Add the matrix to the special "matrix"
mt <- makeCacheMatrix(tm)
## inverse the matrix and print it
im <- cacheSolve(mt)
im
}
##Test the above functions, get the cached inverse matrix
testCache <- function(){
## Create the test matrix
tm <- matrix(c(1, 2, 3, 4), nrow=2, ncol=2)
## Add the matrix to the special "matrix"
mt <- makeCacheMatrix(tm)
## inverse the matrix
im <- cacheSolve(mt)
## get the inverse matrix from the cache and print it. This should also trigger a "getting inverse matrix cached data" message.
cim <- cacheSolve(mt)
cim
}
|
da9ecf2f921125c49264997dbbfaf17da8e51ba5 | f73882ccd9a9a2db72017bca597b06466524b050 | /R/auth.r | ce57994e1c493ff700746d465ccc922cc330a63f | [
"LicenseRef-scancode-us-govt-public-domain",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | usgs-lcmap/client-r | f6e377157194b7db77dbb0041d2a155dfdcdf677 | 929128689b20fd92a7399ba892b86f7c38ed81fd | refs/heads/master | 2021-05-31T04:16:50.261908 | 2016-03-24T04:34:59 | 2016-03-24T04:34:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 557 | r | auth.r | #' Authenticate to LCMAP using ERS
#'
#' @param username username
#' @param password password
#' @param version version
#' @export
#' @examples
#' login("alice", "secret")
#' login("alice", "secret", "2.0")
login <- function (username, password, version) {
if (missing(version)) {
version<-lcmap::defaultAPIVersion
}
cfg<-lcmap::getCfg()
payload<-list(username=cfg$username, password=cfg$password)
result<-lcmap::post(lcmap::routes$loginContext, version, body=payload,
encode="form")
return(result)
}
|
afbd66670a15853a9933724704c27ff680f2f191 | 1aa29f155cdf9bf9b17bcc5699a9bf0c20b82993 | /man/tianComponent.Rd | bdebeb4fcb450dc4db9ed527d3a31e7adbf6f7b6 | [] | no_license | Lucaweihs/SEMID | d165471e59e41ce1437d642d60370854ecf8ddd7 | a079c69bde921f105273e20f40708d3e851ec011 | refs/heads/master | 2023-08-03T08:34:41.337736 | 2023-07-19T12:09:33 | 2023-07-19T12:09:33 | 48,009,470 | 5 | 1 | null | 2023-07-21T09:11:36 | 2015-12-15T00:23:48 | R | UTF-8 | R | false | true | 462 | rd | tianComponent.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MixedGraph.R
\name{tianComponent}
\alias{tianComponent}
\alias{tianComponent.MixedGraph}
\title{Returns the Tian c-component of a node}
\usage{
tianComponent(this, node)
\method{tianComponent}{MixedGraph}(this, node)
}
\arguments{
\item{this}{the mixed graph object}
\item{node}{the node for which to return its c-component}
}
\description{
Returns the Tian c-component of a node
}
|
5df4a27eb69b75868207d5610c87e1dd1c318779 | 257ebc3fac290fb366f26d2559f870d2e0994c8a | /load_libraries.R | 4771135cf9defd64e83a94702a905f7c93c0a116 | [] | no_license | cordura21/risk_consumption_backtest | cbbc51164c4db8f7c1ab95a0bbb3b2177ed3b722 | 7a841a2fa5087e887e5f233b504d52ccbbb5249f | refs/heads/master | 2021-01-18T01:26:07.759629 | 2016-09-13T22:15:48 | 2016-09-13T22:15:48 | 68,021,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 184 | r | load_libraries.R | library(dplyr)
library(lubridate)
library(xts)
library(zoo)
library(TTR)
library(ggplot2)
library(scales)
library(tidyr)
library(PerformanceAnalytics)
library(stringr)
library(readxl)
|
a6c4bf4bb6bdfe8c08d11e2dbced090648e77141 | 34f65d1110083e270d3b5a103e49284fc1295e93 | /code/updateCompletemiRNASeqMetadata.R | d1c0059379139dce0a12d761316cdb3743f348b9 | [] | no_license | kdaily/PCBCSampleMetadata | 7bf884789f76e79642631c13b2e34e23d49dfaba | e12e2a39cd07364f42507eeb5e2ffc7caa54c9f1 | refs/heads/master | 2020-12-24T16:25:06.498568 | 2016-05-04T00:01:02 | 2016-05-04T00:01:02 | 28,197,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 568 | r | updateCompletemiRNASeqMetadata.R | library(plyr)
library(dplyr)
library(synapseClient)
synapseLogin()
tblCurrent <- synGet('syn3219876')
res <- synTableQuery(paste("SELECT * FROM", tblCurrent@properties$id))
bak <- res@values
source("./createCompleteMIRNASeqMetadata.R")
### CAREFUL! THIS WILL DELETE ALL ROWS.
synDeleteRows(res)
tblCurrent <- synGet('syn3219876')
tblNew <- Table(tableSchema=tblCurrent,
values=as.data.frame(tblAll))
tblNew <- synStore(tblNew)
## Just in case, restore from backup of values
# tblNew <- Table(tableSchema=tblCurrent,
# values=bak)
|
c0eea23eb07f4c1de2d629c5f54b3889315a3eeb | fb26c3133d1b44e22a355b3997178f605df195f5 | /cluster_validation.r | a771b4e5bd19fe53fb9cfbd469dbb248e610eff2 | [] | no_license | MwandongaAbel/Clustering | fe8121795dbfefff9cfdb8302e9842c1bf49d48e | 69fbdc36f60cfa6f33e5be7714f81ba54c999537 | refs/heads/master | 2022-11-22T07:43:54.807212 | 2020-07-30T12:02:35 | 2020-07-30T12:02:35 | 283,760,908 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,866 | r | cluster_validation.r | # Clustering Validation......Abel Mwandonga Haro.
install.packages("clustertend")
library(factoextra)
library(clustertend)
data("iris")
head(iris)
#Excluding column species from data set.
gf<-iris[,-5]
# Random data generated from the iris data set
random_gf<-apply(gf,2,function(x){runif(length(x),min(x),(max(x)))})
random_gf<-as.data.frame(random_gf)
# Standardize the data sets
gf<-iris_scaled<-scale(gf)
random_gf<-scale(random_gf)
#Visualization of the data set.
install.packages("backports")
library(backports)
fviz_pca_ind(prcomp(gf), title = "PCA - Iris data",
habillage = iris$Species, palette = "jco",
geom = "point", ggtheme = theme_classic(),
legend = "bottom")
#plot random gf
fviz_pca_ind(prcomp(random_gf),title="PCA_random_data",geom = "point",ggtheme=theme_classic())
# K-means on iris dataset
set.seed(123)
km_res<-kmeans(gf,3)
fviz_cluster(list(data=gf,cluster=km_res$cluster),
ellipse.type = "norm",geom = "point",stand = FALSE,
palette="jco",ggtheme = theme_classic())
# K-means on the random dataset
k_means_random<-kmeans(random_gf,3)
fviz_cluster(list(data=random_gf,cluster=k_means_random$cluster),
ellipse.type = "norm",geom="point",stand = FALSE,
palette="jco",ggtheme = theme_classic())
# Hierarchical clustering on the random dataset
fviz_dend(hclust(dist(random_gf)),k=3,k_colors = "jco",
as.ggplot=TRUE,show_labels = FALSE)
#Hopkins
# Compute Hopkins statistic for iris dataset
set.seed(123)
hopkins(gf,n=nrow(gf)-1)
# Compute Hopkins statistic for a random dataset
hopkins(random_gf,n=nrow(gf)-1)
#visual assessment of cluster tendency (VAT)
fviz_dist(dist(gf),show_labels = FALSE)+
labs(title="iris_data")
fviz_dist(dist(random_gf),show_labels = FALSE)+
labs(title="Random_data")
|
58471ed4b20325e2748381e4c4f72b9870bebdde | 0b197ef046a9bb44e13f3c9637990a40ce6aee82 | /exper/exp0044/rfr_us.R | d08a9e995446a82d10c9168964edd732045e9e7f | [] | no_license | jbrowne6/exper | 52da8ae63faebe295f97e6507328a8b21d459e06 | 10b829ed79d6c2addf1e191b2a6291b4e90e8484 | refs/heads/master | 2021-09-17T06:41:29.141469 | 2018-06-28T18:57:17 | 2018-06-28T18:57:17 | 107,877,985 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,643 | r | rfr_us.R | bestCutForFeature <- function(X){
minVal <- min(X)
maxVal <- max(X)
if(minVal == maxVal){ return(NULL)}
X <- sort(X)
normX <- (X-minVal)/(maxVal-minVal)
sumLeft <- 0
sumRight <- sum(normX)
errLeft <- 0
errRight <- 0
meanLeft <- 0
meanRight <- 0
errCurr <- 0
minErr <- Inf
vectorLength <- length(X)
cutPoint <- NULL
for (m in 1:(vectorLength-1)){
sumLeft <- sumLeft + normX[m]
sumRight <- sumRight - normX[m]
meanLeft <- sumLeft/(m)
meanRight <- sumRight/(vectorLength-m)
errLeft <-sum((normX[1:m]-meanLeft)^2)
errRight <-sum((normX[(m+1):vectorLength]-meanRight)^2)
errCurr <- errLeft + errRight
# Determine if this split is currently the best option
if (errCurr < minErr){
cutPoint <- (X[m] + X[m+1])/2
minErr <- errCurr
}
}
return(c(cutPoint, minErr))
}
rfrus <- function(X, MinParent=1, trees=100, MaxDepth="inf", bagging=.2, replacement=TRUE, FUN=makeA, options=c(ncol(X), round(ncol(X)^.5),1L, 1/ncol(X)), COOB=TRUE, Progress=TRUE){
forest <- vector("list",trees)
BV <- NA # vector in case of ties
BS <- NA # vector in case of ties
MaxDeltaI <- 0
nBest <- 1L
BestIdx <-0L
BestVar <-0L
BestSplitIdx<-0L
BestSplitValue <- 0
w <- nrow(X)
p <- ncol(X)
perBag <- (1-bagging)*w
Xnode<-double(w) # allocate space to store the current projection
SortIdx<-integer(w)
if(object.size(X) > 1000000){
OS<-TRUE
}else{
OS<-FALSE
}
# Calculate the Max Depth and the max number of possible nodes
if(MaxDepth == "inf"){
StopNode <- 2L*w #worst case scenario is 2*(w/(minparent/2))-1
MaxNumNodes <- 2L*w # number of tree nodes for space reservation
}else{
if(MaxDepth==0){
MaxDepth <- ceiling(log2(w))
}
StopNode <- 2L^(MaxDepth)
MaxNumNodes <- 2L^(MaxDepth+1L) # number of tree nodes for space reservation
}
CutPoint <- double(MaxNumNodes)
Children <- matrix(data = 0L, nrow = MaxNumNodes,ncol = 2L)
NDepth <- integer(MaxNumNodes)
matA <- vector("list", MaxNumNodes)
Assigned2Node<- vector("list",MaxNumNodes)
Assigned2Leaf <- vector("list", MaxNumNodes)
ind <- double(w)
min_error <- Inf
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Start tree creation
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
for(treeX in 1:trees){
# intialize values for new tree before processing nodes
CutPoint[] <- 0
Children[] <- 0L
NDepth[]<- 0L #delete this?
NDepth[1]<-1L
CurrentNode <- 1L
NextUnusedNode <- 2L
NodeStack <- 1L
highestParent <- 1L
Assigned2Leaf <- vector("list", MaxNumNodes)
ind[] <- 0L
# Determine bagging set
# Assigned2Node is the set of row indices of X assigned to current node
if(bagging != 0){
if(replacement){
ind<-sample(1:w, w, replace=TRUE)
Assigned2Node[[1]] <- ind
}else{
ind[1:perBag] <- sample(1:w, perBag, replace = FALSE)
Assigned2Node[[1]] <- ind[1:perBag]
}
}else{
Assigned2Node[[1]] <- 1:w
}
# main loop over nodes
while (CurrentNode < NextUnusedNode && CurrentNode < StopNode){
# determine working samples for current node.
NodeRows <- Assigned2Node[CurrentNode]
Assigned2Node[[CurrentNode]]<-NA #remove saved indexes
NdSize <- length(NodeRows[[1L]]) #determine node size
# create projection matrix (sparseM) by calling the custom function FUN
sparseM <- FUN(options)
#isolate objective function
# if node is impure and large enough then attempt to find good split
if (NdSize < MinParent || NDepth[CurrentNode]==MaxDepth || NextUnusedNode+1L >= StopNode || NdSize == 1){
Assigned2Leaf[[CurrentNode]] <- NodeRows[[1L]]
NodeStack <- NodeStack[-1L]
CurrentNode <- NodeStack[1L]
if(is.na(CurrentNode)){
break
}
next
}
min_error <- Inf
cut_val <- 1
BestVar <- 1
# nBest <- 1L
for(q in unique(sparseM[,2])){
#Project input into new space
lrows <- which(sparseM[,2]==q)
Xnode[1:NdSize] <- X[NodeRows[[1L]],sparseM[lrows,1], drop=FALSE]%*%sparseM[lrows,3, drop=FALSE]
#Sort the projection, Xnode, and rearrange Y accordingly
results <- bestCutForFeature(Xnode[1:NdSize])
if (is.null(results)) next
if(results[2] < min_error){
cut_val <- results[1]
min_error <- results[2]
bestVar <- q
}
}#end loop through projections.
if (min_error == Inf){
Assigned2Leaf[[CurrentNode]] <- NodeRows[[1L]]
NodeStack <- NodeStack[-1L]
CurrentNode <- NodeStack[1L]
if(is.na(CurrentNode)){
break
}
next
}
# Recalculate the best projection
lrows<-which(sparseM[,2L]==bestVar)
Xnode[1:NdSize]<-X[NodeRows[[1L]],sparseM[lrows,1], drop=FALSE]%*%sparseM[lrows,3, drop=FALSE]
# find which child node each sample will go to and move
# them accordingly
# changed this from <= to < just in case best split split all values
MoveLeft <- Xnode[1:NdSize] < BestSplitValue
numMove <- sum(MoveLeft)
if (is.null(numMove)){
print("numMove is null")
flush.console()
}
if(is.na(numMove)){
print("numMove is na")
flush.console()
}
#Check to see if a split occured, or if all elements being moved one direction.
if(numMove!=0L && numMove!=NdSize){
# Move samples left or right based on split
Assigned2Node[[NextUnusedNode]] <- NodeRows[[1L]][MoveLeft]
Assigned2Node[[NextUnusedNode+1L]] <- NodeRows[[1L]][!MoveLeft]
#highest Parent keeps track of the highest needed matrix and cutpoint
# this reduces what is stored in the forest structure
if(CurrentNode>highestParent){
highestParent <- CurrentNode
}
# Determine children nodes and their attributes
Children[CurrentNode,1L] <- NextUnusedNode
Children[CurrentNode,2L] <- NextUnusedNode+1L
NDepth[NextUnusedNode]=NDepth[CurrentNode]+1L
NDepth[NextUnusedNode+1L]=NDepth[CurrentNode]+1L
# Pop the current node off the node stack
# this allows for a breadth first traversal
Assigned2Leaf[[CurrentNode]] <- NodeRows[[1L]]
NodeStack <- NodeStack[-1L]
NodeStack <- c(NextUnusedNode, NextUnusedNode+1L, NodeStack)
NextUnusedNode <- NextUnusedNode + 2L
# Store the projection matrix for the best split
matA[[CurrentNode]] <- as.integer(t(sparseM[which(sparseM[,2]==BestVar),c(1,3)]))
CutPoint[CurrentNode] <- BestSplitValue
}else{
# There wasn't a good split so ignore this node and move to the next
NodeStack <- NodeStack[-1L]
}
# Store ClassProbs for this node.
# Only really useful for leaf nodes, but could be used instead of recalculating
# at each node which is how it is currently.
CurrentNode <- NodeStack[1L]
if(is.na(CurrentNode)){
break
}
}
#If input is large then garbage collect prior to adding onto the forest structure.
if(OS){
gc()
}
# save current tree structure to the forest
if(bagging!=0 && COOB){
forest[[treeX]] <- list("CutPoint"=CutPoint[1:highestParent],"Children"=Children[1L:(NextUnusedNode-1L),,drop=FALSE], "matA"=matA[1L:highestParent], "ALeaf"=Assigned2Leaf[1L:(NextUnusedNode-1L)])
}else{
forest[[treeX]] <- list("CutPoint"=CutPoint[1:highestParent],"Children"=Children[1L:(NextUnusedNode-1L),,drop=FALSE], "matA"=matA[1L:highestParent], "ALeaf"=Assigned2Leaf[1L:(NextUnusedNode-1L)])
}
if(Progress){
cat("|")
flush.console()
}
}
return(forest)
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Default option to make projection matrix
#
# this is the randomer part of random forest. The sparseM
# matrix is the projection matrix. The creation of this
# matrix can be changed, but the nrow of sparseM should
# remain p. The ncol of the sparseM matrix is currently
# set to mtry but this can actually be any integer > 1;
# can even greater than p.
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
makeA <- function(options){
p <- options[[1L]]
d <- options[[2L]]
method <- options[[3L]]
if(method == 1L){
rho<-options[[4L]]
nnzs <- round(p*d*rho)
sparseM <- matrix(0L, nrow=p, ncol=d)
sparseM[sample(1L:(p*d),nnzs, replace=F)]<-sample(c(1L,-1L),nnzs,replace=T)
}
#The below returns a matrix after removing zero columns in sparseM.
ind<- which(sparseM!=0,arr.ind=TRUE)
return(cbind(ind,sparseM[ind]))
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Create Distance Matrix
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
dist <- function(X, Forest, maxDepth=0){
n <- nrow(X)
dist <- matrix(0, nrow=n, ncol=n)
numT <- length(Forest)
currBin <- integer(n)
if (maxDepth==0){
for(j in 1:numT){
for(i in 1:n){
currentNode <- 1L
while(Forest[[j]]$Children[currentNode]!=0L){
s<-length(Forest[[j]]$matA[[currentNode]])/2
rotX <-sum(Forest[[j]]$matA[[currentNode]][(1:s)*2]*X[i,Forest[[j]]$matA[[currentNode]][(1:s)*2-1]])
if(rotX<=Forest[[j]]$CutPoint[currentNode]){
currentNode <- Forest[[j]]$Children[currentNode,1L]
}else{
currentNode <- Forest[[j]]$Children[currentNode,2L]
}
}
dist[Forest[[j]]$ALeaf[[currentNode]]] <- dist[Forest[[j]]$ALeaf[[currentNode]]] + 1
for(z in 1:(i-1)){
if(currBin[z] == currentNode){
dist[i,z] <- dist[i,z]+1
dist[z,i] <- dist[z,i]+1
}
}
dist[i,i] <- dist[i,i]+1
}
}
}else{
for(j in 1:numT){
for(i in 1:n){
currentNode <- 1L
depth <- 1L
while(Forest[[j]]$Children[currentNode]!=0L && depth <= maxDepth){
s<-length(Forest[[j]]$matA[[currentNode]])/2
rotX <-sum(Forest[[j]]$matA[[currentNode]][(1:s)*2]*X[i,Forest[[j]]$matA[[currentNode]][(1:s)*2-1]])
if(rotX<=Forest[[j]]$CutPoint[currentNode]){
currentNode <- Forest[[j]]$Children[currentNode,1L]
}else{
currentNode <- Forest[[j]]$Children[currentNode,2L]
}
depth <- depth+1L
}
currBin[i] <- currentNode
if(i>1){
for(z in 1:(i-1)){
if(currBin[z] == currentNode){
dist[i,z] <- dist[i,z]+1
dist[z,i] <- dist[z,i]+1
}
}
}
dist[i,i] <- dist[i,i]+1
}
}
}
return(dist)
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Find Potential Nearest Neighbors Vector
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
distNN <- function(y, X, Forest, maxDepth=0){
dist <- integer(nrow(X))
numT <- length(Forest)
if (maxDepth==0){
maxDepth <- Inf
}
for(j in 1:numT){
currentNode <- 1L
depth <- 1L
while(Forest[[j]]$Children[currentNode]!=0L && depth <= maxDepth){
s<-length(Forest[[j]]$matA[[currentNode]])/2
rotX <-sum(Forest[[j]]$matA[[currentNode]][(1:s)*2]*y[Forest[[j]]$matA[[currentNode]][(1:s)*2-1]])
if(rotX<=Forest[[j]]$CutPoint[currentNode]){
currentNode <- Forest[[j]]$Children[currentNode,1L]
}else{
currentNode <- Forest[[j]]$Children[currentNode,2L]
}
depth <- depth+1L
}
dist[Forest[[j]]$ALeaf[[currentNode]]] <- dist[Forest[[j]]$ALeaf[[currentNode]]] + 1
}
return(dist) #this is the similarity vector
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Find Nearest Neighbors from similarity vector
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
distNNk <- function(y, X, sv, k, adder){
index <- order(sv, decreasing=TRUE)
simCount <- tabulate(sv)
multiplier <- adder
if(sum(simCount) < multiplier+k){
remainingNN <- sum(simCount)
print("Not enough points. Decrease search depth.")
flush.console()
return(NULL)
}else{
remainingNN <- multiplier+k
}
simLength <- length(simCount)
NNindex <- NULL
while (remainingNN >0){
if (remainingNN >= simCount[simLength]){
if(simCount[simLength]>0){
NNindex <- c(NNindex, index[1:simCount[simLength]])
index <- index[-(1:simCount[simLength])]
remainingNN <- remainingNN-simCount[simLength]
}
simLength <- simLength -1
}else{
#NNorder <- order(sqrt(rowSums((y-X[index[1:simCount[simLength]],])^2)))
NNorder <- order(sqrt(rowSums(sweep(X[index[1:simCount[simLength]],],2,y)^2)))
NNindex <- c(NNindex, index[NNorder[1:remainingNN]])
remainingNN = 0
}
}
kNearest <- order(sqrt(rowSums(sweep(X[NNindex,],2,testSamples[1,])^2)))[1:k]
return(NNindex[kNearest])
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Check K-Means
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
CheckKmeans <- function(Y, Yp){
uY <- length(unique(Y))
classCt <- tabulate(Y, uY)
class_order <- order(classCt, decreasing=TRUE)
used_class <-NULL
curr_class <- NA
class_error <- NA
for(z in 1:uY){
Cindex <- which(Y==class_order[z])
subClassCt <- tabulate(Yp[Cindex], uY)
subClass_order <- order(subClassCt, decreasing=TRUE)
if(!is.null(used_class)){
for(m in 1:uY){
curr_class <- subClass_order[m]
if(!(curr_class%in%used_class)){
break
}
}
used_class <- c(used_class, curr_class)
}else{
curr_class <- subClass_order[1]
used_class <- curr_class
}
class_error[z] <- subClassCt[curr_class]/classCt[class_order[z]]
}
print(class_error)
oe <- sum(class_error*classCt[class_order])/length(Y)
cat("the overall error is: ", oe, "\n")
flush.console()
}
#############################Swiss Roll Code###################################
swissRoll <- function(n1, n2 = NULL, size = 6, dim3 = FALSE, rand_dist_fun = NULL, ...) {
### If n2 is NULL, then generate a balanced dataset of size 2*n1
if (is.null(n2)) n2 <- n1
xdim <- ifelse(dim3, 3, 2)
### GROUP 1
# Generate Angles
rho <- runif(n1, 0, size*pi)
# Create Swiss Roll
g1x1 <- rho*cos(rho)
g1x2 <- rho*sin(rho)
### GROUP 2
# Generate Angles
rho <- runif(n2, 0, size*pi)
# Create Inverse Swiss Roll
g2x1 <- -rho*cos(rho)
g2x2 <- -rho*sin(rho)
### Generate the 3rd dimension
if (dim3) {
z_range <- range(c(g1x1, g1x2, g2x1, g2x2))
x3 <- runif(n1 + n2, z_range[1], z_range[2])
}
### If needed random perturbation on the data
### please specify the random generation funciton in R to 'rand_dist_fun'
### and the corresponding parameters in '...'.
### For example,
### rand_dist_fun = rnorm, mean = 0, sd = 0.2
err <- matrix(0, n1 + n2, xdim)
if (!is.null(rand_dist_fun)) err <- matrix(rand_dist_fun(xdim*(n1 + n2), ...), n1 + n2, xdim)
### Output the Swiss Roll dataset
if (dim3) {
out <- data.frame(y = c(rep(0:1, c(n1, n2))), x1 = c(g1x1, g2x1) + err[,1], x2 = c(g1x2, g2x2) + err[,2], x3 = x3 + err[,3])
} else {
out <- data.frame(y = c(rep(0:1, c(n1, n2))), x1 = c(g1x1, g2x1) + err[,1], x2 = c(g1x2, g2x2) + err[,2])
}
out
}
################################################################################
################################################################################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Hartigan's Method
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
findClusters <- function(nearnessMatrix, numClusters=3, numNearestNeighbors=10){
q <- rep(0,numClusters)
clusters <- vector("list", numClusters)
numSamples <- nrow(nearnessMatrix)
numNN <- numNearestNeighbors
randomOrdering <- sample(1:numSamples, numSamples)
# randomOrdering <- 1:numSamples
step <- floor(numSamples/numClusters)
stepStart <- 1
stepEnd <- stepStart+step
for(z in 1:(numClusters-1)){
clusters[[z]] <- randomOrdering[stepStart:stepEnd]
stepStart <- stepEnd+1
stepEnd <- stepStart+step
}
clusters[[numClusters]] <- randomOrdering[stepStart:numSamples]
for(z in 1:numSamples){
nearnessMatrix[z,z] <- 0
}
for(z in 1:numClusters){
for(m in clusters[[z]]){
biggestNN <- order(nearnessMatrix[m,], decreasing=TRUE)[1:numNN]
q[z] <- q[z] + sum(biggestNN%in%clusters[[z]])
}
}
print(paste("initial q", q))
currQ <- rep(0,numClusters)
for(p in 1:30){
for(z in 1:numClusters){
for(m in clusters[[z]]){
biggestNN <- order(nearnessMatrix[m,], decreasing=TRUE)[1:numNN]
for(k in 1:numClusters){
currQ[k] <- sum(biggestNN%in%clusters[[k]])
}
QOrder <- order(currQ, decreasing=TRUE)
if(QOrder[1] != z){
q[z] <- q[z] - currQ[z]
q[QOrder[1]] <- q[QOrder[1]] + currQ[QOrder[1]]
clusters[[z]] <- clusters[[z]][-which(clusters[[z]]==m)]
clusters[[QOrder[1]]] <- c(clusters[[QOrder[1]]], m)
}
}
}
}
print(paste("after 1", q))
return(clusters)
}
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Spectral Cluster
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
specN <- function(distMat, numClust){
Y <- kmeans(distMat, numClust)$cluster
return(Y)
}
#############################################################################
require(compiler)
rfrus <- cmpfun(rfrus)
distNN <- cmpfun(distNN)
createSimilarityMatrix <- function(X, numTrees=100, K=10){
numberSamples <- nrow(X)
similarityMatrix <- matrix(0,nrow= numberSamples, ncol=numberSamples)
forest <- invisible(rfrus(X,trees=numTrees, MinParent=K))
for(z in 1:numberSamples){
NN1 <- distNN(X[z,], X, forest)
similarityMatrix[z,] = NN1
# for(q in 1:numberSamples){ #Why did I do this?
# if(NN1[q]==0){
# similarityMatrix[z,q]<-0
# }
# }
}
return(similarityMatrix)
}
|
a3523fffcdc9627f1c445a5fe2b90f63e1af9bbf | f53e353c54541c9282a9822e1fa23698bf533bd7 | /Statistical experiment/5/5.R | a54be8f9e6e30ad3147d2f1392f3ff0ad340beef | [] | no_license | sakur0zxy/R-practice | acee9b2335077365e70e94fdf4734ed6dee58485 | b8ec1f0a0feddcb16f988e0ca45c9b45b908440b | refs/heads/master | 2020-03-26T05:58:17.014993 | 2018-09-27T08:04:53 | 2018-09-27T08:04:53 | 144,583,426 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,152 | r | 5.R | #12个球中有9个新球3个旧球，第一次从中取出三个使用后放回，
#第二次又取出三个
#1.求第二次取出的三个球都是新球的概率
#2.已知第二次取出的三个球都是新球，求第一次取出的球都是
#新球的概率
#There are 12 balls including 9 new balls and 3 used
#balls. At the first time, randomly select 3 balls
#and return them after playing. At the second time,
#randomly select 3 balls again.
#1.Find the probability that the three balls taken
#out for the second time are all new balls.
#2.It is known that the three balls taken out for
#the second time are all new balls, and the probability
#that the first three balls taken out are new ball,too.
select5<-function(n){
p1=0;p2=0
for(i in 1:n){
x1<-sample(1:12, size=3, replace = FALSE, prob = NULL)
used0=3
used1=used0+sum(x1<=(12-used0))
x2<-sample(1:12, size=3, replace = FALSE, prob = NULL)
if(sum(x2<=(12-used1))==3) p1=p1+1
if(sum(x1<=(12-used0))==3){
if(sum(x2<=(12-used1))==3) p2=p2+1
}
}
rt<-c('P1'=p1/n,'P2'=p2/p1)
rt
}
select5(10000) |
9dc6ff2467aa9ec5ac78b322df73577f60bbcbb0 | 10a72ed9289fbed1832a926ee017902eba6df3bd | /man/gplotgraph.Rd | 26cc1156cec27207ad8bbd92c7e3ba022ef74320 | [
"MIT"
] | permissive | jfontestad/sirgraph | 4a6e9be86c737ac507f5512f0543180374042649 | cf0ec32e7bd4e4250a1c33afa8bbcd4eb5aa22a6 | refs/heads/master | 2022-04-19T04:14:17.496926 | 2020-04-04T20:12:59 | 2020-04-04T20:12:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 301 | rd | gplotgraph.Rd | % Generated by roxygen2 (4.0.0): do not edit by hand
\name{gplotgraph}
\alias{gplotgraph}
\title{gplotgraph}
\usage{
gplotgraph(g)
}
\arguments{
\item{g}{an SIR graph}
}
\value{
make a plot
}
\description{
plot an SIR graph
}
\details{
use ggplot to show an SIR graph
}
\author{
Barry S Rowlingson
}
|
2c1e5d1551d7d8aba2a7bfdfedcb0e45f83a7b1f | e41102477bb433bfb6e49dd6e6bfc7e7333cae0a | /week3/modeling.R | 7baeb4b28322024dd65cc72a35cde10f7836f298 | [] | no_license | ettirapp/coursework | 46950d5ca3b64ca3d63551a02030d4acde2118a7 | 00d6da4925d15bdf99d4b2abecbd8b796867a7cd | refs/heads/master | 2020-06-03T03:14:51.534760 | 2019-07-16T00:59:59 | 2019-07-16T00:59:59 | 191,412,559 | 0 | 0 | null | 2019-06-11T16:45:46 | 2019-06-11T16:45:45 | null | UTF-8 | R | false | false | 7,060 | r | modeling.R | library(scales)
library(broom)
library(modelr)
library(tidyverse)
options(na.action = na.warn)
theme_set(theme_bw())
options(repr.plot.width=4, repr.plot.height=3)
users <- read_tsv(gzfile('users.tsv.gz'))
head(users)
# histogram of the label/regressor variable:
ggplot(users, aes(x = daily.views)) +
geom_histogram(bins = 50) +
scale_x_log10(label=comma, breaks=10^(0:ceiling(log10(max(users$daily.views))))) +
scale_y_continuous(label = comma) +
xlab('Daily pageviews') +
ylab('')
ggplot(data = users, aes(x = age, y = daily.views)) +
geom_point() +
facet_wrap(~ gender) +
xlab('Age') +
ylab('Daily pageviews')
nrow(users)
users <- filter(users, daily.views > 0)
nrow(users)
views_by_age_and_gender <- users %>%
filter(age <= 90) %>%
group_by(age, gender) %>%
summarize(count = n(),
median_daily_views = median(daily.views))
head(views_by_age_and_gender)
options(repr.plot.width=6, repr.plot.height=3)
ggplot(views_by_age_and_gender, aes(x = age, y = median_daily_views, color = gender)) +
geom_line(aes(linetype=gender)) +
geom_point(aes(size = count)) +
xlab('Age') +
ylab('Daily pageviews') +
scale_size_area(guide = F) +
theme(legend.title=element_blank())
model_data <- filter(users, age >= 18 & age <= 65)
options(repr.plot.width=4, repr.plot.height=3)
ggplot(model_data, aes(x = age, y = daily.views)) +
geom_smooth(method = "lm") +
scale_y_log10(breaks = 1:100)
model <- lm(log10(daily.views) ~ age, model_data)
model
summary(model)
tidy(model)
glance(model)
M <- model.matrix(log10(daily.views) ~ age, model_data)
head(M)
tail(M)
plot_data <- model_data %>%
data_grid(age) %>%
add_predictions(model) %>%
mutate(pred = 10^pred)
head(plot_data)
plot_data <- model_data %>%
data_grid(age) %>%
add_predictions(model) %>%
mutate(pred = 10^pred)
head(plot_data)
ggplot(plot_data, aes(x = age, y = pred)) +
geom_line()
plot_data <- model_data %>%
group_by(age) %>%
summarize(count = n(),
geom_mean_daily_views = 10^(mean(log10(daily.views)))) %>%
add_predictions(model) %>%
mutate(pred = 10^pred)
head(plot_data)
ggplot(plot_data, aes(x = age, y = pred)) +
geom_line(aes(y = pred)) +
geom_point(aes(y = geom_mean_daily_views, size = count)) +
scale_size_area(guide = F)
model <- lm(log10(daily.views) ~ age + I(age^2), model_data)
model
tidy(model)
glance(model)
M <- model.matrix(log10(daily.views) ~ age + I(age^2), model_data)
head(M)
plot_data <- model_data %>%
group_by(age) %>%
summarize(count = n(),
geom_mean_daily_views = 10^(mean(log10(daily.views)))) %>%
add_predictions(model) %>%
mutate(pred = 10^pred)
ggplot(plot_data, aes(x = age, y = pred)) +
geom_line(aes(y = pred)) +
geom_point(aes(y = geom_mean_daily_views, size = count)) +
scale_size_area(guide = F)
form <- as.formula(log10(daily.views) ~ gender + age + I(age^2))
M <- model.matrix(form, model_data)
model <- lm(form, model_data)
head(M)
model
options(repr.plot.width=6, repr.plot.height=3)
plot_data <- model_data %>%
group_by(age, gender) %>%
summarize(count = n(),
geom_mean_daily_views = 10^(mean(log10(daily.views)))) %>%
add_predictions(model) %>%
mutate(pred = 10^pred)
ggplot(plot_data, aes(x = age, y = pred, color = gender)) +
geom_line(aes(y = pred)) +
geom_point(aes(y = geom_mean_daily_views, size = count)) +
scale_size_area(guide = F)
form <- as.formula(log10(daily.views) ~ gender * (age + I(age^2)))
M <- model.matrix(form, model_data)
model <- lm(form, model_data)
head(M)
model
plot_data <- model_data %>%
group_by(age, gender) %>%
summarize(count = n(),
geom_mean_daily_views = 10^(mean(log10(daily.views)))) %>%
add_predictions(model) %>%
mutate(pred = 10^pred)
options(repr.plot.width=6, repr.plot.height=3)
ggplot(plot_data, aes(x = age, y = pred, color = gender)) +
geom_line(aes(y = pred)) +
geom_point(aes(y = geom_mean_daily_views, size = count)) +
scale_size_area(guide = F)
#EVALUATING MODELS:
library(tidyverse)
library(scales)
library(modelr)
options(na.action = na.warn)
library(broom)
theme_set(theme_bw())
options(repr.plot.width=4, repr.plot.height=3)
users <- read_tsv(gzfile('users.tsv.gz'))
model_data <- filter(users, daily.views > 0, age >= 18 & age <= 65)
form <- as.formula(log10(daily.views) ~ gender * (age + I(age^2)))
M <- model.matrix(form, model_data)
head(M)
model <- lm(form, model_data)
tidy(model)
glance(model)
plot_data <- model_data %>%
group_by(age, gender) %>%
summarize(count = n(),
geom_mean_daily_views = 10^(mean(log10(daily.views)))) %>%
add_predictions(model) %>%
mutate(pred = 10^pred)
options(repr.plot.width=6, repr.plot.height=3)
ggplot(plot_data, aes(x = age, y = pred, color = gender)) +
geom_line(aes(y = pred)) +
geom_point(aes(y = geom_mean_daily_views, size = count)) +
scale_size_area(guide = F)
options(repr.plot.width=4, repr.plot.height=3)
ggplot(plot_data, aes(x = pred, y = geom_mean_daily_views)) +
geom_point() +
geom_abline(linetype = "dashed") +
xlab('Predicted') +
ylab('Actual')
ggplot(plot_data, aes(x = pred, y = geom_mean_daily_views, color = gender)) +
geom_point() +
geom_abline(linetype = "dashed") +
xlab('Predicted') +
ylab('Actual')
ggplot(plot_data, aes(x = pred, y = geom_mean_daily_views, color = desc(age))) +
geom_point() +
geom_abline(linetype = "dashed") +
xlab('Predicted') +
ylab('Actual') +
facet_wrap(~ gender, scale = "free")
pred_actual <- model_data %>%
add_predictions(model) %>%
mutate(actual = log10(daily.views))
head(pred_actual)
ggplot(pred_actual, aes(x = 10^pred, y = 10^actual)) +
geom_point(alpha = 0.1) +
geom_abline(linetype = "dashed") +
scale_x_log10(label = comma, breaks = seq(0,100,by=10)) +
scale_y_log10(label = comma) +
xlab('Predicted') +
ylab('Actual')
pred_actual %>%
summarize(rmse = sqrt(mean((pred - actual)^2)))
pred_actual %>%
summarize(rmse = sqrt(mean((10^pred - 10^actual)^2)))
pred_actual %>%
summarize(rmse = sqrt(mean((pred - actual)^2)),
cor = cor(pred, actual),
cor_sq = cor^2)
rmse(model, model_data)
rsquare(model, model_data)
pred_actual %>%
summarize(mse = mean((pred - actual)^2),
mse_baseline = mean((mean(actual) - actual)^2),
mse_vs_baseline = (mse_baseline - mse) / mse_baseline,
cor = cor(pred, actual),
cor_sq = cor^2)
K <- 30
form <- as.formula(log10(daily.views) ~ gender * poly(age, K, raw=T))
M <- model.matrix(form, model_data)
head(M)
model <- lm(form, model_data)
glance(model)
tidy(model)
plot_data <- model_data %>%
group_by(age, gender) %>%
summarize(count = n(),
geom_mean_daily_views = 10^(mean(log10(daily.views)))) %>%
add_predictions(model) %>%
mutate(pred = 10^pred)
options(repr.plot.width=6, repr.plot.height=3)
ggplot(plot_data, aes(x = age, y = pred, color = gender)) +
geom_line(aes(y = pred)) +
geom_point(aes(y = geom_mean_daily_views, size = count)) +
scale_size_area(guide = F)
|
cce186bb01a74ee731238f400e87ef3d844adf0e | 6cabc0530af701aea5e5d76c3b0f242479d78140 | /install.R | 92255cf0614c3137035cf68ac4aae8a20e762abf | [
"MIT"
] | permissive | Atheloses/VSB-S8-PS | 06ff0f551124dc692a47cb575a88e9397d3b4506 | fdef214f8169094b6366ce0489f92ef20b460702 | refs/heads/main | 2023-05-28T14:07:59.232669 | 2021-06-04T14:04:26 | 2021-06-04T14:04:26 | 373,856,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 417 | r | install.R | install.packages("readxl")
install.packages("dplyr")
install.packages("openxlsx")
install.packages("moments")
install.packages("lawstat")
install.packages("BSDA")
install.packages("EnvStats")
install.packages("binom")
install.packages("car")
install.packages("dunn.test")
install.packages("lsr")
install.packages("epiR")
install.packages("nortest")
#install.packages('IRkernel')
#IRkernel::installspec(user = TRUE)
|
1fb171ff832e64d1d702a3bf9124b74736ef29a7 | ec78bae636ce940611e2a198539f12c12b330401 | /intro_to_data_modeling/week2/hw_4_2.R | b6e04d0e7c4d4312695ca98c4736589b67526fad | [
"MIT"
] | permissive | bwilson668/gt | cc4495d8f8b0fb2506b9a5e916839c12da1d9080 | d474598425d70f774a6d509761640ebc4516a1f5 | refs/heads/master | 2020-05-30T10:36:11.657344 | 2019-07-03T12:48:36 | 2019-07-03T12:48:36 | 189,676,200 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,917 | r | hw_4_2.R | # QUESTION
#
# Use the R function kmeans to cluster the points as well as possible.
# Report the best combination of predictors, your suggested value of k,
# and how well your best clustering predicts flower type.
# Import Libraries
library(tidyverse)
# Set Seed for Reproducability
set.seed(1234)
# Read the data in
iris <- read.table("_data/iris.txt", header = TRUE, sep = "", dec = ".")
# Data Prep
# Scaled around center due to suggestion in week 1.
# Scaling from 0-1 is good for classification.
# Scaling around center is good for clustering.
iris_scaled <- mutate(
iris,
Sepal.Length = scale(Sepal.Length)[,1],
Sepal.Width = scale(Sepal.Width)[,1],
Petal.Length = scale(Petal.Length)[,1],
Petal.Width = scale(Petal.Width)[,1]
) %>%
select( -Species )
# Determine which predictors should be used
my_cols <- c("#FF0000", "#00FF00", "#0000FF") # RGB
pairs(
iris_scaled, cex = 0.5,
col = my_cols[iris$Species],
lower.panel=NULL
)
###
#
# REPORT
#
# The Green and Blue species are the hardest to distinguish.
# Each of the 4 features look to contribute to the ability of separating the species.
# For my analysis, I'll leave all 4 features in for the clustering.
#
###
k_range <- 1:10
# Loop over all possible Ks
iris_clusters <- sapply(
k_range,
function(k){ kmeans(iris_scaled, k) }
)
# Elbow Curve Plot
plot(
k_range, iris_clusters[5,],
type="b", main="Elbow Curve Plot",
xlab="Number of clusters K",
ylab="Total within-clusters sum of squares"
)
###
#
# Report
#
# Judging by the Elbow Curve, my suggested value of K would be 4 clusters.
#
# Although, we know the dataset has 3 species.
# If these species were previously unknown, then we might name 4 different species.
# However, since we know there are 3, the clustering would not make for the best classifier,
# especially between the blue and green species.
#
### |
dec2bd262af915000f2ef29e69ae39394c765a49 | 38ee778acc8edb66cf5b9649c932ca8f8a154b8c | /study_data-analysis/5. data_visualization/practice01.R | abbea0204b093caca685125cb5c5637c6ed1f3de | [] | no_license | sangm1n/BITLab | 55e73119af544a209cc00676c4efc9d00441211a | 7c40ecaac755facf5fc7b5232d20b6430562cc28 | refs/heads/master | 2023-04-18T06:16:01.093813 | 2021-04-28T05:23:53 | 2021-05-04T07:36:08 | 290,684,489 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,719 | r | practice01.R | # data visualization practice in R by sangmin
# treemap and symbols using state.x77 dataset
us <- data.frame(state.x77, state.division)
head(us)
library(treemap)
state.name <- as.factor(rownames(us))
us.2 <- data.frame(us, state.name)
head(us.2)
treemap(us.2, index=c("state.division", "state.name"),
vSize="Population", vColor="Income", type="dens",
bg.labels="yellow", title="US state")
treemap(us.2, index=c("state.division", "state.name"),
vSize="HS.Grad", vColor="Murder", type="value",
bg.labels="yellow", title="US state")
symbols(us.2$Income, us.2$Illiteracy, circles=us.2$Population,
inches=0.4, fg="grey", bg="green",
xlab="Income", ylab="Illiteracy", main="Income and Illiteracy")
text(us.2$Income, us.2$Illiteracy,
rownames(us), cex=0.6, col="brown")
symbols(us.2$Illiteracy, us.2$Murder, circles=us.2$Area,
inches=0.5, fg="grey", bg="green",
xlab="Illiteracy", ylab="Murder", main="Illiteracy and Murder")
text(us.2$Illiteracy, us.2$Murder,
rownames(us), cex=0.6, col="brown")
# treemap using swiss dataset
head(swiss)
a <- data.frame(subset(swiss, Education <= 6), group="low")
b <- data.frame(subset(swiss, Education >= 13), group="high")
c <- data.frame(subset(swiss, Education > 6 & Education < 13), group="mid")
swiss.name <- rownames(swiss)
swiss.2 <- rbind(a, b, c)
swiss.2 <- data.frame(swiss.2, swiss.name)
swiss.2
treemap(swiss.2, index=c("group", "swiss.name"),
vSize="Fertility", vColor="Agriculture", type="value",
bg.labels="yellow", title="Swiss state name")
treemap(swiss.2, index="swiss.name",
vSize="Catholic", vColor="Examination", type="dens",
title="Swiss state name") |
fad4b0132f39efbe1b62c2cddd18791fb45f142f | 18433088b0f83ad44064bcc50d5900d99b737ec9 | /StartService.R | 946f00c87957ceab4df76ead49c0c82c9e77a3f2 | [] | no_license | ClausBoHansen/Rserve | ff4900b979819781f1c9e37d28f9c5b480810121 | a474251efffb348259c20fee7e933834933f5657 | refs/heads/master | 2020-04-15T17:05:02.451289 | 2019-01-30T13:12:31 | 2019-01-30T13:12:31 | 164,859,901 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 475 | r | StartService.R | # Installation notes
# Install lssl (console)
# apt-get install libssl-dev
# Install Rserve
# install.packages('Rserve',,'http://www.rforge.net/')
#### Load libraries ####
library(Rserve)
library(RserveFunctions)
# Load library with Machine Learning function
#library(testpackage)
load("finalModel.rf.RData")
# Call Prediction example
# Prediction(TrainedModel, 12)
#### Initiate server ####
system("killall -INT Rserve")
# Start Rserve
Rserve(args = "--no-save")
|
426e97b186bd7e32ec393cc532d3b9e8ec95fdf3 | ee4a94d66353c78d326489ce02287bffb993535b | /Gibbs/man/sampling_mu.Rd | 9b55b4486998b356c928a551867419be992a493b | [] | no_license | jeonfect/GibbsSampler | bbf93b23568a895e9bbf1b3dfae257f4bcf68b1a | 8bbb40840dcf901b4831728af7d8a062d6f5bdec | refs/heads/master | 2022-12-31T16:37:55.150318 | 2020-10-15T15:47:22 | 2020-10-15T15:47:22 | 264,549,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 978 | rd | sampling_mu.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GibbsSampler.R
\name{sampling_mu}
\alias{sampling_mu}
\title{Sampling method for within-group sample mean.}
\usage{
sampling_mu(z, theta, mu, Sigma)
}
\arguments{
\item{z}{vector length \code{n} of observation's cluster allocation}
\item{theta}{matrix \verb{n x p} of observation means}
\item{mu}{matrix \verb{k x p} of mean values for each cluster}
\item{Sigma}{matrix \verb{p x p x k} of variances for each cluster}
}
\value{
matrix \verb{k x p} of sampled mu
}
\description{
Sampling method for within-group sample mean.
}
\details{
\code{mu_k} has conditional distribution \verb{N(\\hat\\theta_k,\\Sigma_k/N_k)} where \verb{N_k=\\sum_\{i=1\}^\{N\}I(z_i=k)}. Sampling is done using \code{rmNorm()} from \code{mniw} package.
}
\references{
Martin Lysy and Bryan Yates (2019). mniw: The Matrix-Normal Inverse-Wishart Distribution. R package version 1.0. https://CRAN.R-project.org/package=mniw
}
|
6caeb70a43f0d203932d894c08ba13b7a2c67a7e | 73aac21a9f317c5fccc5a662c7a44b31307e4f93 | /8-pcurve recover AEO.R | 239ebc9e9989cadbaa41a6751dd77c70a52345c9 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | martinasladek/meta-showdown | d76f3e00ee6615ccd7821986277d4008886b483d | 371b41aea50e63158960e4c7136238baa54e1803 | refs/heads/master | 2023-02-23T10:59:31.430984 | 2019-06-14T07:22:25 | 2019-06-14T07:22:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,526 | r | 8-pcurve recover AEO.R | ## ======================================================================
## This code checks the alternative interpretation of p-curve (a la Simonsohn)
## which states that p-curve is supposed to recover the "average true effect of studies submitted to p-curve"
## =====================================================================
library(dplyr)
library(ggplot2)
load("dataFiles/res.wide.RData")
# res.wide$qrp.label <- factor(res.wide$qrpEnv, levels=c("none", "med", "high"), labels=paste0("QRP = ", c("none", "med", "high")), ordered=TRUE)
# res.wide$delta.label <- factor(res.wide$delta, levels=c(0, 0.2, 0.5, 0.8), labels=paste0("delta = ", c(0, 0.2, 0.5, 0.8)), ordered=TRUE)
# res.wide$censor <- factor(res.wide$selProp, levels=unique(res.wide$selProp), labels=paste0("PB = ", unique(res.wide$selProp)))
# ---------------------------------------------------------------------
# Compute summary measures across replications
# use the data set without any reductions (i.e., also keep p-curves with <=3 sign. studies)
PC <- res.wide %>% filter(method=="pcurve", !is.na(kSig_estimate) & kSig_estimate >= 1)
summ.PC <- PC %>% group_by(condition, k, k.label, delta, delta.label, qrpEnv, qrp.label, censor, censor.label, tau, tau.label) %>%
dplyr::summarise(
meanEst.AEO = mean(b0_estimate, na.rm=TRUE),
ME.AEO = mean(b0_estimate - delta.included_mean, na.rm=TRUE),
RMSE.AEO = sqrt(mean((b0_estimate - delta.included_mean)^2, na.rm=TRUE)),
perc2.5.AEO = quantile(b0_estimate, probs=.025, na.rm=TRUE),
perc97.5.AEO = quantile(b0_estimate, probs=.975, na.rm=TRUE),
nSig = mean(kSig_estimate)
)
# average kSig in tau=0 conditions:
summ.PC %>% filter(tau==0, delta == 0, qrpEnv=="none") %>% dplyr::select(1:8, nSig)
# ---------------------------------------------------------------------
# Plot
# order the delta.label factor alphabetically
summ.PC$delta.label2 <- factor(summ.PC$delta.label, levels=sort(levels(summ.PC$delta.label)))
summ.PC$censor.label2 <- factor(paste0("PB = ", summ.PC$censor))
# raw estimates (not posified)
summ.PC %>%
ggplot(aes(x=k.label, y=ME.AEO, shape=delta.label2)) +
geom_point(position=position_dodge(width=0.7)) +
geom_hline(yintercept=0) +
coord_flip(ylim=c(-0.4, 0.25)) +
xlab("k") + ylab("Mean error (relative to average true effect size of studies submitted to p-curve)") +
facet_grid(tau.label~censor.label2~qrp.label) +
guides(shape=guide_legend("True effect size")) + xlab("") +
theme_bw()
ggsave("Plots/ME_AEO_raw.jpg", dpi=120)
|
1c7a44e64b9a73ee6cc30b587d40f4f8d3d6dc3a | 0a3d5398e435fc81a61f832c0921304c72d7dbd5 | /tests/testthat/test-examples.R | 8296ee65662233dd65d0fb35b640b25336fe9181 | [] | no_license | r-lib/pkgload | 609ea95e6f19b51f2ccfed1f71bbb0adbc647283 | 75938cd13f80e912af43d9632cdb54aa0bc9ebff | refs/heads/main | 2023-09-01T00:53:19.446111 | 2023-07-06T07:34:53 | 2023-07-06T08:15:47 | 73,123,753 | 46 | 38 | null | 2023-09-11T13:26:16 | 2016-11-07T21:45:48 | R | UTF-8 | R | false | false | 1,145 | r | test-examples.R | local_load_all_quiet()
test_that("default run_example ignores donttest and dontrun ", {
env <- run_example(test_path("test-examples.Rd"), quiet = TRUE)
expect_equal(env$a, 1)
})
test_that("run donttest when requested", {
env <- run_example(test_path("test-examples.Rd"), run_donttest = TRUE, quiet = TRUE)
expect_equal(env$a, 2)
})
test_that("run dontrun when requested", {
env <- run_example(test_path("test-examples.Rd"), run_dontrun = TRUE, quiet = TRUE)
expect_equal(env$a, 3)
})
test_that("can run example package", {
load_all(test_path("testHelp"))
on.exit(unload(test_path("testHelp")))
env <- dev_example("foofoo", quiet = TRUE)
expect_equal(env$a, 101)
})
test_that("can use system macros", {
load_all(test_path("testHelp"))
on.exit(unload(test_path("testHelp")))
expect_silent(
run_example(
test_path("testHelp", "man", "testSysMacro.Rd"),
quiet = TRUE
)
)
})
test_that("can use extra Rd macros", {
macros <- load_rd_macros("testHelp")
expect_silent(
run_example(
test_path("testHelp", "man", "testCustomMacro.Rd"),
quiet = TRUE, macros = macros
)
)
})
|
bc7af98550efb55329d14b71235388cd23c4000e | 6721e8fd9ee08ed6c4fabd2d6556558779185697 | /man/colorList.Rd | 46497b75b9ee34251c283c09ff5851bebe6559fe | [] | no_license | uschiLaa/galahr | 47befbd4a164a44ecba0615ec65e6bbca13daf73 | 0b7d9b78694e4d711e09987e073bfa9f54987a6c | refs/heads/master | 2021-06-28T08:43:32.326374 | 2020-03-12T01:26:14 | 2020-03-12T01:26:14 | 186,314,758 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 406 | rd | colorList.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotting.R
\name{colorList}
\alias{colorList}
\title{Mapping grouping to color.}
\usage{
colorList(gr)
}
\arguments{
\item{gr}{Vector containing group assignment for each entry.}
}
\value{
Named list containing assigned colors ("color")
and list of colors ("col")
}
\description{
Mapping grouping to color.
}
\keyword{internal}
|
5d6792f9f337009e9d2d4f6abc84de883c1cf9e7 | c4d69c23de8fba861a5f258e13a86a8a769cf81c | /process_script.R | 2144704876eea7c6d1ebfe39ee8e8319505ea3b0 | [] | no_license | guhao0809/VAR | 2e1af7611a8021295f230285d8d3625ac3cf40f2 | 587d73bb05969eb916c10a6df78d3a10cbdc4491 | refs/heads/master | 2020-03-28T05:13:58.439649 | 2018-11-26T03:16:54 | 2018-11-26T03:16:54 | 147,764,116 | 0 | 1 | null | 2018-09-07T03:13:53 | 2018-09-07T03:13:53 | null | UTF-8 | R | false | false | 1,096 | r | process_script.R | # if (length(date.list) < 570){ # date.list
# process_period = ceiling(length(date.list)/2) # date.list
# review_period =floor(length(date.list)/2) # date.list
# } else {
# process_period = 285
# review_period = 285
# }
process_period = 285
review_period = 285
total_days = process_period + review_period
# data_end_day = as.Date(date.list[length(date.list)]) # date.list
data_end_day = date.list[length(date.list)]
data_start_day = date.list[length(date.list)-total_days+1]
# Other Time parameters needed
process_start_day = review_period + 1
temp_result = Lambda.optimize(process_period, selected_index_dt_final)
lambda.var.list = temp_result$lambda.var.list
lambda.cov.list = temp_result$lambda.cov.list
temp_list = Index.var.cov.calculation(selected_index_dt_final, process_period, lambda.var.list, lambda.cov.list)
full_data = temp_list$full_data
setorder(full_data, seccode, date)
final_data = temp_list$final_data
cov_df = temp_list$cov_df
cov.list = temp_list$variance.list
cov.mat = temp_list$cov.mat
cov.series = as.vector(cov.mat)
# matrix(as.vector(cov.series),ncol = 30) |
ec8e3ef164403e4ce215148cf90e92f849f3bf95 | 7f31661d2c24df6ac261f66065f687dbf848a5a5 | /man/form.Rd | 6527108a9a0b563614cfe0f7cf4b05f840f18cff | [] | no_license | cran/sudachir | 0ed0ba2b27f739f3809d38e0178c58bf02958d52 | 9f1251aad5ef5460ddbcb9d3f2aa1677d370a941 | refs/heads/master | 2023-01-14T00:01:41.487373 | 2020-11-10T14:20:02 | 2020-11-10T14:20:02 | 312,241,005 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 669 | rd | form.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/form.R
\name{form}
\alias{form}
\title{Parse tokenized input text}
\usage{
form(x, mode, type, pos = TRUE)
}
\arguments{
\item{x}{Input text vectors}
\item{mode}{Select split mode (A, B, C)}
\item{type}{return form. One of the following "surface", "dictionary",
"normalized", "reading" or "part_of_speech".}
\item{pos}{Include part of speech information with object name.}
}
\description{
Parse tokenized input text
}
\examples{
\dontrun{
form("Tokyo", mode = "B", type = "normalized")
form("Osaka", mode = "B", type = "surface")
form("Hokkaido", mode = "C", type = "part_of_speech")
}
}
|
76b961e4acacb2f40e8c74be7124f8101a1ad537 | db82df76516ffbdd78e5e49da8304c8f872c6385 | /MarkDown_Dynamic Report/in Word/Dynamic Reportin word.R | 78a05b8766026f82aaea9efd18e41e3f1b262fa4 | [] | no_license | Crystal0108W/R | 0023f7ddc392ec882b9114b2bc832dddb5ec7904 | 4d8255cfc85e748ae38136bee87301aea8e53151 | refs/heads/master | 2021-01-17T15:49:25.903400 | 2017-05-13T03:55:36 | 2017-05-13T03:55:36 | 84,112,389 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,339 | r | Dynamic Reportin word.R | #####################Creating Dynamic Repors with R and Markdowns#################################
#R Markdown templates can be used to create HTML, PDF, and MS Word documents.
#ODT and DOCX tem- plates are used to create Open Document and Microsoft Word documents, respectively.
#LaTeX templates are used to create publication-quality PDF documents, including reports, articles, and books
## With R and Microsoft word
install.packages("R2wd")
install.packages("RDCOMClient")
library(R2wd)
library(RDCOMClient)
require(R2wd)
require(car)
df<- Salaries
n <- nrow(df)
fit <- lm(salary~rank*sex, data = df)
aovTable <- Anova(fit, type = 3)
aovTable <- round(as.data.frame(aovTable), 3)
aovTable[is.na(aovTable)] <- ""
wdGet("C:/Users/Crystal/Desktop/Sample Report1.docx", method = "RDCOMClient")
wdGoToBookmark("n")
wdWrite(n)
wdGoToBookmark("aovTable")
wdTable(aovTable, caption = "Two-way Analysis of Variance",
caption.pos = "above", pointsize = 12, autoformat = 4)
wdGoToBookmark("effectsPlot")
myplot <- function(){
require(effects)
par(mar = c(2,2,2,2))
plot(allEffects(fit), main = "")
}
wdPlot(plotfun = myplot, caption = "Mean Effects Plot",
height = 4, width = 5, method = "metafile")
wdSave("C:/Users/Crystal/Desktop/Sample Report1.docx")
wdQuit()
|
58971f74e75ef0207b9eed69253ae4d3c221452b | 34b429c98a64f8a9c43908cc57d6d622d4df1ffc | /man/multi_post.Rd | 29a8f9c6906bf823641615e839ccbb77e90de42f | [] | no_license | mkoohafkan/wqpr-clone | f55212370ff548fab586fc28c3d91fda408f1a28 | 96f17b980125420a0d170dd9d145509c31f60e30 | refs/heads/master | 2023-04-18T03:02:17.881632 | 2021-04-27T16:37:29 | 2021-04-27T16:37:29 | 362,178,533 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 437 | rd | multi_post.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/util.r
\name{multi_post}
\alias{multi_post}
\title{Multi POST}
\usage{
multi_post(service.url, body, stop.on.fail = FALSE)
}
\arguments{
\item{service.url}{The complete URL of the web service.}
\item{body}{The POST field(s) to include in the request body.}
}
\value{
The server response.
}
\description{
Asynchronous WQP POST interface.
}
\keyword{internal}
|
94b98c801af880b560aa4896fc9055bd912c039e | f245521e63b59e37470070092b7d1d38a87b2e48 | /libs/addLetLab.r | b3292c9bbc2d0b2b86a43d8abd3a39906a541e02 | [] | no_license | douglask3/UKESM-land-eval | 3c10d10eba32bcef1e7b2a057db3b22fdf2fd621 | aad3f6902e516590be02585ad926bfe1cf5770bf | refs/heads/master | 2021-08-17T06:32:10.736606 | 2021-07-14T12:57:13 | 2021-07-14T12:57:13 | 242,747,830 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 235 | r | addLetLab.r | addLetLab <- function(let, letAsTitle = TRUE) {
if (!is.null(let)) {
if (letAsTitle) mtext(side = 3, adj = 0.05, let)
else mtext(side = 3, line = -1, adj = 0.1, paste0(let, ')'))
}
}
|
d950e47cf878224d5b22b3d433a07cd0b5b2af46 | 87e1faf49719839550f1249cc14ec3e1da27578d | /man/compareCats.Rd | ab0e517782fe5a439fadc0246feccfda917514d5 | [] | no_license | bryanhanson/HandyStuff | cd9a126d6f344b62ccca79831f174f872136ed00 | d8f292cdbc52f8938a67216a0d63b7168f6b2a00 | refs/heads/master | 2022-10-10T15:56:56.479427 | 2022-07-18T01:38:29 | 2022-07-18T01:38:29 | 1,523,562 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 3,131 | rd | compareCats.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compareCats.R
\name{compareCats}
\alias{compareCats}
\title{Compare Data by Category}
\usage{
compareCats(
formula = NULL,
data = NULL,
cols = NULL,
freckles = FALSE,
method = c("sem", "sem95", "iqr", "mad", "box", "points"),
theme = screenTheme(),
...
)
}
\arguments{
\item{formula}{A formula with both LHS and RHS. The formula should comply
with the usual \code{lattice} conventions, for instance \code{y ~ factor1 |
factor2}.}
\item{data}{A data frame containing the response and categorical/factor
data. A maximum two categories with any number of levels can be plotted at
one time.}
\item{cols}{A vector of colors to be used for factor 1; must be as along as
the levels in factor 1.}
\item{freckles}{Logical; if \code{TRUE}, the actual data points are plotted
as small points, hence the name, jittered slightly in the horizontal
direction only.}
\item{method}{One of \code{c("sem", "sem95", "iqr", "mad", "box",
"points")}. Various methods for computing measures of spread and central
tendency. See the documentation for \code{ChemoSpec::.seXy}.}
\item{theme}{Character; A suitable \code{lattice} theme.
There are two built-in themes which you can use "as is" or modify to your heart's
content. If none is given, \code{\link{screenTheme}} will be used. The other option
provided is \code{\link{posterTheme}}.}
\item{\dots}{Other parameters to be passed downstream.}
}
\value{
A \code{lattice} object. These can be modified by the usual methods
associated with these packages.
}
\description{
This function allows one to generate a nice plot comparing a response from
samples falling into one or two categories with corresponding levels.
\code{lattice} is used to create plots, with options to include the original
data and to use various measures of central tendency and spread. A count of
the number of samples in each level is included. Gives a visual impression
of the data to go along with hypothesis tests.
}
\details{
The supplied data frame is stripped down to just the response and factor
columns, then \code{NAs} are removed; the counts reflect this.
}
\examples{
#
### Set up test data
#
require("ChemoSpec")
require("lattice")
require("latticeExtra")
require("plyr")
mydf <- data.frame(
resp = rnorm(40),
cat1 = sample(LETTERS[1:3], 40, replace = TRUE),
cat2 = sample(letters[1:2], 40, replace = TRUE),
stringsAsFactors = TRUE)
#
### One factor:
#
p <- compareCats(formula = resp~cat1, data = mydf,
method = "sem", freckles = TRUE, poster = FALSE,
cols = c("red", "orange", "blue"))
print(p)
#
### Two factors:
#
p <- compareCats(formula = resp~cat1 | cat2, data = mydf,
method = "sem", freckles = TRUE,
cols = c("red", "orange", "blue"))
print(p)
#
### Interchange the roles of the factors
#
p <- compareCats(formula = resp~cat2 | cat1, data = mydf,
method = "sem", freckles = TRUE,
cols = c("red", "blue"))
print(p)
}
\references{
\url{https://github.com/bryanhanson/HandyStuff}
}
\author{
Bryan A. Hanson, DePauw University. \email{hanson@depauw.edu}
}
\keyword{plot}
\keyword{univariate}
|
cb6384f61ea222ae1586305999e41884830f20de | 297f0c096ee298349bb12d2dd513205df1a19439 | /R programming week 2/pollutantmean-demo.R | 147981a300e867b3c3e56480f085922dd13e1cf7 | [] | no_license | coenschoof/R-Programming | 3f28f48bd5c6d0c4b71bf43e1410c4d431c94d3a | 954ab11898d051d0c5a4e3c6b0ad77dcb4d95eda | refs/heads/main | 2023-01-10T13:38:18.379227 | 2020-11-10T06:50:10 | 2020-11-10T06:50:10 | 311,568,046 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 483 | r | pollutantmean-demo.R | pollutantmean <- function(directory, pollutant, id = 1:332) {
setwd(directory)
listOfFiles <- list.files()
vec <- vector()
for(i in id) {
csvFile <- read.csv(listOfFiles[i])
pollutantColumn <- csvFile[,pollutant]
pollutantColumnWithoutNA <- pollutantColumn[!is.na(pollutantColumn)]
vec <- append(vec, pollutantColumnWithoutNA)
}
mean(vec)
}
pollutantmean("C:\\Users\\coen_\\OneDrive\\Bureaublad\\datasciencecoursera\\specdata", "nitrate") |
65e85d97c5c03babf883ce0b426811017ac0fcc0 | c0931ba541a18095a54e3c2667d02a22d0648015 | /scripts/01_pnv.R | ba44f0b0e5b4166d3abb472018b0355f940cfefe | [] | no_license | juliussebald/formasam | dbd7289aca0350562cd995a64061cc0b73ddc07d | 8927800d1cf3098d1fd1cb47ac9acc00a4073e4b | refs/heads/master | 2023-04-22T06:53:31.893154 | 2021-01-21T08:27:34 | 2021-01-21T08:27:34 | 306,577,923 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,494 | r | 01_pnv.R | # This script loads in the raw simulation data of iLand and
# converts it to the FORMASAM format
# Further it combines the pnv data from iLand with pnv data from LandClim
# and combines everything in one nice dataframe
# Finally it plots the results of the pnv runs nicely
# load packages
library(raster) # version 3.3-13
library(sf) # version 0.9-5
library(tidyverse) # version 1.3.0
library(RSQLite) # version 2.2.0
library(data.table) # version 1.13.0
library(patchwork) # version 1.0.1
# ROSALIA -----------------------------------------------------------------
# bring iLand output in FORMASAM format -----------------------------------
#load coordinates of ressource units
coord <- raster("../../materials/ROSALIA/gis/EnvGrid_Rosalia.asc") %>%
rasterToPoints(.) %>%
as.data.frame(.) %>%
rename(ruid = layer)
# load simulated data
# historic
db.conn <- dbConnect(SQLite(), dbname="../../materials/ROSALIA/output/PNV_ROSALIA_historic.sqlite")
dbListTables(db.conn)
stand_iland_historic <- dbReadTable(db.conn, "dynamicstand") %>%
mutate(model = "ILAND",
AGB = rowSums(.[, c("foliagemass_sum", "stemmass_sum", "branchmass_sum")]))
dbDisconnect(db.conn)
# RCP 45
db.conn <- dbConnect(SQLite(), dbname="../../materials/ROSALIA/output/PNV_ROSALIA_RCP45.sqlite")
dbListTables(db.conn)
stand_iland_rcp45 <- dbReadTable(db.conn, "dynamicstand") %>%
mutate(model = "ILAND",
AGB = rowSums(.[, c("foliagemass_sum", "stemmass_sum", "branchmass_sum")]))
dbDisconnect(db.conn)
# RCP 85
db.conn <- dbConnect(SQLite(), dbname="../../materials/ROSALIA/output/PNV_ROSALIA_RCP85.sqlite")
dbListTables(db.conn)
stand_iland_rcp85 <- dbReadTable(db.conn, "dynamicstand") %>%
mutate(model = "ILAND",
AGB = rowSums(.[, c("foliagemass_sum", "stemmass_sum", "branchmass_sum")]))
dbDisconnect(db.conn)
# bring data in FORMASAM format
# historic
PNV_ILAND_ROSALIA_historic <- stand_iland_historic %>%
dplyr::select(year, rid, species, AGB, model) %>%
rename(ruid = rid) %>%
left_join(coord) %>%
dplyr::select(x, y, ruid, year, species, AGB, model) %>%
mutate(AGB = round(AGB, 3))
# RCP45
PNV_ILAND_ROSALIA_rcp45 <- stand_iland_rcp45 %>%
dplyr::select(year, rid, species, AGB, model) %>%
rename(ruid = rid) %>%
left_join(coord) %>%
dplyr::select(x, y, ruid, year, species, AGB, model) %>%
mutate(AGB = round(AGB, 3))
# RCP85
PNV_ILAND_ROSALIA_rcp85 <- stand_iland_rcp85 %>%
dplyr::select(year, rid, species, AGB, model) %>%
rename(ruid = rid) %>%
left_join(coord) %>%
dplyr::select(x, y, ruid, year, species, AGB, model) %>%
mutate(AGB = round(AGB, 3))
write_csv(PNV_ILAND_ROSALIA_historic, "../../materials/ROSALIA/output/PNV_results/iLand/PNV_ILAND_ROSALIA_historic.csv")
write_csv(PNV_ILAND_ROSALIA_rcp45, "../../materials/ROSALIA/output/PNV_results/iLand/PNV_ILAND_ROSALIA_RCP45.csv")
write_csv(PNV_ILAND_ROSALIA_rcp85, "../../materials/ROSALIA/output/PNV_results/iLand/PNV_ILAND_ROSALIA_RCP85.csv")
rm(list = ls())
# load pnv output --------------------------------------------------------
# iLand
PNV_ILAND_ROSALIA_historic <- read.csv("../../materials/ROSALIA/output/PNV_results/iLand/PNV_ILAND_ROSALIA_historic.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "historic")
PNV_ILAND_ROSALIA_rcp45 <- read.csv("../../materials/ROSALIA/output/PNV_results/iLand/PNV_ILAND_ROSALIA_RCP45.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "RCP45")
PNV_ILAND_ROSALIA_rcp85 <- read.csv("../../materials/ROSALIA/output/PNV_results/iLand/PNV_ILAND_ROSALIA_RCP85.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "RCP85")
PNV_ILAND_ROSALIA <- bind_rows(PNV_ILAND_ROSALIA_historic, PNV_ILAND_ROSALIA_rcp45, PNV_ILAND_ROSALIA_rcp85) %>%
filter(species != "rops")
# LandClim
PNV_LANDCLIM_ROSALIA_historic <- read.csv("../../materials/ROSALIA/output/PNV_results/LandClim/PNV_LANDCLIM_ROSALIA_historic.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "historic") %>%
filter(AGB != 0)
PNV_LANDCLIM_ROSALIA_rcp45 <- read.csv("../../materials/ROSALIA/output/PNV_results/LandClim/PNV_LANDCLIM_ROSALIA_RCP45.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "RCP45") %>%
filter(AGB != 0)
PNV_LANDCLIM_ROSALIA_rcp85 <- read.csv("../../materials/ROSALIA/output/PNV_results/LandClim/PNV_LANDCLIM_ROSALIA_RCP85.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "RCP85" )%>%
filter(AGB != 0)
PNV_LANDCLIM_ROSALIA <- bind_rows(PNV_LANDCLIM_ROSALIA_historic, PNV_LANDCLIM_ROSALIA_rcp45, PNV_LANDCLIM_ROSALIA_rcp85) %>%
mutate(species = case_when(species == "pinucemb" ~ "pice",
species == "betupube" ~ "bepu",
species == "popunigr" ~ "poni",
species == "salialba" ~ "saal",
species == "ilexaqui" ~ "ilaq",
TRUE ~ .$species))
# both models in one data table
PNV_ROSALIA <- setDT(bind_rows(PNV_ILAND_ROSALIA, PNV_LANDCLIM_ROSALIA))
write_csv(PNV_ROSALIA, "../r/pnv_processed/pnv_rosalia.csv")
# landscape plots ---------------------------------------------------------
PNV_ROSALIA <-setDT(read.csv("../r/pnv_processed/pnv_rosalia.csv"))
# define species colours and factor levels
cols <- c("fasy"="#33CC33", "piab"="#006600", "quro"="#FF7F00", "qupe"="#FF9900", "qupu"="#CC9900", "abal"="#003300", "acca"="#F3F781", "acpl"="#86B404", "acps"="#58FAD0", "algl"="#61210B", "alin"="#A4A4A4", "alvi"="#0B3B17", "bepe"="#2E64FE", "bepu"="#FF7F00", "cabe"="#F7BE81", "casa"="#A9F5A9", "coav"="#58D3F7", "frex"="#FF0000", "lade"="#8A4B08", "pice"="#FFB90F", "pini"="#610B21", "pimo"="#000035", "pimu"="#000000", "taba"="#7B4F4B", "ilaq"="#8000FF", "juco"="#DF7401", "pisy"="#B18904", "poni"="#000000", "potr"="#610B5E","saca"="#F5ECCE", "saal"="#00C2A0", "soar"="#E6E0F8", "soau"="#B40404", "tico"="#9F81F7", "tipl"="#8000FF", "ulgl"="#DF7401" )
new_order_gg <- c("ulgl", "tipl", "tico", "soau", "soar", "saca", "saal", "potr", "poni", "pisy", "pini", "pice", "lade", "frex", "coav", "casa","cabe", "bepe", "bepu", "alvi", "alin", "algl", "acps", "acpl", "acca", "abal","qupu", "qupe","quro", "piab", "fasy")
cols_map <- c("01_larch_stonepine"="#FFCC00","02_larch"="#8A4B08","03_subalpine_spruce"="#006600","04_montane_spruce"="#666633","05_spruce_fir"="#0033CC","06_spruce_fir_beech"="#1A8080","07_beech"="#33CC33", "08_oak_hornbeam"="#FF9900","22_silicate_scotspine"="#B18904","23_black_pine"="#610B21", "unclassified"="#D3D3D3")
# create plots
landscape_rosalia <- PNV_ROSALIA %>%
group_by(model, climate, year, species) %>%
summarize(AGB = sum(AGB),
AGB_ha = AGB/1222000) %>%
ungroup(.) %>%
mutate(landscape = "rosalia")
all_rosalia <- ggplot(landscape_rosalia, aes(x = year, y = AGB_ha, fill = species)) +
geom_area() +
scale_fill_manual(values = cols, guide = guide_legend(reverse = TRUE)) +
labs(y = "biomass [t/ha]") +
labs(tag = "A)") +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
plot.title = element_text(hjust = 0.5, size = 22, face = "bold", vjust = 1)) +
theme(plot.background = element_rect(colour = NA)) +
facet_grid(model ~ climate, scales = "free")
pnv_supplement <- all_rosalia + all_dischma + plot_layout(ncol = 1)
ggsave("../../results/figures/supplement/pnv_plots.png", pnv_supplement, width = 7, height = 8.5)
# DISCHMA -----------------------------------------------------------------
rm(list=ls())
# bring iLand output in FORMASAM format ---------------------------------
coord <- raster("../../materials/DISCHMA/gis/EnvGrid_Dischma.asc") %>%
rasterToPoints(.) %>%
as.data.frame(.) %>%
rename(ruid = EnvGrid_Dischma)
db.conn <- dbConnect(SQLite(), dbname="../../materials/DISCHMA/output/PNV_DISCHMA_historic.sqlite")
dbListTables(db.conn)
stand_dischma_historic <- dbReadTable(db.conn, "dynamicstand") %>%
mutate(model = "ILAND",
AGB = rowSums(.[, c("foliagemass_sum", "stemmass_sum", "branchmass_sum")]))
dbDisconnect(db.conn)
db.conn <- dbConnect(SQLite(), dbname="../../materials/DISCHMA/output/PNV_DISCHMA_RCP45.sqlite")
dbListTables(db.conn)
stand_dischma_rcp45 <- dbReadTable(db.conn, "dynamicstand") %>%
mutate(model = "ILAND",
AGB = rowSums(.[, c("foliagemass_sum", "stemmass_sum", "branchmass_sum")]))
dbDisconnect(db.conn)
db.conn <- dbConnect(SQLite(), dbname="../../materials/DISCHMA/output/PNV_DISCHMA_RCP85.sqlite")
dbListTables(db.conn)
stand_dischma_rcp85 <- dbReadTable(db.conn, "dynamicstand") %>%
mutate(model = "ILAND",
AGB = rowSums(.[, c("foliagemass_sum", "stemmass_sum", "branchmass_sum")]))
dbDisconnect(db.conn)
PNV_ILAND_DISCHMA_historic <- stand_dischma_historic %>%
dplyr::select(year, rid, species, AGB, model) %>%
rename(ruid = rid) %>%
left_join(coord) %>%
dplyr::select(x, y, ruid, year, species, AGB, model) %>%
mutate(AGB = round(AGB, 3))
PNV_ILAND_DISCHMA_rcp45 <- stand_dischma_rcp45 %>%
dplyr::select(year, rid, species, AGB, model) %>%
rename(ruid = rid) %>%
left_join(coord) %>%
dplyr::select(x, y, ruid, year, species, AGB, model) %>%
mutate(AGB = round(AGB, 3))
PNV_ILAND_DISCHMA_rcp85 <- stand_dischma_rcp85 %>%
dplyr::select(year, rid, species, AGB, model) %>%
rename(ruid = rid) %>%
left_join(coord) %>%
dplyr::select(x, y, ruid, year, species, AGB, model) %>%
mutate(AGB = round(AGB, 3))
write_csv(PNV_ILAND_DISCHMA_historic, "../../materials/DISCHMA/output/PNV_results/iLand/PNV_ILAND_DISCHMA_historic.csv")
write_csv(PNV_ILAND_DISCHMA_rcp45, "../../materials/DISCHMA/output/PNV_results/iLand/PNV_ILAND_DISCHMA_RCP45.csv")
write_csv(PNV_ILAND_DISCHMA_rcp85, "../../materials/DISCHMA/output/PNV_results/iLand/PNV_ILAND_DISCHMA_RCP85.csv")
rm(list=setdiff(ls(), "all_rosalia"))
# load pnv output --------------------------------------------------------
# iLand
PNV_ILAND_DISCHMA_historic <- read.csv("../../materials/DISCHMA/output/PNV_results/iLand/PNV_ILAND_DISCHMA_historic.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "historic")
PNV_ILAND_DISCHMA_rcp45 <- read.csv("../../materials/DISCHMA/output/PNV_results/iLand/PNV_ILAND_DISCHMA_RCP45.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "RCP45")
PNV_ILAND_DISCHMA_rcp85 <- read.csv("../../materials/DISCHMA/output/PNV_results/iLand/PNV_ILAND_DISCHMA_RCP85.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "RCP85")
PNV_ILAND_DISCHMA <- bind_rows(PNV_ILAND_DISCHMA_historic, PNV_ILAND_DISCHMA_rcp45, PNV_ILAND_DISCHMA_rcp85)
# LandClim
PNV_LANDCLIM_DISCHMA_historic <- read.csv("../../materials/DISCHMA/output/PNV_results/LandClim/PNV_LANDCLIM_DISCHMA_historic.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "historic")
PNV_LANDCLIM_DISCHMA_rcp45 <- read.csv("../../materials/DISCHMA/output/PNV_results/LandClim/PNV_LANDCLIM_DISCHMA_RCP45.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "RCP45")
PNV_LANDCLIM_DISCHMA_rcp85 <- read.csv("../../materials/DISCHMA/output/PNV_results/LandClim/PNV_LANDCLIM_DISCHMA_RCP85.csv",
stringsAsFactors = FALSE) %>%
mutate(climate = "RCP85")
PNV_LANDCLIM_DISCHMA <- bind_rows(PNV_LANDCLIM_DISCHMA_historic, PNV_LANDCLIM_DISCHMA_rcp45, PNV_LANDCLIM_DISCHMA_rcp85) %>%
filter(AGB != 0)
# both models in one data table
PNV_DISCHMA <- setDT(bind_rows(PNV_ILAND_DISCHMA, PNV_LANDCLIM_DISCHMA))
write_csv(PNV_DISCHMA, "pnv_processed/pnv_dischma.csv")
# landscape plots ---------------------------------------------------------
PNV_DISCHMA <- setDT(read.csv("pnv_processed/pnv_dischma.csv"))
# define species colours and factor levels
cols <- c("fasy"="#33CC33", "piab"="#006600", "quro"="#FF7F00", "qupe"="#FF9900", "qupu"="#CC9900", "abal"="#003300", "acca"="#F3F781", "acpl"="#86B404", "acps"="#58FAD0", "algl"="#61210B", "alin"="#A4A4A4", "alvi"="#0B3B17", "bepe"="#2E64FE", "bepu"="#FF7F00", "cabe"="#F7BE81", "casa"="#A9F5A9", "coav"="#58D3F7", "frex"="#FF0000", "lade"="#8A4B08", "pice"="#FFB90F", "pini"="#610B21", "pimo"="#000035", "pimu"="#000000", "taba"="#7B4F4B", "ilaq"="#8000FF", "juco"="#DF7401", "pisy"="#B18904", "poni"="#000000", "potr"="#610B5E","saca"="#F5ECCE", "saal"="#00C2A0", "soar"="#E6E0F8", "soau"="#B40404", "tico"="#9F81F7", "tipl"="#8000FF", "ulgl"="#DF7401" )
new_order_gg <- c("ulgl", "tipl", "tico", "soau", "soar", "saca", "saal", "potr", "poni", "pisy", "pini", "pice", "lade", "frex", "coav", "casa","cabe", "bepe", "bepu", "alvi", "alin", "algl", "acps", "acpl", "acca", "abal","qupu", "qupe","quro", "piab", "fasy")
cols_map=c("01_larch_stonepine"="#FFCC00","02_larch"="#8A4B08","03_subalpine_spruce"="#006600","04_montane_spruce"="#666633","05_spruce_fir"="#0033CC","06_spruce_fir_beech"="#1A8080","07_beech"="#33CC33", "08_oak_hornbeam"="#FF9900","22_silicate_scotspine"="#B18904","23_black_pine"="#610B21", "unclassified"="#D3D3D3")
# create plots
landscape_dischma <- PNV_DISCHMA %>%
group_by(model, climate, year, species) %>%
summarize(AGB = sum(AGB),
AGB_ha = AGB/923000) %>%
ungroup(.) %>%
mutate(landscape = "dischma")
all_dischma <- ggplot(landscape_dischma, aes(x = year, y = AGB_ha, fill = species)) +
geom_area() +
scale_fill_manual(values = cols, guide = guide_legend(reverse = TRUE)) +
labs(y = "biomass [t/ha]") +
theme_bw() +
labs(tag = "B)") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
plot.title = element_text(hjust = 0.5, size = 22, face = "bold", vjust = 1)) +
scale_x_continuous(expand = c(0.0, 0.0)) +
theme(plot.background = element_rect(colour = NA),
legend.position = "none") +
facet_grid(model ~ climate, scales = "free")
# pnv plot for supplement -------------------------------------------------
pnv_supplement <- all_rosalia + all_dischma + plot_layout(ncol = 1)
ggsave("../../results/figures/supplement/pnv_plots.png", pnv_supplement, width = 7, height = 8.5)
|
044c2054ecbf8860e0326c312eb9c4c6d611697d | 4dee9013b0c82214a989b26a86c2ad5b15b48496 | /R/columnLabels.R | a945c06e5d901c6a910c68859302254240f846e6 | [] | no_license | fischuu/GenomicTools | 7997d65c175362acd4e2380c57f5e52e77c01e10 | 67b8b7612c4a5ffc5806be6e0e5492e6948ac49d | refs/heads/master | 2023-05-01T14:02:58.955074 | 2023-04-20T12:12:28 | 2023-04-20T12:12:28 | 60,615,647 | 8 | 2 | null | 2022-10-19T16:41:40 | 2016-06-07T13:34:21 | R | UTF-8 | R | false | false | 846 | r | columnLabels.R | columnLabels <- function(x){
alleles <- sort(unique(unlist(strsplit(unique(x),""))))
# Only missing values:
if((alleles=="X")&&(length(alleles)==1)){
alleles[1] <- "A"
alleles[2] <- "B"
alleles[3] <- "X"
}
# Momomorph
if((alleles!="X")&&(length(alleles)==1)){
alleles[2] <- "B"
alleles[3] <- "X"
}
if(length(alleles)==2){
if(is.element("X",alleles)){
alleles[2] <- "B"
alleles[3] <- "X"
} else {
alleles[3] <- "X"
}
}
genotypes <- c()
hetOpt <- c(paste(alleles[1],alleles[2],sep=""),paste(alleles[2],alleles[1],sep=""))
takeThis <- is.element(hetOpt,x)
if(sum(takeThis)==0) takeThis <- 1
genotypes[1] <- paste(alleles[1],alleles[1],sep="")
genotypes[2] <- hetOpt[takeThis]
genotypes[3] <- paste(alleles[2],alleles[2],sep="")
genotypes[4] <- "XX"
genotypes
}
|
b3bde358e0b86451aa88320fd71d2e5c71c6326c | 515f7bdfc17a76b1d8a5d75a43a3be935a474aae | /man/ElementRecog.Rd | b1f7b49c42dae4782a077d9ec1715ea2e7f97979 | [] | no_license | bhklab/CREAM | 22d6b4942f6c5e097d55b6f341e09413e2c181b1 | 718264747fd4f6886ffcfc260d70fb2b2892b04d | refs/heads/master | 2021-06-03T00:02:07.031151 | 2021-02-11T01:54:32 | 2021-02-11T01:54:32 | 97,014,476 | 14 | 4 | null | 2017-10-24T16:19:35 | 2017-07-12T14:00:38 | R | UTF-8 | R | false | true | 1,332 | rd | ElementRecog.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ElementRecog.R
\name{ElementRecog}
\alias{ElementRecog}
\title{ElementRecog is a function to identify COREs}
\usage{
ElementRecog(InputData, windowSize_Vec, peakNumMax, peakNumMin)
}
\arguments{
\item{InputData}{The input data as a table including chromosome regions
in which the first column is chromosome annotation, and second and third
columns are start and ending positions.}
\item{windowSize_Vec}{Vector of window sizes ordered based on order of CORE}
\item{peakNumMax}{Maximum order of COREs (e.g. maximum number of peaks within COREs)}
\item{peakNumMin}{Minimum order of COREs (e.g. minimum number of peaks within COREs)}
}
\value{
Identified COREs for the given input regions
}
\description{
ElementRecog is a function to identify COREs
}
\examples{
InputData <- read.table(system.file("extdata", "A549_Chr21.bed",
package = "CREAM"), sep="\\t")
colnames(InputData) <- c("chr", "start", "end")
MinLength <- 1000
if(nrow(InputData) < MinLength){
stop(paste( "Number of functional regions is less than ", MinLength,
".", sep = "", collapse = ""))
}
peakNumMin <- 2
WScutoff <- 1.5
WindowVecFinal <- WindowVec(InputData, peakNumMin, WScutoff)
OutputList <- ElementRecog(InputData, WindowVecFinal,
(1+length(WindowVecFinal)), peakNumMin)
}
|
e4ece3182af20922df0048b4abe32420977a7ca1 | 02b794c7cb49497868da94b411ab78e6d9e0162f | /R/summary-method.R | 6b854d85fecfae575c673af23ebcd104e27e9fba | [] | no_license | cran/iCARH | c5fc09a2f23b3253f27149e23c354750b5839e74 | 75dbc3f6d441f4f6d85d8c72b290ea282a514b7c | refs/heads/master | 2021-07-12T01:02:42.197343 | 2020-08-27T06:50:07 | 2020-08-27T06:50:07 | 196,477,121 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,750 | r | summary-method.R | #' @title Summarize and return model parameters
#'
#'@description Group of functions to summarize and return model parameters of interest
#'
#' @describeIn iCARH.params Summary of model parameters
#'
#' @param fit Object returned by iCARH.model
#' @param pars Parameters of interest ("theta","alpha","beta","phi"). All parameters by default.
#' @param path.names Specify pathway names.
#' @param prob Confidence level. Defaults to 0.95.
#' @param use_cache passed to stan summary method.
#' @param digits The number of significant digits for printing out the summary;
#' defaults to 2. The effective sample size is always rounded to integers.
#' @param ... not used currently
#'
#' @return contain summaries for all chains. Included in the summaries are means, standard deviations (Est.Error), effective sample sizes (Eff.Sample), and split Rhats.
#' Monte Carlo standard errors (MC.Error) are also reported.
#'
#' @examples data.sim = iCARH.simulate(4, 10, 14, 8, 2, path.probs=0.3, Zgroupeff=c(0,4),
#' beta.val=c(1,-1,0.5, -0.5))
#' XX = data.sim$XX
#' Y = data.sim$Y
#' Z = data.sim$Z
#' pathways = data.sim$pathways
#' \donttest{
#' rstan_options(auto_write = TRUE)
#' options(mc.cores = 2)
#' fit = iCARH.model(XX, Y, Z, groups=rep(c(0,1), each=5), pathways,
#' control = list(adapt_delta = 0.99, max_treedepth=10), iter = 2, chains = 2)
#' if(!is.null(fit$icarh))
#' iCARH.params(fit)}
#'
#'
#' @importFrom rstan summary
#' @export iCARH.params
iCARH.params <- function(fit, pars=c("theta","alpha","beta","phi"), path.names=NULL, prob = 0.95, use_cache = TRUE, digits=2, ...){
probs = c((1 - prob) / 2, 1 - (1 - prob) / 2)
fit_summary = summary(fit$icarh, pars = pars, probs = probs, use_cache = use_cache)$summary
ci <- paste0( probs * 100, "%")
colnames(fit_summary) <- c("Estimate", "MC.Error", "Est.Error", ci, "Eff.Sample", "Rhat")
xnames = attr(fit$X, "dimnames")[[3]]
ynames = attr(fit$Y, "dimnames")[[3]]
P = dim(iCARH.getPathwaysCoeff(fit))[2]
rhats = iCARH.checkRhats(fit)
cat("\nResponse: ")
if(setequal(fit$drug, c(0,1))) cat(" Binary.\n") else cat(" Continuous.\n")
cat("Data: ")
cat(nrow(fit$X), " time points, ", ncol(fit$X), " observations, ", P, "pathways.\n ")
cat("X has ", length(xnames), " variables, Y has ", length(ynames), " variables.\n")
cat("MCMC samples: ")
cat(fit$icarh@sim$chains, " chains, ", fit$icarh@sim$iter, " iterations each with ",
fit$icarh@sim$warmup, " warmup samples.\n")
if ("theta" %in% pars){
cat("\nTemporal Effects (theta):\n")
tempo = fit_summary[grepl("^theta\\[([0-9]+,*)*\\]$", rownames(fit_summary)),]
rownames(tempo) = xnames
colnames(tempo) = colnames(fit_summary)
print(tempo, digits=digits)
}
if("alpha" %in% pars){
cat("\nTreatment Effect (alpha):\n")
treat = fit_summary[grepl("^alpha\\[([0-9]+,*)*\\]$",rownames(fit_summary)),]
rownames(treat) = xnames
colnames(treat) = colnames(fit_summary)
print(treat, digits=digits)
}
if(("beta" %in% pars) & !is.null(fit$Y) ){
cat("\nEffect of Y variables (beta):\n")
yeff = fit_summary[grepl("^beta\\[([0-9]+,*)*\\]$",rownames(fit_summary)),]
rownames(yeff) = paste0(rep(xnames, each=length(ynames)),"/",ynames)
colnames(yeff) = colnames(fit_summary)
print(yeff, digits=digits)
}
if("phi" %in% pars){
cat("\nPathway Coefficients:\n")
path.names = if(is.null(path.names)) paste("path",1:P) else path.names
path = fit_summary[grepl("^phi\\[([0-9]+,*)*\\]$",rownames(fit_summary)),]
rownames(path) = paste0(rep(path.names, each=2), c("controls", "cases"), sep="/")
colnames(path) = colnames(fit_summary)
print(path, digits=digits)
}
cat("\nWAIC: ", iCARH.waic(fit), ".\n")
return(fit_summary)
}
|
bd2953bea86bbd4cd267fac51ce8b4ef659e6656 | e82101a5856af37a88679820ae82e2cf30afd9b6 | /code/hubway.R | 9cf34461fb8646ef13999251db574791b39c8b32 | [] | no_license | ekonlab/dvux | f3e37ed75c945a4f4ddf1c7c8ade97bac248e8d7 | c781c8a0a1e59c311b28a443bd49387d8dd2a22b | refs/heads/master | 2021-09-03T17:52:18.558210 | 2018-01-10T21:41:52 | 2018-01-10T21:41:52 | 108,869,577 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,840 | r | hubway.R | # Hubway data
options(scipen=999) # avoid scientic notation
# Set up current directory
library(ggplot2)
library(dplyr)
library(tidyr)
library(lubridate)
library(ggmap)
setwd("/Users/albertogonzalez/Dropbox/work_17/bestiario/Mark")
# Import Natgeo water data set and have a initial overiew
hubway_1 = read.csv("hubway_shorttrips-2013.csv")
str(hubway_1)
head(hubway_1)
# Let's start checking some important variables, like the dates range
dates_range = as.data.frame(table(hubway_1$start_date))
str(dates_range)
head(dates_range)
# histogram of trips by day
g0 = ggplot(dates_range,aes(Freq)) + geom_histogram()
g0 + theme_minimal()
# filter one day to visualize its results
one_day = hubway_1 %>%
filter(start_date == "10/1/2013")
# plot maps
q = get_map(location = "boston university", zoom = 13)
q_1 = ggmap(q)
q_1 + geom_point(aes(x=end_statn_long, y=end_statn_lat,size = duration), data=one_day,col = "red",alpha=0.4, shape = 21) + scale_size(range=c(1,10)) + facet_wrap(~subsc_type)
#q_1 + geom_point(aes(x=end_statn_long, y=end_statn_lat,size = duration), data=hubway_1,col = "red",alpha=0.4, shape = 21) + scale_size(range=c(1,10)) + facet_wrap(~start_hour_2)
# long lat by gender, all days
g1 = ggplot(one_day,aes(start_statn_long,start_statn_lat)) + geom_point() + facet_wrap(~gender)
g1 + theme_minimal()
# Add duration as size and facet by gender
g2 = ggplot(one_day,aes(start_statn_long,start_statn_lat, size = duration)) + geom_point() + facet_wrap(~gender)
g2 + theme_minimal()
# Add some alpha to avoid overplotting
g3 = ggplot(one_day,aes(start_statn_long,start_statn_lat, size = duration)) + geom_point(alpha = 0.5) + facet_wrap(~gender)
g3 + theme_minimal()
# Delete circle fill to improve legibility
g4 = ggplot(one_day,aes(start_statn_long,start_statn_lat, size = duration)) + geom_point(shape = 21) + facet_wrap(~gender)
g4 + theme_minimal()
# Add gender as fill
g5 = ggplot(one_day,aes(start_statn_long,start_statn_lat, size = duration, fill = gender)) + geom_point(shape = 21)
g5 + theme_minimal()
# Facet by type of user
g6 = ggplot(one_day,aes(start_statn_long,start_statn_lat, size = duration, fill = gender)) + geom_point(shape = 21) + facet_wrap(~subsc_type)
g6 + theme_minimal()
g7 = ggplot(one_day,aes(start_statn_long,start_statn_lat, size = duration, fill = gender)) + geom_point(shape = 22) + facet_wrap(~subsc_type)
g7 + theme_minimal()
g8 = ggplot(one_day,aes(start_statn_long,start_statn_lat, size = duration, fill = gender)) + geom_point(shape = 23) + facet_wrap(~subsc_type)
g8 + theme_minimal()
# Lets try to assign day of week and study seasonality
head(hubway_1)
str(hubway_1)
# From factor to date
hubway_1$start_date = as.Date(hubway_1$start_date,format = "%m/%d/%Y")
str(hubway_1)
head(hubway_1)
hubway_1$week_day = wday(hubway_1$start_date,label = TRUE)
head(hubway_1)
# Total trips by weekday
trips_by_day = as.data.frame(table(hubway_1$week_day))
trips_by_day
g9 = ggplot(trips_by_day,aes(Var1,Freq)) + geom_bar(stat = "identity")
g9 + theme_minimal()
# Lon / lat, duration as size, gender as color, weekday as facet
g10 = ggplot(hubway_1,aes(start_statn_long,start_statn_lat, size = duration, fill = gender)) + geom_point(shape = 21)
g10 + theme_minimal() + facet_wrap(~week_day)
# Change gender to faceting mode
g11 = ggplot(hubway_1,aes(start_statn_long,start_statn_lat)) + geom_point(shape = 3)
g11 + theme_minimal() + facet_grid(gender~week_day)
# Could the starting hour of the day help us?
# We first need to convert start time factor to date
head(hubway_1)
hubway_1$start_hour_1 = hms(hubway_1$start_time)
hubway_1$start_hour_2 = hour(hubway_1$start_hour_1)
# Let's use 5 variables together in the same viz
g11 = ggplot(hubway_1,aes(start_statn_long,start_statn_lat,fill=gender)) + geom_point(shape = 21) + theme_minimal() + facet_grid(start_hour_2~week_day)
g11
g12 = ggplot(hubway_1,aes(start_statn_long,start_statn_lat,fill=gender)) + geom_point(shape = 21) + theme_minimal() + facet_wrap(~start_hour_2)
g12
# We can also add the month to see if it might show any given pattern
hubway_1$month = month(hubway_1$start_date)
g13 = ggplot(hubway_1,aes(start_statn_long,start_statn_lat,fill=gender)) + geom_point(shape = 21) + theme_minimal() + facet_wrap(~month)
g13
# It's still difficult to spot seasonality patterns, let's try to change
# the geometric shape and pass form circle x/y to line (time series)
g14 = ggplot(hubway_1,aes(start_date,duration, group = gender)) + geom_line() + theme_minimal()
g14
# We need to do some transformations
hubway_2 = hubway_1 %>%
group_by(start_date,gender)%>%
summarise(count = n())
head(hubway_2)
hubway_3 = as.data.frame(hubway_2)
str(hubway_3)
# plot results
g15 = ggplot(hubway_3,aes(start_date,count,group=gender,color=gender)) + geom_line() + theme_minimal()
g15
# Let's add in weekday in the groupings
hubway_4 = hubway_1 %>%
group_by(start_date,gender,week_day)%>%
summarise(count = n())
hubway_5 = as.data.frame(hubway_4)
head(hubway_5)
# plot results
g16 = ggplot(hubway_5,aes(start_date,count,group=gender,color=gender)) + geom_line() + theme_minimal() + facet_wrap(~week_day)
g16
# Let's add in hour in the groupings
hubway_6 = hubway_1 %>%
group_by(start_date,gender,start_hour_2)%>%
summarise(count = n())
hubway_7 = as.data.frame(hubway_6)
head(hubway_7)
# plot results
g17 = ggplot(hubway_7,aes(start_date,count,group=gender,color=gender)) + geom_line() + theme_minimal() + facet_wrap(~start_hour_2)
g17
# Let's add in hour in the groupings
hubway_8 = hubway_1 %>%
group_by(start_date,gender,strt_statn_name)%>%
summarise(count = n())
hubway_9 = as.data.frame(hubway_8)
head(hubway_9)
# plot results
g18 = ggplot(hubway_9,aes(start_date,count,group=gender,color=gender)) + geom_line() + theme_minimal() + facet_wrap(~strt_statn_name)
g18
|
03324218b284fdfa26a87c622760f77f1d2bd6a2 | d14bcd4679f0ffa43df5267a82544f098095f1d1 | /R/plot.groupm.out.R | b3b0cd6924619461b1b63172e6c8f76aabb1e208 | [] | no_license | anhnguyendepocen/SMRD | 9e52aa72a5abe5274f9a8546475639d11f058c0d | c54fa017afca7f20255291c6363194673bc2435a | refs/heads/master | 2022-12-15T12:29:11.165234 | 2020-09-10T13:23:59 | 2020-09-10T13:23:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,995 | r | plot.groupm.out.R | #' @export
plot.groupm.out <-
function (x,
focus.variable,
fixed.other.values,
range.of.focus = range(xmat(data.ld)[[focus.variable]]),
ylim = c(NA, NA),
xlim = c(NA, NA),
xlab = NULL,
ylab = NULL,
grids = F,
title.option = GetSMRDDefault("SMRD.TitleOption"),
response.on.yaxis = T,
dummy.for.fixed = F,
point.from.xmat = NULL,
density.at = "Automatic",
censor.time = NULL,
quant.lines = c(0.1, 0.5, 0.9),
add = F,
plot.quant.labels = T,
my.title = NULL,
include.data = F,...)
{
`if`(!is.onlist("life.data", oldClass(x[[1]])),
groupm.out <- x[[1]],
groupm.out <- x)
data.ld <- groupm.out$data.ld
if (!missing(fixed.other.values)) {
dummy.groupm.out <- get.conditional.groupm.out(focus.variable = focus.variable,
fixed.other.values = fixed.other.values,
groupm.out = groupm.out)
} else {
if (is.null(groupm.out$focus.variable)) groupm.out$focus.variable <- focus.variable
dummy.groupm.out <- groupm.out
}
plot.alt.fit(x = dummy.groupm.out,
ylim = ylim,
xlim = xlim,
xlab = xlab,
ylab = ylab,
grids = grids,
title.option = title.option,
response.on.yaxis = response.on.yaxis,
my.title = my.title,
include.data = include.data,
density.at = density.at,
censor.time = censor.time,
quant.lines = quant.lines,
add = add,
plot.quant.labels = plot.quant.labels,
range.of.focus = range.of.focus,...)
invisible()
}
|
d460636910caac6b257d178de23087e18a105154 | 7503dba6d36a46fa2501bb5dd5c874bc44d4805f | /man/MyWpdf.Rd | 9148a2f0110b8c13edb68c549614589e54efb3a8 | [] | no_license | IvanNavarroCytel/MyPack | ce8f2e358442d374e0ed6317dde29f7f4ada5c7e | 61afc2d17e859d7fc5e338b00202ee04fb52d261 | refs/heads/master | 2021-04-29T21:07:43.821898 | 2018-01-24T17:16:19 | 2018-01-24T17:16:19 | 121,609,216 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 442 | rd | MyWpdf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MyWeibull.R
\name{MyWpdf}
\alias{MyWpdf}
\title{PDF of Weibull distribution}
\usage{
MyWpdf(x, a, b)
}
\arguments{
\item{x}{value.}
\item{a}{Shape parameter.}
\item{b}{Scale parameter.}
}
\value{
Probability density.
}
\description{
This function calculates the probability density for x given ‘a’
as the shape parameter and ‘b’as the scale parameter.
}
|
1280738693c872cbf3545ab27647c1b05025893b | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.machine.learning/man/sagemaker_update_app_image_config.Rd | 7baf95a7d6866fcd0f69382f4bd036d77f28221f | [
"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 | 696 | rd | sagemaker_update_app_image_config.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sagemaker_operations.R
\name{sagemaker_update_app_image_config}
\alias{sagemaker_update_app_image_config}
\title{Updates the properties of an AppImageConfig}
\usage{
sagemaker_update_app_image_config(
AppImageConfigName,
KernelGatewayImageConfig = NULL
)
}
\arguments{
\item{AppImageConfigName}{[required] The name of the AppImageConfig to update.}
\item{KernelGatewayImageConfig}{The new KernelGateway app to run on the image.}
}
\description{
Updates the properties of an AppImageConfig.
See \url{https://www.paws-r-sdk.com/docs/sagemaker_update_app_image_config/} for full documentation.
}
\keyword{internal}
|
031c7b369d42a44b833ebf00533cb21bc8e74553 | f2bfd5ceae6bf32cebc28cf18740a8b44e010e7b | /pkg/retistruct/R/RetinalReconstructedOutline.R | d78ff7da94fe6a055d7e38dd402a67946106ddb5 | [] | no_license | davidcsterratt/retistruct | 602972d127b7119df3fda54ac915228d7ac854d1 | f7075b0a8ac84fdc9773300d553c26a11b45ce2e | refs/heads/master | 2023-08-09T20:08:39.039964 | 2023-07-29T09:27:35 | 2023-07-29T09:27:35 | 25,682,590 | 5 | 7 | null | 2017-07-29T09:14:58 | 2014-10-24T10:05:33 | R | UTF-8 | R | false | false | 11,295 | r | RetinalReconstructedOutline.R | ##' A version of \link{ReconstructedOutline} that is specific to
##' retinal datasets
##'
##' @description A RetinalReconstructedOutline overrides methods of
##' \link{ReconstructedOutline} so that they return data point and
##' landmark coordinates that have been transformed according to the
##' values of \code{DVflip} and \code{side}. When reconstructing, it
##' also computes the \dQuote{Optic disc displacement}, i.e. the
##' number of degrees subtended between the optic disc and the pole.
##'
##' @author David Sterratt
##' @export
RetinalReconstructedOutline <- R6Class("RetinalReconstructedOutline",
inherit = ReconstructedOutline,
public = list(
##' @field EOD Optic disc displacement in degrees
EOD = NULL,
##' @description Get coordinates of corners of pixels of image in spherical
##' coordinates, transformed according to the value of \code{DVflip}
##' @return Coordinates of corners of pixels in spherical coordinates
getIms = function() {
ims <- super$getIms()
if (self$ol$DVflip) {
if (!is.null(ims)) {
ims[,"lambda"] <- -ims[,"lambda"]
}
}
return(ims)
},
##' @description Get location of tear coordinates in spherical coordinates,
##' transformed according to the value of \code{DVflip}
##' @return Location of tear coordinates in spherical coordinates
getTearCoords = function() {
Tss <- super$getTearCoords()
if (self$ol$DVflip) {
for (i in 1:length(Tss)) {
Tss[[i]][,"lambda"] <- -Tss[[i]][,"lambda"]
}
}
return(Tss)
},
##' @param ... Parameters to \code{\link{ReconstructedOutline}}
reconstruct = function(...) {
super$reconstruct(...)
OD <- self$getFeatureSet("LandmarkSet")$getFeature("OD")
if (!is.null(OD)) {
ODmean <- karcher.mean.sphere(OD)
self$EOD <- 90 + ODmean["phi"]*180/pi
}
},
##' @description Get \link{ReconstructedFeatureSet}, transformed
##' according to the value of \code{DVflip}
##' @param type Base type of \link{FeatureSet} as string.
##' E.g. \code{PointSet} returns a \link{ReconstructedPointSet}
getFeatureSet = function(type) {
fs <- super$getFeatureSet(type)
if (self$ol$DVflip) {
if (is.null(self$fst)) {
fst <- fs$clone()
fst$data <-
lapply(fs$data,
function(x) {
x[,"lambda"] <- -x[,"lambda"]
return(x)
})
}
return(fst)
}
return(fs)
}
)
)
##' Plot projection of reconstructed dataset
##' @param r \code{\link{RetinalReconstructedOutline}} object
##' @param transform Transform function to apply to spherical coordinates
##' before rotation
##' @param projection Projection in which to display object,
##' e.g. \code{\link{azimuthal.equalarea}} or \code{\link{sinusoidal}}
##' @param axisdir Direction of axis (North pole) of sphere in external space
##' @param proj.centre Location of centre of projection as matrix with
##' column names \code{phi} (elevation) and \code{lambda} (longitude).
##' @param lambdalim Limits of longitude (in degrees) to display
##' @param datapoints If \code{TRUE}, display data points
##' @param datapoint.means If \code{TRUE}, display Karcher mean of data points.
##' @param datapoint.contours If \code{TRUE}, display contours around
##' the data points generated using Kernel Density Estimation.
##' @param grouped If \code{TRUE}, display grouped data.
##' @param grouped.contours If \code{TRUE}, display contours around
##' the grouped data generated using Kernel Regression.
##' @param landmarks If \code{TRUE}, display landmarks.
##' @param mesh If \code{TRUE}, display the triangular mesh used in reconstruction
##' @param grid If \code{TRUE}, show grid lines
##' @param image If \code{TRUE}, show the reconstructed image
##' @param ids IDs of groups of data within a dataset, returned using
##' \code{getIDs}.
##' @param ... Graphical parameters to pass to plotting functions
##' @method projection RetinalReconstructedOutline
##' @export
projection.RetinalReconstructedOutline <-
function(r,
transform=identity.transform,
projection=azimuthal.equalarea,
axisdir=cbind(phi=90, lambda=0),
proj.centre=cbind(phi=0, lambda=0),
lambdalim=c(-180, 180),
datapoints=TRUE,
datapoint.means=TRUE,
datapoint.contours=FALSE,
grouped=FALSE,
grouped.contours=FALSE,
landmarks=TRUE,
mesh=FALSE,
grid=TRUE,
image=TRUE,
ids=r$getIDs(),
...) {
philim <- c(-90, 90)
colatitude <- FALSE
pole <- TRUE
if (!(identical(projection, sinusoidal) |
identical(projection, orthographic))) {
philim <- c(-90, r$ol$phi0*180/pi)
colatitude <- TRUE
pole <- FALSE
}
if (r$ol$side=="Right") {
labels=c("N", "D", "T", "V")
} else {
labels=c("T", "D", "N", "V")
}
NextMethod(projection=projection,
philim=philim,
labels=labels,
colatitude=TRUE,
grid=FALSE,
mesh=FALSE,
image=image)
## Plot FeatureSets
## Datapoints
if (datapoints) {
message("Plotting points")
fs <- r$getFeatureSet("PointSet")
if (!is.null(fs)) {
projection.ReconstructedPointSet(fs,
phi0=r$phi0,
ids=ids,
transform=transform,
axisdir=axisdir,
projection=projection,
proj.centre=proj.centre,
...)
}
}
## Mean datapoints
if (datapoint.means) {
message("Plotting point means")
fs <- r$getFeatureSet("PointSet")
if (!is.null(fs)) {
Dss.mean <- fs$getMean()
for (id in ids) {
if (!is.null(Dss.mean[[id]])) {
points(projection(rotate.axis(transform(Dss.mean[[id]],
phi0=r$phi0),
axisdir*pi/180),
proj.centre=pi/180*proj.centre),
bg=fs$cols[[id]], col="black",
pch=23, cex=1.5)
}
}
}
}
## Count sets, formerly known as groups
if (grouped) {
message("Plotting counts")
fs <- r$getFeatureSet("CountSet")
if (!is.null(fs)) {
projection.ReconstructedCountSet(fs,
phi0=r$phi0,
ids=ids,
transform=transform,
axisdir=axisdir,
projection=projection,
proj.centre=proj.centre,
...)
}
}
## KDE
if (datapoint.contours) {
message("Plotting point contours")
fs <- r$getFeatureSet("PointSet")
if (!is.null(fs)) {
k <- fs$getKDE()
for (id in ids) {
if (!is.null(k[[id]])) {
css <- k[[id]]$contours
for(cs in css) {
suppressWarnings(lines(projection(rotate.axis(transform(cs,
phi0=r$phi0),
axisdir*pi/180),
lambdalim=lambdalim*pi/180,
lines=TRUE,
proj.centre=pi/180*proj.centre),
col=fs$cols[[id]]))
}
## FIXME: contours need to be labelled
}
}
## Plot locations of highest contours
for (id in ids) {
if (!is.null(k[[id]])) {
suppressWarnings(points(projection(rotate.axis(transform(k[[id]]$maxs,
phi0=r$phi0),
axisdir*pi/180),
proj.centre=pi/180*proj.centre),
pch=22, cex=1, lwd=1,
col="black", bg=fs$cols[[id]]))
}
}
}
}
## KR
if (grouped.contours) {
message("Plotting count contours")
fs <- r$getFeatureSet("CountSet")
if (!is.null(fs)) {
k <- fs$getKR()
for (id in ids) {
if (!is.null(k[[id]])) {
css <- k[[id]]$contours
for(cs in css) {
lines(projection(rotate.axis(transform(cs,
phi0=r$phi0),
axisdir*pi/180),
lambdalim=lambdalim*pi/180,
lines=TRUE,
proj.centre=pi/180*proj.centre),
col=fs$cols[[id]])
}
## FIXME: contours need to be labelled
}
}
## Plot locations of highest contours
for (id in ids) {
if (!is.null(k[[id]])) {
points(projection(rotate.axis(transform(k[[id]]$maxs,
phi0=r$phi0),
axisdir*pi/180),
proj.centre=pi/180*proj.centre),
pch=23, cex=1, lwd=1,
col="black", bg=fs$cols[[id]])
}
}
}
}
## Landmarks
if (landmarks) {
message("Plotting landmarks")
fs <- r$getFeatureSet("LandmarkSet")
if (!is.null(fs)) {
projection.ReconstructedLandmarkSet(fs,
phi0=r$phi0,
ids=ids,
transform=transform,
axisdir=axisdir,
projection=projection,
proj.centre=proj.centre,
...)
}
}
NextMethod(projection=projection,
philim=philim,
labels=labels,
colatitude=TRUE,
grid=grid,
add=TRUE,
image=FALSE,
mesh=mesh)
}
##' @method projection RetinalReconstructedOutline
sphericalplot.RetinalReconstructedOutline <- function(r,
datapoints=TRUE,
ids=r$getIDs(), ...) {
NextMethod()
if (datapoints) {
message("Plotting points")
sphericalplot.ReconstructedPointSet(r,
projection=projection,
ids=ids, ...)
}
}
|
c35c3988c193b298942f277f51c82f20e1234e49 | 68812e0861a476b85115fe6ddea9e9a216c49387 | /man/several.Rd | 40fd4c03e8b7ebced07b7f9e5b6bb5a88b9f5f72 | [
"MIT"
] | permissive | txopen/histoc | 5a895e4593b7486d66e4a31a3f9a7059f01ce0d4 | 86893ebc4396eb568f8ce5c85146aca624fe0af8 | refs/heads/main | 2023-08-24T06:21:32.155839 | 2023-08-04T19:18:11 | 2023-08-04T19:18:11 | 511,302,158 | 0 | 1 | NOASSERTION | 2023-08-04T19:18:13 | 2022-07-06T21:50:33 | R | UTF-8 | R | false | true | 1,757 | rd | several.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mult.R
\name{several}
\alias{several}
\title{Runs several time the function donor_recipient_pairs() as bootstrap.}
\usage{
several(
iteration.number = 10,
df.donors = donors,
df.candidates = candidates,
df.abs = cabs,
algorithm = lima,
n = 0,
seed.number = 123,
check.validity = TRUE,
...
)
}
\arguments{
\item{iteration.number}{Number of times the matchability runs.}
\item{df.donors}{A data frame containing demographics and medical information
for a pool of donors. For \code{uk} algorithm must have their respective columns.}
\item{df.candidates}{A data frame containing demographics and medical information
for a group of waitlisted transplant candidates. For \code{uk} algorithm must have respective columns.}
\item{df.abs}{A data frame with candidates' antibodies.}
\item{algorithm}{The name of the function to use. Valid options are:
\code{lima}, \code{et}, \code{pts}, \code{uk} (without quotation)}
\item{n}{A positive integer to slice the first candidates.}
\item{seed.number}{Seed for new random number.
\code{seed.number} can be \code{NA} in which case no seed is applied.}
\item{check.validity}{Logical to decide whether to validate input.}
\item{...}{all the parameters used on the function algorithm}
}
\value{
Overall statistics obtained from all runs.
}
\description{
Generic function that runs the matchability between all combinations of donors and candidates.
Runs an arbitrary number of times (\code{iteration.number}) to provide statistics.
}
\examples{
\donttest{
several(iteration.number = 10,
df.donors = donors,
df.candidates = candidates,
df.abs = cabs,
algorithm = lima,
n = 0,
seed.number = 123,
check.validity = TRUE)
}
}
|
20788a980ffc18cbe483f7195e80bf767ddddd4c | aab75123aad01298006206831a197533cae83e79 | /Assignment 2 ZK - Subcourse 1 FINAL.R | 933a01f0fafda8fb53f967d877c814f9317b3b9b | [] | no_license | FridaWP/PSYP14_FridaWP_Assignments | 3053a2f01fa6636215996be171516d77fc743d59 | 06abf55485de733281995f960b3cd24c52a0c7c2 | refs/heads/main | 2023-01-29T07:36:15.584361 | 2020-12-04T16:55:31 | 2020-12-04T16:55:31 | 318,578,883 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,392 | r | Assignment 2 ZK - Subcourse 1 FINAL.R | Data_1_sample = read.csv("https://tinyurl.com/ha-dataset1")
library(tidyverse)
library(ggplot2)
library(dplyr)
library(gridExtra)
library(car)
library(lmtest)
library(psych)
library(sandwich)
library(lm.beta)
data_pain <- Data_1_sample
view(data_pain)
data_pain_exclude <- data_pain [-c(93),]
data_pain_changed <- data_pain_exclude %>%
mutate(STAI_trait = replace(STAI_trait, STAI_trait == "3.9", 39))
view(data_pain_changed)
#Data and Model diagnostics:
Plot1 <- data_pain_changed %>%
ggplot() +
aes(x = age,
y = pain) +
geom_point() +
geom_smooth()
Plot1
Plot2 <- data_pain_changed %>%
select(sex, pain) %>%
ggplot() +
aes(x = sex,
y = pain) +
geom_boxplot()
Plot2
Plot3 <- data_pain_changed %>%
ggplot() +
aes(x = STAI_trait,
y = pain) +
geom_point() +
geom_smooth()
Plot3
Plot4 <- data_pain_changed %>%
ggplot() +
aes(x = pain_cat,
y = pain) +
geom_point() +
geom_smooth()
Plot4
Plot5 <- data_pain_changed %>%
ggplot() +
aes(x = mindfulness,
y = pain) +
geom_point() +
geom_smooth()
Plot5
Plot6 <- data_pain_changed %>%
ggplot() +
aes(x = cortisol_serum,
y = pain) +
geom_point() +
geom_smooth()
Plot6
Plot7 <- data_pain_changed %>%
ggplot() +
aes(x = weight,
y = pain) +
geom_point() +
geom_smooth()
Plot7
Plot8 <- data_pain_changed %>%
ggplot() +
aes(x = IQ,
y = pain) +
geom_point() +
geom_smooth()
Plot8
Plot9 <- data_pain_changed %>%
ggplot() +
aes(x = household_income,
y = pain) +
geom_point() +
geom_smooth()
Plot9
grid.arrange(Plot1, Plot2, Plot3, Plot4, Plot5, Plot6, Plot7, Plot8, Plot9, nrow = 3)
data_pain_final <- data_pain_changed %>%
mutate(household_income = replace(household_income, household_income == "-3732", 3732))
view(data_pain_final)
full_model <- lm(pain ~age + sex + STAI_trait + pain_cat + mindfulness + cortisol_serum + weight + IQ + household_income,
data = data_pain_final)
summary(full_model)
data_pain_final %>% ggplot() + aes(x = household_income, y = pain) + geom_point() + geom_smooth(method = "lm")
full_model %>%
plot(which = 5)
full_model %>%
plot(which = 4)
data_pain_final %>% slice(c(3, 102, 113))
#Assumptions of Normality
full_model %>% plot(which = 2 )
#Skew and Kurtosis
full_model_res = enframe(residuals(full_model))
full_model_res %>% ggplot() + aes(x = value) + geom_histogram()
describe(residuals(full_model))
#Assumptions of Linearity
full_model %>% residualPlots()
#Assumptions of Homoscedasticity
full_model %>% plot(which = 3)
full_model %>% ncvTest()
full_model %>% bptest()
#Assumptions of Multicollinearity
full_model %>%
vif()
full_model %>%
summary()
data_pain_final %>% select(pain, age, sex, STAI_trait, pain_cat, mindfulness, cortisol_serum, IQ, weight, household_income) %>%
pairs.panels(col = "Blue", lm = T)
AIC(full_model)
# Backwards regression of the full model
full_model_back = step(full_model, direction = "backward")
summary(full_model_back)
backward_model = lm(pain~age + sex + pain_cat + mindfulness + cortisol_serum + household_income, data = data_pain_final)
theory_based_model = lm(pain ~ age + sex + STAI_trait + pain_cat + mindfulness + cortisol_serum, data = data_pain_final)
summary(theory_based_model)
# Standardised Beta for the backwardmodel
lm.beta(backward_model)
confint(backward_model)
# Comparing initial model (full model) and the backwardsregression model
summary(full_model)
summary(backward_model)
AIC(full_model)
AIC(backward_model)
# Comparing theory-based model and backward model
summary(backward_model)$adj.r.squared
summary(theory_based_model)$adj.r.squared
AIC(theory_based_model, backward_model)
summary(backward_model)
summary(theory_based_model)
# Trying the models on a new dataset
Home_sample_2 = read.csv("https://tinyurl.com/ha-dataset2")
data_pain2 = Home_sample_2
view(data_pain2)
# Predicted values for each model on a new dataset.
pred_theorybased_model <- predict(theory_based_model, data_pain2)
pred_backward_model <- predict(backward_model, data_pain2)
# Calculating the Sum of squared residuals
RSS_theorybased = sum((data_pain2[, "pain"] - pred_theorybased_model)^2)
RSS_backward = sum((data_pain2[, "pain"] - pred_backward_model)^2)
RSS_theorybased
RSS_backward
|
ee97fa150c049ea8f0d40b2072e0a0223ab3b019 | ed25ad1a418c4afc0c3c5ee28108fdf85ff6da48 | /openenbewerkkoffie.R | 6c72abe20f44a73558be5e388138091dc3d97ca7 | [
"MIT"
] | permissive | RMHogervorst/koffie | f67df67cf019c565640c7259abbba79e8647c126 | 0fb2833d381cc91d3423dd5ef12ef3fd584398fa | refs/heads/master | 2016-08-12T14:27:17.273361 | 2016-02-09T16:58:09 | 2016-02-09T16:58:09 | 45,740,643 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,063 | r | openenbewerkkoffie.R | ################################################################
#date: 6-1-15
#by: Roel Hogervorst
#description: het grote koffieproject.
#Rversion:R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet"
# 22-6-15
#R version 3.2.0 (2015-04-16) -- "Full of Ingredients"
# R version 3.2.2 (2015-08-14) -- "Fire Safety"
# aanpassing 31-10-15
################################################################
#klaarmaken van workspace
## Clear R workspace ####
rm(list = ls() ) #dit verwijdert alle bestanden uit de workspace.
#set working directory (mocht je niet in het goede project zitten)
setwd("~/dre-roel map/R/koffie")
#openen van bestanden ####
##hierbij hebben we uit het log gekopieert naar de .txt bestanden
#die laden we.
RoelsKoffie<-read.csv(file="koffie.txt", header=FALSE, as.is=T)
SamenKoffie<-read.csv(file="koffie met dre.txt", header=FALSE, as.is=T)
DreKoffie <-read.csv(file="coffee dre.txt",header=FALSE, as.is=T)
#variabelen toevoegen
RoelsKoffie$cat <-"roel alleen" #categorische variabele
SamenKoffie$cat <- "Samen"
DreKoffie$cat <- "Dre alleen"
#eindresultaat is nu drie dataframes met 3 naamloze kolommen.
Coffee <-rbind(RoelsKoffie, SamenKoffie, DreKoffie) #combineren tot 1 bestand.
colnames(Coffee) <- c("datum", "tijd", "categorie") # namen aan kolommen geven.
Coffee$counter<-1 #koffiecounter toevoegen
### functiechecks is het dataframe correct?####
head(Coffee) #als deze correct is, is de rest ook correct.
head(DreKoffie)
head(RoelsKoffie)
head(SamenKoffie)
unique(Coffee$categorie) #zitten ze er alledrie in.
class(Coffee$datum)
class(Coffee$tijd)
class(Coffee$categorie) #is nu allemaal character.
unique(Coffee$counter) #maar 1.
#einde checks.
library(dplyr)
#kolommen in het juiste type zetten en nieuwe variabelen aanmaken. ####
Coffee$datum <- as.Date(Coffee$datum, "%m-%d-%Y") #verandert datum in date format.
Coffee$datetime<- as.POSIXct(paste(Coffee$datum, Coffee$tijd, sep=" "),
format="%Y-%m-%d %H:%M")
Coffee$dag<-weekdays(Coffee$datum) #creeert variabele dag.
Coffee$getaluur <- gsub("[^[:digit:]]", "", Coffee$tijd) #combineer getallen tot 1.
Coffee$getaluur <- as.numeric(Coffee$getaluur) # verander in getallen.
Coffee$dagdeel[which(Coffee$getaluur <1200)] <- "ochtend" #wanneer getaluur is onder de 1200 dus voor twaalf uur, maak in andere variable ochend
Coffee$dagdeel[which(Coffee$getaluur >= 1200 & Coffee$getaluur <1800)] <- "middag"
Coffee$dagdeel[which(Coffee$getaluur >=1800)] <- "avond"
Coffee$tijd2<-sapply(strsplit(Coffee$tijd,":"),
function(x) {
x <- as.numeric(x)
x[1]+x[2]/60
} ) #van https://stackoverflow.com/questions/5186972/how-to-convert-time-mmss-to-decimal-form-in-r
##checks.
class(Coffee$datetime) #"POSIXct" "POSIXt"
head(Coffee$datetime)
class(Coffee$tijd)
class(Coffee$dag) #character
head(Coffee$dag) #geeft dagen weer in het nederlands.
head(Coffee$getaluur) #check werking: zijn het getallen?
class(Coffee$getaluur) # check type: is het numeric?
head(Coffee)
qplot(dagdeel, data= Coffee)
qplot(dag, data= Coffee)
## einde checks.
##optionele tussenstap om het bestand weg te schrijven####
saveRDS(Coffee, file = "Coffee.Rda")
Coffee <-readRDS(file = "Coffee.Rda") #om het weer in te laden.
rm(DreKoffie, RoelsKoffie, SamenKoffie) #deze hebben we niet meer nodig.
###############
#Grafieken####
library(ggplot2) ##we hebben de ggplot2 nodig voor deze awesome grafieken.
library(dplyr) #hebben we misshien ook nodig
#qplot(dag, getaluur, data = Coffee, color = categorie, alpha = I(1 / 2)) #plot voor koffiemomenten op de dag.
plotdaguur <- qplot(dag, getaluur, data = Coffee, color = categorie) + geom_hline(aes(yintercept= 1200))
# dagen van de week zijn verkeerd.
plotdaguur + scale_x_discrete(
limits=c("maandag","dinsdag","woensdag", "donderdag", "vrijdag",
"zaterdag", "zondag")) +geom_jitter(size=3)
ggsave("wekelijkskoffiegebruik.png", width=6, height=4) #kopieer naar bestand.
qplot(datum, tijd2, data=Coffee)+ geom_hline(aes(yintercept= 12)) #AANPASSING 3-11-15
#dit object bestaat nu uit de data, een 12 uur lijn, en de assen zijn goed.
#g = ggplot(aes (x= dag,y= dagdeel, data = Coffee ))
#g = g + scale_x_discrete(
# limits=c("maandag","dinsdag","woensdag", "donderdag", "vrijdag",
# "zaterdag", "zondag"))
# g = ggplot(data = InsectSprays, aes(y = count, x = spray, fill = spray))
# g = g + geom_violin(colour = "black", size = 2)
# g = g + xlab("Type of spray") + ylab("Insect count")
# g
#per persoon apart.####
Roel <- filter(Coffee, categorie == "roel alleen")
Samen <- filter(Coffee, categorie == "Samen")
#plots
Rplot<- qplot(dag, getaluur, data = Roel)
Rplot + scale_x_discrete(limits=c("maandag","dinsdag","woensdag", "donderdag", "vrijdag",
"zaterdag", "zondag"))
Rplot
ggsave("RoelPlotWeek.png", width=6, height=4) #kopieer naar bestand.
#boxplt van Roel over een week.
Rboxplot<-qplot(dag, getaluur, data = Roel, geom = "boxplot")
Rboxplot + scale_x_discrete(limits=c("maandag","dinsdag","woensdag", "donderdag", "vrijdag",
"zaterdag", "zondag")) + geom_hline(aes(yintercept= 1200))
#werkt niet
sBoxplot<- qplot(dag, getaluur, data = Samen, geom = "boxplot")
sBoxplot + scale_x_discrete(limits=c("maandag","dinsdag","woensdag", "donderdag", "vrijdag",
"zaterdag", "zondag")) + geom_hline(aes(yintercept= 1200))
ggsave("wekelijkskoffiegebruikRoeloverdag.png", width=6, height=4)
#boxplot samen over een week.
Splot<-qplot(dag, getaluur, data = Samen, geom = "boxplot")
Splot + scale_x_discrete(limits=c("maandag","dinsdag","woensdag", "donderdag", "vrijdag",
"zaterdag", "zondag")) + geom_hline(aes(yintercept= 1200))
ggsave("wekelijkskoffiegebruiksamenoverdag.png", width=6, height=4)
#koffie per dag.
Cplotdag<- qplot(dag, data = Coffee, geom = "histogram", color = categorie)
Cplotdag + scale_x_discrete(limits=c("maandag","dinsdag","woensdag", "donderdag", "vrijdag",
"zaterdag", "zondag"))
ggsave("totaalkoffiegebruik.png", width=6, height=4)
#summaries maken?
#dingen die vin deed.
# DENSITY IN GGPLOT OPROEPEN EN DAN FACETTEN OP DAG.#####
plot(density( Coffee[Coffee$dag=="maandag", ]$getaluur, bw=5))
# > plot(density( Coffee[Coffee$dag=="maandag", ]$getaluur, bw=10))
# > plot(density( Coffee[Coffee$dag=="maandag", ]$getaluur, bw=100))
# > plot(density( Coffee[Coffee$dag=="maandag", ]$getaluur, bw=50))
# > plot(density( Coffee[Coffee$dag=="maandag", ]$getaluur, bw=300))
# > plot(density( Coffee[Coffee$dag=="maandag", ]$getaluur, bw=50))
# > plot(density( Coffee[Coffee$dag=="maandag", ]$getaluur, bw=25))
# > plot(density( Coffee[Coffee$dag=="maandag", ]$getaluur, bw=100))
#table(Coffee$dag, Coffee$dagdeel) #werkt, maar ordering is niet goed.
Overzichttabel <- table(factor(Coffee$dag,
levels = c("maandag", "dinsdag", "woensdag", "donderdag", "vrijdag", "zaterdag", "zondag"))
, factor(Coffee$dagdeel, levels = c("ochtend", "middag", "avond")))
Overzichttabel<- as.data.frame.matrix(Overzichttabel ) #omzetten in dataframe
mutate(Overzichttabel, perdag = ochtend + middag + avond)
#KNMI data ####
#tijd koffie is van 2014-11-13 - 2015-6-5
#
#alternatief: nieuws in periode opzoeken en classificeren?
#
library(readr)
KNMI<-read_csv("KNMI_20150605.txt",skip = 24,
col_names = c("STN","YYYYMMDD", "TG", "TN", "TNH", "TX", "TXH", "T10N",
"SQ", "Q", "DR", "RH", "PG", "PX", "PXH"),
trim_ws = TRUE)
#source("C:/Users/roel/Documents/docs/actief/Projecten/paplusdatabestanden/code/nuttigescripts.R")
#eigenschappen_dataset(KNMI)
library(dplyr)
library(lubridate)
KNMI<-KNMI %>%
mutate(datum = as.Date(fast_strptime(as.character(YYYYMMDD), "%Y%m%d")))%>% #zet datum om in date formaat.
mutate(etmaalGemiddeldeTempGradenCelsius = TG/10 , zonneschijnUren = SQ/10,
stralingJoulePerVierkanteCm = Q, EtmaalNeerslagMM = RH/10, gemLuchtdrukHectoPascal = PG/10)%>%
select(datum, etmaalGemiddeldeTempGradenCelsius, zonneschijnUren, stralingJoulePerVierkanteCm,
EtmaalNeerslagMM,gemLuchtdrukHectoPascal )
#missings definieeren.
KNMI$zonneschijnUren[KNMI$zonneschijnUren == -1] <- NA
KNMI$EtmaalNeerslagMM[KNMI$EtmaalNeerslagMM == -1] <- NA
#graph that shit
qplot(datum, etmaalGemiddeldeTempGradenCelsius, data= KNMI,
color = zonneschijnUren)+ scale_fill_brewer(type = "div")
qplot(datum, etmaalGemiddeldeTempGradenCelsius, data= KNMI,
color = stralingJoulePerVierkanteCm) #+ scale_fill_brewer(type = "div")
#Combineer Roel en Weer ####
#Coffee naar per dag bestand.
Roel <- filter(Coffee, categorie == "roel alleen") #idem als hierboven
koffieRoelPerDag<-aggregate( Roel$counter, by =list(datum = Roel$datum), sum)
names(koffieRoelPerDag)[2] <- "aantal"
#combineren to 1 dataset
library(dplyr)
anti_join(koffieRoelPerDag, KNMI, by= "datum") #check, 0 rijen?
koffieWeer<-left_join(koffieRoelPerDag, KNMI, by = "datum") #left want ik hoef geen weer waar ik geen koffie dronk
#plotten
library(ggplot2)
qplot(datum, aantal, data = koffieWeer)+geom_line()
plot(density( koffieWeer$aantal, bw=10))
qplot(datum, etmaalGemiddeldeTempGradenCelsius, data= koffieWeer, color = aantal, size =4, geom = c("point","smooth"))
qplot(datum, EtmaalNeerslagMM, data= koffieWeer, color = aantal, size =3)
qplot(factor(aantal), etmaalGemiddeldeTempGradenCelsius, data = koffieWeer)+ geom_boxplot()
plot <- ggplot(koffieWeer, aes(x = datum, y= etmaalGemiddeldeTempGradenCelsius, color = aantal))
plot = plot + geom_point() + geom_smooth()
plot
#koffie en zon
viool <- ggplot(koffieWeer, aes(factor(aantal), zonneschijnUren))
viool+ geom_violin() + ggtitle("zon per koffieaantal") + xlab("aantal koppen koffie") +ylab("aantal uren zon op de dag")
#luchtdruk
qplot(gemLuchtdrukHectoPascal, data = koffieWeer)
#luchtdruk per koffie aantal.
g<- ggplot(data = koffieWeer ,aes(datum, gemLuchtdrukHectoPascal, aantal))
g + geom_point() + facet_wrap(~ aantal)
#temperatuur
h<- ggplot(data = koffieWeer ,aes(datum, etmaalGemiddeldeTempGradenCelsius, aantal))
h + geom_point() + facet_wrap(~ aantal)
#straling
i<- ggplot(data = koffieWeer ,aes(datum, stralingJoulePerVierkanteCm, aantal))
i + geom_point() + facet_wrap(~ aantal)
#etmaalneerslag
j<- ggplot(data = koffieWeer ,aes(datum, EtmaalNeerslagMM, aantal))
j + geom_point() + facet_wrap(~ aantal)
#beursdata maakt bestand koffieWeerBeurs
library(readr)
library(dplyr)
beurs<- read_csv("YAHOO-INDEX_AEX.csv") #readr package herkent direct als datum
qplot(Date, Close, data = beurs)#plot van closing AEX.
koffieWeerBeurs<-left_join(koffieWeer, beurs, by = c("datum" = "Date"))
#namen: datum, aantal, etmaalGemiddeldeTempGradenCelsius, zonneschijnUren
# stralingJoulePerVierkanteCm, EtmaalNeerslagMM, gemLuchtdrukHectoPascal,
# Open, High, Low, Close, Volume, Adjusted Close
library(ggplot2)
#iets met settings van kleuren is raar. bij errors voer uit:
#theme_set(theme_grey())
k<- ggplot(data = koffieWeerBeurs ,aes(datum, Close, aantal))
k + geom_point() + facet_wrap(~ aantal)
i<- ggplot(data = koffieWeerBeurs ,aes(datum, Close, color = etmaalGemiddeldeTempGradenCelsius))
i + geom_point( size =3) +scale_color_gradient2(high="red") #rood
i + geom_point( size =3) +scale_color_gradientn(colours = rainbow(3)) #hoog contrast regenboog
#plot temperatuur onder koers
library(cowplot)
A<- ggplot(data = koffieWeerBeurs ,aes(datum, Close))+ geom_point()
B<- qplot(datum, etmaalGemiddeldeTempGradenCelsius ,data = koffieWeerBeurs)
plot_grid(A, B, align = "h", nrow = 2)
C<-qplot(datum, aantal, data=koffieWeerBeurs)
plot_grid(A, C, nrow = 2, align = "h")
cor(koffieWeerBeurs$aantal, koffieWeerBeurs$Close, use = "complete.obs")
library(GGally)
ggpairs(data=koffieWeerBeurs,
columns = 2:10,
upper = list(continuous = "density"),
lower = list(combo = "facetdensity")
)
ggscatmat(data = koffieWeerBeurs, columns = 2:10) #eenvoudigste plot
|
01bd21e63f3a83c8b1f5bb19e93a52b7d9967e4c | 029bbab7cbb2b6f1ecd48b4f500a52a72caf86bb | /man/hoad.calib.Rd | d0789fbd11308a1021b3cc7860c1bc6fbdbf42b4 | [] | no_license | cran/LinCal | fdc57d51f1bcd3bcde899b2abd3ec3eddf6a33d9 | 40efd11039a97da600d509d69c2523687d492aed | refs/heads/master | 2022-05-13T06:12:19.302304 | 2022-04-29T21:40:15 | 2022-04-29T21:40:15 | 26,346,284 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 967 | rd | hoad.calib.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hoad_calib.R
\name{hoad.calib}
\alias{hoad.calib}
\title{Bayesian Inverse Linear Calibration Function}
\usage{
hoad.calib(x, y, alpha, y0)
}
\arguments{
\item{x}{numerical vector of regressor measurments}
\item{y}{numerical vector of observation measurements}
\item{alpha}{the confidence interval to be calculated}
\item{y0}{vector of observed calibration value}
}
\description{
\code{hoad.calib} uses an inverse Bayesian approach to estimate an unknown X given observed vector y0 and calculates credible interval estimates.
}
\examples{
X <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10)
Y <- c(1.8,1.6,3.1,2.6,3.6,3.4,4.9,4.2,6.0,5.9,6.8,6.9,8.2,7.3,8.8,8.5,9.5,9.5,10.6,10.6)
hoad.calib(X,Y,0.05,6)
}
\references{
Hoadley, B. (1970). A Bayesian look at Inverse Linear Regression. Journal of the American Statistical Association. 65, 356-369.
}
\keyword{calibration}
\keyword{linear}
|
a0516b0a0c85d8618fe780d572a903b08a61bd82 | a2b36f90d65387e0bfe5791ae1a155268b5ec566 | /Model_2/ann_2.R | 366bbd62b90beb96e398239a8ba5e4a9130adaeb | [] | no_license | rmarlon308/SVM_vs_ANN | 27d3a25319428d3888585cfb9ec3887a3aeb3e8b | 24c8433d6d8cf7f3717e290e8b27b1c9d0576cf0 | refs/heads/main | 2023-02-05T22:37:44.131748 | 2020-12-25T21:54:37 | 2020-12-25T21:54:37 | 324,438,703 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,038 | r | ann_2.R | library(readr)
library(dplyr)
library(keras)
library(tensorflow)
testing = read_csv("/home/marlon/mainfolder/marlon/USFQ/DataMining/10_FinalProject/proyectoFinal/Model_2/dataset/testing.csv")
training = read_csv("/home/marlon/mainfolder/marlon/USFQ/DataMining/10_FinalProject/proyectoFinal/Model_2/dataset/training.csv")
dataset = rbind(training, testing)
dataset$class = as.factor(dataset$class)
dataset$class = as.numeric(dataset$class) - 1
#Normalize the data
summary(dataset)
for(i in 2:ncol(dataset)){
dataset[i] = (dataset[i] - min(dataset[i]))/ (max(dataset[i]) - min(dataset[i])) * (1-0) + 0
}
summary(dataset)
x_data = dataset[, 2:ncol(dataset)]
y_data = dataset[, 1]
#Dimensionality reduction
mean_row = colMeans(x_data)
for(i in 1:nrow(x_data)){
x_data[i, ] = x_data[i, ] - mean_row
}
svd = svd(x_data)
eigVectors = svd$v
eigValues = svd$d^2/sum(svd$d^2)
variance = cumsum(eigValues)/sum(eigValues)
k = NULL
for(i in 1:length(variance)){
if(variance[i] >= 0.95){
k = i
print(k)
break
}
}
k_selected = eigVectors[, 1:k]
proy_data = as.data.frame(as.matrix(x_data) %*% as.matrix(k_selected))
all_data = cbind(y_data, proy_data)
rand_data = all_data[sample(nrow(all_data)),]
#Del csv sacar el mejor modelo
folds = cut(seq(1, nrow(rand_data)), breaks = 10, labels = F)
recall = c()
accuracy = c()
precision = c()
auc = c()
loss = c()
pred_vector = rep(0, nrow(all_data))
real_vector = rand_data$class
history = c()
for(i in 1:10){
test_index = which(folds == i, arr.ind = T)
test_x = as.matrix(rand_data[test_index, 2:ncol(rand_data)])
test_y = rand_data[test_index, 1]
train_x = as.matrix(rand_data[-test_index, 2:ncol(rand_data)])
train_y = rand_data[-test_index, 1]
test_y = to_categorical(test_y)
train_y = to_categorical(train_y)
model = keras_model_sequential()
model %>%
layer_dense(units = 15, activation = 'relu', input_shape = c(ncol(train_x))) %>%
layer_dense(units = 4, activation = 'softmax')
model %>%
compile(loss = 'categorical_crossentropy',
optimizer = optimizer_adam(lr = 0.001),
metrics = c('accuracy', tf$keras$metrics$AUC(), tf$keras$metrics$Precision(), tf$keras$metrics$Recall()))
mymodel = model %>%
fit(train_x,
train_y,
epochs = 150,
batch_size = 32,
validation_split = 0.2
)
eval = model %>%
evaluate(test_x,
test_y)
history = c(history, mymodel)
recall = c(recall, eval[5])
accuracy = c(accuracy, eval[2])
precision = c(precision, eval[4])
auc = c(auc, eval[3])
loss = c(loss, eval[1])
pred = model %>% predict_classes(test_x)
pred_vector[test_index] = pred
}
sprintf("Accuracy Mean: %f SD: %f", mean(accuracy), sd(accuracy))
sprintf("Precision Mean: %f SD: %f", mean(precision), sd(precision))
sprintf("Recall Mean: %f SD: %f", mean(recall), sd(recall))
sprintf("AUC Mean: %f SD: %f", mean(auc), sd(auc))
sprintf("Loss Mean: %f SD: %f", mean(loss), sd(loss))
table(pred_vector, real_vector)
#Grafica Loss
data_loss = NULL
data_loss_val = NULL
for(i in seq(2, 20, by = 2)){
data_loss = rbind(data_loss, as.vector(history[i]$metrics$loss))
data_loss_val = rbind(data_loss_val, as.vector(history[i]$metrics$val_loss))
}
mean_loss = colMeans(data_loss)
mean_loss_val = colMeans(data_loss_val)
loss_data = data.frame(mean_loss, mean_loss_val)
library(ggplot2)
ggplot(loss_data) +
geom_line(aes(x = 1:nrow(loss_data), y = mean_loss), color = "blue") +
geom_line(aes(x = 1:nrow(loss_data), y = mean_loss_val), color = "green")+
xlab("Epochs") + ylab("Loss") + labs(title = "Loss")
library(pROC)
library(caret)
roc.multi = multiclass.roc(real_vector, pred_vector, levels = c(0,1,2,3))
auc(roc.multi)
rs <- roc.multi[['rocs']]
plot.roc(rs[[1]])
sapply(2:length(rs),function(i) lines.roc(rs[[i]],col=i))
|
a7b46a9d70d4225cd14529ddf9d0766f561f37b5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/summarytools/examples/print.summarytools.Rd.R | 713618962291a60a9a725ed4f6aead0e6b688b14 | [] | 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 | 418 | r | print.summarytools.Rd.R | library(summarytools)
### Name: print.summarytools
### Title: Print Method for Objects of Class 'summarytools'.
### Aliases: print.summarytools print view
### Keywords: methods print
### ** Examples
## Not run:
##D data(tobacco)
##D view(dfSummary(tobacco), footnote = NA)
##D
## End(Not run)
data(exams)
print(freq(exams$gender), style = 'rmarkdown')
print(descr(exams), omit.headings = TRUE)
|
5a7f00184814b73afeaa6bc8d7f60741cfe4411f | 8f0431de29762061acb57e06f492d22d5ce2604f | /man/gt_sparkline.Rd | d39c5a991ab2e395f28da158714f798eb04f48fb | [
"MIT"
] | permissive | adamkemberling/gtExtras | 2c3e1a81d5dd97666dedab710d49377a2a7572dd | 40d1e5a006fa67833a702733055c94606f8cffb7 | refs/heads/master | 2023-08-17T11:12:00.431133 | 2021-10-13T16:28:10 | 2021-10-13T16:28:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,911 | rd | gt_sparkline.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gt_sparkline.R
\name{gt_sparkline}
\alias{gt_sparkline}
\title{Add sparklines into rows of a \code{gt} table}
\usage{
gt_sparkline(
gt_object,
column,
type = "sparkline",
width = 30,
line_color = "black",
range_colors = c("red", "blue"),
fill_color = "grey",
bw = NULL,
trim = FALSE,
same_limit = TRUE
)
}
\arguments{
\item{gt_object}{An existing gt table object of class \code{gt_tbl}}
\item{column}{The column wherein the sparkline plot should replace existing data. Note that the data \emph{must} be represented as a list of numeric values ahead of time.}
\item{type}{A string indicating the type of plot to generate, accepts \code{"sparkline"}, \code{"histogram"} or \code{"density"}.}
\item{width}{A number indicating the width of the plot in mm at a DPI of 25.4, defaults to 30}
\item{line_color}{Color for the line, defaults to \code{"black"}. Accepts a named color (eg 'blue') or a hex color.}
\item{range_colors}{A vector of two valid color names or hex codes, the first color represents the min values and the second color represents the highest point per plot. Defaults to \code{c("blue", "blue")}. Accepts a named color (eg \code{'blue'}) or a hex color like \code{"#fafafa"}.}
\item{fill_color}{Color for the fill of histograms/density plots, defaults to \code{"grey"}. Accepts a named color (eg \code{'blue'}) or a hex color.}
\item{bw}{The bandwidth or binwidth, passed to \code{density()} or \code{ggplot2::geom_histogram()}. If \code{type = "density"}, then \code{bw} is passed to the \code{bw} argument, if \code{type = "histogram"}, then \code{bw} is passed to the \code{binwidth} argument.}
\item{trim}{A logical indicating whether to trim the values in \code{type = "density"} to a slight expansion beyond the observable range. Can help with long tails in \code{density} plots.}
\item{same_limit}{A logical indicating that the plots will use the same axis range (\code{TRUE}) or have individual axis ranges (\code{FALSE}).}
}
\value{
An object of class \code{gt_tbl}.
}
\description{
The \code{gt_sparkline} function takes an existing \code{gt_tbl} object and
adds sparklines via the \code{ggplot2}. This is a wrapper around
\code{gt::text_transform()} + \code{ggplot2} with the necessary boilerplate already applied.
}
\section{Figures}{
\if{html}{\figure{ggplot2-sparkline.png}{options: width=50\%}}
}
\section{Function ID}{
1-4
}
\examples{
library(gt)
gt_sparkline_tab <- mtcars \%>\%
dplyr::group_by(cyl) \%>\%
# must end up with list of data for each row in the input dataframe
dplyr::summarize(mpg_data = list(mpg), .groups = "drop") \%>\%
gt() \%>\%
gt_sparkline(mpg_data)
}
\seealso{
Other Plotting:
\code{\link{gt_plt_bar_pct}()},
\code{\link{gt_plt_bar_stack}()},
\code{\link{gt_plt_bar}()},
\code{\link{gt_plt_winloss}()}
}
\concept{Plotting}
|
2bf84a15dfcbf5ebbd7a77f098793943a8a2f8fd | 3bf7d1502b222af53cbda561dd143aaa32a5d538 | /main.R | d5616428d19a8a498deed6174749a1a5ee76febc | [
"MIT"
] | permissive | fdrennan/biggr2 | 90506d9637800d9149d4e6c9b4de21d0a319d9ba | d2c82763bc9331697478dfdc58f11dab6572accc | refs/heads/main | 2023-03-10T22:25:37.241549 | 2021-02-28T07:34:36 | 2021-02-28T07:34:36 | 303,002,767 | 3 | 2 | NOASSERTION | 2021-02-26T17:41:03 | 2020-10-10T22:48:18 | R | UTF-8 | R | false | false | 553 | r | main.R | library(biggr2)
library(glue)
library(readr)
user_data <- read_file("ubuntuinit.sh")
user_data <-
glue_collapse(
c(
user_data,
paste0("echo ", readLines(".Renviron"), ">> /home/ubuntu/.Renviron", collapse = "\n"),
"cd /home/ubuntu && git clone https://github.com/fdrennan/redditstack.git",
"mv /home/ubuntu/.Renviron /home/ubuntu/redditstack/.Renviron",
"sudo chmod 777 -R /home/ubuntu/redditstack"
),
sep = "\n"
)
server <- ec2_instance_create(
user_data = user_data,
InstanceType = "t2.xlarge"
)
|
4f4bd2080cc7490ec7845ffca132ddebd3d1c592 | 8817c24a3fab4de0600244b5880f2de0c7b97f26 | /plot3.R | 8b2602c90d685613bc02d085a503ea3bb1c51ff0 | [] | no_license | wlb0/ExData_Plotting1 | c96ec5b00eae64b0e69013f84a6ea1a6c524021a | 81d217b2f4e1f0c8c9136c3c8e8dab1070b8660f | refs/heads/master | 2020-12-11T06:00:34.825187 | 2015-06-07T16:31:01 | 2015-06-07T16:31:01 | 35,371,703 | 0 | 0 | null | 2015-05-10T13:17:40 | 2015-05-10T13:17:40 | null | UTF-8 | R | false | false | 1,334 | r | plot3.R | ## exdata-014 Assignment 1 part 3
## code for plot 3
## assumes source data has already been downloaded and unzipped to
## csv file "household_power_consumption.txt" in the current directory
if (!require("sqldf")) {
install.packages("sqldf")
}
library(sqldf)
# define wData as a file with indicated format
wData <- file("household_power_consumption.txt")
attr(wData, "file.format") <- list(sep = ";", header = TRUE)
# use sqldf to read it in keeping only rows for the two specified dates
plotData <- sqldf("select * from wData where date = '1/2/2007' or date = '2/2/2007'")
# add column with date and time in proper date/time format,
# update date column to be proper date format
plotData$dateTime <- strptime(paste(plotData$Date,plotData$Time),format="%d/%m/%Y %H:%M:%S")
plotData$Date <- strptime(plotData$Date, "%d/%m/%Y")
# perform plot
png(filename="plot3.png", width=480, height=480)
with(plotData, plot(dateTime,
Sub_metering_1,
type="l",
col="black",
xlab="",
ylab="Energy sub metering",
main=""))
with(plotData, points(dateTime,
Sub_metering_2,
type="l",
col="red"))
with(plotData, points(dateTime,
Sub_metering_3,
type="l",
col="blue"))
legend("topright",
lty=1,
col=c("black", "red", "blue"),
legend=names(plotData[7:9]),
cex=0.95)
dev.off()
|
5b2e3ae525e7eba9149409030ea4bd44bf286709 | 7c4fa47fe62269bad9c8bbdf9edaf8b3180865f9 | /024-maritime_ports_france_2/01_scrape.R | a4214548a5ccb6c1fcae8a32d7fa89f3f3ca061a | [] | no_license | training-datalab/minard | 6236897f64a49979b219ac962b4de9bfd84ce003 | 194f7e537ad0cc16a5ba71ca28773a814e8946ef | refs/heads/master | 2023-03-23T21:11:18.323214 | 2021-03-17T21:57:44 | 2021-03-17T21:57:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,082 | r | 01_scrape.R | library(rvest)
url <- "http://www.worldportsource.com/ports/index/FRA.php"
doc <- read_html(url)
links <- html_nodes(doc,"br+ a") %>%
html_attr("href") %>%
paste0("http://www.worldportsource.com",.)
name <- html_nodes(doc,"br+ a") %>%
html_text()
ports <- tibble(name,link=links) %>% distinct() %>% mutate(link=str_remove(link,"review/"))
ports$lat <- ports$lon <- ports$deglat <- ports$deglon <- ports$port_type <- ports$port_size <- NA
for(i in 1:nrow(ports)){
cat(i,"\r")
th1 <- ports$link[i] %>%
read_html() %>%
html_nodes("th") %>% html_text()
th2 <- ports$link[i] %>%
read_html() %>%
html_nodes(".dash") %>% html_text() %>% .[seq(2,length(.),2)]
ports$deglat[i] <- th2[which(th1=="Latitude:")]
ports$deglon[i] <- th2[which(th1=="Longitude:")]
latlong <- OSMscale::degree(ports$deglat[i],ports$deglon[i],drop = TRUE)
ports$lat[i] <- latlong[1]
ports$lon[i] <- latlong[2]
ports$port_type[i] <- th2[which(th1=="Port Type:")]
ports$port_size[i] <- th2[which(th1=="Port Size:")]
}
write_csv(ports,"data/ports_france.csv")
|
dc573d45f2679a4e4b5fff3c1ccfd0cdb017a72b | 11394cd22cea3b4e644d20564ff4b500018d943e | /scripts/separateAnalysis/checkCellIdent.R | 21db489ecc39f2104a8fdeb5c4f6b9cac4fff284 | [
"MIT"
] | permissive | shunsunsun/single_cell_rna_seq_snakemake | 3d153c9cb7db9988917aff38991217a35940aa64 | f275546eb3bd63d5d535a13407ce47ee36e94cae | refs/heads/master | 2023-04-17T08:44:13.954986 | 2021-04-27T08:13:50 | 2021-04-27T08:13:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 522 | r | checkCellIdent.R | library(Seurat)
se <- readRDS(file="./data/GEJ_QCed_sctNorm_BatchCCA_clustStab/Louvain_clust_k100_pc50_res0.4.rds")
load(file="./data/GEJ_QCed_sctNorm_BatchCCA_clustStab/allcells_ident_scibet.rda")
load(file="./data/GEJ_QCed_sctNorm_BatchCCA_clustStab/Louvain_clust_k100_pc50_res0.4_singleR_label.fine.rda")
se$singleR_label <- clust.pred$labels[match(se$integrated_snn_res.0.4, rownames(clust.pred))]
se$scibet_label <- ci[[2]]
table(se$scibet_label, se$singleR_label)
table(se$scibet_label, se$integrated_snn_res.0.4)
|
be69827fd020ea2e0e1b2ba1e4405bc8bb13f45c | 5fabfb8ce7863ffe6daa91dac2971ee85f9d2ad7 | /man/subsample.Rd | 82dd8ca04ec56e038452770e776581ad2ca34ddd | [] | no_license | Pezhvuk/sumrep | a1ff159aa8b025d15c167e434591788bae8cc4b2 | 275c6981f57b14db51746735f32e1ecf6bf6c486 | refs/heads/master | 2020-04-30T12:14:39.350531 | 2019-01-19T20:44:06 | 2019-01-19T20:44:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 545 | rd | subsample.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Approximation.R
\name{subsample}
\alias{subsample}
\title{Subsample a dataset}
\usage{
subsample(dataset, sample_count, replace = TRUE)
}
\arguments{
\item{dataset}{A data.table, data.frame, or vector object}
\item{sample_count}{Number of samples to retain in the subsampled data.
Samples refers to elements in a vector or rows in a data.table/data.frame}
}
\value{
A subsampled dataset of the same type given by \code{dataset}
}
\description{
Subsample a dataset
}
|
1143c0b1bc2b469b9f734aac5d65581a440e212f | c9cd1cd8ed8904baa6fa7807e9cfc9fc42774d94 | /plot4.R | 2562514897a8ad86e656fda207b9a552ecc6e05a | [] | no_license | BirgitR/ExData_Plotting1 | 274b054c11c63b77d403eb894504bd77fffb4d01 | 72a46ca9c3dc7e5c82c4d498a3671914d4fc613d | refs/heads/master | 2021-01-21T23:45:36.735643 | 2014-09-07T19:39:37 | 2014-09-07T19:39:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,075 | r | plot4.R | hpc <- read.csv("household_power_consumption.txt", sep=";", na.strings="?",
stringsAsFactors=FALSE)
hpc2 <- hpc[min(which(hpc$Date == "1/2/2007")):max(which(hpc$Date == "2/2/2007")),]
datetime<-strptime(paste(hpc2$Date, hpc2$Time), format="%d/%m/%Y %H:%M:%S")
Sys.setlocale("LC_TIME", "C")
par(mfcol=c(2,2),mar=c(4,4,2,2))
plot(x=datetime,y=hpc2$Global_active_power,type="l",xlab="",
ylab="Global Active Power")
plot(datetime,hpc2$Sub_metering_1,xlab="",ylab="Energy sub metering",type="n")
lines(datetime,hpc2$Sub_metering_1,type="l",col="black")
lines(datetime,hpc2$Sub_metering_2,type="l",col="red")
lines(datetime,hpc2$Sub_metering_3,type="l",col="blue")
legend("topright",lty=1,lwd=2,bty="n",cex=.6,col=c("black","red","blue"),
legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"))
plot(x=datetime,y=hpc2$Voltage,type="l",xlab="datetime",
ylab="Voltage")
plot(x=datetime,y=hpc2$Global_reactive_power,type="l",xlab="datetime",
ylab="Global_reactive_power")
dev.copy(png, file="plot4.png",width = 480, height = 480)
dev.off() |
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