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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f72982c7600c2575ff701726b0318a85f4e912a0 | 1f27ad535035612c2731e95cbca8e86b43e043c1 | /R/inla.climate.mu.R | 62a0d0f39f95d29aba8e32bed3804d6b83c82f4f | [] | no_license | eirikmn/INLA.climate | 8417e699d97b24b4d408174c03b8a6896e30d9ee | c467e6e2f9fd2e4341cbc1bf15b9629b82eb7089 | refs/heads/master | 2023-02-08T15:06:14.385088 | 2023-01-27T09:52:09 | 2023-01-27T09:52:09 | 168,170,133 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,780 | r | inla.climate.mu.R | inla.climate.mu = function(result,forcing,quick=FALSE,T0.corr=NULL,nsamples=100000,seed=1234,
print.progress=FALSE,model="fgn"){
catch = tryCatch(attachNamespace("INLA"),error=function(x){})
if(length(find.package("INLA",quiet=TRUE))==0){
stop("This function requires INLA. Please install at www.R-INLA.org or by calling 'install.packages(\"INLA\", repos=c(getOption(\"repos\"), INLA=\"https://inla.r-inla-download.org/R/testing\"), dep=TRUE)' from R.")
}
if(class(result)=="inla.climate"){
climate.res = result$inla.result
if(is.null(T0.corr)){
if(!is.null(result$misc$T0)){
T0.corr = result$misc$T0
}else{
T0.corr=0
}
}
}else if(class(result)=="inla"){
climate.res = result
if(is.null(T0.corr)){
if(!is.null(result$climate.misc$T0)){
T0.corr = result$climate.misc$T0
}else{
T0.corr=0
}
}
}else{
stop("Input 'result' not a valid class.")
}
if(print.progress){
cat("Starting mu Monte Carlo sampling with n=",format(nsamples,scientific=F)," simulations..\n",sep="")
}
set.seed(seed)
inla.seed = as.integer(runif(1)*.Machine$integer.max)
#n=climate.res$misc$configs$contents$length[1]
n=length(forcing)
#x = inla.posterior.sample(nsamples,r,seed=inla.seed) #int.strategy=grid
x = INLA::inla.hyperpar.sample(nsamples,climate.res)
if(dim(x)[2]>4){
model = "ar1"
}
if(model %in% c("fgn","arfima")){
hyperpars = matrix(NA,nrow=nsamples,ncol=4) #c(H,sf,F0,TCR)
hyperpars[,1] = 0.5+0.5/(1+exp(-x[,2]))
hyperpars[,2] = 1/sqrt(exp(x[,3]))
a=3
hyperpars[,3] = x[,4]
#hyperpars[,3] = -a+2*a/(1+exp(-x[,4]))
}else{
hyperpars = matrix(NA,nrow=nsamples,ncol=(2)) #c(H,sf,F0,w1,...,wm,L1,...,Lm)
hyperpars[,1] = 1/sqrt(exp(x[,2]))
a=3
#hyperpars[,2] = -a+2*a/(1+exp(-x[,3]))
hyperpars[,2] = x[,3]
m = (dim(x)[2]-2)/2
ar1.temp= inla.climate.ar1(climate.res,m=m,nsamples=nsamples,seed=seed,print.progress=print.progress)
ww = matrix(NA,nrow=nsamples,ncol=m)
LL = matrix(NA,nrow=nsamples,ncol=m)
if(m == 1){
ww = rep(1,nsamples)
LL = INLA::inla.rmarginal(nsamples,ar1.temp$p$density)-1 #lambda
}else{
for(k in 1:m){
ww[,k] = ar1.temp[[paste0("w",k)]]$samples
LL[,k] = ar1.temp[[paste0("p",k)]]$samples-1 #lambda
}
}
}
tid.start = proc.time()[[3]]
#if(!is.loaded('Rc_mu')){
#print('hallo')
# dyn.load(file.path(.Library,"INLA.climate/libs/Rc_mu.so"))
#dyn.load('./src/colmeansr.so')
#}
if(!quick){
mu.samples=matrix(NA,ncol=n,nrow=nsamples)
}
meansmc=numeric(n)
xsumvec = numeric(n)
x2sumvec =numeric(n)
for(iter in 1:nsamples){
if(model %in% c("fgn","arfima")){
res = .C('Rc_mu',mumeans=as.matrix(meansmc,ncol=1),as.double(forcing),as.integer(length(forcing)),
as.double(hyperpars[iter,1]),as.double(hyperpars[iter,2]),as.double(hyperpars[iter,3]))
}else if(model == "ar1"){
if(!is.loaded('Rc_mu_ar1')){
#dyn.load(file.path(.Library,"INLA.climate/libs/Rc_Q.so"))
dyn.load(file.path("Rc_mu_ar1.so"))
}
if(m == 1){
res = .C('Rc_mu_ar1',mumeans=as.matrix(meansmc,ncol=1),as.double(forcing),as.integer(length(forcing)),as.integer(m),
as.double(1),as.double(LL[iter]),as.double(hyperpars[iter,1]),
as.double(hyperpars[iter,2]))
}else{
res = .C('Rc_mu_ar1',mumeans=as.matrix(meansmc,ncol=1),as.double(forcing),as.integer(length(forcing)),as.integer(m),
as.double(ww[iter,]),as.double(LL[iter,]),as.double(hyperpars[iter,1]),
as.double(hyperpars[iter,2]))
}
}
if(!quick){
mu.samples[iter,]=res$mumeans
}
xsumvec = xsumvec + res$mumeans
x2sumvec = x2sumvec + res$mumeans^2
}
mu.mean = as.numeric(xsumvec/nsamples)
mu.sd=as.numeric(sqrt( 1/(nsamples-1)*( x2sumvec -2*mu.mean*xsumvec + nsamples*mu.mean^2 ) ))
if(!quick){
mu.quant0.025 = numeric(n)
mu.quant0.5 = numeric(n)
mu.quant0.975 = numeric(n)
for(iter in 1:n){
dens = density(mu.samples[,iter])
mu.quant0.025[iter]=INLA::inla.qmarginal(0.025,dens)
mu.quant0.5[iter]=INLA::inla.qmarginal(0.5,dens)
mu.quant0.975[iter]=INLA::inla.qmarginal(0.975,dens)
}
}
tid.slutt = proc.time()[[3]]
tid.mc=tid.slutt-tid.start
if(print.progress){
cat("Finished mu Monte Carlo sampling procedure in ",tid.mc," seconds\n",sep="")
}
ret = list(mean=mu.mean+T0.corr, sd = mu.sd)
if(!quick){
ret$quant0.025=mu.quant0.025+T0.corr
ret$quant0.5=mu.quant0.5+T0.corr
ret$quant0.975=mu.quant0.975+T0.corr
if(model %in% c("fgn","arfima")){
ret$samples=list(mu=mu.samples+T0.corr, H=hyperpars[,1],sigmaf=hyperpars[,2],F0=hyperpars[,3])
}else if(model == "ar1"){
ret$samples=list(mu=mu.samples+T0.corr, sigmaf=hyperpars[,1],F0=hyperpars[,2])
if(m==1){
ret$samples$p = LL+1
}else{
for(k in 1:m){
ret$samples[[paste0("w",k)]] = ww[,k]
}
for(k in 1:m){
ret$samples[[paste0("p",k)]] = LL[,k]+1
}
}
}
}
if(class(result) == "inla.climate"){
if(print.progress){
print("Exporting inla.climate object")
}
result$mu = ret
result$time$mu = tid.mc
result$time$Total = result$time$Total + tid.mc
result$misc$mu.options$nsamples = nsamples
result$misc$mu.options$seed = seed
if(quick){
compute.mu = 2
}else{
compute.mu=1
}
result$misc$mu.options$compute.mu = compute.mu
return(result)
}else{
if(print.progress){
print("Exporting list object")
}
ret$time = tid.mc
return(ret)
}
}
|
29fdf7e79eaf0a213695cda48b17c7f8889fd820 | dc681828c7f3845280e7585574ed376d56b77138 | /CAVIAR/ecaviar_eqtl.R | 868a4217ce826ff8ae3867464eb7a32ca28187bc | [] | no_license | egeoffroy/Advanced_BIOI_Final | 111d2f1185c7a22deced76322beb06aa3d675993 | a0c57391744b1e442a77bbd9c8714c49261f104c | refs/heads/master | 2021-04-11T17:31:50.819852 | 2020-04-07T15:51:40 | 2020-04-07T15:51:40 | 249,040,864 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 617 | r | ecaviar_eqtl.R | library(dplyr)
library(data.table)
eqtl <- fread('coloc/Sig_Height_eQTL_YRI_Height.txt.gz', header = T, stringsAsFactors=F)
eqtl <- unique(eqtl)
for(i in c(1:22)){
snps_1 <- fread(paste('snps_', i, '.txt', sep = ''), header = F, stringsAsFactors=F)
eqtl1 <- eqtl %>% filter(eqtl$variant_id %in% snps_1$V1)
eqtl1$Z <- as.numeric(eqtl1$slope)/as.numeric(eqtl1$slope_se)
eqtl2 <- data.frame(eqtl1$variant_id, eqtl1$Z)
eqtl2 <- unique(eqtl2)
eqtl2 <- eqtl2[!duplicated(eqtl2$eqtl1.variant_id),]
write.table(eqtl2, paste('caviar/eqtl_chr', i '_one_val.txt', sep =''), quote = F, row.names=F, col.names=F)
}
|
664b83c9e5f1fc72348f09305748796f68dbf7bd | e45c6f36a065b6a44e873a773428105de4d3758e | /r-package/R/metadata.R | 59eebcd5b5f53aaf5f0e076c857b529d9e04c267 | [
"MIT"
] | permissive | basedosdados/mais | 080cef1de14376699ef65ba71297e40784410f12 | 2836c8cfad11c27191f7a8aca5ca26b94808c1da | refs/heads/master | 2023-09-05T20:55:27.351309 | 2023-09-02T03:21:02 | 2023-09-02T03:21:02 | 294,702,369 | 376 | 98 | MIT | 2023-08-30T21:17:28 | 2020-09-11T13:26:45 | SQL | UTF-8 | R | false | false | 4,508 | r | metadata.R | #' Search for a dataset by keyword
#' @param search_term keyword for search
#'
#' @return A tibble with search results
#'
#' @importFrom purrr map_chr pluck
#' @importFrom stringr str_replace_all
#' @importFrom rlang .data
#'
#' @export
#' @examples
#'
#' \dontrun{
#'
#' dataset_search("agua")
#' dataset_search("educação")
#'
#'}
#'
#'
dataset_search <- function(search_term) {
bd_request(
endpoint = "dataset_search",
query = list(
resource_type = "bdm_table",
q = search_term,
page_size = 100)) ->
search
tibble::tibble(
dataset_name = purrr::map_chr(
.x = search$datasets,
.f = ~ purrr::pluck(.x, "name") %>%
stringr::str_replace_all("-", "_")),
dataset_tables = purrr::map(
.x = .data$dataset_name,
.f = basedosdados::list_dataset_tables),
url = purrr::map_chr(
.x = search$datasets,
.f = ~ glue::glue("https://basedosdados.org/dataset/{purrr::pluck(.x, 'id')}")),
title = purrr::map_chr(
.x = search$datasets,
.f = ~ purrr::pluck(.x, "title")))
}
#' List tables in a dataset
#' @param dataset_id a dataset name e.g. if addressing table "br_sp_alesp.deputado" then table_id is `br_sp_alesp`
#' @export
#' @importFrom purrr pluck map_chr discard
#' @importFrom dplyr bind_rows
#' @return A tibble listing all tables in a given dataset
#' @examples
#' \dontrun{
#' list_dataset_tables("br_sp_alesp")
#' }
list_dataset_tables <- function(dataset_id) {
bd_request(
endpoint = "bdm_dataset_show",
query = list(
dataset_id = dataset_id)) ->
results
fetch_function <- purrr::possibly(
.f = function(resource) {
tibble::tibble(
name = ifelse(rlang::is_null(resource$name), NA_character_, resource$name),
description = ifelse(rlang::is_null(resource$description), NA_character_, resource$description))
},
otherwise = "Error")
results %>%
purrr::pluck("resources") %>%
purrr::keep(~ .x$resource_type == "bdm_table") %>%
purrr::map(fetch_function) %>%
purrr::reduce(dplyr::bind_rows)
}
#' Get columns in a table
#' @param dataset_id a dataset name e.g. if addressing table "br_sp_alesp.deputado" then table_id is `br_sp_alesp`
#' @param table_id a table name e.g. if addressing table "br_sp_alesp.deputado" then table_id is `deputado`
#'
#' @export
#' @examples
#' \dontrun{
#' get_table_columns("br_sp_alesp", "deputado")
#' }
#' @importFrom httr content
#' @importFrom purrr pluck map reduce
#' @importFrom dplyr bind_rows
#' @return A tibble describing all columns in a table
get_table_columns <- function(
dataset_id,
table_id) {
bd_request(
endpoint = "bdm_table_show",
query = list(
table_id = table_id,
dataset_id = dataset_id)) %>%
purrr::pluck("columns") %>%
purrr::map(tibble::as_tibble) %>%
purrr::reduce(dplyr::bind_rows) %>%
dplyr::select(- c(.data$is_in_staging, .data$is_partition))
}
#' Describe a dataset
#' @param dataset_id a dataset name e.g. if addressing table "br_sp_alesp.deputado" then table_id is `br_sp_alesp`
#'
#' @export
#' @examples
#'
#' \dontrun{
#'
#' get_dataset_description("br_sp_alesp")
#' }
#' @return A tibble describing the specified dataset
get_dataset_description <- function(dataset_id) {
bd_request(
endpoint = "bdm_dataset_show",
query = list(
dataset_id = dataset_id)) ->
result
tibble::tibble(
name = result$name,
title = result$title,
tables = list(list_dataset_tables(dataset_id)),
notes = result$notes)
}
#' Describe a table within a dataset
#'
#' @param dataset_id a dataset name e.g. if addressing table "br_sp_alesp.deputado" then table_id is `br_sp_alesp`
#' @param table_id a table name e.g. if addressing table "br_sp_alesp.deputado" then table_id is `deputado`
#' @export
#' @examples
#' \dontrun{
#' get_table_description("br_sp_alesp", "deputado")
#' }
#' @return A tibble describing the specified table
#'
get_table_description <- function(
dataset_id = ? typed::Character(1),
table_id = ? typed::Character(1)) {
bd_request(
endpoint = "bdm_table_show",
query = list(
dataset_id = dataset_id,
table_id = table_id)) ->
result
tibble::tibble(
dataset_id = dataset_id,
table_id = table_id,
description = result$description,
columns = result %>%
purrr::pluck("columns") %>%
purrr::map(tibble::as_tibble) %>%
purrr::reduce(dplyr::bind_rows) %>%
list())
}
|
e61ba698430e0ccb002ad62814b43e20db085d74 | 9a9bdabe6174b493c712e3a9d3a9a00b3ec1027e | /man/make_dada2_fasta_name.Rd | b391ba1ca0ff4a637fd129a6597a889acfc9d278 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | griffinp/metabarcodedb | 51383f41880280b060affde8b9203bdc6fe82bd3 | a42f16b59d7575051a2a7d060428e41b008cde6f | refs/heads/master | 2021-01-01T15:37:09.011794 | 2017-07-21T02:44:06 | 2017-07-21T02:44:06 | 97,654,579 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 732 | rd | make_dada2_fasta_name.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/file_naming_functions.R
\name{make_dada2_fasta_name}
\alias{make_dada2_fasta_name}
\title{make_dada2_fasta_name}
\usage{
make_dada2_fasta_name(taxon, directory, suffix = "_dada2.fasta")
}
\arguments{
\item{taxon}{taxon name (character string)}
\item{directory}{path to the directory containing the fasta file (character string)}
\item{suffix}{desired suffix for the file. Defaults to "_dada2.fasta"}
}
\description{
This function creates the file path for the dada2-formatted fasta file (fasta format)
}
\examples{
make_mapping_name(taxon="Chlorophyta", directory="~/Documents/metabarcoding/ref_datafiles/", suffix="_dada2.fasta")
}
\keyword{filename}
|
6211daaaeb6b8bcc51272211c8b5dbc20b6ed60d | c2a3647efa1093e8a8d72b8ec65f2ead24060336 | /R/TargetExperimentList-initialize.R | b201f049c6d7d7cbfd26933a055421e2c8973fcb | [] | no_license | gamerino/TarSeqQC | 2363d1ffe1dadcc6f14e552840f90fd79b4a1a30 | ebdf8ca544f5d5073966cf0a16c912ceeac7202b | refs/heads/master | 2021-07-15T03:28:58.735473 | 2020-01-31T11:24:13 | 2020-01-31T11:24:13 | 101,313,918 | 1 | 0 | null | 2017-08-24T16:06:36 | 2017-08-24T16:06:36 | null | UTF-8 | R | false | false | 5,052 | r | TargetExperimentList-initialize.R | #'TargetExperimentList object constructor.
#'
#'\code{initialize} creates the TargetExperimentList object containing the
#'experiment results of several targeted sequencing experiments carried out
#'using a unique bed file.
#'
#'@param .Object TargetExperimentList class.
#'@param TEList List containing all the TargetExperiment objects corresponding
#'to the experiments that will be compared.
#'@param feature Character indicating the name of the feature that will
#'be explored (e.g 'amplicon', 'exon', 'gene').
#'@param attribute Character indicating the name of the attribute that will
#'be explored. Should be 'coverage' or 'medianCounts'.
#'@return TargetExperimentList object.
#'
#'@include TargetExperimentList-TEList.R
#'@exportMethod initialize
#'@docType methods
#'@rdname TargetExperimentList-initialize
#'@import Rsamtools
#'@import GenomicRanges
#'@importFrom stats IQR
#'@importFrom stats aggregate
#'@importFrom stats median
#'@importFrom stats sd
#'@importMethodsFrom BiocGenerics strand
#'@importClassesFrom S4Vectors Rle
#'@importMethodsFrom S4Vectors Rle
#'@importClassesFrom IRanges IRanges
#'@importFrom IRanges IRanges
#'@aliases TargetExperimentList-method
#'@seealso \code{\link{TargetExperimentList}}
#'@note see full example in \code{\link{TargetExperimentList-class}}
#'@family TargetExperimentList
#'@author Gabriela A. Merino \email{gmerino@@bdmg.com.ar}, Cristobal Fresno
#'\email{cfresno@@bdmg.com.ar}, Yanina Murua \email{ymurua@leloir.org.ar},
#'Andrea S. Llera \email{allera@leloir.org.ar} and Elmer A. Fernandez
#'\email{efernandez@bdmg.com.ar}
#'@examples
#'# Defining the set of TargetExperiment objects
#'data(ampliPanel, package="TarSeqQC")
#'data(ampliPanel2, package="TarSeqQC")
#'ampliList<-list(ampliPanel, ampliPanel2)
#'# Defining feature parameter
#'feature<-"amplicon"
#'# Defining attribute parameter
#'attribute<-"coverage"
#'##Calling the constructor
#'object<-TargetExperimentList(TEList=ampliList, attribute=attribute,
#'feature=feature)
#'
setMethod(f="initialize", signature=signature(.Object="TargetExperimentList"),
definition=function(.Object, TEList,feature=NULL, attribute="coverage"){
##Set the different slots
if(nargs() >=2){
if(is.null(names(TEList))){
names(TEList)<-paste("subject", 1:length(TEList), sep="_")
}
if (length(TEList) <2){
stop("The TEList should contain at least two TargetExperiment
objects")
}
# bedFile slot
bed<-getBedFile(TEList[[1]])
bed_df<-as.data.frame(bed)
for (i in 2:length(TEList)){
bed_i<-getBedFile(TEList[[i]])
if(any(bed != bed_i)){
stop("The TargetExperiments should have the same bed file")
}
}
if(!(all(c("seqnames", "start","end","gene") %in% names(bed_df)))){
stop("Error! Bed file should contain at least five columns:
'seqnames', 'start', 'end' and 'gene'")
}
if(any(duplicated(rownames(bed_df)))){
stop("Error! Bed file should one row per feature. The provided file
has duplicated 'name' IDs.")
}
if (any(duplicated(bed_df[,"start"]) & duplicated(bed_df[,"end"]))){
warning("Your bed file have duplicated features. Removing
duplicated ...")
index<-which(duplicated(bed_df[,"start"])& duplicated(bed_df[,"end"]))
bed_df<-bed_df[-index,]
}
.Object@bedFile<-bed
# panels slot
GRpanel<-as.data.frame(getFeaturePanel(TEList[[1]]))
panel<-GRpanel[,!(colnames(GRpanel) %in% c("coverage", "sdCoverage",
"medianCounts", "IQRCounts"))]
panelNames<-colnames(panel)
attribute<-getAttribute(TEList[[1]])
if(!(attribute %in% c("coverage", "medianCounts"))){
stop("Attribute slot should be defined in order to call the
function")
}
for (i in 1:length(TEList)){
panel<-cbind(panel, mcols(getFeaturePanel(TEList[[i]]))[,
attribute])
}
# colnames(panel)<-c(panelNames, paste(attribute,"subject",
# 1:length(TEList), sep="_"))
colnames(panel)<-c(panelNames, paste(attribute,names(TEList), sep="_"))
finalPanel<-GRanges(seqnames=panel[,"seqnames"], ranges=IRanges(start=
panel[,"start"], end=panel[,"end"], names=rownames(panel)),
strand=panel[,"strand"])
mcols(finalPanel)<-panel[,!(names(panel) %in% c("seqnames", "start",
"end", "strand", "width")), drop=FALSE]
.Object@panels <-finalPanel
# feature slot
.Object@feature<-getFeature(TEList[[1]])
# attribute slot
.Object@attribute<-attribute
}else{
.Object@bedFile<-GRanges()
.Object@panels<-GRanges()
.Object@feature<-character(0)
.Object@attribute<-character(0)
}
##Check the object's validity
validObject(.Object)
return(.Object)
})
|
914df6163bf8b22153399f3df8779981089b6d45 | 1313b6a881934a32be3bc23ca950da1571373ebd | /Twiitter Activites - Share Price/TimeSeries Analysis.R | 7382718ed5270a216ae713bef719ee16b5028e28 | [
"MIT"
] | permissive | PKarhade/Projects-ML-DL | 1690d789de061d79cabb62e551b4c997b8554188 | 06c71efcaa570925a4aa8c070a9918a31447f730 | refs/heads/master | 2020-04-22T01:02:11.288272 | 2019-04-22T05:40:07 | 2019-04-22T05:40:07 | 170,001,198 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,452 | r | TimeSeries Analysis.R | library(xts)
library(tseries)
library(TSA)
library(forecast)
data1 <- read.zoo(file="C:\\Users\\Srikanth\\Desktop\\old\\Datamining\\MSFT.csv",header=TRUE,format="%m/%d/%Y",sep=",");
index(data1) = as.yearmon(index(data1))
msft = ts(data1$Close,start=start(data1), end=end(data1),frequency = 12)
time_series = msft
#plot the data, assuming time_series is a ts object
plot(time_series ,col="black", lwd=3)
plot(decompose(time_series))
#create the ARIMA models (see lecture notes)
arima_fit <- Arima(time_series, order= c(1,1,1), seasonal = c(0,0,0))
auto_arima_fit <- auto.arima(time_series, stepwise=FALSE, seasonal=FALSE, approximation=FALSE, D=1)
ets_fit <- ets(time_series)
#We can force a seasonal ets by explictly specifying the model type as shown below
#ets_fit <- ets(time_series, model="ZZM")
#plot the models. red is the hard-coded arima model, blue is the auto-fit arima model, and green is the exponential smoothing model
plot(arima_fit$x,col="black", lwd=3)
lines(fitted(arima_fit),col="red")
lines(fitted(auto_arima_fit),col="blue")
lines(fitted(ets_fit),col="green")
#create the acf plots to determine the level of differencing
acf(time_series)
#create the differenced time series to account for the seasonal pattern
diff_time_series <- diff(time_series, 4)
#create the acf and pacf plots to determine the order of the AR and MA terms
acf(diff_time_series)
pacf(diff_time_series)
#error analysis of the models
summary(arima_fit)
|
edfd3ede40496dd2c42efb0a50d24e8ba44067e8 | 4d391985cb3d99eb4e0d88f777bf70c4dd8d1327 | /06_0_Load_or_compute_species_files.r | e19d507ff5aeb079749e4c85553468c6fa81872e | [] | no_license | naiamh/Predictor_decomposition | f03c1f21967b0bc21dd09b03398e6198957f0748 | 5f937cd425caeca073aabc0f5d8b322d418b1e54 | refs/heads/master | 2020-12-24T18:03:50.140827 | 2015-06-12T23:30:18 | 2015-06-12T23:30:18 | 37,349,456 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,952 | r | 06_0_Load_or_compute_species_files.r | # Scripts to load in species specific data for plotting trailing and leading
# edges.
# If files are missing, relevant models and projections are run
# The script requires that directories have been defined already, as well as species name
# Load occurrence data. Create if it doesn't exist.
occurData = paste(sp.data.dir,"/", mySpecies, ".rdata", sep="")
if(!file.exists(occurData)) {
print(paste("No occurrence data. Extracting from Ecoengine species...",mySpecies))
getOccur(mySpecies=mySpecies, db="Ecoengine", out.dir=sp.data.dir,
in.project=orig.project, out.project=ta.project, save=T)
}
occur = readRDS(occurData)
rm(occurData)
# Load maxent model. Run model if it doesn't exist.
mx.obj = paste(out.dir, mySpecies, mxModelType,'ModelObject', sep="/")
if(!file.exists(mx.obj)) {
print(paste("No maxent model found. Computing for...", mxModelType))
env.files <- list.files(path=pres.clim.data.dir, pattern='CA.img', full.names=FALSE)
env.files = env.files[-grep("xml",env.files)]
runMxModel(mySpecies, mxModelType, env.files, pres.clim.data.dir, sp.data.dir, out.dir)
rm(env.files)
}
mx = readRDS(mx.obj)
rm(mx.obj)
# Load present projection for Bay Area. Project if it doesn't exist.
proj.dir = paste(out.dir, mySpecies, mxModelType, "Projections", sep="/")
pres.pred = paste(proj.dir, "Present_Bay.img", sep="/")
if(!file.exists(pres.pred)) {
print(paste("No present projection. Getting climate data..."))
pres.clim = getClim(env.dir=pres.clim.data.dir, region="Bay", period="HST",mxModelType=mxModelType)
print("Projecting...")
names(pres.clim) = paste(names(pres.clim),"1981_2010_ave_HST_CA",sep="") #match names for mx object
if(!file.exists(proj.dir)) {dir.create(proj.dir, recursive=F)}
predict(mx, pres.clim, filename=paste(proj.dir, "Present_Bay.img", sep="/"), overwrite=F)
rm(pres.clim)
}
present.bay = raster(pres.pred)
rm(pres.pred)
# Load future projections (all scenarios) for Bay Area. Project if they don't exist.
fut.preds = paste(proj.dir, "/", allScenarios, "_Suitability_Bay.img",sep="")
if(!file.exists(fut.preds[1])){
for (i in 1:length(allScenarios)) {
myScenario = allScenarios[i]
future.clim = getClim(env.dir=fut.clim.data.dir, period="Future",
mxModelType=mxModelType, scen=myScenario)
names(future.clim) = paste(names(future.clim),"1981_2010_ave_HST_CA",sep="") #match names for mx object
print(paste("Projecting...", myScenario))
if(!file.exists(proj.dir)) {dir.create(proj.dir, recursive=F)}
predict(mx, future.clim, file=paste(proj.dir, "/", myScenario, "_Suitability_Bay.img", sep=""), overwrite=F)
rm(future.clim,myScenario)
}
}
futures.bay = stack(fut.preds)
rm(fut.preds)
# Set directory to write figures to.
fig.dir = paste(out.dir, mySpecies, "Figures", sep="/")
if(file.exists(fig.dir) == F) {dir.create(fig.dir, recursive=F)}
|
ce3a1fb61bf479964f9a075f82d8e3c78a9127e4 | eab78e955aaee69c14d206c8e5bd76cf246a2372 | /man/render.Rd | 68fef8a199e5ffd1b152863250608f081f917370 | [
"MIT"
] | permissive | rstudio/connectapi | 3ad96af4e7935035658bf09aa8082cae4c68ffb5 | 427ac1fe2eb72d45f6048c376ec94b6c545faf8d | refs/heads/main | 2023-08-08T11:55:20.698746 | 2023-07-10T16:36:27 | 2023-07-10T16:36:27 | 167,249,814 | 34 | 17 | NOASSERTION | 2023-08-03T13:26:37 | 2019-01-23T20:32:57 | R | UTF-8 | R | false | true | 843 | rd | render.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/variant.R
\name{get_variant_renderings}
\alias{get_variant_renderings}
\alias{variant_render}
\title{Render a Variant}
\usage{
get_variant_renderings(variant)
variant_render(variant)
}
\arguments{
\item{variant}{An R6 Variant object. As returned by \code{get_variant()} or \code{get_variant_default()}}
}
\description{
\lifecycle{experimental} Get details about renderings (i.e. render history)
or execute a variant on demand
}
\details{
\itemize{
\item \code{get_variant_renderings()} returns all renderings / content for a particular variant. Returns a \code{tibble}
\item \code{variant_render()} executes a variant on demand. Returns a \code{VariantTask} object
}
}
\seealso{
Other variant functions:
\code{\link{get_variants}()}
}
\concept{variant functions}
|
745fa4463aea41e478d50cff6d4d65ffc5dc7f80 | cc0b3690ac5af79694ab2d78f4c6780a745d7919 | /R/arc.write.R | 852a4c61da9aec953c0f49298878380f3b685a6b | [
"LicenseRef-scancode-warranty-disclaimer",
"Apache-2.0"
] | permissive | amitgajurel/ArcGIS_R_Bridge_raster | 06aa337d9de7129d262a7b5ab225886c9e25fee9 | 763e8d66ec94dcf6a5394c5e7da7aa57c66346fe | refs/heads/master | 2020-03-22T08:39:34.883102 | 2018-04-30T15:32:16 | 2018-04-30T15:32:16 | 139,781,749 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 533 | r | arc.write.R | #' @export
arc.write <- function(path, data, ...)
{
if (inherits(path, "arc.container"))
path <- path@path
stopifnot(is.character(path))
if (missing(data))
data<-NULL
args <- list(path=path, data=data, ...)
overwrite <- args$overwrite
if (is.null(overwrite))
args$overwrite <- FALSE
else
stopifnot(is.logical(overwrite))
if (inherits(data, c("Raster", "arc.raster", "SpatialPixels", "SpatialGrid")))
do.call(.write_raster, args)
else
do.call(.write_feature, args)
}
|
672106ad4efa4409ddf70129c40d4b958d5f0809 | fb56c48278bb20037275d1c1a53b049d4cdf3ae1 | /April_18.R | 3d517f7c2e657e881b2c2e9140fbe8b52cfcfa51 | [] | no_license | rjcmarkelz/rclub | 301306615ecf3852b2dc0b6430ce1b21f3c07c70 | 1d9da7b9c01fb9751407ff01c071ed813f75883a | refs/heads/master | 2021-01-01T19:10:54.366718 | 2016-01-27T01:04:36 | 2016-01-27T01:04:36 | 9,964,173 | 0 | 1 | null | 2016-01-27T01:04:37 | 2013-05-09T17:12:39 | R | UTF-8 | R | false | false | 551 | r | April_18.R | #2013_04_18
#plyr package--- look at hadley
library(plyr)
setwd("~/Documents/Maloof Lab/R_club/plyr-tutorial/examples")
bnames <- read.csv("bnames.csv", stringsAsFactors = FALSE)
head(bnames)
#Proportion of us children that have a name by in the top 100.
head(bnames, n = 100L)
names(bnames)
ddply(bnames, c("name"), summarise, tot = sum(percent))
bnames.2 <- ddply(bnames, c("year"), head = head(bnames, n = 100L))
bnames.2 <- ddply(bnames[1:100,], ~ year)
tail(bnames.2)
bnames.2 <- ddply(bnames, c("year", "sex"), summarise, tot = sum(percent)) |
b0fd6a05feb8b3cbabe13b74d87eb7380d862dbb | 9e4df408b72687493cc23144408868a975971f68 | /SMS_r_prog/function/msy_batch.r | 3343bc16a9e9cf0cfb0ece6cc912605bcfa64c45 | [
"MIT"
] | permissive | ices-eg/wg_WGSAM | 7402ed21ae3e4a5437da2a6edf98125d0d0e47a9 | 54181317b0aa2cae2b4815c6d520ece6b3a9f177 | refs/heads/master | 2023-05-12T01:38:30.580056 | 2023-05-04T15:42:28 | 2023-05-04T15:42:28 | 111,518,540 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,209 | r | msy_batch.r |
######### end user options #########
do.HCR.batch<-function(do.simulation=T,read.condense=T,do.plots=T,read.detailed=T,cutof.year.detailed=2006,NewPlot=T,stochastich=T,YieldPerRecruit=T) {
HCR<-new("FLSMS.predict.control")
HCR@years.wsea[1,1]<-min(refYear)
HCR@years.wsea[2,1]<-max(refYear)
HCR@years.weca<-HCR@years.wsea
HCR@years.propmat[1,1]<-min(refYearPropmat)
HCR@years.propmat[2,1]<-max(refYearPropmat)
HCR@HCR.F.TAC[1]<-0
HCR@rec.noise.input[1]<-0
HCR@inter.year[1]<-1
HCR@rec.noise[1,1]<- recruit.noise.low
HCR@rec.noise[2,1]<- recruit.noise.high
HCR@last.prediction.year<-2026
control<-read.FLSMS.control(file=file.path(data.path,'SMS.dat'))
#recruitment geometric mean
s<-Read.summary.data()
tmp<-subset(s,Year < (control@last.year.model-1) & Quarter==control@rec.season & Age==control@first.age,select=c(Year,N))
GM.mean.log<-mean(log(tmp$N))
GM.mean<-exp(GM.mean.log)
GM.sd.log<-sd(log(tmp$N))
if (stochastich) {
HCR@no.MCMC.iterations<-no.MCMC.iterations
HCR@read.rec.SSB.parm<-0
include.probability<-T
if (YieldPerRecruit) {
HCR@read.rec.SSB.parm<-1
pp<-file.path(data.path,'SSB_R.in')
cat(paste("#model alfa beta std \n",
"3 ",GM.mean.log, 0, GM.sd.log,"\n",sep=' '),file=pp)
include.probability<-F
}
} else {
HCR@no.MCMC.iterations<-1
HCR@read.rec.SSB.parm<-1
if (YieldPerRecruit) {
pp<-file.path(data.path,'SSB_R.in')
cat(paste("#model alfa beta std \n",
"3 ",GM.mean.log, 0, GM.sd.log,"\n",sep=' '),file=pp)
include.probability<-F
} else {
p<-Read.SSB.Rec.data()
pp<-file.path(data.path,'SSB_R.in')
cat("#model alfa beta std \n",file=pp)
p<-subset(p,select=c(model,alfa,beta,std))
p$std<-1E-4
write.table(p,file=pp,append=T,row.names=F,col.names=F)
include.probability<-F
}
}
# make sms.psv file
source(file.path(prog.path,"make_psv.R"))
SMS.option<-" "
first.year.in.mean<-HCR@last.prediction.year-3 # years where output is independent of initial conditions and used as "equilibrium"
last.year.in.mean<-HCR@last.prediction.year-1
#Read refence points from file reference_points.in
ref<-Read.reference.points()
blim<-ref[,"Blim"]
bpa<-ref[,"Bpa"]
T1<-blim
T2<-bpa
# files for output
ssb.out<-'HCR_SSB.dat'
yield.out<-'HCR_yield.dat'
F.out<-'HCR_F.dat'
prob.out<-'HCR_prob.dat'
ssb.out.all<-'mcout_SSB.out.all'
yield.out.all<-'mcout_yield.out.all'
F.out.all<-'mcout_mean_F.out.all'
prob.out.all<-'HCR_prob.dat.all'
if (do.simulation) {
sp.name<-control@species.names
percentiles<-c(0.025,0.05,0.10,0.25,0.50,0.75,0.95,0.975)
# headers in output files
heading<-"targetF"
cat(paste(heading,"Species.n "),file=ssb.out)
cat(paste("SSB",formatC(percentiles*1000,width=3,flag='0'),sep=''),file=ssb.out,append=T); cat('\n',file=ssb.out,append=T)
cat(paste(heading,"Species.n "),file=yield.out)
cat(paste("y",formatC(percentiles*1000,width=3,flag='0'),sep=''),file=yield.out,append=T); cat('\n',file=yield.out,append=T)
cat(paste(heading,"Species.n "),file=F.out)
cat(paste("F",formatC(percentiles*1000,width=3,flag='0'),sep=''),file=F.out,append=T); cat('\n',file=F.out,append=T)
cat(paste(heading,"Species.n "," p.T1 p.T2 \n"),file=prob.out)
iter<-0
i<-0
for (targetF in (targetFs)) i<-1+i
#print(paste("You have asked for",i,"runs. Do you want to continue? (Y/N):"))
#a<-readLines(n=1)
#if(a=='N') stop("Script stopped'")
#print('OK')
tot.rep<-i
for (targetF in (targetFs)) {
iter<-iter+1
print(paste("targetF:",targetF))
print(paste("run no.:",iter, "out of a total of",tot.rep,"runs"))
HCR@constant.F[1]<-targetF
write.FLSMS.predict.control(HCR,SMS=control,file='HCR_options.dat')
#run SMS
shell(paste( file.path(data.path,"sms.exe"),"-mceval",SMS.option,sep=" "), invisible = TRUE)
condense_file<-function(filename){
file<-file.path(data.path,filename)
a<-read.table(file,header=TRUE)
a<-subset(a,Year>cutof.year.detailed)
a<-data.frame(a,targetF=targetF)
file<-paste(file,".all",sep='')
if (iter==1) write.table(a, file =file, row.names = F,col.names = T,quote = F) else write.table(a, file =file, row.names =F,col.names =F, quote =F,append=T)
}
condense_file("mcout_SSB.out")
condense_file("mcout_recruit.out")
condense_file("mcout_mean_F.out")
condense_file("mcout_yield.out")
a<-Read.MCMC.SSB.rec.data()
a<-subset(a,Year>=first.year.in.mean & Year<=last.year.in.mean ,drop=T)
b<-tapply(a$SSB,list(a$Species.n), function(x) quantile(x,probs = percentiles))
for (i in (1:length(b))) {
cat(paste(targetF,i,' '),file=ssb.out,append=TRUE)
cat(b[[i]],file=ssb.out,append=TRUE)
cat('\n',file=ssb.out,append=TRUE)
}
dummy<-by(a,list(a$Species.n),function(x) {
q<-tapply(x$SSB,list(x$Year,x$Repetion,x$Iteration),sum)
sp<-x[1,"Species.n"]
q[q>T2[sp]]<-0
q[q>0]<-1
p.T2<-sum(q)/(dim(q)[1]*dim(q)[2]*dim(q)[3])
q<-tapply(x$SSB,list(x$Year,x$Repetion,x$Iteration),sum)
q[q>T1[sp]]<-0
q[q>0]<-1
p.T1<-sum(q)/(dim(q)[1]*dim(q)[2]*dim(q)[3])
cat(paste(targetF,sp,p.T1,p.T2,'\n'),file=prob.out,append=TRUE)
})
a<-Read.MCMC.F.yield.data(dir=data.path)
a<-subset(a,Year>=first.year.in.mean & Year<=last.year.in.mean ,drop=T)
b<-tapply(a$Yield,list(a$Species.n), function(x) quantile(x,probs = percentiles))
for (i in (1:length(b))) {
cat(paste(targetF,i,' '),file=yield.out,append=TRUE)
cat(b[[i]],file=yield.out,append=TRUE)
cat('\n',file=yield.out,append=TRUE)
}
b<-tapply(a$mean.F,list(a$Species.n), function(x) quantile(x,probs = percentiles))
for (i in (1:length(b))) {
cat(paste(targetF,i,' '),file=F.out,append=TRUE)
cat(b[[i]],file=F.out,append=TRUE)
cat('\n',file=F.out,append=TRUE)
}
}
} # end do.simulations
if (read.condense) {
# read data and options into FLR objects
HCR<-read.FLSMS.predict.control(control=control,file='HCR_options.dat')
sp.name<-control@species.names
ssb<-read.table(ssb.out,header=TRUE)
yield<-read.table(yield.out,header=TRUE)
proba<-read.table(prob.out,header=TRUE)
fi<-read.table(F.out,header=TRUE)
a<-merge(ssb,yield)
a<-merge(a,proba)
a<-merge(a,fi)
condensed<-data.frame(a,targetF.fac=as.factor(a$targetF))
write.csv(condensed,file=file.path(data.path,"condensed_MSY.csv"))
}
if (read.detailed) {
# read data and options into FLR objects
control<-read.FLSMS.control()
#HCR<-read.FLSMS.predict.control(control=control,file='HCR_options.dat')
sp.name<-control@species.names
Years<- c(2010,2012,2014,2030)
ssb<-read.table(ssb.out.all,header=TRUE)
ssb<-subset(ssb,Year %in% Years)
cat("Year targetF p.T2 p.T1 Species.n\n",file=prob.out.all)
dummy<-by(ssb,list(ssb$Species.n),function(x) {
q<-tapply(x$SSB,list(x$Repetion,x$Iteration,x$Year,x$targetF),sum)
sp<-x[1,"Species.n"]
q[q>T2[sp]]<-0
q[q>0]<-1
p.T2<-apply(q,c(3,4),sum)/ (dim(q)[1]*dim(q)[2])
q<-tapply(x$SSB,list(x$Repetion,x$Iteration,x$Year,x$targetF),sum)
q[q>T1[sp]]<-0
q[q>0]<-1
p.T1<-apply(q,c(3,4),sum)/ (dim(q)[1]*dim(q)[2])
a<-arr2dfny(p.T2,name="p.T2")
b<-arr2dfny(p.T1,name="p.T1")
a<-data.frame(merge(a,b),Species.n=sp)
write.table(a,row.names=F,col.names=F,quote=F,file=prob.out.all,append=T)
})
#proba<-read.table(prob.out.all,header=TRUE)
yield<-read.table(yield.out.all,header=TRUE)
yield<-subset(yield,Year %in% Years)
fi<-read.table(F.out.all,header=TRUE)
fi<-subset(fi,Year %in% Years)
a<-merge(ssb,yield)
a<-merge(a,fi)
detailed<-data.frame(a,Year.fac=as.factor(a$Year),targetF.fac=as.factor(a$targetF))
}
if (do.plots) {
# read data and options into FLR objects
control<-read.FLSMS.control()
sp.name<-control@species.names
ssb<-read.table(ssb.out,header=TRUE)
yield<-read.table(yield.out,header=TRUE)
prob<-read.table(prob.out,header=TRUE)
fi<-read.table(F.out,header=TRUE)
a<-merge(ssb,yield)
a<-merge(a,prob)
a<-merge(a,fi)
if (paper) dev<-"wmf" else dev<-"screen"
if (NewPlot) newplot(dev,nox=nox,noy=noy,filename=paste("HCR_",sp.name,sep=''),Portrait=T);
par(mar=c(4,4,3,5)+.1) # c(bottom, left, top, right)
s<-a$SSB500/1000
y<-a$y500/1000
x<-a$targetF
x.lab<-'F(1-2)'
plot(x,s,ylab='SSB & Yield (1000 t)',xlab=x.lab ,ylim=c(min(s,y,0),max(s,y)),lty=1,type='l',lwd=2,col=1,main=NULL)
if (include.probability) {
legend(legendPlace, c('SSB','Yield', paste('p(SSB<',round(T1),'t)')), pch=" 1",lty=c(1,2,3),col=c(1,2,4), lwd=rep(2,3))
} else legend(legendPlace,c('SSB','Yield'),pch=" ",lty=c(1,2),lwd=rep(2,2),col=c(1,2))
lines(x,y,lty=2,lwd=2,col=2)
if (include.probability) {
par(new=T)
plot(x,a$p.T1,axes=F,xlab=x.lab, ylab=' ',lty=3,lwd=2,ylim=c(0,1),type='b',pch="1",col=4)
abline(h=0.05)
axis(side=4)
mtext(side=4,line=3.0,"Probability")
par(xaxs="r")
}
#if (paper) cleanup()
} #end do.plots
}
|
48d835d5ad4f9f2143c5a7338617c3ba8a2cf1e5 | ea5a4eb6717a31b11b78509a672bd6416f825b24 | /assignments/assignment_1/HW1.R | 00fa0e2b40636cd114f6bdccab944cba37a38c2a | [] | no_license | ChaebinIm/MFE_course_financial_time_series_analysis | 5b4d8b110f4ad58a690bd15834a4099042c08494 | 883d706991e47d7c9a00bf171dc0fd46f41ff92c | refs/heads/main | 2023-06-26T12:27:47.390849 | 2021-07-31T12:43:25 | 2021-07-31T12:43:25 | 391,353,382 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,647 | r | HW1.R | ################# Chapter 2 ###########################
# data import
install.packages('readxl')
install.packages("lmtest")
install.packages('fBasics')
install.packages("car")
library(readxl)
library(lmtest)
library(fBasics)
library(car)
setwd('/Users/imchaebin/Desktop/시계열분석/숙제1/data/') # need to be customized
data <- read_excel('capm.xls')
# add log return
# SAP
rsandp <- 100*diff(log(data$SANDP))
data$RSANDP <- c(NA, rsandp)
# Ford stock
ford <- 100*diff(log(data$FORD))
data$RFORD <- c(NA, ford)
data
# US - TBond : annually to monthly
data$USTB3M <- data$USTB3M/12
# Excess return of S&P500
data$ERSANDP <- data$RSANDP - data$USTB3M
data$ERFORD <- data$RFORD - data$USTB3M
# draw plot (ERSANDP, ERFORD)
plot(data$Date, data$ERSANDP, type = 'l', ylim=range(-80, 80), col = 'red') # line
# plot(data$Date, data$ERSANDP, ylim=range(-80, 80), col = 'red') # scatter
par(new=TRUE)
plot(data$Date, data$ERFORD, type = 'l', ylim=range(-80, 80), col = 'blue') # line
# plot(data$Date, data$ERFORD, ylim=range(-80, 80), col = 'blue') # scatter
# simple linear regression
m <- lm(ERFORD ~ ERSANDP, data = data)
coef(m)
summary(m)
plot(ERFORD ~ ERSANDP, data = data)
abline(m, col = 'red')
#Testing normal distribution and independence assumptions
jarqueberaTest(m$resid) #Test residuals for normality
#Null Hypothesis: Skewness and Kurtosis are equal to zero
dwtest(m) #Test for independence of residuals
#Null Hypothesis: Errors are serially UNcorrelated
par(mfrow = c(2, 2))
plot(m)
# linear regression with constraints
linearHypothesis(m, "ERSANDP = 1")
linearHypothesis(m, c("(Intercept) = 1", "ERSANDP = 1"))
|
977e772f319f861215e016a6f74dfa67d5be837f | f5081a73a7e2cae81e35f9c09f9a4b63d402520c | /GlobalVariable/Variable.r | 60c020bbac93527de9baf1d422b2063d3e38f887 | [] | no_license | Alex-OuYang/FinanceAnalysis | e5099f8b4b6a99671cd1051ee791f37ac2997f18 | f8761c56c084399f5c9891d475f1cf9ab6fc76ef | refs/heads/master | 2023-07-26T02:03:12.335062 | 2021-09-05T14:06:35 | 2021-09-05T14:06:35 | 393,606,669 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,125 | r | Variable.r | # 清除所有環境變數
rm(list=ls())
# 初始系統編碼
encodingStr="utf-8"
#Sys.setenv(TZ = "Asia/Taipei")
# 設定初始資料夾位置
projectPathStr="C:/Users/alex1/Desktop/FinanceAnalysis"
setwd(projectPathStr)
# 各檔案列表
# 變數
variablestr=paste0(projectPathStr,"/GlobalVariable/Variable.r")
# Func
funcDBSelectstr=paste0(projectPathStr,"/Function/FuncDBSelect.r")
funcStockInfoToTimeSerialstr=paste0(projectPathStr,"/Function/FuncStockInfoToTimeSerial.r")
funcKDCrossstr=paste0(projectPathStr,"/Function/FuncKDCross.r")
funcGetStockDatastr=paste0(projectPathStr,"/Function/FuncGetStockData.r")
# TradeRule
tradeRuleKD=paste0(projectPathStr,"/TradeRule/KD_Golden.r")
# Finance 參數
stockno='2330'
startDate="2010-01-01"
endDate="2021-08-07"
dateInterval=paste0(startDate,"/",endDate)
stockclass="股票"
# 連接 DB 參數
sqlConnString = "driver={SQL Server};server=ALEX-NB\\SQLEXPRESS;database=Finance;;Trusted_Connection=Yes"
# SQL 語法
sp_sqlstr_SECURITYINFO="EXEC sp_GET_SECURITYINFO "
sp_sqlstr_COMPANYCLASS="EXEC sp_GET_COMPANYCLASS"
sp_sqlstr_COMPANYLIST="EXEC sp_GET_COMPANYLIST "
|
59041dbf13bf24ac64cf4b8d6569cfe16730ffa3 | faaa6f8046404a7e8f616fd0f127441403792b9c | /Extraccion_datos.R | 32d1e9ef6accfe6bf7b9eef4cc60102fa91d6d37 | [
"MIT"
] | permissive | ArrigoCoen/Mexican_vaccination | 577c2b10cfec842c5dd1d16e545216385293f67e | 15d1bd4b68c1597b433693dbcb4a19bb051e75e3 | refs/heads/main | 2023-06-29T17:47:28.005835 | 2021-07-27T14:57:31 | 2021-07-27T14:57:31 | 389,700,814 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,418 | r | Extraccion_datos.R | #-------------------------------------------------------------------------#
# #
# NOMBRE DE ARCHIVO #
# #
#-------------------------------------------------------------------------#
# V1 FECHA
#-------------------------------------------------------------------------#
# OBSERVACIONES:
# 1. Los boletines fueron extraidos de:
# https://www.gob.mx/salud/acciones-y-programas/historico-boletin-epidemiologico
# 2. Parkinson se tiene desde 2014-2 hasta 2019-52
# 3. El Boletin es semanal a partir de 1995 semana 26
# - 2017 tiene muchos archivos extra que fueron borrados
# - 2011 HAY QUE HACER LA CORRECIION DE LOS ARCHIVOS ZIP PARA ESTE ANHO
# - 2011 HAY QUE HACER LA CORRECIION DE LOS ARCHIVOS ZIP PARA ESTE ANHO
# - 2011 HAY QUE HACER LA CORRECIION DE LOS ARCHIVOS ZIP PARA ESTE ANHO
# - 2011 HAY QUE HACER LA CORRECIION DE LOS ARCHIVOS ZIP PARA ESTE ANHO
# - EN 2003 pasaron a formato electronico
# - Faltan cuadros en 2004 semana 7 (falta cuadro 3.2) preguntar a Betty
#-------------------------------------------------------------------------#
# Packages ----------------------------------------------------------------
library(pdftools)
library(manipulate)
library(tidyverse)
#install.packages("manipulate")
# Carpeta del proyecto ----------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
# 1. Home
# 2. Work
# 3. Laptop
# 4. Other
rutas_prueba <- c("/Users/arrigocoen/Dropbox","/Users/useradmin/Dropbox","","")
eleccion_compu <- which(file.exists(rutas_prueba))
# Direcciones de cada computadora
source_raiz <- c("/Users/arrigocoen/Dropbox/1 Proyectos/2020/Asthma/R",# 1. Home
"",# 2. Work
"",# 3. Laptop
"")[eleccion_compu]# 4. Other
setwd(source_raiz)
source("Fn_epidemiologic.R")
# library()
# Function ----------------------------------------------------------------
# Example of extraction ---------------------------------------------------
# De
# https://rstudio-pubs-static.s3.amazonaws.com/415060_553527fd13ed4f30aae0f1e4483aa970.html
cat("\014") # borra consola
rm(list=ls()) # Borra todo
PDF <- pdf_text("oregon_grass_and_legume_seed_crops_preliminary_estimates_2017.pdf") %>%
readr::read_lines() #open the PDF inside your project folder
PDF.grass <-PDF[-c(1:3,6:8,20:35)] # remove lines
PDF.grass
all_stat_lines <- PDF.grass[3:13] %>%
str_squish() %>%
strsplit(split = " ")# remove empty spaces
var_lines <- c("Species", "Acreage", "Yield", "Production", "Price", "Value") # create your variable names
var_lines
# The next line is for some kind of correction that our file doesn't need, that's why I
# commented it
# all_stat_lines[[6]] <- c("Orchard", "grass","15,190","1,046","15,889","225.00","35,750") #change the line 6
df <- plyr::ldply(all_stat_lines) #create a data frame
head(df)
# asoetuh -----------------------------------------------------------------
# My data -----------------------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
pdf_name <- "sem01.pdf"
pdf_name <- "boletin 2019/sem01.pdf"
PDF <- pdf_text(pdf_name) %>%
readr::read_lines() #open the PDF inside your project folder
PDF
length(PDF)
# asoetuh -----------------------------------------------------------------
texto <- "Nonsense? kiss off, geek. what I said is true. I'll have your account terminated."
grep(pattern ="bajo",PDF[1]) # encuentra una palabra en un texto
grep(pattern ="Tatata",texto) # encuentra una palabra en un texto
grep(pattern ="ru",texto) # encuentra una palabra en un texto
i <- 1
linea <- PDF[i]
linea
grep(pattern ="de",linea)
# asueth ------------------------------------------------------------------
i <- 2
linea <- PDF[i]
linea
grep(pattern ="de",linea)
if(length(grep(pattern ="Parkinson",linea))==1) print(i)
# asoetuh -----------------------------------------------------------------
cat("\014") # borra consola
# rm(list=ls()) # Borra todo
intervalo <- 1:1000
intervalo <- 1:length(PDF)
intervalo <- 2621+ 0:1000 # CUADRO DE Parkinson en i=2622
for(i in intervalo) {
linea <- PDF[i]
print(linea)
# if(length(grep(pattern ="CUADRO",linea))==1) {
# print(linea)
# print(i)
# }
}
# asoetuh -----------------------------------------------------------------
anho <- 2018
semana <- 16
semana <- 1
pdf_path <- genera_ruta_boletin(anho,semana)
intervalo <- 1:1000
intervalo <- 2621+ 0:1000 # CUADRO DE Parkinson en i=2622
intervalo <- 1:length(PDF)
for(i in intervalo) {
linea <- PDF[i]
# print(linea)
if(length(grep(pattern ="Neurológicas",linea))==1) {
print(linea)
print(i)
break
}
}
i
text_table <- PDF[i+10:41]
text_table
# soeuh -------------------------------------------------------------------
all_stat_lines <- text_table %>%
str_squish() %>%
strsplit(split = " ")# remove empty spaces
all_stat_lines
i <- 2
n_palabras <- 2
all_stat_lines
# nauht -------------------------------------------------------------------
all_stat_lines <- text_table %>%
str_squish() %>%
strsplit(split = " ")# remove empty spaces
# var_lines <- c("Species", "Acreage", "Yield", "Production", "Price", "Value") # create your variable names
# var_lines
# The next line is for some kind of correction that our file doesn't need, that's why I
# commented it
# all_stat_lines[[6]] <- c("Orchard", "grass","15,190","1,046","15,889","225.00","35,750") #change the line 6
df <- plyr::ldply(all_stat_lines) #create a data frame
head(df)
# Revision de correccion de espacios en lineas ----------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
# set.seed(42)
# Variables pdf
anho <- 2015
anho <- 2017
semana <- 16
semana <- 25 # en 2019 semana 25 BC tiene dos lugares de numeros grandes
semana <- sample(1:52,1)
# Variables tabla
palabra_clave1 <- "Neurológicas"
palabra_clave1 <- "CUADRO 17" # el cuadro 17 es de enfermedades neurologicas
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
semanas <- 21:27
semanas <- 24
semanas <- 1
semanas <- 1:52
semanas <- 40:52
# Error en 2019 sem 24 "PDF error: Invalid Font Weight" pero los valores estan bien salvados
# Error: 2016 sem1 = tabla con longitudes incorrectas
# Error: 2016 = MUCHAS TABLAS con longitudes incorrectas 24-29
anhos <- 2017:2019 # ya fueron revisados para enfermedades neurologicas
anhos <- 2019
anhos <- 2016 # ya fueron revisados para enfermedades neurologicas
anhos <- 2015 # ya fueron revisados para enfermedades neurologicas
correcciones_a_mano_CUADRO_17 <- function(text_table,semana,anho) {
source("Fn_Correcciones_CUADRO_17_anho2016.R")
source("Fn_Correcciones_CUADRO_17_anho2015.R")
if(anho==2015) text_table <- fun_corrige_CUADRO_17_anho2015(text_table,semana,anho)
if(anho==2016) text_table <- fun_corrige_CUADRO_17_anho2016(text_table,semana,anho)
return(text_table)
}
for(anho in anhos) for(semana in semanas) {
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
cat("Trabajando: ",pdf_path,"\n")
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# Correccion cuadro
text_table <- correcciones_a_mano_CUADRO_17(text_table,semana,anho)
# print(text_table)
all_stat_lines <- correccion_espacios_en_lineas(text_table)
print(lengths(all_stat_lines))
# Extrayendo encabezados
encabezado_table <- extrae_encabezado_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(encabezado_table)
}
# otnh --------------------------------------------------------------------
# Correcciones de extraccion de informacion -------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
# Variables tabla
palabra_clave1 <- "Neurológicas"
palabra_clave1 <- "CUADRO 17" # el cuadro 17 es de enfermedades neurologicas
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
longitud_esperada <- 13 # longitud de numero de columnas
semanas <- 2
semanas <- 24:26
semanas <- c(1:3, 20:28)
semanas <- 25:52
semanas <- 1
semanas <- 24
semanas <- 20:30
semanas <- 2:52
anhos <- 2017:2019 # 2017:2019 ya fueron revisados para enfermedades neurologicas f tienen columnas y datos bien
anhos <- 2014
anhos <- 2015:2016 #
anhos <- 2014 # de 2014 de la semana 25 a 52 no hay problema
for(anho in anhos) {
if(anho %in% c(2015,2016)) break # Tablas ya corregidas
texto <- paste0("#--------------------------------------------------------\n",
"#-- fun_corrige_CUADRO_17_anho",anho," --\n",
"#--------------------------------------------------------\n",
"#-- This function was generated by Fn_epidemiologic",
"\n#-- Author = Arrigo Coen \n\n\n",
"\nfun_corrige_CUADRO_17_anho",anho," <- function(text_table,semana,anho) {\n")
for(semana in semanas) {
longitud_esperada <- fun_longitud_esperada(semana,anho)
pdf_path <- genera_ruta_boletin(anho,semana)
cat("Trabajando: ",pdf_path,"\n")
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
print(text_table)
all_stat_lines <- correccion_espacios_en_lineas(text_table)
if(T) {
df <- plyr::ldply(all_stat_lines) #create a data frame
print(head(df))
}
idx_mal <- which(lengths(all_stat_lines)!=longitud_esperada)
if(length(idx_mal)!=0) {
texto <- paste0(texto,"\tif(semana==",semana," && anho==",anho,") {\n")
for(i in idx_mal) {
for(k in 1:10) text_table[i] <- gsub(' ', ' ', text_table[i]) # elimina el signo $
texto <- paste0(texto,"\t\ti <- ",i,
";\n\t\tx <- '",text_table[i],"'\n\t\ttext_table[i] <- x\n")
}
texto <- paste0(texto,"\t}\n")
}
}
texto <- paste0(texto,"\treturn(text_table)\n}")
name_file <- paste0(c("Fn_Correcciones_CUADRO_17_anho",anho,".R"),collapse = "")
fileConn<-file(name_file)
writeLines(texto, fileConn)
close(fileConn)
cat("Se genero el archivo ",name_file,"\n")
}
# cat(texto)
if(F) {
df <- plyr::ldply(all_stat_lines) #create a data frame
head(df)
}
# Indices de todos los cuadros --------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
semanas <- 25:52
semanas <- 52
semanas <- 1:2
semanas <- 1:52
semanas <- 1
anhos <- 2017:2019
anhos <- 2013
imprime_CUADRO_anho_semana(anhos,semanas)
# Cuadros un año ----------------------------------------------------------
# Revisando Titulo general ------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
# set.seed(42)
# Variables pdf
anho <- 2015
anho <- 2017
semana <- 16
semana <- 25 # en 2019 semana 25 BC tiene dos lugares de numeros grandes
semana <- sample(1:52,1)
# Variables tabla
palabra_clave1 <- "Neurológicas"
palabra_clave1 <- "CUADRO 17" # el cuadro 17 es de enfermedades neurologicas
palabra_clave1 <- "CUADRO 3" # el cuadro 17 enfermedades prevenibles
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
semanas <- 21:27
semanas <- 24
semanas <- 1
semanas <- 1:52
# Error en 2019 sem 24 "PDF error: Invalid Font Weight" pero los valores estan bien salvados
# Error: 2016 sem1 = tabla con longitudes incorrectas
# Error: 2016 = MUCHAS TABLAS con longitudes incorrectas
anhos <- 2015 # ya fueron revisados para enfermedades neurologicas
anhos <- 2016 # ya fueron revisados para enfermedades neurologicas
anhos <- 2019
anhos <- 2015:2018 # ya fueron revisados para enfermedades neurologicas
anhos <- 2014:2015 # ya fueron revisados para enfermedades neurologicas
for(anho in anhos) for(semana in semanas) {
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
cat("Trabajando: ",pdf_path,"\n")
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(text_table)
all_stat_lines <- correccion_espacios_en_lineas(text_table)
print(lengths(all_stat_lines))
# Extrayendo encabezados
# encabezado_table <- extrae_encabezado_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(encabezado_table)
}
# astohu ------------------------------------------------------------------
# Correccion nombres 2013 -------------------------------------------------
# Revisor de primera linea ------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
semanas <- 21:27
semanas <- 24
semanas <- 1
semanas <- 1:52
# Error en 2019 sem 24 "PDF error: Invalid Font Weight" pero los valores estan bien salvados
# Error: 2016 sem1 = tabla con longitudes incorrectas
# Error: 2016 = MUCHAS TABLAS con longitudes incorrectas
anhos <- 2015 # ya fueron revisados para enfermedades neurologicas
anhos <- 2018
anhos <- 2016:2019 # ya fueron revisados para enfermedades neurologicas
anhos <- 2015 # ya fueron revisados para enfermedades neurologicas
anhos <- 2014
revisa_linea_1_sem(anhos,semanas)
# asotnu ------------------------------------------------------------------
colnames(df) <- c("Estado","Sem","Acum M","Acum F")
# asoetuh -----------------------------------------------------------------
for(i in 1:length(all_stat_lines)) print(length(all_stat_lines[[i]]))
# Analisis de encabezados de tablas ---------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
# set.seed(42)
# Variables pdf
anho <- 2015
anho <- 2017
semana <- 16
semana <- 25 # en 2019 semana 25 BC tiene dos lugares de numeros grandes
semana <- sample(1:52,1)
# Variables tabla
palabra_clave1 <- "Neurológicas"
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
pdf_path
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
encabezado_table <- extrae_encabezado_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
for(semana in 1:52) {
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
encabezado_table <- extrae_encabezado_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
print(encabezado_table)
}
# asothe ------------------------------------------------------------------
# Extraccion de un cuadro -------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
# set.seed(42)
# Variables pdf
anho <- 2015
anho <- 2017
semana <- 16
semana <- 25 # en 2019 semana 25 BC tiene dos lugares de numeros grandes
semana <- sample(1:52,1)
# Variables tabla
cuadro <- 2
cuadro <- 3.1
palabra_clave1 <- paste0("CUADRO ",as.character(cuadro)) # el cuadro 17 enfermedades prevenibles
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
semanas <- 7
semanas <- 1
semanas <- 1:4
# Error en 2019 sem 24 "PDF error: Invalid Font Weight" pero los valores estan bien salvados
# Error: 2016 sem1 = tabla con longitudes incorrectas
# Error: 2016 = MUCHAS TABLAS con longitudes incorrectas
anhos <- 2015 # ya fueron revisados para enfermedades neurologicas
anhos <- 2016 # ya fueron revisados para enfermedades neurologicas
anhos <- 2019
anhos <- 2015:2018 # ya fueron revisados para enfermedades neurologicas
anhos <- 2005 # ya fueron revisados para enfermedades neurologicas
anhos <- 2005:2019 # ya fueron revisados para enfermedades neurologicas
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
for(anho in anhos) for(semana in semanas) {
n_col_esperado <- n_col_esperado_X_CUADRO(cuadro,semana,anho)
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
cat("Trabajando: ",pdf_path,"\n")
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(text_table)
all_stat_lines <- correccion_espacios_en_lineas(text_table)
# print(lengths(all_stat_lines))
if(any(lengths(all_stat_lines)!=n_col_esperado)) {
print("Posible error en el numero columnas -----------------")
}
# Extrayendo encabezados
# encabezado_table <- extrae_encabezado_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(encabezado_table)
}
# Extraccion un cuadro ----------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
set.seed(42)
# Variables pdf
anho <- 2015
anho <- 2017
semana <- 16
semana <- 25 # en 2019 semana 25 BC tiene dos lugares de numeros grandes
semana <- sample(1:52,1)
semana <- 1
anho <- 2018
# Variables tabla
cuadro <- 2
cuadro <- 3.2
cuadro <- 3.1
palabra_clave1 <- paste0("CUADRO ",as.character(cuadro)) # el cuadro 17 enfermedades prevenibles
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
semanas <- 7
semanas <- 1
semanas <- 1:4
# Error en 2019 sem 24 "PDF error: Invalid Font Weight" pero los valores estan bien salvados
# Error: 2016 sem1 = tabla con longitudes incorrectas
# Error: 2016 = MUCHAS TABLAS con longitudes incorrectas
anhos <- 2015 # ya fueron revisados para enfermedades neurologicas
anhos <- 2016 # ya fueron revisados para enfermedades neurologicas
anhos <- 2019
anhos <- 2015:2018 # ya fueron revisados para enfermedades neurologicas
anhos <- 2005 # ya fueron revisados para enfermedades neurologicas
anhos <- 2010:2019 # ya fueron revisados para enfermedades neurologicas
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
# CORRER ESTA PARTE PARA DESCUBRIR CUALES SON LOS CUADROS CON NUMER DE COLUMNAS INCORRECTOS
for(anho in anhos) for(semana in semanas) {
(n_col_esperado <- n_col_esperado_X_CUADRO(cuadro,semana,anho))
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
cat("Trabajando: ",pdf_path,"\n")
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(text_table)
all_stat_lines <- correccion_espacios_en_lineas(text_table)
# print(lengths(all_stat_lines))
if(any(lengths(all_stat_lines)!=n_col_esperado)) {
print("Posible error en el numero columnas -----------------")
}
# Extrayendo encabezados
encabezado_table <- extrae_encabezado_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
print(encabezado_table)
}
# asoetuh -----------------------------------------------------------------
all_stat_lines <- correccion_espacios_en_lineas(text_table)
zero_value_character <- "-"
# text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
from_text_to_numeric_matrix(all_stat_lines,anho,semana,longitud_tabla,zero_value_character)
# satoeh ------------------------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
set.seed(42)
cuadro <- 4.2; ncol_mat_cuadro <- ncol_CUADRO_X(cuadro,semana=1,anho=2019)
cuadro <- 3.1;
# cuadro <- 3.6; ncol_mat_cuadro <- 11
palabra_clave1 <- paste0("CUADRO ",as.character(cuadro)) # el cuadro 17 enfermedades prevenibles
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
semanas <- 1:4
semanas <- 33
semanas <- 1:52
# ERROR EN Boletines/2010/sem36.pdf CUADRO 3.1
anhos <- 2017
anhos <- 2019
anhos <- 2012:2019
zero_value_character <- "-"
mat_cuadro <- matrix(0,length(anhos)*length(semanas)*longitud_tabla,ncol_mat_cuadro+2) # +2 because the columns year week
anho <- anhos[1]
semana <- semanas[1]
l_main <- list_useful_var(cuadro,anho,semana,anhos,semanas,palabra_clave1,palabra_clave2)
name_file_All_Dat <- paste0(c("Data/All_Dat_",l_main$text_cuadro_guion,".RData"),collapse = "")
load(name_file_All_Dat)
head(BIG_mat_CUADRO)
dim(BIG_mat_CUADRO)
idx_mat_cuadro <- 1
for(anho in anhos) for(semana in semanas) {
index_key <- find_index_key(anho,semana,BIG_mat_CUADRO)
if(length(index_key)!=0) {
cat("Datos de ",l_main$text_cuadro_guion,"anho=",anho," semana ",semana," ya obtenidos\n")
} else {
(n_col_esperado <- ncol_CUADRO_X(cuadro,semana=1,anho=2019))
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
cat("Trabajando: ",pdf_path,"\n")
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(text_table)
text_table <- Correcting_text_table(text_table,l_main)
all_stat_lines <- correccion_espacios_en_lineas(text_table)
mat_dat <- from_text_to_numeric_matrix(all_stat_lines,anho,semana,longitud_tabla,zero_value_character)
BIG_mat_CUADRO <- rbind(BIG_mat_CUADRO,mat_dat)
save(BIG_mat_CUADRO,file=name_file_All_Dat)
cat("Datos de anho-semana-cuadro ACTUALIZADOS")
}
# mat_cuadro[idx_mat_cuadro:(idx_mat_cuadro+longitud_tabla-1),] <- mat_dat
# idx_mat_cuadro <- idx_mat_cuadro + longitud_tabla
# print(mat_dat)
}
s
# aseuh -------------------------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
set.seed(42)
cuadro <- 4.2; ncol_mat_cuadro <- ncol_CUADRO_X(cuadro,semana=1,anho=2019)
cuadro <- 3.1;
# cuadro <- 3.6; ncol_mat_cuadro <- 11
palabra_clave1 <- paste0("CUADRO ",as.character(cuadro)) # el cuadro 17 enfermedades prevenibles
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
semanas <- 1:52
# ERROR EN Boletines/2010/sem36.pdf CUADRO 3.1
anhos <- 2017
anhos <- 2019
anhos <- 2012:2019
zero_value_character <- "-"
mat_cuadro <- matrix(0,length(anhos)*length(semanas)*longitud_tabla,ncol_mat_cuadro+2) # +2 because the columns year week
anho <- anhos[1]
semana <- semanas[1]
l_main <- list_useful_var(cuadro,anho,semana,anhos,semanas,palabra_clave1,palabra_clave2)
names(l_main)
name_file_All_Dat <- paste0(c("Data/All_Dat_",l_main$text_cuadro_guion,".RData"),collapse = "")
load(name_file_All_Dat)
cuadro
estados_to_plot <- 9
estados_to_plot <- "Todos"
col_plot <- 7 # 7 equivale a columna 4, y 11 equivale a 8
col_plot <- 11 # 7 equivale a columna 4, y 11 equivale a 8
head(BIG_mat_CUADRO)
plot_state_info(estados_to_plot,col_plot,BIG_mat_CUADRO)
# imprime_CUADRO_anho_semana(anhos=2019,semanas=1)
# sauhteo -----------------------------------------------------------------
Unirse a la reunión Zoom
https://uammx.zoom.us/j/84954674369
ID de reunión: 849 5467 4369
Código de acceso: 330103
# Relacion estados numero -------------------------------------------------
l_main$state_numbers
# saoethu -----------------------------------------------------------------
imprime_CUADRO_anho_semana(anhos=2010,semanas=52)
# sutha -------------------------------------------------------------------
# Saving all data for a CUADRO --------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
cuadro <- 3.1; ncol_mat_cuadro <- ncol_CUADRO_X(cuadro,semana=1,anho=2019)
Initialize_All_Dat_RData_file(cuadro)
# fun_corrige_CUADRO_4_1_anho2016(text_table,semana,anho)
# all cuadros inizialized -------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
l_main <- Simple_list_useful_var()
for(cuadro in l_main$all_CUADRO_numbers_no_points) {
cat("cuadro",cuadro,"\n")
Initialize_All_Dat_RData_file(cuadro)
}
# astoeuh -----------------------------------------------------------------
load("Data/Dat CUADRO 3_6 2012-2019.RData")
col_plot <- 8 #posibles valores entre 3 y 11 ncol(mat_cuadro)
estados_to_plot <- 7
estados_to_plot <- 1:10
estados_to_plot <- "Todos"
estados_to_plot <- c(1,2,7,16)
# CORREGIR ERROR AL EMPEZAR AQUI
# CORREGIR ERROR AL EMPEZAR AQUI
# CORREGIR ERROR AL EMPEZAR AQUI
# CORREGIR ERROR AL EMPEZAR AQUI
# CORREGIR ERROR AL EMPEZAR AQUI
# CORREGIR ERROR AL EMPEZAR AQUI
# CORREGIR ERROR AL EMPEZAR AQUI
# CORREGIR ERROR AL EMPEZAR AQUI
# CORREGIR ERROR AL EMPEZAR AQUI
plot_state_info(estados_to_plot,col_plot,mat_cuadro)
# Number of columns for 2019 any Cuadro -----------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
set.seed(42)
cuadro <- 4.1
semana <- 52
anho <- 2019
# imprime_CUADRO_anho_semana(anhos,semanas)
ncol_CUADRO_X(cuadro,semana,anho)
# Number of columns for 2019 any Cuadro -----------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
set.seed(42)
cuadro <- 3.2
cuadro <- 4.1
anhos <- c(2005:2010,2012:2019)
anhos <- 2015:2019
semanas <- 1
# imprime_CUADRO_anho_semana(anhos,semanas)
semana <- 52
anho <- 2019
for(anho in anhos) for(semana in semanas) {
cat("Anho",anho," semana ",semana,"CUADRO ",cuadro," num. col.", ncol_CUADRO_X(cuadro,semana,anho),"\n")
}
# Correccion de numeros con espacios --------------------------------------
# Correcciones de extraccion de informacion -------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
# Variables tabla
cuadro <- 3.1
cuadro <- 4.1
# cuadro <- 3.6; ncol_mat_cuadro <- 11
palabra_clave1 <- paste0("CUADRO ",as.character(cuadro))
palabra_clave2 <- "Aguascalientes"
longitud_esperada <- 13 # longitud de numero de columnas
longitud_tabla <- 32 # numero de estados
remplaza_file <- F
semanas <- 2
semana <- 14:20
semanas <- 14:20
anhos <- 2018
genera0_imprime1 <- 0
anho <- anhos[1]
for(anho in anhos) {
texto <- GEN_file_fun_corrige(cuadro,anho,palabra_clave1,palabra_clave2,remplaza_file)
}
cat(texto)
# MAT cuadros con errores -------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
initialize_mat_errores_cuadro()
# asuth -------------------------------------------------------------------
semana <- 1
cuadro <- 1
anho <- 1981
load(file = "Data/Mat_errores_cuadro.RData")
head(mat_errores_cuadro)
mat_errores_cuadro[mat_errores_cuadro[,1]==anho &
mat_errores_cuadro[,2]==semana &
mat_errores_cuadro[,3]==cuadro, ] <- c(T,"ncol wrong")
save(mat_errores_cuadro,file = "Data/Mat_errores_cuadro.RData")
# which(mat_errores_cuadro[,3]==cuadro)
# astoueh -----------------------------------------------------------------
load(file = "Data/Mat_errores_cuadro.RData")
head(mat_errores_cuadro)
which(apply(mat_errores_cuadro[,1:3] == c("2019","4","1"),1,all))
idx <- which(apply(mat_errores_cuadro[,1:2] == c(2019,4),1,all))
idx <- which(apply(mat_errores_cuadro[,1:3] == c(2019,4,13.4),1,all))
idx
mat_errores_cuadro[idx,]
unique(mat_errores_cuadro[,1])
# sotuh -------------------------------------------------------------------
# Works
mat_errores_cuadro <- matrix(1:9,3)
apply(mat_errores_cuadro[,1:3] == c(1,4,7),1,all)
# Correcciones de extraccion de informacion -------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
# Variables tabla
cuadro <- 3.1; ncol_mat_cuadro <- 11
cuadro <- 4.1
# cuadro <- 3.6; ncol_mat_cuadro <- 11
palabra_clave1 <- paste0("CUADRO ",as.character(cuadro))
palabra_clave2 <- "Aguascalientes"
longitud_esperada <- 13 # longitud de numero de columnas
longitud_tabla <- 32 # numero de estados
remplaza_file <- T
semanas <- 2
semana <- 14:20
semanas <- 14:20
anhos <- 2014
anho <- 2016
genera0_imprime1 <- 0
GEN_file_fun_corrige(anho,remplaza_file)
# Revisa correcciones de cuadro -------------------------------------------
review_correcciones_a_mano_CUADRO_17 <- function(text_table,semana,anho) {
source("Fn_Correcciones_CUADRO_17_anho2016.R")
source("Fn_Correcciones_CUADRO_17_anho2015.R")
if(anho==2015) text_table <- fun_corrige_CUADRO_17_anho2015(text_table,semana,anho)
if(anho==2016) text_table <- fun_corrige_CUADRO_17_anho2016(text_table,semana,anho)
return(text_table)
}
for(anho in anhos) for(semana in semanas) {
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
cat("Trabajando: ",pdf_path,"\n")
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# Correccion cuadro
text_table <- correcciones_a_mano_CUADRO_17(text_table,semana,anho)
# print(text_table)
all_stat_lines <- correccion_espacios_en_lineas(text_table)
print(lengths(all_stat_lines))
# Extrayendo encabezados
encabezado_table <- extrae_encabezado_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(encabezado_table)
}
# stoehu ------------------------------------------------------------------
if(F) {
df <- plyr::ldply(all_stat_lines) #create a data frame
head(df)
}
# stnheu ------------------------------------------------------------------
# satoeh ------------------------------------------------------------------
cat("\014") # borra consola
rm(list=ls()) # Borra todo
source("Fn_epidemiologic.R")
set.seed(42)
cuadro <- 3.1; ncol_mat_cuadro <- 11
cuadro <- 4.1; ncol_mat_cuadro <- ncol_CUADRO_X(cuadro,semana=1,anho=2019)
# cuadro <- 3.6; ncol_mat_cuadro <- 11
palabra_clave1 <- paste0("CUADRO ",as.character(cuadro)) # el cuadro 17 enfermedades prevenibles
palabra_clave2 <- "Aguascalientes"
longitud_tabla <- 32 # numero de estados
semanas <- 1:4
semanas <- 33
semanas <- 1:52
# ERROR EN Boletines/2010/sem36.pdf CUADRO 3.1
anhos <- 2012:2019
anhos <- 2017
anhos <- 2017
zero_value_character <- "-"
mat_cuadro <- matrix(0,length(anhos)*length(semanas)*longitud_tabla,ncol_mat_cuadro+2) # +2 because the columns year week
anho <- anhos[1]
semana <- semanas[1]
l_main <- list_useful_var(cuadro,anho,semana,anhos,semanas,palabra_clave1,palabra_clave2)
idx_mat_cuadro <- 1
for(anho in anhos) for(semana in semanas) {
index_key <- find_index_key(anho,semana,BIG_mat_CUADRO)
if(length(index_key)!=0) {
cat("Datos de anho-semana-cuadro ya obtenidos")
} else {
(n_col_esperado <- ncol_CUADRO_X(cuadro,semana=1,anho=2019))
# Path of pdf
pdf_path <- genera_ruta_boletin(anho,semana)
cat("Trabajando: ",pdf_path,"\n")
# Raw text of pdf
PDF <- pdf_to_text(pdf_path)
# Extrayendo encabezados
text_table <- extrae_text_tabla(PDF,palabra_clave1,palabra_clave2,longitud_tabla)
# print(text_table)
text_table <- Correcting_text_table(text_table,l_main)
all_stat_lines <- correccion_espacios_en_lineas(text_table)
mat_dat <- from_text_to_numeric_matrix(all_stat_lines,anho,semana,longitud_tabla,zero_value_character)
BIG_mat_CUADRO <- rbind(BIG_mat_CUADRO,mat_dat)
}
# mat_cuadro[idx_mat_cuadro:(idx_mat_cuadro+longitud_tabla-1),] <- mat_dat
# idx_mat_cuadro <- idx_mat_cuadro + longitud_tabla
# print(mat_dat)
}
(correction_text_cuadro <- gsub('\\.', '_', palabra_clave1))
(text_andos <- paste0(as.character(range(anhos)),collapse = "-"))
(name_file_save <- paste0(c("Data/Dat ",correction_text_cuadro," ",text_andos,".RData"),collapse = ""))
save(mat_cuadro,anhos,cuadro,ncol_mat_cuadro,palabra_clave1,palabra_clave2,longitud_tabla,file=name_file_save)
ncol(mat_dat)
ncol(mat_cuadro)
# nsotauh -----------------------------------------------------------------
|
ecbf0c82c1f3ef1e1e9c50536c8a60fd013efae9 | 2ca96e9d3d4d595682a06de876a8af59113779ba | /cachematrix.R | 4f2f41d4e90efc3fbe7cd7aa021061a88336e5fd | [] | no_license | JeffreyGonlin/ProgrammingAssignment2 | b6b466f56cca51b79114bfd5715c629201f55047 | 23fcd47f9c1ced6985dbd2c602b760f8db11976d | refs/heads/master | 2021-01-16T18:13:19.747397 | 2016-02-21T12:04:15 | 2016-02-21T12:04:15 | 51,803,404 | 0 | 0 | null | 2016-02-16T02:57:09 | 2016-02-16T02:57:09 | null | UTF-8 | R | false | false | 4,350 | r | cachematrix.R | ## Assignment2:
## Write the following functions:
## 1. makeCacheMatrix:
## This function creates a special "matrix" object that can cache its inverse.
## 2. cacheSolve:
## This function computes the inverse of the special "matrix" returned by makeCacheMatrix above.
## If the inverse has already been calculated (and the matrix has not changed),
## then the cachesolve should retrieve the inverse from the cache.
## To create special "YOUR_MATRIX", just enter "YOUR_MATRIX<-makeCacheMatrix".
## It gets added to a list, along with its inverse.
## YOUR_MATRIX and its inverse can be called up by entering "YOUR_MATRIX$get()" or(once solved) "YOUR_MATRIX$getinverse"
## Any number of matrices can be added to the list, but name them distinctly (e.g., YOUR_MATRIX2, YOUR_MATRIX3, etc.)
makeCacheMatrix <- function(x = matrix()) {
nvrs <- NULL ## This first part sets inverse to NULL for a clear start and
set <- function(y) { ## sets the value for YOUR_MATRIX<-makeCacheMatrix in the list.
xp <<- y ## the value of the matrix and its inverse will be held in the
nvrs <<- NULL ## parent environment. The various matrices will not show up as data
} ## but rather as Values, as a List of 4: set, get, setinverse, getinverse.
get <- function() { x }
setinverse <- function(solve) {
nvrs<<-solve(x) }
getinverse <- function() { nvrs }
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## The "cacheSolve" function below is meant to work with "makeCacheMatrix" above.
## First use "makeCacheMatrix" to cache a matrix and its inverse for later return.
## Then enter "cacheSolve(YOUR_MATRIX)" to calculate the inverese or return the cached inverse, if available.
## Cached inverses are returned without the need to repeat the inversion calculations, thus freeng up resources.
cacheSolve <- function(x, ...) {
nvrs <- x$getinverse()
if (!is.null(nvrs)) { ## Checks to see if the inverse has already been solved and is avaiable.
message("getting cached data")
return(nvrs) ## If inverse is available, cached solution gets returned.
}
data <- x$get() ## If not in cache, i.e., this is first time to solve, the matrix is retrieved,
nvrs <- solve(data, ...) ## the inverse calculated and then saved to the list for later recall.
x$setinverse(nvrs)
nvrs
}
## Testing:
## Test using different matrices
matrix1 <- makeCacheMatrix(matrix(1:4, 2, 2))
cacheSolve(matrix1)
matrix2 <- makeCacheMatrix(matrix(4:1, 2, 2))
cacheSolve(matrix2)
## If we want to get either the matrix or its inverse, we can do that.
matrix1$get()
matrix1$getinverse()
## Now call again for the inverse of the matrices.
cacheSolve(matrix1)
cacheSolve(matrix2)
## We see (red message) that the cached solution was used.
## As a check, take the inverse of matrix1, call it matrix1i, and see if we get matrix1 in return.
matrix1i <- makeCacheMatrix(matrix(c(-2,1,1.5,-.5), 2, 2))
cacheSolve(matrix1i)
## From further testing, I found that matrix multiplication doesn't work:
matrix1%*%matrix1i
##bacause the 'matrices' are now lists.
class(matrix1)
## However, one can still multiply them by doing the following:
matrix1solution <- cacheSolve(matrix1)%*%cacheSolve(matrix1i)
matrix1solution
## This shows that matrix1i is indeed the inverse. The solution matrix is class 'matrix'.
class(matrix1solution)
## What if the matrix has changed since the time it was originally "made" with makeCacheMatrix?
## If the matrix changes but is still called the same, cacheSolve won't work. Consider the following example:
## matrix1 is multiplied by 10, then cacheSolve(matrix1) is tried.
matrix1 <- matrix1$get()*10
cacheSolve(matrix1)
## matrix1 has become a new object, as seen under Data in Environemnt.
## An Error message gets returned: "Error in x$getinverse : $ operator is invalid for atomic vectors"
## So if a matrix gets changed, that matrix needs to become listed via makeCacheMatrix before cacheSolve will work for it.
|
1a53edc7c4470fdedec7ee565ef7cb1c0d6c8d2d | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gtkCListColumnTitlePassive.Rd | e4de273a0c443419b05d88acb76601ad15863640 | [] | no_license | lawremi/RGtk2 | d2412ccedf2d2bc12888618b42486f7e9cceee43 | eb315232f75c3bed73bae9584510018293ba6b83 | refs/heads/master | 2023-03-05T01:13:14.484107 | 2023-02-25T15:19:06 | 2023-02-25T15:20:41 | 2,554,865 | 14 | 9 | null | 2023-02-06T21:28:56 | 2011-10-11T11:50:22 | R | UTF-8 | R | false | false | 626 | rd | gtkCListColumnTitlePassive.Rd | \alias{gtkCListColumnTitlePassive}
\name{gtkCListColumnTitlePassive}
\title{gtkCListColumnTitlePassive}
\description{
Causes the specified column title button to become passive, i.e., does
not respond to events, such as the user clicking on it.
\strong{WARNING: \code{gtk_clist_column_title_passive} is deprecated and should not be used in newly-written code.}
}
\usage{gtkCListColumnTitlePassive(object, column)}
\arguments{
\item{\verb{object}}{The \code{\link{GtkCList}} to affect.}
\item{\verb{column}}{The column to make passive, counting from 0.}
}
\author{Derived by RGtkGen from GTK+ documentation}
\keyword{internal}
|
733c1b7ad04ba1f653729ef9afeb2c2b7d00bf61 | db17cf7919d13b04253a44c18129a652034b7148 | /tests/testthat/test-integrate_primitive.R | 06f3c93fdb68968796f9bb6bfa12b3503dc21342 | [
"MIT"
] | permissive | hericks/KDE | 6b286619567ee1bf534c6bb5428dfaddc00330fa | 96fe54291e7a3890c41e00a10b5106813403c7c4 | refs/heads/master | 2022-12-03T10:56:02.986745 | 2020-08-21T22:00:24 | 2020-08-21T22:00:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,685 | r | test-integrate_primitive.R | test_that("integrant should be a function",{
not_fun <- "x"
expect_error(integrate_primitive(not_fun, -1, 1))
a_fun <- function(x) x+1
expect_error(integrate_primitive(a_fun, -1, 1), NA)
})
test_that("support should make sense",{
fun <- rectangular$fun
expect_error(integrate_primitive(fun, rectangular$support[1], rectangular$support[2]), NA)
expect_error(integrate_primitive(fun, 1, -1))
expect_error(integrate_primitive(fun, "-1", 1))
expect_error(integrate_primitive(fun, -1, "-1"))
expect_error(integrate_primitive(fun, c(-1,0), 1))
expect_error(integrate_primitive(fun, -1, c(1,2144)))
})
test_that("support should be finite",{
fun <- rectangular$fun
expect_error(integrate_primitive(fun, -Inf, -1))
expect_error(integrate_primitive(fun, -1, Inf))
})
test_that("subdivisions should be numeric value", {
fun <- rectangular$fun
expect_error(integrate_primitive(fun, -1, 1, subdivisions=1000L),NA)
expect_error(integrate_primitive(fun, -1, 1, subdivisions=1000.2342),NA)
expect_error(integrate_primitive(fun, -1, 1, subdivisions="1000"))
expect_error(integrate_primitive(fun, -1, 1, subdivisions=c(100, 10)))
})
test_that("non integrable functions will not converge with more subdivisions",{
non_integrable_fun <- function(x) 1/x
lower <- 0
upper <- 1
rel_error <- c()
for(i in 1:6){
subdivisions <- 10^i
rel_error <- c(rel_error,integrate_primitive(non_integrable_fun, 0, 1, subdivisions, check=TRUE)$relError)
}
# rel_error does get smaller using more subdivision steps, but it will not get sufficiently small
expect_error(stopifnot(res[length(rel_error)] < 0.01))
})
test_that("integrating integrable functions will converge",{
convergence <- function(x){
for(i in seq_along(x)){
for(j in (i:length(x))){
if(x[i] < x[j] && i != j) return(FALSE)
}
}
return(TRUE)
}
fun <- function(x) x^2 + 2
lower <- 0
upper <- 1
res <- c()
for(i in 1:5){
subdivisions <- 10^i
res <- c(res,integrate_primitive(fun, 0, 1, subdivisions, check=TRUE)$value)
}
true_value <- 2+1/3
expect_true(convergence(abs(res - true_value)))
})
test_that("rel_error has to be boolean value",{
fun <- function(x) 1/x
lower <- 0
upper <- 1
subdivisions <- 1000L
res <- c()
expect_error(integrate_primitive(fun, lower, upper, subdivisions, check=TRUE), NA)
expect_error(integrate_primitive(fun, lower, upper, subdivisions, check=c(TRUE, FALSE)))
expect_error(integrate_primitive(fun, lower, upper, subdivisions, check="TRUE"))
expect_equal(integrate_primitive(fun, lower, upper, subdivisions, check=FALSE)$relError, NULL)
})
|
e10c548f3c0f19cb7b7863df8679438a4959434a | e3c46e15b16f7097c1839dcea45c4a7834062cc2 | /SVM traincode.R | ec9716d6d88a7a1bab4fe26ee1e49e171b206be2 | [] | no_license | jaychoi4830/SMRIbasemodels | 3ae17ac190d4c04730d56c29e69566c91e1a663d | 15e4323bfea40fa7c32a1cbce88a874ee3161efb | refs/heads/main | 2023-03-27T20:24:41.405988 | 2021-03-25T20:21:41 | 2021-03-25T20:21:41 | 351,565,090 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,437 | r | SVM traincode.R | require('e1071')
require(caret)
require(kernlab)
library(e1071)
library(caret)
library(kernlab)
library(pROC)
##SVM Linear Kernal tuning:
set.seed(00100)
linear.tune <- tune.svm(Labels~.,
data = traindt1,
kernel = "linear",
cost = c(0.001,0.01,0.1,1,5,10))
summary(linear.tune)
#list of model performance based on 10-fold CV on different cost levels
best.linearsvm <- linear.tune$best.model
lineartune.test = predict(best.linearsvm, newdata=testdt)
table(lineartune.test, testdt$Labels)
##then caculate accuracy by = (TN+TP)/(TN+TP+FN+FP)
##SVM Radial Kernal tuning:
set.seed(00101)
radial.tune <- tune.svm(Labels~., data = traindt1, kernal = "radial",
gamma = c(0.1, 0.3, 0.5, 1, 2, 3, 4, 5),
cost = c(0.001, 0.01,0.1, 0.5, 1, 10, 30, 50, 70, 100))
summary(radial.tune)
best.radialsvm <- radial.tune$best.model
radialtune.test = predict(best.radialsvm, newdata = testdt)
table(radialtune.test, testdt$Labels)
## calculate accuracy
##SVM Polynomial Kernal tuning:
set.seed(00110)
poly.tune <- tune.svm(Labels~., data = traindt1,
kernal = "polynomial",
degree = c(3,4,5),
coef0 = c(0.1,0.5,1,2,3,4,5))
summary(poly.tune)
best.polysvm <- poly.tune$best.model
polytune.test <- predict(best.polysvm, newdata=testdt)
table(polytune.test, testdt$Labels)
## calculate accuracy
set.seed(01110)
sig.tune <- tune.svm(Labels~., data=traindt1, kernal ="sigmoid",
gamma = c(0.1,0.3,0.5,1,2,3,4,5),
coef0 = c(0.1,0.5,1,2,3,4,5))
summary(sig.tune)
best.sigsvm <- sig.tune$best.model
sigtune.test <- predict(best.sigsvm, newdata = testdt)
table(sigtune.test, testdt$Labels)
## calculate accuracy
## Confusion Matrix
confusionMatrix(sigtune.test, test$labels, positive = "1") #change test
## K-Fold Cross-validation
folds <- createFolds(traindt1$Labels, k =5)
svm.cv = lapply(folds, function(x){
training_fold = traindt[-x,]
test_fold = traindt[x,]
classifier = best.radialsvm
cv_pred = predict(classifier, newdata = test_fold[-1])
cm = table(test_fold[, 1], cv_pred)
accuracy = (cm[1,1] + cm[2,2])/(cm[1,1]+cm[1,2]+cm[2,1]+cm[2,2])
return(accuracy)
})
#mean of accuracy of k-fold cv
accuracy_cv = mean(as.numeric(svm.cv))
accuracy_cv
|
6589dee7ad95c0e7e784fdb4ad092c81b25a711a | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /netrankr/inst/testfiles/checkPairs/libFuzzer_checkPairs/checkPairs_valgrind_files/1612798874-test.R | 0c071e9ed6328f7e845d6af29dbffafec5f5e86a | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 897 | r | 1612798874-test.R | testlist <- list(x = c(NaN, 2.12196353786585e-314, Inf, 1.39068914879695e-309, -5.49817854063981e+303, 2.78542174921362e+180, 6.08113124126605e-13, NaN, -5.82900682303162e+303, 3.24244610965235e-05, -1.27733779809297e+294, 1.08435642727488e-311, NaN, NaN, 9.56817916472489e-303, NaN, 2.12196366434665e-314, NaN, NaN, 2.78231844119784e-309, 1.65436122203656e-24, NaN, NaN, NaN, NaN, 2.04114723650203e-317, NaN, 1.73833895195875e-307, NaN, 3.56011817360252e-307, 1.82359013286078e-314, NaN, 4.77772868874966e-299, 0), y = c(6.48841590713934e-319, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
result <- do.call(netrankr:::checkPairs,testlist)
str(result) |
72ad34d0184f48a14555a84223f27d462f526d1e | abc067dec9d3193de799af17da412abc2c4346ed | /plot1.R | a7301e8f55d763b748502d1529e5bfe8773772aa | [] | no_license | hungerpangg/Exploratory_Data_Analysis_Project1 | d555554c2448b41924948f74a0e7388ca82cf798 | adec65930c17796819fb17e6b987cba7a137c4b8 | refs/heads/master | 2022-12-02T15:44:52.171704 | 2020-08-12T16:56:47 | 2020-08-12T16:56:47 | 287,066,972 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 714 | r | plot1.R | hpc <- read.table("household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?")
#1
#hpc <- hpc[complete.cases(hpc),]
hpc$Date <- as.Date(hpc$Date, format = "%d/%m/%Y")
hpc2 <- subset(hpc, Date == "2007-02-01" | Date == "2007-02-02")
hist(hpc2$Global_active_power, col = "red", main = "Global Active Power", xlab = "Global Active Power (kilowatts)")
dev.copy(png, file = "plot1.png")
dev.off()
#2
hpc2 <- hpc2 %>% mutate(datetime = paste(Date, Time))
hpc2$datetime <- strptime(hpc2$datetime, format = "%Y-%m-%d %H:%M:%S")
plot(hpc2$datetime, hpc2$Global_active_power, type = "s", xlab = "", ylab = "Global Active Power (kilowatts)")
dev.copy(png, file = "plot2.png")
dev.off() |
320edc6dec7c2f2da9c906211bf70d59d3151374 | fca927c08250034c15f3dd3c55251c9f7d48d00a | /pkg/man/shiny.app.Rd | fc5ff0a0e6e8607615be547253ef083352774e32 | [] | no_license | rn1x0n/SwissMortgage | a773b9216d251d994ce08ba8a66ab5308e698663 | 4042797aa95a843020416bdaa19d6f057dd5654c | refs/heads/master | 2021-01-13T01:57:52.629092 | 2013-06-10T20:37:10 | 2013-06-10T20:37:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 314 | rd | shiny.app.Rd | \name{shiny.app}
\alias{shiny.app}
\title{Run Shiny application}
\usage{
shiny.app()
}
\description{
Runs a Swiss Mortgage Calculator Shiny application. This
function normally does not return; interrupt R to stop
the application (usually by pressing Ctrl+C or Esc).
}
\examples{
\dontrun{
shiny.app()
}
}
|
970dc404183241b97fdf6a192022b72f1fe7ea3c | 2e6d94697d4349c2b9f4569acc70d5379fd980d7 | /d_prepareBase.R | 57461bb77a3e0050fd5b3007efae15dfebc904af | [] | no_license | plusy/TitanicLearningR | d5ea50f34cb190376cf7f5e2f10ff6612fae1fdf | 18480b9ce50307b660e9c5605df79976f13fb9b4 | refs/heads/master | 2022-02-26T05:57:45.266933 | 2019-07-29T04:41:53 | 2019-07-29T04:41:53 | 107,499,232 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,083 | r | d_prepareBase.R | .VAR_BEFORE <- ls()
# Descritpion from https://github.com/IamGianluca/titanic
# This is an analysis based on the data made available by Kaggle
# (www.kaggle.com) on the Titanic distaster competition. This work aims to
# predict what sorts of people were more likely to survive.
#
# On the folder text you can find the R Markdown file to generate a pdf
# version of the final report, which summarises the key findings of our
# reseach.
#
# On the folder R code you can find two sub-folders. Raw scripts
# contains all tests we conducted and it is not intended to be a place
# where find perfectly formatted and optimised R code. Whereas final
# scripts include the final version of R code we wrote to conduct
# our analysis. We advice you to give it a read to have a better
# overview of how the analysis has been conducted.
#
# Data contains both the raw dataset we downloaded from Kaggle.com and
# a tiny dataset with variables transformed in the format we used when
# conducting our analysis.
#
#
# VARIABLES DESCRIPTION:
#
# survival Survival
# (0 = No; 1 = Yes)
# pclass Passenger Class
# (1 = 1st; 2 = 2nd; 3 = 3rd)
# name Name
# sex Sex
# age Age
# sibsp Number of Siblings/Spouses Aboard
# parch Number of Parents/Children Aboard
# ticket Ticket Number
# fare Passenger Fare
# cabin Cabin
# embarked Port of Embarkation
# (C = Cherbourg; Q = Queenstown; S = Southampton)
#
#
# SPECIAL NOTES:
#
# Pclass is a proxy for socio-economic status (SES)
# 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower
#
# Age is in Years; Fractional if Age less than One (1)
# If the Age is Estimated, it is in the form xx.5
#
# With respect to the family relation variables (i.e. sibsp and parch)
# some relations were ignored. The following are the definitions used
# for sibsp and parch.
#
# Sibling: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic
# Spouse: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)
# Parent: Mother or Father of Passenger Aboard Titanic
# Child: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic
#
# Other family relatives excluded from this study include cousins,
# nephews/nieces, aunts/uncles, and in-laws. Some children travelled
# only with a nanny, therefore parch=0 for them. As well, some
# travelled with very close friends or neighbors in a village, however,
# the definitions do not support such relations.
# Based on this post: http://wiekvoet.blogspot.com/2015/08/predicting-titanic-deaths-on-kaggle-iv.html
# Load raw data -----------------------------------------------------------
df_RawInput_Train <- read_csv(FILE_SRC_DATA_RAW_TRAIN)
df_RawInput_Train$DataTag <- DATA_TAG_TRAIN
df_RawInput_Test <- read_csv(FILE_SRC_DATA_RAW_TEST)
df_RawInput_Test$DataTag <- DATA_TAG_TEST
df_RawInput_Test$Survived <- NA
df_RawInput <- rbind(df_RawInput_Train,df_RawInput_Test)
df_working <- df_RawInput
# Factorize -----------------------------------------------------------------
colList <- c('PassengerId', 'Pclass', 'Survived', 'Sex')
df_working[colList] <- lapply(df_working[colList], factor)
#Embarked ----------------------------------------------------------
df_working%>%
mutate(Embarked=factor(coalesce(Embarked,"N/A"))) ->
df_working
#Fare ----------------------------------------------------------
MadiaOfAll <- median(df_working$Fare,na.rm=TRUE)
MadiaOfTrain <- median(df_working$Fare[df_working$DataTag==DATA_TAG_TRAIN],na.rm=TRUE)
df_working%>%
mutate(Fare_Adj_MedianOfAll=coalesce(Fare, MadiaOfAll),
Fare_Adj_MedianOfTrain=coalesce(Fare, MadiaOfTrain)) ->
df_working
#Cabin ----------------------------------------------------------
df_working%>%
mutate(Cabin = coalesce(Cabin, "")) ->
df_working
for(cab in LETTERS[1:7]){ # 1:7 -> A:G
df_working[paste0("Cabin_",cab)] <- factor(grepl(cab,df_working$Cabin))
}
df_working%>%
mutate(Cabin_Length = nchar(Cabin),
Cabin_Section = factor(if_else(Cabin_Length==0,
0,
str_count(df_working$Cabin, ' ') + 1))) ->
df_working
df_working%>%
separate(Cabin, sep = " ",
into = c("Cabin_Number"), extra = "drop", remove = FALSE) %>%
mutate(Cabin_Number = coalesce(as.integer(str_replace(Cabin_Number, "[[:alpha:]]", "")), as.integer(0))) %>%
mutate(Cabin_NumberOE = factor(if_else(is.na(Cabin_Number), -1, Cabin_Number%%2))) ->
df_working
#Name ----------------------------------------------------------
df_working%>%
#Name_Title = sapply(Name,function(x) strsplit(as.character(x),'[.,]')[[1]][2]),
separate(Name, sep = '[.,]',
into = c("Name_Last", "Name_Title", "Name_First"), extra = "merge", remove = FALSE) %>%
mutate(Name_TitleSimple = str_trim(Name_Title),
Name_TitleSimple = ifelse(Name_TitleSimple %in% c('Capt','Col','Don','Sir','Jonkheer','Major'),
'Mr',
ifelse(Name_TitleSimple %in% c('Lady','Ms','the Countess','Mlle','Mme','Ms','Dona'),
'Miss',
Name_TitleSimple)),
Name_TitleSimple = factor(Name_TitleSimple),
Name_Title = factor(Name_Title)) ->
df_working
#Ticket ----------------------------------------------------------
df_working%>%
mutate(Ticket_PC = factor(grepl('PC',Ticket)),
Ticket_STON = factor(grepl('STON',Ticket))) ->
df_working
#Age ----------------------------------------------------------
df_working%>%
mutate(Age_ImputeBy35 = coalesce(Age, 35)) %>%
mutate(Age_ImputeBy35_Bin=cut(Age_ImputeBy35, AGE_BIN_CUTOFF))->
df_working
# Save clean data ---------------------------------------------------------
saveRDS(df_working, file = FILE_CLEAN_DATA_BASE_RDS)
write_csv(df_working, FILE_CLEAN_DATA_BASE_CSV)
# Prepare ground Truth ----------------------------------------------------
# Groud truth data from http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv
# Two dup name: Kelly, Mr. James and Connolly, Miss. Kate
read_csv(FILE_SRC_DATA_RAW_TRUTH) %>%
select(name, survived, ticket) %>%
mutate(name = str_replace_all(name, '\"', '')) ->
df_GroundTruth
df_working %>%
mutate(Name = str_replace_all(Name, '\"', '')) %>%
select(Name, Ticket, PassengerId, DataTag) %>%
inner_join(df_GroundTruth, by = c("Name"="name", "Ticket"="ticket" )) %>%
select(PassengerId, GTSurvived = survived, Name, Ticket, DataTag) ->
df_GroundTruth
# if inner join found mismatch
stopifnot(nrow(df_GroundTruth)==nrow(df_working))
write_csv(df_GroundTruth, FILE_CLEAN_DATA_GROUND_TRUTH)
# Clean up ----------------------------------------------------------------
rm(list = setdiff(ls(), .VAR_BEFORE))
rm(.VAR_BEFORE) |
c627f8034d6d30a396bb9e9d45aef6fecb44d0cc | 92b0b62ce2a42cf733f3c564d2a7306a86662ba7 | /plot2.r | c693d36e13491f66475293da97bf18093e343c6c | [] | no_license | naboldyrev/EDA | 1b2c9d3f4a4485d236f7496c1e53a46560133207 | 9250f4b5378b32c99736161ef0b574993024d7b1 | refs/heads/master | 2021-01-22T08:06:55.770065 | 2016-09-19T09:34:26 | 2016-09-19T09:34:26 | 68,553,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,467 | r | plot2.r | #column types
colClss <- c("character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric")
#reading the data directly from the host, interpreting '?' as Na's
zip_file <- paste0(getwd(), '/household_power_consumption.zip')
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", destfile = zip_file, method = "curl")
unzip(zip_file)
consumption <- read.csv("data/household_power_consumption.txt",
colClasses = colClss, sep = ';', na.strings=c("?"),
nrows=2075259)
#concatenating date and time for plots 2-4
consumption$full_date = paste(consumption$Date, consumption$Time, sep=" ")
consumption$full_date = strptime(consumption$full_date, "%d/%m/%Y %H:%M:%S")
#cast the data types
consumption$Date = as.Date(consumption$Date, "%d/%m/%Y")
consumption$Time = as.Date(consumption$Time, "%H:%M:%S")
#constraints for the dates
startDate = as.Date("2007-02-01", "%Y-%m-%d")
endDate = as.Date("2007-02-02", "%Y-%m-%d")
#select subset of data between the startDate and endDate
needed_consumption <- subset(consumption, (Date >= startDate & Date <= endDate))
#plot
png(file = "plot2.png", width=480, height=480)
with(needed_consumption, plot(full_date, Global_active_power, type = "l",
ylab="Global Active Power (Kilowatt)", xlab = ""))
dev.off()
|
e7266be4c8c6d40ae8f33ce20202d90fed888562 | 7ec0c3344e1bffcaa1c7e7380b0618c6995c73ac | /scripts/08_compile-HTML.r | 609d327a0319c6df627234a02ff5aee213917f91 | [] | no_license | jennybc/vanNH | 03afe89da5ff6315727b6cf692076d5ae13b7794 | 52d4c2302fd17cddc944402738ef5d781a1ab204 | refs/heads/master | 2020-06-02T06:25:45.305171 | 2015-06-09T06:34:45 | 2015-06-09T06:34:45 | 18,771,831 | 2 | 1 | null | 2014-04-16T06:30:54 | 2014-04-14T18:36:08 | Python | UTF-8 | R | false | false | 1,976 | r | 08_compile-HTML.r | #!/usr/bin/Rscript
## this script has two purposes
## 1 command line argument processing -- specifically getting the game
## 2 knitting vanNH-nowPlaying.rmd to HTML
## note that purpose #2 can be achieved through a target like this in a Makefile:
# web: vanNH-nowPlaying.rmd
# Rscript -e "library(knitr); knit2html('vanNH-nowPlaying.rmd')"
## but purpose #1 is not easy to address that way; hence this script
library(rmarkdown)
options <- commandArgs(trailingOnly = TRUE)
if(length(options) < 1) {
#game <- "2014-04-12_vanNH-at-pdxST"
#game <- "2014-04-20_sfoDF-at-vanNH"
#game <- "2014-05-10_seaRM-at-vanNH"
#game <- "2014-05-24_pdxST-at-vanNH"
#game <- "2014-05-31_vanNH-at-seaRM"
#game <- "2014-06-07_seaRM-at-vanNH"
#game <- "2014-05-03_sfoDF-at-pdxST"
#game <- "2014-07-19_vanNH-at-wdcCT"
game <- "2015-05-23_vanNH-at-wdcCT"
} else {
game <- options[1]
}
local_html_file <- paste0(game, "_live-stats.html")
local_md_file <- paste0(game, "_live-stats.md")
local_figure_dir <- file.path(paste0(game, "_live-stats_files"), "figure-html")
render('09_make-live-stats.rmd', output_file = local_html_file, quiet = TRUE)
game_html_dir <- file.path("..", "games", game, "09_html")
game_figure_dir <- file.path(game_html_dir, paste0(game, "_live-stats_files"),
"figure-html")
if(!file.exists(game_html_dir)) dir.create(game_html_dir)
if(!file.exists(game_figure_dir)) {
dir.create(game_figure_dir, recursive = TRUE)
} else {
foo <- file.remove(list.files(game_figure_dir, full.names = TRUE))
}
game_md_file <- file.path(game_html_dir, paste0(game, "_live-stats.md"))
game_html_file <- file.path(game_html_dir, paste0(game, "_live-stats.html"))
foo <- file.rename(local_md_file, game_md_file)
#message("wrote ", game_md_file)
foo <- file.rename(local_html_file, game_html_file)
#message("wrote ", game_html_file)
foo <- file.rename(local_figure_dir, game_figure_dir)
foo <- file.remove(dirname(local_figure_dir))
|
9066a8db9d90ecacb4a04553cc4574a4faaa1e15 | 65553a50702fb30c40ec78f75ec305a8fab298f8 | /R/getNeighborhood.R | 9e588fff07fc150e52948f4fb4f5d42ff86b062d | [] | no_license | lzcyzm/Guitar | e22f1b9a34952b48b219a5bfcb8a91a9d1283408 | cb1e1146c86ce8cf9caff505d1b22dca2bc37a3e | refs/heads/master | 2021-01-10T07:42:46.819016 | 2015-10-22T02:55:25 | 2015-10-22T02:55:25 | 44,791,600 | 1 | 0 | null | 2015-10-23T04:54:56 | 2015-10-23T04:54:56 | null | UTF-8 | R | false | false | 1,536 | r | getNeighborhood.R |
getNeighborhood <- function(comp, side=5, Width=1000) {
# transcript info
tx_name <- names(comp)
granges <- unlist(comp)
tx <- granges[tx_name]
strand <- as.character(as.data.frame(strand(tx))[[1]])
chr <- as.character(as.data.frame(seqnames(tx))[[1]])
if (side == 5) {
checkpoints <- rep(1,length(tx))
checkpoints_transcript <- GRanges(seqnames=tx_name,
IRanges(start=checkpoints, end=checkpoints, names=tx_name),
strand=strand)
# convert to genomic coordinates
checkPoints_genomic <- mapFromTranscripts(checkpoints_transcript, comp)
# resize
checkRegion_genomic <- resize(x=checkPoints_genomic, width=Width, fix="end")
} else if (side == 3) {
checkpoints <- sum(width(comp))
checkpoints_transcript <- GRanges(seqnames=tx_name,
IRanges(start=checkpoints, end=checkpoints, names=tx_name),
strand=strand)
# convert to genomic coordinates
checkPoints_genomic <- mapFromTranscripts(checkpoints_transcript, comp)
# resize
checkRegion_genomic <- resize(x=checkPoints_genomic, width=Width, fix="start")
}
# convert to list
names(checkRegion_genomic) <- rep("",length(tx))
sidelist <- split(checkRegion_genomic, tx_name, drop=TRUE)
sidelist <- sidelist[tx_name]
mapped_chr <- as.character(as.data.frame(seqnames(checkRegion_genomic))[[1]])
mcols(sidelist) <- data.frame(mapped_chr)
return(sidelist)
} |
965d40f66cb6bacb0e24e09bac1e62d061321e78 | d165abb74a6c882f89cbacc021ae78da018ea38f | /plot_MAF.R | e09d9c183a14c0815531362167fd8e977e1d8c28 | [] | no_license | bkiranmayee/Scripts | 4fec4f2620b6f86bddb618812e169fa77c9c1c90 | 03c2169956fabc154d9f2d976a76aa5344b56137 | refs/heads/master | 2020-04-25T17:48:21.074457 | 2019-04-17T16:27:22 | 2019-04-17T16:27:22 | 172,961,784 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,029 | r | plot_MAF.R | #!/usr/bin/env Rscript
#Date:June 8 2018
#Author:Kiranmayee Bakshy
# A program to plot densities of MAF/fraction missing per variant of 2 datasets (designed for plink output files like file.frq and file.lmiss)
# The input and output files have to be passed as arguments to this program
# Input = 2 stat files and output = fileplot.pdf
args = commandArgs(trailingOnly=TRUE)
# test if there is at least one argument: if not, return an error
if (length(args)==0) {
stop("At least two arguments must be supplied (input file).\n", call.=FALSE)
} else if (length(args)==2) {
# default output file
args[3] = "out.pdf"
}
library(ggplot2)
library(reshape2)
combined<-readRDS(args[1])
filtered<-readRDS(args[2])
pdf(file=args[3], onefile=T, paper='A4r')
plot(density(combined[[2]], na.rm=T), col="blue", border="black", main="Density plot", xlab=names(filtered[2]))
lines(density(filtered[[2]], na.rm=T), col="red", border="black")
legend("top", c("Combined","Filtered"), lty = c(1,1), col = c("blue","red"))
dev.off()
|
f2cfcbe3b71cff17006ec8d6835ab7a8deb873dd | 9dc3d2ba0bc6ee5cea5b9d1ea1d4ca9296acf103 | /pract_5.R | aa7a6a4f2f58dca8ee28fdf62235e7a124977bb3 | [] | no_license | purva-d/SNA | b043e272c7e4c22d92c67ce2c6f35668babc7f7b | f1ec545ce14504139f672ce2acf339ff93c5038c | refs/heads/master | 2022-12-28T02:51:23.765426 | 2020-10-15T17:51:25 | 2020-10-15T17:51:25 | 290,763,545 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 326 | r | pract_5.R | ##A network as a graph
library(igraph)
ng<-graph.formula(Garth-+Angela,Bill-+Elena,
Elena++Frank,Carol-+Andy,Carol-+Elena,Carol++Dan,
Carol++Bill,Dan++Bill,
Elena-+Daphne,Daphne++Garth)
plot(ng)
##A network as a matrix
get.adjacency(ng)
##A network as an edge list
E(ng)
|
6a16c074e2f3d819993babe89845e117279dfb6b | dc35bc08b2d9eebf783d0a1445d3120b8d83494d | /man/annotate_df.Rd | c4d904d5030afa427f0afd5f29fd2c6dbb7c7d98 | [
"MIT"
] | permissive | hbc/CHBUtils | c83272fd84922fc6fa7e5d34e42b2199552afd5c | a226ceeced2353ee2c7ba2eb22f52bf90c023898 | refs/heads/master | 2021-01-15T15:27:14.308614 | 2017-10-26T18:01:19 | 2017-10-26T18:01:19 | 14,430,820 | 2 | 5 | null | 2017-10-26T18:01:20 | 2013-11-15T17:35:32 | R | UTF-8 | R | false | true | 1,128 | rd | annotate_df.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/annotate_df.R
\name{annotate_df}
\alias{annotate_df}
\title{Annotate Dataframe containing Ensembl IDs with Gene Symbols and Descriptions}
\usage{
annotate_df(df, df_ensemblid_header, biomart_ensembl_dataset,
biomart_ensemblid_filter, biomart_genesymbol_attribute)
}
\arguments{
\item{df}{dataframe, required}
\item{df_ensemblid_header, }{character string containing the name of the dataframe column with ensembl ids, required}
\item{biomart_ensembl_dataset, }{character string describing the biomart dataset to use, required}
\item{biomart_ensemblid_filter, }{character string describing the biomart ensembl id filter to use, required}
\item{biomart_genesymbol_attribute, }{character string describing the biomart gene symbol attribute to obtain, required}
}
\value{
annotated dataframe
}
\description{
Annotate Dataframe containing Ensembl IDs with Gene Symbols and Descriptions
}
\examples{
annotate_df(temp, "id", 'mmusculus_gene_ensembl', "ensembl_gene_id", "mgi_symbol")
}
\seealso{
\code{\link{biomaRt}} used to annotate the dataframe
}
|
9327d57159b7f33f7336c222e1aaa470ce2d5f2d | 616f19c9ecab3330a70d7378f5f73bbbc84ee071 | /cpp/ctrlbars/ctlbmac.r | e688d65bd72a8c908917deee76f0c2c4966c0797 | [] | no_license | rjking58/development | 07dd809da012f736d82fde79c051cc2df328e6b7 | 9566648c6ecfc8b8cb6d166912dd674346da246b | refs/heads/master | 2021-10-11T12:25:31.025767 | 2021-10-01T16:41:06 | 2021-10-01T16:41:06 | 14,648,819 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 3,174 | r | ctlbmac.r | #include "mrc\types.r"
#include "mrc\balloons.r"
#include "resource.h"
#include "systypes.r"
/////////////////////////////////////////////////////////////////////////
// WLM resources
#include "ftab.r"
/////////////////////////////////////////////////////////////////////////
// MFC resources
#include "afxaete.r"
/////////////////////////////////////////////////////////////////////////
// Code fragment resource
#include "CodeFrag.r"
/////////////////////////////////////////////////////////////////////////
// CtrlBars resources
resource 'SIZE' (-1)
{
reserved,
acceptSuspendResumeEvents,
reserved,
canBackground,
doesActivateOnFGSwitch,
backgroundAndForeground,
dontGetFrontClicks,
ignoreAppDiedEvents,
is32BitCompatible,
isHighLevelEventAware,
localAndRemoteHLEvents,
notStationeryAware,
dontUseTextEditServices,
reserved,
reserved,
reserved,
#ifdef _MPPC_
2500 * 1024,
2500 * 1024
#else // 68K Mac
#ifdef _DEBUG
3000 * 1024,
3000 * 1024
#else
2000 * 1024,
2000 * 1024
#endif
#endif
};
resource 'vers' (1)
{
0x01,
0x00,
final,
0x00,
verUS,
"1.0",
"ControlBars 1.0, Copyright \251 Microsoft Corp. 1994-1997"
};
resource 'BNDL' (128)
{
'CTLB',
0,
{
'FREF',
{
0, 128
},
'ICN#',
{
0, IDR_MAINFRAME
}
}
};
type 'CTLB' as 'STR ';
resource 'CTLB' (0)
{
"ControlBars 1.0 Copyright \251 1994-1997 Microsoft Corp."
};
resource 'FREF' (128)
{
'APPL',
0,
""
};
/* Balloon help resources */
resource 'hfdr' (-5696)
{
HelpMgrVersion, hmDefaultOptions, 0, 0,
{
HMSTRResItem {500}
}
};
resource 'hovr' (1000)
{
HelpMgrVersion, hmDefaultOptions, 0, 0,
HMStringItem /* missing items override */
{
"Miscellaneous part of the Microsoft ControlBars "
"Sample Application."
},
{
HMSkipItem {}, /* title bar */
HMSkipItem {}, /* reserved. always skip item here */
HMStringItem /* close box */
{
"Click here to close the Microsoft ControlBars "
"Sample Application."
},
HMStringItem /* zoom box */
{
"Click here to Zoom In or Zoom Out."
},
HMSkipItem {}, /* active app's inactive window */
HMStringItem /* inactive app's window */
{
"This is not part of the Microsoft ControlBars "
"Application. It may be part of the Apple "
"Finder, or some other application."
},
HMSkipItem {} /* outside modal dialog */
}
};
#ifdef _MPPC_
resource 'STR ' (500)
{
"This is the Win32 ControlBars sample application "
"ported to the Power Macintosh using Microsoft VC++ "
"Edition for the Apple Power Macintosh"
};
#else // 68K Mac
resource 'STR ' (500)
{
"This is the Win32 ControlBars sample application "
"ported to the Macintosh using Microsoft VC++ Edition "
"for the Apple Macintosh"
};
#endif
#ifdef _MPPC_
resource 'cfrg' (0) {
{
kPowerPC,
kFullLib,
kNoVersionNum,kNoVersionNum,
0, 0,
kIsApp,kOnDiskFlat,kZeroOffset,kWholeFork,
""
}
};
#endif
|
43c95a99331a5fb54d504a2d377c7a26ca78e983 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/IncomeTax/libFuzzer_IncomeTax/IncomeTax_valgrind_files/1610125576-test.R | a21db92b3fd58fd2d3dce1d4b80e0b53990f6b67 | [] | 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 | 263 | r | 1610125576-test.R | testlist <- list(rates = numeric(0), thresholds = c(4.48309464024909e-120, 4.48309464024909e-120, 4.48309464024909e-120, 1.3906710913539e-309, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), x = 5.0758836746313e-116)
result <- do.call(grattan::IncomeTax,testlist)
str(result) |
826dffb2754a71403f8bdd1b8328e24b2cac2ca2 | bdda050a3713f1ac5b996c2f5c600daecbad920b | /man/convertFlowFrame.Rd | b96b47452f92bdc58690b1a33259fbb983ace2d6 | [
"MIT"
] | permissive | astrolabediagnostics/orloj | dd466acb293447b33426a22766770323b50fee99 | f24b7ef7710e4ba3adaf0d615238bfcd8fe46380 | refs/heads/master | 2021-06-04T16:20:04.772433 | 2021-05-19T14:14:23 | 2021-05-19T14:14:23 | 110,556,030 | 5 | 3 | MIT | 2020-08-09T14:17:33 | 2017-11-13T14:07:22 | R | UTF-8 | R | false | true | 892 | rd | convertFlowFrame.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fcs.R
\name{convertFlowFrame}
\alias{convertFlowFrame}
\title{Convert a FlowCore flow_frame to Astrolabe sample.}
\usage{
convertFlowFrame(experiment, filename, flow_frame)
}
\arguments{
\item{experiment}{An Astrolabe experiment.}
\item{filename}{The name of the FCS file to import.}
\item{flow_frame}{FlowCore flow frame.}
}
\value{
FCS data, in orloj internal FCS list format.
}
\description{
Convert the flow_frame class into orloj's internal FCS list format.
}
\details{
The orloj FCS list format will accumulate more fields as analyses are
applied to it. For example, pre-processing will add a mask to find the
non-bead indices. You can use \code{\link{fcsExprs}} the get the expression
data after applying all of the masks that are in the list.
}
\seealso{
\code{\link{isSample}}, \code{\link{fcsExprs}}
}
|
1252907dc008f1f69d546b324a97245af4263fdf | b28f74d681bb5dfbf34549c82a8c932f77c1b0a8 | /man/aggrNums.Rd | c23fe3d05194b0f0f06d74ee179a2a38072175cd | [
"MIT"
] | permissive | sailfish009/proteoQ | b07e179e9fe27a90fd76cde2ed7caa55e793e9d6 | e6a4fe79a21f9a9106a35d78c2ce42d59e9d82e2 | refs/heads/master | 2022-12-25T20:06:40.340740 | 2020-10-15T20:18:14 | 2020-10-15T20:18:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 477 | rd | aggrNums.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{aggrNums}
\alias{aggrNums}
\title{Summarizes numeric values}
\usage{
aggrNums(f)
}
\arguments{
\item{f}{A function for data summarization.}
}
\description{
\code{aggrNums} summarizes \code{log2FC} and \code{intensity} by the
descriptive statistics of \code{c("mean", "median", "weighted.mean",
"top.3")}
}
\examples{
\donttest{df_num <- aggrNums(median)(df, prot_acc, na.rm = TRUE)}
}
|
ad38a2611a9311087731b87532e364dbe1b1bf8a | 9cf86927ef16f81fb62f9661134a3fc82fe34a00 | /Scripts/Script3.R | e7cccd972e5c19a926555b2876bfd1cc99070dc6 | [] | no_license | esorolla/RepData_PeerAssessment1 | 97e14d70a90b2cc404323967482099b6a491388d | 53436253f7633804ddab5faf88c9f9fea6f9349e | refs/heads/master | 2022-11-24T04:31:21.973535 | 2020-07-15T19:09:09 | 2020-07-15T19:09:09 | 279,573,724 | 0 | 0 | null | 2020-07-14T12:04:08 | 2020-07-14T12:04:08 | null | UTF-8 | R | false | false | 533 | r | Script3.R | ## We aggregate (or group by) the minute intervals of the data set and we
## evaluate the mean value across the days:
timeSeries <- aggregate(data$steps, by=list(interval = data$interval), FUN=mean)
## We plot the requested time series:
plot(timeSeries$interval, timeSeries$x, type = "l", xlab = "interval (minutes)", ylab = "average number of steps")
## We calculate the minute interval where the average number of steps across
## the days is maximum:
reqInterval <- timeSeries$interval[which(timeSeries$x == max(timeSeries$x))]
|
28dd600753a2fa036e271b998dae55f8b11c846e | 902037115141ead7b315e7b63e437ec61c01c2c1 | /R/removeRedundancy.R | e1b922594b612c39c210be9e905d21de092ecfcd | [] | no_license | cran/scrime | 4bdc7e989ba9e648d004ca47cd2d10bb5e78a717 | cf0033dbfe2a6fa807593a460ef4bcb0931db96a | refs/heads/master | 2021-06-02T21:50:17.706604 | 2018-12-01T10:00:03 | 2018-12-01T10:00:03 | 17,699,500 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 173 | r | removeRedundancy.R | `removeRedundancy` <-
function(mat,vec.ia,red=0){
mat<-as.data.frame(mat)
for(i in 1:length(vec.ia))
vec.ia[i]<-evalRedundancy(mat,vec.ia[i],red=red)
vec.ia
}
|
df173cc54b9aa3b513727951cf7ac289b14ce2d0 | 073e4e7c9c2f4822e798f4a56e4ff90b11b5a85c | /Code/baseline_binary_comparison.R | 04515bd098f1c40030758334f35dbfd3399a935d | [] | no_license | peteryzheng/RET_ACCESS | 2cff82bd261beff926affd24798ac02ef2b8775a | ac4e3544d85c90ef723aa3dc433d468515020133 | refs/heads/master | 2022-12-13T08:56:32.229201 | 2020-08-06T04:19:45 | 2020-08-06T04:19:45 | 285,464,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,547 | r | baseline_binary_comparison.R | library(data.table)
library(tidyverse)
baseline.sample.pairs = fread('/ifs/work/bergerm1/zhengy1/RET_all/Sample_mapping/baseline_pairs_121519.tsv')
# pull data for both samples
# DMP.maf <- fread('/ifs/work/bergerm1/zhengy1/dmp/mskimpact/data_mutations_extended.txt') %>%
# filter(Mutation_Status == 'SOMATIC' & Tumor_Sample_Barcode %in% baseline.sample.pairs$DMP_ID) %>% data.table()
maf.dir = '/ifs/work/bergerm1/zhengy1/RET_all/Analysis_files/run_011520/results_combined_permissive/'
access.maf = fread(paste0(gsub('_combined','',maf.dir),'all_sample_maf2maf_oncokb.maf')) %>%
filter(Tumor_Sample_Barcode %in% c(baseline.sample.pairs$`CMO sample ID.plasma`,baseline.sample.pairs$DMP_ID)) %>%
data.table()
x = baseline.sample.pairs$Study_ID[1]
x = 'EDD_ret_pt013'
total.detection.comp = do.call(rbind,lapply(baseline.sample.pairs$Study_ID,function(x){
print(x)
# get all possible mutation called in either or both samples
all.unique.calls = access.maf[Tumor_Sample_Barcode %in% c(baseline.sample.pairs[Study_ID == x]$`CMO sample ID`,
baseline.sample.pairs[Study_ID == x]$DMP_ID)] %>%
transmute(Hugo_Symbol,Chromosome,Start_Position,End_Position,Variant_Classification,Reference_Allele,
Tumor_Seq_Allele2,HGVSp_Short,oncogenic,call_confidence) %>%
filter(call_confidence %in% c('Called','Genotyped')) %>% select(-call_confidence) %>% unique() %>% data.table()
reviewed.calls = fread(paste0(maf.dir,'/',gsub('EDD_|pt','',x),'_table.csv'))[call_confidence == 'High',
.(Hugo_Symbol,Chromosome = as.character(Chromosome),
Start_Position,End_Position,Variant_Classification,
Reference_Allele,Tumor_Seq_Allele2,HGVSp_Short)]
all.unique.calls = merge(all.unique.calls,reviewed.calls,by = c('Hugo_Symbol','Chromosome','Start_Position','End_Position','Variant_Classification',
'Reference_Allele','Tumor_Seq_Allele2','HGVSp_Short'))
# get those all possible calls' genotypes in access sample
access.tmp.maf = access.maf[Tumor_Sample_Barcode == baseline.sample.pairs[Study_ID == x]$`CMO sample ID`] %>%
transmute(Hugo_Symbol,Chromosome,Start_Position,End_Position,Variant_Classification,Reference_Allele,
Tumor_Seq_Allele2,HGVSp_Short,t_alt_count,t_ref_count,t_depth,Tumor_Sample_Barcode,oncogenic,call_confidence) %>%
merge(all.unique.calls,by = c('Hugo_Symbol','Chromosome','Start_Position','End_Position','Variant_Classification',
'Reference_Allele','Tumor_Seq_Allele2','HGVSp_Short','oncogenic'))%>% data.table()
# get those all possible calls' genotypes in DMP sample
dmp.tmp.maf = access.maf[Tumor_Sample_Barcode == baseline.sample.pairs[Study_ID == x]$DMP_ID] %>%
transmute(Hugo_Symbol,Chromosome,Start_Position,End_Position,Variant_Classification,Reference_Allele,
Tumor_Seq_Allele2,HGVSp_Short,t_alt_count,t_ref_count,t_depth,Tumor_Sample_Barcode,oncogenic,call_confidence) %>%
merge(all.unique.calls,by = c('Hugo_Symbol','Chromosome','Start_Position','End_Position','Variant_Classification',
'Reference_Allele','Tumor_Seq_Allele2','HGVSp_Short','oncogenic'))%>% data.table()
detection.comp = merge(dmp.tmp.maf,access.tmp.maf,
by = c('Hugo_Symbol','Chromosome','Start_Position','End_Position','Variant_Classification',
'Reference_Allele','Tumor_Seq_Allele2','HGVSp_Short','oncogenic'),
all = T,suffixes = c('.tumor','.plasma')) %>% rowwise() %>%
mutate(EDD_Patient_ID = x,detection_status = case_when(
call_confidence.tumor != 'Not Called' & call_confidence.plasma != 'Not Called' ~ 'Both',
call_confidence.tumor != 'Not Called' ~ 'Tumor',
call_confidence.plasma != 'Not Called' ~ 'Plasma',
TRUE ~ 'neither'
)) %>% data.table()
}))
# graphing section --------------------------------------------------------
# artifact filtering for plasma
artifacts = total.detection.comp[detection_status == 'Plasma',.N,.(Hugo_Symbol,Chromosome,Start_Position,End_Position,Variant_Classification,Reference_Allele,Tumor_Seq_Allele2,oncogenic)]
total.detection.comp = merge(total.detection.comp,artifacts,by = c('Hugo_Symbol','Chromosome','Start_Position','End_Position','Variant_Classification',
'Reference_Allele','Tumor_Seq_Allele2','oncogenic'), all = T) %>%
filter(N == 1 | is.na(N)) %>% data.table()
# overview of detection status
table(total.detection.comp$detection_status)
# graphing snippet
detection.graph.df = total.detection.comp[,.(oncogenic_mutation = length(which(oncogenic %in% c('Oncogenic','Likely Oncogenic'))),
non_oncogenic_mutation = length(which(!oncogenic %in% c('Oncogenic','Likely Oncogenic')))),detection_status] %>%
melt(id.vars = 'detection_status',variable.name = 'Oncogenicity',value.name = 'Count')
pdf('/ifs/work/bergerm1/zhengy1/RET_all/For_Ezra/RET_Detection/baseline_concordance.pdf',width = 7,height = 7)
ggplot(detection.graph.df) +
geom_bar(aes(x = detection_status,y = Count,fill = Oncogenicity),stat = 'identity') +
scale_fill_brewer(palette = "Pastel2") + theme_classic()
dev.off()
detection.graph.df = total.detection.comp[,.(Count = .N),.(detection_status,Variant_Classification)]
pdf('/ifs/work/bergerm1/zhengy1/RET_all/For_Ezra/RET_Detection/baseline_concordance_class.pdf',width = 7,height = 7)
ggplot(detection.graph.df) +
geom_bar(aes(x = detection_status,y = Count,fill = Variant_Classification),stat = 'identity') +
scale_fill_brewer(palette = "Pastel2") + theme_classic()
dev.off()
total.detection.comp[,.(tumor_only = nrow(.SD[detection_status == 'Tumor']),
plasma_only = nrow(.SD[detection_status == 'Plasma']),
both = nrow(.SD[detection_status == 'Both'])),EDD_Patient_ID]
colourCount = nrow(unique(total.detection.comp[,.(EDD_Patient_ID)]))
getPalette = colorRampPalette(brewer.pal(8, "Set2"))
pdf('/ifs/work/bergerm1/zhengy1/RET_all/For_Ezra/RET_Detection/baseline_VAFs.pdf',width = 10,height = 7)
ggplot(total.detection.comp[detection_status == 'Both']) +
geom_point(aes(x = as.numeric(t_alt_count.tumor)/as.numeric(t_depth.tumor),
y = as.numeric(t_alt_count.plasma)/as.numeric(t_depth.plasma),
color = EDD_Patient_ID,shape = oncogenic)) +
scale_color_manual(values = getPalette(colourCount),name = 'Alteration') + theme_classic() +
xlab('Tumor_VAF') + ylab('Plasma_VAF')
dev.off()
pdf('/ifs/work/bergerm1/zhengy1/RET_all/For_Ezra/RET_Detection/baseline_VAFs_all.pdf',width = 10,height = 7)
ggplot(total.detection.comp) +
geom_point(aes(x = as.numeric(t_alt_count.tumor)/as.numeric(t_depth.tumor),
y = as.numeric(t_alt_count.plasma)/as.numeric(t_depth.plasma),
color = EDD_Patient_ID,shape = oncogenic)) +
scale_color_manual(values = getPalette(colourCount),name = 'Alteration') + theme_classic() +
xlab('Tumor_VAF') + ylab('Plasma_VAF')
dev.off()
# analysis of each category -----------------------------------------------
table(total.detection.comp[detection_status == 'Plasma']$Hugo_Symbol)
|
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9d0c085dd06d44ef7956465c854333f503837ccd | 27b4436fdbab36fd3d7963df408a2d259e8fd909 | /R/internal.R | 892096df7a8cbba0843c9637ed6b557043cb5ba6 | [] | no_license | dklinges9/mcera5 | 962833c6b208d892ddb7e1ef1f9e80b9b7fa6140 | a37074a74f78439118f90c2ee76ab5bafaf7f2ed | refs/heads/master | 2023-07-23T11:09:26.529124 | 2023-07-10T23:13:41 | 2023-07-10T23:13:41 | 260,175,954 | 10 | 4 | null | 2023-09-05T13:51:41 | 2020-04-30T10:01:45 | R | UTF-8 | R | false | false | 10,960 | r | internal.R | #' function to calculate humidity from dew point temperature
#' @param tdew dewpoint temperature (°C)
#' @param tc air temperature (°C)
#' @param p pressure (Pa)
#' @return specific humidity (Kg/Kg)
#' @noRd
humfromdew <- function(tdew, tc, p) {
pk <- p/1000
ea <- 0.6108 * exp(17.27 * tdew / (tdew + 237.3))
e0 <- 0.6108 * exp(17.27 * tc/(tc + 237.3))
s <- 0.622 * e0/pk
hr <- (ea/e0) * 100
hs <- (hr/100) * s
hs
}
#' function to apply a correction to ERA5 temperatures based on proximity to the
#' coast
#' @param tc air temperature (°C)
#' @param landprop single numeric value indicating proportion of grid cell that
#' is land (0 = all sea, 1 = all land)
#' @param cor_fac correction factor to be applied (default to 1.285 for UK)
#' @return air temperature (°C)
#' @noRd
coastal_correct <- function(tc, landprop, cor_fac = 1.285) {
td <- matrix(tc, ncol=24, byrow=T)
tmean <- rep(apply(td, 1, mean), each=24)
m <- (1 - landprop) * cor_fac + 1
tdif <- (tc - tmean) * m
tco <- tmean + tdif
return(tco)
}
#' function to correct radiation for being an average over the hour rather than
#' on the hour
#' @param rad vector of radiation
#' @param tme vector of times
#' @param long longitude
#' @param lat latitude
#' @return vector of radiation
#' @noRd
rad_calc <- function(rad, tme, long, lat) {
bound <- function(x, mn = 0, mx = 1) {
x[x > mx] <- mx
x[x < mn] <- mn
x
}
tme1h <- as.POSIXlt(tme, tz = "UTC", origin = "1970-01-01 00:00")
# interpolate times to 10 mins
tme10min <- as.POSIXlt(seq(from = as.POSIXlt(tme1h[1]-3600),
to = as.POSIXlt(tme1h[length(tme1h)]),
by = "10 min"), origin = "1970-01-01 00:00",
tz = "UTC")
csr10min <- microclima::clearskyrad(tme10min, lat = lat , long = long,
merid = 0, dst = 0)
# mean csr per hour (every 6 values)
csr1h <- tibble::tibble(csr10min) %>%
dplyr::group_by(group = gl(length(csr10min)/6, 6)) %>%
dplyr::summarise(csr1h = mean(csr10min)) %>%
.$csr1h
# watch out for NAs here - need modding?
od <- bound(rad / csr1h)
#if (is.na(od)[1]) od[1] <- mean(od, na.rm = T)
#if (is.na(od)[length(od)]) od[length(od)] <- mean(od, na.rm = T)
od[is.na(od)] <- 0
# hourly time stamps on the half hour
tme1h_2 <- as.POSIXlt(tme1h - 1800, tz = "UTC", origin = "1970-01-01 00:00")
# length to interpolate to
n <- length(tme1h_2) * 2
# work out od values for every half hour
od1h <- stats::spline(tme1h_2, od, n = n)$y
# select just the ones on the hour
od1h <- od1h[seq(2,length(od1h),2)]
# csr on the hour
csr1h_2 <- microclima::clearskyrad(tme1h, lat = lat, long = long, merid = 0, dst = 0)
# calculate corrected rad on the hour
rad_out <- csr1h_2 * od1h
rad_out[is.na(rad_out)] <- 0
return(rad_out)
}
#' function to calculate the position of the 4 nearest neighbours to a point,
#' the xy distance and inverse weight of each.
#' @param long longitude
#' @param lat latitude
#' @return data frame of longitude, latitude, xy distance and inverse weight of
#' each neighbouring point
#' @noRd
focal_dist <- function(long, lat) {
# round to nearest 0.25
x_r <- plyr::round_any(long, .25)
y_r <- plyr::round_any(lat, .25)
# work out locations of the four neighbour points in the ERA5 dataset
if(long >= x_r) {
focal_x <- c(x_r, x_r, x_r + 0.25, x_r + 0.25) } else {
focal_x <- c(x_r - 0.25, x_r - 0.25, x_r, x_r)
}
if(lat >= y_r) {
focal_y <- c(y_r, y_r + 0.25, y_r, y_r + 0.25) } else {
focal_y <- c(y_r - 0.25, y_r, y_r - 0.25, y_r)
}
# work out weighting based on dist between input & 4 surrounding points
x_dist <- abs(long - focal_x)
y_dist <- abs(lat - focal_y)
xy_dist <- sqrt(x_dist^2 + y_dist^2)
focal <- data.frame(x = focal_x, y = focal_y, xy_dist) %>%
dplyr::mutate(., inverse_weight = 1/sum(1/(1/sum(xy_dist) * xy_dist)) * 1/(1/sum(xy_dist) * xy_dist))
return(focal)
}
#' function to process relevant hourly climate data from an ERA5 nc to
#' a data frame
#' @param nc path to nc file downloaded with `request_era5`
#' @param long longitude
#' @param lat latitude
#' @param start_time start time for data required
#' @param end_time end time for data required
#' @param dtr_cor logical value indicating whether to apply a diurnal temperature
#' range correction to air temperature values. Default = `TRUE`.
#' @param dtr_cor_fac numeric value to be used in the diurnal temperature range
#' correction. Default = 1.
#' @return data frame of hourly climate variables
#' @noRd
nc_to_df <- function(nc, long, lat, start_time, end_time, dtr_cor = TRUE,
dtr_cor_fac = 1) {
dat <- tidync::tidync(nc) %>%
tidync::hyper_filter(longitude = longitude == long,
latitude = latitude == lat) %>%
tidync::hyper_tibble() %>%
dplyr::mutate(., obs_time = lubridate::ymd_hms("1900:01:01 00:00:00") + (time * 3600),
timezone = lubridate::tz(obs_time)) %>% # convert to readable times
dplyr::filter(., obs_time >= start_time & obs_time < end_time + 1) %>%
dplyr::rename(., pressure = sp) %>%
dplyr::mutate(., temperature = t2m - 273.15, # kelvin to celcius
lsm = dplyr::case_when(
lsm < 0 ~ 0,
lsm >= 0 ~ lsm),
temperature = dplyr::case_when(
dtr_cor == TRUE ~ coastal_correct(temperature, lsm, dtr_cor_fac),
dtr_cor == FALSE ~ temperature),
humidity = humfromdew(d2m - 273.15, temperature, pressure),
windspeed = sqrt(u10^2 + v10^2),
windspeed = microclima::windheight(windspeed, 10, 2),
winddir = (atan2(u10, v10) * 180/pi + 180)%%360,
cloudcover = tcc * 100,
netlong = abs(msnlwrf) * 0.0036,
downlong = msdwlwrf * 0.0036,
uplong = netlong + downlong,
emissivity = downlong/uplong, # converted to MJ m-2 hr-1
jd = microclima::julday(lubridate::year(obs_time),
lubridate::month(obs_time),
lubridate::day(obs_time)),
si = microclima::siflat(lubridate::hour(obs_time), lat, long, jd, merid = 0)) %>%
dplyr::mutate(., rad_dni = fdir * 0.000001,
rad_glbl = ssrd * 0.000001,
rad_glbl = rad_calc(rad_glbl, obs_time, long, lat), # fix hourly rad
rad_dni = rad_calc(rad_dni, obs_time, long, lat), # fix hourly rad
rad_dif = rad_glbl - rad_dni * si) %>% # converted to MJ m-2 hr-1 from J m-2 hr-1
dplyr::mutate(., szenith = 90 - microclima::solalt(lubridate::hour(obs_time),
lat, long, jd, merid = 0)) %>%
dplyr::select(.,obs_time, temperature, humidity, pressure, windspeed,
winddir, emissivity, cloudcover, netlong, uplong, downlong,
rad_dni, rad_dif, szenith, timezone)
return(dat)
}
# process relevant precipitation data from an ERA5 nc to data frame
#' a function to process relevant precipitation data from an ERA5 nc to
#' a data frame
#' @param nc path to nc file downloaded with `request_era5`
#' @param long longitude
#' @param lat latitude
#' @param start_time start time for data required
#' @param end_time end time for data required
#' @return vector of daily precipitation values
#' @noRd
nc_to_df_precip <- function(nc, long, lat, start_time, end_time) {
dat <- tidync::tidync(nc) %>%
tidync::hyper_filter(longitude = longitude == long,
latitude = latitude == lat) %>%
tidync::hyper_tibble() %>%
dplyr::mutate(., obs_time = lubridate::ymd_hms("1900:01:01 00:00:00") + (time * 3600),
timezone = lubridate::tz(obs_time)) %>% # convert to readable times
dplyr::filter(., obs_time >= start_time & obs_time < end_time + 1) %>%
dplyr::rename(., precipitation = tp) %>%
dplyr::select(., obs_time, precipitation)
return(dat)
}
#' creates a data frame of unique month and year pairs from input
#' start/end times
#' @param start_time start time
#' @param end_time end time
#' @return data frame of unique months and years
#' @noRd
uni_dates <- function(start_time, end_time) {
tme <- seq(as.POSIXlt(start_time), as.POSIXlt(end_time), by = 1)
mon <- lubridate::month(tme)
yea <- lubridate::year(tme)
df <- data.frame(mon,yea) %>%
dplyr::distinct(.)
return(df)
}
#' Combines a series of netCDFs that all have the same spatial extent and
#' set of variables
#' @param filenames a list of filenames for netCDFs you wish to combine
#' @param combined_name the name of the combined netCDF
#' @noRd
combine_netcdf <- function(filenames, combined_name) {
files <- lapply(filenames, function(x) {
ncdf4::nc_open(x)
})
# Pull out first file for reference specs
nc <- files[[1]]
# Create an empty list to populate
vars_list <- vector(mode = "list", length = nc$nvars)
data_list <- vector(mode = "list", length = nc$nvars)
# One variable at a time
for (i in 1:length(names(nc$var))) {
varname <- names(nc$var)[i]
# Get the variable from each of the netCDFs
vars_dat <- lapply(files, function(x) {
ncdf4::ncvar_get(x, varname)
})
# Then bind all of the arrays together using abind, flexibly called via do.call
data_list[[i]] <- do.call(abind::abind, list(... = vars_dat,
along = 3))
# To populate the time dimension, need to pull out the time values from each
# netCDF
timevals <- lapply(files, function(x) {
x$dim$time$vals
})
# Create a netCDF variable
vars_list[[i]] <- ncdf4::ncvar_def(
name = varname,
units = nc$var[varname][[varname]]$units,
# Pull dimension names, units, and values from file1
dim = list(
# Longitude
ncdf4::ncdim_def(nc$dim$longitude$name, nc$dim$longitude$units,
nc$dim$longitude$vals),
# Latitude
ncdf4::ncdim_def(nc$dim$latitude$name, nc$dim$latitude$units,
nc$dim$latitude$vals),
# Time
ncdf4::ncdim_def(nc$dim$time$name, nc$dim$time$units,
# Combination of values of all files
do.call(c, timevals))
))
}
# Create a new file
file_combined <- ncdf4::nc_create(
# Filename from param combined_name
filename = combined_name,
# We need to define the variables here
vars = vars_list)
# And write to it (must write one variable at a time with ncdf4)
for (i in 1:length(names(nc$var))) {
ncdf4::ncvar_put(
nc = file_combined,
varid = names(nc$var)[i],
vals = data_list[[i]])
}
# Finally, close the file
ncdf4::nc_close(file_combined)
}
|
84f913d72932cb7c68f4a52a5f108b378f83311e | 4846b5b3748b6724d7c379dae7572e9fa90a798d | /man/bindingContextDistanceCapR.Rd | 32e76b560b88caa21093e6b8490ca984e8dbb5b3 | [] | no_license | vbusa1/nearBynding | d225bcbdb1541b65c3f01604a1affd8ff51b068a | 9ccf2b0e7fec87c426cf37fe45077d67abef210a | refs/heads/master | 2023-04-07T19:01:47.323219 | 2021-07-30T17:39:58 | 2021-07-30T17:39:58 | 278,680,217 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,491 | rd | bindingContextDistanceCapR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bindingContextDistanceCapR.R
\name{bindingContextDistanceCapR}
\alias{bindingContextDistanceCapR}
\title{bindingContextDistanceCapR}
\usage{
bindingContextDistanceCapR(
dir_stereogene_output = ".",
CapR_prefix = "",
protein_file,
protein_file_input = NULL,
dir_stereogene_output_2 = NULL,
CapR_prefix_2 = "",
protein_file_2,
protein_file_input_2 = NULL,
context = "all",
range = c(-200, 200)
)
}
\arguments{
\item{dir_stereogene_output}{Directory of Stereogene output for first
protein. Default current directory.}
\item{CapR_prefix}{The prefix common to CapR output files of protein_file, if
applicable. Equivalent to output_prefix from runStereogeneOnCapR. Default ""}
\item{protein_file}{A vector of strings with at least one protein file name
to be averaged for calculation of distance. File names must exclude
extensions such as ".bedGraph". All files in the list should be
experimental/biological replicates. Required.}
\item{protein_file_input}{A protein file name of background input to be
subtracted from protein_file signal. File name must exclude extension. Only
one input file is permitted. Optional.}
\item{dir_stereogene_output_2}{Directory of Stereogene output for second
protein. Default current directory.}
\item{CapR_prefix_2}{The prefix common to CapR output files of
protein_file_2, if applicable.Equivalent to output_prefix from r
unStereogeneOnCapR. Default ""}
\item{protein_file_2}{Similar to protein_file. A second vector of at least
one protein file name to be averaged for calculation of distance. File names
must exclude extensions such as ".bedGraph". All files in the list should be
experimental/biological replicates. Required.}
\item{protein_file_input_2}{Similar to protein_file_input. A second protein
file name of background input to be subtracted from protein_file_2 signal.
File name must exclude extension. Only one input file is permitted. Optional.}
\item{context}{The RNA structure context being compared for the two protein
file sets. Acceptable contexts include "all", which sums the distance of all
six contexts, or any of the contexts individually ("bulge", "hairpin",
"stem", "exterior", "multibranch", or "internal"). Default "all"}
\item{range}{A vector of two integers denoting the range upstream and
downstream of the center of protein binding to consider in the comparison.
Ranges that are too small miss the holistic binding context, while large
ranges amplify distal noise in the binding data. Cannot exceed wSize/2 from
write_config. Default c(-200, 200)}
}
\value{
Wasserstein distance between the two protein file sets provided for
the RNA structure context specified, minus the input binding signal if
applicable
}
\description{
Calculate the Wasserstein distance between two replicates' or
two proteins' binding contexts.
}
\note{
Wasserstein distance calculations are reciprocal, so it does not matter
which protein is first or second so long as replicates and input files
correspond to one another.
}
\examples{
## load example StereoGene output
get_outfiles()
## This boring example compares a protein's binding with itself for all
## contexts, therefore the distance is 0
bindingContextDistanceCapR(CapR_prefix = "chr4and5_3UTR",
protein_file = "chr4and5_liftOver",
CapR_prefix_2 = "chr4and5_3UTR",
protein_file_2 = "chr4and5_liftOver")
}
|
3ae1c1d65957cec0ea40ab60337e8d78c379854e | 5f90bac8cc2cf136a3478a2290d9abde7dda745a | /R/normalize.GoldenGate.R | 423ce1d5ae817c4717f6c4d944070d4ce9644460 | [] | no_license | cran/LumiWCluster | 277d23a646575b891d5218a5e69633ff052b50b7 | a1272d73fd1bc89c29da5845d50c7e634b7f4a12 | refs/heads/master | 2020-04-22T03:14:46.756019 | 2010-09-02T00:00:00 | 2010-09-02T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,336 | r | normalize.GoldenGate.R | normalize.GoldenGate <-
function(Probe_ID, beta, det.p.value, design.file, plot.option = FALSE){
if(is.vector(beta) == TRUE) beta <- matrix(beta)
if(is.vector(det.p.value) == TRUE) det.p.value <- matrix(det.p.value)
beta[beta == 0] <- min(beta[ beta > 0],1e-3)/10
beta[beta == 1] <- max(beta[ beta < 1],0.999) + (1 - max(beta[ beta < 1],0.999))/100
matchID <- rep(NA,length(Probe_ID))
for(i in 1:length(matchID)){
matchID[i] <- which(as.character(design.file$Probe_ID) == as.character(Probe_ID[i]))
}
L <- design.file$L[matchID]
GC <- design.file$GC[matchID]
y_raw <- log(t(beta)/(1 - t(beta)))
n <- dim(y_raw)[1]
p <- dim(y_raw)[2]
y <- matrix(0,nrow=n,ncol=p)
w <- apply(log(t(det.p.value)),1,median)
w <- w/sum(w)
g <- apply(log(t(det.p.value)),2,median)
g <- g/sum(g)
L_new <- L
L_new[which(L<=44)] <- 44
L_new[which(L>=57)] <- 57
GC_new <- GC
GC_new[which(GC<=0.4)] <- 0.4
GC_new[which(GC>=0.8)] <- 0.8
L_coef <- 0.04458
GC_coef <- -3.658
for(i in 1:n){
y[i,] <- y_raw[i,] - L_coef*L_new - GC_coef*GC_new + mean(L_coef*L_new) + mean(GC_coef*GC_new)
}
norm.beta <- t(inv_logit(y))
if(plot.option == TRUE ) plotBAnorm(beta, norm.beta, design.file)
return(list(norm.beta = norm.beta, norm.logitbeta = t(y), w = w, g = g))
}
|
6e7c1bdba722dd0fdba107c7c52e3f9b9d2f643a | eb46831a43d55a348e29989d827401f2eb49e30a | /liquididtyt.R | 9107a6506d16dfdd5b84a36165aa8a02d04bea65 | [] | no_license | bplloyd/R-risk-mgmt | 32a0315bdd95790acb4c98cc87c52993a1cd8f9a | ad0e0f3596e5588fb4c483486e271fb98ad942cc | refs/heads/master | 2020-12-13T09:19:32.536834 | 2016-08-03T22:08:14 | 2016-08-03T22:08:14 | 49,309,816 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,386 | r | liquididtyt.R | qry = "WITH
Levels AS
(
select
v.DateReported
, s.Abbreviation 'Name'
, COALESCE(l.Liquidity, 99) 'Level'
, COALESCE(v.MarketValue,0)/a.NetAssets 'Exposure'
from
v_FundSecs_FLASHREPORT AS v
LEFT JOIN v_Assets_UFT AS a ON (v.Fund_UID = a.Fund_UID AND v.DateReported = a.DateReported)
LEFT JOIN HAMF.Liquidity AS l ON ((v.SubAdvised_UID = l.SubAdvised_UID) AND (v.Security_UID = l.Security_UID) AND (l.DateReported = (SELECT MAX(l2.DateREported) FROM Hamf.Liquidity AS l2 WHERE l2.DateReported <= v.DateReported)))
LEFT JOIN HAMF.SubAdvisors AS s ON v.SubAdvised_UID = s.SubAdvised_UID
WHERE
v.DateReported >= '1-22-2016'
AND v.Fund_UID = 784
AND v.Asset_Type IN ('EQ', 'DERV', 'OP', 'FI')
AND l.Liquidity >= 3
)
select
l.DateReported
, l.Name
, l.[Level]
, SUM(COALESCE(l.Exposure,0)) 'Exposure'
from
Levels as l
GROUP BY
l.DateReported
, l.Name
, l.[Level]
ORDER BY
l.DateReported
, l.Name
, l.[Level] DESC"
require(RODBC)
cn = odbcDriverConnect("driver={SQL Server}; server=HAT-SQL-01; database=Hatteras_Sandbox_Tools; trusted_connection=true")
require(xts)
require(data.table)
lsd.liq = data.table(sqlQuery(cn,qry))
lsd.liq$DateReported = as.Date.factor(lsd.liq$DateReported)
lsd.liq_xts = as.xts(dcast.data.table(lsd.liq[, sum(Exposure), by = c("DateReported", "Name")], formula = DateReported ~ Name, value.var = "V1", fun.aggregate = sum))
|
5418eb7403d03b32b0761206690011f8f14839cb | a70d7e23b94219ac412a38f509e386c0c7a51bfe | /data-raw/app/utils.R | 6b05b24bc580aacf0473f9a4eec6dc6f4f238d61 | [] | no_license | nverno/moosedata | 17a6867b567401972b7e9fb38f06dbca5608a15f | 856482d12b4ac3812454969c28aafe7ae21fe576 | refs/heads/master | 2021-01-10T05:47:56.386212 | 2016-01-31T07:42:18 | 2016-01-31T07:42:18 | 46,290,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,053 | r | utils.R | ### utils.R ---
## Filename: utils.R
## Description:
## Author: Noah Peart
## Created: Sat Jan 23 20:37:49 2016 (-0500)
## Last-Updated: Wed Jan 27 23:08:23 2016 (-0500)
## By: Noah Peart
######################################################################
"%||%" <- function(a, b) if (is.null(a)) b else a
################################################################################
##
## Some AFS utils
##
################################################################################
## Store the AFS token information
afs_cache <- new.env()
##' Check that tokens and klog are available
afs_utils <- function() {
progs <- c('klog', 'tokens')
command <- switch(Sys.info()['sysname'], 'Windows'='where', 'type')
all(unlist(lapply(progs, system2, command=command, stdout=FALSE)) == 0L)
}
##' Check the afs cache for tokens
afs_yes <- function() !is.null(afs_cache$tokens)
##' Load data. If can't connect to data, returns and empty data.table.
##' @title Load data
##' @param data name of dataset
load_data <- function(data) {
if (!afs_yes()) {
warning("No AFS tokens, can't load data from AFS.")
return( data.table(x=numeric()) )
}
dat <- sync.afs::get_data(data, sync.afs::get_afs(), dkey)
setnames(dat, names(dat), toupper(names(dat)))
}
##' Parse strings of AFS tokens returned by system call to 'tokens'.
##' @title Parse AFS tokens
##' @param tokens character vector of tokens
##' @return \code{data.table} with token information
afs_parse_tokens <- function(tokens) {
tokens <- strsplit(tokens, '\\s+|@')
out <- lapply(tokens, function(x) {
list(user=sub('([[:alnum:]]+).*', '\\1', x[[2]]),
cell=x[[6]],
expires=as.POSIXct(
paste(c(x[8:9], sub(']', '', x[[10]])), collapse=' '),
format="%b %d %H:%M"))
})
rbindlist(out)
}
##' Query system for tokens
##' @title Retrieve tokens from system
afs_tokens <- function() {
response <- system2("tokens", stdout=TRUE, stderr=TRUE)
has_token <- is.character(response) &&
any((inds <- grepl("tokens for afs@", response)))
if (has_token) response[inds] else ''
}
##' logout of AFS
afs_logout <- function() {
res <- system2('unlog')
afs_cache$tokens <- NULL
afs_cache$error <- NULL
invisible(res == 0L)
}
##' Update the AFS token cache. This could be either a new set of tokens, an error, or
##' an initial check for tokens with the system.
##' @title Update AFS cache
##' @param error Optional error message to cache.
##' @return NULL
afs_update_cache <- function(error) {
token <- afs_tokens()
if (!missing(error)) {
afs_cache$error <- error
if (!nzchar(token)) afs_cache$tokens <- NULL
return( invisible() )
}
afs_cache$error <- NULL # wipe errors
if (!nzchar(token)) {
afs_cache$tokens <- NULL
return( invisible() )
}
afs_cache$tokens <- afs_parse_tokens(token)
invisible()
}
##' Submit AFS credentials
##' @title Submit AFS creds
##' @param user username
##' @param pwd password
##' @param cell cell
afs_submit <- function(user, pwd, cell='northstar.dartmouth.edu') {
if (missing(user) || missing(pwd)) return(FALSE)
res <- system2("klog",
args=c("-principal", user,
"-password", pwd,
"-cell", cell),
stderr=TRUE)
if (!length(res)) {
afs_update_cache()
TRUE
} else {
afs_update_cache(error=res)
FALSE
}
}
##' Format tokens table for html
##' @title Print tokens table
afs_tokens_table <- function() {
if (is.null(afs_cache$tokens)) return()
## res <- formattable(afs_cache$tokens, list(
## expires = formatter(
## 'span',
## style=x ~ style(color = csscolor(gradient(rank(x), 'white', 'red')))
## )
## ))
## paste(as.character(res))
as.character(knitr::kable(afs_cache$tokens, format='html',
caption='Current AFS Tokens'))
}
|
b675c7e0c35dd6653fa8e0dd11f4ad91c016987e | fe0cd547bd325d0026607421d334b25df2d6b15d | /inst/doc/neasg.R | f15c3da9eeeb6b5a224514e7deb2bc28ccd9c198 | [] | no_license | andrew-loh/neaSG | ee800a4922b4c4c755f62c661f9e560a75ebdaee | 2518b9e21163baef559d45c73cf0ba344bca0ab6 | refs/heads/master | 2020-04-10T01:14:16.113645 | 2018-12-17T00:02:36 | 2018-12-17T00:02:36 | 160,709,884 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,685 | r | neasg.R | ## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- message=FALSE, warning=FALSE, eval=FALSE---------------------------
# library(devtools)
# install_github("andrew-loh/neaSG", force = TRUE)
## ---- message=FALSE, warning=FALSE---------------------------------------
library(neaSG)
ls(package:neaSG)
## ---- message=FALSE, warning=FALSE---------------------------------------
get_weatherstns()
## ---- message=FALSE, warning=FALSE---------------------------------------
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_airtemp(from = "2017-12-31", to = "2018-01-04")
head(data)
## ---- message=FALSE, warning=FALSE---------------------------------------
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_humidity(from = "2017-12-31", to = "2018-01-04")
head(data)
## ---- message=FALSE, warning=FALSE---------------------------------------
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_rainfall(from = "2017-12-31", to = "2018-01-04")
head(data)
## ---- message=FALSE, warning=FALSE---------------------------------------
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_UV(from = "2017-12-31", to = "2018-01-04")
head(data)
## ---- message=FALSE, warning=FALSE---------------------------------------
#Dates must be input as characters in a "YYYY-MM-DD" format
data <- get_pollutants(from = "2017-12-31", to = "2018-01-04")
head(data)
## ---- message=FALSE, warning=FALSE---------------------------------------
data <- get_weathersumm(from = "2017-12-31", to = "2018-01-04")
head(data)
|
12d9d373fd616efe1d442d2a5cda66a26e865eb7 | ee0689132c92cf0ea3e82c65b20f85a2d6127bb8 | /23-functions/47c-apply.R | fb592875fc859fbcc430268254c84ad7f8702158 | [] | no_license | DUanalytics/rAnalytics | f98d34d324e1611c8c0924fbd499a5fdac0e0911 | 07242250a702631c0d6a31d3ad8568daf9256099 | refs/heads/master | 2023-08-08T14:48:13.210501 | 2023-07-30T12:27:26 | 2023-07-30T12:27:26 | 201,704,509 | 203 | 29 | null | null | null | null | UTF-8 | R | false | false | 1,754 | r | 47c-apply.R | # Apply Commands
# apply Functions
# #apply ----
#create matrix####
m1 = matrix(1:10,nrow=5)
m1
# create sample data of Matrix : Use ND data : 3 cols, 30 rows
m2 <- matrix(data=cbind(rnorm(30, 0), rnorm(30, 2), rnorm(30, 5)), nrow=30, ncol=3)
m2
# apply is used for structured data - matrix, dataframe with same number of coln & rows
# https://www.r-bloggers.com/using-apply-sapply-lapply-in-r/
# apply Mean on Rowise on all 3 Column
m1
apply(m1, 1, mean)
apply(m1, 2, mean)
# apply function on particular values : count number of negative values
apply(m2, 2, function(x) length(x[x<0]))
m2
apply(m2, 2, function(x) is.numeric(x))
apply(m2, 1, function(x) is.character(x))
apply(m2, 2, function(x) is.vector(x))
apply(m2, 2, function(x) mean(x[x>0]))
# sapply
sapply(1:3, function(x) x^2)
sapply(m[1,], function(x) x^2)
sapply(m[,2], function(x) x^2)
# lapply - return list rather than a vector
lapply(1:3, function(x) x^2)
unlist(lapply(1:3, function(x) x^2))
mean(m[,1])
mean(m[,3])
#https://www.r-bloggers.com/the-apply-command-101/
# tricks
sapply(1:3, function(x) mean(m[,x]))
# dataset name pass as y
sapply(1:3, function(x, y) mean(y[,x]), y=m)
# Means of various colns
meanset = c( mean(m[,1]), mean(m[,2]), mean(m[,3]))
meanset
# fiven no summary five number summary (minimum, lower-hinge, median, upper-hinge, maximum) for the input data
sum1 = apply(m,2,fivenum)
sum1
?fivenum
mean(m[,2])
sum2 = apply(m,2,summary)
sum2
# rowmeans
rowMeans(m)
colMeans(m)
# trimming data
mean(m[,1])
mean(m[,1],trim=0.5)
a1 = array(1:(3*4*2), dim=c(3,4,2))
a1
#5 rows, 4 cols, 3 matrices
apply(a1, 1, sum)
apply(a1, 2, sum)
apply(a1, c(3), sum)
apply(a1, c(1,2), sum)
apply(a1, c(2,3), sum)
apply(a1, c(1,2,3), sum)
apply(a1, c(1,3), sum)
|
1cf7c65b14239d68f7f364c0ae5ec46b6a53be1b | 3d3dca4a42b1777c13108e03ca0e3bfcd9a93205 | /Rfunctions/makeTimeStamp.R | c69635c701fd0057037eac677daef9bf10f14b7a | [] | no_license | BioAimie/AnalyticsWebHub | 90afaaf4abf09ef0799ddaa5bd762233975f2e38 | 4f650049cdbfb2ce41ab072655878a5a7a12d5e7 | refs/heads/master | 2021-03-27T12:31:24.229879 | 2017-07-27T16:50:14 | 2017-07-27T16:50:14 | 74,378,065 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 467 | r | makeTimeStamp.R | makeTimeStamp <- function(timeStamp = Sys.time(), author=NULL, size = 1, color = 'black')
{
library(grid)
stamp <- ifelse(is.null(author), as.character(timeStamp), paste(timeStamp, paste('Created by', author)))
pushViewport(viewport())
grid.text(label = stamp,
x = unit(1,"npc") - unit(2, "mm"),
y = unit(2, "mm"),
just = c("right", "bottom"),
gp = gpar(cex = size, col = color))
popViewport()
}
|
2cc861d3440267e2c1f46c8d8677e6860fa3bf2b | f36b2ad1dc17ec05278f13c7fa72a1fd8343ee19 | /tests/testthat/test-chk-atomic.R | 45ddbb9d8651545d0f44bc30c4a4adcf6339f69e | [
"MIT"
] | permissive | poissonconsulting/chk | 45f5d81df8a967aad6e148f0bff9a9f5b89a51ac | c2545f04b23e918444d4758e4362d20dfaa8350b | refs/heads/main | 2023-06-14T19:32:17.452025 | 2023-05-27T23:53:25 | 2023-05-27T23:53:25 | 199,894,184 | 43 | 3 | NOASSERTION | 2023-01-05T18:50:23 | 2019-07-31T16:42:59 | R | UTF-8 | R | false | false | 460 | r | test-chk-atomic.R | test_that("vld_atomic", {
expect_true(vld_atomic(1))
expect_true(vld_atomic(matrix(1:3)))
expect_true(vld_atomic(character(0)))
expect_true(vld_atomic(NULL))
expect_false(vld_atomic(list(1)))
})
test_that("chk_atomic", {
expect_identical(chk_atomic(1), 1)
expect_invisible(chk_atomic(1))
expect_chk_error(chk_atomic(list(1)), "^`list[(]1[)]` must be atomic[.]$")
expect_chk_error(chk_atomic(list(1), x_name = 1), "^1 must be atomic[.]$")
})
|
0812ac11c1576915c4ae79ae5d086ae1d2238cb1 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/AlphaSimR/man/varP.Rd | 89470d2e87c0126dbbe82b5dc190ae6e894eb8a0 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 604 | rd | varP.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/popSummary.R
\name{varP}
\alias{varP}
\title{Phenotypic variance}
\usage{
varP(pop)
}
\arguments{
\item{pop}{an object of \code{\link{Pop-class}} or \code{\link{HybridPop-class}}}
}
\description{
Returns phenotypic variance for all traits
}
\examples{
#Create founder haplotypes
founderPop = quickHaplo(nInd=10, nChr=1, segSites=10)
#Set simulation parameters
SP = SimParam$new(founderPop)
SP$addTraitA(10)
SP$setVarE(h2=0.5)
#Create population
pop = newPop(founderPop, simParam=SP)
varP(pop)
}
|
d723ee88c075ed21d83f37654885eb727f6f36cf | b47547eae99baa6288cd350d2ef949eaa9faecee | /man/g11_ftest.Rd | 7f489b135b16a5cdf70917e6559d94460edff14a | [] | no_license | gabiitokazu/desperation | a75ed7739b386b02384c1d8f7bc92b6103384f95 | b0957e34661dd476c3702ebf79eac63855196de0 | refs/heads/master | 2023-01-21T08:39:36.094367 | 2020-12-05T05:37:43 | 2020-12-05T05:37:43 | 318,705,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,334 | rd | g11_ftest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/g11_ftest.R
\name{g11_ftest}
\alias{g11_ftest}
\title{Linear Model Function for Group11 - AU STAT6210}
\usage{
g11_ftest(response, covariates)
}
\arguments{
\item{response}{A \code{vector} with the values for the dependent variable (also called outcome).}
\item{covariates}{A \code{matrix} with the values for the independent variable (also called predictors, or explanatory variable).}
\item{Beta}{A \code{matrix} that can be calculated using \code{g11_lm} function.!}
\item{alpha}{A \code{numeric} (double) that sets the alpha coefficient to be used. Has to be between 0 and 1.}
\item{method}{A \code{string} that defines the method used. Options are "Asymptotic" and "Bootstrap", accepts minor misspellings with a warning - which can be both good and bad.}
}
\value{
A \code{list} containing the following attributes:
\describe{
\item{beta}{Estimated coefficients, Linear Regression Model.}
\item{sigma2}{explanation}
\item{variance_beta}{explanation}
\item{ci}{explanation}
}
}
\description{
Function that
}
\examples{
Using data(hubble) from libary(gamair)
g11_lm(hubble$y, hubble$x, alpha = 0.01, method = "Bootstrap")
g11_lm(hubble$y, hubble$x, method = "Asymptotic")
From here, is the ACTUAL function:
}
\author{
Group11
}
|
6e88aaf93c8f8ec2e540e383173ef53e45cc762f | 14f5a7238a5e264a22c59d8864b2084d58b3b29e | /man/columnwise.matrix.norm.Rd | a02d85a3b69d5a7c9630822b24efa2418dd0d4d1 | [] | no_license | dami82/learningMedicine | b3e8dffea4b35e3d94b26e3c8151178c4fb88553 | 25e74bbfd2173da60428ae09ccd9e77b7b4d1553 | refs/heads/master | 2021-01-17T21:01:33.681580 | 2017-11-27T02:32:25 | 2017-11-27T02:32:25 | 84,156,788 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 422 | rd | columnwise.matrix.norm.Rd | \name{columnwise.matrix.norm}
\alias{columnwise.matrix.norm}
\title{Columnwise Matrix Normalization}
\description{Normalize a numeric matrix so that all columns sum up to unity. Requires anumeric matrix as input}
\usage{
columnwise.matrix.norm(mat)
}
\arguments{
\item{mat}{numeric matrix (or numeric data.frame)}
}
\examples{
my.mat <- sapply(1:8, function(i) sample(1:100, 15))
my.mat
columnwise.matrix.norm(my.mat)
}
|
ad4a172efb65e9c6a927c735c5ab7c61f2f21c56 | df9b866147cb0235af9e5290690fbf15b16d2cfd | /man/getUCSC.Rd | 179d0364b95baea7eb6c354cbb483f2ddb1054f0 | [] | no_license | guldenolgun/NoRCE | d3375f46c5ec3810c9cb3033401472993ec3240b | f1ce050b1771c2eb5f33ddc021cf12bdf509b6f0 | refs/heads/master | 2022-10-28T05:41:19.118562 | 2022-10-20T01:06:28 | 2022-10-20T01:06:28 | 187,480,634 | 1 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,287 | rd | getUCSC.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getNearest.R
\name{getUCSC}
\alias{getUCSC}
\title{Get nearest genes for the window of the upstream/downstream region.}
\usage{
getUCSC(
bedfile,
upstream,
downstream,
org_assembly = c("hg19", "hg38", "mm10", "dre10", "rn6", "dm6", "ce11", "sc3")
)
}
\arguments{
\item{bedfile}{Bed formated input gene regions}
\item{upstream}{Maximum upstream distance from the transcription start
region of the input gene}
\item{downstream}{Maximum downstream distance from the transcription end
region of the input gene}
\item{org_assembly}{genomee assembly of interest for the analysis. Possible
assemblies are "mm10" for mouse, "dre10" for zebrafish, "rn6" for rat,
"dm6" for fruit fly, "ce11" for worm, "sc3" for yeast, "hg19" and
"hg38" for human}
}
\value{
genes
}
\description{
When downstream = 0 / upstream = 0, function converts bed formated regions
to HUGO genes
}
\examples{
\dontrun{
regions<-system.file("extdata", "ncRegion.txt", package = "NoRCE")
regionNC <- rtracklayer::import(regions, format = "BED")
neighbour <- getUCSC(bedfile = regionNC,
upstream = 1000,
downstream = 1000,
org_assembly = 'hg19')
}
}
|
6f8c27b676cf0e6cdec1a014c41188bf6bbcfe80 | 7b102f9c8f2e3f9240090d1d67af50333a2ba98d | /gbd_2017/nonfatal_code/headache/split/01_childsplit.R | 36e9048182b9756dd68819255416a59699b2b544 | [] | no_license | Nermin-Ghith/ihme-modeling | 9c8ec56b249cb0c417361102724fef1e6e0bcebd | 746ea5fb76a9c049c37a8c15aa089c041a90a6d5 | refs/heads/main | 2023-04-13T00:26:55.363986 | 2020-10-28T19:51:51 | 2020-10-28T19:51:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,184 | r | 01_childsplit.R | ###########################################################
### Author:
### Date: 10/20/17
### Project: Split Migraine into asym/sym
### Purpose: GBD 2017 Nonfatal Estimation
###########################################################
## SET-UP
rm(list=ls())
if (Sys.info()[1] == "Linux"){
j_root <- "/home/j/"
h_root <- "~/"
} else if (Sys.info()[1] == "Windows"){
j_root <- "J:/"
h_root <- "H:/"
}
library(pacman, lib.loc = FILEPATH)
pacman::p_load(data.table, ggplot2, readr)
## SET OBJECTS
date <- gsub("-", "_", Sys.Date())
repo_dir <- paste0(h_root, FILEPATH)
functions_dir <- paste0(j_root, FILEPATH)
headache_dir <- FILEPATH
sev_dir <- paste0(headache_dir, "sev_draws/")
dir.create(paste0(headache_dir, "probable_sym/", date, "/"))
dir.create(paste0(headache_dir, "definite_sym/", date, "/"))
dir.create(paste0(headache_dir, "probable_asym/", date, "/"))
dir.create(paste0(headache_dir, "definite_asym/", date, "/"))
save_probsym_dir <- paste0(headache_dir, "probable_sym/", date, "/")
save_defsym_dir <- paste0(headache_dir, "definite_sym/", date, "/")
save_probasym_dir <- paste0(headache_dir, "probable_asym/", date, "/")
save_defasym_dir <- paste0(headache_dir, "definite_asym/", date, "/")
draws <- paste0("draw_", 0:999)
prob_me <- 20190
def_me <- 20191
## SOURCE FUNCTIONS
source(paste0(functions_dir, "get_draws.R"))
source(paste0(repo_dir, "job_array.R"))
source(paste0(functions_dir, "get_demographics.R"))
## GET TASK INFORMATION
getit <- job.array.child()
print(commandArgs())
loc_id <- getit[[1]] # grab the unique PARAMETERS for this task id
loc_id <- as.numeric(loc_id)
print(loc_id)
## GET IDS
ids <- get_demographics(gbd_team = "epi")
years <- ids$year_id
sexes <- ids$sex_id
## GET DRAWS
draws_dt <- get_draws(gbd_id_type = "modelable_entity_id", gbd_id = c(def_me, prob_me), source = "epi",
measure_id = c(5, 6), location_id = loc_id, year_id = years, age_group_id = c(6:20, 30:32, 235),
sex_id = sexes, status = "best")
draws_dt[, id := 1]
## GET SEVERITY DRAWS AND FORMAT
files <- list.files(sev_dir)
dates <- substr(files, 1, 10)
dates <- gsub("_", "-", dates)
last_date <- dates[which.max(as.POSIXct(dates))]
last_date <- gsub("-", "_", last_date)
timesym <- as.data.table(read_rds(paste0(sev_dir, last_date, ".rds")))
timesym[, id := 1]
def <- copy(timesym[, .(variable, id, timesymdef)])
def <- dcast(def, id ~ variable, value.var = "timesymdef")
setnames(def, draws, paste0("def_", 0:999))
prob <- copy(timesym[, .(variable, id, timesymprob)])
prob <- dcast(prob, id ~ variable, value.var = "timesymprob")
setnames(prob, draws, paste0("prob_", 0:999))
## MERGE AND CALC DEF
def_draws <- merge(draws_dt[modelable_entity_id == def_me], def, by = "id")
def_draws[, id := NULL]
defsym_draws <- copy(def_draws)
defsym_draws[, (draws) := lapply(0:999, function(x) get(paste0("draw_", x)) * get(paste0("def_", x)))]
defsym_draws[, c(paste0("def_", 0:999), "model_version_id", "modelable_entity_id") := NULL]
defasym_draws <- copy(def_draws)
defasym_draws[, (draws) := lapply(0:999, function(x) get(paste0("draw_", x)) * (1 - get(paste0("def_", x))))]
defasym_draws[, c(paste0("def_", 0:999), "model_version_id", "modelable_entity_id") := NULL]
## MERGE AND CALC PROB
prob_draws <- merge(draws_dt[modelable_entity_id == prob_me], prob, by = "id")
prob_draws[, id := NULL]
probsym_draws <- copy(prob_draws)
probsym_draws[, (draws) := lapply(0:999, function(x) get(paste0("draw_", x)) * get(paste0("prob_", x)))]
probsym_draws[, c(paste0("prob_", 0:999), "model_version_id", "modelable_entity_id") := NULL]
probasym_draws <- copy(prob_draws)
probasym_draws[, (draws) := lapply(0:999, function(x) get(paste0("draw_", x)) * (1 - get(paste0("prob_", x))))]
probasym_draws[, c(paste0("prob_", 0:999), "model_version_id", "modelable_entity_id") := NULL]
## SAVE FILES
write.csv(defsym_draws, paste0(save_defsym_dir, loc_id, ".csv"), row.names = F)
write.csv(defasym_draws, paste0(save_defasym_dir, loc_id, ".csv"), row.names = F)
write.csv(probsym_draws, paste0(save_probsym_dir, loc_id, ".csv"), row.names = F)
write.csv(probasym_draws, paste0(save_probasym_dir, loc_id, ".csv"), row.names = F)
|
ae900237e80ac30fc080bc63c5fd5cedf907893d | 4b5cecbbf56fa2704f2d24c9255adf3dd4df6ff6 | /AppliedDataMining/AppliedDataMining/HW2/2.3/sb41train.R | 934b1aeb29c7e57f86e156edd9ec16cf576ab714 | [] | no_license | keithhickman08/IUH | fa2a8c50eb4ab86f3ea10081a18620e27dc8f021 | 20bc22bdef9523310e1e1b9b6225e6a3eb039d20 | refs/heads/master | 2020-04-02T07:40:22.310044 | 2019-01-04T16:16:45 | 2019-01-04T16:16:45 | 154,208,374 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,528 | r | sb41train.R | #Homework 4
#Problem 4
#Support Vector Machines
??e1071
install.packages("e1071")
library(e1071)
set.seed(1234)
# Creating training and testing data sets: Randomly picking 100 data points from Iris data set
# as training data and the rest of 50 data points will be used as test data.
rndSample <- sample(1:nrow(mydata), 900)
mydata.training <- mydata[rndSample,]
mydata.test <- mydata[-rndSample, ]
s <- svm(Purchase ~ ., mydata.training)
ps <- predict(s, mydata.training)
(cm <- table(ps, mydata.training$Purchase)) #confusion matrix for evaluation
100*(1-sum(diag(cm))/sum(cm)) # the error rate is 14%
# Adjusting some of the parameters of SVM
# In this example we are changing radial kernel to polynomial kernel
# and changing cost from 1 to 10
#cost argument: to specify the cost of a violation to the margin.
#if the cost is small, the margin is large -- more support vectors violating the margin
#if the cost is large, the margin is narrow -- less support vectors violating the margin
# Kernels are used to map linearly non-separable data to higher dimenisonal space so that
# the data can be linearly seperable.
s2 <- svm(Purchase ~ ., mydata.training, cost=10, kernel="polynomial", degree=3)
ps2 <- predict(s2, mydata.training)
(cm2 <- table(ps2, mydata.training$Purchase)) #confusion matrix for evaluation
100*(1-sum(diag(cm2))/sum(cm2))
# the error rate for a cost of 10 is 14%
# the error rate for a cost of 20 is also 14%
# modifying the degree parameter does not substantially improve the error rate. |
90601fe939e33a8628f8d1754d8c226914118e84 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /iBATCGH/R/Scenario2.R | f762e6a7bd1e03c7ae818e35921b9df6f39f166f | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,858 | r | Scenario2.R | Scenario2 <-
function(sigmak=0.1){
g=100 #number of gene expression probes
m=1000 # number of CGH probes
s=100 # number of samples
#State specific mean and variance
mu1=-0.65
mu2=0
mu3=0.65
mu4=1.5
mu=c(mu1,mu2,mu3,mu4)
sigma1=0.1
sigma2=0.1
sigma3=0.1
sigma4=0.2
sigma=c(sigma1,sigma2,sigma3,sigma4)
#Generate xi
A=matrix(c(0.3,0.6,0.095,0.005,0.09,0.818,0.09,0.002,0.095,0.6,0.3,0.005,0.005,0.71,0.005,0.28),nrow=4,byrow=T)
AC=matrix(nrow=4,ncol=4)
for (i in 1:4){
AC[i,1]=A[i,1]
for (j in 2:4){
AC[i,j]=AC[i,(j-1)]+A[i,j]
}
}
A1=A
A1[1,]=c(0.7500, 0.1800, 0.0500, 0.020)
A1[2,]=c(0.4955, 0.0020, 0.4955, 0.007)
A1[3,]=c(0.0200, 0.1800, 0.7000, 0.100)
A1[4,]=c(0.0001, 0.3028, 0.1001, 0.597)
AC1=matrix(nrow=4,ncol=4)
for (i in 1:4){
AC1[i,1]=A1[i,1]
for (j in 2:4){
AC1[i,j]=AC1[i,(j-1)]+A1[i,j]
}
}
xi=matrix(2,nrow=s,ncol=m)
change=c(4:8,100:109,250:259,300,306,380,390,420:426,490:495,500:503,505,525:530,sample(531:1000,197))
change.complete=rep(0,m)
change.complete[change]=1
change.pos.two=which(change.complete==0)
change.partial=sample(change.pos.two[-1],375)
change.complete[change.partial]=2
q=10
for(j in 2:m){
if(change.complete[j]==1){
for(i in 1:s){
temp2=runif(1,0,1)
if(temp2<AC1[xi[i,j-1],1]){
xi[i,j]=1
}
if(AC1[xi[i,j-1],1]<=temp2 && temp2<AC1[xi[i,j-1],2]){
xi[i,j]=2
}
if(AC1[xi[i,j-1],2]<=temp2 && temp2<AC1[xi[i,j-1],3]){
xi[i,j]=3
}
if(AC1[xi[i,j-1],3]<=temp2){
xi[i,j]=4
}
}
}
if(change.complete[j]==2){
samples.to.change=sample(1:s,q)
for(i in 1:q){
temp2=runif(1,0,1)
if(temp2<AC1[xi[samples.to.change[i],j-1],1]){
xi[samples.to.change[i],j]=1
}
if(AC1[xi[samples.to.change[i],j-1],1]<=temp2 && temp2<AC1[xi[samples.to.change[i],j-1],2]){
xi[samples.to.change[i],j]=2
}
if(AC1[xi[samples.to.change[i],j-1],2]<=temp2 && temp2<AC1[xi[samples.to.change[i],j-1],3]){
xi[samples.to.change[i],j]=3
}
if(AC1[xi[samples.to.change[i],j-1],3]<=temp2){
xi[samples.to.change[i],j]=4
}
}
}
}
#Generate X
X=matrix(nrow=s,ncol=m)
for (i in 1:s){
for(j in 1:m){
X[i,j]=rnorm(1,mean=mu[xi[i,j]],sd=sigma[xi[i,j]])
}
}
#Generate beta
beta=matrix(0,nrow=g,ncol=m)
beta[4,change[6:15]]=((-1)^(floor(runif(1,0,2))))*rnorm(10,mean=0.5,sd=0.3)
beta[10,change[16:25]]=((-1)^(floor(runif(1,0,2))))*rnorm(10,mean=0.5,sd=0.3)
#Generate epsilon
epsilon=NULL
for(i in 1:s){
epsilon=rbind(epsilon,rnorm(g,mean=0,sd=sigmak))
}
#Generate intercept
mu.g=rnorm(g,0,sd=0.1)
#Generate Y
Y=xi%*%t(beta)+mu.g+epsilon
##Empirical transition matrix
realA=Tran(xi)
realA[1,]=realA[1,]/sum(realA[1,])
realA[2,]=realA[2,]/sum(realA[2,])
realA[3,]=realA[3,]/sum(realA[3,])
realA[4,]=realA[4,]/sum(realA[4,])
##Beta different from zero
signbeta=which(beta!=0)
#Generate distances between probes
distance=rexp(m-1)
disfix=2*sum(distance)
return(list(Y=Y,X=X,Xi=xi,A=realA,mu=mu,Sd=sigma,coeff=beta,distance=distance,disfix=disfix))
}
|
021b991c29dafc0ba7d9b69d66af463067658f4b | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Letombe/Abduction/aim-50-3_4-yes1-3-90/aim-50-3_4-yes1-3-90.R | 4d5fc2d7dde295bd77f02c07315b1c2b3e197c15 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 628 | r | aim-50-3_4-yes1-3-90.R | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 696
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 696
c
c Input Parameter (command line, file):
c input filename QBFLIB/Letombe/Abduction/aim-50-3_4-yes1-3-90.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 280
c no.of clauses 696
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 696
c
c QBFLIB/Letombe/Abduction/aim-50-3_4-yes1-3-90.qdimacs 280 696 E1 [] 0 50 230 696 NONE
|
cca35e68bf07c83ddcf6e47e86656e58cb6e502c | 918943c4b8b38bee65be66822ecddb6083220fee | /scripts/geno_annot_to_RDS.R | 71df74219a98a6de0f52869de85f7ec87b40aba9 | [] | no_license | drveera/PredictDBPipeline | ad4c0b8e606069663e7a8c699a6249957eba4c9b | 664c1919e3d865841fa6aaf286bb634a42f4c0d1 | refs/heads/master | 2021-04-12T12:22:56.843820 | 2016-11-08T20:16:50 | 2016-11-08T20:16:50 | 94,552,057 | 1 | 0 | null | 2017-06-16T14:31:25 | 2017-06-16T14:31:25 | null | UTF-8 | R | false | false | 338 | r | geno_annot_to_RDS.R | # Turns the parsed genotype annotation file into an RDS file.
# Run from commandline. First arg is the parsed text file, second is the output file.
argv <- commandArgs(trailingOnly = TRUE)
gene_annot <- read.table(argv[1], stringsAsFactors = FALSE, header = TRUE)
rownames(gene_annot) <- gene_annot$gene_id
saveRDS(gene_annot, argv[2])
|
c3255d748e6b2042d71613d88f00959339a7885c | 420cac816c739b8f6a3581c1628d706f7d398beb | /R/intg1.TMLW.r | 1dab80422aff5f43e6bff536d69a5af294ac94db | [] | no_license | cran/RobustAFT | d80a89efb8ffcc80b604d5959893210aab0ae31b | 357b7400ae0a4d0be157b6a46970eb04d8b9ea51 | refs/heads/master | 2023-08-31T10:42:53.415730 | 2023-08-21T16:40:02 | 2023-08-21T17:30:23 | 17,693,388 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 55 | r | intg1.TMLW.r | intg1.TMLW <-
function(x){ (exp(x)-1)*dlweibul(x)}
|
d30b950d4d81861d7b0f3269e07c32b0f9e3a7c0 | 9bd21f21a7e6150e539072ea8405d3a03b4c54d0 | /R/sappp.R | 73ba8ea871b6a6e934c43920f19f900e7d9e7281 | [] | no_license | lucasnell/sappp | dda9c9d1c21721b872890e6cca6166188eced21b | 10d5df07b38303d6e1839bc035681b61a72a08b5 | refs/heads/master | 2022-03-30T04:22:14.862948 | 2020-01-13T17:41:39 | 2020-01-13T17:41:39 | 100,888,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 252 | r | sappp.R |
#' sappp: Simulating Aphid and Parasitoid Populations... with Predation.
#'
#'
#' @section safepug functions:
#'
#' @importFrom Rcpp evalCpp cpp_object_initializer
#' @useDynLib sappp, .registration = TRUE
#'
#' @docType package
#' @name sappp
NULL
|
7bef2f0d456422234a5caaf54b350dbb9c376ba6 | 4d6eae864f95140e6f605c1886245b2a6e1936f6 | /plot3.R | ab72850ea828456cb858e64180c3faeaa993f2cf | [] | no_license | jarrodshingleton/datasciencecoursera | 24ea504dba86e3d256bafe1785260a5e44b8a448 | dbdbaf0b91f754a9954c306c1fc14c5f0a554c28 | refs/heads/master | 2020-07-11T12:56:47.268226 | 2019-09-16T23:45:57 | 2019-09-16T23:45:57 | 204,545,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,033 | r | plot3.R | library(dplyr)
library(lubridate)
library(tidyr)
par(mfrow=c(1,1))
data1<-read.table('Course4/household_power_consumption.txt', sep=';', header=TRUE)
#2007-02-01 and 2007-02-02
data1$Date<-dmy(data1$Date)
data1<-data1%>%
filter(Date>=as.Date("2007-02-01"))%>%
filter(Date<=as.Date("2007-02-02"))
data1$DateTime<-paste(data1$Date, data1$Time)
data1$DateTime<-ymd_hms(data1$DateTime)
##plot1
png("plot3.png", width = 480, height = 480)
##plot3
plot3<-data1[,c("DateTime", "Sub_metering_1", "Sub_metering_2", "Sub_metering_3")]
plot3$Sub_metering_1<-as.numeric(as.character(plot3$Sub_metering_1))
plot3$Sub_metering_2<-as.numeric(as.character(plot3$Sub_metering_2))
with(plot3, plot(DateTime, Sub_metering_1, type="l", ylab="Energy sub metering", xlab=""))
with(plot3, points(DateTime, Sub_metering_2, type="l", col="red"))
with(plot3, points(DateTime, Sub_metering_3, type="l", col="blue"))
legend('topright', col=c('black', 'red', 'blue'),lty=1, legend=c('Sub_metering_1', 'Sub_metering_2', 'Sub_metering_3'))
dev.off()
|
7b313fd078eab3632116645e3d88198c6063675c | c6106b0c33904555119fa42477203ad915e5626d | /man/df.Rd | 37048a5ba0d025827e050a0bf7498b97e4414ec9 | [] | no_license | fanymiles/coindesk | ba8d3f0f869319cb8fff28e5d7f09875d437f2bf | 488b9b0cd804368e7e239c89be5b491640e0bad0 | refs/heads/master | 2021-08-30T12:30:30.424211 | 2017-12-18T00:13:56 | 2017-12-18T00:13:56 | 114,314,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 767 | rd | df.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{df}
\alias{df}
\title{Bitcoin Price Index presented in CNY from 2017-10-01 to 2017-12-01}
\format{The df data frame contains 62 observations on 1 variable.
\describe{
\item{price}{The Bitcoin Price Index, in CNY}
}}
\source{
\url{ https://www.coindesk.com/price/}
}
\usage{
{"df"}
}
\description{
This data provides a small sample of the query result from the get_history() function.
The original query function is set as: get_history(currency = 'CNY',start = '2017-10-01', end = '2017-12-01'), where the currency is specified as CNY. And start date and end date are specified as above.
The rownames of the data frame are repective time.
}
\keyword{datasets}
|
63ac7b982a5787e51135b27f2e7fd87984c99261 | 985217126eedbf793a4a292c488dc6f84096e6ca | /pkg/man/COC.Rd | 7293bb7d598c89ac6d8d76d573251bd5c8f468d8 | [
"MIT"
] | permissive | SNStatComp/GenericValidationRules | fb6bea4c82f604ddd989dc1fe84576fc72377b76 | e80bbf61b067998601c1ed213dde1bc4f5a6b9e1 | refs/heads/master | 2020-06-15T05:47:59.841867 | 2020-06-09T08:19:07 | 2020-06-09T08:19:07 | 195,218,366 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,075 | rd | COC.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/COC.R
\name{COC}
\alias{COC}
\title{Code consistency}
\description{
Check that the codes used in fields are consistent with other codes used in
another field of the same record, the same field in different records of the
same file or in different datasets from the same country.
}
\section{Note}{
The interface proposed in the original document contains redundancies, and it is
easier to express this rule type directly in \pkg{validate} syntax as shown
in the examples below.
}
\examples{
# First example: consistency of TABLE and FREW
library(validate)
data(COCdat)
rules <- validator(
if ( TABLE == "T01" ) FREQ == "A"
, if ( TABLE == "T02" ) FREQ == "Q")
result <- confront(COCdat, rules)
summary(result)
values(result)
as.data.frame(result)
# Second example: consistency of TABLE and FREQ
data(COC2dat)
result <- confront(COC2dat, rules)
summary(result)
values(result)
as.data.frame(result)
# Third example: country must be EL. Envelope data can be passed
# as a single-row data frame
data(COC3dat)
rules <- validator(REPORTING == envelope$Country)
env <- data.frame(Country="EL", stringsAsFactors=FALSE)
result <- confront(COC3dat, rules, ref=list(envelope=env))
summary(result)
values(result)
as.data.frame(result)
# Fourth example: REPORTING country and PARTNER country cannot be the same
data(COC4dat)
# we convert to character as in the original data, these variables are
# different types of 'factor' (categorical) variables.
rules <- validator(as.character(REPORTING) != as.character(PARTNER) )
result <- confront(COC3dat, rules)
summary(result)
values(result)
as.data.frame(result)
}
\references{
\href{../doc/20180202_maintypes.pdf}{Main types of validation rules for ESS data}: COC
}
\seealso{
Other validation-functions: \code{\link{COV}},
\code{\link{FDL}}, \code{\link{FDM}}, \code{\link{FDT}},
\code{\link{RNR}}, \code{\link{RTS}}, \code{\link{RWD}},
\code{\link{VAD}}, \code{\link{VCO}}, \code{\link{VIR}},
\code{\link{VSA}}
}
\concept{validation-functions}
|
e14776f346fbe05c8995a1f408bac1e638300e33 | a1d75e1fb878f2fa43218e78b1361b4f1e125e2d | /House-Price-Prediction-Project.R | 3779db5352373dc672a3a76c2c30d3bc757fb863 | [] | no_license | jesslynne73/Machine-Learning | 4075f5ade1887c12d49936071107d66734309ef6 | ed7a6bd487967c73a653bd2d0cea6fd96725b745 | refs/heads/main | 2023-07-09T02:55:52.610753 | 2021-08-05T21:08:07 | 2021-08-05T21:08:07 | 329,099,558 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,919 | r | House-Price-Prediction-Project.R | # Author: Jess Strait
# Front Matter
rm(list = ls())
library(data.table)
library(Metrics)
# Import and explore data
train <- fread("Stat_380_train.csv")
test <- fread("Stat_380_test.csv")
View(train)
class(train)
# Fill NA values with mean of column
train$LotFrontage <- as.numeric(train$LotFrontage)
test$LotFrontage <- as.numeric(test$LotFrontage)
train[, LotFrontage := replace(LotFrontage, is.na(LotFrontage), 0)]
test[, LotFrontage := replace(LotFrontage, is.na(LotFrontage), 0)]
# Feature engineering: age of house
train$age <- as.numeric(train$YrSold - train$YearBuilt)
test$age <- as.numeric(test$YrSold - test$YearBuilt)
# Write new data to interim folder
fwrite(train,"trainengineer.csv")
fwrite(test, "testengineer.csv")
# Front Matter
rm(list = ls())
library(data.table)
library(Metrics)
library(caret)
library(glmnet)
library(xgboost)
# Import and explore data
train <- fread("trainengineer.csv")
test <- fread("testengineer.csv")
# Initialize variables for dummies
train_y <- train$SalePrice
test$SalePrice <- 0
test_y <- test$SalePrice
# Save ID variables before removal
trainid <- train$Id
testid <- test$Id
train$Id <- NULL
test$Id <- NULL
# Generate dummies and store as matrices for modeling
dummies <- dummyVars(SalePrice ~ ., data = train)
traindummies <- predict(dummies, newdata = train)
testdummies <- predict(dummies, newdata = test)
x <- as.matrix(traindummies)
x_test <- as.matrix(testdummies)
# Explore alpha and lambda combinations for penalized regression
# Setting alpha = 1 implements lasso regression with cross validation
lasso_reg <- cv.glmnet(x, train_y, alpha = 1, family="gaussian")
# Identify best lambda
lambda_best <- lasso_reg$lambda.min
lambda_best
# Adapt model to use best lambda
lasso_model <- glmnet(x, train_y, alpha = 1, lambda = lambda_best, family="gaussian")
# Generate and evaluate training predictions
predictions_train <- predict(lasso_model, s = lambda_best, newx = x)
rmse(train_y, predictions_train)
# 25218.7
# Try other elastic net approaches with alpha = 0.6
# Setting alpha = 0.6
elastic_reg <- cv.glmnet(x, train_y, alpha = 0.6, family="gaussian")
# Identify best lambda
lambda_best <- elastic_reg$lambda.min
lambda_best
# Adapt model to use best lambda
elastic_model <- glmnet(x, train_y, alpha = 0.6, lambda = lambda_best, family="gaussian")
# Generate and evaluate training predictions
predictions_train <- predict(elastic_model, s = lambda_best, newx = x)
rmse(train_y, predictions_train)
# 25220.32
# Try with alpha = 0.8
# Setting alpha = 0.8
elastic_reg <- cv.glmnet(x, train_y, alpha = 0.8, family="gaussian")
# Identify best lambda
lambda_best <- elastic_reg$lambda.min
lambda_best
# Adapt model to use best lambda
elastic_model <- glmnet(x, train_y, alpha = 0.8, lambda = lambda_best, family="gaussian")
# Generate and evaluate training predictions
predictions_train <- predict(elastic_model, s = lambda_best, newx = x)
rmse(train_y, predictions_train)
# 25218.9
# Try with alpha = 0.2
# Setting alpha = 0.2
elastic_reg <- cv.glmnet(x, train_y, alpha = 0.2, family="gaussian")
# Identify best lambda
lambda_best <- elastic_reg$lambda.min
lambda_best
# Adapt model to use best lambda
elastic_model <- glmnet(x, train_y, alpha = 0.2, lambda = lambda_best, family="gaussian")
# Generate and evaluate training predictions
predictions_train <- predict(elastic_model, s = lambda_best, newx = x)
rmse(train_y, predictions_train)
# 25218.8
# Try ridge regression with alpha = 0
# Setting alpha = 0
ridge_reg <- cv.glmnet(x, train_y, alpha = 0, family="gaussian")
# Identify best lambda
lambda_best <- ridge_reg$lambda.min
lambda_best
# Adapt model to use best lambda
ridge_model <- glmnet(x, train_y, alpha = 0, lambda = lambda_best, family="gaussian")
# Generate and evaluate training predictions
predictions_train <- predict(ridge_model, s = lambda_best, newx = x, type="response")
rmse(train_y, predictions_train)
# 2244.33
# Alpha = 1 gave the lowest RMSE and we will proceed with that model.
# Setting alpha = 1
final_reg <- cv.glmnet(x, train_y, alpha = 1, family="gaussian")
# Identify best lambda
lambda_best <- final_reg$lambda.min
lambda_best
# Adapt model to use best lambda
final_model <- glmnet(x, train_y, alpha = 1, lambda = lambda_best, family="gaussian")
# Generate testing predictions
predictions_test <- predict(final_model, s = lambda_best, newx = x_test, type="response")
# Save model
summary(final_model)
saveRDS(final_model, "master_final.model")
# Create submission file
test$Id <- testid
test$SalePrice <- predictions_test
submit <- test[,.(Id, SalePrice)]
fwrite(submit,"submission_final.csv")
# Now explore XGBoost
boosttrain <- xgb.DMatrix(traindummies,label=train_y,missing=NA)
boosttest <- xgb.DMatrix(testdummies,missing=NA)
# Use cross validation to identify tuning parameters as shown in class
tuning <- NULL
# Combinations of parameters tested
# gamma = 0.002, eta = .002, max_depth = 20, min_child_weight = 1, subsample = 1, colsample_bytree = 1, test-rmse = 15630.36
# gamma = 0.002, eta = .002, max_depth = 10, min_child_weight = 1, subsample = 1, colsample_bytree = 1, test-rmse = 15917.03
# gamma = 0.002, eta = .002, max_depth = 15, min_child_weight = 1, subsample = 1, colsample_bytree = 1, test-rmse = 15568.09
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 1, subsample = 1, colsample_bytree = 1, test-rmse = 15429.37
# gamma = 0.002, eta = .1, max_depth = 15, min_child_weight = 1, subsample = 1, colsample_bytree = 1, test-rmse = 15698.66
# gamma = 0.002, eta = .05, max_depth = 15, min_child_weight = 1, subsample = 1, colsample_bytree = 1, test-rmse = 15588.01
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 5, subsample = 1, colsample_bytree = 1, test-rmse = 15518.65
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = 1, colsample_bytree = 1, test-rmse = 15352.95
# gamma = 0.01, eta = .01, max_depth = 15, min_child_weight = 2, subsample = 1, colsample_bytree = 1, test-rmse = 15478.37
# gamma = 0.0001, eta = .01, max_depth = 15, min_child_weight = 2, subsample = 1, colsample_bytree = 1, test-rmse = 15359.76
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = .95, colsample_bytree = 1, test-rmse = 15179.88
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = .9, colsample_bytree = 1, test-rmse = 15258.05
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = .95, colsample_bytree = .9, test-rmse = 15124.55
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = .95, colsample_bytree = .8, test-rmse = 15116.60
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = .95, colsample_bytree = .7, test-rmse = 15083.91
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = .95, colsample_bytree = .5, test-rmse = 15422.06
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = .95, colsample_bytree = .6, test-rmse = 15326.38
# The best parameters we've found are
# gamma = 0.002, eta = .01, max_depth = 15, min_child_weight = 2, subsample = .95, colsample_bytree = .7, test-rmse = 15083.91
parameters <- list( objective = "reg:squarederror",
gamma = 0.002,
booster = "gbtree",
eval_metric = "rmse",
eta = 0.01,
max_depth = 15,
min_child_weight = 2,
subsample = .95,
colsample_bytree = .7,
tree_method = 'hist'
)
XGBm <- xgb.cv(params=parameters,nfold=5,nrounds=10000,missing=NA,data=boosttrain,print_every_n=1, early_stopping_rounds=25)
# Save best results
iter_results <- data.table(t(parameters), best_iter = XGBm$best_iteration, rmse = XGBm$evaluation_log$test_rmse_mean[XGBm$best_iteration])
tuning <- rbind(tuning, iter_results)
fwrite(tuning, "besttuning.csv")
# Fit the model to training data
watchlist <- list(train = boosttrain)
XGBm <- xgb.train(params=parameters,nrounds=143,missing=NA,data=boosttrain,watchlist=watchlist,print_every_n=1)
# Generate and evaluate testing predictions
pred <- predict(XGBm, newdata = boosttest)
rmse(test_y, pred)
# XGBoost did not perform as well as the regression model. We will submit the results from that model to Kaggle.
# Save XGBoost model
summary(XGBm)
saveRDS(XGBm, "boost.model")
# Create submission file
testlast <- NULL
testlast$Id <- testid
test$SalePrice <- pred
submitlast <- test[,.(Id, SalePrice)]
fwrite(submitlast,"submission_XGB.csv")
|
94f39d3bb1200f13445ff2e6f01204693b77856a | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.analytics/man/glue_get_workflow.Rd | 2dea060d1ca405f4b79beb671dcf363ab828bce8 | [
"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 | 600 | rd | glue_get_workflow.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/glue_operations.R
\name{glue_get_workflow}
\alias{glue_get_workflow}
\title{Retrieves resource metadata for a workflow}
\usage{
glue_get_workflow(Name, IncludeGraph = NULL)
}
\arguments{
\item{Name}{[required] The name of the workflow to retrieve.}
\item{IncludeGraph}{Specifies whether to include a graph when returning the workflow
resource metadata.}
}
\description{
Retrieves resource metadata for a workflow.
See \url{https://www.paws-r-sdk.com/docs/glue_get_workflow/} for full documentation.
}
\keyword{internal}
|
e4ca3d197bf01b2164b11cb30349d6f4f662540c | 0363e9059653e5ce2a8fd4dfa1bcfe981072ea82 | /man/rowMads.Rd | d781a696d41242cff34accf43da07e196ba079f6 | [] | no_license | mwrowe/microRutils | 7725bd4d5e2ac60337932f384562ed39abcf86a1 | 654cd867bafe126593089441f63c88906ecf60ed | refs/heads/master | 2021-07-07T19:59:43.732449 | 2021-06-10T16:59:33 | 2021-06-10T16:59:33 | 245,310,935 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 614 | rd | rowMads.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rowZs.R
\name{rowMads}
\alias{rowMads}
\title{rowMads: Fast Calculation of MADs by Row.}
\usage{
rowMads(X, na.rm = T, constant = 1.4826)
}
\arguments{
\item{X}{Numeric matrix, or object that can be coerced to matrix.}
\item{na.rm}{Logical; should NA's be omitted?}
\item{constant}{Scale factor; numeric constant for asymptotically normal consistency.}
}
\description{
rowMads(X) returns median absolute deviation (MAD) of each row of matrix x.
}
\seealso{
\code{\link[stats]{mad}}.
}
\author{
M.W.Rowe, \email{mwr.stats@gmail.com}
}
|
363d89367f0850684cc146da8e5b5351b0cee4a1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rAmCharts/examples/AmStockChart.Rd.R | 5331f9733b62e78a05fbd697303276fdaf710bb0 | [] | 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 | 4,310 | r | AmStockChart.Rd.R | library(rAmCharts)
### Name: initialize,AmStockChart-method
### Title: Initializes an AmStockChart
### Aliases: initialize,AmStockChart-method amStockChart
### setBalloon,AmStockChart,AmBalloonOrMissing-method
### setCategoryAxesSettings setCategoryAxesSettings,AmStockChart-method
### setChartCursorSettings setChartCursorSettings,AmStockChart-method
### setChartScrollbarSettings
### setChartScrollbarSettings,AmStockChart,ChartScrollbarOrMissing-method
### setComparedDataSets setComparedDataSets,AmStockChart-method
### addComparedDataSet
### addComparedDataSet,AmStockChart,DataSetOrMissing-method setDataSets
### setDataSets,AmStockChart-method addDataSet
### addDataSet,AmStockChart,DataSetOrMissing-method setDataSetSelector
### setDataSetSelector,AmStockChart-method setLegendSettings
### setLegendSettings,AmStockChart-method setMainDataSet
### setMainDataSet,AmStockChart,DataSetOrMissing-method setPanels
### setPanels,AmStockChart,list-method addPanel
### addPanel,AmStockChart,StockPanelOrMissing-method setPanelsSettings
### setPanelsSettings,AmStockChart-method setPeriodSelector
### setPeriodSelector,AmStockChart,PeriodSelectorOrMissing-method
### setStockEventsSettings setStockEventsSettings,AmStockChart-method
### setValueAxesSettings setValueAxesSettings,AmStockChart-method
### ** Examples
## No test:
# --- method 'initialize'
new("AmStockChart", theme = "dark")
## End(No test)
## No test:
# --- constructor
amStockChart()
## End(No test)
library(pipeR)
## No test:
# Dummy example
amStockChart() %>>% setBalloon(gridPosition = "start")
## End(No test)
## No test:
# Dummy example
setCategoryAxesSettings(.Object = amStockChart(), gridPosition = "start")
## End(No test)
## No test:
# Dummy example
setChartCursorSettings(.Object = amStockChart(), oneBallOnly = TRUE)
## End(No test)
## No test:
# Dummy example
amchart <- setChartScrollbarSettings(.Object = amStockChart(), enabled = TRUE)
print(amchart)
# equivalent to:
chartScrollbarSettings_obj <- chartScrollbarSettings()
setChartScrollbarSettings(.Object = amStockChart(),
chartScrollbarSettings = chartScrollbarSettings_obj)
## End(No test)
## No test:
# Dummy example
comparedDataSets_ls <- list(dataSet(compared = TRUE), dataSet(compared = TRUE))
setComparedDataSets(.Object = amStockChart(), comparedDataSets = comparedDataSets_ls)
## End(No test)
## No test:
# Dummy example
addComparedDataSet(.Object = amStockChart(), compared = TRUE)
## End(No test)
## No test:
# Dummy example
dataSets_ls <- list(dataSet(compared = FALSE), dataSet(compared = FALSE))
setDataSets(.Object = amStockChart(), dataSets = dataSets_ls)
## End(No test)
## No test:
# Dummy example
addDataSet(.Object = amStockChart(), compared = FALSE)
# equivalent to:
dataSet_obj <- dataSet(compared = FALSE)
addDataSet(.Object = amStockChart(), dataSet = dataSet_obj)
## End(No test)
## No test:
# Dummy example
print(setDataSetSelector(.Object = amStockChart(), width = 180))
# equivalent to:
dataSetSelector_obj <- dataSetSelector(width = 180)
print(setDataSetSelector(.Object = amStockChart(),
dataSetSelector = dataSetSelector_obj))
## End(No test)
## No test:
# Dummy example
setLegendSettings(.Object = amStockChart(), equalWidths = TRUE)
## End(No test)
## No test:
# Dummy example
setMainDataSet(.Object = amStockChart(), showInCompare = TRUE)
## End(No test)
## No test:
# Dummy example
panels_ls <- list(stockPanel(compared = TRUE), stockPanel(compared = TRUE))
setPanels(.Object = amStockChart(), panels = panels_ls)
## End(No test)
## No test:
# Dummy example
chart <- addPanel(.Object = amStockChart(), allowTurningOff = TRUE); print(chart)
# equivalent to:
panel_obj <- panel(allowTurningOff = TRUE)
addPanel(.Object = amStockChart(), panel = panel_obj)
## End(No test)
## No test:
# Dummy example
setPanelsSettings(.Object = amStockChart(), backgroundAlpha = 0)
## End(No test)
## No test:
# Dummy example
setPeriodSelector(.Object = amStockChart(), dateFormat = "DD-MM-YYYY")
## End(No test)
## No test:
# Dummy example
setStockEventsSettings(.Object = amStockChart(), backgroundAlpha = 1)
## End(No test)
## No test:
# Dummy example
setValueAxesSettings(.Object = amStockChart(), autoGridCount = "TRUE")
## End(No test)
|
e209a623af5fa157496295021bb9d83352796262 | 528f588e92ec54c34f29bad39e25b833b6b11366 | /plot1.R | 99632e2a9a594d0867bd1ee930d219c9aff42ad4 | [] | no_license | iuribonna/ExData_Plotting1 | c071ec7cd4aa405d06b2aff1248e20fd605bd558 | e7d6438cf9047fc3e90365d64f4090785f09a261 | refs/heads/master | 2022-12-05T11:44:13.970892 | 2020-08-04T21:19:36 | 2020-08-04T21:19:36 | 285,077,076 | 0 | 0 | null | 2020-08-04T19:20:21 | 2020-08-04T19:20:20 | null | UTF-8 | R | false | false | 1,365 | r | plot1.R | ## Plot 1
## Reads the file in a table
power_data <- read.table("household_power_consumption.txt",skip=1,sep=";")
## Renames the columns in a descrpitive way
names(power_data) <- c("Date","Time","Global_active_power","Global_reactive_power"
,"Voltage","Global_intensity","Sub_metering_1","Sub_metering_2"
,"Sub_metering_3")
## Subset the dataset for the required 2 dates
power_data <- subset(power_data, power_data$Date=="1/2/2007" | power_data$Date =="2/2/2007")
## Sets the dates as variables to format them later
date1 <- "2007-02-01"
date2 <- "2007-02-02"
## Formats the dates as Date class and Time in the posixt format
power_data$Date <- as.Date(power_data$Date, format="%d/%m/%Y")
power_data$Time <- strptime(power_data$Time, format = "%H:%M:%S")
power_data[power_data$Date == date1, "Time"] <- format(power_data[power_data$Date == date1, "Time"], "2007-02-01 %H:%M:%S")
power_data[power_data$Date == date1, "Time"] <- format(power_data[power_data$Date == date1, "Time"], "2007-02-02 %H:%M:%S")
## ------------------------------------------------
## Creates the first plot
hist(as.numeric(power_data$Global_active_power), col="red", main = "Global Active Power", xlab= "Global Active Power (kilowatts)")
## Export to png
dev.copy(png, file="plot1.png", width=480, height=480)
dev.off() ## Close device
|
921f9d427b2a865cc1e04ac91ee03b1ebe19a501 | 960e994f0ba2f7db9821cbad3490a579aaaba136 | /man/probe_profile.Rd | 4f7da525e5f1693ce27f1c868991f1590e1982c1 | [
"MIT"
] | permissive | ouzlim/vjsim | 1a3aaabf5d93bc1be72c9fe069d80f00eb5d8755 | 456d771193463ef00efb91085ef8782ca57f9f21 | refs/heads/master | 2022-11-26T22:14:19.172622 | 2020-08-03T22:39:53 | 2020-08-03T22:39:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,530 | rd | probe_profile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/probing.R
\name{probe_profile}
\alias{probe_profile}
\title{Probe Profile}
\usage{
probe_profile(
mass = 75,
push_off_distance = 0.4,
max_force = 3000,
max_velocity = 4,
time_to_max_activation = 0.3,
change_ratio = seq(0.9, 1.1, length.out = 3),
aggregate = "raw",
external_load = c(-40, -20, 0, 20, 40, 60, 80, 100),
profile_func = get_all_profiles,
...
)
}
\arguments{
\item{mass}{Numeric value. Initial parameter value to be changed using \code{change_ratio}.}
\item{push_off_distance}{Numeric value. Initial parameter value to be changed using \code{change_ratio}}
\item{max_force}{Numeric value. Initial parameter value to be changed using \code{change_ratio}}
\item{max_velocity}{Numeric value. Initial parameter value to be changed using \code{change_ratio}}
\item{time_to_max_activation}{Numeric value. Initial parameter value to be changed using \code{change_ratio}}
\item{change_ratio}{Numeric vector indicating probing change ratios}
\item{aggregate}{How should \code{\link{get_all_profiles}} output be aggregated?
Default is "raw". Other options involve "ratio" and "diff" which use initial
output values}
\item{external_load}{Numeric vector. Default is \code{c(-40, -20, 0, 20, 40, 60, 80)}. Forwarded to \code{\link{vj_profile}}}
\item{profile_func}{Profiling function. Default is \code{\link{get_all_profiles}}. Also use \code{\link{get_FV_profile}},
\code{\link{get_power_profile}}, and \code{\link{get_all_samozino_profiles}}}
\item{...}{Extra argument forwarded to \code{\link{vj_profile}}}
}
\value{
Probing data frame
}
\description{
\code{probe_profile} simulates the vertical jump profiling using \code{\link{vj_profile}} over \code{external_load} loads,
but estimate which parameter brings biggest change in the profile summary metric returned by \code{profile_func}.
This is done by keeping all parameters at initial value, while changing only one parameter. This is then repeated for
all parameters. This way we can answer by changing what parameter for standardize change (\code{change_ratio})
yield biggest change in profile summary metric (e.g. jump height)
}
\examples{
require(tidyverse)
# You call also use get get_all_samozino_profiles() function in the profile_func parameter
profile_probe_data <- probe_profile(
mass = 75,
max_force = 3000,
max_velocity = 3,
time_to_max_activation = 0.3,
time_step = 0.001,
external_load = c(-40, -20, 0, 20, 40, 60, 80, 100),
profile_func = get_all_profiles # Can also use get_all_samozino_profiles
)
plot_data <- gather(profile_probe_data, key = "variable", value = "value", -(1:8)) \%>\%
filter(variable \%in\% c(
"profile_mean_FV.F0",
"profile_mean_FV.V0",
"profile_mean_FV.Pmax",
"profile_mean_FV.Sfv",
"profile_mean_power.Pmax",
"profile_mean_power.Pmax_location",
"profile_mean_power.F0_perc"
))
ggplot(plot_data, aes(x = change_ratio, y = value, color = probing)) +
theme_minimal() +
geom_line() +
facet_wrap(~variable, scales = "free_y") +
xlab("Normalized parameter change") +
ylab(NULL)
# -----------------------------------
# When probing using get_FV_profile or get_power_profile use the following
power_probe_data <- probe_profile(
mass = 75,
max_force = 3000,
max_velocity = 3,
time_to_max_activation = 0.3,
time_step = 0.001,
external_load = c(-40, -20, 0, 20, 40, 60, 80, 100),
profile_func = function(...) list(list = get_power_profile(...))
)
}
|
ac1a47b6e64104ffa85d7b124eaa5f7bbc0342e7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/vegan/examples/anova.cca.Rd.R | e6b58a4f17e6d6e12e954354718b2021b2bb821a | [] | 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 | 542 | r | anova.cca.Rd.R | library(vegan)
### Name: anova.cca
### Title: Permutation Test for Constrained Correspondence Analysis,
### Redundancy Analysis and Constrained Analysis of Principal Coordinates
### Aliases: anova.cca permutest permutest.cca
### Keywords: multivariate htest
### ** Examples
data(varespec, varechem)
mod <- cca(varespec ~ Al + P + K, varechem)
## overall test
anova(mod)
## tests for individual terms
anova(mod, by="term")
anova(mod, by="margin")
## test for adding all environmental variables
anova(mod, cca(varespec ~ ., varechem))
|
d99c0b2bbe2400f922f0023824b1182e68593f65 | 29585dff702209dd446c0ab52ceea046c58e384e | /RHRV/R/BuildTakensVector.R | 02c7bb8a5953b3ce166f1264c013dcaa5c042094 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 602 | r | BuildTakensVector.R | BuildTakensVector <-
function(HRVData, Data, m, tau) {
# -------------------------------------
# Calculates Takens expanded vectors
# -------------------------------------
if (HRVData$Verbose) {
cat("** Creating Takens expanded vectors **\n")
cat(" m: ", m, " Tau: ", tau, "\n", sep="")
}
N = length(Data)
jump = tau
maxjump = (m-1)*jump
jumpsvect = seq(0,maxjump,jump)
numjumps = length(jumpsvect)
numelem = N-maxjump
DataExp = matrix(nrow=numelem,ncol=numjumps)
for (i in 1:numelem) {
DataExp[i,1:numjumps] = Data[jumpsvect+i]
}
return(DataExp)
}
|
6621db2b3e8fbda4783349d44bc37a021866bd30 | 15ce65a306373ce210f8904bb2e3a0697030ffc6 | /man/createAnalysisSettings.Rd | 90a05304607778014273bf9d9920a8f5643bc6c0 | [
"Apache-2.0"
] | permissive | OHDSI/RiskStratifiedEstimation | dd19c1562dcc88078b0b963fe5d49d192deb036e | f4476f8296e5444f35502e5c1e60429a9b0acd19 | refs/heads/main | 2023-07-08T15:41:31.377724 | 2023-03-08T13:19:29 | 2023-03-08T13:19:29 | 136,479,509 | 9 | 5 | Apache-2.0 | 2023-03-14T07:31:53 | 2018-06-07T13:12:58 | R | UTF-8 | R | false | true | 2,784 | rd | createAnalysisSettings.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CreateArgFuns.R
\name{createAnalysisSettings}
\alias{createAnalysisSettings}
\title{Create a parameter defining the performed risk stratified analysis}
\usage{
createAnalysisSettings(
analysisId = NULL,
description = "",
databaseName,
treatmentCohortId,
comparatorCohortId,
outcomeIds,
analysisMatrix = diag(length(outcomeIds)),
mapTreatments,
mapOutcomes,
negativeControlOutcomes = c(),
balanceThreads = 1,
negativeControlThreads = 1,
verbosity = NULL,
saveDirectory = NULL
)
}
\arguments{
\item{analysisId}{The analysis ID.}
\item{description}{Text describing the analysis.}
\item{databaseName}{The name of the database.}
\item{treatmentCohortId}{The cohort definition id of the treatment cohort
in the cohortTable.}
\item{comparatorCohortId}{The cohort definition id of the comparator cohort
in the cohortTable.}
\item{outcomeIds}{The cohort definition ids of the outcome cohorts
in the outcomeTable.}
\item{analysisMatrix}{Boolean matrix defining the outcomes to be assessed
(rows) within risk strata (columns). The order
in columns should match the the order of
\code{outcomeIds}. Default is the diagonal matrix,
which leads to the risk stratified assessment
of only the outcome for which the risk strata
were defined.}
\item{mapTreatments}{Dataframe containing 2 columns: "exposure_id"
with the id numbers of the
treatment and comparator cohorts and "exposure_name"
the cohort names.}
\item{mapOutcomes}{Dataframe containing 2 columns: "outcome_id" with
the cohort names of the outcomes of interest and
"outcome_name" with their names.}
\item{negativeControlOutcomes}{The outcome Ids to be used as negative controls}
\item{balanceThreads}{The number of threads to be used for the estimation
of covariate balance}
\item{negativeControlThreads}{The number of threads to be used for running
the negative control analyses}
\item{verbosity}{Sets the level of the verbosity. If the log level
is at or higher in priority than the logger threshold,
a message will print. The levels are:
\itemize{
\item {DEBUG}{Highest verbosity showing all debug statements}
\item {TRACE}{Showing information about start and end of steps}
\item {INFO}{Show informative information (Default)}
\item {WARN}{Show warning messages}
\item {ERROR}{Show error messages}
\item {FATAL}{Be silent except for fatal errors}
}}
\item{saveDirectory}{The directory name where the results of the analyses will
be stored.}
}
\value{
An analysisSettings object providing the identification information of the analysis.
}
\description{
Create a parameter defining the performed risk stratified analysis
}
|
d6ba3e12baec294fdb56456236e9ba82111251bf | dcf0a19d6beee75169234d35b563a27ce4fb0542 | /R/allele_methods.R | ae5fcfceb89fc4c3506be1d84dd4936ccd37a9dd | [
"MIT"
] | permissive | andremrsantos/htspop | 01ddda7ca4bc217cdd1c9c575eb75120bb8c1dcb | 48df4f3519da0504693a12e3ed0e23be48e0edb7 | refs/heads/master | 2023-07-22T01:41:22.192915 | 2020-05-11T17:37:01 | 2020-05-11T17:37:01 | 154,565,177 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,051 | r | allele_methods.R | #' Allele Count Manipulation
#'
#' Functions to explore and manipule the data from allele count matrices.
#'
#' @param ac Allele count matrix to analyze
#'
#' @name allele_methods
#' @aliases NULL
NULL
#> NULL
#' @rdname allele_methods
#' @export
count_allele <- function(ac) {
if (class(ac) != "allele_count")
stop("`ac` must be an `allele_count`.")
return(ac$count)
}
#' @rdname allele_methods
#' @export
total_allele <- function(ac) {
if (class(ac) != "allele_count")
stop("`ac` must be an `allele_count`.")
return(ac$n)
}
#' @rdname allele_methods
#' @export
invert_allele <- function(ac) {
if (class(ac) != "allele_count")
stop("`ac` must be an `allele_count`.")
new_mtx <- list(count = ac$n - ac$count, n = ac$n)
return(structure(new_mtx, class = "allele_count"))
}
#' @rdname allele_methods
#' @export
population_names <- function(ac) {
if (class(ac) != "allele_count")
stop("`ac` must be an `allele_count`.")
names <- colnames(ac)
if (is.null(names))
return(seq_len(ncol(ac)))
return(names)
}
|
60dec3f9c692c1df43e9e55098efb832968fef21 | ac755a2e6f8e956b75a658f5682847b48025049c | /R/print.catpredi.survival.R | a419abf6d8067fa60cfcc11e3b63d085b7583d62 | [] | no_license | cran/CatPredi | f6dee6f07df42ce07f6269b759fcc1292059b736 | 99c37302aaec362bb3df7dae0b942aee89876753 | refs/heads/master | 2022-01-14T05:28:04.646840 | 2022-01-10T11:43:01 | 2022-01-10T11:43:01 | 106,293,023 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,057 | r | print.catpredi.survival.R | print.catpredi.survival <-
function(x, digits = 4, ...) {
cat("\nCall:\n")
print(x$call)
cat("\n\n*************************************************\n")
method <- switch(x$method,
"addfor" = cat("Addfor Search Algorithm"),
"genetic" = cat("Genetic Search Algorithm"))
conc.index <- switch(x$conc.index,
"cindex" = cat("Concordance C-index"),
"cpe" = cat("Concordance Probability Estimator - CPE"))
cat("\n*************************************************\n\n")
if(x$method == "addfor" & x$conc.index == "cindex") {
if(x$correct.index == TRUE){
cutpoints <- format(x$results$cutpoints, digits = digits, justify = "right")
Cindex <- format(x$results$Cindex, digits = digits, justify = "left")
Cindex.corrected <- format(x$results$Cindex.cor, digits = digits, justify = "left")
p.table <- cbind(cutpoints, Cindex,c(rep("NA",length(x$results$cutpoints)-1),Cindex.corrected))
dimnames(p.table) <- list(rep("", l = length(x$results$Cindex)), c("Optimal cutpoints", "Optimal C-index", "Corrected C-index"))
print(p.table, quote = FALSE, justify = "right")
} else {
cutpoints <- format(x$results$cutpoints, digits = digits, justify = "right")
Cindex <- format(x$results$Cindex, digits = digits, justify = "left")
p.table <- cbind(cutpoints, Cindex)
dimnames(p.table) <- list(rep("", l = length(x$results$Cindex)), c("Optimal cutpoints", "Optimal C-index"))
print(p.table, quote = FALSE, justify = "right")
}
} else if(x$method == "addfor" & x$conc.index == "cpe") {
if(x$correct.index == TRUE){
cutpoints <- format(x$results$cutpoints, digits = digits, justify = "right")
CPE <- format(x$results$CPE, digits = digits, justify = "left")
CPE.corrected <- format(x$results$CPE.cor, digits = digits, justify = "left")
p.table <- cbind(cutpoints, CPE,c(rep("NA",length(x$results$cutpoints)-1),CPE.corrected))
dimnames(p.table) <- list(rep("", l = length(x$results$CPE)), c("Optimal cutpoints", "Optimal CPE", "Corrected CPE"))
print(p.table, quote = FALSE, justify = "right")
} else {
cutpoints <- format(x$results$cutpoints, digits = digits, justify = "right")
CPE <- format(x$results$CPE, digits = digits, justify = "left")
p.table <- cbind(cutpoints, CPE)
dimnames(p.table) <- list(rep("", l = length(x$results$CPE)), c("Optimal cutpoints", "Optimal CPE"))
print(p.table, quote = FALSE, justify = "right")
}
} else if(x$method == "genetic" & x$conc.index == "cindex") {
if(x$correct.index == TRUE){
cutpoints <- cbind(format(sort(x$results$cutpoints), digits = digits, justify = "right"))
dimnames(cutpoints) <- list(rep("", l = length(x$results$cutpoints)), "Optimal cutpoints")
print(cutpoints, quote = FALSE, justify = "right")
cat("\n")
Cindex <- cbind(format(x$results$Cindex, digits = digits, justify = "right"))
dimnames(Cindex) <- list(rep("", l = length(x$results$Cindex)), "Optimal Cindex")
print(Cindex, quote = FALSE, justify = "right")
cat("\n")
Cindex.corrected <- cbind(format(x$results$Cindex.cor, digits = digits, justify = "right"))
dimnames(Cindex.corrected) <- list(rep("", l = length(x$results$Cindex.cor)), "Corrected Cindex")
print(Cindex.corrected, quote = FALSE, justify = "right")
} else {
cutpoints <- cbind(format(sort(x$results$cutpoints), digits = digits, justify = "right"))
dimnames(cutpoints) <- list(rep("", l = length(x$results$cutpoints)), "Optimal cutpoints")
print(cutpoints, quote = FALSE, justify = "right")
cat("\n")
Cindex <- cbind(format(x$results$Cindex, digits = digits, justify = "right"))
dimnames(Cindex) <- list(rep("", l = length(x$results$Cindex)), "Optimal Cindex")
print(Cindex, quote = FALSE, justify = "right")
}
} else {
if(x$correct.index == TRUE) {
cutpoints <- cbind(format(sort(x$results$cutpoints), digits = digits, justify = "right"))
dimnames(cutpoints) <- list(rep("", l = length(x$results$cutpoints)), "Optimal cutpoints")
print(cutpoints, quote = FALSE, justify = "right")
cat("\n")
CPE <- cbind(format(x$results$CPE, digits = digits, justify = "right"))
dimnames(CPE) <- list(rep("", l = length(x$results$CPE)), "Optimal CPE")
print(CPE, quote = FALSE, justify = "right")
cat("\n")
CPE.corrected <- cbind(format(x$results$CPE.cor, digits = digits, justify = "right"))
dimnames(CPE.corrected) <- list(rep("", l = length(x$results$CPE.cor)), "Corrected CPE")
print(CPE.corrected, quote = FALSE, justify = "right")
} else {
cutpoints <- cbind(format(sort(x$results$cutpoints), digits = digits, justify = "right"))
dimnames(cutpoints) <- list(rep("", l = length(x$results$cutpoints)), "Optimal cutpoints")
print(cutpoints, quote = FALSE, justify = "right")
cat("\n")
CPE <- cbind(format(x$results$CPE, digits = digits, justify = "right"))
dimnames(CPE) <- list(rep("", l = length(x$results$CPE)), "Optimal CPE")
print(CPE, quote = FALSE, justify = "right")
}
}
invisible(x)
}
|
961a1a9956b40a38a763917dc31c9397728ca853 | 2f5816f413d45a75914a2a5f4dc5d2b9ac2457da | /Daten/Scleractinia.R | 9ec6429ef74d7a3f7a13f2d28da2ec58377c394a | [] | no_license | FAU-Paleo/Analytical-Palaeobiology | a7d13a18b6ea51e7f3534f776f4ce51a50b38402 | d20639c040203538735b68490f330763cb441470 | refs/heads/master | 2022-11-28T14:40:19.827525 | 2020-08-05T16:54:11 | 2020-08-05T16:54:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,347 | r | Scleractinia.R | # Import einer Datei und Auswertung
remove(list=ls())
# PBDB raw data
myData <- read.table(file="C:/Daten/nceas/PBDB/Scleractinia.csv", header=TRUE, sep=",", as.is=TRUE)
attach(myData)
# Distinct genera in dataset
genus <- occurrences.genus_name
f <- factor(genus)
levels(f)
length(levels(f))
detach(myData)
# PBDB ouput raw data
myData <- read.table(file="C:/Daten/nceas/PBDB/Scleractinia_rawcurve.csv", header=TRUE, sep=",", as.is=TRUE)
# edit(myData)
attach(myData)
age1 <- Base..Ma.
age2 <- Midpoint..Ma.
sg <- Sampled.genera
bg <- Boundary.crosser.genera
rg <- Range.through.genera
e <- Extinction.rate
o <- Origination.rate
co <- Collections
oc <- Occurrences
hist(e, col="red", xlab="Extinction rate", main="Histogram of extinction rates")
# Number of collections
plot(age2, co, xlab="Age (Ma)", ylab="Number of collections", pch=16, type="b", xlim=c(250,0), ylim<-c(-5, 500), frame=F, axes=F)
axis(1, pos=-25, at=seq(0, 250, by=50), labels=c("0", "50", "100", "150", "200", "250")) # for collections
axis(2, pos=251)
# Definition of legend boxes
bt <- -25 # Bottom of rectangle
to <- 5 # Top of rectangle
bt2 <- -5 # Level of text
# Number of occurrences
plot(age2, oc, xlab="Age (Ma)", ylab="Number of occurrences", log="y", pch=16, type="b", xlim=c(250,0), ylim<-c(50, 5000), frame=F, axes=F)
axis(1, pos=42, at=seq(0, 250, by=50), labels=c("0", "50", "100", "150", "200", "250")) # for occurrences
axis(2, pos=251)
# Definition of legend boxes
bt <- 42 # Bottom of rectangle
to <- 60 # Top of rectangle
bt2 <- 50 # Level of text
# Boundary crosser and SIB diversity
lt1=2
lt2=1
plot(age1, bg, type="b", col="blue", xlim=c(250,0), ylim<-c(-5, 160), xlab="Age (Ma)", pch=21, lty=lt1, ylab="Number of genera", frame=F)
points(age2, sg, pch=16, type="b", lty=lt2)
abline(v=199.6, col="red") # Mark Triassic-Jurassic boundary
abline(v=65.5, col="red") # Mark KT boundary
legend(150, 35, bty="n", legend = c("Boundary crossers", "SIB"), lty = lt1:lt2,
col = c("blue", "black"), pch=c(21, 16), title ="Counting methods")
# Definition of legend boxes
bt <- -25 # Bottom of rectangle
to <- 0 # Top of rectangle
bt2 <- -6 # Level of text
# PBDB ouput subsampled data
# Two timers
myData2 <- read.table(file="C:/Daten/nceas/PBDB/Scler_subs_tt.csv", header=TRUE, sep=",", as.is=TRUE)
attach(myData2)
age1 <- Base..Ma.
age2 <- Midpoint..Ma.
tt <- Mean.two.timers.diversity
sg <- Raw.SIB.diversity
cg <- Corrected.SIB.diversity
e <- Extinction.rate
o <- Origination.rate
pairs(myData2)
# Boundary crossers
myData3 <- read.table(file="C:/Daten/nceas/PBDB/Scler_subs_bc.csv", header=TRUE, sep=",", as.is=TRUE)
attach(myData3)
bg <- Mean.boundary.crossers.diversity
age2 <- Midpoint..Ma.
e <- Extinction.rate
o <- Origination.rate
t= e+o
d = o-e
# Turnover rates
lt1=2
lt2=1
plot(age2[t>0], e[t>0],xlim=c(250,0), ylim<-c(0, 0.55), col="red", xlab="Age (Ma)", type="b", pch=21, ylab="Per genus rate")
points(age2[t>0], o[t>0], pch=16, col="green", type="b", lty=lt2)
legend(65, 0.12, bty="n", legend = c("Extinction rate", "Origination rate"), lty = lt2,
col = c("red", "green"), pch=c(21, 16))
abline(v=199.6, col="red") # Mark Triassic-Jurassic boundary
abline(v=65.5, col="red") # Mark KT boundary
# Definition of legend boxes
bt <- -0.25 # Bottom of rectangle
to <- 0.02 # Top of rectangle
bt2 <- 0 # Level of text
# Boundary crosser and SIB diversity
windows(height=5, width=7)
lt1=2
lt2=1
plot(age1, bg, type="b", col="blue", xlim=c(250,0), ylim<-c(-2, 60), xlab="Age (Ma)", pch=21, lty=lt1, ylab="Number of genera", frame=F)
points(age2, sg, pch=16, type="b", lty=lt2)
abline(v=199.6, col="red") # Mark Triassic-Jurassic boundary
abline(v=65.5, col="red") # Mark KT boundary
legend(150, 15, bty="n", legend = c("Boundary crossers", "SIB"), lty = lt1:lt2,
col = c("blue", "black"), pch=c(21, 16), title ="Counting methods")
# Definition of legend boxes
bt <- -10 # Bottom of rectangle
to <- 0 # Top of rectangle
bt2 <- -2 # Level of text
# New plot
windows(height=5, width=7)
plot(age2, sg, xlim=c(250,0), ylim<-c(-2, 60), xlab="Age (Ma)", ylab="Number of genera", type="n")
abline(v=199.6, col="red") # Mark Triassic-Jurassic boundary
abline(v=65.5, col="red") # Mark KT boundary
# Definition of equilibrium states
s1 <- mean(sg[age2<250&age2>160])
s2 <- mean(sg[age2<160&age2>66])
s3 <- mean(sg[age1<66])
ser1 <- sd(sg[age2<250&age2>160])/sqrt(length(sg[age2<250&age2>160]))
ser2 <- sd(sg[age2<160&age2>66])/sqrt(length(sg[age2<160&age2>66]))
ser3 <- sd(sg[age1<66])/sqrt(length(sg[age1<66]))
rect(xleft=240, ybottom=s1-ser1, xright=160, ytop=s1+ser1, col="gray")
rect(xleft=160, ybottom=s2-ser2, xright=66, ytop=s2+ser2, col="gray")
rect(xleft=66, ybottom=s3-ser3, xright=0, ytop=s3+ser3, col="gray")
segments(240, s1, 160, s1)
segments(160, s2, 65.5, s2)
segments(65.5, s3, 0, s3)
points(age2, sg, pch=16, type="b", lty=lt2)
# Definition of legend boxes
bt <- -10 # Bottom of rectangle
to <- 0 # Top of rectangle
bt2 <- -2 # Level of text
# Fetch combined bivalve-coral data for comparison
bdat <- read.table(file="C:/Daten/nceas/PBDB/Combined_curve.csv", header=TRUE, sep=";", as.is=TRUE)
attach(bdat)
lt1=2
lt2=1
plot(age2, Occ_biv, type="b", col="blue", xlim=c(250,0), ylim=c(10, 20000), log="y", xlab="Age (Ma)", pch=21, lty=lt1, ylab="Number of occurrences", frame=F)
points(age2, Occ_cora, pch=16, type="b", log="y", lty=lt2)
abline(v=199.6, col="red") # Mark Triassic-Jurassic boundary
abline(v=65.5, col="red") # Mark KT boundary
legend(150, 35, bty="n", legend = c("Bivalves", "Corals"), lty = lt1:lt2,
col = c("blue", "black"), pch=c(21, 16))
# Definition of legend boxes
bt <- 5 # Bottom of rectangle
to <- 12 # Top of rectangle
bt2 <- 9 # Level of text
# Z-value plot
plot(age2, cor_biv_z, type="b", col="blue", xlim=c(250,0), xlab="Age (Ma)", ylab="Standardized difference corals-bivalves" )
abline(v=199.6, col="red") # Mark Triassic-Jurassic boundary
abline(v=65.5, col="red") # Mark KT boundary
abline(h=0, lty=2)
abline(h=-1.96, lty=2)
# Definition of legend boxes
bt <- -3.32 # Bottom of rectangle
to <- -3 # Top of rectangle
bt2 <- -3.17 # Level of text
# Z-value plot
plot(age2, cor_reef_z, type="b", col="blue", xlim=c(250,0), xlab="Age (Ma)",
ylab="Standardized difference corals-reefs" )
abline(v=199.6, col="red") # Mark Triassic-Jurassic boundary
abline(v=65.5, col="red") # Mark KT boundary
abline(h=0, lty=2)
abline(h=-1.96, lty=2)
# Definition of legend boxes
bt <- -1.22 # Bottom of rectangle
to <- -1.05 # Top of rectangle
bt2 <- -1.12 # Level of text
# Draw boxes
rect(xleft=251, ybottom=bt, xright=199.6, ytop=to) # Triassic
rect(xleft=145.5, ybottom=bt, xright=199.6, ytop=to) # Jurassic
rect(xleft=145.5, ybottom=bt, xright=65.5, ytop=to) # Cretaceous
rect(xleft=23, ybottom=bt, xright=65.5, ytop=to) # Paleogene
rect(xleft=23, ybottom=bt, xright=0, ytop=to) # Neogene
# Add Text for legend of time
text(x=226, y=bt2, labels="Tr")
text(x=175, y=bt2, labels="J")
text(x=105, y=bt2, labels="K")
text(x=45, y=bt2, labels="Pg")
text(x=10, y=bt2, labels="N") |
1d1945f45834582177fed14d56bfe5d8adbc1151 | 183caf378df099da122f65ea9b75002b1e12b774 | /projFocus/ceRNA/processData/step1-2_expVoomNormed.r | 25912d5f38d161c2dd09bd010843d5b9f3836088 | [] | no_license | cwt1/scripts-1 | f58e476ddb2c83e0480856a95a95a644ad3c001c | 061d6592aa6ab11c93363fcb40305a57db05e3f2 | refs/heads/master | 2021-05-28T01:31:30.896133 | 2014-08-25T19:02:37 | 2014-08-25T19:02:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,182 | r | step1-2_expVoomNormed.r | #!/usr/bin/Rscript
#Author: Jing He
#Date:24 Oct,2013
#Last Updated:
#COMMENTS: need edgeR installed;
#input: <string:path you wnat your results to be>
# <string:name of your design file(4 cols, tab delimite:example)
# <string:name of count matrix file>
# <string:name of your output files>
#output: <file:pdf of 2 plots> <file: txt of differetial expresssed genes>
####TODO: need more development
sysInfo = Sys.info()
if(sysInfo['sysname']=="Darwin" ){
source("/Volumes/ifs/home/c2b2/ac_lab/jh3283/scripts/projFocus/ceRNA/projFocusCernaFunctions.R")
rootd = "/Volumes/ifs/data/c2b2/ac_lab/jh3283/projFocus/"
}else if(sysInfo['sysname']=="Linux" ){
source("/ifs/home/c2b2/ac_lab/jh3283/scripts/projFocus/ceRNA/projFocusCernaFunctions.R")
print("working from Linux")
rootd = "/ifs/data/c2b2/ac_lab/jh3283/projFocus/"
}
args = getArgs()
usage = "Usage: Rscript step1-2_expVoomNormed.r --tumor <tumor.mat file> --normal <normal.mat file> "
example = "Example: "
# if(length(args) < 3 || is.null(args)){
# print(usage)
# print(example)
# print(args)
# stop("Input parameter error!")
# }else{
# print(args)
# }
CDT = as.character(paste(unlist(strsplit(date(),split=" "))[c(2,3,5)],collapse="-"))
setwd(system("pwd",intern=T))
cwd = getwd()
# tumor = args['tumor']
# normal = args['normal']
# outExp = paste(tumor, "_", CDT, ".voomNormed.matrix", sep="")
print(paste("current working directory:",cwd))
##-----test
# cwd = getwd()
tumor = "/Volumes//ifs/data/c2b2/ac_lab/jh3283/projFocus/result/03102014/exp/brca_exp_l3_tumor_Mar-21-2014.matrix"
normal = "/Volumes//ifs/data/c2b2/ac_lab/jh3283/projFocus/result/03102014/exp/brca_exp_l3_normal_Mar-21-2014.matrix"
outExp = paste(tumor, "_", CDT, ".voomNormed.matrix", sep="")
outExpN = paste(normal, "_", CDT, ".voomNormed.matrix", sep="")
##----------------------------
getData = function(file,type="T"){
data = read.delim(file,header=T)
gene = unique(unlist(data[,1]))
data = data[-16274,]
data = data[,-1]
rownames(data)=gene
# colnames(data) = vapply(colnames(data),FUN=function(x){substr(x,start=9,stop=16)},'a')
sample = colnames(data)
design = rep(type,ncol(data))
names(design) = sample
return(list(data=data,design=design,gene=gene))
}
##--------#load data
dataT = getData(tumor,type='tumor')
dataN = getData(normal,type='normal')
cntSampleT = ncol(dataT$data)
cntSampleN = ncol(dataN$data)
cntGene = nrow(dataT$data)
dataMat = cbind(dataT$data,dataN$data)
gene = dataT$gene
row.names(dataMat) = gene
design = c(dataT$design,dataN$design)
condition <- factor( design )
##-------------voom transformation
require(limma)
designMat = model.matrix(~design)
dataMatVoom = voom(as.matrix(dataMat),designMat,plot=TRUE)
dataMatVoomTumor = dataMatVoom$E[,which(as.character(design)=="tumor")]
dataMatVoomNormal = dataMatVoom$E[,which(as.character(design)=="normal")]
##---------output
dev.off()
write.table(round(dataMatVoomTumor,digits=4),
outExp,sep="\t",col.names=T,row.names=T,quote=F)
write.table(round(dataMatVoomNormal,digits=4),
outExpN,sep="\t",col.names=T,row.names=T,quote=F)
#--------------------------------------------
|
32351769250220c5d6b03272933a767b4c9a0c2a | 0365e239275b3e08473bef7ae68cbba75137a92f | /5A_CAR_OLS_data_prep_similarity.R | 37ab3a29447c2149374cb5557af7d20f08d35e1d | [] | no_license | KateMMiller/RegenDebtCode | 4a9aacc95c62a742099965a1a63cec00541f7c59 | 2fa9705bf61c3f913932d0da3a29d3f10a90c7b0 | refs/heads/master | 2021-04-26T23:34:17.736231 | 2018-11-03T15:31:54 | 2018-11-03T15:31:54 | 123,821,663 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 18,611 | r | 5A_CAR_OLS_data_prep_similarity.R | #--------------------------------------------
# Spatial Regression on beta diversity
#--------------------------------------------
setwd('C:/temp/GIS')
library(rgdal)
library(raster)
#library(sp)
#library(maptools)
library(dplyr)
options("scipen"=100, "digits"=4)
rasterOptions(timer=TRUE,progress='text', tmpdir='G:/raster_temp') #shows a timer for processes run from raster package
file=('C:/temp/GIS')
#-----------------------------------
# Relate response shapefile point locations to explanatory variables
#-----------------------------------
# Seedling/canopy similarity
seedbeta<-readOGR(dsn=file,layer="Tree_seedling_stems_m2_beta")
seeds.bdf<-data.frame(seedbeta)[,-9]
names(seeds.bdf)
# read in raster and shapefiles to join data with
prec<-raster('Pct_Change_Precip.tif') # raster files not kernel smoothed
tmax<-raster('Pct_Change_Tmax.tif')
deer.den<-readOGR(dsn=file, layer='deer_density_UTMalbers')
deer.den$deer.index<-ifelse(deer.den$deer_dnsty=="red",4,ifelse(deer.den$deer_dnsty=="orange",3,ifelse(deer.den$deer_dnsty=="yellow",2,ifelse(deer.den$deer_dnsty=="green",1,NA))))
deer.sm<-raster('Browse_Impact_kern10k.tif')
inv.sm<-raster('Invasive_Cover_kern10k.tif')
hmod.lc<-raster('nlcd300_utm_clip.tif')
plots<-readOGR(dsn=file, layer="FIA_plots_for_analysis")
relabund.tr<-readOGR(dsn=file, layer="relabund_trees")
relabund.sd<-readOGR(dsn=file, layer="relabund_seedlings")
plots.df<-data.frame(plots)[,-13]
relab.tr.df<-data.frame(relabund.tr)[,-(17:19)] #remove coordinates and optional to keep from getting double copies in merged df
relab.sd.df<-data.frame(relabund.sd)[,-(17:19)] #remove coordinates and optional fields
colnames(relab.sd.df)<-c("PLT_CN","INVYR","ACERUB.sd","ACESAC.sd","CARYA.sd", "EARSUC.sd","FAGGRA.sd","FRAX.sd",
"INVASIVE.sd","LIRTUL.sd","OTHER_SPP.sd","PICEA.sd","PINUS.sd","QUERCUS.sd","SHORT.sd","ULMUS.sd")
# extract values of rasters for each point location in seeds and join point data
prec.ex<-extract(prec,seedbeta,method='bilinear')
tmax.ex<-extract(tmax,seedbeta,method='bilinear')
deer.sm.ex<-extract(deer.sm,seedbeta,method='bilinear')
inv.sm.ex<-extract(inv.sm,seedbeta,method='bilinear')
hmod.ex<-extract(hmod.lc,seedbeta,method='simple')
deer.ex<-sp::over(seedbeta, deer.den)
seeds2<-left_join(seeds.bdf,plots.df[,c("PLT_CN","INVYR","STDSZCD","CANOPY_")], by=c("PLT_CN"))
seeds3<-left_join(seeds2,relab.tr.df, by=c("PLT_CN","INVYR"))
seeds4<-left_join(seeds3,relab.sd.df, by=c("PLT_CN","INVYR"))
# combine datasets
seeds5<-data.frame(seeds4,prec.ex,tmax.ex, deer.sm.ex, inv.sm.ex,hmod.ex, deer.ex[,2])
head(seeds5)
values<-c("train","test")
seeds5$type<-sample(values, nrow(seeds5), TRUE, prob = c(.95,.05)) # split data into training and testing dataset
table(seeds5$type)
coordinates(seeds5)<-~coords.x1+coords.x2
proj4string(seeds5)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
file<-'C:/temp/GIS'
writeOGR(seeds5, dsn=file, layer="Seedlings_m2_beta_with_predictors", driver="ESRI Shapefile", overwrite_layer=T)
#------------------------
# Seedling/canopy similarity
sapbeta<-readOGR(dsn=file,layer="Tree_sapling_stems_m2_beta")
bbox<-readOGR(dsn=file, layer="Bounding_Box")
saps.bdf<-data.frame(sapbeta)[,-9] #remove optional
names(saps.bdf)
# read in sapling relative abundance shapefile
relabund.sap<-readOGR(dsn=file, layer="relabund_saplings")
relab.sap.df<-data.frame(relabund.sap)[,-(17:19)]
colnames(relab.sap.df)<-c("PLT_CN","INVYR","ACERUB.sap","ACESAC.sap","CARYA.sap", "EARSUC.sap","FAGGRA.sap","FRAX.sap",
"INVASIVE.sap","LIRTUL.sap","OTHER_SPP.sap","PICEA.sap","PINUS.sap","QUERCUS.sap","SHORT.sap","ULMUS.sap")
names(relab.sap.df)
# extract values of rasters for each point location in seeds and join point data
prec.ex<-extract(prec,sapbeta,method='bilinear')
tmax.ex<-extract(tmax,sapbeta,method='bilinear')
deer.sm.ex<-extract(deer.sm,sapbeta,method='bilinear')
inv.sm.ex<-extract(inv.sm,sapbeta,method='bilinear')
hmod.ex<-extract(hmod.lc,sapbeta,method='simple')
deer.ex<-sp::over(sapbeta, deer.den)
saps2<-left_join(saps.bdf,plots.df[,c("PLT_CN","INVYR","STDSZCD","CANOPY_")], by=c("PLT_CN"))
saps3<-left_join(saps2,relab.tr.df, by=c("PLT_CN","INVYR"))
saps4<-left_join(saps3,relab.sap.df, by=c("PLT_CN","INVYR"))
# combine datasets
saps5<-data.frame(saps4,prec.ex,tmax.ex, deer.sm.ex, inv.sm.ex,hmod.ex, deer.ex[,2])
values<-c("train","test")
saps5$type<-sample(values, nrow(saps5), TRUE, prob = c(.95,.05)) # split data into training and testing dataset
table(saps5$type)
coordinates(saps5)<-~coords.x1+coords.x2
proj4string(saps5)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
file<-'C:/temp/GIS'
writeOGR(saps5, dsn=file, layer="Saplings_m2_beta_with_predictors", driver="ESRI Shapefile", overwrite_layer=T)
#---------------------------------------
# Clip datasets to mid-Atlantic region for analysis
#---------------------------------------
# Regen without deer impacts
#---------------------------------------
# Clip seedling data to primary area of interest in analysis
seeds<-readOGR(dsn=file,layer="Seedlings_m2_beta_with_predictors")
bbox<-readOGR(dsn=file, layer="Bounding_Box")
seeds.c<-raster::crop(seeds,bbox, snap='in')
plot(seeds.c, col='blue', pch=16)
plot(bbox,add=T)
names(seeds.c)
#View(seeds2)
names(seeds.c)
seeds2<-seeds.c[,c("PLT_CN","INVYR","Sor","Hor","STDSZCD","CANOPY_","ACERUB","ACESAC","CARYA","EARSUC","FAGGRA","FRAX","INVASIVE","LIRTUL",
"OTHER_SPP","PICEA","PINUS","QUERCUS","SHORT","ULMUS","ACERUB_","ACESAC_", "CARYA_s","EARSUC_",
"FAGGRA_","FRAX_sd","INVASIVE_","LIRTUL_","OTHER_SPP_","PICEA_s","PINUS_s","QUERCUS_","SHORT_s","ULMUS_s",
"prec_ex","tmax_ex","inv_sm_","hmod_ex","d____2_","type" )]
names(seeds2)[names(seeds2)=="d____2_"]<-"deer_den"
seeds2$X<-seeds2$coords.x1
seeds2$Y<-seeds2$coords.x2
seeds3<-na.omit(seeds2@data)
nrow(seeds2);nrow(seeds3)
coordinates(seeds3)<-~X+Y
proj4string(seeds3)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
writeOGR(seeds3, dsn=file, layer='Seedlings_m2_beta_with_pred_clip',driver="ESRI Shapefile",overwrite_layer=T)
seeds.df<-as.data.frame(seeds3)
write.csv(seeds.df, "Seedlings_m2_beta_with_pred_df.csv")
# Clip sapling data to primary area of interest in analysis
saps<-readOGR(dsn=file,layer="Saplings_m2_beta_with_predictors")
bbox<-readOGR(dsn=file, layer="Bounding_Box")
saps.c<-raster::crop(saps,bbox, snap='in')
plot(saps.c, col='blue', pch=16)
plot(bbox,add=T)
names(saps.c)
saps2<-saps.c[,c("PLT_CN","INVYR","Sor","Hor","STDSZCD","CANOPY_","ACERUB","ACESAC","CARYA","EARSUC","FAGGRA","FRAX","INVASIVE","LIRTUL",
"OTHER_SPP","PICEA","PINUS","QUERCUS","SHORT","ULMUS","ACERUB_","ACESAC_", "CARYA_s","EARSUC_",
"FAGGRA_","FRAX_sp","INVASIVE_","LIRTUL_","OTHER_SPP_","PICEA_s","PINUS_s","QUERCUS_","SHORT_s","ULMUS_s",
"prec_ex","tmax_ex","inv_sm_","hmod_ex","d____2_","type" )]
names(saps2)[names(saps2)=="d____2_"]<-"deer_den"
saps2$X<-saps2$coords.x1
saps2$Y<-saps2$coords.x2
saps3<-na.omit(saps2@data)
nrow(saps2)-nrow(saps3) # only losing 23 records
coordinates(saps3)<-~X+Y
proj4string(saps3)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
writeOGR(saps3, dsn=file, layer='Saplings_m2_beta_with_pred_clip',driver="ESRI Shapefile",overwrite_layer=T)
saps.df<-as.data.frame(saps3)
write.csv(saps.df, "Saplings_m2_beta_with_pred_df.csv")
#---------------------------------------
# Regen with deer impacts
#---------------------------------------
# Clip seedling data to primary area of interest in analysis
seeds<-readOGR(dsn=file,layer="Seedlings_m2_beta_with_predictors")
bbox<-readOGR(dsn=file, layer="Bounding_Box")
seeds.c<-raster::crop(seeds,bbox, snap='in')
plot(seeds.c, col='blue', pch=16)
plot(bbox,add=T)
names(seeds.c)
names(seeds.c)
seeds2d<-seeds.c[,c("PLT_CN","INVYR","Sor","Hor","STDSZCD","CANOPY_","ACERUB","ACESAC","CARYA","EARSUC","FAGGRA","FRAX","INVASIVE","LIRTUL",
"OTHER_SPP","PICEA","PINUS","QUERCUS","SHORT","ULMUS","ACERUB_","ACESAC_", "CARYA_s","EARSUC_",
"FAGGRA_","FRAX_sd","INVASIVE_","LIRTUL_","OTHER_SPP_","PICEA_s","PINUS_s","QUERCUS_","SHORT_s","ULMUS_s",
"prec_ex","tmax_ex","inv_sm_","hmod_ex","type", "dr_sm_x", "d____2_")]
names(seeds2d)[names(seeds2d)=="d____2_"]<-"deer_den"
seeds2d$X<-seeds2d$coords.x1
seeds2d$Y<-seeds2d$coords.x2
seeds3d<-na.omit(seeds2d@data)
nrow(seeds2)-nrow(seeds3d) #1400 plots lost with deer data included
coordinates(seeds3d)<-~X+Y
proj4string(seeds3d)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
writeOGR(seeds3d, dsn=file, layer='Seedlings_m2_beta_with_deer_pred_clip',driver="ESRI Shapefile",overwrite_layer=T)
seeds.dfd<-as.data.frame(seeds3d)
write.csv(seeds.dfd, "Seedlings_m2_beta_with_deer_pred_df.csv")
# Clip sapling data to primary area of interest in analysis
saps<-readOGR(dsn=file,layer="Saplings_m2_beta_with_predictors")
bbox<-readOGR(dsn=file, layer="Bounding_Box")
saps.c<-raster::crop(saps,bbox, snap='in')
plot(saps.c, col='blue', pch=16)
plot(bbox,add=T)
names(saps.c)
saps2d<-saps.c[,c("PLT_CN","INVYR","Sor","Hor","STDSZCD","CANOPY_","ACERUB","ACESAC","CARYA","EARSUC","FAGGRA","FRAX","INVASIVE","LIRTUL",
"OTHER_SPP","PICEA","PINUS","QUERCUS","SHORT","ULMUS","ACERUB_","ACESAC_", "CARYA_s","EARSUC_",
"FAGGRA_","FRAX_sp","INVASIVE_","LIRTUL_","OTHER_SPP_","PICEA_s","PINUS_s","QUERCUS_","SHORT_s","ULMUS_s",
"prec_ex","tmax_ex","inv_sm_","hmod_ex","type", "dr_sm_x", "d____2_")]
names(saps2d)[names(saps2d)=="d____2_"]<-"deer_den"
saps2d$X<-saps2d$coords.x1
saps2d$Y<-saps2d$coords.x2
saps3d<-na.omit(saps2d@data)
nrow(saps2d)-nrow(saps3d) #1391
coordinates(saps3d)<-~X+Y
proj4string(saps3d)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
writeOGR(saps3d, dsn=file, layer='Saplings_m2_beta_with_deer_pred_clip',driver="ESRI Shapefile",overwrite_layer=T)
saps.dfd<-as.data.frame(saps3d)
write.csv(saps.dfd, "Saplings_m2_beta_with_deer_pred_df.csv")
#-----------------------
# Close R and reopen to clear RAM and temporary files
#----------------------
# START HERE FOR Spatial Regress
#------------------
setwd('C:/temp/GIS')
library(rgdal)
library(raster)
library(spdep)
library(car)
#library(GWmodel)
options("scipen"=100, "digits"=4)
rasterOptions(timer=TRUE,progress='text', tmpdir='G:/raster_temp') #shows a timer for processes run from raster package
file=('C:/temp/GIS')
#-----------------------------
# Seedling vs. Canopy similarity without deer impacts
#-----------------------------
seeds<-read.csv('Seedlings_m2_beta_with_pred_df.csv')[,-c(1)]
seeds2<-subset(seeds,type=="train" ) # select points that are only in the training dataset
names(seeds2)
seeds2$meso.tr<-seeds2$ACERUB+seeds2$ACESAC+seeds2$FAGGRA
seeds2$maple.tr<-seeds2$ACERUB+seeds2$ACESAC
seeds2$oakhick.tr<-seeds2$QUERCUS+seeds2$CARYA
seeds2$meso.sd<-seeds2$ACERUB_+seeds2$ACESAC_+seeds2$FAGGRA_
seeds2$maple.sd<-seeds2$ACERUB_+seeds2$ACESAC_
seeds2$oakhick.sd<-seeds2$QUERCUS_+seeds2$CARYA_s
seeds2$oakhick.sub<-seeds2$oakhick.tr-seeds2$oakhick.sd
seeds2$Sor.lgt<-logit(seeds2$Sor)
seeds2$Hor.lgt<-logit(seeds2$Hor)
seeds2$cancov<-seeds2$CANOPY_/100
#View(seeds2)
# Run OLS model first
names(seeds2)
#View(seeds2)
Sor.lm<-lm(Sor.lgt~SHORT_s+maple.sd+QUERCUS+QUERCUS_+FAGGRA+FAGGRA_+PINUS+PINUS_s+INVASIVE_+deer_den, data=seeds2)
summary(Sor.lm) #r2=0.113
AIC(Sor.lm) #19326
par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
plot(Sor.lm)
par(mfrow=c(1,1))
Hor.lm<-lm(Hor.lgt~tmax_ex+SHORT_s+maple.sd+QUERCUS+QUERCUS_+FAGGRA+FAGGRA_+PINUS+PINUS_s, data=seeds2)
summary(Hor.lm) #r2=0.254
AIC(Hor.lm) #20886
par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
plot(Hor.lm)
par(mfrow=c(1,1))
library(car)
vif(Sor.lm)
vif(Hor.lm) # all are under 2, so no issues of colinearity
library(gstat) #for bubble plot
names(seeds2)
autocor<-data.frame(seeds2$X,seeds2$Y,resids=Sor.lm$residuals)
names(autocor)
coordinates(autocor)<-~seeds2.X+seeds2.Y
proj4string(autocor)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
bubble(autocor,zcol='resids')
var.mod<-variogram(resids~1, data=autocor, alpha=c(0,90,180,270))
plot(var.mod)
autocorH<-data.frame(seeds2$X,seeds2$Y,resids=Hor.lm$residuals)
coordinates(autocorH)<-~seeds2.X+seeds2.Y
proj4string(autocorH)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
bubble(autocorH,zcol='resids')
var.modH<-variogram(resids~1, data=autocorH, alpha=c(0,90,180,270))
plot(var.modH)
#Calc Moran.I
library(ape)
plt.dist<-as.matrix(dist(cbind(seeds2$X,seeds2$Y)))
plt.dist.inv<-1/plt.dist
diag(plt.dist.inv)<-0
Moran.I(autocorH$resids,plt.dist.inv) #checking if residuals are autocorrelated
# significant p-value
#++++++++++++++++++++++++++++++++
#-----------------------------
# Seedling data with deer impacts
#-----------------------------
#++++++++++++++++++++++++++++++++
seedsd<-read.csv('Seedlings_m2_beta_with_deer_pred_df.csv')[,-c(1)]
seeds2d<-subset(seedsd,type=="train" ) # select points that are only in the training dataset
names(seeds2d)
seeds2d$meso.tr<-seeds2d$ACERUB+seeds2d$ACESAC+seeds2d$FAGGRA
seeds2d$maple.tr<-seeds2d$ACERUB+seeds2d$ACESAC
seeds2d$oakhick.tr<-seeds2d$QUERCUS+seeds2d$CARYA
seeds2d$meso.sd<-seeds2d$ACERUB_+seeds2d$ACESAC_+seeds2d$FAGGRA_
seeds2d$maple.sd<-seeds2d$ACERUB_+seeds2d$ACESAC_
seeds2d$oakhick.sd<-seeds2d$QUERCUS_+seeds2d$CARYA_s
seeds2d$Sor.lgt<-logit(seeds2d$Sor)
seeds2d$Hor.lgt<-logit(seeds2d$Hor)
# Run OLS model for Sorenson similarity
Sor.lmd<-lm(Sor.lgt~SHORT_s+maple.sd+QUERCUS+QUERCUS_+FAGGRA+FAGGRA_+PINUS+INVASIVE_+dr_sm_x+deer_den, data=seeds2d)
summary(Sor.lmd) #r2=0.117
AIC(Sor.lmd) #15336
par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
plot(Sor.lmd)
par(mfrow=c(1,1))
# OLS model for Horn similarity
Hor.lmd<-lm(Hor.lgt~tmax_ex+SHORT_s+maple.sd+QUERCUS+QUERCUS_+FAGGRA+FAGGRA_+PINUS+PINUS_s+deer_den, data=seeds2d) #deer dens better than impact
summary(Hor.lmd) #r2=0.265
AIC(Hor.lmd) #16503
par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
plot(Hor.lm)
par(mfrow=c(1,1))
library(gstat) #for bubble plot
names(seeds2d)
length(glob.lm$residuals)
autocor<-data.frame(seeds2d$X,seeds2d$Y,resids=glob.lm$residuals)
names(autocor)
coordinates(autocor)<-~seeds2d.X+seeds2d.Y
proj4string(autocor)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
names(autocor)
bubble(autocor,zcol='resids')
var.mod<-variogram(resids~1, data=autocor, alpha=c(0,90,180,270))
plot(var.mod)
#Calc Moran.I
library(ape)
plt.dist<-as.matrix(dist(cbind(seeds2d$X,seeds2d$Y)))
plt.dist.inv<-1/plt.dist
diag(plt.dist.inv)<-0
Moran.I(autocor$resids,plt.dist.inv) #checking if residuals are autocorrelated
# significant p-value
#----------------------------------
# Sapling data without deer impacts
#----------------------------------
saps<-read.csv('Saplings_m2_beta_with_pred_df.csv')[,-c(1)]
saps2<-subset(saps,type=="train" ) # select points that are only in the training dataset
names(saps2)
saps2$meso.tr<-saps2$ACERUB+saps2$ACESAC+saps2$FAGGRA
saps2$maple.tr<-saps2$ACERUB+saps2$ACESAC
saps2$oakhick.tr<-saps2$QUERCUS+saps2$CARYA
saps2$meso.sd<-saps2$ACERUB_+saps2$ACESAC_+saps2$FAGGRA_
saps2$maple.sap<-saps2$ACERUB_+saps2$ACESAC_
saps2$oakhick.sd<-saps2$QUERCUS_+saps2$CARYA_s
saps2$oakhick.sub<-saps2$oakhick.tr-saps2$oakhick.sd
saps2$Sor.lgt<-logit(saps2$Sor)
saps2$Hor.lgt<-logit(saps2$Hor)
saps2$cancov<-saps2$CANOPY_/100
# OLS model for Sorenson similarity
names(saps2)
Sor.sap.lm<-lm(Sor.lgt~tmax_ex+INVASIVE_+SHORT_s+QUERCUS+QUERCUS_+CARYA+maple.sap+FAGGRA+PINUS_s+STDSZCD+cancov, data=saps2)
summary(Sor.sap.lm) #r2=0.153
AIC(Sor.sap.lm) #18397
par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
plot(Sor.sap.lm)
par(mfrow=c(1,1))
library(gstat) #for bubble plot
autocor<-data.frame(saps2$X,saps2$Y,resids=Sor.sap.lm$residuals)
names(autocor)
coordinates(autocor)<-~saps2.X+saps2.Y
proj4string(autocor)=CRS('+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs')
names(autocor)
library(car)
bubble(autocor,zcol='resids')
var.mod<-variogram(resids~1, data=autocor, alpha=c(0,90,180,270))
plot(var.mod)
#Calc Moran.I
library(ape)
names(saps2)
plt.dist<-as.matrix(dist(cbind(saps2$X,saps2$Y)))
plt.dist.inv<-1/plt.dist
diag(plt.dist.inv)<-0
Moran.I(autocor$resids,plt.dist.inv) #checking if residuals are autocorrelated
# significant p-value
# OLS model for Horn similarity
names(saps2)
Hor.sap.lm<-lm(Hor.lgt~tmax_ex+INVASIVE_+SHORT_s+QUERCUS+QUERCUS_+CARYA+maple.sap+FAGGRA+FAGGRA_+PINUS+PINUS_s+STDSZCD+cancov, data=saps2)
summary(Hor.sap.lm) #r2=0.274
AIC(Hor.sap.lm) #20773
vif(Hor.sap.lm)
#----------------------------------
# Sapling data with deer impacts
#----------------------------------
saps<-read.csv('Saplings_m2_beta_with_deer_pred_df.csv')[,-c(1)]
saps2<-subset(saps,type=="train" ) # select points that are only in the training dataset
saps2$maple.tr<-saps2$ACERUB+saps2$ACESAC
saps2$maple.sap<-saps2$ACERUB_+saps2$ACESAC_
saps2$Sor.lgt<-logit(saps2$Sor)
saps2$Hor.lgt<-logit(saps2$Hor)
saps2$cancov<-saps2$CANOPY_/100
# OLS model for Sorenson similarity
names(saps2)
Sor.sap.lm<-lm(Sor.lgt~tmax_ex+INVASIVE_+SHORT_s+QUERCUS+QUERCUS_+CARYA+maple.sap+FAGGRA+FAGGRA_+PINUS_s+STDSZCD+cancov, data=saps2)
summary(Sor.sap.lm) #r2=0.168
AIC(Sor.sap.lm) #14234
par(mfrow = c(2, 2), oma = c(0, 0, 2, 0))
plot(Sor.sap.lm)
par(mfrow=c(1,1))
Hor.sap.lm<-lm(Hor.lgt~tmax_ex+INVASIVE_+SHORT_s+QUERCUS+QUERCUS_+CARYA+maple.sap+FAGGRA+FAGGRA_+PINUS+PINUS_s+STDSZCD+cancov, data=saps2)
summary(Hor.sap.lm) #r2=0.277
AIC(Hor.sap.lm) #16273
|
b47813daf12b15e147c908cff022694573ee2091 | 1a7f5a2318c3a1e76c2916cb9637509f373dccf0 | /Problem_1.R | c4a5fcdc7ab53809521b45b4eab5d22618734b18 | [] | no_license | feb-uni-sofia/homework-1-r-basics-magivelikova | 539d504c4c2b574d4c6d6415f8c119686c971e1c | e03d3b5a6798068fd1ffc02d268de12e750b02b5 | refs/heads/master | 2021-04-15T18:57:26.111997 | 2018-03-22T18:04:21 | 2018-03-22T18:04:21 | 126,330,392 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 368 | r | Problem_1.R | #a)
x<-c(4,1,1,4)
#b)
y<-c(1,4)
# c) - since y is shorter than x, the elements of y are repeated until y becomes the same length as x and this can only happen if the longer
# vector's length is a multiple of the shorter vector's legth. We get x(4,1,1,4)-y(1,4,1,4)=z(3,-3,0,0)
z <- x-y
#d)
s<-c(x,y)
#e)
rep(s,10)
#f)
rep(s,3)
#g)
seq(7,21)
7:21
#h)
length(seq(7,21))
|
9d2f9bceff2a20169b14d2b30ab0a748c19da630 | f67ac5fcafe4cb26ebc800e6f839de9df402e4d6 | /gather_data.R | 90b61c7fd2e087aa359be4d743b9cd32300b2e2b | [] | no_license | billzichos/Bag-of-Words-Meets-Bags-of-Popcorn | 58c63f2d457eff20f85a78540219349b69c811e2 | 29ff12b8700b27272fc65c1e315edc9a9a06d9aa | refs/heads/master | 2021-01-21T00:52:49.889803 | 2016-05-21T21:31:21 | 2016-05-21T21:31:21 | 36,864,346 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 402 | r | gather_data.R | wd <- "~/GitHub/Bag-of-Words-Meets-Bags-of-Popcorn"
setwd(wd)
source("~/GitHub/Get-Raw-Data/download.R")
downloadSingleKaggleZip("word2vec-nlp-tutorial","unlabeledTrainData.tsv.zip", "unlabeledTrainData.tsv")
downloadSingleKaggleZip("word2vec-nlp-tutorial","testData.tsv.zip", "testData.tsv")
downloadSingleKaggleZip("word2vec-nlp-tutorial","labeledTrainData.tsv.zip", "labeledTrainData.tsv") |
aa17c0c2603b5dd3fc3fe2465521f05362712980 | 86e5242d859ab04bc40e330f075b71c9ef636a91 | /HRAnalytics/hranalytics.r | dd26f47ed143cc732782721cbf3808dbf8412232 | [] | no_license | ujwalk93/Data-Science | 17ba6fb1c1f0d19c96b099eeeb6c2d4383332f70 | 4e84c9f253e5f93f46a1a61d50f62d26f9b634e3 | refs/heads/master | 2020-03-26T10:35:56.134518 | 2019-07-01T12:51:43 | 2019-07-01T12:51:43 | 144,806,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 38,864 | r | hranalytics.r | setwd("D:\\PG_Diploma\\HR Analytics")
#loading all the required libraries
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(reshape2)
library(car)
library(caret)
library(caTools)
library(MASS)
library(lubridate)
library(e1071)
library(cowplot)
#understanding the data based on business perspective
#there are 5 .csv data files used for analysis in this case study
#1.employee_survey_data.csv - contains details from employees about how well they are satisfied with their job and work-life balance
#2.manager_survey_data.csv - contains details from managers about how well employees perform under them
#3.general_data.csv - contains details about employee location and other aspects like employee behaviour and their involvement in job
#4.in_time.csv - contains details regarding in time of employees in calendar year 2015
#5.out_time.csv - contains details regarding out time of employees in calendar year 2015
#business understanding and objective of case study
#A large company XYZ having around 4000 employees has a very high level of attrition.
#This has a significant negative impact on the company.
#The main objective of this case study is to model the probability of attrition using
#logistic regression to understand what are the main factors that cause the high rate of attrition
#The company management can use these results to determine how the high rate of attrition can be reduced
#by making appropriate changes in the workplace.
#1. Data preparation, cleaning, analysis and EDA
#loading all the required dataset files
employee <- read.csv("employee_survey_data.csv",stringsAsFactors=F)
manager <- read.csv("manager_survey_data.csv",stringsAsFactors=F)
general <- read.csv("general_data.csv",stringsAsFactors=F)
intime <- read.csv("in_time.csv",stringsAsFactors=F)
outtime <- read.csv("out_time.csv",stringsAsFactors=F)
#display the file details
str(employee)
str(manager)
str(general)
str(intime)
str(outtime)
#renaming first columns in intime and outtime as EmployeeID to ensure consistency and ease of analysis
colnames(intime)[1]<-"EmployeeID"
colnames(outtime)[1]<-"EmployeeID"
#check for duplicate employee ID values in each data frame
sum(duplicated(employee$EmployeeID))
sum(duplicated(manager$EmployeeID))
sum(duplicated(general$EmployeeID))
sum(duplicated(intime$EmployeeID))
sum(duplicated(outtime$EmployeeID))
#check for missing values
sapply(employee, function(x) length(which(x == '')))
sapply(manager, function(x) length(which(x == '')))
sapply(general, function(x) length(which(x == '')))
sapply(intime, function(x) length(which(x == '')))
sapply(outtime, function(x) length(which(x == '')))
#check for NA values
sapply(employee, function(x) length(which(is.na(x))))
sapply(manager, function(x) length(which(is.na(x))))
sapply(general, function(x) length(which(is.na(x))))
sapply(intime, function(x) length(which(is.na(x))))
sapply(outtime, function(x) length(which(is.na(x))))
#replace NA values in individual columns of the employee dataset with median for ease of analysis
employee$EnvironmentSatisfaction[which(is.na(employee$EnvironmentSatisfaction))]<-median(employee$EnvironmentSatisfaction,na.rm = TRUE)
employee$JobSatisfaction[which(is.na(employee$JobSatisfaction))]<-median(employee$JobSatisfaction,na.rm = TRUE)
employee$WorkLifeBalance[which(is.na(employee$WorkLifeBalance))]<-median(employee$WorkLifeBalance,na.rm = TRUE)
#replace NA values in individual columns of the general dataset with mean for ease of analysis
general$NumCompaniesWorked[which(is.na(general$NumCompaniesWorked))]<-mean(general$NumCompaniesWorked,na.rm = TRUE)
general$TotalWorkingYears[which(is.na(general$TotalWorkingYears))]<-mean(general$TotalWorkingYears,na.rm = TRUE)
#Remove the columns with all the row values as NA
employee <- employee[,colSums(is.na(employee))<nrow(employee)]
manager <- manager[,colSums(is.na(manager))<nrow(manager)]
general <- general[,colSums(is.na(general))<nrow(general)]
#convert all character type columns in general dataframe into factor variables
general[,sapply(general, is.character)] <- lapply(general[,sapply(general, is.character)], as.factor)
#remove EmployeeCount and Over18 columns from general dataframe as all the row values are equal to 1
general <- general[ , -c(8,16)]
#remove StandardHours column from general dataframe as all the row values are equal to 8
general <- general[ , -c(16)]
#check whether EmployeeID values are consistent across different dataframes
setdiff(employee$EmployeeID,general$EmployeeID)
setdiff(manager$EmployeeID,general$EmployeeID)
setdiff(intime$EmployeeID,general$EmployeeID)
setdiff(outtime$EmployeeID,general$EmployeeID)
#convert some numeric variables into categorical (factor) variables as per format given in the data dictionary
general$Education[which(general$Education==1)] <- "Below College"
general$Education[which(general$Education==2)] <- "College"
general$Education[which(general$Education==3)] <- "Bachelor"
general$Education[which(general$Education==4)] <- "Master"
general$Education[which(general$Education==5)] <- "Doctor"
general$Education <- as.factor(general$Education)
employee$EnvironmentSatisfaction[which(employee$EnvironmentSatisfaction==1)] <- "Low"
employee$EnvironmentSatisfaction[which(employee$EnvironmentSatisfaction==2)] <- "Medium"
employee$EnvironmentSatisfaction[which(employee$EnvironmentSatisfaction==3)] <- "High"
employee$EnvironmentSatisfaction[which(employee$EnvironmentSatisfaction==4)] <- "Very High"
employee$EnvironmentSatisfaction <- as.factor(employee$EnvironmentSatisfaction)
employee$JobSatisfaction[which(employee$JobSatisfaction==1)] <- "Low"
employee$JobSatisfaction[which(employee$JobSatisfaction==2)] <- "Medium"
employee$JobSatisfaction[which(employee$JobSatisfaction==3)] <- "High"
employee$JobSatisfaction[which(employee$JobSatisfaction==4)] <- "Very High"
employee$JobSatisfaction <- as.factor(employee$JobSatisfaction)
employee$WorkLifeBalance[which(employee$WorkLifeBalance==1)] <- "Bad"
employee$WorkLifeBalance[which(employee$WorkLifeBalance==2)] <- "Good"
employee$WorkLifeBalance[which(employee$WorkLifeBalance==3)] <- "Better"
employee$WorkLifeBalance[which(employee$WorkLifeBalance==4)] <- "Best"
employee$WorkLifeBalance <- as.factor(employee$WorkLifeBalance)
manager$JobInvolvement[which(manager$JobInvolvement==1)] <- "Low"
manager$JobInvolvement[which(manager$JobInvolvement==2)] <- "Medium"
manager$JobInvolvement[which(manager$JobInvolvement==3)] <- "High"
manager$JobInvolvement[which(manager$JobInvolvement==4)] <- "Very High"
manager$JobInvolvement <- as.factor(manager$JobInvolvement)
manager$PerformanceRating[which(manager$PerformanceRating==1)] <- "Low"
manager$PerformanceRating[which(manager$PerformanceRating==2)] <- "Good"
manager$PerformanceRating[which(manager$PerformanceRating==3)] <- "Excellent"
manager$PerformanceRating[which(manager$PerformanceRating==4)] <- "Outstanding"
manager$PerformanceRating <- as.factor(manager$PerformanceRating)
#merging dataframes for analysis using common attribute (EmployeeID)
hrdata <- merge(general, employee, by="EmployeeID")
hrdata <- merge(hrdata, manager, by="EmployeeID")
sum(is.na(employee$JobSatisfaction))
sum(is.na(employee$WorkLifeBalance))
sum(is.na(manager$JobInvolvement))
sum(is.na(hrdata))
#removing the columns which have na more than 15% in intime and outtime datasets
missing_values_intime <- intime %>% summarise_all(funs(sum(is.na(.))/n()))
missing_values_intime <- gather(missing_values_intime,key='feature',value = 'missing_percentage')
missing_values_outtime <- outtime %>% summarise_all(funs(sum(is.na(.))/n()))
missing_values_outtime <- gather(missing_values_outtime,key='feature',value = 'missing_percentage')
intime_clean_cols <- filter(missing_values_intime,missing_percentage<0.15)
outtime_clean_cols <- filter(missing_values_outtime,missing_percentage<0.15)
intime_clean <- intime[,(colnames(intime) %in% intime_clean_cols$feature)]
outtime_clean <- outtime[,(colnames(outtime) %in% outtime_clean_cols$feature)]
sum(is.na(intime_clean))
sum(is.na(outtime_clean))
ncol(intime_clean)
ncol(intime)
ncol(outtime)
ncol(outtime_clean)
#Convert all the date columns from character to DateTime (POSIX_ct) format
intime_clean[,2:ncol(intime_clean)] <- lapply(intime_clean[,2:ncol(intime_clean)], function(x) as_datetime(x))
outtime_clean[,2:ncol(outtime_clean)] <- lapply(outtime_clean[,2:ncol(outtime_clean)], function(x) as_datetime(x))
x<-outtime_clean[,2:ncol(outtime_clean)]-intime_clean[,2:ncol(intime_clean)]
x[,2:ncol(x)] <- lapply(x[,2:ncol(x)],function(x) as.numeric(x))
avg_hours <- rowMeans(x[,2:ncol(x)],na.rm = T)
#removing prefix X from all columns in intime_clean and outtime_clean
colnames(intime_clean) <- gsub(pattern="X","",colnames(intime_clean))
colnames(outtime_clean) <- gsub(pattern="X","",colnames(outtime_clean))
#convert data from wide to long format using gather() function
in.time_long <- gather(intime_clean, date, timein, EmployeeID)
out.time_long <- gather(outtime_clean, date, timein, EmployeeID)
nrow(x)
length(avg_hours)
hrdata_mergd <- cbind(hrdata,avg_hours)
hrdata_mergd$overtime <- ifelse(hrdata_mergd$avg_hours>8,1,0)
hrdata_mergd$undertime <- ifelse(hrdata_mergd$avg_hours<7,1,0)
#the derived metrics used are avg_hours, leaves, overtime and undertime
#calculating number of leaves for each employee
for(i in 1:ncol(x)){
hrdata_mergd$leaves[i] <- sum(is.na(x[i,]))
}
#collate data into one single file hrdata_mergd
#masterData
str(hrdata_mergd)
##################################################################
# Barcharts for categorical features with stacked Attrition information
bar_theme1<- theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
legend.position="none")
plot_grid(ggplot(hrdata, aes(x=EducationField,fill=Attrition))+ geom_bar(),
ggplot(hrdata, aes(x=BusinessTravel,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=Department,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=Education,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=Gender,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=factor(JobLevel),fill=Attrition))+ geom_bar()+bar_theme1,
align = "h")
ggplot(hrdata, aes(x=EducationField,fill=Attrition))+ geom_bar()
plot_grid(ggplot(hrdata, aes(x=JobRole,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=MaritalStatus,fill=Attrition))+ geom_bar()+bar_theme1,
align = "h")
plot_grid(ggplot(hrdata, aes(x=EnvironmentSatisfaction,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=JobSatisfaction,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=WorkLifeBalance,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=JobInvolvement,fill=Attrition))+ geom_bar()+bar_theme1,
ggplot(hrdata, aes(x=PerformanceRating,fill=Attrition))+ geom_bar()+bar_theme1,
align = "h")
# Histogram and Boxplots for numeric variables
box_theme<- theme(axis.line=element_blank(),axis.title=element_blank(),
axis.ticks=element_blank(), axis.text=element_blank())
box_theme_y<- theme(axis.line.y=element_blank(),axis.title.y=element_blank(),
axis.ticks.y=element_blank(), axis.text.y=element_blank(),
legend.position="none")
#plot_grid(ggplot(hrdata, aes(YearsAtCompany))+ geom_histogram(binwidth = 10),
# ggplot(hrdata, aes(x="",y=YearsAtCompany))+ geom_boxplot(width=0.1)+coord_flip()+box_theme,
# align = "v",ncol = 1)
#plot_grid(ggplot(hrdata, aes(YearsWithCurrManager))+ geom_histogram(binwidth = 20),
# ggplot(hrdata, aes(x="",y=JobInvolvement))+ geom_boxplot(width=0.1)+coord_flip()+box_theme,
# align = "v",ncol = 1)
#plot_grid(ggplot(hrdata, aes(MonthlyIncome))+ geom_histogram(),
# ggplot(hrdata, aes(x="",y=TotalWorkingYears))+ geom_boxplot(width=0.1)+coord_flip()+box_theme,
# align = "v",ncol = 1)
#No outliers in numeric variables
# Boxplots of numeric variables relative to attrition status
plot_grid(ggplot(hrdata, aes(x=Attrition,y=TotalWorkingYears, fill=Attrition))+ geom_boxplot(width=0.2)+
coord_flip() +theme(legend.position="none"),
ggplot(hrdata, aes(x=Attrition,y=PercentSalaryHike, fill=Attrition))+ geom_boxplot(width=0.2)+
coord_flip() + box_theme_y,
ggplot(hrdata, aes(x=Attrition,y=NumCompaniesWorked, fill=Attrition))+ geom_boxplot(width=0.2)+
coord_flip() + box_theme_y,
align = "v",nrow = 1)
#From the plots obtained, it is clear that as the work experience of employees increases the attrition rate decreases
#Also, as the years with current manager increase, attrition rate decreases.
#outliers are not removed from the numeric variables since it may affect the accuracy of final model
######################################################################@@@@@@
#2. Model preparation
#converting all categorical variables (two-level and multi-level) into dummy variables for model building
#converting attrition to dummy data
dummy_1 <- data.frame(model.matrix( ~Attrition, data = hrdata_mergd))
hrdata_mergd$AttritionYes <- dummy_1$AttritionYes
hrdata_mergd$Attrition <- NULL
#Here we are concerned about Attrition rate, so only AttritionYes is considered
#converting BusinessTravel to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~BusinessTravel, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
hrdata_mergd <- cbind(hrdata_mergd[,-3],dummy_1)
#converting Department to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~Department, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
hrdata_mergd <- cbind(hrdata_mergd[,-3],dummy_1)
#converting Education to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~Education, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-4],dummy_1)
#converting EducationField to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~EducationField, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-4],dummy_1)
#converting Gender to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~Gender, data = hrdata_mergd))
str(hrdata_mergd)
hrdata_mergd$GenderMale <- dummy_1$GenderMale
hrdata_mergd$Gender <- NULL
#converting JobRole to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~JobRole, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-5],dummy_1)
#converting MaritalStatus to dummy data
dummy_1 <- data.frame(model.matrix( ~MaritalStatus, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-5],dummy_1)
#converting EnvironmentSatisfaction to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~EnvironmentSatisfaction, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-14],dummy_1)
#converting JobSatisfaction to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~JobSatisfaction, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-14],dummy_1)
#converting WorkLifeBalance to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~WorkLifeBalance, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-14],dummy_1)
#converting JobInvolvement to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~JobInvolvement, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-14],dummy_1)
#converting PerformanceRating to dummy data
str(hrdata_mergd)
dummy_1 <- data.frame(model.matrix( ~PerformanceRating, data = hrdata_mergd))
str(hrdata_mergd)
hrdata_mergd$PerformanceRatingOutstanding <- dummy_1$PerformanceRatingOutstanding
hrdata_mergd$PerformanceRating <- NULL
#converting JobLevel to dummy data
str(hrdata_mergd)
hrdata_mergd$JobLevel <- as.factor(hrdata_mergd$JobLevel)
dummy_1 <- data.frame(model.matrix( ~JobLevel, data = hrdata_mergd))
dummy_1 <- dummy_1[,-1]
str(hrdata_mergd)
hrdata_mergd <- cbind(hrdata_mergd[,-4],dummy_1)
#data with dummy variabels
str(hrdata_mergd)
ncol(hrdata_mergd)
#EnvironmentSatisfaction,JobSatisfaction,WorkLifeBalance,Education
#JobInvolvement,JobLevel,PerformanceRating,Attrition and Gender
#Above variables have been converted into dummy variables.
#Same approach is used for both the two-level and multi-level categorical variables
#This is because for each n-level categorical variable, n-1 dummy variables are created.
#So, it does not matter w.r.t. two-level categorical variables as only one dummy variable is created for each of them.
#Hence, the NULL values obtained as a result (if any) can be ignored
#Here, AttritionYes is used as the dependent variable for modelling as we are only concerned about employees who are actually leaving the company.
################################Check which columns needs to be scaled############
# Feature standardisation
# Normalising continuous features
ind<-c(2:16)
hr_analytics_scaled<-hrdata_mergd
for(i in ind)
{
hr_analytics_scaled[,i]<-scale(x=hrdata_mergd[,i],center = TRUE,scale = TRUE)
}
summary(hr_analytics_scaled)
# separate training and testing data
# 70% data for training and remaining for testing
set.seed(100)
indices = sample.split(hrdata_mergd$EmployeeID, SplitRatio = 0.7)
train = hr_analytics_scaled[indices,]
test = hr_analytics_scaled[-indices,]
########################################################################
#3. Model building
#initial model
model_1<-glm(AttritionYes~.,data=train,family = 'binomial')
summary(model_1)
#variable selection using stepAIC() function
model_2<-stepAIC(model_1, direction="both")
summary(model_2)
vif(model_2)
#build model based on variables selected based on last AIC call
model_3<-glm(formula = AttritionYes ~ YearsAtCompany+JobRoleSales.Executive+PerformanceRatingOutstanding+
MaritalStatusMarried+JobInvolvementVery.High+EnvironmentSatisfactionVery.High+JobLevel5+
PercentSalaryHike+EducationCollege+DepartmentResearch...Development+JobInvolvementLow+
DepartmentSales+JobRoleResearch.Director+BusinessTravelTravel_Rarely+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_3)
vif(model_3)
#Remove variables based on VIF
#Remove YearsAtCompany due to high VIF (4.77) and high p-value (0.12910)
model_4<-glm(formula = AttritionYes ~ JobRoleSales.Executive+PerformanceRatingOutstanding+
MaritalStatusMarried+JobInvolvementVery.High+EnvironmentSatisfactionVery.High+JobLevel5+
PercentSalaryHike+EducationCollege+DepartmentResearch...Development+JobInvolvementLow+
DepartmentSales+JobRoleResearch.Director+BusinessTravelTravel_Rarely+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_4)
vif(model_4)
#Remove BusinessTravelTravel_Rarely due to high VIF (3.762446)
model_5<-glm(formula = AttritionYes ~ JobRoleSales.Executive+PerformanceRatingOutstanding+
MaritalStatusMarried+JobInvolvementVery.High+EnvironmentSatisfactionVery.High+JobLevel5+
PercentSalaryHike+EducationCollege+DepartmentResearch...Development+JobInvolvementLow+
DepartmentSales+JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_5)
vif(model_5)
#Remove DepartmentResearch...Development due to high VIF (4.201548)
model_6<-glm(formula = AttritionYes ~ JobRoleSales.Executive+PerformanceRatingOutstanding+
MaritalStatusMarried+JobInvolvementVery.High+EnvironmentSatisfactionVery.High+JobLevel5+
PercentSalaryHike+EducationCollege+JobInvolvementLow+
DepartmentSales+JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_6)
vif(model_6)
#Remove PercentSalaryHike due to high VIF (2.408234) and high p-value (0.066460)
model_7<-glm(formula = AttritionYes ~ JobRoleSales.Executive+PerformanceRatingOutstanding+
MaritalStatusMarried+JobInvolvementVery.High+EnvironmentSatisfactionVery.High+JobLevel5+
EducationCollege+JobInvolvementLow+
DepartmentSales+JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_7)
vif(model_7)
#Remove MaritalStatusMarried due to high VIF (2.132230) and high p-value (0.082892)
model_8<-glm(formula = AttritionYes ~ JobRoleSales.Executive+PerformanceRatingOutstanding+
JobInvolvementVery.High+EnvironmentSatisfactionVery.High+JobLevel5+
EducationCollege+JobInvolvementLow+
DepartmentSales+JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_8)
vif(model_8)
#Now, remove variables based on p-value as there are quite a few variables having high p-value
#Remove PerformanceRatingOutstanding due to high p-value (0.777756)
model_9<-glm(formula = AttritionYes ~ JobRoleSales.Executive+
JobInvolvementVery.High+EnvironmentSatisfactionVery.High+JobLevel5+
EducationCollege+JobInvolvementLow+DepartmentSales+
JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_9)
vif(model_9)
#Remove DepartmentSales due to high p-value (0.2873)
model_10<-glm(formula = AttritionYes ~ JobRoleSales.Executive+
JobInvolvementVery.High+EnvironmentSatisfactionVery.High+JobLevel5+
EducationCollege+JobInvolvementLow+
JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_10)
vif(model_10)
#Remove JobInvolvementVery.High due to high p-value (0.154716)
model_11<-glm(formula = AttritionYes ~ JobRoleSales.Executive+
EnvironmentSatisfactionVery.High+JobLevel5+
EducationCollege+JobInvolvementLow+
JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_11)
vif(model_11)
#Remove JobLevel5 due to high p-value (0.115706)
model_12<-glm(formula = AttritionYes ~ JobRoleSales.Executive+
EnvironmentSatisfactionVery.High+
EducationCollege+JobInvolvementLow+
JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_12)
vif(model_12)
#Remove JobRoleSales.Executive due to high p-value (0.116878)
model_13<-glm(formula = AttritionYes ~ EnvironmentSatisfactionVery.High+
EducationCollege+JobInvolvementLow+
JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_13)
vif(model_13)
#Remove EnvironmentSatisfactionVery.High due to high p-value (0.085664)
model_14<-glm(formula = AttritionYes ~ EducationCollege+JobInvolvementLow+
JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_14)
vif(model_14)
#Remove EducationCollege due to high p-value (0.085664)
model_15<-glm(formula = AttritionYes ~ JobInvolvementLow+
JobRoleResearch.Director+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_15)
vif(model_15)
#All variables have p-value less than 0.05
#But the number of variables is still too high.
#Hence, variables are removed one by one until all of them have p-value less than 0.001
#Remove JobRoleResearch.Director due to high p-value (0.015714)
model_16<-glm(formula = AttritionYes ~ JobInvolvementLow+TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_16)
vif(model_16)
#Remove JobInvolvementLow due to high p-value (0.007243)
model_17<-glm(formula = AttritionYes ~ TrainingTimesLastYear+
Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial", data = train)
summary(model_17)
vif(model_17)
#Remove TrainingTimesLastYear due to high p-value (0.003009)
model_18<-glm(formula = AttritionYes ~ Age+JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial",data = train)
summary(model_18)
vif(model_18)
#Remove Age due to high p-value (0.00163)
model_19<-glm(formula = AttritionYes ~ JobRoleManufacturing.Director+WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial",data = train)
summary(model_19)
vif(model_19)
#Remove JobRoleManufacturing.Director due to high p-value (0.00109)
model_20<-glm(formula = AttritionYes ~ WorkLifeBalanceGood+JobSatisfactionLow+WorkLifeBalanceBest+
JobSatisfactionVery.High+NumCompaniesWorked+YearsWithCurrManager+WorkLifeBalanceBetter+BusinessTravelTravel_Frequently+
YearsSinceLastPromotion+EnvironmentSatisfactionLow+TotalWorkingYears+MaritalStatusSingle+overtime,
family = "binomial",data = train)
summary(model_20)
vif(model_20)
#now all variables have p-values less than 0.001
#therefore, model_20 is deemed to be the final model
#The major assumption is that p-values of each variable in the final model are less than 0.001
#the method for eliminating variables after each model, is as follows:
#1. If the variables have high VIF and low signifance(p-value>0.05), the variable can be removed
#2. If the variables all have p-values less than 0.05 (or 0.01 if applicable), the variable having the highest VIF is considered (if VIF>3)
#3. If the variables all have VIF less than 2, the variable having the highest p-value is considered if the p-value is more than 0.05
#4. Still, if variables are remaining, they are eliminated in order of p-values until all have p-value<0.001
#5. This means finally variable selection is based on high VIF and then p-values
#From the final model, it is clear that following are the major factors that influence the attrition rate
#1.WorkLifeBalance
#2.JobSatisfaction
#3.NumCompaniesWorked
#4.YearsWithCurrManager
#5.BusinessTravel_Frequently
#6.YearsSinceLastPromotion
#7.EnvironmentSatisfaction
#8.MaritalStatus
#9.overtime
#4. Model evaluation
Predict_1 <- predict(model_20,type = "response",test)
summary(Predict_1)
test$predcit_attrition <- Predict_1
r <- cor(test$AttritionYes,test$predcit_attrition)
rsquared <- cor(test$AttritionYes,test$predcit_attrition)^2
rsquared
ggplot(test, aes(avg_hours, AttritionYes)) + geom_line(aes(colour = "blue" )) + geom_line(aes(x=avg_hours, y=predcit_attrition, colour="red"))
ggplot(test, aes(EmployeeID, AttritionYes)) + geom_line(aes(colour = "blue" )) +
geom_line(aes(x=EmployeeID, y=predcit_attrition, colour="red"))
test_actual_attrition<-factor(ifelse(test$AttritionYes==1,"Yes","No"))
#P_Cutoff>=0.5
test_predict_attrition<-factor(ifelse(Predict_1 >= 0.50, "Yes", "No"))
test_conf <- confusionMatrix(test_predict_attrition, test_actual_attrition, positive = "Yes")
test_conf
#P_Cutoff>=0.4
test_predict_attrition<-factor(ifelse(Predict_1 >= 0.40, "Yes", "No"))
test_conf <- confusionMatrix(test_predict_attrition, test_actual_attrition, positive = "Yes")
test_conf
#Finding the Optimal Probability Cutoff
s = seq(.01,.80,length=100)
OUT = matrix(0,100,3)
perform_fn <- function(cutoff)
{
predicted_attrition <- factor(ifelse(Predict_1 >= cutoff, "Yes", "No"))
conf <- confusionMatrix(predicted_attrition, test_actual_attrition, positive = "Yes")
acc <- conf$overall[1]
sens <- conf$byClass[1]
spec <- conf$byClass[2]
out <- t(as.matrix(c(sens, spec, acc)))
colnames(out) <- c("sensitivity", "specificity", "accuracy")
return(out)
}
for(i in 1:100)
{
OUT[i,] = perform_fn(s[i])
}
dev.off()
op_p_cutoff <- s[which(abs(OUT[,1]-OUT[,2])<0.02)]
op_p_cutoff
#0.1616
test_predict_attrition<-factor(ifelse(Predict_1 >= op_p_cutoff, "Yes", "No"))
conf_final <- confusionMatrix(test_predict_attrition, test_actual_attrition, positive = "Yes")
conf_final
# Confusion Matrix and Statistics
#
# Reference
# Prediction No Yes
# No 2848 171
# Yes 850 540
#
# Accuracy : 0.7684
# 95% CI : (0.7557, 0.7808)
# No Information Rate : 0.8387
# P-Value [Acc > NIR] : 1
#
# Kappa : 0.3822
# Mcnemar's Test P-Value : <2e-16
#
# Sensitivity : 0.7595
# Specificity : 0.7701
# Pos Pred Value : 0.3885
# Neg Pred Value : 0.9434
# Prevalence : 0.1613
# Detection Rate : 0.1225
# Detection Prevalence : 0.3153
# Balanced Accuracy : 0.7648
#
# 'Positive' Class : Yes
#
plot(s, OUT[,1],xlab="Cutoff",ylab="Value",cex.lab=1.5,cex.axis=1.5,ylim=c(0,1),type="l",lwd=2,axes=FALSE,col=2)
axis(1,seq(0,1,length=5),seq(0,1,length=5),cex.lab=1.5)
axis(2,seq(0,1,length=5),seq(0,1,length=5),cex.lab=1.5)
lines(s,OUT[,2],col="darkgreen",lwd=2)
lines(s,OUT[,3],col=4,lwd=2)
box()
legend(0,.50,col=c(2,"darkgreen",4,"darkred"),lwd=c(2,2,2,2),c("Sensitivity","Specificity","Accuracy"))
# Let's choose the optimum cutoff value of 0.1616 for final model
test_cutoff_churn <- factor(ifelse(Predict_1 >=op_p_cutoff, "Yes", "No"))
conf_final <- confusionMatrix(test_cutoff_churn, test_actual_attrition, positive = "Yes")
acc <- conf_final$overall[1]
sens <- conf_final$byClass[1]
spec <- conf_final$byClass[2]
acc
#0.7684
sens
#0.7595
spec
#0.7701
##################################################################################################
### KS -statistic - Test Data ######
test_cutoff_churn <- ifelse(test_actual_attrition=="Yes",1,0)
test_actual_churn <- ifelse(test_predict_attrition=="Yes",1,0)
decile<-c(1:10)
library(ROCR)
#on testing data
pred_object_test<- prediction(test_cutoff_churn, test_actual_churn)
performance_measures_test<- performance(pred_object_test, "tpr", "fpr")
ks_table_test <- attr(performance_measures_test, "y.values")[[1]] -
(attr(performance_measures_test, "x.values")[[1]])
max(ks_table_test)
# 0.3318479
####################################################################
# Lift & Gain Chart
# plotting the lift chart
# Loading dplyr package
require(dplyr)
library(dplyr)
lift <- function(labels , predicted_prob,groups=10) {
if(is.factor(labels)) labels <- as.integer(as.character(labels ))
if(is.factor(predicted_prob)) predicted_prob <- as.integer(as.character(predicted_prob))
helper = data.frame(cbind(labels , predicted_prob))
helper[,"bucket"] = ntile(-helper[,"predicted_prob"], groups)
gaintable = helper %>% group_by(bucket) %>%
summarise_at(vars(labels ), funs(total = n(),
totalresp=sum(., na.rm = TRUE))) %>%
mutate(Cumresp = cumsum(totalresp),
Gain=Cumresp/sum(totalresp)*100,
Cumlift=Gain/(bucket*(100/groups)))
return(gaintable)
}
Churn_decile = lift(test_actual_churn, Predict_1, groups = 10)
Churn_decile
Churn_decile$Gain
Churn_decile$Cumlift
Churn_decile$total
#each bucket indicates the respective decile
plot(x=Churn_decile$Cumlift,y=Churn_decile$bucket,type = 'o')
plot(x=Churn_decile$Gain,y=Churn_decile$bucket,type ='o')
plot(x=Churn_decile$Gain,y=Churn_decile$Cumlift,type ='o')
plot(x=max(decile),y=max(ks_table_test),type = 'o')
#From the above plots obtained, it is clear that the model predicts attrition more accurately as most of the values are accurately predicted by the 4th decile.
#From the optimum probability cut-off (0.1616) and the ROC curve obtained, it is clear that the model is the best fit when it comes to accuracy.
#From the gain and lift charts obtained, it is clear that the model has increasing gain and decreasing lift.
#It is clear that the model predicts attrition more accurately as most of the values are accurately predicted by the 4th decile. |
6459a267104c2d1dc3f7b8bc3f07f7a25008e993 | bb34abb58df1d4d9ef8609a050dffad197256bb4 | /R/mboost_diff_Rsq.R | 467fd2794a6271695c69309781e377b5297264f1 | [] | no_license | hanfu-bios/varsel | 6537cdb12a1dc65ac7d20ed73f62021ba4ccc413 | 1890077d8cbe607a1142b1dd83c9aca264a1f05a | refs/heads/master | 2021-09-07T15:32:52.249826 | 2018-02-25T04:18:32 | 2018-02-25T04:18:32 | 103,572,631 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,233 | r | mboost_diff_Rsq.R |
stat.mboost_diff_Rsq <- function(Xaug, y, max.mstop = 100, bl = c("bbs", "bols", "btree"),
cv.fold = 5, family = Gaussian()){
p = ncol(Xaug) / 2
Xaug = as.data.frame(Xaug)
check.dist <- function(x) grepl(x,family@name)
if (any(unlist(lapply(c("Ada","Binomial","AUC"),check.dist)))) response.type = "binary"
else if (any(unlist(lapply(c("Squared Error","Huber","Absolute Err"),check.dist)))) response.type = "continuous"
else if (any(unlist(lapply(c("Poisson","Gamma","Multinomial"),check.dist)))) response.type = "discrete"
else if (any(unlist(lapply(c("Cox","Weibull","Log Logistic","Lognormal","Gehan","Concordance Probability"),check.dist)))) response.type = "survival"
else stop("unknown family")
if (response.type %in% c("continuous","discrete")){
Xaug$y = as.vector(y)
}
else if (response.type == "binary"){
Xaug$y = as.factor(y)
}
else { # survival
error("R-square for survival data still under development")
}
model = mboost(y ~ ., data = Xaug, control = boost_control(mstop = max.mstop), baselearner = bl, family = family)
cv10f = cv(model.weights(model), type="kfold", B = cv.fold)
cvm = cvrisk(model, folds = cv10f, papply = mclapply)
best.mstop = mstop(cvm)
best.fit = cv.mboost(Xaug, y, best.mstop, family = family, baselearner = bl, cv.fold = cv.fold)
mboost.mse = mean((y-best.fit)^2)
Rsq = mse2Rsq(mboost.mse, y)
mse_j = NULL
Rsq_j = NULL
for (j in 1:(2*p)){
best.pred1 = cv.mboost(Xaug[,-j], y, best.mstop, family = family, baselearner = bl, cv.fold = cv.fold)
mse_j[j] = mean((best.pred1 - y)^2)
Rsq_j[j] = mse2Rsq(mse_j[j], y)
}
Z = abs(Rsq - Rsq_j)
}
mse2Rsq <- function(mse, y) 1 - mse*n/sum((y-mean(y))^2)
cv.mboost <- function(Xaug, y, mstop, family, baselearner = "bbs", cv.fold = 5){
folds_i = sample(rep(1:cv.fold, length.out = n))
X.pred2 = rep(0,n)
for (k2 in 1:cv.fold) {
test_i = which(folds_i == k2)
Xtr = Xaug[-test_i, ]
Xt = Xaug[test_i, ]
fit1 = mboost(y ~ ., data = Xtr, control = boost_control(mstop = mstop), baselearner = baselearner, family = family)
X.pred2[test_i] = predict(fit1, newdata = subset(Xt,select=-y), type = "response")
}
X.pred2
}
|
e89e6a291ded54559e571b81ff6ab4908551ab8d | 7331b465dbed2459478e719a81eac3e15179aa7f | /ejemplo2.R | 40282007047ea736806b023b2c34b65ae8d37f90 | [] | no_license | 920052859/Clases_Progra_R | 572d6b73e0b15e872c75ab97263f11b31acc0e0e | 80dc895cc13a074e9809e14e9d4fa0687490e1fb | refs/heads/master | 2022-10-18T15:19:23.500925 | 2020-06-08T18:36:56 | 2020-06-08T18:36:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 444 | r | ejemplo2.R | getwd()
setwd("C:/Users/DANIPZA/Desktop/pylinR/Rclass")
dir.create("corian")
dir()
help("log")
log(8,base = 2)
vec1 <- 1:5
vec2 <- c(1,4,5,5.7)
vec3 <- c(vec1,vec2)
help("rnorm")
vect4 <- rnorm(5,mean = 5,sd = 2)
vect5 <- sum(vect4)
vect5 <- vect5/5
help("runif")
vec6 <- runif(5,min = 4,max = 7)
help("rchisq")
help("seq")
seq(8,100) # secuencia
help("rep")
rep(9,9) # replicar
getwd()
setwd("C:/Users/DANIPZA/Desktop/pylinR/Rclass") |
24fd6551d3824d7bde0928bbe7e220ac6415f208 | 26e61f22191fb048d9e1eb1074afe13e2a3cbf69 | /getData.R | 5adee4ee2ec51d909c264e786667957aea751824 | [] | no_license | waleedayoub/nhl | 14e3edfb223cf16987b6731655ac2c8eb7934740 | 8975b394097cab2ebfb4dd6318b232997aecd3a3 | refs/heads/master | 2020-05-28T04:06:24.969981 | 2015-06-05T12:07:15 | 2015-06-05T12:07:15 | 35,517,938 | 0 | 0 | null | 2015-06-05T13:14:23 | 2015-05-12T23:36:07 | R | UTF-8 | R | false | false | 1,110 | r | getData.R | setwd('./development/R/nhl/')
library(nhlscrapr)
library(dplyr)
all.games <- full.game.database()
table(all.games$season,all.games$session)
distinct(select(all.games,session))
distinct(select(all.games,season))
# select only the playoff games for 2014/2015 season
filter(all.games, session=='Playoffs', season==20142015)
playoffgames <- filter(all.games, season == 20142015, session=='Playoffs')
head(playoffgames)
# download all the game info for playoff games
compile.all.games(new.game.table=playoffgames)
# figure out how to isolate just the info required for goals/assists
# then iterate through each file and process
load("./nhlr-data/20142015-30216-processed.RData")
game.info$date
summary(game.info)
head(game.info$playbyplay)
game.info$playbyplay[[:3,4]]
summary(game.info$playbyplay)
head(game.info$playbyplay['etype'])
distinct(game.info$playbyplay['etype'])
goaldata <- filter(game.info$playbyplay,etype=='GOAL')
head(goaldata)
goaldata[1,]
summary(goaldata)
goaldata
# create a table with pool participant to player lookup
# merge the 2 tables
# plot the cumulative point haul by day |
c549b4618a51fef24a513a013bc0432ac9d88a05 | 02b127b8e92f7c2af1917c722045b083142d802e | /helpers.R | 25639c2ed441c925823010463c3e1f5adf97c720 | [] | no_license | beegieb/BF4StatsApp | 769b3a28bcad58dc160210ec56791ad3674b3b63 | eeb53cd01bf526a40e0293bf7b65716ded593dc6 | refs/heads/master | 2021-01-19T06:27:20.213286 | 2014-06-23T05:39:07 | 2014-06-23T05:39:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,914 | r | helpers.R | library(caret)
library(shiny)
library(rjson)
platforms <- c("PS3", "Xbox 360")
ovoPlatforms <- c("PC", "PS4", "Xbox One", "PS3", "Xbox 360")
mapNames <- read.csv("data/mapNames.csv")$name
platMap <- function(plat){
switch(plat,
"PS3" = "ps3",
"PS4" = "ps4",
"Xbox 360" = "xbox",
"Xbox One" = "xone",
"PC" = "pc")
}
getPlayerStats <- function(name, plat) {
bf4statsurl <- "http://api.bf4stats.com/api/playerInfo"
opt <- "stats,extra,vehicleCategory,weaponCategory"
name <- gsub(" ", "+", name)
query <- paste0("?plat=", plat, "&name=", name, "&opt=", opt)
player.json <- tryCatch(
readLines(paste0(bf4statsurl, query)),
error=function(e) "failed")
player.json
}
genPlayerStatus <- function(formInput, plat, playerData){
if (formInput == "") {
NULL
} else if (is.null(playerData)) {
p(paste("Failed to find", formInput), style="color:red")
} else if (is.null(playerData$stats)) {
name <- gsub(" ", "%20", formInput)
url <- paste("http://bf4stats.com", plat, name, sep="/")
p("Player stats are stale, please browse to ",
a(href=url, url), " to refresh the players stats", style="color:orange")
} else {
p("Found!", style="color:green")
}
}
genPlayerInput <- function(playerName, platform){
if (playerName == "") { NULL }
else {
pj <- getPlayerStats(playerName, platform)
if (pj == "failed") {
NULL
} else {
fromJSON(pj)
}
}
}
nullPlayer <- data.frame(rank=0, skill=0, kdr=0, wlr=0, spm=0, gspm=0, kpm=0,
sfpm=0, hkp=0, khp=0, accuracy=0, timePlayed=0, kills=0, deaths=0, headshots=0,
shotsFired=0, shotsHit=0, suppressionAssists=0, avengerKills=0, saviorKills=0,
nemesisKills=0, numRounds=0, roundsFinished=0, numWins=0, numLosses=0,
killStreakBonus=0, nemesisStreak=0, resupplies=0, repairs=0, heals=0, revives=0,
longestHeadshot=0, flagDefend=0, flagCaptures=0, killAssists=0, vehicleDestroyed=0,
vehicleDamage=0, dogtagsTaken=0, score.total=0, score.conquest=0, score.award=0,
score.bonus=0, score.unlock=0, score.vehicle=0, score.team=0, score.squad=0,
score.general=0, score.rank=0, score.combat=0, score.assault=0, score.engineer=0,
score.support=0, score.recon=0, score.commander=0, time.assault=0, time.engineer=0,
time.support=0, time.recon=0, time.commander=0, time.vehicle=0,
time.vehicle.precent=0, time.weapon=0, time.weapon.percent=0, stars.assault=0,
stars.engineer=0, stars.support=0, stars.recon=0, stars.commander=0, spm.assault=0,
spm.engineer=0, spm.support=0, spm.recon=0, spm.commander=0, kills.weapon=0,
kills.weapon.percent=0, kills.vehicle=0, kills.vehicle.percent=0, kpm.weapon=0,
kpm.vehicle=0, ribbons=0, medals=0
)
playerDataToDF <- function(pj) {
player <- pj$player
stats <- pj$stats
scores <- stats$scores
kits <- stats$kits
extra <- stats$extra
weap <- pj$weaponCategory
vehi <- pj$vehicleCategory
data.frame(
rank = stats$rank,
skill = stats$skill,
kdr = extra$kdr,
wlr = extra$wlr,
spm = extra$spm,
gspm = extra$gspm,
kpm = extra$kpm,
sfpm = extra$sfpm,
hkp = extra$hkp,
khp = extra$khp,
accuracy = extra$accuracy,
timePlayed = stats$timePlayed,
kills = stats$kills,
deaths = stats$deaths,
headshots = stats$headshots,
shotsFired = stats$shotsFired,
shotsHit = stats$shotsHit,
suppressionAssists = stats$suppressionAssists,
avengerKills = stats$avengerKills,
saviorKills = stats$saviorKills,
nemesisKills = stats$nemesisKills,
numRounds = stats$numRounds,
roundsFinished = extra$roundsFinished,
numWins = stats$numWins,
numLosses = stats$numLosses,
killStreakBonus = stats$killStreakBonus,
nemesisStreak = stats$nemesisStreak,
resupplies = stats$resupplies,
repairs = stats$repairs,
heals = stats$heals,
revives = stats$revives,
longestHeadshot = stats$longestHeadshot,
flagDefend = stats$flagDefend,
flagCaptures = stats$flagCaptures,
killAssists = stats$killAssists,
vehicleDestroyed = stats$vehiclesDestroyed,
vehicleDamage = stats$vehicleDamage,
dogtagsTaken = stats$dogtagsTaken,
score.total = scores$score,
score.conquest = stats$mode[[1]]$score,
score.award = scores$award,
score.bonus = scores$bonus,
score.unlock = scores$unlock,
score.vehicle = scores$vehicle,
score.team = scores$team,
score.squad = scores$squad,
score.general = scores$general,
score.rank = scores$rankScore,
score.combat = scores$combatScore,
score.assault = kits$assault$score,
score.engineer = kits$engineer$score,
score.support = kits$support$score,
score.recon = kits$recon$score,
score.commander = kits$commander$score,
time.assault = kits$assault$time,
time.engineer = kits$engineer$time,
time.support = kits$support$time,
time.recon = kits$recon$time,
time.commander = kits$commander$time,
time.vehicle = extra$vehicleTime,
time.vehicle.precent = extra$vehTimeP,
time.weapon = extra$weaponTime,
time.weapon.percent = extra$weaTimeP,
stars.assault = kits$assault$stars,
stars.engineer = kits$engineer$stars,
stars.support = kits$support$stars,
stars.recon = kits$recon$stars,
stars.commander = kits$commander$stars,
spm.assault = kits$assault$spm,
spm.engineer = kits$engineer$spm,
spm.support = kits$support$spm,
spm.recon = kits$recon$spm,
spm.commander = kits$commander$spm,
kills.weapon = extra$weaKills,
kills.weapon.percent = extra$weaKillsP,
kills.vehicle = extra$vehKills,
kills.vehicle.percent = extra$vehKillsP,
kpm.weapon = extra$weaKpm,
kpm.vehicle = extra$vehKpm,
ribbons = extra$ribbons,
medals = extra$medals
)
}
getPlayerDF <- function(pj) {
if (is.null(pj)) { nullPlayer }
else if (is.null(pj$stats)) { nullPlayer }
else { playerDataToDF(pj) }
}
getTeamAvg <- function(team) {
tot <- nullPlayer
for (p in team) {
tot <- tot + p
}
tot / 12
}
predictResult <- function(team1, team2, map, model) {
d <- getTeamAvg(team1) - getTeamAvg(team2)
d$map <- map
predict(model, d, type="response")
}
getOVOResult <- function(p1, p2, pred, p1Name, p2Name) {
if (is.null(p1) | is.null(p2)) { return() }
rp <- round(pred*100, 2)
if (pred < 0.5) {
winner <- p1Name
scrub <- p2Name
rp <- 100 - rp
} else if (pred > 0.5) {
winner <- p2Name
scrub <- p1Name
} else { return("WTF ... netcode!? You kill trade!") }
getWinMSG(winner, scrub, rp)
}
getWinMSG <- function(winner, loser, confidence) {
msgs <- c(
paste0(loser, " was boned by ", winner, ".... no lube and ", confidence, "% hard"),
paste0(loser, " is such a scrub... ", winner, " is MLG PRO! I'm ",
confidence, "% certain"),
paste0(loser, " is ", confidence, "% lady and ", winner, " is taking dat ass"),
paste0(round(confidence), " out of 100 DICE employees agree, ", loser,
" gets rekt by ", winner),
paste0(winner, " quenched the thirst of ", round(confidence), " African children",
" with the tears of ", loser),
paste0(loser, " calls the whaaambulance ", confidence, "% percent of the time while ",
winner, " eats a carrot."),
paste0("Maybe if ", loser, " spent ", confidence, "% less time playing, ",
"'just for fun' they could avoid getting wrecked by ", winner),
paste0(round(confidence), " alzheimers patients remembered how bad ", loser,
" is after that crushing defeat from ", winner),
paste0("Getting dragged through ", round(confidence), "m of fire is less painful",
" than the beatdown ", winner, " gave ", loser),
paste0(round(confidence), " atheists turned to religion after ", winner, "'s ",
"godly performance when beating ", loser),
paste0(loser, " is like Leonardo DiCrapio in Titanic, while ", winner,
" swims away and marries a $", round(confidence), "million richman"),
paste0("According to Wikipedia ", loser, " died at the majestic hands of ", winner,
"... a total of ", round(confidence), " sources agree"),
paste0(loser, " is hearing ", round(confidence),
" fat ladies sing because he got owned by ", winner),
paste0(loser, " changed sexes after being worked over by ", winner),
paste0("Mommy told ", loser, " there is a ", confidence, "% chance ",
winner, " is his daddy"),
paste0("There is a ", confidence, "% chance ", loser, " is pregnant after ",
winner, " was done with them"),
paste0("The results are in: God favours ", winner, " ", confidence, "% more than ",
loser),
paste0(loser, "'s tight end got loosened by ", confidence, "% after ", winner,
" got done with them"),
paste0(confidence, "% of lesbians said they would gladly chow down on ",
loser, "'s box after that extremely feminine display against ", winner),
paste0("There's a ", confidence, "% chance ", loser, "'s girlfriend will cheat ",
"after witnessing ", winner, "'s glorious performance"),
paste0("There is a ", confidence, "% chance ", loser, " is the taker and ",
winner, " is the giver"),
paste0(loser, "'s life insurance fee just increased by ", confidence, "% after ",
winner, " exposed how much ", loser, " is prone to disaster"),
paste0("Someone call an ambulance because ", winner, " just put ",
round(confidence), " bullets into ", loser, "'s chest")
)
sample(msgs, 1)
}
|
3f5249c430684102cc2e6f0aee2a8853cb248dc3 | e01763b11db2185c5c3f233dea1802f8f84c21c5 | /script-prepadonnees.R | 1f47cfca0423bac9aed9e386bc08f7aaae936271 | [] | no_license | rxlacroix/masterthesis | de118ff9bf14113a0dfbd8595591cba57370fe94 | 8c6bd4e322a7bb355060c1b5b06e0dabd2ef35b2 | refs/heads/master | 2021-01-19T17:38:22.327208 | 2017-08-22T15:31:01 | 2017-08-22T15:31:01 | 101,077,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,684 | r | script-prepadonnees.R | setwd("D:/Switchdrive/Mémoire/DATA")
list.files()
library(stringr)
###############################################################################################
###############################################################################################
# 1. Fichier des applicants par ville via subset et correspondance NUTS3-FUA
###############################################################################################
###############################################################################################
# charger REGPAT-Inventeurs
inventeurs <- read.csv("201602_EPO_Inv_reg.txt", sep= "|")
inventeursEPO_RA <- subset(inventeurs, Reg_code=="FR711"| Reg_code=="FR712" | Reg_code=="FR713"| Reg_code=="FR714" | Reg_code=="FR715" | Reg_code=="FR716")
write.csv(file="inventeursEPO_RA.csv", inventeursEPO_RA)
patipc <- read.csv("201307_Patents_IPC.txt", sep= "|")
# API Google ne gère pas les adresses avec des accents, il faut donc les enlever
# fonction qui supprime les accents
inventeursEPO_RA$Address <- gsub("é","e",inventeursEPO_RA$Address)
inventeursEPO_RA$Address <- gsub("è","e",inventeursEPO_RA$Address)
inventeursEPO_RA$Address <- gsub("â","a",inventeursEPO_RA$Address)
inventeursEPO_RA$Address <- gsub("ô","o",inventeursEPO_RA$Address)
inventeursEPO_RA$Address <- gsub("ê","e",inventeursEPO_RA$Address)
inventeursEPO_RA$Address <- gsub("û","u",inventeursEPO_RA$Address)
inventeursEPO_RA$Address <- gsub("ï","i",inventeursEPO_RA$Address)
inventeursEPO_RA$Address <- gsub("î","i",inventeursEPO_RA$Address)
Unaccent <- function(text) {
text <- gsub("['`^~\"]", " ", text)
text <- iconv(text, to="ASCII//TRANSLIT//IGNORE")
text <- gsub("['`^~\"]", "", text)
return(text)
}
# execution de la fonction sur la colonne address
inventeursEPO_RA$adress2 <- Unaccent(inventeursEPO_RA$Address)
# on enleve l'identifiant du pays du code postal qui gene souvent
inventeursEPO_RA$adress2 <- gsub("F-", "", inventeursEPO_RA$adress2)
# fonction de geolocalisation par l'adresse via API Google
geocodeAdddress <- function(address) {
require(RJSONIO)
url <- "http://maps.google.com/maps/api/geocode/json?address="
url <- URLencode(paste(url, address, sep = "", key="AIzaSyCsjDAVV5C2cVMbGHlOVLT1JXcMI029g30"))
x <- fromJSON(url, simplify = FALSE)
if (x$status == "OK") {
out <- c(x$results[[1]]$geometry$location$lng,
x$results[[1]]$geometry$location$lat)
} else {
out <- NA
}
Sys.sleep(0.20) # API ne peut gérer que 5 requetes par seconde
out
}
# pour i in 1:nombre d'observations, on applique la fonction pour la longitude
for (i in 1001:1500) {
inventeursEPO_RA$lng[i] <- (as.matrix(geocodeAdddress(inventeursEPO_RA$Address[i])))[1]
}
# puis pour la latitude
for (i in 1000:1005){
inventeursEPO_RA$lat[i] <- (as.matrix(geocodeAdddress(inventeursEPO_RA$Address[i])))[2]
}
write.csv(file="inventeursEPO_RA.csv", inventeursEPO_RA)
# ne garder que les adresses européennes
regeuroapp <- subset(regapplicants, Ctry_code == "FI"|Ctry_code == "CH"|Ctry_code == "ES"|
Ctry_code == "IT"|Ctry_code == "FR"|Ctry_code == "GB"|
Ctry_code == "DE"|Ctry_code == "BE"|Ctry_code == "PT"|
Ctry_code == "SE"|Ctry_code == "NO"|Ctry_code == "IE"|
Ctry_code == "GR"|Ctry_code == "DK"|Ctry_code == "AT"|
Ctry_code == "BG"|Ctry_code == "CZ"|Ctry_code == "EE"|
Ctry_code == "HR"|Ctry_code == "HU"|Ctry_code == "LT"|
Ctry_code == "NL"|Ctry_code == "PL"|Ctry_code == "RO"|
Ctry_code == "SI"|Ctry_code == "SK")
# charger correspondance FUA-NUTS3
nutsaero <- read.csv("final_nuts_aero_3.csv", sep = ",")
# renomme la deuxième colonne pour jointure
colnames(nutsaero)[2] <- "Reg_code"
# fusionner sur l'attribut Reg_code
regeuroappvilles <- merge(regeuroapp, nutsaero, by="Reg_code")
# supprimer les colonnes inutiles
regeuroappvilles <- regeuroappvilles[,- c(9:10)]
# statistiques descriptives
summary(regeuroappvilles, maxsum = 11)
# test sur une seule ville (Lyon, code=LYS)
brevetslyon <- subset(regeuroappvilles, CAERO == "LYS")
summary(brevetslyon, maxsum = 11)
###############################################################################################
###############################################################################################
# 2. Fichier des inventeurs par ville via subset et correspondance NUTS3-FUA
###############################################################################################
###############################################################################################
# charger REGPAT-Inventeurs
reginv<- read.csv("201602_EPO_Inv_reg.txt", sep= "|")
# ne garder que les adresses européennes
regeuroinv <- subset(reginv, Ctry_code == "FI"|Ctry_code == "CH"|Ctry_code == "ES"|
Ctry_code == "IT"|Ctry_code == "FR"|Ctry_code == "GB"|
Ctry_code == "DE"|Ctry_code == "BE"|Ctry_code == "PT"|
Ctry_code == "SE"|Ctry_code == "NO"|Ctry_code == "IE"|
Ctry_code == "GR"|Ctry_code == "DK"|Ctry_code == "AT"|
Ctry_code == "BG"|Ctry_code == "CZ"|Ctry_code == "EE"|
Ctry_code == "HR"|Ctry_code == "HU"|Ctry_code == "LT"|
Ctry_code == "NL"|Ctry_code == "PL"|Ctry_code == "RO"|
Ctry_code == "SI"|Ctry_code == "SK")
# charger correspondance FUA-NUTS3
nutsaero <- read.csv("final_nuts_aero_3.csv", sep = ";")
# renomme la deuxième colonne pour jointure
colnames(nutsaero)[2] <- "Reg_code"
# fusionner sur l'attribut Reg_code
regeuroinvvilles <- merge(regeuroinv, nutsaero, by="Reg_code")
# supprimer les colonnes inutiles
regeuroinvvilles <- regeuroinvvilles[,- c(9:10)]
# statistiques descriptives
summary(regeuroinvvilles, maxsum = 11)
# test sur une seule ville (Lyon, code=LYS)
invlyon <- subset(regeuroinvvilles, CAERO == "LYS")
summary(brevetslyon, maxsum = 11)
###############################################################################################
###############################################################################################
# 3. Fichier des localisations des inventeurs via géolocalisation API Google
# test sur la région Rhône-Alpes
###############################################################################################
###############################################################################################
regfranceinv <- subset(reginv, Ctry_code == "FR")
regRAinv <- subset(regfranceinv, Reg_code == "FR711"|Reg_code == "FR712"|Reg_code == "FR713"|Reg_code == "FR714"
|Reg_code == "FR715"|Reg_code == "FR716"|Reg_code == "FR717"|Reg_code == "FR718")
# si besoin autre fichier
# inventeurgeo <- subset(reginv.....)
# API Google ne gère pas les adresses avec des accents, il faut donc les enlever
# fonction qui supprime les accents
Unaccent <- function(text) {
text <- gsub("['`^~\"]", " ", text)
text <- iconv(text, to="ASCII//TRANSLIT//IGNORE")
text <- gsub("['`^~\"]", "", text)
return(text)
}
# exécution de la fonction sur la colonne address
regRAinv$adress2 <- Unaccent(regRAinv$Address)
# on enlève l'identifiant du pays du code postal qui gene souvent
regRAinv$adress2 <- gsub("F-", "", regRAinv$adress2)
# fonction de géolocalisation par l'adresse via API Google
geocodeAdddress <- function(address) {
require(RJSONIO)
url <- "http://maps.google.com/maps/api/geocode/json?address="
url <- URLencode(paste(url, address, sep = ""))
x <- fromJSON(url, simplify = FALSE)
if (x$status == "OK") {
out <- c(x$results[[1]]$geometry$location$lng,
x$results[[1]]$geometry$location$lat)
} else {
out <- NA
}
Sys.sleep(0.20) # API ne peut gérer que 5 requetes par seconde
out
}
# pour i in 1:nombre d'observations, on applique la fonction pour la longitude
for (i in 1:20) {
regRAinv$lng[i] <- (as.matrix(geocodeAdddress(regRAinv$adress2[i])))[1]
}
# puis pour la latitude
for (i in 1:77384){
regRAinv$lat[i] <- (as.matrix(geocodeAdddress(regRAinv$adress2[i])))[2]
}
####################################################################################
# travail sur les citations
citationsEPO <- read.csv("201602_EP_Citations.txt", sep= "|")
regeuroapp <- read.csv ("regeuroapp.csv")
nutsaero <- read.csv("final_nuts_aero_3.csv", sep = ",")
colnames(nutsaero)[2] <- "Reg_code"
regeuroappvilles <- merge(regeuroapp, nutsaero, by="Reg_code")
regeuroappvilles <- regeuroappvilles[,- c(9:10)]
write.csv(regeuroappvilles,"regeuroappvilles.csv")
colnames (regeuroappvilles)[3] <- "Cited_Appln_id"
citedreg <- merge(citationsEPO, regeuroappvilles, by="Cited_Appln_id", suffixes = TRUE)
colnames (regeuroappvilles)[3] <- "Citing_appln_id"
citreg <- merge(citedreg, regeuroappvilles, by="Citing_appln_id", suffixes = TRUE)
colnames (citreg)[25] <- "Target"
colnames (citreg)[37] <- "Source"
citreg <- citreg[,c("Citing_appln_id","Cited_Appln_id","Source","Target")]
write.csv(citreg,"citreg.csv")
citreg <- read.csv("citreg.csv")
links <- citreg[,c(4:5)]
links <- count(links,c("Source","Target"))
net <- graph_from_data_frame(d=links, directed=T)
plot(net)
l <- layout_with_fr(net)
linksAalborg <- subset(citreg, Source=="Aalborg")
linksAarhus <- subset(citreg, Source=="Aarhus")
linksHelsinki <- subset(citreg, Source=="Helsinki")
c <- unique(links$Source)
# fusion data techfield
list.files()
Qualityp <- read.csv("201602_OECD_PATENT_QUALITY_EPO.txt", sep="|")
colnames(Qualityp)[1] <- "Citing_appln_id"
citregtf <- merge(citreg, Qualityp, by="Citing_appln_id")
citregtf <- citregtf[,-c(9:28)]
citregtf <- citregtf[,-2]
citregtf <- citregtf[,-5]
# création de chaque fichier de liens totaux, non filtrés
for (i in c){
x <- subset(citregtf, Source== i)
write.csv(x, file = paste("citstf",i,".csv", sep=""))
}
# pour chaque fichier, liens totaux
for (i in c) {
x <-read.csv(file = paste("citstf",i,".csv", sep=""))
x <- x[,c(4:5)]
x <- count(x,c("Source","Target"))
write.csv(x, file=paste("linksTot",i,".csv", sep=""))
}
# creation de chaque fichier général pour les techfield
d <- unique(citregtf$tech_field)
for (i in 1:35) {
x <- subset(citregtf, tech_field==i)
write.csv(x, paste("citregtf", i,".csv", sep = ""))
}
# creation des links généraux pour les techfields
for (i in 1:35){
x<- read.csv(paste("citregtf", i,".csv", sep = ""))
x <- count(x,c("Source","Target"))
write.csv(x,file=paste("linksTot",i,".csv", sep=""))
}
# creation pour techfield du fichier pour histogramme
for (i in 1:35){
x <- read.csv(paste("citregtf", i,".csv", sep = ""))
y <- count(x, "Source")
y <- y[order(-y$freq),]
colnames(y)[2]<-"number"
colnames(y)[1]<-"letter"
y$frequency <- (y$number/sum(y$number))
y$frequency <- round(y$frequency, digits=4)
y$name <- y$letter
y <- y[1:15,]
write.table(y, file=paste("tffreq", i,".tsv", sep = ""), quote=FALSE, sep='\t', row.names = FALSE)
}
# creation pour techfield du fichier pour histogramme / ville + spécialisation
library("plyr")
library("car")
for (i in c){
x <-read.csv(file = paste("citstf",i,".csv", sep=""))
y <- count(x,"tech_field")
colnames (y)[1]<-"letter"
y$name=y$letter
y$letter<-recode(y$letter,"1='A1'")
y$letter<-recode(y$letter,"2='A2'")
y$letter<-recode(y$letter,"3='A3'")
y$letter<-recode(y$letter,"4='A4'")
y$letter<-recode(y$letter,"5='A5'")
y$letter<-recode(y$letter,"6='A6'")
y$letter<-recode(y$letter,"7='A7'")
y$letter<-recode(y$letter,"8='A8'")
y$letter<-recode(y$letter,"9='B1'")
y$letter<-recode(y$letter,"10='B2'")
y$letter<-recode(y$letter,"11='B3'")
y$letter<-recode(y$letter,"12='B4'")
y$letter<-recode(y$letter,"13='B5'")
y$letter<-recode(y$letter,"14='C1'")
y$letter<-recode(y$letter,"15='C2'")
y$letter<-recode(y$letter,"16='C3'")
y$letter<-recode(y$letter,"17='C4'")
y$letter<-recode(y$letter,"18='C5'")
y$letter<-recode(y$letter,"19='C6'")
y$letter<-recode(y$letter,"20='C7'")
y$letter<-recode(y$letter,"21='C8'")
y$letter<-recode(y$letter,"22='C9'")
y$letter<-recode(y$letter,"23='C10'")
y$letter<-recode(y$letter,"24='C11'")
y$letter<-recode(y$letter,"25='D1'")
y$letter<-recode(y$letter,"26='D2'")
y$letter<-recode(y$letter,"27='D3'")
y$letter<-recode(y$letter,"28='D4'")
y$letter<-recode(y$letter,"29='D5'")
y$letter<-recode(y$letter,"30='D6'")
y$letter<-recode(y$letter,"31='D7'")
y$letter<-recode(y$letter,"32='D8'")
y$letter<-recode(y$letter,"33='E1'")
y$letter<-recode(y$letter,"34='E2'")
y$letter<-recode(y$letter,"35='E3'")
y$name<-recode(y$name,"1='Electrical Machinery, apparatus, energy'")
y$name<-recode(y$name,"2='Audio-Visual Technology'")
y$name<-recode(y$name,"3='Telecommunications'")
y$name<-recode(y$name,"4='Digital Communication'")
y$name<-recode(y$name,"5='Basic Communication Processes'")
y$name<-recode(y$name,"6='Computer Technology'")
y$name<-recode(y$name,"7='IT Methods for Management'")
y$name<-recode(y$name,"8='Semiconductors'")
y$name<-recode(y$name,"9='Optics'")
y$name<-recode(y$name,"10='Measurement'")
y$name<-recode(y$name,"11='Analysis of Biological Materials'")
y$name<-recode(y$name,"12='Control'")
y$name<-recode(y$name,"13='Medical Technology'")
y$name<-recode(y$name,"14='Organic Fine Chemistery'")
y$name<-recode(y$name,"15='Biotechnology'")
y$name<-recode(y$name,"16='Pharmaceuticals'")
y$name<-recode(y$name,"17='Macromolecular chemistery, polymers'")
y$name<-recode(y$name,"18='Food chemistery'")
y$name<-recode(y$name,"19='Basic materials chemistery'")
y$name<-recode(y$name,"20='Materials, metallurgy'")
y$name<-recode(y$name,"21='Surface technology, coating'")
y$name<-recode(y$name,"22='Micro-structural and nano-technology'")
y$name<-recode(y$name,"23='Chemical Engineering'")
y$name<-recode(y$name,"24='Environmental Technology'")
y$name<-recode(y$name,"25='Handling'")
y$name<-recode(y$name,"26='Machine Tools'")
y$name<-recode(y$name,"27='Engines, Pumps, Turbines'")
y$name<-recode(y$name,"28='Textile and Paper Machines'")
y$name<-recode(y$name,"29='Other Special Machines'")
y$name<-recode(y$name,"30='Thermal Processes and Apparatus'")
y$name<-recode(y$name,"31='Mechanical Elements'")
y$name<-recode(y$name,"32='Transport'")
y$name<-recode(y$name,"33='Furniture, Games'")
y$name<-recode(y$name,"34='Other Consumer Goods'")
y$name<-recode(y$name,"35='Civil Engineering'")
y <- y[order(-y$freq),]
colnames (y)[2]<-"number"
y$frequency <- (y$number/sum(y$number))
y$frequency <- round(y$frequency, digits=4)
y$specialisation <- y$frequency
y <- y[order(y$letter),]
y$specialisation[y$letter=='A1']=y$specialisation[y$letter=='A1']/0.0601
y$specialisation[y$letter=='A2']=y$specialisation[y$letter=='A2']/0.0265
y$specialisation[y$letter=='A3']=y$specialisation[y$letter=='A3']/0.0258
y$specialisation[y$letter=='A4']=y$specialisation[y$letter=='A4']/0.0351
y$specialisation[y$letter=='A5']=y$specialisation[y$letter=='A5']/0.0098
y$specialisation[y$letter=='A6']=y$specialisation[y$letter=='A6']/0.0324
y$specialisation[y$letter=='A7']=y$specialisation[y$letter=='A7']/0.0042
y$specialisation[y$letter=='A8']=y$specialisation[y$letter=='A8']/0.0151
y$specialisation[y$letter=='B1']=y$specialisation[y$letter=='B1']/0.0189
y$specialisation[y$letter=='B2']=y$specialisation[y$letter=='B2']/0.0497
y$specialisation[y$letter=='B3']=y$specialisation[y$letter=='B3']/0.0079
y$specialisation[y$letter=='B4']=y$specialisation[y$letter=='B4']/0.0158
y$specialisation[y$letter=='B5']=y$specialisation[y$letter=='B5']/0.0462
y$specialisation[y$letter=='C1']=y$specialisation[y$letter=='C1']/0.0459
y$specialisation[y$letter=='C2']=y$specialisation[y$letter=='C2']/0.0283
y$specialisation[y$letter=='C3']=y$specialisation[y$letter=='C3']/0.0431
y$specialisation[y$letter=='C4']=y$specialisation[y$letter=='C4']/0.0263
y$specialisation[y$letter=='C5']=y$specialisation[y$letter=='C5']/0.0115
y$specialisation[y$letter=='C6']=y$specialisation[y$letter=='C6']/0.0283
y$specialisation[y$letter=='C7']=y$specialisation[y$letter=='C7']/0.0204
y$specialisation[y$letter=='C8']=y$specialisation[y$letter=='C8']/0.0147
y$specialisation[y$letter=='C9']=y$specialisation[y$letter=='C9']/0.0006
y$specialisation[y$letter=='C10']=y$specialisation[y$letter=='C10']/0.0306
y$specialisation[y$letter=='C11']=y$specialisation[y$letter=='C11']/0.0152
y$specialisation[y$letter=='D1']=y$specialisation[y$letter=='D1']/0.0426
y$specialisation[y$letter=='D2']=y$specialisation[y$letter=='D2']/0.0324
y$specialisation[y$letter=='D3']=y$specialisation[y$letter=='D3']/0.0337
y$specialisation[y$letter=='D4']=y$specialisation[y$letter=='D4']/0.0268
y$specialisation[y$letter=='D5']=y$specialisation[y$letter=='D5']/0.0399
y$specialisation[y$letter=='D6']=y$specialisation[y$letter=='D6']/0.0204
y$specialisation[y$letter=='D7']=y$specialisation[y$letter=='D7']/0.0409
y$specialisation[y$letter=='D8']=y$specialisation[y$letter=='D8']/0.0575
y$specialisation[y$letter=='E1']=y$specialisation[y$letter=='E1']/0.0239
y$specialisation[y$letter=='E2']=y$specialisation[y$letter=='E2']/0.0217
y$specialisation[y$letter=='E3']=y$specialisation[y$letter=='E3']/0.0476
y$specialisation <- round(y$specialisation, digits=4)
write.table(y, file=paste("tffreq",i,".tsv", sep=""), quote=FALSE, sep='\t', row.names = FALSE)
}
############## Memoire ############
setwd("C:/Users/rlx/Desktop/memoire_tmp")
list.files()
inventeurs <- read.csv("inventeursEPO_AML.csv", sep=";")
annees <- as.data.frame(table(inventeurs$Année))
colnames(annees)[1]<-"Année"
colnames(annees)[2]<-"Brevets"
annees$Année <- as.numeric(as.character(annees$Année))
plot(annees)
lines(annees)
codes_ipc <- read.csv("201602_EPO_IPC.txt", sep="|")
# jointure des codes IPC pour chaque brevet dont un inventeur est localisé dans AML
brevets_ipc <- merge(inventeurs, codes_ipc, by="Appln_id", all.x = TRUE)
plot(brevets_ipc$IPC, ylim=c(0,1400))
write.csv(brevets_ipc, file="brevets_ipc.csv",row.names = FALSE)
brevets_ipc <- read.csv("brevets_ipc_AML.csv", sep=";", quote="")
ipc <- count(brevets_ipc$IPC)
# on ne garde que les paires id-IPC uniques (pour éviter les doublons dues au co-brevetage)
graph_brevets_ipc <- unique(brevets_ipc[c("Appln_id", "IPC")])
write.csv(graph_brevets_ipc, file="graph_brevets_ipc.csv",row.names = FALSE)
graph_brevets_ipc <- read.csv("graph_brevets_ipc.csv", sep=";")
graph_brevets_ipc$Appln_id <- as.factor(graph_brevets_ipc$Appln_id)
# réseau full-digit
library(igraph)
bip <- graph.data.frame(graph_brevets_ipc)
V(bip)$type <- V(bip)$name %in% graph_brevets_ipc[,2]
## sparse=TRUE is a good idea if you have a large matrix here
v <- get.adjacency(bipartite.projection(bip)[[2]], attr="weight", sparse=FALSE)
## Need to reorder if you want it alphabetically
v[order(rownames(v)), order(colnames(v))]
g <- graph_from_adjacency_matrix(v, mode="undirected", weighted = TRUE, diag = TRUE)
e <- cbind( get.edgelist(g) , round( E(g)$weight, 3 ))
e <- as.data.frame(e)
summary(g)
transitivity(g)
average.path.length(g)
graph.density(g)
summary(degree(g))
summary(graph.strength(g))
edge.betweenness.community(g)
multilevel.community(g)
leading.eigenvector.community(g)
optimal.community(g)
colnames(e)[1]<-"Source"
colnames(e)[2]<-"Target"
colnames(e)[3]<-"Weight"
write.csv(e, "cooc_pat_fulldigits.csv")
# réseau 6 digits
graph_brevets_ipc$IPC <- substr(graph_brevets_ipc$IPC, 0, 6)
graph_brevets_ipc$Appln_id <- as.factor(graph_brevets_ipc$Appln_id)
library(igraph)
bip <- graph.data.frame(graph_brevets_ipc)
V(bip)$type <- V(bip)$name %in% graph_brevets_ipc[,2]
## sparse=TRUE is a good idea if you have a large matrix here
v <- get.adjacency(bipartite.projection(bip)[[2]], attr="weight", sparse=FALSE)
## Need to reorder if you want it alphabetically
v[order(rownames(v)), order(colnames(v))]
g <- graph_from_adjacency_matrix(v, mode="undirected", weighted = TRUE, diag = TRUE)
e <- cbind( get.edgelist(g) , round( E(g)$weight, 3 ))
e <- as.data.frame(e)
colnames(e)[1]<-"Source"
colnames(e)[2]<-"Target"
colnames(e)[3]<-"Weight"
summary(g)
transitivity(g)
average.path.length(g)
graph.density(g)
summary(degree(g))
summary(graph.strength(g))
diameter(g)
summary(clusters(g))
edge.betweenness.community(g)
multilevel.community(g)
leading.eigenvector.community(g)
optimal.community(g)
write.csv(e, "cooc_pat_6digits.csv")
# réseau 4 digits
graph_brevets_ipc$IPC <- substr(graph_brevets_ipc$IPC, 0, 4)
graph_brevets_ipc$Appln_id <- as.factor(graph_brevets_ipc$Appln_id)
library(igraph)
bip <- graph.data.frame(graph_brevets_ipc)
V(bip)$type <- V(bip)$name %in% graph_brevets_ipc[,2]
## sparse=TRUE is a good idea if you have a large matrix here
v <- get.adjacency(bipartite.projection(bip)[[2]], attr="weight", sparse=FALSE)
## Need to reorder if you want it alphabetically
v[order(rownames(v)), order(colnames(v))]
g <- graph_from_adjacency_matrix(v, mode="undirected", weighted = TRUE, diag = TRUE)
e <- cbind( get.edgelist(g) , round( E(g)$weight, 3 ))
e <- as.data.frame(e)
colnames(e)[1]<-"Source"
colnames(e)[2]<-"Target"
colnames(e)[3]<-"Weight"
write.csv(e, "cooc_pat_4digits.csv")
# réseau 3 digits
graph_brevets_ipc$IPC <- substr(graph_brevets_ipc$IPC, 0, 3)
graph_brevets_ipc$Appln_id <- as.factor(graph_brevets_ipc$Appln_id)
library(igraph)
bip <- graph.data.frame(graph_brevets_ipc)
V(bip)$type <- V(bip)$name %in% graph_brevets_ipc[,2]
## sparse=TRUE is a good idea if you have a large matrix here
v <- get.adjacency(bipartite.projection(bip)[[2]], attr="weight", sparse=FALSE)
## Need to reorder if you want it alphabetically
v[order(rownames(v)), order(colnames(v))]
g <- graph_from_adjacency_matrix(v, mode="undirected", weighted = TRUE, diag = TRUE)
e <- cbind( get.edgelist(g) , round( E(g)$weight, 3 ))
e <- as.data.frame(e)
colnames(e)[1]<-"Source"
colnames(e)[2]<-"Target"
colnames(e)[3]<-"Weight"
library(EconGeo)
relatedness.cosine <- relatedness(v, method="Jaccard")
summary(relatedness.cosine)
relatedness.density <- (relatedness.density.int.avg(v, relatedness.cosine))
summary(relatedness.density)
transitivity(g)
average.path.length(g)
graph.density(g)
summary(degree(g))
summary(graph.strength(g))
diameter(g)
clusters(g)
dist <- distance_table(g)
barplot(dist$res, names.arg = seq_along(dist$res))
edge.betweenness.community(g)
multilevel.community(g)
leading.eigenvector.community(g)
optimal.community(g)
write.csv(e, "cooc_pat_3digits.csv")
# to clear now unused memory
gc()
# to remove all files in memory
rm(list = ls(all = TRUE))
|
114308586d067a0f659095577bfd3abeb3527ca2 | 9b0051af4477ce26be267877fbc31f32f01a3a21 | /cachematrix.R | e8ddb45e5ecbe62b03b20679980703248c230695 | [] | no_license | jsl416/ProgrammingAssignment2 | 8c3cdc1a096181a6994481d57d0b30e6e13ee787 | 1835d0b401f6b019fb2d141502102731a38137bb | refs/heads/master | 2021-01-18T09:42:14.956767 | 2014-06-21T19:30:35 | 2014-06-21T19:30:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,167 | r | cachematrix.R | ## ---------------------------------------------------------------------------------------------------------------------
## 2014-06-22 04:27
## Written By: JS Lee
## Purpose of this R code: This R code is to calculate an inverse of a sqaure matrix.
## When the inverse is calculated alreadly, the cached inverse will be returned which saves caculation time.
##----------------------------------------------------------------------------------------------------------------------
## Firstly, "makecacheMatrix" function will create a matrix, which will be saved into a matrix variable.
## Then, it will calculate the inverse of the matrix using "Sovle" function. The result will be set(cached) it as m.
## Finally, The list of four calculation results will be returned, which are set, get, setsolve, getsolve.
## (Input matrix should only be a square matrix.)
makeCacheMatrix <- function(x = matrix()) {
m <- NULL # initialize with NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x # return x, which is the matrix
setsolve <- function(solve) m <<- solve # solve function is applied to get the inverse
getsolve <- function() m # return calculated m
list(set = set, get = get,
setsolve = setsolve,
getsolve = getsolve)
}
## "cacheSolve" function checks getsolve to see if there is any value.
## If there is, it will return m value, which is the inverse of the matrix.
## Otherwise, it will calculate the inverse of the matrix.
cacheSolve <- function(x, ...) {
m <- x$getsolve() ## Return a matrix that is the inverse of 'x'
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get() #This will retrieve the matirx.
m <- solve(data, ...) #It solves to get the inverse of the matrix.
x$setsolve(m) #Set the inverse matrix as m
m #Return m value
}
## End |
b91dfc1c8a0029a51e2de9f447323ef8538d9c58 | a893f063e4fb685c6d959882b74f35cfc17686ee | /exercises/models01.R | 2c51eaf3b5a71721884ebccacbb557e9a97580e7 | [] | no_license | jmarshallnz/intro_to_r | e8ebe29fe4df3d32f9848e79eb75ead3a46bce4c | 35065a9255915f5f9eec4248972560fcbe7ff991 | refs/heads/main | 2023-06-16T10:09:57.485226 | 2021-06-21T04:13:12 | 2021-06-27T23:29:42 | 332,910,494 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 266 | r | models01.R | # Modelling Donkey bodyweights in R
# Load our libraries
library(tidyverse)
# read in the data
donkeys <- read_csv("data/donkeys/donkeys.csv")
# fit a model
model1 <- lm(Bodywt ~ Heartgirth + Umbgirth + Length + Height, data=donkeys)
# assess it
summary(model1)
|
6c6e40e3a9b2109c48e526588e1dddbefd35e381 | e25cd2cce4a5b069406fb87520b7ef9f14a20438 | /phc6711/moves/read.R | e893c060402619baa3dc6994a430f70dac2375da | [] | no_license | joebrew/uf | 415ecd5bb8bad16ba3d93237d7061cbaf6c8dd9c | 7ced5fe71813469c3b2ab4f5ae862664c87e36dd | refs/heads/master | 2016-09-01T23:14:28.734112 | 2015-08-27T14:43:14 | 2015-08-27T14:43:14 | 21,254,912 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,110 | r | read.R | zipfile <- "exportfeb20.zip"
location <- "/home/joebrew/Documents/uf/phc6711/moves"
# SET WD
setwd(location)
# UNZIP MASTER FILE
unzip(zipfile = zipfile)
# UNZIP CSV
unzip(zipfile = 'csv.zip')
# CD INTO THE CSV DAILY FOLDER
setwd('csv/daily')
# READ JUST SUMMARY
setwd('summary')
for (i in dir()){
assign(gsub(".csv", "", i), read.csv(i))
}
# LOOP THROUGH EACH DATAFRAME AND BIND
# Get list of current dataframes
dfs <- names(which(sapply(.GlobalEnv, is.data.frame)))
# Make master dataframe to populate
master <- data.frame(Date = NA,
Activity = NA,
Group = NA,
Duration = NA,
Distance = NA,
Steps = NA,
Calories = NA)
for (i in 1:length(dfs)){
df <- get(dfs[i])
master <- rbind(master, df)
}
# FORMAT MASTER
# make date object
master$Date <- as.Date(master$Date, format = "%m/%d/%y")
# order
master <- master[order(master$Date),]
########################################
# END OF DATA READ IN
#########################################
# BEGINNING OF DATA VIS
library(dplyr)
# Get overall calories by day
temp <- master %>%
group_by(Date) %>%
summarise(Distance = sum(Distance, na.rm = TRUE))
barplot(temp$Distance,
names.arg = temp$Date,
las = 3,
cex.names = 0.6)
# Plot duration just by type
temp <- master %>%
filter(Activity == "walking") %>%
group_by(Date, Activity) %>%
summarise(walk = sum(Duration, na.rm = TRUE))
temp$Activity <- NULL
temp2 <- master %>%
filter(Activity == "cycling") %>%
group_by(Date, Activity) %>%
summarise(bike = sum(Duration, na.rm = TRUE))
temp2$Activity <- NULL
temp3 <- master %>%
filter(Activity == "running") %>%
group_by(Date, Activity) %>%
summarise(run = sum(Duration, na.rm = TRUE))
temp3$Activity <- NULL
tt <- left_join(left_join(temp, temp2), temp3)
mat <- as.matrix(tt[,2:4])
mat <- t(mat) / 60
barplot(mat, beside = TRUE, legend = TRUE, col = c("red", "blue", "green"),
names.arg = tt$Date,
las = 3)
title(main = "Minutes per day")
box("plot")
|
f5f8b8fd799a6790c5b66d1785fc01515e12af54 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /metacoder/inst/testfiles/centroid/AFL_centroid/centroid_valgrind_files/1615765243-test.R | 59e35f965db8b7a98233c186265bfbaa626bb3f1 | [
"MIT"
] | permissive | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 427 | r | 1615765243-test.R | testlist <- list(b = c(1.78388675173214e+127, -40358544454836.7, 5.34324542977894e-305, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
result <- do.call(metacoder:::centroid,testlist)
str(result) |
e6251ad03873ad53e1b34c00b07985b79abd4d70 | 02b25b1ed401fc57f4945a970d2b57f2622e7bf7 | /dose_response_curve/dose_response_curve.R | 128c0c5e247f9e61d020edc76cc691906fd8c939 | [] | no_license | xuyanli2016spring/R_scripts_islj_project | bb361dc865e63def1061bb5dd3e1d7376b5e592c | 20d9742977e0f5f31ae30af67fba8428f1c93668 | refs/heads/master | 2021-01-18T18:27:58.849883 | 2017-04-04T22:14:23 | 2017-04-04T22:14:23 | 86,855,939 | 0 | 1 | null | 2017-04-04T22:14:24 | 2017-03-31T20:01:57 | R | UTF-8 | R | false | false | 2,472 | r | dose_response_curve.R |
# Name: Yanli Xu
# Date: Apr 4th, 2017
# Note: This script creates a dose response curve of mouse and culture experiments.
# Note: ggplot2 library is needed
culture_doses <- c(0.038, 0.045, 0.050, 0.170, 0.200, 0.220, 0.270, 0.300, 0.340, 0.350, 0.360, 0.370, 0.380, 0.390, 0.400, 0.440, 0.450, 0.460, 0.500, 0.600, 0.700, 0.800, 0.900, 1.000)
culture_response <- c(0.005119048, 0.006023810, 0.008464286, 0.051809524, 0.065309524, 0.071726190, 0.106851190, 0.140535714, 0.162523810, 0.177630952, 0.181666667, 0.182047619, 0.191488095, 0.207047619, 0.214642857, 0.240476190, 0.252059524, 0.268297619, 0.287845238, 0.372708333, 0.384726190, 0.408797619, 0.405619048, 0.435285714)
data2 <- data.frame(culture_doses, culture_response)
# for adding minor trick mark
insert_minor <- function(major_labs, n_minor) {labs <-
c( sapply( major_labs, function(x) c(x, rep("", 3) ) ) )
labs[1:(length(labs)-n_minor)]}
ggplot() +
geom_line(data = data1, aes(x = data1$mouse_doses, y = data1$mouse_response, color = "red"), linetype = 6) +
geom_line(data = data2, aes(x = data2$culture_doses, y = data2$culture_response, color = "blue"), linetype = 6) +
xlab("doses") + ylab("nectrig percentage") +
ggtitle("mouse and culture dose response curve") +
theme(axis.text.x = element_text(face = "bold", color = "red", size = 12, angle = 45), axis.text.y = element_text(face = "bold", color = "red", size = 12, angle = 45) ) +
theme(axis.line = element_line(color = "black", size = 1, linetype = "solid")) +
theme(legend.position="top", panel.background = element_blank(), panel.grid.minor = element_blank()) +
theme(plot.title = element_text(family = "Trebuchet MS", color="black", face="bold", size=18, hjust=0)) +
theme(axis.title = element_text(family = "Trebuchet MS", color="black", face="bold", size=14)) +
scale_x_continuous(breaks = seq(0,1,by=0.025), labels = insert_minor(seq(0, 1, 0.1), 3), limits = c(0, 1), expand = c(0.01,0.01)) +
scale_y_continuous(breaks = seq(0,0.5,by=0.025), labels = insert_minor(seq(0, 0.5, 0.1), 3), limits = c(0, 0.5), expand = c(0.01,0.01)) +
theme(legend.title = element_text(colour="blue", size=10, face="bold"), legend.text = element_text(colour="blue", size=10, face="bold"),legend.background = element_rect(fill="lightblue", size=0.5, linetype="solid"), legend.title = element_blank()) +
scale_color_manual(labels = c("mouse", "culture"), values = c("red", "blue")) +
guides(color=guide_legend("Exp_Type"))
|
65b93e133c1879820d0ca1a49e1c60fb4fb890dc | ae29d98e457098c8241dc4ca533911927b3fe47f | /man/zoom.Rd | c29aee730b4ec7ee52139dcadd561958e66f7b38 | [] | no_license | great-northern-diver/loon.ggplot | 1a10c6303f05aca8d9e18431233143a1295f069c | d06a9ad6d53e9a7372745ec4c25ea24e6efc451e | refs/heads/master | 2023-04-14T09:07:03.520916 | 2023-03-15T13:05:44 | 2023-03-15T13:05:44 | 172,743,501 | 23 | 3 | null | 2023-03-15T13:05:46 | 2019-02-26T15:59:58 | R | UTF-8 | R | false | true | 2,518 | rd | zoom.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interactive-ggproto.R
\name{zoom}
\alias{zoom}
\title{Zoom Plot Region}
\usage{
zoom(layerId = NULL, scaleToFun = NULL)
}
\arguments{
\item{layerId}{numerical; which layer to scale the plot by.}
\item{scaleToFun}{scale to function. See details.}
}
\value{
a \code{ggproto} object
}
\description{
Change the visible plot region by scaling to different elements of the display.
}
\details{
Argument \code{layerId} is used for additional plot region settings.
If the \code{layerId} is set as \code{NULL} (default), the region of the
interactive graphics \code{loon} will be determined by the \code{ggplot} object
(i.e. \code{coord_cartesian}, \code{xlim}, etc);
else one can use \code{scaleToFun} to modify the region of the layer.
The \code{scaleToFun} is a function to scale the region.
If it is \code{NULL} (default), based on different layers, different scale functions
will be applied. For example, if the layer is the main graphic model, i.e. \code{l_plot}
\code{l_hist}, then the default \code{scaleToFun} is \code{\link{l_scaleto_plot}}; else
if the layer is a general \code{l_layer} widget, the default \code{scaleToFun} would be
\code{\link{l_scaleto_layer}} (see \code{\link{get_activeGeomLayers}}).
If it is not \code{NULL}, users can select one that precisely tailor their own
problems. The table shows the available \code{scaleToFun} functions
\tabular{ll}{\strong{scale to} \tab \strong{Subfunction}\cr
plot \tab \code{\link{l_scaleto_plot}}\cr
world \tab \code{\link{l_scaleto_world}}\cr
active \tab \code{\link{l_scaleto_active}}\cr
selected \tab \code{\link{l_scaleto_selected}}\cr
layer \tab \code{\link{l_scaleto_layer}}\cr}
Users can also supply their own function, providing its arguments match those
of the functions shown in the above table.
}
\examples{
if(interactive()) {
p <- l_ggplot(mtcars,
mapping = aes(x = hp, y = mpg)) +
geom_point(mapping = aes(color = factor(gear))) +
geom_smooth(data = mtcars[mtcars$gear == 4, ],
method = "lm")
# a scatter plot with a fitted line on 4 gear cars
p
# scale to the second layer (smooth line)
p + zoom(layerId = 2)
# highlight the 3 gear cars
# scale to the selected points
p +
selection(mtcars$gear == 3) +
zoom(layerId = 1,
scaleToFun = loon::l_scaleto_selected)
}
}
\seealso{
\code{\link{active}}, \code{\link{linking}}, \code{\link{selection}},
\code{\link{hover}}, \code{\link{interactivity}}
}
|
bf60f90223b21f305759bc9f5adc010c15607d09 | b37f4d89507ad02ec69a38cece7fb83acf851757 | /decisions.R | da712897661744712b81ae54d0ecb14f8f4dacdf | [] | no_license | ginahixx/sd3 | 4d58e8200c40883566abeb4d44a2d9edfbd73507 | a020dd38acb83ecbd221bf369d9a76e1d879e7b3 | refs/heads/master | 2021-01-25T07:54:56.939514 | 2017-06-07T22:30:10 | 2017-06-07T22:30:10 | 93,684,888 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,161 | r | decisions.R | #functions for Design Decisions section
settingstablename <- "DDSettings"
controlTableName <- "DesignDecisions"
versionflag <- "version"
appflag <- "app"
#functions ---------------------------------
GetMeetingIDs <- function() {
# Connect to the database
db <- dbConnect(MySQL(), dbname = databaseName, host = options()$mysql$host,
port = options()$mysql$port, user = options()$mysql$user,
password = options()$mysql$password)
query <- paste0("SELECT MeetingID FROM ", settingstablename, " Group By MeetingID") #group by in case table structure changes from one line per
data <- dbGetQuery(db, query)
dbDisconnect(db)
return (data)
}
GetSettingsTable <- function(id) {
# Connect to the database
db <- dbConnect(MySQL(), dbname = databaseName, host = options()$mysql$host,
port = options()$mysql$port, user = options()$mysql$user,
password = options()$mysql$password)
query <- paste0("SELECT * FROM ", settingstablename, " where MeetingID = ", id)
data <- dbGetQuery(db, query)
dbDisconnect(db)
return (data)
}
LoadControlData <- function(id){
db <- dbConnect(MySQL(), dbname = databaseName, host = options()$mysql$host,
port = options()$mysql$port, user = options()$mysql$user,
password = options()$mysql$password)
#select * from DesignDecisions inner join MeetingGroups on DDID = DecisionID where MeetingID = 1
query <- paste0("SELECT * FROM ", controlTableName, " inner join MeetingGroups on DDID = DecisionID WHERE MeetingID = ", id)
data <- dbGetQuery(db, query)
dbDisconnect(db)
return (data)
}
LoadComments <- function(tablename, meetingid){
db <- dbConnect(MySQL(), dbname = databaseName, host = options()$mysql$host,
port = options()$mysql$port, user = options()$mysql$user,
password = options()$mysql$password)
query <- paste0("SELECT * FROM ", tablename, " WHERE MeetingID = ", id)
data <- dbGetQuery(db, query)
dbDisconnect(db)
return (data)
}
GetCommentData <- function(tablename, selecteditem, meetingid){
# Connect to the database
db <- dbConnect(MySQL(), dbname = databaseName, host = options()$mysql$host,
port = options()$mysql$port, user = options()$mysql$user,
password = options()$mysql$password)
#cat(file=stderr(), "GetCommentData data has myemail as ", email, ".\n")
downloadfields <- paste0(" comment, PickChoice, concat_ws(', ', firstname, organization) as 'Submitted by' ")
if (selecteditem==""){
query <- paste0("SELECT ", downloadfields, " FROM users INNER JOIN (", desisiontablename, " INNER JOIN ",
tablename, " ON DDID = DecisionID) ON users.email = ", tablename, ".email WHERE MeetingID = ", meetingid)
}else if (selecteditem=="%"){
query <- sprintf(paste0("SELECT %s FROM users INNER JOIN (", desisiontablename, " INNER JOIN ", tablename,
" ON DDID = DecisionID) ON users.email = ", tablename, ".email Where Title like '%s' and MeetingID = ", meetingid),
downloadfields, selecteditem, meetingid)
}else{
query <- sprintf(paste0("SELECT %s FROM users INNER JOIN (", desisiontablename, " INNER JOIN ", tablename,
" ON DDID = DecisionID) ON users.email = ", tablename, ".email Where Title = '%s' and MeetingID = ", meetingid),
downloadfields, selecteditem, meetingid)
}
if(debugmode){
cat(file=stderr(), "in GetCommentData, query is ", query, ".\n")
}
data <- dbGetQuery(db, query)
downloadtable <<- data
dbDisconnect(db)
data
}
#AssembleCommentData(commentsTableName(), MeetingID(), topic, userInfo$user$email, input$comment)
AssembleCommentData <- function(tablename, meetingid, decisiontitle, decisionid, email, comment, pickchoice){
#lookup up commentID
commentCheck <- LoadUserComment(tablename, meetingid, decisiontitle, email)
if(debugmode){
cat(file=stderr(), "in AssembleCommentData nrow(commentCheck) is ", nrow(commentCheck), ", length is ",length(commentCheck), ".\n")
}
#build data
if(nrow(commentCheck)> 0){
if(debugmode){
cat(file=stderr(), "in AssembleCommentData nrow >0 section, nrow(commentCheck) is ", nrow(commentCheck), ".\n")
}
data <- as.list(commentCheck$DCID)
}else {
data <- as.list("")
}
names(data) <- "DCID"
data$DecisionID <- decisionid
data$MeetingID <- meetingid
data$email <-email
data$Comment <- comment
pickoptions <- pickchoice
pickstr <- paste(pickoptions, collapse = ", ")
data$PickChoice <- pickstr
if(debugmode){
cat(file=stderr(), "leaving AssembleCommentData.\n")
}
return(data)
}
# Text retrieval functions ----------------------------------------
#pull together parts for modal message
getmodalHTML <- function(data){
c(data$ModalIntro, data$ContactInfo, "<br><br>")
}
GetInstructionsHTML <- function(data){
c(data$InstructionsText, data$ContactInfo, "<br><br>")
}
GetIntroHTML <- function(data){
c(data$IntroText, "<br>")
}
GetExcelLinkHTML <- function(data){
c(data$ExcelFileLink, "<br>")
}
|
d4a0d9a623ea38b588c5b074004d71bd477388bb | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.security.identity/man/wafregional_get_change_token.Rd | 69c31884357cb22187c64c1d938ec4d11ab98254 | [
"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 | 596 | rd | wafregional_get_change_token.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wafregional_operations.R
\name{wafregional_get_change_token}
\alias{wafregional_get_change_token}
\title{This is AWS WAF Classic documentation}
\usage{
wafregional_get_change_token()
}
\description{
This is \strong{AWS WAF Classic} documentation. For more information, see \href{https://docs.aws.amazon.com/waf/latest/developerguide/classic-waf-chapter.html}{AWS WAF Classic} in the developer guide.
See \url{https://www.paws-r-sdk.com/docs/wafregional_get_change_token/} for full documentation.
}
\keyword{internal}
|
99d22a17a414c3c8c311235d088929ccdbf5ea4d | b8f853e88d5c9f843e3ef0edc940ec1ef206e8ef | /server.R | e2b615f4741ac7904c32af54f01e1da3e2964ae0 | [] | no_license | DBAJAJ89/Text_Analytics | 28af6bbd0717ae9b58c921de2d4081973b70d097 | bebd94013364741e65844bd5253fe43b2c12fab8 | refs/heads/master | 2020-03-16T06:03:10.314544 | 2018-06-02T11:17:27 | 2018-06-02T11:17:27 | 132,546,258 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,504 | r | server.R | #---------------------------------------------------------------------#
#Title: Text Analytics Assignment 2
#Authors: Deepak Bajaj, Richa Kansal, Soumya Panigrahi
#Shiny App: Server.r code
#---------------------------------------------------------------------#
#Start of server code
shinyServer(function(input, output) {
#Module to input data from user
# Input from user is in rective mode since the data should change with every change in input data
Dataset <- reactive({
if (is.null(input$file1)) { return(NULL) } #here we have used file1 since the UI.r has the same name
else{
text<- readLines(input$file1$datapath) #readline function to read the input from user
text = str_replace_all(text, "<.*?>","") # stringr library has the function str_replace_all to clean the corpus
text=text[text!=""] #selecting not null text
return(text) # return cleaned corpus
}
})
# uploading the input module
model = reactive({
if(input$radio==1)
{model = udpipe_load_model("english-ud-2.0-170801.udpipe")}
if(input$radio==2)
{model = udpipe_load_model("spanish-ud-2.0-170801.udpipe")}
if(input$radio==3)
{model = udpipe_load_model("hindi-ud-2.0-170801.udpipe")}
return(model)
})
#passing the input data and english model to the UDpipe annotate function
annot.obj =reactive({
x<-udpipe_annotate(model(),x=Dataset())
x<-as.data.frame(x)
return(x)
})
#--------------tab panel 2 outputs--------------------#
# module to let the user download the annotated data as a csv input
output$downloadData <- downloadHandler(
filename=function(){
"annonated_data.csv" #name of the downloaded file
},
content = function(file) {
write.csv(annot.obj()[,-4],file,row.names=FALSE) #writing the output as a csv after removing the sentence column
})
# module to display the top 100 rows of the annotated corpus. we have intentionally hidden the sentence field
output$dout1 = renderDataTable({
if(is.null(input$file1)) {return (NULL)} #exception handler in case the file is empty
else{
out=annot.obj ()[,-4]
return(head(out,100))
}
})
#--------------tab panel 3 outputs--------------------#
#module to print a wordcloud for nouns
output$plot1 = renderPlot({
if(is.null(input$file1)) {return (NULL)} #exception handler in case the file is empty
else
{
all_nouns=annot.obj() %>% subset(.,upos %in% "NOUN") #filtering the corpus for nouns
top_nouns =txt_freq(all_nouns$lemma) # count of each noun terms
wordcloud(top_nouns$key,top_nouns$freq,min.freq = input$freq, max.words=input$max,colors =brewer.pal(8,"Dark2")) # plotting on a word cloud
}
})
#module to print a wordcloud for verbs
output$plot2 = renderPlot({
if(is.null(input$file1)) {return (NULL)} #exception handler in case the file is empty
else
{
all_verbs=annot.obj() %>% subset(.,upos %in% "VERB") #filtering the corpus for verbs
top_verbs =txt_freq(all_verbs$lemma) # count of each verbs terms
#head(top_verbs,10)
wordcloud(top_verbs$key,top_verbs$freq,min.freq = input$freq, max.words=input$max,colors =brewer.pal(8,"Dark2")) # plotting on a word cloud
}
})
#--------------tab panel 4 output--------------------#
output$plot3 = renderPlot({
if(is.null(input$file1)) {return (NULL)} #exception handler in case the file is empty
else
{
data_cooc<-cooccurrence(
x=subset(annot.obj(),upos %in% input$upos), #collecting required upos from user input and filtering the annonated corpus
term="lemma", #paramerter to specify the extraction terms as lemma
group=c("doc_id","paragraph_id","sentence_id"))
# creation of co-occurrence graph
wordnetwork<- head(data_cooc,50)
wordnetwork<-igraph::graph_from_data_frame(wordnetwork)
#plotting the graph
ggraph(wordnetwork,layout="fr") +
geom_edge_link(aes(width=cooc,edge_alpha=cooc),edge_colour="orange")+
geom_node_text(aes(label=name),col="darkgreen", size=4)+
theme_graph(base_family="Arial Narrow")+
theme(legend.position="none")+
labs(title= "Cooccurrences Plot",subtitle="Nouns & Adjectives")
}
})
})
|
00f3e22c097f8b8fe083f27e94874df5eb71ec3e | ef4b390f70ba2fbdb78c4e68ef7a8dee0eb4c917 | /analysis.R | d69d53fa984c45a8ee01592eeab0eb8c698aaa9b | [] | no_license | sahNarek/CSE_270_FINAL_PROJECT | a4c30fc03e9f0107dd277638afc6e5fca81823de | b650f3a06a7ebfde0d6ae34d7a4236131d0feb2c | refs/heads/master | 2021-07-12T15:28:58.884826 | 2020-11-21T23:58:31 | 2020-11-21T23:58:31 | 220,856,820 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,845 | r | analysis.R | load("data/no_et_games.rda")
load("data/et_agr_94_18.rda")
load("data/legs_info.rda")
load("data/et_minutes.rda")
load("data/po_elos.rda")
packages_list <- c("ggplot2","dplyr", "scales", "DescTools",
"plotly", "stringr", "elo", "caret", "e1071")
install_or_call <- function(list = packages_list){
installed <- installed.packages()[,"Package"]
for( package in packages_list ){
if(!(package %in% installed)){
install.packages(package)
}
do.call(library, list(package))
}
}
install_or_call()
by_id <- function(data, var, id){
return(data[data[var] == id,])
}
all_games <- rbind(games, et_agr)
legs_summary <- legs_info %>%
group_by(TYPE) %>%
summarise(COUNT = n())
pie <- plot_ly(legs_summary, labels = ~TYPE, values = ~COUNT, type = 'pie',
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF'),
hoverinfo = 'text',
text = ~paste(TYPE),
marker = list(line = list(color = '#FFFFFF', width = 1)),
showlegend = F) %>%
layout(title = "DETERMINING LEG'S WINNER IN UCL PO ROUNDS",
xaxis = list(showgrid = F, zeroline = F, showticklabels = F),
yaxis = list(showgrid = F, zeroline = F, showticklabels = F))
pie
et_minutes <- et_minutes %>%
mutate(ROUND = by_id(all_games, "GAME_ID", GAME_ID)$ROUND)
box <- plot_ly(et_minutes, x = ~MINUTE, color = ~ROUND, type = "box") %>%
layout(title = "GOAL SCORING MINUTES IN ALL ROUNDS OF PO",
yaxis = list(showgrid = F, zeroline = F, showticklabels = F),
xaxis = list(showgrid = T, zeroline = F, showticklabels = T))
box
all_games <- all_games %>%
mutate(FTR = ifelse(FTHG > FTAG, "H", ifelse(FTHG < FTAG, "A", "D")),
HTG = (FTHG + ETHG + PTHG),
ATG = (FTAG + ETAG + PTAG),
GR = ifelse(HTG > ATG, "H", ifelse(HTG < ATG, "A", "D")))
games_by_team <- function(data = all_games, team, field) {
var <- "HOMETEAM"
if(field == "A"){
var <- "AWAYTEAM"
}
games <- data[data[var] == team,]
return(length(games))
}
games_in_et <- function(data, team){
h_games <- data %>%
filter(HOMETEAM == team && TYPE == "ET" && LEG == 2)
a_games <- data %>%
filter(AWAYTEAM == team && TYPE == "ET" && LEG == 2)
return(nrow(h_games) + nrow(a_games))
}
standings <- function(data) {
as_ht <- data %>%
group_by(HOMETEAM) %>%
summarise(W = sum(FTR == "H"),
L = sum(FTR == "A"),
D = sum(FTR == "D"),
HTGF = sum(HTHG),
HTGA = sum(HTAG),
FTGF = sum(FTHG),
FTGA = sum(FTAG),
ETGF = sum(ETHG),
ETGA = sum(ETAG))
as_at <- data %>%
group_by(AWAYTEAM) %>%
summarise(W = sum(FTR == "A"),
L = sum(FTR == "H"),
D = sum(FTR == "D"),
HTGF = sum(HTAG),
HTGA = sum(HTHG),
FTGF = sum(FTAG),
FTGA = sum(FTHG),
ETGF = sum(ETAG),
ETGA = sum(ETHG))
table <- data.frame(TEAM = as_ht$HOMETEAM, W = as_ht$W + as_at$W,
D = as_ht$D + as_at$D, L = as_ht$L + as_at$L, FTGF = as_ht$FTGF + as_at$FTGF,
FTGA = as_ht$FTGA + as_at$FTGA, ETGF = as_ht$ETGF + as_at$ETGF,
ETGA = as_ht$ETGA + as_at$ETGA, HTGF = as_ht$HTGF + as_at$HTGF,
HTGA = as_ht$HTGA + as_at$HTGA)
table$M <- table$W + table$D + table$L
table$POINTS <- 3 * table$W + table$D
table$STGF <- table$FTGF - table$HTGF
table$STGA <- table$FTGA - table$HTGA
table$EGM <- sapply(table$TEAM, games_in_et, data = et_agr)
return(table %>%
select(TEAM, M, POINTS ,everything()) %>%
arrange(desc(POINTS)))
}
cl_po_k <- function(table){
table <- table %>%
filter(!(W == 0))
table$FTGD <- table$FTGF - table$FTGA
table$ETGD <- table$ETGF - table$ETGA
table$WPCT <- (table$W + round(table$D / 3)) / (table$M)
table$RATIO <- table$FTGF / table$FTGA
k_model <- lm(log(WPCT) ~ 0 + log(RATIO), data = table)
k <- coefficients(k_model)
table$EWPCT <- table$FTGF ^ k / ((table$FTGF)^k + (table$FTGA)^k)
return(table)
}
table <- standings(data = all_games)
box_1 <- plot_ly(table, x = ~M, type = "box") %>%
layout(title = "NUMBER OF PO MATCHES BY TEAMS",
yaxis = list(showgrid = F, zeroline = F, showticklabels = F),
xaxis = list(showgrid = T, zeroline = F, showticklabels = T))
box_1
wpct_table <- cl_po_k(table)
cor.test(wpct_table$WPCT, wpct_table$FTGD)
# model <- lm(log(WPCT) ~ 0 + log(RATIO), data = table)
# coefficients(model)
# save(table, file = "data/po_table.rda")
# write.csv(table, file = "data/po_table.csv")
p <- ggplot(wpct_table, aes(x = EWPCT, y = WPCT, text = TEAM)) +
geom_point() +
geom_abline(intercept = 0, slope = 1, col = "red")
p <- ggplotly(p)%>%
layout(title = "EXPECTED WIN PERCENTAGE VS ACTUAL",
yaxis = list(showgrid = F, zeroline = F, showticklabels = F),
xaxis = list(showgrid = T, zeroline = F, showticklabels = T))
lucky_teams <- wpct_table %>%
filter(WPCT > EWPCT)
unlucky_teams <- wpct_table %>%
filter(WPCT < EWPCT)
expected_teams <- wpct_table %>%
filter(WPCT == EWPCT)
wpct_table_1 <- wpct_table %>%
filter(M >= 4)
p1 <- ggplot(wpct_table_1, aes(x = EWPCT, y = WPCT, text = TEAM)) +
geom_point() +
geom_abline(intercept = 0, slope = 1, col = "red")
p1 <- ggplotly(p1)%>%
layout(title = "EXPECTED WIN PERCENTAGE VS ACTUAL",
yaxis = list(showgrid = F, zeroline = F, showticklabels = F),
xaxis = list(showgrid = T, zeroline = F, showticklabels = T))
p1
hist <- ggplot(data=et_minutes, aes(x = MINUTE)) +
geom_histogram(breaks=seq(0, 120, by=10),
col="red",
fill="green",
alpha = .2) +
labs(title="Histogram for Goal Minutes", x="Minutes", y="Number of Goals") +
xlim(c(0,120))
hist_ly <- ggplotly(hist)
hist_ly
lucky_teams_1 <- wpct_table_1 %>%
filter(WPCT > EWPCT)
unlucky_teams_1 <- wpct_table_1 %>%
filter(WPCT < EWPCT)
expected_teams_1 <- wpct_table_1 %>%
filter(WPCT == EWPCT)
lucky_teams %>%
anti_join(lucky_teams_1)
all_time_elos <- elo.run(score(FTHG, FTAG) ~ adjust(HOMETEAM, 200) + AWAYTEAM +
k(30 * (all_games$FTHG - all_games$FTAG)), data = all_games)
sort(final.elos(all_time_elos), decreasing = T)
make_final_elos_df <- function(final_elos){
final_elos <- as.data.frame(final_elos)
final_elos$TEAM <- rownames(final_elos)
colnames(final_elos) <- c("ELO", "TEAM")
rownames(final_elos) <- 1:length(final_elos$TEAM)
final_elos <- final_elos[c("TEAM", "ELO")]
return(final_elos)
}
get_elos <- function(final_elos, def_elo = 1500){
starting_elos <- c()
if(length(final_elos) == 0){
starting_elos = def_elo
}
else{
starting_elos <- (def_elo) + (final_elos - def_elo)/2
}
return(starting_elos)
}
calculate_relative_elos <- function(data){
relative_df <- c()
final_elos <- c()
for(season in unique(data$SEASON)){
starting_elos <- get_elos(final_elos, def_elo = 1500)
season_data <- data[data$SEASON == season,]
season_elos <- elo.run(score(FTHG, FTAG) ~ adjust(HOMETEAM, 200) + AWAYTEAM +
k(30 * (all_games$FTHG - all_games$FTAG)), data = all_games,
initial.elos = starting_elos)
season_final_elos <- final.elos(season_elos)
season_elos <- data.frame(SEASON = season,season_elos)
relative_df <- rbind(relative_df, season_elos)
final_elos <- season_final_elos
}
final_elos <- make_final_elos_df(final_elos)
return(list(relative = relative_df, elos_df = final_elos))
}
games_no_rares <- all_games %>%
filter(HOMETEAM %in% unique(po_elos$team),AWAYTEAM %in% unique(po_elos$team))
custom_elos <- calculate_relative_elos(data = games_no_rares)
custom_elos$elos_df <- custom_elos$elos_df %>%
filter(TEAM %in% wpct_table_1$TEAM) %>%
arrange(desc(ELO))
custom_elos$elos_df
gold_minutes <- et_minutes %>%
filter(MINUTE > 90) %>%
group_by(LEG_ID) %>%
summarise(MINUTE = min(MINUTE))
et_minutes %>%
inner_join(gold_minutes, by = c("MINUTE", "LEG_ID"))
gold_goal <- function(data) {
gold_minutes <- data %>%
filter(MINUTE > 90) %>%
group_by(LEG_ID) %>%
summarise(MINUTE = min(MINUTE))
winners <- data %>%
inner_join(gold_minutes, by = c("MINUTE", "LEG_ID"))
return(winners)
}
get_elos_by_date <- function(source, game_date, game_team) {
elos <- source %>%
filter(team == game_team , date <= game_date ) %>%
arrange(desc(date))
return(elos[1,]$rating)
}
neutral_games <- function(data) {
agr_legs <- data %>%
filter(TYPE == "AGR")
elo_teams <- unique(po_elos$team)
result <- c()
for(i in 1:nrow(agr_legs)){
leg <- agr_legs[i,]
team1 <- as.character(leg$TEAM1)
team2 <- as.character(leg$TEAM2)
l2date <- leg$L2D
if(team1 %in% elo_teams && team2 %in% elo_teams){
elo1 <- get_elos_by_date(source = po_elos, game_date = l2date, game_team = team1)
elo2 <- get_elos_by_date(source = po_elos, game_date = l2date, game_team = team2)
wins.1 = elo.prob(elo.A = elo1,
elo.B = elo2)
wins.2 = 1 - wins.1
game <- data.frame(TEAM1 = team1, TEAM2 = team2, WINS.1 = wins.1, WINS.2 = wins.2,
LEG_ID = leg$LEG_ID, ACTUAL_WINNER = leg$WINNER)
result <- rbind(result, game)
}
}
result$TEAM1 <- as.character(result$TEAM1)
result$TEAM2 <- as.character(result$TEAM2)
result <- result %>%
mutate(ACTUAL.1 = ifelse(ACTUAL_WINNER == TEAM1, 1, 0),
ACTUAL.2 = ifelse(ACTUAL_WINNER == TEAM2, 1, 0),
NG_WINNER = ifelse(WINS.1 > WINS.2, TEAM1, TEAM2))
return(result)
}
rand_0_1 <- function() {
return(round(runif(n = 1, min = 0, max = 1)))
}
simulated_games <- neutral_games(data = legs_info)
simulated_games <- simulated_games %>%
mutate(PREDICTED.1 = ifelse(WINS.1 > 0.5,1,
ifelse(WINS.1 < 0.5, 0, rand_0_1())),
PREDICTED.2 = ifelse(WINS.2 > 0.5,1,
ifelse(WINS.2 < 0.5, 0, rand_0_1())))
agr_brier_1 <- BrierScore(pred = simulated_games$WINS.1, resp = simulated_games$ACTUAL.1)
agr_brier_1
agr_conf_1 <- confusionMatrix(data = factor(simulated_games$ACTUAL.1),
reference = factor(simulated_games$PREDICTED.1))
agr_conf_1
golden_goal_winners <- gold_goal(data = et_minutes)
actual_winners <- legs_info %>%
filter(LEG_ID %in% golden_goal_winners$LEG_ID)
et_vs <- games_no_rares %>%
group_by(SEASON) %>%
summarise(HTG = sum(HTHG) + sum(HTAG),
STG = (sum(FTHG) + sum(FTAG)) - HTG,
ETG = sum(ETHG) + sum(ETAG)) %>%
arrange(desc(HTG + STG + ETG))
|
a6524e79a68e47674e3ac94dcbd353dd84deebb9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/kappalab/examples/card.set.func.gen.Rd.R | 84c6f12a6f4300ad35ac43f0c00124becc2a017d | [] | 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 | 367 | r | card.set.func.gen.Rd.R | library(kappalab)
### Name: card.set.func
### Title: Create objects representing cardinal set functions.
### Aliases: card.set.func card.game card.capacity lower.capacity
### upper.capacity uniform.capacity
### Keywords: math
### ** Examples
card.set.func(4:-2)
card.game(c(0,-2:2))
card.capacity(0:5)
lower.capacity(3)
upper.capacity(4)
uniform.capacity(5)
|
136b6f05607feda62ea02ae6644c2ddea69586ba | f026d81ea0042c1b88f5f58f0ceff979d6020664 | /day6/solution.R | fb926dddea561c7d86fb38aa828bcec74b238f0b | [] | no_license | apostaremczak/advent-of-code-2017 | c4832385f122dabbbfeec11c7a9076d7bd449d81 | 36e0f7cfc3379e86f3b08700e269f83010c8a530 | refs/heads/master | 2021-08-31T20:59:32.578569 | 2017-12-22T18:36:44 | 2017-12-22T18:36:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,173 | r | solution.R | # --- Day 6: Memory Reallocation ---
puzzle_input <- read.csv("input.csv", sep = "\t", header = FALSE)
len <- ncol(puzzle_input)
row_to_string <- function(row) {
return(paste(as.character(row), collapse = ","))
}
next_ind <- function(i) {
if (i + 1 <= len) return(i + 1)
else return(1)
}
redistribute <- function(row) {
bank <- max(row[1:len])
bank_ind <- which(row == bank)[1]
row[,bank_ind] <- 0
current_ind <- next_ind(bank_ind)
while(bank) {
row[,current_ind] <- row[,current_ind] + 1
bank <- bank - 1
current_ind <- next_ind(current_ind)
}
return(row)
}
puzzle_input[1, len + 1] <- row_to_string(puzzle_input[1,])
redistribution_count <- 0
while(!any(duplicated(puzzle_input[, len + 1]))) {
last_row <- tail(puzzle_input, n=1)
new_row <- redistribute(last_row[1:len])
redistribution_count <- redistribution_count + 1
puzzle_input[nrow(puzzle_input) + 1,] <- c(new_row, row_to_string(new_row))
}
# --- Part Two ---
last_conf <- puzzle_input[nrow(puzzle_input), len + 1]
duplicates <- which(puzzle_input[,len + 1] == last_conf)
how_many_loops <- duplicates[2] - duplicates[1] |
e5648961297634c5108630b4dff934ccd63fdbad | 2192489e86e8993ac9de7f5c32583c2546e31129 | /plot1.R | 0abec5fd9c1a063b9f301fbac837c1e981a5d625 | [] | no_license | KimMonks/ExData_Plotting1 | e2d4063c5b2d2bd1863498d69993f23d216ad223 | dbecd4b04b01ee9cb021f74c1547f0b37204eedb | refs/heads/master | 2021-01-21T16:31:20.245254 | 2015-07-07T15:09:16 | 2015-07-07T15:09:16 | 38,661,173 | 0 | 0 | null | 2015-07-07T03:16:57 | 2015-07-07T03:16:56 | null | UTF-8 | R | false | false | 468 | r | plot1.R | # Load and extract data
path <- "./household_power_consumption.txt"
#data <- read.table(path, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
data <- read.table(path, header=TRUE, sep=";")
data <- subset(data, Date %in% c("1/2/2007","2/2/2007"))
# Trasnform and plot to file
power <- as.numeric(data$Global_active_power)
png("plot1.png", width=480, height=480)
hist(power, col="red", main="Global Active Power", xlab="Global Active Power (kilowatts)")
dev.off() |
03218a86bda8a7e49a1775936c349c1795247640 | 81e80f2198a64b8ab94ee962489bbc114534ed09 | /plot4.R | c33e80a3630c621ad7974faf9a9731397501b474 | [] | no_license | rajus74/ExData_Plotting1 | c964738a798b794eaced8c246b513b5bc1b8ecae | 163431684d158435f75526efb21c673049bea7fd | refs/heads/master | 2021-01-18T18:57:12.832834 | 2016-02-05T09:55:02 | 2016-02-05T09:55:02 | 51,138,252 | 0 | 0 | null | 2016-02-05T09:44:27 | 2016-02-05T09:44:27 | null | UTF-8 | R | false | false | 793 | r | plot4.R | source("loadSubsetData.R")
png("plot4.png",width = 480,height=480)
par(mfrow=c(2,2))
plot(subsetData$Time, subsetData$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power")
plot(subsetData$Time, subsetData$Voltage, type = "l", xlab = "datetime", ylab = "Voltage")
plot(subsetData$Time, subsetData$Sub_metering_1, type = "l", col = "black", xlab = "", ylab = "Energy sub metering")
lines(subsetData$Time, subsetData$Sub_metering_2, type="l", col="red")
lines(subsetData$Time, subsetData$Sub_metering_3, type="l", col="blue")
legend("topright", legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lwd=2, col=c("black", "red", "blue"))
plot(subsetData$Time, subsetData$Global_reactive_power, type = "l", xlab = "datetime", ylab = "Global_reactive_power")
dev.off() |
3ff7819cd37ea2d272e87e681eebe6386cde554e | 6dcf636b5872580402c291079e8ec1152c5c85d3 | /GeneticsHaplotype/R/simulationModels.R | 20b3e242feb774cf6260b7e86a7be43201c20631 | [] | no_license | sboehringer/GeneticsHaplotype | f9175d66529d0b753dcf7cd3ca9c9dd50f46cabc | ffb217614a70fbb5f46134f5aa71dd1455c9161f | refs/heads/master | 2020-05-16T09:52:50.506237 | 2015-07-31T16:05:28 | 2015-07-31T16:05:28 | 24,718,518 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,427 | r | simulationModels.R | #
# simulationModels.R
#Wed Apr 1 18:16:36 CEST 2015
simulationModelComparisonSingle = function(
# genetic parameters
htfs = rev(vector.std(1:8)), N = 2e2, pedTemplate,
# phenotypes
simulatePhenotypes, useCors = F, usePedIdcs = F,
# models to compare
models, #list(mcmcClass, passPhenotype, coerceFounders)
# other parameters
Nburnin = 1e3L, Nchain = 1e4L, missingness = 0, ...
) {
# diplotypes
d = simulateFromTemplate(pedTemplate, N = N, hfs = htfs);
# phenotypes
phenotypePars = list(...);
cors = NULL;
if (usePedIdcs) phenotypePars = c(phenotypePars, list(pedIdcs = pedsIdcs(d$peds)));
if (useCors) {
cors = pedsCoeffOfRel(d$peds);
phenotypePars = c(phenotypePars, list(cors = cors));
}
y = do.call(simulatePhenotypes, c(list(gts = d$gts[, 1]), phenotypePars));
if (is.matrix(y)) y = y[, 1];
# Covariates
X = model.matrix(~ 1, data.frame(dummy = rep(1, length(y))));
# induce missingness
dMiss = if (missingness > 0) createMissing(d, missing = missingness) else d;
r = lapply(models, function(model) with(model, {
Logs('Analyzing with class: %{mcmcClass}s', 4);
# <p> data set
d0 = dMiss;
cors0 = cors;
if (coerceFounders) {
d0 = createIndependent(dMiss);
cors0 = pedsCoeffOfRel(d0$peds);
}
# <p> reconstructor, instantiate object
R = new(DiplotypeReconstructor, d0$gts, pedsItrios2rcpp(d0$peds));
# <p> arguments for mcmc instantiation
mcmcArgs = list(peds = d0$peds, reconstructor = R, Nburnin = Nburnin, Nchain = Nchain);
if (passPhenotype) mcmcArgs = c(mcmcArgs, list(y = y, X = X));
if (nif(model$passCors)) mcmcArgs = c(mcmcArgs, list(cors = cors0));
mcmc = do.call(new, c(list(Class = mcmcClass), mcmcArgs));
# <p> run chain
mcmc$run();
# <p> summary
r = c(mcmc$summary(), R2 = mcmc$R2(d$gts[, 1]));
r
}));
r
}
simulationModelComparison = function(..., Nrepetition = 5e2L) {
print(date());
print(Nrepetition);
r = Lapply(1:Nrepetition, function(i, ...) simulationModelComparisonSingle(...), ...);
print(date());
r
}
simulationModelComparisonSpec = function(spec, htfs, N, pedTemplate, beta,
Nburnin = 1e2L, Nchain = 1e3L, Nrepetition, missingness = 0) {
args = c(list(htfs = htfs, N = N, pedTemplate = pedTemplate, missingness = missingness,
simulatePhenotype = get(spec$phenotypeFunction), beta = beta,
models = spec$models,
Nrepetition = Nrepetition
), spec$phenotype);
sim = do.call(simulationModelComparison, args);
} |
e36233b486b9a2a8a2ca2edd881c6be279161bdb | d7607d73cd5cc231b44dcb9f6c9c1591b71ed789 | /R/conn.R | c52c3a628394ad3f300a649915ec6e3849ab622d | [] | no_license | cran/msgpack | f595ed6d9ba97c53420462e6d4659367a5d9f59c | 4742bb3a4efdda1b28f432db80c0e0694ee0ac48 | refs/heads/master | 2021-09-07T11:28:41.219445 | 2018-02-22T08:15:35 | 2018-02-22T08:15:35 | 122,456,129 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,185 | r | conn.R | #' Read and write msgpack formatted messages over R connections.
#'
#' A `msgConnection` object decodes msgpack messages from an
#' underlying R raw connection.
#'
#' @param con A [connection] object open in binary mode.
#' @param max_size The largest partial message to store, in
#' bytes. `NA` means do not enforce a limit.
#' @param read_size How many bytes to read at a time.
#' @param ... Unpacking options (see [unpackMsg]).
#' @return `msgConnection()` returns an object of class
#' `msgConnection`.
#'
#' @examples
#' out <- rawConnection(raw(0), open="wb")
#' apply(quakes, 1, function(x) writeMsg(x, out))
#' length(rawConnectionValue(out))
#' inn <- msgConnection(rawConnection(rawConnectionValue(out), open="rb"))
#' readMsg(inn)
#' readMsgs(inn, 3)
#' @export
msgConnection <- function(con, read_size=2^16, max_size=NA, ...) {
partial <- raw(0)
status <- "ok"
bread <- 0
bwrite <- 0
reader <- function(desired) {
# ignore "desired" and just read non-blockingly.
readRaw(con, read_size)
}
readMsgs <- function(n=NA, read_size = parent.env(environment())$read_size) {
if (is.na(n)) n <- .Machine$integer.max
msgs_bytes <- unpackMsgs(partial, n, max_size = max_size, reader = reader)
partial <<- msgs_bytes$remaining
status <<- msgs_bytes$status
bread <<- bread + msgs_bytes$bytes_read
msgs_bytes$msgs
}
doClose <- function(...) {
close(con, ...)
}
#msgConnection object is just the orig object with this
#environment dangled off it.
structure(addClass(con, "msgConnection"), reader = environment())
}
#' @export
summary.msgConnection <- function(object, ...) {
s <- NextMethod("summary")
c(s, list(status = status(object)))
}
#' @rdname msgConnection
#' @export
close.msgConnection <- function(con, ...) {
attr(con, "reader")$doClose(...)
}
catenator <- function(val=c()) {
# An in-memory FIFO type object.
#tracemem(val)
start <- 0
end <- length(val)
function(x, action="store", ..., opts) {
switch(action,
store = {
lx <- length(x)
l <- length(val)
if (lx > 0) {
#check for overflow
if (end + lx > l && start > 0) {
# rotate back to start
if (start > 0 && end != start) {
val[1:(end-start)] <- val[(start+1):end]
}
end <<- end - start
start <<- 0
}
if (end + lx > l) {
# double array length
length(val) <<- max(end + lx, 2 * l);
}
#inject new values
val[ (end + 1):(end + lx) ] <<- x
end <<- end + lx
}
dbg("lx", lx, "start", start, "end", end, "\n")
x
},
read = {
if (end > start) {
val[(start+1):end]
} else val[c()]
},
buf = {
val
},
start = start,
length = end - start,
end = end,
contents = {
list(val, start, end)
},
reset = {
val <<- x
start <<- 0
end <<- length(x)
},
drop = {
if (x <= end - start && x >= 0) {
start <<- start + x
} else {
stop("have ", end - start, ", can't drop ", x)
}
})
}
}
lister <- function(val = list()) {
n <- length(val)
function(x, action="store") {
switch(action,
store = {
if (n > length(val))
length(val) <<- max(1, 2 * length(val))
n <<- n + 1
val[[n]] <<- x
},
read = {
length(val) <<- n
val
},
length = {
n
},
clear = {
n <<- 0
length(val) <<- 0
}
)
}
}
addClass <- function(x, classes) structure(x, class = c(classes, class(x)))
#' @return `partial(con)` returns any data that has been read ahead of
#' the last decoded message.
#' @rdname msgConnection
#' @export
partial <- function(con) UseMethod("partial")
#' @rdname msgConnection
#' @export
partial.msgConnection <- function(con) {
attr(con, "reader")$partial
}
#' @rdname msgConnection
#' @export
#' @param n The maximum number of messages to read. A value of NA
#' means to parse all available messages until end of input.
#' @return `readMsgs(con, n)` returns a list of up to `n` decoded messages.
readMsgs <- function(con, n = NA, ...) {
UseMethod("readMsgs")
}
#' @export
readMsgs.msgConnection <- function(con, n = NA, ...) {
attr(con, "reader")$readMsgs(n, ...)
}
#' @rdname msgConnection
#' @return `status(con)` returns the status of msgpack decoding on the
#' connection. A value of `"ok"` indicates all requested messages
#' were read, `"buffer underflow"` for a non-blocking connection
#' indicates that only part of a message has been received, and
#' `"end of input"` means the last available message has been read.
#' Other values indicate errors encountered in decoding, which will
#' effectively halt reading.
#' @export
status <- function(con) UseMethod("status")
#' @rdname msgConnection
#' @export
status.msgConnection <- function(con) {
attr(con, "reader")$status
}
#' @rdname msgConnection
#' @return `seek(con)` returns the number of bytes that have been
#' successfully read or written, depending on the mode of the
#' connection. (Repositioning is not supported.)
#' @param rw See [seek()].
#' @export
seek.msgConnection <- function(con, rw = summary(con)$mode, ...) {
rw <- pmatch(rw, c("read", "write"), 0L)
switch(rw,
attr(con, "reader")$bread,
attr(con, "reader")$bwrite,
)
}
#' `readMsg(con)` reads exactly one message from a
#' msgConnection, or throws an error.
#'
#' @rdname msgConnection
#' @return `readMsg(con)` returns one decoded message.
#' @export
readMsg <- function(con, ...) {
UseMethod("readMsg", con)
}
#' @export
readMsg.msgConnection <- function(con, ...) {
x <- readMsgs(con, 1, ...)
if (length(x) < 1) {
stop(status(con))
}
x[[1]]
}
#' `writeMsg(x, con)` writes a single message to a msgConnection.
#'
#' @rdname msgConnection
#' @param obj An R object.
#' @export
writeMsg <- function(obj, con, ...) {
UseMethod("writeMsg", con)
}
#' @export
writeMsg.connection <- function(obj, con, ...) {
writeMsgs(list(obj), con, ...)
}
#' @export
writeMsg.msgConnection <- function(obj, con, ...) {
writeMsgs(list(obj), con, ...)
}
#' `writeMsgs(l, conn)` writes a list of
#' messages to a connection. That is, `writeMsg(1:10, conn)` writes one
#' message containing an array, while `writeMsgs(1:10, conn)` writes
#' ten consecutive messages each containing one integer.
#'
#' `writeMsg` will work with any R connection in raw mode, but reading
#' requires a msgConnection object.
#'
#' Because msgpack messages have unpredictable length, the decoder
#' reads ahead in chunks, then finds the boundaries between messages.
#' Therefore when reading over a socket or a fifo it is best to use a
#' nonblocking connection, and it will not work to mix readMsg and
#' readBin on the same connection.
#'
#' If you are reading data from a not completely trusted source you
#' should specify options `max_size` and `max_depth` (see
#' [unpackOpts]). Without it, some deeply nested or cleverly designed
#' messages can cause a stack overflow or out-of-memory error. With
#' these options set, you will get an R exception instead.
#' @rdname msgConnection
#' @param objs A list of R objects.
#' @export
writeMsgs <- function(objs, con, ...) {
UseMethod("writeMsgs", con)
}
#' @export
writeMsgs.connection <- function(objs, con, ...) {
writeRaw(packMsgs(objs, ...), con)
}
#' @export
writeMsgs.msgConnection <- function(objs, con, ...) {
buf <- packMsgs(objs, ...)
result <- writeRaw(buf, con)
attr(con, "reader")$bwrite <- attr(con, "reader")$bwrite + length(buf)
invisible(result)
}
## To support test harness, we use "readRaw" and "writeRaw"
## internally, instead of "readBin" which is not an S3 method
readRaw <- function(con, n, ...) {
UseMethod("readRaw")
}
writeRaw <- function(object, con, ...) {
UseMethod("writeRaw", con)
}
readRaw.connection <- function(con, n) {
# I get errors thrown (sometimes) when reading at the end of
# a nonblocking fifo.
tryCatch({
readBin(con, 'raw', n)
},
error = function(e) {
# warning("Ignoring ", e)
raw(0)
})
}
writeRaw.connection <- function(object, con, ...) {
writeBin(object, con, ...)
}
## An inefficient double ended byte buffer for test harness purposes
rawBuffer <- function(object = raw(0)) {
open <- "r"
bytes <- length(object)
buf <- rawConnection(object, open = "r")
object <- NULL
write <- function(object) {
switch(open,
"w" = {
writeBin(object, buf)
},
"r" = {
data <- readBin(buf, 'raw', bytes - seek(buf))
close(buf)
buf <<- rawConnection(data,
open = "w")
open <<- "w"
writeBin(data, buf)
write(object)
}
)
}
read <- function(n) {
switch(open,
"r" = {
readBin(buf, 'raw', n)
},
"w" = {
##convert a write buffer into a read buffer
val <- rawConnectionValue(buf)
close(buf)
buf <<- rawConnection(val, open = "r")
bytes <<- length(val)
open <<- "r"
read(n)
}
)
}
doClose <- function(n) {
close(buf)
buf <- NULL
}
structure(list(environment()), class = "rawBuffer")
}
writeRaw.rawBuffer <- function(object, con) {
con[[1]]$write(object)
}
readRaw.rawBuffer <- function(con, n) {
con[[1]]$read(n)
}
close.rawBuffer <- function(con) {
con[[1]]$doClose()
}
|
395fa01ccb03b0be67978f4ee0769ad3aa0132ca | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Experiments/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query30_query07_1344n/query30_query07_1344n.R | e55088654c65668efd062c434ef16e1245914e81 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 703 | r | query30_query07_1344n.R | c DCNF-Autarky [version 0.0.1].
c Copyright (c) 2018-2019 Swansea University.
c
c Input Clause Count: 362
c Performing E1-Autarky iteration.
c Remaining clauses count after E-Reduction: 362
c
c Input Parameter (command line, file):
c input filename QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query30_query07_1344n.qdimacs
c output filename /tmp/dcnfAutarky.dimacs
c autarky level 1
c conformity level 0
c encoding type 2
c no.of var 161
c no.of clauses 362
c no.of taut cls 0
c
c Output Parameters:
c remaining no.of clauses 362
c
c QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query30_query07_1344n.qdimacs 161 362 E1 [] 0 69 92 362 NONE
|
a9d62e1275c250b246937c87e1b6497ea183cc85 | 1bff670706a93f577eff6af8ed52dd3275e1534d | /scripts/R/QC/config_db.R | 5100d5a175d62668325c83393ecea4e8ce2b4ca2 | [] | no_license | aodn/aatams | f426d308c553bf5098cbf566f04ba13a9aa3a515 | f21b94497935130f8006e6ef33bce718d197dca0 | refs/heads/master | 2021-05-16T18:24:16.238708 | 2021-05-12T01:18:58 | 2021-05-12T01:18:58 | 10,501,618 | 7 | 3 | null | 2021-04-28T01:22:42 | 2013-06-05T12:04:53 | Groovy | UTF-8 | R | false | false | 234 | r | config_db.R | #file:config_db.R
server_address <- 'server_url'; # e.g. '183.24.245.102'
db_name <- 'db_name'; # e.g. 'aatams'
db_port <- 'db_port'; # e.g. '5432'
db_user <- 'db_username'; # e.g. 'username'
db_password <- 'db_pwd'; # e.g. 'password' |
464437012c3fe3216bc66babc54c20c55231af81 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Amendola-Ricca-Truszczynski/selection-hard/ctrl.e#1.a#3.E#124.A#48.c#.w#5.s#53.asp/ctrl.e#1.a#3.E#124.A#48.c#.w#5.s#53.asp.R | c95ddac8d1f5f19eaa1e6d88b9da0d637085e816 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 91 | r | ctrl.e#1.a#3.E#124.A#48.c#.w#5.s#53.asp.R | 786622ce009c8d70f9104dcd2c86838e ctrl.e#1.a#3.E#124.A#48.c#.w#5.s#53.asp.qdimacs 5131 14878 |
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