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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a4d29cb75b296f97e62ab8cf4d39c31226d4a79e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/multicolor/examples/multi_colour.Rd.R | 15072e0e60c4b21f83e76a91834cf863effdc0ed | [] | 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 | 1,011 | r | multi_colour.Rd.R | library(multicolor)
### Name: multi_colour
### Title: Multi-colour text
### Aliases: multi_colour
### ** Examples
## Not run:
##D multi_colour()
##D
##D multi_colour("ahoy")
##D
##D multi_colour("taste the rainbow",
##D c("mediumpurple",
##D "rainbow",
##D "cyan3"))
##D
##D multi_colour(colours = c(rgb(0.1, 0.2, 0.5),
##D "yellow",
##D rgb(0.2, 0.9, 0.1)))
##D
##D multi_colour(
##D cowsay::animals[["buffalo"]],
##D c("mediumorchid4", "dodgerblue1", "lemonchiffon1"))
##D
##D multi_colour(cowsay:::rms, sample(colours(), 10))
##D
##D # Mystery Bulgarian animal
##D multi_colour(things[[sample(length(things), 1)]],
##D c("white", "darkgreen", "darkred"),
##D direction = "horizontal")
##D
##D # Mystery Italian animal
##D multi_colour(things[[sample(length(things), 1)]],
##D c("darkgreen", "white", "darkred"),
##D direction = "vertical")
## End(Not run)
|
0893a9586814fa5fce035512906081e7321900f5 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/bpcs/man/create_index_with_existing_lookup_table.Rd | 6422df0cd3b5a9ad282d6b58bd6b041f2048fe19 | [
"MIT"
] | permissive | 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 | 970 | rd | create_index_with_existing_lookup_table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bpc_helpers_indexes.R
\name{create_index_with_existing_lookup_table}
\alias{create_index_with_existing_lookup_table}
\title{Create two columns with the indexes for the names
Here we use an existing lookup table. Should be used in predicting}
\usage{
create_index_with_existing_lookup_table(d, player0, player1, lookup_table)
}
\arguments{
\item{d}{A data frame containing the observations. The other parameters specify the name of the columns}
\item{player0}{The name of the column of data data contains player0}
\item{player1}{The name of the column of data data contains player0}
\item{lookup_table}{lookup_table a lookup table data frame}
}
\value{
A dataframe with the additional columns 'player0_index' and 'player1_index' that contains the indexes
}
\description{
Create two columns with the indexes for the names
Here we use an existing lookup table. Should be used in predicting
}
|
80941cf567e7071022a56143e68cae23238ec563 | 767e36f775543750ced47685a5f1df035c12f1c2 | /R/save_as_csv.R | af859e15679217faebee813d662a7a522c4dabca | [] | no_license | clemonster/nameplay | 041c590eb69cc7bbf42b0fce9e06e7f2100a5e85 | e1bd98e75fd8cea82449a1fbb4a4a0ff4706faed | refs/heads/master | 2021-07-12T07:10:48.671766 | 2017-10-16T23:37:52 | 2017-10-16T23:37:52 | 106,831,358 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 889 | r | save_as_csv.R | #' saves a dataframe as csv
#'
#' @param df the data frame we want to save
#' @param path path to the csv file to be created
#' @param row.names row names
#' @param ... all other parameters for write.csv
#'
#' @return nothing
#' @export
#' @importFrom utils write.csv2
#' @importFrom assertthat is.dir
#' @importFrom assertthat is.writeable
#' @importFrom assertthat has_extension
#' @importFrom assertthat not_empty
#'
#'
#' @examples
#' \dontrun{
#' my_df <- as.data.frame(c(1,24,3))
#' save_as_csv(my_df, "..\\..\\somefolder\\save_as_csv_test2.csv")
#' }
#'
save_as_csv <- function(df,path, row.names = FALSE, ...){
is.data.frame(df)
assertthat::is.dir(dirname(path))
assertthat::is.writeable(dirname(path))
assertthat::has_extension(path, "csv")
assertthat::not_empty(df)
write.csv2(x = df, file = path, row.names = row.names, ...)
invisible(normalizePath(path))
}
|
663a94ad0ced6a76da5b375dfdd7dd4c4a80b147 | aaaabb97ca1c7b08a147cd32c3886dae0e805d30 | /Src/Apriori functions.R | dd2279f0b2da9ad2cae10e315fde8342cfdda035 | [
"MIT"
] | permissive | ZyuAFD/DTW_Apriori | 0c69aa4248f80056902cfa2dcee7fb0bb02663c4 | 80412d649eb882b23120bc58bf6d2c9ccbe52ff9 | refs/heads/master | 2021-01-21T22:14:44.460309 | 2018-02-23T18:01:55 | 2018-02-23T18:01:55 | 102,137,999 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,216 | r | Apriori functions.R |
MatchEvtPtn=function(Event_info,AddPC=TRUE)
{
library(arules)
library(stringr)
# min confidence and length need to be specified based on model
minConf=0
minLength=7+AddPC
Event_info %>%
mutate(PC_diff=ifelse(rep(AddPC,nrow(.)),PC_diff,"")) %>%
# These columns has to be ordered alphabetically
mutate(Items=paste(AvgT_diff,DryPd_hr_diff,Dur_hr_diff,EvtP_diff,Jday_diff,PC_diff,
RainPd_hr_diff,
#SoilM_SD_diff,SoilM_Avg_diff,
Dist,sep=','),
Patn=paste(AvgT_diff,DryPd_hr_diff,Dur_hr_diff,EvtP_diff,Jday_diff,PC_diff,
RainPd_hr_diff
#SoilM_SD_diff,SoilM_Avg_diff,
)) %>%
select(Evt_n,Ref_Evt,Items,Patn,Dist) %>%
mutate(Items=gsub(",,",",",Items),
Patn=gsub(" "," ",Patn)) ->Soil_Seq_ts
Event_info %>%
# These columns has to be ordered alphabetically
unite(Items,sort(noquote(colnames(Event_info[,-c('Evt_n','Ref_Evt')]))),sep=',',remove=F) %>%
unite(Patn,sort(noquote(colnames(Event_info[,-c('Evt_n','Ref_Evt','Dist','Items')]))),sep=',')
Soil_Pt=as(lapply(Soil_Seq_ts %>%
select(Items) %>%
unlist %>%
as.list,
function(x) strsplit(x,',') %>% unlist),'transactions')
Pt_feq = apriori(Soil_Pt, parameter=list(support=1/nrow(Soil_Seq_ts), confidence=minConf,target='rules',minlen=minLength),
appearance = list(rhs = c("Dist=TRUE","Dist=FALSE","Dist=Ambiguous"),
default="lhs"))
Pt_freq_df=data.frame(
lhs=sapply(1:Pt_feq@lhs@data@Dim[2],function(x) paste(Pt_feq@lhs@itemInfo$labels[Pt_feq@lhs@data[,x]],collapse = ' ')),
rhs=sapply(1:Pt_feq@rhs@data@Dim[2],function(x) paste(Pt_feq@rhs@itemInfo$labels[Pt_feq@rhs@data[,x]],collapse = ' '))) %>%
cbind(Pt_feq@quality) %>%
mutate(lhs=as.character(lhs),rhs=as.character(rhs))
list(Pt_freq_df,Soil_Seq_ts)%>%
return
}
|
70f71d836dae2c966d0f1082db3e42b92499302e | c255c8e7ed8057413fece1823ffe78ed2b88ba35 | /GettingData.W4.Q4.R | 61840bf63659110eefee6ae314300a4935ee2e14 | [] | no_license | soroosj/Getting-Data | a93b569c834010d8fc687cff8bfb214598300f47 | debfb4bbddf14fda169bb7d82b7cf3d5075e29f6 | refs/heads/master | 2021-05-09T18:52:03.764841 | 2018-03-04T20:23:28 | 2018-03-04T20:23:28 | 119,175,877 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,005 | r | GettingData.W4.Q4.R | #1. load package(s) into R
library(tidyverse)
library(stringr)
#2a. download file(s) to local directory
gdp_url<-"https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FGDP.csv"
download.file(gdp_url,"gdp.csv")
education_url<-"https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2FEDSTATS_Country.csv"
download.file(education_url,"education.csv")
#2b.load file(s) to R
gdp<-read.csv("gdp.csv", skip = 5, header = FALSE, na.strings = c("","NA"), nrows=190, stringsAsFactors = F)
education<-read.csv("education.csv", stringsAsFactors = F)
#3. simplify to required rows, columns
gdp_short <- select (gdp, V1, V2, V4, V5)
education_short <- select (education, CountryCode, Special.Notes)
#4. join tables
combine <- inner_join(gdp_short, education_short, by = c("V1" = "CountryCode"))
#5a. identify countries with June fiscal year end
fiscal <- str_detect(combine$Special.Notes,"Fiscal year end: June 30")
#5b. count countries with June fiscal year end
sum(fiscal) |
0c6bfa9805ab802dbaa407bb666bda512f6dfe95 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rstan/examples/stanfit-method-plot.Rd.R | a7c37416bf9d79761e6df8c5c3fea24b06ce0ad8 | [] | 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 | 572 | r | stanfit-method-plot.Rd.R | library(rstan)
### Name: plot-methods
### Title: Plots for stanfit objects
### Aliases: plot,stanfit-method plot,stanfit,missing-method
### Keywords: methods
### ** Examples
## Not run:
##D library(rstan)
##D fit <- stan_demo("eight_schools")
##D plot(fit)
##D plot(fit, show_density = TRUE, ci_level = 0.5, fill_color = "purple")
##D plot(fit, plotfun = "hist", pars = "theta", include = FALSE)
##D plot(fit, plotfun = "trace", pars = c("mu", "tau"), inc_warmup = TRUE)
##D plot(fit, plotfun = "rhat") + ggtitle("Example of adding title to plot")
## End(Not run)
|
b05a4ea803be156b535a4acd73d2fc741534279c | 502d0505e01e1c1385571cf5fb935630614896de | /man/plotLegend.Rd | 2198d89ab84d943200e54389e2fb1de586e94c04 | [] | no_license | b2slab/FELLA | 43308e802b02b8f566ac26c972dc51e72a340c0e | 53276c4dfcb8cb4b5a688b167ba574d0f85228a6 | refs/heads/master | 2021-03-27T20:53:29.212810 | 2020-10-27T15:33:01 | 2020-10-27T15:33:01 | 24,852,722 | 14 | 4 | null | null | null | null | UTF-8 | R | false | true | 751 | rd | plotLegend.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotLegend.R
\name{plotLegend}
\alias{plotLegend}
\title{Internal function to add a legend to a graph plot}
\usage{
plotLegend(GO.annot = FALSE, cex = 0.75)
}
\arguments{
\item{GO.annot}{Logical, should GO annotations be included?}
\item{cex}{Numeric value, \code{cex} parameter for the function
\code{\link[graphics]{legend}}}
}
\value{
This function is only used for its effect,
so it returns \code{invisible()}
}
\description{
This function adds a legend to a solution plot.
It can include the CC similarity.
}
\examples{
## This function is internal
library(igraph)
g <- barabasi.game(20)
plot(g)
FELLA:::plotLegend()
plot(g)
FELLA:::plotLegend(GO.annot = TRUE)
}
|
5cffb4d345e96ecb081b01183df21343464e1159 | 00ea524f9956e17f045d83e5a78c810042f4c867 | /CAP/man/drugTable1.Rd | b4eb625f76d6ea102b275e9da1fd7951683706b8 | [] | no_license | ABMI/PneumoniaTxPath | 2d81cd4ea6303d72c2f10da5bd3113c475d33bc6 | 468235e560d1e91a2b27f146ef17aa4a9a4dd464 | refs/heads/master | 2023-03-28T04:58:01.534131 | 2021-03-23T02:43:37 | 2021-03-23T02:43:37 | 278,856,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 917 | rd | drugTable1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/drugPathway.R
\name{drugTable1}
\alias{drugTable1}
\title{get table1 about drug exposure}
\usage{
drugTable1(
drugExposureData = drugExposureData,
cohortDatabaseSchema = cohortDatabaseSchema,
cohortTable = cohortTable,
cohortId = cohortId,
conceptSets = conceptSets
)
}
\arguments{
\item{cohortDatabaseSchema}{Schema name where intermediate data can be stored. You will need to have
write priviliges in this schema. Note that for SQL Server, this should
include both the database and schema name, for example 'cdm_data.dbo'.}
\item{cohortTable}{The name of the table that will be created in the work database schema.
This table will hold the exposure and outcome cohorts used in this
study.}
\item{conceptSets}{}
}
\description{
get table1 about drug exposure
}
\details{
Extract concept_id from the json file of concept set
}
|
3538df3940d3e106ac6063333931cd26870a38e5 | b040e78795d547af081f046040d819f1f9f3d6b0 | /script/fun2_transcriptoemIndex.R | 3efa1927d49f0aea0675d52adc6fd89137b9e39e | [] | no_license | ljljolinq1010/developmental_constraints_genome_evolution | ee91616dfc1229104cfa30798399c41f45b200aa | 8b51bb1fbbb75f52b34a47139d8a90bb0f999977 | refs/heads/master | 2021-09-15T02:54:19.033229 | 2018-05-24T13:45:52 | 2018-05-24T13:45:52 | 110,563,844 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,806 | r | fun2_transcriptoemIndex.R | ##### index function #####
index<-function(transcriptome,interestData,interestParameter,orgName,ylim1,ylim2,title) {
## merge two datasets
## paralogs number file only contain genes with at least one paralog, we need add genes without paralog
if (interestParameter=="Paralogs.Number") {
transData<- merge(transcriptome, interestData,all.x=TRUE, by="Ensembl.Gene.ID")
transData$Paralogs.Number <- ifelse(is.na(transData$Paralogs.Number), 0, transData$Paralogs.Number)
}
if (interestParameter!="Paralogs.Number") {
transData<- merge(transcriptome, interestData, by="Ensembl.Gene.ID")
}
cat("\nOverall ",nrow(transData)," genes were used for ", orgName," ", interestParameter," analysis.",sep="")
## choose organism
if (orgName=="D.melanogaster") {
timePoint<-c(2:92)
## totally 91 stages
devTime<-c(paste0(seq(-15,1335,by=15)+150,"m"))
devTime[seq(2,91,by=3)]<-""
devTime[seq(3,91,by=3)]<-""
devTimeColor<-c(rep(myPalette[9],20),rep(myPalette[10],20),rep(myPalette[12],51))
## we separate 91 stages into 3 metastages: early,middle,late
modules = list(early = 1:20, mid = 21:40, late =41:91)
}
if (orgName=="C.elegans") {
timePoint<-c(2:87)
## totally 86 stages
devTime<-c(paste0((c(-50,-30,seq(0,550,by=10),seq(570,840,by=10))+80),"m"))
devTime[seq(2,86,by=3)]<-""
devTime[seq(3,86,by=3)]<-""
devTimeColor<-c(rep("grey",2),rep(myPalette[9],23),rep(myPalette[10],12),rep(myPalette[12],49))
## we separate 86 stages into 4 metastages: maternal, early,middle,late (first two points are before MZT)
modules = list(maternal = 1:2,early = 3:25, mid = 26:37, late =38:86)
}
if (orgName=="D.rerio") {
timePoint<-c(2:107)
## totally 106 stages
devTime<-c(paste0(seq(40,4240,by=40),"m"))
devTime[seq(2,106,by=4)]<-""
devTime[seq(3,106,by=4)]<-""
devTime[seq(4,106,by=4)]<-""
devTimeColor<-c(rep("grey",3),rep(myPalette[9],13),rep(myPalette[10],55),rep(myPalette[12],35))
## we separate 106 stages into 4 metastages: maternal, early,middle,late (first three points are before MZT)
modules = list(maternal = 1:3,early = 4:16, mid = 17:71, late =72:106)
}
if (orgName=="M.musculus") {
timePoint<-c(2:18)
devTime<-c("0.5d","1.5d","2d","3.5","7.5d","8.5d","9d","9.5d","10.5d","11.5d","12.5d","13.5d","14.5d","15.5d","16.5d","17.5d","18.5d")
devTimeColor<-c("grey",rep(myPalette[9],4),rep(myPalette[10],6),rep(myPalette[12],6))
modules = list(maternal = 1, early = 2:5, mid = 6:11, late =12:17 )
}
## plot parameters
if (interestParameter=="omega0" || interestParameter=="omega" ) {
yName<-"TDI"
lineColor<-"blue"
} else if (interestParameter=="Paralogs.Number") {
yName<-"TPI"
lineColor<-"deeppink"
} else if (interestParameter=="Rank") {
yName<-"TAI"
lineColor<-"darkorchid"
}
transIndex<-c()
transIndex<-apply(transData[timePoint], 2, function(x) sum(x*(transData[,interestParameter]))/sum(x))
plot(transIndex)
## bootstrap analysis
cat("\nbootstrap analysis...")
bootGeneID<-replicate(1000,sample(transData$Ensembl.Gene.ID,replace=T))
transIndexBoot<-c()
transIndexBoot1<-c()
for (i in 1:1000) {
tempID<-data.frame(bootGeneID[,i])
names(tempID)<-"Ensembl.Gene.ID"
tempTransData<-merge(tempID,transData,by="Ensembl.Gene.ID")
transIndexBoot1<-apply(tempTransData[timePoint], 2, function(x) sum(x*(tempTransData[,interestParameter]))/sum(x))
transIndexBoot<-rbind(transIndexBoot,transIndexBoot1)
}
## calculate mean index of each module, and compare the mean with wilcox test.
meanIndex<-c()
meanIndex1<-c()
for (i in 1:1000) {
meanIndex1 <- lapply( modules,function(x) mean(transIndexBoot[i,][x]) )
meanIndex <- rbind(meanIndex,meanIndex1)
}
if (length(modules)==3) {
boxplotData<-data.frame(unlist(meanIndex[,1]),unlist(meanIndex[,2]),unlist(meanIndex[,3]))
boxplotName<-c("Early", "Middle","Late")
wt<-wilcox.test(boxplotData[[1]],boxplotData[[2]],alternative = "greater")
}
else {
boxplotData<-data.frame(unlist(meanIndex[,1]),unlist(meanIndex[,2]),unlist(meanIndex[,3]),unlist(meanIndex[,4]))
boxplotName<-c("Maternal","Early", "Middle","Late")
wt<-wilcox.test(boxplotData[[2]],boxplotData[[3]],alternative = "greater")
}
pdf(paste0("result/transcriptomeIndex/logTrans/",title),w=7,h=6)
par(mar=c(7,7,2,2),mgp = c(5, 1, 0))
boxplot(boxplotData,las=2,pch=16,outcex=0.5,boxwex=0.7,notch = T, xaxt = "n",main=orgName,cex.lab=1.5,cex.main=1.5,cex.axis=1.5,
col=lineColor,ylab=bquote(.(yName) ~ (log[2])))
legend("topleft",paste0("p=", signif(wt$p.value,2)),col=1, bty="n",cex=1.5)
mtext(side=1,text = boxplotName,at = c(1:length(modules)),cex=1.5,line = 1.1,col=unique(devTimeColor))
dev.off()
## permutation test
cat("\npermutation test...")
permuParameter<-replicate(1000,sample(transData[,interestParameter],replace=F))
transIndexPermu<-c()
transIndexPermu1<-c()
for (i in 1:1000) {
transIndexPermu1<-apply(transData[timePoint], 2, function(x) sum(x*(permuParameter[,i]))/sum(x))
transIndexPermu<-rbind(transIndexPermu,transIndexPermu1)
}
## calculate mean index
meanIndex<-c()
meanIndex1<-c()
for (i in 1:1000) {
meanIndex1 <- lapply( modules,function(x) mean(transIndexPermu[i,][x]) )
meanIndex <- rbind(meanIndex,meanIndex1)
}
## calculate difference between mean index of early and mean index of middle
if (length(modules)==3) {
earlyMidDif<-(unlist(meanIndex[,1]))-(unlist(meanIndex[,2]))
} else {
earlyMidDif<-(unlist(meanIndex[,2]))-(unlist(meanIndex[,3]))
}
## approximate normal distribution of differences
meanEarlyMid<-mean(earlyMidDif)
varEarlyMid<-var(earlyMidDif)
sdEarlyMid<-sqrt(varEarlyMid)
## compute pValue under hypothesis of hourglass
if (length(modules)==3) {
observedDif<-mean(transIndex[c(unlist(modules[1]))])-mean(transIndex[c(unlist(modules[2]))])
} else {
observedDif<-mean(transIndex[c(unlist(modules[2]))])-mean(transIndex[c(unlist(modules[3]))])
}
pValue<-pnorm(observedDif,meanEarlyMid,sdEarlyMid,lower.tail = F) ## compute the probability of X>earlyMid
## plot
cat("\nplot...")
plotData <- data.frame(transIndex,timePoint)
pdf(paste0("result/transcriptomeIndex/logTrans/",title),w=7,h=6)
par(mfrow=c(1,1))
par(mar=c(7,5,2,2))
print(
ggplot(plotData, aes(x = factor(timePoint, levels = unique(timePoint)),y = transIndex,group = 1))+
geom_ribbon(aes(ymin = apply(transIndexBoot, 2,function(x) quantile(x, probs =0.025)), ## 95% confidence interval
ymax = apply(transIndexBoot, 2,function(x) quantile(x, probs =0.975))), fill = "grey") +
geom_line(lwd = 3,col=lineColor) +ylim(ylim1,ylim2)+
annotate("text", label=paste0("p=",signif(pValue,2)),x=length(plotData$timePoint)/5,y=ylim2,size=7)+
annotate("text", label=paste0("n=",nrow(transData)),x=(length(plotData$timePoint)/5)*4,y=ylim2,size=7)+
ggtitle(orgName) +xlab("Time")+
ylab(bquote(.(yName) ~ (log[2])))+
scale_x_discrete(breaks=plotData$timePoint,labels=devTime)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(),axis.line = element_line(colour = "black"),
plot.title = element_text(color="black", size=20, face="bold",hjust = 0.5),
axis.title.x = element_text(color="black", size=20, face="bold"),
axis.title.y = element_text(color="black", size=20, face="bold"),
axis.text.y = element_text(size=18,color="black"),
axis.text.x = element_text(color=devTimeColor,size=18, angle=270))
)
dev.off()
}
|
9c3ab86c47309cd070df662be59274d485214a59 | 4255a7c5a467c6ade48b0a5699089fe1b2082b5e | /man/check_identical_header.Rd | 352b2fa0b0ee06e6187cfe80fbed453738e1589e | [
"MIT"
] | permissive | jianhong/HMMtBroadPeak | 02978e06fcdf3c14dc4b2a4645018593b8c370b8 | 7a2932fb943e66d9154282b690f8e762967dc539 | refs/heads/main | 2023-06-07T15:15:24.245428 | 2021-06-23T20:35:10 | 2021-06-23T20:35:10 | 353,410,515 | 2 | 2 | null | null | null | null | UTF-8 | R | false | true | 392 | rd | check_identical_header.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/HMMtBroadPeak.R
\name{check_identical_header}
\alias{check_identical_header}
\title{helper function to check the bam file header}
\usage{
check_identical_header(bams)
}
\arguments{
\item{bams}{Bam file of treatments and controls}
}
\value{
seqlengths
}
\description{
helper function to check the bam file header
}
|
2bdd1dcfbb7849392ae7c299179ad50fd370aea5 | e325da8106789cffb6950b1492eb8d1ab6fb41f0 | /R/try-patient-sharing-hhi.R | 8a825b715ee004f1750d2c85398d983507756524 | [
"MIT"
] | permissive | graveja0/health-care-markets | 1aec79caad5155d6586f770cf06446c9d3efe256 | da89d1375f287c2a393b2fbe93411ba6b4c079ae | refs/heads/master | 2023-06-09T17:26:46.548666 | 2023-05-28T17:16:01 | 2023-05-28T17:16:01 | 166,048,364 | 37 | 11 | null | null | null | null | UTF-8 | R | false | false | 6,646 | r | try-patient-sharing-hhi.R | #' ---
#' output: github_document
#' ---
# The objective of this file is to construct physician-level HHI measures.
suppressWarnings(suppressMessages(source(here::here("/R/manifest.R"))))
source(here("R/map-theme.R"))
source(here("R/shared-objects.R"))
source(here("R/get-market-from-x-y-coordinates.R"))
source(here("R/estimate_hhi.R"))
source(here("../../../box/Research-Provider_Networks/networks/R/construct-network-object.R"))
pcsa_info <- foreign::read.dbf(here("public-data/shape-files/dartmouth-hrr-hsa-pcsa/ct_pcsav31.dbf")) %>%
janitor::clean_names() %>%
select(pcsa,pcsa_st, pcsa_l) %>%
mutate_at(vars(pcsa,pcsa_st,pcsa_l),funs(paste0)) %>%
unique()
# ZCTA to PCSA Crosswalk (merges on PCSA state from above)
zcta_to_pcsa <- foreign::read.dbf(here("public-data/shape-files/dartmouth-hrr-hsa-pcsa/zip5_pcsav31.dbf")) %>%
janitor::clean_names() %>%
mutate(pcsa = paste0(pcsa)) %>%
left_join(pcsa_info,"pcsa") %>%
rename(zip_code=zip5)
df_hosp_npi <- read_rds(here("../../../box/Research-Provider_Networks/data/penn-xw-npi/general-acute-care-hospital-npi-crosswalk.rds"))
df_aha <- read_rds(here("output/market-comparisons/01_aha-markets-2017.rds")) %>% # Constructed in R/construct-hosptial-hhi.R
rename(aha_id=id) %>%
select(aha_id,prvnumgrp,sysname,mname,everything()) %>%
left_join(df_hosp_npi,c("aha_id","prvnumgrp")) %>%
filter(!is.na(npi)) %>%
mutate(genacute = 1) %>%
rename(zip_code = hosp_zip_code) %>%
select(npi,genacute,zip_code ) %>%
mutate(npi = paste0(npi)) %>%
tbl_df() #%>%
#mutate(fips_code = paste0(fips_code,width=5,pad="0"))
# Provider Denominator File from SK&A
df_ska <- read_rds(here("output/ska/ska-2017.rds")) %>%
#mutate(primcare = cardiology) %>%
filter(primcare ==1 ) %>%
mutate(npi = paste0(npi)) %>%
select(npi,primcare,zip_code)
df <-
df_ska %>%
bind_rows(df_aha) %>%
mutate(primcare = ifelse(is.na(primcare),0,primcare)) %>%
mutate(genacute = ifelse(is.na(genacute),0,genacute)) %>%
filter(npi!="NA" & !is.na(npi)) %>%
left_join(zcta_to_pcsa,"zip_code")
ff <- quo(genacute) #enquo(source)
tt <- quo(primcare) #enquo(target)
kk <- sym("pcsa") #syms(keep)
cc <- quo(referral_count) #enquo(referral_count)
gg <- quo(pcsa) #enquo(geog)
window = "60"
tmp <-
df %>%
select(npi, !!ff, !!tt,!!!kk) %>%
filter(!!ff == 1 | !!tt == 1) %>%
group_by(npi) %>%
filter(!is.na(npi) & npi != "NA") %>%
ungroup() %>%
group_by(npi) %>%
filter(row_number()==1) %>%
ungroup() %>%
mutate(npi = paste0(npi))
# Docgraph Data
VV <- readRDS(here("../../../box/Research-Provider_Networks/data/careset/node-list-2015.rds")) %>%
mutate(npi = paste0(npi)) %>%
rename(id = node_id) %>%
inner_join(tmp,"npi") %>% data.frame() %>%
mutate(id = as.numeric(paste0(id)))
from_ids <- VV %>% filter(!!ff == 1) %>% pull(id) %>% unique()
to_ids <- VV %>% filter(!!tt == 1) %>% pull(id) %>% unique()
EE <- readRDS(here("../../../box/Research-Provider_Networks/data/careset/edge-list-2015.rds")) %>%
filter(from %in% from_ids & to %in% to_ids) %>% data.frame() %>%
mutate(from = as.numeric(paste0(from)),
to = as.numeric(paste0(to)))
referrals_by_pcsa <-
EE %>%
left_join(VV %>% select(from = id, npi = npi),"from") %>%
left_join(VV %>% select(to = id, to_npi = npi, pcsa=pcsa),"to") %>%
left_join(df_hosp_npi %>% mutate(npi = paste0(npi)),"npi") %>%
group_by(aha_id,prvnumgrp,pcsa) %>%
summarise(primary_care_referrals = sum(pair_count,na.rm=TRUE))
total_referrals <-
referrals_by_pcsa %>%
group_by(aha_id,prvnumgrp) %>%
summarise(total_primary_care_referrals = sum(primary_care_referrals,na.rm=TRUE))
df_aha_market <- read_rds(here("output/market-comparisons/01_aha-markets-2017.rds")) %>% # Constructed in R/construct-hosptial-hhi.R
rename(aha_id=id) %>%
select(aha_id,prvnumgrp,sysname,mname,everything()) %>%
inner_join(total_referrals,c("aha_id","prvnumgrp"))
df_aha_market %>%
ggplot(aes(x = rank(admtot),y=rank(total_primary_care_referrals))) + geom_point(alpha=0.1) +
geom_smooth(se=FALSE, colour="black") +
theme_tufte() +
geom_abline(slope=1,intercept=0,lty=2)
with(df_aha_market,lm(rank(admtot)~rank(total_primary_care_referrals)))
hhi_genacute_pcsa_referral <-
df_aha_market %>%
inner_join(referrals_by_pcsa,c("aha_id","prvnumgrp")) %>%
ungroup() %>%
select(system_id,total_primary_care_referrals,pcsa) %>%
estimate_hhi(id = system_id,
weight = total_primary_care_referrals,
market= pcsa) %>%
rename(hhi_referral = hhi,
total_weight = total_weight)
pcsa_to_county <- read_csv(here("public-data/zcta-to-fips-county/zcta-to-fips-county.csv")) %>%
janitor::clean_names() %>%
filter(row_number() !=1) %>%
mutate(fips_code = county) %>%
select(zip_code = zcta5, fips_code,afact) %>%
mutate(afact = as.numeric(paste0(afact))) %>%
left_join(zcta_to_pcsa,"zip_code") %>%
filter(afact==1) %>%
group_by(pcsa,fips_code) %>%
filter(row_number()==1) %>%
select(pcsa,fips_code) %>%
unique()
pcsa_hhi_aggregated_to_county <-
hhi_genacute_pcsa_referral %>%
inner_join(pcsa_to_county,"pcsa") %>%
mutate(weight = total_weight) %>%
group_by(fips_code) %>%
summarise(hhi_referral = weighted.mean(hhi_referral,weight,na.rm=TRUE))
hhi_foo <-
read_rds(here("output/market-comparisons/01_market-comparisons-county.rds")) %>%
bind_rows() %>%
left_join(pcsa_hhi_aggregated_to_county, "fips_code")
hhi_foo %>%
select(fips_code,hhi_referral,hhi_zip) %>%
ggplot(aes(x=hhi_referral,y=hhi_zip)) +
geom_point(alpha=0.1) +
geom_smooth(se=FALSE)
with(hhi_foo,lm(hhi_referral~hhi_zip))
ms_by_zip <- read_rds(here("output/market-comparisons/01_2017_ZIP-market-shares.rds")) %>%
filter(zip_code=="37203") %>%
mutate(name = ifelse(sysname==""|is.na(sysname),mname,sysname)) %>%
select(zip_code,name,total_cases,market_share,hhi_zip=hhi) %>%
group_by(name) %>%
summarise_at(vars(total_cases),function(x) sum(x,na.rm=TRUE)) %>%
ungroup() %>%
mutate_at(vars(total_cases),function(x) x /sum(x))
referrals_by_zip_code %>% filter(zip_code==37203) %>% arrange(desc(primary_care_referrals)) %>%
left_join(df_aha_market %>% select(aha_id,prvnumgrp,sysname,mname),c("aha_id","prvnumgrp")) %>%
mutate(name = ifelse(sysname=="",mname,sysname)) %>%
group_by(name) %>%
summarise_at(vars(primary_care_referrals),function(x) sum(x,na.rm=TRUE)) %>%
ungroup() %>%
mutate_at(vars(primary_care_referrals),function(x) x /sum(x)) %>%
left_join(ms_by_zip,c("name")) %>%
arrange(desc(primary_care_referrals))
|
f14f0baa747f30e80715cf32f998915aceeb3732 | 110965c3d64b535adfe23c0e409b304767f6941b | /run_analysis.R | d06b52cfada31fb41c49bbf705174a681943af08 | [
"CC0-1.0"
] | permissive | etphonehome2/Getting_and_Cleaning_Data | 4c7dfd50f1534aaa60cb7bfd4c927910e9b6664d | e64af1bff80e3a033a93fcfaff155f3f79df2cac | refs/heads/master | 2016-09-11T13:46:17.703551 | 2015-06-21T03:32:17 | 2015-06-21T03:32:17 | 37,789,445 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,018 | r | run_analysis.R |
# ----------------------------------------------------------------------------------------------------
#
# run_analysis.R
#
# ----------------------------------------------------------------------------------------------------
setwd("I:\\MyData\\Training\\Getting and Cleaning Data\\CourseProject")
getwd()
require("data.table")
require("reshape2")
fileUrl <- "http://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileUrl, destfile = "projectfile.zip", method = "internal")
unzip("projectfile.zip")
list.files(path=".")
# features.txt is the column names for the x-Files
features <- read.table("./UCI HAR Dataset/features.txt",header=FALSE,colClasses="character")
mn <- make.names(features$V2) # make syntactically valid names
# read all files, and assign column names where possible
testData <- read.table("./UCI HAR Dataset/test/X_test.txt",header=FALSE,col.names=mn)
testData_activity <- read.table("./UCI HAR Dataset/test/y_test.txt",header=FALSE,col.names=c("Activity_Id"))
testData_subject <- read.table("./UCI HAR Dataset/test/subject_test.txt",header=FALSE,col.names=c("Subject_Id"))
trainData <- read.table("./UCI HAR Dataset/train/X_train.txt",header=FALSE,col.names=mn)
trainData_activity <- read.table("./UCI HAR Dataset/train/y_train.txt",header=FALSE,col.names=c("Activity_Id"))
trainData_subject <- read.table("./UCI HAR Dataset/train/subject_train.txt",header=FALSE,col.names=c("Subject_Id"))
# yes descriptive activity names to name the activities in the data set
activities <- read.table("./UCI HAR Dataset/activity_labels.txt",header=FALSE)[,2]
testData_activity[,2] <- activities[testData_activity[,1]]
colnames(testData_activity)[2] <- "Activity_Name"
trainData_activity[,2] <- activities[trainData_activity[,1]]
colnames(trainData_activity)[2] <- "Activity_Name"
# merge the training and the test sets to create one data set
testData <-cbind(testData_activity,testData)
testData <-cbind(testData_subject,testData)
trainData <-cbind(trainData_activity,trainData)
trainData <-cbind(trainData_subject,trainData)
oneDataset <-rbind(testData,trainData)
# extracts only the measurements on the mean and standard deviation for each measurement
# find all variable names containg the word "mean" and "std"
data_mean_std <- oneDataset[, grepl("*.mean.*", colnames(oneDataset)) | grepl("*.std.*", colnames(oneDataset))]
data_subj_act <-subset(oneDataset, select=c(Subject_Id, Activity_Id, Activity_Name))
data <- cbind(data_subj_act, data_mean_std)
# reshape the data for processing
id_variables <- c("Subject_Id", "Activity_Id", "Activity_Name")
measure_variables <- setdiff(colnames(data), id_variables)
melt_data <- melt(data, id = id_variables, measure.vars = data_labels)
# aply mean function to dataset using dcast function
tidy_data <- dcast(melt_data, Subject_Id + Activity_Name ~ variable, mean)
write.table(tidy_data, file = "./tidy_data.txt", row.name=FALSE)
|
6afad496bec19dd9bb54e105cb3fdabdf146c9c2 | c9df90ca1c6f04662ab56722d6e5fcfae9586e90 | /R/GetCmat.R | 3736317df61b479f11316ecadac7cb4743bb0bd1 | [] | no_license | HerveAbdi/DistatisR | 5b899bcf565c988cbadb636c0f4520b89f6e0c94 | 96c24d8a03d9f10067a5c5c26042c77d25aa1cc0 | refs/heads/master | 2023-08-29T05:28:14.289849 | 2022-12-05T00:13:45 | 2022-12-05T00:13:45 | 124,935,297 | 6 | 1 | null | 2022-09-01T14:24:29 | 2018-03-12T18:44:45 | R | UTF-8 | R | false | false | 1,117 | r | GetCmat.R | #' @title GetCmat
#' @description Computes the RV coefficient matrix
#' @param CubeCP A 3D array of cross-product matrices
#' @param RV Boolean, if TRUE, GetCmat computes the matrix of the RV coefficients between all the slices of the 3D array, otherwise, GetCmat computes a scalar product.
#' @return A matrix of either RV coefficients or scalar products.
#' @examples
#' \donttest{
#' D3 <- array(c(0, 1, 2, 1, 0, 1, 2, 1, 0,
#' 0, 3, 3, 3, 0, 3, 3, 3, 0),
#' dim = c(3, 3, 2))
#' GetCmat(D3)
#' }
#' @rdname GetCmat
#' @export
GetCmat <- function(CubeCP, RV = TRUE) {
# Compute a C matrix (or RV) from a cube of CP
# get the dimensions
nI <- dim(CubeCP)[1]
nJ <- dim(CubeCP)[3]
# reshape the 3D array as an (nI^2)*(nJ) matrix
CP2 <- array(CubeCP, dim = c(nI * nI, nJ))
C <- t(CP2) %*% CP2 # Scalar Product
if (RV) {
# RV is TRUE we want the RV coefficient instead of the Scalar Product
laNorm <- sqrt(apply(CP2 ^ 2, 2, sum))
C <- C / (t(t(laNorm)) %*% laNorm)
} # end if
rownames(C) <- colnames(C) <- dimnames(CubeCP)[[3]]
return(C)
}
|
8d840be7d39ed3e2aa5c8437e65a0da64505955d | acc0b1c9d24d8784e5b7ec9009400c067b6ba353 | /dataTransformation/R/combineSources.R | 4bd25ef77aecdf1d560209ad81e600420ee8957c | [
"CC0-1.0"
] | permissive | ivozandhuis/dwarsliggers | 611c22699e10605d6bdb43015bb37f365aad0dc5 | 33c051b280ba92cdd9753b34050bf813db52a502 | refs/heads/master | 2023-02-05T02:39:33.181522 | 2023-02-02T08:15:25 | 2023-02-02T08:15:25 | 228,387,452 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,395 | r | combineSources.R | setwd("~/git/dwarsliggers/")
source("dataTransformation/R/transformMedewerkerslijst.R")
source("dataTransformation/R/transformBevolkingsregister.R")
source("dataTransformation/R/transformPersoneelsregister.R")
source("dataTransformation/R/transformBelastingkohier.R")
source("dataTransformation/R/transformRoutes.R")
# combine medewerkerslijst and bevolkingsregister first.
# NB. in this action 19 RPs are removed, because they were not found in de bevolkingsregister.
df <- merge(medewerkerslijst, bevolkingsregister, by = "my_medewerkersnummer")
df <- merge(df, personeelsregister, by = "my_medewerkersnummer", all.x = TRUE)
df <- merge(df, belastingkohier, by = "my_medewerkersnummer", all.x = TRUE)
df <- merge(df, routes, by = "my_medewerkersnummer", all.x = TRUE)
# create dummy "married"
df$gehuwd_dummy[df$huw_st == "H"] <- 1
df$gehuwd_dummy[df$huw_st == "O"] <- 0
df$gehuwd_dummy[df$huw_st == "S"] <- 0
df$gehuwd_dummy[df$huw_st == "W"] <- 0
# replace wrong df$aantal_dienstjaren with average
df$aantal_dienstjaren[(df$leeftijd - df$aantal_dienstjaren) < 12] <- NA
df$aantal_dienstjaren[df$leeftijd == 16] <- 0
aantal_dienstjaren_avg <- aggregate(df$aantal_dienstjaren, list(df$leeftijd), mean, na.rm=TRUE, na.action=NULL)
df <- merge(df, aantal_dienstjaren_avg, by.x = "leeftijd", by.y = "Group.1", all.x = TRUE)
df <- within(df,
aantal_dienstjaren <- ifelse(!is.na(aantal_dienstjaren), aantal_dienstjaren, round(x)))
# remove temp-stuf
rm(aantal_dienstjaren_avg)
df$x <- NULL
# add df$loon_intrapolatie for missing df$loon with averages
# cleanup: list of variables to select
loon_avg1 <- aggregate(loon~functie.x+werkplaats, df[df$loonsoort == "dagloon",], mean)
loon_avg2 <- aggregate(loon~HISCO, df[df$loonsoort == "dagloon",], mean)
names(loon_avg1) <- c("functie.x","werkplaats","loon_avg1")
names(loon_avg2) <- c("HISCO","loon_avg2")
df <- merge(df, loon_avg1, by = c("werkplaats","functie.x"), all.x = TRUE)
df <- merge(df, loon_avg2, by = "HISCO", all.x = TRUE)
# select the best value for loon_intrapolatie,
# first loon, else loon_avg1, else loon_avg2
df$loon[df$loonsoort == "maandloon"] <- NA
count <- df[!is.na(df$loon),]
count <- df[(is.na(df$loon) & !is.na(df$loon_avg1)),]
count <- df[(is.na(df$loon) & is.na(df$loon_avg1) & !is.na(df$loon_avg2)),]
count <- df[(is.na(df$loon) & is.na(df$loon_avg1) & is.na(df$loon_avg2)),]
df <- within(df,
loon_intrapolatie <- ifelse(!is.na(loon), loon, loon_avg1)
)
count <- df[(is.na(df$loon_intrapolatie) & !is.na(df$loon_avg2)),]
df <- within(df,
loon_intrapolatie <- ifelse(is.na(loon_intrapolatie), loon_avg2, loon_intrapolatie)
)
count <- df[is.na(df$loon_intrapolatie),]
df$loon_intrapolatie[is.na(df$loon_intrapolatie)] <- 1.84
rm(loon_avg1)
rm(loon_avg2)
rm(count)
df$loon_avg1 <- NULL
df$loon_avg2 <- NULL
# create dummy for inkomen
df$inkomen_dummy[is.na(df$inkomen)] <- 0
df$inkomen_dummy[!is.na(df$inkomen)] <- 1
# cleanup: list of variables to select
selection = c(
"my_medewerkersnummer",
"aantal_uur",
"achternaam.x",
"functie.x",
"gebdat",
"gebpla",
"amco",
"kleur",
"leeftijd",
"provincie",
"regio",
"regio2",
"geloof",
"gemengd_huw",
"type",
"werkplaats",
"afdeling",
"loon",
"loon_intrapolatie",
"loonsoort",
"inkomen",
"inkomen_dummy",
"aantal_dienstjaren",
"HISCO",
"HISCO_major",
"HISCO_minor",
"HISCO_unit",
"HISCLASS",
"SOCPO",
"huw_st",
"gehuwd_dummy",
"aantal_gezinsleden",
"aantal_kinderen",
"kinderen_dummy",
"gem_leeftijd_kinderen",
"verantwoordelijkheid",
"mijn_wijknaam",
"adres.x",
"afdeling_kohier",
"kiesdistrict_GR1899",
"woningbouwvereniging",
"len",
"t0",
"t1",
"t2",
"t3",
"t4",
"t5",
"aantal_wisselingen"
)
df <- subset(df, select = selection)
# create stakers, volhouders en ontslagen info, derived from df$kleur
# TRUE/FALSE
df$staker <- TRUE
df$staker[df$kleur == 'b'] <- FALSE
df$volhouder <- NA
df$volhouder[df$staker] <- TRUE
df$volhouder[df$kleur == 'w'] <- FALSE
df$ontslagen <- NA
df$ontslagen[df$volhouder] <- TRUE
df$ontslagen[df$kleur == 'g'] <- FALSE
# remove unused levels in all factors
df <- droplevels(df)
# write
write.table(df, "data/constructs/csv/df.csv", sep = ",", row.names=FALSE)
# remove temporary stuff
rm(belastingkohier)
rm(bevolkingsregister)
rm(medewerkerslijst)
rm(personeelsregister)
rm(routes)
|
6fff010c98ffc3ec588f52061a5b15b42e5acf49 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googlebloggerv3.auto/man/Page.author.Rd | 8ca016e1b47abcf0af3b1eb70e1a026f5971e3b1 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 914 | rd | Page.author.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/blogger_objects.R
\name{Page.author}
\alias{Page.author}
\title{Page.author Object}
\usage{
Page.author(Page.author.image = NULL, displayName = NULL, id = NULL,
image = NULL, url = NULL)
}
\arguments{
\item{Page.author.image}{The \link{Page.author.image} object or list of objects}
\item{displayName}{The display name}
\item{id}{The identifier of the Page creator}
\item{image}{The page author's avatar}
\item{url}{The URL of the Page creator's Profile page}
}
\value{
Page.author object
}
\description{
Page.author Object
}
\details{
Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}}
The author of this Page.
}
\seealso{
Other Page functions: \code{\link{Page.author.image}},
\code{\link{Page.blog}}, \code{\link{Page}},
\code{\link{pages.insert}}, \code{\link{pages.patch}},
\code{\link{pages.update}}
}
|
98964aa2a0f743d28fccbea70333c67d0e1ca87e | ffe99240aeea52977d8b08eb1c385d972b7818c8 | /plot1.R | 7cf283e9e2d9b7a7831718a49e07edbc35bee844 | [] | no_license | fridanellros/ExData_Plotting1 | 0e68e6dc9b7bdd30e0d5b2b32baf51f6bdc3b585 | a071dfeb12fbb9809ed572c17faabd0e7fe8e2e0 | refs/heads/master | 2021-01-14T09:45:43.257612 | 2015-04-11T18:43:04 | 2015-04-11T18:43:04 | 33,776,332 | 0 | 0 | null | 2015-04-11T13:17:25 | 2015-04-11T13:17:22 | null | UTF-8 | R | false | false | 688 | r | plot1.R | # LOAD
data <- read.table( "household_power_consumption.txt",
sep = ";",
header = TRUE,
colClasses = c(rep("character",2),rep("numeric",7)),
na.strings = "?")
# PROCESS
require(lubridate)
data$Date <- dmy( data$Date )
dates <- data$Date == ymd("20070201") |
data$Date == ymd("20070202")
# PLOT
png( file = "plot1.png",
width = 480,
height = 480)
hist( data$Global_active_power[ dates ],
breaks = 11,
main = "Global Active Power",
xlab = "Global Active Power (kilowatts)",
ylab = "Frequency",
col = "red",
ylim = c(0,1200))
dev.off() |
664658e371ad3997d9455ff3e13744f44f941b7c | 4f3acdccda11715665419446096b0eb0b35af8c3 | /scripts/Paepalanthus/tree_and_matrix-paep.R | 1c6a6d4f42d943bfc50641dce755ea42da8dec01 | [] | no_license | souzayagob/no_one-size-fits-all | 47e09fcbfafb77488b9b93c28278127b6182f9f9 | e675e7dfa87ba865810ef65b909f7502a0806995 | refs/heads/main | 2023-04-15T17:35:18.408256 | 2022-11-07T15:09:32 | 2022-11-07T15:09:32 | 493,011,704 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,920 | r | tree_and_matrix-paep.R | #This code is intended to prune and manipulate the phylogenetic tree and create a
#presence/absence matrix
#=====================================================================================================#
library(ape)
#=====================================================================================================#
#=======#
# INPUT #
#=======#
#Reading CR dataset
paep_cr <- read.csv("datasets/Paepalanthus/paep_cr.csv", na.strings = c("", NA))
#Reading tree
paep_tree <- read.nexus("trees/Paepalanthus/Paepalanthus_Bayes.nex")
#=====================================================================================================#
#====================#
# STANDARDIZING TREE #
#====================#
#Extracting tip labels
tip_labels <- data.frame("tip_labels" = paep_tree$tip.label,
"std_labels" = NA, stringsAsFactors = FALSE)
#Standardizing tip labels (maintaining varieties)
vars <- tip_labels[grep("var_", tip_labels$tip_labels), 1]
for(i in 1:nrow(tip_labels)){
if(!tip_labels$tip_labels[i] %in% vars){
tip_labels[i, 2] <- paste(strsplit(tip_labels[i, 1],
split = "_")[[1]][c(1,2)], collapse = "_")
} else{
tip_labels[i, 2] <- paste(strsplit(tip_labels[i, 1],
split = "_")[[1]][c(1,2,3,4)], collapse = "_")
}
}
tip_labels$std_labels <- gsub("_var_", "_", tip_labels$std_labels)
#Replacing tip labels in the tree
paep_tree$tip.label <- tip_labels$std_labels
#=================#
# COLLAPSING VARS #
#=================#
#Supressing infraspecific epithet
tip_labels$vars_sup <- NA
for(i in 1:nrow(tip_labels)){
tip_labels[i, "vars_sup"] <- paste(strsplit(tip_labels[i, "std_labels"], "_")[[1]][c(1, 2)],
collapse = "_")
}
paep_tree$tip.label <- tip_labels$vars_sup
#Removing duplicated labels
duplicated_logical <- duplicated(paep_tree$tip.label)
paep_tree$tip.label <- as.character(1:length(paep_tree$tip.label))
for(i in 1:length(paep_tree$tip.label)){
if(duplicated_logical[i] == TRUE)
paep_tree <- drop.tip(paep_tree, paep_tree$tip.label[paep_tree$tip.label == as.character(i)])
}
paep_tree$tip.label <- tip_labels$vars_sup[-which(duplicated_logical)]
#=====================================================================================================#
#===============#
# SAMPLING BIAS #
#===============#
#Removing species not sampled in the tree
paep_cr <- paep_cr[which(paep_cr$gen_sp %in% paep_tree$tip.label), ]
#============#
# Redundancy #
#============#
#Data frames with all samples (all_samples) and with one sample per species per grid (one_sample)
all_samples <- paep_cr[ , which(colnames(paep_cr) %in% c("gen_sp", "id_grid"))]
one_sample <- unique(all_samples, by = c("gen_sp", "id_grid"))
#Redundancy values
redundancy <- tibble("id_grid" = unique(all_samples$id_grid), "richness" = NA, "n" = NA,
"redundancy" = NA)
SR <- plyr::count(one_sample$id_grid)
N <- plyr::count(all_samples$id_grid)
for(i in 1:nrow(redundancy)){
redundancy$richness[i] <- SR$freq[SR$x == redundancy$id_grid[i]]
redundancy$n[i] <- N$freq[N$x == redundancy$id_grid[i]]
}
redundancy$redundancy <- 1-(redundancy$richness/redundancy$n)
#Removing grids with redundancy = 0 (meaning a species per sample)
inv_grids <- redundancy$id_grid[redundancy$redundancy == 0]
paep_cr <- paep_cr[!paep_cr$id_grid %in% inv_grids, ]
#=====================================================================================================#
#==============#
# PRUNING TREE #
#==============#
paep_pruned.tree <- drop.tip(paep_tree,
paep_tree$tip.label[!paep_tree$tip.label %in% paep_cr$gen_sp])
#==========================#
# RESCALING BRANCH LENGTHS #
#==========================#
edge.l <- c()
for(i in 1:length(paep_pruned.tree$edge.length)){
edge.l[i] <- paep_pruned.tree$edge.length[i]/sum(paep_pruned.tree$edge.length)
}
paep_pruned.tree$edge.length <- edge.l
#=======#
# NEXUS #
#=======#
write.nexus(paep_pruned.tree, file = "trees/Paepalanthus/pruned_tree-paep.nex")
#=====================================================================================================#
#========#
# MATRIX #
#========#
#Composing a presence/absence matrix
paep_matrix <- matrix(data = NA, nrow = length(unique(paep_cr$id_grid)),
ncol = length(unique(paep_cr$gen_sp)))
paep_matrix <- as.data.frame(paep_matrix)
colnames(paep_matrix) <- unique(paep_cr$gen_sp)
rownames(paep_matrix) <- unique(paep_cr$id_grid)
for(i in 1:nrow(paep_matrix)){
for(j in 1:ncol(paep_matrix)){
if(colnames(paep_matrix)[j] %in% paep_cr$gen_sp[paep_cr$id_grid == rownames(paep_matrix)[i]]){
paep_matrix[i, j] <- 1
} else {
paep_matrix[i, j] <- 0
}
}
}
#Saving matrix
write.csv(paep_matrix, "matrices/paep_matrix.csv", row.names = TRUE)
|
7c4abeb025bebd5556c119e1540974446d806ad7 | 12676471ec4e7015048e854817b3c253828df917 | /econometrics-001/01__1/15_1.2.4._______R_11-16.R | 8e61a12bcbd8da92b4e12b4be677ac958f91c2e3 | [] | no_license | bdemeshev/coursera_metrics | 9768a61e31e7d1b60edce9dde8e52f47bbd31060 | a689b1a2eed26816b2c5e4fd795136d5f9d1bb4f | refs/heads/master | 2021-11-01T14:15:56.877876 | 2021-10-24T13:16:59 | 2021-10-24T13:16:59 | 23,589,734 | 19 | 58 | null | 2021-07-25T10:51:56 | 2014-09-02T18:07:06 | HTML | UTF-8 | R | false | false | 2,979 | r | 15_1.2.4._______R_11-16.R | # if you see KRAKOZYABRY then do
# File-Reopen with encoding - UTF-8 - (Set as default) - OK
library("psych") # описательные статистики
library("dplyr") # манипуляции с данными
library("ggplot2") # графики
library("GGally") # еще графики
d <- cars # встроенный набор данных по машинам
glimpse(d) # что там?
help(cars) # справка. действует для встроенных наборов данных
head(d) # начало таблички d (первые 6 строк)
tail(d) # хвостик таблички d
describe(d) # среднее, мода, медиана и т.д.
ncol(d) # число столбцов
nrow(d) # число строк
str(d) # структура (похоже на glimpse)
# среднее арифметическое
mean(d$speed)
# создадим новую переменные и поместим их все в табличку d2
d2 <- mutate(d, speed=1.61*speed,
dist=0.3*dist, ratio=dist/speed)
glimpse(d2)
# графики
qplot(data=d2,dist)
qplot(data=d2,dist,xlab="Длина тормозного пути (м)",
ylab="Число машин",main="Данные по машинам 1920х")
qplot(data=d2,speed,dist)
# оценим модель парной регрессии y_i = \beta_1 + \beta_2 x_i + \varepsilon_i
model <- lm(data=d2, dist~speed)
model
coef(model) # оценки бет
residuals(model) # остатки (эпсилон с крышкой)
y_hat <- fitted(model) # прогнозы (игрек с крышкой)
y <- d2$dist # значения зависимой переменной
RSS <- deviance(model) # так называют RSS
TSS <- sum((y-mean(y))^2) # TSS
TSS
R2 <- 1-RSS/TSS
R2
cor(y,y_hat)^2 # квадрат выборочной корреляции
X <- model.matrix(model) # матрица регрессоров
X
# создаем новый набор данных
nd <- data.frame(speed=c(40,60))
nd
# прогнозируем
predict(model,nd)
# добавляем на график линию регрессии
qplot(data=d2,speed,dist) + stat_smooth(method="lm")
t <- swiss # встроенный набор данных по Швейцарии
help(swiss)
glimpse(t)
describe(t)
ggpairs(t) # все диаграммы рассеяния на одном графике
# множественная регрессия
model2 <- lm(data=t,
Fertility~Agriculture+Education+Catholic)
coef(model2) # оценки бет
fitted(model2) # прогнозы
residuals(model2) # остатки
deviance(model2) # RSS
report <- summary(model2)
report
report$r.squared # R^2
# второй способ расчета R^2
cor(t$Fertility,fitted(model2))^2
# создаем новый набор данных
nd2 <- data.frame(Agriculture=0.5,Catholic=0.5,
Education=20)
# прогнозируем
predict(model2,nd2)
|
a98ab60b57ccc32eb2e70b82e2dd4b839b313247 | ae703e19ee171d6182a93946350ee41117c1c235 | /run_analysis.R | 8209ef5c4b639dc7c9ce7af0d15aac8604fd6f6c | [] | no_license | monsteragain/courseProject | f4a72efa0981baa59ca6368fa63c1e0fae102fb0 | 1170ff2faff4193d75a62222a19ad2c15cd8d097 | refs/heads/master | 2016-09-06T15:53:50.949094 | 2014-11-24T00:37:19 | 2014-11-24T00:37:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,518 | r | run_analysis.R |
##Read X_test and X-train data, use 'rbind' to merge the data and store it in 'data' variable
data <- rbind(read.table("UCI HAR Dataset/test/X_test.txt"),read.table("UCI HAR Dataset/train/X_train.txt"))
##Read and rbind subject_test.txt and subject_train.txt
subjects <- rbind(read.table("UCI HAR Dataset/test/subject_test.txt"),read.table("UCI HAR Dataset/train/subject_train.txt"))
##name the column so when we cbind it to data, there'd be no duplicate column names
colnames(subjects) <- "Subjects"
##Read and rbind y_test.txt and y_train.txt
activities <- rbind(read.table("UCI HAR Dataset/test/y_test.txt"),read.table("UCI HAR Dataset/train/y_train.txt"))
##name the column so when we cbind it to data, there'd be no duplicate column names
colnames(activities) <- "Activities"
##make a vector 'features' with the names from 'features.txt'
features <- as.vector(read.table("UCI HAR Dataset/features.txt")[ ,2])
##use grep to find the positions of features that contain 'mean()' or 'std()'
featurepos <- grep('mean\\(\\)|std\\(\\)',features)
##use 'featurepos' to extract only the columns with measurements on the mean and standard deviation from 'data'
data <- data[ ,featurepos]
##apply 'featurepos' to 'features' to make a vector of measurement names, then make syntactically valid names with 'make.name'
featurenames <- make.names(features[featurepos],unique = TRUE, allow_ = FALSE)
##clean the names with some substitutions to make readable descriptive names
featurenames <- gsub('\\.','',featurenames) ##remove dots
featurenames <- gsub('mean','Mean',featurenames) ##capitalize first letter of
##'mean' to make it more readable
featurenames <- gsub('std','Std',featurenames) ##capitalize first letter of
##'std' to make it more readable
featurenames <- gsub('BodyBody','Body',featurenames) ##correcting themismatch in the
##original authors features names as pointed by David Hood at
##https://class.coursera.org/getdata-009/forum/thread?thread_id=89#comment-656
##name the columns in 'data'
colnames(data) <- featurenames
##cbind subjects and activities to data - thus we merge the measurements with subjects and activities
data <- cbind(data,c(subjects,activities))
##read activity labels from file
activityLabels <- as.vector(read.table("UCI HAR Dataset/activity_labels.txt")[ ,2])
##turn the numbers in data$Activities column into factors then change the level
##names (this changes it in the same column)- as suggested by David Hood at
##https://class.coursera.org/getdata-009/forum/thread?thread_id=141#post-523
data$Activities <- factor(data$Activities, labels = activityLabels)
##Now getting to create the tidy data set
##Create a vector of strings to be used as colnames in the resulting data frame
colnames <- colnames(data)
##adding 'Avg' to every var name to reflect the fact that we calculate
##the average of means and standard deviations
colnames[1] <- paste0(colnames[1],"Avg")
##create an empty dataframe to store the tidy data
##the method to create empty df with column names by Floo0 at
##http://stackoverflow.com/a/26614741
df <- read.table(text='',col.names=colnames(data))
##loop through subjects
for (i in 1:30) {
##find indices of rows for the given subject
##NB! I'm not using if condition to loop through all the rows of data
##as 'which' is supposed to be a faster and less resourceful way
rows_subjects <- which(data$Subjects==i)
for (a in 1:6) {
##find a subset of row indices that contain the given activity label
##keep in mind, it finds indices inside data set subsetted by rows_subjects,
##not inside original 'data'
rows_activities <- which(data[rows_subjects,"Activities"]==activityLabels[a])
##finaly, get rows in the original 'data' with a given subject and activity
rows <- rows_subjects[rows_activities]
##create a vector with averages for each of 66 columns with measurements
##plus the subject label - I do not include activityLabel here as it
##is a string and will coerce all the vector into a character class
vec <- c(as.vector(colMeans(data[rows,1:66])),i)
rownumber <- nrow(df) + 1
df[rownumber,1:67] <- vec
df[rownumber,68] <- activityLabels[a]
}
}
df |
cb0b9d35dc8accdfe1e650568dba50d00a347046 | 4042c33db4606452e80bf20e763d388be2785372 | /pkg/R/nw_setup.R | 5378bca81149cfc9253c8f35d905807f919d6b46 | [] | no_license | RodrigoAnderle/EvoNetHIV | 992a5b735f8fac5255ea558ec2758251634ffaab | 4bef435b0c5739d6baba9f80c93ac046e540647e | refs/heads/master | 2023-08-25T14:01:38.754647 | 2021-09-24T16:48:29 | 2021-09-24T16:48:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 831 | r | nw_setup.R | #' @export
nw_setup <- function(params){
nw <- setup_initialize_network(params)
#--------------------------------------------------------------
#estimate initial network (create argument list, then call fxn)
netest_arg_list <- list(
nw = nw,
formation = as.formula(params$nw_form_terms),
target.stats = params$target_stats,
coef.form = params$nw_coef_form,
constraints = as.formula(params$nw_constraints),
verbose = FALSE,
coef.diss = dissolution_coefs( dissolution = as.formula(params$dissolution),
duration = params$relation_dur,
d.rate = params$d_rate) )
sim <- do.call(EpiModel::netest, netest_arg_list)
if(params$hyak_par==T){
save(sim,file="estimated_nw.RData")
}
return(sim)
}
|
e9d810b63d2955520d9c9fb01446737545319358 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.storage/man/efs_create_mount_target.Rd | f6b5669c0b63f314c5a0b71cf28d153550af6372 | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 6,582 | rd | efs_create_mount_target.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/efs_operations.R
\name{efs_create_mount_target}
\alias{efs_create_mount_target}
\title{Creates a mount target for a file system}
\usage{
efs_create_mount_target(FileSystemId, SubnetId, IpAddress,
SecurityGroups)
}
\arguments{
\item{FileSystemId}{[required] The ID of the file system for which to create the mount target.}
\item{SubnetId}{[required] The ID of the subnet to add the mount target in.}
\item{IpAddress}{Valid IPv4 address within the address range of the specified subnet.}
\item{SecurityGroups}{Up to five VPC security group IDs, of the form \code{sg-xxxxxxxx}. These must
be for the same VPC as subnet specified.}
}
\value{
A list with the following syntax:\preformatted{list(
OwnerId = "string",
MountTargetId = "string",
FileSystemId = "string",
SubnetId = "string",
LifeCycleState = "creating"|"available"|"updating"|"deleting"|"deleted",
IpAddress = "string",
NetworkInterfaceId = "string",
AvailabilityZoneId = "string",
AvailabilityZoneName = "string",
VpcId = "string"
)
}
}
\description{
Creates a mount target for a file system. You can then mount the file
system on EC2 instances by using the mount target.
You can create one mount target in each Availability Zone in your VPC.
All EC2 instances in a VPC within a given Availability Zone share a
single mount target for a given file system. If you have multiple
subnets in an Availability Zone, you create a mount target in one of the
subnets. EC2 instances do not need to be in the same subnet as the mount
target in order to access their file system. For more information, see
\href{https://docs.aws.amazon.com/efs/latest/ug/how-it-works.html}{Amazon EFS: How it Works}.
In the request, you also specify a file system ID for which you are
creating the mount target and the file system's lifecycle state must be
\code{available}. For more information, see
\code{\link[=efs_describe_file_systems]{describe_file_systems}}.
In the request, you also provide a subnet ID, which determines the
following:
\itemize{
\item VPC in which Amazon EFS creates the mount target
\item Availability Zone in which Amazon EFS creates the mount target
\item IP address range from which Amazon EFS selects the IP address of the
mount target (if you don't specify an IP address in the request)
}
After creating the mount target, Amazon EFS returns a response that
includes, a \code{MountTargetId} and an \code{IpAddress}. You use this IP address
when mounting the file system in an EC2 instance. You can also use the
mount target's DNS name when mounting the file system. The EC2 instance
on which you mount the file system by using the mount target can resolve
the mount target's DNS name to its IP address. For more information, see
\href{https://docs.aws.amazon.com/efs/latest/ug/how-it-works.html#how-it-works-implementation}{How it Works: Implementation Overview}.
Note that you can create mount targets for a file system in only one
VPC, and there can be only one mount target per Availability Zone. That
is, if the file system already has one or more mount targets created for
it, the subnet specified in the request to add another mount target must
meet the following requirements:
\itemize{
\item Must belong to the same VPC as the subnets of the existing mount
targets
\item Must not be in the same Availability Zone as any of the subnets of
the existing mount targets
}
If the request satisfies the requirements, Amazon EFS does the
following:
\itemize{
\item Creates a new mount target in the specified subnet.
\item Also creates a new network interface in the subnet as follows:
\itemize{
\item If the request provides an \code{IpAddress}, Amazon EFS assigns that
IP address to the network interface. Otherwise, Amazon EFS
assigns a free address in the subnet (in the same way that the
Amazon EC2 \code{CreateNetworkInterface} call does when a request
does not specify a primary private IP address).
\item If the request provides \code{SecurityGroups}, this network interface
is associated with those security groups. Otherwise, it belongs
to the default security group for the subnet's VPC.
\item Assigns the description
\verb{Mount target fsmt-id for file system fs-id } where \code{fsmt-id}
is the mount target ID, and \code{fs-id} is the \code{FileSystemId}.
\item Sets the \code{requesterManaged} property of the network interface to
\code{true}, and the \code{requesterId} value to \code{EFS}.
}
Each Amazon EFS mount target has one corresponding requester-managed
EC2 network interface. After the network interface is created,
Amazon EFS sets the \code{NetworkInterfaceId} field in the mount target's
description to the network interface ID, and the \code{IpAddress} field
to its address. If network interface creation fails, the entire
\code{\link[=efs_create_mount_target]{create_mount_target}} operation fails.
}
The \code{\link[=efs_create_mount_target]{create_mount_target}} call returns only
after creating the network interface, but while the mount target state
is still \code{creating}, you can check the mount target creation status by
calling the \code{\link[=efs_describe_mount_targets]{describe_mount_targets}}
operation, which among other things returns the mount target state.
We recommend that you create a mount target in each of the Availability
Zones. There are cost considerations for using a file system in an
Availability Zone through a mount target created in another Availability
Zone. For more information, see \href{https://aws.amazon.com/efs/}{Amazon EFS}. In addition, by always using a mount
target local to the instance's Availability Zone, you eliminate a
partial failure scenario. If the Availability Zone in which your mount
target is created goes down, then you can't access your file system
through that mount target.
This operation requires permissions for the following action on the file
system:
\itemize{
\item \code{elasticfilesystem:CreateMountTarget}
}
This operation also requires permissions for the following Amazon EC2
actions:
\itemize{
\item \code{ec2:DescribeSubnets}
\item \code{ec2:DescribeNetworkInterfaces}
\item \code{ec2:CreateNetworkInterface}
}
}
\section{Request syntax}{
\preformatted{svc$create_mount_target(
FileSystemId = "string",
SubnetId = "string",
IpAddress = "string",
SecurityGroups = list(
"string"
)
)
}
}
\examples{
\dontrun{
# This operation creates a new mount target for an EFS file system.
svc$create_mount_target(
FileSystemId = "fs-01234567",
SubnetId = "subnet-1234abcd"
)
}
}
\keyword{internal}
|
546bbb8ec1401c0c4cf5145d22b6769595981b48 | 84af362583c9562a8a5225986d877b6a5b26ef0a | /man/yf_collection_get.Rd | 5558a04c24571b1267403191e1ea218d75b6239b | [
"MIT"
] | permissive | ropensci/yfR | 945ef3f23a6f04560072d7436dd5c28e0f809462 | b2f2c5c9f933e933821b5a92763c98f155b401bd | refs/heads/main | 2023-05-23T19:12:44.048601 | 2023-02-16T10:48:15 | 2023-02-16T10:48:15 | 375,024,106 | 19 | 6 | NOASSERTION | 2023-01-30T17:18:11 | 2021-06-08T13:44:11 | HTML | UTF-8 | R | false | true | 1,548 | rd | yf_collection_get.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/yf_collections.R
\name{yf_collection_get}
\alias{yf_collection_get}
\title{Downloads a collection of data from Yahoo Finance}
\usage{
yf_collection_get(
collection,
first_date = Sys.Date() - 30,
last_date = Sys.Date(),
do_parallel = FALSE,
do_cache = TRUE,
cache_folder = yf_cachefolder_get(),
...
)
}
\arguments{
\item{collection}{A collection to fetch data (e.g. "SP500", "IBOV", "FTSE" ).
See function \code{\link{yf_get_available_collections}} for finding all
available collections}
\item{first_date}{The first date of query (Date or character as YYYY-MM-DD)}
\item{last_date}{The last date of query (Date or character as YYYY-MM-DD)}
\item{do_parallel}{Flag for using parallel or not (default = FALSE).
Before using parallel, make sure you call function future::plan() first.
See <https://furrr.futureverse.org/> for more details.}
\item{do_cache}{Use cache system? (default = TRUE)}
\item{cache_folder}{Where to save cache files?
(default = yfR::yf_cachefolder_get() )}
\item{...}{Other arguments passed to \code{\link{yf_get}}}
}
\value{
A data frame with financial prices from collection
}
\description{
This function will use a set collection of YF data, such as index components
and will download all data from Yahoo Finance using \code{\link{yf_get}}.
}
\examples{
\donttest{
df_yf <- yf_collection_get(collection = "IBOV",
first_date = Sys.Date() - 30,
last_date = Sys.Date()
)
}
}
|
178bfd8b213b596ac0015afb6e7f8cd850aea5a1 | c0a0e4ee67152f89ea3bdb22a0752946c969ebf2 | /scripts/happ_maier_merz.R | 2245b9c6213a0b61eec26a00a2b34093b1341653 | [] | no_license | PirateGrunt/cotor_correlation | eb1fac9fdc96523ea06c09d234b115fc09a2af3b | 398158b82818e12153dba1858060e61a2de5bd2b | refs/heads/master | 2020-03-26T11:48:17.772123 | 2018-09-24T00:36:08 | 2018-09-24T00:36:08 | 144,860,602 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,886 | r | happ_maier_merz.R | library(tidyverse)
library(magrittr)
munge_triangle <- function(str_in, lags) {
tbl_triangle <- str_in %>%
str_replace_all('\\.', '') %>%
str_split('\n', simplify = TRUE) %>%
str_split('[:space:]+', simplify = TRUE) %>%
extract(, -1) %>%
as_tibble() %>%
map_dfc(as.numeric)
names(tbl_triangle) <- c('ay', lags)
tbl_triangle %>%
tidyr::gather(lag, incremental_paid, -ay, na.rm = TRUE)
}
lags <- 0:10
triangle_a <- ' 0 14.492 7.746 949 467 814 234 1.718 104 15 49 4
1 17.017 9.251 945 750 33 196 1.144 18 10 1 11
2 19.563 12.265 1.302 1.099 967 1.237 1.838 1.093 276 643 0
3 21.632 13.249 963 136 25 172 27 39 7 0 0
4 22.672 9.677 1.779 153 95 1.265 31 1 45 0 8
5 23.062 13.375 2.200 327 2.273 20 23 0 0 2 0
6 23.588 11.713 1.660 4.569 1.662 256 0 39 0 32 2
7 21.758 12.300 1.685 1.266 -28 -41 -49 -45 -26 0
8 20.233 9.197 932 644 754 2.452 161 34 0
9 24.984 12.632 1.931 415 1.730 163 113 33
10 24.260 13.555 1.585 1.679 123 147 32
11 20.616 11.430 2.932 516 558 37
12 18.814 11.499 3.363 1.711 97
13 18.563 13.492 16.370 2.704
14 18.457 11.089 2.064
15 19.533 9.833
16 17.620'
triangle_b <- ' 0 16.651 8.206 -468 -152 5 4 6 0 1 0 0
1 16.292 8.129 1.713 195 426 35 68 50 55 0 11
2 16.658 10.566 1.736 618 1.272 442 572 15 38 749 2
3 19.715 10.690 968 2.098 154 197 100 63 3.519 24 149
4 21.220 8.815 2.969 896 151 1.146 26 20 0 0 2
5 21.302 10.582 2.237 952 7 1.183 14 0 26 70 -9
6 17.201 7.493 1.574 518 1.685 25 2 56 -8 71 7
7 15.835 11.668 1.728 122 493 -10 4 1 1 0
8 17.560 8.550 2.373 908 1.163 389 127 501 2
9 21.051 12.279 2.387 690 372 2.024 14 0
10 20.368 9.832 1.285 460 43 186 0
11 18.623 11.160 942 892 8 28
12 18.112 12.040 1.662 5.654 979
13 17.744 10.346 2.134 599
14 17.993 8.956 869
15 19.082 11.403
16 17.809'
tbl_triangle <- triangle_a %>%
munge_triangle(lags)
|
3ff8666170141d482e8424423dd49caeef861b22 | 1eee16736f5560821b78979095454dea33b40e98 | /thirdParty/HiddenMarkov.mod/R/mmglm1.R | 765bb029b2ad2d20d3e3e92d5abecc2db5a9282d | [] | no_license | karl616/gNOMePeaks | 83b0801727522cbacefa70129c41f0b8be59b1ee | 80f1f3107a0dbf95fa2e98bdd825ceabdaff3863 | refs/heads/master | 2021-01-21T13:52:44.797719 | 2019-03-08T14:27:36 | 2019-03-08T14:27:36 | 49,002,976 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 700 | r | mmglm1.R | "mmglm1" <-
function (y, Pi, delta, glmfamily, beta, Xdesign,
sigma=NA, nonstat=TRUE, size=NA, msg=TRUE){
if (msg)
message("NOTE: 'mmglm1' and its methods are still under development and may change.")
if (glmfamily$family=="binomial" & all(is.na(size)))
stop("Argument size must be specified when fitting a binomial model.")
if (glmfamily$family=="binomial" | glmfamily$family=="poisson") discrete <- TRUE
else discrete <- FALSE
x <- c(list(y=y, Pi=Pi, delta=delta, glmfamily=glmfamily,
beta=beta, Xdesign=Xdesign, sigma=sigma,
size=size, nonstat=nonstat, discrete=discrete))
class(x) <- c("mmglm1")
return(x)
}
|
65546608f6e6e44b56dc5e0f3c3547835ea872f4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/starma/examples/stcov.Rd.R | e4bc8a309a2040a086e3d66af6878920b0e7a5b9 | [] | 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 | 329 | r | stcov.Rd.R | library(starma)
### Name: stcov
### Title: Space-time covariance function
### Aliases: stcov
### Keywords: starma stcov
### ** Examples
data(nb_mat) # Get neighbourhood matrices
data <- matrix(rnorm(9400), 100, 94)
# Compute covariance between 2-nd and 1-st order neighbours, at time lag 5
stcov(data, blist, 2, 1, 5)
|
b78363943c6e47cc32431b9bf2a1d13f4582e7da | 301bd6d09d7aa29572e944755e1966832db29066 | /cachematrix.R | bfe0fee55dbd46179aefbe39b15f6478aa605514 | [] | no_license | chewlwb/ProgrammingAssignment2 | a001cd2d30c70bb4a865d26091f126b466fef1f4 | 53fcc6b651dbe7b65eee71d4fc12c6bfb2e03f88 | refs/heads/master | 2021-01-18T10:27:07.349087 | 2015-03-21T13:02:24 | 2015-03-21T13:02:24 | 32,588,762 | 0 | 0 | null | 2015-03-20T14:35:37 | 2015-03-20T14:35:35 | null | UTF-8 | R | false | false | 2,376 | r | cachematrix.R | ## makeCacheMatrix creates a special "matrix, which is really a
## list containing a function to
## set the value of the matrix
## get the value of the matrix
## set the value of the inverse of matrix
## get the value of the inverse of matrix
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL ## initialize inv so that makeCache will work for first time
## set function sets x to y and inv to null within its environment
set <- function(y) {
x <<- y
inv <<- NULL
}
# the get function assigns a matrix to it. This function
# is useful only when worked with the cacheSolve function.
get <- function () x
#this function sets the value ('inverse') to inv in the makeCacheMatrix
# enironment. This function is useful only when CacheSolve is called second time
# because the inverse is stored in setinv function and doesn't need to compute it again.
setinv <- function(inverse) inv <<- inverse
getinv <- function() inv
#constructs a named list of functions within the environment in the makeCacheMatrix
list(set = set, get = get,
setinv = setinv,
getinv = getinv)
}
## cacheSolve calculates the inverse of the special "matrix"
## created with makeCacheMatrix.
## However, it first checks to see if the inverse has already been calculated in the getinv function.
## If so, it gets the inverse from the cache and skips the computation.
## Otherwise, it calculates the inverse of the data and sets the value of the inverse
## in the cache via the setinv function.
## Goes to the makeCachematrix environment and assigns the
## 'inv' value from that environment to this one.
## If the makeCacheMatrix environment has been evaluated
## before, the function prints the message and
## the value of inv (the cached inverse).
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
inv <- x$getinv()
if (!is.null(inv)){
message("getting cached data")
return(inv)
}
## If the inverse has never been evaluated before,
## puts the x-matrix into a local variable called 'data'
## Calculate the inverse of the matrix x by calling
## solve function on the data local variable.
## Assign the calculated inverse to the MakeCacheMatrix
## environment using the 'setinv' function.
## Display the calculated inverse.
data <- x$get()
inv <- solve(data, ...)
x$setinv(inv)
inv
}
|
d281a9dc0cf0341025687f1bc6786f26a24eb955 | c95d61a58f83ea3beae1949c2e4cd8e5f1186c01 | /BM-challenge/tabs/11. pyro.R | 13eb4d2c7a35991e9440ba81842a57e4c2ea600c | [] | no_license | TMBish/TMBisc | d9da0e75320b47a5e539f6124430dc49cef61e4f | 79378a7e18e02afaf9679ef8f914d9b21f39e453 | refs/heads/master | 2023-02-22T19:14:11.210224 | 2023-02-16T06:55:09 | 2023-02-16T06:55:09 | 172,413,835 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,844 | r | 11. pyro.R | pyroTab =
fluidRow(
column(width = 8,
tabBox(
title = "Pyro: wood chopping and fire construction",
id = "tabset1", height = "600", width = 12,
tabPanel("Challenge Description",
HTML(
"<strong> <big> Overview </big> </strong> <br/>
<p> This is a time-trial. Each participant will recieve:<br/>
1. One log; <br/>
2. A fire lighter; <br/>
3. 3 sheets of newspaper; and <br/>
4. An axe. <br/>
Your timer stops when your fire is going to the satisfaction of the chief justice: Tom B.
</p> <br/> <br/>
<strong> <big> What constitutes participation? </big> </strong> <br/>
<p> A piece of the log must be alight. </p> <br/> <br/>
<strong> <big> How do I win bonus points? </big> </strong> <br/>
<p> NA </p>")),
tabPanel("Results",
hotable("hotable11"),
br(),
actionButton("calc11","calculate",styleclass="primary",size="mini"),
actionButton("up11","upload",styleclass="danger"))
)
),
column(width = 4,
valueBox("Team/Individual", "Individual", icon = icon("user"), width = NULL, color = "olive"),
valueBox("Min. Participants", 4, icon = icon("users"), width = NULL, color = "olive"),
valueBox("Points Avaliable", 100, icon = icon("money"), width = NULL, color = "olive"),
valueBox("Bonus Points?", NA, icon = icon("asterisk"), width = NULL, color = "olive")
)
)
calc11 = function(hdf) {
power = ifelse(hdf$Fire.Rank == 0,0,(10-hdf$Fire.Rank)^2)
scaled = power / (sum(power)/100)
pyroScore = round(scaled,0)
hdf$Score = pyroScore
return(hdf)
} |
e61f2bb4c0058ec2cb9f35359088b5ac231e0672 | ac3738c440117527327ec58affb38f7d887adc91 | /scripts/allelefreqs.R | b8f37af14084be239e1781cebb806035d21c0abe | [] | no_license | jpgerke/KrugQx | 7e27bbf725563413b5694432bec5f84056cb91df | c83b874b7bf71d45ca07c0765fe8231b121f6ccf | refs/heads/master | 2020-04-05T23:29:30.991478 | 2015-08-16T03:01:28 | 2015-08-16T03:01:28 | 38,621,692 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 860 | r | allelefreqs.R | #!/usr/bin/Rscript
rm(list=ls())
load_obj <- function (f) {
env <- new.env()
nm <- load(f, env)[1]
return(env[[nm]])
}
# allelefreqs <- function (filename) {
# myobj <- load_obj(filename)
# return(myobj)
# }
#
# getfreq <- function (geno) {
# return(sum(geno) / (length(geno) *2 ))
# }
#
# calc_freqs <- function (filename) {
#
# alldata <- load_obj(filename)
# mycols <- alldata[,-1]
# freqs <- apply(mycols, 2, getfreq)
# return(freqs)
# }
#running into memory problems running the whole thing so loop by chromosome
allnames = list()
for (x in 1:10) {
filename <- paste("../data/Namfreqs/cut2uniqueAD_NAM_KIDS_chr", x, ".raw.RData", sep='')
myobj <- load_obj(filename)
mynames = names(myobj)
allnames[[x]] = mynames
}
rm(myobj)
rm(mynames)
fullnames = do.call(c, allnames)
write(fullnames, file = "../data/Namfreqs/GWAS_SNPs.txt")
|
bd951252aea0f2ea994e837fb369a4b705b47e40 | 83d93f6ff2117031ba77d8ad3aaa78e099657ef6 | /R/gtimer.R | 4621d839fad7c48d9bef3e252955b660a0f779a9 | [] | no_license | cran/gWidgets2 | 64733a0c4aced80a9722c82fcf7b5e2115940a63 | 831a9e6ac72496da26bbfd7da701b0ead544dcc1 | refs/heads/master | 2022-02-15T20:12:02.313167 | 2022-01-10T20:12:41 | 2022-01-10T20:12:41 | 17,696,220 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,260 | r | gtimer.R | ##' @include methods.R
NULL
##' Basic timer widget
##'
##' Calls FUN every ms/1000 seconds. A timer is stopped through its \code{stop_timer} method which is called using OO style: \code{obj$stop_timer()}.
##' @param ms interval in milliseconds
##' @param FUN FUnction to call. Has one argument, data passed in
##' @param data passed to function
##' @param one.shot logical. If TRUE, called just once, else repeats
##' @param start logical. If FALSE, started by \code{start_timer} OO method. (Call \code{obj$start_time()}).
##' @param toolkit gui toolkit to dispatch into
##' @export
##' @rdname gtimer
##' @examples
##' \dontrun{
##' i <- 0
##' FUN <- function(data) {i <<- i + 1; if(i > 10) a$stop_timer(); print(i)}
##' a <- gtimer(1000, FUN)
##' ##
##' ## a one shot timer is run only once
##' FUN <- function(data) message("Okay, I can breathe now")
##' hold_breath <- gtimer(1000*60, FUN, one.shot=TRUE)
##' }
gtimer <- function(ms, FUN, data=NULL, one.shot=FALSE, start=TRUE, toolkit=guiToolkit()) {
.gtimer(toolkit=toolkit, ms=ms, FUN=FUN, data=data, one.shot=one.shot, start=start)
}
##' S3 generic for dispatch
##'
##' @export
##' @rdname gtimer
.gtimer <- function(toolkit, ms, FUN, data=NULL, one.shot=FALSE, start=TRUE) UseMethod(".gtimer")
|
cddaac299a25a670be8829d4fbdcb55d8c6001ca | e1632c4e03c43bf56894760274e0a4c891395645 | /ms_initialize_adjacency_matrix.R | 2d5ccad5c0b4f0798daf0c4b0db9ced273fe06b9 | [] | no_license | johnchower/ticket_to_ride | 0c5596df43bb0a438600dc0b96b934ed3d1312e7 | 9d76fcb714b9e7da8c9501fef36e3e6e975211b8 | refs/heads/master | 2021-01-19T05:30:00.026202 | 2016-07-27T00:22:34 | 2016-07-27T00:22:34 | 64,262,098 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 422 | r | ms_initialize_adjacency_matrix.R |
library(dplyr)
library(magrittr)
city_df <- read.csv("city_graph.csv", stringsAsFactors = F) %>%
select(city_1)
city_df %<>%
{
matrix(
0
, nrow = nrow(.), ncol = length(.$city_1)
, dimnames = list(1:length(.$city_1), .$city_1)
) %>%
as.data.frame
} %>%
{
cbind(data.frame(city_1 = city_df$city_1,.))
}
city_df %<>%
edit
write.csv(city_df, "city_adjacency_matrix.csv")
|
cc9f5566a634ce8f05ac34c4a714bca9bcca1581 | 02203a5e1487c6bf95647b38038e2428c261aad7 | /R/qm.R | 65da8d5ce1e7f9f86ffb214e8942e7ce7f0802db | [] | no_license | cran/gdalUtils | 8793292640f94d0f8960804f0ba9d4b5099baad7 | 9aa3955becca0970f98513ca20e4bff56be44d81 | refs/heads/master | 2021-06-07T07:27:23.972525 | 2020-02-13T19:10:02 | 2020-02-13T19:10:02 | 17,696,298 | 3 | 8 | null | null | null | null | UTF-8 | R | false | false | 302 | r | qm.R | #' qm
#'
#' Wraps an input in quotation marks.
#'
#' @param x Character or Numeric.
#'
#' @return A character string that begins and ends with quotation marks.
#' @author Jonathan A. Greenberg
#'
#' @examples {
#' qm("Hi!")
#' qm(42)
#' }
#' @export
qm <- function(x)
{
paste('"',x,'"',sep="")
} |
8a893dbaf0becf74dee174f62a96b15e31defc4f | 589df3701d3012b0335d0f9e6090163ac160493c | /fn-train.r | 3f7383e0c2417549a4673d8f528ca999ad9ffc08 | [] | no_license | brentonk/ppp-replication | e8cdbef86723e6448d8efeecb36819c6ab9f6141 | 00e856ce3f054dfaeec9cf6fa1e6170c49df24f9 | refs/heads/master | 2020-04-28T14:04:50.412568 | 2019-04-22T21:15:31 | 2019-04-22T21:15:31 | 175,327,182 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,907 | r | fn-train.r | ################################################################################
###
### Functions to train a Super Learner on the imputed CINC data
###
################################################################################
library("caret")
library("dplyr")
library("foreach")
library("tidyr")
## Take a list of arguments and return either a list of trained models or a
## matrix of out-of-fold predicted probabilities
##
## Used in both the inner loop and the middle loop, which is why we need the
## flexibility in terms of the return value -- only need probabilities in the
## inner loop, but need the full model in the outer loop
args_to_train <- function(arg_list,
common_args,
tr_control = trainControl(),
data_train,
data_test = NULL,
id = NULL,
for_probs = FALSE,
allow_no_tune = TRUE,
logfile = NULL)
{
## Not allowing parallelization, since this function is always inside an
## imputation loop
ans <- foreach (args = arg_list) %do% {
## Assemble arguments to pass to train()
##
## First element of each element of `args` must be `form`, containing
## the model formula
train_args <- c(args,
common_args,
list(trControl = tr_control))
train_args$data <- data_train
## Don't resample if the model doesn't need to be tuned
##
## Can be switched off (e.g., if we want CV estimates of accuracy for
## non-tuned models) via the `allow_no_tune` argument
if (allow_no_tune && isTRUE(train_args$doNotTune)) {
train_args$trControl$method <- "none"
}
train_args$doNotTune <- NULL
## Train the model
fit <- do.call(caret:::train.formula, train_args)
## Remove cross-validation indices, which aren't needed for further
## prediction and may take up a lot of space
fit$control$index <- NULL
fit$control$indexOut <- NULL
## Calculate out-of-fold probabilities, if requested
##
## In this case, the probabilities will be returned as output, not the
## trained model
if (for_probs) {
pred <- predict(fit, newdata = data_test, type = "prob")
## Hack to normalize predicted probabilities from avNNet models --
## should be fixed in an upcoming version of caret
##
## See https://github.com/topepo/caret/issues/261
if (fit$method == "avNNet") {
pred <- pred / rowSums(pred)
}
## Include observation IDs for mapping back into original dataset
pred$id <- id
fit <- pred
}
if (!is.null(logfile)) {
cat(args$method, as.character(Sys.time()), "\n",
file = logfile, append = TRUE)
}
fit
}
## Process output
names(ans) <- names(arg_list)
ans
}
## Objective function for choosing weights on each model to minimize the
## log-loss of out-of-fold predicted probabilities
ll_ensemble_weights <- function(w, pr_mat)
{
## Compute the last weight -- not including in `w` because constrOptim()
## doesn't do equality constraints and must be initialized on the
## interior
w <- c(w, 1 - sum(w))
## Calculate predicted probability of the observed outcome for each
## observation
pred <- drop(pr_mat %*% w)
## Log-likelihood of each observation is log(w * p_i)
##
## Multiplied by -1 since constrOptim() minimizes, and divided by number
## of observations for consistency with mnLogLoss()
-1 * sum(log(pred)) / length(pred)
}
## Derivative w.r.t. w_j is the sum of
## (p_j - p_J) / (w * p_i)
## across observations i
grad_ensemble_weights <- function(w, pr_mat)
{
## Calculate each w * p_i
w <- c(w, 1 - sum(w))
pred <- drop(pr_mat %*% w)
## Subtract p_J from the preceding columns
pr_mat <- sweep(pr_mat,
1,
pr_mat[, ncol(pr_mat)],
"-")
pr_mat <- pr_mat[, -ncol(pr_mat)]
## Divide by each w * p_i
pr_mat <- sweep(pr_mat,
1,
pred,
"/")
## Multiplied by -1 again since constrOptim() minimizes
-1 * colSums(pr_mat) / length(pred)
}
## Learn optimal ensemble weights for a matrix of out-of-fold predicted
## probabilities of observed outcomes
learn_ensemble <- function(probs, outer.eps = 1e-8)
{
## Constraints for weight selection:
## w_j >= 0 for all j
## -w_1 - ... - w_(J-1) + 1 >= 0 [ensures w_J is positive]
n_models <- ncol(probs)
ui <- rbind(diag(n_models - 1),
-1)
ci <- c(rep(0, n_models - 1), -1)
## Perform constrained out-of-fold log-likelihood maximization
##
## Starting value gives equal weight to all models
theta_start <- rep(1/n_models, n_models - 1)
names(theta_start) <- head(colnames(probs), n_models - 1)
ensemble_optim <- constrOptim(theta = theta_start,
f = ll_ensemble_weights,
grad = grad_ensemble_weights,
ui = ui,
ci = ci,
outer.eps = outer.eps,
pr_mat = probs)
ensemble_optim
}
train_weights <- function(dat,
n_folds,
arg_list,
common_args,
tr_control)
{
## Generate cross-validation folds
##
## We're going to train each model *within* each fold as well, in order to
## get the out-of-sample probabilities we need to learn the ensemble weights
cv_folds <- createFolds(y = dat$Outcome,
k = n_folds,
list = TRUE)
## Calculate out-of-fold predicted probabilities for each candidate model
all_probs <- foreach (fold = seq_len(n_folds)) %do% {
cat("FOLD", fold, as.character(Sys.time()), "\n")
## Retrieve the training and test sets corresponding to the given fold
fold_train <- dat[-cv_folds[[fold]], , drop = FALSE]
fold_test <- dat[cv_folds[[fold]], , drop = FALSE]
## Train each specified model and calculate the predicted probability of
## each out-of-fold outcome
fold_probs <- args_to_train(arg_list = arg_list,
common_args = common_args,
tr_control = tr_control,
data_train = fold_train,
data_test = fold_test,
id = cv_folds[[fold]],
for_probs = TRUE,
allow_no_tune = TRUE,
logfile = "")
fold_probs
}
## Collapse the out-of-fold predictions from each model into a single data
## frame, each with observations in the same order as the original data
##
## The result is a list of said data frames, the same length as the number
## of models being trained
all_probs_collapse <- foreach (model = seq_along(arg_list)) %do% {
## Retrieve relevant components of `all_probs`
oof_preds <- foreach (fold = seq_len(n_folds), .combine = "rbind") %do% {
all_probs[[fold]][[model]]
}
## Place in order of original data using the id variable
oof_preds <- oof_preds[order(oof_preds$id), ]
oof_preds
}
names(all_probs_collapse) <- names(arg_list)
## Form a matrix of out-of-fold probabilities of *observed* outcomes
obs_probs <- foreach (model = all_probs_collapse, .combine = "cbind") %do% {
model$obs <- dat$Outcome
## Translate relevant element of `all_probs_collapse` into key-value
## format
pred <- gather_(model,
key_col = "Outcome",
value_col = "prob",
gather_cols = levels(dat$Outcome))
## Extract the predicted values for the observed outcomes only
pred <- filter(pred,
as.character(obs) == as.character(Outcome))
## Once again place in original order
pred <- pred[order(pred$id), ]
pred$prob
}
colnames(obs_probs) <- names(all_probs_collapse)
## Calculate optimal ensemble weights
imputation_weights <- learn_ensemble(obs_probs)
## To estimate the bias of the minimum CV error via Tibshirani &
## Tibshirani's method, calculate the difference in CV error on each fold
## for the chosen weights versus the weights that would be optimal for that
## fold alone
losses <- foreach (fold = seq_len(n_folds), .combine = "rbind") %do% {
## Subset the matrix of out-of-fold probabilities of observed outcomes
## to the relevant fold only
obs_probs_fold <- obs_probs[cv_folds[[fold]], , drop = FALSE]
## Log loss using the weights chosen on the full dataset
loss_global <- ll_ensemble_weights(w = imputation_weights$par,
pr_mat = obs_probs_fold)
## Log loss using the weights that would be optimal for this fold alone
loss_local <- learn_ensemble(obs_probs_fold)$value
c(global = loss_global,
local = loss_local)
}
bias_min_cv <- mean(losses[, "global"] - losses[, "local"])
list(out_probs = obs_probs,
weights = imputation_weights,
bias_min_cv = bias_min_cv,
all_probs = all_probs_collapse)
}
|
dbdefc902ffe5924d71c7c4a790ecead1d20aa8f | 03e2a998e7764557d8333cd7063c1a06a5b2ec73 | /R/session script.R | 6d00adbb67e8001eedaf82422d8ed17b7fdda0f6 | [] | no_license | jordache-ramjith/Project-1 | 0dee5e8da6d89a737a9fa26fea58b2f4a48472c6 | f4f9f90970d379ec9a0e0463ff64f6142e0e88de | refs/heads/master | 2021-09-09T12:57:34.224794 | 2018-03-16T10:22:48 | 2018-03-16T10:22:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,532 | r | session script.R | library(usethis)
#a function of the package usethis
edit_r_profile()
#this will allow us to use the package in future sessionswithout needing to call it from library
if (interactive()){
suppressMessages(require(devtools))
suppressMessages(require(usethis))
}
create_package("iemand")
###Go to package description and change etails
##type the following in the package script
#use_mit_license(name="Jo Ramjith") creates an MIT license, obviously get the license for yourself
use_r(name="create_age") #to link to a function called create_age
use_git_config(user.name="Jordache1986", user.email="jordache.uct@gmail.com")
use_git()
creat_age()
#inside gitbash send to server
?iemand
#no documentation
#create and R file for documentation
use_package_doc()
#link it to package
document()
?document
######session 2######
#a unit test to not break the code
use_testthat()
context("test-create_age.R")
test_that("create_age returns an integer",{
expect_is(create_age(),"integer")})
use_package(package="praise")
use_package(package="magrittr")
#a function to make UPPERCASE
use_r("praise_nicely")
praise_nicely("everyone")
?praise_nicely
#####using data in your R package - including a function
###UCT github account - to share private repositories within your own institute
##create a data folder
##save data in package folder
##run prepare_name_data file
#restart
#run new package
library(notiemand)
head(sa_names)
##Now we WANT TO MAKE A FUNCTION THAT USES THE DATA
#Function is called sa_names
|
953f51e30fb22910ed9dc6fd3d755bd4553cd2c2 | 46a1b4c7844e1287ce184ff4fb01fe74626460c1 | /tests/runTests.R | 37ef9b9fc5e2add033d4bd905607946c8134275d | [] | no_license | LihuaJulieZhu/NADfinder | 7d3d810e5f35102efa69967a87e70d1dd64aad5c | 2cf769212c7049274d84d9657eade65570a61384 | refs/heads/master | 2020-03-06T18:20:10.973542 | 2019-03-22T16:39:49 | 2019-03-22T16:39:49 | 127,005,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 238 | r | runTests.R | require("NADfinder") || stop("unable to load Package:NADfinder")
require("SummarizedExperiment") ||
stop("unable to load Package:SummarizedExperiment")
require("testthat") || stop("unable to load testthat")
#test_check("NADfinder")
|
9936e277ab13176eccf1971cd1c0a5e8eda3cdf3 | fa3b78a6076dc9e4d46a087c6ee0f7d7e1d97f99 | /GPX2CSV.R | 0206b25d612040849ff029b0f173f0961cc8d8c9 | [
"MIT"
] | permissive | quantixed/GPXanalysis | 012202e1dfa0a92950c25a33f3ab9af93b6f0b78 | 2e8072aaf52f5c497af01bc2fe69908b97a58e60 | refs/heads/master | 2021-08-16T00:23:21.301564 | 2021-01-19T22:16:32 | 2021-01-19T22:16:32 | 77,203,820 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,389 | r | GPX2CSV.R | library(XML)
library(lubridate)
library(raster)
shift.vec <- function (vec, shift) {
if(length(vec) <= abs(shift)) {
rep(NA ,length(vec))
}else{
if (shift >= 0) {
c(rep(NA, shift), vec[1:(length(vec)-shift)]) }
else {
c(vec[(abs(shift)+1):length(vec)], rep(NA, abs(shift))) } } }
# Parse the GPX file
gpxfile <- file.choose()
pfile <- htmlTreeParse(gpxfile,error = function (...) {}, useInternalNodes = T)
elevations <- as.numeric(xpathSApply(pfile, path = "//trkpt/ele", xmlValue))
times <- xpathSApply(pfile, path = "//trkpt/time", xmlValue)
coords <- xpathSApply(pfile, path = "//trkpt", xmlAttrs)
lats <- as.numeric(coords["lat",])
lons <- as.numeric(coords["lon",])
geodf <- data.frame(lat = lats, lon = lons,time = times)
rm(list=c("elevations", "lats", "lons", "pfile", "times", "coords"))
geodf$lat.p1 <- shift.vec(geodf$lat, -1)
geodf$lon.p1 <- shift.vec(geodf$lon, -1)
geodf$dist.to.prev <- apply(geodf, 1, FUN = function (row) {
pointDistance(c(as.numeric(row["lon.p1"]),
as.numeric(row["lat.p1"])),
c(as.numeric(row["lon"]), as.numeric(row["lat"])),
lonlat = T)
})
geodf$time <- strptime(geodf$time, format = "%Y-%m-%dT%H:%M:%OS")
geodf$time.p1 <- shift.vec(geodf$time, -1)
geodf$time.diff.to.prev <- as.numeric(difftime(geodf$time.p1, geodf$time))
head(geodf)
write.csv(geodf,'geodf.csv') |
955267cd03aaadfcdff7ae7e5509d0dcb2578619 | a2564d11f5b60532ad6c3ab2a850ffb996879819 | /man/run_from_file.Rd | 2ba1f934cc41fc1111d06edcfceaf59779e71b3e | [] | no_license | marcrr/BgeeCall | 6aace1d3be82bfb6e291f1b04c6330a60924fb7e | c85802f1a90f76d701899c9e1962fb5a76bc74e0 | refs/heads/master | 2020-04-20T20:21:25.778673 | 2019-02-01T18:57:39 | 2019-02-01T18:57:39 | 169,074,371 | 0 | 0 | null | 2019-02-04T12:21:51 | 2019-02-04T12:21:51 | null | UTF-8 | R | false | true | 795 | rd | run_from_file.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/runCallsPipeline.R
\name{run_from_file}
\alias{run_from_file}
\title{generate present/absent calls from a file}
\usage{
run_from_file(myAbundanceMetadata = new("KallistoMetadata"),
myBgeeMetadata = new("BgeeMetadata"), userMetadataFile)
}
\arguments{
\item{myAbundanceMetadata}{A Reference Class BgeeMetadata object (optional)}
\item{myBgeeMetadata}{A Reference Class BgeeMetadata object (optional)}
\item{userMetadataFile}{A tsv file describing all user libraries for which present/absent calls have to be generated}
}
\description{
Main function allowing to generate present/absent calls for some libraries described in a file. Each line of the file describes one RNA-Seq library.
}
\author{
Julien Wollbrett
}
|
a8d1342ca89392709d8c8efe7deb43b409bdb35f | d220633376ccc1cb5cbe654a69d0459792c4d2ce | /01-Data_Functions.R | 652f2285a4b09a48011c12d44ee68bc8da681f7e | [
"Apache-2.0"
] | permissive | jamiahuswalton/Analysis_Dissertation_Scripts | 5b93ffbf25f31a517c42768d4927d88cece424c3 | dbec4e82fb09f7f6c0ac2da4b5ca05db6c99c08b | refs/heads/master | 2020-04-07T03:41:46.070652 | 2019-07-01T15:04:09 | 2019-07-01T15:04:09 | 158,026,449 | 0 | 0 | Apache-2.0 | 2019-05-03T15:47:50 | 2018-11-17T21:28:30 | HTML | UTF-8 | R | false | false | 52,693 | r | 01-Data_Functions.R | # Remove teams from raw data (Clean raw data) ----
remove_a_team_from_raw_data <- function(data, teams_to_be_removed, name_of_team_number_col){
clean_data <- data
for(team in teams_to_be_removed){
team_to_remove_from_inventory <- which(clean_data[,name_of_team_number_col] == team)
clean_data <- clean_data[-team_to_remove_from_inventory,]
}
return(clean_data)
}
# Remove measures with a give value ----
remove_measures_with_given_value <- function(data_set, col_name, value){
rows_to_move <- which(as.vector(data_set[,col_name]) == value)
return(data_set[-rows_to_move,])
}
# Clean inventory table----
clean_inventory_data_by_removing_game_enteries <- function(inventory_data, column_to_look_into, value_to_remove){
inventory_entries_to_remove <- which(inventory_data[,column_to_look_into] == value_to_remove)
return(inventory_data[-inventory_entries_to_remove,])
}
# Count the total number of errors commited by a team (regarless of if this rule was broken before) ----
total_number_of_errors_team <- function(data_errors, teamNum, condition){
team_errors_data <- data_errors %>%
filter(teamnumber == teamNum & expcondition == condition)
return(length(team_errors_data[,"ID"]))
}
# Function to calculate the total error rate (i.e., Duration / total errors) for a team Units: Sec / Error.
# Duration is retruned if error count is 0.
error_rate_team <- function(data_position, data_errors, teamNum, condition){
error_count_team <- total_number_of_errors_team(data_errors, teamNum, condition)
data_team <- data_position %>% filter(teamnumber == teamNum & playernum == 1 & expcondition == condition)
data_team_last_line <- tail(data_team,1)
duration_team<- data_team_last_line[1,"duration"]
if(error_count_team == 0){
return(duration_team)
} else if(error_count_team > 0){
return(duration_team / error_count_team)
} else if(error_count_team < 0){
message<- paste("error_rate_team: the error count generated for team", teamNum, "in condition", condition)
stop(message)
}
}
# Count the total number of errors commited by a player (regarless of if this rule was broken before) ----
total_number_of_errors_individual <- function(data_errors, teamNum, player, condition){
ind_errors_data <- data_errors %>%
filter(teamnumber == teamNum & playernum == player & expcondition == condition)
total_errors_individual <- length(ind_errors_data[,"ID"])
return(total_errors_individual)
}
# Function to calculate the total error rate (i.e., Duration / total errors) for an individual. Units: Sec / Error
# Duration is retruned if error count is 0.
error_rate_ind <- function(data_position, data_errors, teamNum, playerNum, condition){
error_count_ind <- total_number_of_errors_individual(data_errors, teamNum, playerNum, condition)
data_ind <- data_position %>% filter(teamnumber == teamNum & playernum == playerNum & expcondition == condition)
data_ind_last_line <- tail(data_ind,1)
duration_ind<- data_ind_last_line[1,"duration_ind"]
if(error_count_ind == 0){
return(duration_ind)
} else if(error_count_ind > 0){
return(duration_ind / error_count_ind)
} else if(error_count_ind < 0){
message<- paste("error_rate_ind: the error count generated for player", playerNum, "in team", teamNum, "in condition", condition)
stop(message)
}
}
# Factor the columns ----
re_factor_columns <- function(userData, columnNames){
factorData <- userData
for(column in columnNames){
print(column)
factorData[,column] <- factor(factorData[,column])
}
return(factorData)
}
# Generate team number list in a given data set ----
list_of_team_numbers <- function(data, team_number_col_name){
return(as.numeric(levels(factor(as.vector(data[,team_number_col_name])))))
}
# Count the number of times a game strategy was used ----
strategy_count_vector<- function(position_data, experimentalcondition, teamnumber_current, playernumber_one, playernumber_two, playernumber_three, strategy_barrier_distance){
#Need to get the position Data for all players (3 players)
is_player_1 <- as.vector(position_data$expcondition == experimentalcondition & position_data$teamnumber == teamnumber_current &
position_data$playernum == playernumber_one)
is_player_2<- as.vector(position_data$expcondition == experimentalcondition & position_data$teamnumber == teamnumber_current &
position_data$playernum == playernumber_two)
is_player_3<- as.vector(position_data$expcondition == experimentalcondition & position_data$teamnumber == teamnumber_current &
position_data$playernum == playernumber_three)
is_team_current<- as.vector(position_data$expcondition == experimentalcondition & position_data$teamnumber == teamnumber_current)
pos_player_1<- position_data[is_player_1,]
pos_player_2<- position_data[is_player_2,]
pos_player_3<- position_data[is_player_3,]
#This is the length of the position data. They will not have the same number of enteries due to time issues. The number of strategies checked will be equal to the lowest number
number_of_checkes <- min(c(length(pos_player_1[,1]), length(pos_player_2[,1]), length(pos_player_3[,1])))
#Make sure they are in the correct order (The ID value at the top should be the lowest)
#Calculate calculate the centroiz value for X and Z (Y value does not change in unity)
x_1 <- as.vector(pos_player_1$pos_x[1:number_of_checkes])
x_2 <- as.vector(pos_player_2$pos_x[1:number_of_checkes])
x_3 <- as.vector(pos_player_3$pos_x[1:number_of_checkes])
z_1 <- as.vector(pos_player_1$pos_z[1:number_of_checkes])
z_2 <- as.vector(pos_player_2$pos_z[1:number_of_checkes])
z_3 <- as.vector(pos_player_3$pos_z[1:number_of_checkes])
x_centroid<- (x_1 + x_2 + x_3)/3
z_centroid<- (z_1 + z_2 + z_3)/3
#Calculate the distance from each player to the centroid
player_1_distance_from_centroid<- sqrt((x_1 - x_centroid)^2 + (z_1 - z_centroid)^2)
player_2_distance_from_centroid<- sqrt((x_2 - x_centroid)^2 + (z_2 - z_centroid)^2)
player_3_distance_from_centroid<- sqrt((x_3 - x_centroid)^2 + (z_3 - z_centroid)^2)
#determine the strategy being used based on distance
strategy_vectors<- seq(1:number_of_checkes)
for(index in seq(1:number_of_checkes)){
#Performing math operations on logic vectors turns TRUEs in 1's and FALSE's into 0's
check_distances<- c(player_1_distance_from_centroid[index] < strategy_barrier_distance, player_2_distance_from_centroid[index] < strategy_barrier_distance,
player_3_distance_from_centroid[index] < strategy_barrier_distance)
#1 = "Go Together", 2 = "Go Alone", 3 = "Mix"
if(sum(check_distances) == 3){
#Go Together - this means that everyone is within the strategy border
strategy_vectors[index]<- 1
} else if(sum(check_distances) == 0){
paste("Go Alone")
#Go Alone - this means that everyone was outside the strategy border
strategy_vectors[index] <- 2
} else {
paste("Mix")
#Mix - this means that some were inside the borader and others were outside the border
strategy_vectors[index] <- 3
}
}
# 1 = "Go Together", 2 = "Go Alone", 3 = "Mix"
count_go_together <- sum(strategy_vectors == 1)
count_go_alone <- sum(strategy_vectors == 2)
count_mix <- sum(strategy_vectors == 3)
return(c(count_go_together, count_go_alone, count_mix))
}
# Counts the number of utterences for a player in a given condition ----
utterance_count_for_a_player<- function(positionData, condition, team_number, player_num){
#Get players table data
is_player_1 <- as.vector(positionData$expcondition == condition & positionData$teamnumber == team_number &
positionData$playernum == player_num)
current_player <- positionData[is_player_1,]
#Determine the number of utterences
current_player_transmission_set<- current_player$istransmitting
#Need a variable that indicates if the player is currently talking
isTalking <- FALSE
#Utterance count
utterance_count <- 0
# Count the number of utterances
# An utterance is defined as a consecutive set of checks were the intervale value is great than 0, (i.e., ~.25)
for(index in seq(from = 1, to = length(current_player_transmission_set))){
#get current check interval
current_check_interval<- current_player_transmission_set[index]
#If the index is 1, then this means it is the first check. The first check is different than the following checks
if(index != 1){
# This means that the current index is not one. As a result, there should have been a previosu check value
# Get the previous value
previous_check_interval<- current_player_transmission_set[index-1]
if(current_check_interval > 0){
#This means that the player was talking during this interval check
#Is this the last vector index?
if(index != length(current_player_transmission_set)){
# This is not the last vaule in the vector
#If the previous value was 0, that means that the player just started talking. We set the isTalking value to true
if(previous_check_interval == 0.00){
#This means that the player just started talking
} else if(previous_check_interval > 0){
#This means that the paleyer is continuing to talk
}
} else {
# This is the last value in the vector.
#Since this is the last vector value and the value of it is greater than 0, this means the player was last talking before the game ended.
#This will count as a utterence. The loop should end after this current pass
utterance_count <- utterance_count + 1
}
#The player is talking during this check
isTalking <- TRUE
} else if(current_check_interval == 0){
# Was the player talking previously?
if(isTalking){
#This means that the player was talking previously but is not longer talking. This is the end of an utterance, so count it
utterance_count <- utterance_count + 1
} else{
#This means that the player was not talkin previously and is still not talking
}
#The player is no longer talking
isTalking <- FALSE
} else {
#isTalking <- FALSE
}
} else if(index == 1){
# This means that this is the first index value.
#If the player is talking, set isTalking to true
if(current_check_interval > 0){
isTalking <- TRUE
}
}
}
# Return utterance count for this player in a given condition
return(utterance_count)
}
# Generate the IDs for a player on a given team (Assuming there are 3 members per team)----
generate_player_id <- function(playernum, teamnum){
# This function assumes that player IDs are assigned in sequential order.
# For example, in team 1, player 1 is P1, 2 is p2, and P3. Team 2, player 1 is P4, 2 is P5, 3 is P6
max_player_ID_value <- 3 * teamnum # 3 is hardcoded because there should have only three elements
palyer_ID <- ""
if(playernum == 3){
player_ID_num <- max_player_ID_value
if(player_ID_num < 10){
palyer_ID <- paste("P0", player_ID_num, sep = "")
} else if(player_ID_num >= 10){
palyer_ID <- paste("P", player_ID_num, sep = "")
}
} else if(playernum == 2 ){
player_ID_num <- max_player_ID_value - 1
if(player_ID_num < 10){
palyer_ID <- paste("P0", player_ID_num, sep = "")
} else if(player_ID_num >= 10){
palyer_ID <- paste("P", player_ID_num, sep = "")
}
} else if(playernum == 1) {
player_ID_num <- max_player_ID_value - 2
if(player_ID_num < 10){
palyer_ID <- paste("P0", player_ID_num, sep = "")
} else if(player_ID_num >= 10){
palyer_ID <- paste("P", player_ID_num, sep = "")
}
}
return(palyer_ID)
}
#Function to retrive the NASA TLX value for a specific scale, for a specific player, in a specific team, in a specific condition
scale_value_NASA_TLX <- function (TLX_table, teamNum, playerNum, condition, scale){
player_tlx <- TLX_table %>%
filter(Team == teamNum, Player == playerNum, Condition == condition)
if(length(player_tlx[,scale] == 1)){
return(player_tlx[,scale])
} else if(length(player_tlx[,scale]) == 0){
stop(paste("No TLX data found for player", playerNum, "in team", teamNum, "in condition", condition))
} else {
stop(paste("There is not exactly 1 entery for player", playerNum, "in team", teamNum, "in condition", condition))
}
}
# Return familiary value for team
familiarity_value<- function(data_familiar, team_num, team_column_name){
team_data <- data_familiar %>%
filter(.data[[team_column_name]]== team_num)
num_of_entries<- length(team_data[[team_column_name]])
if(num_of_entries == 1){
return(team_data[["Familiarity"]])
}
}
# Retrive demographic value
demographic_value_get <- function(demographic_data, key_rand_playerID_data, player_Id_value, demographic_value){
rand_num <- key_rand_playerID_data %>%
filter(ID == player_Id_value)
if(length(rand_num[[1]]) == 1){
# There should only be one value.
player_demo_data <- demographic_data %>%
filter(Rand == rand_num$Random_num)
if(length(rand_num[[1]]) == 1){
return(player_demo_data[[demographic_value]])
} else{
# This means there is more than one value. That is a problem.
message <- paste("There are multiple deomgraphic entries for the player with the ID", player_Id_value, ".")
stop(message)
}
} else{
# This means there is more than one value. That is a problem for demographic data.
message <- paste("The player with the ID", player_Id_value, "does not have a rand number value.")
stop(message)
}
}
# Get post-session value
post_session_survey_value<- function(data_post_session, team, player, condition, survey_value){
player_data<- data_post_session %>%
filter(Condition == condition, Team == team, Player == player)
if(length(player_data[[1]]) != 1){
message <- paste("Could not find post session survey data for player", player, "in team", team, "for condition", condition)
stop(message)
}
return(player_data[[survey_value]])
}
# Get overall post-session value
overall_post_session_survey_value<- function(overall_post_data, key_rand_playerID_data, player_Id_value, value_name){
rand_num_player<- key_rand_playerID_data %>%
filter(player_Id_value == ID)
if(length(rand_num_player[["Random_num"]]) == 1){
player_rand_value<- rand_num_player[1,"Random_num"]
} else{
message<- paste("Rand key data table has more than or less than one entery with the ID:", player_Id_value)
stop(message)
}
player_data<- overall_post_data%>%
filter(Rand == player_rand_value)
if(length(player_data[["Rand"]])== 1){
return(player_data[[value_name]])
} else{
message<- paste("The overal post survey has more than or less than one entry that has a Random number value of", player_rand_value)
stop(message)
}
}
# Find session order ----
session_order_number <- function(teamNum, counter_balance_set_dataframe, condition){
set_index <- teamNum %% length(counter_balance_set_dataframe) #If this equal 0 then that means this team used the last set
if(set_index == 0){
#This means that this team used the last set
set_index = length(counter_balance_set_dataframe)
}
# Select the correct session order list
session_set_in_order <- counter_balance_set_dataframe[,set_index] # Assumes the set is in a data frame
#Find the index of the session order and that is the session order (e.g., an index of 3 means this session was the 3rd session)
current_session_order <- which(condition == session_set_in_order)
return(current_session_order)
}
# Find the counterbalance set order
set_counter_balance_number <- function(teamNum, counter_balance_set_dataframe){
set_index <- teamNum %% length(counter_balance_set_dataframe)
if(set_index == 0){
#This means that this team used the last set
set_index = length(counter_balance_set_dataframe)
}
return(set_index)
}
# Total distance traveled by a player ----
total_distance_traveled_by_player<- function(position_data, experimentalcondition, teamnumber_current, playerNum, col_name_X, col_name_y){
# Player data
player_data_index <- which(position_data$teamnumber == teamnumber_current &
position_data$playernum == playerNum &
position_data$expcondition == experimentalcondition)
x <- position_data[player_data_index,col_name_X]
y <- position_data[player_data_index,col_name_y]
x.1 <- x[1:length(x) - 1]
x.2 <- x[2:length(x)]
y.1 <- y[1:length(y)-1]
y.2 <- y[2:length(y)]
total_distance <- sum(sqrt((x.2 - x.1)^2 + (y.2 - y.1)^2))
return(total_distance)
}
# Total distance traveled by team ----
total_distance_traveled_by_team <- function(position_data, experimentalcondition, teamnumber_current, col_name_X, col_name_y){
# Assuming there are three members in each team
distance_traveled_by_each_player <- rep(x = 0, times = 3)
for(player in seq(1,3)){
distance_traveled_by_each_player[player] <- total_distance_traveled_by_player(position_data, experimentalcondition, teamnumber_current, player, col_name_X, col_name_y)
}
return(sum(distance_traveled_by_each_player))
}
# Total items (correct and incorrect)(team and individual) collected by a player in a given session
total_items_collected_in_session_by_individual <- function(inventory_data, team_num, player_num, condition_name){
inventory_data_filtered <- inventory_data %>%
filter(teamnumber == team_num & expcondition == condition_name & playernum == player_num & itemid != -1)
return(length(inventory_data_filtered[,1]))
}
# Function to calculate the Collection Rate (i.e., duration / total items collected): Sec / Error. ----
# Duration is retruned if error count is 0.
collection_rate_ind <- function(data_position, data_inventory, teamnum, playernumber, condition){
# This is the item collection rate for an individual
# The units for this value is Sec / item.
# This takes into account the total items (incorrect or correct) collected by the individual
total_items_collected <- total_items_collected_in_session_by_individual(data_inventory, teamnum, playernumber, condition)
player_data <- data_position %>% filter(teamnumber == teamnum & playernum == playernumber & expcondition == condition)
player_data_last_line <- tail(player_data, 1)
duration_ind <- player_data_last_line[1,"duration_ind"]
if(total_items_collected == 0){
return(duration_ind)
} else if(total_items_collected > 0){
return(duration_ind / total_items_collected )
} else if(total_items_collected < 0){
message<- paste("collection_rate_ind: the total item count for player", playerNum, "in team", teamNum, "in condition", condition)
stop(message)
}
}
# Total correct items (team and individual items) collected by a player in a given session ----
total_correct_items_collected_in_session_by_individual <- function(inventory_data, team_num, player_num, condition_name){
inventory_data_filtered <- inventory_data %>%
filter(playernum == player_num & teamnumber == team_num & expcondition == condition_name & itemid != -1 & boughtcorrectly == 1)
return(length(inventory_data_filtered[,"itemid"]))
}
# Function to calculate the Collection Rate for correct items (i.e., duration / total items collected): Sec / Item.----
# Duration is retruned if error count is 0.
collection_rate_correct_items_ind <- function(data_position, data_inventory, teamnum, playernumber, condition){
# This is the item collection rate for an individual
# The units for this value is Sec / item.
# This takes into account the total correct items (team and individual) collected by the individual
total_items_collected <- total_correct_items_collected_in_session_by_individual(data_inventory, teamnum, playernumber, condition)
player_data <- data_position %>% filter(teamnumber == teamnum & playernum == playernumber & expcondition == condition)
player_data_last_line <- tail(player_data, 1)
duration_ind <- player_data_last_line[1,"duration_ind"]
if(total_items_collected == 0){
return(duration_ind)
} else if(total_items_collected > 0){
return(duration_ind / total_items_collected)
} else if(total_items_collected < 0){
message<- paste("collection_rate_correct_items_ind: the total correct item count for player", playerNum, "in team", teamNum, "in condition", condition)
stop(message)
}
}
# Total items (correct and incorrect)(team and individual) collected by a team in a given session ----
total_items_collected_in_session_by_team <- function(data_inventory, team_num, condition_name){
inventory_data_filtered <- data_inventory %>%
filter(teamnumber == team_num & expcondition == condition_name & itemid != -1)
return(length(inventory_data_filtered[,"itemid"]))
}
# Function to calculate the Collection Rate (i.e., duration / total items collected): Sec / Error. ----
# Duration is retruned if error count is 0.
collection_rate_team <- function(data_position, data_inventory, teamnum, condition){
# This is the item collection rate for a team
# The units for this value is Sec / item.
# This takes into account the total items (incorrect or correct) collected by the team
total_items_collected <- total_items_collected_in_session_by_team(data_inventory, teamnum, condition)
team_data <- data_position %>% filter(teamnumber == teamnum & playernum == 1, expcondition == condition)
team_data_last_line <- tail(team_data, 1)
duration_team <- team_data_last_line[1,"duration"]
if(total_items_collected == 0){
return(duration_team)
} else if(total_items_collected > 0){
return(duration_team / total_items_collected)
} else if(total_items_collected < 0){
message<- paste("collection_rate_team: the total item count for team", teamNum, "in condition", condition)
stop(message)
}
}
# Total correct items (team and individual items) collected by a team in a given session ----
total_correct_items_collected_in_session_by_team <- function(data_inventory, team_num, condition_name){
inventory_data_filtered <- data_inventory %>%
filter(teamnumber == team_num & expcondition == condition_name & itemid != -1 & boughtcorrectly == 1)
return(length(inventory_data_filtered[,"itemid"]))
}
# Function to calculate the Collection Rate for correct items (i.e., duration / total items collected): Sec / Item. ----
# Duration is retruned if error count is 0.
collection_rate_correct_items_team <- function(data_position, data_inventory, teamnum, condition){
# This is the item collection rate for a team
# The units for this value is Sec / item.
# This takes into account the total correct items (team and individual) collected by a team
total_items_collected <- total_correct_items_collected_in_session_by_team(data_inventory, teamnum, condition)
team_data <- data_position %>% filter(teamnumber == teamnum & playernum == 1, expcondition == condition)
team_data_last_line <- tail(team_data, 1)
duration_team <- team_data_last_line[1,"duration"]
if(total_items_collected == 0){
return(duration_team)
} else if(total_items_collected > 0){
return(duration_team / total_items_collected)
} else if(total_items_collected < 0){
message<- paste("collection_rate_correct_items_team: the total correct item count for team", teamNum, "in condition", condition)
stop(message)
}
}
# Check to see if the random numbers in demographics surveys match the random numbers in the post surveys ----
is_demographic_rand_num_in_post_survey <- function(post_session_table, demo_survey, team_col_name, player_col_name, rand_num_col_name){
#team_col_name : for the post_session_table
#rand_num_col_name: This name should be the same for the post_session_table survey and the demo_survey is the name in the
is_in_post_survey <- T
# Make sure post session data is good
post_session_survey_is_good <- is_post_session_data_correct(post_session_table, team_col_name, player_col_name, rand_num_col_name)
if(!post_session_survey_is_good){
message <- paste("Something wrong with post_session survey.")
stop(message)
}
rand_num_list_demo <- as.vector(demo_survey[,rand_num_col_name])
if(sum(duplicated(rand_num_list_demo)) > 0){ # If there is more than one of the same rand number, then something is wrong
message <- paste("There are duplicate rand num values.")
is_in_post_survey <- F
stop(message)
}
for(rand_num in rand_num_list_demo){
rand_in_post_survey <- rand_num == as.vector(post_session_table[,rand_num_col_name])
if(sum(rand_in_post_survey) == 0){
message <- paste("The rand number ", rand_num, " was not found in the post session", sep = "")
stop(message)
}
}
return(is_in_post_survey)
}
# Check to make sure TLX survey is correct (i.e., make sure every player in the key has the correct number of TLX values 4) ----
is_TLX_survey_correct <- function(data_tlx, rand_num_list){
is_valid <- T
for (rand in rand_num_list) {
data_tlxTemp <- data_tlx %>%
filter(Rand.Num == rand)
if(length(data_tlxTemp[[1]]) != 4){
message <- paste("There is not exactly 4 entries in the TLX for random number ", rand)
is_valid <- F
stop(message)
break
}
}
return(is_valid)
}
# Check to make sure each rand number in the post survey responses are the same for each player in each team ( for all of the conditions) ----
is_post_session_data_correct <- function(post_session_data, team_number_column_name, player_num_col_name, rand_num_col_name){
is_correct <- T
team_list <- as.integer(levels(factor(post_session_data[,team_number_column_name]))) # The teams that are in the data set
for(team in team_list){
for(player in c(1,2,3)){
team_col <- as.vector(post_session_data[,team_number_column_name])
player_num_col <- as.vector(post_session_data[,player_num_col_name])
player_index <- which(team_col == team & player_num_col == player)
num.of.enteries <- length(player_index)
if(num.of.enteries != 4){ # -- Check to see if there is exactly 4 entiers, if more/less than 4 then something is wrong
message <- paste("There is either more than or less than 4 data points for team ", team, " and player ", player, sep = "")
is_correct <- F
stop(message)
}
# -- Pick first value and make sure it is the same throughout
player_data <- post_session_data[player_index,]
first.rand.num <- player_data[1, rand_num_col_name]
all.rand.num <- player_data[,rand_num_col_name]
if(sum(all.rand.num != first.rand.num) > 0){
# This means one or more rand numbers enteries are not the same as the others
message <- paste("One or more of the rand number enteries are not the same as the other enteriesf. Team ", team,
", player ", player, ", rand ", first.rand.num, sep = "")
is_correct <- F
stop(message)
}
# Check to make sure the player numbers are correct
team_index <- which(team_col == team)
team_data <- post_session_data[team_index,]
player_col <- as.vector(team_data[,"Player"]) # Get the player colum (i.e., 1,2, or 3)
player_factors <- levels(factor(player_col))
# print(paste("Factors: ", player_factors))
for(player in player_factors){ # Check to make sure there are 4 enteries for each player
num_of_enteries_for_player <- sum(player == player_col)
if(num_of_enteries_for_player !=4){
message <- paste("There is more or less than 4 enteries for player ", player, " in team ", team, ".", sep = "")
is_correct <-F
stop(message)
}
}
}
}
return(is_correct)
}
# Generate aggragate data (final team score, final individual score, ) ----
generate_aggragate_data <- function(team_numbers, condition_list, clean_position_data, clean_error_data, clean_invent_data, clean_demo_data, clean_familiarity_data, clean_overall_post_data,
player_num_list, strategy_barrier_dis, counter_balance_set, col.names, names_TLX, names_PostSession, names_Overall_Postsession,
key_rand_player_data, names_demographic ){
# Final data output
number_of_columns<- length(col.names)
data_output_final<- matrix(0, nrow = 0, ncol = number_of_columns)
# Give column names
colnames(data_output_final)<- col.names
for(team in team_numbers){
# Counter balance set number for the team
counter_balance_set_num <- set_counter_balance_number(team, counter_balance_set)
# Familiarity value for team
familiarity_of_team<- as.character(familiarity_value(clean_familiarity_data,team, "ï..Team")) # Note sure why the column name is "ï..Team" in stead of "Team".
for(condition in condition_list){
#Count the number of times strategies were used
current_strategy_vector<- strategy_count_vector(position_data = clean_position_data,
experimentalcondition = condition,
teamnumber_current = team,
playernumber_one = player_num_list[1],
playernumber_two = player_num_list[2],
playernumber_three = player_num_list[3],
strategy_barrier_distance = strategy_barrier_dis)
current_go_together_count<- current_strategy_vector[1]
current_go_alone_count <- current_strategy_vector[2]
current_mix<- current_strategy_vector[3]
#Get the total utterence count for a team
player_utterance_count_list<- rep(x = 0, times = length(player_num_list))
#Get the total number of errors for team
total_errors_team <- total_number_of_errors_team(clean_error_data, team, condition)
#Find the count for each palyer (i.e., index 1 is player 1)
for(player in player_num_list){
current_player_utterence_count <- utterance_count_for_a_player(positionData = clean_position_data, condition, team, player)
#ErrorCheck
if(length(current_player_utterence_count) > 1){
stop("utterance_count_for_a_player() function produceted a vector that had a length greater than 1. Should only be 1 element.")
}
player_utterance_count_list[player] <- current_player_utterence_count[1] #This is hard codded because there should only be one value
}
#Total Distance traveled - Team
total_dis_team <- total_distance_traveled_by_team(position_data = clean_position_data, condition, team, "pos_x", "pos_z")
# Get player 1 data
is_player_1<- as.vector(clean_position_data$expcondition == condition & clean_position_data$teamnumber == team &
clean_position_data$playernum == 1) #Use the team score recorded from player 1
player_data_1<- clean_position_data[is_player_1,]
last_line_1 <- length(player_data_1[,1]) #Last line index
#Team Score
team_final_score<- as.vector(player_data_1[last_line_1,"teamscore"])
#Team time remaining
time_remaining_team <- as.vector(player_data_1[last_line_1,"gametimer"])
# Collection rate for team in a condition (correct and incorrect items)(team and individual)
team_collection_rate <- collection_rate_team(clean_position_data, clean_invent_data, team, condition)
# Collection rate for team in a condition (correct items) (team and individual items)
team_collection_rate_correct_items <- collection_rate_correct_items_team(clean_position_data, clean_invent_data, team, condition)
# Error rate for the team
team_error_rate <- error_rate_team(clean_position_data, clean_error_data, team, condition)
for(player in player_num_list){
# The next step is to add all of the values for each cloumn
is_player<- as.vector(clean_position_data$expcondition == condition & clean_position_data$teamnumber == team &
clean_position_data$playernum == player)
player_data<- clean_position_data[is_player,]
#The order of the variables is determined by the cloumn name vector - col_names
#Last Line in Data Table
last_line<- length(player_data[,1]) #Last line index
# Set the target feedback and if there was feedback present
if(condition == "A"){
target_for_feedback<- c("None")
} else if(condition == "B"){
target_for_feedback<- c("Ind")
} else if (condition == "C"){
target_for_feedback<- c("Team")
} else if (condition == "D"){
#The only other condition is condition D
target_for_feedback<- c("Ind_Team")
} else {
stop("A condition value was not recognized.")
}
# Get the time remaining team and individual
time_remaining_ind <- as.vector(player_data[last_line,"gametimer_ind"])
#Get the Individual Score
individual_final_score<- as.vector(player_data[last_line,"individualscore"])
#Correct Individual Items Collected
correct_individual_items_collected<- as.vector(player_data[last_line, "ci_ind"])
#Incorrect Individual Items Collected
incorrect_individual_item_collected<- as.vector(player_data[last_line, "ii_ind"])
#errors Individual
errors_individual_unique<- as.vector(player_data[last_line, "error_ind"])
#Get the total number of errors for player
total_errors_ind <- total_number_of_errors_individual(data_errors = clean_error_data, team, player, condition)
# Get total distance traveled by player
total_dis_ind <- total_distance_traveled_by_player(position_data = clean_position_data, condition, team, player, "pos_x", "pos_z")
#Correct Team Items Collected
correct_team_items_collected<- as.vector(player_data[last_line, "ci_team"])
#Incorrect Team Items collected
incorrect_team_item_collected<- as.vector(player_data[last_line, "ii_team"])
#Errors Team
errors_team_unique<- as.vector(player_data[last_line, "error_team"])
#Transmission
total_transmition_ind<- sum(as.vector(player_data$istransmitting))
#Utterance count
current_utterance_count_player <- player_utterance_count_list[player]
current_utterance_count_team <- sum(player_utterance_count_list)
# total team transmission time
current_team_index <- which(condition == clean_position_data[,"expcondition"] & team == clean_position_data[,"teamnumber"])
current_team_position_data <- clean_position_data[current_team_index,]
total_transmition_team <- sum(current_team_position_data$istransmitting)
#Dominate Strategy. The dominitate strategy was the strategy used the highest number of times
if(max(current_strategy_vector) == current_go_together_count){
dominate_strategy_used<- go_together_name
} else if (max(current_strategy_vector) == current_go_alone_count){
dominate_strategy_used<- go_alone_name
} else if (max(current_strategy_vector) == current_mix){
dominate_strategy_used<- mix_name
} else {
stop("The max count in the strategy vector does not equal any of the selected strategy counts")
}
# Find player id
current_player_id <- generate_player_id(player,team)
# Demographic survey data
demo_values<- vector()
for(name in names_demographic){
# Need to make the the values a character because there are different types of values.
value<- as.character(demographic_value_get(clean_demo_data, key_rand_player_data, current_player_id, name))
demo_values<- append(demo_values, value)
}
#---------------------------------------------------------------
#Find the session order
current_session_order <- session_order_number(team, counter_balance_set, condition)
#---------------------------------------------------------------
# Collection rate for individual (correct and incorrect)(team and individual items)
individual_collection_rate <- collection_rate_ind(clean_position_data, clean_invent_data, team, player, condition)
# Collection rate for individual (correct items)(team and individual items)
individual_collection_rate_correct_items <- collection_rate_correct_items_ind(clean_position_data, clean_invent_data, team, player, condition)
# TLX values for player
TLX_values<- vector()
for(name in names_TLX){
value<- scale_value_NASA_TLX(NASA_TLX_table, team, player, condition, name)
TLX_values<- append(TLX_values,value)
}
# Post-Session Survey values
Post_Session_Values<- vector()
for (name in names_PostSession) {
index<- which(names_PostSession == name)
value<- as.character(post_session_survey_value(post_session_table, team, player,condition, name))
Post_Session_Values[index] <- value
}
# Overall post-session survey values
Overall_Post_Session_Values<- vector()
for (name in names_Overall_Postsession) {
value <- as.character(overall_post_session_survey_value(clean_overall_post_data, key_rand_player_data, current_player_id, name))
Overall_Post_Session_Values<- append(Overall_Post_Session_Values, value)
}
# Error Rate for individual
ind_error_rate <- error_rate_ind(clean_position_data, clean_error_data, team, player, condition)
#This should be the same as the col_names variable above.
data_output_final<- rbind(data_output_final,
c(team,
familiarity_of_team,
condition,
player,
current_player_id,
current_session_order,
counter_balance_set_num,
target_for_feedback,
time_remaining_ind,
time_remaining_team,
individual_final_score,
team_final_score,
correct_individual_items_collected,
incorrect_individual_item_collected,
individual_collection_rate,
individual_collection_rate_correct_items,
errors_individual_unique,
total_errors_ind,
ind_error_rate,
total_dis_ind,
correct_team_items_collected,
incorrect_team_item_collected,
team_collection_rate,
team_collection_rate_correct_items,
errors_team_unique,
total_errors_team,
team_error_rate,
total_dis_team,
total_transmition_ind,
total_transmition_team,
current_utterance_count_player,
current_utterance_count_team,
current_go_together_count,
current_go_alone_count,
current_mix,
dominate_strategy_used,
TLX_values,
Post_Session_Values,
Overall_Post_Session_Values,
demo_values))
}
}
# Progress Bar
team_index <- which(team == team_numbers)
progress(team_index / length(team_numbers) * 100)
}
return(data_output_final)
}
# Generate figures for dependent variables with specified x variables ----
generate_figures_team <- function(Data, num_of_teams, figure_titles, y_values_team, y_labels_team, x_values, x_labels_team, plot_types, filelocation){ # Need to change the variables
previous_wd_location <- getwd()
setwd(filelocation)
# What is the N for Teams
N_teams <- num_of_teams
# The N text to add to title for teams
N_teams_full_text <- paste("(N = ", N_teams, ")", sep = "")
for(y_current in y_values_team){
for (x_current in x_values){
index_for_y <- which(y_current == y_values_team)
index_for_x <- which(x_current == x_values)
for(plot in plot_types){
x_label <- x_labels_team[index_for_x]
y_label <- y_labels_team[index_for_y]
figure_title <- figure_titles[index_for_y]
# print(paste("x: ", x_current," ","y: ", y_current, " ", "Plot: ", plot, " ", "Label: ", x_label," ", "Label (y): ", y_label , sep = ""))
filename_graph <- paste("team_",y_label,"_by_",x_label,"_",plot,".png", sep = "")
if(plot == "Group_Bar"){
Data_ordered <- Data %>%
mutate(position = rank(-Data[,y_current], ties.method="first"))
ggplot(data = Data_ordered, aes_string(x = x_current, y = y_current, fill = "Team", group = "position")) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = paste(figure_title, N_teams_full_text) , x = x_label, y = y_label) +
guides(fill=guide_legend(title="Team"))
ggsave(filename = filename_graph)
} else if(plot == "Boxplot"){
ggplot(data = Data, aes_string(x = x_current, y = y_current)) +
geom_boxplot() +
labs(title = paste(figure_title, N_teams_full_text), x = x_label, y = y_label)
ggsave(filename = filename_graph)
} else if(plot == "Point_plot"){
ggplot(data = Data, aes_string(x = x_current, y = y_current)) +
geom_point() +
labs(title = paste(figure_title, N_teams_full_text), x = x_label, y = y_label)
ggsave(filename = filename_graph)
}
}
}
# Progress Bar
y_index <- which(y_current == y_values_team)
progress(y_index / length(y_values_team) * 100)
}
setwd(previous_wd_location)
}
# Generate figures for dependent variables with specified x variables (Individual) ----
generate_figures_ind <- function(Data, num_of_players, figure_titles, y_values_ind, y_labels_ind, x_values_ind, x_labels_ind, plot_types_ind, filelocation){
previous_wd_location <- getwd()
# What is the N for Inds
N_ind <- num_of_players
# The N text to add to title for Inds
N_ind_full_text <- paste("(N = ", N_ind, ")", sep = "")
for(y_current in y_values_ind){
for (x_current in x_values_ind){
index_for_y <- which(y_current == y_values_ind)
index_for_x <- which(x_current == x_values_ind)
for(plot in plot_types_ind){
x_label <- x_labels_ind[index_for_x]
y_label <- y_labels_ind[index_for_y]
figure_title <- figure_titles[index_for_y]
filename_graph <- paste("ind_",y_label,"_by_",x_label,"_",plot,".png", sep = "")
if(plot == "Group_Bar"){
Data_filtered<- Data %>%
mutate(position = rank(-Data[,y_current], ties.method="first"))
# print(paste("team_",y_label,"_by_",x_label,"_",plot, sep = ""))
ggplot(data = Data_filtered, aes_string(x = x_current, y = y_current, fill = "Player_ID", group = "position")) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = paste(figure_title, N_ind_full_text) , x = x_label, y = y_label) +
guides(fill=FALSE)
ggsave(filename = filename_graph)
} else if(plot == "Boxplot"){
ggplot(data = Data, aes_string(x = x_current, y = y_current)) +
geom_boxplot() +
labs(title = paste(figure_title, N_ind_full_text), x = x_label, y = y_label)
ggsave(filename = filename_graph)
} else if(plot == "Point_plot"){
ggplot(data = Data, aes_string(x = x_current, y = y_current)) +
geom_point() +
labs(title = paste(figure_title, N_ind_full_text), x = x_label, y = y_label)
ggsave(filename = filename_graph)
}
}
}
# Progress Bar
y_index <- which(y_current == y_values_ind)
progress(y_index / length(y_values_ind) * 100)
}
setwd(previous_wd_location)
}
# Model the data for the team level anlysis ----
model_data_Target_Session <- function(df, dependent, model.type, is.team, is.robust){
if(is.team){
if(model.type == "null" && !is.robust){
lmer(data = df, as.formula(paste(dependent,"~ 1 + (1|Team)")))
} else if(model.type == "All"){
lmer(data = df, as.formula(paste(dependent,"~ Target * SessionOrder + (1|Team)")))
} else if(model.type == "NoInteraction" && !is.robust){
lmer(data = df, as.formula(paste(dependent,"~ Target + SessionOrder + (1|Team)")))
} else if(model.type == "NoInteraction_NoTarget" && !is.robust){
lmer(data = df, as.formula(paste(dependent,"~ SessionOrder + (1|Team)")))
} else if(model.type == "NoInteraction_NoSession" && !is.robust){
lmer(data = df, as.formula(paste(dependent,"~ Target + (1|Team)")))
} else if(model.type == "null" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ 1 + (1|Team)")))
} else if(model.type == "All" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ Target * SessionOrder + (1|Team)")))
} else if(model.type == "NoInteraction" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ Target + SessionOrder + (1|Team)")))
} else if(model.type == "NoInteraction_NoTarget" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ SessionOrder + (1|Team)")))
} else if(model.type == "NoInteraction_NoSession" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ Target + (1|Team)")))
} else{
stop("Model.type not supported")
}
} else {
# Run this code if individual level model
if(model.type == "null" && !is.robust){
lmer(data = df, as.formula(paste(dependent,"~ 1 + (1|Team) + (1| Player_ID)")))
} else if(model.type == "All" && !is.robust){
lmer(data = df, as.formula(paste(dependent,"~ Target * SessionOrder + (1|Team) + (1| Player_ID)")))
} else if(model.type == "NoInteraction" && !is.robust){
lmer(data = df, as.formula(paste(dependent,"~ Target + SessionOrder + (1|Team) + (1| Player_ID)")))
} else if(model.type == "NoInteraction_NoTarget" && !is.robust){
lmer(data = df, as.formula(paste(dependent,"~ SessionOrder + (1|Team) + (1| Player_ID)")))
} else if(model.type == "NoInteraction_NoSession"){
lmer(data = df, as.formula(paste(dependent,"~ Target + (1|Team) + (1| Player_ID)")))
} else if(model.type == "null" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ 1 + (1|Team) + (1| Player_ID)")))
} else if(model.type == "All" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ Target * SessionOrder + (1|Team) + (1| Player_ID)")))
} else if(model.type == "NoInteraction" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ Target + SessionOrder + (1|Team) + (1| Player_ID)")))
} else if(model.type == "NoInteraction_NoTarget" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ SessionOrder + (1|Team) + (1| Player_ID)")))
} else if(model.type == "NoInteraction_NoSession" && is.robust){
rlmer(data = df, as.formula(paste(dependent,"~ Target + (1|Team) + (1| Player_ID)")))
} else{
stop("Model.type not supported")
}
}
}
# Multiple plot function ----
# Function found: http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
# Test ----
# # The goal of this logic is to retrive values from the overall post survey
# overall_post_data<- overall_post_session_table
# key_rand_playerID_data<- Rand_num_key
# player_Id_value<- "P20"
# value_name<- "did_your_ind_perform_change_over_time_why_whyNot"
#
# rand_num_player<- key_rand_playerID_data %>%
# filter(player_Id_value == ID)
#
# if(length(rand_num_player[["Random_num"]]) == 1){
# player_rand_value<- rand_num_player[1,"Random_num"]
# } else{
# message<- paste("Rand key data table has multiple enteries with the same ID:", player_Id_value)
# stop(message)
# }
#
# player_data<- overall_post_data%>%
# filter(Rand == player_rand_value)
#
# if(length(player_data[["Rand"]])== 1){
# player_data[[value_name]]
# } else{
# message<- paste("The overal post survey has more than one entry that has a Random number value of", player_rand_value)
# stop(message)
# }
#
#
# overall_post_session_survey_value<- function(overall_post_data, key_rand_playerID_data, player_Id_value, value_name){
# rand_num_player<- key_rand_playerID_data %>%
# filter(player_Id_value == ID)
#
# if(length(rand_num_player[["Random_num"]]) == 1){
# player_rand_value<- rand_num_player[1,"Random_num"]
# } else{
# message<- paste("Rand key data table has multiple enteries with the same ID:", player_Id_value)
# stop(message)
# }
#
# player_data<- overall_post_data%>%
# filter(Rand == player_rand_value)
#
# if(length(player_data[["Rand"]])== 1){
# return(player_data[[value_name]])
# } else{
# message<- paste("The overal post survey has more than one entry that has a Random number value of", player_rand_value)
# stop(message)
# }
# }
#
# overall_post_session_survey_value(overall_post_session_table, Rand_num_key, "P20", "did_your_ind_perform_change_over_time_why_whyNot")
|
739ed41832ae801c46cee3d42b6c0360ef4c1690 | 3ae034f636da3885d76ed09f03222520d557f8b9 | /inst/tinytest/test_Utilities.R | 5918a4b143d6f2cac66bd746bb443c80270d2e4c | [] | no_license | vlcvboyer/FITfileR | e32b03762ac55bfb6ac8e38aeb0f7ed6dadf549c | 90f52e716022123a02ecd2c44bdb73967d3f467c | refs/heads/master | 2023-07-09T05:02:09.914129 | 2021-08-11T08:11:07 | 2021-08-11T08:11:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 460 | r | test_Utilities.R |
############################################################
## convert double representing a uint32 into a raw vector ##
############################################################
## Should fail with negative or too large values
expect_error(FITfileR:::.uintToBits(-1))
expect_error(FITfileR:::.uintToBits(2^32))
expect_true(
is(FITfileR:::.uintToBits(10), "raw")
)
expect_equal(
FITfileR:::.binaryToInt(FITfileR:::.uintToBits(10)),
10
)
|
a5b54d67017b80a6d2d232c4d6e41bab390f2b7d | 426a32c372432dd23462b558aa24c5e100cbbcab | /man/AlleleRetain-package.Rd | 7112da0a79ca1045a22e618ff7c2226890f19fef | [] | no_license | cran/AlleleRetain | 6e2922aa67a095effbc38e9c9822a8faa4d93e0b | 5d47ba944a152f41220c92d4dd525ec95a07e990 | refs/heads/master | 2021-06-06T14:13:37.494126 | 2018-01-11T18:29:41 | 2018-01-11T18:29:41 | 17,677,695 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 602 | rd | AlleleRetain-package.Rd | \name{AlleleRetain}
\alias{AlleleRetain}
\title{ Allele Retention, Inbreeding, and Demography }
\description{
Simulates the effect of management or demography on allele retention
and inbreeding accumulation in bottlenecked populations of animals
with overlapping generations.
}
\details{
Typically, the user will run \code{aRetain}, then \code{aRetain.summary} to assess characteristics of the simulated population. \code{indiv.summary}, \code{pedigree.summary} (requires package \bold{pedigree}), \code{LRS.summary}, and \code{agerepro.summary} will provide further output.
} |
e51ab8fa20023bded079966ed61355bd9c994fc0 | d69bc1641e7e83034660f0408b3e8fd65cfced80 | /weighted_jaccard/GAGE_simulation/plot_sim_error_weighted_jaccard.R | de72929229d0000bfc09dc2ee929c6f3074324e7 | [] | no_license | cchu70/perfect_mapper | 1e9cf48d62bcd79c2b02d903ab48460e59c17d09 | eca3850b499c3d62a73be35ca0075db90b870dcf | refs/heads/master | 2020-06-17T09:16:32.039692 | 2019-08-16T15:35:16 | 2019-08-16T15:35:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,027 | r | plot_sim_error_weighted_jaccard.R | # Rscript to plot the performance of weighted jaccard weighting schemes, table output from Simulated Reads Weighted Jaccard Performance Percentages
library(ggplot2)
args = commandArgs(trailingOnly=T)
out=args[1] # Output name (no type ending)
table=args[2] # Weighted Jaccard Performance Percentage output
vary_weights=read.table(table, header=T)
# Combine the weight schemes
vary_weights$weight_scheme = paste(vary_weights$V5,vary_weights$V6)
# Plot
ggplot(data=vary_weights, aes(x=V1, y=V2, col=weight_scheme)) + geom_point() + ylim(0,1) + xlab("Error rate introduced") + ylab("Retention Rate") + theme_bw()
# Subset or change the coloring schemes to view different combinations
# Examples
# Plot only the performances of weighting schemes where the non-uniqmer weight is 0
# ggplot(data=subset(vary_weights, (V6 == 0)), aes(x=V1, y=V2, col=paste(V3,V4))) + geom_point() + ylim(0,1) + theme_bw() + labs(fill = "Weight 0 for non-uniq-mers")
ggsave(file=paste(out,'.plot_sim_error_weighted_jaccard.png', sep="")) |
8eff69ad7578600ae7b70c482f59b97e85c615da | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.management/man/licensemanagerusersubscriptions_list_instances.Rd | 83fd88300bc7a010acb659c9b15f441e503de5c7 | [
"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 | 921 | rd | licensemanagerusersubscriptions_list_instances.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/licensemanagerusersubscriptions_operations.R
\name{licensemanagerusersubscriptions_list_instances}
\alias{licensemanagerusersubscriptions_list_instances}
\title{Lists the EC2 instances providing user-based subscriptions}
\usage{
licensemanagerusersubscriptions_list_instances(
Filters = NULL,
MaxResults = NULL,
NextToken = NULL
)
}
\arguments{
\item{Filters}{An array of structures that you can use to filter the results to those
that match one or more sets of key-value pairs that you specify.}
\item{MaxResults}{Maximum number of results to return in a single call.}
\item{NextToken}{Token for the next set of results.}
}
\description{
Lists the EC2 instances providing user-based subscriptions.
See \url{https://www.paws-r-sdk.com/docs/licensemanagerusersubscriptions_list_instances/} for full documentation.
}
\keyword{internal}
|
2d53c36519a1e9b370f285960183ab6757180328 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spatstat/examples/closing.Rd.R | 5a8fc11a11ac1b19a9ec3fb5482bd6b9b1b52d22 | [] | 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 | 304 | r | closing.Rd.R | library(spatstat)
### Name: closing
### Title: Morphological Closing
### Aliases: closing closing.owin closing.ppp closing.psp
### Keywords: spatial math
### ** Examples
v <- closing(letterR, 0.25)
plot(v, main="closing")
plot(letterR, add=TRUE)
plot(closing(cells, 0.1))
points(cells)
|
8f1bdb02e7c21fd7a024fd55d91dc5248f8b3b3b | 2d58f4d5eae4139a72ab36c255238536486b4ff7 | /GameGP/R/Classification.R | d8ac8ff5f64e1b55957dd790b6a3c28c0eee5d33 | [] | no_license | ahle-pro/datamining | 39758cc665e7fc359d6d404ad1340f0300902e37 | ba8e149dfd5b9d8eb65c0055ecae256d06250b69 | refs/heads/master | 2021-08-31T07:08:20.252750 | 2017-12-20T16:02:53 | 2017-12-20T16:02:53 | 114,905,772 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,504 | r | Classification.R | library("readr", lib.loc="~/R/win-library/3.3")
library("data.table")
library(dplyr)
setwd("D:/projects/Data Mining")
dataSrc <- readRDS("data/dataStat.rds")
pdf("report/program_classif.pdf")
getPrograms_X<-function(df){
# create a table
table = data.table(df)# table1: table with less columns from source data
table <- table[,.(.N), by=Programid] # do on all row, produce the count, and group by the first columns
#tUserFreq <- tUserFreq[order(N)]# table2: data groupeby users
plot(tbPrograms, main="No. of exercises on each program dist.",sub="N = 5.8M obs., db = GamePro", xlab="Programid", ylab="No. of exercises")
return(table)
}
tbPrograms = getPrograms_X(dataSrc)
tabular1<-function(table){
# declare variables
ret <- data.frame("Name"=character(), "Value" = character(), stringsAsFactors = FALSE)
# count No. of the programs
ret[nrow(ret)+1,] <- c("No. of programs", nrow(table))
#sort
table = table[order(-N)]
# output
plot.new()
grid.draw(grid.table(ret))
#output
tb_head = head(table)
colnames(tb_head)[2] <- "No. of exercises"
plot.new()
grid.draw(grid.table(tb_head))
}
tabular1(tbPrograms)
dev.off()
build_save_data <- function(){
dataP4 = dataSrc[dataSrc$Programid == 4, ]
saveRDS(dataP4, "data/dataP4.rds")
dataP13 = dataSrc[dataSrc$Programid == 13, ]
saveRDS(dataP13, "data/dataP13.rds")
dataP14 = dataSrc[dataSrc$Programid == 14, ]
saveRDS(dataP14, "data/dataP14.rds")
}
#build_save_data()
|
c807701c283d3e0565cda0d1e7e6928a10fc9861 | 6666615e41404182c1b0421c49c80e0fe223ab78 | /1_master.R | a3805bb47d4784da96187b62f2e71ee480fb5b39 | [] | no_license | PerinatalLab/PubMed_AbstractMinining_for_Genes | d8891a5dd56f3696baf48d801ee561b3640cb21d | c736a0084ba1f451baf0035e59e853e69cc17a64 | refs/heads/master | 2016-09-05T12:56:57.670311 | 2015-07-12T22:26:29 | 2015-07-12T22:26:29 | 37,733,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 843 | r | 1_master.R | #!/usr/bin/Rscript
# passes arguments to the bash script that takes care of PubMed abstracts
setwd("~/Biostuff/MOBA_GESTAGE_GWAS/PREGNANCY_GENES/PubMed_2015Jun_GYNECOLOGY")
bash_script = "./1_extract_Abstracts_from_PubMed_output.sh"
# abstracts that are downloaded from PubMed
file_list1 = list.files("./PubMed_RAW/")
file_list = file_list1[grep("SKIN|INTEST|MUSCL|HEART|LIVER|LUNG",file_list1)]
# naming convention: PubMed_webSearch_RAW_CERVIX_GENES_2014Jun17_n1362.txt
for (i in 1:length(file_list)) {
file_name = file_list[i]
print(file_name)
name_chunks = unlist(strsplit(file_name,"_"))
name_chunks[3] = "DIGESTED"
name_chunks[7] = unlist(strsplit(name_chunks[7],"\\."))[1] # get rid of .txt
new_name = paste(name_chunks,sep="_",collapse="_")
cmnd = paste(bash_script,file_name,new_name,sep=" ")
system(cmnd,ignore.stdout = F)
}
|
20c7ce76c73bf1c2855450cbca1094249bd98597 | c91350b98d6c2d6c067cd796742795847c6fd631 | /vignettes/man/spectrum.id-set-PsmDetail-method.Rd | c55fe314cf9effe17c75925b17c767421c2c489c | [] | no_license | gccong/ddiR-sirius | 127673f8fca158449e50dafc462ec78c234a5d55 | 6b1792d06e6ff094349e89b5cbee7144763b932d | refs/heads/master | 2021-01-19T04:18:49.242352 | 2015-12-13T22:29:01 | 2015-12-13T22:29:01 | 62,987,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 429 | rd | spectrum.id-set-PsmDetail-method.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/psm.R
\docType{methods}
\name{spectrum.id<-,PsmDetail-method}
\alias{spectrum.id<-,PsmDetail-method}
\title{Replaces a PSM spectrum.id}
\usage{
\S4method{spectrum.id}{PsmDetail}(object) <- value
}
\arguments{
\item{object}{a ProteinDetail}
\item{value}{the spectrum.id}
}
\description{
Replaces a PSM spectrum.id
}
\author{
Jose A. Dianes
}
|
6a51a74fa77a7e568d038b42f6911cdc86dc3606 | b6fd15efff8945a1f0b8501f422cbe6d9f4c23e9 | /Anul III/Sem 2/Tehnici de Simulare/Proiect/cod.R | e2ce02209df7af216865608d06e7a3601cce5e09 | [] | no_license | andreim9816/Facultate | 729a14fb60755e36fca01355162253f088fabf3e | 8eae7f73fd8ab5fd11de3f1fa744a1837abd49e0 | refs/heads/master | 2023-03-06T09:59:53.477012 | 2022-03-11T22:47:17 | 2022-03-11T22:47:17 | 175,485,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,051 | r | cod.R | library(shiny)
library(shinythemes)
# Functie corespunzatoare intensitatii procesului Poisson
LAMBDA <- function(t) {
if (0 <= t & t < 3 * 60) {
c <- -t ** 2 - 2 * t + 25
} else if (3 * 60 <= t & t <= 5 * 60) {
c <- 14
} else if (5 * 60 < t & t < 9 * 60) {
c <- -0.05 * (t ** 2) - 0.2 * t + 12
} else {
c <- 11
}
return (c)
}
# Functie ce simuleaza momentul primei sosiri a unui client dupa momentul de timp s
# corespunzatoare unui proces Poisson neomogen cu functia de intensitate lambda si
# maximul ei mLambda
GET_TS <- function(s, lambda, mLambda) {
ts <- t <- s
while (TRUE) {
# Simuleaza 2 valori uniforme in intervalul [0,1]
U1 <- runif(1)
U2 <- runif(1)
# Noul moment de timp t
t <- t - 1 / mLambda * log(U1)
# Daca valoarea aleatoare uniforma este mai mica decat raportul dintre functia de intensitate
# in momentul respectiv si valoarea maxima a functiei intensitate, atunci ne oprim
if (U2 <= lambda(t) / mLambda) {
ts <- t
break
}
}
ts
}
# Functie care simuleaza o variabila aleatoare exponentiala de parametru lambda folosind metoda inversa
SIMULATE_EXP <- function(lambda) {
# Genereaza o variabila aleatoare uniforma pe intervalul [0,1]
u <- runif(1)
# Construieste variabila exponentiala de parametru lambda
x <- -1 / lambda * log(u)
return (x)
}
# Functie care simuleaza o variabila aleatoare Poisson de parametru lambda = 6
# prin metoda inversa.
GET_Y1 <- function(lambda = 6) {
# Se foloseste de relatia de recurenta p_j+1 = p_j * lambda / (j + 1)
# Contor
i <- 0
# Probabilitatea curenta
p <- exp(1) ^ (-lambda)
# Valoarea functiei de repartitie in punctul curent
F <- p
u <- runif(1, 0, 1)
while (u >= F) {
# Updatam noile valori ale probabilitatilor si functiei de repartitie
p <- p * lambda / (i + 1)
F <- F + p
i <- i + 1
}
return (i)
}
# Functie care simuleaza o variabila aleatoare data prin densitatea de probabilitate
# folosind metoda respingeri
GET_Y2 <- function(x) {
# Am ales g(y) = e^x, x > 0
# Am calculat maximul functiei h(x) = f(x)/g(x),iar acesta este c = 32
c <- 32
while (T) {
# Simuleaza o variabila aleatoare exponentiala de parametru lambda = 1
y <- SIMULATE_EXP(1)
# Simuleaza o variabila aleatoare uniforma cu valori in [0,1]
u <- runif(1)
if (u <= 2 / (61 * c) * y * exp(y - y ^ 2 / 61)) {
return(y)
}
}
}
# Functia care simuleaza intregul sistem
# t_max -> timpul (exprimat in minute) in care functioneaza centrul de vaccinare
# lambda -> functia de intensitate a procesului poisson neomogen care simuleaza venirea clientilor
# c_max -> lungimea maxima a cozii de asteptare pentru care un client pleaca
RUN_SIMULATION <- function(t_max = 720, lambda = LAMBDA, c_max = 20) {
# Vectorii de output
# Momentul de timp in care un client ajunge in sistem
A1 <- c()
# Momentul de timp in care un client ajunge la serverul 2
A2 <- c()
# momentul de timp in care un client paraseste sistemul
D <- c()
# timpul maxim de asteptare dupa care un pacient pleaca
timp_asteptare_max <- 60
# valoarea maxima a functiei de intensitate
max_value <- 26
# numarul de doze dintr-o zi
nr_doze <- 100
# Generam primul moment la care ajunge un client
ta <- GET_TS(0, lambda, max_value)
# Variabile contor ce retin date despre starea sistemului
t <- na <- nd <- n1 <- n2 <- 0
t1 <- t2 <- Inf
# Variabile indecsi pt fiecare vector ce marcheaza primul pacient care e in asteptare pentru coada de la primul server, respectiv al 2-lea server
indexA1 <- 1
indexA2 <- 0
# Numarul de pacienti care au ajuns la fiecare server, in total
k1 <- 0
k2 <- 0
# Numarul de clienti pierduti la coada la fiecare server
serv_1_pierdut <- 0
serv_2_pierdut <- 0
# Cat timp functioneaza centrul de vaccinare (si mai sunt doze destule)
while (t < t_max && nr_doze > 0) {
#Soseste un client nou
if (ta == min(ta, t1, t2)) {
t <- ta
if (ta < t_max) {
k1 <- k1 + 1
#A venit un client nou, adaugam in vectorii de output
A1 <- append(A1, t)
A2 <- append(A2, 0)
D <- append(D, 0)
#Punem in coada
na <- na + 1
n1 <- n1 + 1
# Generam urmatorul timp la care vine un client
ta <- GET_TS(t, lambda, max_value)
#Daca e singurul client genereaza t1 (momentul de timp la care clientul de la serverul 1 termina cu acesta)
if (n1 == 1) {
t1 <- t + GET_Y1()
}
# Daca lungimea cozii e prea mare, clientul pleaca. Marcam in vectorii de output cu valori negative
if(n1 > c_max) {
n1 <- n1 - 1
A1[na] <- -t
A2[na] <- -t
D[na] <- -t
serv_1_pierdut <- serv_1_pierdut + 1
}
}
} else if (t1 < ta && t1 <= t2) { # Daca clientul de la serverul 1 termina primul
# Verificam daca primul pacient de la coada asteapta de prea mult timp
t <- t1
if (indexA1 <= k1 &&
((t - A1[indexA1]) > timp_asteptare_max)) {
# Actualizam vectorii de output pentru pacientii care au asteptat prea mult si au plecat.
# Marcam cu -t in vectorii A2 si D pentru a sti momentul in care un pacient a plecat
A2[indexA1] <- -t
D[indexA1] <- -t
indexA1 <- indexA1 + 1
n1 <- n1 - 1
if (n1 == 0) {
t1 <- Inf
}
} else {
k2 <- k2 + 1
# Terminam de procesat un client pana ajunge altul in serverul 1
# Scade numarul de clienti ramasi in coada
n1 <- n1 - 1
# Creste in serv2
n2 <- n2 + 1
# Adaugam timpul la care ajunge clientul la al II-lea server
A2[indexA1] <- t
indexA1 <- indexA1 + 1
if (n1 == 0) {
# Daca serverul ramane gol reinitializeaza
t1 <- Inf
} else {
# Altfel, pentru urmatorul client din coada, genereaza timpul pentru taskul lui
t1 <- t + GET_Y1()
}
if (n2 == 1) {
# Daca clientul terminat de serverul 1 e singurul in serverul 2 genereaza timpul pentru taskul lui
t2 <- t + GET_Y2()
} else if(n2 > c_max) {
# Daca sunt prea multi clienti la coada, clientul pleaca
A2[indexA1] <- -t
D[indexA1] <- -t
n2 <- n2 - 1
serv_2_pierdut <- serv_2_pierdut + 1
}
}
} else if (t2 < ta && t2 < t1) {
# Serverul 2 termina de procesat client inainte sa primim persoane noi sau sa terminam pe cineva in s1
t <- t2
nd <- nd + 1
# Scadem numarul de doze ramase
nr_doze <- nr_doze - 1
# Scade numarul de clienti din coada
n2 <- n2 - 1
indexA2 <- indexA2 + 1
if (n2 == 0) {
t2 <- Inf
}
#adaugam timpul la care pleaca persoana din centrul de vaccinare
D[nd] <- t
if (n2 >= 1 && n2 <= c_max) {
#daca mai avem persoane de procesat, genereaza timpul pentru taskul lor
t2 <- t + GET_Y2()
} else if(n2 >= 1 && n2 > c_max) {
# coada este prea mare, pleaca clientul
D[indexA2] <- -t
n2 <- n2 - 1
serv_2_pierdut <- serv_2_pierdut + 1
}
}
}
res <- data.frame(A1, A2, D, nr_doze, n1, n2)
return(res)
}
result <- RUN_SIMULATION()
print("~~~~~~~~~~~~")
print(result)
print("________")
print(result$A1)
print("____________")
print(result$A2)
print("________________")
print(result$D)
# Functie corespunzatoare intensitatii procesului Poisson, in momentul in care
# programul incepe cu o ora mai devreme
LAMBDA_INCEPUT <- function(t) {
if (0 <= t & t < 4 * 60) {
c <- -t ** 2 - 2 * t + 25
} else if (4 * 60 <= t & t <= 6 * 60) {
c <- 14
} else if (6 * 60 < t & t < 10 * 60) {
c <- -0.05 * (t ** 2) - 0.2 * t + 12
} else {
c <- 11
}
return (c)
}
# Define UI
ui <- fluidPage(theme = shinytheme("cerulean"),
navbarPage(
"Simulare vaccinare Covid-19",
tabPanel("Pagina pricipala",
# Cerinta 1
mainPanel(
h2("Determinarea timpului minim, maxim si mediu petrecut de o persoana ce asteapta sa se vaccineze\n"),
br(),
column(10,
tableOutput("table1"))
),
br(),
# Cerinta 2
mainPanel(
h2("Determinarea numarului mediu de persoane vaccinate intr-un interval de timp"),
br(),
sidebarLayout(
# Sidebar with a slider input
sidebarPanel(sliderInput("oraSt", "Ora inceput:",
min = 8, max = 20,
value = 8),
sliderInput("oraEnd", "Ora sfarsit:",
min = 8, max = 20,
value = 20),
sliderInput("nrSim2", "Numar simulari:",
min = 5, max = 20,
value = 10)
),
# Show a plot of the generated distribution
mainPanel(
plotOutput("plotClientiTotal")
)
),
h3(textOutput("medie2"))
),
# Suplimentar nr doze
br(),
mainPanel(
h2("Numarul de doze de vaccin"),
h3(textOutput("persNevaccinate")),
plotOutput("plotNrDozePierdute"),
h3(textOutput("medieDoze"))
),
# Cerinta 3
mainPanel(
h2("Determinarea primului moment de timp la care pleaca o persoana"),
h4("Studiul a fost realizat pe 10 simulari"),
column(8,
tableOutput("tablePlecare"))
),
# Cerinta 4
mainPanel(
h2("Determinarea numarului mediu de persoane care au plecat datorita timpului de asteptare"),
h4("Studiul a fost realizat pe 10 simulari"),
column(10,
tableOutput("tablePlecareMediu"))
),
# Cerinta 5
mainPanel(
h2("Determinarea numarului de doze suplimentare administrate"),
br(),
sidebarLayout(
# Sidebar with a slider input
sidebarPanel(sliderInput("nrSim5", "Numar simulari:",
min = 10, max = 50, #todo: dim minima trebuie sa fie maximul curent
value = 20),
radioButtons(inputId="choice", label="Ce se modifica?",
choices=c("Se incepe programul mai devreme cu o ora" = 1,
"Se prelungeste programul cu o ora" = 2,
"Se mareste dimensiunea maxima a cozii de asteptare" = 3)),
sliderInput("dimCoada", "Dimensiune maxima a cozii de asteptare:",
min = 20, max = 100, #todo: dim minima trebuie sa fie maximul curent
value = 20)),
# Show a plot of the generated distribution
mainPanel(
plotOutput("plotCastig")
)
),
h3(textOutput("medie5"))
)
)
) # navbarPage
) # fluidPage
TIMP_IN_SISTEM <- function(ta, td) {
# Cerinta 1: determinarea timpilor maximi, minimi si medii de asteptare pentru fiecare server
tmax <- 0
tmin <- Inf
tmed <- 0
num <- 0
for(i in 1:length(ta)) {
if(td[i] > 0 && ta[i] > 0) { # nu luam in calcul persoanele care au plecat inainte sa ajunga la servere
tmed <- tmed + (td[i] - ta[i])
num <- num + 1
}
if(td[i] > 0 && ta[i] > 0 && td[i] - ta[i] > tmax)
tmax <- td[i] - ta[i]
else if(td[i] > 0 && ta[i] > 0 && td[i] - ta[i] < tmin)
tmin <- td[i] - ta[i]
}
tmed <- tmed / num
# print("TIMPI ASTEPTARE")
# print(tmax)
# print(tmin)
# print(tmed)
res <- c(tmax, tmin, tmed)
return(res)
}
# Define server function
server <- function(input, output) {
# Cerinta 1
res1 <- RUN_SIMULATION()
server <- c("Completare fisa", "Vaccinare")
# Pentru primul server, timpul de asteptare este momentul cand intra in asteptare la serverul 2 - cand intra in sistem
s1 <- TIMP_IN_SISTEM(res1$A1, res1$A2)
# Pentru al doilea server, timpul de asteptare este momentul cand iese din sistem(D) - cand intra in al doilea server
s2 <- TIMP_IN_SISTEM(res1$A2, res1$D)
timp_maxim <- c(paste0(toString(round(s1[1],2)), " min"), paste0(toString(round(s2[1],2)), " min"))
timp_minim <- c(paste0(toString(round(s1[2],2)), " min"), paste0(toString(round(s2[2],2)), " min"))
timp_mediu <- c(paste0(toString(round(s1[3],2)), " min"), paste0(toString(round(s2[3],2)), " min"))
print("in cerinta 1")
print(server)
print(timp_maxim)
print(timp_minim)
print(timp_mediu)
df1 <- data.frame("Server"=server, "Timp minim"=timp_minim, "Timp maxim"=timp_maxim, "Timp mediu"=timp_mediu)
# Cerinta 1
output$table1 <- renderTable({df1})
# Cerinta 2
output$plotClientiTotal <- renderPlot({
oraSt <- input$oraSt
oraEnd <- input$oraEnd
nrSim <- input$nrSim2
x <- c()
y <- c()
med <- 0
if(oraSt <= oraEnd) {
for(i in 1 : nrSim) {
res <- RUN_SIMULATION()
nrPers <- 0
for(j in 1 : length(res$D)) {
if(((oraSt - 8) * 60) < res$D[j] && res$D[j] <= ((oraEnd - oraSt) * 60)) # nr persoane vaccinate intr-un interval de timp
nrPers <- nrPers + 1
}
x <- append(x, i)
y <- append(y, nrPers)
med <- med + nrPers
}
med <- med / nrSim
plot(x, y, col = "green",main="Persoane care se vaccineaza", xlab="Indexul simularii", ylab="Numarul persoane", pch = 19)
abline(h=med, col="magenta")
legend("topright", legend=c("Numar persoane vaccinate", "Media nr de persoane vaccinate"),
col=c("green", "magenta"), lty=c(NA, 1), pch=c(19, NA))
output$medie2 <- renderText({paste0("Numarul mediu de persoane vaccinate este de ", toString(round(med, 2)),".")})
}
})
# Cerinta 3
idx <- c(1:10) # realizam pe 10 simulari
timp_plecare <- c()
for(i in 1:10) {
res <- RUN_SIMULATION()
t <- Inf
for(j in 1 : length(res$A2))
if(res$A1[j] < 0 || res$A2[j] < 0 || res$D[j] < 0) { # a plecat de la una din cele 2 cozi sau nu avea loc la coada
t <- abs(res$A1[j])
break
}
timp_plecare <- append(timp_plecare, paste0(toString(round(t, 2)), " min"))
}
df2 <- data.frame(idx, timp_plecare)
# Cerinta 3
output$tablePlecare <- renderTable({df2})
# Suplimentar
output$persNevaccinate <- renderText({
res <- RUN_SIMULATION()
nevaccinati <- 0
if(res$nr_doze == 0) {
nevaccinati <- res$n1[1] + res$n2[1]
}
paste0("Dintr-un total de 100 de doze, numarul persoanelor ramase nevaccinate din cauza numarului insuficient de doze este ", toString(nevaccinati), ".")})
output$plotNrDozePierdute <- renderPlot({
x <- c(1:10)
y <- c()
med <- 0
for(i in 1:10) {
res <- RUN_SIMULATION()
y <- append(y, res$nr_doze[1])
med <- med + res$nr_doze[1]
}
med <- med / 10
plot(x, y, main="Numar de doze pierdute", xlab="Indexul simularii", ylab="Nr doze pierdute", pch = 19, col="green")
abline(h=med, col="magenta")
legend("topright", legend=c("Nr doze pierdute", "Medie nr doze pierdute"),
col=c("green", "magenta"), lty=c(NA, 1), pch=c(19, NA))
output$medieDoze <- renderText({paste0("Numarul mediu de doze pierdute este de ", toString(round(med, 2)),".")})
})
# Cerinta 4
server_1 <- 0
for(i in 1 : 10) {
res <- RUN_SIMULATION()
nr <- 0
for(j in 1 : length(res$A1)) {
if(res$A1[j] > 0 && res$A2[j] < 0 && res$D[j] < 0)
nr <- nr + 1
}
server_1 <- server_1 + nr
}
server_1 <- server_1 / 10
server_2 <- 0
df3 <- data.frame(server_1, server_2)
# Cerinta 4
output$tablePlecareMediu <- renderTable({df3})
# Cerinta 5
output$plotCastig <- renderPlot({
nrSim <- input$nrSim5
dimCoada <- input$dimCoada
ch <- input$choice
x <- c()
y <- c()
med <- 0
if(ch == 1) {
for(i in 1 : nrSim) {
res <- RUN_SIMULATION(13 * 60, LAMBDA_INCEPUT)
x <- append(x, i)
supl <- 0
for(j in 1:length(res$D)) {
if(res$D[j] > 0 && res$D[j] < 60) #s-au vaccinat in prima ora
supl <- supl + 1
}
y <- append(y, supl)
med <- med + supl
}
med <- med / nrSim
}
else if(ch == 2) {
for(i in 1 : nrSim) {
res <- RUN_SIMULATION(13 * 60)
x <- append(x, i)
supl <- 0
for(j in 1:length(res$D)) {
if(res$D[j] > 0 && res$D[j] <= 780 && res$D[j] >= 720) #s-au vaccinat in ultima ora
supl <- supl + 1
}
y <- append(y, supl)
med <- med + supl
}
med <- med / nrSim
}
else if(ch == 3) {
for(i in 1 : nrSim) {
res1 <- RUN_SIMULATION(c_max=dimCoada)
res2 <- RUN_SIMULATION()
nr_doze_res1 <- length(res1$D[res1$D > 0])
nr_doze_res2 <- length(res2$D[res2$D > 0])
x <- append(x, i)
supl <- 0
if(nr_doze_res1 > nr_doze_res2)
supl <- nr_doze_res1 - nr_doze_res2
y <- append(y, supl)
med <- med + supl
}
med <- med / nrSim
}
plot(x, y, col = "green",main="Numarul suplimentar de doze administrate obtinute pentru fiecare simulare", xlab="Indexul simularii", ylab="Numarul suplimentar de doze",
pch = 19)
abline(h=med, col="magenta")
legend("topright", legend=c("Numar suplimentar de doze", "Media nr suplimentar de doze"),
col=c("green", "magenta"), lty=c(NA, 1), pch=c(19, NA))
output$medie5 <- renderText({paste0("Numarul de doze administrate suplimentar este de ", toString(round(med, 2)),".")})
})
}
# Creeaza serverul Shiny App
shinyApp(ui = ui, server = server)
|
813205c69f8b5c500632368fe99032f2dbe806a1 | 29585dff702209dd446c0ab52ceea046c58e384e | /OligoSpecificitySystem/R/security.r | f2640c2cc323920a4a04cdb8670524dae506d455 | [] | 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 | 632 | r | security.r | "security"<-function(){
if (length(txt4)==1 & length(txt1)==1 & length(txt2)==1) tkmessageBox(message="Error. Please, load before at least 2 databases")
if (length(txt4)==1 & length(txt1)==1 & length(txt2)==1) stop()
if (length(txt4)==1 & length(txt1)==1 & length(txt3)==1) tkmessageBox(message="Error. Please, load before at least 2 databases")
if (length(txt4)==1 & length(txt1)==1 & length(txt3)==1) stop()
if (length(txt4)==1 & length(txt3)==1 & length(txt2)==1) tkmessageBox(message="Error. Please, load before at least 2 databases")
if (length(txt4)==1 & length(txt3)==1 & length(txt2)==1) stop()
} |
dc31ebf5deb5a55ee42e0c536c3613982ed2fad5 | 5c90adf11c63ef8a8f05e310d72d61edcb8f31d3 | /avg_duration.R | e6c8a048789d2944dade5a312789f2491de53051 | [] | no_license | JohnnyFilip/Average_game_duration | fecb098d1583061194548c4fdaa35d372ac56225 | 7bb32ea2a116d700a74afed6761dd6b17932f8b0 | refs/heads/master | 2021-01-17T23:22:32.781817 | 2017-03-29T05:53:23 | 2017-03-29T05:53:23 | 84,218,892 | 0 | 0 | null | 2017-03-07T16:29:32 | 2017-03-07T16:02:28 | null | UTF-8 | R | false | false | 7,576 | r | avg_duration.R | library(jsonlite)
library(plotly)
library(magrittr)
library(plyr)
library(tidyr)
# AVerage game duration by patch
avg1 <- fromJSON("https://api.opendota.com/api/explorer?sql=SELECT%0Apatch%20%2C%0Around(sum(duration)%3A%3Anumeric%2Fcount(1)%2C%202)%20avg%2C%0Acount(distinct%20matches.match_id)%20count%2C%0Asum(case%20when%20(player_matches.player_slot%20%3C%20128)%20%3D%20radiant_win%20then%201%20else%200%20end)%3A%3Afloat%2Fcount(1)%20winrate%2C%0Asum(duration)%20sum%2C%0Amin(duration)%20min%2C%0Amax(duration)%20max%2C%0Around(stddev(duration)%2C%202)%20stddev%0AFROM%20matches%0AJOIN%20match_patch%0AUSING%20(match_id)%0AJOIN%20leagues%0AUSING(leagueid)%0AJOIN%20player_matches%0AUSING(match_id)%0ALEFT%20JOIN%20notable_players%0AUSING(account_id)%0ALEFT%20JOIN%20teams%0AUSING(team_id)%0AJOIN%20heroes%0AON%20player_matches.hero_id%20%3D%20heroes.id%0AWHERE%20TRUE%0AAND%20duration%20IS%20NOT%20NULL%0AGROUP%20BY%20patch%0AHAVING%20count(distinct%20matches.match_id)%20%3E%200%0AORDER%20BY%20avg%20DESC%2Ccount%20DESC%20NULLS%20LAST%0ALIMIT%20150")
avg1a <- avg1$rows %>% arrange(desc(patch))
print(avg1a[avg1a$patch > 7.00,])
avg_patch <- mean(as.numeric(avg1a[avg1a$patch > 7.00,]$avg))/60
print(avg_patch)
p_avg_patch <-
plot_ly(
avg1a,
x = avg1a$patch,
y = as.numeric(avg1a$avg)/60,
name = 'Avg_game_duration',
type = 'bar',
marker = list(
color = 'rgb(158,202,225)',
line = list(color = 'rgb(8,48,107)',
width = 1.5)
)
) %>%
layout(yaxis = list(range = c(32,44), title = 'Average_game_duration'), xaxis = list(title = 'Patch'))
# Average game duration by league
avg2 <- fromJSON("https://api.opendota.com/api/explorer?sql=SELECT%0Aleagues.name%20leaguename%2C%0Around(sum(duration)%3A%3Anumeric%2Fcount(1)%2C%202)%20avg%2C%0Acount(distinct%20matches.match_id)%20count%0AFROM%20matches%0AJOIN%20match_patch%0AUSING%20(match_id)%0AJOIN%20leagues%0AUSING(leagueid)%0AJOIN%20player_matches%0AUSING(match_id)%0ALEFT%20JOIN%20notable_players%0AUSING(account_id)%0ALEFT%20JOIN%20teams%0AUSING(team_id)%0AJOIN%20heroes%0AON%20player_matches.hero_id%20%3D%20heroes.id%0AWHERE%20TRUE%0AAND%20duration%20IS%20NOT%20NULL%0AAND%20match_patch.patch%20%3D%20%277.00%27%20OR%20match_patch.patch%20%3D%20%277.01%27%20OR%20match_patch.patch%20%3D%20%277.02%27%0AGROUP%20BY%20leagues.name%0AHAVING%20count(distinct%20matches.match_id)%20%3E%200%0AORDER%20BY%20avg%20DESC%2Ccount%20DESC%20NULLS%20LAST%0ALIMIT%20150")
avg2a <- avg2$rows %>% arrange(desc(avg))
avg2a$average <- as.numeric(avg2a$avg)/60
avg_league <- avg2a[avg2a$count >= 26,] %>% subset(select = -avg)
print(avg_league)
# AVerage game duration by team
avg3 <- fromJSON("https://api.opendota.com/api/explorer?sql=SELECT%0Ateams.name%20%2C%0Around(sum(duration)%3A%3Anumeric%2Fcount(1)%2C%202)%20avg%2C%0Acount(distinct%20matches.match_id)%20count%2C%0Asum(case%20when%20(player_matches.player_slot%20%3C%20128)%20%3D%20radiant_win%20then%201%20else%200%20end)%3A%3Afloat%2Fcount(1)%20winrate%0AFROM%20matches%0AJOIN%20match_patch%0AUSING%20(match_id)%0AJOIN%20leagues%0AUSING(leagueid)%0AJOIN%20player_matches%0AUSING(match_id)%0ALEFT%20JOIN%20notable_players%0AUSING(account_id)%0ALEFT%20JOIN%20teams%0AUSING(team_id)%0AJOIN%20heroes%0AON%20player_matches.hero_id%20%3D%20heroes.id%0AWHERE%20TRUE%0AAND%20duration%20IS%20NOT%20NULL%0AAND%20match_patch.patch%20%3D%20%277.00%27%20OR%20match_patch.patch%20%3D%20%277.01%27%20OR%20match_patch.patch%20%3D%20%277.02%27%0AGROUP%20BY%20teams.name%0AHAVING%20count(distinct%20matches.match_id)%20%3E%200%0AORDER%20BY%20avg%20DESC%2Ccount%20DESC%20NULLS%20LAST%0ALIMIT%20150")
avg3a <- avg3$rows %>% arrange(desc(avg)) %>% drop_na(name)
avg3a$average <- as.numeric(avg3a$avg)/60
avg_team <- avg3a[avg3a$count >= 10,] %>% subset(select = -avg)
print(avg_team)
# Average game duration head to head
team_list <- fromJSON('https://api.opendota.com/api/teams')
avg_h2h <- function(team1_id, team2_id){
team1_id <- 111474
team2_id <- 1838315
teams <- 'https://api.opendota.com/api/explorer?sql=select%20match_id%2C%20start_time%2C%20duration%0Afrom%20matches%0AWHERE%20(radiant_team_id%3Dlight%20AND%20dire_team_id%3Ddark)%0AOR%20(radiant_team_id%3Ddark%20AND%20dire_team_id%3Dlight)%0A'
teams_new <- gsub('light', team1_id, gsub('dark', team2_id, teams))
avg4 <- fromJSON(teams_new)
avg4a <- avg4$rows[avg4$rows$start_time > 1481583600,]
avg <- mean(avg4a$duration)/60
return(list(nrow(avg4a), avg))
}
# AVerage game duration Kiev
average_kiev <- function() {
avg5 <-
fromJSON(
"https://api.opendota.com/api/explorer?sql=SELECT%0Ateams.name%20%2C%0Around(sum(duration)%3A%3Anumeric%2Fcount(1)%2C%202)%20avg%2C%0Acount(distinct%20matches.match_id)%20count%2C%0Asum(case%20when%20(player_matches.player_slot%20%3C%20128)%20%3D%20radiant_win%20then%201%20else%200%20end)%3A%3Afloat%2Fcount(1)%20winrate%2C%0Asum(duration)%20sum%2C%0Amin(duration)%20min%2C%0Amax(duration)%20max%2C%0Around(stddev(duration)%2C%202)%20stddev%0AFROM%20matches%0AJOIN%20match_patch%0AUSING%20(match_id)%0AJOIN%20leagues%0AUSING(leagueid)%0AJOIN%20player_matches%0AUSING(match_id)%0ALEFT%20JOIN%20notable_players%0AUSING(account_id)%0ALEFT%20JOIN%20teams%0AUSING(team_id)%0AJOIN%20heroes%0AON%20player_matches.hero_id%20%3D%20heroes.id%0AWHERE%20TRUE%0AAND%20duration%20IS%20NOT%20NULL%0AAND%20matches.leagueid%20%3D%205157%0AGROUP%20BY%20teams.name%0AHAVING%20count(distinct%20matches.match_id)%20%3E%200%0AORDER%20BY%20avg%20DESC%2Ccount%20DESC%20NULLS%20LAST%0ALIMIT%20150"
)
avg5a <- avg5$rows %>% drop_na(name)
avg5a$avg <- as.numeric(avg5a$avg) / 60
teams_kiev <- select(avg5a, c(name, count, avg))
avg_kiev <- mean(as.numeric(avg5a$avg))
list(teams_kiev, sum(teams_kiev$count), avg_kiev)
}
# AVerage game duration DAC - main tournament
average_dac <- function() {
avg6 <-
fromJSON(
"https://api.opendota.com/api/explorer?sql=SELECT%0Ateams.name%20%2C%0Around(sum(duration)%3A%3Anumeric%2Fcount(1)%2C%202)%20avg%2C%0Acount(distinct%20matches.match_id)%20count%2C%0Asum(case%20when%20(player_matches.player_slot%20%3C%20128)%20%3D%20radiant_win%20then%201%20else%200%20end)%3A%3Afloat%2Fcount(1)%20winrate%2C%0Asum(duration)%20sum%2C%0Amin(duration)%20min%2C%0Amax(duration)%20max%2C%0Around(stddev(duration)%2C%202)%20stddev%0AFROM%20matches%0AJOIN%20match_patch%0AUSING%20(match_id)%0AJOIN%20leagues%0AUSING(leagueid)%0AJOIN%20player_matches%0AUSING(match_id)%0ALEFT%20JOIN%20notable_players%0AUSING(account_id)%0ALEFT%20JOIN%20teams%0AUSING(team_id)%0AJOIN%20heroes%0AON%20player_matches.hero_id%20%3D%20heroes.id%0AWHERE%20TRUE%0AAND%20duration%20IS%20NOT%20NULL%0AAND%20matches.leagueid%20%3D%205197%0AAND%20matches.start_time%20%3E%3D%201490565600%0AGROUP%20BY%20teams.name%0AHAVING%20count(distinct%20matches.match_id)%20%3E%200%0AORDER%20BY%20avg%20DESC%2Ccount%20DESC%20NULLS%20LAST%0ALIMIT%20150"
)
avg6a <- avg6$rows %>% drop_na(name)
avg6a$avg <- as.numeric(avg6a$avg) / 60
teams_dac <- select(avg6a, c(name, count, avg))
avg_dac <- mean(as.numeric(avg6a$avg))
list(teams_dac, sum(teams_dac$count), avg_dac)
}
write.csv(avg1a, file = 'D:\\RTS\\Scripts\\avg_patch.csv')
write.table(avg_league, file = 'D:\\RTS\\Scripts\\avg_league.csv', sep = ' ', row.names = FALSE)
write.table(avg_match, file = 'D:\\RTS\\Scripts\\avg_match.csv', sep = ' ', row.names = FALSE)
write.csv(team_list, file = 'D:\\RTS\\Scripts\\team_list.csv')
|
2b1fc92f180f128e588a237ea11f659b01a53a61 | 9096176d4a3a6305e08250fa1a7c9b5c2930f20f | /scripts/usa.R | 6bc25e2b6059c6a573b49ef8292cb550aaa1c158 | [] | no_license | cg0lden/subnational_distributions_BFA | 6c9ad7ff1be1ce39a34a6e60724ae3a659c54364 | 4a8e3fb69691ee9b85be3ab9a064df5dc998bd30 | refs/heads/master | 2023-05-05T11:33:42.087553 | 2021-05-25T19:39:35 | 2021-05-25T19:39:35 | 300,038,711 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,900 | r | usa.R | # NHANES data cleaning for SPADE
# Created by Simone Passarelli on 11/23/2020
library(tidyverse)
library(haven)
library(here)
library(janitor)
# Clean day 1 data and sync names. Keep necessary vars only
nhanes_day1 <- read_xpt(here("data", "raw", "United States", "DR1IFF_J.XPT")) %>%
clean_names() %>% mutate(mday=1) %>%
rename(id=seqn, code=dr1ifdcd, amount=dr1igrms, vita=dr1ivara, calc=dr1icalc,
b12a=dr1ivb12, b12b=dr1ib12a, zinc=dr1izinc, epa=dr1ip205, dha=dr1ip226, iron=dr1iiron, weight1=wtdrd1, weight2=wtdr2d) %>%
select(id, mday, code, amount, vita, b12a, b12b, zinc, iron, calc, epa, dha, weight1, weight2)
#Clean day 2 data and sync names. Keep necessary vars only
nhanes_day2 <- read_xpt(here("data", "raw", "United States", "DR2IFF_J.XPT")) %>%
clean_names() %>% mutate(mday=2) %>%
rename(id=seqn, code=dr2ifdcd, amount=dr2igrms, vita=dr2ivara, calc=dr2icalc,
b12a=dr2ivb12, b12b=dr2ib12a, zinc=dr2izinc, epa=dr2ip205, dha=dr2ip226, iron=dr2iiron, weight1=wtdrd1, weight2=wtdr2d) %>%
select(id, mday, code, amount, vita, b12a, b12b, zinc, iron, calc, epa, dha, weight1, weight2)
#append day 1 and day 2 data
nhanes <- rbind(nhanes_day1, nhanes_day2)
# Merge in and combine food group codes from GDQS when they are available
nhanes_gdqs_meat <- read_dta(here("data", "raw", "NHANES", "FPED_DR_1516.dta")) %>%
select(DR1IFDCD, DESCRIPTION, DR1I_PF_MEAT, DR1I_PF_CUREDMEAT) %>%
clean_names() %>%
rename(code=dr1ifdcd) %>%
filter(code >=20000000 & code < 30000000 , dr1i_pf_meat > 0 | dr1i_pf_curedmeat>0 ) %>%
mutate(red = case_when(dr1i_pf_meat > 0 ~ 1,
TRUE ~ 0)) %>%
mutate(processed = case_when(dr1i_pf_curedmeat > 0 ~ 1 , TRUE ~ 0)) %>%
select(code, red, processed) %>%
distinct()
#Load demographic data for age and sex variables
usa_merge <- read_xpt(here("data", "raw", "NHANES", "DEMO_J.XPT")) %>%
clean_names() %>%
rename( age=ridageyr, sex=riagendr, id=seqn) %>%
dplyr::select( age, sex, id) %>%
distinct()
nhanes1 <- merge(nhanes, nhanes_gdqs_meat, by.all="code", all.x=T)
# see how many meat values weren't classified by the gdqs codes
meat_test <- nhanes1 %>% filter(code >=20000000 & code < 30000000) %>% filter(processed==0 & red==0)
# None, they were all classified
usa_nut <- nhanes1 %>%
mutate(red_meat = case_when(red >0 ~ amount,
TRUE ~ 0),
processed_meat = case_when(processed >0 ~ amount, TRUE ~ 0)) %>%
group_by(id, mday) %>%
summarize(b12 = sum(b12a, b12b),
iron = sum(iron),
zinc = sum(zinc),
vita = sum(vita),
calc = sum(calc),
red_meat = sum(red_meat),
processed_meat = sum(processed_meat),
omega_3 = sum(epa, dha),
weight1 = weight1,
weight2=weight2) %>% distinct()
# Rename and format variables for spade
usa_spade <- usa_nut %>%
left_join(usa_merge, by=c("id")) %>%
group_by(id, mday) %>%
distinct() %>%
ungroup() %>%
dplyr::select(id, weight1, weight2, age, sex, mday, b12, iron, zinc, vita, calc, omega_3, red_meat, processed_meat) %>%
mutate(id=as.integer(id))
# Check for missing or differen ages
usa_missings <- usa_spade[is.na(usa_spade$age), ] # shows you the missings
usa_missings
#No missing ages
#Replace any cases where the ages are different for the same individual
ids_data <- unique(usa_spade$id)
for (idid in ids_data){
data.id <- usa_spade[usa_spade$id == idid, ]
if(nrow(data.id) > 1){
usa_spade[usa_spade$id == idid,"age"] <-
min(usa_spade[usa_spade$id == idid,"age"])
}
}
# Update sampling weights
# We want to include both the day 1 and the day 2 data, so we should use the day 1
# Change id type to be numeric
save(usa_spade, file=here("data", "processed", "usa"), replace)
|
475e509e545facda920869dcaeb7ef1ddc3a1b7e | df8e6ea4375e01082e05966c39aca2fa7c733aef | /R-Code/makePlot.R | 80ae5a9388b16626f8739566cde528fd33016a68 | [] | no_license | vrrani/IntegrativeRegressionNetwork | 9b1669fbe9d3c7a63251f3b91d94e4b809825d10 | 2c41b39a592eae0dd4d8af6c534a88d3c73b07db | refs/heads/main | 2023-01-08T01:01:37.346132 | 2020-11-01T10:32:05 | 2020-11-01T10:32:05 | 308,850,692 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,214 | r | makePlot.R | maxIter <- 100
for( method in c("fused", "GFLasso", "Lasso", "SGL", "SIOL") ) {
maxX <- 0
for( i in 1:length( Wset ) ) {
X <- Wset[[i]][[method]]
maxX <- max( maxX, max(X[upper.tri(X)]) )
}
print( sprintf("%s %f", method, maxX) )
pdf( sprintf('figure_Breast_%s.pdf',method),width=9,height=13)
par( mfrow=c(2,1) )
iv <- seq( 0, maxX, maxX / 40.0 )
ivAxis <- seq( 0, maxX, maxX / 20.0 )
plot( 0, type='n', xlim=c(0,maxX), ylim=c(0,log(2*10^5,10)), xaxt="n", ylab="log10(num. edges)", xlab="weight", title = method )
axis(1, at = sprintf("%.4f",iv) , las=2, cex.axis=0.8)
for( i in 2:maxIter ) {
lines( iv, log( getNumEdges( Wset[[i]][[method]], iv ), 10 ), col='gray' )
}
lines( iv, log( getNumEdges(Wset[[1]][[method]], iv),10), lwd=1.5, col='red')
plot( 0, type='n', xlim=c(maxX,0), ylim=c(600,0), xaxt="n", ylab="num. component", xlab="weight", title = method )
axis(1, at = sprintf("%.4f",iv) , las=2, cex.axis=0.8)
for( i in 2:maxIter ) {
lines( iv, getMaxComponent( Wset[[i]][[method]], iv ), col='gray' )
}
lines( iv, getMaxComponent(Wset[[1]][[method]], iv), lwd=1.5, col='red')
dev.off()
}
|
b5ced5b9b8f86e36900cc66f1d9d3645e2f8d152 | 84f5ac25a17b16191b40d979b4d4fc0bc21b0b9e | /man/key_sentiment_jockers.Rd | 560a7b5b57adc9ba379c185b57a57f5659cdaa7d | [] | no_license | cran/lexicon | 1d23c81e51c828020110b9f7d2d01980bdf78bf3 | 03761ddba87f3ac1dd6af743508a7e8303be061b | refs/heads/master | 2021-01-12T01:03:22.432056 | 2019-03-21T09:40:03 | 2019-03-21T09:40:03 | 78,337,774 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 725 | rd | key_sentiment_jockers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hash_sentiment_jockers.R
\docType{data}
\name{key_sentiment_jockers}
\alias{key_sentiment_jockers}
\title{Jockers Sentiment Key}
\format{An object of class \code{data.frame} with 10748 rows and 2 columns.}
\usage{
key_sentiment_jockers
}
\description{
A dataset containing an imported version of Jocker's (2017)
sentiment lookup table used in \pkg{syuzhet}.
}
\details{
\itemize{
\item word. Words
\item value. Sentiment values ranging between -1 and 1.
}
}
\references{
Jockers, M. L. (2017). Syuzhet: Extract sentiment and plot arcs
from Text. Retrieved from https://github.com/mjockers/syuzhet
}
\keyword{datasets}
|
4213b6ecd475b99aabd53351ff49dd386ab5acd8 | 7ac8a44774d4e3f69fc5ffa55bdde5903c722e7f | /R/Visualizing_data.R | afc3de4930aa3dcfdbc0d4126cb6a7b1c031e485 | [] | no_license | emmaSkarstein/Citizen_Science_Skarstein_master | e75910b931629a759f599f8660238bec0e3af3b3 | 73a8163d577456eefd7c685ea3d21adc52a4a44d | refs/heads/master | 2021-07-09T19:32:45.701585 | 2020-10-20T13:23:19 | 2020-10-20T13:23:19 | 207,277,325 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,741 | r | Visualizing_data.R |
library(sf)
library(sp)
library(ggplot2)
library(ggpubr)
library(dplyr)
library(fishualize)
library(hexbin)
library(tidyr)
library(knitr)
library(summarytools)
library(pander)
library(maps)
library(maptools)
library(PointedSDMs)
library(patchwork)
library(inlabru)
library(colorspace)
source("R/Model_visualization_functions.R")
source("R/loading_map_obs_covs.R")
# Setting fish species
fish_sp <- "trout"
#fish_sp <- "perch"
#fish_sp <- "char"
#fish_sp <- "pike"
if(fish_sp == "trout"){
lat_name <- "Salmo trutta"
}else if(fish_sp == "perch"){
lat_name <- "Perca fluviatilis"
}else if(fish_sp == "char"){
lat_name <- "Salvelinus alpinus"
}else{
lat_name <- "Esox lucius"
}
norway <- ggplot2::map_data("world", region = "Norway(?!:Svalbard)")
norway <- dplyr::setdiff(norway, filter(norway, subregion == "Jan Mayen"))
# Showing mesh
ggplot() +
geom_polygon(data = norway, aes(long, lat, group = group),
color=NA, fill = NA) + coord_quickmap() +
gg(Mesh$mesh, int.color = "darkorange2", edge.color = "gray20") +
theme_bw() +
theme(axis.title = element_blank())
ggsave("figs/meshplot.pdf")
# Looking at the observations
## Observation map
p2 <- ggplot(data.frame(trout_artsobs)) +
geom_polygon(data = norway, aes(long, lat, group = group),
color="black", fill = "grey93") + coord_quickmap() +
geom_point(aes(x = decimalLongitude, y = decimalLatitude),
color = "darkorange2", size = 0.5) +
theme_bw() +
theme(axis.title = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(),
axis.ticks.x = element_blank(), axis.text.x = element_blank()) +
ggtitle("Artsobservasjoner")
p1 <- ggplot(data.frame(trout_survey)) +
geom_polygon(data = norway, aes(long, lat, group = group),
color="black", fill = "grey93") + coord_quickmap() +
geom_point(aes(x = decimalLongitude, y = decimalLatitude, color = occurrenceStatus),
size = 0.5) +
scale_color_manual(values=c("cyan4", "darkorange2"), labels = c("Absent", "Present")) +
guides(colour = guide_legend(override.aes = list(size=2)))+
theme_bw() +
theme(axis.title = element_blank(), legend.title = element_blank(),
legend.position = "left", axis.ticks.x = element_blank(),
axis.text.x = element_blank()) +
ggtitle("Fish status survey")
hex2 <- ggplot(data.frame(trout_artsobs), aes(x = decimalLongitude, y = decimalLatitude)) +
geom_polygon(data = norway, aes(long, lat, group = group),
color='gray93', fill = 'gray93') + coord_quickmap() +
geom_hex() +
scale_fill_continuous_sequential(palette = "Teal") +
theme_bw() +
theme(axis.title = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank())
hex1 <- ggplot(data.frame(trout_survey), aes(x = decimalLongitude, y = decimalLatitude)) +
geom_polygon(data = norway, aes(long, lat, group = group),
color='gray93', fill = 'gray93') + coord_quickmap() +
geom_hex() +
scale_fill_continuous_sequential(palette = "Teal") +
theme_bw() +
theme(axis.title = element_blank(), legend.position = "left")
(p1 + p2)/
(hex1 + hex2)
ggsave(paste0("figs/pointhex_", fish_sp, ".pdf"), height = 6, width = 8)
# Hex map of all species
all_sp <- bind_rows(Trout = data.frame(trout_artsobs), Perch = data.frame(perch_artsobs),
Char = data.frame(char_artsobs), Pike = data.frame(pike_artsobs),
.id = "species")
all_hex <- ggplot(all_sp, aes(x = decimalLongitude, y = decimalLatitude)) +
geom_polygon(data = norway, aes(long, lat, group = group),
color='gray80', fill = 'gray80') + coord_quickmap() +
geom_hex() +
#geom_polygon(data = norway, aes(long, lat, group = group), color = "black", fill = NA) +
scale_fill_continuous_sequential(palette = "Teal") +
facet_wrap(vars(species), nrow = 1) +
theme_bw() +
theme(axis.title = element_blank(), strip.text.x = element_text(size = 14))
all_hex
#ggsave("figs/hex_all_sp.pdf", width = 9, height = 3)
ggsave("figs/hex_all_sp.png", height = 4, width = 10)
# Point maps of survey all species
all_sp_survey <- bind_rows(Trout = data.frame(trout_survey), Perch = data.frame(perch_survey),
Char = data.frame(char_survey), Pike = data.frame(pike_survey),
.id = "species")
all_points <- ggplot(all_sp_survey, aes(x = decimalLongitude, y = decimalLatitude)) +
geom_polygon(data = norway, aes(long, lat, group = group),
color="black", fill = "grey93") + coord_quickmap() +
geom_point(aes(x = decimalLongitude, y = decimalLatitude, color = occurrenceStatus),
alpha = 0.8, size = 0.3) +
scale_color_manual(values=c("cyan4", "darkorange2"), labels = c("Absent", "Present")) +
facet_wrap(vars(species), nrow = 1) +
guides(colour = guide_legend(override.aes = list(size=2))) +
theme_bw() +
theme(axis.title = element_blank(), strip.text.x = element_text(size = 14), legend.title = element_blank())
all_points
ggsave("figs/points_all_sp.png", height = 4, width = 10)
all_hex / all_points
ggsave("figs/points_hex_all_sp.png", height = 8, width = 12)
## Number of observations per year
top_four <- Data_artsobs_df %>% filter(species %in% c("Perca fluviatilis", "Salmo trutta", "Salvelinus alpinus", "Esox lucius"))
time_counts <- dplyr::count(top_four, year, species)
ggplot(time_counts, aes(x = year, y = n, fill = species)) +
geom_bar(stat = "identity", position = position_stack(reverse = FALSE)) +
theme_bw() +
theme(axis.title = element_blank()) +
scale_fill_viridis_d() +
geom_vline(xintercept = 1996, linetype = "dashed", color = "black", size = 1)
ggsave("figs/timeline.pdf", height = 2, width = 7)
# Explanatory variables
#Covariates <- readRDS("R/output/Covariates.RDS")
Cov_long <- tidyr::gather(data.frame(Covariates), key = variable, value = value, area_km2:log_catchment)
## Explanatory variables on a map
plot_exp_var <- function(var){
ggplot(Cov_long %>% dplyr::select(variable, value, decimalLatitude, decimalLongitude) %>%
filter(variable==var)) +
geom_polygon(data = norway, aes(long, lat, group = group),
color = "grey", fill = "grey") + coord_quickmap() +
geom_point(aes(x = decimalLongitude, y = decimalLatitude, color = value),
alpha = 0.8, size = 0.2) +
scale_color_continuous_sequential(palette = "OrRd") +
facet_grid(cols = vars(variable), labeller = labeller(variable = cov.labs)) +
theme_bw() +
theme(axis.title = element_blank(), legend.title = element_blank(),
strip.text.x = element_text(size = 14))
}
Cov_names <- unique(Cov_long$variable)
Use_p <- c("log_area", "log_catchment", "eurolst_bio10", "SCI", "log_perimeter",
"distance_to_road", "HFP")
Use_interesting <- c("eurolst_bio10", "distance_to_road", "HFP")
cov.labs <- c("Log area", "Log catchment", "Temperature",
"Shoreline complexity index", "Log perimeter",
"Distance to road", "Human footprint index")
names(cov.labs) <- Use_p
var_plots <- lapply(Use_interesting, plot_exp_var)
(var_plots[[1]] + var_plots[[2]] + var_plots[[3]])
#ggsave("figs/covariates_on_map_interesting.pdf", height = 4, width = 10)
ggsave("figs/covariates_on_map_interesting.png", height = 4, width = 10)
## Explanatory variables histograms
ggplot(Cov_long %>% filter(variable %in% Use_p), aes(x = value)) +
geom_histogram(fill = "cyan4", color = "cyan4") +
facet_wrap(~variable, scales = 'free_x', nrow = 2, labeller = labeller(variable = cov.labs)) +
theme_bw() +
theme(axis.title = element_blank())
ggsave("figs/covariate_histograms.pdf", width = 7, height = 4)
|
d05c4bed32dd7d7c665434a8202aa54e6d1fd4f3 | 9132996d08213cdf27c8f6d444e3f5b2cfdcfc85 | /tests/testthat/test_all_positive.R | d8353640de47042f909073fc4ede00d7ea98b13b | [] | no_license | prioritizr/prioritizr | 152013e81c1ae4af60d6e326e2e849fb066d80ba | e9212a5fdfc90895a3638a12960e9ef8fba58cab | refs/heads/main | 2023-08-08T19:17:55.037205 | 2023-08-08T01:42:42 | 2023-08-08T01:42:42 | 80,953,648 | 119 | 30 | null | 2023-08-22T01:51:19 | 2017-02-04T22:45:17 | R | UTF-8 | R | false | false | 3,382 | r | test_all_positive.R | test_that("x = default", {
expect_tidy_error(all_positive(new_waiver()), "recognized")
})
test_that("x = numeric", {
expect_true(all_positive(c(0, 1, 2, NA)))
expect_true(all_positive(c(0L, 1L, 2L, NA)))
expect_false(all_positive(c(-1, NA, 0)))
expect_false(all_positive(c(-1L, NA, 0L)))
expect_error(
assert(all_positive(c(0, -1, 2, NA))),
"negative"
)
})
test_that("x = Matrix", {
expect_true(all_positive(Matrix::Matrix(c(0, 1, 2, NA))))
expect_false(all_positive(Matrix::Matrix(c(-1, NA, 0))))
expect_error(
assert(all_positive(Matrix::Matrix(c(-1, NA, 0)))),
"negative"
)
})
test_that("x = matrix", {
expect_true(all_positive(matrix(c(0, 1, 2, NA))))
expect_false(all_positive(matrix(c(-1, NA, 0))))
expect_error(
assert(all_positive(matrix(c(-1, NA, 0)))),
"negative"
)
})
test_that("x = data.frame", {
expect_true(all_positive(data.frame(x = c(0, 1, NA), y = c(0L, 2L, NA))))
expect_false(all_positive(data.frame(x = c(0, 1, NA), y = c(-1, 1, 2))))
expect_error(
assert(all_positive(data.frame(x = c(0, 1, NA), y = c(-1, 1, 2)))),
"negative"
)
})
test_that("x = sf", {
# create data
g <- sf::st_sfc(list(sf::st_point(c(1, 0)), sf::st_point(c(0, 1))))
x <- sf::st_as_sf(tibble::tibble(x = c(0, NA), y = c(0, NA), geom = g))
y <- sf::st_as_sf(tibble::tibble(x = c(0, NA), y = c(-1, 2), geom = g))
# tests
expect_true(all_positive(x))
expect_false(all_positive(y))
expect_error(assert(all_positive(y)), "negative")
})
test_that("x = Spatial", {
# create data
g <- sf::st_sfc(list(sf::st_point(c(1, 0)), sf::st_point(c(0, 1))))
x <- sf::st_as_sf(tibble::tibble(x = c(0, NA), y = c(2, NA), geom = g))
y <- sf::st_as_sf(tibble::tibble(x = c(0, NA), y = c(-1, 2), geom = g))
# tests
expect_true(all_positive(sf::as_Spatial(x)))
expect_false(all_positive(sf::as_Spatial(y)))
expect_error(
assert(all_positive(sf::as_Spatial(y))),
"negative"
)
})
test_that("x = SpatRaster", {
expect_true(all_positive(terra::rast(matrix(c(0, 1, 2, NA)))))
expect_false(all_positive(terra::rast(matrix(c(-1, NA, 0)))))
expect_error(
assert(all_positive(terra::rast(matrix(c(-1, NA, 0))))),
"negative"
)
})
test_that("x = Raster", {
expect_true(all_positive(raster::raster(matrix(c(0, 1, 2, NA)))))
expect_false(all_positive(raster::raster(matrix(c(-1, NA, 0)))))
expect_error(
assert(all_positive(raster::raster(matrix(c(-1, NA, 0))))),
"negative"
)
})
test_that("x = ZonesSpatRaster", {
# create data
z1 <- zones(
terra::rast(matrix(c(0, 1, 2, NA))),
terra::rast(matrix(c(0, 5, 2, NA)))
)
z2 <- zones(
terra::rast(matrix(c(0, 1, 2, NA))),
terra::rast(matrix(c(0, 1, -2, NA)))
)
# tests
expect_true(all_positive(z1))
expect_false(all_positive(z2))
expect_error(
assert(all_positive(z2)),
"negative"
)
})
test_that("x = ZonesRaster", {
# create data
expect_warning(
z1 <- zones(
raster::raster(matrix(c(0, 1, 2, NA))),
raster::raster(matrix(c(0, 5, 2, NA)))
),
"deprecated"
)
expect_warning(
z2 <- zones(
raster::raster(matrix(c(0, 1, 2, NA))),
raster::raster(matrix(c(0, 1, -2, NA)))
),
"deprecated"
)
# tests
expect_true(all_positive(z1))
expect_false(all_positive(z2))
expect_error(
assert(all_positive(z2)),
"negative"
)
})
|
628e7c1c1fbaa335ccbee2760e61a18abd5a4ac8 | 1983b21fb4d2dafa7f5d0e1196e5889253a60206 | /Codes/data_frame.r | 6be0716d993fca4c0d086ac73cddb2c147b8f942 | [
"Apache-2.0"
] | permissive | hackwithabhishek/R-Programming-Knowledge | 7348cc376a935732e6e3003502a6cf0a51181ec2 | 365664003655964b25bf84aee2e1c706efdff83f | refs/heads/main | 2023-03-19T06:11:14.443380 | 2021-03-19T11:36:42 | 2021-03-19T11:36:42 | 346,687,501 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,400 | r | data_frame.r | #basics of data frame
S.No=1:5
df<-data.frame("S.no"=S.No,
"Name"=c("John","Marry","Joseph","Mohan","Rihanna"),
"Age"=c(32,26,20,43,26))
str(df)
df1<-data.frame("S.no"=S.No,
"Name"=c("John","Marry","Joseph","Mohan","Rihanna"),
"Age"=c(32,26,20,43,26),
"Start_date"=as.Date(c("2012-01-01","2013-09-23","2014-11-15","2014-05-11","2015-03-27")))
str(df1)
df1
#total noof rows
nrow(df1)
#total no. of cols
length(df1)
dim(df1)
names(df1)
df2<-data.frame("S.no"=S.No,
"Name"=c("John","Marry","Joseph","Mohan","Rihanna"),
"Age"=c(32,26,20,43,26),
"Start_date"=as.Date(c("2012-01-01","2013-09-23","2014-11-15","2014-05-11","2015-03-27")),
stringsAsFactors = T)
#How to Acces data
#use either [,[[ or $ operator to acces col of df.
df1["Nme"]#op will in the form df
df1[["Nme"]] #op will be in the from vector
df1$Name
df[2]
df1[[2]]
df1[["Name"]][2]
df1$Start_data[2]
#error
df1[2][2]
df1["Nme"][2]
df1$Start_date[2]
df1[2,2]
df1[,2]
df1[c(1,3),]
df1[3:5,c(1,4)]
df1[.-3]
#install .packages("tibble")
#add col
dept<-c("IT","HR","IT","Finance","Management")
df1$Dept<-dept
#remove col from
df1<-df1[,-5]
df1$Dept<-NULL
df1
#add col is using cbind funtion
emp_id=101:105
df1<-cbind(df1,emp_id)
df1 |
85e495b6287a16b991238dfedbbf681c551ba1e4 | cf1f32300d37750c4c74ea0b44c61888fd3a9a2e | /R/04_graphing.R | 4ce4f1f92afd092e030728fcd19a633bcdaf8875 | [] | no_license | UPGo-McGill/city_summaries | f47f1e654b0da4827f00fa1a180b9e2ecbcb97d3 | ece792a27ba6e57b2bd13375d2fa07b7f161e7d1 | refs/heads/master | 2020-05-30T23:13:20.805172 | 2019-06-06T14:40:55 | 2019-06-06T14:40:55 | 190,011,487 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,429 | r | 04_graphing.R | ############## GRAPHING #################################
source("R/01_helper_functions.R")
# Set up timeframes
year_prior <- as.POSIXlt(End_date)
year_prior$year <- year_prior$year - 1
year_prior_prior <- as.POSIXlt(year_prior)
year_prior_prior$year <- year_prior$year - 1
# Active airbnb listings over time
figure2 <- ggplot(daily %>%
group_by(Date) %>%
summarize(Listings = n())) +
geom_line(aes(Date, Listings)) +
theme_minimal()
#ggsave("output/figure2.jpg")
# host revenue
host_revenue<-
daily %>%
filter(Date >= year_prior, Date <= End_date, Status == "R") %>%
group_by(Airbnb_HID) %>%
summarize(rev = sum(Price)) %>%
filter(rev > 0) %>%
summarize(
`Top 1%` = sum(rev[rev > quantile(rev, c(0.99))] / sum(rev)),
`Top 5%` = sum(rev[rev > quantile(rev, c(0.95))] / sum(rev)),
`Top 10%` = sum(rev[rev > quantile(rev, c(0.90))] / sum(rev))) %>%
gather(`Top 1%`, `Top 5%`, `Top 10%`, key = "percentile", value = "value") %>%
mutate(percentile = factor(percentile, levels = c('Top 1%', 'Top 5%', 'Top 10%')))
figure3 <-
ggplot(host_revenue)+
geom_bar(mapping = aes(x = percentile, y = value, fill = percentile), stat = "identity")+
theme_minimal()+
scale_fill_manual("Percentile", values = alpha(c("lightblue", "blue", "darkblue"), .6))+
theme(axis.title.y = element_blank()) +
theme(axis.title.x = element_blank())
#ggsave("output/figure3.jpg")
|
39d4a3f062d0ce4b52308bcb0ec439856b1b64a5 | dcac5a647b01cce1d99ee5ab1bd1c14c2eb0467e | /R/sl_R/crawlers/R/twlottery.R | 45f272ecc3580dfe7b5d10d2bf1efb46d93f5e9d | [] | no_license | slchangtw/R_Basics | de9dbef37660b18acb7471968891cd944cabc660 | 6a7b67558ed8cfee5f403fd1301b505680a82071 | refs/heads/master | 2021-06-16T23:31:11.051427 | 2017-05-28T14:37:04 | 2017-05-28T14:37:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,849 | r | twlottery.R | #' ---
#' title: "台灣彩券"
#' author: ""
#' date: "`r Sys.Date()`"
#' output:
#' html_document:
#' toc: yes
#' ---
#+ include=FALSE
knitr::opts_chunk$set(eval = FALSE)
#' ## Load Packages
library(httr)
library(rvest)
library(magrittr)
#' ## 台灣彩券 (GET) {.tabset}
#' ### Fill In
url <- "http://www.taiwanlottery.com.tw/lotto/Lotto649/history.aspx"
res <- GET(url = url)
doc <- content(res, as = "text", encoding = "UTF-8") %>%
read_html() %>%
html_nodes(xpath = <Fill In>) %>%
html_text() # get text from xml nodeset
doc_cleaned <- doc %>% gsub(<Fill In>, "", .)
dat <- matrix(doc_cleaned, ncol = <Fill In>, byrow = TRUE) %>%
.[, -c(13:19)] %>%
set_colnames(c('期別', '開獎日', '兌獎截止', '銷售金額', '獎金總額',
'獎號_1', '獎號_2', '獎號_3', '獎號_4', '獎號_5', '獎號_6',
'特別號' ,'頭獎_中獎注數', '貳獎_中獎注數', '參獎_中獎注數',
'肆獎_中獎注數', '伍獎_中獎注數', '陸獎_中獎注數',
'柒獎_中獎注數', '普獎_中獎注數', '頭獎_單注獎金',
'貳獎_單注獎金', '參獎_單注獎金', '肆獎_單注獎金',
'伍獎_單注獎金', '陸獎_單注獎金', '柒獎_單注獎金',
'普獎_單注獎金', '頭獎_累積至次期獎', '貳獎_累積至次期獎金',
'參獎_累積至次期獎金', '肆獎_累積至次期獎金', '伍獎_累積至次期獎金')) %>%
as.data.frame(stringAsFactors = FALSE)
#' ### Answer
url <- "http://www.taiwanlottery.com.tw/lotto/Lotto649/history.aspx"
res <- GET(url = url)
doc <- content(res, as = "text", encoding = "UTF-8") %>%
read_html() %>%
html_nodes(xpath = "//td/span") %>%
html_text() # get text from xml nodeset
doc_cleaned <- doc %>% gsub(",|\r\n|\\s", "", .)
dat <- matrix(doc_cleaned, ncol = 40, byrow = TRUE) %>%
.[, -c(13:19)] %>%
set_colnames(c('期別', '開獎日', '兌獎截止', '銷售金額', '獎金總額',
'獎號_1', '獎號_2', '獎號_3', '獎號_4', '獎號_5', '獎號_6',
'特別號' ,'頭獎_中獎注數', '貳獎_中獎注數', '參獎_中獎注數',
'肆獎_中獎注數', '伍獎_中獎注數', '陸獎_中獎注數',
'柒獎_中獎注數', '普獎_中獎注數', '頭獎_單注獎金',
'貳獎_單注獎金', '參獎_單注獎金', '肆獎_單注獎金',
'伍獎_單注獎金', '陸獎_單注獎金', '柒獎_單注獎金',
'普獎_單注獎金', '頭獎_累積至次期獎', '貳獎_累積至次期獎金',
'參獎_累積至次期獎金', '肆獎_累積至次期獎金', '伍獎_累積至次期獎金')) %>%
as.data.frame(stringAsFactors = FALSE)
#' ## 台灣彩券 (POST) {.tabset}
#' ### Fill In
get_lottery <- function(<Fill In>, <Fill In>) {
url <- "http://www.taiwanlottery.com.tw/lotto/Lotto649/history.aspx"
# get view state and event validation
res_g <- GET(url = url)
view_state <- content(res_g) %>%
html_nodes("<Fill In>") %>%
html_attr("<Fill In>")
event_validation <- content(res_g) %>%
html_nodes("<Fill In>") %>%
html_attr("<Fill In>")
form <- list(
'__EVENTTARGET' = '',
'__EVENTARGUMENT' = '',
'__LASTFOCUS' = '',
'__VIEWSTATE' = <Fill In>,
'__VIEWSTATEGENERATOR' = 'C3E8EA98',
'__EVENTVALIDATION' = <Fill In>,
'Lotto649Control_history$DropDownList1' = '2',
'Lotto649Control_history$chk' = 'radYM',
'Lotto649Control_history$dropYear' = <Fill In>,
'Lotto649Control_history$dropMonth' = <Fill In>,
'Lotto649Control_history$btnSubmit' = '查詢')
res_p <- POST(url = url,
body = form,
encode = "form")
doc <- content(res_p, as = "text", encoding = "UTF-8") %>%
read_html() %>%
html_nodes(xpath = "//td/span") %>%
html_text() %>%
gsub(",|\r\n|\\s", "", .)
dat <- matrix(doc, ncol = 40, byrow = TRUE) %>%
.[, -c(13:19)] %>% ## don't need the order of winning numbers
as_tibble() %>%
set_colnames(c('期別', '開獎日', '兌獎截止', '銷售金額', '獎金總額',
'獎號_1', '獎號_2', '獎號_3', '獎號_4', '獎號_5', '獎號_6',
'特別號' ,'頭獎_中獎注數', '貳獎_中獎注數', '參獎_中獎注數',
'肆獎_中獎注數', '伍獎_中獎注數', '陸獎_中獎注數',
'柒獎_中獎注數', '普獎_中獎注數', '頭獎_單注獎金',
'貳獎_單注獎金', '參獎_單注獎金', '肆獎_單注獎金',
'伍獎_單注獎金', '陸獎_單注獎金', '柒獎_單注獎金',
'普獎_單注獎金', '頭獎_累積至次期獎', '貳獎_累積至次期獎金',
'參獎_累積至次期獎金', '肆獎_累積至次期獎金', '伍獎_累積至次期獎金')) %>%
as.data.frame(stringAsFactors = FALSE)
return(dat)
}
#' ### Answer
get_lottery <- function(year, month) {
url <- "http://www.taiwanlottery.com.tw/lotto/Lotto649/history.aspx"
# get view state and event validation
res_g <- GET(url = url)
view_state <- content(res_g) %>%
html_nodes("#__VIEWSTATE") %>%
html_attr("value")
event_validation <- content(res_g) %>%
html_nodes("#__EVENTVALIDATION") %>%
html_attr("value")
form <- list(
'__EVENTTARGET' = '',
'__EVENTARGUMENT' = '',
'__LASTFOCUS' = '',
'__VIEWSTATE' = view_state,
'__VIEWSTATEGENERATOR' = 'C3E8EA98',
'__EVENTVALIDATION' = event_validation,
'Lotto649Control_history$DropDownList1' = '2',
'Lotto649Control_history$chk' = 'radYM',
'Lotto649Control_history$dropYear' = year,
'Lotto649Control_history$dropMonth' = month,
'Lotto649Control_history$btnSubmit' = '查詢')
res_p <- POST(url = url,
body = form,
encode = "form")
doc <- content(res_p, as = "text", encoding = "UTF-8") %>%
`Encoding<-`("UTF-8") %>%
read_html() %>%
html_nodes(xpath = "//td/span") %>%
html_text() %>%
gsub(",|\r\n|\\s", "", .)
dat <- matrix(doc, ncol = 40, byrow = TRUE) %>%
.[, -c(13:19)] %>% ## don't need the order of winning numbers
set_colnames(c('期別', '開獎日', '兌獎截止', '銷售金額', '獎金總額',
'獎號_1', '獎號_2', '獎號_3', '獎號_4', '獎號_5', '獎號_6',
'特別號' ,'頭獎_中獎注數', '貳獎_中獎注數', '參獎_中獎注數',
'肆獎_中獎注數', '伍獎_中獎注數', '陸獎_中獎注數',
'柒獎_中獎注數', '普獎_中獎注數', '頭獎_單注獎金',
'貳獎_單注獎金', '參獎_單注獎金', '肆獎_單注獎金',
'伍獎_單注獎金', '陸獎_單注獎金', '柒獎_單注獎金',
'普獎_單注獎金', '頭獎_累積至次期獎', '貳獎_累積至次期獎金',
'參獎_累積至次期獎金', '肆獎_累積至次期獎金', '伍獎_累積至次期獎金')) %>%
as.data.frame(stringAsFactors = FALSE)
return(dat)
}
#' ## Notes
#' 1. If the data is not table-like, extract it by DOM selector.
#' 2. Use a GET request to get the value of viewstate.
#' 3. Set the value in the body, and get data by a POST request |
052d7c15ea95b46a9c4e8336c232eaf6ca14a659 | d1d9c9a8197f8ae7060c1b2eea02c62e4529c6ad | /PDS/Slides/scratch.R | 59e39c96a80c9d55aeb20b1ef8fcea2a7845c423 | [] | no_license | jmontgomery/jmontgomery.github.io | bd0ec322b7416ef811ff5265b7b8edcb9337fe8b | a9a01ef49b6cff50b8a6fd3a4ca58ba5684fe426 | refs/heads/master | 2021-09-23T09:17:22.152539 | 2021-09-12T19:23:01 | 2021-09-12T19:23:01 | 99,852,460 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 674 | r | scratch.R | library(shiny)
runExample("01_hello") # a histogram
###
setwd("~/GitHub/jmontgomery.github.io/PDS/Datasets/SenateForecast")
senateData<-read.csv("PollingCandidateData92-16.csv")
stateName<-"North Carolina"
stateData<-senateData %>%
filter(state==sateName & cycle==2016) %>%
select(c("Poll", "daysLeft", "Candidateidentifier")) %>%
group_by(Candidateidentifier)%>%
glimpse()
library(ggplot2)
thisPlot<-ggplot(stateData,
mapping=aes(x=daysLeft, y=Poll, color=Candidateidentifier)) +
geom_point() +
geom_smooth() +
ggtitle(paste0("2016 Senate Election in", stateName)) +
labs(y="Poll results", x="Days Till Election")
print(thisPlot)
###
|
f9796f19840ddc1ba349357be3e30d60f26de638 | 9d941f38054390183c6215f47bb29db422d0c876 | /man/acf2.Rd | f98fafd9311f3ed60c3e406f657ed7b8ea617249 | [] | no_license | egedib/astsa | e9b54be69a517be48d8cd56daf0062adcec7df65 | 2a5e8d69e2f0ed62dca2fd37ed34a514fc8ed96f | refs/heads/master | 2020-05-03T10:01:04.206943 | 2019-03-30T14:43:07 | 2019-03-30T14:43:07 | 178,569,127 | 0 | 0 | null | 2019-03-30T14:28:24 | 2019-03-30T14:28:24 | null | UTF-8 | R | false | false | 3,358 | rd | acf2.Rd | \name{acf2}
\alias{acf2}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Plot and print ACF and PACF of a time series
}
\description{
Produces a simultaneous plot (and a printout) of the sample ACF and PACF on the same scale.
The zero lag value of the ACF is removed.
}
\usage{
acf2(series, max.lag=NULL, plot=TRUE,
main=paste("Series: ", deparse(substitute(series))),
na.action = na.pass, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{series}{The data. Does not have to be a time series object.
}
\item{max.lag}{
Maximum lag. Can be omitted. Defaults to \eqn{\sqrt{n} + 10} unless \eqn{n < 60}.
If the series is seasonal, this will be at least 4 seasons by default.
}
\item{plot}{If FALSE, no graph is produced but the values are still printed.
}
\item{main}{Title of graphic; defaults to name of series.
}
\item{na.action}{How to handle missing data; default is \code{na.pass}
}
\item{...}{ Additional arguments passed to \code{acf} }
}
%\details{
%% ~~ If necessary, more details than the description above ~~
%}
\value{\item{ACF}{The sample ACF}
\item{PACF}{The sample PACF}
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
}
\details{This is basically a wrapper for \code{acf()} provided in \code{tseries}. The error bounds are approximate white noise bounds, \eqn{0 \pm 2/\sqrt{n}}; no other option is given.
}
\references{\url{http://www.stat.pitt.edu/stoffer/tsa4/}
%% ~put references to the literature/web site here ~
}
\author{
D.S. Stoffer
}
%\note{
%This is bacisally a front end for \code{acf()} and \code{pacf()} provided in \code{tseries}.
%}
%% ~Make other sections like Warning with \section{Warning }{....} ~
%%\seealso{\code{\link{acf1}}
%% ~~objects to See Also as \code{\link{help}}, ~~~}
\examples{
acf2(rnorm(100))
acf2(rnorm(100), 25, main='') # no title
acf2(rnorm(100), plot=FALSE)[,'ACF'] # print only ACF
}
%% ##-- or do help(data=index) for the standard data sets.
%%
%% ## The function is currently defined as
%% function(series,max.lag=NULL){
%% num=length(series)
%% if (num > 49 & is.null(max.lag)) max.lag=ceiling(10+sqrt(num))
%% if (num < 50 & is.null(max.lag)) max.lag=floor(5*log10(num))
%% if (max.lag > (num-1)) stop("Number of lags exceeds number of observations")
%% ACF=acf(series, max.lag, plot=FALSE)$acf[-1]
%% PACF=pacf(series, max.lag, plot=FALSE)$acf
%% LAG=1:max.lag/frequency(series)
%% minA=min(ACF)
%% minP=min(PACF)
%% U=2/sqrt(num)
%% L=-U
%% minu=min(minA,minP,L)-.01
%% old.par <- par(no.readonly = TRUE)
%% par(mfrow=c(2,1), mar = c(3,3,2,0.8),
%% oma = c(1,1.2,1,1), mgp = c(1.5,0.6,0))
%% plot(LAG, ACF, type="h",ylim=c(minu,1),
%% main=paste("Series: ",deparse(substitute(series))))
%% abline(h=c(0,L,U), lty=c(1,2,2), col=c(1,4,4))
%% plot(LAG, PACF, type="h",ylim=c(minu,1))
%% abline(h=c(0,L,U), lty=c(1,2,2), col=c(1,4,4))
%% on.exit(par(old.par))
%% ACF<-round(ACF,2); PACF<-round(PACF,2)
%% return(cbind(ACF, PACF))
%% }
%}
%% % Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ts}
|
37ff6460327ec26830206139837aacaa8f714928 | e08c16082eec850673cce70157cb71f55531d0b0 | /code/plot2.R | b8c7eff17e2575afec8d0bbc5c2dbf003d337c1b | [] | no_license | helenwan/ExData_Plotting1 | 723d3a100dc588d30fcfe4506955c463db7b377c | 4fdd973e2321507ece7864f72eef72748409be8c | refs/heads/master | 2021-01-22T07:03:46.363918 | 2014-05-11T23:21:10 | 2014-05-11T23:21:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 577 | r | plot2.R | # only read in data from 2007-02-01 and 2007-02-02
data <- read.csv(pipe('egrep \'^Date|^[1-2]/2/2007\' household_power_consumption.txt'), header=T, sep=';')
# open 480x480 pixel png file called plot2.png
png(file="plot2.png", width=480, height=480, units = "px")
# create new column of DateTime objects
data$DateTime <- strptime(paste(data$Date, data$Time, sep=" "), format="%d/%m/%Y %H:%M:%S")
# create line plot of Global Active Power with appropriate labels
plot(data$DateTime, data$Global_active_power, type="l", ylab="Global Active Power (kilowatts)", xlab="")
dev.off() |
29106a3e114af9d825b81419f570cc8fa997382e | 84a1906fcf51e71e12fc6c04dfae9bb4e5d314ce | /Rscripts/PCA.R | 0703f4cbb329c4f199aa68e4656f8b1860f69fe6 | [] | no_license | RyanSchu/gwasqc_pipeline | 4224fcd4af2e000fbc8c5a71cb3e44d4ccaed048 | bece21065427fd30f257a63fb58ee3fac2b22ba4 | refs/heads/master | 2020-03-24T18:44:52.514828 | 2019-03-18T19:23:29 | 2019-03-18T19:23:29 | 142,898,699 | 2 | 4 | null | 2018-09-21T14:57:39 | 2018-07-30T16:08:27 | Shell | UTF-8 | R | false | false | 3,828 | r | PCA.R | library(dplyr)
library(tidyr)
library(ggplot2)
library(argparse)
parser <- ArgumentParser()
parser$add_argument("--hapmapdir", help="directory where all the hapmap files are written")
parser$add_argument("--val",help="full path to eigenvalue file")
parser$add_argument("--vec",help="full path to eigenvector file")
parser$add_argument("--fam", help="full path to the fam file youd like to use")
parser$add_argument("-o", "--outputdir", help="directory where you would like to output your plots")
parser$add_argument("--pop", help="full path to the ")
args <- parser$parse_args()
"%&%" = function(a,b) paste (a,b,sep="")
pcaplots <- args$outputdir %&% "/merged_pca_plots.pdf"
if (!is.null(args$pop)){
hapmappopinfo <- read.table(args$pop) %>% select (V1,V3)
} else if (grepl("19",args$hapmapdir, fixed=TRUE)) {
hapmappopinfo <- read.table(args$hapmapdir %&% "/pop_HM3_hg19_forPCA.txt") %>% select (V1,V3)
} else if (grepl( "18",args$hapmapdir, fixed=TRUE)) {
hapmappopinfo <- read.table(args$hapmapdir %&% "/pop_HM3_hg18_forPCA.txt") %>% select (V1,V3)
}
colnames(hapmappopinfo) <- c("pop","IID")
fam <- read.table(args$fam) %>% select (V1,V2)
colnames(fam) <- c("FID","IID")
popinfo <- left_join(fam,hapmappopinfo,by="IID")
popinfo <- mutate(popinfo, pop=ifelse(is.na(pop),'GWAS', as.character(pop)))
table(popinfo$pop)
pcs <- read.table(args$vec,header=T)
pcdf <- data.frame(popinfo, pcs[,3:ncol(pcs)])
gwas <- filter(pcdf,pop=='GWAS')
hm3 <- filter(pcdf, grepl('NA',IID))
eval <- scan(args$val)[1:10]
skree<-round(eval/sum(eval),3)
skree<-cbind.data.frame(skree,c(1,2,3,4,5,6,7,8,9,10))
colnames(skree)<-c("percent_var", "PC")
pdf(pcaplots)
ggplot(data=skree, aes(x=PC, y=percent_var)) + geom_point() + geom_line() + scale_x_continuous(breaks = 1:10) + ggtitle("Proportion of variance explained")
#PCA Plot 1 (PC1 vs PC2)
ggplot() + geom_point(data=pcdf,aes(x=PC1,y=PC2,col=pop,shape=pop)) + theme_bw() + scale_colour_brewer(palette="Set1") + ggtitle("PC1 vs PC2")
#PCA Plot 2 (PC1 vs PC3)
ggplot() + geom_point(data=pcdf,aes(x=PC1,y=PC3,col=pop,shape=pop)) + theme_bw() + scale_colour_brewer(palette="Set1") + ggtitle("PC1 vs PC3")
#PCA Plot 1 (PC2 vs PC3)
ggplot() + geom_point(data=pcdf,aes(x=PC2,y=PC3,col=pop,shape=pop)) + theme_bw() + scale_colour_brewer(palette="Set1") + ggtitle("PC2 vs PC3")
#PCA with HAPMAP populations
yri <- filter(pcdf,pop=='YRI')
uPC1 <- mean(yri$PC1) + 5*sd(yri$PC1)
lPC1 <- mean(yri$PC1) - 5*sd(yri$PC1)
uPC2 <- mean(yri$PC2) + 5*sd(yri$PC2)
lPC2 <- mean(yri$PC2) - 5*sd(yri$PC2)
ggplot() + geom_point(data=gwas,aes(x=PC1,y=PC2,col=pop,shape=pop))+geom_point(data=hm3,aes(x=PC1,y=PC2,col=pop,shape=pop))+ theme_bw() +geom_vline(xintercept=c(uPC1,lPC1)) +geom_hline(yintercept=c(uPC2,lPC2)) + ggtitle("Assuming homogeneous, non-admixed")
inclusion <- gwas[gwas$PC1 >= lPC1,]
inclusion <- inclusion[inclusion$PC1 <= uPC1,]
inclusion <- inclusion[inclusion$PC2 >= lPC2,]
inclusion <- inclusion[inclusion$PC2 <= uPC2,]
samples <- inclusion[,1:2]
table(inclusion$pop)
dim(samples)[1]
dim(gwas)[1]-dim(samples)[1]
ggplot() + geom_point(data=gwas,aes(x=PC1,y=PC2,col=gwas$IID %in% samples$IID,shape=gwas$IID %in% samples$IID))+geom_point(data=hm3,aes(x=PC1,y=PC2,col=pop,shape=pop))+ theme_bw() + ggtitle("Assuming homogeneous, non-admixed")
dev.off()
#write.table(samples, args$QCdir %&% "/PCA/GWAS_PCA.txt",quote=F,row.names=F,col.names=F)
#afrpcs <- read.table("/home/angela/px_yri_chol/QC/QCStep6/QCStep6e/QCStep6e.evec",skip=1)
#afrcdf <- afrpcs %>% rename(PC1=V2,PC2=V3,PC3=V4,PC4=V5,PC5=V6,PC6=V7,PC7=V8,PC8=V9,PC9=V10,PC10=V11) %>% mutate(pop=ifelse(grepl("TC",V1),"GWAS","GWAS"))
#eval <- scan("/home/angela/px_yri_chol/QC/QCStep6/QCStep6e/QCStep6e.eval")[1:10]
#round(eval/sum(eval),3)
|
d3281b1c0f6adde22099337c2e3811e1ee6d94c8 | 42231220c4194fcef7c12819b72bb6c53c0b68d2 | /man/UMRactiveSet_trust.Rd | 8d60a68eebd83c43f6e4a534eb2cad04417141b1 | [] | no_license | cran/UMR | d710c18c2f42ee71981925aee02f37dc724b17fc | 2ce99462b3ef013db95fa703af901f280a5f9fd4 | refs/heads/master | 2023-07-11T06:36:57.461127 | 2021-08-14T08:00:09 | 2021-08-14T08:00:09 | 292,347,400 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,789 | rd | UMRactiveSet_trust.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/UMRactiveSet_trust.R
\name{UMRactiveSet_trust}
\alias{UMRactiveSet_trust}
\title{An active set approach to minimizing objective in Unlinked Monotone
Regression}
\usage{
UMRactiveSet_trust(
yy,
ww_y = NULL,
grad,
hess,
UMR_curv,
CDF,
init,
counts = rep(1, length(init)),
stepsize,
MM,
tol_end = 1e-04,
tol_collapse,
printevery,
filename
)
}
\arguments{
\item{yy}{Y (response) observation vector (numeric)}
\item{ww_y}{Weights (nonnegative, sum to 1) corresponding to yy. Samelength as yy. Or NULL in which yy are taken as being evenly weighted.}
\item{grad}{Is function(mm, ww_m). (Will be defined based on yy [and maybe ww_y] before being passed in.) Returns vector of length(mm). Gradient of objective function.}
\item{hess}{Is function(mm, ww_m). (Will be defined based on yy [and maybe ww_y] before being passed in.) Returns matrix of dimensions length(mm) by length(mm). Hessian of objective function.}
\item{UMR_curv}{A curvature function object (giving mathfrak(C) in the paper; and related to "C" in the paper). See UMR_curv_generic() and examples. This is generally a "curried" version of UMR_curv_generic with densfunc and BBp passed in.}
\item{CDF}{This is the error (cumulative) distribution function, a function object. Function accepting vector or matrix arguments.}
\item{init}{Initial value of estimate ('mm'). Vector, length may be different than length(yy). See 'counts' input.}
\item{counts}{Together 'init' and 'counts' serve as the initialization; the implied initial vector is rep.int(init, counts).}
\item{stepsize}{Stepsize for moving out of saddle points.}
\item{MM}{A number of iterations. May not use them all. MM is not
exactly the total number of iterations used in the sense that within
each of MM iterations, we will possibly run another algorithm which
may take up to MM iterations (but usually takes many fewer).}
\item{tol_end}{Used as tolerance at various points . Generally algorithm (and
some subalgorithms) end once sum(abs(mm-mmprev)) < tol, or you hit MM
iterations.}
\item{tol_collapse}{Collapsing roughly equal mm values into each other.}
\item{printevery}{integer value (generally << MM). Every 'printevery'
iterations, a count will be printed and the output saved.}
\item{filename}{filename (path) to save output to.}
}
\description{
An active set approach to minimizing objective in Unlinked Monotone
Regression
}
\details{
Uses first order (gradient) for optimization, and uses certain
second derivative computations to leave saddle points. See
Balabdaoui, Doss, and Durot (2021). Note that yy and mm (i.e., number
covariates) may have different length.
}
|
8a1a0ee75bf291c71e0801b33732200af362f3c7 | 89d8f5e676682c487f2143afa352251a7ee11a1a | /man/getPhyloNames.Rd | b0f17b1b48246dc3eff91f65d89df397b8b9d8b4 | [
"MIT"
] | permissive | galacticpolymath/galacticEdTools | 36d5cb580877c36f03f6f80f8d0e400c8a5d5a3d | 32be95593eda122bfe359f724ddd17acad1d647d | refs/heads/main | 2023-04-11T22:25:43.780344 | 2022-07-08T23:33:08 | 2022-07-08T23:33:08 | 379,769,787 | 0 | 0 | NOASSERTION | 2021-07-06T02:02:09 | 2021-06-24T01:22:04 | R | UTF-8 | R | false | true | 1,456 | rd | getPhyloNames.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getPhyloNames.R
\name{getPhyloNames}
\alias{getPhyloNames}
\title{getPhyloNames}
\usage{
getPhyloNames(speciesNames, nameType, clearCache = F, quiet = T)
}
\arguments{
\item{speciesNames}{a name or vector of common or scientific names of organisms of interest (in quotes)}
\item{nameType}{what type of name are you supplying? Either "sci" or "common"}
\item{clearCache}{delete the cached phylonamescache.rds file saved in tempdir()? default=F}
\item{quiet}{suppress verbose feedback from the taxize package? Default=T}
}
\description{
Find matching scientific or common names for the common or scientific names provided and cache the results for efficient retrieval. This is a convenience wrapper for the \code{\link[taxize]{sci2comm}} and \code{\link[taxize]{comm2sci}} functions.
}
\details{
Depending on what nameType you supply, getPhyloNames will use the sci2comm or comm2sci function to look for the matching taxonomic name. It first searches the NCBI database, and if that doesn't return a result, it will try the Encyclopedia of Life (EOL) database. This function relies on an internal helper function \code{\link{getPhyloNames_noCache}}, though you will generally not need to use it. The advantage of getPhyloNames is that it caches results, since the database APIs can be a little slow, so you will not need to keep looking up the same names over and over again.
}
|
59f719c958a7bdf40eeb5212979f657ea2b4ad3a | 5cd797823ac3e1b404b71b758cfceab9d41fd1b6 | /MASA_Sentiment_Analysis.R | 1933a25b6a5015a9edd5697f89510d13ac33ef4a | [] | no_license | matthewfarant/News-Tweets-Sentiment | ffec9707b73dd3fc724a2ac364be396899433531 | 313195c6b6f865a5204dbdbb9044cc1b8e532f03 | refs/heads/master | 2023-06-28T19:59:12.028962 | 2021-08-07T13:24:26 | 2021-08-07T13:24:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,974 | r | MASA_Sentiment_Analysis.R | ########### PACKAGES #############
if (!require('rtweet')) install.packages('rtweet'); library('rtweet')
if (!require('tidyverse')) install.packages('tidyverse'); library('tidyverse')
if (!require('tidytext')) install.packages('tidytext'); library('tidytext')
if (!require('textclean')) install.packages('textclean'); library('textclean')
if (!require('sentimentr')) install.packages('sentimentr'); library('sentimentr')
if (!require('lubridate')) install.packages('lubridate'); library('lubridate')
if (!require('stringr')) install.packages('stringr'); library('stringr')
if (!require('tseries')) install.packages('tseries'); library('tseries')
if (!require('lmtest')) install.packages('lmtest'); library('lmtest')
if (!require('ggpubr')) install.packages('ggpubr'); library('ggpubr')
if (!require('scales')) install.packages('scales'); library('scales')
########## SCRAPING TWEETS ######################
tweet_cnn<-get_timeline(user='@cnnbrk', n = 3200)
tweet_nyt<-get_timeline(user='@nytimes', n= 3200)
tweet_bbc<-get_timeline(user='@BBCBreaking',n=3200)
tweet_bloom<-get_timeline(user='@business',n=3200)
tweet_nbc<-get_timeline(user='@BreakingNews',n=3200)
tweet_wsj<-get_timeline(user='@wsj',n=3200)
#################################################
keywords<-c('Covid-19','coronavirus','corona','Covid','ncov','2019-ncov','SARS-CoV-2','lockdown')
############ KEYWORDS FILTER ###########################
corona_cnn<-tweet_cnn %>%
filter(grepl(paste(keywords, collapse="|"), text, ignore.case = TRUE))
corona_nyt<-tweet_nyt %>%
filter(grepl(paste(keywords, collapse="|"), text, ignore.case = TRUE))
corona_bbc<-tweet_bbc %>%
filter(grepl(paste(keywords, collapse="|"), text, ignore.case = TRUE))
corona_bloom<-tweet_bloom %>%
filter(grepl(paste(keywords, collapse="|"), text, ignore.case = TRUE))
corona_nbc<-tweet_nbc %>%
filter(grepl(paste(keywords, collapse="|"), text, ignore.case = TRUE))
corona_wsj<-tweet_wsj %>%
filter(grepl(paste(keywords, collapse="|"), text, ignore.case = TRUE))
#############################################
corona_cnn %>%
mutate(created_at=as.Date(created_at)) %>%
dplyr::select(created_at,text)->corona_cnn
corona_nyt %>%
mutate(created_at=as.Date(created_at)) %>%
dplyr::select(created_at,text)->corona_nyt
corona_bbc %>%
mutate(created_at=as.Date(created_at)) %>%
dplyr::select(created_at,text)->corona_bbc
corona_bloom %>%
mutate(created_at=as.Date(created_at)) %>%
dplyr::select(created_at,text)->corona_bloom
corona_nbc %>%
mutate(created_at=as.Date(created_at)) %>%
dplyr::select(created_at,text)->corona_nbc
corona_wsj %>%
mutate(created_at=as.Date(created_at)) %>%
dplyr::select(created_at,text)->corona_wsj
############ TOKEN NORMALIZATION #####################
corona_cnn$text %>%
str_to_lower() %>%
replace_contraction() %>%
replace_symbol() %>%
replace_url() %>%
strip()->corona_cnn$text
corona_nyt$text %>%
str_to_lower() %>%
replace_contraction() %>%
replace_symbol() %>%
replace_url() %>%
strip()->corona_nyt$text
corona_bbc$text %>%
str_to_lower() %>%
replace_contraction() %>%
replace_symbol() %>%
replace_url() %>%
strip()->corona_bbc$text
corona_bloom$text %>%
str_to_lower() %>%
replace_contraction() %>%
replace_symbol() %>%
replace_url() %>%
strip()->corona_bloom$text
corona_nbc$text %>%
str_to_lower() %>%
replace_contraction() %>%
replace_symbol() %>%
replace_url() %>%
strip()->corona_nbc$text
corona_wsj$text %>%
str_to_lower() %>%
replace_contraction() %>%
replace_symbol() %>%
replace_url() %>%
strip()->corona_wsj$text
###############################################
corona_bbc %>%
filter(created_at>=as.Date('2020-01-01'))->corona_bbc
corona_nbc %>%
filter(created_at>=as.Date('2020-01-01'))->corona_nbc
sentiment_cnn %>%
filter(created_at>=as.Date('2020-01-01'))->sentiment_cnn
###############################################
sentiment_cnn<-cbind(corona_cnn,sentiment_by(corona_cnn$text))
sentiment_nyt<-cbind(corona_nyt,sentiment_by(corona_nyt$text))
sentiment_bbc<-cbind(corona_bbc,sentiment_by(corona_bbc$text))
sentiment_bloom<-cbind(corona_bloom,sentiment_by(corona_bloom$text))
sentiment_nbc<-cbind(corona_nbc,sentiment_by(corona_nbc$text))
sentiment_wsj<-cbind(corona_wsj,sentiment_by(corona_wsj$text))
View(sentiment_wsj)
#SUPER CLEANING
keywords2<-c('crisis','positive','highest','top','death','new covid','new coronavirus')
for(i in 1:nrow(sentiment_cnn)){
if(grepl(paste(keywords2, collapse="|"),
sentiment_cnn$text[i],
ignore.case = TRUE)&
sentiment_cnn$ave_sentiment[i]>=0){
sentiment_cnn$ave_sentiment[i]<-sentiment_cnn$ave_sentiment[i]*(-1)
}
}
for(i in 1:nrow(sentiment_bbc)){
if(grepl(paste(keywords2, collapse="|"),sentiment_bbc$text[i], ignore.case = TRUE)&
sentiment_bbc$ave_sentiment[i]>=0){
sentiment_bbc$ave_sentiment[i]<-sentiment_bbc$ave_sentiment[i]*(-1)
}
}
for(i in 1:nrow(sentiment_nbc)){
if(grepl(paste(keywords2, collapse="|"),sentiment_nbc$text[i], ignore.case = TRUE)&
sentiment_nbc$ave_sentiment[i]>=0){
sentiment_nbc$ave_sentiment[i]<-sentiment_nbc$ave_sentiment[i]*(-1)
}
}
keyword3<-c('vaccine','low','lowest')
for(i in 1:nrow(sentiment_cnn)){
if(grepl(paste(keyword3,collapse="|"),sentiment_cnn$text[i],ignore.case = TRUE)&
sentiment_cnn$ave_sentiment[i]<0){
sentiment_cnn$ave_sentiment[i]<-sentiment_cnn$ave_sentiment[i]*(-1)
}
}
for(i in 1:nrow(sentiment_bbc)){
if(grepl(paste(keyword3,collapse="|"),sentiment_bbc$text[i],ignore.case = TRUE)&
sentiment_bbc$ave_sentiment[i]<0){
sentiment_bbc$ave_sentiment[i]<-sentiment_bbc$ave_sentiment[i]*(-1)
}
}
for(i in 1:nrow(sentiment_nbc)){
if(grepl(paste(keyword3,collapse="|"),sentiment_nbc$text[i],ignore.case = TRUE)&
sentiment_nbc$ave_sentiment[i]<0){
sentiment_nbc$ave_sentiment[i]<-sentiment_nbc$ave_sentiment[i]*(-1)
}
}
############## SENTIMENT ANALYSIS ####################
sentiment_cnn %>%
group_by(created_at) %>%
summarize(sentiment=mean(ave_sentiment)) %>%
mutate(Status=ifelse(sentiment>0,'Positive','Negative')) %>%
ggplot(aes(x=created_at,y=sentiment,fill=Status)) +
geom_col()+
scale_x_date(breaks='1 month',labels=date_format('%B'))+
labs(x='Month',y='Sentiment',title='Sentiment Analysis of @CNNbrk Tweets',subtitle='Polarity analysis on tweets that contain coronavirus related words',
caption='Source: Twitter')
sentiment_nyt %>%
group_by(created_at) %>%
summarize(sentiment=mean(ave_sentiment)) %>%
mutate(Status=ifelse(sentiment>0,'Positive','Negative')) %>%
ggplot(aes(x=created_at,y=sentiment,fill=Status)) +
geom_col()
sentiment_bbc %>%
group_by(created_at) %>%
summarize(sentiment=mean(ave_sentiment)) %>%
mutate(Status=ifelse(sentiment>0,'Positive','Negative')) %>%
ggplot(aes(x=created_at,y=sentiment,fill=Status)) +
geom_col()+
scale_x_date(breaks='1 month',labels=date_format('%B'))+
labs(x='Month',y='Sentiment',title='Sentiment Analysis of @BBCBreaking Tweets',subtitle='Polarity analysis on tweets that contain coronavirus related words',
caption='Source: Twitter')
sentiment_bloom %>%
group_by(created_at) %>%
summarize(sentiment=mean(ave_sentiment)) %>%
mutate(Status=ifelse(sentiment>0,'Positive','Negative')) %>%
ggplot(aes(x=created_at,y=sentiment,fill=Status)) +
geom_col()
sentiment_nbc %>%
group_by(created_at) %>%
summarize(sentiment=mean(ave_sentiment)) %>%
mutate(Status=ifelse(sentiment>0,'Positive','Negative')) %>%
ggplot(aes(x=created_at,y=sentiment,fill=Status)) +
geom_col()+
scale_x_date(breaks='1 month',labels=date_format('%B'))+
labs(x='Month',y='Sentiment',title='Sentiment Analysis of @BreakingNews Tweets',subtitle='Polarity analysis on tweets that contain coronavirus related words',
caption='Source: Twitter')
###########################################################
sentiment_cnn %>%
group_by(created_at) %>%
summarize(sentiment=mean(ave_sentiment))->sentiment_cnn_sum
sentiment_bbc %>%
group_by(created_at) %>%
summarize(sentiment=mean(ave_sentiment))->sentiment_bbc_sum
sentiment_nbc %>%
group_by(created_at) %>%
summarize(sentiment=mean(ave_sentiment))->sentiment_nbc_sum
################### CNN BBC JOINED #######################
sentiment_cnn_sum %>%
dplyr::select(created_at,sentiment) %>%
left_join(sentiment_bbc_sum,by='created_at',suffix=c('_cnn','_bbc')) %>%
left_join(sentiment_nbc_sum,by='created_at') %>%
rename(sentiment_nbc=sentiment)->sentiment_cnn_bbc_joined
sentiment_cnn_bbc_joined<-mutate(sentiment_cnn_bbc_joined,sentiment_mean=rowMeans(
dplyr::select(sentiment_cnn_bbc_joined,starts_with("sentiment_")),na.rm = TRUE))
View(sentiment_cnn_bbc_joined) # includes nbc
#############EMOTION #############################
emotion_cnn<-emotion_by(corona_cnn$text)
emotion_cnn %>%
filter(ave_emotion>0) %>%
group_by(emotion_type) %>%
summarize(emotion_count=sum(emotion_count)) %>%
arrange(desc(emotion_count)) %>%
head(8) %>%
mutate(emotion_type=fct_reorder(emotion_type,emotion_count)) %>%
ggplot(aes(emotion_type,emotion_count))+
geom_col(fill='#EC2029')+
coord_flip()+
labs(y='Word Count',x='Emotion Type',title='CNN Breaking News')->emotion_plot_cnn
emotion_bbc<-emotion_by(corona_bbc$text)
emotion_bbc %>%
filter(ave_emotion>0) %>%
group_by(emotion_type) %>%
summarize(emotion_count=sum(emotion_count)) %>%
arrange(desc(emotion_count)) %>%
head(8) %>%
mutate(emotion_type=fct_reorder(emotion_type,emotion_count)) %>%
ggplot(aes(emotion_type,emotion_count))+
geom_col(fill='black')+
coord_flip()+
labs(x='Emotion Type', y= "Word Count", title='BBC Breaking News')->emotion_plot_bbc
emotion_nyt<-emotion_by(corona_nyt$text)
emotion_nyt %>%
filter(ave_emotion>0) %>%
group_by(emotion_type) %>%
summarize(emotion_count=sum(emotion_count)) %>%
arrange(desc(emotion_count)) %>%
head(8)%>%
mutate(emotion_type=fct_reorder(emotion_type,emotion_count)) %>%
ggplot(aes(emotion_type,emotion_count))+
geom_col()+
coord_flip()+
labs(x="Emotion Type",y="Word Count",title='New York Times')->emotion_plot_nyt
emotion_bloom<-emotion_by(corona_bloom$text)
emotion_bloom %>%
filter(ave_emotion>0) %>%
group_by(emotion_type) %>%
summarize(emotion_count=sum(emotion_count)) %>%
arrange(desc(emotion_count)) %>%
head(8) %>%
mutate(emotion_type=fct_reorder(emotion_type,emotion_count)) %>%
ggplot(aes(emotion_type,emotion_count))+
geom_col(fill='#0000FF')+
coord_flip()+
labs(x='Emotion Type',y="Word Count",title='Bloomberg')->emotion_plot_bloom
emotion_nbc<-emotion_by(corona_nbc$text)
emotion_nbc %>%
filter(ave_emotion>0) %>%
group_by(emotion_type) %>%
summarize(emotion_count=sum(emotion_count)) %>%
arrange(desc(emotion_count)) %>%
head(8) %>%
mutate(emotion_type=fct_reorder(emotion_type,emotion_count)) %>%
ggplot(aes(emotion_type,emotion_count))+
geom_col(fill='#FF9900')+
coord_flip()+
labs(x='Emotion Type',y="Word Count",title='NBC Breaking News')->emotion_plot_nbc
emotion_wsj<-emotion_by(corona_wsj$text)
emotion_wsj %>%
filter(ave_emotion>0) %>%
group_by(emotion_type) %>%
summarize(emotion_count=sum(emotion_count)) %>%
arrange(desc(emotion_count)) %>%
head(8) %>%
mutate(emotion_type=fct_reorder(emotion_type,emotion_count)) %>%
ggplot(aes(emotion_type,emotion_count))+
geom_col(fill='#edb879')+
coord_flip()+
labs(x='Emotion Type',y="Word Count",title='Wall Street Journal')->emotion_plot_wsj
ggarrange(emotion_plot_bbc,emotion_plot_cnn,emotion_plot_nyt,emotion_plot_bloom,emotion_plot_nbc,emotion_plot_wsj,
ncol=3,nrow=3)
################## VIX ########################
vix<-read.csv(file.choose())
vix$Date<-as.Date(vix$Date,format = '%m/%d/%Y')
vix<-vix %>%
filter(Date>=as.Date('2020-01-01'))
changevix<-c()
for(i in 1:nrow(vix)){
changevix[i]<-(vix$VIX.Close[i+1]-vix$VIX.Close[i])/vix$VIX.Close[i]
}
vix<-vix %>% mutate(changevix=changevix)
####################################################################
sentiment_cnn_bbc_joined %>%
inner_join(vix,by=c('created_at'='Date'))->vix_sent_joined
####################################################################
vix_sent_joined$Status<-ifelse(vix_sent_joined$sentiment_mean>0
,'Positive','Negative')
ggplot(vix_sent_joined)+
geom_col(aes(created_at,sentiment_mean,fill=Status))+
geom_line(aes(created_at,changevix),size=1,alpha=0.6)+
labs(x='Month',y='Mean Polarity & VIX',title='Tweets\' Polarity vs. VIX*',caption='*VIX data downloaded from CBOE website')
#adf test
broom::tidy(adf.test(vix_sent_joined$sentiment_mean))
broom::tidy(adf.test(na.omit(vix_sent_joined$VIX.Close)))
#kpss
broom::tidy(kpss.test(vix_sent_joined$sentiment_mean))
broom::tidy(kpss.test(vix_sent_joined$changevix))
#ccf
ccf(vix_sent_joined$sentiment_mean,na.omit(vix_sent_joined$changevix))
|
e457933921c8afbedaa3104fb210a44604051162 | 184180d341d2928ab7c5a626d94f2a9863726c65 | /valgrind_test_dir/do_divisible2-test.R | 87cc6554857277ccbade5708b614f8607bf48d2e | [] | no_license | akhikolla/RcppDeepStateTest | f102ddf03a22b0fc05e02239d53405c8977cbc2b | 97e73fe4f8cb0f8e5415f52a2474c8bc322bbbe5 | refs/heads/master | 2023-03-03T12:19:31.725234 | 2021-02-12T21:50:12 | 2021-02-12T21:50:12 | 254,214,504 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 223 | r | do_divisible2-test.R | function (x, nThread = 1L)
{
e <- get("data.env", .GlobalEnv)
e[["do_divisible2"]][[length(e[["do_divisible2"]]) + 1]] <- list(x = x,
nThread = nThread)
.Call("_hutilscpp_do_divisible2", x, nThread)
}
|
af5635179f3b974bc2bde2dc4be67b85826876c9 | f5fdd56afdca7f8d9165ddfac54d6d005e3ffa0c | /RBioDeg.R | 07584915e06da85e38b8f8386402fd2478f0b395 | [] | no_license | pjkowalczyk/PhysicochemicalPropertyPredictions | 866e04eb8cb869f7eeabffe7424840fec29b2a77 | 4a8020c4c0dd36abf8989ceb55591f36a117dd4e | refs/heads/main | 2022-12-24T19:05:19.187489 | 2020-10-08T15:50:55 | 2020-10-08T15:50:55 | 302,376,811 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,052 | r | RBioDeg.R | library(rcdk)
library(tidyverse)
library(magrittr)
library(purrr)
library(stringr)
library(caret)
library(corrplot)
library(ggplot2)
library(ggthemes)
library(pROC)
library(egg)
# read data
## training data
train <-
read.csv('cache/TR_RBioDeg_1197_descrs.csv',
header = TRUE,
stringsAsFactors = FALSE) %>%
select(-X,-CAS,-ROMol,-SMILES,-ID) %>%
select(Ready_Biodeg, everything()) %>%
na.omit()
train$Ready_Biodeg <- ifelse(train$Ready_Biodeg > 0.5, 'RB', 'NRB')
X_train <- train %>%
select(-Ready_Biodeg)
y_train <- train %>%
select(Ready_Biodeg) %>%
data.frame()
## test data
test <-
read.csv('cache/TST_RBioDeg_411_descrs.csv',
header = TRUE,
stringsAsFactors = FALSE) %>%
select(-X,-CAS,-ROMol,-SMILES,-ID) %>%
select(Ready_Biodeg, everything()) %>%
na.omit()
test$Ready_Biodeg <- ifelse(test$Ready_Biodeg > 0.5, 'RB', 'NRB')
X_test <- test %>%
select(-Ready_Biodeg)
y_test <- test %>%
select(Ready_Biodeg) %>%
data.frame()
# curate data
## near-zero variance descriptors
nzv <- nearZeroVar(X_train, freqCut = 100/0)
X_train <- X_train[ , -nzv]
### and
X_test <- X_test[ , -nzv]
## highly correlated descriptors
correlations <- cor(X_train)
alles_plot <- corrplot::corrplot(correlations, order = 'hclust', tl.cex = 0.6)
highCorr <- findCorrelation(correlations, cutoff = 0.85)
X_train <- X_train[ , -highCorr]
### and
X_test <- X_test[ , -highCorr]
correlations <- cor(X_train)
noCorr_plot <- corrplot::corrplot(correlations, order = 'hclust', tl.cex = 0.8)
## linear combinations
comboInfo <- findLinearCombos(X_train) # returns NULL
X_train <- X_train[ , -comboInfo$remove]
### and
X_test <- X_test[ , -comboInfo$remove]
## center & scale descriptors
preProcValues <- preProcess(X_train, method = c("center", "scale"))
X_trainTransformed <- predict(preProcValues, X_train)
### and
X_testTransformed <- predict(preProcValues, X_test)
# models
## support vector machines
set.seed(350)
ctrl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 10,
## repeated ten times
repeats = 10,
classProbs = TRUE)
library(kernlab)
sigmaRangeReduced <- sigest(as.matrix(X_trainTransformed))
svmGridReduced <- expand.grid(.sigma = sigmaRangeReduced[1],
.C = 2^(seq(-4, 4)))
trainSet <- cbind(y_train, X_trainTransformed)
svmModel <- train(Ready_Biodeg ~ .,
data = trainSet,
method = 'svmRadial',
metric = 'ROC',
tuneGrid = svmGridReduced,
fit = FALSE,
trControl = ctrl)
y_predict <- predict(svmModel, newdata = X_testTransformed) %>%
data.frame()
colnames(y_predict) <- c('Predicted')
confusionMatrix(data = as.factor(y_predict$Predicted),
reference = as.factor(y_test$Ready_Biodeg),
positive = 'NRB')
library(pROC)
y_test$endpt <- ifelse(y_test$Ready_Biodeg == 'NRB', 0, 1)
y_predict$endpt <- ifelse(y_predict$Predicted == 'NRB', 0, 1)
rocCurve <- roc(response = y_test$endpt,
predictor = y_predict$endpt)
auc(rocCurve)
plot(rocCurve, legacy.axes = TRUE)
#####...#####...#####...#####
library(randomForest)
library(caret)
library(e1071)
# read data
## training data
train <-
read.csv('cache/TR_RBioDeg_1197_descrs.csv',
header = TRUE,
stringsAsFactors = FALSE) %>%
select(-X,-CAS,-ROMol,-SMILES,-ID) %>%
select(Ready_Biodeg, everything()) %>%
na.omit()
train$Ready_Biodeg <- ifelse(train$Ready_Biodeg > 0.5, 'RB', 'NRB')
## test data
test <-
read.csv('cache/TST_RBioDeg_411_descrs.csv',
header = TRUE,
stringsAsFactors = FALSE) %>%
select(-X,-CAS,-ROMol,-SMILES,-ID) %>%
select(Ready_Biodeg, everything()) %>%
na.omit()
test$Ready_Biodeg <- ifelse(test$Ready_Biodeg > 0.5, 'RB', 'NRB')
## bind train and test, by row
alles <- rbind(train, test)
## data splitting
set.seed(350)
trainIndex <- createDataPartition(alles$Ready_Biodeg, p = .8,
list = FALSE,
times = 1)
train <- alles[trainIndex, ]
test <- alles[-trainIndex, ]
X_train <- train %>%
select(-Ready_Biodeg)
y_train <- train %>%
select(Ready_Biodeg) %>%
data.frame() %>%
mutate(Ready_Biodeg = as.factor(Ready_Biodeg))
X_test <- test %>%
select(-Ready_Biodeg)
y_test <- test %>%
select(Ready_Biodeg) %>%
data.frame() %>%
mutate(Ready_Biodeg = as.factor(Ready_Biodeg))
# curate data
## near-zero variance descriptors
nzv <- nearZeroVar(X_train, freqCut = 100/0)
X_train <- X_train[ , -nzv]
### and
X_test <- X_test[ , -nzv]
## highly correlated descriptors
correlations <- cor(X_train)
corrplot::corrplot(correlations, order = 'hclust')
highCorr <- findCorrelation(correlations, cutoff = 0.85)
X_train <- X_train[ , -highCorr]
### and
X_test <- X_test[ , -highCorr]
correlations <- cor(X_train)
corrplot::corrplot(correlations, order = 'hclust')
## linear combinations
comboInfo <- findLinearCombos(X_train) # returns NULL
X_train <- X_train[ , -comboInfo$remove]
### and
X_test <- X_test[ , -comboInfo$remove]
# 10 fold; repeat 3 times
control <- trainControl(method='repeatedcv',
number=10,
repeats=3)
# metric: Accuracy
metric <- "Accuracy"
mtry <- sqrt(ncol(X_train))
tunegrid <- expand.grid(.mtry=mtry)
data2model <- cbind(y_train, X_train)
rf_default <- train(
Ready_Biodeg ~ .,
data = data2model,
method = 'rf',
metric = 'Accuracy',
tuneGrid = tunegrid,
trControl = control
)
print(rf_default)
y_predict <- predict(rf_default, newdata = X_test) %>%
data.frame()
colnames(y_predict) <- c('Predicted')
confusionMatrix(data = as.factor(y_predict$Predicted),
reference = as.factor(y_test$Ready_Biodeg),
positive = 'NRB')
library(doParallel)
cores <- 3
registerDoParallel(cores = cores)
mtry <- sqrt(ncol(X_train))
#ntree: Number of trees to grow.
ntree <- 3
control <- trainControl(
method = 'repeatedcv',
number = 10,
repeats = 3,
search = 'random'
)
#
rf_random <- train(Ready_Biodeg ~ .,
data = data2model,
method = 'rf',
metric = 'Accuracy',
tuneLength = 15,
trControl = control)
print(rf_random)
plot(rf_random)
#
control <- trainControl(method='repeatedcv',
number=10,
repeats=3,
search='grid')
#
tunegrid <- expand.grid(.mtry = (1:15))
rf_gridsearch <- train(Ready_Biodeg ~ .,
data = data2model,
method = 'rf',
metric = 'Accuracy',
tuneGrid = tunegrid)
|
2ca81b092ee85269c9f2a3c37b0ea69b172b247e | e61d4e17b5683e6c5c79588588aa302f24b03863 | /R_Test_File.R | 984c37602333bd3c13486fcfb62eb40caf1bde0b | [] | no_license | Joseph-C-Fritch/web_scrape_project | 89466e585b3e10dab0be11e1d2d7c803945d1962 | 0d56f349421d8f564a4ade6ce15c4bda7be11407 | refs/heads/master | 2020-04-22T02:05:24.170732 | 2019-02-12T19:06:26 | 2019-02-12T19:06:26 | 170,035,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 86 | r | R_Test_File.R | library(dplyr)
xrp_price_df = read.csv('./xrp_price.csv', stringsAsFactors = 'False') |
5b482b6e30a86e55e4de3286146a293f7373c612 | 342bd673b3cf5f3477eac4c23c7ba8b32d6e7062 | /shiny/server_1_loadModels/server_1_loadModels.R | d6bd4514d53fdccb4942183a6c9218ab78827d19 | [] | no_license | GRSEB9S/eSDM | 4f3575b77fdffe4ebad35028e5dd67aef49da1af | a26b9ac92127346fe0d9e22c3eb6376b8ff88a77 | refs/heads/master | 2021-07-13T06:35:28.689723 | 2017-10-19T18:26:26 | 2017-10-19T18:26:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,993 | r | server_1_loadModels.R | ### Code for loading models and converting them to SPDFs
###############################################################################
### Flag for if any model predictions are loaded
output$loadModels_display_flag <- reactive({
length(vals$models.ll) != 0
})
outputOptions(output, "loadModels_display_flag", suspendWhenHidden = FALSE)
### Flag for if any model predictions are selected in the table
output$loaded_models_selected_flag <- reactive({
isTruthy(input$models_loaded_table_rows_selected)
})
outputOptions(output, "loaded_models_selected_flag", suspendWhenHidden = FALSE)
###############################################################################
### Delete selected model
observeEvent(input$model_remove_execute, {
idx <- as.numeric(input$models_loaded_table_rows_selected)
validate(
need(length(idx) > 0,
paste("Error: Please select one or more sets",
"of model predictions to remove"))
)
#########################################################
### Remove the reactiveValue info for selected set(s) of model predicitons
vals$models.ll <- vals$models.ll[-idx]
vals$models.orig <- vals$models.orig[-idx]
vals$models.pix <- vals$models.pix[-idx]
vals$models.names <- vals$models.names[-idx]
vals$models.data.names <- vals$models.data.names[-idx]
vals$models.pred.type <- vals$models.pred.type[-idx]
vals$models.specs <- vals$models.specs[-idx]
if (length(vals$models.names) == 0) vals$models.names <- NULL
if (length(vals$models.pred.type) == 0) vals$models.pred.type <- NULL
#########################################################
# Handle other places this data was used
### If these predictions were previewed, hide preview
### Else, adjust vals idx
if (!is.null(vals$models.plotted.idx)) {
if (any(idx %in% vals$models.plotted.idx)) {
shinyjs::hide("model_pix_preview_plot", time = 0)
vals$models.plotted.idx <- NULL
} else {
idx.adjust <- sapply(vals$models.plotted.idx, function(i) {
sum(idx < i)
})
vals$models.plotted.idx <- vals$models.plotted.idx - idx.adjust
validate(
need(all(vals$models.plotted.idx > 0),
"Error: While deleting original model(s), error 1")
)
}
}
### Remove evaluation metrics if they're calculated for original model preds
# TODO: make this so it only removes the metrics of models being removed
if (!is.null(vals$eval.models.idx)) {
if (!is.null(vals$eval.models.idx[[1]])){
vals$eval.models.idx <- NULL
vals$eval.metrics <- list()
vals$eval.metrics.names <- NULL
}
}
### If these predictions were pretty-plotted, reset and hide pretty plot
### Else, adjust vals idx
if (!is.null(vals$pretty.plotted.idx)) {
if (any(idx %in% vals$pretty.plotted.idx[[1]])) {
shinyjs::hide("pretty_plot_plot", time = 0)
vals$pretty.params.list <- list()
vals$pretty.plotted.idx <- NULL
} else {
idx.adjust <- sapply(vals$pretty.plotted.idx[[1]], function(i) {
sum(idx < i)
})
vals$pretty.plotted.idx[[1]] <- vals$pretty.plotted.idx[[1]] - idx.adjust
validate(
need(all(vals$pretty.plotted.idx[[1]] > 0),
"Error: While deleting 1+ original model(s), error 2")
)
}
}
})
###############################################################################
# Reset 'Prediction value type' to 'Relative' if new file is loaded
observe({
input$model_csv_file
updateSelectInput(session, "model_csv_pred_type", selected = 2)
})
observe({
input$model_gis_raster_file
updateSelectInput(session, "model_gis_raster_pred_type", selected = 2)
})
observe({
input$model_gis_shp_files
updateSelectInput(session, "model_gis_shp_pred_type", selected = 2)
})
observe({
input$model_gis_gdb_load
updateSelectInput(session, "model_gis_gdb_pred_type", selected = 2)
})
############################################################################### |
c5fa95924f4d22ac1b4e3a33f51714206aae5e3e | 7be74683083a548d47b31fa6cb826c061c1d2aea | /non-linear/non_linear_lab_6.R | 65f4d2275c45672e68edb9a225d40331fa041742 | [] | no_license | AnatoliiStepaniuk/ISLR | 32b7ab6c9fed20c92dba36016c474b4d9b697ce9 | f613a1c56a594f4594c813cbe8f35b55d4263f99 | refs/heads/master | 2021-01-09T06:35:46.439090 | 2017-02-12T13:22:29 | 2017-02-12T13:22:29 | 81,018,010 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,449 | r | non_linear_lab_6.R | library(ISLR)
set.seed(1)
# (a)
all.deltas <- rep(NA, 10)
for(i in 1:10){
glm.fit <- glm(wage~poly(age,i), data=Wage)
all.deltas[i] <- cv.glm(glm.fit, data=Wage, K=10)$delta[2]
}
min.delta <- min(all.deltas)
d <- which.min(all.deltas)
par(mfrow=c(1,1))
plot(x=1:10,y=all.deltas,col="red",type="l",ylab = "CV error",xlab="Polynomial degree")
title("Choosing polynomial degree")
fit1 <- lm(wage~poly(age,1), data=Wage)
fit2 <- lm(wage~poly(age,2), data=Wage)
fit3 <- lm(wage~poly(age,3), data=Wage)
fit4 <- lm(wage~poly(age,4), data=Wage)
fit5 <- lm(wage~poly(age,5), data=Wage)
fit6 <- lm(wage~poly(age,6), data=Wage)
fit7 <- lm(wage~poly(age,7), data=Wage)
fit8 <- lm(wage~poly(age,8), data=Wage)
fit9 <- lm(wage~poly(age,9), data=Wage)
fit10 <- lm(wage~poly(age,10), data=Wage)
anova(fit1,fit2,fit3,fit4,fit5,fit6,fit6,fit7,fit8,fit9,fit10)
# Anova proved that CV determined polynom degree = 4 is a reasonable value
# b
all.step.deltas <- rep(NA, 9)
for(i in 2:10){
Wage$age.cut <- cut(age,i)
fit.step <- glm(wage~age.cut,data = Wage)
all.step.deltas[i-1] <- cv.glm(Wage, fit.step,K=10)$delta[2]
}
Wage$age.cut<-NULL
best.cut.n <- which.min(all.step.deltas)
fit.step <- glm(wage~cut(age,best.cut.n),data = Wage)
age.range <- range(Wage$age)
age.grid <- (age.range[1]:age.range[2])
pred.step <- predict(fit.step, data.frame(age=age.grid))
plot(Wage$age, Wage$wage,col="darkgrey")
lines(age.grid, pred.step, type = "l", col="red")
|
1f7dc4bf0127c9db91762ebeaff289f2c4ef1bb2 | cb01200aef78010bbce2477944aec53357334de4 | /script work/working scripts.R | 16c347eaae4ba088b67a554da3e175453e2f617c | [] | no_license | S-AI-F/Open-Geo-KPI-App | 4a5a14891ac94deb3671943673d35acbb8250815 | a78da0578c519979efc9c418c44a7c2c70674491 | refs/heads/master | 2023-06-28T10:48:36.702859 | 2021-07-21T11:22:34 | 2021-07-21T11:22:34 | 263,057,928 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,096 | r | working scripts.R |
library(shinyjs)
install.packages("shinyjs")
# get map data
geodata_world <- ne_countries()
# rename country code and name variables
names(geodata_world)[names(geodata_world) == "iso_a3"] <- "iso3c"
names(geodata_world)[names(geodata_world) == "name"] <- "NAME"
# get worldbank data
attribute_data <- wb(
indicator = c("EN.ATM.PM25.MC.M3", "SP.POP.TOTL"),
return_wide = TRUE,
POSIXct = TRUE
)
attribute_data_world <- wb(
indicator = c(# people indicators
"SP.POP.TOTL","SE.PRM.ENRR","SL.TLF.CACT.ZS","SH.STA.MMRT",
# porverty and inequality indicators
"SI.POV.NAHC", "SI.POV.GINI",
# Environment
"AG.LND.AGRI.ZS","EN.ATM.CO2E.PC","EN.ATM.PM25.MC.M3",
# Economy
"NY.GDP.MKTP.CD","NE.GDI.TOTL.KD.ZG","SL.GDP.PCAP.EM.KD",
# States and markets
"GC.REV.XGRT.CN", "GC.XPN.TOTL.CN","MS.MIL.XPND.GD.ZS","GB.XPD.RSDV.GD.ZS",
# Global links
"DT.DOD.DECT.CD","SM.POP.NETM"),
return_wide = TRUE,
POSIXct = TRUE
)
attribute_data_world = attribute_data_world %>%
dplyr::rename(# people indicators
Population_Total = SP.POP.TOTL,
School_enrollment_primary = SE.PRM.ENRR,
Labor_force_participation_rate = SL.TLF.CACT.ZS,
Maternal_mortality_ratio = SH.STA.MMRT,
# porverty and inequality indicators
Poverty_headcount_ratio = SI.POV.NAHC,
GINI_index = SI.POV.GINI,
# Environment
Agricultural_land = AG.LND.AGRI.ZS,
CO2_emission = EN.ATM.CO2E.PC,
PM2.5_air_pollution = EN.ATM.PM25.MC.M3,
# Economy
GDP = NY.GDP.MKTP.CD,
Gross_capital_formation = NE.GDI.TOTL.KD.ZG,
GDP_per_person_employed = SL.GDP.PCAP.EM.KD,
# States and markets
Government_revenue = GC.REV.XGRT.CN,
Government_Expense = GC.XPN.TOTL.CN,
Military_expenditure = MS.MIL.XPND.GD.ZS,
Research_and_development_expenditure = GB.XPD.RSDV.GD.ZS,
# Global Links
External_debt_stocks = DT.DOD.DECT.CD,
Net_migration = SM.POP.NETM)
head(attribute_data)
# get worldbank data
exposure <- wb(
indicator = "EN.ATM.PM25.MC.M3",
return_wide = TRUE,
POSIXct = TRUE
)
head(exposure)
EN.ATM.PM25.MC.M3 <- wb(
indicator = "EN.ATM.PM25.MC.M3",
return_wide = TRUE,
POSIXct = TRUE
)
population <- wb(
indicator = "SP.POP.TOTL",
return_wide = TRUE,
POSIXct = TRUE
)
head(population)
attribute_data_world <- exposure %>%
full_join(population[,c("iso3c","date","SP.POP.TOTL")], by = c("iso3c","date"))
KPI_start_end_date_fill = function(KPIlist){
KPI_start_end_date = data.frame(indicator = KPIlist)
}
KPI_start_end_date = data.frame(indicator = c("EN.ATM.PM25.MC.M3", "SP.POP.TOTL"))
KPI_start_end_date[KPI_start_end_date$indicator == "EN.ATM.PM25.MC.M3", "stratdate"] = min(EN.ATM.PM25.MC.M3$date)
KPI_start_end_date[KPI_start_end_date$indicator == "EN.ATM.PM25.MC.M3", "enddate"] = max(EN.ATM.PM25.MC.M3$date)
KPI_start_end_date = data.frame(KPI = as.character(),
startdate = as.character(),
enddate = as.character())
kpi_list = c("EN.ATM.PM25.MC.M3","SP.POP.TOTL")
for(i in 1:length(kpi_list)){
KPI_name = kpi_list[1]
KPI_start_end_date[,"KPI"] = KPI_name
KPI_start_end_date[,"startdate"] = min()
}
# merge with attribute data with geodata
map_data_world = sp::merge(geodata_world,
attribute_data_world,
by = "iso3c",
duplicateGeoms = TRUE)
map_data_world = map_data_world[!is.na(map_data_world@data$date_ct), ]
date_vector_world = seq.Date(from = min(map_data_world@data$date_ct),
to = max(map_data_world@data$date_ct),
by = "years")
# get selected date from animation
selected_date_world = date_vector_world[10]
# get selected kpi from kpi filters
selected_kpi_world = "SP.POP.TOTL"
# discretization param
# discr_param = "KPIcolor_binpal"
discr_param = "KPIcolor_qpal"
binpal <- colorBin("YlOrRd",
map_data_world@data[, selected_kpi_world],
5,
pretty = FALSE)
qpal = colorQuantile(palette = "YlOrRd",
domain = map_data_world@data[, selected_kpi_world],
n = 5)
Specify_legend_colpal = function(discr_param){
if(discr_param == "KPIcolor_binpal"){
legend_pal = binpal
}
else if(discr_param == "KPIcolor_qpal"){
legend_pal = qpal
}
return(legend_pal)
}
legend_pal = Specify_legend_colpal(discr_param = discr_param)
# filter map data based on selected filters
map_data_selected_world = map_data_world[map_data_world@data$date_ct == selected_date_world, ]
toto = map_data_world@data[ ,selected_kpi_world]
# discretize indicator value and fill color
map_data_selected_world@data$KPIcolor_qpal = qpal(map_data_selected_world@data[,selected_kpi_world])
map_data_selected_world@data$KPIcolor_binpal = binpal(map_data_selected_world@data[,selected_kpi_world])
# generte filteres map
leaflet(map_data_selected_world) %>%
clearControls() %>%
addTiles() %>%
addPolygons(stroke = TRUE,
smoothFactor = 0.3,
fillOpacity = 0.8,
color = "gray",
dashArray = "3",
weight = 1,
opacity = 0.8,
fillColor = ~legend_pal(map_data_selected_world@data[,selected_kpi_world]),
# fillColor = ~binpal(map_data_selected_world@data[,selected_kpi_world]),
highlightOptions = highlightOptions(weight = 2, color = "grey", fillOpacity = 0.7, bringToFront = TRUE),
label = ~paste0(NAME,": ",prettyNum(map_data_selected_world@data[ ,selected_kpi_world],
format = "f",
big.mark = ","))) %>%
addLegend(pal = legend_pal,
values = ~ map_data_selected_world@data[ ,selected_kpi_world],
opacity = 0.5,
title = selected_kpi_world)
addLegend(pal = binpal,
values = ~ map_data_selected_world@data[ ,selected_kpi_world],
opacity = 0.5,
title = selected_kpi_world)
|
1a17533e2828adf65f2accfc305d619d5a849303 | 0f56baca4f5779b5e389311a658f9e6c06617502 | /man/preds.Rd | 8fc0ff93c96f0a40fce47fa4ec8ce4600c38a102 | [] | no_license | cfree14/datalimited2 | 4e01d6547b6735ee71403da228af6efee4df6621 | 152a352899585951ff09b52d26181f22d3e09dda | refs/heads/master | 2023-08-17T12:23:13.694125 | 2023-08-04T14:09:57 | 2023-08-04T14:09:57 | 115,050,731 | 16 | 5 | null | null | null | null | UTF-8 | R | false | true | 605 | rd | preds.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{preds}
\alias{preds}
\title{RAM Legacy Database catch-only status predictions}
\format{A data frame with 161 rows (stocks) and 57 variables including B/BMSY estimates
and status estimates.}
\usage{
preds
}
\description{
A dataset containing the catch and biomass time series for USA Southern
New England/Mid-Atlantic (SNE/MA) Yellowtail flounder (\emph{Pleuronectes ferruginea}) from 1973-2014.
This dataset is included for additional testing of the \pkg{datalimited2} package.
}
\keyword{datasets}
|
1a813667600961627fb0e8c4cc10e8515ed7f353 | 6ae2d6b27576cc8c75a7e02256db410e5007a8b2 | /tests/testthat/test-dir2.R | ffcfa0332ccdfd9a4c15d85455fe8fcfb7da25bb | [] | no_license | HughParsonage/hutils | 3c2cec1b1a01179219deb47df0dc9e6a3a127435 | 11e6f828876bbc87a42d43a0ee8084ee6d9a6765 | refs/heads/master | 2023-02-20T04:27:01.996815 | 2023-02-10T05:18:04 | 2023-02-10T05:18:04 | 88,268,552 | 11 | 2 | null | 2022-04-13T08:31:57 | 2017-04-14T13:09:05 | R | UTF-8 | R | false | false | 2,181 | r | test-dir2.R | context("test-dir2")
test_that("Error handling", {
skip_if_not(identical(.Platform$OS.type, "windows"))
library(data.table)
expect_error(dir2(.dont_use = TRUE),
regexp = "Windows")
expect_error(dir2(path = raw(1)),
regexp = "`path` was a raw, but must be a string",
fixed = TRUE)
expect_error(dir2(path = data.table(x = 1)),
regexp = "`path` was a data.table data.frame, but must be a string",
fixed = TRUE)
expect_error(dir2(path = character(0)),
regexp = "`path` was length 0",
fixed = TRUE)
theDir <- "a"
while (dir.exists(theDir)) {
theDir <- paste0(theDir, "x")
}
skip_if(dir.exists(theDir))
expect_error(dir2(path = theDir),
regexp = "does not exist")
})
test_that("Error handling (non-Windows)", {
skip_on_os("windows")
expect_error(dir2(.dont_use = FALSE),
regexp = "Windows")
})
test_that("dir2 works", {
skip_if_not(identical(.Platform$OS.type, "windows"))
skip_only_on_cran()
tempd <- tempfile()
dir.create(tempd)
file.create(file.path(tempd, "abc.csv"))
file.create(file.path(tempd, "def.csv"))
y <- dir(path = tempd, recursive = FALSE)
z <- dir2(path = tempd, recursive = FALSE)
expect_equal(length(z), length(y))
z <- dir2(path = tempd, file_ext = ".csv")
expect_equal(length(z), 2L)
z <- dir2(path = tempd, file_ext = "*.csv")
expect_equal(length(z), 2L)
z1 <- dir2(path = tempd, file_ext = "*.csv", pattern = "^a")
expect_equal(length(z1), 1L)
zp <- dir2(path = tempd, file_ext = "*.csv", pattern = "^a", perl = TRUE)
expect_equal(length(zp), 1L)
zp <- dir2(path = tempd, file_ext = "*.csv", pattern = "^a", perl = FALSE)
expect_equal(length(zp), 1L)
zfx <- dir2(path = tempd, file_ext = ".csv", pattern = "^a", fixed = TRUE)
expect_equal(length(zfx), 0L)
zic <- dir2(path = tempd, file_ext = ".csv", pattern = "^A", ignore.case = TRUE)
expect_equal(length(zic), 1L)
})
test_that("Nil files", {
skip_if_not(identical(.Platform$OS.type, "windows"))
z <- dir2(file_ext = "*.qqq")
expect_equal(length(z), 0L)
z <- dir2(file_ext = ".qqq")
})
|
171d70d85fb338ce68ec30b7f1d27a95e39de2b9 | bec2aef1fa0722ad373f0a51bcf119c3028855aa | /chap03/mpg.R | f189af1ac171d5dca057c1d3916401a904c8f07f | [] | no_license | floraOuO/R | 751a275163758716806752e98f1a4cd3f06f6cc2 | 4cace0f4158513b08701c2c4f9987d2c1803d8f6 | refs/heads/master | 2020-04-28T04:02:56.088121 | 2019-03-22T09:07:50 | 2019-03-22T09:07:50 | 174,962,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 470 | r | mpg.R | library(ggplot2)
library(sqldf)
x=c("a","a","b","c","d","d")
qplot(x)
mpg
sqldf("
select distinct manufacturer from mpg
")
sqldf("
select count(*) from mpg
")
sqldf("
select manufacturer,round(avg(cty),2) as cty_avg,round(avg(hwy),2) as hwy_avg from mpg
group by manufacturer
order by avg(cty) desc
") ->mpg_ctyhwy_mean
mpg_ctyhwy_mean
qplot(mpg_ctyhwy_mean$manufacturer,mpg_ctyhwy_mean$cty_avg)
qplot(mpg$hwy)
table(mpg$drv)
|
7f6858b6b727fbafe58b028eab287c25809c5a28 | 2a4b11fd17377f5cf06ef1f89585c71266b8b1c9 | /descr_stat_barpie.R | efc571e185d5d21c76d9061d971350c728a08757 | [] | no_license | AhyonJeon/covid19-petitons-analysis | 6ca1f3017dcfe127e936c5824d28e5df5dc4e8b5 | ed69de78646352dd071fc53c5ed0f22bf3a1072f | refs/heads/master | 2023-04-18T22:48:22.591345 | 2021-04-18T06:56:04 | 2021-04-18T06:56:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,147 | r | descr_stat_barpie.R | # 라이브러리
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(reshape2)
library(data.table) # setdt() 사용하기 위해
library(plyr) #desc()를 사용하기 위해서 필요하다.
# 코로나 전후 청원데이터 불러오기
before <- read_csv("before_covid_petition_enc.csv")
after <- read_csv("after_covid_petition_enc.csv")
total<- rbind(before, after)
# category 확인
sort(unique(before$category))
sort(unique(after$category))
length(unique(before$category))
# 간단한 데이터 확인
summary(before)
summary(after)
# 카테고리별 청원건수 빈도와 비율 확인후 데이터프레임 생성
before_df<- cbind(freq= sort(table(before$category), decreasing =T), relative= prop.table(table(before$category)))
after_df<- cbind(freq= sort(table(after$category), decreasing =T), relative= prop.table(table(after$category)))
before_df<- data.frame(before_df)
## rownames가 카테고리로 되어있어 카테고리를 열 변수로 빼고 새로운 행이름을 설정
setDT(before_df, keep.rownames = TRUE)[]
## 변수명이 rn으로 빼진 카테고리 변수명을 category로 재설정
before_df<- rename(before_df, category=rn)
## df에 period 변수 생성
before_df<- before_df %>%
mutate(period="before")
after_df<- data.frame(after_df)
setDT(after_df, keep.rownames = TRUE)[]
after_df<- rename(after_df, category=rn)
after_df<- after_df %>%
mutate(period="after")
# 카테고리별 청원동의 인원수 변수 생성
temp<- before %>%
group_by(category) %>%
summarise(AgreeSum= sum(numOfAgrees))
before_df<- merge(before_df, temp, by='category')
temp2<- after %>%
group_by(category) %>%
summarise(AgreeSum= sum(numOfAgrees))
after_df<- merge(after_df, temp2, by='category')
total_df= rbind(after_df,before_df)
# 막대 그래프
# 빈도 건수
ggplot(total_df, aes(x= category, y= freq, fill=period))+
geom_bar(stat="identity",position= position_dodge2(reverse = TRUE))+
scale_fill_brewer(palette="Set2")+
labs(title="코로나 전후 카테고리 별 청원 건수")+
theme_bw()+
theme(plot.title = element_text(hjust = 0.5, face='bold', size = 15))+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5 ))+
theme(legend.text = element_text(size = 8))+
guides(fill=guide_legend(reverse=TRUE))
ggsave("category_freq_bar.jpg", dpi = 300) # ggplot를 저장합니다.
# 비율
ggplot(total_df, aes(x= category, y= relative, fill=period))+
geom_bar(stat="identity",position= position_dodge2(reverse = TRUE))+
scale_fill_brewer(palette="Set2")+
labs(title="코로나 전후 카테고리 별 청원 비율")+
theme_bw()+
theme(plot.title = element_text(hjust = 0.5, face='bold', size = 15))+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5 ))+
theme(legend.text = element_text(size = 8))+
guides(fill=guide_legend(reverse=TRUE))
ggsave("category_rela_bar.jpg", dpi = 300) # ggplot를 저장합니다.
# 파이 그래프
ggplot(before_df, aes(x="", y= -freq, fill= reorder(category,-freq)))+
geom_bar(stat="identity")+
coord_polar("y")+
theme_void()+
labs(title="코로나 전 카테고리 별 청원 건수")+
theme(plot.title = element_text(hjust = 0.6, face='bold', size = 15))
ggsave("category_freq_before_pie.jpg", dpi = 300) # ggplot를 저장합니다.
ggplot(after_df, aes(x="", y= -freq, fill= reorder(category,-freq)))+
geom_bar(stat="identity")+
coord_polar("y")+
theme_void()+
labs(title="코로나 후 카테고리 별 청원 건수")+
theme(plot.title = element_text(hjust = 0.6, face='bold', size = 15))
ggsave("category_freq_after_pie.jpg", dpi = 300) # ggplot를 저장합니다.
# 다른 파이차트
## plot the outside pie and shades of subgroups
lab <- with(before_df, sprintf('%s: %s',category, freq))
pie(before_df$freq, border = NA,
labels = lab, cex = 0.5)
# scatter plot
ggplot(total_df, aes(x= freq, y= AgreeSum, color=category)) +
geom_point()+
labs(title="청원건수에 따른 청원동의 인원수")+
theme(plot.title = element_text(hjust = 0.6, face='bold', size = 15))+
theme_bw()
ggsave("agreesum_freq_scatter.jpg", dpi = 300)
|
986d8a1510c88a86a12321588e39dda5898f48c4 | 2d320d9af4bed14cc1bbfceb0e8014dcf6a82fe7 | /Chapter5/auxFun/F_genNormal.R | d323383c7b32fb54da9e6d2e4122586902849cec | [] | no_license | sthawinke/DoctoralThesis | a5be71706a63d650f24c2a4bc43dd549c627e887 | 924b9c93ffa1b2939a93d8e88a372d1ce65efb09 | refs/heads/master | 2020-12-13T22:53:05.279324 | 2020-01-21T13:35:24 | 2020-01-21T13:35:24 | 234,554,358 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,061 | r | F_genNormal.R | #' Generate normal data
genNormal = function(estList, nPop, n, p, FC, TPR){
if((n %% nPop)!=0) stop("Choose even group sizes!")
groupIndComp = rep(seq_len(nPop), times = n/nPop)
p = min(p, length(estList["coef",]))
## Sample parameters
coefSampled0 = sample(estList["coef",], p)
nOTUs = round(p*TPR) #DA taxa
meanSampled0 = lapply(integer(nPop), function(x){
OTUids = sample(names(coefSampled0), nOTUs, replace = FALSE)
coefSampled0[OTUids] = coefSampled0[OTUids]+FC # Add fold change up
indTP <- names(coefSampled0) %in% OTUids
newTaxaNames <- paste0(names(coefSampled0)[indTP], "-TPup")
names(coefSampled0)[indTP] <- newTaxaNames
coefSampled0
})
meanSampled = simplify2array(meanSampled0)[,groupIndComp]
dataMat = t(matrix(rnorm(n*p, mean = meanSampled, sd = estList["sd",]), ncol = n, nrow = p))
colnames(dataMat) = rownames(meanSampled)
rownames(dataMat) = paste0("Group", groupIndComp, "_", seq_len(n))
list(dataMat = dataMat, meanSampled = meanSampled0)
} |
bed8feb65862e2eab2edae1f008ed24bb23720cb | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SpatialNP/examples/spatial.location.Rd.R | 282b7d28bd3e44393397d4bb31812b0e150524b0 | [] | 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 | 501 | r | spatial.location.Rd.R | library(SpatialNP)
### Name: Spatial location
### Title: Multivariate location estimates based on spatial signs and
### signed ranks
### Aliases: ae.hl.estimate ae.spatial.median spatial.location
### Keywords: multivariate nonparametric
### ** Examples
A<-matrix(c(1,2,-3,4,3,-2,-1,0,4),ncol=3)
X<-matrix(rnorm(3000),ncol=3)%*%t(A)
spatial.location(X,score="signrank")
spatial.location(X,score="sign")
#compare with:
colMeans(X)
ae.hl.estimate(X,shape=A%*%t(A))
ae.hl.estimate(X,shape=FALSE)
|
d827cc2793a3520ddd1ba3d7629ab58ed7ad3a7e | 1e6970ee8f6b77a0881a2561c2b2836da0d66ac2 | /R/celsius_to_kelvin.R | 4ba68fa9c376366eeeacabc63286c27a6d69758e | [] | no_license | brilu815/tempConvert | 9fd89c80fc434c171454b05dc9481b723bbf170e | 675b4d859d964238ee06e78a7d0917af3adb2284 | refs/heads/master | 2021-01-18T22:29:32.843677 | 2016-11-02T00:58:48 | 2016-11-02T00:58:48 | 72,590,089 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 126 | r | celsius_to_kelvin.R | celsius_to_kelvin = function(temp_c){
temp_f = celsius_to_fahr(temp_c)
temp_k = fahr_to_kelvin(temp_f)
return(temp_k)
} |
94014347ec8fef263030514bd26997e69eb25ddb | ea319f995c73653e2e43a1c0b0924757b7e19304 | /rscripts/submit_createApsimSims.R | 7bafaeb25a008d0f688834afaecfb9a73f9f6391 | [] | no_license | cla473/AG_WHEATTEMP | 0d25c943c08fcb752be8b49250ffb4a711c0c97e | 383459818dae42883ae1f9134710bdb3b1999408 | refs/heads/master | 2020-03-23T04:16:40.890878 | 2019-03-27T22:02:21 | 2019-03-27T22:02:21 | 139,104,642 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,808 | r | submit_createApsimSims.R | #Builds simulations using data from GridSoil.7z (needs to be extracted and converted first) and Ross' met files
rm(list = ls())
library(tidyverse)
library(xml2)
apsimLoc <- "//OSM/CBR/AG_WHEATTEMP/work/ApsimNG-LC/apsimx/"
baseApsimxFile <- "//OSM/CBR/AG_WHEATTEMP/work/ApsimNG-LC/prep/BaseWithCultivars.apsimx"
metLoc <- "//OSM/CBR/AG_WHEATTEMP/work/ApsimNG-LC/met"
soilLoc <- "//OSM/CBR/AG_WHEATTEMP/work/ApsimNG-test/APSIM_prep/SoilXML"
gridData <- read.csv("//OSM/CBR/AG_WHEATTEMP/work/ApsimNG-LC/metGrid_Soil.csv", sep=',', header=TRUE)
# gridData <- head(gridData, 5)
simFiles <- paste(apsimLoc, gridData$simFile, sep = "")
soilFiles <- paste(soilLoc, "/", gridData$soilLong * 100, gridData$soilLat * 100, ".soil", sep = "")
#these don't need the path, as we are just updating the reference in the apsimx file
metFiles <- as.character(gridData$metFile)
df <- data.frame(sims=simFiles, mets=metFiles, soils=soilFiles, stringsAsFactors = FALSE)
i <- 1
par_generate <- function(i, df, baseApsimxFile) {
library(tidyverse)
library(xml2)
simName <- df$sims[i] #What is the name of apsimx file that will be created
met <- df$mets[i] #get the name of the met file
#Now read base the Apsim File
sim <- read_xml(baseApsimxFile)
#update the met file name
xml_set_text(xml_find_first(sim, ".//Weather/FileName"), met)
#replace the soil data
soilData <- read_xml(df$soils[i])
xml_replace(xml_find_first(sim, ".//Soil"), soilData)
write_xml(sim, simName)
}
library(parallel)
library(snow)
# Get cluster from pearcey
cl <- getMPIcluster()
if (is.null(cl)) {
# Create a cluster for local
cl <- makeCluster(parallel::detectCores() - 1, type = 'SOCK')
}
parLapply(cl, seq_len(nrow(df)), par_generate, df, baseApsimxFile)
|
3dea5db7934056ed2a22ef6cf34c89474c855336 | 1d8e6bb24a98389c46db0db658db539f7917c6ea | /problems/problem002.R | a8483611143f7dd0b395cd333d6764c9a817ceae | [] | no_license | parksw3/projectEuler | 6609c5950344494476976ad4464f75264b8e5b48 | 133c4915d59d3ce43ab869fe6bf99e51158f135d | refs/heads/master | 2020-04-06T06:54:48.299332 | 2016-08-31T12:29:59 | 2016-08-31T12:29:59 | 65,112,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 140 | r | problem002.R | fib <- c(1,2)
i <- 2
while(fib[i] < 4e6){
new.fib <- fib[i] + fib[i-1]
fib <- c(fib, new.fib)
i <- i + 1
}
sum(fib[fib%%2 == 0]) |
f9e1827acbf230f7013ff46afbea3df7ab5f3e39 | f753c3187e80b854c1600ef93ac3006851535bbc | /R/buoyData.R | 51d73c64b9409e9d889c76e5dac5761497d09e5f | [] | no_license | NOAA-EDAB/buoydata | 42055c8b5e55919927a9fce7ef069c6d7351bf04 | ad6eceaf1c6c7f998db623f79fbcf8127d620529 | refs/heads/master | 2023-02-25T10:40:04.467643 | 2021-01-28T16:38:53 | 2021-01-28T16:38:53 | 238,990,643 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 337 | r | buoyData.R | #' buoydata: Extract buoy data from ndbc
#'
#'Tools to aid in extraction of data from the nbdc website
#'
#'\itemize{
#'\item Get_ functions to get data.
#'\item functions to manipulate the data, aggregate the data
#'}
#'
#'To learn more about using \code{buoydata} click the index below
#'
#'
#' @docType package
#' @name buoydata
NULL
|
65fee7d83bfaa1adb8c2eb64ff68e280189f1f86 | 8ac91b65f4b3674d5fa514b304940712bab78dc8 | /plot4.R | dd5b3b685a1262684297fd39a258f5bcb75bd51a | [] | no_license | adanlp/ExData_Plotting1 | 1ad01af7129cb2bd4a4d624fb4201fc79e75ad9b | a9f3cafac5494d013263ac5b4995ab72664d9a93 | refs/heads/master | 2021-05-14T02:09:16.548604 | 2018-01-08T00:34:48 | 2018-01-08T00:34:48 | 116,588,050 | 0 | 0 | null | 2018-01-07T18:11:49 | 2018-01-07T18:11:49 | null | UTF-8 | R | false | false | 1,869 | r | plot4.R | # plot4.R
#
# Construct the corresponding plot for Plotting Assignment 1
# for Exploratory Data Analysis. In this case, plot4.png
#
# Define the source of data and a name for our file
my_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
my_file <- "household_power_consumption.zip"
# If not exist, download and unzip the file (avoid downloading every time)
if (!file.exists(my_file)){
download.file(my_url, my_file)
unzip(my_file)
}
# Load the data only for the desired dates (2007-02-01 and 2007-02-02)
my_data <- read.table("household_power_consumption.txt", sep=";",
skip=66637, nrows=2879)
# Set column names and convert Date column to date type
my_cols <- c("Date","Time","Global_active_power","Global_reactive_power",
"Voltage","Global_intensity","Sub_metering_1","Sub_metering_2",
"Sub_metering_3")
colnames(my_data) <- my_cols
my_data <- transform(my_data, Date=as.Date(Date, "%d/%m/%Y"))
my_data$Time <- paste(my_data$Date, my_data$Time)
my_data <- transform(my_data, Time=strptime(Time, "%Y-%m-%d %H:%M:%S"))
# Generates the plot with following options:
# 4 plots (2x2)
# output file=plot4.png
par(mfrow=c(2,2))
with(my_data,{
plot(x=Time, y=Global_active_power, type="l", ylab="Global Active Power", xlab="")
plot(x=Time, y=Voltage, type="l", ylab="Voltage", xlab="datetime")
plot(x=Time, y=Sub_metering_1, type="l", ylab="Energy sub metering", xlab="")
lines(x=Time, y=Sub_metering_2, col="red")
lines(x=Time, y=Sub_metering_3, col="blue")
legend("topright",
pch=151,
col=c("black","red","blue"),
legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),
bty="n")
plot(x=Time, y=Global_reactive_power, type="l", ylab="Global_reactive_power", xlab="datetime")
})
dev.copy(png, file="plot4.png")
dev.off()
|
1bc4646fbf6b0fedb5dc3c5cec6f2963802069c1 | 2cb79ce691200221321447a586864810a138a562 | /workflow/scripts/figures/disagreement_gender.R | 1a45629f4458583abdcc3368725c0bda43c4ea71 | [
"MIT"
] | permissive | murrayds/sci-text-disagreement | 8cc76bf829577eadc9058a170b65af5b6249d63c | e70d5a16a9b770a4110980e9bacd90f361065d8a | refs/heads/master | 2023-06-05T08:47:54.587986 | 2022-01-03T18:48:45 | 2022-01-03T18:48:45 | 288,233,573 | 2 | 1 | MIT | 2021-08-20T14:15:14 | 2020-08-17T16:42:15 | TSQL | UTF-8 | R | false | false | 3,858 | r | disagreement_gender.R | #
# disagreement_gender.R
#
# author: Dakota Murray
#
# Plots a barchoart of the gender differences in disagreement
#
source("scripts/figures/themes.R")
source("scripts/common.R")
FIG.HEIGHT <- 6
FIG.WIDTH <- 5
library(dplyr)
library(ggplot2)
library(tidyr)
library(readr)
suppressPackageStartupMessages(require(optparse))
# Command line arguments
option_list = list(
make_option(c("--input"), action="store", default=NA, type="character",
help="Path to file containing disagreement by gender data"),
make_option(c("-o", "--output"), action="store", default=NA, type="character",
help="Path to save output image")
) # end option_list
opt = parse_args(OptionParser(option_list=option_list))
# Load the gender data (obtained from the SQL server) and
# filter to the appropriate validity threshold, citation sentence
# type, and measure.
gender <- read_csv(opt$input, col_types = cols()) %>%
filter(type == "citances") %>%
filter(threshold == 80)
#
# The code here is a little messy, but basically we need two seaprate
# dataframes, one for the 'count', and the other for the share of disagreement
counts <- gender %>%
filter(measure == "n_citances_valid") %>%
gather(key, n_intext, F1, F2, M1, M2)
# Now, the second (disagreement) data frame
disagreement <- gender %>%
filter(measure == "perc_citances_valid") %>%
gather(key, perc_intext, F1, F2, M1, M2) %>%
left_join(counts, by = c("LR_main_field", "key")) %>%
mutate(
key = recode(key,
'F1' = 'female first',
'F2' = 'female last',
'M1' = 'male first',
'M2' = 'male last'
),
perc_intext = perc_intext * 100
) %>%
separate(key, into = c("gender", "authorship")) %>%
mutate(
authorship = recode(authorship,
"first" = "First Author",
"last" = "Last Author"),
field = factor(LR_main_field, levels = field_long_levels()),
field = factor(field, labels = field_long_labels()),
gender = factor(gender, levels = c("male", "female"), labels = c("men", "women"))
) %>%
filter(field != "All") %>%
group_by(authorship, field) %>%
mutate(
# Calculate the ratio of the share of disagreement between
# the genders
ratio = lag(perc_intext) / (perc_intext)
)
# Build the plot
plot <- disagreement %>%
ggplot(aes(x = gender, y = perc_intext, fill = field, group = interaction(authorship, field))) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9), color = "black", aes(alpha = gender)) +
geom_text(aes(label = ifelse(is.na(ratio), NA, paste0(sprintf("%.2f", round(ratio, 2)), "x"))),
position = position_dodge(width = 0.9),
vjust = -1.5, hjust = -0.5
) +
# Add the number of disagreement citances
geom_text(position = position_dodge(width = 0.9),
aes(label = formatC(n_intext, format = "d", big.mark = ","),
y = 0,
group = interaction(authorship, field)),
vjust = -0.5,
alpha = 1,
size = 3
) +
facet_grid(field~authorship, switch = "y", scales = "free") +
scale_y_continuous(expand = expand_scale(mult = c(0, .5)), position = "right") +
scale_alpha_manual(values = c(0.1, 1)) +
scale_fill_manual(values = tail(field_long_colors(), 5)) +
guides(fill = F, alpha = F) +
theme_dakota() +
theme(
axis.title.x = element_blank(),
panel.border = element_rect(size = 0.5, fill = NA),
axis.title.y = element_text(face = "bold"),
legend.position = "bottom",
panel.grid.major.x = element_blank(),
strip.text.y.left = element_text(angle = 0, hjust = 0),
axis.text.x = element_text(face = "bold", angle = 45, hjust = 1),
) +
ylab("% disagreement")
# Save the plot
ggsave(opt$output, plot, height = FIG.HEIGHT, width = FIG.WIDTH)
|
4d90a5018c3d85f57ec547241fda6f952ecc5fe8 | 57612442b949ad63fdf319006fa32b50270b8e89 | /best.R | 07c4812859f49d2358caff316ea97af63607026b | [] | no_license | zieka/r_programming | add79e86d272ea90982c6c6dbd612df5b0b38ce5 | 77a9368ecd6fd97d49d83f8a8eab8be26fb17a1d | refs/heads/master | 2021-01-10T22:03:44.188911 | 2014-06-22T01:21:59 | 2014-06-22T01:21:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,362 | r | best.R | #
# best.R
#
# Copyright (C) 2014 Kyle Scully
#
# Author(s)/Maintainer(s):
# Kyle Scully
#
# Function returns a character vector with the name of the hospital
# that has the best (i.e. lowest) 30-day mortality for the specified
# outcome in that state. The hospital name is the name provided in
# the Hospital.Name variable. The outcomes can be one of
# “heart attack”, “heart failure”, or “pneumonia”.
#
best <- function(state, outcome) {
#Read outcome data
data <- read.csv("./data/outcome-of-care-measures.csv", colClasses = "character")
#Check that state and outcome are valid
valid_states <- unique(data$State)
valid_outcomes <- c("heart attack", "heart failure", "pneumonia")
if (!(state %in% valid_states)){
stop("invalid state")
}
if (!(outcome %in% valid_outcomes)){
stop("invalid outcome")
}
data <- data[data$State == state, ]
suppressWarnings(data[, c(11, 17, 23)] <- sapply(data[, c(11, 17, 23)], as.numeric))
#Order the data
data <- data[order(data[, 2]), ]
#Return hospital name in that state with lowest 30-day death rate
if (outcome == "heart attack"){
data[which.min(data[, 11]), "Hospital.Name"]
}
else if (outcome == "heart failure"){
data[which.min(data[, 17]), "Hospital.Name"]
}
else if (outcome=="pneumonia"){
data[which.min(data[, 23]), "Hospital.Name"]
}
}
|
60e543b71368c432ec3f9875fb263f5177323d86 | 118960cf373053fe99ca322d17e47b07fe6eb4c9 | /R_scripts/hdf5_lib.R | b47ea3f0bab20b3377d816c4dde96a658da4bad4 | [
"CC-BY-4.0"
] | permissive | DeplanckeLab/ASAP | 19b7926586db7beae943b1b232d399dc5376bd78 | 5795b7d7329b303973f4549cd78d0a7ae00bd91e | refs/heads/master | 2023-08-25T06:24:26.055799 | 2023-08-22T15:38:39 | 2023-08-22T15:38:39 | 86,680,770 | 21 | 12 | null | null | null | null | UTF-8 | R | false | false | 2,903 | r | hdf5_lib.R | require(rhdf5) # For handling Loom files # loomR is deprecated
error.json <- function(displayed) {
stats <- list()
stats$displayed_error = displayed
write(toJSON(stats, method="C", auto_unbox=T), file = paste0(output_dir,"/output.json"), append=F)
close_all()
stop(displayed)
}
# Close All handles
close_all <- function() {
h5closeAll()
}
# Close File Handle
close_file <- function(handle) {
H5Fflush(handle)
H5Fclose(handle)
}
# Open the Loom file while handling potential locking
open_with_lock <- function(loom_filename, mode) {
if(mode == "r") mode <- "H5F_ACC_RDONLY"
if(mode == "r+") mode <- "H5F_ACC_RDWR"
repeat{ # Handle the lock of the file
tryCatch({
return(H5Fopen(name = loom_filename, flags = mode))
}, error = function(err) {
if(!grepl("Unable to open file", err$message)) error.json(err$message)
})
message("Sleeping 1sec for file lock....")
time_idle <<- time_idle + 1
Sys.sleep(1)
}
}
# Delete a dataset if exists
delete_dataset <- function(handle, dataset_path) {
tryCatch({
h5delete(file = handle, name = dataset_path)
}, error = function(err) {
if(!grepl("Specified link doesn't exist.", err$message)) error.json(err$message)
})
}
add_matrix_dataset <- function(handle, dataset_path, dataset_object, storage.mode_param="double", chunk_param=c(min(dim(dataset_object)[1], 64), min(dim(dataset_object)[2], 64)), level_param = 2) {
if(is.null(dim(dataset_object))) error.json("Cannot write this dataset, it is not a matrix!")
delete_dataset(handle, dataset_path)
out <- h5createDataset(file = handle, dataset = dataset_path, dims = dim(dataset_object), storage.mode = storage.mode_param, chunk=chunk_param, level=level_param)
h5write(file = handle, obj = dataset_object, name=dataset_path)
}
add_array_dataset <- function(handle, dataset_path, dataset_object, storage.mode_param="double", chunk_param=c(min(length(dataset_object), 64)), level_param = 2) {
if(!is.null(dim(dataset_object))) error.json("Cannot write this dataset, it is not an array!")
delete_dataset(handle, dataset_path)
out <- h5createDataset(file = handle, dataset = dataset_path, dims = length(dataset_object), storage.mode = storage.mode_param, chunk=chunk_param, level=level_param)
h5write(file = handle, obj = dataset_object, name=dataset_path)
}
# Fetch dataset if exists or return NULL
fetch_dataset <- function(handle, dataset_path, transpose = F) {
handle_dataset <- NULL
tryCatch({
handle_dataset <- H5Dopen(h5loc = handle, name = dataset_path)
out <- H5Dread(handle_dataset)
H5Dclose(handle_dataset)
if(transpose) out <- t(out)
if(length(dim(out)) == 2) return(as.data.frame(out))
return(as.vector(out))
}, error = function(err) {
if(!is.null(handle_dataset)) H5Dclose(handle_dataset)
if(grepl("Can't open object", err$message)) return(NULL)
else error.json(err$message)
})
}
|
54146459cf21b4cc108ca9b4cd17e6e89e6894ed | 31deaad1827ec16f80447a5e06c601d354e034a6 | /inst/shiny-examples/testapp/app.R | 772934593f14402e86ab64dd95c307c747ebe1d8 | [] | no_license | schliebs/trollR | 1976355e538d749b4ceb85763b4fd1fa864de462 | 365b65ea7f5b5af0fec2d45ead9867e40e1cec03 | refs/heads/master | 2020-03-11T14:04:17.049910 | 2018-04-19T17:03:20 | 2018-04-19T17:03:20 | 130,043,175 | 10 | 1 | null | null | null | null | UTF-8 | R | false | false | 864 | r | app.R | library(shiny)
source("modules.R")
ui <- fixedPage(
h2("Module example"),
actionButton("insertBtn", "Insert module")
)
server <- function(input, output, session) {
observeEvent(input$insertBtn, {
btn <- input$insertBtn
insertUI(
selector = "h2",
where = "beforeEnd",
ui = tagList(
h4(paste("Module no.", btn)),
linkedScatterUI(paste0("scatters", btn)),
textOutput(paste0("summary", btn))
)
)
df <- callModule(linkedScatter,
paste0("scatters", btn),
reactive(mpg),
left = reactive(c("cty", "hwy")),
right = reactive(c("drv", "hwy"))
)
output$summary <- renderText({
sprintf("%d observation(s) selected",
nrow(dplyr::filter(df(), selected_)))
})
})
}
shinyApp(ui, server)
|
c4dd937e7516e0e6f4073c37404dc1becc9df92d | eb61601e2a01b8ffe2b039cd3f4002ab4530fae0 | /roller/tests/testthat/test-check-times.R | aca0fee89fa93d3df6ac16f65042dede68ca9bde | [] | no_license | ronishen86/hw-stat133 | 7fc597fca74ad4365a1d6db4526b794d3eee2449 | 769d836a2d17f65b8e997b8d7b6ada18e0d8cd97 | refs/heads/master | 2020-03-30T00:19:11.536294 | 2018-12-01T20:14:44 | 2018-12-01T20:14:44 | 150,516,127 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 279 | r | test-check-times.R | context("Test check roll arguments")
test_that("check_times works with ok vectors", {
expect_true(check_times(1))
expect_true(check_times(33))
})
test_that("check_times fails with invalid numbers", {
expect_error(check_times(.5))
expect_error(check_times("a"))
}) |
583b89d241d7ec1f76d78a5e94e8b80cef4b5e11 | 11b20d7df0aecb79ba43e83ec271362da0cdc928 | /R/reporting.R | d5ccee59aa3b5973ef86e9fa1df725f3f436ed06 | [] | no_license | pinusm/Mmisc | 63b5b8ce5e7262ae14dc102ebe06b5d990a14d3f | 509a248d3564b557ef2c5eee6ca47a3a2a9b60ab | refs/heads/master | 2021-06-06T06:44:00.360151 | 2021-06-03T06:42:31 | 2021-06-03T06:42:31 | 138,485,843 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,995 | r | reporting.R | #' convert numeric p-values to strings for reporting, without leading zero
#' @param p a p-value in numeric value
#' @export
pvalue.correct <- function(p){
p <- p %>% as.numeric
pvalue = dplyr::case_when(p < 0.001 ~ "< .001",
p < 0.01 ~ "< .01",
p < 0.05 ~ "< .05",
TRUE ~ p %>% round(2) %>% formatC(digits = 2, format = "f") %>% as.character() %>% str_replace("0.", ".")
)
return(pvalue)
}
#' ggplot2 APA-style
#'
#' theme modified version of papaja's theme_apa()
#'
#' @param base_size base_size
#' @param base_family base_family
#' @param box box
#' @importFrom grDevices windowsFonts
#' @export
#
theme_apa <- function(base_size = 14, base_family = "", box = FALSE)
{
grDevices::windowsFonts(Times = grDevices::windowsFont("TT Times New Roman"))
adapted_theme <- ggplot2::theme_bw(base_size, base_family) +
ggplot2::theme(plot.title = ggplot2::element_text(size = ggplot2::rel(1.1),
margin = ggplot2::margin(0, 0, ggplot2::rel(14),
0)), axis.title = ggplot2::element_text(size = ggplot2::rel(1.1)),
axis.title.x = ggplot2::element_text(margin = ggplot2::margin(ggplot2::rel(18),
0, 0, 0)), axis.title.y = ggplot2::element_text(margin = ggplot2::margin(0,
ggplot2::rel(18), 0, 0)), axis.ticks.length = ggplot2::unit(ggplot2::rel(6),
"points"), axis.text = ggplot2::element_text(size = ggplot2::rel(0.9)),
axis.text.x = ggplot2::element_text(margin = ggplot2::margin(ggplot2::rel(6),
0, 0, 0)), axis.text.y = ggplot2::element_text(margin = ggplot2::margin(0,
ggplot2::rel(8), 0, 0)), axis.line.x = ggplot2::element_line(),
axis.line.y = ggplot2::element_line(), legend.title = ggplot2::element_text(),
legend.key = ggplot2::element_rect(fill = NA, color = NA),
legend.key.width = ggplot2::unit(ggplot2::rel(20),
"points"), legend.key.height = ggplot2::unit(ggplot2::rel(25),
"points"), legend.spacing = ggplot2::unit(ggplot2::rel(18),
"points"), panel.spacing = ggplot2::unit(ggplot2::rel(16),
"points"), panel.grid.major.x = ggplot2::element_line(size = NA),
panel.grid.minor.x = ggplot2::element_line(size = NA),
panel.grid.major.y = ggplot2::element_line(size = NA),
panel.grid.minor.y = ggplot2::element_line(size = NA),
strip.background = ggplot2::element_rect(fill = NA,
color = NA), strip.text.x = ggplot2::element_text(size = ggplot2::rel(1.1),
margin = ggplot2::margin(0, 0, ggplot2::rel(16),
0)), strip.text.y = ggplot2::element_text(size = ggplot2::rel(1.1),
margin = ggplot2::margin(0, 0, 0, ggplot2::rel(16))))
if (box) {
adapted_theme <- adapted_theme + ggplot2::theme(panel.border = ggplot2::element_rect(color = "black"))
}
else {
adapted_theme <- adapted_theme + ggplot2::theme(panel.border = ggplot2::element_blank())
}
adapted_theme
}
#' convert string p-values to strings for reporting, without leading zero
#' @param p a p-value in string value
#' @export
pvalue.report <- function(p){
pvalue <- p %>% formatC(digits = 2, format = "f")
if (p < 0.001) {
pvalue <- " < .001"
} else {
if (p < 0.01) {
pvalue <- " < .001"
} else {
if (p < 0.05) {
pvalue <- " < .05"
} else {
pvalue <- paste0(" = ", gsub(pattern = "0\\.", replacement = "\\.", as.character(p %>% formatC(digits = 2, format = "f"))))
}
}
}
paste0("*p*", pvalue)
}
#' report cocor's different of correlations
#' @param cor_p a cocor object
#' @export
#'
cor_diff_report <- function(cor_p){
pvalue <- pvalue.report(cor_p$fisher1925$p.value)
cohensQ <- round(psych::fisherz(cor_p$fisher1925$estimate[1]) - psych::fisherz(cor_p$fisher1925$estimate[2]),2)
return(paste0("*z* = ",round(cor_p$fisher1925$statistic,2),", ", pvalue, ", *Cohen's q* = ", cohensQ))
}
#' report var.test's different of variances
#' @param var_d a var.test's different of variances object
#' @export
var_diff_report <- function(var_d){
pvalue <- pvalue.report(var_d$p.value)
DFnum <- var_d$parameter[["num df"]]
DFdenom <- var_d$parameter[["denom df"]]
return(paste0("*F*$\\textsubscript{(",DFnum,",",DFdenom,")}$ = ",round(var_d$statistic,2),", ", pvalue))
}
#' report chi-square different of proportions
#' @param var_d a chi-square different of proportions object
#' @export
chi_prop_diff_report <- function(var_d){
pvalue <- pvalue.report(var_d$p.value)
DFnum <- var_d$parameter[["num df"]]
DFdenom <- var_d$parameter[["denom df"]]
return(paste0("*F*$\\textsubscript{(",DFnum,",",DFdenom,")}$ = ",round(var_d$statistic,2),", ", pvalue))
}
#' give M and SD per group, with the 'apa' package, on the results of t_test()
#'
#' @param t_test an apa::t_test object
#' @param x the name of a group in the apa::t_test object
#' @export
apa.desc <- function(t_test, x){
group <- as.character(x)
group_data <- t_test$data[[group]]
mean <- mean(group_data, na.rm = T)
sd <- sd(group_data, na.rm = T)
return(paste0("(*M* = ",round(mean,2),", *SD* = ", round(sd,2),")"))
}
#' report correlation with BF, created by cor.bf()
#'
#' @param corObject a cor.bf() object
#' @param BF01 should the BF be 10, or 01 based
#' @export
report_cor.bf <- function(corObject , BF01 = F) {
BFtype <- "10"
BFvalue <- ifelse("jzs_med" %in% class(corObject$bf), corObject$bf$BayesFactor,
ifelse("BFBayesFactor" %in% class(corObject$bf),BayesFactor::extractBF(corObject$bf)$bf, #corObject$bf@bayesFactor$bf,
NA)
)
if (BF01) {
BFtype <- "01"
BFvalue <- 1 / BFvalue
}
paste0(apa::apa(corObject$cor),", $\\textit{BF}_\\textit{",BFtype,"}$ = ",BFvalue %>% round(2))
}
#' convert numbers to literal numbers
#'
#' based on https://github.com/ateucher/useful_code/blob/master/R/numbers2words.r
#'
#' @param x a number in numeric format
#' @export
numbers2words <- function(x){
## Function by John Fox found here:
## http://tolstoy.newcastle.edu.au/R/help/05/04/2715.html
## Tweaks by AJH to add commas and "and"
helper <- function(x){
digits <- rev(strsplit(as.character(x), "")[[1]])
nDigits <- length(digits)
if (nDigits == 1) as.vector(ones[digits])
else if (nDigits == 2)
if (x <= 19) as.vector(teens[digits[1]])
else trim(paste(tens[digits[2]],
Recall(as.numeric(digits[1]))))
else if (nDigits == 3) trim(paste(ones[digits[3]], "hundred and",
Recall(makeNumber(digits[2:1]))))
else {
nSuffix <- ((nDigits + 2) %/% 3) - 1
if (nSuffix > length(suffixes)) stop(paste(x, "is too large!"))
trim(paste(Recall(makeNumber(digits[
nDigits:(3*nSuffix + 1)])),
suffixes[nSuffix],"," ,
Recall(makeNumber(digits[(3*nSuffix):1]))))
}
}
trim <- function(text){
#Tidy leading/trailing whitespace, space before comma
text = gsub("^\ ", "", gsub("\ *$", "", gsub("\ ,",",",text)))
#Clear any trailing " and"
text = gsub(" and$","",text)
#Clear any trailing comma
gsub("\ *,$","",text)
}
makeNumber <- function(...) as.numeric(paste(..., collapse = ""))
#Disable scientific notation
opts <- options(scipen = 100)
on.exit(options(opts))
ones <- c("", "one", "two", "three", "four", "five", "six", "seven",
"eight", "nine")
names(ones) <- 0:9
teens <- c("ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen",
"sixteen", " seventeen", "eighteen", "nineteen")
names(teens) <- 0:9
tens <- c("twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty",
"ninety")
names(tens) <- 2:9
x <- round(x)
suffixes <- c("thousand", "million", "billion", "trillion")
if (length(x) > 1) return(trim(sapply(x, helper)))
helper(x)
}
#' a helper for GGally's ggpairs
#'
#' @param data data
#' @param mapping mapping
#' @param ... ...
#' @export
plot_trend_lines <- function(data, mapping, ...){
p <- ggplot2::ggplot(data = data, mapping = mapping) +
ggplot2::geom_point() +
ggplot2::geom_smooth(method=stats::loess, fill="#A4A4A4", color="#A4A4A4", ...) +
ggplot2::geom_smooth(method=stats::lm, fill="#2E2E2E", color="#2E2E2E", ...)
p
}
#' convert numeric p-values to asterik stars
#'
#' @param p a p-value in numeric format
#' @export
pvalue.stars <- function (p)
{
p <- p %>% as.numeric
pstar = dplyr::case_when(
p < 0.001 ~ "***",
p < 0.01 ~ "**",
p < 0.05 ~ "*",
TRUE ~ "")
return(pstar)
}
|
6d52fe9cd5121068c0dc449b0c3c30ff090d76bb | 3176c7f008fabb7a406241149808fc2f7cacec4d | /man/minExactMatch.Rd | fad2020bc4f28e0d448bc2f8cf3d7b8e5f94f19b | [
"MIT"
] | permissive | markmfredrickson/optmatch | 712f451829c128c4f1fd433e2d527bb7160e8fcc | 51e0b03a30420179149be254262d7d6414f3d708 | refs/heads/master | 2023-05-31T05:11:01.917928 | 2023-04-06T13:18:58 | 2023-04-06T13:18:58 | 1,839,323 | 37 | 18 | NOASSERTION | 2023-01-26T18:45:56 | 2011-06-02T20:49:32 | R | UTF-8 | R | false | true | 1,527 | rd | minExactMatch.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/feasible.R
\name{minExactMatch}
\alias{minExactMatch}
\title{Find the minimal exact match factors that will be feasible for a
given maximum problem size.}
\usage{
minExactMatch(x, scores = NULL, width = NULL, maxarcs = 1e+07, ...)
}
\arguments{
\item{x}{The object for dispatching.}
\item{scores}{Optional vector of scores that will be checked against a caliper width.}
\item{width}{Optional width of a caliper to place on the scores.}
\item{maxarcs}{The maximum problem size to attempt to fit.}
\item{...}{Additional arguments for methods.}
}
\value{
A factor grouping units, suitable for \code{\link{exactMatch}}.
}
\description{
The \code{\link{exactMatch}} function creates a smaller matching problem by
stratifying observations into smaller groups. For a problem that is larger
than maximum allowed size, \code{minExactMatch} provides a way to find the
smallest exact matching problem that will allow for matching.
}
\details{
\code{x} is a formula of the form \code{Z ~ X1 + X2}, where
\code{Z} is indicates treatment or control status, and \code{X1} and \code{X2} are variables
can be converted to factors. Any additional arguments are passed to \code{\link{model.frame}}
(e.g., a \code{data} argument containing \code{Z}, \code{X1}, and \code{X2}).
The the arguments \code{scores} and \code{width} must be passed together.
The function will apply the caliper implied by the scores and the width while
also adding in blocking factors.
}
|
9858828a1deea58ea918c1e92826fad376d24c47 | 718de5af14276062cf57b96c2ed3f32a0fc74312 | /man/clusterGenes.Rd | 7a2e21e28411476b1b91e339ce24b873da43d196 | [] | no_license | cole-trapnell-lab/monocle | 34976ee5f1d7960245b30aa1ef07ff16cbb97092 | 5ad275623499b1830863d36b6a7db334460948bd | refs/heads/master | 2021-01-10T22:53:24.172037 | 2016-05-15T20:14:22 | 2016-05-15T20:14:22 | 70,340,167 | 2 | 1 | null | 2016-10-08T15:25:47 | 2016-10-08T15:25:46 | null | UTF-8 | R | false | false | 908 | rd | clusterGenes.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{clusterGenes}
\alias{clusterGenes}
\title{Plots the minimum spanning tree on cells.}
\usage{
clusterGenes(expr_matrix, k, method = function(x) { as.dist((1 -
cor(t(x)))/2) }, ...)
}
\arguments{
\item{expr_matrix}{a matrix of expression values to cluster together}
\item{k}{how many clusters to create}
\item{method}{the distance function to use during clustering}
\item{...}{extra parameters to pass to pam() during clustering}
}
\value{
a pam cluster object
}
\description{
Plots the minimum spanning tree on cells.
}
\examples{
\dontrun{
full_model_fits <- fitModel(HSMM[sample(nrow(fData(HSMM_filtered)), 100),], modelFormulaStr="expression~sm.ns(Pseudotime)")
expression_curve_matrix <- responseMatrix(full_model_fits)
clusters <- clusterGenes(expression_curve_matrix, k=4)
plot_clusters(HSMM_filtered[ordering_genes,], clusters)
}
}
|
7233d52a6f0727e89f249ae4ae763114022eac24 | 9032d2c38dd8060bce037372e585de09e9a813d9 | /man/Gcontrol.Rd | 74e701ee6afc090328b320e7020d5b79e7c0bd2a | [] | no_license | huihualee/DLMtool | 34e2dde7b978d71bf356c995d829601d12c6b809 | 2cbec6a21ddf04e44455c6bafde5910a568ffeb9 | refs/heads/master | 2021-01-16T22:27:15.259416 | 2014-09-11T00:00:00 | 2014-09-11T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 946 | rd | Gcontrol.Rd | \name{Gcontrol}
\alias{Gcontrol}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
G-control harvest control rule
}
\description{
A harvest control rule proposed by Carl Walters that uses trajectory in inferred surplus production to make upward/downward adjustments to quota recommendations
}
\usage{
Gcontrol(x, DLM, reps = 100, yrsmth = 10, gg = 2, glim = c(0.5, 2))
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
A position in data-limited methods data object
}
\item{DLM}{
A data-limited methods data object
}
\item{reps}{
The number of quota samples
}
\item{yrsmth}{
The number of years over which to smooth catch and biomass data
}
\item{gg}{
A gain parameter
}
\item{glim}{
A constraint limiting the maximum level of change in quota recommendations
}
}
\references{
Made-up for this package.
}
\author{
C. Walters and T. Carruthers
}
|
631fdd249d065a532a42c517c7b2980b22537e3d | 0e19f4fc4f1908d238934e421a1de8480654ca42 | /HorseRacingPrediction/LogisticRegression/Regression.r | 6d08c7f63729c44e9ed3be117da7590a23fe1814 | [
"CC-BY-2.0"
] | permissive | nitingautam/sa2014 | a7aaf7499463933b57c815afacd4291722a02346 | e8574ffb76c92860e1f13f2895c4423cd3aaa4ca | refs/heads/master | 2021-01-15T18:08:30.516493 | 2015-01-07T13:56:06 | 2015-01-07T13:56:06 | 30,122,732 | 5 | 0 | null | 2015-01-31T19:05:04 | 2015-01-31T19:05:04 | null | UTF-8 | R | false | false | 363 | r | Regression.r | # Load the data and take a look at it.
data <- read.csv("C:\\Users\\Gary\\Documents\\Presentations\\SoftwareArchitect\\HorseRacingPrediction\\LogisticRegression\\Data.csv");
head(data);
# Predict win/lose based on mile time and weight
logit <- glm(Win~MileTime+Weight, data=data, family="binomial");
summary(logit);
# "Decode" the coefficients
exp(coef(logit)) |
a3af06b64acc3dac9f3b88006f402f518b3d5861 | cd8a77f7c6c09c32ffa6205cac6ba295bb97d838 | /cachematrix.R | e81371461bd8c80711b3cb76f604d0b9f425b5b2 | [] | no_license | tamasfrisch/ProgrammingAssignment2 | 16233dc529d4e4452abb0b4bdf14dedad4963f9f | e648e38c3d821ec9f621d0132facd83ade87a860 | refs/heads/master | 2020-12-25T20:54:10.811425 | 2014-09-16T23:51:21 | 2014-09-16T23:51:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,356 | r | cachematrix.R | ## Coursera Programming Assignment 2
## Scoping exercise
## makeCacheMatrix generates a list, taking a matrix as an input
## The list contains 4 functions, basically methods that set and get the matrix and its cached inverse (invMatrix)
## Note that there is no checking of the values in the setinverse function.
makeCacheMatrix <- function(x = matrix()) {
invMatrix <-NULL
set <- function (y){
x <<-y
invMatrix <<- NULL
}
get <- function () x
setinverse <- function (inverse) invMatrix <<- inverse
getinverse <-function () invMatrix
list (set=set, get=get, setinverse=setinverse, getinverse=getinverse)
}
## cacheSolve expects an input generated by the makeCacheMatrix function; that is a list with 4 functions
## Then it checks whether the getinverse method has a value in the closure; if it does, it returns the cached value
## If the value does not exist (setinverse has not been called yet), it calls the setinverse function and sets it.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
invMatrix <- x$getinverse()
if (!is.null(invMatrix)) {
message ("Getting cached data")
return(invMatrix)
}
data <- x$get()
invMatrix <- solve(data, ...)
x$setinverse(invMatrix)
invMatrix
}
|
886d873acbe5f1c8f78bbbc52712de445d9db27e | 2cf5744042a9802bc019c0557848db8fbfda0d39 | /man/MRIaggr-plotLesion3D.Rd | a3a1fb412fd4d77293df90a72b61e459c5ce6b7c | [] | no_license | cran/MRIaggr | bcc874f1253ab7b168e4a6d68bc66e8556b7d330 | 099c3227ac60fdad71aa5c1b79bf53b91a92e177 | refs/heads/master | 2021-01-21T21:47:16.132229 | 2015-12-23T23:44:19 | 2015-12-23T23:44:19 | 31,946,742 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,992 | rd | MRIaggr-plotLesion3D.Rd | \name{plotLesion3D}
\title{3D plot of the lesion}
\alias{plotLesion3D}
\alias{plotLesion3D,MRIaggr-method}
\description{
Make a 3D plot of the lesion. (experimental version)
}
\usage{
\S4method{plotLesion3D}{MRIaggr}(object, mask, edge = FALSE, Neighborhood = "3D_N6",
numeric2logical = FALSE, spatial_res = c(1,1,1), xlim = NULL,
ylim = NULL, zlim = NULL, type.plot = "shapelist3d", px_max = 10000,
radius = 1, type = "s", col = "red", col.edge = "black")
}
\arguments{
\item{object}{an object of class \code{\linkS4class{MRIaggr}}. REQUIRED.}
\item{mask}{the binary contrast parameter indicating the lesion. \emph{character}. REQUIRED.}
\item{edge}{should the edges of the lesion be ploted instead of the core ? \emph{logical}.}
\item{Neighborhood}{the type of neighbourhood used to defined the edges. \emph{character}.}
\item{numeric2logical}{should \code{mask} be convert to logical ? \emph{logical}.}
\item{spatial_res}{a dilatation factor for the coordinates. \emph{positive numeric vector of size 3}.}
\item{xlim}{the x limits of the plot. \emph{numeric vector of size 2} or \code{NULL} leading to automatic setting of the x limits.}
\item{ylim}{the y limits of the plot. \emph{numeric vector of size 2} or \code{NULL} leading to automatic setting of the y limits.}
\item{zlim}{the z limits of the plot. \emph{numeric vector of size 2} or \code{NULL} leading to automatic setting of the z limits.}
\item{type.plot}{the type of plot to be displayed. Can be \code{"plot3d"} or \code{"shapelist3d"}.}
\item{px_max}{the maximum number of points that can be ploted. \emph{integer}.}
\item{radius}{the radius of spheres. \emph{numeric}. See \code{plot3d} for more details.}
\item{type}{the type of item to plot. \emph{character}. See \code{plot3d} for more details.}
\item{col}{the color of the core of the lesion. \emph{character}.}
\item{col.edge}{the color of the edge of the lesion. \emph{character}.}
}
\details{
ARGUMENTS: \cr
the \code{Neighborhood} argument can be a \emph{matrix} or an \emph{array} defining directly the neighbourhood to use (i.e the weight of each neighbor)
or a name indicating which type of neighbourhood should be used (see the details section of \code{\link{initNeighborhood}}).
FUNCTION: \cr
This functions uses the \code{plot3d} or \code{shapelist3d} and thus require the \emph{rgl} package to work. This package is not automatically loaded by the \code{MRIaggr} package.
}
\value{
None.
}
\examples{
## load a MRIaggr object
data("MRIaggr.Pat1_red", package = "MRIaggr")
\dontrun{
if(require(rgl)){
# global view
plotLesion3D(MRIaggr.Pat1_red, mask = "MASK_T2_FLAIR_t2", spatial_res = c(1.875,1.875,6),
numeric2logical = TRUE)
# by slice
plotLesion3D(MRIaggr.Pat1_red, mask = "MASK_T2_FLAIR_t2", spatial_res = c(1.875,1.875,6),
type.plot = "plot3d",
numeric2logical = TRUE)
}
}
}
\concept{plot.}
\keyword{methods}
|
9a172a862b18638a712cf2eb6e9f3ebe3fda6ff4 | e42721b2bf31675a294e14b2d59111a1b83de58b | /R/GSE27034.R | bc41246f917d383784f554bfd402af374aa21345 | [] | no_license | szymczak-lab/DataPathwayGuidedRF | d60fdd2a07cf69359f147b7956a27b03d1b9dd02 | 8af8869378e6aae27b727f1417e51c30564f4a34 | refs/heads/master | 2020-12-11T06:42:48.292639 | 2020-01-27T09:16:50 | 2020-01-27T09:16:50 | 233,790,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 746 | r | GSE27034.R | #' GSE27034
#'
#' This is a preprocessed hallmark data set with COAGULATION as target pathway.
#' A Genome U133 Plus 2.0 Array is utilized to analyze peripheral artery disease in blood tissue. The study was performed in an unpaired design.
#'
#'
#' @docType data
#' @keywords datasets
#' @name GSE27034
#' @usage data(GSE27034)
#' @format A Summarized Experiment object with 19014 genes and 36 samples (19 cases and 17 controls).
#' The column outcome in the colData corresponds to the outcome that was used in the paper.
#' @references Masud, R., Shameer, K., Dhar, A., Ding, K., and Kullo, I. J. (2012). Gene expression profiling of peripheral blood mononuclear cells in the setting of peripheral arterial disease. J Clin Bioinf , 2, 6.
NULL
|
1caf576d45ec5e28392471afa6a7b7946157b581 | b3db9e15f1112a4ac9d58213396d6a6fff5e8e6f | /man/lasso.Rd | c8dd2ce62b932e46feebcdb47c40ff43ca902ee8 | [] | no_license | Yannuo10/BIS557-HW4 | 700300059bef7ce37090430d0db0e5b51f01f66d | 9d558ffab0dbb8308a91eb37157d25e51a70f60b | refs/heads/main | 2023-01-16T05:06:56.773266 | 2020-11-23T08:17:25 | 2020-11-23T08:17:25 | 315,206,771 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 602 | rd | lasso.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lasso_py.R
\name{lasso}
\alias{lasso}
\title{lasso() function}
\usage{
lasso(form, df, contrasts = NULL, lambda)
}
\arguments{
\item{form}{a formula;}
\item{df}{a data frame used for the function;}
\item{contrasts}{a list of contrasts for factor variables}
\item{lambda}{a constant for penalty}
}
\description{
to build a lasso regression model using python code
}
\examples{
data(iris)
library(reticulate)
library(casl)
fit_model <- lasso(Sepal.Length ~ ., iris, contrasts = list(Species = "contr.sum"), lambda = 0.1)
}
|
e4a7413b9a7b392f8d934deecd99a6c1dc7d1407 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/fDMA/R/fDMA.R | 299acc8b78d2cac82ce128b3110d5f6ddd66ecf3 | [] | 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 | false | 34,298 | r | fDMA.R |
fDMA <- function (y,x,alpha,lambda,initvar,W=NULL,initial.period=NULL,V.meth=NULL,kappa=NULL,
gprob=NULL,omega=NULL,model=NULL,parallel=NULL,m.prior=NULL,mods.incl=NULL,
DOW=NULL,DOW.nmods=NULL,DOW.type=NULL,DOW.limit.nmods=NULL,progress.info=NULL,
forced.models=NULL,forbidden.models=NULL,forced.variables=NULL,
bm=NULL,small.c=NULL,fcores=NULL,mods.check=NULL,red.size=NULL,
av=NULL)
{
### requires "forecast", "parallel", "stats" and "xts" packages
### y - a numeric or a column matrix of a dependent variable,
### if y is a xts object, then plots will have time index on the x axis
### x - a matrix of independent variables (drivers), different columns correspond to different variables
### alpha - a forgetting factor between 0 and 1 used in probabilities estimations
### lambda - a forgetting factor between 0 and 1 used in variance approximations,
### or numeric vector of forgetting factors between 0 and 1
### initvar - initial variance in the state space equation
### W - a method for setting the initial values of variance for the models equations,
### W = "reg" corresponds to the method based on the linear regression as in the paper by Raftery et al. (2010),
### alternatively an arbitrary positive number can be specified,
### by default the method of Raftery et al. (2010) is used
### initial.period - a number of observation since which MSE and MAE are computed,
### by default the whole sample is used, i.e., initial.period = 1
### V.meth - a method for the state space equation variance updating,
### V.meth = "rec" corresponds to the recursive moment estimator, as in the paper by Raftery et al. (2010),
### V.meth = "ewma" corresponds to the exponentially weighted moving average,
### by default V.meth = "rec" is used
### kappa - a parameter in the exponentially weighted moving average, between 0 and 1,
### used if V.meth = "ewma"
### gprob - a matrix of Google probabilities, columns should correspond to columns of x
### omega - a parameter between 0 and 1 used in probabilities estimations,
### used if gprob is specified
### model - model = "dma" for Dynamic Model Averaging, model = "dms" for Dynamic Model Selection,
### or model = "med" for Median Probability Model,
### by default model = "dma" is used
### parallel - indicate whether parallel computations should be used,
### by default parallel = FALSE
### m.prior - a parameter for general model prior (Mitchell and Beauchamp, 1988),
### by default m.prior = 0.5, which corresponds to the uniform distribution
### mods.incl - a matrix indicating which models should be used for estimation,
### the first column indicates inclusion of a constant,
### by default all possible models with a constant are used
### DOW - a threshold for Dynamic Occam's Window (Onorante and Raftery, 2016),
### should be a number between 0 and 1,
### if DOW = 0, then no Dynamic Occam's Window is applied,
### by default DOW = 0
### DOW.nmods - initial number of models for Dynamic Occam's Window,
### should be less than the number of all possible models and larger than or equal to 2,
### they are randomly chosen,
### if DOW.nmods = 0, then initially models with exactly one variable are taken,
### by default DOW.nmods = 0
### DOW.type - DOW.type = "r" for DMA-R (Onorante and Raftery, 2016),
### DOW.type = "e" for DMA-E,
### by default DOW.type = "r"
### bm - indicate whether benchmark forecast should be computed,
### by default bm = FALSE
### DOW.limit.nmods - the limit of number of models used in Dynamic Occam's Window
### progress.info - applicable only if Dynamic Occam's Window is used,
### if progress.info = TRUE number of round and number of models are printed,
### by default progress.info = FALSE
### small.c - a small constant added to posterior model probabilities,
### to prevent possible reduction them to 0 due to computational issues,
### by default small.c is taken as in small constant as in Eq. (17)
### in Raftery et al. (2010)
### forced.models - matrix of models which if Dynamic Occam's Window is used
### have to be always included, similar as mods.incl,
### by default forced.models = NULL
### forbidden.models - matrix of models which should not be used
### in Dynamic Occam's Window, similar as mods.incl,
### by default forbidden.models = NULL
### forced.variables - vector of variables which must be present in each model
### in Dynamic Occam's Window, first slot indicates constant,
### by default forced.variables = NULL
### fcores - used only if parallel = TRUE, otherwise ignored,
### indicates the number of cores that should not be used,
### by default fcores = 1
### mods.check - indicates if models specified by the user should be checked,
### by default mods.check = FALSE
### red.size - indicates if outcomes should be reduced to save memory,
### by default red.size = FALSE
### av - av = "dma" corresponds to the original DMA averaging scheme,
### av = "mse" corresponds to averaging based on Mean Squared Error,
### av = "hr1" corresponds to averaging based on Hit Ratio, assuming time-series are in levels,
### av = "hr2" corresponds to averaging based on Hit Ratio, assuming time-series represent changes,
### by default av = "dma"
###################################### checking initial parameters
if (missing(y)) { stop("please, specify y") }
if (missing(x)) { stop("please, specify x") }
if (is.xts(x)) { x <- as.matrix(x) }
if (is.xts(y)) { y <- as.matrix(y) }
if (! (is.numeric(y) || is.matrix(y))) { stop("y must be numeric or matrix") }
if (is.matrix(y) && ! (ncol(y) == 1)) { stop("y must be a one column matrix") }
if (! is.matrix(x)) { stop("x must be a matrix") }
if (is.null(colnames(x)))
{
colnames(x) <- colnames(x, do.NULL = FALSE, prefix = "X")
warning('column names of x were automatically created')
}
if (anyNA(colnames(x))) { stop("x must have column names") }
if (is.matrix(y) && is.null(colnames(y)))
{
warning('column name of y was automatically created')
colnames(y) <- colnames(y, do.NULL = FALSE, prefix = "Y")
}
if (is.matrix(y) && anyNA(colnames(y)))
{
warning('column name of y was automatically created')
colnames(y) <- "Y1"
}
if (! length(y) == nrow(x)) { stop("y and x must have the same number of observations") }
if (anyNA(y)) { stop("missing values in y") }
if (anyNA(x)) { stop("missing values in x") }
if (missing(alpha)) { stop("please, specify alpha") }
if (! missing(alpha) && ! is.numeric(alpha)) { stop("alpha must be numeric") }
if ((alpha <= 0) || (alpha > 1)) { stop("alpha must be greater than 0, and less than or equal to 1") }
if (missing(lambda)) { stop("please, specify lambda") }
if (! missing(lambda) && ! is.numeric(lambda)) { stop("lambda must be numeric or numeric vector") }
if ((any(lambda <= 0)) || (any(lambda > 1))) { stop("lambda must be greater than 0, and less than or equal to 1") }
if (missing(initvar)) { stop("please, specify initvar (i.e., initial variance)") }
if (! missing(initvar) && ! is.numeric(initvar)) { stop("initvar must be numeric") }
if (initvar <= 0) { stop("variance (initvar) must be positive") }
if (is.null(W)) { W <- "reg" }
if (! ((W == "reg") || is.numeric(W)) ) { stop("please, specify correct W (i.e., initial variance)") }
if (is.numeric(W) && (W <= 0)) { stop("variance (W) must be positive") }
if (W == "reg") { W <- NULL }
if (is.null(initial.period)) { initial.period <- 1 }
if (! is.numeric(initial.period)) { stop("initial.period must be numeric") }
if ((initial.period <= 0) || (initial.period > length(y))) { stop("initial.period must be greater than or equal to 1, and less than the number of observations") }
if (is.null(V.meth)) { V.meth <- "rec" }
if (V.meth == "rec" && !is.null(kappa)) { stop("kappa is used only if V.meth is set to ''ewma''") }
if (! V.meth %in% c("rec","ewma")) { stop("please, specify correct V.meth") }
if (V.meth == "ewma" && is.null(kappa)) { stop("please, specify kappa") }
if (V.meth == "ewma" && ! is.numeric(kappa)) { stop("kappa must be numeric") }
if ( (V.meth == "ewma") && ( (kappa < 0) || (kappa > 1) ) ) { stop("kappa must be between 0 and 1") }
if ( ! is.null(gprob) && ! is.matrix(gprob) ) { stop("gprob must be a matrix") }
if ( ! is.null(gprob) && !(length(y) == nrow(gprob)) ) { warning("time-series of gprob and x differ in length") }
if (! is.null(gprob))
{
gprob.ind <- nrow(x) - nrow(gprob) + 1
}
else
{
gprob.ind <- nrow(x) + 1
}
if ( ! is.null(gprob) && !(ncol(x) == ncol(gprob)) ) { stop("gprob and x must have the same number of columns") }
if ( ! is.null(gprob) && anyNA(gprob) ) { stop("missing values in gprob") }
if ( (! is.null(gprob)) && ( (length(gprob[gprob<0]) > 0) || (length(gprob[gprob>1]) > 0) ) )
{
stop("values of gprob must be greater than or equal to 0, and less than or equal to 1")
}
if ( (! is.null(gprob)) && (is.null(omega) ) ) { stop("please, specify omega") }
if (! is.null(omega) && ! is.numeric(omega)) { stop("omega must be numeric") }
if (! is.null(omega) && ( (omega < 0) || (omega > 1) ) ) { stop("omega must be greater than or equal to 0, and less than or equal to 1") }
if (is.null(model)) { model <- "dma" }
if (! model %in% c("dma","dms","med")) { stop("please, specify correct model: ''dma'', ''dms'', or ''med''") }
if (is.null(parallel)) { parallel <- FALSE }
if (! is.logical(parallel)) { stop("parallel must be logical, i.e., TRUE or FALSE") }
if (is.null(m.prior)) { m.prior <- 0.5 }
if (! is.null(m.prior) && ! is.numeric(m.prior)) { stop("m.prior must be numeric") }
if ((m.prior <= 0) || (m.prior >= 1)) { stop("m.prior must be greater than 0, and less than 1") }
if (is.null(mods.incl))
{
all.mods <- TRUE
mods.incl <- expand.grid(rep.int(list(0:1), ncol(x)))
mods.incl <- as.matrix(cbind(rep.int(1,nrow(mods.incl)),mods.incl))
}
else
{
all.mods <- FALSE
}
if (is.null(mods.check)) { mods.check <- FALSE }
if (mods.check == TRUE)
{
if ((! is.null (mods.incl)) && (! is.matrix(mods.incl))) { stop("mods.incl must be a matrix") }
if (is.matrix(mods.incl) && (! (ncol(mods.incl) == (ncol(x)+1)))) { stop("columns of mods.incl do not correspond to variables specified by columns of x") }
if (is.matrix(mods.incl) && (length(mods.incl[!(mods.incl %in% c(0,1))]) > 0)) { stop("mods.incl should contain only 0 and 1") }
if (is.matrix(mods.incl) && any(duplicated(mods.incl))) { stop("mods.incl contain duplicated models") }
if (is.matrix(mods.incl))
{
test <- FALSE
test.row <- rep.int(0,ncol(mods.incl))
for (i in 1:nrow(mods.incl))
{
if (identical(test.row,mods.incl[i,])) { test <- TRUE }
}
if (test == TRUE) { stop("mods.incl contain a model with no variables") }
}
}
if ( (all.mods == TRUE ) && (ncol(x)>29) ) { stop("max number of variables, in case of using all possible models, is 29") }
if ( (all.mods == FALSE ) && (nrow(mods.incl)>2^29) ) { stop("max number of models is 2^29") }
if (all.mods == FALSE && nrow(mods.incl) == 2^ncol(x)) { all.mods <- TRUE }
if (is.null(DOW))
{
threshold <- 0
}
if (all.mods == FALSE && model == "med") { stop("Median Probability Model can be applied only if all possible models with a constant are used, i.e, mods.incl is not specified") }
if (!is.null(DOW) && !is.numeric(DOW)) { stop("DOW must be numeric") }
if (!is.null(DOW) && ((DOW < 0) || (DOW > 1))) { stop("DOW must be between 0 and 1") }
if (!is.null(DOW))
{
threshold <- DOW
}
if (!is.null(DOW) && is.null(DOW.nmods)) { DOW.nmods <- 0 }
if (!is.null(DOW.nmods) && !is.numeric(DOW.nmods)) { stop("DOW.nmods must be numeric") }
if (!is.null(DOW.nmods) && ((DOW.nmods < 2 && !DOW.nmods == 0) || DOW.nmods > nrow(mods.incl))) { stop("DOW.nmods must be greater than or equal to 2, and less than or equal to the number of all possible models") }
if (is.null(DOW.type)) { DOW.type <- "r" }
if (! DOW.type %in% c("r","e")) { stop("please, specify correct DOW.type: ''r'', or ''e''") }
if (!is.null(DOW) && DOW.type %in% c("r","e") && !model == "dma") { stop("Dynamic Occam's Window can be applied only to Dynamic Model Averaging, i.e., model must be ''dma''") }
if (is.null(bm)) { bm <- FALSE }
if (! is.logical(bm)) { stop("bm must be logical, i.e., TRUE or FALSE") }
if (!is.null(DOW.limit.nmods) && !is.numeric(DOW.limit.nmods)) { stop("DOW.limit.nmods must be numeric") }
if (!is.null(DOW.limit.nmods) && DOW.limit.nmods < 2) { stop("DOW.limit.nmods must be greater than or equal to 2") }
rm(all.mods)
if (length(lambda)>1 && model=="med") stop("multiple lambdas cannot be used for Median Probability Model")
if (length(lambda)>1 && !threshold==0) stop("multiple lambdas cannot be used with Dynamic Occam's Window")
if (is.null(progress.info)) { progress.info <- FALSE }
if (! is.logical(progress.info)) { stop("progress.info must be logical, i.e., TRUE or FALSE") }
if (! is.null(small.c) && ! is.numeric(small.c)) { stop("small.c must be a (small) number") }
if (! is.null(small.c) && (small.c<0)) { stop("small.c must be positive") }
if (! is.null(forced.models)) { forced.models <- forced.models[!duplicated(forced.models),,drop=FALSE] }
if (! is.null(forbidden.models)) { forbidden.models <- forbidden.models[!duplicated(forbidden.models),,drop=FALSE] }
if (is.null(fcores)) { fcores <- 1 }
if (is.null(red.size)) { red.size <- FALSE }
lambda <- unique(lambda)
lambda <- sort(lambda,decreasing=TRUE)
mods.incl <- matrix(as.logical(mods.incl), dim(mods.incl))
if (! is.null(forced.models)) { forced.models <- matrix(as.logical(forced.models), dim(forced.models)) }
if (! is.null(forbidden.models)) { forbidden.models <- matrix(as.logical(forbidden.models), dim(forbidden.models)) }
if (! is.null(forced.variables)) { forced.variables <- as.logical(forced.variables) }
if (is.null(av)) { av <- "dma" }
###################################################################
###################################################################
################################################ basic DMA function
f.basicDMA <- function (y,x,alpha,lambda,initvar,W=NULL,kappa=NULL,
gprob=NULL,omega=NULL,model=NULL,parallel=NULL,
m.prior=NULL,mods.incl=NULL,small.c=NULL)
{
################################ setting initial values for DMA
len <- length(lambda)
exp.lambda <- vector()
### small constant as in Eq. (17) in Raftery et al. (2010)
if (is.null(small.c))
{
c <- 0.001 * (1/(len*(2^ncol(x))))
}
else
{
c <- small.c
}
### initialization of \pi_{0|0} as in Raftery et al. (2010)
if (m.prior == 0.5)
{
pi1 <- as.vector(rep.int(1/(nrow(mods.incl)*len),len*nrow(mods.incl)))
}
else
{
pi1 <- vector()
for (i in 1:nrow(mods.incl))
{
pi1[i] <- m.prior^(sum(mods.incl[i,])) * (1-m.prior)^(ncol(mods.incl)-sum(mods.incl[i,]))
}
pi1 <- rep(pi1,len)
pi1 <- pi1 / sum(pi1)
}
### yhat.all - predictions from all regression models used in averaging
### post - posterior predictive model probabilities
###
### in DMA all models are averaged, in DMS the model with highest
### posterior predictive model probability is chosen,
### for MED see Barbieri and Berger (2004)
if (model == "dma")
{
if (red.size==FALSE)
{
post <- as.vector(rep.int(0,len*nrow(mods.incl)))
yhat.all <- matrix(0,ncol=len*nrow(mods.incl),nrow=1)
}
else
{
post <- as.vector(rep.int(0,len*ncol(mods.incl)))
yhat.all <- NA
vm <- as.matrix(apply(mods.incl,1,sum))
exp.var <- vector()
}
}
if (model == "dms" || model == "med")
{
post <- vector()
p.incl <- matrix(0,ncol=ncol(mods.incl),nrow=1)
}
ydma <- vector()
thetas.exp <- as.vector(rep.int(0, ncol(mods.incl)))
###############################################################
############################ recursive estimation of sub-models
f.param <- function(i)
{
i.old <- i
i <- i.old %% nrow(mods.incl)
if (i==0) { i <- nrow(mods.incl) }
i.l <- 1 + ( (i.old - 1) %/% nrow(mods.incl) )
if (length(which(mods.incl[i,-1,drop=FALSE]==1))>0)
{
xx <- x[,which(mods.incl[i,-1,drop=FALSE]==1),drop=FALSE]
if (mods.incl[i,1]==1)
{
c.incl <- TRUE
}
else
{
c.incl <- FALSE
}
}
else
{
xx <- matrix(,ncol=0,nrow=length(y))
c.incl <- TRUE
}
out <- tvp(y=y,x=xx,V=initvar,lambda=lambda[i.l],W=W,kappa=kappa,c=c.incl)
out[[4]] <- NULL
return(out)
}
if (parallel == TRUE)
{
ncores <- detectCores() - fcores
cl <- makeCluster(ncores)
registerDoParallel(cl)
est.models <- foreach(i=seq(len*nrow(mods.incl)),.packages=c("xts","fDMA")) %dopar%
{
f.param(i)
}
stopCluster(cl)
rm(cl,ncores)
}
else
{
est.models <- lapply(seq(len*nrow(mods.incl)),f.param)
}
############################################## models averaging
### modification as in Koop and Onorante (2014)
f.pi2.g <- function(i)
{
if (length(which(mods.incl[i,-1,drop=FALSE]==1))>0)
{
p1 <- gprob[t-gprob.ind+1,which(mods.incl[i,-1,drop=FALSE]==1)]
}
else
{
p1 <- 1
}
if (length(which(mods.incl[i,-1,drop=FALSE]==0))>0)
{
p2 <- 1-gprob[t-gprob.ind+1,which(mods.incl[i,-1,drop=FALSE]==0)]
}
else
{
p2 <- 1
}
p1 <- prod(p1)
p2 <- prod(p2)
if (p1==0) { p1 <- 0.001 * (1/(2^ncol(mods.incl))) }
if (p2==0) { p2 <- 0.001 * (1/(2^ncol(mods.incl))) }
return( p1*p2 )
}
f.pdens <- function(i)
{
return(.subset2(.subset2(est.models,i),3)[t])
}
f.mse <- function(i)
{
out.mse <- mean((y[1:t] - .subset2(.subset2(est.models,i),1)[1:t])^2)
if (out.mse==0) { out.mse <- 0.001 * (1/(2^ncol(mods.incl))) }
return(1/out.mse)
}
f.hr1 <- function(i)
{
out.hr <- hit.ratio(y=y[1:t],y.hat=.subset2(.subset2(est.models,i),1)[1:t],d=FALSE)
if (out.hr==0) { out.hr <- 0.001 * (1/(2^ncol(mods.incl))) }
return(out.hr)
}
f.hr2 <- function(i)
{
out.hr <- hit.ratio(y=y[1:t],y.hat=.subset2(.subset2(est.models,i),1)[1:t],d=TRUE)
if (out.hr==0) { out.hr <- 0.001 * (1/(2^ncol(mods.incl))) }
return(out.hr)
}
f.yhat <- function(i)
{
return(.subset2(.subset2(est.models,i),1)[t])
}
f.thetas <- function(i)
{
i.old <- i
i <- i.old %% nrow(mods.incl)
if (i==0) { i <- nrow(mods.incl) }
theta.i.tmp <- as.vector(mods.incl[i,])
theta.i.tmp[theta.i.tmp==1] <- .subset2(.subset2(est.models,i.old),2)[t,]
return(theta.i.tmp)
}
for (t in 1:nrow(x))
{
if (t<gprob.ind)
{
pi2 <- (pi1^alpha + c) / (sum((pi1)^alpha + c))
}
else
{
pi2.g <- unlist(lapply(seq(nrow(mods.incl)),f.pi2.g))
pi2.g <- pi2.g / sum(pi2.g)
pi2.g <- pi2.g / len
pi2.g <- rep(pi2.g,len)
pi1sum <- sum((pi1)^alpha + c)
pi2 <- omega * ( (pi1^alpha + c) / pi1sum ) + (1-omega) * pi2.g
rm(pi1sum,pi2.g)
}
if (model == "dma")
{
if (red.size==FALSE)
{
post <- rbind(post,pi2)
}
else
{
f.split.pi2 <- function(i.col)
{
col.s <- ( ( i.col - 1 ) * nrow(mods.incl) ) + 1
col.e <- col.s + nrow(mods.incl) - 1
return(pi2[col.s:col.e])
}
pi2.temp <- lapply(1:len,f.split.pi2)
pi2.temp <- Reduce('+', pi2.temp)
pi2.temp <- as.vector(pi2.temp)
post <- rbind(post,pi2.temp %*% mods.incl)
exp.var[t] <- pi2.temp %*% vm
rm(pi2.temp)
}
}
if (model == "dms")
{
j.m <- which.max(pi2)
post[t] <- pi2[j.m]
j.m.new <- j.m %% nrow(mods.incl)
if (j.m.new==0) { j.m.new <- nrow(mods.incl) }
p.incl <- rbind(p.incl,mods.incl[j.m.new,])
}
if (model == "med")
{
j.m <- as.vector(pi2 %*% mods.incl)
j.m1 <- which(j.m >= 0.5)
j.m <- as.vector(rep.int(0, ncol(mods.incl)))
j.m[j.m1] <- 1
j.m <- which(apply(mods.incl, 1, function(x) all(x == j.m)))
rm(j.m1)
post[t] <- pi2[j.m]
p.incl <- rbind(p.incl,mods.incl[j.m,])
}
yhat <- unlist(lapply(seq(len*nrow(mods.incl)),f.yhat))
if (model == "dma")
{
if (red.size==FALSE) { yhat.all <- rbind(yhat.all,yhat) }
}
if (model == "dma")
{
ydma[t] <- crossprod(pi2,yhat)
thetas <- t(sapply(seq(len*nrow(mods.incl)),f.thetas))
thetas.exp <- rbind(thetas.exp,pi2 %*% thetas)
exp.lambda[t] <- as.numeric(crossprod(pi2,rep(lambda,each=nrow(mods.incl))))
}
if (model == "dms" || model == "med")
{
ydma[t] <- yhat[j.m]
thetas <- f.thetas(j.m)
thetas.exp <- rbind(thetas.exp,thetas)
exp.lambda[t] <- lambda[1 + (j.m - 1) %/% nrow(mods.incl)]
rm(j.m)
}
if (av=="dma") { pdens <- unlist(lapply(seq(len*nrow(mods.incl)),f.pdens)) }
if (av=="mse") { pdens <- unlist(lapply(seq(len*nrow(mods.incl)),f.mse)) }
if (av=="hr1")
{
if (t==1)
{
pdens <- rep(1,len*nrow(mods.incl))
}
else
{
pdens <- unlist(lapply(seq(len*nrow(mods.incl)),f.hr1))
}
}
if (av=="hr2") { pdens <- unlist(lapply(seq(len*nrow(mods.incl)),f.hr2)) }
pi1 <- (pi2 * pdens) / as.numeric(crossprod(pi2,pdens))
}
rm(est.models)
######################################################## output
thetas.exp <- thetas.exp[-1,,drop=FALSE]
if (model == "dma")
{
post <- as.matrix(post[-1,,drop=FALSE])
if (red.size==FALSE)
{
yhat.all <- yhat.all[-1,,drop=FALSE]
f.split <- function(i.col)
{
col.s <- ( ( i.col - 1 ) * nrow(mods.incl) ) + 1
col.e <- col.s + nrow(mods.incl) - 1
return(post[,col.s:col.e])
}
post <- lapply(1:len,f.split)
post <- Reduce('+', post)
post <- as.matrix(post)
}
if (red.size==FALSE) { exp.var <- NA }
return(list(post,yhat.all,thetas.exp,ydma,pdens,NA ,pi1,thetas,yhat,pi2,exp.lambda, NA, exp.var))
}
if (model == "dms" || model == "med")
{
p.incl <- p.incl[-1,,drop=FALSE]
return(list(post,NA ,thetas.exp,ydma,pdens,p.incl,pi1,thetas,yhat,pi2,exp.lambda))
}
}
################################################## end of basic DMA
###################################################################
###################################################################
if (threshold==0)
{
comput <- f.basicDMA(y=y,x=x,alpha=alpha,lambda=lambda,initvar=initvar,W=W,kappa=kappa,
gprob=gprob,omega=omega,model=model,parallel=parallel,
m.prior=m.prior,mods.incl=mods.incl,small.c=small.c)
}
else
{
##### Dynamic Occam's Window as in Onorante and Raftery (2016)
##############################################################
ydma.dow <- vector()
post.dow <- as.vector(rep.int(0, ncol(mods.incl)))
thetas.exp.dow <- as.vector(rep.int(0,ncol(mods.incl)))
exp.var.dow <- vector()
### selection of initial models
if (!(DOW.nmods == 0))
{
mods.incl <- mods.incl[sample.int(nrow(mods.incl),size=DOW.nmods),]
}
else
{
mods.incl <- as.matrix(rbind(rep.int(0,ncol(x)),diag(1,nrow=ncol(x),ncol=ncol(x))))
mods.incl <- as.matrix(cbind(rep.int(1,ncol(x)+1),mods.incl))
mods.incl <- matrix(as.logical(mods.incl), dim(mods.incl))
}
init.mods <- mods.incl
nms <- vector()
for (T in 1:nrow(x))
{
nms[T] <- nrow(mods.incl)
if (progress.info==TRUE)
{
print(paste('round:',T))
print(paste('models:',nms[T]))
}
if (parallel==TRUE && nms[T]>2^10)
{
dopar <- TRUE
}
else
{
dopar <- FALSE
}
T.dma <- f.basicDMA(y=y[1:T,,drop=FALSE],x=x[1:T,,drop=FALSE],alpha=alpha,lambda=lambda,initvar=initvar,W=W,kappa=kappa,
gprob=gprob,omega=omega,model=model,parallel=dopar,
m.prior=m.prior,mods.incl=mods.incl,small.c=small.c)
yhat <- T.dma[[9]]
thetas <- T.dma[[8]]
T.dma[[8]] <- NA
pi1 <- T.dma[[7]]
if (DOW.type == "r")
{
pi2.temp <- T.dma[[10]]
pi2.temp[which(pi1<(threshold*max(pi1)))] <- 0
pi2.temp <- pi2.temp / sum(pi2.temp)
ydma.dow[T] <- crossprod(pi2.temp,yhat)
post.dow <- rbind(post.dow,pi2.temp %*% mods.incl)
exp.var.dow[T] <- pi2.temp %*% (as.matrix(apply(mods.incl,1,sum)))
thetas <- pi2.temp %*% thetas
thetas.exp.dow <- rbind(thetas.exp.dow,thetas)
rm(pi2.temp)
}
if (DOW.type == "e")
{
ydma.dow[T] <- crossprod(T.dma[[10]],yhat)
post.dow <- rbind(post.dow,T.dma[[10]] %*% mods.incl)
exp.var.dow[T] <- T.dma[[10]] %*% (as.matrix(apply(mods.incl,1,sum)))
thetas <- T.dma[[10]] %*% thetas
thetas.exp.dow <- rbind(thetas.exp.dow,thetas)
}
### models' space update
mod.red <- mods.incl[which(pi1>=(threshold*max(pi1))),,drop=FALSE]
if (!is.matrix(mod.red)) { mod.red <- t(as.matrix(mod.red)) }
if (!is.null(DOW.limit.nmods))
{
if (nrow(mod.red)>DOW.limit.nmods)
{
ind.red <- sort(pi1,decreasing=TRUE,index.return=TRUE)$ix
ind.red <- ind.red[1:DOW.limit.nmods]
mod.red <- mods.incl[ind.red,,drop=FALSE]
rm(ind.red)
}
}
if (!is.matrix(mod.red)) { mod.red <- t(as.matrix(mod.red)) }
mod.exp <- mod.red
unm <- matrix(TRUE,nrow(mod.red),1)
for (i.col in 2:ncol(mod.red))
{
mod.exp.temp <- mod.red
mod.exp.temp[,i.col] <- xor(mod.red[,i.col],unm)
mod.exp <- rbind(mod.exp,mod.exp.temp)
}
if (! is.null(forced.models))
{
mod.exp <- rbind(mod.exp,forced.models)
}
if (! is.null(forced.variables))
{
for (i.var in which(forced.variables==1))
{
mod.exp[,i.var] <- TRUE
}
}
mod.exp <- unique(mod.exp)
if (! is.null(forbidden.models))
{
mod.exp <- rbind(forbidden.models,mod.exp)
mod.exp <- mod.exp[!duplicated(mod.exp),,drop=FALSE]
mod.exp <- mod.exp[-(1:nrow(forbidden.models)),,drop=FALSE]
}
if (T<nrow(x)) { mods.incl <- mod.exp }
rm(mod.red,mod.exp,mod.exp.temp)
}
comput <- list(post.dow[-1,,drop=FALSE],NA,thetas.exp.dow[-1,,drop=FALSE],ydma.dow,T.dma[[5]])
comput[[11]] <- T.dma[[11]]
comput[[12]] <- exp.var.dow
rm(post.dow,ydma.dow,thetas.exp.dow,T.dma)
}
############################################################ output
###################################################################
ydma <- comput[[4]]
comput[[4]] <- NA
pdens <- comput[[5]]
comput[[5]] <- NA
if (length(lambda)>1)
{
exp.lambda <- comput[[11]]
}
else
{
exp.lambda <- rep.int(lambda,nrow(x))
}
if (model == "dma")
{
if (threshold==0)
{
post <- comput[[1]]
}
yhat.all <- comput[[2]]
if (threshold==0)
{
if (red.size==FALSE)
{
colnames(yhat.all) <- seq(1,length(lambda)*nrow(mods.incl))
rownames(yhat.all) <- rownames(x)
}
}
}
thetas.exp <- as.matrix(comput[[3]])
colnames(thetas.exp) <- c("const",colnames(x))
if (model == "dma")
{
if (threshold==0)
{
if (red.size==FALSE)
{
post.inc <- post %*% mods.incl
}
else
{
post.inc <- post
post <- NA
}
}
else
{
post.inc <- comput[[1]]
}
}
if (model == "dms" || model == "med")
{
post.inc <- comput[[6]]
}
colnames(post.inc) <- c("const",colnames(x))
if (model == "dma")
{
if (threshold==0)
{
if (red.size==FALSE)
{
exp.var <- post %*% (as.matrix(apply(mods.incl,1,sum)))
}
else
{
exp.var <- as.matrix(comput[[13]])
}
}
else
{
exp.var <- as.matrix(comput[[12]])
}
}
if (model == "dms" || model == "med")
{
exp.var <- as.matrix(rowSums(post.inc))
}
if (model == "dms" || model == "med")
{
post <- as.matrix(comput[[1]])
yhat.all <- NA
}
mse <- (mean((y[initial.period:length(y)] - ydma[initial.period:length(y)])^2))^(1/2)
mae <- mean(abs(y[initial.period:length(y)] - ydma[initial.period:length(y)]))
naive.mse <- (mean((diff(as.numeric(y[initial.period:length(as.numeric(y))])))^2))^(1/2)
naive.mae <- mean(abs(diff(as.numeric(y[initial.period:length(as.numeric(y))]))))
if (bm == TRUE)
{
arima <- auto.arima(as.numeric(y))$residuals[initial.period:length(as.numeric(y))]
arima.mse <- (mean((arima)^2))^(1/2)
arima.mae <- mean(abs(arima))
}
else
{
arima.mse <- NA
arima.mae <- NA
}
benchmarks <- rbind(cbind(naive.mse,arima.mse),cbind(naive.mae,arima.mae))
rownames(benchmarks) <- c("RMSE", "MAE")
colnames(benchmarks) <- c("naive", "auto ARIMA")
if (model == "dma")
{
mod.type <- "DMA"
}
if (model == "dms")
{
mod.type <- "DMS"
}
if (model == "med")
{
mod.type <- "MED"
}
if (is.null(kappa)) { kappa <- NA }
if (is.null(omega)) { omega <- NA }
if (is.null(W)) { W <- "reg" }
if (threshold==0)
{
DOW.nmods <- NA
DOW.type <- NA
}
if (length(lambda)>1) { lambda <- paste(lambda,collapse=" ") }
if (threshold==0)
{
temp <- list(ydma, post.inc, mse, mae, mods.incl+0, post, exp.var, thetas.exp,
cbind(alpha,lambda,initvar,mod.type,W,initial.period,V.meth,kappa,omega,m.prior,threshold,DOW.nmods,DOW.type),
yhat.all, y, benchmarks, NA, NA, pdens,exp.lambda)
}
else
{
colnames(init.mods) <- colnames(post.inc)
rownames(init.mods) <- seq(1,nrow(init.mods))
temp <- list(ydma, post.inc, mse, mae, mods.incl+0, NA, exp.var, thetas.exp,
cbind(alpha,lambda,initvar,mod.type,W,initial.period,V.meth,kappa,omega,m.prior,threshold,DOW.nmods,DOW.type),
yhat.all, y, benchmarks, init.mods+0, nms, pdens,exp.lambda)
}
rownames(temp[[2]]) <- rownames(x)
colnames(temp[[5]]) <- c("const", colnames(x))
if (threshold==0 & (!any(is.na(temp[[6]])))) { rownames(temp[[6]]) <- rownames(x) }
if (model == "dma")
{
if (threshold==0 & (!any(is.na(temp[[6]])))) { colnames(temp[[6]]) <- seq(1,nrow(mods.incl)) }
}
if (model == "dms" || model == "med")
{
colnames(temp[[6]]) <- "mod. prob."
}
rownames(temp[[7]]) <- rownames(x)
rownames(temp[[8]]) <- rownames(x)
colnames(temp[[9]]) <- c("alpha","lambda","initvar","model type","W","initial period","V.meth","kappa","omega","m.prior","DOW threshold","DOW.nmods","DOW.type")
rownames(temp[[9]]) <- "parameters"
names(temp) <- c("y.hat","post.incl","RMSE","MAE","models","post.mod","exp.var","exp.coef.","parameters",
"yhat.all.mods","y","benchmarks","DOW.init.mods","DOW.n.mods.t","p.dens.","exp.lambda")
class(temp) <- "dma"
return(temp)
}
|
2ba52d2323b30132bf106e43b4483b2f648a217b | d06f4860f0815281085689b706a923740476b386 | /landing/code/newsletter/src/Create_report.R | 8db61e73c24f675c2dac3dc561a503c8bb24197a | [
"Apache-2.0"
] | permissive | jacobgreen4477/withmakers.github.io | b94d3a9e6247e161328224761047ad3478f4e436 | 28d3b68572b195f9bc84a44f32d31228dd31a16b | refs/heads/master | 2022-01-08T21:31:08.236198 | 2019-05-15T11:55:35 | 2019-05-15T11:55:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,320 | r | Create_report.R | # title : Write weekly report
# author : Hyunseo Kim
# path
path_local <- "C:/Users/user/Documents/ds/2econsulting/newsletter/"
path_git <- "C:/Users/user/Documents/2econsulting.github.io/data/newsletter/"
# Set today and wordcloud's url, subject
today <- gsub("-","",Sys.Date())
fileName <- paste0(path_local, "input/text.csv")
text <- read.csv(fileName)
kor <- paste(text$word[1], text$word[2], text$word[3])
wordcloud <- paste0("https://raw.githubusercontent.com/2econsulting/2econsulting.github.io/master/data/newsletter/output/report/wordcloud_",today,".png")
subject <- paste0(gsub("-",".",Sys.Date()),". ",kor)
# load data
fileName <- paste0(path_git,"input/total_",today,".csv")
total <- read.csv(fileName)
total$X <- NULL
# necessary function
filename <- paste0(path_local,"src/text_mining_function.R")
source(filename)
# Get existing information
kaggle_info <- exist_info(total, "kaggle")
datacamp_info <- exist_info(total, "datacamp")
vidhya_info <- exist_info(total, "vidhya")
mastery_info <- exist_info(total, "mastery")
# Make markdown code
kaggle_report <- link_make(kaggle_info)
datacamp_report <- link_make(datacamp_info)
vidhya_report <- link_make(vidhya_info)
mastery_report <- link_make(mastery_info)
# Write the first paragragh
report_head <- paste0('---\n',
'layout: post\n',
'title: ', subject,'\n',
'category: weekly report\n',
'tags: [Machine learning, Data Science article]\n',
'no-post-nav: true\n',
'---\n\n',kor,' ([wordcloud](',wordcloud,'))\n')
# Write kaggle's markdown
kaggle_body <- paste0('\n<br>\n\n#### Kaggle Blog NEWS TITLE\n\n',
kaggle_report)
# Write datacamp's markdown
datacamp_body <- paste0('\n<br>\n\n#### Data Camp NEWS TITLE\n\n',
datacamp_report)
# Write vidhya's markdown
vidhya_body <- paste0('\n<br>\n\n#### Analytics Vidhya NEWS TITLE\n\n',
vidhya_report)
# Write mastery's markdown
mastery_body <- paste0('\n<br>\n\n#### Machine Learning Mastery NEWS TITLE\n\n',
mastery_report)
# Conbine all markdown
total_body <- paste0(report_head,kaggle_body,datacamp_body,vidhya_body,mastery_body,'\n<br>\n')
# Save markdown
fileName <- paste0("C:/Users/user/Documents/2econsulting.github.io/_posts/2018/",Sys.Date(),"-newsletter.md")
write.table(total_body, fileName, row.names = FALSE, col.names = FALSE, quote = FALSE, fileEncoding = "UTF-8")
gc()
|
5f47a415ea9fccacd149f5bc77d0f0114047bdd6 | 142283d08a3e4d48c76f3837a891f528bc1900c4 | /man/get_providers_table.Rd | 7e7224769db830b298ed4430129b8d49bc6294b3 | [] | no_license | pachevalier/WidukindR | 6f57731c86f2ada9214381642a51cdee5aa4dd84 | f1c467956274bf9fec5e5d8ce6fe0643e5b8c629 | refs/heads/master | 2021-01-01T03:55:23.953998 | 2017-04-06T15:53:47 | 2017-04-06T15:53:47 | 56,228,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 357 | rd | get_providers_table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_providers.R
\name{get_providers_table}
\alias{get_providers_table}
\title{Get Providers Table}
\usage{
get_providers_table()
}
\value{
a tibble
}
\description{
This function returns a data frame with data on providers
}
\examples{
get_providers_table()
}
\keyword{providers}
|
7733b9ffe18da8cc11f890774363c4f3479f1a0c | e853edce6dc05ffa97588aba87cefad284acc8d4 | /Rank 1/1_data_cleaner_basic.R | f543f649c2b0f025a2ba8965d34c52868c9b4137 | [
"MIT"
] | permissive | BenJamesbabala/The_Ultimate_Student_Hunt | 3cbf66d8f55985da0e9b51252263617cc312d404 | edef8e81a07f7aacf67caf290ceaf5f3dba79b8d | refs/heads/master | 2021-05-04T08:42:02.427769 | 2016-10-07T09:30:05 | 2016-10-07T09:30:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,184 | r | 1_data_cleaner_basic.R | library(caTools)
library(lubridate)
#---------------------------------
# Reading the train and test sets.
#---------------------------------
train <- read.csv('Train.csv', stringsAsFactors = FALSE)
test <- read.csv("Test.csv", stringsAsFactors = FALSE)
#--------------------------------------------------------------
# Selecting the target and dropping it from the training frame.
#--------------------------------------------------------------
target <- train$Footfall
train <- train[, 1:17]
proper_feature_names <- function(input_table){
#--------------------------------------------
# This function normalizes the column names.
# INPUT -- Table with messed up column names.
# OUTPUT -- Table with proper column names.
#--------------------------------------------
colnames(input_table) <- tolower(colnames(input_table))
colnames(input_table) <- gsub('([[:punct:]])|\\s+','_',colnames(input_table))
while (any(grepl("__",colnames(input_table),fixed = TRUE)) == TRUE){
colnames(input_table) <- gsub("__","_",colnames(input_table),fixed = TRUE)
}
colnames(input_table) <- gsub("\\*$", "",colnames(input_table))
return(input_table)
}
dummygen <- function(new_table, original_table, dummified_column, column_values, new_name){
#---------------------------------------------------------------------------------------
# This function generates dummies from a categorical variable and adds them to a table.
# INPUT 1. -- The new cleaned table -- I will attach the dummies.
# INPUT 2. -- The original table that is being cleaned.
# INPUT 3. -- The column that has the strings.
# INPUT 4. -- The unique values in the column encoded.
# INPUT 5. -- The new name of the columns.
# OUTPUT -- The new table with the dummy variables.
#---------------------------------------------------------------------------------------
i <- 0
for (val in column_values){
i <- i + 1
new_variable <- data.frame(matrix(0, nrow(new_table), 1))
new_variable[original_table[,dummified_column] == val, 1] <- 1
colnames(new_variable) <- paste0(new_name, i)
new_table <- cbind(new_table,new_variable)
}
return(new_table)
}
#------------------------------------------------------------
# Normalizing the feature names in the train and test tables.
#------------------------------------------------------------
train <- proper_feature_names(train)
test <- proper_feature_names(test)
data_munger <- function(input_table){
#------------------------------------
# This function cleans the data.
# INPUT -- The table to be cleaned.
# OUTPUT -- The cleaned numeric table.
#------------------------------------
#----------------------------------------------
# Defining a target table for the cleaned data.
#----------------------------------------------
new_table <- data.frame(matrix(0, nrow(input_table), 1))
new_table[, 1] <- input_table$id
#-----------------------------------------------------
# The first variable is an artifical ID.
#-----------------------------------------------------
colnames(new_table) <- c("id")
#----------------------------
# Park ID dummy generation.
#----------------------------
park_id <- c(12:39)
new_table <- dummygen(new_table, input_table, "park_id", park_id, "park_id_")
#------------------------------------------------------
# Generating a proper day variable in the input table.
#------------------------------------------------------
input_table$monkey_day <- paste0(substr(input_table$date, 7, 10),"-",substr(input_table$date, 4, 5),"-",substr(input_table$date, 1, 2))
#-------------------------------------
# Generating a day of week indicator.
#-------------------------------------
input_table$days <- lubridate::wday(input_table$monkey_day)
#---------------------------------------
# Generating a day of year categorical.
#---------------------------------------
new_table$super_monkey_day <- yday(input_table$monkey_day)
#--------------------------------------
# Generating a day of month categorical.
#--------------------------------------
new_table$hyper_monkey_day <- mday(input_table$monkey_day)
#---------------------------------------------------
# Creating dummies from the day of week categorical.
#---------------------------------------------------
days <- c(1:7)
new_table <- dummygen(new_table, input_table, "days", days, "week_days_")
#-----------------------------------------------------------------------
# Days simple solution -- this is biased, but works as a biweekly proxy.
#-----------------------------------------------------------------------
new_table$date <- yday(input_table$date)
#------------------------
# Month dummy variables.
#------------------------
input_table$first_two <- substr(input_table$date, 6, 7)
first_two <- c("01", "02", "03", "04", "05", "06",
"07", "08", "09", "10", "11", "12")
new_table <- dummygen(new_table, input_table, "first_two", first_two, "first_two_")
#----------------------------------------------------
# Extracting the numeric variables the way they are.
#----------------------------------------------------
columns_to_extract_exactly <- c("direction_of_wind",
"average_breeze_speed",
"max_breeze_speed",
"min_breeze_speed",
"var1",
"average_atmospheric_pressure",
"max_atmospheric_pressure",
"min_atmospheric_pressure",
"min_ambient_pollution",
"max_ambient_pollution",
"average_moisture_in_park",
"max_moisture_in_park",
"min_moisture_in_park")
sub_table <- input_table[, columns_to_extract_exactly]
new_table <- cbind(new_table, sub_table)
#---------------------------------------------------------------------
# Creating moving window standard deviation variables for the different parks.
#------------------------------------------------------------------------------
names_to_use <- colnames(sub_table)
keys <- unique(input_table$park_id)
for (i in 1:ncol(sub_table)){
for (k in keys){
sub_table[input_table$park_id == k, i] <- runsd(sub_table[input_table$park_id == k, i], 4, endrule = "constant")
}
}
colnames(sub_table) <- paste0("sd_", names_to_use)
new_table <- cbind(new_table, sub_table)
#----------------------------------------------------------------
# Creating moving window mean variables for the different parks.
#----------------------------------------------------------------
keys <- unique(input_table$park_id)
for (i in 1:ncol(sub_table)){
for (k in keys){
sub_table[input_table$park_id == k, i] <- runmean(sub_table[input_table$park_id == k, i], 4, endrule = "constant")
}
}
colnames(sub_table) <- paste0("mean_", names_to_use)
new_table <- cbind(new_table, sub_table)
#-----------------------------------------------------------------
# Creating moving window maxima variables for the different parks.
#-----------------------------------------------------------------
keys <- unique(input_table$park_id)
for (i in 1:ncol(sub_table)){
for (k in keys){
sub_table[input_table$park_id == k, i] <- runmax(sub_table[input_table$park_id == k, i], 7, endrule = "constant")
}
}
colnames(sub_table) <- paste0("max_", names_to_use)
new_table <- cbind(new_table, sub_table)
#-----------------------------------------------------------------
# Creating moving window minima variables for the different parks.
#-----------------------------------------------------------------
keys <- unique(input_table$park_id)
for (i in 1:ncol(sub_table)){
for (k in keys){
sub_table[input_table$park_id == k, i] <- runmin(sub_table[input_table$park_id == k, i], 7, endrule="constant")
}
}
colnames(sub_table) <- paste0("min_", names_to_use)
new_table <- cbind(new_table, sub_table)
#----------------------------
# Creating location dummies.
#----------------------------
location_type <- c(1:4)
new_table <- dummygen(new_table, input_table, "location_type", location_type, "location_type_")
return(new_table)
}
#--------------------------------------------
# Creating the cleaned test and train tables.
#--------------------------------------------
new_train <- data_munger(train)
new_test <- data_munger(test)
#-----------------------------------------
# Dumping the tables and arget variables.
#-----------------------------------------
write.csv(new_train, file = "train.csv", row.names = FALSE)
write.csv(new_test, file = "test.csv", row.names = FALSE)
write.csv(target, file = "target.csv", row.names = FALSE)
|
52f3a8e2c984db3d28bdc4cc78a3bf5f5c28c74f | 5845a56588bef70f613a610d23f7833069f6dab2 | /pkg/R/alignment_plot.R | 49170cf5cf48d32b9371e0540851563f889f5ae0 | [] | no_license | DrRoad/dataquality | fdc537b0d2e71785253bf8b3423dbd022a3177ff | 96d930ef6469b73b8eb6a4db29273772f457bc2a | refs/heads/master | 2022-01-27T07:03:35.073584 | 2013-11-25T10:05:39 | 2013-11-25T10:05:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,835 | r | alignment_plot.R | alignment.plot <- function(src.nrow, ref.nrow, matched.nrow, src.name="source", ref.name="reference") {
require(RColorBrewer)
require(grid)
grid.newpage()
datnames <- c(src.name, ref.name)
datwidths <- convertWidth(stringWidth(datnames),unitTo="npc", valueOnly=TRUE)
parts <- c(src.nrow - matched.nrow,
matched.nrow,
ref.nrow - matched.nrow)
total <- sum(parts)
heights <- parts/total
ys <- c(cumsum(c(0,heights))[1:3],1)
numberwidth <- convertWidth(stringWidth("10000000"),unitTo="npc", valueOnly=TRUE)
pushViewport(viewport(layout=grid.layout(3, 6,
widths=unit(c(numberwidth,numberwidth,1,1,numberwidth,numberwidth), c("npc", "npc", "null", "null", "npc", "npc")),
heights=unit(c(1.5,1, .5), c("lines", "null", "lines")))))
cellplot(1, 3, e={
grid.text(datnames[1])
})
cellplot(1, 4, e={
grid.text(datnames[2])
})
ys_numbers <- (ys[2:4]+ys[1:3])/2
brewer.set1 <- brewer.pal(9, name="Set1")
cellplot(2, 1, e={
grid.text(parts[1:2], x=.9, y=ys_numbers[1:2], just="right")
})
src_perc <- round(parts[1:2]/sum(parts[1:2])*100, 2)
ref_perc <- round(parts[2:3]/sum(parts[2:3])*100, 2)
cellplot(2, 2, e={
grid.text(paste0("(", src_perc, "%)"), x=.9, y=ys_numbers[1:2], just="right")
})
cellplot(2, 5, e={
grid.text(parts[2:3], x=.9, y=ys_numbers[2:3], just="right")
})
cellplot(2, 6, e={
grid.text(paste0("(", ref_perc, "%)"), x=.9, y=ys_numbers[2:3], just="right")
})
cellplot(2, 3, e={
grid.rect(y=ys[1], height=sum(heights[1:2]), gp=gpar(fill=brewer.set1[2], col=NA), just=c("bottom"))
})
cellplot(2, 4, e={
grid.rect(y=ys[2], height=sum(heights[2:3]), gp=gpar(fill=brewer.set1[3], col=NA), just=c("bottom"))
})
cellplot(2, 3:4, e={
grid.polyline(y=rep(ys[2:3], each=2), x=rep(c(0,1), 2), id=rep(1:2, each=2))
})
}
|
06eb716b3cccd621f92896fec284adb9e4c4a556 | 0ad2a36dcd4191ac4da79933f66f08855489b934 | /man/kmModel.Rd | 835d1b9f0d0ed22c0eef16b0e72c4402085ab343 | [] | no_license | schiffner/mobKriging | 75f3f035b70d314361f3f117c492cbcb399007b4 | 3fac343fd86cac72f032b2a148c62d4dfde8f20d | refs/heads/master | 2021-01-22T01:04:40.175978 | 2014-08-13T20:17:21 | 2014-08-13T20:17:21 | 22,139,892 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,571 | rd | kmModel.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\docType{data}
\name{kmModel}
\alias{kmModel}
\alias{kmModel-class}
\title{\code{kmModel}}
\format{\preformatted{Formal class 'StatModel' [package "modeltools"] with 5 slots
..@ name : chr "kriging model"
..@ dpp :function (formula, data = list(), subset = NULL, na.action = NULL, frame = NULL,
enclos = sys.frame(sys.nframe()), other = list(), designMatrix = TRUE,
responseMatrix = TRUE, setHook = NULL, ...)
..@ fit :function (object, weights = NULL, noise.var = NULL, km.args = NULL, ...)
..@ predict :function (object, newdata = NULL, ...)
..@ capabilities:Formal class 'StatModelCapabilities' [package "modeltools"] with 2 slots
.. .. ..@ weights: logi FALSE
.. .. ..@ subset : logi FALSE
}}
\usage{
kmModel
}
\value{
Slot \code{fit} returns an object of class \code{kmModel}.
}
\description{
An object of class \code{\linkS4class{StatModel}} that provides infra-structure for an unfitted Kriging model.
}
\examples{
## We use the first example in the documentation of function km
if (require(DiceKriging)) {
d <- 2L
x <- seq(0, 1, length = 4L)
design <- expand.grid(x1 = x, x2 = x)
y <- apply(design, 1, branin)
df <- data.frame(y = y, design)
## Fitting the model using kmModel:
# data pre-processing
mf <- dpp(kmModel, y ~ ., data = df)
# no trend (formula = ~ 1)
m1 <- fit(kmModel, mf)
# linear trend (formula = ~ x1 + x2)
m1 <- fit(kmModel, mf, formula = ~ .)
# predictions on the training data
# recommended: improved version of predict for models fitted with objects
# of class StatModel
Predict(m1, type = "UK")
# also possible
predict(m1, type = "UK")
## This is equivalent to:
# no trend (formula = ~ 1)
m2 <- km(design = design, response = y)
# linear trend (formula = ~ x1 + x2)
m2 <- km(formula = ~ ., design = design, response = y)
# predictions on the training data
predict(m2, newdata = design, type = "UK")
## extract information
coef(m1)
residuals(m1)
logLik(m1)
## diagnostic plots
plot(m1)
}
}
\references{
Roustant, O., Ginsbourger, D. and Deville, Y. (2012), DiceKriging, DiceOptim: Two R packages for the analysis of computer
experiments by Kriging-based metamodeling and optimization.
\emph{Journal of Statistical Software}, \bold{51(1)}, \url{http://www.jstatsoft.org/}.
}
\seealso{
\code{\linkS4class{StatModel}}, \code{\link[DiceKriging]{km}}, \code{\link[modeltools]{Predict}}.
}
\keyword{datasets}
|
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