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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
85d0d85610f25c8374f8b10204ee616956ccb312 | 60aee3139de2c74fee465fd27c5ae52355b90306 | /code/deseq_to_function.R | 9e5df8bf1fe581391936e8fdc16abeae43cf6fe1 | [] | no_license | jflater/incubation_manuscript | 2f9fc4a35a52f4537a60ffae73383e9b9fb40c87 | 950128b4e1e1d88c9733a260102882378007ae9e | refs/heads/master | 2020-04-11T04:20:00.155974 | 2020-01-13T21:09:07 | 2020-01-13T21:09:07 | 161,508,762 | 1 | 1 | null | 2018-12-12T16:49:34 | 2018-12-12T15:37:50 | R | UTF-8 | R | false | false | 7,571 | r | deseq_to_function.R | library(phyloseq)
library(tidyverse)
library(DESeq2)
inc.physeq <- readRDS("data/RDS/incubation_physeq_Aug18.RDS")
tree <- read.tree("data/tree.nwk")
inc.physeq <- merge_phyloseq(inc.physeq, tree)
#Rename treatments to more informative titles
data <- data.frame(sample_data(inc.physeq)) %>%
mutate(treatment = recode(treatment,
'Control' = 'Reference',
'CompAlfa' = 'Mix')) %>%
mutate(C_N = C_flash / N_flash, Inorganic_N = NH3 + NO3) %>%
mutate(TreatmentAndDay = paste(treatment, day))
rownames(data) <- data$i_id
sample_data(inc.physeq) <- data
sample_data(inc.physeq)$day <- as.factor(sample_data(inc.physeq)$day)
sample_data(inc.physeq)$treatment <- as.character(sample_data(inc.physeq)$treatment)
inc.physeq.data <- data.frame(sample_data(inc.physeq))
inc.physeq.data$response.group[inc.physeq.data$day == "0"] <-
"baseline"
inc.physeq.data$response.group[inc.physeq.data$day %in% c("7", "14", "21")] <-
"early"
inc.physeq.data$response.group[inc.physeq.data$day %in% c("35", "49", "97")] <-
"late"
inc.physeq.data <- inc.physeq.data %>%
mutate(Treatment_Response = paste(treatment, response.group, sep = '_'))
rownames(inc.physeq.data) <- data$i_id
sample_data(inc.physeq) <- inc.physeq.data
no.unclass <- subset_taxa(inc.physeq, !Phylum=="Bacteria_unclassified")
no.unclass <- subset_taxa(no.unclass, !Genus=="Gp6_unclassified")
inc.physeq <- no.unclass
who_diff_day <- function(DDS, choice1, choice2, phy.object){
res = results(DDS, contrast = c("response.group", choice1, choice2), cooksCutoff = FALSE)
#plotCounts(AlfalfaDDS, gene="OTU_311", intgroup="day")
#Use above line to check if an OTU is increasing or decreasing depending on order of contrast
alpha = 0.01
#alpha = 0.1
sigtab = res[which(res$padj < alpha), ]
sigtab = cbind(as(sigtab, "data.frame"), as(tax_table(phy.object)[rownames(sigtab), ], "matrix"))
theme_set(theme_bw())
scale_fill_discrete <- function(palname = "Set1", ...) {
scale_fill_brewer(palette = palname, ...)
}
# Phylum order
x = tapply(sigtab$log2FoldChange, sigtab$Phylum, function(x) max(x))
x = sort(x, TRUE)
sigtab$Phylum = factor(as.character(sigtab$Phylum), levels=names(x))
# Genus order
x = tapply(sigtab$log2FoldChange, sigtab$Genus, function(x) max(x))
x = sort(x, TRUE)
sigtab$Genus = factor(as.character(sigtab$Genus), levels=names(x))
#ggplot(sigtab, aes(x=Genus, y=log2FoldChange, color=phylum)) + geom_point(size=2) +
# theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=1.0)) +
# ggtitle("Day 0 to Day 7")
return(sigtab)
}
# function plot log2FoldChange
log_plot <- function(sigtab,t1){
sigtab <- sigtab %>%
rownames_to_column(var = "OTU") %>%
filter(log2FoldChange >= 2)
ggplot(sigtab, aes(x=Genus, y=log2FoldChange, color=Phylum)) + geom_point(size=2) +
coord_flip() +
ggtitle(t1)
}
#################
alf.physeq <- subset_samples(inc.physeq, Treatment_Response %in% c("Alfalfa_early", "Reference_early")) %>%
filter_taxa(function(x) sum(x) >= 3, T)
log.plot.early.alf <- alf.physeq %>%
phyloseq_to_deseq2( ~ treatment) %>%
DESeq(test = "Wald", fitType = "local") %>%
who_diff_day("Alfalfa", "Reference", alf.physeq) %>%
log_plot("Alfalfa OTUS in early group that are significantly changing compared to reference early")
log.plot.early.alf
png("Figures/log.plot.earlyvref.alf",height=5,width=6,units='in',res=300)
plot(plot)
dev.off()
alf.late.physeq <- subset_samples(inc.physeq, Treatment_Response %in% c("Alfalfa_late", "Reference_late")) %>%
filter_taxa(function(x) sum(x) >= 3, T)
log.plot.late.alf <- alf.late.physeq %>%
phyloseq_to_deseq2( ~ treatment) %>%
DESeq(test = "Wald", fitType = "local") %>%
who_diff_day("Alfalfa", "Reference", alf.late.physeq) %>%
log_plot("Alfalfa OTUS in late group that are significantly changing compared to reference late group")
log.plot.late.alf
early.alf.otus <- log.plot.early.alf$data %>%
rownames_to_column() %>%
select(OTU, Phylum, Genus, log2FoldChange) %>%
filter(log2FoldChange >= 2) %>%
arrange(desc(log2FoldChange))
early.alf.otus
saveRDS(early.alf.otus, "data/early.alfvref.otus.rds")
late.alf.otus <- log.plot.late.alf$data %>%
rownames_to_column() %>%
select(OTU, Phylum, Genus, log2FoldChange) %>%
filter(log2FoldChange >= 2)
late.alf.otus
saveRDS(late.alf.otus, "data/late.alfvref.otus.rds")
############
comp.physeq <- subset_samples(inc.physeq, Treatment_Response %in% c("Compost_early", "Reference_early")) %>%
filter_taxa(function(x) sum(x) >= 3, T) %>%
tax_glom(taxrank = "Genus")
log.plot.early.comp <- comp.physeq %>%
phyloseq_to_deseq2( ~ treatment) %>%
DESeq(test = "Wald", fitType = "local") %>%
who_diff_day("Compost", "Reference", comp.physeq) %>%
log_plot("Compost OTUS in early group that are significantly changing compared to reference early")
log.plot.early.comp
png("Figures/log.plot.earlyvref.comp",height=5,width=6,units='in',res=300)
plot(plot)
dev.off()
comp.late.physeq <- subset_samples(inc.physeq, Treatment_Response %in% c("Compost_late", "Reference_late")) %>%
filter_taxa(function(x) sum(x) >= 3, T) %>%
tax_glom(taxrank = "Genus")
log.plot.late.comp <- comp.late.physeq %>%
phyloseq_to_deseq2( ~ treatment) %>%
DESeq(test = "Wald", fitType = "local") %>%
who_diff_day("Compost", "Reference", comp.late.physeq) %>%
log_plot("Compost OTUS in late group that are significantly changing compared to late reference group")
log.plot.late.comp
early.comp.otus <- log.plot.early.comp$data %>%
rownames_to_column() %>%
select(OTU, Phylum, Genus, log2FoldChange) %>%
filter(log2FoldChange >= 2) %>%
arrange(desc(log2FoldChange))
early.comp.otus
saveRDS(early.comp.otus, "data/early.compvref.otus.rds")
late.comp.otus <- log.plot.late.comp$data %>%
rownames_to_column() %>%
select(OTU, Phylum, Genus, log2FoldChange) %>%
filter(log2FoldChange >= 2)
late.comp.otus
saveRDS(late.comp.otus, "data/late.compvref.otus.rds")
################
mix.physeq <- subset_samples(inc.physeq, Treatment_Response %in% c("Mix_early", "Reference_early")) %>%
filter_taxa(function(x) sum(x) >= 3, T) %>%
tax_glom(taxrank = "Genus")
log.plot.early.mix <- mix.physeq %>%
phyloseq_to_deseq2( ~ treatment) %>%
DESeq(test = "Wald", fitType = "local") %>%
who_diff_day("Mix", "Reference", mix.physeq) %>%
log_plot("Mix OTUS in early group that are significantly changing compared to early reference")
log.plot.early.mix
png("Figures/log.plot.earlyvref.mix",height=5,width=6,units='in',res=300)
plot(plot)
dev.off()
mix.late.physeq <- subset_samples(inc.physeq, Treatment_Response %in% c("Mix_late", "Reference_late")) %>%
filter_taxa(function(x) sum(x) >= 3, T) %>%
tax_glom(taxrank = "Genus")
log.plot.late.mix <- mix.late.physeq %>%
phyloseq_to_deseq2( ~ treatment) %>%
DESeq(test = "Wald", fitType = "local") %>%
who_diff_day("Mix", "Reference", mix.late.physeq) %>%
log_plot("Mix OTUS in late group that are significantly changing compared to late reference")
log.plot.late.mix
early.mix.otus <- log.plot.early.mix$data %>%
rownames_to_column() %>%
select(OTU, Phylum, Genus, log2FoldChange) %>%
filter(log2FoldChange >= 2) %>%
arrange(desc(log2FoldChange))
early.mix.otus
saveRDS(early.mix.otus, "data/early.mixvref.otus.rds")
late.mix.otus <- log.plot.late.mix$data %>%
rownames_to_column() %>%
select(OTU, Phylum, Genus, log2FoldChange) %>%
filter(log2FoldChange >= 2)
late.mix.otus
saveRDS(late.mix.otus, "data/late.mixvref.otus.rds") |
a978df24cea4bc9efaaa332dd92e95f2518e09cf | 92e828aeed0eb4203cd1890fbdcee430d6cc9773 | /src/linear_regression.R | 3202120702f6cfab9bcb5c83c5f6f1c76fb3d8fb | [
"MIT"
] | permissive | dy-lin/stat540-project | 46d9ee4f4638dbe933bdb607f3f8232e872a6847 | 20f040d9399ab5b9df0341b317e0f22eefafe5e8 | refs/heads/master | 2022-11-18T03:27:53.049420 | 2020-07-19T22:47:39 | 2020-07-19T22:47:39 | 259,114,940 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,467 | r | linear_regression.R | #author: Almas K.
#date : 2020-04-02
'This script is for linear regression in R'
## Libraries
library(tidyverse)
library(reshape2)
library(stringr)
library(here)
library(lumi)
library(limma)
library(FDb.InfiniumMethylation.hg19)
## Import Data
male_metadata <- read_csv(here("data","processed_data","male_metadata.csv"))
data_limma <- readRDS(here("data","raw_data","data_for_limma.rds"))
tsv_file <- read_tsv(here("data","raw_data","EPIC.hg38.manifest.tsv.gz")) #annotation file
## Convert Betas to M values
data_mval <- beta2m(data_limma)
## Edit Metadata
male_metadata$cancer <- as.factor(male_metadata$cancer)
male_metadata$cancer_type <- as.factor(male_metadata$cancer_type)
##Custom Annotation Function for limma:
genes_annotator <- function(t_tab){
annotated <-tsv_file %>%
filter(probeID %in% rownames(t_tab)) %>%
dplyr::select(CpG_chrm,probeID,gene,gene_HGNC)
t_table_joined <-t_tab %>%
mutate(probeID=rownames(t_tab)) %>%
left_join(.,annotated, by="probeID")
t_table_joined
}
## Limma using Primary vs Metastatic cancer and age
d_matrix2 <- model.matrix(~cancer_type*age,male_metadata)
lm_fit2 <- lmFit(data_mval,d_matrix2)
eBayes2 <- eBayes(lm_fit2)
## Annotate Limma Results
v2_t_table <- topTable(eBayes2,coef = "cancer_typeprimary") ## Primary genes coefficient
v2_t_table <- genes_annotator(v2_t_table)
knitr::kable(v2_t_table)
v2_t_table2 <- topTable(eBayes2,coef = "cancer_typeprimary:age")
v2_t_table2 <- genes_annotator(v2_t_table2)
knitr::kable(v2_t_table2)
v2_t_table3 <- topTable(eBayes2,coef = "age")
v2_t_table3 <- genes_annotator(v2_t_table3)
knitr::kable(v2_t_table3)
## Look for missing gene names in v2_t_table using the 450K annotation and find them
hm450 <- getPlatform(platform='HM450', genome='hg19')
annot450 <- FDb.InfiniumMethylation.hg19::getNearestGene(hm450)
annot450 <- annot450 %>% mutate(probeID=rownames(annot450))
NA_genes <- v2_t_table %>%
filter(is.na(gene))
found_gene <- annot450 %>%
filter(probeID %in%(NA_genes$probeID)) ## only found label for one of them
v2_t_table <- v2_t_table %>%
mutate(gene=(if_else(probeID %in% found_gene$probeID,found_gene$nearestGeneSymbol,gene)))
## Saving the Csv results for Annotated top table genes
write_csv(v2_t_table,here("results","final","primary_topGenes_limma2.csv"))
write_csv(v2_t_table3,here("results","revised","age_top_genes_limma2.csv"))
write_csv(v2_t_table2,here("results","revised","ageandprimary_top_genes_limma2.csv")) |
c9cf5cff4048d919256b4a0e40508cc4ecc0052e | b4fa3f92a666972e8147eaccc268e775c983df70 | /run_analysis.R | e81bded4ca70c9ca75c3c3c6fa0a252509cf9450 | [] | no_license | mdon89/Cleaning_Data_Quiz_4 | b9bc58deb7301476aed3bc739f041c70b6c708cd | a93831c6ea938f823b0d5084144fec282e7af352 | refs/heads/master | 2021-04-15T17:13:12.353786 | 2018-03-24T00:10:33 | 2018-03-24T00:10:33 | 126,549,024 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,345 | r | run_analysis.R | #Set working directory
setwd("C:/Users/mdon/datasciencecoursera/Cleaning data quiz 4/UCI HAR Dataset)
#list file names
filenames <- list.files()[c(5,6,9,10,11,12)]
#read in all test documents and assign to a dataframe
List <- lapply(filenames, read.table, fill = TRUE)
#Assign names to data frames
newnames <- gsub(".txt", "",filenames)
names(List) <- newnames
#Unlist List in Global environment
list2env(List, .GlobalEnv)
#Merge datasets with cbind for data rames with the same number or rows
Mergeddata <- cbind(subject_test, y_test, X_test)
Mergeddatatrain <- cbind(subject_train, y_train, X_train)
#rbind two dataframes above to give one data source
df1 <- rbind(Mergeddata, Meregeddatatrain)
#Change first 2 column names
colnames(df1)[1] <- "Subject"
colnames(df1)[2] <- "Y"
#Add column headings to rest of df1 dataframe from features file
df1columnheadings <- features$V2
#Convert column headings to character
df1columnheadings <- as.character(df1columnheadings)
colnames(df1)[3:563] <- df1columnheadings
#Find which columns are mean or standard deviation so heading contains mean() or std()
std <- grep("std()", colnames(df1), fixed = TRUE)
mean <- grep("mean()", colnames(df1), fixed = TRUE)
#subset df1 on mean and std columns
meanstddf1 <- df1[,c(1,2,mean,std)]
#read in activity labels document
activity_labels <- read.table("activity_labels.txt")
#assign labels to corresponding numbers in column Y of dataframe. Lookup Y values and match to activities to create new vector of activities
newlabel <- as.character(sapply(meanstddf1$Y, function(x) activity_labels$V2[match(x, activity_labels$V1)]))
#Substitute numbers with names of activities
meanstddf1$Y <- newlabel
#Replace column name Y with "activity names"
colnames(meanstddf1)[2] <- "Activity_Names"
#Rename column variables so they are more understandable. Replace t with time and f with freq
renamet <- gsub("^t", "time",colnames(meanstddf1)[3:68])
renametf <- gsub("^f", "freq",renamet)
colnames(meanstddf1)[3:68] <- renametf
#create 2nd data frame with tidy data. One mean per subject and activity of each variable. Group by Activity name and subject then take mean of variables
res = meanstddf1 %>% group_by(Subject, Activity_Names) %>%
+ summarise_each(funs(mean(.)))
|
593ab22daa252c044f13958653419661c3ebd112 | fbff64c4ca06554868cbd7f74fa2c56e9884896b | /cachematrix.R | 05487019eb2724a6d18accaacd5244de35df4541 | [] | no_license | ssuzuki323/ProgrammingAssignment2 | be683ef02ab9454f74427f36368a54e3ec785743 | a80a63079ad0304ab6c3643b49424e30ca58abee | refs/heads/master | 2021-01-22T17:15:06.286648 | 2015-04-12T22:16:38 | 2015-04-12T22:16:38 | 33,834,894 | 0 | 0 | null | 2015-04-12T21:38:00 | 2015-04-12T21:38:00 | null | UTF-8 | R | false | false | 1,407 | r | cachematrix.R | ## makeCacheMatrix creates a special "matrix" that can cache its inverse. cacheSolve computes
## the inverse of the special "matrix" but first checks to see if the inverse has already been
## cached, in which case it would skip the computation and return the inverse matrix.
## Create a special "matrix" object that can cache its inverse
## The returned object is a list containing functions to set the value of the matrix,
## get the value of the matrix, set the value of the inverse matrix, and get the value
## of the inverse matrix.
makeCacheMatrix <- function(x = matrix()) {
i <- NULL
set <- function(y) {
x <<- y
i <<- NULL
}
get <- function() x
setinverse <- function(inverse) i <<- inverse
getinverse <- function() i
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## Computes the inverse of matrix x. The function first checks to see if inverse has
## already been cached, in which case the function skips the computation and returns the
## inverse matrix. Otherwise, the function calculates the inverse matrix and sets the value
## of the inverse in the cache using setinverse function.
cacheSolve <- function(x, ...) {
## Return a matrix that is the inverse of 'x'
i <- x$getinverse()
if(!is.null(i)) {
message("getting cached data")
return(i)
}
data <- x$get()
i <- solve(data, ...)
x$setinverse(i)
i
}
|
954491424980e0b24397c07566cd89cd3c36a183 | 6b78fcf74c68d33be7b52414e273eeeb8eeb2e7d | /01_split_data.R | 81d19ba3238bae1e386671ab7f5a31bd62ed4e90 | [] | no_license | zpeng1989/TCGA_CNV_sumamry_diff | 51189a7427c714c5f22fd696e15a69d714e1c300 | 6a70724709395a4458689669bff90d026778e1a0 | refs/heads/master | 2020-03-25T09:59:19.295740 | 2018-08-14T05:07:18 | 2018-08-14T05:07:18 | 143,681,178 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 704 | r | 01_split_data.R | args<-commandArgs(T)
cancer = args[1]
##主要目标，分割样本；
input_data_path = paste0('/work/home/zhangp/mission/09_deep_sumamry/data_used/CNV/',cancer,'.txt')
input_data <- read.delim(input_data_path,header=F)
sample_id = names(table(input_data[,1]))
for (one_sample_id in sample_id){
#print(one_sample_id)
one_data_table <- input_data[which(input_data[,1]==one_sample_id),]
print(dim(one_data_table))
print(head(one_data_table))
write_table_path <- paste0('/work/home/zhangp/mission/12_CNV_deep_sumamry/split_data/all_data_test/',cancer,'/',one_sample_id,'.txt')
#print(write_table_path)
write.table(one_data_table,write_table_path,sep='\t',quote=F,row.names=F,col.names=F)
}
|
182b9c6221bd71e6d9caa672e37c5e2ce84a5fac | f422c73b787ae891bcc155e3f0867973ef3dc949 | /shiny_weather/server.R | 3e6d43263e6423fd217e0f528d911761d736bd5f | [] | no_license | karafede/WeatherMaps_R | 209510016e88cda99d400543ac50ac06903d4905 | 2d391645d4d0a34ffe20f1ba36c6ff5994956a00 | refs/heads/master | 2016-09-14T01:31:35.262014 | 2016-06-06T16:13:29 | 2016-06-06T16:13:29 | 59,492,926 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,436 | r | server.R |
library(shiny)
library(dplyr)
library(leaflet)
###############################################################
shinyServer(function(input, output) {
finalMap_rain <- reactive({
map_rain <- leaflet() %>%
addTiles() %>%
setView(18, 38, 4) %>%
# addProviderTiles("Stamen.TonerBackground", options = providerTileOptions(opacity = 1)) %>%
addProviderTiles("OpenWeatherMap.Rain", options = providerTileOptions(opacity = 0.35))
})
finalMap_temperature <- reactive({
map_temperature <- leaflet() %>%
addTiles() %>%
setView(18, 38, 4) %>%
addProviderTiles("OpenWeatherMap.Temperature", options = providerTileOptions(opacity = 0.35))
})
finalMap_clouds <- reactive({
map_clouds <- leaflet() %>%
# addTiles() %>%
setView(18, 38, 4) %>%
addProviderTiles("Thunderforest.TransportDark", options = providerTileOptions(opacity = 1)) %>%
addProviderTiles("OpenWeatherMap.Clouds", options = providerTileOptions(opacity = 0.35))
})
finalMap_wind <- reactive({
map_wind <- leaflet() %>%
addTiles() %>%
setView(18, 38, 4) %>%
addProviderTiles("OpenWeatherMap.Wind", options = providerTileOptions(opacity = 0.35))
})
finalMap_pressure <- reactive({
map_pressure <- leaflet() %>%
addTiles() %>%
setView(18, 38, 4) %>%
addProviderTiles("OpenWeatherMap.PressureContour", options = providerTileOptions(opacity = 0.35))
})
finalMap_AOD <- reactive({
map_AOD <- leaflet() %>%
addTiles() %>%
setView(18, 38, 4) %>%
addProviderTiles("NASAGIBS.ModisTerraAOD", options = providerTileOptions(opacity = 0.35))
})
finalMap_Chlorophyll <- reactive({
map_Chlorophyll <- leaflet() %>%
addTiles() %>%
setView(18, 38, 4) %>%
addProviderTiles("NASAGIBS.ModisTerraChlorophyll", options = providerTileOptions(opacity = 0.35))
})
# Return to client
output$myMap_rain = renderLeaflet(finalMap_rain())
output$myMap_temperature = renderLeaflet(finalMap_temperature())
output$myMap_clouds = renderLeaflet(finalMap_clouds())
output$myMap_wind = renderLeaflet(finalMap_wind())
output$myMap_pressure = renderLeaflet(finalMap_pressure())
output$myMap_AOD = renderLeaflet(finalMap_AOD())
output$myMap_Chlorophyll = renderLeaflet(finalMap_Chlorophyll())
})
|
aeda9596c197c39e640ed0c481fad2605a3c05fb | c1958ff3ca2b10a041fadaaa3503724f194631ae | /plot3.R | e93268d69f2431b6e4a1574dcf82e2b13dfb8e52 | [] | no_license | pbrogan12/ExData_Plotting1 | 63e9c48be8a7937a3b7f33e6a48bc431d1b83f80 | 795a4781855ca302e85774d4a903d8f46cc85181 | refs/heads/master | 2021-01-24T03:11:40.013244 | 2014-06-08T23:23:16 | 2014-06-08T23:23:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,029 | r | plot3.R | data <- read.table("~/Downloads/household_power_consumption.txt",header=T,sep=";",na.strings="?",stringsAsFactors=F)
data <- data[which(data$Date=="1/2/2007" | data$Date=="2/2/2007"),]
png("plot3.png",width = 480, height = 480)
plot(strptime(paste(data$Date,data$Time),format = "%d/%m/%Y %H:%M:%S"),data$Sub_metering_1,type='n',ylab="Energy sub meeting", xlab="")
points(strptime(paste(data$Date,data$Time),format = "%d/%m/%Y %H:%M:%S"),data$Sub_metering_2,type='n')
points(strptime(paste(data$Date,data$Time),format = "%d/%m/%Y %H:%M:%S"),data$Sub_metering_3,type='n')
lines(strptime(paste(data$Date,data$Time),format = "%d/%m/%Y %H:%M:%S"),data$Sub_metering_1,type='l')
lines(strptime(paste(data$Date,data$Time),format = "%d/%m/%Y %H:%M:%S"),data$Sub_metering_2,type='l',col="red")
lines(strptime(paste(data$Date,data$Time),format = "%d/%m/%Y %H:%M:%S"),data$Sub_metering_3,type='l',col="blue")
legend("topright",legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), lty=1,lwd=1,col=c("black","red","blue"))
dev.off()
|
88abfc3a0a0d5a3e141269027e5c0eae8f1db059 | bfec4871cd31a5d5b52bbd1dc22f977724952bbc | /tests/testthat/testMMM.R | edea33435ab092cec653e2bb8ee804c09b1f17ae | [] | no_license | ekstroem/mommix | a51cd361d973616d8b4faef453d92e20f09607d4 | 4b9601ee6e31b97ee4d5a0da274c647b6e4187e2 | refs/heads/master | 2021-05-05T00:57:00.022881 | 2019-01-06T21:16:45 | 2019-01-06T21:16:45 | 119,524,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,325 | r | testMMM.R | context("test results for MMM")
set.seed(1)
p <- 0.7
N <- 100
x <- rnorm(N)
x2 <- rnorm(N)
y <- rnorm(N, mean=x)
y[1:(N*p)] <- rnorm(N*p, mean=0, sd=.25)
DF <- data.frame(x, x2, y)
res <- moment_mixture_model(DF, y ~ x + x2)
res2 <- moment_mixture_model(DF, y ~ x + x2, weight="square")
test_that("computations are consistant", {
expect_equivalent(coef(res), c(0.1654199, 0.8233733, -0.2667224), tolerance=1e-4)
expect_equivalent(coef(res2), c(0.1495042, 0.8259178, -0.2469152), tolerance=1e-4)
expect_equivalent(res$alpha,0.3041905, tolerance=1e-4)
expect_equivalent(res2$alpha,0.3162248, tolerance=1e-4)
} )
|
41858f9e70de4799c9f92f4fb9bd0e03c1886d52 | b2360322fc78847770c990a55a2e0859f328c520 | /projects/MachineLearning/code/LinearRegressionForPrediction.R | 942d506b75f56d0c6141e6df2165a7b503077969 | [] | no_license | senthil-lab/RWork | 6739546116a67cd8a6e95d5760d230f3f7432643 | 3754a2c4f50c39b9be189a48c7ee2087ce2e248c | refs/heads/master | 2020-12-18T18:19:09.299030 | 2020-10-04T18:28:28 | 2020-10-04T18:28:28 | 235,481,973 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,064 | r | LinearRegressionForPrediction.R | library(tidyverse)
library(HistData)
galton_heights <- GaltonFamilies %>%
filter(childNum == 1 & gender == "male") %>%
select(father, childHeight) %>%
rename(son = childHeight)
head(galton_heights)
library(caret)
y <- galton_heights$son
test_index <- createDataPartition(y, times = 1, p = 0.5, list = FALSE)
train_set <- galton_heights %>% slice(-test_index)
test_set <- galton_heights %>% slice(test_index)
avg <- mean(train_set$son)
avg
mean((avg - test_set$son)^2)
# fit linear regression model
fit <- lm(son ~ father, data = train_set)
fit$coef
son_height_pred <- 35.4251130 + 0.5080324 * test_set$father
mean((son_height_pred - test_set$son)^2)
y_hat <- fit$coef[1] + fit$coef[2]*test_set$father
mean((y_hat - test_set$son)^2)
# Using the predict function
y_hat <- predict(fit, test_set)
mean((y_hat - test_set$son)^2)
# read help files
?predict.lm
?predict.glm
#Exercise
# set.seed(1) # if using R 3.5 or earlier
set.seed(1, sample.kind="Rounding") # if using R 3.6 or later
n <- 100
Sigma <- 9*matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2)
dat <- MASS::mvrnorm(n = 100, c(69, 69), Sigma) %>%
data.frame() %>% setNames(c("x", "y"))
nrow(dat)
set.seed(1, sample.kind="Rounding")
res <- sapply(seq(1:100),function(x1) {
test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
train_set <- dat %>% slice(-test_index)
test_set <- dat %>% slice(test_index)
fit <- lm(y ~ x, data = train_set)
y_hat <- predict(fit, test_set)
RMSE(y_hat, test_set$y)
})
mean(res)
sd(res)
# set.seed(1) # if using R 3.5 or earlier
set.seed(1, sample.kind="Rounding") # if using R 3.6 or later
rmse <- replicate(100, {
test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
train_set <- dat %>% slice(-test_index)
test_set <- dat %>% slice(test_index)
fit <- lm(y ~ x, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
})
mean(rmse)
sd(rmse)
set.seed(1, sample.kind="Rounding") # if using R 3.6 or later
res <- sapply(c(100, 500, 1000, 5000, 10000),function(x1) {
n <- x1
Sigma <- 9*matrix(c(1.0, 0.5, 0.5, 1.0), 2, 2)
dat <- MASS::mvrnorm(n = x1, c(69, 69), Sigma) %>%
data.frame() %>% setNames(c("x", "y"))
rmse <- replicate(100, {
test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
train_set <- dat %>% slice(-test_index)
test_set <- dat %>% slice(test_index)
fit <- lm(y ~ x, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
})
c(mean(rmse), sd(rmse))
})
res
mean(res[,1])
sd(res[,1])
# set.seed(1) # if using R 3.5 or earlier
set.seed(1, sample.kind="Rounding") # if using R 3.6 or later
n <- 100
Sigma <- 9*matrix(c(1.0, 0.95, 0.95, 1.0), 2, 2)
dat <- MASS::mvrnorm(n = 100, c(69, 69), Sigma) %>%
data.frame() %>% setNames(c("x", "y"))
head(dat)
set.seed(1, sample.kind="Rounding") # if using R 3.6 or later
rmse <- replicate(100, {
test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
train_set <- dat %>% slice(-test_index)
test_set <- dat %>% slice(test_index)
fit <- lm(y ~ x, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
})
mean(rmse)
sd(rmse)
# set.seed(1) # if using R 3.5 or earlier
set.seed(1, sample.kind="Rounding") # if using R 3.6 or later
Sigma <- matrix(c(1.0, 0.75, 0.75, 0.75, 1.0, 0.25, 0.75, 0.25, 1.0), 3, 3)
dat <- MASS::mvrnorm(n = 100, c(0, 0, 0), Sigma) %>%
data.frame() %>% setNames(c("y", "x_1", "x_2"))
cor(dat)
head(dat)
set.seed(1, sample.kind="Rounding")
test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
train_set <- dat %>% slice(-test_index)
test_set <- dat %>% slice(test_index)
fit <- lm(y ~ x_1, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
set.seed(1, sample.kind="Rounding")
fit <- lm(y ~ x_2, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
set.seed(1, sample.kind="Rounding")
fit <- lm(y ~ x_1 + x_2, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
set.seed(1, sample.kind="Rounding") # if using R 3.6 or later
Sigma <- matrix(c(1.0, 0.75, 0.75, 0.75, 1.0, 0.95, 0.75, 0.95, 1.0), 3, 3)
dat <- MASS::mvrnorm(n = 100, c(0, 0, 0), Sigma) %>%
data.frame() %>% setNames(c("y", "x_1", "x_2"))
set.seed(1, sample.kind="Rounding")
test_index <- createDataPartition(dat$y, times = 1, p = 0.5, list = FALSE)
train_set <- dat %>% slice(-test_index)
test_set <- dat %>% slice(test_index)
fit <- lm(y ~ x_1, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
set.seed(1, sample.kind="Rounding")
fit <- lm(y ~ x_2, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
set.seed(1, sample.kind="Rounding")
fit <- lm(y ~ x_1 + x_2, data = train_set)
y_hat <- predict(fit, newdata = test_set)
sqrt(mean((y_hat-test_set$y)^2))
|
09cb736a6407f943b21116021b2e1c57fda5312f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/EMMIXskew/examples/ddmvt.Rd.R | d41a7d1cc276b95110c6645ad8da5076510bb063 | [] | 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 | 311 | r | ddmvt.Rd.R | library(EMMIXskew)
### Name: ddmvt
### Title: The Multivariate t-Distribution
### Aliases: ddmvt rdmvt
### Keywords: cluster datasets
### ** Examples
n <- 100
p <- 2
mean <- rep(0,p)
cov <- diag(p)
nu <- 3
set.seed(3214)
x <- rdmvt( n,p,mean,cov,nu)
den <- ddmvt(x ,n,p,mean,cov,nu)
|
1059ed7919435c56c471f7790539417fa71493cd | 7efa216193f994c69c6566eb6f23418ec3b03706 | /rejection_sampling.R | 430c9d2a4341d12ee2c754a40b533aca5d2ffa5e | [] | no_license | Sirius081/MCMC | bd4952077572de7806c0a0819ea2f2b457f95e36 | 742a96b116c38f8ff7551ec980d6638329bce440 | refs/heads/master | 2021-01-20T02:37:37.086023 | 2017-04-26T02:49:04 | 2017-04-26T02:49:04 | 89,430,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 607 | r | rejection_sampling.R | #p is gaussian(0,1)
#q is uniform(min,max)
rejection_sampling_gau=function(mean,variance){
N=100000
x=vector(length=N)
min_v=mean-3*variance
max_v=mean+3*variance
k=max-min
print(min_v)
print(max_v)
i=1
count=0
while(i<=N){
z=runif(1,min_v,max_v) #sample from q
p_z=dnorm(z,mean,variance) #p(z)
q_z=dunif(z,min_v,max_v) #q(z)
u=runif(1,min=0,max=k*q_z)#sample from uniform(0,k*q(z))
if(p_z>=u){ #accept
x[i]=z
i=i+1
}
}
hist(x,breaks = 50,freq = F)
lines(density(rnorm(N*10,0,1)))
}
rejection_sampling_gau(0,1) |
9a925cc50bb6a3c2520ab0f06a6aa35a7379b9ac | 854175b2bd9411866784aba87b959dad603cf6e9 | /R/capture_ping.R | 7bd790a8d6e31d301d2869148571acc78a67ef0f | [
"MIT"
] | permissive | JesseVent/pingers | e43ddf27c8f8012d3e245982e5771091a3b411be | 1fc9ddaa201de08c1187fa68ee3a3b2f0de04c70 | refs/heads/master | 2021-08-15T20:28:42.676962 | 2018-10-31T12:05:01 | 2018-10-31T12:05:01 | 145,440,455 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,515 | r | capture_ping.R | #' Ping Server
#'
#' Ping a server to capture response details
#' @name ping_capture
#'
#' @param server IP address or URL of server
#' @param count Number of times to ping server
#'
#' @return dataframe with ping results
#' @export
#'
#' @importFrom dplyr "%>%"
#' @importFrom stringr str_replace_all
#' @importFrom tibble tibble
#'
#' @examples
#' \dontrun{
#' dest <- get_destinations(top_n = 1)
#' ping_res <- ping_capture(dest$ip[1], 10)
#' print(ping_res)
#' }
ping_capture <- function(server, count) {
sys_os <- .Platform$OS.type
if (sys_os == "unix") {
ping_query <- paste("ping", server, "-c", count)
} else {
ping_query <- paste("ping", server, "-n", count)
}
d <- system(ping_query, intern = TRUE)
n <- length(d) %>% as.numeric()
ping_list <- list(d[2:(n - 4)])
packet_loss <- d[n - 1]
ping_stats <- d[n]
timestamp <- Sys.time()
## Strip out ping statistics
stats <- stringr::str_replace_all(ping_stats, "[^0-9|./]", "") %>% strsplit("/")
ping_min <- stats[[1]][4] %>% as.numeric()
ping_avg <- stats[[1]][5] %>% as.numeric()
ping_max <- stats[[1]][6] %>% as.numeric()
ping_stddev <- stats[[1]][7] %>% as.numeric()
## Strip out packet loss
pkt <- stringr::str_replace_all(packet_loss, "[^0-9|,.]", "") %>% strsplit(",")
packets_sent <- pkt[[1]][1] %>% as.numeric()
packets_back <- pkt[[1]][2] %>% as.numeric()
packet_loss <- pkt[[1]][3] %>% as.numeric()
packets_lost <- packets_sent - packets_back
loss_rate <- ((packets_sent - packets_back) / packets_sent) * 100
pres <-
tibble::tibble(
timestamp,
server,
packets_sent,
packets_back,
packet_loss,
packets_lost,
loss_rate,
ping_min,
ping_avg,
ping_max,
ping_stddev,
ping_list
)
pres$packets_sent[is.na(pres$packets_sent)] <- 100
pres$packets_back[is.na(pres$packets_back)] <- 0
pres$loss_rate[is.na(pres$loss_rate)] <- 100
pres$packets_lost[is.na(pres$packets_lost)] <- 100
pres$ping_stddev[is.na(pres$ping_stddev)] <- 0
pres$ping_min[is.na(pres$ping_min)] <- 0
pres$ping_max[is.na(pres$ping_max)] <- 0
pres$ping_avg[is.na(pres$ping_avg)] <- 0
pres$loss_rate <- ((pres$packets_sent - pres$packets_back) / pres$packets_sent * 100)
pres$packet_loss[is.na(pres$packet_loss)] <- pres$loss_rate
pres$packets_lost <- pres$packets_sent - pres$packets_back
return(pres)
} |
d9b42af3f4bb3d9aefd4c56d161ba03d8b0bb532 | 737d67e2884b33324003a7303351f5abf2c419e6 | /OutlierAnalysis/outlierMatrixProtein_gluC.R | 793e59f63d6bd1c526b810f501e19eb8dda014f8 | [
"Apache-2.0"
] | permissive | bwbai/ATLANTiC | ad576ec305120a8b5012d08cad7f11a0c4ab217e | c831ac3e0c6ae0ed9be130a1959b50a6bc41ce2f | refs/heads/master | 2022-04-19T18:42:15.671658 | 2020-04-21T11:41:52 | 2020-04-21T11:41:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,257 | r | outlierMatrixProtein_gluC.R | source("https://raw.githubusercontent.com/mengchen18/RFunctionCollection/master/outliers.R")
library(openxlsx)
library(matrixStats)
if (!dir.exists("Res/20180328_outlierAnalysis"))
dir.create("Res/20180328_outlierAnalysis")
t1 <- read.xlsx("../Manuscript/current/supplementTables/table_S3_Intensity_of_proteins.xlsx", sheet = 7)
an <- read.xlsx("../Manuscript/current/supplementTables/table_S3_Intensity_of_proteins.xlsx", sheet = 6)
nam <- t1$ID
t1$ID <- NULL
t1 <- apply(t1, 2, as.numeric)
rownames(t1) <- nam
i <- findOutlier(t1, foldthresh = 5, pvalue = 0.1, reachLowBound = FALSE, window = 0.5)
getab <- function(m, a, col = make.names(c("Gene.names", "iBAQ", "Majority protein IDs", "Protein names"))) {
i <- findOutlier(m, foldthresh = 5, pvalue = 0.1, reachLowBound = FALSE, window = 0.5)
ls <- lapply(names(i$outlierIndexColumns), function(ii) {
ir <- i$outlierIndexColumns[[ii]]
d <- a[ir, col]
if (nrow(d) == 0)
return(d)
d$cellline <- ii
d
})
do.call(rbind, ls)
}
tab <- getab(t1, a = an)
tab$cellline <- paste0(tab$cellline, "_NCI60_GluC")
write.table(tab, file = "Res/20180328_outlierAnalysis/outlierTableProtein_GluC.txt",
col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t")
|
a9dab08b26c070fb959183717c747c0812d4bccd | d64d88d896260aac75f0ed98a01c7e8e02005063 | /server.R | c0b4322809062589dfdf9499da89314910334e63 | [] | no_license | neeagle/CourseraDDP | 025aa557875e6fa5fb7a865b396cabf0ae93708a | 1f8dd2c9a6f981736f7516c5d2350c38c69537cf | refs/heads/master | 2020-12-25T08:59:40.106663 | 2016-08-15T04:04:27 | 2016-08-15T04:04:27 | 65,699,493 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 982 | r | server.R | library(shiny)
library(zipcode)
library(ggmap)
data(zipcode)
zip_price_rent <- read.csv(file="Zip_PriceToRentRatio_AllHomes052016.csv")
map<-get_map(location='united states', zoom=4, maptype = "toner", source='google',color='color')
zipmerge <- merge(zip_price_rent, zipcode, by.x="RegionName", by.y="zip")
g <- ggmap(map) + geom_point(aes(x=longitude, y=latitude, show_guide = TRUE, colour=log(as.numeric(1/X2016.05))), data=zipmerge, na.rm=TRUE) + scale_color_gradient(low="yellow", high="purple")
shinyServer(
function(input, output) {
dataInput <- reactive({zipcode[zipcode$zip==input$select,]
})
lon <- reactive({zipcode[zipcode$zip==input$select,]$longitude[1]})
lat <- reactive({zipcode[zipcode$zip==input$select,]$latitude[1]})
output$oid1 <- renderPrint({dataInput()})
output$gmap <- renderPlot( {
#zcor <- dataInput()
#p <- g + geom_point(aes(x=zcor$longitude[1], y=zcor$latitude[1], size=15), colour=20)
print(g)
})
}
) |
08ada3e6f8fe9a9bf6dab1dc608b6114197f2175 | ba094e0fc15739a634ecb3fb7703ab52d0ee1925 | /Mario.R | 7842f2369cd6f9bf8b37c68cd0c27cc97b899f5b | [] | no_license | vina1998/TidyTuesday05-25 | 0951463fe004b3c010211e3c8443b0da6059439a | 098eca6451c87526ac79f6e640abfe09aadcd6a1 | refs/heads/master | 2023-06-19T22:48:38.494303 | 2021-07-19T08:39:16 | 2021-07-19T08:39:16 | 371,938,912 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,672 | r | Mario.R | records <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-05-25/records.csv')
drivers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-05-25/drivers.csv')
library(tidyverse)
library(ggimage)
library(viridis)
library(extrafont)
loadfonts()
#where is the line between analysis and visualizations lol
drivers <- drivers %>% drop_na()
#prep data
df <- drivers %>%
count(year) %>%
rowwise() %>%
mutate(
w = round(runif(1, 0, 3)),
h = runif(1, 0, 3)) %>%
ungroup()
#starting line
starting_line <- data.frame(
fyear = df$year
) %>%
mutate(
r = row_number(),
c = list(c(1:1))
) %>%
unnest(c(c, r)) %>%
rowwise %>%
mutate(
x = list(c + c(0, 0.9, 0.9, 0)),
y = list(c * 0.5 + c(fyear - 0.45, fyear, fyear + 0.9, fyear + 0.45))
) %>%
unnest(c(x, y))
#track:
ggplot(df) +
annotate("polygon", x = c(-20, 15, 10, -20), y = c(1997, 1997, 2021, 2021), fill = "grey30", color = "grey30") +
annotate("polygon", x = c(15, 22.5, 22.5, 10), y = c(1997, 1997, 2021, 2021), fill = "grey30", color = "grey30") +
annotate("polygon", x = c(-20, 15, 10, -20), y = c(2020.3, 2020.3, 2030, 2030), fill = "palegreen3", color = "palegreen3") +
annotate("polygon", x = c(15, 22.5, 22.5, 10), y = c(2020.3, 2020.3, 2030, 2030), fill = "palegreen3", color = "palegreen3") +
annotate("line", x = c(-15,-17), y=2010, colour="white", size=2) +
annotate("line", x = c(-10,-12), y=2010, colour="white", size=2) +
annotate("line", x = c(-8,-6), y=2010, colour="white", size=2) +
annotate("line", x = c(-5,-3), y=2010, colour="white", size=2) +
annotate("line", x = c(-2,0), y=2010, colour="white", size=2) +
annotate("line", x = c(2,4), y=2010, colour="white", size=2) +
annotate("line", x = c(6,8), y=2010, colour="white", size=2) +
annotate("line", x = c(10,12), y=2010, colour="white", size=2) +
annotate("line", x = c(14,16), y=2010, colour="white", size=2) +
annotate("line", x = c(18,20), y=2010, colour="white", size=2) +
geom_polygon(data = starting_line, aes(x = x - 21, y = y - 1.9, group = interaction(c, r)), fill = "white", color = "black", size= 1.5) +
annotate("polygon", x = c(-20, 15, 10, -20), y = c(1987.3, 1987.3, 1997, 1997), fill = "palegreen3", color = "palegreen3") +
annotate("polygon", x = c(15, 22.5, 22.5, 10), y = c(1987.3, 1987.3, 1997, 1997), fill = "palegreen3", color = "palegreen3") +
geom_curve(aes(x = -15 - w, y = year - w * 0.4, xend = n, yend = 2001 + h, color = year, size = n), curvature = 0) +
scale_size_continuous(range = c(0.1, 3)) + scale_color_viridis_c(option = "inferno") +
annotate("polygon", x = c(-19, -15, -12, -16), y = c(2028, 2028, 2029, 2029), fill = "yellow1", color = "grey20") +
annotate("polygon", x = c(-19, -15, -15, -19), y = c(2022, 2022, 2028, 2028), fill = "yellow3", color = "grey20") +
annotate("polygon", x = c(-15, -12, -12, -15), y = c(2022, 2024, 2029, 2028), fill = "yellow4", color = "grey20") +
annotate("text", x = -16, y = 2026, hjust = 1, vjust = 1, label = "?", size = 15, color = "white", family= "Impact") +
annotate("text", x = -13, y = 2027, hjust = 1, vjust = 1, label = "?", size = 15, color = "seashell3", family= "Impact") +
annotate("polygon", x = c(13, 17, 20, 16), y = c(2028, 2028, 2029, 2029), fill = "blue1", color = "grey20") +
annotate("polygon", x = c(13, 17, 17, 13), y = c(2022, 2022, 2028, 2028), fill = "blue3", color = "grey20") +
annotate("polygon", x = c(17, 20, 20, 17), y = c(2022, 2024, 2029, 2028), fill = "blue4", color = "grey20") +
annotate("polygon", x = c(-19, -15, -12, -16), y = c(1995, 1995, 1996, 1996), fill = "red1", color = "grey20") +
annotate("polygon", x = c(-19, -15, -15, -19), y = c(1989, 1989, 1995, 1995), fill = "red3", color = "grey20") +
annotate("polygon", x = c(-15, -12, -12, -15), y = c(1989, 1991, 1996, 1995), fill = "red4", color = "grey20") +
annotate("polygon", x = c(13, 17, 20, 16), y = c(1995, 1995, 1996, 1996), fill = "green1", color = "grey20") +
annotate("polygon", x = c(13, 17, 17, 13), y = c(1989, 1989, 1995, 1995), fill = "green3", color = "grey20") +
annotate("polygon", x = c(17, 20, 20, 17), y = c(1989, 1991, 1996, 1995), fill = "green4", color = "grey20") +
annotate("text", x = -16, y = 1993, hjust = 1, vjust = 1, label = "?", size = 15, color = "white", family= "Impact") +
annotate("text", x = -13, y = 1994, hjust = 1, vjust = 1, label = "?", size = 15, color = "seashell3", family= "Impact") +
annotate("text", x = 16, y = 1993, hjust = 1, vjust = 1, label = "?", size = 15, color = "white", family= "Impact") +
annotate("text", x = 19, y = 1994, hjust = 1, vjust = 1, label = "?", size = 15, color = "seashell3", family= "Impact") +
annotate("text", x = 16, y = 2026, hjust = 1, vjust = 1, label = "?", size = 15, color = "white", family= "Impact") +
annotate("text", x = 19, y = 2027, hjust = 1, vjust = 1, label = "?", size = 15, color = "seashell3", family= "Impact") +
annotate("text", x = 9.5, y = 2025, hjust = 1, vjust = 1, label = "Number of records by year", size = 10, family= "Comic Sans MS",color = "white") +
annotate("text", x = 17.5, y = 2022, hjust = 1, vjust = 1, label = "Interesting observation:overlap in number of records obtained between earliest and later years", size= 5, family= "Comic Sans MS",color = "black") +
annotate("text", x = 17.5, y = 2019, hjust = 1, vjust = 1, label = "Source: Mario Kart World Records, Graphics: Thivina Thanabalan", size= 5, family= "Comic Sans MS",color = "grey48") +
scale_x_continuous(breaks = 0:20, labels = ifelse(0:20 %% 5 == 0, 0:20, ""), limits = c(-20, 23)) +
theme_void() +
theme(legend.position = "none",axis.ticks.y = element_line(color = "yellow"),
axis.ticks.length.y = unit(0.5, "line", "lemonchiffon4"),
axis.text.y= element_text(colour="lemonchiffon4"),
axis.ticks.x = element_line(color = "lemonchiffon4"),
axis.ticks.length.x = unit(0.5, "line", "lemonchiffon4"),
axis.text.x = element_text(colour="lemonchiffon4")) +
theme(plot.background = element_rect(fill = "lemonchiffon", colour=NA)) +
geom_image(aes(x = n, y = 2002 + h - 0.5, image = "https://www.pngkey.com/png/full/1007-10074038_bananas-transparent-mario-kart.png"), size = 0.035, by = "height") +
geom_image(aes(x = 1, y = 2027, image = "https://www.pngkey.com/png/full/140-1403727_super-mario-kart-png-file-mario-kart-super.png"), size = 0.1, by = "height") +
ggsave(path = "png", filename = "track.png", dpi = 128, width = 15, height =8.4 )
|
7d838c2a24fed29632bda716dcb0a45fff268204 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/seasonal/examples/outlier.Rd.R | 6cf16a1805cf69e819406060aa4234966653809b | [] | 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 | 184 | r | outlier.Rd.R | library(seasonal)
### Name: outlier
### Title: Outlier Time series
### Aliases: outlier
### ** Examples
## Not run:
##D x <- seas(AirPassengers)
##D outlier(x)
## End(Not run)
|
cf71f8773ff285b889f877978b110e5a9ec46e0d | 9992af6db68a9d3a92844b83cf992210da05cc32 | /AnalisisRendimientoRiesgo.R | c193d8fd2e09062bbf20ec6dcfcf1bbce89fbc6d | [] | no_license | cristinacambronero/CovarianzaR | bcaa06f763ef13220e1090995f2e989663ebf19a | e899f3234ca19ccff410d838e6ecff81d17bf5bf | refs/heads/master | 2020-04-13T12:53:38.498218 | 2015-07-26T09:42:44 | 2015-07-26T09:42:44 | 39,720,639 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,317 | r | AnalisisRendimientoRiesgo.R | numAleatorios<-c(round(abs(rnorm(10)*100))) ##10 número aleatorios
CIAleatoria<-CI_Empresas[,numAleatorios] ##Obtenemos los valores close de 10 empresas
CIRend<-(CIAleatoria[2,1:length(CIAleatoria)]-CIAleatoria[1,1:length(CIAleatoria)])/CIAleatoria[1,1:length(CIAleatoria)]
##Calculamos la rentabilidad de cada serie temporal
for(i in 3:length(CIAleatoria[,1])){
aux<-(CIAleatoria[1,1:length(CIAleatoria)]-CIAleatoria[i-1,1:length(CIAleatoria)])/CIAleatoria[i-1,1:length(CIAleatoria)]
CIRend = rbind(CIRend,aux)
}
##Calculamos la media de los rendimientos
mu = colMeans(CIRend[,-1])
##Y la matriz de covarianza de los rendimientos
bigsig = cov(CIRend[,-1])
m = nrow(bigsig)-1
##Asignamos aleatoriamente mesos entre 0 y 1 con suma total 1 para las 10 empresas
w = diff(c(0,sort(runif(m)), 1));
##Multiplicamos los pesos por la media
rb = sum(w*mu);
##Y por la matriz de covarianza
sb = sum(w*bigsig*w);
##Definimos un número de iteraciones
N = 2000
##E iteramos para calcular la curva de la cartera eficiente
for (j in 2:N) {
w = diff(c(0,sort(runif(m)), 1));
r = sum(w*mu); rb = rbind(rb,r);
s = sum(w*bigsig*w); sb = rbind(sb,s);
##Obtenemos la desviació tipica.
sb = sqrt(sb)
##Y dibujamos la gráfica
plot(sb,rb,col="blue",ylab="E(r)", xlab="Sigma",main="Cartera Eficiente")
|
696880d105a43c61d27d2b92111eaf40c6f39df2 | 0d93a95face0469ca653511e689738794eed05d8 | /script/ResampledWells.R | e5520cb749dc8b6d8eea69abb64dd7c205a1fd5c | [] | no_license | xxmissingnoxx/As-measurement-code | fefa5b1ad176f1958a7d08686019935a9a760567 | a5325fc5ba5ae2433e27d1e74d88003b5117c593 | refs/heads/master | 2023-02-15T18:07:30.470404 | 2021-01-17T06:47:18 | 2021-01-17T06:47:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,527 | r | ResampledWells.R | # model for the 271 resampled wells
######################################
library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
setwd("/Users/YulingYao/Documents/Research/arsenic")
data_repeated308=read.csv("./data/4. repeated308.csv")
data_repeated14_15=read.csv("./data/2014vs15.csv",sep=',')
head(data_repeated308)
head(data_repeated14_15)
y1= data_repeated308$Arsenic_lab2000
y2= data_repeated308$Arsenic_lab2014
y3=data_repeated14_15$As2015
depth=data_repeated14_15$Depth.2000
loc_x=data_repeated14_15$X2000
loc_y=data_repeated14_15$Y2000
scale_map=max(loc_x)-min(loc_x)
loc_x=(loc_x-min(loc_x))/scale_map
loc_y=(loc_y-min(loc_y))/scale_map
loc_x=loc_x[complete.cases(y3)]
loc_y=loc_y[complete.cases(y3)]
y1=log(y1[complete.cases(y3)])
y2=log(y2[complete.cases(y3)])
y3=log(y3[complete.cases(y3)])
# hyper-parameter is from optimization *(Type-2 MAP)
stan_fit_mu=stan(file="gp_map_mu.stan", iter=2000,
data = list(N=length(y1),y1=y1, y2=y2, y3=y3,
loc_X=cbind(loc_x,loc_y),
rho= 0.04169228,
alpha=1.06221590, sigma=1.16859570))
# hyper-parameter is from optimization *(Type-2 MAP)
stan_fit=stan(file="gp_resampled.stan", iter=2000, init=ttt,
data = list(N=length(y1),y1=y1, y2=y2, y3=y3,
loc_X=cbind(loc_x,loc_y), spline_degree=3,
rho= 0.04169228,
alpha=1.06221590, sigma=1.16,
num_knots=10, knots= unname( quantile(c(y1,y2,y3) ,probs=seq(from=0, to=1, length.out = 10))) ,
N_grid=24, f_grid= sort( c( seq(-0.8, 6.6, length.out = 20) , log (c(40,50,60,80)))) ))
sss=extract(stan_fit, pars=c("mu", "mu0"))
sss2=extract(stan_fit_mu, pars=c("mu", "mu0"))
f_grid= (sort( c( seq(-0.8, 6.6, length.out = 20) , log (c(40,50,60,80)))))
f12= sss$change_grid
ppb50=c()
ppb100=c()
ppb10=c()
for( i in 1:length(f_grid)){
ppb10[i]= mean(f_grid[i]+ f12[,i] + rnorm(dim(f12)[1], 0, 1.16859570) > log(10))
ppb50[i]= mean(f_grid[i]+ f12[,i] + rnorm(dim(f12)[1], 0, 1.16859570) > log(50))
ppb100[i]= mean(f_grid[i]+ f12[,i] + rnorm(dim(f12)[1], 0, 1.16859570) > log(100))
}
for( i in 1:24){
f12[,i]=f12[,i]+sss$sigma_change*rnorm(4000,0,1)
}
library(RColorBrewer)
red_c=brewer.pal(n = 4, name = "YlOrRd")[2:4]
# graphing
pdf("~/Desktop/spline.pdf", height=2.6, width=7.5)
layout(matrix(c(1:3),nrow=1), width = c(1.2,1,0.6),height = c(1))
f_grid= (sort( c( seq(-0.8, 6.6, length.out = 20) , log (c(40,50,60,80)))))
par(oma=c(1.5,2.7,2,0), pty='m',mar=c(1,1.5,1,1) ,mgp=c(1.5,0.25,0), lwd=0.5,tck=-0.01, cex.axis=1, cex.lab=0.6, cex.main=0.7)
plot(f_grid, colMeans(sss$change_grid), axes=F,ylab="",xlab="",type='l',col="#8F2727",lwd=1.5, ylim=c(-1.39,1.39 ) , xlim= log(c(0.9,1000)) , xaxs='i' )
axis(2, las=2,lwd=0.5,at=log(c(1/4, 1/2, 1,2, 4)), labels = c("1/4" , "1/2" ,"1\n no change" ,2, 4 ) )
abline(h=0,col= 'darkgreen', lwd=0.8 )
zz=apply(sss$change_grid, 2, quantile, c(0.975, 0.75, 0.25, 0.025))
polygon(x=c(f_grid,rev(f_grid)), y=c(zz[1,],rev(zz[4,] ) ), border=NA,col= adjustcolor( "#B97C7C", alpha.f = 0.45), xpd=F)
polygon(x=c(f_grid,rev(f_grid)), y=c(zz[2,],rev(zz[3,] ) ), border=NA,col= adjustcolor( "#B97C7C", alpha.f = 0.45), xpd=F)
axis(1, padj=-0.1,lwd=0.5,at=log(c(0.1,1,10,50,100,1000)), labels = c(expression(10^-1), 1 , 10, 50,expression(10^2),expression(10^3) ) )
lines(f_grid, colMeans(sss$change_grid),col="#8F2727",lwd=1.5, xpd=F)
box(lwd=0.5,bty='l')
abline(h=log(c(1/4,1/2,2 ,4)), lty=2, col='grey40')
abline(v= log( c(10,50, 100)), lty=2, col='grey40')
mtext(1, line=1.3,text="baseline As in 2000 (ppb)",cex=0.8)
mtext(3, text="expected multiplicative change in 2014/15",cex=0.8,line=1)
abline(v= ( c(100, 200,300, 400)), lty=2, col='grey40')
plot(f_grid, ppb10, col=red_c[1], lwd=1.5, type='l' , axes=F,ylab="",xlab="",yaxs='i', xpd=T, ylim=c(0,1), xlim=log( c(1,800)))
lines(f_grid, ppb50, col=red_c[2], xpd=T,lwd=1.5)
lines(f_grid, ppb100, col=red_c[3], xpd=T,lwd=1.5)
axis(1, padj=-0.1,lwd=0.5,at=log(c(0.1,1,10,50,100,1000)), labels = c(expression(10^-1), 1 , 10, 50,expression(10^2),expression(10^3) ) )
axis(2, las=2,lwd=0.5,at=c(0,0.5,1))
abline(h=c(0.25,0.5,0.75), lty=2, col='grey40')
#text(x= c(1.8, 3.5, 5.5), y=c(0.45,0.54,0.55), col= red_c, labels = c(">10", ">50", ">100") , cex=1)
text(x= (c(1.8, 3.6, 5.8)), y=c(0.45,0.54,0.55), col= red_c, labels = c(">10", ">50", ">100 ppb") , cex=1)
mtext(1, line=1.3,text="baseline As in 2000 (ppb)",cex=0.8)
mtext(3, text="probablity of excessing safe levels \n in 15 years",cex=0.8, line=0.5)
abline(v= log( c(10,50, 100)), lty=2, col='grey40')
abline(v= ( c(100,200,300,400)), lty=2, col='grey40')
plot(c(0,0), axes=F,ylab="",xlab="",xaxs='i', xpd=T,
xlim=c(0.5, 2.2), ylim=c(30,75))
points(c(1,2), y= c(mean( exp(sss2$mu0)) , mean( exp(sss2$mu) ) ), pch=18, col="#8F2727", cex=2)
abline(h=c(30,40,50,60,70), col='gray40', lty=2)
lines(x=c(1,1), y= as.vector( quantile( exp(sss2$mu0), c(0.975, 0.025) )), lwd=1, col="#B97C7C" )
lines(x=c(1,1), y= as.vector( quantile(exp(sss2$mu0), c(0.75, 0.25) )), lwd=3, col="#8F2727" )
lines(x=c(2,2), y= as.vector( quantile(exp(sss2$mu), c(0.975, 0.025) )), lwd=1, col="#B97C7C" )
lines(x=c(2,2), y= as.vector( quantile( exp(sss2$mu), c(0.75, 0.25) )), lwd=3, col="#8F2727" )
axis(2, las=2, at=c(30,50,70))
mtext(3, text="spatial mean",cex=0.8, line=0.5)
text(x= c(1.3, 1.7), y=c(53,41), col= 1, labels = c(" 2000", "14-15 ") , cex=1)
dev.off()
|
086c05399ac8cbd5398a1f7e7251b9db0ac33e05 | 3a5086aa8460c8253cf45a49be933be875bca99f | /cachematrix.R | 5e436983545c201c81758278eae58c34e104af00 | [] | no_license | MarinePGT/ProgrammingAssignment2 | d6d7d882a58b26f92924cf5c7bc5de4a40ecd935 | 659b94f96ee05b69f7afea23a429b2ddac020594 | refs/heads/master | 2020-04-06T04:11:20.435325 | 2015-10-21T17:10:22 | 2015-10-21T17:10:22 | 44,611,405 | 0 | 0 | null | 2015-10-20T14:20:52 | 2015-10-20T14:20:52 | null | UTF-8 | R | false | false | 1,574 | r | cachematrix.R | ### Coursera R programming
##Programming Assignment 2: Caching the Inverse of a Matrix
# ========================================================
# (21st October 2015)
# Author: M Pouget
## functions:
# 1.makeCacheMatrix: fct creates "matrix" object that cache its inverse
# 2.cacheSolve: fct computes inverse of the "matrix" returned by makeCacheMatrix
# If inverse already calculated (and matrix not changed),
# then cachesolve retrieves inverse from cache
# set working directory
setwd("C://Users/mpouget/Desktop/Coursera/assigment2")
### 1
makeCacheMatrix <- function(x = matrix()) {
## return: a list containing functions to
## 1. set the matrix
## 2. get the matrix
## 3. set the inverse
## 4. get the inverse
inv <- NULL
setmatrix <- function(y) {
x <<- y
inv <<- NULL
}
getmatrix = function() x
setinverse = function(inverse) inv <<- inverse
getinverse = function() inv
list(setmatrix = setmatrix, getmatrix = getmatrix,
setinverse = setinverse, getinverse = getinverse)
}
dump("makeCacheMatrix", file="makeCacheMatrix.R")
### 2
cacheSolve <- function (x=matrix(), ...) {
## return: inverse of the original matrix input to makeCacheMatrix()
inv <- x$getinverse()
if(!is.null(inv)){
message("getting cached data")
return(inv)
}
y <- x$getmatrix()
inv <- solve(y, ...)
x$setinverse(inv)
inv
}
dump("cacheSolve",file="cacheSolve.R")
source("cachematrix.R")
source("cacheSolve.R")
|
19e6a33dc6254bab3907f5b92c6605311a07574e | a1d2e832de47d6c00ff49ba47f02a61e9d157210 | /RandomUserTest.R | 0d6a3dff0590a9bc6015d9b1dbac11a295f48d08 | [] | no_license | KelseyChetnik/MigrateTest | 25ee73301ff9aab19f10661683f02ad65701ac87 | 823caa2a0460e4984a775ef5b6a38e0ace6f1295 | refs/heads/master | 2023-01-28T08:33:51.891568 | 2020-12-08T15:24:17 | 2020-12-08T15:24:17 | 318,604,223 | 0 | 1 | null | 2020-12-08T15:13:35 | 2020-12-04T18:32:53 | R | UTF-8 | R | false | false | 78 | r | RandomUserTest.R | # random user test
print("I'm a random user and I want to change your code!")
|
f8d81f316a29cdabbb3a24f82c65aaf10a368781 | 72119643f835f5d00531048b647fe79a581748b3 | /R/summary_stats.R | 2f602694685114c602697a3ff0eb9d890c172020 | [
"MIT"
] | permissive | Diaz-Oscar/tidycyte | e8dd78031569c8451b04bf1bc3fa3c4103654ba7 | 94a4933fefb57846eacc4e8ee0f9541461f5cd46 | refs/heads/master | 2023-08-20T22:38:54.789143 | 2021-10-08T04:29:38 | 2021-10-08T04:29:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,383 | r | summary_stats.R | #' Calculate summary statistics for tidycyte data
#'
#' Gives n, mean, standard deviation, standard error of the mean, and confidence interval.
#'
#' @param .data A data frame containing tidycyte data.
#' @param measurevar <[`data-masking`][dplyr::dplyr_data_masking]> The masked name of the column containing tidycyte values. Typically this will be named \code{value}.
#' @param ... <[`data-masking`][dplyr::dplyr_data_masking]> The masked names of the grouping variables for summary statistics. Values sharing each of these parameters will be grouped together for calculating statistics. Typically will be \code{elapsed,treatment,cell,metric}.
#' @param .ci Numeric value specifying the confidence interval range as a fraction. Defaults to 0.95.
#' @param .na.rm A boolean that indicates whether to ignore \code{NA} values. Defaults to FALSE.
#' @keywords tidycyte summary standard error statistics
#' @export
#' @examples
#' df %>% summary_stats(value,elapsed,treatment,cell,metric,.ci = 0.95)
summary_stats <- function(.data, measurevar, ..., .ci = 0.95, .na.rm = FALSE) {
options(dplyr.summarise.inform = F)
.data %>%
group_by(...) %>%
summarise(n = n(),
mean = mean({{measurevar}}, na.rm = .na.rm),
sd = sd({{measurevar}}, na.rm = .na.rm),
se = sd/sqrt(n),
ci = se * qt(.ci/2 + 0.5, n-1)) %>%
ungroup()
}
|
1620d330fc2ece4d3e1d3b84ebd7b46d6d1894a1 | 0d7a326479fbddc1f8405a49fb6dcdd7690e6165 | /file23.R | 09406f069005ff0b081ba9375fb4b5213e1281f3 | [] | no_license | jinishashah1998/R-practice | 0019ba6787f3ebf84a7d9594b7e7cd66ac062145 | 94d726a171c00e1b0c7b2334593883c36cd38528 | refs/heads/master | 2022-10-19T18:52:05.728503 | 2020-06-10T21:28:04 | 2020-06-10T21:28:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,676 | r | file23.R | library(dslabs)
library(tidyverse)
set.seed(1986) #for R 3.5 or earlier
#if using R 3.6 or later, use `set.seed(1986, sample.kind="Rounding")` instead
n <- round(2^rnorm(1000, 8, 1))
set.seed(1) #for R 3.5 or earlier
#if using R 3.6 or later, use `set.seed(1, sample.kind="Rounding")` instead
mu <- round(80 + 2*rt(1000, 5))
range(mu)
schools <- data.frame(id = paste("PS",1:1000),
size = n,
quality = mu,
rank = rank(-mu))
schools %>% top_n(10, quality) %>% arrange(desc(quality))
set.seed(1) #for R 3.5 or earlier
#if using R 3.6 or later, use `set.seed(1, sample.kind="Rounding")` instead
mu <- round(80 + 2*rt(1000, 5))
scores <- sapply(1:nrow(schools), function(i){
scores <- rnorm(schools$size[i], schools$quality[i], 30)
scores
})
schools <- schools %>% mutate(score = sapply(scores, mean))
schools %>% top_n(10, score) %>% arrange(desc(score)) %>% select(id, size, score)
schools %>% summarize(median(size))
schools %>% top_n(10, score) %>% summarize(median(size))
schools %>% top_n(-10, score) %>% summarize(median(size))
overall <- mean(sapply(scores, mean))
alpha <- 25
schools5 <- schools %>%
mutate(score_dev = overall + (score - overall) * size / (size + alpha)) %>%
arrange(desc(score_dev))
# mutate(quality_new = score_dev-80)
schools5 %>%
top_n(10, score_dev)
overall <- mean(sapply(scores, mean))
alpha <- 135
schools5 <- schools %>%
mutate(score_dev = overall + (score - overall) * size / (size + alpha)) %>%
arrange(desc(score_dev))
# mutate(quality_new = score_dev-80)
schools5 %>%
top_n(10, score_dev) |
8509fefe79a61d54ff58df4ea1b57ed98fcc8bac | 3d328d07eac93537021760bfdf9d5afc1943fdfd | /ui.R | 188aa4b04fe9ef1fdb8cfdb135ba032ebb9246c2 | [] | no_license | fss14142/DevelopingDataProductsProject | b360de61fc65c2c4058aa3b468a44bf15fbfd9ae | fd62654c7ba6b71761a5553f0e818c53c3612eae | refs/heads/master | 2021-01-18T13:58:26.744358 | 2014-11-23T10:51:38 | 2014-11-23T10:51:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,750 | r | ui.R | require(shiny)
require(rCharts)
require(httr)
source("global.R")
shinyUI(
#bootstrapPage(
fluidPage(
title = "Bird distribution data from eBird.",
titlePanel("Migrating birds observations across the US, using data from Ebird."),
sidebarLayout(
sidebarPanel(
#h5('See the App Documentation tab in the main panel.'),
includeHTML("abouDocumentation.html"),
h4('State selection:'),
selectInput(inputId = 'stateSel', label = '',
choices = sort(states$name), selected = 'Arkansas'),
h4('Bird species selection:'),
selectInput(inputId = 'speciesSel', label = '',
choices = birdSpecies$comName, selected = birdSpecies$comName[1]),
h4('Bird species selection:'),
p('Only the observations from the past 30 days are available from Ebird. Use the
slider to choose how many days you want.'),
sliderInput(inputId = 'daysSel', label = 'Select a number of days',
min = 1, max = 30, value = 7, step = 1),
h4(''),
imageOutput('birdPic', height=200)
),
mainPanel(
tabsetPanel(
tabPanel("State summary and Map",
textOutput('stateSummary'),
mapOutput('map_container')
),
tabPanel("App Documentation", includeMarkdown("documentation.Rmd"))
#tabPanel("Table", tableOutput("table"))
)
)
# mainPanel(
# textOutput('stateSummary'),
# mapOutput('map_container')
# )
# )
)
)
) |
4eb322a6a592457d9234c0ec5a65d3b73c223dd9 | 7172e02a32a1046ec1a2ec0a8631f0ae3d2dd761 | /R/data_functions.R | 1751a6690df5bce0682e4ec3e78c855a4e0f61d0 | [
"MIT"
] | permissive | nps-jeff/OZAB | 24358d5fbb17b0c50778b66f1fd2c9c2986441ad | f98d494630e4fb38903a09e32fd2aec6bcfb0441 | refs/heads/master | 2023-03-03T20:52:48.088345 | 2021-02-11T19:14:52 | 2021-02-11T19:14:52 | 337,487,343 | 0 | 0 | NOASSERTION | 2021-02-11T19:14:53 | 2021-02-09T17:46:01 | null | UTF-8 | R | false | false | 2,686 | r | data_functions.R | #' NPS Data Format Transformation
#'
#' @param nps_data Tibble containing NPS Formatted Data
#' @param covariate_cols Columns which refer to covariates such as Year, Location, Grade, etc.
#'
#' @return Long format tibble
#' @export
#'
#' @examples
#' \dontrun{
#' pivot_nps_data(nps_data_ex, c(Year, Loc_Name, SCOSA))
#' }
#' @importFrom rlang .data
pivot_nps_data <- function(nps_data, covariate_cols = NULL){
tidyr::pivot_longer(
data = nps_data,
cols = -c({{ covariate_cols }}),
names_to = 'Species',
values_to = 'Cover Class'
) %>%
dplyr::mutate(
`Cover Class` = forcats::fct_rev(forcats::as_factor(.data$`Cover Class`))
)
}
#' Add Presence Column
#'
#' @param df Tibble containing Cover Class Data
#' @param cover_class_col Column Name for Cover Class Data
#' @param absence_value Value for Absence -- Assumed to be Zero
#'
#' @return Provided tibble with column of logicals indicating presence / absence appended
#' @export
#'
#' @examples
#' @importFrom rlang .data
add_presence <- function(df, cover_class_col = .data$`Cover Class`, absence_value = 0){
df %>%
dplyr::mutate(Presence = ifelse({{ cover_class_col }} == absence_value, FALSE, TRUE))
}
compose_ozab_data <- function(df, presence_formula, abundance_formula, cutpoint_scheme){
## Check if response of presence_formula is in dataframe
presence_response_col <- all.vars(presence_formula)[1]
if(!(presence_response_col %in% names(df))){
stop(glue::glue('Response of presence-absence formula, { presence_response_col }, not found in provided data'))
}
## Check if response of abundance_formula is in dataframe
abundance_response_col <- all.vars(abundance_formula)[1]
if(!(abundance_response_col %in% names(df))){
stop(glue::glue('Response of abundance formula, { abundance_response_col }, not found in provided data'))
}
## Make sure a single column is not response for both abundance and presence formula
if(presence_response_col == abundance_response_col){
stop('Response columns of abundance and presence-absence cannot be identical')
}
## Make sure presence_column only has two levels
## TODO
## Data Composition
y <- as.numeric(df[[presence_response_col]]) * as.numeric(df[[abundance_response_col]])
N <- length(y)
presence_matrix <- as.matrix(modelr::model_matrix(df, presence_formula))
Kp <- ncol(presence_matrix)
abundance_matrix <- as.matrix(modelr::model_matrix(df, abundance_formula))
Ka <- ncol(abundance_matrix)
c <- cutpoint_scheme
K <- length(c) + 1
list(
N = N,
K = K,
c = c,
y = y,
Kp = Kp,
Xp = presence_matrix,
Ka = Ka,
Xa = abundance_matrix
)
}
|
43f93eaacc4f4576534cbaa1cab2ef440816fb94 | 7da7a83763b7c4b4ab9608ffec1599d30f673662 | /R/race-wrapper.R | e3a23aa8275abe1ce78db5711663ba6843b808aa | [] | no_license | abeham/irace | 57dfaf83b8c6891e01a5bbdcec7e7927fb40c254 | 90467edf1ac9fc3950b76780c5fba90c62c3d309 | refs/heads/master | 2020-03-14T03:19:24.959619 | 2017-08-03T16:34:51 | 2017-08-03T16:34:51 | 131,417,353 | 0 | 0 | null | 2018-04-28T14:39:37 | 2018-04-28T14:39:37 | null | UTF-8 | R | false | false | 15,111 | r | race-wrapper.R | # FIXME: This is needed because race.R is not divided in two-stages
# run/evaluate like irace is, so there is no way to communicate data
# from the first stage to the second.
#
# FIXME: In fact, we should use this trick also in irace.R to avoid
# pass-by-copy-on-write of huge matrices and data.frames and instead
# pass-by-reference an environment containing those.
.irace <- new.env()
buildCommandLine <- function(values, switches)
{
irace.assert(length(values) == length(switches))
command <- ""
# FIXME: This probably can be implemented faster with apply() and
# paste(collapse=" "). But how to get the index i in that case?
for (i in seq_along(values)) {
value <- values[i]
if (!is.na(value)) {
command <- paste0(command, " ", switches[i], value)
}
}
return(command)
}
# This function tries to convert a, possibly empty, character vector into a
# numeric vector.
parse.output <- function(outputRaw, verbose)
{
if (verbose) { cat (outputRaw, sep = "\n") }
# Initialize output as raw. If it is empty stays like this.
output <- outputRaw
# strsplit crashes if outputRaw == character(0)
if (length(outputRaw) > 0) {
output <- strsplit(trim(outputRaw), "[[:space:]]+")[[1]]
}
# suppressWarnings to avoid messages about NAs introduced by coercion
output <- suppressWarnings (as.numeric (output))
return (output)
}
target.error <- function(err.msg, output, scenario, target.runner.call,
target.evaluator.call = NULL)
{
if (!is.null(target.evaluator.call)) {
err.msg <- paste0(err.msg, "\n", .irace.prefix,
"The call to targetEvaluator was:\n", target.evaluator.call)
}
if (!is.null(target.runner.call)) {
err.msg <- paste0(err.msg, "\n", .irace.prefix,
"The call to targetRunner was:\n", target.runner.call)
}
if (is.null(output$outputRaw)) {
# Message for a function call.
# FIXME: Ideally, we should print the list as R would print it.
output$outputRaw <- toString(output)
advice.txt <- paste0(
"This is not a bug in irace, but means that something failed in ",
"a call to the targetRunner or targetEvaluator functions provided by the user.",
" Please check those functions carefully.")
} else {
# Message for an external script.
advice.txt <- paste0(
"This is not a bug in irace, but means that something failed when",
" running the command(s) above or they were terminated before completion.",
" Try to run the command(s) above from the execution directory '",
scenario$execDir, "' to investigate the issue.")
}
irace.error(err.msg, "\n", .irace.prefix,
"The output was:\n", paste(output$outputRaw, collapse = "\n"),
"\n", .irace.prefix, advice.txt)
}
check.output.target.evaluator <- function (output, scenario, target.runner.call = NULL)
{
if (!is.list(output)) {
target.error ("The output of targetEvaluator must be a list",
list(), scenario, target.runner.call = target.runner.call)
return(NULL)
}
err.msg <- output$error
if (is.null(err.msg)) {
if (is.null(output$cost)) {
err.msg <- "The output of targetEvaluator must be one number 'cost'!"
} else if (is.na (output$cost)) {
err.msg <- "The output of targetEvaluator is not numeric!"
} else if (is.infinite(output$cost)) {
err.msg <- "The output of targetEvaluator is not finite!"
}
}
if (!is.null(err.msg)) {
target.error (err.msg, output, scenario,
target.runner.call = target.runner.call,
target.evaluator.call = output$call)
}
}
exec.target.evaluator <- function (experiment, num.configurations, all.conf.id,
scenario, target.runner.call)
{
output <- .irace$target.evaluator(experiment, num.configurations, all.conf.id,
scenario, target.runner.call)
check.output.target.evaluator (output, scenario, target.runner.call = target.runner.call)
return (output)
}
target.evaluator.default <- function(experiment, num.configurations, all.conf.id,
scenario, target.runner.call)
{
configuration.id <- experiment$id.configuration
instance.id <- experiment$id.instance
seed <- experiment$seed
instance <- experiment$instance
execDir <- scenario$execDir
debugLevel <- scenario$debugLevel
targetEvaluator <- scenario$targetEvaluator
if (as.logical(file.access(targetEvaluator, mode = 1))) {
irace.error ("targetEvaluator", shQuote(targetEvaluator),
"cannot be found or is not executable!\n")
}
cwd <- setwd (execDir)
# FIXME: I think we don't even need to paste the args, since system2 handles this by itself.'
args <- paste(configuration.id, instance.id, seed, instance, num.configurations, all.conf.id)
output <- runcommand(targetEvaluator, args, configuration.id, debugLevel)
setwd (cwd)
cost <- time <- NULL
err.msg <- output$error
if (is.null(err.msg)) {
v.output <- parse.output(output$output, verbose = (scenario$debugLevel >= 2))
if (length(v.output) != 1) {
err.msg <- paste0("The output of targetEvaluator must be one number 'cost'!")
} else {
cost <- v.output[1]
}
}
return(list(cost = cost,
error = err.msg, outputRaw = output$output,
call = paste(targetEvaluator, args)))
}
check.output.target.runner <- function (output, scenario)
{
if (!is.list(output)) {
output <- list()
err.msg <- paste0("The output of targetRunner must be a list")
target.error (err.msg, output, scenario, target.runner.call = NULL)
return(output)
}
err.msg <- output$error
if (is.null(err.msg)) {
if (is.null.or.na(output$cost))
output$cost <- NULL
if (is.null.or.na(output$time))
output$time <- NULL
# When targetEvaluator is provided targetRunner must return only the time.
if (!is.null(.irace$target.evaluator)) {
if (scenario$maxTime > 0 && is.null(output$time)) {
err.msg <- paste0("The output of targetRunner must be one number 'time'!")
} else if (!is.null(output$cost)) {
err.msg <- paste0("The output of targetRunner must be empty or just one number 'time'!")
}
} else if (scenario$maxTime > 0 && (is.null (output$cost) || is.null(output$time))) {
err.msg <- paste0("The output of targetRunner must be two numbers 'cost time'!")
} else if (scenario$maxExperiments > 0 && is.null (output$cost)) {
err.msg <- paste0("The output of targetRunner must be one number 'cost'!")
} else if (!is.null(output$time) && output$time < 0) {
err.msg <- paste0("The value of time returned by targetRunner cannot be negative (", output$time, ")!")
}
if (!is.null (output$cost)) {
if (is.na(output$cost)) {
err.msg <- paste0("The cost returned by targetRunner is not numeric!")
} else if (is.infinite(output$cost)) {
err.msg <- paste0("The cost returned by targetRunner is not finite!")
}
}
if (!is.null (output$time)) {
if (is.na(output$time)) {
err.msg <- paste0("The time returned by targetRunner is not numeric!")
} else if (is.infinite(output$time)) {
err.msg <- paste0("The time returned by targetRunner is not finite!")
}
}
# Fix too small time.
output$time <- if (is.null(output$time)) NA else max(output$time, 0.01)
}
if (!is.null(err.msg)) {
target.error (err.msg, output, scenario, target.runner.call = output$call)
}
return (output)
}
# This function invokes target.runner. When used on a remote node by Rmpi,
# environments do not seem to be shared and the default value is evaluated too
# late, thus we have to pass .irace$target.runner explicitly.
exec.target.runner <- function(experiment, scenario,
target.runner = .irace$target.runner)
{
doit <- function(experiment, scenario)
{
x <- target.runner(experiment, scenario)
return (check.output.target.runner (x, scenario))
}
retries <- scenario$targetRunnerRetries
while (retries > 0) {
output <- try (doit(experiment, scenario))
if (!inherits(output, "try-error") && is.null(output$error)) {
return (output)
}
irace.note("Retrying (", retries, " left).\n")
retries <- retries - 1
}
output <- doit(experiment, scenario)
return (output)
}
target.runner.default <- function(experiment, scenario)
{
debugLevel <- scenario$debugLevel
configuration.id <- experiment$id.configuration
instance.id <- experiment$id.instance
seed <- experiment$seed
configuration <- experiment$configuration
instance <- experiment$instance
switches <- experiment$switches
targetRunner <- scenario$targetRunner
if (as.logical(file.access(targetRunner, mode = 1))) {
irace.error ("targetRunner ", shQuote(targetRunner), " cannot be found or is not executable!\n")
}
args <- paste(configuration.id, instance.id, seed, instance,
buildCommandLine(configuration, switches))
output <- runcommand(targetRunner, args, configuration.id, debugLevel)
cost <- time <- NULL
err.msg <- output$error
if (is.null(err.msg)) {
v.output <- parse.output(output$output, verbose = (scenario$debugLevel >= 2))
if (length(v.output) > 2) {
err.msg <- paste0("The output of targetRunner should not be more than two numbers!")
} else if (length(v.output) == 1) {
if (!is.null(scenario$targetEvaluator)) {
time <- v.output[1]
} else {
cost <- v.output[1]
}
} else if (length(v.output) == 2) {
cost <- v.output[1]
time <- v.output[2]
}
}
return(list(cost = cost, time = time,
error = err.msg, outputRaw = output$output,
call = paste(targetRunner, args)))
}
execute.experiments <- function(experiments, scenario)
{
parallel <- scenario$parallel
mpi <- scenario$mpi
execDir <- scenario$execDir
if (!isTRUE (file.info(execDir)$isdir)) {
irace.error ("Execution directory '", execDir, "' is not found or not a directory\n")
}
cwd <- setwd (execDir)
on.exit(setwd(cwd), add = TRUE)
target.output <- vector("list", length(experiments))
if (!is.null(scenario$targetRunnerParallel)) {
# User-defined parallelization
target.output <-
scenario$targetRunnerParallel(experiments, exec.target.runner, scenario = scenario)
} else if (scenario$batchmode != 0) {
target.output <- cluster.lapply (experiments, scenario = scenario)
} else if (parallel > 1) {
if (mpi) {
if (scenario$loadBalancing) {
target.output <- Rmpi::mpi.applyLB(experiments, exec.target.runner,
scenario = scenario,
target.runner = .irace$target.runner)
} else {
# Without load-balancing, we need to split the experiments into chunks
# of size parallel.
target.output <- unlist(use.names = FALSE,
tapply(experiments,
ceiling(1:length(experiments) / parallel),
Rmpi::mpi.apply, exec.target.runner,
scenario = scenario,
target.runner = .irace$target.runner))
}
# FIXME: if stop() is called from mpi.applyLB, it does not
# terminate the execution of the parent process, so it will
# continue and give more errors later. We have to terminate
# here, but is there a nicer way to detect this and terminate?
if (any(sapply(target.output, inherits, "try-error"))) {
# FIXME: mclapply has some bugs in case of error. In that
# case, each element of the list does not keep the output of
# each configuration and repetitions may occur.
cat(unique(unlist(target.output[sapply(
target.output, inherits, "try-error")])), file = stderr(), sep = "")
irace.error("A slave process terminated with a fatal error")
}
} else {
if (.Platform$OS.type == 'windows') {
irace.assert(!is.null(.irace$cluster))
if (scenario$loadBalancing) {
target.output <-
parallel::parLapplyLB(.irace$cluster, experiments, exec.target.runner,
scenario = scenario)
} else {
target.output <-
parallel::parLapply(.irace$cluster, experiments, exec.target.runner,
scenario = scenario)
}
# FIXME: if stop() is called from parLapply, then the parent
# process also terminates, and we cannot give further errors.
} else {
target.output <-
parallel::mclapply(experiments, exec.target.runner,
# FALSE means load-balancing.
mc.preschedule = !scenario$loadBalancing,
mc.cores = parallel,
scenario = scenario)
# FIXME: if stop() is called from mclapply, it does not
# terminate the execution of the parent process, so it will
# continue and give more errors later. We have to terminate
# here, but is there a nicer way to detect this and terminate?
if (any(sapply(target.output, inherits, "try-error"))
|| any(sapply(target.output, is.null))) {
# FIXME: mclapply has some bugs in case of error. In that
# case, each element of the list does not keep the output of
# each configuration and repetitions may occur.
cat(unique(unlist(
target.output[sapply(
target.output, inherits, "try-error")])), file = stderr())
irace.error("A child process triggered a fatal error")
}
}
}
} else {
# One process, all sequential
for (k in seq_along(experiments)) {
target.output[[k]] <- exec.target.runner(experiments[[k]], scenario = scenario)
}
}
return(target.output)
}
execute.evaluator <- function(experiments, scenario, target.output, configurations.id)
{
## FIXME: We do not need the configurations.id argument:
# configurations.id <- sapply(experiments, function(x) x[["id.configuration"]])
all.conf.id <- paste(configurations.id, collapse = " ")
## Evaluate configurations sequentially
for (k in seq_along(experiments)) {
output <- exec.target.evaluator(experiment = experiments[[k]],
num.configurations = length(configurations.id),
all.conf.id, scenario = scenario,
target.runner.call = target.output[[k]]$call)
target.output[[k]]$cost <- output$cost
if (is.null(target.output[[k]]$call))
target.output[[k]]$call <- output$call
if (is.null(target.output[[k]]$time))
target.output[[k]]$time <- output$time
}
return(target.output)
}
|
72e60a756b692289faaabd33d356a39ec26d7d5b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/naniar/examples/miss_scan_count.Rd.R | 66a23ae926977e401c8e1e6ef158c0f32d7439d4 | [] | 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 | 564 | r | miss_scan_count.Rd.R | library(naniar)
### Name: miss_scan_count
### Title: Search and present different kinds of missing values
### Aliases: miss_scan_count
### ** Examples
dat_ms <- tibble::tribble(~x, ~y, ~z,
1, "A", -100,
3, "N/A", -99,
NA, NA, -98,
-99, "E", -101,
-98, "F", -1)
miss_scan_count(dat_ms,-99)
miss_scan_count(dat_ms,c(-99,-98))
miss_scan_count(dat_ms,c("-99","-98","N/A"))
miss_scan_count(dat_ms,common_na_strings)
|
1f500f6d35237a9aff0f851e8cacf51af2261228 | c77069c2dc6dbf3f9449a44e06d70b540a1912b5 | /man/fitRMU_MHmcmc.Rd | aa93adcdbd305dca7baf378e841639fe90549668 | [] | no_license | cran/phenology | 62b323a9231c3701568de58c57a804e043abe6a2 | 991d2c35dcbcf1fcff23cbcc0c2f82b74a868dfb | refs/heads/master | 2023-04-15T03:37:51.464388 | 2023-04-01T09:10:02 | 2023-04-01T09:10:02 | 17,698,504 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,452 | rd | fitRMU_MHmcmc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fitRMU_MHmcmc.R
\name{fitRMU_MHmcmc}
\alias{fitRMU_MHmcmc}
\title{Run the Metropolis-Hastings algorithm for RMU.data}
\usage{
fitRMU_MHmcmc(
result = stop("An output from fitRMU() must be provided"),
n.iter = 10000,
parametersMCMC = stop("A parameter set from fitRMU_MHmcmc_p() must be provided"),
n.chains = 1,
n.adapt = 0,
thin = 1,
adaptive = FALSE,
adaptive.lag = 500,
adaptive.fun = function(x) {
ifelse(x > 0.234, 1.3, 0.7)
},
trace = FALSE,
traceML = FALSE,
intermediate = NULL,
filename = "intermediate.Rdata",
previous = NULL
)
}
\arguments{
\item{result}{An object obtained after a SearchR fit}
\item{n.iter}{Number of iterations for each step}
\item{parametersMCMC}{A set of parameters used as initial point for searching with information on priors}
\item{n.chains}{Number of replicates}
\item{n.adapt}{Number of iterations before to store outputs}
\item{thin}{Number of iterations between each stored output}
\item{adaptive}{Should an adaptive process for SDProp be used}
\item{adaptive.lag}{Lag to analyze the SDProp value in an adaptive content}
\item{adaptive.fun}{Function used to change the SDProp}
\item{trace}{TRUE or FALSE or period, shows progress}
\item{traceML}{TRUE or FALSE to show ML}
\item{intermediate}{Period for saving intermediate result, NULL for no save}
\item{filename}{If intermediate is not NULL, save intermediate result in this file}
\item{previous}{Previous result to be continued. Can be the filename in which intermediate results are saved.}
}
\value{
A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used
}
\description{
Run the Metropolis-Hastings algorithm for RMU.data.\cr
The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed.\cr
I recommend thin=1 because the method to estimate SE uses resampling.\cr
As initial point is maximum likelihood, n.adapt = 0 is a good solution.\cr
The parameters intermediate and filename are used to save intermediate results every 'intermediate' iterations (for example 1000). Results are saved in a file of name filename.\cr
The parameter previous is used to indicate the list that has been save using the parameters intermediate and filename. It permits to continue a mcmc search.\cr
These options are used to prevent the consequences of computer crash or if the run is very very long and computer processes at time limited.\cr
}
\details{
fitRMU_MHmcmc runs the Metropolis-Hastings algorithm for RMU.data (Bayesian MCMC)
}
\examples{
\dontrun{
library("phenology")
RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana",
"Galibi.Suriname",
"Irakumpapy.French.Guiana"),
se=c("se_Yalimapo.French.Guiana",
"se_Galibi.Suriname",
"se_Irakumpapy.French.Guiana"))
data.AtlanticW <- data.frame(Year=c(1990:2000),
Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA,
6542, 5678, 1243, NA, 1566, 1566),
se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA,
230, 129, 167, NA, 145, 20),
Galibi.Suriname=c(276, 275, 290, NA, 267,
542, 678, NA, 243, 156, 123),
se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2,
4.3, 2.3, NA, 10.3, 10.1, 8.9),
Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA,
3542, 2678, 243, NA, 566, 566),
se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA,
130, 29, 67, NA, 15, 20))
cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW,
colname.year="Year", model.trend="Constant",
model.SD="Zero")
pMCMC <- fitRMU_MHmcmc_p(result=cst, accept=TRUE)
fitRMU_MCMC <- fitRMU_MHmcmc(result = cst, n.iter = 10000,
parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE)
}
}
\seealso{
Other Fill gaps in RMU:
\code{\link{CI.RMU}()},
\code{\link{fitRMU_MHmcmc_p}()},
\code{\link{fitRMU}()},
\code{\link{logLik.fitRMU}()},
\code{\link{plot.fitRMU}()}
}
\author{
Marc Girondot
}
\concept{Fill gaps in RMU}
|
98c5a17ad3db89f02efe36fb614f78c601da3d9b | a32d706b41bdce3e777a157d4aafeefbf4f01237 | /GeneraAnalysis.R | dc25d64a84c5f57099ad8b6f73f6ec6c44294063 | [] | no_license | levisimons/SCCWRP | 8873f3cca4b780f4ca8dcde52442c03e817323dd | 240a162517ed743842a0ad327132aeec87434700 | refs/heads/master | 2022-12-15T21:03:21.785069 | 2022-12-05T20:45:16 | 2022-12-05T20:45:16 | 110,313,993 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,287 | r | GeneraAnalysis.R | library("plyr")
library(dplyr)
library("ggplot2")
library(lubridate)
library("ape")
library("vegan")
library("microbiome")
library(data.table)
library(tidyr)
library(ggplot2)
library(vegan)
library(scales)
library(grid)
library(reshape2)
library(phyloseq)
library(reshape2)
setwd("~/Desktop/SCCWRP")
#Read in site data containing biological counts, water chemistry, and land usage values.
GISBioData <- read.table("CAGISBioData.csv", header=TRUE, sep=",",as.is=T,skip=0,fill=TRUE,check.names=FALSE)
#Filter out to taxonomic groups of interest.
GISBioData <- subset(GISBioData, MeasurementType == "benthic macroinvertebrate relative abundance")
#Remove duplicate measures.
GISBioData <- GISBioData[!duplicated(GISBioData[,c("UniqueID","FinalID","Count")]),]
#Read in sample metadata.
SCCWRP <- read.table("CSCI.csv", header=TRUE, sep=",",as.is=T,skip=0,fill=TRUE,check.names=FALSE)
#Merge in altitude.
GISBioData <- join(GISBioData,SCCWRP[,c("UniqueID","altitude")],by=c("UniqueID"))
#Add the number of genera per sample
taxaBySample <- count(GISBioData,UniqueID)
colnames(taxaBySample) <- c("UniqueID","nTaxa")
GISBioData <- join(GISBioData,taxaBySample,by=c("UniqueID"))
#Add the number of samples per watershed
WSBySample <- as.data.frame(table(SCCWRP$Watershed))
colnames(WSBySample) <- c("Watershed","NSamples")
GISBioData <- join(GISBioData,WSBySample,by=c("Watershed"))
#Read in watershed metadata
#HUC2 = State level, HUC4 = super-regional level, HUC6 = regional level, HUC8 = local level
Watersheds <- read.table("SCCWRPWatersheds.tsv", header=TRUE, sep="\t",as.is=T,skip=0,fill=TRUE,check.names=FALSE)
Watersheds$Watershed <- gsub('\\s+', '', Watersheds$Watershed)
#Merge in watershed area
GISBioData <- join(GISBioData,Watersheds,by=c("Watershed"))
#Read in functional feeding group for each taxon.
#Abbreviations used in denoting functional feeding groups are as follows ( http://www.safit.org/Docs/CABW_std_taxonomic_effort.pdf ):
#P= predator MH= macrophyte herbivore OM= omnivore
#PA= parasite PH= piercer herbivore XY= xylophage (wood eater)
#CG= collector-gatherer SC= scraper
#CF= collector filterer SH= shredder
FFG <- read.table("metadata.csv", header=TRUE, sep=",",as.is=T,skip=0,fill=TRUE,check.names=FALSE)
# Filter data so only known functional feeding groups are kept.
FFG <- subset(FFG, FunctionalFeedingGroup != "")
# Generate functional feeding group data frame.
FFG <- FFG[,c("FinalID","LifeStageCode","FunctionalFeedingGroup")]
FFG <- subset(FFG,LifeStageCode=="L" | LifeStageCode=="X" | FinalID=="Hydrophilidae" | FinalID=="Hydraenidae")
FFG <- FFG[!duplicated(FFG$FinalID),]
#Merge in functional feeding groups into sample data.
GISBioData <- join(GISBioData,FFG[,c("FinalID","FunctionalFeedingGroup")],by=c("FinalID"))
FFGCounts <- na.omit(as.data.frame(unique(GISBioData$FunctionalFeedingGroup)))
colnames(FFGCounts) <- c("FunctionalFeedingGroup")
FFGCounts$FunctionalFeedingGroup <- as.character(as.factor(FFGCounts$FunctionalFeedingGroup))
FFGCounts <- arrange(FFGCounts,FunctionalFeedingGroup)
#Add column containing the sum of taxa, by functional feeding groups, within each sample.
tmp <- GISBioData[,c("UniqueID","FunctionalFeedingGroup","Count")]
colnames(tmp) <- c("UniqueID","FunctionalFeedingGroup","FFGCount")
tmp <- aggregate(tmp$FFGCount, by=list(Category=tmp$UniqueID,tmp$FunctionalFeedingGroup), FUN=sum)
colnames(tmp) <- c("UniqueID","FunctionalFeedingGroup","FFGCount")
tmp <- arrange(tmp,UniqueID,FunctionalFeedingGroup)
GISBioData <- join(GISBioData,tmp,by=c("UniqueID","FunctionalFeedingGroup"))
#Find watersheds with a larger enough set of samples for downstream analysis.
sampleMin <- 25
GISBioDataLWS <- subset(GISBioData,NSamples>=sampleMin)
selected <- GISBioDataLWS
#Get unique genera for the heavily sampled statewide data set.
GeneraCounts <- na.omit(as.data.frame(unique(selected$FinalID)))
colnames(GeneraCounts) <- c("FinalID")
tmp <- selected[,c("UniqueID","FinalID","Count")]
#Construct a table of counts by genera in a Phyloseq otu table format
otudata <- arrange(GeneraCounts,FinalID)
for(ID in unique(selected$UniqueID)){
tmp2 <- subset(tmp,UniqueID==ID)
tmp2 <- tmp2[,c("FinalID","Count")]
colnames(tmp2) <- c("FinalID",ID)
tmp2 <- merge(tmp2,GeneraCounts,by=c("FinalID"),all=TRUE)
otudata <- cbind(otudata,tmp2)
otudata <- otudata[,!duplicated(colnames(otudata))]
}
#Create Phyloseq object with the OTU table, sample factors, and taxonomic data.
otumat <- t(otudata)
colnames(otumat) <- otumat[c(1),]
otumat <- otumat[-c(1),]
otumat[is.na(otumat)] <- 0
otumat <- as.data.frame(otumat)
sampleNames <- rownames(otumat)
otumat <- sapply(otumat,as.numeric)
rownames(otumat) <- sampleNames
OTU = otu_table(otumat,taxa_are_rows = FALSE)
taxmat <- as.matrix(GeneraCounts)
rownames(taxmat) <- as.data.frame(taxmat)$FinalID
TAX = tax_table(taxmat)
samplemat <- selected[,c("UniqueID","HUC2","HUC4","HUC6","HUC8","LU_2000_5K","Year","altitude")]
samplemat <- samplemat[!duplicated(samplemat),]
samplemat$HUC2 <- as.factor(samplemat$HUC2)
samplemat$HUC4 <- as.factor(samplemat$HUC4)
samplemat$HUC6 <- as.factor(samplemat$HUC6)
samplemat$HUC8 <- as.factor(samplemat$HUC8)
samplemat$LU_2000_5K <- as.numeric(as.character(samplemat$LU_2000_5K))
samplemat$Year <- as.factor(samplemat$Year)
samplemat$altitude <- as.numeric(as.character(samplemat$altitude))
row.names(samplemat) <- samplemat$UniqueID
sampledata <- sample_data(samplemat)
physeq <- phyloseq(OTU,TAX,sampledata)
#What factors are driving variations in alpha and beta diversity?
#Samples are comprised of the relative abundances of taxa by genera.
set.seed(1)
test <- subset_samples(physeq)
#test <- transform_sample_counts(test, function(x) x/sum(x))
testDF <- as(sample_data(test), "data.frame")
testDF$HUC2 <- as.factor(testDF$HUC2)
testDF$HUC4 <- as.factor(testDF$HUC4)
testDF$HUC6 <- as.factor(testDF$HUC6)
testDF$HUC8 <- as.factor(testDF$HUC8)
levels(alphaDF$HUC8) <- unique(alphaDF$HUC8)
alphaDF <- GISBioData[,c("UniqueID","HUC2","HUC4","HUC6","HUC8","altitude","LU_2000_5K","Year","nTaxa","CSCI")]
alphaDF <- alphaDF[!duplicated(alphaDF),]
alphaDF$HUC2 <- as.factor(alphaDF$HUC2)
alphaDF$HUC4 <- as.factor(alphaDF$HUC4)
alphaDF$HUC6 <- as.factor(alphaDF$HUC6)
alphaDF$HUC8 <- as.factor(alphaDF$HUC8)
levels(alphaDF$HUC8) <- unique(alphaDF$HUC8)
summary(aov(nTaxa~HUC2+HUC4+HUC6+HUC8+altitude+LU_2000_5K+Year,data=alphaDF))
testBeta <- adonis(distance(test,method="jaccard") ~ HUC2+HUC4+HUC6+HUC8+altitude+LU_2000_5K+Year, data=testDF, permutations=1000)
testBeta
#Determine land use deciles for the full state data set.
LUdf <- GISBioData[,c("UniqueID","LU_2000_5K")]
LUdf <- LUdf[!duplicated(LUdf),]
LUquantile <- quantile(LUdf$LU_2000_5K,probs=seq(0,1,0.1))
#Determine altitude deciles for the full state data set.
Altitudedf <- GISBioData[,c("UniqueID","altitude")]
Altitudedf <- Altitudedf[!duplicated(Altitudedf),]
Altitudequantile <- quantile(Altitudedf$altitude,probs=seq(0,1,0.1))
#Compute trends in beta diversity groups of samples set by
#altitude, year, and land use.
GeneraTrends <- data.frame()
for(i in 1:length(LUquantile)){
if(i>1){
LULow <- LUquantile[i-1]
LUHigh <- LUquantile[i]
MidLU <- 0.5*(LULow+LUHigh)
for(j in 1:length(Altitudequantile)){
if(j>1){
AltitudeLow <- Altitudequantile[j-1]
AltitudeHigh <- Altitudequantile[j]
MidAltitude <- 0.5*(AltitudeLow+AltitudeHigh)
sampleDF <- subset(GISBioData,LU_2000_5K >= LULow & LU_2000_5K <= LUHigh & altitude >= AltitudeLow & altitude <= AltitudeHigh)
if(length(unique(sampleDF$UniqueID))>2){
physeqLUYear <- subset_samples(physeq,LU_2000_5K >= LULow & LU_2000_5K <= LUHigh & altitude >= AltitudeLow & altitude <= AltitudeHigh)
if(nrow(otu_table(physeqLUYear)) > 2){
sampleBeta <- distance(physeqLUYear,method="bray")
print(paste(MidLU,MidAltitude,length(unique(sampleDF$UniqueID)),mean(sampleBeta),sd(sampleBeta)))
row <- t(as.data.frame(c(MidLU,MidAltitude,length(unique(sampleDF$UniqueID)),mean(sampleBeta),sd(sampleBeta))))
GeneraTrends <- rbind(row,GeneraTrends)
}
}
}
}
}
}
colnames(GeneraTrends) <- c("MidLU","MidAltitude","NSamples","MeanBeta","SDBeta")
rownames(GeneraTrends) <- 1:nrow(GeneraTrends)
|
f10905bc35c1f8b60eb00e2628b3c853ad9be799 | b51a4d510afd3aa852e446290d7bba9695ae8e72 | /products/ui.R | 36cbcafa875999ec710551bf3dd947fd8aca183b | [
"MIT"
] | permissive | sharksmhi/SHARK4R | 8ed9e733b3cab91862401cc233d1fb503187d26d | acb28d26660f7daa45c7f2395329a67e038534dd | refs/heads/master | 2023-08-29T23:07:56.765132 | 2021-10-25T06:25:23 | 2021-10-25T06:25:23 | 270,549,422 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 508 | r | ui.R | library(SHARK4R)
library(shiny)
library(htmltools)
library(leaflet)
library(DT)
library(rmarkdown)
shinyUI(pageWithSidebar(
headerPanel("SHARK4R QC"),
sidebarPanel(
helpText(),
fileInput('file1', 'Choose ZIP File',
accept=c('.zip')),
tags$hr(),
leafletOutput("mymap"),
tags$hr(),
downloadButton('report', 'Generate report')
),
mainPanel(
DT::DTOutput(outputId = "table")
)
)) |
369e0605cb9a5238633818b75548f72ca99b9ec2 | da44d6b825853e76fa449ff1d477c8c4b4748f84 | /man/vizectionExampleGenes.Rd | 7ecf668059b7199fb3c38374635c51935048a131 | [] | no_license | shamansim/Vizection | fde7f22552cf1bcb487dcb2a50d72d2c0064fb59 | 37246952e43d2a1a9132087ba8b92718ddf625e8 | refs/heads/master | 2020-12-25T14:38:25.793023 | 2017-10-11T14:43:51 | 2017-10-11T14:43:51 | 64,266,012 | 0 | 2 | null | 2017-10-11T14:44:05 | 2016-07-27T01:12:26 | R | UTF-8 | R | false | true | 521 | rd | vizectionExampleGenes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vizectionServerFunctions.R
\name{vizectionExampleGenes}
\alias{vizectionExampleGenes}
\title{vizection example \dQuote{genes} table}
\usage{
vizectionExampleGenes()
}
\description{
Toy example for the \dQuote{genes} table to be used in vizection.
}
\details{
This is for debugging purposes; the example does not reflect well
the kind of real data that is expected.
}
\examples{
summary(vizectionExampleGenes())
head(vizectionExampleGenes())
}
|
7c965ccc9e7c7780c676dcb12953fe40417d76b1 | 644c06fefbf06d8b6b96e7ca4e9f39607b9dc83a | /plot1.R | a6538f2707002c1a1b31b9c258a8ada7a3277d64 | [] | no_license | Wil1999/ExData_Plotting1 | 162984a609e1f235fbcdd13f348573253edc1abd | 73c9c77ada0f227daecf052857e4e40db44f0598 | refs/heads/master | 2022-12-02T15:12:22.522399 | 2020-08-17T00:10:30 | 2020-08-17T00:10:30 | 287,833,174 | 0 | 0 | null | 2020-08-15T22:17:19 | 2020-08-15T22:17:18 | null | UTF-8 | R | false | false | 642 | r | plot1.R | # Unzip the data
fileurl = "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileurl,destfile = "data.zip",method = "curl")
unzip("data.zip")
# Loading and tyding data
power <- read.table("household_power_consumption.txt",sep = ";", header = TRUE, stringsAsFactors = FALSE, dec = ".")
# Loading of the subset
subpower <- power[power$Date %in% c("1/2/2007","2/2/2007"),]
gactivep <- as.numeric(subpower$Global_active_power)
# Plots the data "Global Active Power"
png("plot1.png")
hist(gactivep,col = "red",xlab = "Global Active Power (kilowatts)",main = "Global Active Power" )
dev.off()
|
e46a67c7cfa92518d775eee777affa301bfc8a5a | 1a19c6916b7171f8e893d89bded8766d10936719 | /man/fars_summarize_years.Rd | 4239d891e61f98fd9a2c054a7e2bb8004f4593d9 | [] | no_license | richcmwang/Fars | b7604e3626dde8555c57e84ad8c45d69fada5b4d | 40912d938e13b1b5060701286fe62e33cb8cec2d | refs/heads/master | 2021-01-22T11:20:51.788174 | 2017-05-29T21:57:38 | 2017-05-29T21:57:38 | 92,684,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 708 | rd | fars_summarize_years.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fars_functions.R
\name{fars_summarize_years}
\alias{fars_summarize_years}
\title{Show a summary of accidents during given years}
\usage{
fars_summarize_years(years)
}
\arguments{
\item{years}{a vector of integers}
}
\value{
a tibble showing summary of number of accidents by month
for each input year
}
\description{
Show a summary of accidents during given years
}
\note{
"dplyr" package is required. If input years contains a year where no data exists,
then the function will returns summary for valid years and a warning of invalid year.
error and warning message are returned when the list of years contains no valid year.
}
|
79cacd0351bd26bb47c4422fe136b446a2887dd2 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/solvebio/examples/DatasetExport.delete.Rd.R | 3516f347ba9fd4916b5e8f8bd5bc259ea15a23fe | [] | 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 | 199 | r | DatasetExport.delete.Rd.R | library(solvebio)
### Name: DatasetExport.delete
### Title: DatasetExport.delete
### Aliases: DatasetExport.delete
### ** Examples
## Not run:
##D DatasetExport.delete(<ID>)
## End(Not run)
|
047e069ab7e2067520ce062f12657e35659d0ee5 | 12eab6d1208f29f6a4210cee0a712e8304240814 | /mrds/R/is.linear.logistic.R | 003e503dd87ce1649806402b6c773c4262c090c1 | [] | no_license | jlaake/mrds | c8920ffdd91c411f2bfb91a15abd786f8b0b1d0e | 64bb11a5682a2fd56611cef9860be476ea5f14be | refs/heads/master | 2021-01-18T06:25:57.563386 | 2013-12-18T19:13:48 | 2013-12-18T19:13:48 | 2,009,699 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,705 | r | is.linear.logistic.R | #' Collection of functions for logistic detection functions
#'
#' These functions are used to test whether a logistic detection function is a
#' linear function of distance (\code{is.linear.logistic}) or is constant
#' (varies by distance but no other covariates) \code{is.logistic.constant}).
#' Based on these tests, the most appropriate manner for integrating the
#' detection function with respect to distance is chosen. The integrals are
#' needed to estimate the average detection probability for a given set of
#' covariates.
#'
#' If the logit is linear in distance then the integral can be computed
#' analytically. If the logit is constant or only varies by distance then only
#' one integral needs to be computed rather than an integral for each
#' observation.
#'
#' @param xmat data matrix
#' @param g0model logit model
#' @param zdim number of columns in design matrix
#' @param width transect width
#' @return Logical TRUE if condition holds and FALSE otherwise
#' @author Jeff Laake
#' @keywords utility
is.linear.logistic <- function(xmat,g0model,zdim,width){
xmat$distance <- rep(width/2, nrow(xmat))
beta <- rep(1,zdim)
logit1 <- mean(beta %*% t(setcov(xmat, g0model)$cov))
xmat$distance <- rep(width, nrow(xmat))
logit2 <- mean(beta %*% t(setcov(xmat, g0model)$cov))
xmat$distance <- rep(0, nrow(xmat))
logit0 <- mean( beta %*% t(setcov(xmat, g0model)$cov))
if(logit1-logit0==0){
integral.numeric <- FALSE
}else if((logit2-logit0)/(logit1-logit0) <= 2.00001 &
(logit2-logit0)/(logit1-logit0) >= 1.99999){
integral.numeric <- FALSE
}else{
integral.numeric <- TRUE
}
return(integral.numeric)
}
|
5044d1b51604e7e5dc8c2aa2318880755e589a59 | 8466ef1c1a8697e0409c8d2894ef74644d00f155 | /packages.R | a852f8270792e52ddcfa315cec6c977baf9eee4d | [] | no_license | MilesMcBain/carbon_au | 1b17df25dbb44ea5fbc91268f1007630cb806850 | c03044830818a1551f395e19be27f7e17482590d | refs/heads/master | 2020-12-14T07:59:09.785595 | 2020-03-06T10:12:55 | 2020-03-06T10:12:55 | 234,681,059 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 264 | r | packages.R | ## library() calls go here
library(conflicted)
library(dotenv)
library(drake)
library(rvest)
library(tidyverse)
library(stringr)
library(curl)
library(glue)
library(pdftools)
conflict_prefer("pluck", "purrr")
conflict_prefer("filter", "dplyr")
library(rmarkdown)
|
058c856942ff19555071ea6dfa8eb5bb269dbec1 | fb0f8dacbfc6f7afb90d5f1a5da9626119104697 | /man/scmap.Rd | c2cbd439edcf7e0d81519840d38ece26575d3487 | [] | no_license | dewittpe/sccm | 1777cebddb4042843db85a8a10eea099bddb24a4 | dfbd83862461fd59116ee3b6a05d75319c0fd9a8 | refs/heads/master | 2021-06-18T01:27:49.423285 | 2021-02-18T19:11:12 | 2021-02-18T19:11:12 | 67,158,240 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,060 | rd | scmap.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scmap.R
\name{scmap}
\alias{scmap}
\title{Schwarz-Christoffel Mapping (Disk to Polygon)}
\usage{
scmap(x, wc, nptsq = 12)
}
\arguments{
\item{x}{\code{sccm_ch} or \code{sccm_pg}, object.}
\item{wc}{the center of the polygon, i.e., where the origin will be mapped
to. The default is c(mean(x), mean(y))}
\item{nptsq}{the number of points per subinterval used in Gauss-Jacobi
quadrature.}
}
\value{
an object
}
\description{
Generates the Schwarz-Christoffel mapping from the unit disk to the provided
2D polygon.
}
\details{
These functions might be used later to allow end users to have more control
over the mmappings. Until then, it would be best for end users to use
\code{\link{d2p}} for disk to polygon,
\code{\link{p2d}} for polygon to disk, and
\code{\link{p2p}} for mapping one polygon to another via a disk.
}
\seealso{
\code{\link{d2p}} for disk to polygon,
\code{\link{p2d}} for polygon to disk, and
\code{\link{p2p}} for mapping one polygon to another via a disk.
}
|
82af3b78e696aedca5ee8ace08cdf7bee20220e2 | 6d734eeb3982656c23de22c7a0d2d8f9902417bb | /analysis/crop_field_extent.R | e1f6373a27dfc579e654aa413b1ca6b34be1e9d2 | [] | no_license | weecology/TreeSegmentation | cadc8fa79dd5e5b24a1f4b898ce709134c5461af | cf10386eaf0d5da286a855d7484d57918d607d29 | refs/heads/master | 2022-10-21T07:49:52.103033 | 2022-10-07T03:41:03 | 2022-10-07T03:41:03 | 130,730,841 | 8 | 3 | null | 2019-03-15T18:09:21 | 2018-04-23T17:15:37 | HTML | UTF-8 | R | false | false | 959 | r | crop_field_extent.R | #Find crops
library(raster)
library(TreeSegmentation)
rgb_filename<-"/home/b.weinstein/TreeSegmentation/data/field_crowns/2018_HARV_5_733000_4698000_image_crop.tif"
basename <- stringr::str_match(rgb_filename,"/(\\w+).tif")[,2]
#CROP CHM
crop_target_CHM(siteID="MLBS",rgb_filename,year="2018",tif_base_dir="/orange/ewhite/NeonData",save_base_dir="/orange/ewhite/b.weinstein/NEON")
#crop Hyperspectral 3 band
ext <- raster::extent(raster::raster(rgb_filename))
easting <- as.integer(ext@xmin/1000)*1000
northing <- as.integer(ext@ymin/1000)*1000
geo_index <- paste(easting,northing,sep="_")
crop_target_hyperspectral(siteID="HARV",
rgb_filename=rgb_filename,
geo_index=geo_index,
false_color=TRUE,
year="2018",
h5_base_dir="/orange/ewhite/NeonData/",
save_base_dir="/orange/ewhite/b.weinstein/NEON/")
|
3d545783244fc8047f4d6c264c782864540510e8 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610131239-test.R | 78d7e5ef0e4de0c2efaf9f582f43fae6fafa7a9e | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 202 | r | 1610131239-test.R | testlist <- list(a = 0L, b = 0L, x = c(-488505344L, -488505344L, 2748927L, -1L, -256L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L ))
result <- do.call(grattan:::anyOutside,testlist)
str(result) |
3e6021a8909ecad9a44bbb3fcfd252c5680b8674 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/CLVTools/R/f_s3generics_clvfittedtransactions_plot.R | 4ec06c09f75c3765b916790c041bed2dd7a59198 | [] | 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 | 13,810 | r | f_s3generics_clvfittedtransactions_plot.R |
#' @title Plot expected and actual repeat transactions
#' @param x The fitted clv model to plot
#' @param newdata An object of class clv.data for which the plotting should be made with the fitted model. If none or NULL is given, the plot is made for the data on which the model was fit.
#' @param transactions Whether the actual observed repeat transactions should be plotted.
#' @param cumulative Whether the cumulative expected (and actual) transactions should be plotted.
#' @param plot Whether a plot should be created or only the assembled data is returned.
#' @param label Character string to label the model in the legend
#' @template template_param_predictionend
#' @template template_param_verbose
#' @template template_param_dots
#'
#' @description
#' Plot the actual repeat transactions and overlay it with the repeat transaction as predicted
#' by the fitted model. Currently, following previous literature, the in-sample unconditional
#' expectation is plotted in the holdout period. In the future, we might add the option to also
#' plot the summed CET for the holdout period as an alternative evaluation metric.
#'
#' @template template_details_predictionend
#'
#' @details Note that only whole periods can be plotted and that the prediction end might not exactly match \code{prediction.end}.
#' See the Note section for more details.
#'
#' @template template_details_newdata
#'
#'
#' @note Because the unconditional expectation for a period is derived as the difference of
#' the cumulative expectations calculated at the beginning and at end of the period,
#' all timepoints for which the expectation is calculated are required to be spaced exactly 1 time unit apart.
#'
#' If \code{prediction.end} does not coincide with the start of a time unit, the last timepoint
#' for which the expectation is calculated and plotted therefore is not \code{prediction.end}
#' but the start of the first time unit after \code{prediction.end}.
#'
#'
#' @seealso \code{\link[CLVTools:plot.clv.fitted.spending]{plot}} for spending models
#'
#' @return
#' An object of class \code{ggplot} from package \code{ggplot2} is returned by default.
#' If the parameter \code{plot} is \code{FALSE}, the data that would have been melted and used to
#' create the plot is returned. It is a \code{data.table} which contains the following columns:
#' \item{period.until}{The timepoint that marks the end (up until and including) of the period to which the data in this row refers.}
#' \item{Number of Repeat Transactions}{The number of actual repeat transactions in
#' the period that ends at \code{period.until}. Only if \code{transactions} is \code{TRUE}.}
#' \item{"Name of Model" or "label"}{The value of the unconditional expectation for the period that ends on \code{period.until}.}
#'
#' @examples
#' \donttest{
#'
#' data("cdnow")
#'
#' # Fit ParetoNBD model on the CDnow data
#' pnbd.cdnow <- pnbd(clvdata(cdnow, time.unit="w",
#' estimation.split=37,
#' date.format="ymd"))
#'
#' # Plot actual repeat transaction, overlayed with the
#' # expected repeat transactions as by the fitted model
#' plot(pnbd.cdnow)
#'
#' # Plot cumulative expected transactions of only the model
#' plot(pnbd.cdnow, cumulative=TRUE, transactions=FALSE)
#'
#' # Plot forecast until 2001-10-21
#' plot(pnbd.cdnow, prediction.end = "2001-10-21")
#'
#' # Plot until 2001-10-21, as date
#' plot(pnbd.cdnow,
#' prediction.end = lubridate::dym("21-2001-10"))
#'
#' # Plot 15 time units after end of estimation period
#' plot(pnbd.cdnow, prediction.end = 15)
#'
#' # Save the data generated for plotting
#' # (period, actual transactions, expected transactions)
#' plot.out <- plot(pnbd.cdnow, prediction.end = 15)
#'
#' # A ggplot object is returned that can be further tweaked
#' library("ggplot2")
#' gg.pnbd.cdnow <- plot(pnbd.cdnow)
#' gg.pnbd.cdnow + ggtitle("PNBD on CDnow")
#'
#' }
#'
# # Compose plot from separate model plots
# # pnbd vs bgnbd
# p.m1 <- plot(pnbd.cdnow, transactions = TRUE)
#
# # static cov model
# p.m2 <- plot(pnbd.cdnow.cov, transactions = FALSE)
# p.m1 + geom_line(mapping=p.m2$mapping, data=p.m2$data,
# color="blue")
#' @importFrom graphics plot
#' @include class_clv_fitted.R
#' @method plot clv.fitted.transactions
#' @aliases plot
#' @export
plot.clv.fitted.transactions <- function (x, prediction.end=NULL, newdata=NULL, cumulative=FALSE, transactions=TRUE, label=NULL, plot=TRUE, verbose=TRUE,...) {
period.until <- period.num <- NULL
# Check if can plot -----------------------------------------------------------------------------------------
# Cannot plot if there are any NAs in any of the prediction.params
clv.controlflow.check.prediction.params(clv.fitted = x)
# Newdata ------------------------------------------------------------------------------------------------
# Because many of the following steps refer to the data stored in the fitted model,
# it first is replaced with newdata before any other steps are done
if(!is.null(newdata)){
# check newdata
clv.controlflow.check.newdata(clv.fitted = x, user.newdata = newdata, prediction.end=prediction.end)
# Replace data in model with newdata
# Deep copy to not change user input
x@clv.data <- copy(newdata)
# Do model dependent steps of adding newdata
x <- clv.model.process.newdata(clv.model = x@clv.model, clv.fitted=x, verbose=verbose)
}
# Check inputs ------------------------------------------------------------------------------------------------------
err.msg <- c()
err.msg <- c(err.msg, .check_user_data_single_boolean(b=cumulative, var.name="cumulative"))
err.msg <- c(err.msg, .check_user_data_single_boolean(b=plot, var.name="plot"))
err.msg <- c(err.msg, .check_user_data_single_boolean(b=verbose, var.name="verbose"))
err.msg <- c(err.msg, .check_user_data_single_boolean(b=transactions, var.name="transactions"))
err.msg <- c(err.msg, check_user_data_predictionend(clv.fitted=x, prediction.end=prediction.end))
err.msg <- c(err.msg, check_user_data_emptyellipsis(...))
if(!is.null(label)) # null is allowed = std. model name
err.msg <- c(err.msg, .check_userinput_single_character(char=label, var.name="label"))
check_err_msg(err.msg)
# do fitted object specific checks (ie dyncov checks cov data length)
clv.controlflow.plot.check.inputs(obj=x, prediction.end=prediction.end, cumulative=cumulative,
plot=plot, label.line=label, verbose=verbose)
# Define time period to plot -----------------------------------------------------------------------------------------
# Use table with exactly defined periods as reference and to save all generated data
# End date:
# Use same prediction.end date for clv.data (actual transactions) and clv.fitted (unconditional expectation)
# If there are not enough transactions for all dates, they are set to NA (= not plotted)
dt.dates.expectation <- clv.time.expectation.periods(clv.time = x@clv.data@clv.time, user.tp.end = prediction.end)
tp.data.start <- dt.dates.expectation[, min(period.until)]
tp.data.end <- dt.dates.expectation[, max(period.until)]
if(verbose)
message("Plotting from ", tp.data.start, " until ", tp.data.end, ".")
if(clv.data.has.holdout(x@clv.data)){
if(tp.data.end < x@clv.data@clv.time@timepoint.holdout.end){
warning("Not plotting full holdout period.", call. = FALSE, immediate. = TRUE)
}
}else{
if(tp.data.end < x@clv.data@clv.time@timepoint.estimation.end){
warning("Not plotting full estimation period.", call. = FALSE, immediate. = TRUE)
}
}
# Get expectation values -----------------------------------------------------------------------------------------
dt.expectation <- clv.controlflow.plot.get.data(obj=x, dt.expectation.seq=dt.dates.expectation,
cumulative=cumulative, verbose=verbose)
if(length(label)==0)
label.model.expectation <- x@clv.model@name.model
else
label.model.expectation <- label
setnames(dt.expectation,old = "expectation", new = label.model.expectation)
# Get repeat transactions ----------------------------------------------------------------------------------------
if(transactions){
label.transactions <- "Actual Number of Repeat Transactions"
dt.repeat.trans <- clv.controlflow.plot.get.data(obj=x@clv.data, dt.expectation.seq=dt.dates.expectation,
cumulative=cumulative, verbose=verbose)
setnames(dt.repeat.trans, old = "num.repeat.trans", new = label.transactions)
}
# Plot data, if needed --------------------------------------------------------------------------------------------
# Merge data for plotting
# To be sure to have all dates, merge data on original dates
dt.dates.expectation[, period.num := NULL]
if(transactions){
dt.dates.expectation[dt.expectation, (label.model.expectation) := get(label.model.expectation), on="period.until"]
dt.dates.expectation[dt.repeat.trans, (label.transactions) := get(label.transactions), on="period.until"]
dt.plot <- dt.dates.expectation
}else{
dt.dates.expectation[dt.expectation, (label.model.expectation) := get(label.model.expectation), on="period.until"]
dt.plot <- dt.dates.expectation
}
# data.table does not print when returned because it is returned directly after last [:=]
# " if a := is used inside a function with no DT[] before the end of the function, then the next
# time DT or print(DT) is typed at the prompt, nothing will be printed. A repeated DT or print(DT)
# will print. To avoid this: include a DT[] after the last := in your function."
dt.plot[]
# Only plot if needed
if(!plot)
return(dt.plot)
if(transactions)
line.colors <- setNames(object = c("black", "red"),
nm = c(label.transactions, label.model.expectation))
else
line.colors <- setNames(object = "red", nm = label.model.expectation)
# Plot table with formatting, label etc
return(clv.controlflow.plot.make.plot(dt.data = dt.plot, clv.data = x@clv.data, line.colors = line.colors))
}
#' @importFrom ggplot2 ggplot aes geom_line geom_vline labs theme scale_fill_manual guide_legend element_text element_rect element_blank element_line rel
clv.controlflow.plot.make.plot <- function(dt.data, clv.data, line.colors){
# cran silence
period.until <- value <- variable <- NULL
# Melt everything except what comes from the standard expectation table
meas.vars <- setdiff(colnames(dt.data), c("period.num", "period.until"))
data.melted <- melt(data=dt.data, id.vars = c("period.until"),
variable.factor = FALSE, na.rm = TRUE,
measure.vars = meas.vars)
p <- ggplot(data = data.melted, aes(x=period.until, y=value, colour=variable)) + geom_line()
# Add holdout line if there is a holdout period
if(clv.data.has.holdout(clv.data)){
p <- p + geom_vline(xintercept = as.numeric(clv.data@clv.time@timepoint.holdout.start),
linetype="dashed", show.legend = FALSE)
}
# Variable color and name
p <- p + scale_fill_manual(values = line.colors,
aesthetics = c("color", "fill"),
guide = guide_legend(title="Legend"))
# Axis and title
p <- p + labs(x = "Date", y= "Number of Repeat Transactions", title= paste0(clv.time.tu.to.ly(clv.time=clv.data@clv.time), " tracking plot"),
subtitle = paste0("Estimation end: ", clv.time.format.timepoint(clv.time=clv.data@clv.time, timepoint=clv.data@clv.time@timepoint.estimation.end)))
p <- p + theme(
plot.title = element_text(face = "bold", size = rel(1.5)),
text = element_text(),
panel.background = element_blank(),
panel.border = element_blank(),
plot.background = element_rect(colour = NA),
axis.title = element_text(face = "bold",size = rel(1)),
axis.title.y = element_text(angle=90,vjust =2),
axis.title.x = element_text(vjust = -0.2),
axis.text = element_text(),
axis.line = element_line(colour="black"),
axis.ticks = element_line(),
panel.grid.major = element_line(colour="#d2d2d2"),
panel.grid.minor = element_blank(),
legend.key = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.title = element_text(face="italic"),
strip.background=element_rect(colour="#d2d2d2",fill="#d2d2d2"),
strip.text = element_text(face="bold", size = rel(0.8)))
return(p)
}
# . clv.controlflow.plot.get.data ---------------------------------------------------------------
setMethod(f="clv.controlflow.plot.get.data", signature = signature(obj="clv.fitted.transactions"), definition = function(obj, dt.expectation.seq, cumulative, verbose){
expectation <- i.expectation <- NULL
# Pass copy of expectation table file because will be modified and contain column named expecation
dt.model.expectation <- clv.model.expectation(clv.model=obj@clv.model, clv.fitted=obj, dt.expectation.seq=copy(dt.expectation.seq),
verbose = verbose)
# Only the expectation data
dt.model.expectation <- dt.model.expectation[, c("period.until", "expectation")]
if(cumulative)
dt.model.expectation[, expectation := cumsum(expectation)]
# add expectation to plot data
# name columns by model
dt.expectation.seq[dt.model.expectation, expectation := i.expectation, on = "period.until"]
return(dt.expectation.seq)
})
#' @exportMethod plot
#' @include class_clv_fitted.R
#' @rdname plot.clv.fitted.transactions
setMethod("plot", signature(x="clv.fitted.transactions"), definition = plot.clv.fitted.transactions)
|
b51ab8fd93124ca6e032258df7db6602e2f37230 | 84dcc770d7766a3171efe7aa46e50dbcb496c9b5 | /CUSUM/version4/cut_grid.R | 4c3a83f2aced5bd74452e7ea4ce2db41ddd153e7 | [] | no_license | xinzhang-nac/Spatial_CUSUM | 0b36e1a04526ea26578d86c41a2eaf1ffa980023 | 677eedf773af01525d788f2d2ee9932a1d8f4541 | refs/heads/master | 2020-04-09T03:58:59.994822 | 2019-05-15T14:31:29 | 2019-05-15T14:31:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 233 | r | cut_grid.R | grid.point <- within(grid.point, {
grp.x = cut(x, (0:10)/10, labels = FALSE)
grp.y = cut(y, (0:10)/10, labels = FALSE)
})
plot(y ~ x, data = grid.point, pch = (15:25)[grp.x], col = grp.y)
abline(v = (1:9)/10)
abline(h = (1:9)/10) |
935dc4d5e117a44dad7d8832722f73e42e7d0fdf | 79457aaae83a0b3914a38874c10907440e0dfc61 | /man/dimension_values.Rd | e8b3918e176c4de1ea08037841ae1cee2fdc1fd4 | [] | permissive | appelmar/gdalcubes | be9786b36fbe4e25a5c0245968634f57a40752ad | 2134f769454e147660e7a73c61afa14219de20b4 | refs/heads/master | 2023-08-07T20:56:02.442579 | 2023-07-25T06:36:46 | 2023-07-25T06:36:46 | 148,130,790 | 74 | 7 | MIT | 2023-03-23T19:56:08 | 2018-09-10T09:25:01 | C++ | UTF-8 | R | false | true | 1,315 | rd | dimension_values.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cube.R
\name{dimension_values}
\alias{dimension_values}
\title{Query coordinate values for all dimensions of a data cube}
\usage{
dimension_values(obj, datetime_unit = NULL)
}
\arguments{
\item{obj}{a data cube proxy (class cube), or a data cube view object}
\item{datetime_unit}{unit used to format values in the datetime dimension, one of "Y", "m", "d", "H", "M", "S", defaults to the unit of the cube.}
}
\value{
list with elements t,y,x
}
\description{
Dimension values give the coordinates along the spatial and temporal axes of a data cube.
}
\examples{
# create image collection from example Landsat data only
# if not already done in other examples
if (!file.exists(file.path(tempdir(), "L8.db"))) {
L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
".TIF", recursive = TRUE, full.names = TRUE)
create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE)
}
L8.col = image_collection(file.path(tempdir(), "L8.db"))
v = cube_view(extent=list(left=388941.2, right=766552.4,
bottom=4345299, top=4744931, t0="2018-04", t1="2018-06"),
srs="EPSG:32618", nx = 497, ny=526, dt="P1M")
dimension_values(raster_cube(L8.col, v))
}
|
326d570cb7510eb82c1f56d46593051f4839f964 | 1087dc5af15ff0944cf595d877f5ef6d2d951936 | /tests/testthat/test-opart-gaussian.R | 8969ce628d0740413508ef000be19cd5d0e055cc | [] | no_license | cran/opart | 3777c97e8995ffef3a5bef6f53e7bc2b048689f2 | 3868f22cf67ab10c6051f92e6dd89dfd2e76ffc2 | refs/heads/master | 2020-12-22T01:48:33.382168 | 2019-09-04T12:40:02 | 2019-09-04T12:40:02 | 236,634,128 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,313 | r | test-opart-gaussian.R | library(testthat)
context("opart-gaussian")
library(opart)
data(neuroblastoma, package="neuroblastoma")
#tests for invalid scenarios
test_that("opart gives error for negative penalty", {
expect_error({
opart_gaussian(c(1,2,3), -2)
}, "penalty value must be greater than 0",
fixed=TRUE)
})
test_that("opart gives error when data points are less than 1", {
expect_error({
opart_gaussian(c(), 1)
}, "data vector must have atleast one element",
fixed=TRUE)
})
test_that("opart gives error when data vector has missing(NA) values", {
expect_error({
opart_gaussian(c(1,NA,2,3), 1)
}, "data vector has missing(NA) values",
fixed=TRUE)
})
test_that("opart gives error when data vector contains non-numeric values", {
expect_error({
opart_gaussian(c("a", "b", 1, 3), 1)
}, "data vector must contains finite numeric values",
fixed=TRUE)
})
test_that("opart gives error when data vector contains infinity", {
expect_error({
opart_gaussian(c(Inf, 1,2), 1)
}, "data vector must contains finite numeric values",
fixed=TRUE)
})
#test for zero penalty
res <- opart_gaussian(c(1,2,3,4,5), 0)
test_that("all the data points are segment ends", {
expect_equal(res$end.vec, c(1,2,3,4,5))
})
#test for large penalty
res <- opart_gaussian(c(1,2,3,4,5), 1000000)
test_that("all the data points in one segment", {
expect_equal(res$end.vec, 5)
})
#test for large data
res <- opart_gaussian(c(1:1000), 0)
test_that("all the data points as segment ends", {
expect_equal(res$end.vec, c(1:1000))
})
#test for correctness
res <- opart_gaussian(c(1,2,3,4), 1)
test_that("cost vector should match manual calculation", {
expect_equal(res$cost.vec, c(-1, -4.5, -12.5, -28))
})
#test for neuroblastoma data set with profile.id = 1 and chromosome = 1
selProfile <- subset(neuroblastoma$profiles, profile.id=="1" & chromosome=="1")
nrows <- nrow(selProfile)
#test for large penalty
res <- opart_gaussian(selProfile$logratio, 10000000)
test_that("all the data points in one segment", {
expect_equal(res$end.vec, nrows)
})
#test for small penalty
res <- opart_gaussian(selProfile$logratio, 1)
test_that("all the data points are segment ends", {
expect_equal(length(res$end.vec), 4)
})
|
653233b02be0ceffe044e4d8dabd79c3fb6cfb35 | e2a88b360595b345d64f1ae2ded271ac0f39203b | /cachematrix.R | 52cf7004ff4d90c438f7642a42ec3cd273c7c97f | [] | no_license | arkim42/ProgrammingAssignment2 | 958cf4c368df9919a2f8fbf30b6bbc7d89fb88de | 80961bdc7a0858673fe052295433ebc04c55da76 | refs/heads/master | 2022-11-13T11:02:27.069156 | 2020-06-19T21:20:54 | 2020-06-19T21:20:54 | 273,445,411 | 0 | 0 | null | 2020-06-19T08:35:17 | 2020-06-19T08:35:16 | null | UTF-8 | R | false | false | 1,194 | r | cachematrix.R | ## The first function, makeCacheMatrix creates a special "matrix",
## which is reall a list containing a function to
## 1. set : set the value of the matrix
## 2. get: get the value of the matrix
## 3. setinverse : set the value of the inverse of the matrix
## 4. getinverse : get the value of the inverse of the matrix
makeCacheMatrix <- function(x = matrix()) {
iv <- NULL
set <- function(y) {
x <<- y
iv <<- NULL
}
get <- function() x
setinverse <- function(inverse) iv <<- inverse
getinverse <- function() iv
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## The following function calculates the inverse of the special "matrix"
## created with the above function. However, it first checks to see if
## the inverse has already been calculated. If so, it gets the inverse
## from the cache and skips the computation. Otherwise, it calculates
## the inverse of the data and sets the value of the inverse in the cache
## via the setinverse function.
cacheSolve <- function(x, ...) {
iv <- x$getinverse()
if (!is.null(iv)) {
message("getting cached data")
return (iv)
}
data <- x$get()
iv <- solve(data, ...)
x$setinverse(iv)
iv
}
|
a5d01f1c07df08f3eca5c210ec3bf9ddf498d658 | ae4a257cc62e8ee4b90fda7e877750e59c0d8ba3 | /man/plotSingleTS.Rd | 17378a3b9ab57a653359e102909aea858c1c4c25 | [] | no_license | davidlamcm/Rtoolbox | 824a5f189c5e3ec89f841fe4e73f85605d894d6a | 238a6881f4df4a47365aeb0a2786fed62899e537 | refs/heads/master | 2021-01-19T14:09:35.775475 | 2018-08-22T15:16:44 | 2018-08-22T15:16:44 | 88,128,106 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 300 | rd | plotSingleTS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fun_plotSingleTS.R
\name{plotSingleTS}
\alias{plotSingleTS}
\title{this function will be deprecated
wrapper for ggplot}
\usage{
plotSingleTS(ts, title)
}
\description{
this function will be deprecated
wrapper for ggplot
}
|
85fd0a52ca45b212e75a9c840865d0034c3e3c9d | 9ff5451498def880e07a1f568cf58a116a1d6abb | /cachematrix.R | 145356cc4296b90f237f6c3a615ce665bd57241b | [] | no_license | daniellins/ProgrammingAssignment2 | f570690716696640c4b8812546008acff4177eff | 970d4e44091605b87344e37856536489b0d517b6 | refs/heads/master | 2021-01-16T21:49:13.900652 | 2015-02-22T06:34:32 | 2015-02-22T06:34:32 | 31,153,720 | 0 | 0 | null | 2015-02-22T05:26:49 | 2015-02-22T05:26:48 | null | UTF-8 | R | false | false | 1,685 | r | cachematrix.R | ## makeCacheMatrix: Store and retrieve a object (matrix) in the cache.
## Params: a matrix
## Result: a special object with the functions below:
## 1- set the value of the matrix (set)
## 2- get the value of the matrix (get)
## 3- set the value of the solve (setInverse)
## 4- get the value of the solve (getInverse)
## Author: Daniel Lins
## Date:02/21/2015
## Version:1.0
makeCacheMatrix <- function(x = matrix()) {
cachedInverseMatrix <- NULL
set <- function(y){
x <<- y
cachedInverseMatrix <<- NULL
}
get <- function() x
setInverse <- function(inverseMatrix) cachedInverseMatrix <<- inverseMatrix
getInverse <- function() cachedInverseMatrix
list(set=set, get=get, setInverse=setInverse, getInverse=getInverse)
}
## cacheSolve: Calculates the inverse (solve) of the matrix. However, it first
## checks if the result has already been calculated and stored in the cache.
## If yes, it gets the result from the cache.
## Params: Special object (output of MakeCacheMatrix())
## Return: a matrix that is the inverse of the original matrix.
## Author: Daniel Lins
## Date:02/21/2015
## Version:1.0
cacheSolve <- function(x, ...) {
inverseMatrix <- x$getInverse()
if(!is.null(inverseMatrix)){
message("Object already in Cache. Retrieving cached data...")
return (inverseMatrix)
}
originalMatrix <-x$get()
inverseMatrix <- solve(originalMatrix)
x$setInverse(inverseMatrix)
return(inverseMatrix)
}
|
f161750daa45a085349f201e11657affcb24c468 | 8f6927b75d22a4f24958477c0bb23e3962126ae1 | /Workbook_6/template_function_macrophage_filtered.R | 1141cd25eee05b124d062c3750ee6db184ce38d2 | [] | no_license | NCI-VB/pavlakis_TNBC_hetIL-15 | 9f160ab9e11291922515d99f4c3600fdd648870a | efbb02c019ef16079b278e8be628abd176508f42 | refs/heads/main | 2023-04-15T12:42:41.160189 | 2023-03-14T15:36:26 | 2023-03-14T15:36:26 | 606,222,869 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,656 | r | template_function_macrophage_filtered.R | # [scRNA-Seq][CCBR] Filter Seurat Object by Metadata (ec3f23f9-bcba-4f3a-8a08-8ba611fbb6c7): v114
macrophage_filtered <- function(Annotate_Cell_Types, Metadata_Table, Sample_Names) {
## Libraries
suppressMessages(library(ggplot2))
suppressMessages(library(Seurat))
suppressWarnings(library(gridExtra))
suppressMessages(library(grid))
suppressMessages(library(gridBase))
suppressMessages(library(cowplot))
suppressMessages(library(RColorBrewer))
suppressMessages(library(colorspace))
suppressMessages(library(tidyverse))
## Inputs.
so <- Annotate_Cell_Types$value
reduction = "tsne"
doCiteSeq <- FALSE
#image: png
imageType = "png"
Keep <- FALSE
plotinteractive = FALSE
seed = 10
metacols = "SCT_snn_res_0_6"
cols1 <- c("aquamarine3","salmon1","lightskyblue3","plum3","darkolivegreen3","goldenrod1","burlywood2","gray70","firebrick2","steelblue","palegreen4","orchid4","darkorange1","yellow","sienna","palevioletred1","gray60","cyan4","darkorange3","mediumpurple3","violetred2","olivedrab","darkgoldenrod2","darkgoldenrod","gray40","palegreen3","thistle3","khaki1","deeppink2","chocolate3","paleturquoise3","wheat1","lightsteelblue","salmon","sandybrown","darkolivegreen2","thistle2","gray85","orchid3","darkseagreen1","lightgoldenrod1","lightskyblue2","dodgerblue3","darkseagreen3","forestgreen","lightpink2","mediumpurple4","lightpink1","thistle","navajowhite","lemonchiffon","bisque2","mistyrose","gray95","lightcyan3","peachpuff2","lightsteelblue2","lightyellow2","moccasin","gray80","antiquewhite2","lightgrey")
samples = eval(parse(text=gsub('\\[\\]','c()','c("Control","Treated")')))
if (length(samples) == 0) {
samples = unique(SO@meta.data$sample_name)
}
## Replace dots in metadata column names with underscores.
colnames(so@meta.data) = gsub("\\.", "_", colnames(so@meta.data))
## If you have protien data, then ...
if (doCiteSeq) {
reduction =paste("protein_", reduction,sep='')
}
## Set image dimensions within Vector.
imageWidth = 2000 * 2
imageHeight = 2000
dpi = 300
## Set image format (png or svg) based on user input.
if (imageType == 'png') {
png(
filename = "macrophage_filtered.png",
width = imageWidth,
height = imageHeight,
units = "px",
pointsize = 4,
bg = "white",
res = dpi,
type = "cairo")
} else {
library(svglite)
svglite::svglite(
file = "macrophage_filtered.png",
width = round(imageWidth/dpi,digits=2),
height = round(imageHeight/dpi,digits=2),
pointsize = 1,
bg = "white")
}
## Original color-picking code.
n <- 2e3
set.seed(seed)
ourColorSpace <- colorspace::RGB(runif(n), runif(n), runif(n))
ourColorSpace <- as(ourColorSpace, "LAB")
distinctColorPalette <-function(k=1,seed) {
currentColorSpace <- ourColorSpace@coords
# Set iter.max to 20 to avoid convergence warnings.
set.seed(seed)
km <- kmeans(currentColorSpace, k, iter.max=20)
colors <- unname(hex(LAB(km$centers)))
return(colors)
}
## User-selected metadata column is used to set idents.
Filter.orig = so@meta.data[[metacols[1]]]
colname <- metacols[1]
ident_of_interest = as.factor(so@meta.data[[colname]])
names(ident_of_interest)=names(so@active.ident)
so@active.ident <- as.factor(vector())
so@active.ident <- ident_of_interest
## Get colors from user parameter and add more if the default list is too short.
if(class(so@meta.data[[metacols[1]]]) != "numeric"){
q = length(levels(as.factor(Filter.orig)))
if(length(cols1) < q) {
r = q - length(cols1)
more_cols = distinctColorPalette(r,10)
cols1 <- c(cols1, more_cols)
}
names(cols1) <- levels(as.factor(Filter.orig))
## Keep or remove cells based on user input values.
if (Keep) {
subsetValue <- c("0","1","2","6","8","9","11","12")
metaCol <- unique(so@meta.data[[metacols[1]]])
print("Missing values:")
print(setdiff(subsetValue,metaCol))
subsetValue <- intersect(metaCol,subsetValue)
} else {
metaCol <- unique(so@meta.data[[colname]])
valsToRemove <- c("0","1","2","6","8","9","11","12")
subsetValue <- setdiff(metaCol, valsToRemove)
}
## Subset Seurat object.
#SO.sub <-SubsetData(so, ident.use=subsetValue)
SO.sub <- subset(so, idents = subsetValue)
## Log output of tables of cell types by samples before and after filtes.
print("Breakdown of filtered data:")
print(table(so@meta.data[[metacols[1]]],so@meta.data$orig_ident))
cat("\n")
print("After Filtering:")
print(table(SO.sub@meta.data[[metacols[1]]],SO.sub@meta.data$orig_ident))
## Set filter for the subsetted SO.
SO.sub@meta.data[[colname]] <- as.factor(as.character(SO.sub@meta.data[[colname]])) #Relevel Factors
Filter.sub = SO.sub@meta.data[[colname]]
## More color stuff.
#Set colors for unfiltered and filtered data by sample name:
n = length(levels(as.factor(Filter.sub)))
idx = vector("list", n)
names(idx) <- levels(as.factor(Filter.sub))
for (i in 1:n) {
id = Filter.orig %in% levels(as.factor(Filter.sub))[i]
idx[[i]] <- rownames(so@meta.data)[id]
}
cols2 <- cols1[levels(as.factor(Filter.sub))]
## Make before and after plots.
title <- paste0("filtered by ", metacols[1], " and split by ", metacols[2])
p1 = DimPlot(so, reduction=reduction,
group.by=colname,
pt.size=0.1) +
theme_classic() +
scale_color_manual(values=cols1) +
theme(legend.position="right") +
guides(colour=guide_legend(ncol=1,override.aes = list(size = 2))) +
ggtitle(colname)
p2 = DimPlot(so, reduction=reduction,
cells.highlight = idx,
cols.highlight= rev(cols2[1:n]),
sizes.highlight = 0.5) +
theme_classic() +
theme(legend.position="right")+
guides(colour=guide_legend(ncol=1,reverse=TRUE,override.aes = list(size = 2))) +
ggtitle(title)
## Else, filter on numeric data with a user defined threshold and direction.
} else {
filterDirection <-"greater than"
metaCol <- unique(so@meta.data[["SCT_snn_res_0_6"]])
value <- 0.5
if (filterDirection =="greater than") {
SO.sub <- subset(so, subset = SCT_snn_res_0_6 > 0.5)
} else {
SO.sub <- subset(so, subset = SCT_snn_res_0_6 < 0.5)
}
drawtsne <- function(SO,reduction,scalecol,colgrad){
SO.clus <- SO@meta.data[[m]]
p1 <- DimPlot(SO, reduction = reduction, group.by = "ident")
class(p1$data$ident) <- "numeric"
if(reduction=="tsne"){
clusmat=data.frame(umap1=p1$data$tSNE_1,umap2=p1$data$tSNE_2, clusid=as.numeric(SO@meta.data[[m]]))
} else if(reduction=="umap"){
clusmat=data.frame(umap1=p1$data$UMAP_1,umap2=p1$data$UMAP_2, clusid=as.numeric(SO@meta.data[[m]]))
} else if (reduction=="pca"){
clusmat=data.frame(umap1=p1$data$PC_1,umap2=p1$data$PC_2, clusid=as.numeric(SO@meta.data[[m]]))
} else if (reduction=="protein_tsne"){
clusmat=data.frame(umap1=p1$data$protein_tsne_1,umap2=p1$data$protein_tsne_2, clusid=as.numeric(SO@meta.data[[m]]))
} else if (reduction=="protein_umap"){
clusmat=data.frame(umap1=p1$data$protein_umap_1,umap2=p1$data$protein_umap_2, clusid=as.numeric(SO@meta.data[[m]]))
} else {
clusmat=data.frame(umap1=p1$data$protein_pca_1,umap2=p1$data$protein_pca_2, clusid=as.numeric(SO@meta.data[[m]]))
}
clusmat %>% group_by(clusid) %>% summarise(umap1.mean=mean(umap1), umap2.mean=mean(umap2)) -> umap.pos
title=as.character(m)
clusmat %>% dplyr::arrange(clusid) -> clusmat
p2 <- ggplot(clusmat, aes(x=umap1, y=umap2)) +
theme_bw() +
theme(legend.title=element_blank()) +
geom_point(aes(colour=clusid),alpha=0.5,shape = 20,size=0.1) +
#scale_color_gradientn(colours = c("blue4", "lightgrey", "red"), values = scales::rescale(c(min, midpt2,midpt,midpt3, max), limits = c(0, 1))) +
#scale_color_gradientn(colours = c("blue4", "lightgrey", "red"), values = c(min, midpt2,midpt,midpt3, max)) +
#scale_color_gradientn(colors=brewer.pal(n = 5, name = "RdBu"), values = scales::rescale(c(min, midpt2,midpt,midpt3, max))) +
scale_color_gradientn(colors=brewer.pal(n = 5, name = colgrad), values = scalecol) +
guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank()) +
ggtitle(title) +
xlab("umap-1") + ylab("umap-2")
return(p2)
}
m = metacols
clusid = so@meta.data[[m]]
maxclus = max(clusid)
clusmid = 0.01/maxclus
min = min(clusid)
midpt1 = 0.99*value
midpt = value
midpt2 = 1.01*value
max = max(clusid)
colpoints <- c(min,midpt1,midpt,midpt2,max)
colpoints <- scales::rescale(colpoints,c(0,1))
p1 <- drawtsne(so,reduction,colpoints,"RdBu")
clusid = scales::rescale(SO.sub@meta.data[[m]], to=c(0,1))
clus.quant=quantile(clusid[clusid>0],probs=c(0,.25,.5,.75,1))
min = clus.quant[1]
midpt = clus.quant[3]
midpt3 = clus.quant[2]
midpt4 = clus.quant[4]
max = clus.quant[5]
colpoints2 <- c(min,midpt3,midpt,midpt4,max)
p2 <- drawtsne(SO.sub,reduction,colpoints2,"Blues")
}
## If interactive plot requested, then ...
if (plotinteractive == TRUE) {
gp1 <- ggplotly(p1)
gp2 <- ggplotly(p2)
p <- subplot(gp1, gp2, nrows=2)
print(p)
}
## Else, print non-interactive plot.
else {
print(plot_grid(p1,p2,nrow=1))
}
## Return the subsetted Seurat object.
return(list(value=SO.sub))
}
## Commented out below on 7/28/21 because it had started failing in training. It is believed to be vestigial. -- Josh M.
#################################################
## Global imports and functions included below ##
#################################################
#
# }
#
#
# Functions defined here will be available to call in
# the code for any table.
print("template_function_macrophage_filtered.R #########################################################################")
library(plotly);library(ggplot2);library(jsonlite);
currentdir <- getwd()
rds_output <- paste0(currentdir,'/rds_output')
var_Annotate_Cell_Types<-readRDS(paste0(rds_output,"/var_Annotate_Cell_Types.rds"))
Input_is_Seurat_count <- 0
for(item in var_Annotate_Cell_Types){ if (class(item)=="Seurat"){Input_is_Seurat_count = Input_is_Seurat_count + 1}}
if(Input_is_Seurat_count == 0 ){
var_Annotate_Cell_Types<-as.data.frame(var_Annotate_Cell_Types)}else{var_Annotate_Cell_Types <- var_Annotate_Cell_Types}
currentdir <- getwd()
rds_output <- paste0(currentdir,'/rds_output')
var_Metadata_Table<-readRDS(paste0(rds_output,"/var_Metadata_Table.rds"))
Input_is_Seurat_count <- 0
for(item in var_Metadata_Table){ if (class(item)=="Seurat"){Input_is_Seurat_count = Input_is_Seurat_count + 1}}
if(Input_is_Seurat_count == 0 ){
var_Metadata_Table<-as.data.frame(var_Metadata_Table)}else{var_Metadata_Table <- var_Metadata_Table}
currentdir <- getwd()
rds_output <- paste0(currentdir,'/rds_output')
var_Sample_Names<-readRDS(paste0(rds_output,"/var_Sample_Names.rds"))
Input_is_Seurat_count <- 0
for(item in var_Sample_Names){ if (class(item)=="Seurat"){Input_is_Seurat_count = Input_is_Seurat_count + 1}}
if(Input_is_Seurat_count == 0 ){
var_Sample_Names<-as.data.frame(var_Sample_Names)}else{var_Sample_Names <- var_Sample_Names}
invisible(graphics.off())
var_macrophage_filtered<-macrophage_filtered(var_Annotate_Cell_Types,var_Metadata_Table,var_Sample_Names)
invisible(graphics.off())
saveRDS(var_macrophage_filtered, paste0(rds_output,"/var_macrophage_filtered.rds"))
|
f58221a0a3dba8bf2e184566efc46fabb316c40f | c8879eea856503907a134000df4ef1acf1aa5751 | /cachematrix.R | eee08040943da2b90dd083084687cb764ace4c64 | [] | no_license | mlg3672/ProgrammingAssignment2 | 3922ba5ac4eb1f63c5ac33f6db5bd973cdbf3645 | 4ae59d5b257801f0c93013e356e8e709b0687a23 | refs/heads/master | 2020-04-04T23:55:15.852721 | 2015-04-20T06:05:19 | 2015-04-20T06:05:19 | 34,235,305 | 0 | 0 | null | 2015-04-20T03:14:55 | 2015-04-20T03:14:55 | null | UTF-8 | R | false | false | 919 | r | cachematrix.R | ## the functions below makeCacheMatrix and cacheSolve
# executes 4 actions
## (1) get the value of matrix x
## (2) set the value of the matrix
## (3) get the value of the inverse matrix
## (4) set the value of the inverse matrix
makeCacheMatrix <- function(x = matrix()) {
## this function returns a list
m<- NULL
set <-function(y) {
x<<-y
m<<-NULL
}
get <- function() x
getInverse <- solve(x)
setInverse <- function(inverse) m<<-inverse
list(set = set, get = get,
setInverse = setInverse,getInverse = getInverse)
}
## the cacheSolve function calculates matrix inverse
# created with makeCacheMatrix, first checks to see if
# matrix has already been created
cacheSolve <- function(x,...) {
m<-makeCacheMatrix(x)$getInverse
m<-NULL
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data<-makeCacheMatrix(x)$get()
m <-solve(data)
makeCacheMatrix(x)$setInverse(m)
m
}
|
7c7b47fdd7405aa7c014c38a3bf2d77bbb6b3685 | 8c126aade4c65ce684ee5eeeae10d6e6efa2fbf5 | /cachematrix.R | a4a99b909ce48764677c0c3c92c09bfd056398f2 | [] | no_license | ccali930/ProgrammingAssignment2 | afb1c670ffba9eb9b15105865b3ec6efdefc951d | 8dfe6e71f39e04ee93760fbc8b4f6e9db8fda8cd | refs/heads/master | 2020-04-05T23:24:57.111579 | 2014-08-21T03:16:57 | 2014-08-21T03:16:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,100 | r | cachematrix.R | ## makeCacheMatrix and cacheSolve are two functions that can be used together to create a matrix,
## perform a calculation of the inverse of the matrix, then cache the value of the inverse so it can be
## easily called
## Creats a matrix object and functions for getting and setting the inverse of the
## matrix object
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinv <- function(solve) m <<- solve
getinv <- function() m
list(set = set, get = get,
setinv = setinv,
getinv = getinv)
}
## Takes inverse of the matrix and caches it if the inverse has not yet been taken
cacheSolve <- function(x, ...) {
m <- x$getinv()
if(!is.null(m)) {
message("getting cached data")
return(m)
}
data <- x$get()
m <- solve(data)
x$setinv(m)
m
## Return a matrix that is the inverse of 'x'
}
|
f780daf541116d6350eb39f81a1c165542bfcbb0 | ebeaf5e8dd0de49dd34d7a49b4a168d322c92716 | /man/select.k.func.Rd | c235e52d8516fbba88cd4e8e56ba7863245871d9 | [] | no_license | cran/EXRQ | d82b4c185019aae8767aba404f969faef244dbd6 | cfaff6808a8effda090131b4c6868e27e73555fe | refs/heads/master | 2021-01-20T18:58:30.027434 | 2016-07-06T23:48:41 | 2016-07-06T23:48:41 | 62,774,040 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,413 | rd | select.k.func.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/EXRQ.R
\name{select.k.func}
\alias{select.k.func}
\title{Selection of the Tuning Parameter k}
\usage{
select.k.func(y, x, Lam.y, lam, a, max.tau, grid.k, n)
}
\arguments{
\item{y}{a vector of n untransformed responses}
\item{x}{a n x p matrix of n observations and p predictors}
\item{Lam.y}{a vector of n power-transformed responses}
\item{lam}{the power-transformation parameter}
\item{a}{location shift parameter in the power transformation (introduced to avoid negative y values)}
\item{max.tau}{the upper bound of the intermediate quantile levels}
\item{grid.k}{the grid for the number of upper order statistics involved in Hill estimator}
\item{n}{the number of observations}
}
\value{
the selected k is returned
}
\description{
This function selects the tuning parameter k, the number of upper order statistics involved in Hill estimator of EVI among a grid of points
following the method described in Section 3.3 of Wang and Li (2013). The method selects k as the value that minimizes the discrepancy between
the estimated x-dependent EVI on the transformed scale and lam times the estimated x-dependent EVI on the original scale
}
\references{
Wang, H. and Li, D. (2013). Estimation of conditional high quantiles through power transformation. Journal of the American Statistical Association, 108, 1062-1074.
}
|
f79d68f383f8ecfac83facd0647e26ba2e788fa2 | 007c850db5e6cf12cba2007526002ecc897e2f27 | /man/TS.Rd | bbc7ec78d783daa19bc0a63f959bfbb73662f970 | [] | no_license | rdshankar/RImmPort | 12cf59a049f434fd7d4fb5735d507b17b52b28b0 | 5b74cc64d969c353cf7abd2f33fc1dd09b2e4c4a | refs/heads/master | 2021-01-17T13:34:11.016039 | 2016-03-13T03:20:33 | 2016-03-13T03:20:33 | 42,664,085 | 5 | 2 | null | 2015-10-13T19:38:57 | 2015-09-17T15:21:27 | R | UTF-8 | R | false | true | 1,347 | rd | TS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TrialSummary.R
\name{TS}
\alias{TS}
\title{Trial Summary Domain Variables}
\description{
{
\tabular{ll}{
\strong{Variable Name} \tab \strong{Variable Label} \cr
STUDYID \tab Study Identifier \cr
DOMAIN \tab Domain Abbreviation \cr
TSSEQ \tab Sequence Number \cr
TSPARMCD \tab Trial Summary Parameter Short Name \cr
TSPARM \tab Trial Summary Parameter \cr
TSVAL \tab Parameter Value
}
}
}
\note{
The following table enumerates the values in TSPARMCD and TSPARM variables {
\tabular{ll}{
\strong{TSPARMCD} \tab \strong{TSPARM} \cr
TITLE \tab Trial Title \cr
DESCR \tab Trial Description \cr
INDIC \tab Trial Indication \cr
TRT \tab Investigational Therapy or Treatment \cr
HYPOTHS \tab Trial Hypotheses \cr
SSTDTC \tab Study Start Date \cr
SENDTC \tab Study End Date \cr
PLANSUB \tab Planned Number of Subjects \cr
ACTSUB \tab Actual Number of Subjects \cr
AGEMAX \tab Planned Maximum Age of Subjects \cr
AGEMIN \tab Planned Minimum Age of Subjects \cr
AGEU \tab Age Units \cr
SEXPOP \tab Sex of Participants \cr
SPONSOR \tab Clinical Study Sponsor \cr
PUBRLDAT \tab Public Release Date \cr
ISTRIAL \tab Study Type \cr
RESFOCUS \tab Trial Research Focus
}
}
}
|
f7ec22944141962da7bd8a08496c1469766a6a5c | abfdbe31cc8413c0b3af32e7fa2f04966a933b84 | /Exploration_Stuff.R | 087480caff6f4ead95e3a779aac58dc3ddcdaa46 | [] | no_license | ZTerechshenko/Event-Data | 0ceeada80cfa771f173dc8cb820e0e075ee053dc | 23f84a06e3846b1497746fb259fec82c095ddc63 | refs/heads/master | 2021-09-15T04:11:06.256587 | 2017-12-14T07:03:39 | 2018-05-25T14:41:56 | 106,856,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,009 | r | Exploration_Stuff.R |
#### Script for processing Phoenix event data and creating summary graphics
#### load packages ####
#install.packages("ggplot2")
#install.packages("dplyr")
library(ggplot2)
library(dplyr)
#### Read NYT Phoenix ####
NYT <- read.csv(file ="Phoenix/PhoenixNYT_1945-2005.csv")
#head(NYT)
#nrow(NYT)
# Convert to proper date format
NYT <- NYT %>% mutate(date = as.Date(story_date, format="%m/%d/%Y") )
#str(NYT$date)
##### NYT Phoenix counts over time #####
# Create NYT Phoenix Geolocated Subset
NYT_Geo <- NYT %>% filter(!is.na(lat))
# Summarize count by day, all NYT
NYT_count_all <- count(NYT, story_date)
# Summarize count by day, just Geo
NYT_Geo_count <- count(NYT_Geo, story_date)
# Join the Geo count to get both in same tibble
NYT_count <- NYT_count_all %>%
full_join(NYT_Geo_count, by = "story_date") %>%
mutate(story_date = as.Date(story_date, format="%m/%d/%Y") ) %>%
rename(all = n.x) %>%
rename(geo = n.y)
#remove used datasets
rm(NYT_count_all)
rm(NYT_Geo_count)
head(NYT_count)
#### Read FBIS Phoenix ####
SWB <- read.csv(file ="Phoenix/PhoenixSWB_1979-2015.csv")
#head(SWB)
#nrow(SWB)
# Convert to proper date format
SWB <- SWB %>% mutate(date = as.Date(story_date, format="%m/%d/%Y") )
# Create SWB Phoenix Geolocated Subset
SWB_Geo <- SWB %>% filter(!is.na(lat))
# Summarize count by day, all SWB
SWB_count_all <- count(SWB, story_date)
# Summarize count by day, just Geo
SWB_Geo_count <- count(SWB_Geo, story_date)
# Join the Geo count to get both in same tibble
SWB_count <- SWB_count_all %>%
full_join(SWB_Geo_count, by = "story_date") %>%
mutate(story_date = as.Date(story_date, format="%m/%d/%Y") ) %>%
rename(all = n.x) %>%
rename(geo = n.y)
#remove used datasets
rm(SWB_count_all)
rm(SWB_Geo_count)
head(SWB_count)
#### Read FBIS Phoenix ####
SWB <- read.csv(file ="Phoenix/PhoenixSWB_1979-2015.csv")
#head(SWB)
#nrow(SWB)
# Convert to proper date format
SWB <- SWB %>% mutate(date = as.Date(story_date, format="%m/%d/%Y") )
#### SWB count, time series ####
# line chart
ggplot(NYT_count, aes(x = story_date, y = all)) +
geom_line(alpha = .5 ) +
scale_x_date(breaks = seq( as.Date("1945-01-01"),
as.Date("2006-12-31"),
by = "5 years"),
date_minor_breaks = "1 year",
date_labels = "%Y") +
labs( title ="Phoenix NYT Events",
x = "Time",
y = "Daily Event Count") +
theme( title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12) )
# Bar chart
ggplot(NYT_count, aes(x = story_date, y = all)) +
geom_bar(stat = "identity",
alpha = .6,
fill = "blue") +
scale_y_continuous(limits = c(0, 150)) +
scale_x_date(breaks = seq( as.Date("1945-01-01"),
as.Date("2006-12-31"),
by = "5 years"),
date_minor_breaks = "1 year",
date_labels = "%Y") +
labs( title ="Phoenix NYT Events",
x = "Time",
y = "Daily Event Count") +
theme( title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12) )
# Histogram showing missing dates
ggplot(NYT_count, aes(x = story_date)) +
geom_histogram(binwidth = 10,
alpha = .6,
fill = "blue") +
scale_x_date(breaks = seq( as.Date("1945-01-01"),
as.Date("2006-12-31"),
by = "5 years"),
date_minor_breaks = "1 year",
date_labels = "%Y") +
labs( title ="Phoenix NYT Events - Missing Records",
x = "Time") +
theme( title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12) )
# Events per day histogram
ggplot(NYT_count) +
geom_histogram(aes(x = all),
binwidth = 5) +
labs( title ="Histogram of Events per Day") +
theme( title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12) )
#### NYT 1961 anomaly ####
ggplot(NYT_count, aes(x = story_date, y = all)) +
geom_bar(stat = "identity",
alpha = .6,
fill = "blue") +
scale_x_date( limits = c( as.Date("1960-06-01"),
as.Date("1962-01-01")),
date_breaks = "2 month",
date_minor_breaks = "1 month",
date_labels = "%b-%Y") +
scale_y_continuous(limits = c(0, 200)) +
annotate("rect",
xmin = as.Date("1961-01-01"),
xmax = as.Date("1961-11-16"),
ymin = 152,
ymax = 165,
alpha = .8,
fill = "firebrick1")+
annotate("text",
x = as.Date("1961-06-15"),
y = 160,
label= "January 1 to November 16",
color = "white") +
labs( title ="1961 Anamoly",
x = "Time",
y = "Daily Event Count") +
theme( title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12) )
#### NYT 1978 anomaly ####
ggplot(NYT_count, aes(x = story_date, y = all)) +
geom_bar(stat = "identity",
alpha = .6,
fill = "blue") +
scale_x_date( limits = c( as.Date("1978-06-01"),
as.Date("1978-12-31")),
date_breaks = "1 month",
date_minor_breaks = "1 month",
date_labels = "%b-%Y") +
scale_y_continuous(limits = c(0, 200)) +
annotate("rect",
xmin = as.Date("1978-08-09"),
xmax = as.Date("1978-11-06"),
ymin = 152,
ymax = 165,
alpha = .8,
fill = "firebrick1")+
annotate("text",
x = as.Date("1978-10-01"),
y = 160,
label= "August 9 to November 11",
color = "white") +
labs( title ="1978 Anamoly",
x = "Time",
y = "Daily Event Count") +
theme( title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12) )
#### NYT Zoom in on random sample ####
ggplot(NYT_count, aes(x = story_date, y = all)) +
geom_bar(stat = "identity",
fill = "blue") +
scale_x_date( limits = c( as.Date("1985-01-01"),
as.Date("1987-12-31")),
date_breaks = "3 months",
date_minor_breaks = "1 month",
date_labels = "%b-%Y") +
scale_y_continuous(limits = c(0, 200)) +
labs( title ="Random Area",
x = "Time",
y = "Daily Event Count") +
theme( title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 10) )
# annotate("segment",
# x = as.Date("1961-01-01"),
# xend = as.Date("1961-01-01"),
# y = 0,
# yend = 165,
# alpha = .8,
# colour = "firebrick1",
# linetype = 2)+
ggplot(NYT, aes(x=date)) + geom_histogram(bins = 56)
ggplot(NYT, aes(x=date)) +
geom_freqpoly( bins = 56)
ggplot(NYT_Geo, aes(x=date)) +
geom_freqpoly( bins = 56)
|
850ac2696aef128fcf530b531f4692e32d9fe590 | a5b8244731689344004c67af107b1a531f7e9e2f | /src/00_data_wrangle/03_identify_drops.R | f40b9059f0efa0a4833f65ef897bcbb834301c5a | [] | no_license | jvenzor23/DefensiveCoverageNet | 4efcb0f36d6806c71a1750fa9b58ba63c55e3929 | 85eef09aeede123aa32cb8ad3a8075cd7b7f3e43 | refs/heads/master | 2023-02-13T22:14:23.396421 | 2021-01-07T22:52:32 | 2021-01-07T22:52:32 | 317,361,746 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,339 | r | 03_identify_drops.R | # This code animates a given play in the player tracking data, while
# also displaying the epa values for all receivers (targeted receiver
# shown in red)
# Clean workspace
rm(list=ls())
# Setting Working Directory
setwd("~/Desktop/NFL_BIG_DATA_BOWL_2021/inputs/")
# Calling Necessary Libraries
library(tidyverse)
library(dplyr)
library(ggplot2)
library(lubridate)
library(reticulate)
library(rootSolve)
library(modeest)
library(gganimate)
library(magick)
# Reading in The Data -----------------------------------------------------
players = read.csv("~/Desktop/CoverageNet/inputs/players.csv")
games = read.csv("~/Desktop/CoverageNet/inputs/games.csv")
plays = read.csv("~/Desktop/CoverageNet/inputs/plays.csv")
targeted_receiver = read.csv("~/Desktop/CoverageNet/inputs/targetedReceiver.csv")
# Finding Drops -----------------------------------------------------------
setwd("~/Desktop/CoverageNet/src/00_data_wrangle/outputs/")
files = dir()[startsWith(dir(), "week")]
drops_tot = data.frame()
for(file in files){
pbp_data = read.csv(paste0("~/Desktop/CoverageNet/src/00_data_wrangle/outputs/",
file))
pbp_data_incompletions = pbp_data %>%
inner_join(plays %>%
filter(passResult == "I") %>%
distinct(gameId, playId)) %>%
filter(event == "pass_arrived")
incompletions_off_target = plays %>%
inner_join(pbp_data %>%
distinct(gameId, playId)) %>%
filter(passResult == "I") %>%
distinct(gameId, playId) %>%
anti_join(pbp_data_incompletions %>%
distinct(gameId, playId))
pbp_data_targeted_receiver = pbp_data_incompletions %>%
inner_join(targeted_receiver,
by = c("gameId", "playId", "nflId" = "targetNflId"))
pbp_data_football = pbp_data_incompletions %>%
filter(is.na(nflId)) %>%
dplyr::select(gameId, playId, frameId, x, y) %>%
rename(x_football = x,
y_football = y)
pbp_data_defense = pbp_data_incompletions %>%
filter(!IsOnOffense,
!is.na(nflId)) %>%
dplyr::select(gameId, playId, frameId, x, y)
receiver_close_enough = pbp_data_targeted_receiver %>%
inner_join(pbp_data_football) %>%
mutate(receiver_football_dist = sqrt((x - x_football)^2 + (y - y_football)^2)) %>%
filter(receiver_football_dist <= 1.5,
y > 0,
y < 53 + 1/3,
x > 0,
x < 120)
defenders_close_enough = pbp_data_defense %>%
inner_join(pbp_data_football) %>%
mutate(receiver_football_dist = sqrt((x - x_football)^2 + (y - y_football)^2)) %>%
filter(receiver_football_dist <= 1.5) %>%
distinct(gameId, playId)
defenders_close_enough_to_receiver = pbp_data_targeted_receiver %>%
inner_join(pbp_data_defense %>%
rename(x_def = x,
y_def = y)) %>%
mutate(receiver_defender_dist = sqrt((x - x_def)^2 + (y - y_def)^2)) %>%
filter(receiver_defender_dist <= 1.5) %>%
distinct(gameId, playId)
drops_plays = receiver_close_enough %>%
anti_join(defenders_close_enough) %>%
anti_join(defenders_close_enough_to_receiver) %>%
distinct(gameId, playId, nflId, displayName)
drops_tot = rbind(drops_plays,
drops_tot)
}
# QA ----------------------------------------------------------------------
player_drops = drops_tot %>%
group_by(displayName, nflId) %>%
summarize(drops = n()) %>%
arrange(desc(drops))
setwd("~/Desktop/NFL_PBP_DATA/")
pbp_data_2018 = read_csv("reg_pbp_2018.csv", col_types = cols()) %>%
dplyr::select(play_id, game_id, interception_player_name,
pass_defense_1_player_name,
pass_defense_2_player_name) %>%
rename(gameId = game_id,
playId = play_id)
pbus = pbp_data_2018 %>%
filter(!is.na(pass_defense_1_player_name))
ints = pbp_data_2018 %>%
filter(!is.na(interception_player_name))
# should be 0!
drops_and_pbu = drops_tot %>%
inner_join(pbus)
drops_and_ints = drops_tot %>%
inner_join(ints)
drops_clean = drops_tot %>%
anti_join(drops_and_pbu) %>%
distinct(gameId, playId)
write.csv(drops_clean,
"~/Desktop/CoverageNet/src/00_data_wrangle/helper_tables/drops.csv",
row.names = FALSE)
|
c371c509788868a706291d345227b0d627752249 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/yum/examples/load_and_simplify.Rd.R | 25bfda535555708dfa814edaa8d880cc1799aca0 | [] | 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 | 437 | r | load_and_simplify.Rd.R | library(yum)
### Name: load_and_simplify
### Title: Load YAML fragments in one or multiple files and simplify them
### Aliases: load_and_simplify load_and_simplify_dir
### ** Examples
yum::load_and_simplify(text="
---
firstObject:
id: firstFragment
---
Outside of YAML
---
otherObjectType:
-
id: secondFragment
parentId: firstFragment
-
id: thirdFragment
parentId: firstFragment
---
Also outside of YAML");
|
56b3e0729b873babea32c465d1a769e6b6198b45 | 659d3ecae15a0673b0fb5ca2298209071e226a4b | /man/bounds.r.Rd | fb1ae216e4d9b502b6f3721943fe9ef592723135 | [] | no_license | dstanley4/intervalTraining | c1c431c5114c4d4dafd7874f32ea7ee2630edd2a | adb3c31d04e1ca55ec8700e6b1c8d55be9f3dc2a | refs/heads/master | 2020-05-19T22:20:13.489805 | 2019-05-31T13:42:53 | 2019-05-31T13:42:53 | 185,244,262 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 502 | rd | bounds.r.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ci_functions.R
\name{bounds.r}
\alias{bounds.r}
\title{Obtain the range of sample correlations for a given population correlation and sample size}
\usage{
bounds.r(samples, level = 0.95)
}
\arguments{
\item{level}{Confidence level (0 to 1), default .95}
\item{pop_r}{Population correlation}
\item{n}{Sample size}
}
\description{
Obtain the range of sample correlations for a given population correlation and sample size
}
|
9c487573fd095a48385de6916778a10f17217f1d | d4cd3909b5c5ff996e405a9dbcdb830a9f18599f | /networkSetosa.R | f9dc6658df014eabde11a52085151d81fc0697e0 | [] | no_license | jevzimite/Projects.compiled | 6bb39df27ed44871c240fea4408967248f76293d | df1fdcaa12bf8d339a2ca782e28c425a44c12409 | refs/heads/main | 2023-05-01T13:30:24.549058 | 2021-05-25T22:11:07 | 2021-05-25T22:11:07 | 332,641,146 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,015 | r | networkSetosa.R | # spc = readline(prompt = "What species would you like to build a network for?")
# spc = as.character(spc)
spc = "setosa"
df = iris
df = iris %>% filter(Species == spc)
df$Species = NULL
round(cor(df),2)
cmb = combn(colnames(df), 2)
from = cmb[1,]
to = cmb[2,]
netdf = data.frame(from, to)
weights = c(.74,
.27,
.28,
.18,
.23,
.33)
netdf = cbind(netdf, weights)
routes_igraph <- graph_from_data_frame(d = netdf, vertices = colnames(df), directed = FALSE)
plot(routes_igraph)
routes_igraph_tidy <- as_tbl_graph(routes_igraph)
ggraph(routes_igraph_tidy, layout = "linear" ) +
# geom_edge_arc(aes(colour = 2))+
geom_node_point(size = 33, color = 2) +
geom_edge_arc(aes(width = netdf$weights), alpha = 0.75, linetype = 7, color = 2) +
scale_edge_width(range = c(.1, 5)) +
geom_node_text(aes(label = colnames(df)), repel = 0, color = "black", fontface = 2) +
labs(edge_width = "Correlation") + ylim(c(-.1,1.2))+xlim(c(-.2,5))+
theme_graph()
|
71d4397a345b9eb90a85e99313a536837a64623d | 8edf0521ebc0ca53ec618d6d220c47c851caaa71 | /man/SSplotMovementRates.Rd | edc834dc053cc8ed922b19fc79f872d12018c1b6 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | amart/r4ss | 9b730038ee4c4b6d38aaabe81b6ad9fddf0eb4f3 | fbccbace9a70e846401d32577aeab9f25cb31ba5 | refs/heads/master | 2021-01-17T06:03:03.172272 | 2020-10-04T01:38:14 | 2020-10-04T01:38:14 | 24,735,775 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,005 | rd | SSplotMovementRates.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SSplotMovementRates.R
\name{SSplotMovementRates}
\alias{SSplotMovementRates}
\title{Plot movement rates from model output}
\usage{
SSplotMovementRates(
replist,
plot = TRUE,
print = FALSE,
subplots = 1:2,
plotdir = "default",
colvec = "default",
ylim = "default",
legend = TRUE,
legendloc = "topleft",
moveseas = "all",
min.move.age = 0.5,
pwidth = 6.5,
pheight = 5,
punits = "in",
res = 300,
ptsize = 10,
cex.main = 1,
verbose = TRUE
)
}
\arguments{
\item{replist}{list created by \code{\link{SS_output}}}
\item{plot}{plot to active plot device?}
\item{print}{print to PNG files?}
\item{subplots}{which subplots to create}
\item{plotdir}{where to put the plots (uses model directory by default)}
\item{colvec}{vector of colors for each movement rate in the plot}
\item{ylim}{optional input for y range of the plot. By default plot ranges
from 0 to 10\% above highest movement rate (not including fish staying in an
area).}
\item{legend}{add a legend designating which color goes with which pair of
areas?}
\item{legendloc}{location passed to legend function (if used)}
\item{moveseas}{choice of season for which movement rates are shown}
\item{min.move.age}{Minimum age of movement (in future will come from Report file)}
\item{pwidth}{width of plot}
\item{pheight}{height of plot}
\item{punits}{units for PNG file}
\item{res}{resolution for PNG file}
\item{ptsize}{point size for PNG file}
\item{cex.main}{Character expansion parameter for plot titles}
\item{verbose}{Print information on function progress.}
}
\description{
Plots estimated movement rates in final year for each area/season with movement as
reported in Report.sso. If movement is time-varying, an additional figure shows
pattern across years.
}
\examples{
\dontrun{
SSplotMovementRates(myreplist)
}
}
\seealso{
\code{\link{SS_output}}, \code{\link{SSplotMovementRates}},
}
\author{
Ian Taylor
}
|
c5f410d52fc5bf285cc1b3bcb1bbf2f4c3e89ca9 | 2b81de418ef2616e2f8be9dfa8be9ff785d28431 | /other_analyses_not_in_paper/round_2/round_2_scoring.R | 641f90211d3af800a5137bed19ac5ad6c2208108 | [
"Apache-2.0"
] | permissive | Sage-Bionetworks/IDG-DREAM-Challenge-Analysis | 006538ac946e0fc13b03c9c48be111b03efdf2c8 | 19710fcd1714c938312048f78b768ebbe91f6cd9 | refs/heads/master | 2023-03-28T02:11:24.834047 | 2021-03-31T00:14:33 | 2021-03-31T00:14:33 | 157,908,491 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,513 | r | round_2_scoring.R | source('R/bootstrap.R')
library(reticulate)
library(tidyverse)
library(doMC)
doMC::registerDoMC(cores = detectCores())
use_python("/usr/local/bin/python2")
synapse <- import("synapseclient")
syn <- synapse$Synapse()
synutils <- synapse$utils
syn$login()
source_python('https://raw.githubusercontent.com/Sage-Bionetworks/IDG-DREAM-Challenge-Analysis/master/round_1b/evaluation_metrics_python2.py?token=AE3WNSGB6AYUBWIOM75LP4S4ZB2WK')
spearman_py <- function(gold, pred){
gold_py <- gold %>% np_array()
pred_py <- pred %>% np_array()
spearman(gold_py, pred_py)
}
rmse_py <- function(gold, pred){
gold_py <- gold %>% np_array()
pred_py <- pred %>% np_array()
rmse(gold_py, pred_py)
}
fv <- syn$tableQuery("select id, submissionId AS objectId, teamId, userId from syn18513076")$filepath %>%
read_csv()
leaderboard <- read_csv(syn$get("syn18520916")$path) %>% full_join(fv)
get_path <- function(id){
syn$get(id)$path
}
gold <- read_csv(syn$get("syn18421225")$path)
disqualified_submissions <- c("9686285")
####SPEARMAN ANALYSIS
best_spearman_by_team <- leaderboard %>%
group_by(submitterId) %>%
top_n(1, spearman) %>%
ungroup() %>%
arrange(-spearman) %>%
filter(!objectId %in% disqualified_submissions)
paths <- map_chr(best_spearman_by_team$id, get_path)
best_path <- paths[1]
other_paths <- paths[-1]
results_spearman <- finalScoring(predictions = other_paths,
predictionColname = 'pKd_[M]_pred',
predictionIds = best_spearman_by_team$objectId[-1],
goldStandard = gold,
goldStandardColname = 'pKd_[M]',
bestPrediction = best_path,
bestPredictionId = best_spearman_by_team$objectId[1],
keyColumns = colnames(gold)[1:6],
doParallel = TRUE,
scoreFun = spearman_py)
tidy_bayes <- tibble('objectId' = colnames(results_spearman$bootstrappedScores),
'bayes' = results_spearman$bayes)
# tidy_bayes$bayes[tidy_bayes$objectId == 9686285] <- 0
tidy_res <- results_spearman$bootstrappedScores %>%
as.data.frame %>%
tidyr::gather(objectId, bootstrappedScore) %>%
left_join(tidy_bayes) %>%
left_join(leaderboard %>% mutate(objectId = as.character(objectId)))
ggplot(data = tidy_res) +
geom_boxplot(aes(x = reorder(objectId, bootstrappedScore, fun = median), y = bootstrappedScore,
color = cut(bayes, c(-Inf, 3, 5,Inf))), outlier.shape = NA)+
coord_flip() +
scale_color_manual(values = c("#30C31E","#FFCA3E","#FF3E3E"),
name = "Bayes Factor",
labels = c("<3","3-5",">5")) +
labs(x = "Submission", y = "Bootstrapped Spearman") +
theme_minimal() +
theme(axis.text.y = element_text(size = 10))
ggplot(data = tidy_res %>% filter(bayes <20)) +
geom_boxplot(aes(x = reorder(objectId, bootstrappedScore, fun = median), y = bootstrappedScore,
color = cut(bayes, c(-Inf, 3, 5,Inf))), outlier.shape = NA)+
coord_flip() +
geom_label(data = tidy_res %>% select(objectId, bayes, average_auc) %>%
distinct %>%
filter(bayes <5),
aes(x = objectId, label = paste0("AUC: ", round(average_auc, 3))), y = 0.35) +
scale_color_manual(values = c("#30C31E","#FFCA3E","#FF3E3E"),
name = "Bayes Factor",
labels = c("<3","3-5",">5")) +
labs(x = "Submission", y = "Bootstrapped Spearman") +
theme_minimal() +
theme(axis.text.y = element_text(size = 10))
####RMSE ANALYSIS
best_rmse_by_team <- leaderboard %>%
group_by(submitterId) %>%
top_n(1, -rmse) %>%
ungroup() %>%
arrange(rmse)
paths <- map_chr(best_rmse_by_team$id, get_path)
best_path <- paths[1]
other_paths <- paths[-1]
results_rmse <- finalScoring(predictions = other_paths,
predictionColname = 'pKd_[M]_pred',
predictionIds = best_rmse_by_team$objectId[-1],
goldStandard = gold,
goldStandardColname = 'pKd_[M]',
bestPrediction = best_path,
bestPredictionId = best_rmse_by_team$objectId[1],
keyColumns = colnames(gold)[1:6],
doParallel = TRUE,
scoreFun = rmse_py,
largerIsBetter = F)
tidy_bayes <- tibble('objectId' = colnames(results_rmse$bootstrappedScores),
'bayes' = results_rmse$bayes)
tidy_res_rmse <- results_rmse$bootstrappedScores %>%
as.data.frame %>%
tidyr::gather(objectId, bootstrappedScore) %>%
left_join(tidy_bayes) %>%
left_join(leaderboard %>% mutate(objectId = as.character(objectId)))
ggplot(data = tidy_res_rmse) +
geom_boxplot(aes(x = reorder(objectId, -bootstrappedScore, fun = median), y = log10(bootstrappedScore),
color = cut(bayes, c(-Inf, 3, 5, 20, Inf))), outlier.shape = NA)+
coord_flip() +
scale_color_manual(values = c("#30C31E","#FFCA3E","#FF3E3E",'#000000'),
name = "Bayes Factor",
labels = c("<3","3-5",">5")) +
labs(x = "Submission", y = "log10(Bootstrapped RMSE)") +
theme_minimal() +
theme(axis.text.y = element_text(size = 10))
ggplot(data = tidy_res_rmse %>% filter(bayes <20)) +
geom_boxplot(aes(x = reorder(objectId, -bootstrappedScore, fun = median), y = log10(bootstrappedScore),
color = cut(bayes, c(-Inf, 3, 5, 20, Inf))), outlier.shape = NA)+
coord_flip() +
geom_label(data = tidy_res_rmse %>% select(objectId, bayes, average_auc) %>%
distinct %>%
filter(bayes <5),
aes(x = objectId, label = paste0("AUC: ", round(average_auc, 3))), y = 0.0050) +
scale_color_manual(values = c("#30C31E","#FFCA3E","#FF3E3E",'#000000'),
name = "Bayes Factor",
labels = c("<3","3-5",">5")) +
labs(x = "Submission", y = "log10(Bootstrapped RMSE)") +
theme_minimal() +
theme(axis.text.y = element_text(size = 10))
###comparison of rounds
r1b <- syn$tableQuery('select * from syn18487972')$asDataFrame()
r1 <- syn$tableQuery('SELECT * FROM syn17054253')$asDataFrame()
length(unique(leaderboard$submitterId[leaderboard$submitterId %in% c(r1b$submitterId)]))
length(unique(leaderboard$submitterId[!leaderboard$submitterId %in% c(r1b$submitterId)]))
length(unique(leaderboard$submitterId[leaderboard$userId %in% c(r1$userId)]))
length(unique(leaderboard$submitterId[!leaderboard$userId %in% c(r1$userId)]))
|
3818cbe7a82281444a613070d933f49a8456e9be | 50e4e1244a771566be1a3cb3c20c735338a33e13 | /run-cdn-ccc-experiments.R | e2a887879b10593c86230c1c40a2b7a9dd1eb275 | [] | no_license | llorenc/cdn-ccc | 023a075afa522d81ee22c1466684fbed923caae5 | ab0b6110250d45bfab8ce093cb917120b674c69d | refs/heads/master | 2021-01-01T05:27:43.463093 | 2016-05-23T18:05:23 | 2016-05-23T18:05:23 | 57,230,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,991 | r | run-cdn-ccc-experiments.R | #################################################################################
## Simulation of CDN CCC.
## (c) Llorenç Cerdà, 2016
#################################################################################
source("r6-cdn-ccc.R")
source("run-simulation.R")
library('ggplot2')
## library('utils') # Rprof()
## Rprof()
##
## Simulation parameters
##
debug.events <- F # TRUE
number.of.proxies <- 10
simu.time <- 300 # seconds
## Proxies configuration
bw1 <- 10e6/8 # proxies with lower Bw
bw2 <- 20e6/8 # proxies with medium Bw
bw3 <- 100e6/8 # proxies with higher Bw
bw1.num <- round(number.of.proxies/3)
bw2.num <- round(number.of.proxies/3)
bw3.num <- number.of.proxies-2*round(number.of.proxies/3)
proxy.access.bw <- # Bw in Bps (Bytes per second) of each proxy
c(rep(bw1, len=bw1.num),
rep(bw2, len=bw2.num),
rep(bw3, len=bw3.num))
##fname <- paste0(format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), ".trace")
sla.bw <- 1 # in Mbps
alpha <- 1
##
## testing
##
debug.events <- F
simu.time <- 20 # seconds
zrr <- run.simulation(
proxy.access.bw=proxy.access.bw,
sla.bw=sla.bw/alpha, # SLA Bw in Mbps
cdn.load=0.5,
background.load=0, #0.05,
Rweight=1e10, # gamma in equation (3)
zrr.update.time=1,
owd.mean=0,
# owd.mean=20,
owd.coef.var=0.1,
choose.proxy.type='roundrobin',
max.queue.len=300,
seed=0 # simulation seed
)
zrr$Qreq
zrr$Rqueue
zrr$Penalty
## print the queue of one proxy
zrr$proxies[[1]]$print.queues()
zrr$proxies[[2]]$print.queues()
zrr$proxies[[3]]$print.queues()
zrr$proxies[[4]]$print.queues()
zrr$proxies[[6]]$print.queues()
##
## run simulations varying weights (w) and update interval (ut)
##
simu.res <- list()
for(ut in c(0.1, 0.5, 1)) {
for(w in c(1, 10, 100)) {
## for(ut in c(1)) {
## for(w in c(10, 100)) {
fname <- paste0(format(Sys.time(), "%Y-%m-%d-t"),
ut, "w", w, "s", simu.time, ".trace")
message(fname)
##
zrr <- run.simulation(
fname=fname,
sla.bw=sla.bw*1.5, # SLA Bw in Bps (Bytes per second)
cdn.load=0.5,
background.load=0.01,
Rweight=w*1000*sla.bw, # alpha in equation (3)
Pweight=w*10*sla.bw, # Penalty weight
zrr.update.time=ut,
seed=0 # simulation seed
)
simu.res <- c(simu.res, list(list(zrr=zrr, ut=ut, w=w)))
}
}
##
## check one trace
##
fname <- '2016-04-25-t0.1w100s600.trace'
##
trace.ta <- read.table(header=T, gzfile(paste0(fname, '.gz')))
trace.ta <- cbind(trace.ta, Bw=NA)
trace.ta$Bw[trace.ta$proxy %in% 1:bw1.num] <- as.character(bw1*8e-6)
trace.ta$Bw[trace.ta$proxy %in% (bw1.num+1):(bw1.num+bw2.num)] <- as.character(bw2*8e-6)
trace.ta$Bw[trace.ta$proxy %in% (bw1.num+bw2.num+1):number.of.proxies] <- as.character(bw3*8e-6)
ggplot(data=trace.ta, aes(QLen, color=Bw)) +
stat_ecdf() +
xlab("Queue Length") + ylab("ECDF")
## trace.ta.cdn$proxy <- as.factor(trace.ta.cdn$proxy)
trace.ta.cdn <- trace.ta[trace.ta$type=='cdn',]
ggplot(data=trace.ta.cdn, aes(throughput*8e-6, color=Bw)) +
stat_ecdf() +
scale_x_log10() +
geom_vline(xintercept = sla.bw*8e-6) + annotation_logticks(sides="b") +
xlab("throughput [Mbps] (log scale)") + ylab("ECDF")
## print Q and R queues of the ZRR
zrr$Qreq
zrr$Rqueue
zrr$Penalty
## print the queue of one proxy
zrr$proxies[[1]]$print.queues()
zrr$proxies[[2]]$print.queues()
zrr$proxies[[3]]$print.queues()
zrr$proxies[[4]]$print.queues()
zrr$proxies[[6]]$print.queues()
##
## facet_grid of all traces
##
trace.ta.all <- data.frame()
for(ut in c(0.1, 0.5, 1)) {
for(w in c(1, 10, 100)) {
## for(ut in c(1)) {
## for(w in c(10, 100)) {
fname <- paste0("2016-04-25-t",
ut, "w", w, "s", simu.time, ".trace")
trace.ta.all <- rbind(trace.ta.all,
cbind(read.table(header=T, gzfile(paste0(fname, '.gz'))),
ut=ut, w=w, Link=NA))
}
}
trace.ta.all$Link[trace.ta.all$proxy %in% 1:bw1.num] <- as.character(bw1*8e-6)
trace.ta.all$Link[trace.ta.all$proxy %in% (bw1.num+1):(bw1.num+bw2.num)] <- as.character(bw2*8e-6)
trace.ta.all$Link[trace.ta.all$proxy %in% (bw1.num+bw2.num+1):number.of.proxies] <- as.character(bw3*8e-6)
trace.ta.all.cdn <- trace.ta.all[trace.ta.all$type=='cdn',]
ggplot(data=trace.ta.all.cdn, aes(throughput*8e-6, color=Link)) +
facet_grid(ut ~ w) +
stat_ecdf() +
scale_x_log10() +
geom_vline(xintercept = sla.bw*8e-6) + annotation_logticks(sides="b") +
xlab("throughput [Mbps] (log scale)") + ylab("ECDF")
ggsave(file="throughput-ecdf.pdf")
ggplot(data=trace.ta.all, aes(QLen, color=Link)) +
facet_grid(ut ~ w) +
stat_ecdf() +
xlab("Queue Length") + ylab("ECDF")
ggsave(file="queue-length-ecdf.pdf")
## summaryRprof(filename = "Rprof.out")
|
73b738db3cca33ee53fdffffacafb55766befb20 | 7244d6ecc30aeda541713f7310a28382645a5999 | /R/ag.ds.histogram.R | 6fbc35109cab9babdf16a6c8c16ab6dd5a59b950 | [] | no_license | datashield/ag.dev.cl | 52cab8e410c1b67a98b55222ad7edf9cb7a53423 | 717a75ad29bac2ad3848770a76e5390186b0fbdd | refs/heads/master | 2021-01-19T08:24:05.981000 | 2013-09-01T01:05:59 | 2013-09-01T01:05:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,969 | r | ag.ds.histogram.R | #'
#' @title Plots a histogram
#' @description This function plots histogram of the given data values.
#' It calls a datashield server side function that produces the
#' histogram objects to plot. The objects to plot do not contain bins with
#' counts < 5. The function allows for the user to plot disctinct histograms
#' (one for each study) or a combine histogram that merges the single plots.
#' @param opals a list of opal object(s) obtained after login in to opal servers;
#' these objects hold also the data assign to R, as \code{dataframe}, from opal
#' datasources.
#' @param xvect vector of values for which the histogram is desired.
#' @param type a character which represent the type of graph to display.
#' If \code{type} is set to 'combine', a histogram that merges the single
#' plot is displayed. Each histogram is plotted separately if If \code{type}
#' is set to 'split'.
#' @return one or more histogram plot depending on the argument \code{type}
#' @author Gaye, A.
#' @export
#' @examples {
#'
#' # load that contains the login details
#' data(logindata)
#'
#' # login and assign specific variable(s)
#' myvar <- list("LAB_TSC")
#' opals <- ag.ds.login(logins=logindata,assign=TRUE,variables=myvar)
#'
#' # Example 1: plot a combined histogram of the variable 'LAB_TSC' - default behaviour
#' ag.ds.histogram(opals=opals, xvect=quote(D$LAB_TSC))
#'
#' # Example 2: Plot the histograms separately (one per study)
#' ag.ds.histogram(opals=opals, xvect=quote(D$LAB_TSC), type="split")
#'
#' # Example 3: Plot the histograms of the first and second study
#' ag.ds.histogram(opals=opals[1:2], xvect=quote(D$LAB_TSC), type="split")
#'
#' # Example 4: Plot the histogram of the third study only
#' ag.ds.histogram(opals=opals[3], xvect=quote(D$LAB_TSC), type="split")
#' }
#'
ag.ds.histogram <- function(opals=opals, xvect=NULL, type="combine"){
if(is.null(opals)){
cat("\n\n ALERT!\n")
cat(" No valid opal object(s) provided.\n")
cat(" Make sure you are logged in to valid opal server(s).\n")
stop(" End of process!\n\n", call.=FALSE)
}
if(is.null(xvect)){
cat("\n\n ALERT!\n")
cat(" Please provide a valid numeric vector\n")
stop(" End of process!\n\n", call.=FALSE)
}
# get the name of the variable used for the histogram
variable <- strsplit(deparse(xvect), "\\$", perl=TRUE)[[1]][2]
# call the function that checks the variables are available and not empty
vars2check <- list(xvect)
opals <- ag.ds.checkvar(opals, vars2check)
# get the range from each studyand produce the 'global' range
cally1 <- call("ag.range.ds", xvect)
ranges <- datashield.aggregate(opals, cally1)
minrs <- c()
maxrs <- c()
for(i in 1:length(ranges)){
minrs <- append(minrs, ranges[[i]][1])
maxrs <- append(maxrs, ranges[[i]][2])
}
range.arg <- c(min(minrs), max(maxrs))
# call the function that produces the histogram object to plot
cally2 <- call("ag.histogram.ds", xvect, range.arg[1], range.arg[2])
hist.objs <- vector("list", length(opals))
asterix2plot <- vector("list", length(opals))
for(i in 1: length(opals)){
output <- datashield.aggregate(opals[i], cally2)
hist.objs[[i]] <- output[[1]]$histobject
asterix2plot[[i]] <- output[[1]]$aterix2plot
}
# combine the histogram objects
# 'breaks' and 'mids' are the same for all studies
global.counts <- rep(0, length(hist.objs[[i]]$counts))
global.density <- rep(0, length(hist.objs[[i]]$density))
global.intensities <- rep(0, length(hist.objs[[i]]$intensities))
for(i in 1:length(opals)){
global.counts <- global.counts + hist.objs[[i]]$counts
global.density <- global.density + hist.objs[[i]]$density
global.intensities <- global.intensities + hist.objs[[i]]$intensities
}
global.density <- global.density/3
global.intensities <- global.intensities/3
# generate the combined histogram object to plot
combined.histobject <- hist.objs[[1]]
combined.histobject$counts <- global.counts
combined.histobject$density <- global.density
combined.histobject$intensities <- global.intensities
# plot the individual histograms on the same graph
# if the argument 'type'="combine" plot a combined histogram and if 'type'="split" plot single histograms separately
if(type=="combine"){
colour <- "red"
par(mfrow=c(1,1))
plot(combined.histobject,col=colour, xlab=variable, main='Histogram of the pooled data')
}else{
if(type=="split"){
# set the graph area and plot
ll <- length(opals)
colour <- rainbow(ll)
if(ll > 1){
if((ll %% 2) == 0){ numr <- ll/2 }else{ numr <- (ll+1)/2}
numc <- 2
par(mfrow=c(numr,numc))
for(i in 1:ll){
plot(hist.objs[[i]], col=colour[i], xlab=variable, main=paste("Histogram of ", names(opals)[i], sep=""))
# if there are cells with count > 0 and < mention them as an '*' on the graph
if(length(asterix2plot[[i]]) > 0){
text(asterix2plot[[i]], rep(10, length(asterix2plot[[i]])), "*", pos=3, cex=1.2)
xpos <- min(hist.objs[[i]]$breaks, na.rm=TRUE)
ypos <- max(hist.objs[[i]]$counts, na.rm=TRUE)
text(xpos, ypos, "'*' Cells were 0 < count < 5", pos=3, cex=1.2)
}
}
}else{
par(mfrow=c(1,1))
plot(hist.objs[[1]], col=colour[1], xlab=variable, main=paste("Histogram of ", names(opals)[1], sep=""))
# if there are cells with count > 0 and < mention them as an '*' on the graph
if(length(asterix2plot[[1]]) > 0){
text(asterix2plot[[1]], rep(10, length(asterix2plot[[1]])), "*", pos=3, cex=1.2)
xpos <- min(hist.objs[[1]]$breaks, na.rm=TRUE)
ypos <- max(hist.objs[[1]]$counts, na.rm=TRUE)
text(xpos, ypos, "'*' Cells were 0 < count < 5", pos=3, cex=1.2)
}
}
}else{
stop('Function argument "type" has to be either "combine" or "split"')
}
}
}
|
a09c71e864db6acd6bd9c042293e0e7f402eac66 | cad6f58627cdf4ccbb1363ae81bde60e4aeb65fb | /man/plot.param.Rd | 6fc67c7af598bbaa61909e5eaa05eeb789655386 | [] | no_license | WangTJ/glidars | d011a7f94778e6d62f6243e963d702a3b0857878 | bcee3000b1afdcff501ea218f7d72dcca829bc1e | refs/heads/master | 2021-08-06T20:43:15.353332 | 2021-01-19T01:37:49 | 2021-01-19T01:37:49 | 239,854,611 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 654 | rd | plot.param.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/func_visual.R
\name{plot.param}
\alias{plot.param}
\title{Plot function for class "param"}
\usage{
\method{plot}{param}(beta, lambda.index = NULL)
}
\arguments{
\item{beta}{beta is of class "param"}
\item{lambda.index}{when the param beta contains multiple values of the tuning parameter, the user need to
specify the index of the tuning parameter to be plotted}
}
\description{
This function plot the coefficients as an object "param" (see \code{\link{as.param}} for details) on a 2-way
T/N table, using the darkness of the color to represent the values of coefficients.
}
|
43401dea30849edf827a45a67b7aac7f00ae3331 | fb37979cd9880d94815d60a76caeb2d51b90585d | /R/describe.R | 0f8af8302a65145ac2817c6876fd0880441c6763 | [] | no_license | cran/onewaytests | 6ffcb87235f3b6557c96544a2f5c127d6eacf404 | 00eb041971b92468ff56a01dd84796832e5bb46b | refs/heads/master | 2023-01-29T09:10:25.715174 | 2023-01-18T14:00:02 | 2023-01-18T14:00:02 | 41,308,667 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,923 | r | describe.R |
describe <- function(formula, data){
data <- model.frame(formula, data)
dp <- as.character(formula)
DNAME <- paste(dp[[2L]], "and", dp[[3L]])
if (any(colnames(data)==dp[[3L]])==FALSE) stop("The name of group variable does not match the variable names in the data. The group variable must be one factor.")
if (any(colnames(data)==dp[[2L]])==FALSE) stop("The name of response variable does not match the variable names in the data.")
y = data[[dp[[2L]]]]
group = data[[dp[[3L]]]]
if (!(is.factor(group)|is.character(group))) stop("The group variable must be a factor or a character.")
if (is.character(group)) group <- as.factor(group)
if (!is.numeric(y)) stop("The response must be a numeric variable.")
if (length(y)!=length(group)) stop("Lengths of y and group variables are not equal.")
x.levels <- levels(factor(group))
y.NAs <- NULL
for (i in x.levels) {
y.NAs[i] <- length(y[group == i][is.na(y[group == i])])
}
completeObs <- complete.cases(y)
y <- y[completeObs]
group <- group[completeObs]
y.skew <- y.kurtosis <-y.means <- y.n <- y.medians <- y.firstqs <- y.thirdqs <- y.mins <- y.maxs <- y.sds <- NULL
for (i in x.levels) {
y.means[i] <- mean(y[group == i])
y.n[i] <- length(y[group == i])
y.medians[i] <- median(y[group == i])
y.firstqs[i] <- as.numeric(quantile(y[group == i])[2])
y.thirdqs[i] <- as.numeric(quantile(y[group == i])[4])
y.mins[i] <- min(y[group == i])
y.maxs[i] <- max(y[group == i])
y.sds[i] <- sd(y[group == i])
y.skew[i] <- skewness(y[group == i])
y.kurtosis[i] <- kurtosis(y[group == i])
}
out=as.data.frame(cbind(y.n,y.means,y.sds,y.medians,y.mins,y.maxs,y.firstqs,y.thirdqs,y.skew,y.kurtosis,y.NAs))
colnames(out) = c("n", "Mean", "Std.Dev", "Median", "Min", "Max", "25th","75th","Skewness","Kurtosis","NA")
return(out)
}
|
6c213353bf3d5bcae9d28dc5aa5e5d7e8a9cce7a | 31d82d53c384486bd569f7f11f37308eba9f7774 | /tests/testthat/testCreate.R | 0505ecc6c759261646c08617a5615695059de3a5 | [] | no_license | mirsinarm/big.data.frame | 228fc762990e1d966e9f90074f319530fc3e5291 | 2b71c2e3efb80ea8d8fe29ea16349c26af734e6b | refs/heads/master | 2020-05-30T11:02:46.212306 | 2014-12-15T15:37:08 | 2014-12-15T15:37:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,165 | r | testCreate.R | context("Creation and characteristics")
test_that("Creating and characteristics 1", {
x <- big.data.frame(10, c("double", "character"),
names=c("first", "second"),
init=list(-1.23, "A"),
maxchar=c(NA, 10))
y <- data.frame(first=rep(-1.23, 10), second=rep("A", 10),
stringsAsFactors=FALSE)
expect_that(x[], equals(y))
expect_that(x[,1:2], equals(y[,1:2]))
expect_that(x[,1], equals(y[,1]))
expect_that(x$first, equals(y$first))
expect_that(x[,1,drop=FALSE], equals(y[,1,drop=FALSE]))
expect_that(x[,2], equals(y[,2]))
expect_that(x$second, equals(y$second))
expect_that(x[,2,drop=FALSE], equals(y[,2,drop=FALSE]))
expect_that(nrow(x), equals(nrow(y)))
expect_that(ncol(x), equals(ncol(y)))
expect_that(length(x), equals(length(y)))
expect_that(dim(x), equals(dim(y)))
})
test_that("Creating and characteristics 2", {
z <- big.data.frame(10, c("double", "character"),
names=c("first", "second"),
init=list(-1.23, "A"),
maxchar=c(NA, 10),
location="testdir")
y <- data.frame(first=rep(-1.23, 10), second=rep("A", 10),
stringsAsFactors=FALSE)
x <- attach.big.data.frame("testdir")
expect_that(x[], equals(y))
expect_that(x[,1:2], equals(y[,1:2]))
expect_that(x[,1], equals(y[,1]))
expect_that(x$first, equals(y$first))
expect_that(x[,1,drop=FALSE], equals(y[,1,drop=FALSE]))
expect_that(x[,2], equals(y[,2]))
expect_that(x$second, equals(y$second))
expect_that(x[,2,drop=FALSE], equals(y[,2,drop=FALSE]))
expect_that(nrow(x), equals(nrow(y)))
expect_that(ncol(x), equals(ncol(y)))
expect_that(length(x), equals(length(y)))
expect_that(dim(x), equals(dim(y)))
expect_that(dim(x), equals(dim(y)))
})
test_that("Creating and characteristics 3", {
x <- big.data.frame(10, c("double", "character"),
names=c("first", "second"),
init=list(-1.23, "A"),
maxchar=c(NA, 10))
y <- data.frame(first=rep(-1.23, 10), second=rep("A", 10),
stringsAsFactors=FALSE)
expect_that(x[], equals(y))
expect_that(x[,1:2], equals(y[,1:2]))
expect_that(x$first, equals(y$first))
expect_that(x[,1,drop=FALSE], equals(y[,1,drop=FALSE]))
expect_that(x$second, equals(y$second))
expect_that(x[,2,drop=FALSE], equals(y[,2,drop=FALSE]))
expect_that(nrow(x), equals(nrow(y)))
expect_that(ncol(x), equals(ncol(y)))
expect_that(length(x), equals(length(y)))
expect_that(dim(x), equals(dim(y)))
})
test_that("Extractions 1", {
x <- big.data.frame(10, c("double", "character"),
names=c("first", "second"),
init=list(-1.23, "A"),
maxchar=c(NA, 10))
y <- data.frame(first=rep(-1.23, 10), second=rep("A", 10),
stringsAsFactors=FALSE)
expect_that(x[], equals(y))
expect_that(x[1,], equals(y[1,])) # this fails ... no idea why ... print, class, typeof all look identical
#expect_that(x[1,], equals(y[1,])) # not working; need to fix
expect_that(x[1:2,], equals(y[1:2,]))
expect_that(x[-c(1:2),], equals(y[-c(1:2),])) # removes the last two rows instead of the first two rows
expect_that(x[1,1], equals(y[1,1]))
expect_that(x[1:2,1], equals(y[1:2,1]))
expect_that(x[-c(1:2),1], equals(y[-c(1:2),1]))
expect_that(x[1,2], equals(y[1,2]))
expect_that(x[1:2,2], equals(y[1:2,2]))
expect_that(x[-c(1:2),2], equals(y[-c(1:2),2]))
})
###########################
# big.read.table tests
# Make the data frame
wh <- data.frame(rep(c(1L:26L), 20), rep(letters, 20), rep(rnorm(26), 20))
names(wh) <- c("int", "factor", "num")
write.csv(wh, "withheader.csv", row.names=F)
write.table(wh, "withoutheader.csv", sep=",", row.names=FALSE, col.names=FALSE)
# Reading CSV
test_that("CSV with header, reading in in 1 chunk", {
x <- big.read.table(file="withheader.csv", header=TRUE)
y <- read.csv("withheader.csv", header=TRUE, as.is=TRUE)
expect_that(x[], equals(y)) # check everything
expect_that(x[,1:2], equals(y[,1:2])) # check columns
expect_that(nrow(x), equals(nrow(y))) # check nrow: count number of rows
expect_that(ncol(x), equals(ncol(y))) # check ncol: count number of cols
expect_that(length(x), equals(length(y))) # check length
expect_that(dim(x), equals(dim(y))) # check dim
})
test_that("CSV without header, reading in in 1 chunk", {
x <- big.read.table(file="withoutheader.csv", header=FALSE)
y <- read.csv("withoutheader.csv", header=FALSE, as.is=TRUE)
expect_that(x[], equals(y)) # check everything
expect_that(x[,1:2], equals(y[,1:2])) # check columns
expect_that(nrow(x), equals(nrow(y))) # check nrow: count number of rows
expect_that(ncol(x), equals(ncol(y))) # check ncol: count number of cols
expect_that(length(x), equals(length(y))) # check length
expect_that(dim(x), equals(dim(y))) # check dim
})
test_that("CSV with header, reading in in multiple chunks", {
# chunks are in size of 100 rows
x <- big.read.table(file="withheader.csv", header=TRUE, nrows=100)
y <- read.csv("withheader.csv", header=TRUE, as.is=TRUE)
expect_that(x[], equals(y)) # check everything
expect_that(x[,1:2], equals(y[,1:2])) # check columns
expect_that(nrow(x), equals(nrow(y))) # check nrow: count number of rows
expect_that(ncol(x), equals(ncol(y))) # check ncol: count number of cols
expect_that(length(x), equals(length(y))) # check length
expect_that(dim(x), equals(dim(y))) # check dim
})
test_that("CSV without header, reading in in multiple chunks", {
# chunks are in size of 100 rows
x <- big.read.table(file="withoutheader.csv", header=FALSE, nrows=100)
y <- read.csv("withoutheader.csv", header=FALSE, as.is=TRUE)
expect_that(x[], equals(y)) # check everything
expect_that(x[,1:2], equals(y[,1:2])) # check columns
expect_that(nrow(x), equals(nrow(y))) # check nrow: count number of rows
expect_that(ncol(x), equals(ncol(y))) # check ncol: count number of cols
expect_that(length(x), equals(length(y))) # check length
expect_that(dim(x), equals(dim(y))) # check dim
})
|
ba6d1f6d6ccc20054598ab24406d9d04d92fba8c | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.security.identity/man/iam_delete_ssh_public_key.Rd | 3e5039fd98a2dbc2b5fddf2da6ddcf7e80a02c62 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,053 | rd | iam_delete_ssh_public_key.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iam_operations.R
\name{iam_delete_ssh_public_key}
\alias{iam_delete_ssh_public_key}
\title{Deletes the specified SSH public key}
\usage{
iam_delete_ssh_public_key(UserName, SSHPublicKeyId)
}
\arguments{
\item{UserName}{[required] The name of the IAM user associated with the SSH public key.
This parameter allows (through its \href{https://en.wikipedia.org/wiki/Regex}{regex pattern}) a string of characters
consisting of upper and lowercase alphanumeric characters with no
spaces. You can also include any of the following characters: _+=,.@-}
\item{SSHPublicKeyId}{[required] The unique identifier for the SSH public key.
This parameter allows (through its \href{https://en.wikipedia.org/wiki/Regex}{regex pattern}) a string of characters
that can consist of any upper or lowercased letter or digit.}
}
\description{
Deletes the specified SSH public key.
See \url{https://www.paws-r-sdk.com/docs/iam_delete_ssh_public_key/} for full documentation.
}
\keyword{internal}
|
e2f3a6577be120f0a2f8c090a6baa63e278d11b8 | 96ec24aba788f6cb1ce17c619809715515db7788 | /network/run_genLevel_prep.R | 5c5d02511f334461d0f473690a4e0737c39cac0d | [] | no_license | hawaiiDimensions/geb_paper | 96fdca932be53a6daeac6a34c9cf0cb5ac31222a | 031ba3d23e7db7bf0f874d1b7cd852005f34b0a8 | refs/heads/master | 2021-01-10T11:36:05.975874 | 2015-12-04T17:11:49 | 2015-12-04T17:11:49 | 47,417,181 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 806 | r | run_genLevel_prep.R | library(bipartite)
library(vegan)
library(igraph)
source('netFuns.R')
## get site info and specify which sites to analyze
site.info <- read.csv('data/siteInfo.csv', header=TRUE, as.is=TRUE)
good.sites <- c('Kil', 'Koh', 'Hal', 'KoK')
## load data
bimat <- lapply(good.sites,
function(i) {
as.matrix(read.csv(sprintf('data/%s_unconmat.csv', i),
row.names=1, header=TRUE, as.is=TRUE))
})
## aggregate by genus
bimat <- lapply(bimat,
function(x) {
x <- aggregate(as.data.frame(t(x)), list(gsub('\\..*', '', colnames(x))), sum)
rownames(x) <- x[, 1]
x <- x[, -1]
as.matrix(t(x))
})
## run parameters
nsim <- 999 |
445e8f5b5049707450735ebd8f19b9a9c7a8a8be | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tea/examples/avhill.Rd.R | 325d44ee6e743ad871d7726ed8d2c0ede7c944b0 | [] | 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 | 135 | r | avhill.Rd.R | library(tea)
### Name: avhill
### Title: Averaged Hill Plot
### Aliases: avhill
### ** Examples
data(danish)
avhill(danish)
|
e8506b5a67544707095a1d5998c2ae1d798cc37b | c452a79f3cc26d1d154292706c12d7d174767511 | /1.Pre-process_microarry_data/1.ArrayExpress_multiple_tissue_multiple_chip.R | f50025cea5076872606a4ffc2f40645f3e7f3f12 | [] | no_license | hamelpatel/brain_expression | af1fea5e3776082221a3cd20779442615342fd3a | cb25bb42e432c44add9b89cf2b5247b1f776c0e3 | refs/heads/master | 2020-04-06T06:50:20.525551 | 2017-07-06T08:16:28 | 2017-07-06T08:16:28 | 58,650,399 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,132 | r | 1.ArrayExpress_multiple_tissue_multiple_chip.R |
##########################################################################################################################################
#### ####
### ###
## ##
# DOWNLOAD E-GEOD-3790 #
## ##
### ###
#### ####
##########################################################################################################################################
# QC PIPELINE VERSION: 1.0
# DATE: 30/01/2017
# ARRAY EXPRESS NUMBER: E-GEOD-3790
# DISORDER: Huntingdon's Disease
# MICROARRAY PLATFORM: Affymetrix
# EXPRESSION CHIP: HG-U133A + HG-U133B
# NUMBER OF SAMPLES:
# TISSUE: Cerebellum, Frontal Cortex, Caudate nucleus
#
# NOTES -
# ONLY READING RAW DATA AND CREATEING R OBJECT
# e.g given using E-GEOD-3970 dataset
#
##### SET PARAMETERS #####
rm(list=ls())
options=(stringAsFactors=FALSE)
##### SET DIRECTORIES ####
work_dir="/media/hamel/Workspace/Dropbox/Projects/Brain_expression/1.Data/1.Re-process_with_new_pipeline/NON-AD-NEUROLOGICAL/Huntingdons_Disease/E-GEOD-3790"
setwd(work_dir)
# create directory for raw data
dir.create(paste(work_dir,"Raw_Data", sep="/"))
raw_dir=paste(work_dir,"Raw_Data", sep="/")
##### LOAD LIBRARIES ####
library(ArrayExpress)
library(affy)
##### DOWNLOAD RAW DATA #####
setwd(raw_dir)
#data_raw=getAE("E-GEOD-3790", type = "raw")
#data_raw=getAE("E-GEOD-3790", type = "processed")
data_raw=getAE("E-GEOD-3790", type = "full")
##### CREATE R EXPRESSION OBJECT FROM RAW DATA #####
# METHOD 1 - convert MAGE-TAB files into expresssion set - USING RAW DATA
expression_data = ae2bioc(mageFiles = data_raw)
expression_data
# # METHOD 2 - convert MAGE-TAB files into expresssion set - USING RAW DATA
#
# expression_data<-ReadAffy()
#
# METHOD 3 - convert MAGE-TAB files into expresssion set - USING PROCESSED DATA
#
# cnames=getcolproc(data_raw)
#
# cnames
#
# expression_data=procset(data_raw, cnames[2])
#
# expression_data
##### SPLIT DATA INTO CHIPS ######
table((pData(expression_data[[1]]))$Factor.Value..DiseaseState.)
table((pData(expression_data[[1]]))$Characteristics..OrganismPart.)
table((pData(expression_data[[2]]))$Factor.Value..DiseaseState.)
table((pData(expression_data[[2]]))$Characteristics..OrganismPart.)
#create objects
Affy_U133A<-expression_data[[1]]
Affy_U133B<-expression_data[[2]]
##### BACKGROUND CORRECT DATA #####
Affy_U133A_bc<-mas5(Affy_U133A)
Affy_U133B_bc<-mas5(Affy_U133B)
##### CREATE SUB DIRECTORIES FOR EACH CHIP AN TISSUE AND SAVE #####
# create directory for each chip and write expression table
setwd(work_dir)
dir.create(paste(work_dir,"Affy_U133A", sep="/"))
Affy_U133A_dir=paste(work_dir,"Affy_U133A", sep="/")
dir.create(paste(work_dir,"Affy_U133B", sep="/"))
Affy_U133B_dir=paste(work_dir,"Affy_U133B", sep="/")
setwd(Affy_U133A_dir)
save(Affy_U133A_bc, file="E-GEOD-3790_Affy_U133A_bc.Rdata")
setwd(Affy_U133B_dir)
save(Affy_U133B_bc, file="E-GEOD-3790_Affy_U133B_bc.Rdata")
# create directory for cerebellum + frontal lobe in each chip
setwd(Affy_U133A_dir)
dir.create(paste(Affy_U133A_dir,"Cerebellum", sep="/"))
dir.create(paste(Affy_U133A_dir,"Frontal_Lobe", sep="/"))
setwd(Affy_U133B_dir)
dir.create(paste(Affy_U133B_dir,"Cerebellum", sep="/"))
dir.create(paste(Affy_U133B_dir,"Frontal_Lobe", sep="/"))
|
d2062a890943f00bf5d3a9935180788cf7aa7356 | 35fac53e593ad39a3c092f84fa0d88fa11f46fa7 | /man/add_quotes.Rd | acd91cee09fc1d078ca704110686d4a3dd65aa3b | [] | no_license | gastonstat/cranium | a916a61595e7432385a410e5a90fe21e6a41cae4 | 1ba7a60fd7b3ef62c2d2f38d66151494f382f837 | refs/heads/master | 2020-05-24T13:24:13.206253 | 2015-08-26T00:03:01 | 2015-08-26T00:03:01 | 23,559,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 429 | rd | add_quotes.Rd | \name{add_quotes}
\alias{add_quotes}
\title{Add Quotes}
\usage{
add_quotes(str)
}
\arguments{
\item{str}{character string}
}
\value{
quoted character string
}
\description{
Adds double quotation marks around a character string
}
\details{
This function is used to create the JSON file for package
dependencies and reverse dependencies
}
\examples{
\dontrun{
s = 'quotations'
add_quotes(s)
}
}
\keyword{internal}
|
6b0ceb3fda39dd0dad84345f76312833267014e4 | 91903e21fa8472ae02cc94485ec3c0a52d725b68 | /Course Project 1/plot4.R | 56b9ec272b4b9b6c04be26959b4926f24c2a734e | [] | no_license | lycansoul/ExData_Plotting1 | 9ca9d70d73c1928b081c8d3ef220477aefa35a15 | 1326f2b73bba3daf31d275579a896f3c4aab0583 | refs/heads/master | 2021-01-18T10:26:31.821832 | 2014-07-11T14:00:15 | 2014-07-11T14:00:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,066 | r | plot4.R | #Reading data
data <- read.table("./eda/household_power_consumption.txt",header = TRUE,sep = ";",na.strings= "?")
datatest <- data
data[,1] <- as.character(data[,1])
data[,2] <- as.character(data[,2])
#Cleaning data
targetdate <- c("1/2/2007","2/2/2007")
ndata <- subset(data, data[,1] %in% targetdate)
ndata <- na.omit(ndata)
ndata[,10] <- paste(ndata[,1],ndata[,2])
names(ndata)[10] <- "Paste"
DT <- strptime(ndata[,10], "%d/%m/%Y %H:%M:%S")
Sys.setlocale("LC_TIME","C")
#plot4
png(filename = "plot4.png",width = 480, height = 480)
par(mfrow = c(2,2))
plot(DT, ndata[,3], xlab="",ylab= "Global Active Power (kilowatts)",type= "l")
plot(DT, ndata[,5], xlab="datetime",ylab= "Voltage",type= "l")
plot(DT, ndata[,7], xlab="",ylab= "Energy sub metering",type= "l")
lines(DT, ndata[,8], col = "red")
lines(DT,ndata[,9],col = "blue")
legend("topright", col = c("black", "red", "blue"), lty= "solid", bty="n",
legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3") )
plot(DT, ndata[,4], xlab="datetime",ylab= "Global_reactive_power",type= "l")
dev.off() |
aa1d537d644c0b7a8b79a474521fff2558f1623c | 339eaa009c2f9f3870c8ef2bdb1bf708e0cfcf09 | /dlmSmc/FuncPoisson.R | c14a783ac28a882e308a3ed6f76461aaa2aee9a9 | [] | no_license | miltonluaces/data_science_in_R | ac9ea2b78b817ec01c0383afd8ef9ca4e4c84316 | 872c3ce1ab2497d57febaeb24590b561260f3067 | refs/heads/master | 2021-06-30T20:30:16.997520 | 2021-02-27T16:03:41 | 2021-02-27T16:03:41 | 226,901,456 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,409 | r | FuncPoisson.R | # FUNCTIONS DLM-SMC WITH POISSON DISTRIBUTION
# ==========================================================================================================
library(dlm)
library(mvtnorm)
# Poisson DLM-SMC. Estimate Poisson parameter lambda for each time t of a time series using DLM with SMC algorithm
#-----------------------------------------------------------------------------------------------------------------
# Paramets : the time series Data, F & G DLM matrices, m0 & C0 initial distribution, V & W error terms and nW
# Returns : an array of estimation of lambda for each time t
DlmSmcPoisson = function(Data, F, G, m0, C0, V, W, nW) {
#Declarations
nD = length(Data)
nF = length(F)
A = array(0, dim=c(nF,nW,nD+1))
InvArav = matrix(0, nD+1,nW)
m = array(0, dim=c(nF,nW,nD+1))
C = array(0, dim=c(nF,nF,nW,nD+1))
w = matrix(0,nD+1,nW)
theta = array(0, dim=c(nF,nW,nD+1))
thetaT = array(0, dim=c(nF,nW,nD))
thetaHat = matrix(0,nF,nD)
LambdaHat = numeric(0);
#Initial values
w[1,] = 1/nW
m[,,1] = m0
C[,,,1] = C0
y = c(0, Data)
for(i in 1:nW) {
v = rmvnorm(1, m[,i,1], C[,,i,1])
theta[,i,1] = t(v)
}
#DLM steps
for( t in 2:(nD+1)) {
for(i in 1:nW) {
#Prior states at time t
R = G %*% C[,,i,t-1] %*% t(G) + W
a = G %*% m[,i,t-1]
A[,i,t] = exp(t(F) %*% a)[1,1] * F
InvArav[t,i] = solve(t(A[,i,t]) %*% R %*% A[,i,t] + V)
#i.d.f. mean: m_t = E(t_t|t_t-1,y_t) = G * m_t-1 * R_t * A_t * (A'_t * R_t * A_t + V)^-1 * (y_t - mG)
m[,i,t] = a + R %*% A[,i,t] %*% InvArav[t,i] %*% (y[t] - exp(t(F) %*% a))
#i.d.f. cov: C_t = Var(t_t|t_t-1,y_t) = R_t - R_t * A_t * (A'_t * R_t * A_t + V)^-1 * A'_t * R_t
C[,,i,t] = R - (R %*% A[,i,t] %*% InvArav[t,i]) %*% t(A[,i,t]) %*% R
#draw theta_t = g(theta_t | theta_t-1, y_t)
theta[,i,t] = rmvnorm(1, m[,i,t], C[,,i,t])
#importance density function: multivariate normal for Kalman filter
impDens = dmvnorm(theta[,i,t], m[,i,t], C[,,i,t])
#Posterior states at time t
#t_t = G(t_t-1) + w_t
probTt = dmvnorm(theta[,i,t], G %*% m[,i,t-1], R)
#y_t|t_t = p(y_t, Poisson(F' t_t))
probYt = dpois(round(y[t]), exp(t(F) %*% theta[,i,t]))
#calculate importance weights
w[t,i] = w[t-1,i] * probYt * probTt / impDens
}
#normalize importance weights (sum to 1)
w[t,] = w[t,]/(sum(w[t,]))
#algorithm to avoid weight degradation
nEff = 1/crossprod(w[t,])
if(nEff < nW/3) {
index = sample(nW, nW, replace=TRUE, prob = w[t,])
theta[,,t] = theta[,index,t]
w[t,] = 1/nW
}
for(i in 1:nW) { thetaT[,i,t-1] = w[t,i] * theta[,i,t] }
for(f in 1:nF) { thetaHat[f, t-1] = sum(thetaT[f,,t-1]) }
# integral : int[ p(y_t| t_t) * p(t_t|D_t-1)]
lambdaHat = exp(t(F) %*% thetaHat[,t-1]);
LambdaHat = c(LambdaHat, lambdaHat);
}
LambdaHat
}
# One-step forecast samples. Generates Poisson forecast samples for each time t given some lambda estimations
#------------------------------------------------------------------------------------------------------------
# Paramets : number of samples nSamples and array of estimations of Lambda (from DlmSmcPoisson)
# Returns : a matrix with columns of nSamples for each time t of the time series
PoissonFcstSamples = function(nSamples, LambdaHat) {
nD = length(LambdaHat);
yHat = matrix(nrow=nSamples,ncol=nD);
for(t in 2:(nD+1)) {
yh = rpois(nSamples, LambdaHat[t-1]);
yHat[,t-1] = yh;
}
yHat
}
# One-step forecast mean for each time t
#-----------------------------------------------------------------------------------------------------------
# Paramets : n forecast samples for each time t (from PoissonFcstSamples)
# Returns : an array with one-step forecast for each point of the history
FcstMean = function(fcstSamples) {
fc = NULL
for(i in 1:length(fcstSamples[1,])) {
m = mean(fcstSamples[,i])
fc = c(fc, m)
}
fc
}
|
886d2923db2c4c422a9dffff54242c28c1cc5fd4 | 6dabb731d76e567e9a89ec8545006e4d89b54355 | /tests/testthat/test-EdgeConditions.R | b51feee0be520a0b63f47783849110aa15d4ff37 | [
"MIT"
] | permissive | diego-urgell/BinSeg | d9b93e25ac87782d49bd390a6e26c760bd95f884 | a14827f76072f38e75d2e741d4f478ddf9e4a163 | refs/heads/main | 2023-07-07T16:52:28.760409 | 2021-08-22T05:23:14 | 2021-08-22T05:23:14 | 375,764,096 | 5 | 1 | MIT | 2021-08-25T05:45:40 | 2021-06-10T16:33:12 | R | UTF-8 | R | false | false | 1,052 | r | test-EdgeConditions.R | # Title : TODO
# Objective : TODO
# Created by: diego.urgell
# Created on: 02/08/21
library(BinSeg)
test_that("Change in mean + Single data point -> Zero cost and trivial mean", {
ans <- BinSeg::BinSegModel(100, "BS", "mean_norm", 1)
expect_equal(logLik(ans), 0)
expect_equal(ans@models_summary[["before_mean"]], 100)
})
test_that("Change in mean and variance + Two data points -> Trivial params", {
ans <- BinSeg::BinSegModel(c(100, 50), "BS", "meanvar_norm", 1, 2)
expect_equal(ans@models_summary[["before_mean"]], 75)
expect_equal(ans@models_summary[["before_var"]], 625)
})
test_that(desc="Binary Segmentation + Change in mean and variace: Test 4 - No possible segmentation", {
data <- c(1.1, 1, 2, 2, 2)
ans <- BinSeg::BinSegModel(data, "BS", "meanvar_norm", 1, 2)
expect_equal(sort(cpts(ans)), 5)
})
test_that(desc="Binary Segmentation + Change in variace: Test 2 - Variance constraint", {
data <- c(1.1, 1, 1, -5)
ans <- BinSeg::BinSegModel(data, "BS", "var_norm", 1, 2)
expect_equal(sort(cpts(ans)), 4)
})
|
762e4c6b1debe9946a04f5f10d44e1f93c80daa1 | e77609cd32fe8c55002636ba5285edeed6708b20 | /006-sensitivity-analysis-one-parameter/R/006-packages.R | 30a70024e8daaed4b456249aca63927f433b687b | [
"CC-BY-4.0"
] | permissive | sahirbhatnagar/raqc | 6acac11e9a0bc7fd1e270cd3f35ceb646e43c6f5 | fe313b2e05fd9e26fbc2e085bc0b93dfbbd32611 | refs/heads/master | 2020-05-21T11:42:31.324618 | 2019-05-27T03:22:35 | 2019-05-27T03:22:35 | 186,035,633 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 115 | r | 006-packages.R | ## ---- required-packages ----
if (!require("pacman")) install.packages("pacman")
pacman::p_load(knitr, here)
|
f9248216c671db8b0d84d4fcd8e65a1c9d364a85 | 8bb13ff1ae4e4ed5afa98f7590949fb3bea7130a | /Analisis_Todo.R | 84becec55d9e4aad7633d9f40bcf89c208d7f51a | [] | no_license | Fedeayl/Libro-Elecciones | 97110cc5b41820f0afaa2eedf29910d9bc76ed48 | d3829c6b7f70ef8b391f7dc5514b25a163255e03 | refs/heads/main | 2023-01-30T21:11:35.061504 | 2020-12-16T23:35:00 | 2020-12-16T23:35:00 | 319,621,846 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,798 | r | Analisis_Todo.R | ################### ELECCIONES INTERNAS ###################
Base <- rio::import(here::here("Internas hojas_interna2019.xlsx"))
head(Base)
Datos <- Base[Base$TipoHoja == "ODN", c(1,2,4,6,7)]
Datos <- unique(Datos)
# Hojas por sublema (ODN)
sum(Datos$PartidoPolitico == "Frente Amplio")
sum(Datos$PartidoPolitico == "Partido Nacional")
sum(Datos$PartidoPolitico == "Partido Colorado")
sum(Datos$PartidoPolitico == "Cabildo Abierto")
Base2 <- rio::import(here::here("Internas totales-generales-por-circuito.xlsx"))
Datos2 <- Base2
Tabla1 <- cbind(Habilitados = xtabs(Total_Habilitados ~ Departamento, Datos2),
Emitidos= xtabs(Total_Votos_Emitidos ~ Departamento, Datos2),
Anulados = xtabs(Total_Anulados ~ Departamento, Datos2),
Blanco = xtabs(Total_En_Blanco ~ Departamento, Datos2)) # Crea la base general de habilitados, blancos, etc
Base3 <- rio::import(here::here("Interna desglose-de-votos.xlsx"))
Datos3 <- Base3[,c(1,2,5,6,8)]
Datos3 <- Datos3[Datos3$TIPO_REGISTRO == "HOJA_ODN",]
Tabla2 <- xtabs(CANTIDAD_VOTOS ~ DEPARTAMENTO + LEMA, Datos3) # Tabla de votos por lema por departamento
Tabla1 <- reshape::melt(Tabla1)
names(Tabla1) <- c("DEPARTAMENTO", "LEMA", "value")
Tabla2 <- reshape::melt(Tabla2)
Resultados <- bind_rows(Tabla1, Tabla2)
ResultadosINT <- reshape::cast(Resultados, formula = DEPARTAMENTO~...)
# Resultados Frente Amplio por departamento
DatosFA <- Datos3[Datos3$LEMA == "Frente Amplio",]
TablaFA <- xtabs(CANTIDAD_VOTOS ~ DEPARTAMENTO + DESCRIPCIÓN_1, DatosFA) # Deagrega por candidato FA
#Resultados Partido Nacional por departamento
DatosPN <- Datos3[Datos3$LEMA == "Nacional",]
TablaPN <- xtabs(CANTIDAD_VOTOS ~ DEPARTAMENTO + DESCRIPCIÓN_1, DatosPN) # Deagrega por candidato PN
#Resultados Partido Colorado por departamento
DatosPC <- Datos3[Datos3$LEMA == "Partido Colorado",]
TablaPC <- xtabs(CANTIDAD_VOTOS ~ DEPARTAMENTO + DESCRIPCIÓN_1, DatosPC) # Deagrega por candidato PC
#################### ELECCIONES NACIONALES #####################
# Carga de datos
NacionalesHAB <- rio::import(here::here("totales-generales.xlsx")) # Datos nulos, blanco, etc
Nacionales <- rio::import(here::here("desglose-de-votos.xlsx")) # Datos por Lema
# Crear las tablas
Tabla1 <- xtabs(CantidadVotos ~ Departamento + Lema, data=Nacionales) # Tabla cruzada Depto vs Lema
Tabla1b <- cbind(EnBlanco = xtabs(TotalEnBlanco ~ Departamento, data = NacionalesHAB),
Anulados = xtabs(TotalAnulados ~ Departamento, data = NacionalesHAB),
Emitidos = xtabs(TotalVotosEmitidos ~ Departamento, data = NacionalesHAB),
Habilitados = xtabs(TotalHabilitados ~ Departamento, data = NacionalesHAB)
) # Tabla cruzada, blanco, anulado, por depto
Tabla1 <- as.data.frame(reshape::melt(Tabla1)) # Cambio de formato para combinar
Tabla1b <- as.data.frame(reshape::melt(Tabla1b))
names(Tabla1b) <- c("Departamento", "Lema", "value") # Asignar mismos nombres a Tabla2 que a Tabla1
Tabla <- rbind(Tabla1, Tabla1b) # Combinar ambas talas
Tabla <- reshape::cast(Tabla, ... ~ Lema) # Reformatear para que quede la salida deseada
TablaNAC1 <- dplyr::bind_rows(Tabla, colSums(Tabla), .id=NULL) # Resultados generales
### Se quiere obtener una tabla de votos por sublema
# Crear una base con hojas por sublema
Sublemas <- rio::import(here::here("integracion-de-hojas.xlsx"))
Sublemas <- cbind.data.frame(Lema = Sublemas$PartidoPolitico, Hoja=Sublemas$Numero, Sublema=Sublemas$Sublema)
Sublemas <- unique(Sublemas)
Sublemas <- Sublemas[Sublemas$Sublema != "No aplica", ]
## FRENTE AMPLIO - Crear una tabla de votos por sublema del FA
SubFA <- Sublemas[Sublemas$Lema == "Frente Amplio",]
Tabla2 <- as.data.frame(xtabs(CantidadVotos ~ Descripcion1 + Lema, data=Nacionales)) # Tabla cruzada Lema vs Hoja
SsFA <- Tabla2[Tabla2$Lema == "Partido Frente Amplio",] # Selecciona solamente las entradas "Frente Amplio"
SsFA <- SsFA[SsFA$Freq != 0,] # Elimina las entradas con ceros
names(SsFA) <- c("Hoja", "Lema","Freq") # Asigna nombres a las categorías para el merge siguiente
datos <- merge(SsFA, SubFA, by = "Hoja")
a <- dplyr::group_by(datos, Sublema, Lema.x, Lema.y)
Frente <- unique(dplyr::summarise(a, Sublema, Votos=sum(Freq)))
Frente <- Frente[c(1,4)]
Frente <- as.data.frame(dplyr::arrange(Frente, desc(Frente$Votos)))# Ordenar decreciente
Frente2<- cbind(as.data.frame(rep("Frente Amplio", length(Frente$Sublema))), Frente)
names(Frente2) <- c("Lema", "Sublema", "Votos")
Frente2
## PARTIDO NACIONAL
SubPN <- Sublemas[Sublemas$Lema == "Partido Nacional",]
Tabla2 <- as.data.frame(xtabs(CantidadVotos ~ Descripcion1 + Lema, data=Nacionales)) # Tabla cruzada Lema vs Hoja
SsPN <- Tabla2[Tabla2$Lema == "Partido Nacional",] # Selecciona solamente las entradas "Frente Amplio"
SsPN <- SsPN[SsPN$Freq != 0,] # Elimina las entradas con ceros
names(SsPN) <- c("Hoja", "Lema","Freq") # Asigna nombres a las categorías para el merge siguiente
datos <- merge(SsPN, SubPN, by = "Hoja")
a <- dplyr::group_by(datos, Sublema, Lema.x, Lema.y)
PNacional <- unique(dplyr::summarise(a, Sublema, Votos=sum(Freq)))
PNacional <- PNacional[c(1,4)]
PNacional <- as.data.frame(dplyr::arrange(PNacional, desc(PNacional$Votos)))# Ordenar decreciente
PNacional2<- cbind(as.data.frame(rep("P.Nacional", length(PNacional$Sublema))), PNacional)
names(PNacional2) <- c("Lema", "Sublema", "Votos")
PNacional2
## PARTIDO COLORADO
SubPC <- Sublemas[Sublemas$Lema == "Partido Colorado",]
Tabla2 <- as.data.frame(xtabs(CantidadVotos ~ Descripcion1 + Lema, data=Nacionales)) # Tabla cruzada Lema vs Hoja
SsPC <- Tabla2[Tabla2$Lema == "Partido Colorado",] # Selecciona solamente las entradas "Frente Amplio"
SsPC <- SsPC[SsPC$Freq != 0,] # Elimina las entradas con ceros
names(SsPC) <- c("Hoja", "Lema","Freq") # Asigna nombres a las categorías para el merge siguiente
datos <- merge(SsPC, SubPC, by = "Hoja")
a <- dplyr::group_by(datos, Sublema, Lema.x, Lema.y)
PColorado <- unique(dplyr::summarise(a, Sublema, Votos=sum(Freq)))
PColorado <- PColorado[c(1,4)]
PColorado <- as.data.frame(dplyr::arrange(PColorado, desc(PColorado$Votos)))# Ordenar decreciente
PColorado2 <- cbind(as.data.frame(rep("P.Colorado", length(PColorado$Sublema))), PColorado)
names(PColorado2) <- c("Lema", "Sublema", "Votos")
PColorado2
VotosSublemas <- dplyr::bind_rows(Frente2, PNacional2, PColorado2)
######################## REFORMA ##############################
BaseP <- rio::import(here::here("PLEBISCITO.xlsx"))
DatosP <- BaseP[, c(3,7,10,11,12,13,14)]
names(DatosP)
TablaSI <- cbind("Habilitados" = xtabs(HABILITADO~ DEPTO, DatosP),
"Emitidos" = xtabs(T_EMITIDOS~ DEPTO, DatosP),
"Blanco" = xtabs(EN_BLANCO~ DEPTO, DatosP),
"Anulados" = xtabs(ANULADOS~ DEPTO, DatosP),
"Solo SI" = xtabs(SOLO_POR_SI~ DEPTO, DatosP),
"Papeleta" = xtabs(`Papeleta por SI`~ DEPTO, DatosP))
######################## BALOTAJE ##############################
BaseB <- rio::import(here::here("balotaje-2019.xlsx"))
DatosB <- BaseB
names(DatosB)
TablaBalotaje <- cbind("Martínez-Villar" = xtabs(Total_Martinez_Villar ~ Departamento, DatosB),
"Lacalle-Argimón" = xtabs(Total_Lacalle_Pou_Argimon ~ Departamento, DatosB),
"Hablitados" = xtabs(Total_Habilitados ~ Departamento, DatosB),
"Emitidos" = xtabs(Total_Votos_Emitidos ~ Departamento, DatosB),
"Blanco" = xtabs(Total_EN_Blanco ~ Departamento, DatosB),
"Anulado" = xtabs(Total_Anulados ~ Departamento, DatosB))
library(xlsx)
write.xlsx(ResultadosINT, file="Resultados.xlsx", sheetName="Internas-General2",row.names=FALSE)
write.xlsx(TablaFA, file="Resultados.xlsx", sheetName="Internas-FA", append=TRUE, row.names=FALSE)
write.xlsx(TablaPN, file="Resultados.xlsx", sheetName="Internas-PN", append=TRUE, row.names=FALSE)
write.xlsx(TablaPC, file="Resultados.xlsx", sheetName="Internas-PC", append=TRUE, row.names=FALSE)
write.xlsx(TablaNAC1, file="Resultados.xlsx", sheetName="Nacional-General", append=TRUE, row.names=FALSE)
write.xlsx(Frente2, file="Resultados.xlsx", sheetName="Nacional-FA", append=TRUE, row.names=FALSE)
write.xlsx(PNacional2, file="Resultados.xlsx", sheetName="Nacional-PN", append=TRUE, row.names=FALSE)
write.xlsx(PColorado2, file="Resultados.xlsx", sheetName="Nacional-PC", append=TRUE, row.names=FALSE)
write.xlsx(VotosSublemas, file="Resultados.xlsx", sheetName="Nacional-SUBLEMAS", append=TRUE, row.names=FALSE)
write.xlsx(TablaSI, file="Resultados.xlsx", sheetName="Reforma", append=TRUE, row.names=FALSE)
write.xlsx(TablaBalotaje, file="Resultados.xlsx", sheetName="Balotaje", append=TRUE, row.names=FALSE)
|
1df5a9d252449b9ce4075cc16d885c699d4ee2d1 | a1e3f742d80a225e9a2a35e8e88b3054f5408037 | /R/big.gomp.R | 410cad3178dece34236e2c607d32620ee4b28027 | [] | no_license | cran/MXM | 7590471ea7ed05944f39bf542c41a07dc831d34f | 46a61706172ba81272b80abf25b862c38d580d76 | refs/heads/master | 2022-09-12T12:14:29.564720 | 2022-08-25T07:52:40 | 2022-08-25T07:52:40 | 19,706,881 | 0 | 4 | null | null | null | null | UTF-8 | R | false | false | 17,905 | r | big.gomp.R | big.gomp <- function(target = NULL, dataset, tol = qchisq(0.95, 1) + log(dim(x)[1]), test = "testIndFisher", method = "ar2") {
tic <- proc.time()
oop <- options(warn = -1)
on.exit( options(oop) )
if ( is.null(target) ) {
if (test == "censIndCR" | test == "censIndWR") {
y <- survival::Surv(dataset[, 1], dataset[, 2])
x <- bigmemory::sub.big.matrix(dataset, firstCol = 3)
} else {
y <- dataset[, 1]
x <- bigmemory::sub.big.matrix(dataset, firstCol = 2)
}
} else y <- target
n <- dim(x)[1]
phi <- NULL
if ( test == "testIndFisher" ) {
tic <- proc.time()
######### SSE
if ( method == "sse" ) {
rho <- Rfast::Var(y) * (n - 1)
r <- cor(y, x[])
sel <- which.max(abs(r))
sela <- sel
res <- .lm.fit(x[, sel, drop = FALSE], y)$residuals
rho[2] <- sum(res^2)
i <- 2
while ( (rho[i - 1] - rho[i])/(rho[i - 1]) > tol & i < n ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max(abs(r))
sela <- c(sela, sel)
res <- .lm.fit(x[, sela], y)$residuals
rho[i] <- sum(res^2)
ind[sela] <- 0
} ## end while ( (rho[i - 1] - rho[i])/(rho[i - 1]) > tol & i < n )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Vars", "|sse|")
######### loglik
} else if ( method == "loglik" ) {
rho <- n * log( Rfast::Var(y) )
r <- cor(y, x[])
sel <- which.max( abs(r) )
sela <- sel
res <- .lm.fit(x[, sel, drop = FALSE], y)$residuals
rho[2] <- n * log( sum(res^2)/(n - i) )
i <- 2
while ( rho[i - 1] - rho[i] > tol & i < n ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max(abs(r))
sela <- c(sela, sel)
res <- .lm.fit(x[, sela], y)$residuals
rho[i] <- n * log( sum(res^2)/(n - i) )
} ## end while ( rho[i - 1] - rho[i] > tol & i < n )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len] + n * log(2 * pi) + n )
colnames(res) <- c("Vars", "BIC")
######### adjusted R-square
} else if (method == "ar2") {
down <- Rfast::Var(y) * (n - 1)
rho <- 0
r <- cor(y, x[])
sel <- which.max( abs(r) )
sela <- sel
res <- .lm.fit(x[, sel, drop = FALSE], y)$residuals
r2 <- 1 - sum(res^2)/down
rho[2] <- 1 - (1 - r2) * (n - 1)/(n - 2)
i <- 2
while ( rho[i] - rho[i - 1] > tol & i < n ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max(abs(r))
sela <- c(sela, sel)
res <- .lm.fit(x[, sela], y)$residuals
r2 <- 1 - sum(res^2)/down
rho[i] <- 1 - (1 - r2) * (n - 1)/(n - i - 1)
} ## end while ( rho[i] - rho[i - 1] > tol & i < n )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Vars", "adjusted R2")
}
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else {
if (test == "testIndGamma") {
tic <- proc.time()
mod <- glm( y ~ 1, family = Gamma(log) )
rho <- mod$deviance
phi <- summary(mod)[[ 14 ]]
res <- y - fitted(mod)
ela <- as.vector( cor(res, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- glm(y ~ x[, sela], family = Gamma(log) )
res <- mod$residuals
rho[2] <- mod$deviance
phi[2] <- summary(mod)[[ 14 ]]
i <- 2
while ( (rho[i - 1] - rho[i]) / phi[i] > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- glm(y ~ x[, sela], family = Gamma(log) )
res <- y - fitted(mod)
rho[i] <- mod$deviance
phi[i] <- summary(mod)[[ 14 ]]
} ## end while ( (rho[i - 1] - rho[i]) > tol )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi[1:len], res = res)
} else if ( test == "testIndNormLog" ) {
tic <- proc.time()
ini <- Rfast::normlog.mle(y)
m <- ini$param[1]
rho <- sum( (y - ini$param[1])^2 )
phi <- mod$devi/(n - 2)
ela <- cor(y - m, x[])
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- Rfast::normlog.reg(y, x[, sel])
res <- y - exp( mod$be[1] + x[, sel] * mod$be[2] )
rho[2] <- mod$deviance
phi[2] <- mod$devi/(n - 2)
i <- 2
while ( (rho[i - 1] - rho[i]) / phi[i] > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- Rfast::normlog.reg(y, x[, sela])
res <- y - as.vector( exp( mod$be[1] + x[, sela] %*% mod$be[-1] ) )
rho[i] <- mod$deviance
phi[i] <- mod$deviance/(n - length(mod$be) )
}
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi[1:len], res = res)
} else if ( test == "testIndLogistic" ) {
tic <- proc.time()
n <- dim(x)[1]
p <- sum(y)/n
rho <- -2 * (n * p * log(p) + (n - n * p) * log(1 - p))
ela <- as.vector( cor(y - p, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- Rfast::glm_logistic(x[, sel], y)
est <- exp(-mod$be[1] - x[, sel] * mod$be[2])
res <- y - 1/(1 + est)
rho[2] <- mod$devi
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- Rfast::glm_logistic(x[, sela], y)
est <- as.vector(exp(-mod$be[1] - x[, sela] %*% mod$be[-1]))
res <- y - 1/(1 + est)
rho[i] <- mod$devi
}
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else if (test == "testIndQBinom") {
tic <- proc.time()
p <- sum(y)/n
y0 <- 1 - y
rho <- 2 * sum(y * log(y/p), na.rm = TRUE) + 2 * sum(y0 * log(y0/(1 - p)), na.rm = TRUE)
phi <- 1
ela <- as.vector( cor(y - p, x[]) )
sel <- which.max(abs(ela))
sela <- sel
names(sela) <- NULL
mod <- Rfast::prop.reg(y, x[, sel], varb = "glm")
est <- exp(-mod$info[1, 1] - x[, sel] * mod$info[2, 1])
p <- 1/(1 + est)
res <- y - p
rho[2] <- 2 * sum(y * log(y/p), na.rm = TRUE) + 2 * sum(y0 * log(y0/(1 - p)), na.rm = TRUE)
phi[2] <- mod$phi
i <- 2
while ((rho[i - 1] - rho[i])/phi[i] > tol) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max(abs(r))
sela <- c(sela, sel)
mod <- Rfast::prop.reg(y, x[, sela], varb = "glm")
est <- as.vector( exp(-mod$info[1, 1] - x[, sela] %*% mod$info[-1, 1]) )
p <- 1/(1 + est)
res <- y - p
rho[i] <- 2 * sum(y * log(y/p), na.rm = TRUE) + 2 * sum(y0 * log(y0/(1 - p)), na.rm = TRUE)
phi[i] <- mod$phi
}
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi[1:len], res = res)
} else if ( test == "testIndPois" ) {
tic <- proc.time()
m <- sum(y)/n
rho <- 2 * sum(y * log(y), na.rm = TRUE) - 2 * n * m * log(m)
ela <- as.vector( cor(y - m, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- Rfast::glm_poisson(x[, sel], y)
res <- y - exp( mod$be[1] + x[, sel] * mod$be[2] )
rho[2] <- mod$devi
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- Rfast::glm_poisson(x[, sela], y)
res <- y - as.vector( exp( mod$be[1] + x[, sela] %*% mod$be[-1] ) )
rho[i] <- mod$devi
}
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else if ( test == "testIndQPois" ) {
tic <- proc.time()
m <- sum(y)/n
rho <- 2 * sum(y * log(y), na.rm = TRUE) - 2 * n * m * log(m)
phi <- 1
ela <- as.vector( cor(y - m, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- Rfast::qpois.reg(x[, sel], y)
phi[2] <- mod$phi
res <- y - exp( mod$be[1, 1] + x[, sel] * mod$be[2, 1] )
rho[2] <- mod$devi
i <- 2
while ( (rho[i - 1] - rho[i]) / phi[i] > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- Rfast::qpois.reg(x[, sela], y)
res <- y - as.vector( exp( mod$be[1, 1] + x[, sela] %*% mod$be[-1, 1] ) )
rho[i] <- mod$devi
phi[i] <- mod$phi
}
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi[1:len], res = res)
} else if (test == "testIndNB") {
tic <- proc.time()
mod <- MASS::glm.nb(y ~ 1)
rho <- - 2 * as.numeric( logLik(mod) )
res <- y - fitted(mod)
ela <- as.vector( cor(res, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- MASS::glm.nb(y ~ x[, sela], control = list(epsilon = 1e-08, maxit = 100, trace = FALSE) )
res <- mod$residuals
rho[2] <- - 2 * as.numeric( logLik(mod) )
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- MASS::glm.nb( y ~ x[, sela], control = list(epsilon = 1e-08, maxit = 100, trace = FALSE) )
res <- y - fitted(mod)
rho[i] <- - 2 * as.numeric( logLik(mod) )
} ## end while ( (rho[i - 1] - rho[i]) > tol )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else if ( test == "testIndMMReg") {
tic <- proc.time()
mod <- MASS::rlm(y ~ 1, method = "MM", maxit = 2000)
rho <- - 2 * as.numeric( logLik(mod) )
res <- mod$residuals
ela <- as.vector( cor(res, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- MASS::rlm(y ~ x[, sela], method = "MM", maxit = 2000 )
res <- mod$residuals
rho[2] <- - 2 * as.numeric( logLik(mod) )
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- try( MASS::rlm(y ~ x[, sela], method = "MM", maxit = 2000 ), silent = TRUE )
if ( identical( class(mod), "try-error" ) ) {
rho[i] <- rho[i - 1]
} else {
res <- mod$residuals
rho[i] <- - 2 * as.numeric( logLik(mod) )
}
} ## end while ( (rho[i - 1] - rho[i]) > tol )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else if ( test == "testIndRQ") {
tic <- proc.time()
mod <- quantreg::rq(y ~ 1)
rho <- - 2 * as.numeric( logLik(mod) )
res <- mod$residuals
ela <- as.vector( cor(res, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- quantreg::rq(y ~ x[, sela])
res <- mod$residuals
rho[2] <- - 2 * as.numeric( logLik(mod) )
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- try( quantreg::rq(y ~ x[, sela]), silent = TRUE )
if ( identical( class(mod), "try-error" ) ) {
rho[i] <- rho[i - 1]
} else {
res <- mod$residuals
rho[i] <- - 2 * as.numeric( logLik(mod) )
}
} ## end while ( (rho[i - 1] - rho[i]) > tol )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else if (test == "testIndOrdinal") {
tic <- proc.time()
mod <- MASS::polr(y ~ 1)
rho <- - 2 * as.numeric( logLik(mod) )
res <- ord.resid(y, mod$fitted.values)
ela <- as.vector( cor(res, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- MASS::polr(y ~ x[, sela])
res <- ord.resid(y, mod$fitted.values)
rho[2] <- - 2 * as.numeric( logLik(mod) )
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- MASS::polr(y ~ x[, sela])
res <- ord.resid(y, mod$fitted.values)
rho[i] <- - 2 * as.numeric( logLik(mod) )
} ## end while ( (rho[i - 1] - rho[i]) > tol )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else if ( test == "testIndTobit") {
tic <- proc.time()
mod <- survival::survreg(y ~ 1, dist = "gaussian")
rho <- - 2 * as.numeric( logLik(mod) )
res <- resid(mod)
ela <- as.vector( cor(res, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- survival::survreg(y ~ x[, sela], dist = "gaussian" )
res <- resid(mod)
rho[2] <- - 2 * as.numeric( logLik(mod) )
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- try( survival::survreg(y ~ x[, sela], dist = "gaussian" ), silent = TRUE )
if ( identical( class(mod), "try-error" ) ) {
rho[i] <- rho[i - 1]
} else {
res <- resid(mod)
rho[i] <- - 2 * as.numeric( logLik(mod) )
r[sela] <- NA
}
} ## end while ( (rho[i - 1] - rho[i]) > tol )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else if ( test == "censIndCR") {
tic <- proc.time()
mod <- survival::coxph(y ~ 1)
rho <- - 2 * summary( mod)[[1]]
res <- mod$residuals ## martingale residuals
ela <- as.vector( cor(res, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- survival::coxph(y ~ x[, sela] )
res <- mod$residuals
rho[2] <- - 2 * as.numeric( logLik(mod) )
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- try( survival::coxph(y ~ x[, sela] ), silent = TRUE )
if ( identical( class(mod), "try-error" ) ) {
rho[i] <- rho[i - 1]
} else {
res <- mod$residuals ## martingale residuals
rho[i] <- - 2 * as.numeric( logLik(mod) )
}
} ## end while ( (rho[i - 1] - rho[i]) > tol )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} else if ( test == "censIndWR") {
tic <- proc.time()
mod <- survival::survreg(y ~ 1)
rho <- - 2 * as.numeric( logLik(mod) )
res <- resid(mod)
ela <- as.vector( cor(res, x[]) )
sel <- which.max( abs(ela) )
sela <- sel
names(sela) <- NULL
mod <- survival::survreg(y ~ x[, sela], control = list(iter.max = 10000) )
res <- resid(mod)
rho[2] <- - 2 * as.numeric( logLik(mod) )
if ( is.na(rho[2]) ) rho[2] <- rho[1]
i <- 2
while ( (rho[i - 1] - rho[i]) > tol ) {
i <- i + 1
r <- cor(res, x[])
r[sela] <- NA
sel <- which.max( abs(r) )
sela <- c(sela, sel)
mod <- try( survival::survreg(y ~ x[, sela], control = list(iter.max = 10000) ), silent = TRUE )
if ( identical( class(mod), "try-error" ) ) {
rho[i] <- rho[i - 1]
} else {
res <- resid(mod)
rho[i] <- - 2 * as.numeric( logLik(mod) )
}
} ## end while ( (rho[i - 1] - rho[i]) > tol )
len <- length(sela)
res <- cbind(c(0, sela[-len]), rho[1:len])
colnames(res) <- c("Selected Vars", "Deviance")
runtime <- proc.time() - tic
result <- list(runtime = runtime, phi = phi, res = res)
} ## end if (test == "testIndNB")
} ## end if else (test == "testIndFisher")
result
}
|
286912104797eea66d53533f613cb031b0cb6b64 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/fungible/examples/seBetaCor.Rd.R | f24ce0b7b08478035f68a340d5b8f3675fe8d3b7 | [] | 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 | 603 | r | seBetaCor.Rd.R | library(fungible)
### Name: seBetaCor
### Title: Standard Errors and CIs for Standardized Regression Coefficients
### from Correlations
### Aliases: seBetaCor
### Keywords: Statistics
### ** Examples
R <- matrix(c(1.0000, 0.3511, 0.3661,
0.3511, 1.0000, 0.4359,
0.3661, 0.4359, 1.0000), 3, 3)
rxy <- c(0.5820, 0.6997, 0.7621)
Nobs <- 46
out <- seBetaCor(R = R, rxy = rxy, Nobs = Nobs)
# 95% CIs for Standardized Regression Coefficients:
#
# lbound estimate ubound
# beta_1 0.107 0.263 0.419
# beta_2 0.231 0.391 0.552
# beta_3 0.337 0.495 0.653
|
8dcec43341b0d19eea3180c38053023163fc78ac | be86308f9cd95be51b4f83ec06895fa1bd4c49e7 | /R/separateBlocks.R | bd245821abe71e0bc4b9d002dbf2218d3b05ce9d | [] | no_license | duncantl/CodeDepends | 7f60ad0b4e3513dd4e2e723778f44bce39e47fd6 | 878fd24850d28037640cb21fbbf3922b51f996df | refs/heads/master | 2022-05-16T00:25:38.573235 | 2022-04-25T22:11:20 | 2022-04-25T22:11:20 | 3,998,946 | 75 | 16 | null | 2020-01-14T19:30:08 | 2012-04-11T22:17:31 | R | UTF-8 | R | false | false | 1,082 | r | separateBlocks.R | separateExpressionBlocks =
#
# This allows us to take a script that is made up of
# blocks and unravel them into a list of individual top-level
# calls. It does not unravel the individual calls, just the
# top-level blocks. And it leaves top-level calls outside of {}
# alone.
#
# This allows us to start with a block script and then to move
# to individual calls. In this way, we can work at higher resolution
# and process/evaluate individual, top-level expressions rather than entire blocks.
# We can easily compute the dependencies for either the blocks or the calls
# and so by converting the blocks to calls, we work with not aggregated groups of calls
# but individual ones directly.
function(blocks)
{
tmp = lapply(blocks, function(x) {
if(is(x, "{"))
as.list(x[-1])
# unlist(x, recursive = FALSE)
else
x
})
if(all(is.na(names(tmp))))
names(tmp) = NULL
unlist(tmp, recursive = FALSE)
}
|
cd6636849043776aa1715a38da0c310a9b95d5d2 | 41fb06c29999266568b0084075d33b2370f8de4a | /man/removeComments.Rd | 59d94beb9f140a3dd6cecf6579ce53567dfcb88d | [] | no_license | eloytf/caeasciiR | e3813dd0a3831bd76dee7000fbf0fae1d7652cf3 | 80b1f2f67f5e9a22c81b9f5ecb30fe78d57c4d46 | refs/heads/master | 2021-09-13T03:59:27.905751 | 2018-04-24T18:56:12 | 2018-04-24T18:56:12 | 106,620,964 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 518 | rd | removeComments.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{removeComments}
\alias{removeComments}
\title{A Function for removing commented lines from a character vector of expanded bulk lines.}
\usage{
removeComments(lines)
}
\arguments{
\item{lines}{character vector with the bulk lines}
}
\value{
a character vector of non-commented bulk lines.
}
\description{
This function allows you remove commented lines, returning a character vector of the non-commented lines
}
\examples{
}
|
e52ead0529268a26907a8b6a4e41565a9f60bba0 | 9571c840a5ea7ad52e68e87b928be9eba864317d | /plot3.R | e779fa6f08f7f01cbc02ccddcda772a2c4c1ba62 | [] | no_license | fabioks/ExData_Plotting1 | 6907fa0ab24b95f9cb137ddd6d9a45604131044c | 8f5d4c69f582710bc41a9bdffc9b0bf1cc8ebc83 | refs/heads/master | 2021-01-21T09:18:10.106728 | 2015-12-13T20:36:12 | 2015-12-13T20:36:12 | 47,931,351 | 0 | 0 | null | 2015-12-13T18:35:28 | 2015-12-13T18:35:28 | null | UTF-8 | R | false | false | 1,565 | r | plot3.R | #Setting file name
file <- "household_power_consumption.zip"
#Download file if it was not already downloaded
if(!file.exists(file))
{
URL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(URL, file)
}
##Checking if file exists, if not, unzip the data
if(!file.exists("household_power_consumption"))
{
unzip(file)
}
#Reading the required data only
data <- read.table("household_power_consumption.txt", header = FALSE, skip = 66637, nrows = 2880, sep = ";",
na.strings = "?", stringsAsFactors = FALSE, dec = ".")
#Configuring column names
names <- c("Date", "Time", "Global_active_power", "Global_reactive_power", "Voltage", "Global_intensity",
"Sub_metering_1", "Sub_metering_2", "Sub_metering_3")
#Setting column names
colnames(data) <- names
#Converting date column do Date
data$Date <- as.Date(data$Date, format = "%d/%m/%Y")
#Creating new column with date and time
DateTime <- paste(data$Date, data$Time)
data$DateTime <- as.POSIXct(DateTime)
#Plotting
plot(data$Sub_metering_1 ~ data$DateTime, type = "l", ylab = "Energy sub metering", xlab = "")
lines(data$Sub_metering_2 ~ data$DateTime, type = "l", col = "red")
lines(data$Sub_metering_3 ~ data$DateTime, type = "l", col = "blue")
legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, lwd=2.5,
col=c("black", "red", "blue"), cex = .75)
#Saving to file png
dev.copy(png, filename = "plot3.png", height = 480, width = 480)
#Shutting off device
dev.off()
|
0b518cb62a55ab88a7d70e8daf8e95c060e6d2e0 | fbb2d5e4032e46e82a4f5d7a061654a0deb371d1 | /man/dot-estimateZcutoff.Rd | 2d8478c235669b574e86eeb784bf74373795c97b | [
"MIT"
] | permissive | quevedor2/CCLid | 23a59d973db2d4cdb6884fecea85b00099fbccc9 | 460744aafc2127251a8d2b943d0475cbf15cb0d4 | refs/heads/master | 2022-11-13T17:33:51.166853 | 2020-07-09T19:48:56 | 2020-07-09T19:48:56 | 232,857,878 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 642 | rd | dot-estimateZcutoff.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils_drift.R
\name{.estimateZcutoff}
\alias{.estimateZcutoff}
\title{.estimateZcutoff}
\usage{
.estimateZcutoff(seg, data.type = "baf", verbose = FALSE)
}
\arguments{
\item{seg}{an output dataframe from a CNA object}
\item{data.type}{Data type, supports only 'baf' at this time}
\item{verbose}{Verbose, default = FALSE}
}
\value{
Returned seg with $t and $seg.diff
}
\description{
Creates a theoreticla framework for difference
between BAFs, and then compares the observed z-differences
against the theoretical diff to look for differences
}
\keyword{internal}
|
ffe35201a6939b599763c1fb6aeb09874e8a51d3 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/7702_0/rinput.R | 5c754709e8fcab3315e4d307ab2f7cc77d40a79b | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("7702_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="7702_0_unrooted.txt") |
56160204dd8ed422272d979df0efd9b385c6a609 | e7f2c1765eaf6f2da87693ad1a0198a976320a1a | /ui.R | e0f73adbbfe2698e4bee859f6db32d9f8c427222 | [] | no_license | angeliflavio/FAOStatistics | 2610966fc71bad39b80fdd5a20a383136be389cc | 2193c606916dfdf890695f4326bee2473a6a937d | refs/heads/master | 2021-05-16T04:30:49.191559 | 2017-10-14T21:15:25 | 2017-10-14T21:15:25 | 106,080,625 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,219 | r | ui.R | library(shiny)
library(shinyBS)
library(markdown)
apple <- read.csv('apple.csv',sep = ',')
wine <- read.csv('wine.csv',sep = ',')
shinyUI(
navbarPage(theme=shinythemes::shinytheme('cerulean'),title='Statistiche FAO',
tabPanel('Mele',
tabsetPanel(
tabPanel('Mappa',
br(),
sidebarLayout(
sidebarPanel(
selectInput('measuremap','Misura',
choices = c('Produzione (ton)'= 'Production',
'Superficie (ettari)'='Area harvested',
'Resa (hg/ettaro)'='Yield'),
selected = 'Produzione'),
selectInput('yearmap','Anno',
choices = seq(1961,2014),selected = 2014),
br(),
tags$a(href='http://www.fao.org','Fonte FAO')
),
mainPanel(
br(),
htmlOutput('worldmap'))
)
),
tabPanel('Grafico',
br(),
sidebarLayout(
sidebarPanel(
selectInput('countryapplemotion','Paesi',
choices = c('Tutti',as.vector(unique(apple$Area))),
selected = 'Tutti',
multiple = T),
bsTooltip('countryapplemotion','Selezione singola o multipla',
options = list(container = "body")),
br(),
tags$a(href='http://www.fao.org','Fonte FAO')
),
mainPanel(
br(),
htmlOutput('motionapple')
)
)
),
tabPanel('Grafico storico',
br(),
sidebarLayout(
sidebarPanel(
selectInput('measuretimeline','Misura',
choices = c('Produzione (ton)'= 'Production',
'Superficie (ettari)'='Area harvested',
'Resa (hg/ettaro)'='Yield'),
selected = 'Produzione (ton)'),
selectInput('countrytimeline','Paesi',
choices = as.vector(unique(apple$Area)),
selected = 'Italy',
multiple = T),
bsTooltip('countrytimeline','Selezione singola o multipla',
options = list(container = "body")),
br(),
tags$a(href='http://www.fao.org','Fonte FAO')
),
mainPanel(
br(),
htmlOutput('timeline'))
)
),
tabPanel('Tabella',
br(),
dataTableOutput('table'))
)),
tabPanel('Vino',
tabsetPanel(
tabPanel('Mappa',
br(),
sidebarLayout(
sidebarPanel(
selectInput('yearmapwine','Anno',
choices = seq(1961,2014),selected = 2014),
br(),
tags$a(href='http://www.fao.org','Fonte FAO')
),
mainPanel(
br(),
htmlOutput('worldmapwine'))
)),
tabPanel('Grafico',
br(),
sidebarLayout(
sidebarPanel(
selectInput('countrywinemotion','Paesi',
choices = c('Tutti',as.vector(unique(wine$Area))),
selected = 'Tutti',
multiple = T),
bsTooltip('countrywinemotion','Selezione singola o multipla',
options = list(container = "body")),
br(),
tags$a(href='http://www.fao.org','Fonte FAO')
),
mainPanel(
br(),
htmlOutput('motionwine')
)
)),
tabPanel('Grafico storico',
br(),
sidebarLayout(
sidebarPanel(
selectInput('countrytimelinewine','Paesi',
choices = as.vector(unique(wine$Area)),
selected = 'Italy',
multiple = T),
bsTooltip('countrytimelinewine','Selezione singola o multipla',
options = list(container = "body")),
br(),
tags$a(href='http://www.fao.org','Fonte FAO')
),
mainPanel(
br(),
htmlOutput('timelinewine'))
)),
tabPanel('Tabella',
br(),
dataTableOutput('tablewine'))
)),
br(),
br(),
tags$a(img(src='github.png'),href='https://github.com/angeliflavio/FAOStatistics'))
)
#add year selection
#add value type (produciton, area harvested, yield )
|
d3651e0c7137f62ce8f727880745a2250a5f743e | 3da6a7aa6f30ebe1dc9f2a3479da0e52b74c7a0b | /demographicDetection/man/gender_detect.Rd | 4d5f4c485e11a46da60761acb55e5142abc2d5d2 | [] | no_license | ensoesie/digitalpophealth | eed712a4fd20403f568678f83181362cd0d8c4a8 | 58cf871a29ccc6fb4fbb509428dfe08a61c08652 | refs/heads/master | 2020-03-20T16:15:56.189783 | 2018-09-18T19:05:34 | 2018-09-18T19:05:34 | 137,534,425 | 2 | 1 | null | 2019-11-12T21:11:19 | 2018-06-15T21:44:16 | R | UTF-8 | R | false | true | 568 | rd | gender_detect.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gender_detect.R
\name{gender_detect}
\alias{gender_detect}
\title{Return the gender estimate for a name}
\usage{
gender_detect(name, method = "ssa")
}
\arguments{
\item{name}{A character value containing a name}
}
\value{
gend_value A string value containing the estimated gender of a name
}
\description{
Uses the gender package, with US Social Security Administration Data, to estimate gender based on a first name
}
\examples{
gender_detect("Jessica")
}
\keyword{gender,}
\keyword{text}
|
2ee97400bd794d978ad5500a2e9c1f1a1492c9ac | 787231fb66ebaaad9b71268b4c1a76e90fd460d8 | /software/step5.1_create_alpha_diveristy.R | f59838f15aba2d40f3591eb6ac727f1c300b9ff2 | [] | no_license | wyc9559/miQTL_cookbook | e2bf28b6c6c089d75c813cb926dbd9c29a26d7e3 | 5ef8396ea46760179d892caef88b1e64d14d242a | refs/heads/master | 2022-04-07T05:05:02.452893 | 2020-02-17T14:46:52 | 2020-02-17T14:46:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,403 | r | step5.1_create_alpha_diveristy.R | options = commandArgs(trailingOnly = TRUE)
taxonomy_file = options[1]
phenotype_file = options[2]
coupling_file = options[3]
if(!require(vegan)){
install.packages("vegan")
library(vegan)
}
tax = read.table(taxonomy_file,header=T,row.names=1,sep="\t")
phenos = read.table(phenotype_file,header = T,row.names=1,sep="\t")
phenos = phenos[!apply(phenos,1,function(x){any(is.na(x))}),,drop=F]
if (exists("coupling_file")&file.exists(coupling_file)){
coupling = read.table(coupling_file,colClasses = "character")
has_both = (coupling[,1] %in% rownames(phenos)) & (coupling[,2] %in% rownames(tax))
coupling= coupling[has_both,]
tax = tax[coupling[,2],]
phenos = phenos[coupling[,1],,drop = FALSE]
rownames(tax) = rownames(phenos)
tax = tax[rownames(tax)[grep("[.][0-9]+$",rownames(tax),invert=T)],]
phenos = phenos[rownames(tax),,drop = FALSE]
} else {
tax = tax[intersect(rownames(tax),rownames(phenos)),]
phenos = phenos[rownames(tax),,drop = FALSE]
}
tax = tax[,grep("^genus[.]",colnames(tax))]
shannon = diversity(tax,index = "shannon")
simpson = diversity(tax,index = "simpson")
invsimpson = diversity(tax,index = "invsimpson")
all_div = data.frame(row.names = rownames(tax),shannon = shannon, simpson = simpson,invsimpson = invsimpson)
corrected_data = apply(all_div,2,function(x){
x.subset = x[!is.na(x)]
phenos.subset = phenos[!is.na(x),,drop = FALSE]
phenos.subset.matrix = data.matrix(phenos.subset)
if(ncol(phenos.subset)==ncol(phenos.subset.matrix)){
phenos.subset = phenos.subset[,apply(phenos.subset.matrix,2,sd) !=0,drop = FALSE]
}
x.resid = resid(lm(x.subset ~ .,data = phenos.subset))
x[!is.na(x)] = x.resid+100
x[is.na(x)] = 0
return(x)
})
corrected_data = as.data.frame(t(corrected_data))
corrected_data2 = data.frame(rownames(corrected_data),corrected_data)
colnames(corrected_data2)[1] = "-"
annot = data.frame(platform = "RDP",
HT12v3.ArrayAddress = rownames(corrected_data2),
Gene = rownames(corrected_data2),
Chr = 4,
ChrStart = 1000,
ChrEnd = 1000)
colnames(annot)[2] = "HT12v3-ArrayAddress"
write.table(corrected_data2, file = "alpha_div.txt",sep="\t",row.names = F,quote = F)
write.table(annot,file = "alpha_div.txt.annot",sep="\t",row.names=F,quote = F)
|
dd1c38d9307b9b5a87a40f392822f5220788b6ab | f197e1cfd48f7d0b55b79fdc2e084ca9cce85b02 | /R/prof.R | 9bdfa259229981fe99ba0b76d64a4006fd49327a | [] | no_license | lenamax2355/Rprofiling | 61696211ceff4ef7830cae0b47efcd27861f74d5 | 308e46de533f050e3acaa8156e6321da09f6894f | refs/heads/master | 2022-01-06T13:58:43.926179 | 2018-09-14T15:33:27 | 2018-09-14T15:33:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,710 | r | prof.R | ####--------------------------------##
## Global Function
get_data_types <- function(data){
x <- (lapply(data,class))
x_frame <- (data.frame(unlist(x)))
names(x_frame) <- c("Data_Type")
x_frame %>%
dplyr::group_by(Data_Type) %>%
dplyr::count()
}
###----------------------------------##
## Global Function
create_html <- function(html){
template <- "<!DOCTYPE html>
<html lang='en'>
<head>
<title>Rprofiling</title>
<meta charset='utf-8'>
<meta name='viewport' content='width=device-width, initial-scale=1'>
<link rel='stylesheet' href='https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css'>
<script src='https://ajax.googleapis.com/ajax/libs/jquery/3.2.1/jquery.min.js'></script>
<script src='https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js''></script>
<style>
.table-hover tbody tr:hover td {
background-color: #008080;
color : white;
}
</style>
</head>
<body>
<div class='container'>"
end_temp <- "</div>
</body>
</html>"
html <- stringr::str_c(template, html, end_temp)
html <- gsub("<table>", '<table class="table table-hover table-bordered">',html)
tempDir <- tempfile()
dir.create(tempDir)
html_file <- file.path(tempDir, "report.html")
writeLines(html,html_file)
viewer <- getOption("viewer")
if (!is.null(viewer)){
viewer(html_file)
utils::browseURL(html_file)
}
else
utils::browseURL(html_file)
}
#' Information about datasets
#'
#' @param data is the dataset that you want to digest. dataset class must be dataframe or tibble.
#'
#' @return Returns information on variables, observation, missing values and uniquness.
#' @export dsetinfo
#'
#' @examples destinfo(mtcars)
dsetinfo <- function(data){
df <- dsetinfo_(data)
description <- "Dataset Information"
res <- knitr::kable(df, format = "html",caption = description)
create_html(res)
}
dsetinfo_ <- function(data){
if(class(data) != "data.frame"){
return("Sorry this function only works for data frames as of now")
}else{
num_var <- as.character(length(names(data)))
num_obs <- nrow(data)
t_missing <- sum(!complete.cases(data))
t_missing_percentage <- round((sum(!complete.cases(data))/nrow(data) * 100),2)
unique_percentage <- format(round(nrow(unique(data))/nrow(data) * 100,2), nsmall=2)
df <- data.frame(Info = c("Number of variables","Number of observation","Total Missing","Total Missing(%)","Unique Row(%)"),
Value = c(num_var,num_obs,t_missing,paste0(t_missing_percentage,"%"),paste0(unique_percentage, "%")))
}
}
#' Information on the variables
#'
#' @param data is the dataset that you want to digest. dataset class must be dataframe or tibble.
#'
#' @return Returns the count of data_types.
#' @export var_types
#'
#' @examples var_types(mtcars)
var_types <- function(data){
if(class(data) != "data.frame"){
return("Sorry this function only works for data frames as of now")
}else{
df <- data.frame(get_data_types(data))
description <- "Variable Types"
res <- knitr::kable(df, format = "html", caption = description)
create_html(res)
}
}
#' Sample of dataset
#'
#' @param data is the dataset that you want to digest. dataset class must be dataframe or tibble.
#'
#' @return Returns a sample(20 rows if dataset is more than 20 rows) of the dataset
#' @export sample_data
#'
#' @examples sample_data(mtcars)
sample_data <- function(data){
if(class(data) != "data.frame"){
return("Sorry this function only works for data frames as of now")
}else{
data <- head(data,15)
description <- "Data Sample"
res <- knitr::kable(data, format = "html",caption = description)
create_html(res)
}
}
#' Summary of dataset
#'
#' @param data is the dataset that you want to digest. dataset class must be dataframe or tibble.
#'
#' @return Returns a summary of the dataset of each
#' @export
#'
#' @examples data_summary(mtcars)
data_summary <- function(data){
if(class(data) != "data.frame"){
return("Sorry this function only works for data frames as of now")
}else{
data <- summary(data)
description <- "Data Summary"
res <- knitr::kable(data, format = "html", caption = description)
create_html(res)
}
}
#' Visualization of missing data
#'
#' @param data is the dataset that you want to digest. dataset class must be dataframe or tibble.
#'
#' @return Return a graph that sows the variables and rows with missing data
#' @export
#'
#' @examples vis_mdata(mtcars)
vis_mdata <- function(data){
if(class(data) != "data.frame"){
return("Sorry this function only works for data frames as of now")
}else{
Amelia::missmap(data,main="Visualization of missing Data",y.cex = 0.9, x.cex = 0.9)
}
}
#' Generates full profile of the dataframe passed
#'
#' @param data is the dataset that you want to digest. dataset class must be dataframe or tibble.
#'
#' @return Returns a full digestion(profile) of the data
#' @export
#'
#' @examples full_profile(mtcars)
full_profile <- function(data){
info <- knitr::kable(x = dsetinfo_(data), format = "html", caption = "Dataset Information")
x <- get_data_types(data)
var <- knitr::kable(x,format = "html", caption = "Variable Types")
d_summary <- knitr::kable(summary(data), format = "html", caption = "Data Summary")
samp <- knitr::kable(x=head(data,20),format = "html", caption = "Data Sample")
#png("missing.png")
Amelia::missmap(data,main="Visualization of missing Data",y.cex = 0.9, x.cex = 0.9)
# dev.off()
img <- "<strong>Check your 'Plots tab for the graph'</strong>"
res <- paste(info,var,d_summary,samp,"Graph of missing Values", img,sep="<hr>")
create_html(res)
}
|
901912a79cfd37cb9368c2a15df49c64b969c6b8 | 53acd1eff5a7523085a71c6553e914999b46fb7b | /tests/testthat.R | a0417f9c7ab63b33096c4c90ff714ce64aa750f0 | [
"MIT"
] | permissive | Helseatlas/data | 33ee21c497c45a2b5be7fae18e535d9e66c01a23 | b18a235a580573b71623c91e9c012400290475a5 | refs/heads/master | 2021-06-24T21:09:54.761949 | 2020-05-02T13:24:12 | 2020-05-02T13:24:12 | 183,443,098 | 0 | 0 | MIT | 2020-05-20T09:11:59 | 2019-04-25T13:45:25 | R | UTF-8 | R | false | false | 44 | r | testthat.R | library(data)
testthat::test_check("data")
|
42346e60802e8019683094b52f5f671d3bd88d6b | 4a7718b5618d75bdcfb3fb71324569d0d11ac749 | /man/load_multi_data.Rd | 69ae14d43e3d2403de40fe3ee4cd92bfad28357f | [] | no_license | EwaMarek/FindReference | 859676f1744ea2333714634fd420d6b91b367956 | eedc8c80809b6f3e4439999bac4cb09ec2b228f2 | refs/heads/master | 2018-08-01T08:39:05.326467 | 2018-06-02T17:05:06 | 2018-06-02T17:05:06 | 104,148,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,518 | rd | load_multi_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/load_multi_data.R
\name{load_multi_data}
\alias{load_multi_data}
\title{Loading microarray data from a list of experiments downloaded from ArrayExpress database}
\usage{
load_multi_data(dane, ExpInfoTable, sdrfFiles)
}
\arguments{
\item{dane}{A list of lists from downloadAE function.}
\item{platforma}{A character vector indicating platform for each experiment. Supported platforms are 'Affymetrix' and 'Agilent'.}
\item{donotread}{A list of character vectors indicating which microarrays should not be loaded for each experiment. Default is NA,
which means that all microarrays listed in dane$rawFiles will be loaded.
Note that list's length must be the same as the length of list 'dane'.}
}
\value{
Function returns a list with two elements. The first one is a list of objects specific for array platform with raw expression data.
The second element is a list of data frames with loaded .sdrf files for each experiment.
}
\description{
\code{load_multi_data} function loads data for a list of microarray experiments downloaded from ArrayExpress database.
}
\details{
This function is designed to read in microarray data from many experiments. For more detail see \code{load_data} function help.
}
\examples{
\dontrun{
### load all microarrays for two experiments
to_download = c("E-GEOD-21066", "E-MTAB-966")
platforms = c("Affymetrix", "Agilent")
dane = downloadAE(to_download, getwd())
loaded_experiments = load_multi_data(dane, platforms)
### do not load some microarrays from the first experiment
to_download = c("E-GEOD-21066", "E-MTAB-966")
platforms = c("Affymetrix", "Agilent")
dane = downloadAE(to_download, getwd())
# for second experiment all microarrays should be loaded
# -> the second element of the list is NA
unwanted_arrays = list(c("GSM526680.CEL", "GSM526756.CEL"), NA)
# or (which could be useful when there are a lot of experiments)
unwanted_arrays = rep(list(list(NA)), length(dane))
unwanted_arrays[[1]] = c("GSM526680.CEL", "GSM526756.CEL")
loaded_experiment = load_multi_data(dane, platforms, donotread = unwanted_arrays)
### when you downloaded data with downloadAE
### but didn't assign the output into variable
to_download = c("E-GEOD-21066", "E-MTAB-966")
platforms = c("Affymetrix", "Agilent")
downloadAE(to_download, getwd())
# read in data saved by downloadAE function
dane = readRDS('dataAE.rds')
loaded_experiment = load__multi_data(dane, platforms)
}
}
\seealso{
\code{\link{load_data}}
}
|
d104423281d81f0f2189c26b188d1cf6974d9f9c | 4dd05c5789fbf09aeb6fdc1229ccf9b8b3caf511 | /R/downloadSitemap.R | 952da39d5342ada926e0e865a0fc09ddb299d6bc | [
"MIT"
] | permissive | dschmeh/seoR | 2d553d3a06aea53c572d30cb9cd2c3a91e2eb8b1 | 9908f41b3f026930bde83028b8669f70d3a1030d | refs/heads/master | 2023-01-04T10:07:53.267464 | 2022-12-21T12:33:59 | 2022-12-21T12:33:59 | 114,384,766 | 39 | 7 | MIT | 2018-06-13T12:59:18 | 2017-12-15T15:29:25 | R | UTF-8 | R | false | false | 846 | r | downloadSitemap.R | #' Function to download the Sitemap.xml of a given Webpage
#'
#' This function allows to Download the Sitemap.xml of a given Webpage
#' @param sitemap The Sitemap you want to download. This can also be a Index-Sitemap
#' downloadSitemap()
#' @examples
#' downloadSitemap("http://ohren-reinigen.de/sitemap.xml")
downloadSitemap <- function(sitemap) {
#Input check
if(sitemapxml_check(sitemap) != TRUE) {
warning("Please check you provided Sitemap")
}
#Get the Sitemap
s <- XML::xmlToDataFrame(sitemap)
#check if Sitemap is Index Sitemap
if ((nrow(s) / sum(grepl("xml", s$loc))) > 0.8) {
loop <- 0
full_sm <- NULL
for (i in 1:nrow(s)) {
sm <- XML::xmlToDataFrame(as.character(s$loc[i]))
full_sm <- rbind(full_sm, sm)
}
} else {
full_sm <- s
}
return(full_sm)
}
|
12f86bd624011dc2b74de685443c7aa8fc56c8be | 6c4a6643847fab3b6bb38cfa0c914f353c678214 | /QRMC-3HS_Fig3B/QRMC-3HS_Fig3B.R | 9fcc292825945bfca39d004d13d7cd6909a8e568 | [] | no_license | BulgarelliD-Lab/Microbiota_mapping | 2693af905cc1ae23c8771c744c990513106043cd | c69cdb61409d7513e8b2e4a7dfcbcf5843caaac6 | refs/heads/main | 2023-04-08T05:09:09.889259 | 2022-06-21T15:53:23 | 2022-06-21T15:53:23 | 429,454,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,883 | r | QRMC-3HS_Fig3B.R | #####################################################################################
#Ref to the ARTICLE
#
# Code to compute calculations presented in:https://www.biorxiv.org/content/10.1101/2021.12.20.472907v3
# Figure 3B
#
# c.m.z.escuderomartinez@dundee.ac.uk
# d.bulgarelli@dundee.ac.uk
#############################################################
# Clean-up the memory and start a new session
#############################################################
rm(list=ls())
dev.off()
#############################################################
# Libraries required
#############################################################
library("DESeq2")
library("ggplot2")
library("vegan")
library ("ape")
library("dplyr")
library("scales")
library("VennDiagram")
library("tidyverse")
library("forcats")
library("PMCMRplus")
library("phyloseq")
#retrieve R and package versions and compare to the uploaded file in GitHub for the reproducibility of the code
sessionInfo()
#set the working directory
setwd("")
getwd()
#################################################
################################################
##Fig. 3B Fungal community
###############################################
##############################################
#Import data picked with JH23 DADA2
JH23_data_phyloseq_DADA2 <-readRDS("JH23_dada2.rds")
JH23_data_phyloseq_sample <- JH23_data_phyloseq_DADA2
JH23_data_phyloseq_sample
sample_names(JH23_data_phyloseq_sample)
rank_names(JH23_data_phyloseq_sample)
JH23_tax_table<-tax_table(JH23_data_phyloseq_sample)
dim(tax_table(JH23_data_phyloseq_sample))
##################################################################
#Pre-processing: remove Chloroplast and Mitochondria but retain NA
#################################################################
JH23_taxa_raw<-tax_table(JH23_data_phyloseq_sample)
#write.table(JH23_taxa_raw, file="JH23_taxa_raw_taxonomy_info.txt", sep="\t")
#Remove chloroplasts but retain"NA" JH23
JH23_no_chlor <-subset_taxa(JH23_data_phyloseq_DADA2, (Order!="Chloroplast") | is.na(Order))
JH23_no_chlor
#Remove mitochondria but retains "NA"
JH23_no_plants <-subset_taxa(JH23_no_chlor, (Family!="Mitochondria") | is.na(Family))
JH23_no_plants
colnames (tax_table(JH23_no_plants))
rownames (tax_table(JH23_no_plants))
dim(tax_table(JH23_no_plants))
#########################################################################
# Remove ASVs assigned to NA at phylum level
#########################################################################
JH23_phyloseq_DADA2_no_plants_1 <- subset_taxa (JH23_no_plants, Phylum!= "NA")
JH23_phyloseq_DADA2_no_plants_1
#########################################################################
# Abundance threshold: 20 reads, set as 2% the minimum number of samples
#########################################################################
JH23_phyloseq_DADA2_no_plants_10K <- prune_samples(sample_sums(JH23_phyloseq_DADA2_no_plants_1)>=10000, JH23_phyloseq_DADA2_no_plants_1)
JH23_phyloseq_DADA2_no_plants_10K
JH23_phyloseq_DADA2_no_plants_2 = filter_taxa(JH23_phyloseq_DADA2_no_plants_10K, function(x) sum(x > 20) > (0.02 *length(x)), TRUE)
JH23_phyloseq_DADA2_no_plants_2
sort(sample_sums(JH23_phyloseq_DADA2_no_plants_2))
JH23_taxa<-tax_table(JH23_phyloseq_DADA2_no_plants_2 )
#write.table(JH23_taxa, file="JH23_taxa_info.txt", sep="\t")
##ratio filtered reads/total reads
ratio <- sum(sample_sums(JH23_phyloseq_DADA2_no_plants_2))/sum(sample_sums(JH23_phyloseq_DADA2_no_plants_1))*100
ratio
#########################################################################
# Import a simplified mapping file
########################################################################
#Import the simplified mapping file
design <- read.delim("JH23_Map.txt", sep = "\t", header=TRUE, row.names=1)
JH23_map <- sample_data(design)
sample_data(JH23_phyloseq_DADA2_no_plants_2)<-JH23_map
JH23_phyloseq_DADA2_no_plants_2
#########################################################################
# Aggregate samples at genus and family level (Note: NArm set to false to keep NA at Genus level)
########################################################################
JH23_data_phyloseq_genus <- tax_glom(JH23_phyloseq_DADA2_no_plants_2, taxrank= "Genus", NArm=FALSE, bad_empty=c(NA, "", " ", "\t"))
JH23_genus_taxa<-tax_table(JH23_data_phyloseq_genus)
#write.table(JH23_genus_taxa, file="JH23_genus_taxa_taxonomy_info.txt", sep="\t")
JH23_data_phyloseq_family <- tax_glom(JH23_phyloseq_DADA2_no_plants_2, taxrank= "Family", NArm=FALSE, bad_empty=c(NA, "", " ", "\t"))
#Compare the two objects
#ASVs
JH23_phyloseq_DADA2_no_plants_2
sort(sample_sums(JH23_phyloseq_DADA2_no_plants_2))
#Genus
JH23_data_phyloseq_genus
sort(sample_sums(JH23_data_phyloseq_genus))
#Family
JH23_data_phyloseq_family
sort(sample_sums(JH23_data_phyloseq_family))
####################################################################################
# Rarefy at an even sequencing depth (10,600) and "freeze" these objects for downstream analyses
#####################################################################################
#ASVs : ignore the warnings, the object will be saved right after
JH23_rare_ASV_10K <- rarefy_even_depth(JH23_phyloseq_DADA2_no_plants_2, 10200)
#saveRDS(JH23_rare_ASV_10K, file ="JH23_rare_ASV_10K.rds")
JH23_rare_ASV_10K<- readRDS("JH23_rare_ASV_10K.rds")
######################################################################
######################################################################
## Figure 3B_Constrained ordination
#####################################################################
#####################################################################
JH23_rhizosphere<- subset_samples(JH23_rare_ASV_10K, Genotype == "Barke" |Genotype == "52"|Genotype == "17")
JH23_rhizosphere
JH23_rare_sqrt<-transform_sample_counts(JH23_rhizosphere, function (x) sqrt(x))
#constrained ordination: constrained for genotype
#rarefied data
JH23_rhizosphere_CAP <- ordinate(JH23_rare_sqrt, "CAP", "bray", ~ Genotype)
plot_ordination(JH23_rare_sqrt, JH23_rhizosphere_CAP, color = "Genotype")
#assign shapes to Soil type and color to Sample type
p=plot_ordination(JH23_rare_sqrt, JH23_rhizosphere_CAP , color = "Genotype")
p = p + geom_point(size = 6, alpha = 0.80, shape =17)
p = p + scale_colour_manual(values = c("#56B4E9","#E69F00","#0072B2"))
p + ggtitle("CAP 16S data, Bray distance-rarefied samples")
#extract the mapping file
design_rhizosphere <- design[colnames(otu_table(JH23_rhizosphere)), ]
#BC distance
BC <- phyloseq::distance(JH23_rhizosphere, "bray")
adonis(BC ~ Genotype, data= design_rhizosphere , permutations = 5000)
adonis(BC ~ Batch*Genotype , data=design_rhizosphere , permutation =5000)
############################################################################################################################
##End |
851c5fd0414524e13155aa2b6d5b557847015c92 | 7a35a5103521e220d69036f9a71b60d9055052e7 | /man/namespaceImportMethods.Rd | 8658fa9aa78a3a8e07bde6f258d6b3e143f3b285 | [] | no_license | granatb/RapeR | da7205c6f07442ee03e53511b636858f3d0c8bbf | 799b289c09ec024e7186cbf338bfe539c6ed7334 | refs/heads/master | 2020-05-20T14:36:24.817925 | 2019-05-15T14:56:26 | 2019-05-15T14:56:26 | 185,624,078 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 303 | rd | namespaceImportMethods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/namespaceImportMethods.R
\name{namespaceImportMethods}
\alias{namespaceImportMethods}
\title{fun_name}
\usage{
namespaceImportMethods(params)
}
\arguments{
\item{param}{fun_name}
}
\description{
kolejna funkcja podmieniona
}
|
62cd6e61c34d2f12171cf58a17ded351f11f9235 | e41e39e46ccaa44f104f8a817932824dbe4fb0c0 | /R_script/1/quant_pub.R | d5a293771f1fc01ce76b8d8ff9629e0ff99a642a | [] | no_license | yoshimura19/samples | fd59758e4431565047d306ad865fb000d2f4df4b | fc4cf50a583029016ddcb9eb9476c464c52900f1 | refs/heads/master | 2021-07-18T00:23:02.252666 | 2017-10-26T19:21:09 | 2017-10-26T19:21:09 | 108,371,681 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,148 | r | quant_pub.R | library(quantreg)
data(engel)
qr <- rq(foodexp~income,data=engel,tau=seq(0,1,0.25))
summary(qr)
qr
data6 <- read.csv("/Users/serubantesu2/Downloads/675cross.csv",header= T,sep=",",na.strings=".",fileEncoding="cp932")
attach(data6)
data6
ls(data6)
data6
data7 <- read.csv("/Users/serubantesu2/Downloads/try2-2.csv",header= T,sep=",",na.strings=".",fileEncoding="cp932")
attach(data7)
ls(data7)
data7
data8 <- read.csv("/Users/serubantesu2/Downloads/make717public_panel.csv",header= T,sep=",",na.strings=".",fileEncoding="cp932")
attach(data8)
ls(data8)
forPSM <- model.frame(id ~ oldid + year + misOrder + kubun + ken + shi
+ name + forcheck + med_cost_per1man1day
+ med_cost + general_beds + use_rate_general_beds
+ ave_stay_general + ave_impatient_per1day + out_patient
+ patient + outpatient_rate + assets_building_100beds
+ doctors_100beds + nurses_100beds + pharmacist_100beds
+ DPCdummyH20 + fixedeffect
, data = data7)
for717panel <- model.frame(id ~ year + kubun + ken + shi
+ name + forcheck + med_cost_per1man1day
+ medical_cost + general_beds + use_rate_general_beds
+ ave_stay_general + ave_inpatient_per1day #+out_patient
+ outpatient_rate + assets_building_100beds #+ patient
+ doctors_100beds + nurses_100beds + pharmacist_100beds
#+ fixedeffect + DPCdummyH20
, data = data8)
length(for717panel$id)
write.table(forPSM, file="forPSM.txt", quote=F, col.names=T,append=F)
editeddata <- model.frame(DPCdummyH20 ~ fe + id + med_cost_per1man1day
+ general_beds + use_rate_general_beds
+ ave_stay_general + ave_impatient_per1day + outpatient_rate
+ assets_building_100beds + doctors_100beds + pharmacist_100beds
, data=data6)
ed <- editeddata
write(ed$id, file="ed.txt")
write.table(editeddata, file="ed.txt", quote=F, col.names=T, append=F)
reg <- lm(ed$med_cost_per1man1day ~ ed$DPCdummyH20 + ed$general_beds + ed$use_rate_general_beds
+ ed$ave_stay_general + ed$ave_impatient_per1day + ed$outpatient_rate
+ ed$assets_building_100beds + ed$doctors_100beds + ed$pharmacist_100beds
, data=editeddata)
summary(reg)
qr <- rq(ed$med_cost_per1man1day ~ ed$general_beds + ed$use_rate_general_beds
+ ed$ave_stay_general + ed$ave_impatient_per1day + ed$outpatient_rate
+ ed$assets_building_100beds + ed$doctors_100beds + ed$pharmacist_100beds
, data=editeddata, tau=seq(0,1,0.25))
summary(qr)
summary.rq(qr)
plot(qr)
qr
options(scipen=10)
qr <- rq(med_cost_per1man1day ~ general_beds + use_rate_general_beds
+ ave_stay_general + ave_impatient_per1day + outpatient_rate
+ assets_building_100beds + doctors_100beds + pharmacist_100beds
#+ nurses_100beds
, data=data7, tau=seq(0,1,0.25))
|
d8970e048fcd2706512283e4c1f9b4e4e0b2f002 | bdd73b70c77512ad645b546a2f9bdb6c6b96f60f | /analyses/1 simulate_data.R | 6dec9d22f06acfa716d2d538edca92ae87591ffd | [
"MIT"
] | permissive | lnsongxf/eta-simulations | 1e0e0703e1143c4930627659eb47e44ba503f09c | aee8587d481bf121c6da809ae3161a41529816a1 | refs/heads/master | 2020-04-25T18:46:24.249519 | 2018-08-28T19:52:50 | 2018-08-28T19:52:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,339 | r | 1 simulate_data.R | setwd("C:/Users/eri2005/Desktop")
####simulations for data####
library(mvtnorm)
library(ez)
library(reshape)
##rmvnorm(n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)),
## method=c("eigen", "svd", "chol"), pre0.9_9994 = FALSE)
####things to simulate####
##rotate around N values start at 20 in each level, then add 5 as you go
##keep means steady 2.5, 3, 3.5, etc.
##rotate around the number of levels 3-6
##rotate around SDs making eta small medium large
##rotate around correlated error 0, .1, 3., .5, .7, .9
####create blank data from for data####
totalsims = 1224*1000
mydata = data.frame(N = 1:totalsims)
####keep track of the simulation rounds####
round = 0
####loop over N values here####
##loop a
Nloops = seq(20, 100, 6)
for (a in 1:length(Nloops)) {
simulate = 0
####loop over M and levels values here####
##this loop will give you different numbers of means for the different number of levels
##loops b
levels = 3:6
for (b in 1:length(levels)) {
topmean = c(3.5, 4.0, 4.5, 5)
Means = seq(2.5, topmean[b], .5)
####loop over SD values here####
##loop c
SDloops = c(5, 3, 1)
for (c in 1:length(SDloops)) {
####loop over correlations here####
##loop d
corloops = c(0, .1, .3, .5, .7, .9)
for (d in 1:length(corloops)) {
####simulate 1000 rounds of data####
for (e in 1:1000) {
####make the data here####
##here we are going to want to make the cor / SD matrix pattern
##but that will depends on the number of levels
##might have to do it by if statements? not very elegant
if(levels[b] == 3) {
sigma = matrix(c(SDloops[c],corloops[d],corloops[d],
corloops[d],SDloops[c],corloops[d],
corloops[d],corloops[d],SDloops[c]), nrow = 3, ncol = 3)
}
if(levels[b] == 4) {
sigma = matrix(c(SDloops[c],corloops[d],corloops[d],corloops[d],
corloops[d],SDloops[c],corloops[d],corloops[d],
corloops[d],corloops[d],SDloops[c],corloops[d],
corloops[d],corloops[d],corloops[d],SDloops[c]), nrow = 4, ncol = 4)
}
if(levels[b] == 5) {
sigma = matrix(c(SDloops[c],corloops[d],corloops[d],corloops[d],corloops[d],
corloops[d],SDloops[c],corloops[d],corloops[d],corloops[d],
corloops[d],corloops[d],SDloops[c],corloops[d],corloops[d],
corloops[d],corloops[d],corloops[d],SDloops[c],corloops[d],
corloops[d],corloops[d],corloops[d],corloops[d],SDloops[c]), nrow = 5, ncol = 5)
}
if(levels[b] == 6) {
sigma = matrix(c(SDloops[c],corloops[d],corloops[d],corloops[d],corloops[d],corloops[d],
corloops[d],SDloops[c],corloops[d],corloops[d],corloops[d],corloops[d],
corloops[d],corloops[d],SDloops[c],corloops[d],corloops[d],corloops[d],
corloops[d],corloops[d],corloops[d],SDloops[c],corloops[d],corloops[d],
corloops[d],corloops[d],corloops[d],corloops[d],SDloops[c],corloops[d],
corloops[d],corloops[d],corloops[d],corloops[d],corloops[d],SDloops[c]), nrow = 6, ncol = 6)
}
dataset = rmvnorm(Nloops[a], Means, sigma)
##here we are simulating 1-7 Likert type data
##take off the digits
##take out the out of range values
dataset = round(dataset, digits = 0)
dataset[ dataset < 1 ] = 1
dataset[ dataset > 7 ] = 7
####put in the basic statistics here####
round = round + 1
simulate = simulate + 1
mydata$N[round] = Nloops[a]
mydata$levels[round] = levels[b]
mydata$stdev[round] = SDloops[c]
mydata$correl[round] = corloops[d]
mydata$simnum[round] = simulate
####begin RM ANOVA one way####
dataset = as.data.frame(dataset)
dataset$partno = as.factor(1:nrow(dataset))
longdataset = melt(dataset,
id = "partno",
measured = colnames(longdataset[1:(ncol(longdataset)-1), ]))
rmoutput = ezANOVA(data = longdataset,
wid = partno,
within = variable,
dv = value,
type = 3,
detailed = T)
mydata$RM1.dfm[round] = rmoutput$ANOVA$DFn[2]
mydata$RM1.dfr[round] = rmoutput$ANOVA$DFd[2]
mydata$RM1.ssm.p[round] = rmoutput$ANOVA$SSn[1]
mydata$RM1.ssm.main[round] = rmoutput$ANOVA$SSn[2]
mydata$RM1.ssr.p[round] = rmoutput$ANOVA$SSd[1]
mydata$RM1.ssr.main[round] = rmoutput$ANOVA$SSd[2]
mydata$RM1.F[round] = rmoutput$ANOVA$F[2]
mydata$RM1.ges[round] = rmoutput$ANOVA$ges[2]
####begin RM ANOVA two way####
tempstuff = longdataset
randomvalues = rnorm(Nloops[a], mean(Means), 1)
tempstuff$value = tempstuff$value + randomvalues
tempstuff$value = round(tempstuff$value, digits = 0)
tempstuff$value[ tempstuff$value < 1 ] = 1
tempstuff$value[ tempstuff$value > 7 ] = 7
doublermdata = rbind(longdataset, tempstuff)
level1 = as.numeric(gl(2, Nloops[a]/2, nrow(longdataset), labels = c("1", "2")))
level2 = 3 - as.numeric(gl(2, Nloops[a]/2, nrow(longdataset), labels = c("2", "1")))
doublermdata$rmlevel2 = as.factor(c(level1,level2))
rmoutput2 = ezANOVA(data = doublermdata,
wid = partno,
within = .(variable, rmlevel2),
dv = value,
type = 3,
detailed = T)
mydata$RM2.dfm[round] = rmoutput2$ANOVA$DFn[2]
mydata$RM2.dfr[round] = rmoutput2$ANOVA$DFd[2]
mydata$RM2.ssm.p[round] = rmoutput2$ANOVA$SSn[1]
mydata$RM2.ssm.main[round] = rmoutput2$ANOVA$SSn[2]
mydata$RM2.ssm.other[round] = rmoutput2$ANOVA$SSn[3]
mydata$RM2.ssm.interact[round] = rmoutput2$ANOVA$SSn[4]
mydata$RM2.ssr.p[round] = rmoutput2$ANOVA$SSd[1]
mydata$RM2.ssr.main[round] = rmoutput2$ANOVA$SSd[2]
mydata$RM2.ssr.other[round] = rmoutput2$ANOVA$SSd[3]
mydata$RM2.ssr.interact[round] = rmoutput2$ANOVA$SSd[4]
mydata$RM2.F[round] = rmoutput2$ANOVA$F[2]
mydata$RM2.ges[round] = rmoutput2$ANOVA$ges[2]
####begin MIXED ANOVA two way####
longdataset$level2 = gl(2, Nloops[a]/2, nrow(longdataset), labels = c("level 1", "level 2"))
mixedoutput = ezANOVA(data = longdataset,
wid = partno,
within = variable,
between = level2,
dv = value,
type = 3,
detailed = T)
mydata$MIX.dfm[round] = mixedoutput$ANOVA$DFn[3]
mydata$MIX.dfr[round] = mixedoutput$ANOVA$DFd[3]
mydata$MIX.ssm.p[round] = mixedoutput$ANOVA$SSn[1]
mydata$MIX.ssm.other[round] = mixedoutput$ANOVA$SSn[2]
mydata$MIX.ssm.main[round] = mixedoutput$ANOVA$SSn[3]
mydata$MIX.ssm.interact[round] = mixedoutput$ANOVA$SSn[4]
mydata$MIX.ssr.p[round] = mixedoutput$ANOVA$SSd[1]
mydata$MIX.ssr.other[round] = mixedoutput$ANOVA$SSd[2]
mydata$MIX.ssr.main[round] = mixedoutput$ANOVA$SSd[3]
mydata$MIX.ssr.interact[round] = mixedoutput$ANOVA$SSd[4]
mydata$MIX.F[round] = mixedoutput$ANOVA$F[3]
mydata$MIX.ges[round] = mixedoutput$ANOVA$ges[3]
####begin BN ANOVA one way####
longdataset$bnpartno = as.factor(1:nrow(longdataset))
bnoutput = ezANOVA(data = longdataset,
wid = bnpartno,
between = variable,
dv = value,
type = 3,
detailed = T)
mydata$BN1.dfm[round] = bnoutput$ANOVA$DFn[2]
mydata$BN1.dfr[round] = bnoutput$ANOVA$DFd[2]
mydata$BN1.ssm.p[round] = bnoutput$ANOVA$SSn[1]
mydata$BN1.ssm.main[round] = bnoutput$ANOVA$SSn[2]
mydata$BN1.ssr.p[round] = bnoutput$ANOVA$SSd[1]
mydata$BN1.ssr.main[round] = bnoutput$ANOVA$SSd[2]
mydata$BN1.F[round] = bnoutput$ANOVA$F[2]
mydata$BN1.ges[round] = bnoutput$ANOVA$ges[2]
####begin BN ANOVA two way####
bnoutput2 = ezANOVA(data = longdataset,
wid = bnpartno,
between = .(variable, level2),
dv = value,
type = 3,
detailed = T)
mydata$BN2.dfm[round] = bnoutput2$ANOVA$DFn[2]
mydata$BN2.dfr[round] = bnoutput2$ANOVA$DFd[2]
mydata$BN2.ssm.p[round] = bnoutput2$ANOVA$SSn[1]
mydata$BN2.ssm.main[round] = bnoutput2$ANOVA$SSn[2]
mydata$BN2.ssm.other[round] = bnoutput2$ANOVA$SSn[3]
mydata$BN2.ssm.interact[round] = bnoutput2$ANOVA$SSn[4]
mydata$BN2.ssr.all[round] = bnoutput2$ANOVA$SSd[1]
mydata$BN2.F[round] = bnoutput2$ANOVA$F[2]
mydata$BN2.ges[round] = bnoutput2$ANOVA$ges[2]
} ##end sim loop
filename = paste(round, ".csv", sep = "")
datalines = (abs(round-999):round)
write.csv(mydata[ datalines, ], file = filename)
} ##end N loop
} ##end levels loop
} ##end SD loop
} ##end cor loop
|
44320d61d1bbabc4f611a541f13cb29e70673228 | 0c7f7d067297fce49cf8eacd2efbaeb3b441f6dc | /decision_tree/decisionTree.R | eaa367019672eb5e6eb2c15fd1b35413eb28c5ca | [] | no_license | AngeloDamiani/CodingMachineLearning | f7ff418de38c26c1f61845661ce18a2305124d67 | a36bdc878bc32dd0a5b182e5216a02fcf4ca3935 | refs/heads/master | 2020-03-14T02:13:22.494524 | 2018-05-04T08:43:30 | 2018-05-04T08:43:30 | 131,394,986 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,194 | r | decisionTree.R | rm(list=ls())
this.dir <- dirname(parent.frame(2)$ofile)
setwd(this.dir)
##################### MATH FUNCTIONS ######################################
gini <- function(rows){
nrows = nrow(rows)
already_seen = vector()
impurity = 1
label_index = ncol(rows)
for (label in 1:nrows){
current_label = rows[label, label_index]
if(!(current_label %in% already_seen)){
lbl_count = sum(rows[,label_index] == current_label)
impurity = impurity - (lbl_count/nrows)^2
already_seen = c(already_seen, current_label)
}
}
impurity
}
information_gain <- function(uncertainty, left_partition, right_partition){
nright = nrow(right_partition)
nleft = nrow(left_partition)
ntotal = nleft + nright
uncertainty - (nleft/ntotal)*gini(left_partition) - (nright/ntotal)*gini(right_partition)
}
###################### QUESTION DEFINITION ##################################
Question <- setRefClass("Question",
fields = list(var = "numeric", point = "ANY"),
methods = list(
match = function(sample) {
val = sample[var]
if(is.numeric(point)){
result = val >= point
}
else{
result = val == point
}
result
}
)
)
####################### TREE ELEMENTS ########################################
Node <- setRefClass("Node",
fields = list()
)
QuestionNode <- setRefClass("QuestionNode",
fields = list(question = "Question", left_son = "Node", right_son = "Node"),
contains = c("Node")
)
Leaf <- setRefClass("Leaf",
fields = list(solution = "ANY"),
contains = c("Node")
)
###################### TREE BUILDING FUNCTIONS ###############################
split <- function(dataset, question){
true_partition = vector()
false_partition = vector()
for (row in 1:nrow(dataset)){
if (question$match(dataset[row,]) == TRUE){
true_partition = rbind(true_partition, dataset[row,])
}
else{
false_partition = rbind(false_partition, dataset[row,])
}
}
partitions = list(true_partition, false_partition)
}
get_best_splitting_question <- function(dataset){
best_question = NULL
current_uncertainty = gini(dataset)
max_gain = 0
for (clmn in 1:(ncol(dataset)-1)){
for (row in 1:nrow(dataset)){
new_quest = Question(var=clmn, point=dataset[row,clmn])
splitting = split(dataset, new_quest)
true_rows = splitting[[1]]
false_rows = splitting[[2]]
if (length(true_rows) == 0 | length(false_rows) == 0) next
gain = information_gain(current_uncertainty, true_rows, false_rows)
if (gain >= max_gain){
max_gain = gain
best_question = new_quest
}
}
}
best_question
}
decision_tree_building <- function(dataset){
result = NULL
impurity = gini(dataset)
if(impurity == 0){
result = Leaf(solution=dataset[1,ncol(dataset)])
}
else{
best_question = get_best_splitting_question(dataset)
splitting = split(dataset, best_question)
true_branch = splitting[[1]]
false_branch = splitting[[2]]
left_branch = decision_tree_building(true_branch)
right_branch = decision_tree_building(false_branch)
result = QuestionNode(question=best_question, left_son=left_branch, right_son=right_branch)
}
result
}
classify <- function(node, sample){
result = NULL
if (class(node)[[1]] == "Leaf"){
result = node$solution
}
else{
if(node$question$match(sample)){
result = classify(node$left_son, sample)
}
else{
result = classify(node$right_son, sample)
}
}
result
}
################### MAIN ################################################################
data <- read.csv(file="./irisdataset.data", header=FALSE, sep=",")
dataset = vector()
for(i in 1:(ncol(data))){
dataset = cbind(dataset, data[[i]])
}
print(dataset)
tree = decision_tree_building(dataset)
# Three samples has been removed from iris dataset and put here
test1 = c(5.2,3.4,1.4,0.2) # "1" expected (Iris Setosa)
test2 = c(5.7,2.8,4.1,1.3) # "2" expected (Iris Versicolor)
test3 = c(7.9,3.8,6.4,2.0) # "3" expected (Iris Virginica)
print(classify(tree, test1))
print(classify(tree, test2))
print(classify(tree, test3))
|
3817cb91d99bfd47945349144373d2e67314b713 | 2dfe5ece80ef0cde27bc3369f08d9798188e69b5 | /PL.R | a75e7953b3b41b9736b12597a4e7b04486852752 | [] | no_license | iamkk11/EPL-Prediction | c31de33503fb595d658ec032a80f8245ea517375 | ade3542cbfc3146072d26d1d4cbd7e107639748c | refs/heads/master | 2022-12-10T19:02:13.258482 | 2020-09-11T14:21:16 | 2020-09-11T14:21:16 | 105,903,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,767 | r | PL.R | set.seed(1)
v='PL2'
p=paste('/home/kevin/Downloads/',v,sep='')
#Combine files from trace folders into 1 data frame
library('dplyr',warn.conflicts=FALSE)
#Defining function to merge traces
combine = function(mypath){
filenames=list.files(path=mypath, full.names=TRUE)
datalist = lapply(filenames, function(x){read.csv(file=x,sep=',')})
Reduce(function(x,y) {bind_rows(x,y)}, datalist)}
#Reading each trace file and combining them
a=combine(p)
attach(a)
#teams
library('sqldf',warn.conflicts=FALSE)
t=sqldf("SELECT Date,B365H,B365D,B365A,LBH,LBD,LBA,FTR FROM a WHERE
HomeTeam='Chelsea' AND AwayTeam='Man City'ORDER BY Date")
s=sqldf("SELECT FTR AS 'Result',COUNT(FTR) AS 'Counts' FROM t GROUP BY FTR")
p=sqldf("SELECT Result,Counts,ROUND(Counts*100.0/(SELECT SUM(Counts) FROM s),0) AS 'Percentage'
FROM s GROUP BY Result")
#b=t[-10,]
#b=t
#Factorization
b$FTR=factor(b$FTR)
#Training and test
library('varhandle')
gtrain=b
gtest<-data.frame(gtrain[,c(1:6)])
gtestlabel<-c(unfactor(gtrain$FTR))
attach(gtrain)
#naive bayes
library('e1071',warn.conflicts=FALSE)
naive_bayes_model<-naiveBayes(FTR ~ ., data = gtrain)
naive_bayes_predictions<-predict(naive_bayes_model, newdata=gtest)
naive_bayes_accuracy=round(mean(naive_bayes_predictions==gtestlabel),2)*100
#classification tree
library('party',warn.conflicts=FALSE)
ctree_model<- ctree(FTR ~ ., data = gtrain,controls=ctree_control(minsplit=30,minbucket=10,maxdepth=5))
ctree_predictions <- predict(ctree_model,newdata=gtest,type='response')
ctree_accuracy=round(mean(ctree_predictions==gtestlabel),2)*100
odds=data.frame(B365H='0.727',B365D='2.75',B365A='4',LBH='0.727',LBD='2.6',LBA='3.75')
predict(naive_bayes_model,newdata=odds,type = 'class',interval=predict)
|
f457a3c0f65291ab43d1e2cd776dd076da6e031a | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /paws.common/man/new_request.Rd | eaa86e50083d94a86e66d0c6b9fbdbdb876054ea | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,251 | rd | new_request.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/request.R
\name{new_request}
\alias{new_request}
\title{Return an API request object}
\usage{
new_request(client, operation, params, data, dest = NULL)
}
\arguments{
\item{client}{A service client, e.g. from \code{new_service}.}
\item{operation}{An operation, e.g. from \code{new_operation}.}
\item{params}{A populated input object.}
\item{data}{An empty output object.}
\item{dest}{Control where the response body is written}
}
\description{
Return an API request object with everything needed to make a request.
}
\examples{
\dontrun{
# Make a request object for the S3 ListBuckets operation.
metadata <- list(
endpoints = list("*" = list(endpoint = "s3.{region}.amazonaws.com", global = FALSE)),
service_name = "s3"
)
client <- new_service(metadata, new_handlers("restxml", "s3"))
op <- new_operation("ListBuckets", "GET", "/", list())
params <- list()
data <- tag_add(list(Buckets = list()), list(type = "structure"))
req <- new_request(client, op, params, data)
}
}
\seealso{
Other API request functions:
\code{\link{new_handlers}()},
\code{\link{new_operation}()},
\code{\link{new_service}()},
\code{\link{send_request}()}
}
\concept{API request functions}
|
bddb86db08d7eea410dbb0e4e69091e001e0fd22 | 0d4c415c43ef34498dc2e5fbb58080c3401d194b | /R/dropSetup.R | d0704865813f016c253f6b647eddb0a757fd10d5 | [] | no_license | OliverDietrich/MasterThesis | 4faca4e62f9a36f392cbc61072cab834a09dad47 | 3167ef11f75744fb9ee6d366ad3c576a0e06de9b | refs/heads/master | 2020-05-06T13:43:58.249981 | 2019-11-26T09:56:36 | 2019-11-26T09:56:36 | 180,152,871 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,270 | r | dropSetup.R | ######################################################################
######### Setup Seurat object from Cell Ranger output files ##########
#--------------------------------------------------------------------#
########### R script by Oliver Dietrich on 17 April 2019 #############
######################################################################
# read input arguments from the command line
args <- commandArgs(trailingOnly = TRUE)
DSname <- args[1]
arg2 <- args[2]
arg3 <- args[3]
# load dependencies
library(tidyverse)
library(Seurat)
library(hdf5r)
set.seed(154)
# check if the input is correct, otherwise abort script
if( arg2 %in% c("raw", "filtered")) { type <- arg2 ; print(paste("Type:", type)) } else {
print("Error. Please specify data type ('raw' or 'filtered'). Usage: DS type format") ; quit(status = 10) }
if( arg3 %in% c("h5", "mtx") ) { format <- arg3 ; print(paste("Format:", format)) } else {
print("Error. Please specify data format ('h5' or 'mtx'). Usage: DS type format") ; quit(status = 10) }
# check to which project the dataset belongs
HOME <- paste0("/home/", Sys.getenv("USER"), "/Data/")
if(file.exists(paste0(HOME, "projects")) == TRUE) {
projects <- read.table(file = paste0("/home/", Sys.getenv("USER"), "/Data/", "projects"), sep = "\t", header = TRUE)
projectName <- as.character(projects$project[str_detect(projects$datasets, DSname)])
} else { print("Error. Projects file not available or path corrupt.") ; quit(status = 12) }
dir.create(paste0(HOME, projectName, "/analysis/Input"), recursive = TRUE)
# load the data and convert to Seurat object, depends on the file format (mtx or hdf5)
if( format %in% "h5") {
print("Error. The Hdf5 format cannot be used yet. Please refer to the matrix format instead.") ; quit(status = 13)
# IN CONSTRUCTION ...
# pathIN <- paste0(pathIN, projectName, "/datasets/", DSname, "/outs/", type, "_feature_bc_matrix.h5")
# DS_data <- Read10X_h5(pathIN)
# DS <- CreateSeuratObject(DS_data)
} else {
# set path to data
pathIN <- paste0(HOME, projectName, "/datasets/", DSname, "/outs/", type, "_feature_bc_matrix/")
### test the input, must lead from different inputs to same output
print("Checking and sorting input")
inputFolder <- paste0(HOME, projectName, "/analysis/Input/")
if(file.exists(paste0(pathIN, "/genes.tsv")) == TRUE) {
# load genes table, copy to features table
genes <- read.table(file = paste0(pathIN, "genes.tsv"), sep = "\t", header = FALSE)
features <- genes
# remove "/" and "_" from gene names and replaces with "-", necessary for printing out gene names
levels(features$V2) <- gsub("/", "-", levels(features$V2)) ; levels(features$V2) <- gsub("_", "-", levels(features$V2))
# change column names, does not depend on columns (only two)
# change column names, depends on the number of columns
if (length(colnames(features)) == 2) {colnames(features) <- c("ensID", "feature") }
if (length(colnames(features)) == 3) {colnames(features) <- c("ensID", "feature", "type") }
# save features table to file
write.table(features, file = paste0(inputFolder, "features.tsv"), sep = "\t", row.names = FALSE, col.names = TRUE)
} else {
# load features table
features <- read.table(file = paste0(pathIN, "features.tsv"), sep = "\t", header = FALSE)
# remove "/" and "_" from gene names and replaces with "-", necessary for printing out gene names
levels(features$V2) <- gsub("/", "-", levels(features$V2)) ; levels(features$V2) <- gsub("_", "-", levels(features$V2))
# change column names, depends on the number of columns
if (length(colnames(features)) == 2) {colnames(features) <- c("ensID", "feature") }
if (length(colnames(features)) == 3) {colnames(features) <- c("ensID", "feature", "type") }
# save features table to input files + copy as genes.tsv
write.table(features, file = paste0(inputFolder, "features.tsv"), sep = "\t", row.names = FALSE, col.names = TRUE)
write.table(features, file = paste0(pathIN, "genes.tsv"), sep = "\t", row.names = FALSE, col.names = FALSE)
}
print("Converting matrix to Seurat object")
# read matrix from file
DS <- Read10X(data.dir = pathIN)
# change feature names to ensemble ID
DS@Dimnames[[1]] <- as.character(features$ensID)
# convert to seurat object
DS <- CreateSeuratObject(DS, project = DSname)
### add metadata based on datasets file, necessary for distinguishing merged datasets and later visualizations
print("Adding metadata")
if(file.exists(paste0(HOME, projectName, "/datasets/datasets")) == TRUE) {
datasets <- read.table(paste0(HOME, projectName, "/datasets/datasets"), sep = "\t", header = TRUE)
} else { print("Datasets file not available, aborting.") ; quit(status = 14)}
# add information for single dataset
if (levels(DS@meta.data$orig.ident) %in% datasets$dataset) {
for (i in colnames(datasets)) {
DS@meta.data[i] <- datasets[match(DS@meta.data$orig.ident, datasets$dataset),i] } # end of for loop
} # end of if statement
# add information for multiple (merged) datasets
if (str_detect(head(rownames(DS@meta.data), 1), "-") == TRUE) {
DS@meta.data$order <- as.factor(str_extract(string = rownames(DS@meta.data), "\\d"))
# detect which datasets are being analyzed and subset the datasets table
a <- which(str_detect(levels(DS@meta.data$orig.ident), as.character(datasets$dataset)) == TRUE)
b <- datasets[a,]
rownames(b) <- seq(1, length(rownames(b)))
# add information to DS@meta.data (order is assumed by name, e.g. D6, E2, E12, F5, ...)
for (i in colnames(datasets)) {
DS@meta.data[i] <- b[match(DS@meta.data$order, rownames(b)), i] } # end of for loop
} # end of if statement
# write R dataset to .Rds file
print("Saving to file")
pathOUT <- paste0(HOME, projectName, "/analysis/", DSname, "_", Sys.Date(), "/")
dir.create(paste0(pathOUT, "RDS"), recursive = TRUE)
info <- data.frame("parameter" = c("type", "format"), "value" = c(type, format))
write.table(info, file = paste0(pathOUT, "RDS/setupInfo"), sep = "\t")
saveRDS(DS, paste0(pathOUT, "RDS/", DSname, ".Rds"))
# produce table to set values for image generation
parameters <- c("pointSize","position","binSize","color","fill","shape","alpha","titleSize","axisTitle","axisXsize",
"axisXangle","axisXhjust","axisXvjust","axisYsize","axisYangle","axisYhjust","axisYvjust","axisLineSize",
"axisTicks","legendPositionX","legendPositionY","legendTitle","legendText","legendKeySize","legendBgrdCol",
"guideSize","fileType","dpi","width","height")
boxplot <- c(1,"jitter",NA,"grey50","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
smoothDensity <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
0.7,0.7,0,35,35,"white",5,"png",350,10,10)
scatter <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
hvgPlot <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"none","none",0,35,35,"white",5,"png",350,10,10)
PCAplot <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
PCAloadings <- c(3,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
tSNE <- c(2,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
UMAP <- c(2,"jitter",NA,"dataset","dataset",NA,0.5,35,0,0,0,0.5,0,0,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
DM <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
barplot <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
sankey <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
DEheatmap <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
GO <- c(1,"jitter",NA,"dataset","dataset",NA,0.5,35,35,35,0,0.5,0,35,0,0,0.5,1,1,
"right","none",0,35,35,"white",5,"png",350,10,10)
imageStats <- data.frame(parameters = parameters, boxplot = boxplot, smoothDensity = smoothDensity, scatter = scatter,
hvgPlot=hvgPlot, PCAplot=PCAplot, PCAloadings=PCAloadings, tSNE=tSNE, UMAP=UMAP, DM=DM,
barplot=barplot, sankey=sankey, DEheatmap=DEheatmap, GO=GO)
dir.create(paste0(pathOUT, "Input"))
write.table(imageStats, file = paste0(pathOUT, "Input/imageStats.csv"), sep = ",", row.names = FALSE)
print(paste("Finished. Seurat object for", DSname, "has been created and stored in", pathOUT))
} # end of else, mtx format
sessionInfo()
quit(status = 0)
|
51c08b5d18e7c8f78891e9e354c9d5c6c4ee2111 | 3a764372a998e48807ac0fb7c2c2eaa91324ed35 | /Plot5.R | 10763038795ca5e4568455a3e8d56adf2046363e | [] | no_license | gnupate/EDA-Assignment2 | f8bb0c00527948bdf614d2ccd0f1908621652ab7 | 4748c0c287aa35c46a8f914fd5545db186989064 | refs/heads/master | 2020-04-19T21:57:36.532058 | 2015-07-25T23:54:42 | 2015-07-25T23:54:42 | 39,645,095 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,252 | r | Plot5.R | library(dplyr) ## use the dplyr library
## NOTE: I have not saved the data to github, it should be unzipped into the directrory where this script is run
## ensure that the data is there
files <- dir()
if(is.element("summarySCC_PM25.rds",files)) { #the data exists, go ahead and work
## read the data into a dataframe
sourceData <- readRDS("summarySCC_PM25.rds")
## I'm using the ON-ROAD data under the assumption that vehicles means motor vehicles driven on roads
## (not boats, trains, planes, etc,)
vehicleData <- filter(sourceData, type == "ON-ROAD", fips == 24510)
year = c(1999,2002,2005,2008)
emissions = c(0,0,0,0)
df = data.frame(year , emissions)
## fill in the dataframe for each year
counter = 1
for(i in year) {
df$emissions[counter] <- colSums(select(filter(vehicleData, year == i),Emissions))
counter <- counter + 1
}
## use the png filter to write to a graphics file device
png(file="plot5.png")
plot(df, main="Annual Vehicle Related Emmissions across Baltimore", type="l")
dev.off()
} else { # datafile isn't there ...
print("Expected datafile not found, please ensure it is in the current directory and retry")
} |
2e2f73cb5450a1680db28fbb42452720b8912e03 | a2bcf83193959276f7bd8db656db53e58e875a26 | /steal-the-moon/stealTheMoonSim/man/stealTheMoonSim.Rd | bfeb2828e28c48bad9c2ed6781c44e4b2435999e | [] | no_license | PieceMaker/presentation-lsdg-docker | 2b3c22fafdfe390eb9e60499c9083b4c1d461065 | 09aa739cfad27583d344cbae6a653dcb5700ce99 | refs/heads/master | 2020-04-21T06:16:15.830018 | 2019-02-09T05:03:32 | 2019-02-09T05:03:32 | 169,361,528 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 503 | rd | stealTheMoonSim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stealTheMoonSim.R
\name{stealTheMoonSim}
\alias{stealTheMoonSim}
\title{Simple example simulation for use in demonstrating rminions package.}
\usage{
stealTheMoonSim(fuelParams, solarFlareParams, flareSurfaceParams,
flareAdditionalFuelParams, surfaceFuelDependenceCopParams, asteroidParams,
asteroidAdditionalFuelParams)
}
\description{
Simple example simulation for use in demonstrating rminions package.
}
|
24dbc2ef30113949e62e3fa595985e261fb3d7b4 | dd0969134fdc71e65bcb8380ac03a14cbc680cc2 | /R/getFlexFile.R | 0d761d91bda332aa43d52806a06aa15f0176b9ae | [] | no_license | cran/icesDatras | ae26fd615ad95aecb37d9f330bbef65d8985cd09 | bb39967b7c89a6734ceb314e88248ac230e54235 | refs/heads/master | 2023-05-26T11:16:22.608725 | 2023-05-08T08:40:05 | 2023-05-08T08:40:05 | 78,541,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,644 | r | getFlexFile.R | #' Get Flex File
#'
#' Get all information in HH plus estimates of Door Spread, Wing Spread and
#' Swept Area per square km. Only available for NS-IBTS survey.
#'
#' @param survey the survey acronym, e.g. NS-IBTS.
#' @param year the year of the survey, e.g. 2010.
#' @param quarter the quarter of the year the survey took place, i.e. 1, 2, 3 or 4.
#'
#' @return A data frame.
#'
#' @seealso
#' \code{\link{getDATRAS}} supports querying many years and quarters in one function call.
#'
#' \code{\link{getHHdata}} get haul data
#'
#' \code{\link{icesDatras-package}} gives an overview of the package.
#'
#' @author Adriana Villamor.
#'
#' @examples
#' \dontrun{
#' flex <- getFlexFile(survey = "NS-IBTS", year = 2020, quarter = 1)
#' str(flex)
#'
#' # error checking examples:
#' flex <- getFlexFile(survey = "NS_IBTS", year = 2016, quarter = 1)
#' flex <- getFlexFile(survey = "NS-IBTS", year = 2030, quarter = 1)
#' flex <- getFlexFile(survey = "NS-IBTS", year = 2016, quarter = 6)
#' }
#' @export
getFlexFile <- function(survey, year, quarter) {
# check survey name
if (!checkSurveyOK(survey)) return(FALSE)
# check year
if (!checkSurveyYearOK(survey, year, checksurvey = FALSE)) return(FALSE)
# check quarter
if (!checkSurveyYearQuarterOK(survey, year, quarter, checksurvey = FALSE, checkyear = FALSE)) return(FALSE)
# read url and parse to data frame
url <-
sprintf(
"https://datras.ices.dk/WebServices/DATRASWebService.asmx/getFlexFile?survey=%s&year=%i&quarter=%i",
survey, year, quarter)
out <- readDatras(url)
out <- parseDatras(out)
out
}
|
c35d30d451bdfd98a2c152b3ef2bb25dfa726e90 | e67c99cc4146142e0b9210a7a908e0b7998f2cef | /man/race.describe.Rd | 58d0916f96486ead545a967caa34798218511bd2 | [] | no_license | cran/race | 888998a1a9ad03c6aca6928f84d2bf8109c8fce6 | c1d8cbcfe91ff2e0c34b068f8522da612eea6ec9 | refs/heads/master | 2016-09-16T10:05:42.835799 | 2012-04-05T00:00:00 | 2012-04-05T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,508 | rd | race.describe.Rd | % ------------------------------------------------------- -*- mode: Rd; -*- %
% race.describe.Rd Describe a candidate %
% ------------------------------------------------------------------------- %
% ========================================================================= %
% Racing methods for the selection of the best %
% ------------------------------------------------------------------------- %
% Copyright (C) 2003 Mauro Birattari %
% ========================================================================= %
% This program is free software; you can redistribute it and/or modify it %
% under the terms of the GNU General Public License as published by the %
% Free Software Foundation; either version 2 of the License, or (at your %
% option) any later version. %
% %
% This program is distributed in the hope that it will be useful, but %
% WITHOUT ANY WARRANTY; without even the implied warranty of %
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU %
% General Public License for more details. %
% %
% You should have received a copy of the GNU General Public License along %
% with this program; if not, write to the Free Software Foundation, Inc., %
% 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. %
% ========================================================================= %
% ========================================================================= %
% Mauro BIRATTARI %
% IRIDIA - ULB, CP 194/6 %
% Av. F. D. Roosevelt 50 mbiro@ulb.ac.be %
% 1050 Brussels, Belgium http://iridia.ulb.ac.be/~mbiro %
% ========================================================================= %
\name{race.describe}
\alias{race.describe}
\title{Describe a candidate}
\description{The function \preformatted{race.describe(candidate,data)}
may be provided by the user for giving a description of a
candidate. It's definition has to be given in the same file in which
the functions \code{race.wrapper} and \code{race.info} are defined.
The name of such file has to be passed as first argument to the
function \code{race}. }
\arguments{
\item{candidate}{The candidate for which a description is to be
returned.}
\item{data}{It is the object of type \code{list} (possibly empty)
returned by \code{\link{race.init}}, if the latter is defined by the
user.}
}
\value{The function \code{race.describe} should return an object
describing the selected candidate. Such object will be printed by
\code{race} through the function \code{print}.}
\examples{
# Please have a look at the function `race.describe'
# defined in the file `example-wrapper.R':
local({
source(file.path(system.file(package="race"),
"examples","example-wrapper.R"),local=TRUE);
print(race.describe)})
}
\author{Mauro Birattari}
\seealso{\code{\link{race}}, \code{\link{race.init}}}
\keyword{misc}
|
3479e8484ff7abf4a0cf2aee41d3be7a779d45db | c4a362b0a4dbf892dc257e25e5df07f8ff9a7c2f | /R/centerVAR1data.r | 58199b726c840d46a13079a8282fe82446276928 | [] | no_license | cran/ragt2ridges | 232f816a2817a9a31f86d4ea815fb43381b613b2 | 5c4c2ebb6adfb5d3e955ca3df6aca80596f43143 | refs/heads/master | 2020-12-24T07:41:30.024419 | 2020-01-28T14:30:02 | 2020-01-28T14:30:02 | 58,468,203 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,000 | r | centerVAR1data.r | centerVAR1data <- function(Y){
########################################################################
#
# DESCRIPTION:
# within-individual, covariate-wise centering of the data.
#
# ARGUMENTS:
# -> Y : Three-dimensional array containing the data. The
# first, second and third dimensions correspond to
# covariates, time and samples, respectively. The
# data are assumed to centered covariate-wise.
#
# DEPENDENCIES:
# ...
#
# NOTES:
# ...
#
########################################################################
# input checks
if (!is(Y, "array")){
stop("Input (Y) is of wrong class.")
}
if (length(dim(Y)) != 3){
stop("Input (Y) is of wrong dimensions: either covariate, time or sample dimension is missing.")
}
# centering
for (i in 1:dim(Y)[3]){
Y[1:dim(Y)[1], 1:dim(Y)[2],i] <-
sweep(Y[,,i,drop=TRUE], 1,
apply(Y[,,i,drop=TRUE], 1, mean, na.rm=TRUE))
}
return(Y)
}
|
05531cb116fe1e6fe565ef22b78a31fa1a5d9fc2 | f0b1bc9d37d67113311f9b09dd559b9abbec166f | /inst/tests/tReadVar3.R | 22fc1fca62e848a3ec8be0de5fa8d12311a5d2ee | [] | no_license | GAMS-dev/gdxrrw-miro | b0a8f28c730eaa02fb63887a6832d861f48914c5 | 91486406f60986429b385cf37b2741648ac5b2e2 | refs/heads/master | 2023-04-03T17:53:31.082850 | 2023-03-15T17:24:04 | 2023-03-15T17:24:04 | 219,473,354 | 2 | 1 | null | 2021-01-28T09:19:28 | 2019-11-04T10:19:39 | C | UTF-8 | R | false | false | 27,563 | r | tReadVar3.R | #### test rgdx reading a 3-dim variable
#### test form=['sparse','full'] X [filtered,unfiltered]
#### ['l','m','lo','up','s']
#### wanted lists produced with dump("listName",file="")
if (! require(gdxrrwMIRO)) stop ("gdxrrw package is not available")
if (0 == igdx(silent=TRUE)) stop ("the gdx shared library has not been loaded")
source ("chkSame.R")
reqIdent <- TRUE
iUels <- c("i1", "i2")
iCard <- length(iUels)
jUels <- c("j1", "j2")
jCard <- length(jUels)
kUels <- c("k1", "k2")
kCard <- length(kUels)
fields <- c('l','m','lo','up','s')
nFields <- length(fields)
domains <- c("i","j","k")
domainsf <- c("i","j","k","_field")
cart <- list(iUels,jUels,kUels)
cartN <- list('i'=iUels,'j'=jUels,'k'=kUels)
cartf <- list(iUels,jUels,kUels,fields)
cartfN <- list('i'=iUels,'j'=jUels,'k'=kUels,'_field'=fields)
lev <- 1
mar <- 2
low <- 3
upp <- 4
sca <- 5
tryCatch({
print ("testing rgdx on variable reads")
rgdx('?')
fnIn <- "tReadVar3.gdx"
if (! file_test ('-f', fnIn)) {
stop (paste("FAIL: File", fnIn, "does not exist"))
}
### ---------- reading form=sparse, no filter
# all
t <- matrix(c( 1,1,1,lev, 0
,1,1,1,mar, 0
,1,1,1,low, 0
,1,1,1,upp, 525
,1,1,1,sca, 1
,1,1,2,lev, 1
,1,1,2,mar, 0.25
,1,1,2,low, 0
,1,1,2,upp, Inf
,1,1,2,sca, 1
,1,2,1,lev, 10
,1,2,1,mar, 0
,1,2,1,low,-Inf
,1,2,1,upp, Inf
,1,2,1,sca, 1
,1,2,2,lev, 11
,1,2,2,mar, 0.25
,1,2,2,low, 100
,1,2,2,upp, Inf
,1,2,2,sca, 1
,2,1,1,lev, 100
,2,1,1,mar, 0
,2,1,1,low, 0
,2,1,1,upp, 0
,2,1,1,sca, 1
,2,1,2,lev, 101
,2,1,2,mar, 0
,2,1,2,low, 0
,2,1,2,upp, Inf
,2,1,2,sca, 1
,2,2,1,lev, 110
,2,2,1,mar, 0
,2,2,1,low, 0
,2,2,1,upp, Inf
,2,2,1,sca, 10
,2,2,2,lev, 6
,2,2,2,mar, 0
,2,2,2,low, 6
,2,2,2,upp, 6
,2,2,2,sca, 1
), nrow=40, ncol=5, byrow=T)
xwantA <- list(name="x", type="variable", dim=3L,
val=t,
form="sparse",
uels=list(iUels,jUels,kUels,fields),
domains=domainsf, domInfo="full",
field='all',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',field='alL'))
chk <- chkRgdxRes (x, xwantA, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'all',unfiltered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='sparse',field='alL'),squeeze=F)
chk <- chkRgdxRes (x, xwantA, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'all',unfiltered,squeeze=F) failed:",chk$msg))
}
# level
xwantL <- list(name="x", type="variable", dim=3L,
val=matrix(c( 1,1,2, 1
,1,2,1, 10
,1,2,2, 11
,2,1,1, 100
,2,1,2, 101
,2,2,1, 110
,2,2,2, 6), nrow=7, ncol=4, byrow=T),
form="sparse",
uels=list(iUels,jUels,kUels),
domains=domains, domInfo="full",
field='l',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',field='L'))
chk <- chkRgdxRes (x, xwantL, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'L',unfiltered) failed:",chk$msg))
}
xwantL$val <- matrix(c( 1,1,1, 0
,1,1,2, 1
,1,2,1, 10
,1,2,2, 11
,2,1,1, 100
,2,1,2, 101
,2,2,1, 110
,2,2,2, 6), nrow=8, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',field='L'),squeeze=F)
chk <- chkRgdxRes (x, xwantL, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'L',unfiltered,squeeze=F) failed:",chk$msg))
}
# marginal
xwantM <- list(name="x", type="variable", dim=3L,
val=matrix(c( 1,1,2, .25
,1,2,2, .25), nrow=2, ncol=4, byrow=T),
form="sparse",
uels=list(iUels,jUels,kUels),
domains=domains, domInfo="full",
field='m',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',field='M'))
chk <- chkRgdxRes (x, xwantM, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'M',unfiltered) failed:",chk$msg))
}
xwantM$val <- matrix(c( 1,1,1, 0
,1,1,2, .25
,1,2,1, 0
,1,2,2, .25
,2,1,1, 0
,2,1,2, 0
,2,2,1, 0
,2,2,2, 0
), nrow=8, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',field='M'),squeeze=F)
chk <- chkRgdxRes (x, xwantM, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'M',unfiltered,squeeze=F) failed:",chk$msg))
}
# lower
xwantLo <- list(name="x", type="variable", dim=3L,
val=matrix(c( 1,2,1, -Inf
,1,2,2, 100
,2,2,2, 6), nrow=3, ncol=4, byrow=T),
form="sparse",
uels=list(iUels,jUels,kUels),
domains=domains, domInfo="full",
field='lo',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',field='lo'))
chk <- chkRgdxRes (x, xwantLo, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'lo',unfiltered) failed:",chk$msg))
}
xwantLo$val <- matrix(c( 1,1,1, 0
,1,1,2, 0
,1,2,1, -Inf
,1,2,2, 100
,2,1,1, 0
,2,1,2, 0
,2,2,1, 0
,2,2,2, 6), nrow=8, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',field='lo'),squeeze=F)
chk <- chkRgdxRes (x, xwantLo, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'lo',unfiltered,squeeze=F) failed:",chk$msg))
}
# upper
xwantUp <- list(name="x", type="variable", dim=3L,
val=matrix(c( 1,1,1, 525
,2,1,1, 0
,2,2,2, 6), nrow=3, ncol=4, byrow=T),
form="sparse",
uels=list(iUels,jUels,kUels),
domains=domains, domInfo="full",
field='up',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',field='up'))
chk <- chkRgdxRes (x, xwantUp, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'up',unfiltered) failed:",chk$msg))
}
xwantUp$val <- matrix(c( 1,1,1, 525
,1,1,2, Inf
,1,2,1, Inf
,1,2,2, Inf
,2,1,1, 0
,2,1,2, Inf
,2,2,1, Inf
,2,2,2, 6), nrow=8, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',field='up'),squeeze=F)
chk <- chkRgdxRes (x, xwantUp, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'up',unfiltered,squeeze=F) failed:",chk$msg))
}
# scale
xwantS <- list(name="x", type="variable", dim=3L,
val=matrix(c( 2,2,1, 10), nrow=1, ncol=4, byrow=T),
form="sparse",
uels=list(iUels,jUels,kUels),
domains=domains, domInfo="full",
field='s',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',field='s'))
chk <- chkRgdxRes (x, xwantS, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'s',unfiltered) failed:",chk$msg))
}
xwantS$val <- matrix(c( 1,1,1, 1
,1,1,2, 1
,1,2,1, 1
,1,2,2, 1
,2,1,1, 1
,2,1,2, 1
,2,2,1, 10
,2,2,2, 1), nrow=8, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',field='s'),squeeze=F)
chk <- chkRgdxRes (x, xwantS, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'s',unfiltered,squeeze=F) failed:",chk$msg))
}
### ---------- reading form=sparse, filtered
# all
f <- list(c('i2'),jUels,kUels)
t <- matrix(c( 1,1,1,lev, 100
,1,1,1,mar, 0
,1,1,1,low, 0
,1,1,1,upp, 0
,1,1,1,sca, 1
,1,1,2,lev, 101
,1,1,2,mar, 0
,1,1,2,low, 0
,1,1,2,upp, Inf
,1,1,2,sca, 1
,1,2,1,lev, 110
,1,2,1,mar, 0
,1,2,1,low, 0
,1,2,1,upp, Inf
,1,2,1,sca, 10
,1,2,2,lev, 6
,1,2,2,mar, 0
,1,2,2,low, 6
,1,2,2,upp, 6
,1,2,2,sca, 1
), nrow=20, ncol=5, byrow=T)
xwantA <- list(name="x", type="variable", dim=3L,
val=t,
form="sparse",
uels=list(f[[1]],f[[2]],f[[3]],fields),
domains=domainsf, domInfo="filtered",
field='all',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f,field='all'))
chk <- chkRgdxRes (x, xwantA, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'all',filtered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f,field='all'),squeeze=F)
chk <- chkRgdxRes (x, xwantA, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'all',filtered,squeeze=F) failed:",chk$msg))
}
# level
f <- list(c('i2'),jUels,kUels)
xwantL <- list(name="x", type="variable", dim=3L,
val=matrix(c( 1,1,1, 100
,1,1,2, 101
,1,2,1, 110
,1,2,2, 6), nrow=4, ncol=4, byrow=T),
form="sparse",
uels=f, domains=domains, domInfo="filtered",
field='l',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f))
chk <- chkRgdxRes (x, xwantL, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'L',filtered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f),squeeze=F)
chk <- chkRgdxRes (x, xwantL, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'L',filtered,squeeze=F) failed:",chk$msg))
}
# marginal
f <- list(iUels,jUels,kUels)
xwantM <- list(name="x", type="variable", dim=3L,
val=matrix(c( 1,1,2, .25
,1,2,2, .25), nrow=2, ncol=4, byrow=T),
form="sparse",
uels=f, domains=domains, domInfo="filtered",
field='m',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f,field='M'))
chk <- chkRgdxRes (x, xwantM, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'M',filtered) failed:",chk$msg))
}
xwantM$val <- matrix(c( 1,1,1, 0
,1,1,2, .25
,1,2,1, 0
,1,2,2, .25
,2,1,1, 0
,2,1,2, 0
,2,2,1, 0
,2,2,2, 0
), nrow=8, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f,field='M'),squeeze=F)
chk <- chkRgdxRes (x, xwantM, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'M',filtered,squeeze=F) failed:",chk$msg))
}
# lower
f <- list(iUels,c('j2'),kUels)
xwantLo <- list(name="x", type="variable", dim=3L,
val=matrix(c( 1,1,1, -Inf
,1,1,2, 100
,2,1,2, 6), nrow=3, ncol=4, byrow=T),
form="sparse",
uels=f, domains=domains, domInfo="filtered",
field='lo',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f,field='lo'))
chk <- chkRgdxRes (x, xwantLo, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'lo',filtered) failed:",chk$msg))
}
xwantLo$val <- matrix(c( 1,1,1, -Inf
,1,1,2, 100
,2,1,1, 0
,2,1,2, 6), nrow=4, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f,field='lo'),squeeze=F)
chk <- chkRgdxRes (x, xwantLo, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'lo',filtered,squeeze=F) failed:",chk$msg))
}
# upper
f <- list(iUels,jUels,kUels)
xwantUp <- list(name="x", type="variable", dim=3L,
val=matrix(c( 1,1,1, 525
,2,1,1, 0
,2,2,2, 6), nrow=3, ncol=4, byrow=T),
form="sparse",
uels=f, domains=domains, domInfo="filtered",
field='up',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f,field='UP'))
chk <- chkRgdxRes (x, xwantUp, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'up',filtered) failed:",chk$msg))
}
xwantUp$val <- matrix(c( 1,1,1, 525
,1,1,2, +Inf
,1,2,1, +Inf
,1,2,2, +Inf
,2,1,1, 0
,2,1,2, +Inf
,2,2,1, +Inf
,2,2,2, 6), nrow=8, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',uels=f,field='UP'),squeeze=F)
chk <- chkRgdxRes (x, xwantUp, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'up',filtered,squeeze=F) failed:",chk$msg))
}
# scale
f <- list(iUels,jUels,kUels)
xwantS <- list(name="x", type="variable", dim=3L,
val=matrix(c( 2,2,1, 10), nrow=1, ncol=4, byrow=T),
form="sparse",
uels=f, domains=domains, domInfo="filtered",
field='s',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='sparse',field='S',uels=f))
chk <- chkRgdxRes (x, xwantS, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'S',filtered) failed:",chk$msg))
}
xwantS$val <- matrix(c( 1,1,1, 1
,1,1,2, 1
,1,2,1, 1
,1,2,2, 1
,2,1,1, 1
,2,1,2, 1
,2,2,1, 10
,2,2,2, 1), nrow=8, ncol=4, byrow=T)
x <- rgdx(fnIn,list(name='x',form='sparse',field='S',uels=f),squeeze=F)
chk <- chkRgdxRes (x, xwantS, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'S',filtered,squeeze=F) failed:",chk$msg))
}
### ---------- reading form=full, no filter
# all
v <- array(0,c(iCard,jCard,kCard,nFields),dimnames=cartfN)
for (i in 1:iCard) {
for (j in 1:jCard) {
for (k in 1:kCard) {
v[i,j,k,'l'] <- 100 * (i-1) + 10 * (j-1) + (k-1)
v[i,j,k,'up'] <- Inf
v[i,j,k,'s'] <- 1
}
}
}
v['i2','j2','k2',c('l','lo','up')] <- 6
v['i1',c('j1','j2'),'k2','m'] <- 0.25
v['i1','j2','k1','lo'] <- -Inf
v['i1','j2','k2','lo'] <- 100
v['i1','j1','k1','up'] <- 525
v['i2','j1','k1','up'] <- 0
v['i2','j2','k1','s'] <- 10
xwantA <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=cartfN,
domains=domainsf, domInfo="full",
field='all',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',field='all'))
chk <- chkRgdxRes (x, xwantA, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'all',full,unfiltered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',field='all'),squeeze=F)
chk <- chkRgdxRes (x, xwantA, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'all',full,unfiltered,squeeze=F) failed:",chk$msg))
}
# level
v <- array(0,c(iCard,jCard,kCard),dimnames=cartN)
for (i in 1:iCard) {
for (j in 1:jCard) {
for (k in 1:kCard) {
v[i,j,k] <- 100 * (i-1) + 10 * (j-1) + (k-1)
}
}
}
v['i2','j2','k2'] <- 6
xwantL <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=cartN,
domains=domains, domInfo="full",
field='l',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full'))
chk <- chkRgdxRes (x, xwantL, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'L',full,unfiltered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full'),squeeze=F)
chk <- chkRgdxRes (x, xwantL, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'L',full,unfiltered,squeeze=F) failed:",chk$msg))
}
# marginal
v <- array(0,c(iCard,jCard,kCard),dimnames=cartN)
v['i1','j1',2] <- .25
v['i1','j2',2] <- .25
xwantM <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=cartN,
domains=domains, domInfo="full",
field='m',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',field='m'))
chk <- chkRgdxRes (x, xwantM, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'M',full,unfiltered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',field='m'),squeeze=F)
chk <- chkRgdxRes (x, xwantM, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'M',full,unfiltered,squeeze=F) failed:",chk$msg))
}
# lower
v <- array(0,c(iCard,jCard,kCard),dimnames=cartN)
v['i1','j2','k1'] <- -Inf
v['i1','j2','k2'] <- 100
v['i2','j2','k2'] <- 6
xwantLo <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=cartN,
domains=domains, domInfo="full",
field='lo',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',field='lo'))
chk <- chkRgdxRes (x, xwantLo, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'lo',full,unfiltered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',field='lo'),squeeze=F)
chk <- chkRgdxRes (x, xwantLo, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'lo',full,unfiltered,squeeze=F) failed:",chk$msg))
}
# upper
v <- array(+Inf,c(iCard,jCard,kCard),dimnames=cartN)
v['i1','j1','k1'] <- 525
v['i2','j1','k1'] <- 0
v['i2','j2','k2'] <- 6
xwantUp <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=cartN, domains=domains, domInfo="full",
field='up',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',field='up'))
chk <- chkRgdxRes (x, xwantUp, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'up',full,unfiltered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',field='up'),squeeze=F)
chk <- chkRgdxRes (x, xwantUp, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'up',full,unfiltered,squeeze=F) failed:",chk$msg))
}
# scale
v <- array(1,c(iCard,jCard,kCard),dimnames=cartN)
v[2,2,1] <- 10
xwantS <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=cartN,
domains=domains, domInfo="full",
field='s',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',field='s'))
chk <- chkRgdxRes (x, xwantS, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'s',full,unfiltered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',field='s'),squeeze=F)
chk <- chkRgdxRes (x, xwantS, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'s',full,unfiltered,squeeze=F) failed:",chk$msg))
}
### ---------- reading form=full, filtered
# all
f <- list(c('i2'),jUels,kUels)
dnames <- f ; dnames[[4]] <- fields ; names(dnames) <- domainsf
v <- array(0,c(1,jCard,kCard,nFields),dimnames=dnames)
for (j in 1:jCard) {
for (k in 1:kCard) {
v['i2',j,k,'l'] <- 100 + 10 * (j-1) + (k-1)
v['i2',j,k,'up'] <- Inf
v['i2',j,k,'s'] <- 1
}
}
v['i2','j2','k2',c('l','lo','up')] <- 6
v['i2','j1','k1','up'] <- 0
v['i2','j2','k1','s'] <- 10
xwantA <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=dnames,
domains=domainsf, domInfo="filtered",
field='all',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',field='all',uels=f))
chk <- chkRgdxRes (x, xwantA, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'all',full,filtered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',field='all',uels=f),squeeze=F)
chk <- chkRgdxRes (x, xwantA, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'all',full,filtered,squeeze=F) failed:",chk$msg))
}
# level
f <- list(c('i2'),jUels,kUels)
dnames <- f ; names(dnames) <- domains
v <- array(0,c(1,jCard,kCard),dimnames=dnames)
for (j in 1:jCard) {
for (k in 1:kCard) {
v[1,j,k] <- 100 + 10 * (j-1) + (k-1)
}
}
v['i2','j2','k2'] <- 6
xwantL <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=dnames, domains=domains, domInfo="filtered",
field='l',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',uels=f))
chk <- chkRgdxRes (x, xwantL, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'L',full,filtered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',uels=f),squeeze=F)
chk <- chkRgdxRes (x, xwantL, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'L',full,filtered,squeeze=F) failed:",chk$msg))
}
# marginal
f <- cart
dnames <- f ; names(dnames) <- domains
v <- array(0,c(iCard,jCard,kCard),dnames)
v['i1',jUels,'k2'] <- 0.25
xwantM <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=dnames, domains=domains, domInfo="filtered",
field='m',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',uels=f,field='M'))
chk <- chkRgdxRes (x, xwantM, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'M',full,filtered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',uels=f,field='M'),squeeze=F)
chk <- chkRgdxRes (x, xwantM, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'M',full,filtered,squeeze=F) failed:",chk$msg))
}
# lower
f <- list(iUels,c('j2'),kUels)
dnames <- f ; names(dnames) <- domains
v <- array(0,c(iCard,1,kCard),dimnames=dnames)
v['i1','j2','k1'] <- -Inf
v['i1','j2','k2'] <- 100
v['i2','j2','k2'] <- 6
xwantLo <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=dnames, domains=domains, domInfo="filtered",
field='lo',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',uels=f,field='lo'))
chk <- chkRgdxRes (x, xwantLo, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'lo',full,filtered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',uels=f,field='lo'),squeeze=F)
chk <- chkRgdxRes (x, xwantLo, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'lo',full,filtered,squeeze=F) failed:",chk$msg))
}
# upper
f <- cart
dnames <- f ; names(dnames) <- domains
v <- array(Inf,c(iCard,jCard,kCard),dimnames=dnames)
v['i1','j1','k1'] <- 525
v['i2','j1','k1'] <- 0
v['i2','j2','k2'] <- 6
xwantUp <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=dnames, domains=domains, domInfo="filtered",
field='up',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',uels=f,field='up'))
chk <- chkRgdxRes (x, xwantUp, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'up',full,filtered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',uels=f,field='up'),squeeze=F)
chk <- chkRgdxRes (x, xwantUp, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'up',full,filtered,squeeze=F) failed:",chk$msg))
}
# scale
f <- cart
dnames <- f ; names(dnames) <- domains
v <- array(1,c(iCard,jCard,kCard),dimnames=dnames)
v['i2','j2','k1'] <- 10
xwantS <- list(name="x", type="variable", dim=3L,
val=v,
form="full",
uels=dnames, domains=domains, domInfo="filtered",
field='s',
varTypeText='positive', typeCode=GMS_VARTYPE$POSITIVE)
x <- rgdx(fnIn,list(name='x',form='full',uels=f,field='s'))
chk <- chkRgdxRes (x, xwantS, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'s',full,filtered) failed:",chk$msg))
}
x <- rgdx(fnIn,list(name='x',form='full',uels=f,field='s'),squeeze=F)
chk <- chkRgdxRes (x, xwantS, T, reqIdent=reqIdent)
if (!chk$same) {
stop (paste("test rgdx(x,'s',full,filtered,squeeze=F) failed:",chk$msg))
}
print ("test of rgdx on variable reads passed")
TRUE ## all tests passed: return TRUE
},
error = function(ex) { print ("test of rgdx on variable reads failed"); print(ex) ; FALSE }
)
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