R/stat_load_data.R
In kwanjeeraw/metabox: metabox
#'Upload Data
#'@description Upload Data
#'@usage load_aggregated_data(file, type, ...)
#'@param file the file in read.csv or read.xlsx2.
#'@param type a string of file name ended either with .xlsx or .csv.
#'@param ... Additional arguments for xlsx::read.xlsx2 or read.csv.
#'@details
#'
#'@return a list of three data frames: "expression"(sample in row), "feature"(compoud in row) and "phenotype"(sample in row).
#'@author Sili Fan \email{fansili2013@gmail.com}
#'@seealso \code{\link{load_expression_data}}, \code{\link{load_expression_data}}, \code{\link{load_expression_data}}
#'@examples
#'load_aggregated_data(input$inputID,startRow=2)
#'@export
stat_load_data = function(file,sheetIndex = NULL, from_example=NULL){ # returns a expression data frame(eData),
# file = "C:\\Users\\Sili Fan\\Desktop\\statistics courses\\Statistical analysis datasets\\mx 107155 B\\mx 107155 _lung cancer tissue_summer course_08-2015_submit.xlsx"
# t test
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two independent group\\mx_274941_Francisco Portell_human cells_06-2016_submit.xlsx"
# ANOVA
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\one way ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# two way ANOVA 2*2
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\\\data\\two way ANOVA\\mx 255530 Jan Schilling_Project 1_ mouse serum_04-2016_submit_4.29.2016.xlsx"
# two way ANOVA 3*4
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two way ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# paired t test
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two paired group\\mx_274941_Francisco Portell_human cells_06-2016_submit.xlsx"
# one way repeated ANOVA
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\one way repeated ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# two way repeated ANOVA 2*2
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two way repeated ANOVA\\mx 255530 Jan Schilling_Project 1_ mouse serum_04-2016_submit_4.29.2016.xlsx"
# two way repeated ANOVA 3*4
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two way repeated ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# mixed ANOVA 2*2
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\mixed ANOVA\\mx 255530 Jan Schilling_Project 1_ mouse serum_04-2016_submit_4.29.2016.xlsx"
# mixed ANOVA 3*4
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\mixed ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# metabolomics data for manuscript
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\manuscript\\mx 107155 _study 112912 TRDRP LC7 NYU Lung Tissue Miyamoto 10113.xlsx"
# gene data for manuscript
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\manuscript\\GeneExpression_GSE32863.xlsx"
# loess norm
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\loessnorm.xlsx"
# temp
# file = "C:\\Users\\Sili Fan\\Desktop\\WORK\\WCMC\\projects\\mx 271133_Nicolas Villarino_cat plasma_07-2016_submit\\mx 271133_Nicolas Villarino_cat plasma_07-2016_submit.xlsx"
library(data.table)
if(is.null(from_example) | length(from_example)==0 | is.na(from_example) | from_example=="undefined"){ # If user want to use the example data set.
if(length(sheetIndex)==0 | sheetIndex==""){
sheetIndex = 1
nosheetinput= T
}else{
nosheetinput = F
}
if(grepl("xlsx", file)){
d <- openxlsx::read.xlsx(file, sheet = sheetIndex,colNames = FALSE)
}else if(grepl("csv", file)){
# file = "C:\\Users\\fansi\\Downloads\\val (18).csv"
d <- data.table::fread(file)
}
# d = data.table(d)
# as.numeric(names(table(sapply(d,function(x){sum(is.na(x))})[1:15]))[1]) #count number of NA in the first 15 columns. The most of the
d[d==""] <- NA
#### fData
fData <- d[!is.na(d[,1]),c(which(is.na(d[1,])),sum(is.na(d[1,]))+1)] # The first row and column is critical of formating the data.
colnames(fData) = as.character(fData[1,]); fData = data.frame(fData[-1,],stringsAsFactors = F);rownames(fData) = 1:nrow(fData);
#### pData
pData <- d[is.na(d[,1]),!is.na(d[1,])]
pData <- t(pData); colnames(pData) = pData[1,]; pData = data.frame(pData[-1,],stringsAsFactors = F)
#### eData
eData <- d[!is.na(d[,1]),!is.na(d[1,])][-1,-1]
eData <- sapply(eData, as.numeric)
colnames(eData) = rownames(pData); rownames(eData) = fData[,1]
eData <- data.frame(t(eData),stringsAsFactors = F)
# remove any unwanted character in columns of eData, fData and pData to _.
colnames(eData) = gsub("([_])|[[:punct:]]", "_", colnames(eData))
colnames(fData) = gsub("([_])|[[:punct:]]", "_", colnames(fData))
colnames(pData) = gsub("([_])|[[:punct:]]", "_", colnames(pData))
# remove all the NA. And replace NA with "NA" Otherwise DataTables will give error.datatables warning requested unknown parameter
eData[is.na(eData)]="NA"
fData[is.na(fData)]="NA"
pData[is.na(pData)]="NA"
for(i in 1:nrow(pData)){
for(j in 1:ncol(pData)){
pData[i,j] = gsub("\\+|~|-", " ", pData[i,j])
}
}
if(sum(!c("phenotype_index","subjectID","feature_index")%in%c(colnames(pData),colnames(fData)))>0){
message = paste0("The data uploaded doesn't have ",
paste(c("phenotype_index","subjectID","feature_index")[!
c("phenotype_index","subjectID","feature_index")%in%c(colnames(pData),colnames(fData))],collapse = ", ")," and they are added automatically. You can examine them by ")
}else{
message = NULL
}
if(!"phenotype_index"%in%colnames(pData)){
pData$phenotype_index = 1:nrow(pData)
}
if(!"subjectID"%in%colnames(pData)){
pData$subjectID = 1:nrow(pData)
}
if(!"feature_index"%in%colnames(fData)){
fData$feature_index = 1:nrow(fData)
}
if(!"KnownorUnknown"%in%colnames(fData)){
fData$KnownorUnknown = rep(T,nrow(fData))
}
# check format.
#1. cannot have missing value.
num_of_missing = vector()
for(i in 1:ncol(eData)){
num_of_missing[i] = sum(eData[,i]=="NA")
}
if(sum(num_of_missing>0)){
message = paste(message, fData$feature_index[num_of_missing>0], "th feature contains missing value and they have been removed!")
}
eData = eData[, num_of_missing == 0]
fData = fData[num_of_missing == 0, ]
#2. cannot have constant value
constant_feature = sapply(eData, sd) == 0
if(sum(constant_feature,na.rm = T)>0){
message = paste(message, fData$feature_index[constant_feature], "th feature is constant and they have been removed!")
}
eData = eData[, !constant_feature]
fData = fData[!constant_feature, ]
if(is.null(message)){
if(nosheetinput){
message = paste0("Success!" )
}else{
message = paste0("Success!")
}
writeLines(message,"messages.txt")
}else{
writeLines(message,"messages.txt")
}
if(sum(duplicated(pData$subjectID[pData$subjectID>0]))){
duplicatedID = T
}else{
duplicatedID = F
}
fData = fData[,!colnames(fData)%in%"mass_spec"]
rownames(pData) = as.character(1:nrow(pData))
eData[eData==0] = 1
result <- list(expression = eData, feature = fData, phenotype = pData, duplicatedID=duplicatedID)
# e = e_ori = eData; p = p_ori = pData; f = fData;
# e_after_sample_normalization = e
# if(log_para=="exp"){
# log_para = exp(1)
# }
# log_para = as.numeric(log_para)
# e_after_transformation = log(e_after_sample_normalization,base = log(exp(log_para)))
# e_after_scaling = stat_pareto_scale(e_after_transformation)
# e = e_after_scaling
return(result)
}else{
if(from_example=="Two Independent Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(t_test_example)
}else if(from_example=="Multi-Independent Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(ANOVA_example)
}else if(from_example=="3*4-Independent Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(two_way_ANOVA_3_4_example)
}else if(from_example=="2*2-Independent Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(two_way_ANOVA_2_2_example)
}else if(from_example=="Two Paired Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(paired_t_test_example)
}else if(from_example=="Multi-Paired Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(paired_ANOVA_example)
}else if(from_example=="3*4-Paired Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(two_way_repeated_ANOVA_3_4_example)
}else if(from_example=="2*2-Paired Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(two_way_repeated_ANOVA_2_2_example)
}else if(from_example=="3*4-Mixed Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(mixed_two_way_ANOVA_3_4_example)
}else if(from_example=="2*2-Mixed Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(mixed_two_way_ANOVA_2_2_example)
}
}
}
kwanjeeraw/metabox documentation built on May 20, 2019, 7:07 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#'Upload Data
#'@description Upload Data
#'@usage load_aggregated_data(file, type, ...)
#'@param file the file in read.csv or read.xlsx2.
#'@param type a string of file name ended either with .xlsx or .csv.
#'@param ... Additional arguments for xlsx::read.xlsx2 or read.csv.
#'@details
#'
#'@return a list of three data frames: "expression"(sample in row), "feature"(compoud in row) and "phenotype"(sample in row).
#'@author Sili Fan \email{fansili2013@gmail.com}
#'@seealso \code{\link{load_expression_data}}, \code{\link{load_expression_data}}, \code{\link{load_expression_data}}
#'@examples
#'load_aggregated_data(input$inputID,startRow=2)
#'@export
stat_load_data = function(file,sheetIndex = NULL, from_example=NULL){ # returns a expression data frame(eData),
# file = "C:\\Users\\Sili Fan\\Desktop\\statistics courses\\Statistical analysis datasets\\mx 107155 B\\mx 107155 _lung cancer tissue_summer course_08-2015_submit.xlsx"
# t test
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two independent group\\mx_274941_Francisco Portell_human cells_06-2016_submit.xlsx"
# ANOVA
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\one way ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# two way ANOVA 2*2
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\\\data\\two way ANOVA\\mx 255530 Jan Schilling_Project 1_ mouse serum_04-2016_submit_4.29.2016.xlsx"
# two way ANOVA 3*4
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two way ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# paired t test
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two paired group\\mx_274941_Francisco Portell_human cells_06-2016_submit.xlsx"
# one way repeated ANOVA
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\one way repeated ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# two way repeated ANOVA 2*2
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two way repeated ANOVA\\mx 255530 Jan Schilling_Project 1_ mouse serum_04-2016_submit_4.29.2016.xlsx"
# two way repeated ANOVA 3*4
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\two way repeated ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# mixed ANOVA 2*2
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\mixed ANOVA\\mx 255530 Jan Schilling_Project 1_ mouse serum_04-2016_submit_4.29.2016.xlsx"
# mixed ANOVA 3*4
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\mixed ANOVA\\mx 69088_HepG2 cells_Hirahatake & Meissen_high fructose_summer course_08-2015_submit.xlsx"
# metabolomics data for manuscript
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\manuscript\\mx 107155 _study 112912 TRDRP LC7 NYU Lung Tissue Miyamoto 10113.xlsx"
# gene data for manuscript
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\manuscript\\GeneExpression_GSE32863.xlsx"
# loess norm
# file = "C:\\Users\\Sili Fan\\Documents\\GitHub\\MetaBoxDiv2\\data\\loessnorm.xlsx"
# temp
# file = "C:\\Users\\Sili Fan\\Desktop\\WORK\\WCMC\\projects\\mx 271133_Nicolas Villarino_cat plasma_07-2016_submit\\mx 271133_Nicolas Villarino_cat plasma_07-2016_submit.xlsx"
library(data.table)
if(is.null(from_example) | length(from_example)==0 | is.na(from_example) | from_example=="undefined"){ # If user want to use the example data set.
if(length(sheetIndex)==0 | sheetIndex==""){
sheetIndex = 1
nosheetinput= T
}else{
nosheetinput = F
}
if(grepl("xlsx", file)){
d <- openxlsx::read.xlsx(file, sheet = sheetIndex,colNames = FALSE)
}else if(grepl("csv", file)){
# file = "C:\\Users\\fansi\\Downloads\\val (18).csv"
d <- data.table::fread(file)
}
# d = data.table(d)
# as.numeric(names(table(sapply(d,function(x){sum(is.na(x))})[1:15]))[1]) #count number of NA in the first 15 columns. The most of the
d[d==""] <- NA
#### fData
fData <- d[!is.na(d[,1]),c(which(is.na(d[1,])),sum(is.na(d[1,]))+1)] # The first row and column is critical of formating the data.
colnames(fData) = as.character(fData[1,]); fData = data.frame(fData[-1,],stringsAsFactors = F);rownames(fData) = 1:nrow(fData);
#### pData
pData <- d[is.na(d[,1]),!is.na(d[1,])]
pData <- t(pData); colnames(pData) = pData[1,]; pData = data.frame(pData[-1,],stringsAsFactors = F)
#### eData
eData <- d[!is.na(d[,1]),!is.na(d[1,])][-1,-1]
eData <- sapply(eData, as.numeric)
colnames(eData) = rownames(pData); rownames(eData) = fData[,1]
eData <- data.frame(t(eData),stringsAsFactors = F)
# remove any unwanted character in columns of eData, fData and pData to _.
colnames(eData) = gsub("([_])|[[:punct:]]", "_", colnames(eData))
colnames(fData) = gsub("([_])|[[:punct:]]", "_", colnames(fData))
colnames(pData) = gsub("([_])|[[:punct:]]", "_", colnames(pData))
# remove all the NA. And replace NA with "NA" Otherwise DataTables will give error.datatables warning requested unknown parameter
eData[is.na(eData)]="NA"
fData[is.na(fData)]="NA"
pData[is.na(pData)]="NA"
for(i in 1:nrow(pData)){
for(j in 1:ncol(pData)){
pData[i,j] = gsub("\\+|~|-", " ", pData[i,j])
}
}
if(sum(!c("phenotype_index","subjectID","feature_index")%in%c(colnames(pData),colnames(fData)))>0){
message = paste0("The data uploaded doesn't have ",
paste(c("phenotype_index","subjectID","feature_index")[!
c("phenotype_index","subjectID","feature_index")%in%c(colnames(pData),colnames(fData))],collapse = ", ")," and they are added automatically. You can examine them by ")
}else{
message = NULL
}
if(!"phenotype_index"%in%colnames(pData)){
pData$phenotype_index = 1:nrow(pData)
}
if(!"subjectID"%in%colnames(pData)){
pData$subjectID = 1:nrow(pData)
}
if(!"feature_index"%in%colnames(fData)){
fData$feature_index = 1:nrow(fData)
}
if(!"KnownorUnknown"%in%colnames(fData)){
fData$KnownorUnknown = rep(T,nrow(fData))
}
# check format.
#1. cannot have missing value.
num_of_missing = vector()
for(i in 1:ncol(eData)){
num_of_missing[i] = sum(eData[,i]=="NA")
}
if(sum(num_of_missing>0)){
message = paste(message, fData$feature_index[num_of_missing>0], "th feature contains missing value and they have been removed!")
}
eData = eData[, num_of_missing == 0]
fData = fData[num_of_missing == 0, ]
#2. cannot have constant value
constant_feature = sapply(eData, sd) == 0
if(sum(constant_feature,na.rm = T)>0){
message = paste(message, fData$feature_index[constant_feature], "th feature is constant and they have been removed!")
}
eData = eData[, !constant_feature]
fData = fData[!constant_feature, ]
if(is.null(message)){
if(nosheetinput){
message = paste0("Success!" )
}else{
message = paste0("Success!")
}
writeLines(message,"messages.txt")
}else{
writeLines(message,"messages.txt")
}
if(sum(duplicated(pData$subjectID[pData$subjectID>0]))){
duplicatedID = T
}else{
duplicatedID = F
}
fData = fData[,!colnames(fData)%in%"mass_spec"]
rownames(pData) = as.character(1:nrow(pData))
eData[eData==0] = 1
result <- list(expression = eData, feature = fData, phenotype = pData, duplicatedID=duplicatedID)
# e = e_ori = eData; p = p_ori = pData; f = fData;
# e_after_sample_normalization = e
# if(log_para=="exp"){
# log_para = exp(1)
# }
# log_para = as.numeric(log_para)
# e_after_transformation = log(e_after_sample_normalization,base = log(exp(log_para)))
# e_after_scaling = stat_pareto_scale(e_after_transformation)
# e = e_after_scaling
return(result)
}else{
if(from_example=="Two Independent Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(t_test_example)
}else if(from_example=="Multi-Independent Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(ANOVA_example)
}else if(from_example=="3*4-Independent Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(two_way_ANOVA_3_4_example)
}else if(from_example=="2*2-Independent Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(two_way_ANOVA_2_2_example)
}else if(from_example=="Two Paired Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(paired_t_test_example)
}else if(from_example=="Multi-Paired Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(paired_ANOVA_example)
}else if(from_example=="3*4-Paired Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(two_way_repeated_ANOVA_3_4_example)
}else if(from_example=="2*2-Paired Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(two_way_repeated_ANOVA_2_2_example)
}else if(from_example=="3*4-Mixed Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(mixed_two_way_ANOVA_3_4_example)
}else if(from_example=="2*2-Mixed Group Data"){
writeLines("Success!","messages.txt", sep = "")
return(mixed_two_way_ANOVA_2_2_example)
}
}
}
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