R/dataprocess.R
In Wei-V-Zhou/KMPPc: KMPPc: K-M Plot for Pan-cancer
Defines functions
dataprocess
Documented in
dataprocess
#' dataprocess
#'
#' dataprocess for TCGA
#'
#' By default, this function you can filter some dirty data to prepare for kmplot.
#'
#' @param clinicaldata The data can be loaded by the results of dataexplore. You can load("survival_inputdata.Rdata") to get both of datasets.
#' @param exprSet The data can be loaded by the results of dataexplore. You can load("survival_inputdata.Rdata") to get both of datasets.
#' @param x_axis This argument is the x axis for time.
#' @param y_axis This argument is the y axis for status.
#' @return There is a clean data after dataprocess.
#' @export
#' @import ggplot2 stringr ggpubr ggstatsplot
#' @author Wei Zhou <247328181@@qq.com>
#' @examples
#' \dontrun{
#' ## Plot the kmplot automatically
#' kmplot()
#'
#' ## Plot kmfit using "specified" style
#' kmplot(plotType = "specified")
#' }
dataprocess <- function(clinicaldata = NULL, exprSet = NULL, x_axis = NULL, y_axis = NULL) {
## 0. prepare environment and load libraries
# rm(list = ls())
# gc()
# set.seed(12345)
# graphics.off()
# options(stringsAsFactors = FALSE)
# load packages
# pkgs <- c("ggplot2", "stringr", "ggpubr", "ggstatsplot", "export")
# installpkgs <- function(pkgs){
# new.pkgs <- pkgs[!(pkgs %in% installed.packages()[ , "Package"])]
# if (length(new.pkgs))
# BiocManager::install(new.pkgs, ask = F, update = F)
# sapply(pkgs, require, character.only = T)
# }
# installpkgs(pkgs)
# lapply(pkgs, library, character.only = T)
## 2. data preproceeding
# load data
if (is.null(clinicaldata) || is.null(exprSet)) {
load("../data/survival_inputdata.Rdata")
} else {
print("Please guarantee your two files are choose_clinicaldata and exprSet, respectively!")
cat("***Notation: Or you will load the survival_inputdata.Rdata on your own!")
}
# view the clinical data
clinicaldata_view <- as.matrix(colnames(myclinicaldata))
choose_columns <- c(clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "DFS_MONTHS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "DFS_STATUS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "DSS_MONTHS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "DSS_STATUS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "OS_MONTHS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "OS_STATUS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "PFS_MONTHS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "PFS_STATUS")))])
cat("This clinical dataset only has the following data: \n***Notation: x_axis presents time, y_axis presents status; please choose the corresponding data!\n")
for (i in 1:length(choose_columns)) {
cat(c(i, ": ", choose_columns[i], "\n"))
}
# read the clinical information
# choose x_axis
if (is.null(x_axis)) {
if(interactive()){
repeat{
ANSWER <- readline("Please input the x_axis number (time): ")
num <- as.numeric(ANSWER)
if(is.na(num) || num <= 0 || num > length(choose_columns)){
print("Please type right number format!")
} else if (num > 0 && num <= length(choose_columns)) {
break;
}
}
x_axis <- choose_columns[num]
}
} else {
if (! x_axis %in% choose_columns) {
cat("Warning! The input of x_axis is not in the clinical dataset!\n***Please type right format!\n")
x_axis = NULL
stop("Input again! Or you can eliminate x_axis and follow the tips.")
}
}
# choose y_axis
if (is.null(y_axis)) {
if(interactive()){
repeat{
ANSWER <- readline("Please input the y_axis number (status): ")
num <- as.numeric(ANSWER)
if(is.na(num) || num <= 0 || num > length(choose_columns)){
print("Please type right number format!")
} else if (num > 0 && num <= length(choose_columns)) {
if (x_axis != choose_columns[num]) {
break;
} else {
print("y_axis can't be the same with x_axis!")
}
}
}
y_axis <- choose_columns[num]
}
} else if (x_axis == y_axis) {
stop("x_axis can't be the same with y_axis!")
} else {
if (! y_axis %in% choose_columns) {
cat("Warning! The input of y_axis is not in the clinical dataset!\n***Please type right format!\n")
x_axis = NULL
stop("Input again! Or you can eliminate y_axis and follow the tips.")
}
}
# filter the data
choose_column = c(x_axis, y_axis)
choose_clinicaldata = myclinicaldata[ , choose_column]
dat1 <- choose_clinicaldata[!is.na(choose_clinicaldata[ , 1]), ]
dat2 <- cbind(dat1, exprSet[rownames(dat1), ])
geneName <- names(exprSet)
colnames(dat2)[3] <- geneName
# save the filtered data
if(interactive()){
repeat{
ANSWER <- readline("Save the clinical data and related exprset data[y/n]: ")
num <- trimws(tolower(ANSWER), which = c("both", "left", "right"), whitespace = "[ \t\r\n]")
if (num == "y" || num == "yes") {
print("Save successfully!")
filename = paste0(geneName, "_clinical_expr_data_", Sys.Date(), ".csv")
write.csv(dat2, file = filename)
break;
} else if (num == "n" || num == "no") {
break;
} else {
print("Wrong format! Please type y or n!")
}
}
}
# expressed genes plot
p <- ggboxplot(dat2, x = y_axis, y = geneName, color = y_axis, palette = "jco", add = "jitter")
p + stat_compare_means(method = "t.test")
group <- paste0(geneName, "_group")
dat2$group = ifelse(dat2[ , geneName] > median(dat2[ , names(exprSet)]), 'high', 'low')
# dat2$group = ifelse(dat2[ , names(exprSet)] > quantile(dat2[ , names(exprSet)])[4], 'high', 'low')
# save the data
save(dat2, file = "../data/kmplotdata.Rdata")
# plot the expression img
dat2$geneName = dat2$HTRA1
ggbetweenstats(data = dat2, x = group, y = geneName, xlab = "Patient group", ylab = paste0(geneName, "_expression"))
# save the image
# if(interactive()){
# repeat{
# ANSWER <- readline("Save the expression picture?[y/n]: ")
# num <- trimws(tolower(ANSWER), which = c("both", "left", "right"), whitespace = "[ \t\r\n]")
# if (num == "y" || num == "yes") {
# print("Save successfully!")
# filename = paste0(geneName, "_clinical_expr_data_", Sys.Date(), ".pptx")
# graph2ppt(file = filename, width = 7, height = 5)
# break;
# } else if (num == "n" || num == "no") {
# break;
# } else {
# print("Wrong format! Please type y or n!")
# }
# }
# }
}
# sessionInfo()
# R version 3.6.3 (2020-02-29)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 10 x64 (build 18363)
#
# Matrix products: default
#
# locale:
# [1] LC_COLLATE=Chinese (Simplified)_China.936 LC_CTYPE=Chinese (Simplified)_China.936
# [3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C
# [5] LC_TIME=Chinese (Simplified)_China.936
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] export_0.2.2 ggstatsplot_0.1.3 ggpubr_0.2.4 magrittr_1.5 stringr_1.4.0
# [6] ggplot2_3.2.1
#
# loaded via a namespace (and not attached):
# [1] tidyselect_0.2.5 lme4_1.1-21 robust_0.4-18.1
# [4] htmlwidgets_1.5.1 grid_3.6.3 munsell_0.5.0
# [7] codetools_0.2-16 future_1.15.1 miniUI_0.1.1.1
# [10] withr_2.1.2 Brobdingnag_1.2-6 metaBMA_0.6.2
# [13] colorspace_1.4-1 uuid_0.1-2 knitr_1.26
# [16] rstudioapi_0.10 stats4_3.6.3 DescTools_0.99.30
# [19] robustbase_0.93-5 officer_0.3.6 ggsignif_0.6.0
# [22] rcompanion_2.3.7 listenv_0.7.0 labeling_0.3
# [25] emmeans_1.4.2 rstan_2.19.2 repr_1.0.1
# [28] mnormt_1.5-5 MCMCpack_1.4-4 farver_2.0.1
# [31] bridgesampling_0.7-2 coda_0.19-3 vctrs_0.2.0
# [34] generics_0.0.2 TH.data_1.0-10 metafor_2.1-0
# [37] xfun_0.11 R6_2.4.1 BayesFactor_0.9.12-4.2
# [40] manipulateWidget_0.10.0 reshape_0.8.8 logspline_2.1.15
# [43] assertthat_0.2.1 promises_1.1.0 scales_1.1.0
# [46] multcomp_1.4-10 ggExtra_0.9 gtable_0.3.0
# [49] multcompView_0.1-7 globals_0.12.4 processx_3.4.1
# [52] mcmc_0.9-6 sandwich_2.5-1 rlang_0.4.1
# [55] MatrixModels_0.4-1 EMT_1.1 zeallot_0.1.0
# [58] systemfonts_0.1.1 splines_3.6.3 TMB_1.7.16
# [61] lazyeval_0.2.2 broom_0.5.2 inline_0.3.15
# [64] rgl_0.100.30 reshape2_1.4.3 abind_1.4-5
# [67] modelr_0.1.5 crosstalk_1.0.0 backports_1.1.5
# [70] httpuv_1.5.2 rsconnect_0.8.16 tools_3.6.3
# [73] psych_1.8.12 ellipsis_0.3.0 stargazer_5.2.2
# [76] WRS2_1.0-0 ez_4.4-0 Rcpp_1.0.3
# [79] plyr_1.8.4 base64enc_0.1-3 jmvcore_1.0.8
# [82] purrr_0.3.3 ps_1.3.0 prettyunits_1.0.2
# [85] pbapply_1.4-2 cowplot_1.0.0 zoo_1.8-6
# [88] LaplacesDemon_16.1.1 haven_2.2.0 ggrepel_0.8.1
# [91] cluster_2.1.0 furrr_0.1.0 data.table_1.12.6
# [94] openxlsx_4.1.3 flextable_0.5.6 SparseM_1.77
# [97] lmtest_0.9-37 mvtnorm_1.0-11 broomExtra_0.0.6
# [100] sjmisc_2.8.2 matrixStats_0.54.0 evaluate_0.14
# [103] hms_0.5.2 mime_0.9 xtable_1.8-4
# [106] rio_0.5.16 sjstats_0.17.7 pairwiseComparisons_0.1.2
# [109] broom.mixed_0.2.4 readxl_1.3.1 gridExtra_2.3
# [112] rstantools_2.0.0 compiler_3.6.3 tibble_2.1.3
# [115] crayon_1.3.4 minqa_1.2.4 StanHeaders_2.19.0
# [118] htmltools_0.4.0 mgcv_1.8-31 mc2d_0.1-18
# [121] pcaPP_1.9-73 later_1.0.0 tidyr_1.0.0
# [124] libcoin_1.0-5 rrcov_1.4-7 expm_0.999-4
# [127] sjlabelled_1.1.1 jmv_1.0.8 MASS_7.3-51.4
# [130] boot_1.3-23 Matrix_1.2-18 car_3.0-5
# [133] cli_1.1.0 parallel_3.6.3 insight_0.7.0
# [136] forcats_0.4.0 pkgconfig_2.0.3 fit.models_0.5-14
# [139] statsExpressions_0.1.1 coin_1.3-1 foreign_0.8-72
# [142] skimr_2.0.1 xml2_1.2.2 paletteer_0.2.1
# [145] ggcorrplot_0.1.3 webshot_0.5.2 rvg_0.2.2
# [148] estimability_1.3 callr_3.3.2 digest_0.6.25
# [151] parameters_0.3.0 rmarkdown_1.17 cellranger_1.1.0
# [154] nortest_1.0-4 gdtools_0.2.1 curl_4.2
# [157] shiny_1.4.0.2 gtools_3.8.1 quantreg_5.52
# [160] modeltools_0.2-22 rjson_0.2.20 nloptr_1.2.1
# [163] lifecycle_0.1.0 nlme_3.1-142 jsonlite_1.6.1
# [166] carData_3.0-3 groupedstats_0.1.0 pillar_1.4.2
# [169] ggsci_2.9 lattice_0.20-38 loo_2.1.0
# [172] fastmap_1.0.1 DEoptimR_1.0-8 pkgbuild_1.0.6
# [175] survival_3.1-8 glue_1.3.1 bayestestR_0.4.0
# [178] zip_2.0.4 stringi_1.4.3 performance_0.4.0
# [181] rsample_0.0.5 dplyr_0.8.3
#============================#
# Musician: Resonance #
# Date: 2020/03/16 #
# Revised author: Resonance #
# 1st Time: 2020/03/17 #
# 2nd Time: 2020/05/27 #
# 3rd Time: 2020/06/03 #
#============================#
Wei-V-Zhou/KMPPc documentation built on Sept. 5, 2020, 6:21 a.m.
R Package Documentation
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Defines functions dataprocess
Documented in dataprocess
#' dataprocess
#'
#' dataprocess for TCGA
#'
#' By default, this function you can filter some dirty data to prepare for kmplot.
#'
#' @param clinicaldata The data can be loaded by the results of dataexplore. You can load("survival_inputdata.Rdata") to get both of datasets.
#' @param exprSet The data can be loaded by the results of dataexplore. You can load("survival_inputdata.Rdata") to get both of datasets.
#' @param x_axis This argument is the x axis for time.
#' @param y_axis This argument is the y axis for status.
#' @return There is a clean data after dataprocess.
#' @export
#' @import ggplot2 stringr ggpubr ggstatsplot
#' @author Wei Zhou <247328181@@qq.com>
#' @examples
#' \dontrun{
#' ## Plot the kmplot automatically
#' kmplot()
#'
#' ## Plot kmfit using "specified" style
#' kmplot(plotType = "specified")
#' }
dataprocess <- function(clinicaldata = NULL, exprSet = NULL, x_axis = NULL, y_axis = NULL) {
## 0. prepare environment and load libraries
# rm(list = ls())
# gc()
# set.seed(12345)
# graphics.off()
# options(stringsAsFactors = FALSE)
# load packages
# pkgs <- c("ggplot2", "stringr", "ggpubr", "ggstatsplot", "export")
# installpkgs <- function(pkgs){
# new.pkgs <- pkgs[!(pkgs %in% installed.packages()[ , "Package"])]
# if (length(new.pkgs))
# BiocManager::install(new.pkgs, ask = F, update = F)
# sapply(pkgs, require, character.only = T)
# }
# installpkgs(pkgs)
# lapply(pkgs, library, character.only = T)
## 2. data preproceeding
# load data
if (is.null(clinicaldata) || is.null(exprSet)) {
load("../data/survival_inputdata.Rdata")
} else {
print("Please guarantee your two files are choose_clinicaldata and exprSet, respectively!")
cat("***Notation: Or you will load the survival_inputdata.Rdata on your own!")
}
# view the clinical data
clinicaldata_view <- as.matrix(colnames(myclinicaldata))
choose_columns <- c(clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "DFS_MONTHS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "DFS_STATUS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "DSS_MONTHS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "DSS_STATUS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "OS_MONTHS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "OS_STATUS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "PFS_MONTHS")))],
clinicaldata_view[which(!is.na(str_extract(clinicaldata_view, "PFS_STATUS")))])
cat("This clinical dataset only has the following data: \n***Notation: x_axis presents time, y_axis presents status; please choose the corresponding data!\n")
for (i in 1:length(choose_columns)) {
cat(c(i, ": ", choose_columns[i], "\n"))
}
# read the clinical information
# choose x_axis
if (is.null(x_axis)) {
if(interactive()){
repeat{
ANSWER <- readline("Please input the x_axis number (time): ")
num <- as.numeric(ANSWER)
if(is.na(num) || num <= 0 || num > length(choose_columns)){
print("Please type right number format!")
} else if (num > 0 && num <= length(choose_columns)) {
break;
}
}
x_axis <- choose_columns[num]
}
} else {
if (! x_axis %in% choose_columns) {
cat("Warning! The input of x_axis is not in the clinical dataset!\n***Please type right format!\n")
x_axis = NULL
stop("Input again! Or you can eliminate x_axis and follow the tips.")
}
}
# choose y_axis
if (is.null(y_axis)) {
if(interactive()){
repeat{
ANSWER <- readline("Please input the y_axis number (status): ")
num <- as.numeric(ANSWER)
if(is.na(num) || num <= 0 || num > length(choose_columns)){
print("Please type right number format!")
} else if (num > 0 && num <= length(choose_columns)) {
if (x_axis != choose_columns[num]) {
break;
} else {
print("y_axis can't be the same with x_axis!")
}
}
}
y_axis <- choose_columns[num]
}
} else if (x_axis == y_axis) {
stop("x_axis can't be the same with y_axis!")
} else {
if (! y_axis %in% choose_columns) {
cat("Warning! The input of y_axis is not in the clinical dataset!\n***Please type right format!\n")
x_axis = NULL
stop("Input again! Or you can eliminate y_axis and follow the tips.")
}
}
# filter the data
choose_column = c(x_axis, y_axis)
choose_clinicaldata = myclinicaldata[ , choose_column]
dat1 <- choose_clinicaldata[!is.na(choose_clinicaldata[ , 1]), ]
dat2 <- cbind(dat1, exprSet[rownames(dat1), ])
geneName <- names(exprSet)
colnames(dat2)[3] <- geneName
# save the filtered data
if(interactive()){
repeat{
ANSWER <- readline("Save the clinical data and related exprset data[y/n]: ")
num <- trimws(tolower(ANSWER), which = c("both", "left", "right"), whitespace = "[ \t\r\n]")
if (num == "y" || num == "yes") {
print("Save successfully!")
filename = paste0(geneName, "_clinical_expr_data_", Sys.Date(), ".csv")
write.csv(dat2, file = filename)
break;
} else if (num == "n" || num == "no") {
break;
} else {
print("Wrong format! Please type y or n!")
}
}
}
# expressed genes plot
p <- ggboxplot(dat2, x = y_axis, y = geneName, color = y_axis, palette = "jco", add = "jitter")
p + stat_compare_means(method = "t.test")
group <- paste0(geneName, "_group")
dat2$group = ifelse(dat2[ , geneName] > median(dat2[ , names(exprSet)]), 'high', 'low')
# dat2$group = ifelse(dat2[ , names(exprSet)] > quantile(dat2[ , names(exprSet)])[4], 'high', 'low')
# save the data
save(dat2, file = "../data/kmplotdata.Rdata")
# plot the expression img
dat2$geneName = dat2$HTRA1
ggbetweenstats(data = dat2, x = group, y = geneName, xlab = "Patient group", ylab = paste0(geneName, "_expression"))
# save the image
# if(interactive()){
# repeat{
# ANSWER <- readline("Save the expression picture?[y/n]: ")
# num <- trimws(tolower(ANSWER), which = c("both", "left", "right"), whitespace = "[ \t\r\n]")
# if (num == "y" || num == "yes") {
# print("Save successfully!")
# filename = paste0(geneName, "_clinical_expr_data_", Sys.Date(), ".pptx")
# graph2ppt(file = filename, width = 7, height = 5)
# break;
# } else if (num == "n" || num == "no") {
# break;
# } else {
# print("Wrong format! Please type y or n!")
# }
# }
# }
}
# sessionInfo()
# R version 3.6.3 (2020-02-29)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 10 x64 (build 18363)
#
# Matrix products: default
#
# locale:
# [1] LC_COLLATE=Chinese (Simplified)_China.936 LC_CTYPE=Chinese (Simplified)_China.936
# [3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C
# [5] LC_TIME=Chinese (Simplified)_China.936
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] export_0.2.2 ggstatsplot_0.1.3 ggpubr_0.2.4 magrittr_1.5 stringr_1.4.0
# [6] ggplot2_3.2.1
#
# loaded via a namespace (and not attached):
# [1] tidyselect_0.2.5 lme4_1.1-21 robust_0.4-18.1
# [4] htmlwidgets_1.5.1 grid_3.6.3 munsell_0.5.0
# [7] codetools_0.2-16 future_1.15.1 miniUI_0.1.1.1
# [10] withr_2.1.2 Brobdingnag_1.2-6 metaBMA_0.6.2
# [13] colorspace_1.4-1 uuid_0.1-2 knitr_1.26
# [16] rstudioapi_0.10 stats4_3.6.3 DescTools_0.99.30
# [19] robustbase_0.93-5 officer_0.3.6 ggsignif_0.6.0
# [22] rcompanion_2.3.7 listenv_0.7.0 labeling_0.3
# [25] emmeans_1.4.2 rstan_2.19.2 repr_1.0.1
# [28] mnormt_1.5-5 MCMCpack_1.4-4 farver_2.0.1
# [31] bridgesampling_0.7-2 coda_0.19-3 vctrs_0.2.0
# [34] generics_0.0.2 TH.data_1.0-10 metafor_2.1-0
# [37] xfun_0.11 R6_2.4.1 BayesFactor_0.9.12-4.2
# [40] manipulateWidget_0.10.0 reshape_0.8.8 logspline_2.1.15
# [43] assertthat_0.2.1 promises_1.1.0 scales_1.1.0
# [46] multcomp_1.4-10 ggExtra_0.9 gtable_0.3.0
# [49] multcompView_0.1-7 globals_0.12.4 processx_3.4.1
# [52] mcmc_0.9-6 sandwich_2.5-1 rlang_0.4.1
# [55] MatrixModels_0.4-1 EMT_1.1 zeallot_0.1.0
# [58] systemfonts_0.1.1 splines_3.6.3 TMB_1.7.16
# [61] lazyeval_0.2.2 broom_0.5.2 inline_0.3.15
# [64] rgl_0.100.30 reshape2_1.4.3 abind_1.4-5
# [67] modelr_0.1.5 crosstalk_1.0.0 backports_1.1.5
# [70] httpuv_1.5.2 rsconnect_0.8.16 tools_3.6.3
# [73] psych_1.8.12 ellipsis_0.3.0 stargazer_5.2.2
# [76] WRS2_1.0-0 ez_4.4-0 Rcpp_1.0.3
# [79] plyr_1.8.4 base64enc_0.1-3 jmvcore_1.0.8
# [82] purrr_0.3.3 ps_1.3.0 prettyunits_1.0.2
# [85] pbapply_1.4-2 cowplot_1.0.0 zoo_1.8-6
# [88] LaplacesDemon_16.1.1 haven_2.2.0 ggrepel_0.8.1
# [91] cluster_2.1.0 furrr_0.1.0 data.table_1.12.6
# [94] openxlsx_4.1.3 flextable_0.5.6 SparseM_1.77
# [97] lmtest_0.9-37 mvtnorm_1.0-11 broomExtra_0.0.6
# [100] sjmisc_2.8.2 matrixStats_0.54.0 evaluate_0.14
# [103] hms_0.5.2 mime_0.9 xtable_1.8-4
# [106] rio_0.5.16 sjstats_0.17.7 pairwiseComparisons_0.1.2
# [109] broom.mixed_0.2.4 readxl_1.3.1 gridExtra_2.3
# [112] rstantools_2.0.0 compiler_3.6.3 tibble_2.1.3
# [115] crayon_1.3.4 minqa_1.2.4 StanHeaders_2.19.0
# [118] htmltools_0.4.0 mgcv_1.8-31 mc2d_0.1-18
# [121] pcaPP_1.9-73 later_1.0.0 tidyr_1.0.0
# [124] libcoin_1.0-5 rrcov_1.4-7 expm_0.999-4
# [127] sjlabelled_1.1.1 jmv_1.0.8 MASS_7.3-51.4
# [130] boot_1.3-23 Matrix_1.2-18 car_3.0-5
# [133] cli_1.1.0 parallel_3.6.3 insight_0.7.0
# [136] forcats_0.4.0 pkgconfig_2.0.3 fit.models_0.5-14
# [139] statsExpressions_0.1.1 coin_1.3-1 foreign_0.8-72
# [142] skimr_2.0.1 xml2_1.2.2 paletteer_0.2.1
# [145] ggcorrplot_0.1.3 webshot_0.5.2 rvg_0.2.2
# [148] estimability_1.3 callr_3.3.2 digest_0.6.25
# [151] parameters_0.3.0 rmarkdown_1.17 cellranger_1.1.0
# [154] nortest_1.0-4 gdtools_0.2.1 curl_4.2
# [157] shiny_1.4.0.2 gtools_3.8.1 quantreg_5.52
# [160] modeltools_0.2-22 rjson_0.2.20 nloptr_1.2.1
# [163] lifecycle_0.1.0 nlme_3.1-142 jsonlite_1.6.1
# [166] carData_3.0-3 groupedstats_0.1.0 pillar_1.4.2
# [169] ggsci_2.9 lattice_0.20-38 loo_2.1.0
# [172] fastmap_1.0.1 DEoptimR_1.0-8 pkgbuild_1.0.6
# [175] survival_3.1-8 glue_1.3.1 bayestestR_0.4.0
# [178] zip_2.0.4 stringi_1.4.3 performance_0.4.0
# [181] rsample_0.0.5 dplyr_0.8.3
#============================#
# Musician: Resonance #
# Date: 2020/03/16 #
# Revised author: Resonance #
# 1st Time: 2020/03/17 #
# 2nd Time: 2020/05/27 #
# 3rd Time: 2020/06/03 #
#============================#
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