xuefliang

 library(tidyverse) library(hablar) library(janitor) options(scipen=200) tst <- read.csv("D:\\ph.csv") %>%    clean_names() %>%    distinct() %>%    str_c(sep="','") %>%      str_trim() %>%    str_replace_all("[\"]","'") %>%    str_remove_all("[\n]")  

 library(rstatix)  library(ggpubr)  # For easy data-visualizatio library(gtsummary) library(tidyverse) iris %>%    group_by(Species) %>%    get_summary_stats(Sepal.Length, Sepal.Width, type = "common") iris %>% get_summary_stats(type = "common") iris %>%   group_by(Species) %>%    get_summary_stats(Sepal.Length, show=c('n'))    df <- ToothGrowth df$dose <- as.factor(df$dose) # 比较2个独立组别 df %>%    t_test(len ~ supp, paired = FALSE)  -> stat.test # 分组数据：在按照「dose」分组后比较 supp 水平： stat.test <- df %>%   group_by(dose) %>%   t_test(len ~ supp) %>%   adjust_pvalue() %>%   add_significance("p.adj") # 比较配对样本 stat.test <- df %>%    t_test(len ~ supp, paired = TRUE) stat.test <- df %>%  # 成对比较：如果分组变量包含多于2个分类，会自动执行成对比较 pairwise.test <- df %>% t_test(len ~ dose) # 基于参考组的成对比较 stat.test <- df %>% t_test(len ~ dose, ref.group = "0.5") # 基于总体水平的成对比较 # T-test stat.test <- df %>% t_test(le

安装包   if (!requireNamespace("BiocManager", quietly = TRUE))      install.packages("BiocManager") BiocManager::install(c("GSEABase","GSVA","clusterProfiler" ),ask = F,update = F) BiocManager::install(c("GEOquery","limma","impute" ),ask = F,update = F) BiocManager::install(c("org.Hs.eg.db","hgu133plus2.db" ),ask = F,update = F) BiocManager::install("GDCRNATools") if (! require (tinyarray))devtools::install_github( "xjsun1221/tinyarray" ,upgrade =  F )