1 ggplot作图不显示中文

theme_bw(base_family = "STKaiti")

# 或者

library(showtext); showtext_auto()

在主题里设置字体，具体可用字体可搜索

2 四舍五入保留两位小数，末尾为0也显示

sprintf("%.2f",round(Estimate, 2))

Estimate换成具体数值

3 数据探索

summmary() #所有变量进行描述
pairs() #所有变量两两散点图

4 多重线性回归教程

多重线性回归教程

5 查看描述性分析的一个简单写法

ftable(xtabs(~var3+var1+var2,data = dat))
var1 代表行 var2代表列 var3分层

6 误差棒

误差棒

dat<- read_excel('dat.xlsx') %>% 
  mutate(BMI = 体重/(身高*0.01)^2,
         组别 = ifelse(组别==0,'实验组','对照组')) %>% 
  dplyr::select(c(1,29:34))

df <- dat[,c(1,6:7)]


df <- pivot_longer(df, cols = c(2:3), names_to = "condition", values_to = "value") 
df$condition <- factor(df$condition)
df$组别 <- factor(df$组别)
data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  #data_sum <- rename(data_sum, c("mean" = varname))
  return(data_sum)
}
df2 <- data_summary(df, varname="value", 
                    groupnames=c("组别", "condition"))
names(df2)[c(2,3)] <-  c('阶段',"value")
df2$阶段 <- ifelse(df2$阶段=='抑郁','治疗前','治疗后')

ggplot(df2, aes(x=组别, y=value, fill=阶段)) + 
  geom_bar(stat="identity", color="black", 
           position=position_dodge()) +
  geom_errorbar(aes(ymin=value-sd, ymax=value+sd), width=.2,
                position=position_dodge(.9)) +
  theme_minimal(base_family = "Times") +  
  theme(panel.grid = element_blank(),
        panel.border = element_blank(),
        panel.background = element_rect(fill = NA),  
        plot.background = element_rect(fill = NA,colour = NA),
        plot.title = element_text(hjust = 0.5),
        axis.text.y = element_text(face = "bold")) +  
  labs(x = NULL, y = '均值',title = "两组抑郁评分变化")+
  scale_fill_manual(values=c('#999999','#CCCCCC'))

7 长数据转化

library(tidyr)

# 创建一个宽格式数据框
wide_data <- data.frame(
  id = 1:3,
  var1_1 = c(1, 2, 3),
  var1_2 = c(4, 5, 6),
  var2_1 = c(7, 8, 9),
  var2_2 = c(10, 11, 12)
)

# 使用pivot_longer函数将数据转换为长格式
long_data <- pivot_longer(wide_data, cols = starts_with("var"), 
                          names_to = "variable", values_to = "value")

# 查看转换后的长格式数据
print(long_data)

9 ggplot 拼图技巧

library(patchwork) 
(p1 + p2 + p3 + p4) + 
  plot_annotation(
    tag_levels = 'A'  # 设置标签为A, B, C, D
  )

10 GitHub上下载R包本地安装

> library(devtools)
载入需要的程序包：usethis
> devtools::install_local("D:/Download/sl3-master.zip")

11 个人常用的YAML

---
title: "R 语言中常见 bug 处理收集"
author: "Congyuan He (hcy)"
date: "`r Sys.Date()`"
# 全局设置公式靠左 (针对 PDF)
classoption: fleqn
output:
  html_document:
    toc: true
    toc_float:
      collapsed: false    # 侧边栏默认展开
    number_sections: true
    theme: flatly
    highlight: tango      # 代码高亮风格
    code_folding: show    # 默认显示代码，但允许点击折叠
    self_contained: true  # 方便单个文件传阅
---

12 图片保存

tiff( paste0('./outcome/cor_',save_name,'.tiff'), width = 3600, height = 3600, res = 300)

corrplot(cor_matrix,
         method = "color",
         type = "upper",
         tl.cex = 0.9,
         tl.col = "black",
         tl.srt = 45,
         addCoef.col = "black",
         number.cex = 0.8,
         col = colorRampPalette(c("#377EB8", "white", "#B22222"))(100),
         mar = c(0, 0, 3, 0) )

dev.off()