本文参考ggpubr包官方文档https://rpkgs.datanovia.com/ggpubr/index.html及http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/
ggpubr 包是基于ggplot2包，非R数据专业人员可用ggpubr包绘制图表

下载安装ggpubr

install.packages("ggpubr")

或者使用以下命令

if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")

ggpubr可绘制大部分我们常用的图形

分布图（Distribution）
- 密度分布图以及边际地毯线并添加平均值线（Density plot with mean lines and marginal rug）
- 带有均值线和边际地毯线的直方图（Histogram plot with mean lines and marginal rug）
箱线图和小提琴图（Box plots and violin plots）
- 具有不同点分布的箱线图（Box plots with jittered points）
- 小提琴图内添加箱线图（Violin plots with box plots inside）
条形图（Bar plots）
- 有序的条形图（Ordered bar plots）
- 偏差图（Deviation graphs）
点图（Dot charts）
- 棒棒糖图表（Lollipop chart）
- 偏差图（Deviation graphs）
- 克利夫兰点图（Cleveland’s dot plot）

首先：

分布图——密度分布图

library(ggpubr)  #加载ggubr, 同时也要安装加载ggplot2, magrittr 这两个包
#> Le chargement a nécessité le package : ggplot2
#> Le chargement a nécessité le package : magrittr
# Create some data format 设置数值
# :::::::::::::::::::::::::::::::::::::::::::::::::::
set.seed(1234) 
# set.seed 用于设定随机数种子， 
#一个特定的种子可以产生一个特定的伪随机序列，
#这个函数的主要目的，是让你的模拟能够可重复出现，
#因为很多时候我们需要取随机数，但这段代码再跑一次的时候，
#结果就不一样了，如果需要重复出现同样的模拟结果的话，
#就可以用set.seed()。在调试程序或者做展示的时候，
#结果的可重复性是很重要的，所以随机数种子也就很有必要。
#参考：https://blog.csdn.net/vencent_cy/article/details/50350020 

wdata = data.frame(
   sex = factor(rep(c("F", "M"), each=200)),
   weight = c(rnorm(200, 55), rnorm(200, 58)))
head(wdata, 4)        #设置一个数据框，包含sex因子，weight 向量，输出前5行
#>   sex   weight
#> 1   F 53.79293
#> 2   F 55.27743
#> 3   F 56.08444
#> 4   F 52.65430

# Density plot with mean lines and marginal rug
#密度分布图以及边际地毯线并添加平均值线
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex") 按性别更改轮廓和填充颜色
# Use custom palette  使用自定义调色板
ggdensity(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))

# ggdensity绘图，设定x轴，添加平均线。
# rug =TRUE 是添加边缘地毯线，
# 如果rug=F，则不添加，可以看以下两张图的区别

分布图——直方图

# Histogram plot with mean lines and marginal rug
# 带有均值线和边际地毯线的直方图
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex")
# Use custom color palette #使用自定义调色板
gghistogram(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))

其次：

箱线图及小提琴图—— 具有不同点分布的箱线图

# Load data  加载ToothGrowth数据，
# 它描述了维生素C对豚鼠牙齿生长的影响。 
# 使用三种剂量水平的维生素C（0.5mg,1mg和2 mg）
# 和两种递送方法[橙汁（OJ）或抗坏血酸（VC）]中的每一种
data("ToothGrowth")
df <- ToothGrowth
head(df, 4)  #输出前4行
#>    len supp dose
#> 1  4.2   VC  0.5
#> 2 11.5   VC  0.5
#> 3  7.3   VC  0.5
#> 4  5.8   VC  0.5

# Box plots with jittered points
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline colors by groups: dose
# Use custom color palette
# Add jitter points and change the shape by groups
 p <- ggboxplot(df, x = "dose", y = "len",
                color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
                add = "jitter", shape = "dose")
 p

 # Add p-values comparing groups  每组添加p值
 # Specify the comparisons you want 设置你想比较的任意组
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
p + stat_compare_means(comparisons = my_comparisons)+   stat_compare_means(label.y = 50)
# Add pairwise comparisons p-value 添加成对p值
         # Add global p-value   添加全局p值

箱线图及小提琴图—— 小提琴图内添加箱线图

# Violin plots with box plots inside
#小提琴图内添加箱线图
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose 以计量填充
# add boxplot with white fill color 
ggviolin(df, x = "dose", y = "len", fill = "dose",
         palette = c("#00AFBB", "#E7B800", "#FC4E07"), #颜色随机
         add = "boxplot", add.params = list(fill = "white"))+ #添加内部箱线图
  stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ 
# Add significance levels  添加显著水平
  stat_compare_means(label.y = 50)                        
              # Add global the p-value  添加全局影响因子

第三

条形图—— 有序的条形图

# Load data   加载内置数据集 mtcars
data("mtcars")
dfm <- mtcars
# Convert the cyl variable to a factor  将cyl 变量转换为因子
dfm$cyl <- as.factor(dfm$cyl)
# Add the name colums    添加name列
dfm$name <- rownames(dfm) 
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "cyl")])
#>                                name    wt  mpg cyl
#> Mazda RX4                 Mazda RX4 2.620 21.0   6
#> Mazda RX4 Wag         Mazda RX4 Wag 2.875 21.0   6
#> Datsun 710               Datsun 710 2.320 22.8   4
#> Hornet 4 Drive       Hornet 4 Drive 3.215 21.4   6
#> Hornet Sportabout Hornet Sportabout 3.440 18.7   8
#> Valiant                     Valiant 3.460 18.1   6

ggbarplot(dfm, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group 
不按组排序
          x.text.angle = 90           # Rotate vertically x axis texts
          )

ggbarplot(dfm, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in dscending order
          sort.by.groups = TRUE,      # Sort inside each group 按组排序
          x.text.angle = 90           # Rotate vertically x axis texts
          )

条形图—— 偏差图

# Calculate the z-score of the mpg data
dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"), 
                     levels = c("low", "high"))
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")])
#>                                name    wt  mpg      mpg_z mpg_grp cyl
#> Mazda RX4                 Mazda RX4 2.620 21.0  0.1508848    high   6
#> Mazda RX4 Wag         Mazda RX4 Wag 2.875 21.0  0.1508848    high   6
#> Datsun 710               Datsun 710 2.320 22.8  0.4495434    high   4
#> Hornet 4 Drive       Hornet 4 Drive 3.215 21.4  0.2172534    high   6
#> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345     low   8
#> Valiant                     Valiant 3.460 18.1 -0.3302874     low   6

ggbarplot(dfm, x = "name", y = "mpg_z",
          fill = "mpg_grp",           # change fill color by mpg_level
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",           # Sort the value in ascending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 90,          # Rotate vertically x axis texts
          ylab = "MPG z-score",
          xlab = FALSE,
          legend.title = "MPG Group"
          )

Rotate the plot: use rotate = TRUE and sort.val = “desc”  转换角度
ggbarplot(dfm, x = "name", y = "mpg_z",
          fill = "mpg_grp",           # change fill color by mpg_level
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "desc",          # Sort the value in descending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 90,          # Rotate vertically x axis texts
          ylab = "MPG z-score",
          legend.title = "MPG Group",
          rotate = TRUE,
          ggtheme = theme_minimal()
          )

最后

点图——棒棒糖点图

ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "ascending",                        # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           ggtheme = theme_pubr()                        # ggplot2 theme
           )

设置参数

Sort in decending order. sorting = “descending”.
Rotate the plot vertically, using rotate = TRUE.
Sort the mpg value inside each group by using group = “cyl”.
Set dot.size to 6.
Add mpg values as label. label = “mpg” or label = round(dfm$mpg).

ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = TRUE,                                # Rotate vertically
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 9, 
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr()                        # ggplot2 theme
           )

**点图——偏差图

Use y = “mpg_z”
Change segment color and size: add.params = list(color = “lightgray”, size = 2)



ggdotchart(dfm, x = "name", y = "mpg_z",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           add.params = list(color = "lightgray", size = 2), # Change segment color and size
           group = "cyl",                                # Order by groups
           dot.size = 6,                                 # Large dot size
           label = round(dfm$mpg_z,1),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 9, 
                             vjust = 0.5),               # Adjust label parameters
           ggtheme = theme_pubr()                        # ggplot2 theme
           )+
  geom_hline(yintercept = 0, linetype = 2, color = "lightgray")

Color y text by groups. Use y.text.col = TRUE.

ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                # Color by groups
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
           sorting = "descending",                       # Sort value in descending order
           rotate = TRUE,                                # Rotate vertically
           dot.size = 2,                                 # Large dot size
           y.text.col = TRUE,                            # Color y text by groups
           ggtheme = theme_pubr()                        # ggplot2 theme
           )+
  theme_cleveland()                                      # Add dashed grids

以上。

入门生信最快方式请搜索生信技能树

生信技能树全球公益巡讲
https://mp.weixin.qq.com/s/E9ykuIbc-2Ja9HOY0bn_6g
B站公益74小时生信工程师教学视频合辑https://mp.weixin.qq.com/s/IyFK7l_WBAiUgqQi8O7Hxw
招学徒
https://mp.weixin.qq.com/s/KgbilzXnFjbKKunuw7NVfw

ggpubr 可视化R包

首先：

分布图——密度分布图

分布图——直方图

其次：

箱线图及小提琴图—— 具有不同点分布的箱线图

箱线图及小提琴图—— 小提琴图内添加箱线图

第三

条形图—— 有序的条形图

条形图—— 偏差图

最后

点图——棒棒糖点图

**点图——偏差图

入门生信最快方式请搜索生信技能树

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