用ggpubr包繪制圖表

本文參考ggpubr包官方文檔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數(shù)據(jù)專業(yè)人員可用ggpubr包繪制圖表

下載安裝ggpubr

install.packages("ggpubr")

或者使用以下命令

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

ggpubr可繪制大部分我們常用的圖形

  • 分布圖（Distribution）
    • 密度分布圖以及邊際地毯線并添加平均值線（Density plot with mean lines and marginal rug）
    • 帶有均值線和邊際地毯線的直方圖（Histogram plot with mean lines and marginal rug）
  • 箱線圖和小提琴圖（Box plots and violin plots）
    • 具有不同點(diǎn)分布的箱線圖（Box plots with jittered points）
    • 小提琴圖內(nèi)添加箱線圖（Violin plots with box plots inside）
  • 條形圖（Bar plots）
    • 有序的條形圖（Ordered bar plots）
    • 偏差圖（Deviation graphs）
  • 點(diǎn)圖 （Dot charts）
    • 棒棒糖圖表（Lollipop chart）
    • 偏差圖（Deviation graphs）
    • 克利夫蘭點(diǎn)圖（Cleveland’s dot plot）

首先：

分布圖——密度分布圖

library(ggpubr)  #加載ggubr, 同時(shí)也要安裝加載ggplot2, magrittr 這兩個(gè)包
#> Le chargement a nécessité le package : ggplot2
#> Le chargement a nécessité le package : magrittr
# Create some data format 設(shè)置數(shù)值
# :::::::::::::::::::::::::::::::::::::::::::::::::::
set.seed(1234) 
# set.seed 用于設(shè)定隨機(jī)數(shù)種子， 
#一個(gè)特定的種子可以產(chǎn)生一個(gè)特定的偽隨機(jī)序列，
#這個(gè)函數(shù)的主要目的，是讓你的模擬能夠可重復(fù)出現(xiàn)，
#因?yàn)楹芏鄷r(shí)候我們需要取隨機(jī)數(shù)，但這段代碼再跑一次的時(shí)候，
#結(jié)果就不一樣了，如果需要重復(fù)出現(xiàn)同樣的模擬結(jié)果的話，
#就可以用set.seed()。在調(diào)試程序或者做展示的時(shí)候，
#結(jié)果的可重復(fù)性是很重要的，所以隨機(jī)數(shù)種子也就很有必要。
#參考：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)        #設(shè)置一個(gè)數(shù)據(jù)框，包含sex因子，weight 向量，輸出前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
#密度分布圖以及邊際地毯線并添加平均值線
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex") 按性別更改輪廓和填充顏色
# Use custom palette  使用自定義調(diào)色板
ggdensity(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))

# ggdensity繪圖，設(shè)定x軸，添加平均線。
# rug =TRUE 是添加邊緣地毯線，
# 如果rug=F，則不添加，可以看以下兩張圖的區(qū)別
image
image

分布圖——直方圖

# Histogram plot with mean lines and marginal rug
# 帶有均值線和邊際地毯線的直方圖
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex")
# Use custom color palette #使用自定義調(diào)色板
gghistogram(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#00AFBB", "#E7B800"))
image

其次：

箱線圖及小提琴圖—— 具有不同點(diǎn)分布的箱線圖

# Load data  加載ToothGrowth數(shù)據(jù)，
# 它描述了維生素C對(duì)豚鼠牙齒生長(zhǎng)的影響。 
# 使用三種劑量水平的維生素C（0.5mg,1mg和2 mg）
# 和兩種遞送方法[橙汁（OJ）或抗壞血酸（VC）]中的每一種
data("ToothGrowth")
df <- ToothGrowth
head(df, 4)  #輸出前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
image
 # Add p-values comparing groups  每組添加p值
 # Specify the comparisons you want 設(shè)置你想比較的任意組
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 添加成對(duì)p值
         # Add global p-value   添加全局p值
image

箱線圖及小提琴圖—— 小提琴圖內(nèi)添加箱線圖

# Violin plots with box plots inside
#小提琴圖內(nèi)添加箱線圖
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose 以計(jì)量填充
# add boxplot with white fill color 
ggviolin(df, x = "dose", y = "len", fill = "dose",
         palette = c("#00AFBB", "#E7B800", "#FC4E07"), #顏色隨機(jī)
         add = "boxplot", add.params = list(fill = "white"))+ #添加內(nèi)部箱線圖
  stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ 
# Add significance levels  添加顯著水平
  stat_compare_means(label.y = 50)                        
              # Add global the p-value  添加全局影響因子
image

第三

條形圖—— 有序的條形圖

# Load data   加載內(nèi)置數(shù)據(jù)集 mtcars
data("mtcars")
dfm <- mtcars
# Convert the cyl variable to a factor  將cyl 變量轉(zhuǎn)換為因子
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 
不按組排序
          x.text.angle = 90           # Rotate vertically x axis texts
          )
image
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 按組排序
          x.text.angle = 90           # Rotate vertically x axis texts
          )
image

條形圖—— 偏差圖

# 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"
          )
image
Rotate the plot: use rotate = TRUE and sort.val = “desc”  轉(zhuǎn)換角度
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()
          )
image

最后

點(diǎn)圖——棒棒糖點(diǎn)圖

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
           )
image

設(shè)置參數(shù)

  • 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
           )
image

**點(diǎn)圖——偏差圖

  • Use y = “mpg_z”
  • Change segment color and size: add.params = list(color = “l(fā)ightgray”, 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")
image

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
image

以上 。

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