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包摹恨，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)序列尊剔，
#這個(gè)函數(shù)的主要目的埂材，是讓你的模擬能夠可重復(fù)出現(xiàn)亦歉，
#因?yàn)楹芏鄷r(shí)候我們需要取隨機(jī)數(shù)恤浪，但這段代碼再跑一次的時(shí)候，
#結(jié)果就不一樣了肴楷，如果需要重復(fù)出現(xiàn)同樣的模擬結(jié)果的話水由，
#就可以用set.seed()。在調(diào)試程序或者做展示的時(shí)候赛蔫，
#結(jié)果的可重復(fù)性是很重要的砂客，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)框呵恢，包含sex因子鞠值，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繪圖渗钉，設(shè)定x軸彤恶，添加平均線。
# rug =TRUE 是添加邊緣地毯線鳄橘，
# 如果rug=F声离，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)瘫怜，
# 它描述了維生素C對(duì)豚鼠牙齒生長(zhǎng)的影響术徊。
# 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 。
