R语言中科研绘图神器ggpubr
在整理图像的时候会让人觉得苦恼的就是怎么让绘图风格统一、画风高级。今天就跟大家介绍一个画图的神器，据说可以直接赢得主编的好感。此包就是ggpubr，一个联合ggplot2包进行绘图工具包。首先看下包的安装：
install.packages("ggpubr")接下来我们通过实例来深度剖析下此包：
1. 气泡图：描述一个矩阵数据每个节点大小
##载入数据
my_cols <- c("#0D0887FF", "#6A00A8FF", "#B12A90FF",
"#E16462FF", "#FCA636FF", "#F0F921FF")
# Standard contingency table
#:::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Read a contingency table: housetasks
# Repartition of 13 housetasks in the couple
data <- read.delim(
system.file("demo-data/housetasks.txt", package = "ggpubr"),
row.names = 1
)
###基础绘制
ggballoonplot(data)
###改变气泡的颜色
ggballoonplot(data, color = "#0073C2FF", fill = "#0073C2FF")
###根据值改变颜色
ggballoonplot(data, fill = "value")+scale_fill_gradientn(colors = my_cols)
###改变形状
ggballoonplot(data, fill = "value", shape = 23)+gradient_fill(c("blue", "white", "red"))
###固定形状大小，通过值改变颜色
ggballoonplot(data, fill = "value", color = "lightgray",size = 10, show.label = TRUE)+ gradient_fill(c("blue", "white", "red"))
2. 柱状图
####载入数据
df <- data.frame(dose=c("D0.5", "D1", "D2"),
len=c(4.2, 10, 29.5))
df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
dose=rep(c("D0.5", "D1", "D2"),2),
len=c(6.8, 15, 33, 4.2, 10, 29.5))
df3 <- ToothGrowth
###基础绘制
ggbarplot(df, x = "dose", y = "len", width = 0.5)
###改变坐标轴方向
ggbarplot(df, "dose", "len", orientation = "horiz")
###改变排序
ggbarplot(df, "dose", "len",order = c("D2", "D1", "D0.5"))
###修改柱子颜色并添加标签
ggbarplot(df, "dose", "len",
fill = "steelblue", color = "steelblue",
label = TRUE, lab.pos = "in", lab.col = "white")
##添加分组
ggbarplot(df, "dose", "len", color = "dose",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
###改变填充方式
ggbarplot(df, "dose", "len",
fill = "dose", color = "dose",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
###多组数据绘制（垂直分组）
ggbarplot(df2, "dose", "len",
fill = "supp", color = "supp", palette = "Paired",
label = TRUE, lab.col = "white", lab.pos = "in")
###多组数据绘制（水平分组）
ggbarplot(df2, "dose", "len",
fill = "supp", color = "supp", palette = "Paired",
label = TRUE,
position = position_dodge(0.7))
###添加误差线
ggbarplot(df3, x = "dose", y = "len", color = "supp",
add = "mean_se", palette = c("#00AFBB", "#E7B800"),
position = position_dodge())
###添加误差线的折线图
ggline(df3, x = "dose", y = "len", color = "supp",
add = "mean_se", palette = c("#00AFBB", "#E7B800"))
###添加抖动散点折线图
ggline(df3, x = "dose", y = "len", color = "supp",
add = c("mean_se", "jitter"), palette = c("#00AFBB", "#E7B800"))
3. 箱线图
###载入数据
data("ToothGrowth")
df <- ToothGrowth
###基础绘制
ggboxplot(df, x = "dose", y = "len", width = 0.8)
###修改颜色
ggboxplot(df, "dose", "len",
color = "black", fill = "gray")
###修改排序
ggboxplot(df, "dose", "len",
order = c("2", "1", "0.5"))
###改变方向
ggboxplot(df, "dose", "len", orientation = "horizontal")
###添加缺口
ggboxplot(df, x = "dose", y = "len",
notch = TRUE)
###添加散点
ggboxplot(df, x = "dose", y = "len",
add = "dotplot")
###添加形状分组
ggboxplot(df, x = "dose", y = "len",
add = "jitter", shape = "dose")
###修改颜色
ggboxplot(df, "dose", "len",
color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
add = "jitter", shape = "dose")
###修改填充方式
ggboxplot(df, "dose", "len",
fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"))
###多组数据绘制
ggboxplot(df, "dose", "len", color = "supp",
palette = c("#00AFBB", "#E7B800"))
###选择局部数据绘制
ggboxplot(df, "dose", "len",
select = c("0.5", "2"))
###配对箱线图
ggpaired(df3, x = "supp", y = "len",
color = "supp", line.color = "gray", line.size = 0.4,
palette = "npg")
###添加差异分析P值
p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
color = "supp", palette = "npg", add = "jitter")
# Add p-value
p + stat_compare_means()
###添加其它分析方法
p + stat_compare_means(method = "t.test")
###添加组建横线
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
ggboxplot(ToothGrowth, x = "dose", y = "len",
color = "dose", palette = "npg")+
# Add pairwise comparisons p-value
stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+
stat_compare_means(label.y = 45) # Add global Anova p-value
###设置参考组，并标注与其他组差异分析结果
ggboxplot(ToothGrowth, x = "dose", y = "len",
color = "dose", palette = "npg")+
stat_compare_means(method = "anova", label.y = 40)+ # Add global p-value
stat_compare_means(aes(label = ..p.signif..),
method = "t.test", ref.group = "0.5")
###多组数据同时比较
p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
color = "supp", palette = "npg",
add = "jitter",
facet.by = "dose", short.panel.labs = FALSE)
# Use only p.format as label. Remove method name.
p + stat_compare_means(
aes(label = paste0("p = ", ..p.format..))
)
4. 密度图绘制
###数据载入
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each=200)),
weight = c(rnorm(200, 55), rnorm(200, 58)))
###基础绘制
ggdensity(wdata, x = "weight", fill = "lightgray",
add = "mean", rug = TRUE)
###分组统计
ggdensity(wdata, x = "weight",
add = "mean", rug = TRUE,
color = "sex", palette = c("#00AFBB", "#E7B800"))
###改变填充模式
ggdensity(wdata, x = "weight",
add = "mean", rug = TRUE,
color = "sex", fill = "sex",
palette = c("#00AFBB", "#E7B800"))
###密度图添加文本注释
# Density plot
density.p <- ggdensity(iris, x = "Sepal.Length",
fill = "Species", palette = "jco")
# Text plot
text <- paste("iris data set gives the measurements in cm",
"of the variables sepal length and width",
"and petal length and width, respectively,",
"for 50 flowers from each of 3 species of iris.",
"The species are Iris setosa, versicolor, and virginica.", sep = " ")
text.p <- ggparagraph(text, face = "italic", size = 12)
# Arrange the plots on the same page
ggarrange(density.p, text.p,
ncol = 1, nrow = 2,
heights = c(1, 0.3))
###密度图添加表格
# Combine density plot and summary table
#:::::::::::::::::::::::::::::::::::::
# Density plot of "Sepal.Length"
density.p <- ggdensity(iris, x = "Sepal.Length",
fill = "Species", palette = "jco")
# Draw the summary table of Sepal.Length
# Descriptive statistics by groups
stable <- desc_statby(iris, measure.var = "Sepal.Length",
grps = "Species")
stable <- stable[, c("Species", "length", "mean", "sd")]
stable.p <- ggtexttable(stable, rows = NULL,
theme = ttheme("mOrange"))
# Arrange the plots on the same page
ggarrange(density.p, stable.p,
ncol = 1, nrow = 2,
heights = c(1, 0.5))
###密度直方图
gghistogram(wdata, x = "weight", fill = "lightgray",
add = "mean", rug = TRUE)
##添加分组，修改颜色
gghistogram(wdata, x = "weight",
add = "mean", rug = TRUE,
color = "sex", fill = "sex",
palette = c("#00AFBB", "#E7B800"))
###密度曲线和直方图合并
gghistogram(wdata, x = "weight",
add = "mean", rug = TRUE,
fill = "sex", palette = c("#00AFBB", "#E7B800"),
add_density = TRUE)
###累计密度函数
ggecdf(wdata, x = "weight",
color = "sex", linetype = "sex",
palette = c("#00AFBB", "#E7B800"))
###QQ图
ggqqplot(wdata, x = "weight",
color = "sex", palette = c("#00AFBB", "#E7B800"))
5. 圆环图同饼状图（ggpie）
###数据载入
df <- data.frame(
group = c("Male", "Female", "Child"),
value = c(25, 25, 50))
###基础绘制
ggdonutchart(df, "value", label = "group")
###添加分组信息和修改颜色
# Use custom color palette
ggdonutchart(df, "value", label = "group",
fill = "group", color = "white",
palette = c("#00AFBB", "#E7B800", "#FC4E07") )
###修改标签内容
labs <- paste0(df$group, " (", df$value, "%)")
ggdonutchart(df, "value", label = labs,
fill = "group", color = "white",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
###修改标签位置和颜色
ggdonutchart(df, "value", label = labs,
lab.pos = "in", lab.font = "white",
fill = "group", color = "white",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
6. 点图
###载入数据
data("mtcars")
df <- mtcars
df$cyl <- as.factor(df$cyl)
df$name <- rownames(df)
df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
dose=rep(c("D0.5", "D1", "D2"),2),
len=c(6.8, 15, 33, 4.2, 10, 29.5))
# 基础绘制
ggdotchart(df, x = "name", y ="mpg",
ggtheme = theme_bw())
###根据分组展示不同的颜色
ggdotchart(df, x = "name", y = "mpg",
group = "cyl", color = "cyl",
palette = c('#999999','#E69F00','#56B4E9'),
rotate = TRUE,
sorting = "descending",
ggtheme = theme_bw(),
y.text.col = TRUE )
###多组展示
ggdotchart(df2, x = "dose", y = "len",
color = "supp", size = 3,
add = "segment",
add.params = list(color = "lightgray", size = 1.5),
position = position_dodge(0.3),
palette = "jco",
ggtheme = theme_pubclean()
)
7. 散点图
###数据载入
data("ToothGrowth")
df <- ToothGrowth
###基础绘制
ggdotplot(df, x = "dose", y = "len",
add = "mean_sd")
###添加箱线图
ggdotplot(df, x = "dose", y = "len",
add = "boxplot")
###添加分组颜色
ggdotplot(df, "dose", "len",
add = "boxplot",
color = "dose", fill = "dose",
palette = c("#00AFBB", "#E7B800", "#FC4E07"))
##添加小提琴图
ggdotplot(df, x = "dose", y = "len",
add = c("violin", "mean_sd"))
##多组点图
ggdotplot(df, "dose", "len", fill = "supp", color = "supp",
palette = c("#00AFBB", "#E7B800"))
8. 基因差异分布火山图
###数据载入
data(diff_express)
# 基础绘制
ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"),
fdr = 0.05, fc = 2, size = 0.4,
palette = c("#B31B21", "#1465AC", "darkgray"),
genenames = as.vector(diff_express$name),
legend = "top", top = 20,
font.label = c("bold", 11),
font.legend = "bold",
font.main = "bold",
ggtheme = ggplot2::theme_minimal())
###选择特别的基因展示
ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"),
fdr = 0.05, fc = 2, size = 0.4,
genenames = as.vector(diff_express$name),
ggtheme = ggplot2::theme_minimal(),
top = 0, label.select = c("BUB1", "CD83")
)
9. 散点图
###载入数据
data("mtcars")
df <- mtcars
df$cyl <- as.factor(df$cyl)
###基础散点图绘制
ggscatter(df, x = "wt", y = "mpg",
color = "black", shape = 21, size = 3, # Points color, shape and size
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)
####基于单独函数添加相关性分析结果
sp <- ggscatter(df, x = "wt", y = "mpg",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE # Add confidence interval
)
# Add correlation coefficient
sp + stat_cor(method = "pearson", label.x = 3, label.y = 30)
###通过阈值标注P值
sp + stat_cor(p.accuracy = 0.001, r.accuracy = 0.01)
###添加LOESS拟合曲线
ggscatter(df, x = "wt", y = "mpg",
add = "loess", conf.int = TRUE)
###根据值大小变化散点图
ggscatter(df, x = "wt", y = "mpg",
color = "#00AFBB", size = "qsec")
###根据不同值进行颜色变化散点图
ggscatter(df, x = "wt", y = "mpg", color = "cyl",
palette = c("#00AFBB", "#E7B800", "#FC4E07") )
###为各点添加标签
df$name <- rownames(df)
ggscatter(df, x = "wt", y = "mpg",
color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"),
label = "name", repel = TRUE)
##为分组数据添加相关性分析结果
sp <- ggscatter(df, x = "wt", y = "mpg",
color = "cyl", palette = "jco",
add = "reg.line", conf.int = TRUE)
sp + stat_cor(aes(color = cyl), label.x = 3)
###添加密度分布的散点图
ggscatterhist(
iris, x = "Sepal.Length", y = "Sepal.Width",
color = "Species", size = 3, alpha = 0.6,
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
margin.params = list(fill = "Species", color = "black", size = 0.2)
)
10. 小提琴图
###数据载入
data("ToothGrowth")
df <- ToothGrowth
###基础绘制
ggviolin(df, x = "dose", y = "len")
###添加箱线图
ggviolin(df, x = "dose", y = "len",
add = "boxplot")
###设置颜色
ggviolin(df, "dose", "len", fill = "dose",
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
add = "boxplot", add.params = list(fill = "white"))
###多组绘制
ggviolin(df, "dose", "len", color = "supp",
palette = c("#00AFBB", "#E7B800"), add = "boxplot")
欢迎大家学习交流！
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原始发表：2022-06-26，如有侵权请联系 cloudcommunity@tencent.com 删除
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