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ggpubr是我经常会用到的R包#xff0c;它傻瓜式的画图方式对很多初次接触R绘图的人来…欢迎大家关注全网生信学习者系列 WX公zhong号生信学习者 Xiao hong书生信学习者 知hu生信学习者 CDSN生信学习者2 介绍
ggpubr是我经常会用到的R包它傻瓜式的画图方式对很多初次接触R绘图的人来讲是很友好的。该包有个stat_compare_means函数可以做组间假设检验分析。
安装R包
install.packages(ggpubr)
devtools::devtools::install_github(kassambara/ggpubr)
library(ggpubr)
plotdata - data.frame(sex factor(rep(c(F, M), each200)),weight c(rnorm(200, 55), rnorm(200, 58)))
密度图density
ggdensity(plotdata, x weight,add mean, rug TRUE, # x轴显示分布密度color sex, fill sex,palette c(#00AFBB, #E7B800)) 柱状图histogram
gghistogram(plotdata, x weight,bins 30,add mean, rug TRUE,color sex, fill sex,palette c(#00AFBB, #E7B800)) 箱线图boxplot
df - ToothGrowth
head(df)
my_comparisons - list( c(0.5, 1), c(1, 2), c(0.5, 2) )
ggboxplot(df, x dose, y len,color dose, palette c(#00AFBB, #E7B800, #FC4E07),add jitter, shape dose)stat_compare_means(comparisons my_comparisons) # Add pairwise comparisons p-valuestat_compare_means(label.y 50) 小提琴图violin
ggviolin(df, x dose, y len, fill dose,palette c(#00AFBB, #E7B800, #FC4E07),add boxplot, add.params list(fill white))stat_compare_means(comparisons my_comparisons, label p.signif) # Add significance levelsstat_compare_means(label.y 50) 点图dotplot
ggdotplot(ToothGrowth, x dose, y len,color dose, palette jco, binwidth 1) 有序条形图 ordered bar plots
data(mtcars)
dfm - mtcars
dfm$cyl - as.factor(dfm$cyl)
dfm$name - rownames(dfm)
head(dfm[, c(name, wt, mpg, cyl)])
ggbarplot(dfm, x name, y mpg,fill cyl, # change fill color by cylcolor white, # Set bar border colors to whitepalette jco, # jco journal color palett. see ?ggparsort.val asc, # Sort the value in dscending ordersort.by.groups TRUE, # Sort inside each groupx.text.angle 90) # Rotate vertically x axis texts 偏差图Deviation graphs
dfm$mpg_z - (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg)
dfm$mpg_grp - factor(ifelse(dfm$mpg_z 0, low, high), levels c(low, high))
# Inspect the data
head(dfm[, c(name, wt, mpg, mpg_z, mpg_grp, cyl)])
ggbarplot(dfm, x name, y mpg_z,fill mpg_grp, # change fill color by mpg_levelcolor white, # Set bar border colors to whitepalette jco, # jco journal color palett. see ?ggparsort.val asc, # Sort the value in ascending ordersort.by.groups FALSE, # Dont sort inside each groupx.text.angle 90, # Rotate vertically x axis textsylab MPG z-score,rotate FALSE,xlab FALSE,legend.title MPG Group) 棒棒糖图 lollipop chart
ggdotchart(dfm, x name, y mpg,color cyl, # Color by groupspalette c(#00AFBB, #E7B800, #FC4E07), # Custom color palettesorting descending, # Sort value in descending orderadd segments, # Add segments from y 0 to dotsrotate TRUE, # Rotate verticallygroup cyl, # Order by groupsdot.size 6, # Large dot sizelabel round(dfm$mpg), # Add mpg values as dot labelsfont.label list(color white, size 9, vjust 0.5), # Adjust label parametersggtheme theme_pubr()) # ggplot2 theme 偏差图Deviation graph
ggdotchart(dfm, x name, y mpg_z,color cyl, # Color by groupspalette c(#00AFBB, #E7B800, #FC4E07), # Custom color palettesorting descending, # Sort value in descending orderadd segments, # Add segments from y 0 to dotsadd.params list(color lightgray, size 2), # Change segment color and sizegroup cyl, # Order by groupsdot.size 6, # Large dot sizelabel round(dfm$mpg_z,1), # Add mpg values as dot labelsfont.label list(color white, size 9, vjust 0.5), # Adjust label parametersggtheme theme_pubr()) # ggplot2 themegeom_hline(yintercept 0, linetype 2, color lightgray) 散点图scatterplot
df - datasets::iris
head(df)
ggscatter(df, x Sepal.Width, y Sepal.Length, palette jco, shape Species, add reg.line,color Species, conf.int TRUE) 添加回归线的系数
ggscatter(df, x Sepal.Width, y Sepal.Length, palette jco, shape Species, add reg.line,color Species, conf.int TRUE)stat_cor(aes(colorSpecies),method pearson, label.x 3) 添加聚类椭圆 concentration ellipses
data(mtcars)
dfm - mtcars
dfm$cyl - as.factor(dfm$cyl)
dfm$name - rownames(dfm)
p1 - ggscatter(dfm, x wt, y mpg,color cyl, palette jco,shape cyl,ellipse TRUE)
p2 - ggscatter(dfm, x wt, y mpg,color cyl, palette jco,shape cyl,ellipse TRUE,ellipse.type convex)
cowplot::plot_grid(p1, p2, align hv, nrow 1) 添加mean和stars
ggscatter(dfm, x wt, y mpg,color cyl, palette jco,shape cyl,ellipse TRUE, mean.point TRUE,star.plot TRUE) 显示点标签
dfm$name - rownames(dfm)
p3 - ggscatter(dfm, x wt, y mpg,color cyl, palette jco,label name,repel TRUE)
p4 - ggscatter(dfm, x wt, y mpg,color cyl, palette jco,label name,repel TRUE,label.select c(Toyota Corolla, Merc 280, Duster 360))
cowplot::plot_grid(p3, p4, align hv, nrow 1) 气泡图bubble plot
ggscatter(dfm, x wt, y mpg,color cyl,palette jco,size qsec, alpha 0.5)scale_size(range c(0.5, 15)) # Adjust the range of points size 连线图 lineplot
p1 - ggbarplot(ToothGrowth, x dose, y len, add mean_se,color supp, palette jco, position position_dodge(0.8))stat_compare_means(aes(group supp), label p.signif, label.y 29)
p2 - ggline(ToothGrowth, x dose, y len, add mean_se,color supp, palette jco)stat_compare_means(aes(group supp), label p.signif, label.y c(16, 25, 29))
cowplot::plot_grid(p1, p2, ncol 2, align hv) 添加边沿图 marginal plots
library(ggExtra)
p - ggscatter(iris, x Sepal.Length, y Sepal.Width,color Species, palette jco,size 3, alpha 0.6)
ggMarginal(p, type boxplot) 第二种添加方式: 分别画出三个图然后进行组合
sp - ggscatter(iris, x Sepal.Length, y Sepal.Width,color Species, palette jco,size 3, alpha 0.6, ggtheme theme_bw())
xplot - ggboxplot(iris, x Species, y Sepal.Length, color Species, fill Species, palette jco,alpha 0.5, ggtheme theme_bw()) rotate()
yplot - ggboxplot(iris, x Species, y Sepal.Width,color Species, fill Species, palette jco,alpha 0.5, ggtheme theme_bw())
sp - sp rremove(legend)
yplot - yplot clean_theme() rremove(legend)
xplot - xplot clean_theme() rremove(legend)
cowplot::plot_grid(xplot, NULL, sp, yplot, ncol 2, align hv, rel_widths c(2, 1), rel_heights c(1, 2)) 上图主图和边沿图之间的space太大第三种方法能克服这个缺点
library(cowplot)
# Main plot
pmain - ggplot(iris, aes(x Sepal.Length, y Sepal.Width, color Species))geom_point()ggpubr::color_palette(jco)
# Marginal densities along x axis
xdens - axis_canvas(pmain, axis x)geom_density(data iris, aes(x Sepal.Length, fill Species),alpha 0.7, size 0.2)ggpubr::fill_palette(jco)
# Marginal densities along y axis
# Need to set coord_flip TRUE, if you plan to use coord_flip()
ydens - axis_canvas(pmain, axis y, coord_flip TRUE)geom_boxplot(data iris, aes(x Sepal.Width, fill Species),alpha 0.7, size 0.2)coord_flip()ggpubr::fill_palette(jco)
p1 - insert_xaxis_grob(pmain, xdens, grid::unit(.2, null), position top)
p2 - insert_yaxis_grob(p1, ydens, grid::unit(.2, null), position right)
ggdraw(p2) 第四种方法通过grob设置
# Scatter plot colored by groups (Species)
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
sp - ggscatter(iris, x Sepal.Length, y Sepal.Width,color Species, palette jco,size 3, alpha 0.6)
# Create box plots of x/y variables
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Box plot of the x variable
xbp - ggboxplot(iris$Sepal.Length, width 0.3, fill lightgray) rotate() theme_transparent()
# Box plot of the y variable
ybp - ggboxplot(iris$Sepal.Width, width 0.3, fill lightgray) theme_transparent()
# Create the external graphical objects
# called a grop in Grid terminology
xbp_grob - ggplotGrob(xbp)
ybp_grob - ggplotGrob(ybp)
# Place box plots inside the scatter plot
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
xmin - min(iris$Sepal.Length); xmax - max(iris$Sepal.Length)
ymin - min(iris$Sepal.Width); ymax - max(iris$Sepal.Width)
yoffset - (1/15)*ymax; xoffset - (1/15)*xmax
# Insert xbp_grob inside the scatter plot
sp annotation_custom(grob xbp_grob, xmin xmin, xmax xmax, ymin ymin-yoffset, ymax yminyoffset) # Insert ybp_grob inside the scatter plotannotation_custom(grob ybp_grob,xmin xmin-xoffset, xmax xminxoffset, ymin ymin, ymax ymax) 二维密度图 2d density
sp - ggscatter(iris, x Sepal.Length, y Sepal.Width,color lightgray)
p1 - sp geom_density_2d()
# Gradient color
p2 - sp stat_density_2d(aes(fill ..level..), geom polygon)
# Change gradient color: custom
p3 - sp stat_density_2d(aes(fill ..level..), geom polygon)gradient_fill(c(white, steelblue))
# Change the gradient color: RColorBrewer palette
p4 - sp stat_density_2d(aes(fill ..level..), geom polygon) gradient_fill(YlOrRd)
cowplot::plot_grid(p1, p2, p3, p4, ncol 2, align hv) 混合图
混合表、字体和图
# Density plot of Sepal.Length
#::::::::::::::::::::::::::::::::::::::
density.p - ggdensity(iris, x Sepal.Length, fill Species, palette jco)
# Draw the summary table of Sepal.Length
#::::::::::::::::::::::::::::::::::::::
# Compute descriptive statistics by groups
stable - desc_statby(iris, measure.var Sepal.Length,grps Species)
stable - stable[, c(Species, length, mean, sd)]
# Summary table plot, medium orange theme
stable.p - ggtexttable(stable, rows NULL, theme ttheme(mOrange))
# Draw text
#::::::::::::::::::::::::::::::::::::::
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 text, face italic, size 11, color black)
# Arrange the plots on the same page
ggarrange(density.p, stable.p, text.p, ncol 1, nrow 3,heights c(1, 0.5, 0.3)) 注释table在图上
density.p - ggdensity(iris, x Sepal.Length, fill Species, palette jco)
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))
density.p annotation_custom(ggplotGrob(stable.p),xmin 5.5, ymin 0.7,xmax 8) systemic information
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
locale:
[1] LC_COLLATEChinese (Simplified)_China.936 LC_CTYPEChinese (Simplified)_China.936
[3] LC_MONETARYChinese (Simplified)_China.936 LC_NUMERICC
[5] LC_TIMEChinese (Simplified)_China.936
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.4.0 ggplot2_3.3.2
loaded via a namespace (and not attached):[1] zip_2.0.4 Rcpp_1.0.3 cellranger_1.1.0 pillar_1.4.6 compiler_3.6.1 forcats_0.5.0 [7] tools_3.6.1 digest_0.6.27 lifecycle_0.2.0 tibble_3.0.4 gtable_0.3.0 pkgconfig_2.0.3
[13] rlang_0.4.8 openxlsx_4.2.3 ggsci_2.9 rstudioapi_0.10 curl_4.3 haven_2.3.1
[19] rio_0.5.16 withr_2.1.2 dplyr_1.0.2 generics_0.0.2 vctrs_0.3.4 hms_0.5.3
[25] grid_3.6.1 tidyselect_1.1.0 glue_1.4.2 data.table_1.13.2 R6_2.4.1 rstatix_0.6.0
[31] readxl_1.3.1 foreign_0.8-73 carData_3.0-4 farver_2.0.3 tidyr_1.0.0 purrr_0.3.3
[37] car_3.0-10 magrittr_1.5 scales_1.1.0 backports_1.1.10 ellipsis_0.3.1 abind_1.4-5
[43] colorspace_1.4-1 ggsignif_0.6.0 labeling_0.4.2 stringi_1.4.3 munsell_0.5.0 broom_0.7.2
[49] crayon_1.3.4
