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ggstatsplot是ggplot2包的扩展包，可以同时输出美观的图片和统计分析结果，对于经常做统计分析或者生信人来说非常有用。
一 准备数据
gapminder 数据集包含1952到2007年间(5年间隔)的142个国家的life expectancy, GDP per capita, 和 population信息
#载入绘图R包
library(ggstatsplot)
#载入gapminder 数据集
library(gapminder)
head(gapminder)
image.png
ggstatsplot-R包含有很多绘图函数（文末会给出），本文仅展示ggbetweenstats函数使用方法。
二 ggbetweenstats 绘图
1 基本绘图展示
显示2007年每个continent的预期寿命分布情况，并统计一下不同大陆之间平均预期寿命的是否有差异？差异是否显著？
#设置种子方便复现
set.seed(123)
# Oceania数据太少，去掉后分析
ggstatsplot::ggbetweenstats(
data = dplyr::filter(
.data = gapminder::gapminder,
year == 2007, continent != "Oceania"
),
x = continent,
y = lifeExp,
nboot = 10,
messages = FALSE
)
img
可以看到图中展示出了2007年每个continent的预期寿命分布的箱线图，点图和小提琴图，均值，样本数；并且图形最上方给出了模型的一些统计量信息（整体）。
统计信息意义如下图所示：
img
注：该函数根据分组变量中的个数自动决定是选择独立样本t检验(2组)还是单因素方差分析(3组或更多组)
2 添加统计值
上方给出了整体的检验P值，下面两两之间比较，并添加检验统计量
set.seed(123)
ggstatsplot::ggbetweenstats(
data = dplyr::filter(
.data = gapminder::gapminder,year == 2007, continent != "Oceania"),
x = continent,y = lifeExp,
nboot = 10,
messages = FALSE,
effsize.type = "unbiased", # type of effect size (unbiased = omega)
partial = FALSE, # partial omega or omega?
pairwise.comparisons = TRUE, # display results from pairwise comparisons
pairwise.display = "significant", # display only significant pairwise comparisons
pairwise.annotation = "p.value", # annotate the pairwise comparisons using p-values
p.adjust.method = "fdr", # adjust p-values for multiple tests using this method
)
img
3 图形美化
添加标题和说明，x轴和y轴标签，标记，离群值，更改主题以及调色板。
set.seed(123)
# plot
gapminder %>% # dataframe to use
ggstatsplot::ggbetweenstats(
data = dplyr::filter(.data = ., year == 2007, continent != "Oceania"),
x = continent, # grouping/independent variable
y = lifeExp, # dependent variables
xlab = "Continent", # label for the x-axis
ylab = "Life expectancy", # label for the y-axis
plot.type = "boxviolin", # type of plot ,"box", "violin", or "boxviolin"
type = "parametric", # type of statistical test , p (parametric), np ( nonparametric), r(robust), bf (Bayes Factor).
effsize.type = "biased", # type of effect size
nboot = 10, # number of bootstrap samples used
bf.message = TRUE, # display bayes factor in favor of null hypothesis
outlier.tagging = TRUE, # whether outliers should be flagged
outlier.coef = 1.5, # coefficient for Tukey's rule
outlier.label = country, # label to attach to outlier values
outlier.label.color = "red", # outlier point label color
mean.plotting = TRUE, # whether the mean is to be displayed
mean.color = "darkblue", # color for mean
messages = FALSE, # turn off messages
ggtheme = ggplot2::theme_gray(), # a different theme
package = "yarrr", # package from which color palette is to be taken
palette = "info2", # choosing a different color palette
title = "Comparison of life expectancy across continents (Year: 2007)",
caption = "Source: Gapminder Foundation"
) + # modifying the plot further
ggplot2::scale_y_continuous(
limits = c(35, 85),
breaks = seq(from = 35, to = 85, by = 5)
)
img
三 其他绘图函数
| Function | Plot | Description |
|---|---|---|
ggbetweenstats |
violin plots | for comparisons between groups/conditions |
ggwithinstats |
violin plots | for comparisons within groups/conditions |
gghistostats |
histograms | for distribution about numeric variable |
ggdotplotstats |
dot plots/charts | for distribution about labeled numeric variable |
ggpiestats |
pie charts | for categorical data |
ggbarstats |
bar charts | for categorical data |
ggscatterstats |
scatterplots | for correlations between two variables |
ggcorrmat |
correlation matrices | for correlations between multiple variables |
ggcoefstats |
dot-and-whisker plots | for regression models |
四 更多请参照官方文档
https://indrajeetpatil.github.io/ggstatsplot/index.html
◆ ◆ ◆ ◆ ◆
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【觉得不错，右下角点个“在看”，期待您的转发，谢谢！】
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