ggstatsplot: 基于ggplot2语法具有统计信息的图形

介绍

在典型的探索性数据分析工作流程中，数据可视化和统计建模是两个不同的阶段：可视化技术为建模提供直观图形界面信息，而根据建模的结果又可以使用不同的可视化方法。 ggstatsplot包的中心思想是：将这两个阶段以具有统计细节的图形形式组合成一个阶段，这使得数据探索更加简单快捷。

功能模块

ggstatsplot包含众多的函数模块，其中主要在小提琴图、柱状图、散点图以及百分比图等可视化上。

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 and meta-analysis

统计方法

支持的统计方法有：参数、非参数、贝叶斯、t-test、方差分析、相关分析和列联表等分析（parametric, nonparametric, robust, and bayesian versions of t-test/anova, correlation analyses, contingency table
analysis, meta-analysis, and regression analyses）。

Functions	Description	Parametric	Non-parametric	Robust	Bayes Factor
`ggbetweenstats`	Between group/condition comparisons	Yes	Yes	Yes	Yes
`ggwithinstats`	Within group/condition comparisons	Yes	Yes	Yes	Yes
`gghistostats`, `ggdotplotstats`	Distribution of a numeric variable	Yes	Yes	Yes	Yes
`ggcorrmat`	Correlation matrix	Yes	Yes	Yes	Yes
`ggscatterstats`	Correlation between two variables	Yes	Yes	Yes	Yes
`ggpiestats`, `ggbarstats`	Association between categorical variables	Yes	`NA`	`NA`	Yes
`ggpiestats`, `ggbarstats`	Equal proportions for categorical variable levels	Yes	`NA`	`NA`	Yes
`ggcoefstats`	Regression model coefficients	Yes	Yes	Yes	Yes
`ggcoefstats`	Random-effects meta-analysis	Yes	No	Yes	Yes

图形展示统计信息如下

安装

在window系统下，通过CRAN直接安装已完成版本；

install.packages("ggstatsplot")

# development version 
remotes::install_github(
  repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
  dependencies = TRUE, # installs packages which ggstatsplot depends on
  upgrade_dependencies = TRUE # updates any out of date dependencies
)

Linux下安装，需要安装较多的依赖包(centos 8)；

# ubuntu : sudo apt-get
sudo yum install libmpfr-dev libmpfr-doc libmpfr4 libmpfr4-dbg

# biomanager installation
BiocManager::install("ggstatsplot")

实例

ggbetweenstats

该函数可创建小提琴图，箱形图或两者的混合，并在图形中包含组间或条件间的统计检验的结果。

set.seed(123)
library(ggplot2)
library(ggstatsplot)

# plot
ggbetweenstats(
  data = ToothGrowth,
  x = supp,
  y = len,
  notch = TRUE,    # show notched box plot
  mean.ci = TRUE,  # whether to display confidence interval for means
  k = 3,           # number of decimal places for statistical results
  outlier.tagging = TRUE,   # whether outliers need to be tagged
  outlier.label = dose,     # variable to be used for the outlier tag
  xlab = "Supplement type", # label for the x-axis variable
  ylab = "Tooth length",    # label for the y-axis variable
  title = "The Effect of Vitamin C on Tooth Growth",  # title text for the plot
  ggtheme = ggthemes::theme_fivethirtyeight(),        # choosing a different theme
  ggstatsplot.layer = FALSE,   # turn off `ggstatsplot` theme layer
  package = "wesanderson",     # package from which color palette is to be taken
  palette = "Darjeeling1"      # choosing a different color palette
)

# add group facet 
# for reproducibility
set.seed(123)
ggstatsplot::grouped_ggbetweenstats(
  data = dplyr::filter(
    .data = ggstatsplot::movies_long,
    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
  ),
  x = mpaa,
  y = length,
  grouping.var = genre, # grouping variable
  ggsignif.args = list(textsize = 4, tip_length = 0.01),
  p.adjust.method = "bonferroni", # method for adjusting p-values for multiple comparisons
  # adding new components to `ggstatsplot` default
  ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
  k = 3,
  title.prefix = "Movie genre",
  caption = substitute(paste(italic("Source"), ": IMDb (Internet Movie Database)")),
  palette = "default_jama",
  package = "ggsci",
  plotgrid.args = list(nrow = 2),
  title.text = "Differences in movie length by mpaa ratings for different genres"
)

ggscatterstats

该函数可创建散点图并包含坐标分布的统计信息的图形。

# for reproducibility
set.seed(123)

# plot
ggstatsplot::grouped_ggscatterstats(
  data = dplyr::filter(
    .data = ggstatsplot::movies_long,
    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
  ),
  x = rating,
  y = length,
  grouping.var = genre, # grouping variable
  label.var = title,
  label.expression = length > 200,
  xfill = "#E69F00",
  yfill = "#8b3058",
  xlab = "IMDB rating",
  title.prefix = "Movie genre",
  ggtheme = ggplot2::theme_grey(),
  ggplot.component = list(
    ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
  ),
  plotgrid.args = list(nrow = 2),
  title.text = "Relationship between movie length by IMDB ratings for different genres"
)

ggpiestats

This function creates a pie chart for categorical or nominal variables with results from contingency table analysis (Pearson’s chi-squared test for between-subjects design and McNemar’s chi-squared test for within-subjects design) included in the subtitle of the plot

# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggpiestats(
  data = data.frame(
    "before" = c("Approve", "Approve", "Disapprove", "Disapprove"),
    "after" = c("Approve", "Disapprove", "Approve", "Disapprove"),
    counts = c(794, 150, 86, 570),
    check.names = FALSE
  ),
  x = before,
  y = after,
  counts = counts,
  title = "Survey results before and after the intervention",
  label = "both",
  paired = TRUE, # within-subjects design
  package = "wesanderson",
  palette = "Royal1"
)

引用

ggstatsplot tutorial

参考文章如引起任何侵权问题，可以与我联系，谢谢。