简介
安装
基本函数
ggbetweenstats
grouped_ggbetweenstats
gghistostats
ggdotplotstats
ggcorrmat
ggscatterstats
ggcoefstats
ggstatsplot是ggplot2包的扩展，用于创建图标本身及详细的统计结果。在典型的探索性数据分析工作流中，数据可视化和统计建模是两个不同的阶段。ggstatsplot的核心思想很简单:以包含详细统计细节的图形形式将这两个阶段合并为一个阶段，这使得数据探索更简单、更快。
使用时需要引用：
“Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167
只用一行代码，该函数就产生了关于描述性统计、统计推断、效应量估计及其不确定性、两两比较、贝叶斯假设检验、贝叶斯后验估计及其不确定性的详细信息。
library(ggstatsplot)
## Registered S3 method overwritten by 'parameters':
## method from
## format.parameters_distribution datawizard
## You can cite this package as:
## Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
## Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167
ggbetweenstats(iris, Species, Sepal.Length)
这个包很大程度上依赖easystats和statsExpressions包，感兴趣的小伙伴可以自己查看或者等我更新。:smile
# 2选1
install.packages("ggstatsplot")
remotes::install_github("IndrajeetPatil/ggstatsplot")
用于组间比较。下面是一个4组间比较的例子。
library(gapminder)
dplyr::glimpse(x = gapminder::gapminder)
## Rows: 1,704
## Columns: 6
## $ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", ~
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, ~
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, ~
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8~
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12~
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, ~
## 可信区间是通过bootstrapping方法产生的，因此需要指定种子数
set.seed(123)
ggbetweenstats(
data = dplyr::filter(gapminder::gapminder, year == 2007, continent != "Oceania"),
x = continent,
y = lifeExp
)
还可以通过超多参数自定义图形！
library(ggplot2)
set.seed(123)
ggbetweenstats(
data = dplyr::filter(gapminder, year == 2007, continent != "Oceania"),
x = continent, ## 横坐标/自变量
y = lifeExp, ## 纵坐标/因变量
type = "robust", ## 4选1,：parametric,nonparametric，robust，bayes
xlab = "Continent",
ylab = "Life expectancy",
plot.type = "boxviolin", ## 3选1，box,violin,boxviolin
outlier.tagging = TRUE, ## 是否标注离群值
outlier.coef = 1.5, ## coefficient for Tukey's rule
outlier.label = country, ## label to attach to outlier values
outlier.label.args = list(color = "red"), ## outlier point label color
ggtheme = ggplot2::theme_minimal(), ## 换主题
package = "yarrr", ## 提供配色方案的R包，可以通过View(paletteer::palettes_d_names)查看支持的R包
palette = "info2", ## 配色方案
title = "Comparison of life expectancy across continents (Year: 2007)",
caption = "Source: Gapminder Foundation"
) + # 加ggplot2语法
scale_y_continuous(
limits = c(35, 85),
breaks = seq(from = 35, to = 85, by = 5)
)
提供一个combine_plots()函数用于多个图形的组合：
set.seed(123)
df_year <- dplyr::filter(gapminder::gapminder, year == 2007 | year == 1957)
## parametric t-test and box plot
p1 <- ggbetweenstats(
data = df_year,
x = year,
y = lifeExp,
xlab = "Year",
ylab = "Life expectancy",
plot.type = "box",
type = "p",
conf.level = 0.99,
title = "Parametric test",
package = "ggsci",
palette = "nrc_npg"
)
## Mann-Whitney U test (nonparametric t) and violin plot
p2 <- ggbetweenstats(
data = df_year,
x = year,
y = lifeExp,
xlab = "Year",
ylab = "Life expectancy",
plot.type = "violin",
type = "np",
conf.level = 0.99,
title = "Non-parametric Test (violin plot)",
package = "ggsci",
palette = "uniform_startrek"
)
## combining the individual plots into a single plot
combine_plots(
list(p1, p2),
plotgrid.args = list(nrow = 1),
annotation.args = list(
title = "Comparison of life expectancy between 1957 and 2007",
caption = "Source: Gapminder Foundation"
)
)
这个函数很牛逼，类似于先分组再画图，比如你想根据一个变量分组，然后画多张图，放在一个图形中。有点类似group_by的感觉，但不是一样的。
下面介绍的所有函数都是支持这一特性的！
set.seed(123)
gapminder::gapminder %>%
dplyr::filter(year %in% c(1967, 1987, 2007), continent != "Oceania") %>%
grouped_ggbetweenstats(
x = continent,
y = lifeExp,
grouping.var = year,
xlab = "Continent",
ylab = "Life expectancy",
pairwise.display = "significant", ## 只显示显著的
p.adjust.method = "fdr",
package = "ggsci",
palette = "default_jco",
outlier.tagging = TRUE,
outlier.label = country,
## 用于拼图的参数
annotation.args = list(title = "Changes in life expectancy across continents (1967-2007)"),
plotgrid.args = list(nrow = 3)
)
这个图非常棒，分别展示了1967年，1987年，2007年的不同地区，人均寿命的变化！
这个图形也可以通过ggbetweenstats和purrr包实现。
还有一个ggwithinstats函数和grouped_ggwithinstats，形式和上面两个完全一样，就不介绍了。
用于可视化直方图，当然也有一个grouped_gghistostats()函数用于分组画图。
set.seed(123)
gghistostats(
data = psych::sat.act,
x = ACT, ## 数值型
xlab = "ACT Score",
title = "Distribution of ACT Scores",
test.value = 20,
caption = "Data courtesy of: SAPA project (https://sapa-project.org)"
)
画点图。当然也是支持grouped_ggdotplotstats
set.seed(123)
library(ggstatsplot)
df <- dplyr::filter(ggplot2::mpg, cyl %in% c("4", "6"))
paletter_vector <-
paletteer::paletteer_d(
palette = "palettetown::venusaur",
n = nlevels(as.factor(df$manufacturer)),
type = "discrete"
)
ggdotplotstats(
data = df,
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
test.value = 15.5,
point.args = list(
shape = 16,
color = paletter_vector,
size = 5
),
title = "Distribution of mileage of cars",
ggtheme = ggplot2::theme_dark()
)
可视化性关矩阵，之前介绍过一个全能的相关矩阵可视化R包：corrplot。两者在功能上有很多重复，不过今天这个在拼图方面很有优势。
set.seed(123)
ggcorrmat(
data = dplyr::sample_frac(ggplot2::diamonds, size = 0.05),
cor.vars = c(carat, depth:z), ## 选择变量
cor.vars.names = c(
"carat",
"total depth",
"table",
"price",
"length (in mm)",
"width (in mm)",
"depth (in mm)"
),
ggcorrplot.args = list(outline.color = "black", hc.order = TRUE)
)
是不是看起来很美观，其实借用了ggcorrplot包。🤪
接下来看看比较有意思的组合图形：grouped_ggcorrplot
set.seed(123)
grouped_ggcorrmat(
## 和`ggcorrmat`相关的参数
data = ggplot2::diamonds,
type = "bayes",
grouping.var = cut,
## 和`combine_plots`有关的参数
plotgrid.args = list(nrow = 3),
annotation.args = list(
tag_levels = "a",
title = "Relationship between diamond attributes and price across cut",
caption = "Dataset: Diamonds from ggplot2 package"
)
)
颜值还可以吧？
散点图，适合两个变量的相关性探索，自动添加边际图形，支持更换图形类型，支持grouped_ggscatterstats()。
set.seed(123)
ggscatterstats(
data = movies_long,
x = budget,
y = rating,
xlab = "Budget (in millions of US dollars)", ## label for the x-axis
ylab = "Rating on IMDB", ## label for the y-axis
label.var = title,
label.expression = rating < 5 & budget > 100, ## 哪些点打标签
point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
xfill = "#CC79A7", ## fill for marginals on the x-axis
yfill = "#009E73", ## fill for marginals on the y-axis
title = "Relationship between movie budget and IMDB rating",
caption = "Source: www.imdb.com"
)
## Registered S3 method overwritten by 'ggside':
## method from
## +.gg ggplot2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
画饼图。
set.seed(123)
ggpiestats(
data = Titanic_full,
x = Survived,
y = Sex,
title = "Passenger survival on the Titanic by gender",
caption = "Source: Titanic survival dataset",
legend.title = "Survived?",
ggtheme = ggplot2::theme_grey(),
palette = "category10_d3",
package = "ggsci",
k = 3, ## 结果中的小数点位置
perc.k = 1 ## 百分比中的小数点位置
) +
ggplot2::theme(
plot.title = ggplot2::element_text(
color = "black",
size = 14,
hjust = 0
)
)
可视化模型的可信区间，支持100多个常见的模型！！比如各种回归模型，生存分析，方差分析等等，非常全面！
# 查看支持的模型
head(insight::supported_models())
## [1] "aareg" "afex_aov" "AKP" "Anova.mlm" "aov" "aovlist"
# 随便尝试一个
library(survival)
set.seed(123)
afit <- survival::aareg(
formula = Surv(time, status) ~ age + sex + ph.ecog,
data = lung,
dfbeta = TRUE
)
# plot
ggcoefstats(
x = afit,
title = "Aalen's additive regression model",
subtitle = "(for censored data)",
k = 3
)
非常好看！
看到这里都是真爱，这个包还有很多细节都没介绍到，比如对于不同的数据会如何选取合适的统计方法，各种自定义的选项等等，这些在官网[1]都有详细的介绍，每个函数都会以表格的形式列出来，大家感兴趣的可以自行查看哦。
ggstatsplot官网: https://indrajeetpatil.github.io/ggstatsplot/index.html
以上就是今天的内容，希望对你有帮助哦！欢迎点赞、在看、关注、转发！
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