Survival

美图神器ggstatsplot-专为学术论文而生

在CRAN(comprehensive R Achive Netwokrk)中已有13000多个R包了简单讲ggstatsplot能够提供更为丰富信息的包,其实就是画出高质量的图不需要我们花费过多的精力去调整绘图细节,举个例子一般的探索性数据分过程析包括数据可视化与数据统计两个部分，而ggstatsplot正是达到两者结合的目的

举例说明

组间比较-ggbetweenstats

library(ggstatsplot)

## Warning: package 'ggstatsplot' was built under R version 3.6.1

## Registered S3 methods overwritten by 'broom.mixed':
##   method         from 
##   augment.lme    broom
##   augment.merMod broom
##   glance.lme     broom
##   glance.merMod  broom
##   glance.stanreg broom
##   tidy.brmsfit   broom
##   tidy.gamlss    broom
##   tidy.lme       broom
##   tidy.merMod    broom
##   tidy.rjags     broom
##   tidy.stanfit   broom
##   tidy.stanreg   broom

## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car

library(ggplot2)

p代表参数检验，np代表非参数 mpaa是分类变量，y是数值型变量

head(movies_long)

## # A tibble: 6 x 8
##   title                        year length budget rating  votes mpaa  genre
##   <chr>                       <int>  <int>  <dbl>  <dbl>  <int> <fct> <fct>
## 1 Shawshank Redemption, The    1994    142     25    9.1 149494 R     Drama
## 2 Lord of the Rings: The Ret~  2003    251     94    9   103631 PG-13 Acti~
## 3 Lord of the Rings: The Fel~  2001    208     93    8.8 157608 PG-13 Acti~
## 4 Lord of the Rings: The Two~  2002    223     94    8.8 114797 PG-13 Acti~
## 5 Pulp Fiction                 1994    168      8    8.8 132745 R     Drama
## 6 Schindler's List             1993    195     25    8.8  97667 R     Drama

ggbetweenstats(
  data = movies_long,
  x = mpaa, # > 2 groups
  y = rating,
  type = "p", # default
  messages = FALSE
)

> 默认参数绘图

ggbetweenstats(
  data = movies_long,
  x = mpaa,
  y = rating
)

## Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.

##

## Note: Shapiro-Wilk Normality Test for rating : p-value = < 0.001

##

## Note: Bartlett's test for homogeneity of variances for factor mpaa : p-value = 0.004

##

配对比较 pairwise.display参数控制曾现的比较,ns无意义,all，所有,s有意义的

ggbetweenstats(
  data = movies_long,
  x = mpaa,
  y = rating,
  type = "np",
  mean.ci = TRUE,
  pairwise.comparisons = TRUE,
  pairwise.display = "s",
  p.adjust.method = "fdr",
  messages = FALSE
)

> 调整颜色，主题，可信区间调整，突出值标记 > confi.level：可信区间调整,ggtheme主题，pallete：颜色调用 > outlier:超出界限标记

ggbetweenstats(
  data = movies_long,
  x = mpaa,
  y = rating,
  type = "r",
  conf.level = 0.99,
  pairwise.comparisons = TRUE,
  pairwise.annotation = "p", 
  outlier.tagging = TRUE,
  outlier.label = title,
  outlier.coef = 2,
  ggtheme = hrbrthemes::theme_ipsum_tw(),
  palette = "Darjeeling2",
  package = "wesanderson",
  messages = FALSE
)

ggwithinstats组内比较

图还是非常美观，就不去细讲每个参数了，需要时调用即可，这也是作者的意图

ggwithinstats(
  data = WRS2::WineTasting,
  x = Wine, # > 2 groups
  y = Taste,
  pairwise.comparisons = TRUE,
  pairwise.annotation = "p",
  ggtheme = hrbrthemes::theme_ipsum_tw(),
  ggstatsplot.layer = FALSE,
  messages = FALSE
)

Survival_03

Chard Liu

2019年7月11日

美图神器ggstatsplot-专为学术论文而生

举例说明

ggwithinstats组内比较

相关性图-ggscatterstats