收集和总结tidyverse中的步骤后保持因子顺序

编程问答 2022-06-08

问题描述

我有一百多个变量，我正在尝试为其计算频率和百分比。如何在输出中保持每个变量值的因子顺序？请注意，为数据集外部的每个变量指定顺序不切实际，因为我有100多个变量。

示例数据：

df <- data.frame(gender=factor(c("male","female","male",NA),levels=c("male","female")),disease=factor(c("yes","yes","no",levels=c("yes","no")))
df
  gender disease
1   male     yes
2 female     yes
3   male      no
4   <NA>    <NA>

尝试：

df %>% gather(key,value,factor_key = T) %>%
  group_by(key,value) %>% 
  summarise(n=n()) %>%
  ungroup() %>%
  group_by(key) %>%
  mutate(percent=n/sum(n))

输出：

# A tibble: 6 x 4
# Groups:   key [2]
  key     value      n percent
  <fct>   <chr>  <int>   <dbl>
1 gender  female     1    0.25
2 gender  male       2    0.5 
3 gender  NA         1    0.25
4 disease no         1    0.25
5 disease yes        2    0.5 
6 disease NA         1    0.25

所需的输出将按性别将性别排序为男性，女性，将疾病排序为是，否。

解决方法

更新：如果您使用ivot_longer（新聚集），它将保留因子水平！您还可以在pivot_longer中使用参数names_transform和values_transform来微调列类型。

library(tidyverse)
df <- data.frame(gender=factor(c("male","female","male",NA),levels=c("male","female")),disease=factor(c("yes","yes","no",levels=c("yes","no")))

df %>% 
  pivot_longer(everything()) %>%
  group_by(name,value) %>% 
  summarise(n=n(),.groups = "drop_last") %>%
  mutate(percent=n/sum(n))
#> # A tibble: 6 x 4
#> # Groups:   name [2]
#>   name    value      n percent
#>   <chr>   <fct>  <int>   <dbl>
#> 1 disease yes        2    0.5 
#> 2 disease no         1    0.25
#> 3 disease <NA>       1    0.25
#> 4 gender  male       2    0.5 
#> 5 gender  female     1    0.25
#> 6 gender  <NA>       1    0.25

由reprex软件包（v0.3.0）于2020-10-16创建

由于收集会删除值变量的因数，并且汇总也可能会删除数据框属性，因此您必须重新添加它们。您可以通过阅读并组合以下因子级别，以半自动方式重新添加它们：

library(tidyverse)
df <- data.frame(gender=factor(c("male","no")))

df %>% 
  gather(key,value,factor_key = T) %>%
  group_by(key,value) %>% 
  summarise(n=n()) %>%
  ungroup() %>%
  group_by(key) %>%
  mutate(percent=n/sum(n),value = factor(value,levels = df %>% map(levels) %>% unlist())) %>%
  arrange(key,value)
#> Warning: attributes are not identical across measure variables;
#> they will be dropped
#> `summarise()` regrouping output by 'key' (override with `.groups` argument)
#> # A tibble: 6 x 4
#> # Groups:   key [2]
#>   key     value      n percent
#>   <fct>   <fct>  <int>   <dbl>
#> 1 gender  male       2    0.5 
#> 2 gender  female     1    0.25
#> 3 gender  <NA>       1    0.25
#> 4 disease yes        2    0.5 
#> 5 disease no         1    0.25
#> 6 disease <NA>       1    0.25

由reprex软件包（v0.3.0）于2020-10-16创建

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