在 tidyverse 中收集和总结步骤后保持因子顺序

Question

我试图计算一百多个变量的频率和百分比。如何维护输出中每个变量值的因子顺序？请注意，为数据集外的每个变量指定顺序是不切实际的，因为我有超过 100 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", NA), 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

期望的输出将性别排序为男性、女性，疾病排序为是、否。

Answer 1

更新：如果您使用 pivot_longer（新聚集），它会保留因子水平！您还可以在 pivot_longer.

中 fine-tune 带有参数 names_transform 和 values_transform 的列类型

library(tidyverse)
df <- data.frame(gender=factor(c("male", "female", "male", NA), levels=c("male", "female")),
                 disease=factor(c("yes","yes","no", NA), 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 包 (v0.3.0) 创建于 2020-10-16

因为 gather 删除了值变量的因子并且 summarize 似乎也删除了数据框属性，所以您必须 re-add 它们。您可以 re-add 通过读入并组合因子水平，将它们 semi-automated 成 semi-automated：

library(tidyverse)
df <- data.frame(gender=factor(c("male", "female", "male", NA), levels=c("male", "female")),
                 disease=factor(c("yes","yes","no", NA), levels=c("yes", "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 包 (v0.3.0) 创建于 2020-10-16

在 tidyverse 中收集和总结步骤后保持因子顺序

Keeping factor order after gather and summarise steps in tidyverse

r

dplyr

r-factor

tidyr