Question

我有一个数据集，其中每个变量的均值和sd为列，但我想将其转换为“长”格式：

library(tidyverse)

iris %>% 
  group_by(Species) %>% 
  summarize_all(list(mean = mean, sd = sd))
#> # A tibble: 3 x 9
#>   Species Sepal.Length_me~ Sepal.Width_mean Petal.Length_me~
#>   <fct>              <dbl>            <dbl>            <dbl>
#> 1 setosa              5.01             3.43             1.46
#> 2 versic~             5.94             2.77             4.26
#> 3 virgin~             6.59             2.97             5.55
#> # ... with 5 more variables: Petal.Width_mean <dbl>,
#> #   Sepal.Length_sd <dbl>, Sepal.Width_sd <dbl>, Petal.Length_sd <dbl>,
#> #   Petal.Width_sd <dbl>


# Desired output:
#
# tribble(~Species, ~Variable, ~Mean, ~SD
#         #-------------------------------
#         ...     )

我觉得tidyr::gather可以在这里很好地使用，但是，我不确定每个键具有两个值的语法如何工作。还是我需要使用两个收集器并对其进行列绑定？

Answer 1

要转换后summarise_all数据，您可以执行以下操作

df %>%
    gather(key, val, -Species) %>%
    separate(key, into = c("Variable", "metric"), sep = "_") %>%
    spread(metric, val)
## A tibble: 12 x 4
#   Species    Variable      mean    sd
#   <fct>      <chr>        <dbl> <dbl>
# 1 setosa     Petal.Length 1.46  0.174
# 2 setosa     Petal.Width  0.246 0.105
# 3 setosa     Sepal.Length 5.01  0.352
# 4 setosa     Sepal.Width  3.43  0.379
# 5 versicolor Petal.Length 4.26  0.470
# 6 versicolor Petal.Width  1.33  0.198
# 7 versicolor Sepal.Length 5.94  0.516
# 8 versicolor Sepal.Width  2.77  0.314
# 9 virginica  Petal.Length 5.55  0.552
#10 virginica  Petal.Width  2.03  0.275
#11 virginica  Sepal.Length 6.59  0.636
#12 virginica  Sepal.Width  2.97  0.322

但是从一开始就将数据从宽转换为长实际上更快，更短

iris %>%
    gather(Variable, val, -Species) %>%
    group_by(Species, Variable) %>%
    summarise(Mean = mean(val), SD = sd(val))
## A tibble: 12 x 4
## Groups:   Species [?]
#   Species    Variable      Mean    SD
#   <fct>      <chr>        <dbl> <dbl>
# 1 setosa     Petal.Length 1.46  0.174
# 2 setosa     Petal.Width  0.246 0.105
# 3 setosa     Sepal.Length 5.01  0.352
# 4 setosa     Sepal.Width  3.43  0.379
# 5 versicolor Petal.Length 4.26  0.470
# 6 versicolor Petal.Width  1.33  0.198
# 7 versicolor Sepal.Length 5.94  0.516
# 8 versicolor Sepal.Width  2.77  0.314
# 9 virginica  Petal.Length 5.55  0.552
#10 virginica  Petal.Width  2.03  0.275
#11 virginica  Sepal.Length 6.59  0.636
#12 virginica  Sepal.Width  2.97  0.322

Answer 2

这是pivot_longer开发版本中tidyr的一个选项。

library(dplyr)
library(tidyr) #tidyr_0.8.3.9000
df %>%
   rename_at(-1, ~ str_replace(., "(.*)_(.*)", "\\2_\\1")) %>% 
   pivot_longer(-Species, names_to = c(".value", "Variable"), names_sep = "_")
# A tibble: 12 x 4
#   Species    Variable      mean    sd
#   <fct>      <chr>        <dbl> <dbl>
# 1 setosa     Sepal.Length 5.01  0.352
# 2 setosa     Sepal.Width  3.43  0.379
# 3 setosa     Petal.Length 1.46  0.174
# 4 setosa     Petal.Width  0.246 0.105
# 5 versicolor Sepal.Length 5.94  0.516
# 6 versicolor Sepal.Width  2.77  0.314
# 7 versicolor Petal.Length 4.26  0.470
# 8 versicolor Petal.Width  1.33  0.198
# 9 virginica  Sepal.Length 6.59  0.636
#10 virginica  Sepal.Width  2.97  0.322
#11 virginica  Petal.Length 5.55  0.552
#12 virginica  Petal.Width  2.03  0.275

数据

data(iris)
df <- iris %>% 
        group_by(Species) %>% 
        summarize_all(list(mean = mean, sd = sd))

tidyr：每个键收集两个值

2 个答案:

数据