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映射到输出tibble中的突变爆炸行数

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C.Robin · 技术社区 · 2 年前

假设我有这样的数据:

d <- tibble::tribble(
  ~sit_comfy_sofa_1, ~sit_comfy_sofa_2, ~sit_comfy_sofa_3, ~sit_comfy_sofa_4, ~sit_comfy_couch_1, ~sit_comfy_couch_2, ~sit_comfy_couch_3, ~sit_comfy_couch_4, ~sit_comfy_settee_1, ~sit_comfy_settee_2, ~sit_comfy_settee_3, ~sit_comfy_settee_4,
                 1L,                0L,                0L,                0L,                 0L,                 1L,                 0L,                 0L,                  0L,                  0L,                  1L,                  0L,
                 0L,                0L,                0L,                1L,                 0L,                 0L,                 0L,                 1L,                  0L,                  1L,                  0L,                  0L,
                 0L,                1L,                0L,                0L,                 1L,                 0L,                 0L,                 0L,                  1L,                  0L,                  0L,                  0L,
                 0L,                0L,                1L,                0L,                 0L,                 0L,                 1L,                 0L,                  0L,                  0L,                  0L,                  1L
  )

此tibble有三个“类别”列,其中一个用于 _sofa_ ,一个用于 _couch_ ,和一个用于 _settee_ 。我试图查看每个类别,并构造一个新变量,该变量具有基于类别==1内的每个列是否为1的条件值。

我编写此函数是为了尝试:

cleaning_fcn <- function(.df, .x){
  .df %>% 
    mutate(!!sym(paste0("explain_", .x)) := case_when(
      !!sym(paste0("sit_comfy_", .x ,"_1")) == 1 ~ "Just better",
      !!sym(paste0("sit_comfy_", .x, "_2")) == 1 ~ "Nice shape",
      !!sym(paste0("sit_comfy_", .x ,"_3")) == 1 ~ "Like the color",
      !!sym(paste0("sit_comfy_", .x ,"_4")) == 1 ~ "Nice material"),
      !!sym(paste0("explain_", .x)) := factor(!!sym(paste0("explain_", .x)), 
                                               levels = c("Just better", "Nice shape",
                                                          "Like the color", "Nice material")))
}

然而,当我称之为tibble时,我最终得到了一个tibble,它的行数是原始tibble的3倍。

require(tidyverse)

purrr::map_dfr(
    .x = tidyselect::all_of(c("sofa", "couch", "settee")),
    .f = ~ cleaning_fcn(.df = d, .x))

有人能看出我哪里错了吗?

从本质上讲,我想实现与下面代码相同的功能,但理想情况下,它将是一个函数(通常重复次数要少得多):

d <- d %>% 
  mutate(explain_sofa = case_when(
    sit_comfy_sofa_1 == 1 ~ "Just better",
    sit_comfy_sofa_2 == 1 ~ "Nice shape",
    sit_comfy_sofa_3 == 1 ~ "Like the color",
    sit_comfy_sofa_4 == 1 ~ "Nice material"),
    explain_sofa = factor(explain_sofa, levels = c("Just better", "Nice shape",
                                                   "Like the color", "Nice material")))
d <- d %>% 
  mutate(explain_couch = case_when(
    sit_couch_sofa_1 == 1 ~ "Just better",
    sit_couch_sofa_2 == 1 ~ "Nice shape",
    sit_couch_sofa_3 == 1 ~ "Like the color",
    sit_couch_sofa_4 == 1 ~ "Nice material"),
    explain_couch = factor(explain_couch, levels = c("Just better", "Nice shape",
                                                   "Like the color", "Nice material")))

d <- d %>% 
  mutate(explain_settee = case_when(
    sit_settee_sofa_1 == 1 ~ "Just better",
    sit_settee_sofa_2 == 1 ~ "Nice shape",
    sit_settee_sofa_3 == 1 ~ "Like the color",
    sit_settee_sofa_4 == 1 ~ "Nice material"),
    explain_settee = factor(explain_settee, levels = c("Just better", "Nice shape",
                                                    "Like the color", "Nice material")))

1 回复 | 直到 2 年前

stefan 2 年前

使用 map_dfr 您正在创建 list 数据帧,每个类别一个,然后按行绑定。因此,最终得到的数据帧的行数是原来的3倍。一种选择是使用 purrr::reduce 相反:

library(tidyverse)

purrr::reduce(.x = c("sofa", "couch", "settee"), .f = cleaning_fcn, .init = d)
#> # A tibble: 4 Ã 15
#>   sit_comfy_sofa_1 sit_comfy_sofa_2 sit_comfy_sofa_3 sit_comfy_sofa_4
#>              <int>            <int>            <int>            <int>
#> 1                1                0                0                0
#> 2                0                0                0                1
#> 3                0                1                0                0
#> 4                0                0                1                0
#> # â¹ 11 more variables: sit_comfy_couch_1 <int>, sit_comfy_couch_2 <int>,
#> #   sit_comfy_couch_3 <int>, sit_comfy_couch_4 <int>, sit_comfy_settee_1 <int>,
#> #   sit_comfy_settee_2 <int>, sit_comfy_settee_3 <int>,
#> #   sit_comfy_settee_4 <int>, explain_sofa <fct>, explain_couch <fct>,
#> #   explain_settee <fct>