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r - 将数据文件和标签文件组合在一起，在 R 中拥有一个带有标签的数据框

问题描述

我有两个数据框，一个是调查数据（data.csv），另一个是标签数据（label.csv）。这是示例数据（我的原始数据有大约 150 个变量）

#sample data

df <- tibble::tribble(
  ~id, ~House_member, ~dob, ~age_quota, ~work, ~sex, ~pss,
  1L,            4L,  1983L,  2L,        2L,     1,      1,
  2L,            1L,  1940L,  7L,        2L,     1,      2,
  3L,            2L,  1951L,  5L,        6L,     1,      1,
  4L,            4L,  1965L,  2L,        2L,     1,      4,
  5L,            3L,  1965L,  2L,        3L,     1,      1,
  6L,            1L,  1951L,  3L,        1L,     1,      3,
  7L,            1L,  1955L,  1L,        1L,     1,      3,
  8L,            4L,  1982L,  2L,        2L,     2,      5,
  9L,            2L,  1990L,  2L,        4L,     2,      3,
  10L,            2L,  1953L, 3L,        2L,     2,      4
)


#sample label data
label <- tibble::tribble(
                ~variable, ~value,                           ~label,
           "House_member",     NA, "How many people live with you?",
           "House_member",     1L,                       "1 person",
           "House_member",     2L,                      "2 persons",
           "House_member",     3L,                      "3 persons",
           "House_member",     4L,                      "4 persons",
           "House_member",     5L,                      "5 persons",
           "House_member",     6L,                      "6 persons",
           "House_member",     7L,                      "7 persons",
           "House_member",     8L,                      "8 persons",
           "House_member",     9L,                      "9 persons",
           "House_member",    10L,                     "10 or more",
                    "dob",     NA,                  "date of brith",
              "age_quota",     NA,                      "age_quota",
              "age_quota",     1L,                          "10-14",
              "age_quota",     2L,                          "15-19",
              "age_quota",     3L,                          "20-29",
              "age_quota",     4L,                          "30-39",
              "age_quota",     5L,                          "40-49",
              "age_quota",     6L,                          "50-70",
              "age_quota",     7L,                           "70 +",
                   "work",     NA,        "what is your occupation?",
                   "work",     1L,                      "full time",
                   "work",     2L,                      "part time",
                   "work",     3L,                        "retired",
                   "work",     4L,                        "student",
                   "work",     5L,                      "housewife",
                   "work",     6L,                     "unemployed",
                   "work",     7L,                          "other",
                   "work",     8L,                   "kid under 15",
                    "sex",     NA,                        "gender?",
                    "sex",     1L,                            "Man",
                    "sex",     2L,                          "Woman",
                    "pss",     NA,       "How often do you use PS?",
                    "pss",     1L,                          "Daily",
                    "pss",     2L,         "several times per week",
                    "pss",     3L,                  "once per week",
                    "pss",     4L,         "several time per month",
                    "pss",     5L,                          "Rarly"
           )

我想知道有什么方法可以将这些文件组合在一起，形成一个带有标签的数据框，例如SPSS's 样式格式（dbl+lbl 格式）。我知道labelled可以将值标签添加到未标记向量的包，例如以下示例：

v <- labelled::labelled(c(1,2,2,2,3,9,1,3,2,NA), c(yes = 1, maybe = 2, no = 3))

我希望有一种比一个一个地为每个变量添加标签更好/更快的方法。

标签： rdataframetidyversepurrrr-haven

解决方案

另一种imap_dfc解决方案：

library(tidyverse)

df %>% imap_dfc(~{ 
                  label[label$variable==.y,c('label','value')] %>%
                  deframe() %>% # to named vector
                  haven::labelled(.x,.)
                 })

# A tibble: 10 x 7
          id  House_member       dob age_quota           work       sex                        pss
   <int+lbl>     <int+lbl> <int+lbl> <int+lbl>      <int+lbl> <dbl+lbl>                  <dbl+lbl>
 1         1 4 [4 persons]      1983 2 [15-19] 2 [part time]  1 [Man]   1 [Daily]                 
 2         2 1 [1 person]       1940 7 [70 +]  2 [part time]  1 [Man]   2 [several times per week]
 3         3 2 [2 persons]      1951 5 [40-49] 6 [unemployed] 1 [Man]   1 [Daily]                 
 4         4 4 [4 persons]      1965 2 [15-19] 2 [part time]  1 [Man]   4 [several time per month]
 5         5 3 [3 persons]      1965 2 [15-19] 3 [retired]    1 [Man]   1 [Daily]                 
 6         6 1 [1 person]       1951 3 [20-29] 1 [full time]  1 [Man]   3 [once per week]         
 7         7 1 [1 person]       1955 1 [10-14] 1 [full time]  1 [Man]   3 [once per week]         
 8         8 4 [4 persons]      1982 2 [15-19] 2 [part time]  2 [Woman] 5 [Rarly]                 
 9         9 2 [2 persons]      1990 2 [15-19] 4 [student]    2 [Woman] 3 [once per week]         
10        10 2 [2 persons]      1953 3 [20-29] 2 [part time]  2 [Woman] 4 [several time per month]

已使用tibble::deframe且haven::labelled包含在tidyverse

替换后的速度比较filter/select通过直接访问label：

Waldi <- function() {
df %>% imap_dfc(~{ 
    label[label$variable==.y,c('label','value')] %>%
      deframe() %>% # to named vector
      haven::labelled(.x,.)})}

Waldi_old <- function() {   
    df %>% imap_dfc(~{ 
      label %>% filter(variable==.y) %>%
        select(label, value) %>%
        deframe() %>% # to named vector
        haven::labelled(.x,.)
    })}

#EDIT : Included TIC33() for-loop solution

microbenchmark::microbenchmark(TIC3(),Waldi(),Anil(),TIC1(),Waldi_old(),Sinh())
Unit: microseconds
        expr     min       lq      mean   median       uq     max neval   cld
      TIC3()   688.0   871.80   982.280   920.95  1005.55  1801.6   100 a    
     Waldi()  1345.5  1543.60  1804.758  1635.45  1893.75  4306.8   100  b   
      Anil()  4006.8  4476.65  5188.519  4862.95  5439.10 10163.6   100   c  
      TIC1()  3898.2  4278.80  5009.927  4774.95  5277.05 12916.2   100   c  
 Waldi_old() 18712.3 20091.75 21756.140 20609.35 22169.75 33359.8   100    d 
      Sinh() 22730.9 24093.45 25931.412 24946.00 26614.00 38735.3   100     e

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