按行检查 NA，然后聚合它们的列名

Question

我目前正在努力逐行获取关于哪些变量结果 well/which 是 NA 的信息。下面，我展示了我的一部分数据的 example/dput 输出。

head(big_test)
# A tibble: 3 x 19
     id ctr_n   ctr    yr    mn  nvvi ENP_nat ENP_avg ENP_wght inflation1 inflation2 inflation3 inflation4 PSNS  PSNS_s PSNS_w
  <dbl> <chr> <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>    <dbl> <chr>      <chr>      <chr>      <chr>      <chr>  <dbl> <chr> 
1  1854 Isra~   376  2019     4     1    3.50    3.50     3.50 NA         NA         NA         NA         NA     0.962 NA    
2  1855 Isra~   376  2019     9     1    2.51    2.51     2.51 NA         NA         NA         NA         NA     0.992 NA    
3  1856 Isra~   376  2020     3     1    3.78    3.78     3.78 NA         NA         NA         NA         NA     0.999 NA    
# ... with 3 more variables: PSNS_sw <chr>, local_E <dbl>, cst_tot <dbl>

dput(big_test)
structure(list(id = c(1854, 1855, 1856), ctr_n = c("Israel", 
"Israel", "Israel"), ctr = c(376, 376, 376), yr = c(2019, 2019, 
2020), mn = c(4, 9, 3), nvvi = c(1, 1, 1), ENP_nat = c(3.50348063163162, 
2.51319610127466, 3.78468892335972), ENP_avg = c(3.50348063163162, 
2.51319610127466, 3.78468892335972), ENP_wght = c(3.50348063163162, 
2.51319610127466, 3.78468892335972), inflation1 = c("NA", "NA", 
"NA"), inflation2 = c("NA", "NA", "NA"), inflation3 = c("NA", 
"NA", "NA"), inflation4 = c("NA", "NA", "NA"), PSNS = c("NA", 
"NA", "NA"), PSNS_s = c(0.961748183147869, 0.992275075925835, 
0.998547438416594), PSNS_w = c("NA", "NA", "NA"), PSNS_sw = c("NA", 
"NA", "NA"), local_E = c(1, 1, 1), cst_tot = c(1, 1, 1)), row.names = c(NA, 
-3L), class = c("tbl_df", "tbl", "data.frame"))

编辑：这里的 NAs 用引号引起来，这是不合适的。我认为问题出在写入 .xlsx；下面的 dput 输出中列出了正确的版本，NA 上没有引号。

如您所见，数据在国家层面通过选举分开，这里的每一行都应该是唯一的（即以色列，2019 年，第 4 个月）。 我想创建一个字符列，列出此输出中缺少的变量。这是所需列的示例：

desired_output <- tibble(missing_vars=paste("inflation1","inflation2","inflation3","inflation4","etc",sep=";"))

head(desired_output)
# A tibble: 1 x 1
  missing_vars                                   
  <chr>                                          
1 inflation1;inflation2;inflation3;inflation4;etc

因此，我想知道是否有某种循环可以对唯一选举进行切片，查看缺失的列，然后将缺失的列向量化？这对于自动化至关重要，因为对于相同 country/year，某些变量可能会在其他变量存在的情况下丢失。我试图对它们进行计数，但我不知道如何将这些列名称列为字符列。

感谢您的帮助。谢谢！

Answer 1

如@akrun 所述，您有“NA”字符串而不是 NA。修复后，您可以定义一个缺失的函数并将其应用于每一行以创建一个新变量：

big_test <- structure(list(id = c(1854, 1855, 1856), ctr_n = c("Israel", 
"Israel", "Israel"), ctr = c(376, 376, 376), yr = c(2019, 2019, 
2020), mn = c(4, 9, 3), nvvi = c(1, 1, 1), ENP_nat = c(3.50348063163162, 
2.51319610127466, 3.78468892335972), ENP_avg = c(3.50348063163162, 
2.51319610127466, 3.78468892335972), ENP_wght = c(3.50348063163162, 
2.51319610127466, 3.78468892335972), inflation1 = c(NA, NA, 
NA), inflation2 = c(NA, NA, NA), inflation3 = c(NA, 
NA, NA), inflation4 = c(NA, NA, NA), PSNS = c(NA, 
NA, NA), PSNS_s = c(0.961748183147869, 0.992275075925835, 
0.998547438416594), PSNS_w = c(NA, NA, NA), PSNS_sw = c(NA, 
NA, NA), local_E = c(1, 1, 1), cst_tot = c(1, 1, 1)), row.names = c(NA, 
-3L), class = c("tbl_df", "tbl", "data.frame"))

missing <- function(x) {
  idx <- is.na(unlist(x))
  paste(colnames(big_test)[idx], collapse=", ")
}

big_test$missing <- apply(big_test, 1, missing)

big_test$missing
#> [1] "inflation1, inflation2, inflation3, inflation4, PSNS, PSNS_w, PSNS_sw"
#> [2] "inflation1, inflation2, inflation3, inflation4, PSNS, PSNS_w, PSNS_sw"
#> [3] "inflation1, inflation2, inflation3, inflation4, PSNS, PSNS_w, PSNS_sw"

Answer 2

这是一个带有 tidyverse 的选项，我们在其中使用 pivot_longer 重塑为 'long' 格式，按 row_number()、paste 列名称分组'value'

中有缺失值的地方

library(dplyr)
library(tidyr)
library(stringr)
big_test %>%
     select(starts_with('inflation')) %>% 
     mutate(rn = row_number()) %>% 
     pivot_longer(cols = -rn) %>% 
     group_by(rn) %>%
     summarise(missing_vars = str_c(name[is.na(value)], collapse=";"),
          .groups = 'drop') %>%
     select(-rn)

不整形，一个选项是rowwise/c_across

big_test %>% 
   rowwise %>% 
   transmute(missing_vars = str_c(names(select(cur_data(), 
      starts_with('inflation')))[which(c_across(starts_with('inflation')) 
        == 'NA')], collapse=";"))

这里是比较(==)和"NA"。如果是真实的 NA，请使用 is.na 而不是 ==

按行检查 NA，然后聚合它们的列名

Checking for NAs by Row, then Aggregating their Column Names

r

slice

dataframe

na

dplyr