根據列名稱從應用功能中排除列

Question

我有一些數據：

df <- data.frame(v1 = c('word',NA,'word','word',NA,'word','word',NA,'word','word'), 
                 v1_open = c('word',NA,'word','word',NA,'word','word',NA,'word','word'),
                 v2 = c('word','word',NA,'word','word',NA,'word','word',NA,'word'), 
                 v2_open = c('word','word',NA,'word','word',NA,'word','word',NA,'word'))

我正在使用apply將包含NA的觀察值更改為包含1的其他觀察值：

df <- t(apply(df,1,function(x){
  ifelse(is.na(x) ,0,1)
}))

回報

      v1 v1_open v2 v2_open
 [1,]  1       1  1       1
 [2,]  0       0  1       1
 [3,]  1       1  0       0
 [4,]  1       1  1       1
 [5,]  0       0  1       1
 [6,]  1       1  0       0
 [7,]  1       1  1       1
 [8,]  0       0  1       1
 [9,]  1       1  0       0
[10,]  1       1  1       1

我想修改apply函數以排除名稱包含文本'_open'的列，從而導致：

      v1 v1_open v2 v2_open
 [1,]  1    word  1    word  
 [2,]  0    NA    1    word  
 [3,]  1    word  0    NA    
 [4,]  1    word  1    word  
 [5,]  0    NA    1    word  
 [6,]  1    word  0    NA    
 [7,]  1    word  1    word  
 [8,]  0    NA    1    word  
 [9,]  1    word  0    NA    
[10,]  1    word  1    word  

如何才能做到這一點？

Answer 1

能做：

library(dplyr)

df %>%
  mutate_at(vars(-contains("_open")),
            ~ +(!is.na(.)))

輸出：

   v1 v1_open v2 v2_open
1   1    word  1    word
2   0    <NA>  1    word
3   1    word  0    <NA>
4   1    word  1    word
5   0    <NA>  1    word
6   1    word  0    <NA>
7   1    word  1    word
8   0    <NA>  1    word
9   1    word  0    <NA>
10  1    word  1    word

Answer 2

我們可以將is.na直接應用於is.na列的子集，而無需任何循環，然后更新列

nm1 <- grep("_open", names(df), value = TRUE, invert = TRUE)
df[nm1] <- +(!is.na(df[nm1]))
df
#   v1 v1_open v2 v2_open
#1   1    word  1    word
#2   0    <NA>  1    word
#3   1    word  0    <NA>
#4   1    word  1    word
#5   0    <NA>  1    word
#6   1    word  0    <NA>
#7   1    word  1    word
#8   0    <NA>  1    word
#9   1    word  0    <NA>
#10  1    word  1    word

Answer 3

如果您的列在.*和.*_open之間交替，那么您可以簡單地通過TRUE, FALSE對列進行子集化，即

df[c(TRUE, FALSE)] <- +(!is.na(df[c(TRUE, FALSE)]))

df
#   v1 v1_open v2 v2_open
#1   1    word  1    word
#2   0    <NA>  1    word
#3   1    word  0    <NA>
#4   1    word  1    word
#5   0    <NA>  1    word
#6   1    word  0    <NA>
#7   1    word  1    word
#8   0    <NA>  1    word
#9   1    word  0    <NA>
#10  1    word  1    word