R 使用mutate和gsub将特定列中的所有值替换为NA
在我的数据框中，我想将某些列中的所有值替换为NAR 使用mutate和gsub将特定列中的所有值替换为NA,r,dplyr,gsub,mutate,across,R,Dplyr,Gsub,Mutate,Across,在我的数据框中，我想将某些列中的所有值替换为NA Test2 ID Sex Location Obs1 Obs4 Obs5 1 291978 FEMALE 2 16.5 4836 0.563636364 2 292429 FEMALE 2 20.2 5428 0.584158416 3 292466 FEMALE 2 19.2 48 0.005208333 4 293656 FEMALE 2 1
Test2
ID Sex Location Obs1 Obs4 Obs5
1 291978 FEMALE 2 16.5 4836 0.563636364
2 292429 FEMALE 2 20.2 5428 0.584158416
3 292466 FEMALE 2 19.2 48 0.005208333
4 293656 FEMALE 2 15.8 2904 0.417721519
5 291993 FEMALE 2 18.1 6194 0.900552486
6 293263 FEMALE 2 17.9 616 0.078212291
7 290200 FEMALE 2 16.7 792 0.107784431
8 292511 FEMALE 2 18.3 4992 0.568306011
9 291510 FEMALE 2 18.6 350 0.037634409
10 293711 FEMALE 2 18.2 264 0.032967033
11 295234 FEMALE 2 16.5 216 0.036363636
12 293839 FEMALE 2 15.0 4114 0.806666667
13 291057 FEMALE 2 16.7 56 0.005988024
14 295094 FEMALE 2 16.5 3154 0.503030303
15 295562 FEMALE 2 14.7 966 0.142857143
16 292381 FEMALE 2 17.4 1980 0.258620690
17 289765 FEMALE 2 17.8 3492 0.544943820
18 293871 FEMALE 2 18.2 3760 0.516483516
19 291076 FEMALE 2 16.8 88 0.011904762
20 293851 FEMALE 2 16.1 2242 0.366459627
> Obs <- c('Obs1')
非常感谢 我建议采用
base RObs#Data
df <- structure(list(ID = c(291978L, 292429L, 292466L, 293656L, 291993L,
293263L, 290200L, 292511L, 291510L, 293711L, 295234L, 293839L,
291057L, 295094L, 295562L, 292381L, 289765L, 293871L, 291076L,
293851L), Sex = c("FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE",
"FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE",
"FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE",
"FEMALE"), Location = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Obs1 = c(16.5, 20.2,
19.2, 15.8, 18.1, 17.9, 16.7, 18.3, 18.6, 18.2, 16.5, 15, 16.7,
16.5, 14.7, 17.4, 17.8, 18.2, 16.8, 16.1), Obs4 = c(4836L, 5428L,
48L, 2904L, 6194L, 616L, 792L, 4992L, 350L, 264L, 216L, 4114L,
56L, 3154L, 966L, 1980L, 3492L, 3760L, 88L, 2242L), Obs5 = c(0.563636364,
0.584158416, 0.005208333, 0.417721519, 0.900552486, 0.078212291,
0.107784431, 0.568306011, 0.037634409, 0.032967033, 0.036363636,
0.806666667, 0.005988024, 0.503030303, 0.142857143, 0.25862069,
0.54494382, 0.516483516, 0.011904762, 0.366459627)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20"))
#数据
df我建议采用base R
方法，在Obs
中定义要替换的列：
#Data
df <- structure(list(ID = c(291978L, 292429L, 292466L, 293656L, 291993L,
293263L, 290200L, 292511L, 291510L, 293711L, 295234L, 293839L,
291057L, 295094L, 295562L, 292381L, 289765L, 293871L, 291076L,
293851L), Sex = c("FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE",
"FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE",
"FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE", "FEMALE",
"FEMALE"), Location = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Obs1 = c(16.5, 20.2,
19.2, 15.8, 18.1, 17.9, 16.7, 18.3, 18.6, 18.2, 16.5, 15, 16.7,
16.5, 14.7, 17.4, 17.8, 18.2, 16.8, 16.1), Obs4 = c(4836L, 5428L,
48L, 2904L, 6194L, 616L, 792L, 4992L, 350L, 264L, 216L, 4114L,
56L, 3154L, 966L, 1980L, 3492L, 3760L, 88L, 2242L), Obs5 = c(0.563636364,
0.584158416, 0.005208333, 0.417721519, 0.900552486, 0.078212291,
0.107784431, 0.568306011, 0.037634409, 0.032967033, 0.036363636,
0.806666667, 0.005988024, 0.503030303, 0.142857143, 0.25862069,
0.54494382, 0.516483516, 0.011904762, 0.366459627)), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20"))
#数据
df或使用在
> Obs = c("Obs1", "Obs4")
> df %>% mutate_at(Obs, function(x) x = NA)
ID Sex Location Obs1 Obs4 Obs5
1 291978 FEMALE 2 NA NA 0.563636364
2 292429 FEMALE 2 NA NA 0.584158416
3 292466 FEMALE 2 NA NA 0.005208333
4 293656 FEMALE 2 NA NA 0.417721519
5 291993 FEMALE 2 NA NA 0.900552486
6 293263 FEMALE 2 NA NA 0.078212291
7 290200 FEMALE 2 NA NA 0.107784431
8 292511 FEMALE 2 NA NA 0.568306011
9 291510 FEMALE 2 NA NA 0.037634409
10 293711 FEMALE 2 NA NA 0.032967033
11 295234 FEMALE 2 NA NA 0.036363636
12 293839 FEMALE 2 NA NA 0.806666667
13 291057 FEMALE 2 NA NA 0.005988024
14 295094 FEMALE 2 NA NA 0.503030303
15 295562 FEMALE 2 NA NA 0.142857143
16 292381 FEMALE 2 NA NA 0.258620690
17 289765 FEMALE 2 NA NA 0.544943820
18 293871 FEMALE 2 NA NA 0.516483516
19 291076 FEMALE 2 NA NA 0.011904762
20 293851 FEMALE 2 NA NA 0.366459627
或者在
处使用mutate\u：
> Obs = c("Obs1", "Obs4")
> df %>% mutate_at(Obs, function(x) x = NA)
ID Sex Location Obs1 Obs4 Obs5
1 291978 FEMALE 2 NA NA 0.563636364
2 292429 FEMALE 2 NA NA 0.584158416
3 292466 FEMALE 2 NA NA 0.005208333
4 293656 FEMALE 2 NA NA 0.417721519
5 291993 FEMALE 2 NA NA 0.900552486
6 293263 FEMALE 2 NA NA 0.078212291
7 290200 FEMALE 2 NA NA 0.107784431
8 292511 FEMALE 2 NA NA 0.568306011
9 291510 FEMALE 2 NA NA 0.037634409
10 293711 FEMALE 2 NA NA 0.032967033
11 295234 FEMALE 2 NA NA 0.036363636
12 293839 FEMALE 2 NA NA 0.806666667
13 291057 FEMALE 2 NA NA 0.005988024
14 295094 FEMALE 2 NA NA 0.503030303
15 295562 FEMALE 2 NA NA 0.142857143
16 292381 FEMALE 2 NA NA 0.258620690
17 289765 FEMALE 2 NA NA 0.544943820
18 293871 FEMALE 2 NA NA 0.516483516
19 291076 FEMALE 2 NA NA 0.011904762
20 293851 FEMALE 2 NA NA 0.366459627
下面是如何使用mutate
和跨
执行此操作
cols\u na%
变异（跨越（一个或多个（cols\u na），~na））
以下是使用变异
和跨
的方法
cols\u na%
变异（跨越（一个或多个（cols\u na），~na））
Oh，忘了提到我想要保持列名相同。Test2[，Obs]Test2[Obs]哦，忘了提到我想要保持列名相同。Test2[，Obs]Test2[Obs]记住，mutate
的美妙之处在于你不必设置匿名函数！您可以只执行~NA
而不是函数（x）x=NA
。另外，mutate_at（）
已被取代。请记住，mutate
的妙处在于您不必设置匿名函数！您可以只执行~NA
而不是函数（x）x=NA
。此外，mutate_at（）
也已被取代。
ID Sex Location Obs1 Obs4 Obs5
1 291978 FEMALE 2 <NA> 4836 0.563636364
2 292429 FEMALE 2 <NA> 5428 0.584158416
3 292466 FEMALE 2 <NA> 48 0.005208333
4 293656 FEMALE 2 <NA> 2904 0.417721519
5 291993 FEMALE 2 <NA> 6194 0.900552486
6 293263 FEMALE 2 <NA> 616 0.078212291
7 290200 FEMALE 2 <NA> 792 0.107784431
8 292511 FEMALE 2 <NA> 4992 0.568306011
9 291510 FEMALE 2 <NA> 350 0.037634409
10 293711 FEMALE 2 <NA> 264 0.032967033
11 295234 FEMALE 2 <NA> 216 0.036363636
12 293839 FEMALE 2 <NA> 4114 0.806666667
13 291057 FEMALE 2 <NA> 56 0.005988024
14 295094 FEMALE 2 <NA> 3154 0.503030303
15 295562 FEMALE 2 <NA> 966 0.142857143
16 292381 FEMALE 2 <NA> 1980 0.258620690
17 289765 FEMALE 2 <NA> 3492 0.544943820
18 293871 FEMALE 2 <NA> 3760 0.516483516
19 291076 FEMALE 2 <NA> 88 0.011904762
20 293851 FEMALE 2 <NA> 2242 0.366459627
> Obs = c("Obs1", "Obs4")
> df %>% mutate_at(Obs, function(x) x = NA)
ID Sex Location Obs1 Obs4 Obs5
1 291978 FEMALE 2 NA NA 0.563636364
2 292429 FEMALE 2 NA NA 0.584158416
3 292466 FEMALE 2 NA NA 0.005208333
4 293656 FEMALE 2 NA NA 0.417721519
5 291993 FEMALE 2 NA NA 0.900552486
6 293263 FEMALE 2 NA NA 0.078212291
7 290200 FEMALE 2 NA NA 0.107784431
8 292511 FEMALE 2 NA NA 0.568306011
9 291510 FEMALE 2 NA NA 0.037634409
10 293711 FEMALE 2 NA NA 0.032967033
11 295234 FEMALE 2 NA NA 0.036363636
12 293839 FEMALE 2 NA NA 0.806666667
13 291057 FEMALE 2 NA NA 0.005988024
14 295094 FEMALE 2 NA NA 0.503030303
15 295562 FEMALE 2 NA NA 0.142857143
16 292381 FEMALE 2 NA NA 0.258620690
17 289765 FEMALE 2 NA NA 0.544943820
18 293871 FEMALE 2 NA NA 0.516483516
19 291076 FEMALE 2 NA NA 0.011904762
20 293851 FEMALE 2 NA NA 0.366459627