使用 mutate 和 gsub 将特定列中的所有值替换为 NA
Replace all values to NA in specific columns using mutate and gsub
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 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
首先，我想指定哪些列的值应该替换为 NA。这可以只是一列，也可以是多列。这就是为什么我更喜欢将它放入向量中。
> Obs <- c('Obs1')
然后，我尝试将 'Obs1' 列中的所有值替换为 NA，使用：
> deselect <- Test2 %>% mutate(across(paste(Obs), gsub(".*",NA,paste(Obs))))
但是，它给了我这个错误：
Error: Problem with `mutate()` input `..1`.
x Problem with `across()` input `.fns`.
i Input `.fns` must be NULL, a function, a formula, or a list of functions/formulas.
i Input `..1` is `across(paste(Obs), gsub(".*", NA, paste(Obs)))`.
Run `rlang::last_error()` to see where the error occurred.
有人知道如何在 mutate 中使用 gsub 吗？或者我应该使用其他方法？
非常感谢！
我建议采用 base R 方法，您可以在 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"))
下一个代码：
#Define cols
Obs <- c('Obs1')
#Assign
index <- which(names(df) %in% Obs)
df[,index] <- gsub(".*",NA,df[,index])
输出：
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
或使用mutate_at:
> 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
这是使用 mutate 和 across 的方法。
cols_na <- c("wt", "hp")
mtcars %>%
mutate(across(one_of(cols_na), ~ NA))
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 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
首先，我想指定哪些列的值应该替换为 NA。这可以只是一列，也可以是多列。这就是为什么我更喜欢将它放入向量中。
> Obs <- c('Obs1')
然后，我尝试将 'Obs1' 列中的所有值替换为 NA，使用：
> deselect <- Test2 %>% mutate(across(paste(Obs), gsub(".*",NA,paste(Obs))))
但是，它给了我这个错误：
Error: Problem with `mutate()` input `..1`.
x Problem with `across()` input `.fns`.
i Input `.fns` must be NULL, a function, a formula, or a list of functions/formulas.
i Input `..1` is `across(paste(Obs), gsub(".*", NA, paste(Obs)))`.
Run `rlang::last_error()` to see where the error occurred.
有人知道如何在 mutate 中使用 gsub 吗？或者我应该使用其他方法？
非常感谢！
我建议采用 base R 方法，您可以在 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"))
下一个代码：
#Define cols
Obs <- c('Obs1')
#Assign
index <- which(names(df) %in% Obs)
df[,index] <- gsub(".*",NA,df[,index])
输出：
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
或使用mutate_at:
> 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
这是使用 mutate 和 across 的方法。
cols_na <- c("wt", "hp")
mtcars %>%
mutate(across(one_of(cols_na), ~ NA))