我有一堆具有不同变量的数据框。我想将它们读入 R 并将列添加到那些缺少一些变量的列中，以便它们都具有一组通用的标准变量，即使有些变量未被观察到。
换句话说...当列不存在时，有没有办法在 tidyverse 中添加
NAtop_speedmpgMazda RX4library(tidyverse)
mtcars %>%
as_tibble() %>%
rownames_to_column("car") %>%
mutate(top_speed = ifelse("top_speed" %in% names(.), top_speed, NA),
mpg = ifelse("mpg" %in% names(.), mpg, NA)) %>%
select(car, top_speed, mpg, everything())
# # A tibble: 32 x 13
# car top_speed mpg cyl disp hp drat wt qsec vs am gear carb
# <chr> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 Mazda RX4 NA 21 6 160.0 110 3.90 2.620 16.46 0 1 4 4
# 2 Mazda RX4 Wag NA 21 6 160.0 110 3.90 2.875 17.02 0 1 4 4
# 3 Datsun 710 NA 21 4 108.0 93 3.85 2.320 18.61 1 1 4 1
# 4 Hornet 4 Drive NA 21 6 258.0 110 3.08 3.215 19.44 1 0 3 1
# 5 Hornet Sportabout NA 21 8 360.0 175 3.15 3.440 17.02 0 0 3 2
# 6 Valiant NA 21 6 225.0 105 2.76 3.460 20.22 1 0 3 1
# 7 Duster 360 NA 21 8 360.0 245 3.21 3.570 15.84 0 0 3 4
# 8 Merc 240D NA 21 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# 9 Merc 230 NA 21 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# 10 Merc 280 NA 21 6 167.6 123 3.92 3.440 18.30 1 0 4 4
另一个选项，不需要使用 tibble 的
add_columnlibrary(tibble)
cols <- c(top_speed = NA_real_, nhj = NA_real_, mpg = NA_real_)
add_column(mtcars, !!!cols[setdiff(names(cols), names(mtcars))])
我们可以创建一个辅助函数来创建列
fncols <- function(data, cname) {
add <-cname[!cname%in%names(data)]
if(length(add)!=0) data[add] <- NA
data
}
fncols(mtcars, "mpg")
fncols(mtcars, c("topspeed","nhj","mpg"))
您可以像这样使用
rowwiselibrary(tidyverse)
mtcars %>%
tbl_df() %>%
rownames_to_column("car") %>%
rowwise() %>%
mutate(top_speed = ifelse("top_speed" %in% names(.), top_speed, NA),
mpg = ifelse("mpg" %in% names(.), mpg, NA)) %>%
select(car, top_speed, mpg, everything())
如果您有一个包含要检查的所有名称的空数据框，则可以使用
bind_rows我使用
purrr:map_dfrtibblecolumns = c("top_speed", "mpg") %>%
map_dfr( ~tibble(!!.x := logical() ) )
# A tibble: 0 x 2
# ... with 2 variables: top_speed <lgl>, mpg <lgl>
bind_rows(columns, mtcars)
# A tibble: 32 x 12
top_speed mpg cyl disp hp drat wt qsec vs am gear carb
<lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 NA 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 NA 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 NA 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
根据 Hadley (2023)，我们应该使用
dplyr::bind_rows()# Case when column exists
dplyr::tibble(x='a',y='b') %>% dplyr::bind_rows(dplyr::tibble(y=character()))
# Case when column does not exist
dplyr::tibble(x='a') %>% dplyr::bind_rows(dplyr::tibble(y=character()))
原始答案（从 dplyr_1.1.0 开始已过时）： 或者，您可以将
dplyr::union_all()# Case when column exists
dplyr::tibble(x='a',y='b') %>% dplyr::union_all(dplyr::tibble(y=character()))
# Case when column does not exist
dplyr::tibble(x='a') %>% dplyr::union_all(dplyr::tibble(y=character()))
尝试以下方法，
library(tidyverse)
mtcars %>%
tbl_df() %>%
rownames_to_column("car") %>%
mutate(top_speed = if ("top_speed" %in% names(.)){return(top_speed)}else{return(NA)},
mpg = if ("mpg" %in% names(.)){return(mpg)}else{return(NA)}) %>%
select(car, top_speed, mpg, everything())
# A tibble: 32 x 13
car top_speed mpg cyl disp hp drat wt qsec vs am gear carb
<chr> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Mazda RX4 NA 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
2 Mazda RX4 Wag NA 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
3 Datsun 710 NA 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
4 Hornet 4 Drive NA 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
5 Hornet Sportabout NA 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
6 Valiant NA 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
7 Duster 360 NA 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
8 Merc 240D NA 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
9 Merc 230 NA 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
10 Merc 280 NA 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
# ... with 22 more rows
我认为 ifelse() 不会从对象继承类。
如果您已经有一个包含所有必需列的数据框，请说
library(tidyverse)
df_with_required_columns =
mtcars %>%
mutate(top_speed = NA_real_) %>%
select(top_speed, mpg)
然后你可以简单地
bind_rowsmtcars %>%
rownames_to_column("car") %>%
bind_rows( df_with_required_columns %>% filter(F) ) %>%
select(car, top_speed, mpg, everything())
请注意，缺失的列将采用
df_with_required_columns您可以将新 data.frame 的列与填充 NA 的假完整 data.frame 绑定，重命名重复的列，然后仅过滤原始名称。
# your default complete vector of col names
standard.variables = names(mtcars)
# prep
default=mtcars %>% mutate_all(.funs=function(x) NA)
# treat with a data.frame missing 3 columns
test=mtcars %>% select(-mpg, -disp, -am)
bind_cols(test, default) %>% setNames(make.names(names(.), unique=TRUE)) %>%
select_(.dots=standard.variables) %>% head(2)
#### mpg cyl disp hp drat wt qsec vs am gear carb
#### 1 NA 6 NA 110 3.9 2.620 16.46 0 NA 4 4
#### 2 NA 6 NA 110 3.9 2.875 17.02 0 NA 4 4
基于@Joris C. 的回答。这是一个管道友好的解决方案，不需要预先创建
clsmtcars %>%
# an inline anonymous function to add the needed columns
(function(.df){
cls <- c("top_speed", "nhj") # columns I need
# adding cls columns with NAs if not present in the piped data.frame
.df[cls[!(cls %in% colnames(.df))]] = NA
return(.df)
})