R: Remove multiple empty columns of character vari

问题:

I have a data frame where all the variables are of character type. Many of the columns are completely empty, i.e. only the variable headers are there, but no values. Is there any way to subset out the empty columns?

回答1:

If your empty columns are really empty character columns, something like the following should work. It will need to be modified if your "empty" character columns include, say, spaces.

Sample data:

mydf <- data.frame(
  A = c("a", "b"),
  B = c("y", ""),
  C = c("", ""),
  D = c("", ""),
  E = c("", "z")
)
mydf
#   A B C D E
# 1 a y      
# 2 b       z

Identifying and removing the "empty" columns.

mydf[!sapply(mydf, function(x) all(x == ""))]
#   A B E
# 1 a y  
# 2 b   z

---

Alternatively, as recommended by @Roland:

> mydf[, colSums(mydf != "") != 0]
  A B E
1 a y  
2 b   z

回答2:

You can do either of the following:

emptycols <- sapply(df, function (k) all(is.na(k)))
df <- df[!emptycols]

or:

emptycols <- colSums(is.na(df)) == nrow(df)
df <- df[!emptycols]

If by empty you mean they are "", the second approach can be adapted like so:

emptycols <- colSums(df == "") == nrow(df)

回答3:

I have a similar situation -- I'm working with a large public records database but when I whittle it down to just the date range and category that I need, there are a ton of columns that aren't in use. Some are blank and some are NA.

The selected answer: https://stackoverflow.com/a/17672737/233467 didn't work for me, but this did:

df[!sapply(df, function (x) all(is.na(x) | x == ""))]

回答4:

It depends what you mean by empty: Is it NA or "", or can it even be " "? Something like this might work:

df[,!apply(df, 2, function(x) all(gsub(" ", "", x)=="", na.rm=TRUE))]

回答5:

If you're talking about columns where all values are NA, use remove_empty("cols") from the janitor package.

If you have character vectors where every value is the empty string "", you can first convert those values to NA throughout your data.frame with na_if from the dplyr package:

dat <- data.frame(
  x = c("a", "b", "c"),
  y = c("", "", ""),
  z = c(NA, NA, NA),
  stringsAsFactors = FALSE
)

dat
#>   x y  z
#> 1 a   NA
#> 2 b   NA
#> 3 c   NA

library(dplyr)
library(janitor)

dat %>%
  mutate_all(funs(na_if(., ""))) %>%
  remove_empty("cols")
#>   x
#> 1 a
#> 2 b
#> 3 c

回答6:

Here is something that can be modified to exclude columns containing any variables specied.

newdf= df[, apply(df, 2, function(x) !any({is.na(x) | x== "" | 
x== "-4"} ) )] 

回答7:

If you know the column indices, you can use

df[,-c(3, 5, 7)]

This will omit columns 3, 5, 7.