I have a number of columns that I would like to remove from a data frame. I know that we can delete them individually using something like:
df$x <- NULL
<
Out of interest, this flags up one of R's weird multiple syntax inconsistencies. For example given a two-column data frame:
df <- data.frame(x=1, y=2)
This gives a data frame
subset(df, select=-y)
but this gives a vector
df[,-2]
This is all explained in ?[
but it's not exactly expected behaviour. Well at least not to me...
If you have a large data.frame
and are low on memory use [
. . . . or rm and within to remove columns of a data.frame
, as subset
is currently (R 3.6.2) using more memory - beside the hint of the manual to use subset
interactively.
getData <- function() {
n <- 1e7
set.seed(7)
data.frame(a = runif(n), b = runif(n), c = runif(n), d = runif(n))
}
DF <- getData()
tt <- sum(.Internal(gc(FALSE, TRUE, TRUE))[13:14])
DF <- DF[setdiff(names(DF), c("a", "c"))] ##
#DF <- DF[!(names(DF) %in% c("a", "c"))] #Alternative
#DF <- DF[-match(c("a","c"),names(DF))] #Alternative
sum(.Internal(gc(FALSE, FALSE, TRUE))[13:14]) - tt
#0.1 MB are used
DF <- getData()
tt <- sum(.Internal(gc(FALSE, TRUE, TRUE))[13:14])
DF <- subset(DF, select = -c(a, c)) ##
sum(.Internal(gc(FALSE, FALSE, TRUE))[13:14]) - tt
#357 MB are used
DF <- getData()
tt <- sum(.Internal(gc(FALSE, TRUE, TRUE))[13:14])
DF <- within(DF, rm(a, c)) ##
sum(.Internal(gc(FALSE, FALSE, TRUE))[13:14]) - tt
#0.1 MB are used
DF <- getData()
tt <- sum(.Internal(gc(FALSE, TRUE, TRUE))[13:14])
DF[c("a", "c")] <- NULL ##
sum(.Internal(gc(FALSE, FALSE, TRUE))[13:14]) - tt
#0.1 MB are used
Provide the data frame and a string of comma separated names to remove:
remove_features <- function(df, features) {
rem_vec <- unlist(strsplit(features, ', '))
res <- df[,!(names(df) %in% rem_vec)]
return(res)
}
Usage:
remove_features(iris, "Sepal.Length, Petal.Width")
There's also the subset
command, useful if you know which columns you want:
df <- data.frame(a = 1:10, b = 2:11, c = 3:12)
df <- subset(df, select = c(a, c))
UPDATED after comment by @hadley: To drop columns a,c you could do:
df <- subset(df, select = -c(a, c))
list(NULL) also works:
dat <- mtcars
colnames(dat)
# [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
# [11] "carb"
dat[,c("mpg","cyl","wt")] <- list(NULL)
colnames(dat)
# [1] "disp" "hp" "drat" "qsec" "vs" "am" "gear" "carb"
Another dplyr
answer. If your variables have some common naming structure, you might try starts_with()
. For example
library(dplyr)
df <- data.frame(var1 = rnorm(5), var2 = rnorm(5), var3 = rnorm (5),
var4 = rnorm(5), char1 = rnorm(5), char2 = rnorm(5))
df
# var2 char1 var4 var3 char2 var1
#1 -0.4629512 -0.3595079 -0.04763169 0.6398194 0.70996579 0.75879754
#2 0.5489027 0.1572841 -1.65313658 -1.3228020 -1.42785427 0.31168919
#3 -0.1707694 -0.9036500 0.47583030 -0.6636173 0.02116066 0.03983268
df1 <- df %>% select(-starts_with("char"))
df1
# var2 var4 var3 var1
#1 -0.4629512 -0.04763169 0.6398194 0.75879754
#2 0.5489027 -1.65313658 -1.3228020 0.31168919
#3 -0.1707694 0.47583030 -0.6636173 0.03983268
If you want to drop a sequence of variables in the data frame, you can use :
. For example if you wanted to drop var2
, var3
, and all variables in between, you'd just be left with var1
:
df2 <- df1 %>% select(-c(var2:var3) )
df2
# var1
#1 0.75879754
#2 0.31168919
#3 0.03983268