Reputation: 3278
I have a very large list comprised of data frames, every element of the list is a different data frame, where each column is comprised of different types of variables, and data frames of different lengths. I want to subset the data frames in this list, and keep only those columns have classes 'integer' or 'numeric', while keeping the data frame structure (so seemingly no 'lapply').
A MRE follows:
x1 <- c(1,2,3,4)
y1 <- c(letters[1:4])
z1 <- as.integer(c(0, 1, 0, 1))
df1 <- data.frame(x1,y1,z1)
str(df1)
x2 <- c(0, 1, 2, 3,4 )
y2 <- as.integer(c(0, 1, 0, 1, 0))
z2 <- c(letters[1:5])
df2 <- data.frame(x2,y2,z2)
str(df2)
list12 <- list(df1, df2)
str(list12)
#the following have not worked or returned errors:
#list12<- sapply(list12, function (x) subset(x, select = class %in% c('character', 'factor'), drop =FALSE))
#Error in match(x, table, nomatch = 0L) :
# 'match' requires vector arguments
#list12 <- list12[sapply(list12, function(x) subset(x, select x %in% class is.numeric(x) || is.integer(x))]
#unexpected symbol
#list12 <- list12[, sapply(list12, function(x) is.numeric(x) || is.integer(x))]
# incorrect number of dimensions
#list12 <- sapply(list12, function(x) subset(x, select = class is.numeric(x) || is.integer(x))
#unexpected symbol
My expected result is a list of 2 data frames, with only columns that contain integers or numeric classes
Upvotes: 4
Views: 1299
Reputation: 44658
I like David's answer (+1), but using sapply()
feels more natural to me.
lapply(list12, function(x) x[sapply(x, is.numeric)])
Upvotes: 1
Reputation: 92292
Another option is to use Filter
within lapply
lapply(list12, Filter, f = is.numeric)
# [[1]]
# x1 z1
# 1 1 0
# 2 2 1
# 3 3 0
# 4 4 1
#
# [[2]]
# x2 y2
# 1 0 0
# 2 1 1
# 3 2 0
# 4 3 1
# 5 4 0
Upvotes: 3
Reputation: 24490
Just try:
lapply(list12,function(x) x[vapply(x,class,"") %in% c("integer","numeric")])
Upvotes: 1