How do I subset column variables in DF1 based on the important variables I got in DF2?

Question

I have 2 df's like this

ID = c('x1','x2','x5')
df1 <- data.frame(ID)

x1 = c(1,2,3,4,5)
x2 = c(11,12,13,14,15)
x3 = c(21,22,23,24,25)
x4 = c(31,32,33,34,35)
x5 = c(41,42,43,44,45)
df2 <- data.frame(x1,x2,x3,x4,x5)

Desired output

  x1 x2 x5
1  1 11 41
2  2 12 42
3  3 13 43
4  4 14 44
5  5 15 45

I would like my new dataset to contain only those variables that are identified in df1 as important (i.e: x1,x2,x5) with the values from df2.

In this simple dataset, I know I could do this but just removing x3,x4 in df2 but ideally I would like to apply it to a larger data set where I have more than 100 variables and hence would like to do it programatically.

Answer 1

I can't find a dupe so here goes- simply subset by the values of as.character(df1$ID) as in

df2[as.character(df1$ID)] ## Or just `df2[df1$ID]` if its already a character
#   x1 x2 x5
# 1  1 11 41
# 2  2 12 42
# 3  3 13 43
# 4  4 14 44
# 5  5 15 45

The reason for as.character is in order to avoid sub-setting by df1$ID underlying storage mode (integer) rather by it's levels

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Though this question is tagged with data.table, so we could also do this by reference (if we have a data.table)- no need to convert to character

setDT(df2)[, setdiff(names(df2), df1$ID) := NULL]
df2
#    x1 x2 x5
# 1:  1 11 41
# 2:  2 12 42
# 3:  3 13 43
# 4:  4 14 44
# 5:  5 15 45