如何一次性将多列添加到data.frame？

Question

I have following dataframe and vector:

ddf = data.frame(a=rep(1,10), b=rep(2,10))
xx = c("c", "d", "e", "f")

How can I new empty columns which are named with items in xx ?

I tried following but it does not work:

ddf = cbind(ddf, data.frame(xx))
Error in data.frame(..., check.names = FALSE) : 
  arguments imply differing number of rows: 10, 4

Following also does not work:

for(i in 1:length(xx)){
    ddf$(xx[i]) = ""  
}

Error: unexpected '(' in:
"for(i in 1:length(xx)){
ddf$("
 }
Error: unexpected '}' in "}"

Answer 1

This will get you there:

ddf[xx] <- NA

#   a b  c  d  e  f
#1  1 2 NA NA NA NA
#2  1 2 NA NA NA NA
#3  1 2 NA NA NA NA
#...

You can't directly use something like ddf$xx because this will try to assign to a column called xx rather than interpreting xx . You need to use [ and [<- functions, using the square brackets when you are dealing with a character string/vector - like ddf["columnname"] or ddf[c("col1","col2")] , or a stored vector like your ddf[xx] .

The reason why it selects columns is because data.frames are lists essentially:

is.list(ddf)
#[1] TRUE

as.list(ddf)
#$a
# [1] 1 1 1 1 1 1 1 1 1 1
# 
#$b
# [1] 2 2 2 2 2 2 2 2 2 2

...with each column corresponding to a list entry. So if you don't use a comma to specify a row, like ddf["name",] or a column like ddf[,"name"] , you get the column by default.

---

In the case that you are working with a 0-row dataset, you can not use a value like NA as the replacement. Instead, replace with list(character(0)) where character(0) can be substituted for numeric(0) , integer(0) , logical(0) etc, depending on the class you want for your new columns.

ddf <- data.frame(a=character())
xx <- c("c", "d", "e", "f")
ddf[xx] <- list(character(0))
ddf
#[1] a c d e f
#<0 rows> (or 0-length row.names)

Answer 2

This seems to succeed:

> cbind(ddf, setNames( lapply(xx, function(x) x=NA), xx) )
   a b  c  d  e  f
1  1 2 NA NA NA NA
2  1 2 NA NA NA NA
3  1 2 NA NA NA NA
4  1 2 NA NA NA NA
5  1 2 NA NA NA NA
6  1 2 NA NA NA NA
7  1 2 NA NA NA NA
8  1 2 NA NA NA NA
9  1 2 NA NA NA NA
10 1 2 NA NA NA NA