refer to range of columns by name in R

Question

I need help with something that might be fairly simple in R. I want to refer to a range of columns in a data frame (e.g., extracting a few select variables). However, I don't know their column numbers. Normally, if I wanted to extract columns 4-10 i would say mydata[,4:10].

However, given that I don't know the column numbers, I would want to refer to them by name. Is there an easy way to do this? in sas or spss it is fairly easy to refer to a range of variables by name. Alternatively, is there an easy way to figure out which column number corresponds to a variable name in R?

Answer 1

A column number can be identified from a column name within a data frame as follows:

which(colnames(mydf)=="a")

where mydf is a data frame and a is the name of the column the column number is required for.

(Source)

This can be used to create a column range:

firstcol = which(colnames(x)=="a")
lastcol = which(colnames(x)=="b")

mydf[c(firstcol:lastcol)]

Answer 2

Getting a range of columns can be done in several ways. subset(data.frame, select = name4:name10), works but is quite long. I used that before I got annoyed writing long commands for a simple thing. I made a function to tackle the naming columns / not remembering column numbers in large data frames:

coln <- function(X){
  y <- rbind(seq(1,ncol(X)))
  colnames(y) <- colnames(X)
rownames(y) <- "col.number"
  return(y)} 

Here is how it works:

df <- data.frame(a = 1:10, b =10:1, c = 1:10)
coln(df)
           a b c
col.number 1 2 3

Now you can call them with numbers and still look at names.

Answer 3

Use %in% in combination with names(). It's useful for grabbing a group of columns from a data frame. You can negate the expression when you want to keep just a subset and drop the rest. Type ?"%in%" at the R Console prompt for more details.

set.seed(1234)
mydf <- data.frame(A = runif(5, 1, 2),
                   B = runif(5, 3, 4),
                   C = runif(5, 5, 6),
                   D = runif(5, 7, 8),
                   E = runif(5, 9, 10))
mydf

keep.cols <- c('A','D','E')
mydf[, names(mydf) %in% keep.cols]
drop.cols <- c('A','B','C')
mydf[, !names(mydf) %in% drop.cols]

The data frame:

> mydf
         A        B        C        D        E
1 1.113703 3.640311 5.693591 7.837296 9.316612
2 1.622299 3.009496 5.544975 7.286223 9.302693
3 1.609275 3.232551 5.282734 7.266821 9.159046
4 1.623379 3.666084 5.923433 7.186723 9.039996
5 1.860915 3.514251 5.292316 7.232226 9.218800

A subset of columns:

> mydf[, names(mydf) %in% keep.cols]
         A        D        E
1 1.113703 7.837296 9.316612
2 1.622299 7.286223 9.302693
3 1.609275 7.266821 9.159046
4 1.623379 7.186723 9.039996
5 1.860915 7.232226 9.218800

Keeping a subset of columns and dropping the rest:

> mydf[, !names(mydf) %in% drop.cols]
         D        E
1 7.837296 9.316612
2 7.286223 9.302693
3 7.266821 9.159046
4 7.186723 9.039996
5 7.232226 9.218800

Answer 4

I think I figured it out, but it's a bit ornery. Here's an example using mtcars to get the columns between hp and vs. do.call usually means there is a simpler way, though.

mtcars[do.call(seq, as.list(match(c("hp", "vs"), colnames(mtcars))))]

Answer 5

Here is a fun little function that combines the ideas behind Largh's answer with a handy function call. To use it, just enter

call.cols(mydata, "firstvarname", "lastvarname")

call.cols <- function(df, startvar, endvar) {
  col.num <- function(df){
    var.nums <- seq(1,ncol(df))
    names(var.nums) <- colnames(df)      
    return(var.nums)
  } 

 start.num <- as.numeric(col.num(df)[startvar])
 end.num <- as.numeric(col.num(df)[endvar])
 range.num <- start.num:end.num
 return(df[range.num]) 
}

I plan to expand this to use for scale creation for psychometric research.