I have a large data set and I would like to read specific columns or drop all the others.
data <- read.dta("file.dta")
I select the columns that I'm not interested in:
var.out <- names(data)[!names(data) %in% c("iden", "name", "x_serv", "m_serv")]
and than I'd like to do something like:
for(i in 1:length(var.out)) {
paste("data$", var.out[i], sep="") <- NULL
}
to drop all the unwanted columns. Is this the optimal solution?
You should use either indexing or the subset function. For example :
R> df <- data.frame(x=1:5, y=2:6, z=3:7, u=4:8)
R> df
x y z u
1 1 2 3 4
2 2 3 4 5
3 3 4 5 6
4 4 5 6 7
5 5 6 7 8
Then you can use the which function and the - operator in column indexation :
R> df[ , -which(names(df) %in% c("z","u"))]
x y
1 1 2
2 2 3
3 3 4
4 4 5
5 5 6
Or, much simpler, use the select argument of the subset function : you can then use the - operator directly on a vector of column names, and you can even omit the quotes around the names !
R> subset(df, select=-c(z,u))
x y
1 1 2
2 2 3
3 3 4
4 4 5
5 5 6
Note that you can also select the columns you want instead of dropping the others :
R> df[ , c("x","y")]
x y
1 1 2
2 2 3
3 3 4
4 4 5
5 5 6
R> subset(df, select=c(x,y))
x y
1 1 2
2 2 3
3 3 4
4 4 5
5 5 6