I understand the answer in R to repetitive things is usually "apply()" rather than loop. Is there a better R-design pattern for a nasty bit of code I create frequently?

So, pulling tabular data from HTML, I usually need to change the data type, and end up running something like this, to convert the first column to date format (from decimal), and columns 2-4 from character strings with comma thousand separators like "2,400,000" to numeric "2400000."

X[,1] <- decYY2YY(as.numeric(X[,1]))
X[,2] <- as.numeric(gsub(",", "", X[,2]))
X[,3] <- as.numeric(gsub(",", "", X[,3]))
X[,4] <- as.numeric(gsub(",", "", X[,4]))

I don't like that I have X[,number] repeated on both the left and ride sides here, or that I have basically the same statement repeated for 2-4.

Is there a very R-style way of making X[,2] less repetitive but still loop-free? Something that sort of says "apply this to columns 2,3,4---a function that reassigns the current column to a modified version in place?"

I don't want to create a whole, repeatable cleaning function, really, just a quick anonymous function that does this with less repetition.

• r
• coding-style
• apply

Assuming X is a data frame, I would do:

X[2:4] <- lapply(X[2:4], function (x) as.numeric(gsub(",", "", x)))

Something like

comma2numeric <- function(x) { as.numeric(gsub(",","",x)) }
X[,2:4] <- apply(X[,2:4],2,comma2numeric)

is a start. transform is a good modify-in-place idiom, but it operates with names rather than with column numbers.

edited: missing close-parenthesis in line 1