Efficiently transform multiple columns of a data frame

Question

I have a data frame, and I want to transform all columns (say, take the logs or whatever) with columns that match a certain name. So in the example below, I want to take the log of X.1 and X.2, but not Y or Z.1.

df <- data.frame(
  Y = sample(0:1, 10, replace = TRUE),
  X.1 = sample(1:10),
  X.2 = sample(1:10),
  Z.1 = sample(151:160)
)

# option 1, won't work for dozens of fields
df$X.1 <- log(df$X.1)
df$X.2 <- log(df$X.2)

Is there a good, efficient way to do this when the dataframe is several gigabtyes?

Answer 1

In the case of functions that will return a data.frame:

cols <- c("X.1","X.2")
df[cols] <- log(df[cols])

Otherwise you will need to use lapply or a loop over the columns. These solutions will be slower than the solution above, so only use them if you must.

df[cols] <- lapply(df[cols], function(x) c(NA,diff(x)))
for(col in cols) {
  df[col] <- c(NA,diff(df[col]))
}

Answer 2

vars <- c("X.1", "X.2")

df[vars] <- lapply(df[vars], log)