Mutliply several columns of a dataframe by a factor (scalar)

Question

I have a very basic problem and can't find a solution, so sorry in advance for the beginner question.

I have a data frame with several ID columns and 30 numerical columns. I want to multiply all values of those 30 columns with the same factor. I want to keep the the rest of the data frame unchanged. I figured that dplyr and transmute_all or transmute_at are my friends, but I can't find a way to express the function Column1:Column30 * factor. All examples given use simple functions like mean and that doesn't help me with the expression.

Answer 1

I would use mutate_at. For example:

library(dplyr)

mtcars %>% 
  mutate_at(vars(mpg:qsec),
            .funs = funs(. * 3))

Answer 2

I'll give a solution with data.table, the dplyr version should be close to identical.

library(data.table)
# convert to data.table format to use data.table syntax
setDT(my_df) 

# .SD refers to all the columns mentioned in the .SDcols argument
#   (all columns by default when this argument is not specified)
# - instead of using backticks around *, you could use quotes: "*"
my_df[ , lapply(.SD, `*`, factor), .SDcols = Column1:Column30]

On some made-up data

set.seed(0123498)
# create fake data
DT = setDT(replicate(8, rnorm(5), simplify = FALSE))
DT
#            V1          V2         V3          V4         V5         V6        V7         V8
# 1: -0.2685077 -1.06491111  0.7307661  0.09880937  0.2791274 -0.5589676 1.5320685  0.4730013
# 2:  1.0783236 -0.17810929 -0.2578453  0.95940860  1.0990367 -0.6983235 0.9530062 -1.3800769
# 3:  1.1730611 -0.48828441 -1.6314077 -0.76117268 -0.5753245 -0.7370099 0.3982160 -0.8088035
# 4:  0.2060451 -0.07105785 -1.1878591 -0.83464592  2.1872117 -0.4390479 0.1428239  1.2634280
# 5:  1.6142695  0.46381602  0.5315299  2.34790945 -1.2977851  1.0428450 1.9292390  0.5337248
scalar = 3
DT[ , lapply(.SD, "*", scalar), .SDcols = V4:V6]
#            V4         V5        V6
# 1:  0.2964281  0.8373822 -1.676903
# 2:  2.8782258  3.2971101 -2.094970
# 3: -2.2835180 -1.7259734 -2.211030
# 4: -2.5039378  6.5616352 -1.317144
# 5:  7.0437283 -3.8933554  3.128535