How do I sweep specific columns with dplyr?

Question

An incredibly common operation for my type of data is applying a normalisation factor to all columns. This can be done efficiently using sweep or scale

Answer 1

Given akrun's encouragement, let me post what I did as an answer here. I just intuitively thought that you might want to ask R to indicate columns with a same name to do this mutate_each. For instance, if . indicates the column, A, I thought another column named A from another data.frame might be something dplyr might like. So, I created a data frame for factors then used mutate_each. It seems that the outcome is right. Since I have no technical background, I am afraid that I cannot really provide any explanation. I hope you do not mind that.

factors <- data.frame(A = 1, B = 1.2, C = 0.8, D = 0.75)

mutate_at(data, vars(A:D), funs(. / foo$.))

# By the time I answered this question, the following was working.
# But mutate_each() is now deprecated.

# mutate_each(data, funs(. / factors$.), A:D)

#  ID Type    A           B      C          D
#1  1    X    3   0.8333333   3.75   5.333333
#2  2    X  174 106.6666667  82.50  76.000000
#3  3    X    6   1.6666667   2.50   5.333333
#4  4    Y 1377 849.1666667 312.50 334.666667
#5  5    Y  537 353.3333333 161.25 165.333333
#6  6    Y  173 115.8333333  50.00  50.666667

EDIT

This also works. Given data frame is a special case of list, this is not perhaps surprising.

# Experiment
foo <- list(A = 1, B = 1.2, C = 0.8, D = 0.75)

mutate_at(data, vars(A:D), funs(. / foo$.))

# mutate_each(data, funs(. / foo$.), A:D)

#  ID Type    A           B      C          D
#1  1    X    3   0.8333333   3.75   5.333333
#2  2    X  174 106.6666667  82.50  76.000000
#3  3    X    6   1.6666667   2.50   5.333333
#4  4    Y 1377 849.1666667 312.50 334.666667
#5  5    Y  537 353.3333333 161.25 165.333333
#6  6    Y  173 115.8333333  50.00  50.666667

Answer 2

From dplyr 1.0.0, you can do:

data %>%
 rowwise() %>%
 mutate(across(A:D)/factors)

     ID Type      A       B      C      D
  <dbl> <chr> <dbl>   <dbl>  <dbl>  <dbl>
1     1 X         3   0.833   3.75   5.33
2     2 X       174 107.     82.5   76   
3     3 X         6   1.67    2.5    5.33
4     4 Y      1377 849.    312.   335.  
5     5 Y       537 353.    161.   165.  
6     6 Y       173 116.     50     50.7