How do I sweep specific columns with dplyr?
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
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