Compute variable according to factor levels - r

Question

I am kind of new to R and programming in general. I am currently strugling with a piece of code for data transformation and hope someone can take a little bit of time to help me.
Below a reproducible exemple :
# Data
a <- c(rnorm(12, 20))
b <- c(rnorm(12, 25))
f1 <- rep(c("X","Y","Z"), each=4) #family
f2 <- rep(x = c(0,1,50,100), 3) #reference and test levels
dt <- data.frame(f1=factor(f1), f2=factor(f2), a,b)
#library loading
library(tidyverse)
Goal : Compute all values (a,b) using a reference value. Calculation should be : a/a_ref with a_ref = a when f2=0 depending on the family (f1 can be X,Y or Z).
I tried to solve this by using this code :
test <- filter(dt, f2!=0) %>% group_by(f1) %>%
mutate("a/a_ref"=a/(filter(dt, f2==0) %>% group_by(f1) %>% distinct(a) %>% pull))
I get :
test results
as you can see a is divided by a_ref. But my script seems to recycle the use of reference values (a_ref) regardless of the family f1.
Do you have any suggestion so A is computed with regard of the family (f1) ?
Thank you for reading !
EDIT
I found a way to do it 'manualy'
filter(dt, f1=="X") %>% mutate("a/a_ref"=a/(filter(dt, f1=="X" & f2==0) %>% distinct(a) %>% pull()))
f1 f2 a b a/a_ref
1 X 0 21.77605 24.53115 1.0000000
2 X 1 20.17327 24.02512 0.9263973
3 X 50 19.81482 25.58103 0.9099366
4 X 100 19.90205 24.66322 0.9139422
the problem is that I'd have to update the code for each variable and family and thus is not a clean way to do it.

Answer 1

# use this to reproduce the same dataset and results
set.seed(5)
# Data
a <- c(rnorm(12, 20))
b <- c(rnorm(12, 25))
f1 <- rep(c("X","Y","Z"), each=4) #family
f2 <- rep(x = c(0,1,50,100), 3) #reference and test levels
dt <- data.frame(f1=factor(f1), f2=factor(f2), a,b)
#library loading
library(tidyverse)
dt %>%
group_by(f1) %>% # for each f1 value
mutate(a_ref = a[f2 == 0], # get the a_ref and add it in each row
"a/a_ref" = a/a_ref) %>% # divide a and a_ref
ungroup() %>% # forget the grouping
filter(f2 != 0) # remove rows where f2 == 0
# # A tibble: 9 x 6
# f1 f2 a b a_ref `a/a_ref`
# <fctr> <fctr> <dbl> <dbl> <dbl> <dbl>
# 1 X 1 21.38436 24.84247 19.15914 1.1161437
# 2 X 50 18.74451 23.92824 19.15914 0.9783583
# 3 X 100 20.07014 24.86101 19.15914 1.0475490
# 4 Y 1 19.39709 22.81603 21.71144 0.8934042
# 5 Y 50 19.52783 25.24082 21.71144 0.8994260
# 6 Y 100 19.36463 24.74064 21.71144 0.8919090
# 7 Z 1 20.13811 25.94187 19.71423 1.0215013
# 8 Z 50 21.22763 26.46796 19.71423 1.0767671
# 9 Z 100 19.19822 25.70676 19.71423 0.9738257
You can do this for more than one variable using:
dt %>%
group_by(f1) %>%
mutate_at(vars(a:b), funs(./.[f2 == 0])) %>%
ungroup()
Or generally use vars(a:z) to use all variables between a and z as long as they are one after the other in your dataset.
Another solution could be using mutate_if like:
dt %>%
group_by(f1) %>%
mutate_if(is.numeric, funs(./.[f2 == 0])) %>%
ungroup()
Where the function will be applied to all numeric variables you have. The variables f1 and f2 will be factor variables, so it just excludes those ones.