R: Caculating new variable by category with a different equation for each category

Question

I have been searching an answer to this for a while but nothing seems to fit me.

I have a dataframe called "data" that looks like this:

Species	Length	Weight
A	15	0
A	8	0
B	20	0
C	4	0
B	11	0
B	16	0
C	13	0

What I would like to do is to calculate the weight of each individual of a species based on their length ant fill the Weigth column with the results. For that purpose, I have already obtained an equation for each species, since each species has a different length-weight relation. Lets say those length-weight equations look as follows:

• "A" species: weight=length*2
• "B" species: weight=length^3
• "C" species: weight=length/5

So, as a result, I would like to have have a dataframe that looks like this:

Species	Length	Weight
A	15	30
A	8	16
B	20	8000
C	4	0.8
B	11	1331
B	16	4096
C	13	3.6

I have tried to do it this way:

Species <- c("A", "A", "B", "C", "B","B","C")
Length <- c(15,8,20,4,11,16,13)
Weight <- c(0,0,0,0,0,0,0)
data <- data.frame(Species, Length, Weight)

for(i in 1:length(data$Length)){if(data$Species[i]=="A"){
data$Weight[i]<-data$Length[i]*2
}else if(data$Species[i]=="B"){
data$Weight[i]<-data$Length[i]^3
}else if(data$Species=="C"){
data$Weight[i]<-data$Length[i]/5
} else {data$Weight[i]<-"NA"}
}

The code runs without error, but the data$Weigth column is not filled with the results. However, when I tried the code without using for() and replacing [i] with a row number (for example [4]), it did the correct thing and fills the Weight column with the result for that row.

In reality I have 40 species and over 5000 observations, so running the code for each row individually is not an option.

Any suggestions? Maybe there is an easier way but I don´t see it? Any help will be much appreciated.

Thanks

Answer 1

In base R you could do:

transform(df, 
  Weight = Vectorize(\(x, type) switch(type, A = x*2, B= x^3, C =x/5))(Length, Species))

  Species Length Weight
1       A     15   30.0
2       A      8   16.0
3       B     20 8000.0
4       C      4    0.8
5       B     11 1331.0
6       B     16 4096.0
7       C     13    2.6

library(tidyverse)
df %>%
  mutate(Weight = case_when(Species == 'A' ~Length * 2,
                            Species == 'B' ~ Length ^ 3,
                            Species == 'C' ~ Length / 5))