R: calculate rates within grouped values in dplyr [duplicate] - r

Question

This question already has answers here:
Summarizing by subgroup percentage in R
(2 answers)
Closed 2 years ago.
Say we have a dataframe looking like this one below:
month issue amount
Jan withdrawal 250
Jan delay 120
Jan other 65
Feb withdrawal 189
Feb delay 122
Feb other 89
My goal is to tweak this in order to get a dataframe giving me the percentages of each value in issue related to each month. Plainly speaking, my desired output should look like follows:
month issue rate
Jan withdrawal 57.47
Jan delay 27.59
Jan other 14.94
Feb withdrawal 47.25
Feb delay 30.50
Feb other 22.25
I've tried helping myself with dplyr but my attempts had all been unsuccessful so far.

Answer 1

library(tidyverse)
df <- read.table(text = "month issue amount
Jan withdrawal 250
Jan delay 120
Jan other 65
Feb withdrawal 189
Feb delay 122
Feb other 89", header = T)
df
#> month issue amount
#> 1 Jan withdrawal 250
#> 2 Jan delay 120
#> 3 Jan other 65
#> 4 Feb withdrawal 189
#> 5 Feb delay 122
#> 6 Feb other 89
df %>%
group_by(month) %>%
mutate(rate = amount / sum(amount, na.rm = T) * 100)
#> # A tibble: 6 x 4
#> # Groups: month [2]
#> month issue amount rate
#> <chr> <chr> <int> <dbl>
#> 1 Jan withdrawal 250 57.5
#> 2 Jan delay 120 27.6
#> 3 Jan other 65 14.9
#> 4 Feb withdrawal 189 47.2
#> 5 Feb delay 122 30.5
#> 6 Feb other 89 22.2
df %>%
group_by(month) %>%
mutate(rate = prop.table(amount) * 100)
#> # A tibble: 6 x 4
#> # Groups: month [2]
#> month issue amount rate
#> <chr> <chr> <int> <dbl>
#> 1 Jan withdrawal 250 57.5
#> 2 Jan delay 120 27.6
#> 3 Jan other 65 14.9
#> 4 Feb withdrawal 189 47.2
#> 5 Feb delay 122 30.5
#> 6 Feb other 89 22.2
Created on 2021-01-26 by the reprex package (v0.3.0)
using data.table
library(data.table)
setDT(df)
df[, rate := prop.table(amount) * 100, by = list(month)]
df
#> month issue amount rate
#> 1: Jan withdrawal 250 57.47126
#> 2: Jan delay 120 27.58621
#> 3: Jan other 65 14.94253
#> 4: Feb withdrawal 189 47.25000
#> 5: Feb delay 122 30.50000
#> 6: Feb other 89 22.25000
Created on 2021-01-26 by the reprex package (v0.3.0)

Answer 2

Try the answer here . it might be what you're looking for. Code is pasted below for your convenience.
library(dplyr)
group_by(df, group) %>% mutate(percent = value/sum(value))
In your case it would probably be something like :
library(dplyr)
group_by(df, month) %>% mutate(rate= amount/sum(amount))
Or alternatively :
group_by(df, month) %>% transmute(issue, rate= amount/sum(amount))