How can I best use dplyr to subset data and create relative frequency tables?

Question

I'm using the iris data set to learn how to use dplyr, and am trying to create a relative frequency table that looks like this:

Petal.Width	.1	.2	.3	.4	.5	.6	1	1.1	1.2	1.3	1.4	1.5	1.6	1.7	1.8
Species
setosa	0.10	0.58	0.14	0.14	0.02	0.02	0	0	0	0	0	0	0	0	0
versicolor	0	0	0	0	0	0	0.14	0.06	0.10	0.26	0.14	0.02	0.20	0.04	0.06

I'm struggling to group the observations by species, and then produce relative frequencies on a species by species basis.

I'm guessing it'll have to be something using group_by, mutate, and count, but the closest thing I could find online was this:

my_data %>% 
    group_by(Petal.Width,Species) %>% 
    summarise(n = n()) %>%
    ungroup %>% 
    mutate(total = sum(n), rel.freq = n / total)

This was still not quite what I was looking for as it is the total number of observations, not the number per species.

Any help is appreciated greatly!

Answer 1

Something like this?

Not sure about the "wide" format though; I'd be inclined to keep it as long (omit the pivot_wider step).

library(dplyr)
library(tidyr)

iris %>% 
  count(Species, Petal.Width) %>% 
  group_by(Species) %>% 
  mutate(p = n/sum(n)) %>% 
  ungroup() %>% 
  select(-n) %>% 
  pivot_wider(names_from = "Petal.Width", values_from = "p")

Result:

Species    `0.1` `0.2` `0.3` `0.4` `0.5` `0.6`   `1` `1.1` `1.2` `1.3` `1.4` `1.5` `1.6` `1.7` `1.8` `1.9`   `2` `2.1` `2.2` `2.3` `2.4` `2.5`
  <fct>      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 setosa       0.1  0.58  0.14  0.14  0.02  0.02 NA    NA     NA   NA    NA    NA    NA    NA    NA     NA   NA    NA    NA    NA    NA    NA   
2 versicolor  NA   NA    NA    NA    NA    NA     0.14  0.06   0.1  0.26  0.14  0.2   0.06  0.02  0.02  NA   NA    NA    NA    NA    NA    NA   
3 virginica   NA   NA    NA    NA    NA    NA    NA    NA     NA   NA     0.02  0.04  0.02  0.02  0.22   0.1  0.12  0.12  0.06  0.16  0.06  0.06