Summarize occurrences by area and then by custom groups

Question

I have below dataset that takes a 2 column dataset and creates age group categories depending on stated CustomerAge.

    library(tidyverse)
    
    df <- 
      read.table(textConnection("Area   CustomerAge
    A 28 
    A 40
    A 70
    A 19
    B 13
    B 12
    B 72
    B 90"), header=TRUE)
    

df2 <- df %>% 
  mutate(
    # Create categories
    Customer_Age_Group = dplyr::case_when(
      CustomerAge <= 18            ~ "0-18",
      CustomerAge > 18 & CustomerAge <= 60 ~ "19-60",
      CustomerAge > 60             ~ ">60"
    ))

What I am looking to achieve is an output summary that looks like the below:

Area	Customer_Age_Group	Occurrences
A	0-18	0
A	19-59	3
A	>60	1
B	0-18	2
B	19-59	0
B	>60	2

Answer 1

To include also 0 occurences you need count() , ungroup() and complete() :

df2 %>% group_by(Area, Customer_Age_Group,.drop = FALSE) %>% 
count() %>% 
ungroup() %>% 
complete(Area, Customer_Age_Group, fill=list(n=0))

This will show also 0 occurences.

To sort for Area and Age group:

df2 %>% group_by(Area, Customer_Age_Group,.drop = FALSE) %>% 
count() %>% 
ungroup() %>% 
complete(Area, Customer_Age_Group, fill=list(n=0)) %>% 
arrange(Area, parse_number(Customer_Age_Group))

Answer 2

group_by and summarise is what you're looking for.

df2 %>% group_by(Area, Customer_Age_Group) %>% summarise(Occurences = n())

However note that this won't show categories with zero occurences in your data set.