Average of transposed counts in dplyr, is it possible?

[Slavek]

Hi, I have this simple df with DealerName and Region. I have done this:

source <- data.frame(
  stringsAsFactors = FALSE,
  DealerName = c("aaa","aaa","aaa","bbb",
                 "bbb","bbb","ccc","ccc","ccc","ccc","ccc","ccc",
                 "ddd","ddd","ddd","ddd","ddd","ddd","ddd","ddd"),
  Region = c("East Midlands",
             "East Midlands","East Midlands","East of England","East of England",
             "East of England","East of England","East of England",
             "East of England","East of England","Greater London",
             "Greater London","Greater London","Greater London",
             "Greater London","Greater London","Greater London",
             "Greater London","Greater London","Greater London"),
  Drive.Time = c(20,15,20,18,12,15,20,15,
                 20,10,18,12,15,20,15,20,18,12,15,10)
)

source

library(dplyr)
result <- source %>%
  group_by(DealerName, Region) %>%
  summarise(cnt = n()) 
result

library(tidyr)
result.table <- result %>%
  select(DealerName, Region, cnt)  %>%
  spread(Region, cnt)
result.table

but is it possible to find an average count for each Region?
Very easy in Excel and East Midlands is 3, East of England 3.5 and Greater London 5

[Leon]

Like so:

> result |> ungroup() |> group_by(Region) |> summarise(mu = mean(cnt))
# A tibble: 3 × 2
  Region             mu
  <chr>           <dbl>
1 East Midlands     3  
2 East of England   3.5
3 Greater London    5  

?