R-中断休息，并按组统计出现的次数

Question

I have a data frame that looks like this:

dat <- structure(list(Geocode = c("1100015", "1100023", "1100031", "1100049", 
"1100056", "1100064", "1100072", "1100080", "1100098", "1100106", 
"1100114", "1100122", "1100130", "1100148", "1100155", "1100189", 
"1100205", "1100254", "1100262", "1100288", "1100296", "1100304", 
"1100320", "1100338", "1100346", "1100379", "1100403", "1100452", 
"1100502", "1100601"), Location = c("Alta Floresta D'oeste, RO", 
"Ariquemes, RO", "Cabixi, RO", "Cacoal, RO", "Cerejeiras, RO", 
"Colorado Do Oeste, RO", "Corumbiara, RO", "Costa Marques, RO", 
"Espigo D'oeste, RO", "Guajar-Mirim, RO", "Jaru, RO", "Ji-Paran, RO", 
"Machadinho D'oeste, RO", "Nova Brasilndia D'oeste, RO", "Ouro Preto Do Oeste, RO", 
"Pimenta Bueno, RO", "Porto Velho, RO", "Presidente Mdici, RO", 
"Rio Crespo, RO", "Rolim De Moura, RO", "Santa Luzia D'oeste, RO", 
"Vilhena, RO", "So Miguel Do Guapor, RO", "Nova Mamor, RO", "Alvorada D'oeste, RO", 
"Alto Alegre Dos Parecis, RO", "Alto Paraso, RO", "Buritis, RO", 
"Novo Horizonte Do Oeste, RO", "Cacaulandia, RO"), Region = c("Norte", 
"Norte", "Norte", "Norte", "Norte", "Norte", "Norte", "Norte", 
"Norte", "Norte", "Sul", "Sul", "Sul", "Sul", "Sul", 
"Sul", "Sul", "Sul", "Sul", "Sul", "Nordeste", "Nordeste", 
"Nordeste", "Nordeste", "Nordeste", "Nordeste", "Nordeste", "Nordeste", "Nordeste", 
"Nordeste"), Population = c(25578L, 104401L, 6355L, 87226L, 17986L, 
18817L, 8842L, 16651L, 32385L, 46632L, 55738L, 130419L, 37167L, 
21592L, 39924L, 37512L, 502748L, 22557L, 3750L, 56242L, 8532L, 
91801L, 23933L, 27600L, 17063L, 13940L, 20210L, 37838L, 10276L, 
6367L)), .Names = c("Geocode", "Location", "Region", "Population"
), row.names = c(NA, 30L), class = "data.frame")

It shows the population of some cities, as well as the region that the cities pertain to.

I need to classify the population into breaks ( breaks=c(0,50000,100000) ), and then find the counts of cities according to the breaks, both as a whole (all regions) and separating by region.

The resulting data frame should look like this (random, hypothetical values):

Class                  Region       Count
[0-50000]               Norte        7
[50000-100000]          Norte        3
[>100000]               Norte        0
[0-50000]               Sul          5
[50000-100000]          Sul          4
[>100000]               Sul          1
[0-50000]               Nordeste     4
[50000-100000]          Nordeste     5
[>100000]               Nordeste     1
[0-50000]               All          16
[50000-100000]          All          12
[>100000]               All          2

Any help appreciated.

Answer 1

By using cut and dplyr

dat$Class=cut(dat$Population,c(0,50000,100000,Inf),labels=c('0-50000','50000-100000','>100000'))
library(dplyr)
d1=dat%>%group_by(Class,Region)%>%summarise(count=n())
d2=dat%>%group_by(Class)%>%summarise(count=n(),Region='All')
bind_rows(d1,d2)

          Class   Region count
         <fctr>    <chr> <int>
 1      0-50000 Nordeste     9
 2      0-50000    Norte     8
 3      0-50000      Sul     6
 4 50000-100000 Nordeste     1
 5 50000-100000    Norte     1
 6 50000-100000      Sul     2
 7      >100000    Norte     1
 8      >100000      Sul     2
 9      0-50000      All    23
10 50000-100000      All     4
11      >100000      All     3

Answer 2

Here is a quick and dirty method, might update this later to make it more clean and avoid having to bind_rows()

Try the following:

library(tidyverse)

dat_1 <- dat %>% 
  mutate(population_breaks = case_when(Population <= 50000 ~ "0-50000",
                                       Population >= 50000 & Population <= 100000 ~ "50000-100000",
                                       Population >= 100000 ~ ">100000")) %>% 
  group_by(population_breaks) %>% 
  count(Region)

dat_2 <- dat %>% 
  mutate(population_breaks = case_when(Population <= 50000 ~ "0-50000",
                                       Population >= 50000 & Population <= 100000 ~ "50000-100000",
                                       Population >= 100000 ~ ">100000")) %>% 
  group_by(population_breaks) %>% 
  count(population_breaks) %>% 
  mutate(Region = "All")

bind_rows(dat_1, dat_2)  

Which returns:

# A tibble: 11 x 3
# Groups:   population_breaks [3]
   population_breaks   Region     n
               <chr>    <chr> <int>
 1           0-50000 Nordeste     9
 2      50000-100000 Nordeste     1
 3           >100000    Norte     1
 4           0-50000    Norte     8
 5      50000-100000    Norte     1
 6           >100000      Sul     2
 7           0-50000      Sul     6
 8      50000-100000      Sul     2
 9           >100000      All     3
10           0-50000      All    23
11      50000-100000      All     4