按组切割并计算出现次数的R语言-断点法

Question

我有一个数据框，长这样：

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")

它显示了一些城市的人口以及这些城市所属的地区。

我需要将人口分类为间断点(breaks=c(0,50000,100000))，并根据间断点找到城市的计数，无论是整体还是按地区分开。

生成的数据框应如下所示(随机的、假设的值):

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

需要帮助，非常感谢。

- thiagoveloso

Answer 1

通过使用 cut 和 dplyr 来实现。

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

- BENY

---

或者，在创建Class之后，使用bind_rows(count(dat, Region, Class), count(dat, Class, Region = "all"))也可以。 - undefined

很酷，而且有 @jazzurro 的贡献，更好了 :) - undefined

@thiagoveloso 这个更好 :-) - undefined

Answer 2

这里提供了一种快速而简单的方法，可能会在以后进行更新以使其更加干净，并避免使用bind_rows()。

请尝试以下步骤：

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)  

这将返回：

# 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

- tyluRp