按多列（日期、列）分组，然后按 R 中的第三列排名

Question

我有包含 Date、GEO、VALUE 的数据库。我想先按 Date 排序，然后按 GEO 排序，最后按 VALUE.

降序创建排名

这是我的df1，如果有任何NA应该排在最后。

我的输出应该是

structure(list(Date = structure(c(2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("01/01/2020", 
"01/01/2021"), class = "factor"), GEO = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Barrie", 
"Toronto"), class = "factor"), NAICS = structure(c(14L, 17L, 
8L, 10L, 14L, 17L, 8L, 10L, 14L, 17L, 12L, 6L, 14L, 17L, 12L, 
6L), .Label = c("Accomd. & food serv.", "Agriculture", "Bus., build.,& other supp. services", 
"Construction", "Educational services", "Fin., Insur., Real est., Rental, & lease", 
"Forest.,Fish.,Mining.,Quar.,Oil & Gas", "Health care & Social Assis", 
"Info., culture & rec.", "Manufacturing", "Other services (except pub. admin)", 
"Prof., Sci.,and Tech. Serv", "Public administration", "Total", 
"Transp. & warehousing", "Utilities", "Wholesale and retail trade"
), class = "factor"), VALUE = c(114.5, 17.6, 15.1, 14.4, 117.6, 
17.1, NA, NA, 3393.4, 520.5, 443.9, 393.4, 3221.8, 486.2, 
414.2, 400.5), rank = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L, 1L, 2L, 3L, 4L)), class = "data.frame", row.names = c(NA, 
-16L))

我的代码不能正常工作，因为它不是按日期排序，然后是地理位置，然后是值。

df1 %>%
  arrange(GEO, desc(VALUE)) %>%
  group_by(GEO) %>% 
  mutate(rank = row_number()) -> df1

Answer 1

根据您的描述，您应该先按 GEO 进行分组，然后再按 Date:

进行分组

library(dplyr)

df %>%
  group_by(GEO, Date) %>%
  arrange(desc(VALUE), .by_group = TRUE) %>% 
  mutate(rank.new = row_number())

# A tibble: 16 x 6
# Groups:   GEO, Date [4]
   Date       GEO     NAICS                                     VALUE  rank rank.new
   <fct>      <fct>   <fct>                                     <dbl> <int>    <int>
 1 01/01/2020 Barrie  Total                                     118.      1        1
 2 01/01/2020 Barrie  Wholesale and retail trade                 17.1     2        2
 3 01/01/2020 Barrie  Health care & Social Assis                 NA       3        3
 4 01/01/2020 Barrie  Manufacturing                              NA       4        4
 5 01/01/2021 Barrie  Total                                     114.      1        1
 6 01/01/2021 Barrie  Wholesale and retail trade                 17.6     2        2
 7 01/01/2021 Barrie  Health care & Social Assis                 15.1     3        3
 8 01/01/2021 Barrie  Manufacturing                              14.4     4        4
 9 01/01/2020 Toronto Total                                    3222.      1        1
10 01/01/2020 Toronto Wholesale and retail trade                486.      2        2
11 01/01/2020 Toronto Prof., Sci.,and Tech. Serv                414.      3        3
12 01/01/2020 Toronto Fin., Insur., Real est., Rental, & lease  400.      4        4
13 01/01/2021 Toronto Total                                    3393.      1        1
14 01/01/2021 Toronto Wholesale and retail trade                520.      2        2
15 01/01/2021 Toronto Prof., Sci.,and Tech. Serv                444.      3        3
16 01/01/2021 Toronto Fin., Insur., Real est., Rental, & lease  393.      4        4

按多列（日期、列）分组，然后按 R 中的第三列排名

Group by multiple columns (Date, Column), then rank by third Column in R

r

dplyr