The armgin package summarises all combinations of grouping variables at once. For example, vote counts by
* voting place, ward, constituency, party, candidate
* voting place, ward, constituency, party
* voting place, ward, constituency
* voting place, ward,
* voting place
Or with method = "combination" and grouping by only voting place, ward
and constituency (for brevity)
* voting place, ward, constituency
* voting place, ward
* voting place, constituency
* ward, constituency
* voting place
* ward
* constituency
Groups are defined as normal by dplyr::group_by() and then piped into
armgin::margins().
library(dplyr)
library(armgin)
mtcars %>%
group_by(cyl, gear, am) %>%
margins(mpg = mean(mpg),
hp = min(hp)) %>%
print(n = Inf)
#> # A tibble: 21 x 5
#> # Groups: cyl [3]
#> cyl gear am mpg hp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 NA NA 26.7 52
#> 2 6 NA NA 19.7 105
#> 3 8 NA NA 15.1 150
#> 4 4 3 NA 21.5 97
#> 5 4 4 NA 26.9 52
#> 6 4 5 NA 28.2 91
#> 7 6 3 NA 19.8 105
#> 8 6 4 NA 19.8 110
#> 9 6 5 NA 19.7 175
#> 10 8 3 NA 15.0 150
#> 11 8 5 NA 15.4 264
#> 12 4 3 0 21.5 97
#> 13 4 4 0 23.6 62
#> 14 4 4 1 28.0 52
#> 15 4 5 1 28.2 91
#> 16 6 3 0 19.8 105
#> 17 6 4 0 18.5 123
#> 18 6 4 1 21 110
#> 19 6 5 1 19.7 175
#> 20 8 3 0 15.0 150
#> 21 8 5 1 15.4 264Output individual data frames with bind = FALSE.
mtcars %>%
group_by(cyl, gear, am) %>%
margins(mpg = mean(mpg),
hp = min(hp),
bind = FALSE)
#> [[1]]
#> # A tibble: 3 x 3
#> # Groups: cyl [3]
#> cyl mpg hp
#> <dbl> <dbl> <dbl>
#> 1 4 26.7 52
#> 2 6 19.7 105
#> 3 8 15.1 150
#>
#> [[2]]
#> # A tibble: 8 x 4
#> # Groups: cyl, gear [8]
#> cyl gear mpg hp
#> <dbl> <dbl> <dbl> <dbl>
#> 1 4 3 21.5 97
#> 2 4 4 26.9 52
#> 3 4 5 28.2 91
#> 4 6 3 19.8 105
#> 5 6 4 19.8 110
#> 6 6 5 19.7 175
#> 7 8 3 15.0 150
#> 8 8 5 15.4 264
#>
#> [[3]]
#> # A tibble: 10 x 5
#> # Groups: cyl, gear, am [10]
#> cyl gear am mpg hp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 3 0 21.5 97
#> 2 4 4 0 23.6 62
#> 3 4 4 1 28.0 52
#> 4 4 5 1 28.2 91
#> 5 6 3 0 19.8 105
#> 6 6 4 0 18.5 123
#> 7 6 4 1 21 110
#> 8 6 5 1 19.7 175
#> 9 8 3 0 15.0 150
#> 10 8 5 1 15.4 264Please don’t use this package. You can easily implement the functions yourself. If you really want to use a package then use groupie, which is the same but better.
The trick is to treat sets of grouping variables as ‘data’, map over
them to create copies of your actual data grouped by each set of
variables, and then map again with a summarise() function or whatever.
library(dplyr)
library(purrr)
grouping_vars <- c("vs", "am", "carb")
map(grouping_vars, group_by_at, .tbl = mtcars) %>%
map(summarise,
'6 cylinder' = sum(cyl == 6),
'Large disp' = sum(disp >= 100),
'low gears' = sum(gear <= 4))
#> [[1]]
#> # A tibble: 2 x 4
#> vs `6 cylinder` `Large disp` `low gears`
#> <dbl> <int> <int> <int>
#> 1 0 3 18 14
#> 2 1 4 9 13
#>
#> [[2]]
#> # A tibble: 2 x 4
#> am `6 cylinder` `Large disp` `low gears`
#> <dbl> <int> <int> <int>
#> 1 0 4 19 19
#> 2 1 3 8 8
#>
#> [[3]]
#> # A tibble: 6 x 4
#> carb `6 cylinder` `Large disp` `low gears`
#> <dbl> <int> <int> <int>
#> 1 1 2 4 7
#> 2 2 0 8 8
#> 3 3 0 3 3
#> 4 4 4 10 9
#> 5 6 1 1 0
#> 6 8 0 1 0The groupie package also wraps the arrangements package to create sets of grouping variables. This again is very simple to do yourself.
You can install the development version from GitHub with devtools or remotes.
install.packages("devtools")
devtools::install_github("nacnudus/armgin")What are the salaries of various jobs, grades and professions in the UK’s Joint Nature Conservation Committee?
library(armgin)
library(dplyr)
library(tidyr)
library(readr)
# organogram.csv is from "https://data.gov.uk/sites/default/files/organogram/joint-nature-conservation-committee/30/9/2018/20180930%20JNCC-junior.csv",
organogram <-
read_csv("organogram.csv") %>%
replace_na(list(Grade = "Other"))
#> Parsed with column specification:
#> cols(
#> `Parent Department` = col_character(),
#> Organisation = col_character(),
#> Unit = col_character(),
#> `Reporting Senior Post` = col_character(),
#> Grade = col_character(),
#> `Payscale Minimum (£)` = col_double(),
#> `Payscale Maximum (£)` = col_double(),
#> `Generic Job Title` = col_character(),
#> `Number of Posts in FTE` = col_double(),
#> `Professional/Occupational Group` = col_character()
#> )It could be a hierarchy with the whole JNCC Organisation at the top, then Grade within that, then Profession as the bottom grouping.
organogram %>%
group_by(Organisation, Grade, `Professional/Occupational Group`) %>%
margins(min = min(`Payscale Minimum (£)`),
max = max(`Payscale Maximum (£)`),
fte = sum(`Number of Posts in FTE`)) %>%
mutate_if(is.character, replace_na, "All") %>%
print(n = Inf)
#> `mutate_if()` ignored the following grouping variables:
#> Column `Organisation`
#> # A tibble: 33 x 6
#> # Groups: Organisation [1]
#> Organisation Grade `Professional/Occupation… min max fte
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Joint Nature Conserv… All All 16284 63271 179.
#> 2 Joint Nature Conserv… AA All 16284 17560 1
#> 3 Joint Nature Conserv… AO All 18515 21226 9.3
#> 4 Joint Nature Conserv… H All 27398 33159 62
#> 5 Joint Nature Conserv… O All 22143 26945 19.6
#> 6 Joint Nature Conserv… Other All 44671 63271 25.8
#> 7 Joint Nature Conserv… S All 35175 41889 61.1
#> 8 Joint Nature Conserv… AA Knowledge and Informatio… 16284 17560 1
#> 9 Joint Nature Conserv… AO Finance 18515 21226 1
#> 10 Joint Nature Conserv… AO Human Resources 18515 21226 1
#> 11 Joint Nature Conserv… AO Information Technology 18515 21226 1
#> 12 Joint Nature Conserv… AO Knowledge and Informatio… 18515 21226 5.3
#> 13 Joint Nature Conserv… AO Science and Engineering 18515 21226 1
#> 14 Joint Nature Conserv… H Communications 27398 33159 1.5
#> 15 Joint Nature Conserv… H Finance 27398 33159 1.89
#> 16 Joint Nature Conserv… H Human Resources 27398 33159 2
#> 17 Joint Nature Conserv… H Knowledge and Informatio… 27398 33159 8.64
#> 18 Joint Nature Conserv… H Science and Engineering 27398 33159 48.0
#> 19 Joint Nature Conserv… O Communications 22143 26945 1
#> 20 Joint Nature Conserv… O Finance 22143 26945 1
#> 21 Joint Nature Conserv… O Human Resources 22143 26945 1.6
#> 22 Joint Nature Conserv… O Knowledge and Informatio… 22143 26945 5
#> 23 Joint Nature Conserv… O Science and Engineering 22143 26945 11
#> 24 Joint Nature Conserv… Other Finance 44671 53196 1
#> 25 Joint Nature Conserv… Other Human Resources 44671 53196 1
#> 26 Joint Nature Conserv… Other Information Technology 44671 53196 2
#> 27 Joint Nature Conserv… Other Knowledge and Informatio… 44671 53196 2
#> 28 Joint Nature Conserv… Other Science and Engineering 44671 63271 19.8
#> 29 Joint Nature Conserv… S Communications 35175 41889 2
#> 30 Joint Nature Conserv… S Finance 35175 41889 2
#> 31 Joint Nature Conserv… S Human Resources 35175 41889 0.69
#> 32 Joint Nature Conserv… S Information Technology 35175 41889 1
#> 33 Joint Nature Conserv… S Science and Engineering 35175 41889 55.4Or it could be a different hierarchy, with Grade grouped inside Profession.
organogram %>%
group_by(Organisation, `Professional/Occupational Group`, Grade) %>%
margins(min = min(`Payscale Minimum (£)`),
max = max(`Payscale Maximum (£)`),
fte = sum(`Number of Posts in FTE`)) %>%
mutate_if(is.character, replace_na, "All") %>%
print(n = Inf)
#> `mutate_if()` ignored the following grouping variables:
#> Column `Organisation`
#> # A tibble: 33 x 6
#> # Groups: Organisation [1]
#> Organisation `Professional/Occupation… Grade min max fte
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Joint Nature Conserv… All All 16284 63271 179.
#> 2 Joint Nature Conserv… Communications All 22143 41889 4.5
#> 3 Joint Nature Conserv… Finance All 18515 53196 6.89
#> 4 Joint Nature Conserv… Human Resources All 18515 53196 6.29
#> 5 Joint Nature Conserv… Information Technology All 18515 53196 4
#> 6 Joint Nature Conserv… Knowledge and Informatio… All 16284 53196 21.9
#> 7 Joint Nature Conserv… Science and Engineering All 18515 63271 135.
#> 8 Joint Nature Conserv… Communications H 27398 33159 1.5
#> 9 Joint Nature Conserv… Communications O 22143 26945 1
#> 10 Joint Nature Conserv… Communications S 35175 41889 2
#> 11 Joint Nature Conserv… Finance AO 18515 21226 1
#> 12 Joint Nature Conserv… Finance H 27398 33159 1.89
#> 13 Joint Nature Conserv… Finance O 22143 26945 1
#> 14 Joint Nature Conserv… Finance Other 44671 53196 1
#> 15 Joint Nature Conserv… Finance S 35175 41889 2
#> 16 Joint Nature Conserv… Human Resources AO 18515 21226 1
#> 17 Joint Nature Conserv… Human Resources H 27398 33159 2
#> 18 Joint Nature Conserv… Human Resources O 22143 26945 1.6
#> 19 Joint Nature Conserv… Human Resources Other 44671 53196 1
#> 20 Joint Nature Conserv… Human Resources S 35175 41889 0.69
#> 21 Joint Nature Conserv… Information Technology AO 18515 21226 1
#> 22 Joint Nature Conserv… Information Technology Other 44671 53196 2
#> 23 Joint Nature Conserv… Information Technology S 35175 41889 1
#> 24 Joint Nature Conserv… Knowledge and Informatio… AA 16284 17560 1
#> 25 Joint Nature Conserv… Knowledge and Informatio… AO 18515 21226 5.3
#> 26 Joint Nature Conserv… Knowledge and Informatio… H 27398 33159 8.64
#> 27 Joint Nature Conserv… Knowledge and Informatio… O 22143 26945 5
#> 28 Joint Nature Conserv… Knowledge and Informatio… Other 44671 53196 2
#> 29 Joint Nature Conserv… Science and Engineering AO 18515 21226 1
#> 30 Joint Nature Conserv… Science and Engineering H 27398 33159 48.0
#> 31 Joint Nature Conserv… Science and Engineering O 22143 26945 11
#> 32 Joint Nature Conserv… Science and Engineering Other 44671 63271 19.8
#> 33 Joint Nature Conserv… Science and Engineering S 35175 41889 55.4Or there could be no hierarchy, with all combinations of Organisation, Grade and Profession equally valid subsets.
organogram %>%
group_by(Organisation, Grade, `Professional/Occupational Group`) %>%
margins(min = min(`Payscale Minimum (£)`),
max = max(`Payscale Maximum (£)`),
fte = sum(`Number of Posts in FTE`),
method = "combination") %>%
mutate_if(is.character, replace_na, "All") %>%
print(n = Inf)
#> `mutate_if()` ignored the following grouping variables:
#> Column `Organisation`
#> # A tibble: 77 x 6
#> # Groups: Organisation [2]
#> Organisation Grade `Professional/Occupation… min max fte
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Joint Nature Conserv… All All 16284 63271 179.
#> 2 <NA> AA All 16284 17560 1
#> 3 <NA> AO All 18515 21226 9.3
#> 4 <NA> H All 27398 33159 62
#> 5 <NA> O All 22143 26945 19.6
#> 6 <NA> Other All 44671 63271 25.8
#> 7 <NA> S All 35175 41889 61.1
#> 8 <NA> All Communications 22143 41889 4.5
#> 9 <NA> All Finance 18515 53196 6.89
#> 10 <NA> All Human Resources 18515 53196 6.29
#> 11 <NA> All Information Technology 18515 53196 4
#> 12 <NA> All Knowledge and Informatio… 16284 53196 21.9
#> 13 <NA> All Science and Engineering 18515 63271 135.
#> 14 Joint Nature Conserv… AA All 16284 17560 1
#> 15 Joint Nature Conserv… AO All 18515 21226 9.3
#> 16 Joint Nature Conserv… H All 27398 33159 62
#> 17 Joint Nature Conserv… O All 22143 26945 19.6
#> 18 Joint Nature Conserv… Other All 44671 63271 25.8
#> 19 Joint Nature Conserv… S All 35175 41889 61.1
#> 20 Joint Nature Conserv… All Communications 22143 41889 4.5
#> 21 Joint Nature Conserv… All Finance 18515 53196 6.89
#> 22 Joint Nature Conserv… All Human Resources 18515 53196 6.29
#> 23 Joint Nature Conserv… All Information Technology 18515 53196 4
#> 24 Joint Nature Conserv… All Knowledge and Informatio… 16284 53196 21.9
#> 25 Joint Nature Conserv… All Science and Engineering 18515 63271 135.
#> 26 <NA> AA Knowledge and Informatio… 16284 17560 1
#> 27 <NA> AO Finance 18515 21226 1
#> 28 <NA> AO Human Resources 18515 21226 1
#> 29 <NA> AO Information Technology 18515 21226 1
#> 30 <NA> AO Knowledge and Informatio… 18515 21226 5.3
#> 31 <NA> AO Science and Engineering 18515 21226 1
#> 32 <NA> H Communications 27398 33159 1.5
#> 33 <NA> H Finance 27398 33159 1.89
#> 34 <NA> H Human Resources 27398 33159 2
#> 35 <NA> H Knowledge and Informatio… 27398 33159 8.64
#> 36 <NA> H Science and Engineering 27398 33159 48.0
#> 37 <NA> O Communications 22143 26945 1
#> 38 <NA> O Finance 22143 26945 1
#> 39 <NA> O Human Resources 22143 26945 1.6
#> 40 <NA> O Knowledge and Informatio… 22143 26945 5
#> 41 <NA> O Science and Engineering 22143 26945 11
#> 42 <NA> Other Finance 44671 53196 1
#> 43 <NA> Other Human Resources 44671 53196 1
#> 44 <NA> Other Information Technology 44671 53196 2
#> 45 <NA> Other Knowledge and Informatio… 44671 53196 2
#> 46 <NA> Other Science and Engineering 44671 63271 19.8
#> 47 <NA> S Communications 35175 41889 2
#> 48 <NA> S Finance 35175 41889 2
#> 49 <NA> S Human Resources 35175 41889 0.69
#> 50 <NA> S Information Technology 35175 41889 1
#> 51 <NA> S Science and Engineering 35175 41889 55.4
#> 52 Joint Nature Conserv… AA Knowledge and Informatio… 16284 17560 1
#> 53 Joint Nature Conserv… AO Finance 18515 21226 1
#> 54 Joint Nature Conserv… AO Human Resources 18515 21226 1
#> 55 Joint Nature Conserv… AO Information Technology 18515 21226 1
#> 56 Joint Nature Conserv… AO Knowledge and Informatio… 18515 21226 5.3
#> 57 Joint Nature Conserv… AO Science and Engineering 18515 21226 1
#> 58 Joint Nature Conserv… H Communications 27398 33159 1.5
#> 59 Joint Nature Conserv… H Finance 27398 33159 1.89
#> 60 Joint Nature Conserv… H Human Resources 27398 33159 2
#> 61 Joint Nature Conserv… H Knowledge and Informatio… 27398 33159 8.64
#> 62 Joint Nature Conserv… H Science and Engineering 27398 33159 48.0
#> 63 Joint Nature Conserv… O Communications 22143 26945 1
#> 64 Joint Nature Conserv… O Finance 22143 26945 1
#> 65 Joint Nature Conserv… O Human Resources 22143 26945 1.6
#> 66 Joint Nature Conserv… O Knowledge and Informatio… 22143 26945 5
#> 67 Joint Nature Conserv… O Science and Engineering 22143 26945 11
#> 68 Joint Nature Conserv… Other Finance 44671 53196 1
#> 69 Joint Nature Conserv… Other Human Resources 44671 53196 1
#> 70 Joint Nature Conserv… Other Information Technology 44671 53196 2
#> 71 Joint Nature Conserv… Other Knowledge and Informatio… 44671 53196 2
#> 72 Joint Nature Conserv… Other Science and Engineering 44671 63271 19.8
#> 73 Joint Nature Conserv… S Communications 35175 41889 2
#> 74 Joint Nature Conserv… S Finance 35175 41889 2
#> 75 Joint Nature Conserv… S Human Resources 35175 41889 0.69
#> 76 Joint Nature Conserv… S Information Technology 35175 41889 1
#> 77 Joint Nature Conserv… S Science and Engineering 35175 41889 55.4Or each one of Organisation, Grade and Profession could be summarised individually.
organogram %>%
group_by(Organisation, Grade, `Professional/Occupational Group`) %>%
margins(min = min(`Payscale Minimum (£)`),
max = max(`Payscale Maximum (£)`),
fte = sum(`Number of Posts in FTE`),
method = "individual") %>%
mutate_if(is.character, replace_na, "All") %>%
print(n = Inf)
#> `mutate_if()` ignored the following grouping variables:
#> Column `Organisation`
#> # A tibble: 13 x 6
#> # Groups: Organisation [2]
#> Organisation Grade `Professional/Occupation… min max fte
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Joint Nature Conserv… All All 16284 63271 179.
#> 2 <NA> AA All 16284 17560 1
#> 3 <NA> AO All 18515 21226 9.3
#> 4 <NA> H All 27398 33159 62
#> 5 <NA> O All 22143 26945 19.6
#> 6 <NA> Other All 44671 63271 25.8
#> 7 <NA> S All 35175 41889 61.1
#> 8 <NA> All Communications 22143 41889 4.5
#> 9 <NA> All Finance 18515 53196 6.89
#> 10 <NA> All Human Resources 18515 53196 6.29
#> 11 <NA> All Information Technology 18515 53196 4
#> 12 <NA> All Knowledge and Informatio… 16284 53196 21.9
#> 13 <NA> All Science and Engineering 18515 63271 135.Please note that the ‘armgin’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.