A one-liner for when they want everything by everything.
Some people want everything summarised by everything else. Who are we to refuse? So here’s a one-liner to deliver what they want, and you can take the rest of 2019 off.
[This is also useful in Shiny apps to replace a slow summarise() with fast filter().]
armgin::margins() is a function in the armgin R package to summarise 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 placeOr with hierarchy = FALSE 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
* constituencyGroups 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 264You 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"))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)
# A tibble: 33 x 6
# Groups: Organisation [1]
Organisation Grade `Professional/Occupat… min max fte
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Joint Nature Conse… All All 16284 63271 179.
2 Joint Nature Conse… AA All 16284 17560 1
3 Joint Nature Conse… AO All 18515 21226 9.3
4 Joint Nature Conse… H All 27398 33159 62
5 Joint Nature Conse… O All 22143 26945 19.6
6 Joint Nature Conse… Other All 44671 63271 25.8
7 Joint Nature Conse… S All 35175 41889 61.1
8 Joint Nature Conse… AA Knowledge and Informa… 16284 17560 1
9 Joint Nature Conse… AO Finance 18515 21226 1
10 Joint Nature Conse… AO Human Resources 18515 21226 1
11 Joint Nature Conse… AO Information Technology 18515 21226 1
12 Joint Nature Conse… AO Knowledge and Informa… 18515 21226 5.3
13 Joint Nature Conse… AO Science and Engineeri… 18515 21226 1
14 Joint Nature Conse… H Communications 27398 33159 1.5
15 Joint Nature Conse… H Finance 27398 33159 1.89
16 Joint Nature Conse… H Human Resources 27398 33159 2
17 Joint Nature Conse… H Knowledge and Informa… 27398 33159 8.64
18 Joint Nature Conse… H Science and Engineeri… 27398 33159 48.0
19 Joint Nature Conse… O Communications 22143 26945 1
20 Joint Nature Conse… O Finance 22143 26945 1
21 Joint Nature Conse… O Human Resources 22143 26945 1.6
22 Joint Nature Conse… O Knowledge and Informa… 22143 26945 5
23 Joint Nature Conse… O Science and Engineeri… 22143 26945 11
24 Joint Nature Conse… Other Finance 44671 53196 1
25 Joint Nature Conse… Other Human Resources 44671 53196 1
26 Joint Nature Conse… Other Information Technology 44671 53196 2
27 Joint Nature Conse… Other Knowledge and Informa… 44671 53196 2
28 Joint Nature Conse… Other Science and Engineeri… 44671 63271 19.8
29 Joint Nature Conse… S Communications 35175 41889 2
30 Joint Nature Conse… S Finance 35175 41889 2
31 Joint Nature Conse… S Human Resources 35175 41889 0.69
32 Joint Nature Conse… S Information Technology 35175 41889 1
33 Joint Nature Conse… S Science and Engineeri… 35175 41889 55.4 Or 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)
# A tibble: 33 x 6
# Groups: Organisation [1]
Organisation `Professional/Occupat… Grade min max fte
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Joint Nature Conse… All All 16284 63271 179.
2 Joint Nature Conse… Communications All 22143 41889 4.5
3 Joint Nature Conse… Finance All 18515 53196 6.89
4 Joint Nature Conse… Human Resources All 18515 53196 6.29
5 Joint Nature Conse… Information Technology All 18515 53196 4
6 Joint Nature Conse… Knowledge and Informa… All 16284 53196 21.9
7 Joint Nature Conse… Science and Engineeri… All 18515 63271 135.
8 Joint Nature Conse… Communications H 27398 33159 1.5
9 Joint Nature Conse… Communications O 22143 26945 1
10 Joint Nature Conse… Communications S 35175 41889 2
11 Joint Nature Conse… Finance AO 18515 21226 1
12 Joint Nature Conse… Finance H 27398 33159 1.89
13 Joint Nature Conse… Finance O 22143 26945 1
14 Joint Nature Conse… Finance Other 44671 53196 1
15 Joint Nature Conse… Finance S 35175 41889 2
16 Joint Nature Conse… Human Resources AO 18515 21226 1
17 Joint Nature Conse… Human Resources H 27398 33159 2
18 Joint Nature Conse… Human Resources O 22143 26945 1.6
19 Joint Nature Conse… Human Resources Other 44671 53196 1
20 Joint Nature Conse… Human Resources S 35175 41889 0.69
21 Joint Nature Conse… Information Technology AO 18515 21226 1
22 Joint Nature Conse… Information Technology Other 44671 53196 2
23 Joint Nature Conse… Information Technology S 35175 41889 1
24 Joint Nature Conse… Knowledge and Informa… AA 16284 17560 1
25 Joint Nature Conse… Knowledge and Informa… AO 18515 21226 5.3
26 Joint Nature Conse… Knowledge and Informa… H 27398 33159 8.64
27 Joint Nature Conse… Knowledge and Informa… O 22143 26945 5
28 Joint Nature Conse… Knowledge and Informa… Other 44671 53196 2
29 Joint Nature Conse… Science and Engineeri… AO 18515 21226 1
30 Joint Nature Conse… Science and Engineeri… H 27398 33159 48.0
31 Joint Nature Conse… Science and Engineeri… O 22143 26945 11
32 Joint Nature Conse… Science and Engineeri… Other 44671 63271 19.8
33 Joint Nature Conse… Science and Engineeri… S 35175 41889 55.4 Or 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`),
hierarchy = FALSE) %>%
mutate_if(is.character, replace_na, "All") %>%
print(n = Inf)
# A tibble: 77 x 6
# Groups: Organisation [2]
Organisation Grade `Professional/Occupat… min max fte
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Joint Nature Conse… 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 Informa… 16284 53196 21.9
13 <NA> All Science and Engineeri… 18515 63271 135.
14 Joint Nature Conse… AA All 16284 17560 1
15 Joint Nature Conse… AO All 18515 21226 9.3
16 Joint Nature Conse… H All 27398 33159 62
17 Joint Nature Conse… O All 22143 26945 19.6
18 Joint Nature Conse… Other All 44671 63271 25.8
19 Joint Nature Conse… S All 35175 41889 61.1
20 Joint Nature Conse… All Communications 22143 41889 4.5
21 Joint Nature Conse… All Finance 18515 53196 6.89
22 Joint Nature Conse… All Human Resources 18515 53196 6.29
23 Joint Nature Conse… All Information Technology 18515 53196 4
24 Joint Nature Conse… All Knowledge and Informa… 16284 53196 21.9
25 Joint Nature Conse… All Science and Engineeri… 18515 63271 135.
26 <NA> AA Knowledge and Informa… 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 Informa… 18515 21226 5.3
31 <NA> AO Science and Engineeri… 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 Informa… 27398 33159 8.64
36 <NA> H Science and Engineeri… 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 Informa… 22143 26945 5
41 <NA> O Science and Engineeri… 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 Informa… 44671 53196 2
46 <NA> Other Science and Engineeri… 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 Engineeri… 35175 41889 55.4
52 Joint Nature Conse… AA Knowledge and Informa… 16284 17560 1
53 Joint Nature Conse… AO Finance 18515 21226 1
54 Joint Nature Conse… AO Human Resources 18515 21226 1
55 Joint Nature Conse… AO Information Technology 18515 21226 1
56 Joint Nature Conse… AO Knowledge and Informa… 18515 21226 5.3
57 Joint Nature Conse… AO Science and Engineeri… 18515 21226 1
58 Joint Nature Conse… H Communications 27398 33159 1.5
59 Joint Nature Conse… H Finance 27398 33159 1.89
60 Joint Nature Conse… H Human Resources 27398 33159 2
61 Joint Nature Conse… H Knowledge and Informa… 27398 33159 8.64
62 Joint Nature Conse… H Science and Engineeri… 27398 33159 48.0
63 Joint Nature Conse… O Communications 22143 26945 1
64 Joint Nature Conse… O Finance 22143 26945 1
65 Joint Nature Conse… O Human Resources 22143 26945 1.6
66 Joint Nature Conse… O Knowledge and Informa… 22143 26945 5
67 Joint Nature Conse… O Science and Engineeri… 22143 26945 11
68 Joint Nature Conse… Other Finance 44671 53196 1
69 Joint Nature Conse… Other Human Resources 44671 53196 1
70 Joint Nature Conse… Other Information Technology 44671 53196 2
71 Joint Nature Conse… Other Knowledge and Informa… 44671 53196 2
72 Joint Nature Conse… Other Science and Engineeri… 44671 63271 19.8
73 Joint Nature Conse… S Communications 35175 41889 2
74 Joint Nature Conse… S Finance 35175 41889 2
75 Joint Nature Conse… S Human Resources 35175 41889 0.69
76 Joint Nature Conse… S Information Technology 35175 41889 1
77 Joint Nature Conse… S Science and Engineeri… 35175 41889 55.4 If you see mistakes or want to suggest changes, please create an issue on the source repository.
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/nacnudus/duncangarmonsway, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Garmonsway (2019, Jan. 2). Duncan Garmonsway: New package {armgin} to summarise all margins of data frames. Retrieved from https://nacnudus.github.io/duncangarmonsway/posts/2019-01-02-new-package-armgin-to-summarise-all-margins-of-data-frames/
BibTeX citation
@misc{garmonsway2019new,
author = {Garmonsway, Duncan},
title = {Duncan Garmonsway: New package {armgin} to summarise all margins of data frames},
url = {https://nacnudus.github.io/duncangarmonsway/posts/2019-01-02-new-package-armgin-to-summarise-all-margins-of-data-frames/},
year = {2019}
}