pct_routine: Calculate percentage by group.
In extdplyr: Data Manipulation Extensions of 'Dplyr' and 'Tidyr'
Description
Usage
Arguments
Functions
Examples
Description
pct_routine works like count except that it
returns group percentages instead of counts. tally_pct is a underlying
utility function that corresponds to tally. As the name implies, it
also returns percentage.
Usage
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Arguments
data
A data.frame or tbl.
...
Variables to group by, see group_by.
wt
Column name of weights.
ret_name
Character of the variable name returned.
rebase
Whether to remove the missing values in the percentage, e.g.
rebase the percentage so that NAs in the last group are excluded.
ungroup
Whether to ungroup the returned table.
vars
A character vector of variable names to group by.
Functions
-
pct_routine_: SE version of pct_routine.
-
tally_pct: NSE version of tally_pct_.
-
tally_pct_: Underlying SE function of pct_routine_ without
options for groups.
Examples
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data(esoph)
esoph
pct_routine(esoph, agegp, alcgp)
pct_routine(esoph, agegp, alcgp, wt = ncases)
# Crate new grouping variables
pct_routine(esoph, agegp, low_alcgp = alcgp %in% c("0-39g/day", "40-79"))
# This examples shows how rebase works
if (require(dplyr)) {
iris %>%
mutate(random_missing = ifelse(rnorm(n()) > 0, NA, round(Sepal.Length))) %>%
group_by(Species, random_missing) %>%
tally_pct(wt = Sepal.Width, rebase = TRUE)
}
Example output
agegp alcgp tobgp ncases ncontrols
1 25-34 0-39g/day 0-9g/day 0 40
2 25-34 0-39g/day 10-19 0 10
3 25-34 0-39g/day 20-29 0 6
4 25-34 0-39g/day 30+ 0 5
5 25-34 40-79 0-9g/day 0 27
6 25-34 40-79 10-19 0 7
7 25-34 40-79 20-29 0 4
8 25-34 40-79 30+ 0 7
9 25-34 80-119 0-9g/day 0 2
10 25-34 80-119 10-19 0 1
11 25-34 80-119 30+ 0 2
12 25-34 120+ 0-9g/day 0 1
13 25-34 120+ 10-19 1 1
14 25-34 120+ 20-29 0 1
15 25-34 120+ 30+ 0 2
16 35-44 0-39g/day 0-9g/day 0 60
17 35-44 0-39g/day 10-19 1 14
18 35-44 0-39g/day 20-29 0 7
19 35-44 0-39g/day 30+ 0 8
20 35-44 40-79 0-9g/day 0 35
21 35-44 40-79 10-19 3 23
22 35-44 40-79 20-29 1 14
23 35-44 40-79 30+ 0 8
24 35-44 80-119 0-9g/day 0 11
25 35-44 80-119 10-19 0 6
26 35-44 80-119 20-29 0 2
27 35-44 80-119 30+ 0 1
28 35-44 120+ 0-9g/day 2 3
29 35-44 120+ 10-19 0 3
30 35-44 120+ 20-29 2 4
31 45-54 0-39g/day 0-9g/day 1 46
32 45-54 0-39g/day 10-19 0 18
33 45-54 0-39g/day 20-29 0 10
34 45-54 0-39g/day 30+ 0 4
35 45-54 40-79 0-9g/day 6 38
36 45-54 40-79 10-19 4 21
37 45-54 40-79 20-29 5 15
38 45-54 40-79 30+ 5 7
39 45-54 80-119 0-9g/day 3 16
40 45-54 80-119 10-19 6 14
41 45-54 80-119 20-29 1 5
42 45-54 80-119 30+ 2 4
43 45-54 120+ 0-9g/day 4 4
44 45-54 120+ 10-19 3 4
45 45-54 120+ 20-29 2 3
46 45-54 120+ 30+ 4 4
47 55-64 0-39g/day 0-9g/day 2 49
48 55-64 0-39g/day 10-19 3 22
49 55-64 0-39g/day 20-29 3 12
50 55-64 0-39g/day 30+ 4 6
51 55-64 40-79 0-9g/day 9 40
52 55-64 40-79 10-19 6 21
53 55-64 40-79 20-29 4 17
54 55-64 40-79 30+ 3 6
55 55-64 80-119 0-9g/day 9 18
56 55-64 80-119 10-19 8 15
57 55-64 80-119 20-29 3 6
58 55-64 80-119 30+ 4 4
59 55-64 120+ 0-9g/day 5 10
60 55-64 120+ 10-19 6 7
61 55-64 120+ 20-29 2 3
62 55-64 120+ 30+ 5 6
63 65-74 0-39g/day 0-9g/day 5 48
64 65-74 0-39g/day 10-19 4 14
65 65-74 0-39g/day 20-29 2 7
66 65-74 0-39g/day 30+ 0 2
67 65-74 40-79 0-9g/day 17 34
68 65-74 40-79 10-19 3 10
69 65-74 40-79 20-29 5 9
70 65-74 80-119 0-9g/day 6 13
71 65-74 80-119 10-19 4 12
72 65-74 80-119 20-29 2 3
73 65-74 80-119 30+ 1 1
74 65-74 120+ 0-9g/day 3 4
75 65-74 120+ 10-19 1 2
76 65-74 120+ 20-29 1 1
77 65-74 120+ 30+ 1 1
78 75+ 0-39g/day 0-9g/day 1 18
79 75+ 0-39g/day 10-19 2 6
80 75+ 0-39g/day 30+ 1 3
81 75+ 40-79 0-9g/day 2 5
82 75+ 40-79 10-19 1 3
83 75+ 40-79 20-29 0 3
84 75+ 40-79 30+ 1 1
85 75+ 80-119 0-9g/day 1 1
86 75+ 80-119 10-19 1 1
87 75+ 120+ 0-9g/day 2 2
88 75+ 120+ 10-19 1 1
# A tibble: 24 x 3
# Groups: agegp [6]
agegp alcgp pct
<ord> <ord> <dbl>
1 25-34 0-39g/day 0.2666667
2 25-34 40-79 0.2666667
3 25-34 80-119 0.2000000
4 25-34 120+ 0.2666667
5 35-44 0-39g/day 0.2666667
6 35-44 40-79 0.2666667
7 35-44 80-119 0.2666667
8 35-44 120+ 0.2000000
9 45-54 0-39g/day 0.2500000
10 45-54 40-79 0.2500000
# ... with 14 more rows
# A tibble: 24 x 3
# Groups: agegp [6]
agegp alcgp pct
<ord> <ord> <dbl>
1 25-34 0-39g/day 0.00000000
2 25-34 40-79 0.00000000
3 25-34 80-119 0.00000000
4 25-34 120+ 1.00000000
5 35-44 0-39g/day 0.11111111
6 35-44 40-79 0.44444444
7 35-44 80-119 0.00000000
8 35-44 120+ 0.44444444
9 45-54 0-39g/day 0.02173913
10 45-54 40-79 0.43478261
# ... with 14 more rows
# A tibble: 12 x 3
# Groups: agegp [6]
agegp low_alcgp pct
<ord> <lgl> <dbl>
1 25-34 FALSE 0.4666667
2 25-34 TRUE 0.5333333
3 35-44 FALSE 0.4666667
4 35-44 TRUE 0.5333333
5 45-54 FALSE 0.5000000
6 45-54 TRUE 0.5000000
7 55-64 FALSE 0.5000000
8 55-64 TRUE 0.5000000
9 65-74 FALSE 0.5333333
10 65-74 TRUE 0.4666667
11 75+ FALSE 0.3636364
12 75+ TRUE 0.6363636
Loading required package: dplyr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
# A tibble: 10 x 3
# Groups: Species [3]
Species random_missing pct
<fctr> <dbl> <dbl>
1 setosa 4 0.04632588
2 setosa 5 0.88658147
3 setosa 6 0.06709265
4 versicolor 5 0.04244482
5 versicolor 6 0.85398981
6 versicolor 7 0.10356537
7 virginica 5 0.04237288
8 virginica 6 0.57966102
9 virginica 7 0.26271186
10 virginica 8 0.11525424
extdplyr documentation built on April 20, 2020, 9:04 a.m.
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Description Usage Arguments Functions Examples
Description
pct_routine works like count except that it
returns group percentages instead of counts. tally_pct is a underlying
utility function that corresponds to tally. As the name implies, it
also returns percentage.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
Arguments
data |
A |
... |
Variables to group by, see |
wt |
Column name of weights. |
ret_name |
Character of the variable name returned. |
rebase |
Whether to remove the missing values in the percentage, e.g. rebase the percentage so that NAs in the last group are excluded. |
ungroup |
Whether to ungroup the returned table. |
vars |
A character vector of variable names to group by. |
Functions
-
pct_routine_: SE version ofpct_routine. -
tally_pct: NSE version oftally_pct_. -
tally_pct_: Underlying SE function ofpct_routine_without options for groups.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | data(esoph)
esoph
pct_routine(esoph, agegp, alcgp)
pct_routine(esoph, agegp, alcgp, wt = ncases)
# Crate new grouping variables
pct_routine(esoph, agegp, low_alcgp = alcgp %in% c("0-39g/day", "40-79"))
# This examples shows how rebase works
if (require(dplyr)) {
iris %>%
mutate(random_missing = ifelse(rnorm(n()) > 0, NA, round(Sepal.Length))) %>%
group_by(Species, random_missing) %>%
tally_pct(wt = Sepal.Width, rebase = TRUE)
}
|
Example output
agegp alcgp tobgp ncases ncontrols
1 25-34 0-39g/day 0-9g/day 0 40
2 25-34 0-39g/day 10-19 0 10
3 25-34 0-39g/day 20-29 0 6
4 25-34 0-39g/day 30+ 0 5
5 25-34 40-79 0-9g/day 0 27
6 25-34 40-79 10-19 0 7
7 25-34 40-79 20-29 0 4
8 25-34 40-79 30+ 0 7
9 25-34 80-119 0-9g/day 0 2
10 25-34 80-119 10-19 0 1
11 25-34 80-119 30+ 0 2
12 25-34 120+ 0-9g/day 0 1
13 25-34 120+ 10-19 1 1
14 25-34 120+ 20-29 0 1
15 25-34 120+ 30+ 0 2
16 35-44 0-39g/day 0-9g/day 0 60
17 35-44 0-39g/day 10-19 1 14
18 35-44 0-39g/day 20-29 0 7
19 35-44 0-39g/day 30+ 0 8
20 35-44 40-79 0-9g/day 0 35
21 35-44 40-79 10-19 3 23
22 35-44 40-79 20-29 1 14
23 35-44 40-79 30+ 0 8
24 35-44 80-119 0-9g/day 0 11
25 35-44 80-119 10-19 0 6
26 35-44 80-119 20-29 0 2
27 35-44 80-119 30+ 0 1
28 35-44 120+ 0-9g/day 2 3
29 35-44 120+ 10-19 0 3
30 35-44 120+ 20-29 2 4
31 45-54 0-39g/day 0-9g/day 1 46
32 45-54 0-39g/day 10-19 0 18
33 45-54 0-39g/day 20-29 0 10
34 45-54 0-39g/day 30+ 0 4
35 45-54 40-79 0-9g/day 6 38
36 45-54 40-79 10-19 4 21
37 45-54 40-79 20-29 5 15
38 45-54 40-79 30+ 5 7
39 45-54 80-119 0-9g/day 3 16
40 45-54 80-119 10-19 6 14
41 45-54 80-119 20-29 1 5
42 45-54 80-119 30+ 2 4
43 45-54 120+ 0-9g/day 4 4
44 45-54 120+ 10-19 3 4
45 45-54 120+ 20-29 2 3
46 45-54 120+ 30+ 4 4
47 55-64 0-39g/day 0-9g/day 2 49
48 55-64 0-39g/day 10-19 3 22
49 55-64 0-39g/day 20-29 3 12
50 55-64 0-39g/day 30+ 4 6
51 55-64 40-79 0-9g/day 9 40
52 55-64 40-79 10-19 6 21
53 55-64 40-79 20-29 4 17
54 55-64 40-79 30+ 3 6
55 55-64 80-119 0-9g/day 9 18
56 55-64 80-119 10-19 8 15
57 55-64 80-119 20-29 3 6
58 55-64 80-119 30+ 4 4
59 55-64 120+ 0-9g/day 5 10
60 55-64 120+ 10-19 6 7
61 55-64 120+ 20-29 2 3
62 55-64 120+ 30+ 5 6
63 65-74 0-39g/day 0-9g/day 5 48
64 65-74 0-39g/day 10-19 4 14
65 65-74 0-39g/day 20-29 2 7
66 65-74 0-39g/day 30+ 0 2
67 65-74 40-79 0-9g/day 17 34
68 65-74 40-79 10-19 3 10
69 65-74 40-79 20-29 5 9
70 65-74 80-119 0-9g/day 6 13
71 65-74 80-119 10-19 4 12
72 65-74 80-119 20-29 2 3
73 65-74 80-119 30+ 1 1
74 65-74 120+ 0-9g/day 3 4
75 65-74 120+ 10-19 1 2
76 65-74 120+ 20-29 1 1
77 65-74 120+ 30+ 1 1
78 75+ 0-39g/day 0-9g/day 1 18
79 75+ 0-39g/day 10-19 2 6
80 75+ 0-39g/day 30+ 1 3
81 75+ 40-79 0-9g/day 2 5
82 75+ 40-79 10-19 1 3
83 75+ 40-79 20-29 0 3
84 75+ 40-79 30+ 1 1
85 75+ 80-119 0-9g/day 1 1
86 75+ 80-119 10-19 1 1
87 75+ 120+ 0-9g/day 2 2
88 75+ 120+ 10-19 1 1
# A tibble: 24 x 3
# Groups: agegp [6]
agegp alcgp pct
<ord> <ord> <dbl>
1 25-34 0-39g/day 0.2666667
2 25-34 40-79 0.2666667
3 25-34 80-119 0.2000000
4 25-34 120+ 0.2666667
5 35-44 0-39g/day 0.2666667
6 35-44 40-79 0.2666667
7 35-44 80-119 0.2666667
8 35-44 120+ 0.2000000
9 45-54 0-39g/day 0.2500000
10 45-54 40-79 0.2500000
# ... with 14 more rows
# A tibble: 24 x 3
# Groups: agegp [6]
agegp alcgp pct
<ord> <ord> <dbl>
1 25-34 0-39g/day 0.00000000
2 25-34 40-79 0.00000000
3 25-34 80-119 0.00000000
4 25-34 120+ 1.00000000
5 35-44 0-39g/day 0.11111111
6 35-44 40-79 0.44444444
7 35-44 80-119 0.00000000
8 35-44 120+ 0.44444444
9 45-54 0-39g/day 0.02173913
10 45-54 40-79 0.43478261
# ... with 14 more rows
# A tibble: 12 x 3
# Groups: agegp [6]
agegp low_alcgp pct
<ord> <lgl> <dbl>
1 25-34 FALSE 0.4666667
2 25-34 TRUE 0.5333333
3 35-44 FALSE 0.4666667
4 35-44 TRUE 0.5333333
5 45-54 FALSE 0.5000000
6 45-54 TRUE 0.5000000
7 55-64 FALSE 0.5000000
8 55-64 TRUE 0.5000000
9 65-74 FALSE 0.5333333
10 65-74 TRUE 0.4666667
11 75+ FALSE 0.3636364
12 75+ TRUE 0.6363636
Loading required package: dplyr
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
# A tibble: 10 x 3
# Groups: Species [3]
Species random_missing pct
<fctr> <dbl> <dbl>
1 setosa 4 0.04632588
2 setosa 5 0.88658147
3 setosa 6 0.06709265
4 versicolor 5 0.04244482
5 versicolor 6 0.85398981
6 versicolor 7 0.10356537
7 virginica 5 0.04237288
8 virginica 6 0.57966102
9 virginica 7 0.26271186
10 virginica 8 0.11525424
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