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tests/testthat/_snaps/by_factor.md
In crosstable: Crosstables for Descriptive Analyses
showNA with NA in by
Code
x0 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, times = c(0, 100, 200, 400))
as.data.frame(x0)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
10 cyl Number of cylinders NA 2 2 1
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x1 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, showNA = "no", times = c(0, 100, 200, 400))
as.data.frame(x1)
Output
.id label variable straight vshaped
1 am Transmission auto 2 (18.18%) 9 (81.82%)
2 am Transmission manual 7 (53.85%) 6 (46.15%)
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%)
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%)
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%)
10 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15)
11 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15)
12 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11)
13 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4)
14 surv Dummy survival (disp/am) Median survival 95.1 <NA>
Code
x2 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, showNA = "ifany", times = c(0, 100, 200, 400))
as.data.frame(x2)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
10 cyl Number of cylinders NA 2 2 1
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x3 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, showNA = "always", times = c(0, 100, 200, 400))
as.data.frame(x3)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 am Transmission NA 0 0 0
4 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
5 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
6 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
7 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
8 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
9 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
10 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
11 cyl Number of cylinders NA 2 2 1
12 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
14 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
15 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
16 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
showNA without NA in by
Code
x0 = crosstable(mtcars3, c(vs, mpg, cyl, surv), by = am, times = c(0, 100, 200, 400))
as.data.frame(x0)
Output
.id label variable auto manual
1 vs Engine straight 2 (22.22%) 7 (77.78%)
2 vs Engine vshaped 9 (60.00%) 6 (40.00%)
3 vs Engine NA 8 0
4 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
5 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
6 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
7 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
8 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%)
9 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%)
10 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%)
11 cyl Number of cylinders NA 2 3
12 surv Dummy survival (disp/am) t=0 1.00 (0/19) 1.00 (0/13)
13 surv Dummy survival (disp/am) t=100 1.00 (0/19) 0.62 (5/8)
14 surv Dummy survival (disp/am) t=200 1.00 (0/14) 0.15 (6/2)
15 surv Dummy survival (disp/am) t=400 1.00 (0/4) 0 (2/0)
16 surv Dummy survival (disp/am) Median survival <NA> 120.3
Code
x1 = crosstable(mtcars3, c(vs, mpg, cyl, surv), by = am, showNA = "no", times = c(0, 100, 200, 400))
as.data.frame(x1)
Output
.id label variable auto manual
1 vs Engine straight 2 (22.22%) 7 (77.78%)
2 vs Engine vshaped 9 (60.00%) 6 (40.00%)
3 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
4 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
5 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
6 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
7 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%)
8 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%)
9 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%)
10 surv Dummy survival (disp/am) t=0 1.00 (0/19) 1.00 (0/13)
11 surv Dummy survival (disp/am) t=100 1.00 (0/19) 0.62 (5/8)
12 surv Dummy survival (disp/am) t=200 1.00 (0/14) 0.15 (6/2)
13 surv Dummy survival (disp/am) t=400 1.00 (0/4) 0 (2/0)
14 surv Dummy survival (disp/am) Median survival <NA> 120.3
Code
x2 = crosstable(mtcars3, c(vs, mpg, cyl, surv), by = am, showNA = "ifany", times = c(0, 100, 200, 400))
as.data.frame(x2)
Output
.id label variable auto manual
1 vs Engine straight 2 (22.22%) 7 (77.78%)
2 vs Engine vshaped 9 (60.00%) 6 (40.00%)
3 vs Engine NA 8 0
4 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
5 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
6 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
7 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
8 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%)
9 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%)
10 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%)
11 cyl Number of cylinders NA 2 3
12 surv Dummy survival (disp/am) t=0 1.00 (0/19) 1.00 (0/13)
13 surv Dummy survival (disp/am) t=100 1.00 (0/19) 0.62 (5/8)
14 surv Dummy survival (disp/am) t=200 1.00 (0/14) 0.15 (6/2)
15 surv Dummy survival (disp/am) t=400 1.00 (0/4) 0 (2/0)
16 surv Dummy survival (disp/am) Median survival <NA> 120.3
Code
x3 = crosstable(mtcars3, c(vs, mpg, cyl, surv), by = am, showNA = "always", times = c(0, 100, 200, 400))
as.data.frame(x3)
Output
.id label variable auto manual NA
1 vs Engine straight 2 (22.22%) 7 (77.78%) 0
2 vs Engine vshaped 9 (60.00%) 6 (40.00%) 0
3 vs Engine NA 8 0 0
4 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9 no NA
5 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4] no NA
6 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2) no NA
7 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0) no NA
8 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%) 0
9 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%) 0
10 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%) 0
11 cyl Number of cylinders NA 2 3 0
12 surv Dummy survival (disp/am) t=0 1.00 (0/19) 1.00 (0/13) <NA>
13 surv Dummy survival (disp/am) t=100 1.00 (0/19) 0.62 (5/8) <NA>
14 surv Dummy survival (disp/am) t=200 1.00 (0/14) 0.15 (6/2) <NA>
15 surv Dummy survival (disp/am) t=400 1.00 (0/4) 0 (2/0) <NA>
16 surv Dummy survival (disp/am) Median survival <NA> 120.3 <NA>
total
Code
x0 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, times = c(0, 100, 200, 400))
as.data.frame(x0)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
10 cyl Number of cylinders NA 2 2 1
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x1 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, total = "none", times = c(0, 100, 200, 400))
as.data.frame(x1)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
10 cyl Number of cylinders NA 2 2 1
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x2 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, total = "row", times = c(0, 100, 200, 400))
as.data.frame(x2)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0 13 (40.62%)
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4 10.4 / 33.9
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1] 19.2 [15.4;22.8]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3) 20.1 (6.0)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0) 32 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2 10 (37.04%)
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3 4 (14.81%)
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2 13 (48.15%)
10 cyl Number of cylinders NA 2 2 1 5
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8) 1.00 (0/32)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8) 0.84 (5/27)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4) 0.64 (6/16)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0) 0.50 (2/4)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA> <NA>
Code
x3 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, total = "col", times = c(0, 100, 200, 400))
as.data.frame(x3)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8
4 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
5 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
6 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
7 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
8 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
9 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
10 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
11 cyl Number of cylinders NA 2 2 1
12 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8
13 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
14 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
15 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
16 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
17 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x4 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, total = "both", times = c(0, 100, 200, 400))
as.data.frame(x4)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4 10.4 / 33.9
5 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1] 19.2 [15.4;22.8]
6 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3) 20.1 (6.0)
7 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0) 32 (0)
8 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2 10 (37.04%)
9 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3 4 (14.81%)
10 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2 13 (48.15%)
11 cyl Number of cylinders NA 2 2 1 5
12 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
13 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8) 1.00 (0/32)
14 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8) 0.84 (5/27)
15 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4) 0.64 (6/16)
16 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0) 0.50 (2/4)
17 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA> <NA>
Margins without totals
Code
x0 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none")
as.data.frame(x0)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
4 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
5 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x1 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "row")
as.data.frame(x1)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
4 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
5 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x2 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "col")
as.data.frame(x2)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (22.22%) 9 (60.00%) 8
2 am Transmission manual 7 (77.78%) 6 (40.00%) 0
3 cyl Number of cylinders 4 7 (100.00%) 1 (7.69%) 2
4 cyl Number of cylinders 6 0 (0%) 1 (7.69%) 3
5 cyl Number of cylinders 8 0 (0%) 11 (84.62%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x3 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "cell")
as.data.frame(x3)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (8.33%) 9 (37.50%) 8
2 am Transmission manual 7 (29.17%) 6 (25.00%) 0
3 cyl Number of cylinders 4 7 (35.00%) 1 (5.00%) 2
4 cyl Number of cylinders 6 0 (0%) 1 (5.00%) 3
5 cyl Number of cylinders 8 0 (0%) 11 (55.00%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x4 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "none")
as.data.frame(x4)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 9 8
2 am Transmission manual 7 6 0
3 cyl Number of cylinders 4 7 1 2
4 cyl Number of cylinders 6 0 1 3
5 cyl Number of cylinders 8 0 11 2
6 cyl Number of cylinders NA 2 2 1
Code
x5 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "all")
as.data.frame(x5)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (8.33% / 18.18% / 22.22%) 9 (37.50% / 81.82% / 60.00%) 8
2 am Transmission manual 7 (29.17% / 53.85% / 77.78%) 6 (25.00% / 46.15% / 40.00%) 0
3 cyl Number of cylinders 4 7 (35.00% / 87.50% / 100.00%) 1 (5.00% / 12.50% / 7.69%) 2
4 cyl Number of cylinders 6 0 (0% / 0% / 0%) 1 (5.00% / 100.00% / 7.69%) 3
5 cyl Number of cylinders 8 0 (0% / 0% / 0%) 11 (55.00% / 100.00% / 84.62%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x6 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = 1:2)
as.data.frame(x6)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18% / 22.22%) 9 (81.82% / 60.00%) 8
2 am Transmission manual 7 (53.85% / 77.78%) 6 (46.15% / 40.00%) 0
3 cyl Number of cylinders 4 7 (87.50% / 100.00%) 1 (12.50% / 7.69%) 2
4 cyl Number of cylinders 6 0 (0% / 0%) 1 (100.00% / 7.69%) 3
5 cyl Number of cylinders 8 0 (0% / 0%) 11 (100.00% / 84.62%) 2
6 cyl Number of cylinders NA 2 2 1
Margins with totals
Code
x0 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both")
as.data.frame(x0)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x1 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "row")
as.data.frame(x1)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x2 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "col")
as.data.frame(x2)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (22.22%) 9 (60.00%) 8 19 (59.38%)
2 am Transmission manual 7 (77.78%) 6 (40.00%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (100.00%) 1 (7.69%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0%) 1 (7.69%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0%) 11 (84.62%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x3 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "cell")
as.data.frame(x3)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (8.33%) 9 (37.50%) 8 19 (59.38%)
2 am Transmission manual 7 (29.17%) 6 (25.00%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (35.00%) 1 (5.00%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0%) 1 (5.00%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0%) 11 (55.00%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x4 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "none")
as.data.frame(x4)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 9 8 19
2 am Transmission manual 7 6 0 13
3 am Transmission Total 9 15 8 32
4 cyl Number of cylinders 4 7 1 2 10
5 cyl Number of cylinders 6 0 1 3 4
6 cyl Number of cylinders 8 0 11 2 13
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 15 8 32
Code
x5 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "all")
as.data.frame(x5)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (8.33% / 18.18% / 22.22%) 9 (37.50% / 81.82% / 60.00%) 8 19 (59.38%)
2 am Transmission manual 7 (29.17% / 53.85% / 77.78%) 6 (25.00% / 46.15% / 40.00%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (35.00% / 87.50% / 100.00%) 1 (5.00% / 12.50% / 7.69%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0% / 0% / 0%) 1 (5.00% / 100.00% / 7.69%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0% / 0% / 0%) 11 (55.00% / 100.00% / 84.62%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x6 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = 1:2)
as.data.frame(x6)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18% / 22.22%) 9 (81.82% / 60.00%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85% / 77.78%) 6 (46.15% / 40.00%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (87.50% / 100.00%) 1 (12.50% / 7.69%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0% / 0%) 1 (100.00% / 7.69%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0% / 0%) 11 (100.00% / 84.62%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Percent pattern
Code
x0 = crosstable(mtcars3, cyl, percent_digits = 0, total = TRUE, showNA = "always", percent_pattern = PERCENT_PATTERN)
as.data.frame(x0)
Output
.id label variable value
1 cyl Number of cylinders 4 N=10\nCell: p = 37% (10/27) [95%CI 22%; 56%]\nCol: p = 37% (10/27) [95%CI 22%; 56%]\nRow:p = 37% (10/27) [95%CI 22%; 56%]
2 cyl Number of cylinders 6 N=4\nCell: p = 15% (4/27) [95%CI 6%; 32%]\nCol: p = 15% (4/27) [95%CI 6%; 32%]\nRow:p = 15% (4/27) [95%CI 6%; 32%]
3 cyl Number of cylinders 8 N=13\nCell: p = 48% (13/27) [95%CI 31%; 66%]\nCol: p = 48% (13/27) [95%CI 31%; 66%]\nRow:p = 48% (13/27) [95%CI 31%; 66%]
4 cyl Number of cylinders NA 5
5 cyl Number of cylinders Total 32 (100%)
Code
x1 = crosstable(mtcars3, cyl, by = am, percent_digits = 0, total = TRUE, showNA = "always", percent_pattern = PERCENT_PATTERN)
as.data.frame(x1)
Output
.id label variable auto manual NA Total
1 cyl Number of cylinders 4 N=3\nCell: p = 11% (3/27) [95%CI 4%; 28%]\nCol: p = 18% (3/17) [95%CI 6%; 41%]\nRow:p = 30% (3/10) [95%CI 11%; 60%] N=7\nCell: p = 26% (7/27) [95%CI 13%; 45%]\nCol: p = 70% (7/10) [95%CI 40%; 89%]\nRow:p = 70% (7/10) [95%CI 40%; 89%] 0 10 (37%)
2 cyl Number of cylinders 6 N=3\nCell: p = 11% (3/27) [95%CI 4%; 28%]\nCol: p = 18% (3/17) [95%CI 6%; 41%]\nRow:p = 75% (3/4) [95%CI 30%; 95%] N=1\nCell: p = 4% (1/27) [95%CI 1%; 18%]\nCol: p = 10% (1/10) [95%CI 2%; 40%]\nRow:p = 25% (1/4) [95%CI 5%; 70%] 0 4 (15%)
3 cyl Number of cylinders 8 N=11\nCell: p = 41% (11/27) [95%CI 25%; 59%]\nCol: p = 65% (11/17) [95%CI 41%; 83%]\nRow:p = 85% (11/13) [95%CI 58%; 96%] N=2\nCell: p = 7% (2/27) [95%CI 2%; 23%]\nCol: p = 20% (2/10) [95%CI 6%; 51%]\nRow:p = 15% (2/13) [95%CI 4%; 42%] 0 13 (48%)
4 cyl Number of cylinders NA 2 3 0 5
5 cyl Number of cylinders Total 19 (59%) 13 (41%) <NA> 32 (100%)
Code
x2 = crosstable(mtcars3, c(mpg, vs, cyl), by = c(am, dummy), percent_digits = 0, total = TRUE, showNA = "always", percent_pattern = PERCENT_PATTERN)
as.data.frame(x2)
Output
.id label variable am=auto & dummy=dummy am=manual & dummy=dummy NA Total
1 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9 no NA 10.4 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4] no NA 19.2 [15.4;22.8]
3 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2) no NA 20.1 (6.0)
4 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0) no NA 32 (0)
5 vs Engine straight N=2\nCell: p = 8% (2/24) [95%CI 2%; 26%]\nCol: p = 18% (2/11) [95%CI 5%; 48%]\nRow:p = 22% (2/9) [95%CI 6%; 55%] N=7\nCell: p = 29% (7/24) [95%CI 15%; 49%]\nCol: p = 54% (7/13) [95%CI 29%; 77%]\nRow:p = 78% (7/9) [95%CI 45%; 94%] 0 9 (38%)
6 vs Engine vshaped N=9\nCell: p = 38% (9/24) [95%CI 21%; 57%]\nCol: p = 82% (9/11) [95%CI 52%; 95%]\nRow:p = 60% (9/15) [95%CI 36%; 80%] N=6\nCell: p = 25% (6/24) [95%CI 12%; 45%]\nCol: p = 46% (6/13) [95%CI 23%; 71%]\nRow:p = 40% (6/15) [95%CI 20%; 64%] 0 15 (62%)
7 vs Engine NA 8 0 0 8
8 vs Engine Total 19 (59%) 13 (41%) <NA> 32 (100%)
9 cyl Number of cylinders 4 N=3\nCell: p = 11% (3/27) [95%CI 4%; 28%]\nCol: p = 18% (3/17) [95%CI 6%; 41%]\nRow:p = 30% (3/10) [95%CI 11%; 60%] N=7\nCell: p = 26% (7/27) [95%CI 13%; 45%]\nCol: p = 70% (7/10) [95%CI 40%; 89%]\nRow:p = 70% (7/10) [95%CI 40%; 89%] 0 10 (37%)
10 cyl Number of cylinders 6 N=3\nCell: p = 11% (3/27) [95%CI 4%; 28%]\nCol: p = 18% (3/17) [95%CI 6%; 41%]\nRow:p = 75% (3/4) [95%CI 30%; 95%] N=1\nCell: p = 4% (1/27) [95%CI 1%; 18%]\nCol: p = 10% (1/10) [95%CI 2%; 40%]\nRow:p = 25% (1/4) [95%CI 5%; 70%] 0 4 (15%)
11 cyl Number of cylinders 8 N=11\nCell: p = 41% (11/27) [95%CI 25%; 59%]\nCol: p = 65% (11/17) [95%CI 41%; 83%]\nRow:p = 85% (11/13) [95%CI 58%; 96%] N=2\nCell: p = 7% (2/27) [95%CI 2%; 23%]\nCol: p = 20% (2/10) [95%CI 6%; 51%]\nRow:p = 15% (2/13) [95%CI 4%; 42%] 0 13 (48%)
12 cyl Number of cylinders NA 2 3 0 5
13 cyl Number of cylinders Total 19 (59%) 13 (41%) <NA> 32 (100%)
Percent pattern - Ultimate
Code
x1 = crosstable(mtcars3, cyl, by = vs, percent_digits = 0, total = TRUE, showNA = "always", percent_pattern = ULTIMATE_PATTERN)
as.data.frame(x1)
Output
.id label variable straight vshaped NA Total
1 cyl Number of cylinders 4 N=7\nCell: p = 35% (7/20) [2e+01%; 57%]\nCol: p = 100% (7/7) [65%; 100%]\nRow: p = 88% (7/8) [53%; 98%]\n\nCell (NA): p = 22% (7/32) [11%; 39%]\nCol (NA): p = 78% (7/9) [45%; 94%]\nRow (NA): p = 70% (7/10) [40%; 89%] N=1\nCell: p = 5% (1/20) [9e-01%; 24%]\nCol: p = 8% (1/13) [1%; 33%]\nRow: p = 12% (1/8) [2%; 47%]\n\nCell (NA): p = 3% (1/32) [1%; 16%]\nCol (NA): p = 7% (1/15) [1%; 30%]\nRow (NA): p = 10% (1/10) [2%; 40%] 2 N=10\nCol: p = 37% (10/27) [22%; 56%]\nCol (NA): p = 31% (10/32) [18%; 49%]
2 cyl Number of cylinders 6 N=0\nCell: p = 0% (0/20) [1e-15%; 16%]\nCol: p = 0% (0/7) [0%; 35%]\nRow: p = 0% (0/1) [0%; 79%]\n\nCell (NA): p = 0% (0/32) [0%; 11%]\nCol (NA): p = 0% (0/9) [0%; 30%]\nRow (NA): p = 0% (0/4) [0%; 49%] N=1\nCell: p = 5% (1/20) [9e-01%; 24%]\nCol: p = 8% (1/13) [1%; 33%]\nRow: p = 100% (1/1) [21%; 100%]\n\nCell (NA): p = 3% (1/32) [1%; 16%]\nCol (NA): p = 7% (1/15) [1%; 30%]\nRow (NA): p = 25% (1/4) [5%; 70%] 3 N=4\nCol: p = 15% (4/27) [6%; 32%]\nCol (NA): p = 12% (4/32) [5%; 28%]
3 cyl Number of cylinders 8 N=0\nCell: p = 0% (0/20) [1e-15%; 16%]\nCol: p = 0% (0/7) [0%; 35%]\nRow: p = 0% (0/11) [0%; 26%]\n\nCell (NA): p = 0% (0/32) [0%; 11%]\nCol (NA): p = 0% (0/9) [0%; 30%]\nRow (NA): p = 0% (0/13) [0%; 23%] N=11\nCell: p = 55% (11/20) [3e+01%; 74%]\nCol: p = 85% (11/13) [58%; 96%]\nRow: p = 100% (11/11) [74%; 100%]\n\nCell (NA): p = 34% (11/32) [20%; 52%]\nCol (NA): p = 73% (11/15) [48%; 89%]\nRow (NA): p = 85% (11/13) [58%; 96%] 2 N=13\nCol: p = 48% (13/27) [31%; 66%]\nCol (NA): p = 41% (13/32) [26%; 58%]
4 cyl Number of cylinders NA 2 2 1 5
5 cyl Number of cylinders Total N=9\nRow: p = 38% (9/24) [21%; 57%]\nRow (NA): p = 28% (9/32) [16%; 45%] N=15\nRow: p = 62% (15/24) [43%; 79%]\nRow (NA): p = 47% (15/32) [31%; 64%] 8 N=32\nP: 100% [89%; 100%]\nP (NA): 100% [89%; 100%]
Code
x2 = crosstable(mtcars3, cyl, by = vs, percent_digits = 0, total = TRUE, showNA = "no", percent_pattern = ULTIMATE_PATTERN)
as.data.frame(x2)
Output
.id label variable straight vshaped Total
1 cyl Number of cylinders 4 N=7\nCell: p = 35% (7/20) [2e+01%; 57%]\nCol: p = 100% (7/7) [65%; 100%]\nRow: p = 88% (7/8) [53%; 98%]\n\nCell (NA): p = 35% (7/20) [2e+01%; 57%]\nCol (NA): p = 100% (7/7) [65%; 100%]\nRow (NA): p = 88% (7/8) [53%; 98%] N=1\nCell: p = 5% (1/20) [9e-01%; 24%]\nCol: p = 8% (1/13) [1%; 33%]\nRow: p = 12% (1/8) [2%; 47%]\n\nCell (NA): p = 5% (1/20) [9e-01%; 24%]\nCol (NA): p = 8% (1/13) [1%; 33%]\nRow (NA): p = 12% (1/8) [2%; 47%] N=8\nCol: p = 40% (8/20) [22%; 61%]\nCol (NA): p = 40% (8/20) [2e+01%; 61%]
2 cyl Number of cylinders 6 N=0\nCell: p = 0% (0/20) [1e-15%; 16%]\nCol: p = 0% (0/7) [0%; 35%]\nRow: p = 0% (0/1) [0%; 79%]\n\nCell (NA): p = 0% (0/20) [1e-15%; 16%]\nCol (NA): p = 0% (0/7) [0%; 35%]\nRow (NA): p = 0% (0/1) [0%; 79%] N=1\nCell: p = 5% (1/20) [9e-01%; 24%]\nCol: p = 8% (1/13) [1%; 33%]\nRow: p = 100% (1/1) [21%; 100%]\n\nCell (NA): p = 5% (1/20) [9e-01%; 24%]\nCol (NA): p = 8% (1/13) [1%; 33%]\nRow (NA): p = 100% (1/1) [21%; 100%] N=1\nCol: p = 5% (1/20) [1%; 24%]\nCol (NA): p = 5% (1/20) [9e-01%; 24%]
3 cyl Number of cylinders 8 N=0\nCell: p = 0% (0/20) [1e-15%; 16%]\nCol: p = 0% (0/7) [0%; 35%]\nRow: p = 0% (0/11) [0%; 26%]\n\nCell (NA): p = 0% (0/20) [1e-15%; 16%]\nCol (NA): p = 0% (0/7) [0%; 35%]\nRow (NA): p = 0% (0/11) [0%; 26%] N=11\nCell: p = 55% (11/20) [3e+01%; 74%]\nCol: p = 85% (11/13) [58%; 96%]\nRow: p = 100% (11/11) [74%; 100%]\n\nCell (NA): p = 55% (11/20) [3e+01%; 74%]\nCol (NA): p = 85% (11/13) [58%; 96%]\nRow (NA): p = 100% (11/11) [74%; 100%] N=11\nCol: p = 55% (11/20) [34%; 74%]\nCol (NA): p = 55% (11/20) [3e+01%; 74%]
4 cyl Number of cylinders Total N=7\nRow: p = 35% (7/20) [18%; 57%]\nRow (NA): p = 35% (7/20) [18%; 57%] N=13\nRow: p = 65% (13/20) [43%; 82%]\nRow (NA): p = 65% (13/20) [43%; 82%] N=20\nP: 100% [84%; 100%]\nP (NA): 100% [84%; 100%]
Unique numeric
Code
x0 = crosstable(mtcars3, gear)
as.data.frame(x0)
Output
.id label variable value
1 gear gear 1 10 (38.46%)
2 gear gear 2 11 (42.31%)
3 gear gear 3 5 (19.23%)
4 gear gear NA 6
Code
x1 = crosstable(mtcars3, carb)
as.data.frame(x1)
Output
.id label variable value
1 carb Number of carburetors Min / Max 1.0 / 8.0
2 carb Number of carburetors Med [IQR] 2.0 [2.0;4.0]
3 carb Number of carburetors Mean (std) 2.8 (1.6)
4 carb Number of carburetors N (NA) 32 (0)
Code
x2 = crosstable(mtcars3, carb, unique_numeric = 9)
as.data.frame(x2)
Output
.id label variable value
1 carb Number of carburetors 1 7 (21.88%)
2 carb Number of carburetors 2 10 (31.25%)
3 carb Number of carburetors 3 3 (9.38%)
4 carb Number of carburetors 4 10 (31.25%)
5 carb Number of carburetors 6 1 (3.12%)
6 carb Number of carburetors 8 1 (3.12%)
By dummy
Code
x0 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = dummy)
as.data.frame(x0)
Output
.id label variable dummy
1 am Transmission auto 19 (59.38%)
2 am Transmission manual 13 (40.62%)
3 mpg Miles/(US) gallon Min / Max 10.4 / 33.9
4 mpg Miles/(US) gallon Med [IQR] 19.2 [15.4;22.8]
5 mpg Miles/(US) gallon Mean (std) 20.1 (6.0)
6 mpg Miles/(US) gallon N (NA) 32 (0)
7 cyl Number of cylinders 4 10 (37.04%)
8 cyl Number of cylinders 6 4 (14.81%)
9 cyl Number of cylinders 8 13 (48.15%)
10 cyl Number of cylinders NA 5
11 surv Dummy survival (disp/am) t=71.1 0.97 (1/32)
12 surv Dummy survival (disp/am) t=75.7 0.94 (1/31)
13 surv Dummy survival (disp/am) t=78.7 0.91 (1/30)
14 surv Dummy survival (disp/am) t=79 0.88 (1/29)
15 surv Dummy survival (disp/am) t=95.1 0.84 (1/28)
16 surv Dummy survival (disp/am) t=108 0.81 (1/27)
17 surv Dummy survival (disp/am) t=120.1 0.81 (0/26)
18 surv Dummy survival (disp/am) t=120.3 0.78 (1/25)
19 surv Dummy survival (disp/am) t=121 0.75 (1/24)
20 surv Dummy survival (disp/am) t=140.8 0.75 (0/23)
21 surv Dummy survival (disp/am) t=145 0.71 (1/22)
22 surv Dummy survival (disp/am) t=146.7 0.71 (0/21)
23 surv Dummy survival (disp/am) t=160 0.64 (2/20)
24 surv Dummy survival (disp/am) t=167.6 0.64 (0/18)
25 surv Dummy survival (disp/am) t=225 0.64 (0/16)
26 surv Dummy survival (disp/am) t=258 0.64 (0/15)
27 surv Dummy survival (disp/am) t=275.8 0.64 (0/14)
28 surv Dummy survival (disp/am) t=301 0.58 (1/11)
29 surv Dummy survival (disp/am) t=304 0.58 (0/10)
30 surv Dummy survival (disp/am) t=318 0.58 (0/9)
31 surv Dummy survival (disp/am) t=350 0.58 (0/8)
32 surv Dummy survival (disp/am) t=351 0.50 (1/7)
33 surv Dummy survival (disp/am) t=360 0.50 (0/6)
34 surv Dummy survival (disp/am) t=400 0.50 (0/4)
35 surv Dummy survival (disp/am) t=440 0.50 (0/3)
36 surv Dummy survival (disp/am) t=460 0.50 (0/2)
37 surv Dummy survival (disp/am) t=472 0.50 (0/1)
38 surv Dummy survival (disp/am) Median survival <NA>
Code
x1 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = dummy, showNA = TRUE)
as.data.frame(x1)
Output
.id label variable dummy NA
1 am Transmission auto 19 (59.38%) 0
2 am Transmission manual 13 (40.62%) 0
3 am Transmission NA 0 0
4 mpg Miles/(US) gallon Min / Max 10.4 / 33.9 no NA
5 mpg Miles/(US) gallon Med [IQR] 19.2 [15.4;22.8] no NA
6 mpg Miles/(US) gallon Mean (std) 20.1 (6.0) no NA
7 mpg Miles/(US) gallon N (NA) 32 (0) no NA
8 cyl Number of cylinders 4 10 (37.04%) 0
9 cyl Number of cylinders 6 4 (14.81%) 0
10 cyl Number of cylinders 8 13 (48.15%) 0
11 cyl Number of cylinders NA 5 0
12 surv Dummy survival (disp/am) t=71.1 0.97 (1/32) <NA>
13 surv Dummy survival (disp/am) t=75.7 0.94 (1/31) <NA>
14 surv Dummy survival (disp/am) t=78.7 0.91 (1/30) <NA>
15 surv Dummy survival (disp/am) t=79 0.88 (1/29) <NA>
16 surv Dummy survival (disp/am) t=95.1 0.84 (1/28) <NA>
17 surv Dummy survival (disp/am) t=108 0.81 (1/27) <NA>
18 surv Dummy survival (disp/am) t=120.1 0.81 (0/26) <NA>
19 surv Dummy survival (disp/am) t=120.3 0.78 (1/25) <NA>
20 surv Dummy survival (disp/am) t=121 0.75 (1/24) <NA>
21 surv Dummy survival (disp/am) t=140.8 0.75 (0/23) <NA>
22 surv Dummy survival (disp/am) t=145 0.71 (1/22) <NA>
23 surv Dummy survival (disp/am) t=146.7 0.71 (0/21) <NA>
24 surv Dummy survival (disp/am) t=160 0.64 (2/20) <NA>
25 surv Dummy survival (disp/am) t=167.6 0.64 (0/18) <NA>
26 surv Dummy survival (disp/am) t=225 0.64 (0/16) <NA>
27 surv Dummy survival (disp/am) t=258 0.64 (0/15) <NA>
28 surv Dummy survival (disp/am) t=275.8 0.64 (0/14) <NA>
29 surv Dummy survival (disp/am) t=301 0.58 (1/11) <NA>
30 surv Dummy survival (disp/am) t=304 0.58 (0/10) <NA>
31 surv Dummy survival (disp/am) t=318 0.58 (0/9) <NA>
32 surv Dummy survival (disp/am) t=350 0.58 (0/8) <NA>
33 surv Dummy survival (disp/am) t=351 0.50 (1/7) <NA>
34 surv Dummy survival (disp/am) t=360 0.50 (0/6) <NA>
35 surv Dummy survival (disp/am) t=400 0.50 (0/4) <NA>
36 surv Dummy survival (disp/am) t=440 0.50 (0/3) <NA>
37 surv Dummy survival (disp/am) t=460 0.50 (0/2) <NA>
38 surv Dummy survival (disp/am) t=472 0.50 (0/1) <NA>
39 surv Dummy survival (disp/am) Median survival <NA> <NA>
Code
x2 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = dummy2)
as.data.frame(x2)
Output
.id label variable dummy NA
1 am Transmission auto 11 (100.00%) 8
2 am Transmission manual 13 (100.00%) 0
3 mpg Miles/(US) gallon Min / Max 10.4 / 33.9 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 20.4 [15.2;23.6] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 20.5 (6.7) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 24 (0) 8 (0)
7 cyl Number of cylinders 4 8 (100.00%) 2
8 cyl Number of cylinders 6 1 (100.00%) 3
9 cyl Number of cylinders 8 11 (100.00%) 2
10 cyl Number of cylinders NA 4 1
11 surv Dummy survival (disp/am) t=71.1 0.96 (1/24) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=75.7 0.92 (1/23) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=78.7 0.88 (1/22) 1.00 (0/8)
14 surv Dummy survival (disp/am) t=79 0.83 (1/21) 1.00 (0/8)
15 surv Dummy survival (disp/am) t=95.1 0.79 (1/20) 1.00 (0/8)
16 surv Dummy survival (disp/am) t=108 0.75 (1/19) 1.00 (0/8)
17 surv Dummy survival (disp/am) t=120.1 0.75 (0/18) 1.00 (0/8)
18 surv Dummy survival (disp/am) t=120.3 0.71 (1/17) 1.00 (0/8)
19 surv Dummy survival (disp/am) t=121 0.66 (1/16) 1.00 (0/8)
20 surv Dummy survival (disp/am) t=140.8 0.66 (0/15) 1.00 (0/8)
21 surv Dummy survival (disp/am) t=145 0.62 (1/15) 1.00 (0/7)
22 surv Dummy survival (disp/am) t=146.7 0.62 (0/14) 1.00 (0/7)
23 surv Dummy survival (disp/am) t=160 0.53 (2/14) 1.00 (0/6)
24 surv Dummy survival (disp/am) t=167.6 0.53 (0/12) 1.00 (0/6)
25 surv Dummy survival (disp/am) t=225 0.53 (0/12) 1.00 (0/4)
26 surv Dummy survival (disp/am) t=258 0.53 (0/12) 1.00 (0/3)
27 surv Dummy survival (disp/am) t=275.8 0.53 (0/11) 1.00 (0/3)
28 surv Dummy survival (disp/am) t=275.8 0.53 (0/11) 1.00 (0/3)
29 surv Dummy survival (disp/am) t=301 0.47 (1/9) 1.00 (0/2)
30 surv Dummy survival (disp/am) t=304 0.47 (0/8) 1.00 (0/2)
31 surv Dummy survival (disp/am) t=318 0.47 (0/7) 1.00 (0/2)
32 surv Dummy survival (disp/am) t=350 0.47 (0/6) 1.00 (0/2)
33 surv Dummy survival (disp/am) t=351 0.38 (1/5) 1.00 (0/2)
34 surv Dummy survival (disp/am) t=360 0.38 (0/4) 1.00 (0/2)
35 surv Dummy survival (disp/am) t=400 0.38 (0/4) 1.00 (0/0)
36 surv Dummy survival (disp/am) t=440 0.38 (0/3) 1.00 (0/0)
37 surv Dummy survival (disp/am) t=460 0.38 (0/2) 1.00 (0/0)
38 surv Dummy survival (disp/am) t=472 0.38 (0/1) 1.00 (0/0)
39 surv Dummy survival (disp/am) Median survival 301 <NA>
Code
x3 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = dummy2, showNA = FALSE)
as.data.frame(x3)
Output
.id label variable dummy
1 am Transmission auto 11 (100.00%)
2 am Transmission manual 13 (100.00%)
3 mpg Miles/(US) gallon Min / Max 10.4 / 33.9
4 mpg Miles/(US) gallon Med [IQR] 20.4 [15.2;23.6]
5 mpg Miles/(US) gallon Mean (std) 20.5 (6.7)
6 mpg Miles/(US) gallon N (NA) 24 (0)
7 cyl Number of cylinders 4 8 (100.00%)
8 cyl Number of cylinders 6 1 (100.00%)
9 cyl Number of cylinders 8 11 (100.00%)
10 surv Dummy survival (disp/am) t=71.1 0.96 (1/24)
11 surv Dummy survival (disp/am) t=75.7 0.92 (1/23)
12 surv Dummy survival (disp/am) t=78.7 0.88 (1/22)
13 surv Dummy survival (disp/am) t=79 0.83 (1/21)
14 surv Dummy survival (disp/am) t=95.1 0.79 (1/20)
15 surv Dummy survival (disp/am) t=108 0.75 (1/19)
16 surv Dummy survival (disp/am) t=120.1 0.75 (0/18)
17 surv Dummy survival (disp/am) t=120.3 0.71 (1/17)
18 surv Dummy survival (disp/am) t=121 0.66 (1/16)
19 surv Dummy survival (disp/am) t=145 0.62 (1/15)
20 surv Dummy survival (disp/am) t=160 0.53 (2/14)
21 surv Dummy survival (disp/am) t=258 0.53 (0/12)
22 surv Dummy survival (disp/am) t=275.8 0.53 (0/11)
23 surv Dummy survival (disp/am) t=301 0.47 (1/9)
24 surv Dummy survival (disp/am) t=304 0.47 (0/8)
25 surv Dummy survival (disp/am) t=318 0.47 (0/7)
26 surv Dummy survival (disp/am) t=350 0.47 (0/6)
27 surv Dummy survival (disp/am) t=351 0.38 (1/5)
28 surv Dummy survival (disp/am) t=400 0.38 (0/4)
29 surv Dummy survival (disp/am) t=440 0.38 (0/3)
30 surv Dummy survival (disp/am) t=460 0.38 (0/2)
31 surv Dummy survival (disp/am) t=472 0.38 (0/1)
32 surv Dummy survival (disp/am) Median survival 301
By multiple
Code
x0 = crosstable(mtcars3, c(mpg, gear), by = c(cyl, am, vs))
as.data.frame(x0)
Output
.id label variable cyl=4 & am=auto & vs=straight cyl=6 & am=auto & vs=straight cyl=8 & am=auto & vs=straight cyl=NA & am=auto & vs=straight cyl=4 & am=manual & vs=straight cyl=6 & am=manual & vs=straight cyl=8 & am=manual & vs=straight cyl=NA & am=manual & vs=straight cyl=4 & am=auto & vs=vshaped cyl=6 & am=auto & vs=vshaped cyl=8 & am=auto & vs=vshaped cyl=NA & am=auto & vs=vshaped cyl=4 & am=manual & vs=vshaped cyl=6 & am=manual & vs=vshaped cyl=8 & am=manual & vs=vshaped cyl=NA & am=manual & vs=vshaped cyl=4 & am=auto & vs=NA cyl=6 & am=auto & vs=NA cyl=8 & am=auto & vs=NA cyl=NA & am=auto & vs=NA cyl=4 & am=manual & vs=NA cyl=6 & am=manual & vs=NA cyl=8 & am=manual & vs=NA cyl=NA & am=manual & vs=NA
1 mpg Miles/(US) gallon Min / Max 21.5 / 21.5 NA / NA NA / NA 21.4 / 21.4 21.4 / 33.9 NA / NA NA / NA 22.8 / 22.8 NA / NA NA / NA 10.4 / 19.2 NA / NA 26.0 / 26.0 19.7 / 19.7 15.0 / 15.8 21.0 / 21.0 22.8 / 24.4 17.8 / 19.2 14.3 / 16.4 18.7 / 18.7 NA / NA NA / NA NA / NA NA / NA
2 mpg Miles/(US) gallon Med [IQR] 21.5 [21.5;21.5] NA [NA;NA] NA [NA;NA] 21.4 [21.4;21.4] 30.4 [28.1;31.9] NA [NA;NA] NA [NA;NA] 22.8 [22.8;22.8] NA [NA;NA] NA [NA;NA] 15.2 [13.3;15.5] NA [NA;NA] 26.0 [26.0;26.0] 19.7 [19.7;19.7] 15.4 [15.2;15.6] 21.0 [21.0;21.0] 23.6 [23.2;24.0] 18.1 [18.0;18.6] 15.3 [14.8;15.9] 18.7 [18.7;18.7] NA [NA;NA] NA [NA;NA] NA [NA;NA] NA [NA;NA]
3 mpg Miles/(US) gallon Mean (std) 21.5 (NA) NA (NA) NA (NA) 21.4 (NA) 29.3 (4.5) NA (NA) NA (NA) 22.8 (NA) NA (NA) NA (NA) 14.6 (2.9) NA (NA) 26.0 (NA) 19.7 (NA) 15.4 (0.6) 21.0 (0) 23.6 (1.1) 18.4 (0.7) 15.3 (1.5) 18.7 (NA) NA (NA) NA (NA) NA (NA) NA (NA)
4 mpg Miles/(US) gallon N (NA) 1 (0) 0 (0) 0 (0) 1 (0) 6 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 9 (0) 0 (0) 1 (0) 1 (0) 2 (0) 2 (0) 2 (0) 3 (0) 2 (0) 1 (0) 0 (0) 0 (0) 0 (0) 0 (0)
5 gear Number of forward gears 3 1 (6.67%) 0 (0%) 0 (0%) 1 (6.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (60.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (6.67%) 2 (13.33%) 1 (6.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
6 gear Number of forward gears 4 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (41.67%) 0 (0%) 0 (0%) 1 (8.33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (16.67%) 2 (16.67%) 2 (16.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
7 gear Number of forward gears 5 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 1 (20.00%) 2 (40.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Code
x1 = crosstable(mtcars3, c(mpg, gear), by = c(cyl, am, vs), showNA = FALSE)
as.data.frame(x1)
Output
.id label variable cyl=4 & am=auto & vs=straight cyl=6 & am=auto & vs=straight cyl=8 & am=auto & vs=straight cyl=NA & am=auto & vs=straight cyl=4 & am=manual & vs=straight cyl=6 & am=manual & vs=straight cyl=8 & am=manual & vs=straight cyl=NA & am=manual & vs=straight cyl=4 & am=auto & vs=vshaped cyl=6 & am=auto & vs=vshaped cyl=8 & am=auto & vs=vshaped cyl=NA & am=auto & vs=vshaped cyl=4 & am=manual & vs=vshaped cyl=6 & am=manual & vs=vshaped cyl=8 & am=manual & vs=vshaped cyl=NA & am=manual & vs=vshaped
1 mpg Miles/(US) gallon Min / Max 21.5 / 21.5 NA / NA NA / NA 21.4 / 21.4 21.4 / 33.9 NA / NA NA / NA 22.8 / 22.8 NA / NA NA / NA 10.4 / 19.2 NA / NA 26.0 / 26.0 19.7 / 19.7 15.0 / 15.8 21.0 / 21.0
2 mpg Miles/(US) gallon Med [IQR] 21.5 [21.5;21.5] NA [NA;NA] NA [NA;NA] 21.4 [21.4;21.4] 30.4 [28.1;31.9] NA [NA;NA] NA [NA;NA] 22.8 [22.8;22.8] NA [NA;NA] NA [NA;NA] 15.2 [13.3;15.5] NA [NA;NA] 26.0 [26.0;26.0] 19.7 [19.7;19.7] 15.4 [15.2;15.6] 21.0 [21.0;21.0]
3 mpg Miles/(US) gallon Mean (std) 21.5 (NA) NA (NA) NA (NA) 21.4 (NA) 29.3 (4.5) NA (NA) NA (NA) 22.8 (NA) NA (NA) NA (NA) 14.6 (2.9) NA (NA) 26.0 (NA) 19.7 (NA) 15.4 (0.6) 21.0 (0)
4 mpg Miles/(US) gallon N (NA) 1 (0) 0 (0) 0 (0) 1 (0) 6 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 9 (0) 0 (0) 1 (0) 1 (0) 2 (0) 2 (0)
5 gear Number of forward gears 3 1 (9.09%) 0 (0%) 0 (0%) 1 (9.09%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (81.82%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
6 gear Number of forward gears 4 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (62.50%) 0 (0%) 0 (0%) 1 (12.50%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (25.00%)
7 gear Number of forward gears 5 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 1 (20.00%) 2 (40.00%) 0 (0%)
Code
x2 = crosstable(mtcars3, c(mpg, gear), by = c(cyl, am, vs), total = TRUE)
as.data.frame(x2)
Output
.id label variable cyl=4 & am=auto & vs=straight cyl=6 & am=auto & vs=straight cyl=8 & am=auto & vs=straight cyl=NA & am=auto & vs=straight cyl=4 & am=manual & vs=straight cyl=6 & am=manual & vs=straight cyl=8 & am=manual & vs=straight cyl=NA & am=manual & vs=straight cyl=4 & am=auto & vs=vshaped cyl=6 & am=auto & vs=vshaped cyl=8 & am=auto & vs=vshaped cyl=NA & am=auto & vs=vshaped cyl=4 & am=manual & vs=vshaped cyl=6 & am=manual & vs=vshaped cyl=8 & am=manual & vs=vshaped cyl=NA & am=manual & vs=vshaped cyl=4 & am=auto & vs=NA cyl=6 & am=auto & vs=NA cyl=8 & am=auto & vs=NA cyl=NA & am=auto & vs=NA cyl=4 & am=manual & vs=NA cyl=6 & am=manual & vs=NA cyl=8 & am=manual & vs=NA cyl=NA & am=manual & vs=NA Total
1 mpg Miles/(US) gallon Min / Max 21.5 / 21.5 NA / NA NA / NA 21.4 / 21.4 21.4 / 33.9 NA / NA NA / NA 22.8 / 22.8 NA / NA NA / NA 10.4 / 19.2 NA / NA 26.0 / 26.0 19.7 / 19.7 15.0 / 15.8 21.0 / 21.0 22.8 / 24.4 17.8 / 19.2 14.3 / 16.4 18.7 / 18.7 NA / NA NA / NA NA / NA NA / NA 10.4 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 21.5 [21.5;21.5] NA [NA;NA] NA [NA;NA] 21.4 [21.4;21.4] 30.4 [28.1;31.9] NA [NA;NA] NA [NA;NA] 22.8 [22.8;22.8] NA [NA;NA] NA [NA;NA] 15.2 [13.3;15.5] NA [NA;NA] 26.0 [26.0;26.0] 19.7 [19.7;19.7] 15.4 [15.2;15.6] 21.0 [21.0;21.0] 23.6 [23.2;24.0] 18.1 [18.0;18.6] 15.3 [14.8;15.9] 18.7 [18.7;18.7] NA [NA;NA] NA [NA;NA] NA [NA;NA] NA [NA;NA] 19.2 [15.4;22.8]
3 mpg Miles/(US) gallon Mean (std) 21.5 (NA) NA (NA) NA (NA) 21.4 (NA) 29.3 (4.5) NA (NA) NA (NA) 22.8 (NA) NA (NA) NA (NA) 14.6 (2.9) NA (NA) 26.0 (NA) 19.7 (NA) 15.4 (0.6) 21.0 (0) 23.6 (1.1) 18.4 (0.7) 15.3 (1.5) 18.7 (NA) NA (NA) NA (NA) NA (NA) NA (NA) 20.1 (6.0)
4 mpg Miles/(US) gallon N (NA) 1 (0) 0 (0) 0 (0) 1 (0) 6 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 9 (0) 0 (0) 1 (0) 1 (0) 2 (0) 2 (0) 2 (0) 3 (0) 2 (0) 1 (0) 0 (0) 0 (0) 0 (0) 0 (0) 32 (0)
5 gear Number of forward gears 3 1 (6.67%) 0 (0%) 0 (0%) 1 (6.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (60.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (6.67%) 2 (13.33%) 1 (6.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 15 (46.88%)
6 gear Number of forward gears 4 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (41.67%) 0 (0%) 0 (0%) 1 (8.33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (16.67%) 2 (16.67%) 2 (16.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 12 (37.50%)
7 gear Number of forward gears 5 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 1 (20.00%) 2 (40.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (15.62%)
8 gear Number of forward gears Total 1 (3.12%) 0 (0%) 0 (0%) 1 (3.12%) 6 (18.75%) 0 (0%) 0 (0%) 1 (3.12%) 0 (0%) 0 (0%) 9 (28.12%) 0 (0%) 1 (3.12%) 1 (3.12%) 2 (6.25%) 2 (6.25%) 2 (6.25%) 3 (9.38%) 2 (6.25%) 1 (3.12%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 32 (100.00%)
Code
x3 = crosstable(mtcars3, c(mpg, vs, cyl), by = c(am, dummy))
as.data.frame(x3)
Output
.id label variable am=auto & dummy=dummy am=manual & dummy=dummy
1 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
3 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
4 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
5 vs Engine straight 2 (22.22%) 7 (77.78%)
6 vs Engine vshaped 9 (60.00%) 6 (40.00%)
7 vs Engine NA 8 0
8 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%)
9 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%)
10 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%)
11 cyl Number of cylinders NA 2 3
Code
x4 = crosstable(mtcars3, c(mpg, vs, cyl, dummy, surv, hp_date, qsec_posix, diff, cyl3), by = c(am, gear), total = TRUE, times = c(100, 200), followup = TRUE)
as.data.frame(x4)
Output
.id label variable am=auto & gear=3 am=manual & gear=3 am=auto & gear=4 am=manual & gear=4 am=auto & gear=5 am=manual & gear=5 Total
1 mpg Miles/(US) gallon Min / Max 10.4 / 21.5 NA / NA 17.8 / 24.4 21.0 / 33.9 NA / NA 15.0 / 30.4 10.4 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 15.5 [14.5;18.4] NA [NA;NA] 21.0 [18.8;23.2] 25.1 [21.3;30.9] NA [NA;NA] 19.7 [15.8;26.0] 19.2 [15.4;22.8]
3 mpg Miles/(US) gallon Mean (std) 16.1 (3.4) NA (NA) 21.1 (3.1) 26.3 (5.4) NA (NA) 21.4 (6.7) 20.1 (6.0)
4 mpg Miles/(US) gallon N (NA) 15 (0) 0 (0) 4 (0) 8 (0) 0 (0) 5 (0) 32 (0)
5 vs Engine straight 2 (22.22%) 0 (0%) 0 (0%) 6 (66.67%) 0 (0%) 1 (11.11%) 9 (37.50%)
6 vs Engine vshaped 9 (60.00%) 0 (0%) 0 (0%) 2 (13.33%) 0 (0%) 4 (26.67%) 15 (62.50%)
7 vs Engine NA 4 0 4 0 0 0 8
8 vs Engine Total 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
9 cyl Number of cylinders 4 1 (10.00%) 0 (0%) 2 (20.00%) 5 (50.00%) 0 (0%) 2 (20.00%) 10 (37.04%)
10 cyl Number of cylinders 6 1 (25.00%) 0 (0%) 2 (50.00%) 0 (0%) 0 (0%) 1 (25.00%) 4 (14.81%)
11 cyl Number of cylinders 8 11 (84.62%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (15.38%) 13 (48.15%)
12 cyl Number of cylinders NA 2 0 0 3 0 0 5
13 cyl Number of cylinders Total 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
14 dummy dummy dummy 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
15 dummy dummy Total 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
16 surv Dummy survival (disp/am) t=100 1.00 (0/15) <NA> 1.00 (0/4) 0.50 (4/4) <NA> 0.80 (1/4) 0.84 (5/27)
17 surv Dummy survival (disp/am) t=200 1.00 (0/14) <NA> 1.00 (0/0) 0 (4/0) <NA> 0.40 (2/2) 0.64 (6/16)
18 surv Dummy survival (disp/am) Median follow up [min ; max] 318 [120.1 ; 472] <NA> 157.15 [140.8 ; 167.6] NA [Inf ; 160] <NA> NA [Inf ; 351] 304 [120.1 ; 472]
19 surv Dummy survival (disp/am) Median survival <NA> <NA> <NA> 93.5 <NA> 145 <NA>
20 hp_date Some nonsense date Min / Max 2010-04-08 - 2010-09-03 NA / NA 2010-03-04 - 2010-05-04 2010-02-22 - 2010-04-21 NA / NA 2010-04-02 - 2010-12-02 2010-02-22 - 2010-12-02
21 hp_date Some nonsense date Med [IQR] 2010-06-30 [2010-05-31;2010-08-04] NA [NA;NA] 2010-04-20 [2010-03-04;2010-05-04] 2010-03-21 [2010-03-07;2010-04-20] NA [NA;NA] 2010-06-25 [2010-04-24;2010-09-22] 2010-05-04 [2010-04-06;2010-06-30]
22 hp_date Some nonsense date Mean (std) 2010-06-26 (1.6 months) NA (NA) 2010-04-11 (29.0 days) 2010-03-25 (24.2 days) NA (NA) 2010-07-15 (3.4 months) 2010-05-27 (2.3 months)
23 hp_date Some nonsense date N (NA) 15 (0) 0 (0) 4 (0) 8 (0) 0 (0) 5 (0) 32 (0)
24 qsec_posix Date+time Min / Max 2010-01-16 10:50:24 - 2010-01-21 06:16:48 NA / NA 2010-01-19 08:12:00 - 2010-01-23 22:36:00 2010-01-17 12:02:24 - 2010-01-20 22:36:00 NA / NA 2010-01-15 13:00:00 - 2010-01-17 22:36:00 2010-01-15 13:00:00 - 2010-01-23 22:36:00
25 qsec_posix Date+time Med [IQR] 2010-01-18 11:04:48 [2010-01-18 01:28:48;2010-01-19 01:00:00] NA [NA;NA] 2010-01-20 11:48:00 [2010-01-19 08:12:00;2010-01-21 01:00:00] 2010-01-19 15:31:12 [2010-01-18 01:28:48;2010-01-19 22:36:00] NA [NA;NA] 2010-01-16 13:00:00 [2010-01-15 15:24:00;2010-01-17 17:48:00] 2010-01-18 18:02:24 [2010-01-17 21:52:48;2010-01-19 22:36:00]
26 qsec_posix Date+time Mean (std) 2010-01-18 17:36:28 (1.3 days) NA (NA) 2010-01-21 01:36:00 (2.0 days) 2010-01-19 11:26:24 (1.2 days) NA (NA) 2010-01-16 16:21:36 (1.1 days) 2010-01-18 21:22:12 (1.8 days)
27 qsec_posix Date+time N (NA) 15 (0) 0 (0) 4 (0) 8 (0) 0 (0) 5 (0) 32 (0)
28 diff Difftime hp_date-qsec_posix (days) Min / Max 77.0 / 229.6 NA / NA 42.0 / 104.7 33.5 / 93.5 NA / NA 74.3 / 320.4 33S / 5M 20S
29 diff Difftime hp_date-qsec_posix (days) Med [IQR] 162.0 [132.9;192.1] NA [NA;NA] 88.1 [64.6;104.2] 60.7 [46.2;91.0] NA [NA;NA] 159.5 [96.1;249.5] 1M 44S [1M 16S;2M 42S]
30 diff Difftime hp_date-qsec_posix (days) Mean (std) 158.4 (48.8) NA (NA) 80.7 (30.0) 65.4 (25.0) NA (NA) 180.0 (103.9) 2M 9S (1M 10S)
31 diff Difftime hp_date-qsec_posix (days) N (NA) 15 (0) 0 (0) 4 (0) 8 (0) 0 (0) 5 (0) 32 (0)
32 cyl3 cyl3 FALSE 13 (48.15%) 0 (0%) 4 (14.81%) 5 (18.52%) 0 (0%) 5 (18.52%) 27 (100.00%)
33 cyl3 cyl3 NA 2 0 0 3 0 0 5
34 cyl3 cyl3 Total 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
By multiple (formula)
Code
x = crosstable(mtcars3, mpg + gear ~ I(am == "auto") + vs, total = TRUE)
as.data.frame(x)
Output
.id label variable I(am == "auto")=FALSE & vs=straight I(am == "auto")=TRUE & vs=straight I(am == "auto")=FALSE & vs=vshaped I(am == "auto")=TRUE & vs=vshaped I(am == "auto")=FALSE & vs=NA I(am == "auto")=TRUE & vs=NA Total
1 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 21.4 / 21.5 15.0 / 26.0 10.4 / 19.2 NA / NA 14.3 / 24.4 10.4 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 30.4 [25.1;31.4] 21.4 [21.4;21.5] 20.4 [16.8;21.0] 15.2 [13.3;15.5] NA [NA;NA] 18.4 [17.5;20.1] 19.2 [15.4;22.8]
3 mpg Miles/(US) gallon Mean (std) 28.4 (4.8) 21.4 (0.1) 19.8 (4.0) 14.6 (2.9) NA (NA) 19.0 (3.3) 20.1 (6.0)
4 mpg Miles/(US) gallon N (NA) 7 (0) 2 (0) 6 (0) 9 (0) 0 (0) 8 (0) 32 (0)
5 gear Number of forward gears 3 0 (0%) 2 (13.33%) 0 (0%) 9 (60.00%) 0 (0%) 4 (26.67%) 15 (46.88%)
6 gear Number of forward gears 4 6 (50.00%) 0 (0%) 2 (16.67%) 0 (0%) 0 (0%) 4 (33.33%) 12 (37.50%)
7 gear Number of forward gears 5 1 (20.00%) 0 (0%) 4 (80.00%) 0 (0%) 0 (0%) 0 (0%) 5 (15.62%)
8 gear Number of forward gears Total 7 (21.88%) 2 (6.25%) 6 (18.75%) 9 (28.12%) 0 (0%) 8 (25.00%) 32 (100.00%)
get_percent_pattern()
Code
get_percent_pattern()
Output
$body
{n} ({p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(na = TRUE)
Output
$body
[1] "{n} ({p_row_na})"
$total_row
[1] "{n} ({p_col_na})"
$total_col
[1] "{n} ({p_row_na})"
$total_all
[1] "{n} ({p_tot_na})"
Code
get_percent_pattern(c("cells", "row", "column"))
Output
$body
{n} ({p_tot} / {p_col} / {p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(c("cells", "row", "column"), na = TRUE)
Output
$body
[1] "{n} ({p_tot_na} / {p_col_na} / {p_row_na})"
$total_row
[1] "{n} ({p_col_na})"
$total_col
[1] "{n} ({p_row_na})"
$total_all
[1] "{n} ({p_tot_na})"
Code
get_percent_pattern(margin = TRUE)
Output
$body
[1] "{n} ({p_row} / {p_col})"
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = 1)
Output
$body
{n} ({p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = c(1, 0, 2))
Output
$body
{n} ({p_tot} / {p_row} / {p_col})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = 1:2)
Output
$body
{n} ({p_row} / {p_col})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = 2:1)
Output
$body
{n} ({p_row} / {p_col})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = "row")
Output
$body
{n} ({p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = c("row", "cells", "column"))
Output
$body
{n} ({p_tot} / {p_col} / {p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
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showNA with NA in by
Code
x0 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, times = c(0, 100, 200, 400))
as.data.frame(x0)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
10 cyl Number of cylinders NA 2 2 1
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x1 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, showNA = "no", times = c(0, 100, 200, 400))
as.data.frame(x1)
Output
.id label variable straight vshaped
1 am Transmission auto 2 (18.18%) 9 (81.82%)
2 am Transmission manual 7 (53.85%) 6 (46.15%)
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%)
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%)
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%)
10 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15)
11 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15)
12 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11)
13 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4)
14 surv Dummy survival (disp/am) Median survival 95.1 <NA>
Code
x2 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, showNA = "ifany", times = c(0, 100, 200, 400))
as.data.frame(x2)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
10 cyl Number of cylinders NA 2 2 1
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x3 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, showNA = "always", times = c(0, 100, 200, 400))
as.data.frame(x3)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 am Transmission NA 0 0 0
4 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
5 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
6 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
7 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
8 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
9 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
10 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
11 cyl Number of cylinders NA 2 2 1
12 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
14 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
15 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
16 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
showNA without NA in by
Code
x0 = crosstable(mtcars3, c(vs, mpg, cyl, surv), by = am, times = c(0, 100, 200, 400))
as.data.frame(x0)
Output
.id label variable auto manual
1 vs Engine straight 2 (22.22%) 7 (77.78%)
2 vs Engine vshaped 9 (60.00%) 6 (40.00%)
3 vs Engine NA 8 0
4 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
5 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
6 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
7 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
8 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%)
9 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%)
10 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%)
11 cyl Number of cylinders NA 2 3
12 surv Dummy survival (disp/am) t=0 1.00 (0/19) 1.00 (0/13)
13 surv Dummy survival (disp/am) t=100 1.00 (0/19) 0.62 (5/8)
14 surv Dummy survival (disp/am) t=200 1.00 (0/14) 0.15 (6/2)
15 surv Dummy survival (disp/am) t=400 1.00 (0/4) 0 (2/0)
16 surv Dummy survival (disp/am) Median survival <NA> 120.3
Code
x1 = crosstable(mtcars3, c(vs, mpg, cyl, surv), by = am, showNA = "no", times = c(0, 100, 200, 400))
as.data.frame(x1)
Output
.id label variable auto manual
1 vs Engine straight 2 (22.22%) 7 (77.78%)
2 vs Engine vshaped 9 (60.00%) 6 (40.00%)
3 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
4 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
5 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
6 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
7 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%)
8 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%)
9 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%)
10 surv Dummy survival (disp/am) t=0 1.00 (0/19) 1.00 (0/13)
11 surv Dummy survival (disp/am) t=100 1.00 (0/19) 0.62 (5/8)
12 surv Dummy survival (disp/am) t=200 1.00 (0/14) 0.15 (6/2)
13 surv Dummy survival (disp/am) t=400 1.00 (0/4) 0 (2/0)
14 surv Dummy survival (disp/am) Median survival <NA> 120.3
Code
x2 = crosstable(mtcars3, c(vs, mpg, cyl, surv), by = am, showNA = "ifany", times = c(0, 100, 200, 400))
as.data.frame(x2)
Output
.id label variable auto manual
1 vs Engine straight 2 (22.22%) 7 (77.78%)
2 vs Engine vshaped 9 (60.00%) 6 (40.00%)
3 vs Engine NA 8 0
4 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
5 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
6 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
7 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
8 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%)
9 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%)
10 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%)
11 cyl Number of cylinders NA 2 3
12 surv Dummy survival (disp/am) t=0 1.00 (0/19) 1.00 (0/13)
13 surv Dummy survival (disp/am) t=100 1.00 (0/19) 0.62 (5/8)
14 surv Dummy survival (disp/am) t=200 1.00 (0/14) 0.15 (6/2)
15 surv Dummy survival (disp/am) t=400 1.00 (0/4) 0 (2/0)
16 surv Dummy survival (disp/am) Median survival <NA> 120.3
Code
x3 = crosstable(mtcars3, c(vs, mpg, cyl, surv), by = am, showNA = "always", times = c(0, 100, 200, 400))
as.data.frame(x3)
Output
.id label variable auto manual NA
1 vs Engine straight 2 (22.22%) 7 (77.78%) 0
2 vs Engine vshaped 9 (60.00%) 6 (40.00%) 0
3 vs Engine NA 8 0 0
4 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9 no NA
5 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4] no NA
6 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2) no NA
7 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0) no NA
8 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%) 0
9 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%) 0
10 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%) 0
11 cyl Number of cylinders NA 2 3 0
12 surv Dummy survival (disp/am) t=0 1.00 (0/19) 1.00 (0/13) <NA>
13 surv Dummy survival (disp/am) t=100 1.00 (0/19) 0.62 (5/8) <NA>
14 surv Dummy survival (disp/am) t=200 1.00 (0/14) 0.15 (6/2) <NA>
15 surv Dummy survival (disp/am) t=400 1.00 (0/4) 0 (2/0) <NA>
16 surv Dummy survival (disp/am) Median survival <NA> 120.3 <NA>
total
Code
x0 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, times = c(0, 100, 200, 400))
as.data.frame(x0)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
10 cyl Number of cylinders NA 2 2 1
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x1 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, total = "none", times = c(0, 100, 200, 400))
as.data.frame(x1)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
10 cyl Number of cylinders NA 2 2 1
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x2 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, total = "row", times = c(0, 100, 200, 400))
as.data.frame(x2)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0 13 (40.62%)
3 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4 10.4 / 33.9
4 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1] 19.2 [15.4;22.8]
5 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3) 20.1 (6.0)
6 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0) 32 (0)
7 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2 10 (37.04%)
8 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3 4 (14.81%)
9 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2 13 (48.15%)
10 cyl Number of cylinders NA 2 2 1 5
11 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8) 1.00 (0/32)
12 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8) 0.84 (5/27)
13 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4) 0.64 (6/16)
14 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0) 0.50 (2/4)
15 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA> <NA>
Code
x3 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, total = "col", times = c(0, 100, 200, 400))
as.data.frame(x3)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8
4 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4
5 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1]
6 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3)
7 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0)
8 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
9 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
10 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
11 cyl Number of cylinders NA 2 2 1
12 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8
13 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8)
14 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8)
15 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4)
16 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0)
17 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA>
Code
x4 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = vs, total = "both", times = c(0, 100, 200, 400))
as.data.frame(x4)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 10.4 / 26.0 14.3 / 24.4 10.4 / 33.9
5 mpg Miles/(US) gallon Med [IQR] 27.3 [21.5;30.4] 15.5 [14.8;19.4] 18.4 [17.5;20.1] 19.2 [15.4;22.8]
6 mpg Miles/(US) gallon Mean (std) 26.8 (5.1) 16.6 (4.2) 19.0 (3.3) 20.1 (6.0)
7 mpg Miles/(US) gallon N (NA) 9 (0) 15 (0) 8 (0) 32 (0)
8 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2 10 (37.04%)
9 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3 4 (14.81%)
10 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2 13 (48.15%)
11 cyl Number of cylinders NA 2 2 1 5
12 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
13 surv Dummy survival (disp/am) t=0 1.00 (0/9) 1.00 (0/15) 1.00 (0/8) 1.00 (0/32)
14 surv Dummy survival (disp/am) t=100 0.44 (5/4) 1.00 (0/15) 1.00 (0/8) 0.84 (5/27)
15 surv Dummy survival (disp/am) t=200 0.17 (2/1) 0.73 (4/11) 1.00 (0/4) 0.64 (6/16)
16 surv Dummy survival (disp/am) t=400 0.17 (0/0) 0.52 (2/4) 1.00 (0/0) 0.50 (2/4)
17 surv Dummy survival (disp/am) Median survival 95.1 <NA> <NA> <NA>
Margins without totals
Code
x0 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none")
as.data.frame(x0)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
4 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
5 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x1 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "row")
as.data.frame(x1)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0
3 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2
4 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3
5 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x2 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "col")
as.data.frame(x2)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (22.22%) 9 (60.00%) 8
2 am Transmission manual 7 (77.78%) 6 (40.00%) 0
3 cyl Number of cylinders 4 7 (100.00%) 1 (7.69%) 2
4 cyl Number of cylinders 6 0 (0%) 1 (7.69%) 3
5 cyl Number of cylinders 8 0 (0%) 11 (84.62%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x3 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "cell")
as.data.frame(x3)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (8.33%) 9 (37.50%) 8
2 am Transmission manual 7 (29.17%) 6 (25.00%) 0
3 cyl Number of cylinders 4 7 (35.00%) 1 (5.00%) 2
4 cyl Number of cylinders 6 0 (0%) 1 (5.00%) 3
5 cyl Number of cylinders 8 0 (0%) 11 (55.00%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x4 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "none")
as.data.frame(x4)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 9 8
2 am Transmission manual 7 6 0
3 cyl Number of cylinders 4 7 1 2
4 cyl Number of cylinders 6 0 1 3
5 cyl Number of cylinders 8 0 11 2
6 cyl Number of cylinders NA 2 2 1
Code
x5 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = "all")
as.data.frame(x5)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (8.33% / 18.18% / 22.22%) 9 (37.50% / 81.82% / 60.00%) 8
2 am Transmission manual 7 (29.17% / 53.85% / 77.78%) 6 (25.00% / 46.15% / 40.00%) 0
3 cyl Number of cylinders 4 7 (35.00% / 87.50% / 100.00%) 1 (5.00% / 12.50% / 7.69%) 2
4 cyl Number of cylinders 6 0 (0% / 0% / 0%) 1 (5.00% / 100.00% / 7.69%) 3
5 cyl Number of cylinders 8 0 (0% / 0% / 0%) 11 (55.00% / 100.00% / 84.62%) 2
6 cyl Number of cylinders NA 2 2 1
Code
x6 = crosstable(mtcars3, c(am, cyl), by = vs, total = "none", margin = 1:2)
as.data.frame(x6)
Output
.id label variable straight vshaped NA
1 am Transmission auto 2 (18.18% / 22.22%) 9 (81.82% / 60.00%) 8
2 am Transmission manual 7 (53.85% / 77.78%) 6 (46.15% / 40.00%) 0
3 cyl Number of cylinders 4 7 (87.50% / 100.00%) 1 (12.50% / 7.69%) 2
4 cyl Number of cylinders 6 0 (0% / 0%) 1 (100.00% / 7.69%) 3
5 cyl Number of cylinders 8 0 (0% / 0%) 11 (100.00% / 84.62%) 2
6 cyl Number of cylinders NA 2 2 1
Margins with totals
Code
x0 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both")
as.data.frame(x0)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x1 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "row")
as.data.frame(x1)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18%) 9 (81.82%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85%) 6 (46.15%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (87.50%) 1 (12.50%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0%) 1 (100.00%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0%) 11 (100.00%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x2 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "col")
as.data.frame(x2)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (22.22%) 9 (60.00%) 8 19 (59.38%)
2 am Transmission manual 7 (77.78%) 6 (40.00%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (100.00%) 1 (7.69%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0%) 1 (7.69%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0%) 11 (84.62%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x3 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "cell")
as.data.frame(x3)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (8.33%) 9 (37.50%) 8 19 (59.38%)
2 am Transmission manual 7 (29.17%) 6 (25.00%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (35.00%) 1 (5.00%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0%) 1 (5.00%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0%) 11 (55.00%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x4 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "none")
as.data.frame(x4)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 9 8 19
2 am Transmission manual 7 6 0 13
3 am Transmission Total 9 15 8 32
4 cyl Number of cylinders 4 7 1 2 10
5 cyl Number of cylinders 6 0 1 3 4
6 cyl Number of cylinders 8 0 11 2 13
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 15 8 32
Code
x5 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = "all")
as.data.frame(x5)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (8.33% / 18.18% / 22.22%) 9 (37.50% / 81.82% / 60.00%) 8 19 (59.38%)
2 am Transmission manual 7 (29.17% / 53.85% / 77.78%) 6 (25.00% / 46.15% / 40.00%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (35.00% / 87.50% / 100.00%) 1 (5.00% / 12.50% / 7.69%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0% / 0% / 0%) 1 (5.00% / 100.00% / 7.69%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0% / 0% / 0%) 11 (55.00% / 100.00% / 84.62%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Code
x6 = crosstable(mtcars3, c(am, cyl), by = vs, total = "both", margin = 1:2)
as.data.frame(x6)
Output
.id label variable straight vshaped NA Total
1 am Transmission auto 2 (18.18% / 22.22%) 9 (81.82% / 60.00%) 8 19 (59.38%)
2 am Transmission manual 7 (53.85% / 77.78%) 6 (46.15% / 40.00%) 0 13 (40.62%)
3 am Transmission Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
4 cyl Number of cylinders 4 7 (87.50% / 100.00%) 1 (12.50% / 7.69%) 2 10 (37.04%)
5 cyl Number of cylinders 6 0 (0% / 0%) 1 (100.00% / 7.69%) 3 4 (14.81%)
6 cyl Number of cylinders 8 0 (0% / 0%) 11 (100.00% / 84.62%) 2 13 (48.15%)
7 cyl Number of cylinders NA 2 2 1 5
8 cyl Number of cylinders Total 9 (37.50%) 15 (62.50%) 8 32 (100.00%)
Percent pattern
Code
x0 = crosstable(mtcars3, cyl, percent_digits = 0, total = TRUE, showNA = "always", percent_pattern = PERCENT_PATTERN)
as.data.frame(x0)
Output
.id label variable value
1 cyl Number of cylinders 4 N=10\nCell: p = 37% (10/27) [95%CI 22%; 56%]\nCol: p = 37% (10/27) [95%CI 22%; 56%]\nRow:p = 37% (10/27) [95%CI 22%; 56%]
2 cyl Number of cylinders 6 N=4\nCell: p = 15% (4/27) [95%CI 6%; 32%]\nCol: p = 15% (4/27) [95%CI 6%; 32%]\nRow:p = 15% (4/27) [95%CI 6%; 32%]
3 cyl Number of cylinders 8 N=13\nCell: p = 48% (13/27) [95%CI 31%; 66%]\nCol: p = 48% (13/27) [95%CI 31%; 66%]\nRow:p = 48% (13/27) [95%CI 31%; 66%]
4 cyl Number of cylinders NA 5
5 cyl Number of cylinders Total 32 (100%)
Code
x1 = crosstable(mtcars3, cyl, by = am, percent_digits = 0, total = TRUE, showNA = "always", percent_pattern = PERCENT_PATTERN)
as.data.frame(x1)
Output
.id label variable auto manual NA Total
1 cyl Number of cylinders 4 N=3\nCell: p = 11% (3/27) [95%CI 4%; 28%]\nCol: p = 18% (3/17) [95%CI 6%; 41%]\nRow:p = 30% (3/10) [95%CI 11%; 60%] N=7\nCell: p = 26% (7/27) [95%CI 13%; 45%]\nCol: p = 70% (7/10) [95%CI 40%; 89%]\nRow:p = 70% (7/10) [95%CI 40%; 89%] 0 10 (37%)
2 cyl Number of cylinders 6 N=3\nCell: p = 11% (3/27) [95%CI 4%; 28%]\nCol: p = 18% (3/17) [95%CI 6%; 41%]\nRow:p = 75% (3/4) [95%CI 30%; 95%] N=1\nCell: p = 4% (1/27) [95%CI 1%; 18%]\nCol: p = 10% (1/10) [95%CI 2%; 40%]\nRow:p = 25% (1/4) [95%CI 5%; 70%] 0 4 (15%)
3 cyl Number of cylinders 8 N=11\nCell: p = 41% (11/27) [95%CI 25%; 59%]\nCol: p = 65% (11/17) [95%CI 41%; 83%]\nRow:p = 85% (11/13) [95%CI 58%; 96%] N=2\nCell: p = 7% (2/27) [95%CI 2%; 23%]\nCol: p = 20% (2/10) [95%CI 6%; 51%]\nRow:p = 15% (2/13) [95%CI 4%; 42%] 0 13 (48%)
4 cyl Number of cylinders NA 2 3 0 5
5 cyl Number of cylinders Total 19 (59%) 13 (41%) <NA> 32 (100%)
Code
x2 = crosstable(mtcars3, c(mpg, vs, cyl), by = c(am, dummy), percent_digits = 0, total = TRUE, showNA = "always", percent_pattern = PERCENT_PATTERN)
as.data.frame(x2)
Output
.id label variable am=auto & dummy=dummy am=manual & dummy=dummy NA Total
1 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9 no NA 10.4 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4] no NA 19.2 [15.4;22.8]
3 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2) no NA 20.1 (6.0)
4 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0) no NA 32 (0)
5 vs Engine straight N=2\nCell: p = 8% (2/24) [95%CI 2%; 26%]\nCol: p = 18% (2/11) [95%CI 5%; 48%]\nRow:p = 22% (2/9) [95%CI 6%; 55%] N=7\nCell: p = 29% (7/24) [95%CI 15%; 49%]\nCol: p = 54% (7/13) [95%CI 29%; 77%]\nRow:p = 78% (7/9) [95%CI 45%; 94%] 0 9 (38%)
6 vs Engine vshaped N=9\nCell: p = 38% (9/24) [95%CI 21%; 57%]\nCol: p = 82% (9/11) [95%CI 52%; 95%]\nRow:p = 60% (9/15) [95%CI 36%; 80%] N=6\nCell: p = 25% (6/24) [95%CI 12%; 45%]\nCol: p = 46% (6/13) [95%CI 23%; 71%]\nRow:p = 40% (6/15) [95%CI 20%; 64%] 0 15 (62%)
7 vs Engine NA 8 0 0 8
8 vs Engine Total 19 (59%) 13 (41%) <NA> 32 (100%)
9 cyl Number of cylinders 4 N=3\nCell: p = 11% (3/27) [95%CI 4%; 28%]\nCol: p = 18% (3/17) [95%CI 6%; 41%]\nRow:p = 30% (3/10) [95%CI 11%; 60%] N=7\nCell: p = 26% (7/27) [95%CI 13%; 45%]\nCol: p = 70% (7/10) [95%CI 40%; 89%]\nRow:p = 70% (7/10) [95%CI 40%; 89%] 0 10 (37%)
10 cyl Number of cylinders 6 N=3\nCell: p = 11% (3/27) [95%CI 4%; 28%]\nCol: p = 18% (3/17) [95%CI 6%; 41%]\nRow:p = 75% (3/4) [95%CI 30%; 95%] N=1\nCell: p = 4% (1/27) [95%CI 1%; 18%]\nCol: p = 10% (1/10) [95%CI 2%; 40%]\nRow:p = 25% (1/4) [95%CI 5%; 70%] 0 4 (15%)
11 cyl Number of cylinders 8 N=11\nCell: p = 41% (11/27) [95%CI 25%; 59%]\nCol: p = 65% (11/17) [95%CI 41%; 83%]\nRow:p = 85% (11/13) [95%CI 58%; 96%] N=2\nCell: p = 7% (2/27) [95%CI 2%; 23%]\nCol: p = 20% (2/10) [95%CI 6%; 51%]\nRow:p = 15% (2/13) [95%CI 4%; 42%] 0 13 (48%)
12 cyl Number of cylinders NA 2 3 0 5
13 cyl Number of cylinders Total 19 (59%) 13 (41%) <NA> 32 (100%)
Percent pattern - Ultimate
Code
x1 = crosstable(mtcars3, cyl, by = vs, percent_digits = 0, total = TRUE, showNA = "always", percent_pattern = ULTIMATE_PATTERN)
as.data.frame(x1)
Output
.id label variable straight vshaped NA Total
1 cyl Number of cylinders 4 N=7\nCell: p = 35% (7/20) [2e+01%; 57%]\nCol: p = 100% (7/7) [65%; 100%]\nRow: p = 88% (7/8) [53%; 98%]\n\nCell (NA): p = 22% (7/32) [11%; 39%]\nCol (NA): p = 78% (7/9) [45%; 94%]\nRow (NA): p = 70% (7/10) [40%; 89%] N=1\nCell: p = 5% (1/20) [9e-01%; 24%]\nCol: p = 8% (1/13) [1%; 33%]\nRow: p = 12% (1/8) [2%; 47%]\n\nCell (NA): p = 3% (1/32) [1%; 16%]\nCol (NA): p = 7% (1/15) [1%; 30%]\nRow (NA): p = 10% (1/10) [2%; 40%] 2 N=10\nCol: p = 37% (10/27) [22%; 56%]\nCol (NA): p = 31% (10/32) [18%; 49%]
2 cyl Number of cylinders 6 N=0\nCell: p = 0% (0/20) [1e-15%; 16%]\nCol: p = 0% (0/7) [0%; 35%]\nRow: p = 0% (0/1) [0%; 79%]\n\nCell (NA): p = 0% (0/32) [0%; 11%]\nCol (NA): p = 0% (0/9) [0%; 30%]\nRow (NA): p = 0% (0/4) [0%; 49%] N=1\nCell: p = 5% (1/20) [9e-01%; 24%]\nCol: p = 8% (1/13) [1%; 33%]\nRow: p = 100% (1/1) [21%; 100%]\n\nCell (NA): p = 3% (1/32) [1%; 16%]\nCol (NA): p = 7% (1/15) [1%; 30%]\nRow (NA): p = 25% (1/4) [5%; 70%] 3 N=4\nCol: p = 15% (4/27) [6%; 32%]\nCol (NA): p = 12% (4/32) [5%; 28%]
3 cyl Number of cylinders 8 N=0\nCell: p = 0% (0/20) [1e-15%; 16%]\nCol: p = 0% (0/7) [0%; 35%]\nRow: p = 0% (0/11) [0%; 26%]\n\nCell (NA): p = 0% (0/32) [0%; 11%]\nCol (NA): p = 0% (0/9) [0%; 30%]\nRow (NA): p = 0% (0/13) [0%; 23%] N=11\nCell: p = 55% (11/20) [3e+01%; 74%]\nCol: p = 85% (11/13) [58%; 96%]\nRow: p = 100% (11/11) [74%; 100%]\n\nCell (NA): p = 34% (11/32) [20%; 52%]\nCol (NA): p = 73% (11/15) [48%; 89%]\nRow (NA): p = 85% (11/13) [58%; 96%] 2 N=13\nCol: p = 48% (13/27) [31%; 66%]\nCol (NA): p = 41% (13/32) [26%; 58%]
4 cyl Number of cylinders NA 2 2 1 5
5 cyl Number of cylinders Total N=9\nRow: p = 38% (9/24) [21%; 57%]\nRow (NA): p = 28% (9/32) [16%; 45%] N=15\nRow: p = 62% (15/24) [43%; 79%]\nRow (NA): p = 47% (15/32) [31%; 64%] 8 N=32\nP: 100% [89%; 100%]\nP (NA): 100% [89%; 100%]
Code
x2 = crosstable(mtcars3, cyl, by = vs, percent_digits = 0, total = TRUE, showNA = "no", percent_pattern = ULTIMATE_PATTERN)
as.data.frame(x2)
Output
.id label variable straight vshaped Total
1 cyl Number of cylinders 4 N=7\nCell: p = 35% (7/20) [2e+01%; 57%]\nCol: p = 100% (7/7) [65%; 100%]\nRow: p = 88% (7/8) [53%; 98%]\n\nCell (NA): p = 35% (7/20) [2e+01%; 57%]\nCol (NA): p = 100% (7/7) [65%; 100%]\nRow (NA): p = 88% (7/8) [53%; 98%] N=1\nCell: p = 5% (1/20) [9e-01%; 24%]\nCol: p = 8% (1/13) [1%; 33%]\nRow: p = 12% (1/8) [2%; 47%]\n\nCell (NA): p = 5% (1/20) [9e-01%; 24%]\nCol (NA): p = 8% (1/13) [1%; 33%]\nRow (NA): p = 12% (1/8) [2%; 47%] N=8\nCol: p = 40% (8/20) [22%; 61%]\nCol (NA): p = 40% (8/20) [2e+01%; 61%]
2 cyl Number of cylinders 6 N=0\nCell: p = 0% (0/20) [1e-15%; 16%]\nCol: p = 0% (0/7) [0%; 35%]\nRow: p = 0% (0/1) [0%; 79%]\n\nCell (NA): p = 0% (0/20) [1e-15%; 16%]\nCol (NA): p = 0% (0/7) [0%; 35%]\nRow (NA): p = 0% (0/1) [0%; 79%] N=1\nCell: p = 5% (1/20) [9e-01%; 24%]\nCol: p = 8% (1/13) [1%; 33%]\nRow: p = 100% (1/1) [21%; 100%]\n\nCell (NA): p = 5% (1/20) [9e-01%; 24%]\nCol (NA): p = 8% (1/13) [1%; 33%]\nRow (NA): p = 100% (1/1) [21%; 100%] N=1\nCol: p = 5% (1/20) [1%; 24%]\nCol (NA): p = 5% (1/20) [9e-01%; 24%]
3 cyl Number of cylinders 8 N=0\nCell: p = 0% (0/20) [1e-15%; 16%]\nCol: p = 0% (0/7) [0%; 35%]\nRow: p = 0% (0/11) [0%; 26%]\n\nCell (NA): p = 0% (0/20) [1e-15%; 16%]\nCol (NA): p = 0% (0/7) [0%; 35%]\nRow (NA): p = 0% (0/11) [0%; 26%] N=11\nCell: p = 55% (11/20) [3e+01%; 74%]\nCol: p = 85% (11/13) [58%; 96%]\nRow: p = 100% (11/11) [74%; 100%]\n\nCell (NA): p = 55% (11/20) [3e+01%; 74%]\nCol (NA): p = 85% (11/13) [58%; 96%]\nRow (NA): p = 100% (11/11) [74%; 100%] N=11\nCol: p = 55% (11/20) [34%; 74%]\nCol (NA): p = 55% (11/20) [3e+01%; 74%]
4 cyl Number of cylinders Total N=7\nRow: p = 35% (7/20) [18%; 57%]\nRow (NA): p = 35% (7/20) [18%; 57%] N=13\nRow: p = 65% (13/20) [43%; 82%]\nRow (NA): p = 65% (13/20) [43%; 82%] N=20\nP: 100% [84%; 100%]\nP (NA): 100% [84%; 100%]
Unique numeric
Code
x0 = crosstable(mtcars3, gear)
as.data.frame(x0)
Output
.id label variable value
1 gear gear 1 10 (38.46%)
2 gear gear 2 11 (42.31%)
3 gear gear 3 5 (19.23%)
4 gear gear NA 6
Code
x1 = crosstable(mtcars3, carb)
as.data.frame(x1)
Output
.id label variable value
1 carb Number of carburetors Min / Max 1.0 / 8.0
2 carb Number of carburetors Med [IQR] 2.0 [2.0;4.0]
3 carb Number of carburetors Mean (std) 2.8 (1.6)
4 carb Number of carburetors N (NA) 32 (0)
Code
x2 = crosstable(mtcars3, carb, unique_numeric = 9)
as.data.frame(x2)
Output
.id label variable value
1 carb Number of carburetors 1 7 (21.88%)
2 carb Number of carburetors 2 10 (31.25%)
3 carb Number of carburetors 3 3 (9.38%)
4 carb Number of carburetors 4 10 (31.25%)
5 carb Number of carburetors 6 1 (3.12%)
6 carb Number of carburetors 8 1 (3.12%)
By dummy
Code
x0 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = dummy)
as.data.frame(x0)
Output
.id label variable dummy
1 am Transmission auto 19 (59.38%)
2 am Transmission manual 13 (40.62%)
3 mpg Miles/(US) gallon Min / Max 10.4 / 33.9
4 mpg Miles/(US) gallon Med [IQR] 19.2 [15.4;22.8]
5 mpg Miles/(US) gallon Mean (std) 20.1 (6.0)
6 mpg Miles/(US) gallon N (NA) 32 (0)
7 cyl Number of cylinders 4 10 (37.04%)
8 cyl Number of cylinders 6 4 (14.81%)
9 cyl Number of cylinders 8 13 (48.15%)
10 cyl Number of cylinders NA 5
11 surv Dummy survival (disp/am) t=71.1 0.97 (1/32)
12 surv Dummy survival (disp/am) t=75.7 0.94 (1/31)
13 surv Dummy survival (disp/am) t=78.7 0.91 (1/30)
14 surv Dummy survival (disp/am) t=79 0.88 (1/29)
15 surv Dummy survival (disp/am) t=95.1 0.84 (1/28)
16 surv Dummy survival (disp/am) t=108 0.81 (1/27)
17 surv Dummy survival (disp/am) t=120.1 0.81 (0/26)
18 surv Dummy survival (disp/am) t=120.3 0.78 (1/25)
19 surv Dummy survival (disp/am) t=121 0.75 (1/24)
20 surv Dummy survival (disp/am) t=140.8 0.75 (0/23)
21 surv Dummy survival (disp/am) t=145 0.71 (1/22)
22 surv Dummy survival (disp/am) t=146.7 0.71 (0/21)
23 surv Dummy survival (disp/am) t=160 0.64 (2/20)
24 surv Dummy survival (disp/am) t=167.6 0.64 (0/18)
25 surv Dummy survival (disp/am) t=225 0.64 (0/16)
26 surv Dummy survival (disp/am) t=258 0.64 (0/15)
27 surv Dummy survival (disp/am) t=275.8 0.64 (0/14)
28 surv Dummy survival (disp/am) t=301 0.58 (1/11)
29 surv Dummy survival (disp/am) t=304 0.58 (0/10)
30 surv Dummy survival (disp/am) t=318 0.58 (0/9)
31 surv Dummy survival (disp/am) t=350 0.58 (0/8)
32 surv Dummy survival (disp/am) t=351 0.50 (1/7)
33 surv Dummy survival (disp/am) t=360 0.50 (0/6)
34 surv Dummy survival (disp/am) t=400 0.50 (0/4)
35 surv Dummy survival (disp/am) t=440 0.50 (0/3)
36 surv Dummy survival (disp/am) t=460 0.50 (0/2)
37 surv Dummy survival (disp/am) t=472 0.50 (0/1)
38 surv Dummy survival (disp/am) Median survival <NA>
Code
x1 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = dummy, showNA = TRUE)
as.data.frame(x1)
Output
.id label variable dummy NA
1 am Transmission auto 19 (59.38%) 0
2 am Transmission manual 13 (40.62%) 0
3 am Transmission NA 0 0
4 mpg Miles/(US) gallon Min / Max 10.4 / 33.9 no NA
5 mpg Miles/(US) gallon Med [IQR] 19.2 [15.4;22.8] no NA
6 mpg Miles/(US) gallon Mean (std) 20.1 (6.0) no NA
7 mpg Miles/(US) gallon N (NA) 32 (0) no NA
8 cyl Number of cylinders 4 10 (37.04%) 0
9 cyl Number of cylinders 6 4 (14.81%) 0
10 cyl Number of cylinders 8 13 (48.15%) 0
11 cyl Number of cylinders NA 5 0
12 surv Dummy survival (disp/am) t=71.1 0.97 (1/32) <NA>
13 surv Dummy survival (disp/am) t=75.7 0.94 (1/31) <NA>
14 surv Dummy survival (disp/am) t=78.7 0.91 (1/30) <NA>
15 surv Dummy survival (disp/am) t=79 0.88 (1/29) <NA>
16 surv Dummy survival (disp/am) t=95.1 0.84 (1/28) <NA>
17 surv Dummy survival (disp/am) t=108 0.81 (1/27) <NA>
18 surv Dummy survival (disp/am) t=120.1 0.81 (0/26) <NA>
19 surv Dummy survival (disp/am) t=120.3 0.78 (1/25) <NA>
20 surv Dummy survival (disp/am) t=121 0.75 (1/24) <NA>
21 surv Dummy survival (disp/am) t=140.8 0.75 (0/23) <NA>
22 surv Dummy survival (disp/am) t=145 0.71 (1/22) <NA>
23 surv Dummy survival (disp/am) t=146.7 0.71 (0/21) <NA>
24 surv Dummy survival (disp/am) t=160 0.64 (2/20) <NA>
25 surv Dummy survival (disp/am) t=167.6 0.64 (0/18) <NA>
26 surv Dummy survival (disp/am) t=225 0.64 (0/16) <NA>
27 surv Dummy survival (disp/am) t=258 0.64 (0/15) <NA>
28 surv Dummy survival (disp/am) t=275.8 0.64 (0/14) <NA>
29 surv Dummy survival (disp/am) t=301 0.58 (1/11) <NA>
30 surv Dummy survival (disp/am) t=304 0.58 (0/10) <NA>
31 surv Dummy survival (disp/am) t=318 0.58 (0/9) <NA>
32 surv Dummy survival (disp/am) t=350 0.58 (0/8) <NA>
33 surv Dummy survival (disp/am) t=351 0.50 (1/7) <NA>
34 surv Dummy survival (disp/am) t=360 0.50 (0/6) <NA>
35 surv Dummy survival (disp/am) t=400 0.50 (0/4) <NA>
36 surv Dummy survival (disp/am) t=440 0.50 (0/3) <NA>
37 surv Dummy survival (disp/am) t=460 0.50 (0/2) <NA>
38 surv Dummy survival (disp/am) t=472 0.50 (0/1) <NA>
39 surv Dummy survival (disp/am) Median survival <NA> <NA>
Code
x2 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = dummy2)
as.data.frame(x2)
Output
.id label variable dummy NA
1 am Transmission auto 11 (100.00%) 8
2 am Transmission manual 13 (100.00%) 0
3 mpg Miles/(US) gallon Min / Max 10.4 / 33.9 14.3 / 24.4
4 mpg Miles/(US) gallon Med [IQR] 20.4 [15.2;23.6] 18.4 [17.5;20.1]
5 mpg Miles/(US) gallon Mean (std) 20.5 (6.7) 19.0 (3.3)
6 mpg Miles/(US) gallon N (NA) 24 (0) 8 (0)
7 cyl Number of cylinders 4 8 (100.00%) 2
8 cyl Number of cylinders 6 1 (100.00%) 3
9 cyl Number of cylinders 8 11 (100.00%) 2
10 cyl Number of cylinders NA 4 1
11 surv Dummy survival (disp/am) t=71.1 0.96 (1/24) 1.00 (0/8)
12 surv Dummy survival (disp/am) t=75.7 0.92 (1/23) 1.00 (0/8)
13 surv Dummy survival (disp/am) t=78.7 0.88 (1/22) 1.00 (0/8)
14 surv Dummy survival (disp/am) t=79 0.83 (1/21) 1.00 (0/8)
15 surv Dummy survival (disp/am) t=95.1 0.79 (1/20) 1.00 (0/8)
16 surv Dummy survival (disp/am) t=108 0.75 (1/19) 1.00 (0/8)
17 surv Dummy survival (disp/am) t=120.1 0.75 (0/18) 1.00 (0/8)
18 surv Dummy survival (disp/am) t=120.3 0.71 (1/17) 1.00 (0/8)
19 surv Dummy survival (disp/am) t=121 0.66 (1/16) 1.00 (0/8)
20 surv Dummy survival (disp/am) t=140.8 0.66 (0/15) 1.00 (0/8)
21 surv Dummy survival (disp/am) t=145 0.62 (1/15) 1.00 (0/7)
22 surv Dummy survival (disp/am) t=146.7 0.62 (0/14) 1.00 (0/7)
23 surv Dummy survival (disp/am) t=160 0.53 (2/14) 1.00 (0/6)
24 surv Dummy survival (disp/am) t=167.6 0.53 (0/12) 1.00 (0/6)
25 surv Dummy survival (disp/am) t=225 0.53 (0/12) 1.00 (0/4)
26 surv Dummy survival (disp/am) t=258 0.53 (0/12) 1.00 (0/3)
27 surv Dummy survival (disp/am) t=275.8 0.53 (0/11) 1.00 (0/3)
28 surv Dummy survival (disp/am) t=275.8 0.53 (0/11) 1.00 (0/3)
29 surv Dummy survival (disp/am) t=301 0.47 (1/9) 1.00 (0/2)
30 surv Dummy survival (disp/am) t=304 0.47 (0/8) 1.00 (0/2)
31 surv Dummy survival (disp/am) t=318 0.47 (0/7) 1.00 (0/2)
32 surv Dummy survival (disp/am) t=350 0.47 (0/6) 1.00 (0/2)
33 surv Dummy survival (disp/am) t=351 0.38 (1/5) 1.00 (0/2)
34 surv Dummy survival (disp/am) t=360 0.38 (0/4) 1.00 (0/2)
35 surv Dummy survival (disp/am) t=400 0.38 (0/4) 1.00 (0/0)
36 surv Dummy survival (disp/am) t=440 0.38 (0/3) 1.00 (0/0)
37 surv Dummy survival (disp/am) t=460 0.38 (0/2) 1.00 (0/0)
38 surv Dummy survival (disp/am) t=472 0.38 (0/1) 1.00 (0/0)
39 surv Dummy survival (disp/am) Median survival 301 <NA>
Code
x3 = crosstable(mtcars3, c(am, mpg, cyl, surv), by = dummy2, showNA = FALSE)
as.data.frame(x3)
Output
.id label variable dummy
1 am Transmission auto 11 (100.00%)
2 am Transmission manual 13 (100.00%)
3 mpg Miles/(US) gallon Min / Max 10.4 / 33.9
4 mpg Miles/(US) gallon Med [IQR] 20.4 [15.2;23.6]
5 mpg Miles/(US) gallon Mean (std) 20.5 (6.7)
6 mpg Miles/(US) gallon N (NA) 24 (0)
7 cyl Number of cylinders 4 8 (100.00%)
8 cyl Number of cylinders 6 1 (100.00%)
9 cyl Number of cylinders 8 11 (100.00%)
10 surv Dummy survival (disp/am) t=71.1 0.96 (1/24)
11 surv Dummy survival (disp/am) t=75.7 0.92 (1/23)
12 surv Dummy survival (disp/am) t=78.7 0.88 (1/22)
13 surv Dummy survival (disp/am) t=79 0.83 (1/21)
14 surv Dummy survival (disp/am) t=95.1 0.79 (1/20)
15 surv Dummy survival (disp/am) t=108 0.75 (1/19)
16 surv Dummy survival (disp/am) t=120.1 0.75 (0/18)
17 surv Dummy survival (disp/am) t=120.3 0.71 (1/17)
18 surv Dummy survival (disp/am) t=121 0.66 (1/16)
19 surv Dummy survival (disp/am) t=145 0.62 (1/15)
20 surv Dummy survival (disp/am) t=160 0.53 (2/14)
21 surv Dummy survival (disp/am) t=258 0.53 (0/12)
22 surv Dummy survival (disp/am) t=275.8 0.53 (0/11)
23 surv Dummy survival (disp/am) t=301 0.47 (1/9)
24 surv Dummy survival (disp/am) t=304 0.47 (0/8)
25 surv Dummy survival (disp/am) t=318 0.47 (0/7)
26 surv Dummy survival (disp/am) t=350 0.47 (0/6)
27 surv Dummy survival (disp/am) t=351 0.38 (1/5)
28 surv Dummy survival (disp/am) t=400 0.38 (0/4)
29 surv Dummy survival (disp/am) t=440 0.38 (0/3)
30 surv Dummy survival (disp/am) t=460 0.38 (0/2)
31 surv Dummy survival (disp/am) t=472 0.38 (0/1)
32 surv Dummy survival (disp/am) Median survival 301
By multiple
Code
x0 = crosstable(mtcars3, c(mpg, gear), by = c(cyl, am, vs))
as.data.frame(x0)
Output
.id label variable cyl=4 & am=auto & vs=straight cyl=6 & am=auto & vs=straight cyl=8 & am=auto & vs=straight cyl=NA & am=auto & vs=straight cyl=4 & am=manual & vs=straight cyl=6 & am=manual & vs=straight cyl=8 & am=manual & vs=straight cyl=NA & am=manual & vs=straight cyl=4 & am=auto & vs=vshaped cyl=6 & am=auto & vs=vshaped cyl=8 & am=auto & vs=vshaped cyl=NA & am=auto & vs=vshaped cyl=4 & am=manual & vs=vshaped cyl=6 & am=manual & vs=vshaped cyl=8 & am=manual & vs=vshaped cyl=NA & am=manual & vs=vshaped cyl=4 & am=auto & vs=NA cyl=6 & am=auto & vs=NA cyl=8 & am=auto & vs=NA cyl=NA & am=auto & vs=NA cyl=4 & am=manual & vs=NA cyl=6 & am=manual & vs=NA cyl=8 & am=manual & vs=NA cyl=NA & am=manual & vs=NA
1 mpg Miles/(US) gallon Min / Max 21.5 / 21.5 NA / NA NA / NA 21.4 / 21.4 21.4 / 33.9 NA / NA NA / NA 22.8 / 22.8 NA / NA NA / NA 10.4 / 19.2 NA / NA 26.0 / 26.0 19.7 / 19.7 15.0 / 15.8 21.0 / 21.0 22.8 / 24.4 17.8 / 19.2 14.3 / 16.4 18.7 / 18.7 NA / NA NA / NA NA / NA NA / NA
2 mpg Miles/(US) gallon Med [IQR] 21.5 [21.5;21.5] NA [NA;NA] NA [NA;NA] 21.4 [21.4;21.4] 30.4 [28.1;31.9] NA [NA;NA] NA [NA;NA] 22.8 [22.8;22.8] NA [NA;NA] NA [NA;NA] 15.2 [13.3;15.5] NA [NA;NA] 26.0 [26.0;26.0] 19.7 [19.7;19.7] 15.4 [15.2;15.6] 21.0 [21.0;21.0] 23.6 [23.2;24.0] 18.1 [18.0;18.6] 15.3 [14.8;15.9] 18.7 [18.7;18.7] NA [NA;NA] NA [NA;NA] NA [NA;NA] NA [NA;NA]
3 mpg Miles/(US) gallon Mean (std) 21.5 (NA) NA (NA) NA (NA) 21.4 (NA) 29.3 (4.5) NA (NA) NA (NA) 22.8 (NA) NA (NA) NA (NA) 14.6 (2.9) NA (NA) 26.0 (NA) 19.7 (NA) 15.4 (0.6) 21.0 (0) 23.6 (1.1) 18.4 (0.7) 15.3 (1.5) 18.7 (NA) NA (NA) NA (NA) NA (NA) NA (NA)
4 mpg Miles/(US) gallon N (NA) 1 (0) 0 (0) 0 (0) 1 (0) 6 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 9 (0) 0 (0) 1 (0) 1 (0) 2 (0) 2 (0) 2 (0) 3 (0) 2 (0) 1 (0) 0 (0) 0 (0) 0 (0) 0 (0)
5 gear Number of forward gears 3 1 (6.67%) 0 (0%) 0 (0%) 1 (6.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (60.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (6.67%) 2 (13.33%) 1 (6.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
6 gear Number of forward gears 4 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (41.67%) 0 (0%) 0 (0%) 1 (8.33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (16.67%) 2 (16.67%) 2 (16.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
7 gear Number of forward gears 5 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 1 (20.00%) 2 (40.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Code
x1 = crosstable(mtcars3, c(mpg, gear), by = c(cyl, am, vs), showNA = FALSE)
as.data.frame(x1)
Output
.id label variable cyl=4 & am=auto & vs=straight cyl=6 & am=auto & vs=straight cyl=8 & am=auto & vs=straight cyl=NA & am=auto & vs=straight cyl=4 & am=manual & vs=straight cyl=6 & am=manual & vs=straight cyl=8 & am=manual & vs=straight cyl=NA & am=manual & vs=straight cyl=4 & am=auto & vs=vshaped cyl=6 & am=auto & vs=vshaped cyl=8 & am=auto & vs=vshaped cyl=NA & am=auto & vs=vshaped cyl=4 & am=manual & vs=vshaped cyl=6 & am=manual & vs=vshaped cyl=8 & am=manual & vs=vshaped cyl=NA & am=manual & vs=vshaped
1 mpg Miles/(US) gallon Min / Max 21.5 / 21.5 NA / NA NA / NA 21.4 / 21.4 21.4 / 33.9 NA / NA NA / NA 22.8 / 22.8 NA / NA NA / NA 10.4 / 19.2 NA / NA 26.0 / 26.0 19.7 / 19.7 15.0 / 15.8 21.0 / 21.0
2 mpg Miles/(US) gallon Med [IQR] 21.5 [21.5;21.5] NA [NA;NA] NA [NA;NA] 21.4 [21.4;21.4] 30.4 [28.1;31.9] NA [NA;NA] NA [NA;NA] 22.8 [22.8;22.8] NA [NA;NA] NA [NA;NA] 15.2 [13.3;15.5] NA [NA;NA] 26.0 [26.0;26.0] 19.7 [19.7;19.7] 15.4 [15.2;15.6] 21.0 [21.0;21.0]
3 mpg Miles/(US) gallon Mean (std) 21.5 (NA) NA (NA) NA (NA) 21.4 (NA) 29.3 (4.5) NA (NA) NA (NA) 22.8 (NA) NA (NA) NA (NA) 14.6 (2.9) NA (NA) 26.0 (NA) 19.7 (NA) 15.4 (0.6) 21.0 (0)
4 mpg Miles/(US) gallon N (NA) 1 (0) 0 (0) 0 (0) 1 (0) 6 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 9 (0) 0 (0) 1 (0) 1 (0) 2 (0) 2 (0)
5 gear Number of forward gears 3 1 (9.09%) 0 (0%) 0 (0%) 1 (9.09%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (81.82%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
6 gear Number of forward gears 4 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (62.50%) 0 (0%) 0 (0%) 1 (12.50%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (25.00%)
7 gear Number of forward gears 5 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 1 (20.00%) 2 (40.00%) 0 (0%)
Code
x2 = crosstable(mtcars3, c(mpg, gear), by = c(cyl, am, vs), total = TRUE)
as.data.frame(x2)
Output
.id label variable cyl=4 & am=auto & vs=straight cyl=6 & am=auto & vs=straight cyl=8 & am=auto & vs=straight cyl=NA & am=auto & vs=straight cyl=4 & am=manual & vs=straight cyl=6 & am=manual & vs=straight cyl=8 & am=manual & vs=straight cyl=NA & am=manual & vs=straight cyl=4 & am=auto & vs=vshaped cyl=6 & am=auto & vs=vshaped cyl=8 & am=auto & vs=vshaped cyl=NA & am=auto & vs=vshaped cyl=4 & am=manual & vs=vshaped cyl=6 & am=manual & vs=vshaped cyl=8 & am=manual & vs=vshaped cyl=NA & am=manual & vs=vshaped cyl=4 & am=auto & vs=NA cyl=6 & am=auto & vs=NA cyl=8 & am=auto & vs=NA cyl=NA & am=auto & vs=NA cyl=4 & am=manual & vs=NA cyl=6 & am=manual & vs=NA cyl=8 & am=manual & vs=NA cyl=NA & am=manual & vs=NA Total
1 mpg Miles/(US) gallon Min / Max 21.5 / 21.5 NA / NA NA / NA 21.4 / 21.4 21.4 / 33.9 NA / NA NA / NA 22.8 / 22.8 NA / NA NA / NA 10.4 / 19.2 NA / NA 26.0 / 26.0 19.7 / 19.7 15.0 / 15.8 21.0 / 21.0 22.8 / 24.4 17.8 / 19.2 14.3 / 16.4 18.7 / 18.7 NA / NA NA / NA NA / NA NA / NA 10.4 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 21.5 [21.5;21.5] NA [NA;NA] NA [NA;NA] 21.4 [21.4;21.4] 30.4 [28.1;31.9] NA [NA;NA] NA [NA;NA] 22.8 [22.8;22.8] NA [NA;NA] NA [NA;NA] 15.2 [13.3;15.5] NA [NA;NA] 26.0 [26.0;26.0] 19.7 [19.7;19.7] 15.4 [15.2;15.6] 21.0 [21.0;21.0] 23.6 [23.2;24.0] 18.1 [18.0;18.6] 15.3 [14.8;15.9] 18.7 [18.7;18.7] NA [NA;NA] NA [NA;NA] NA [NA;NA] NA [NA;NA] 19.2 [15.4;22.8]
3 mpg Miles/(US) gallon Mean (std) 21.5 (NA) NA (NA) NA (NA) 21.4 (NA) 29.3 (4.5) NA (NA) NA (NA) 22.8 (NA) NA (NA) NA (NA) 14.6 (2.9) NA (NA) 26.0 (NA) 19.7 (NA) 15.4 (0.6) 21.0 (0) 23.6 (1.1) 18.4 (0.7) 15.3 (1.5) 18.7 (NA) NA (NA) NA (NA) NA (NA) NA (NA) 20.1 (6.0)
4 mpg Miles/(US) gallon N (NA) 1 (0) 0 (0) 0 (0) 1 (0) 6 (0) 0 (0) 0 (0) 1 (0) 0 (0) 0 (0) 9 (0) 0 (0) 1 (0) 1 (0) 2 (0) 2 (0) 2 (0) 3 (0) 2 (0) 1 (0) 0 (0) 0 (0) 0 (0) 0 (0) 32 (0)
5 gear Number of forward gears 3 1 (6.67%) 0 (0%) 0 (0%) 1 (6.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 9 (60.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (6.67%) 2 (13.33%) 1 (6.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 15 (46.88%)
6 gear Number of forward gears 4 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (41.67%) 0 (0%) 0 (0%) 1 (8.33%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (16.67%) 2 (16.67%) 2 (16.67%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 12 (37.50%)
7 gear Number of forward gears 5 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (20.00%) 1 (20.00%) 2 (40.00%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (15.62%)
8 gear Number of forward gears Total 1 (3.12%) 0 (0%) 0 (0%) 1 (3.12%) 6 (18.75%) 0 (0%) 0 (0%) 1 (3.12%) 0 (0%) 0 (0%) 9 (28.12%) 0 (0%) 1 (3.12%) 1 (3.12%) 2 (6.25%) 2 (6.25%) 2 (6.25%) 3 (9.38%) 2 (6.25%) 1 (3.12%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 32 (100.00%)
Code
x3 = crosstable(mtcars3, c(mpg, vs, cyl), by = c(am, dummy))
as.data.frame(x3)
Output
.id label variable am=auto & dummy=dummy am=manual & dummy=dummy
1 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
3 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
4 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
5 vs Engine straight 2 (22.22%) 7 (77.78%)
6 vs Engine vshaped 9 (60.00%) 6 (40.00%)
7 vs Engine NA 8 0
8 cyl Number of cylinders 4 3 (30.00%) 7 (70.00%)
9 cyl Number of cylinders 6 3 (75.00%) 1 (25.00%)
10 cyl Number of cylinders 8 11 (84.62%) 2 (15.38%)
11 cyl Number of cylinders NA 2 3
Code
x4 = crosstable(mtcars3, c(mpg, vs, cyl, dummy, surv, hp_date, qsec_posix, diff, cyl3), by = c(am, gear), total = TRUE, times = c(100, 200), followup = TRUE)
as.data.frame(x4)
Output
.id label variable am=auto & gear=3 am=manual & gear=3 am=auto & gear=4 am=manual & gear=4 am=auto & gear=5 am=manual & gear=5 Total
1 mpg Miles/(US) gallon Min / Max 10.4 / 21.5 NA / NA 17.8 / 24.4 21.0 / 33.9 NA / NA 15.0 / 30.4 10.4 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 15.5 [14.5;18.4] NA [NA;NA] 21.0 [18.8;23.2] 25.1 [21.3;30.9] NA [NA;NA] 19.7 [15.8;26.0] 19.2 [15.4;22.8]
3 mpg Miles/(US) gallon Mean (std) 16.1 (3.4) NA (NA) 21.1 (3.1) 26.3 (5.4) NA (NA) 21.4 (6.7) 20.1 (6.0)
4 mpg Miles/(US) gallon N (NA) 15 (0) 0 (0) 4 (0) 8 (0) 0 (0) 5 (0) 32 (0)
5 vs Engine straight 2 (22.22%) 0 (0%) 0 (0%) 6 (66.67%) 0 (0%) 1 (11.11%) 9 (37.50%)
6 vs Engine vshaped 9 (60.00%) 0 (0%) 0 (0%) 2 (13.33%) 0 (0%) 4 (26.67%) 15 (62.50%)
7 vs Engine NA 4 0 4 0 0 0 8
8 vs Engine Total 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
9 cyl Number of cylinders 4 1 (10.00%) 0 (0%) 2 (20.00%) 5 (50.00%) 0 (0%) 2 (20.00%) 10 (37.04%)
10 cyl Number of cylinders 6 1 (25.00%) 0 (0%) 2 (50.00%) 0 (0%) 0 (0%) 1 (25.00%) 4 (14.81%)
11 cyl Number of cylinders 8 11 (84.62%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 2 (15.38%) 13 (48.15%)
12 cyl Number of cylinders NA 2 0 0 3 0 0 5
13 cyl Number of cylinders Total 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
14 dummy dummy dummy 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
15 dummy dummy Total 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
16 surv Dummy survival (disp/am) t=100 1.00 (0/15) <NA> 1.00 (0/4) 0.50 (4/4) <NA> 0.80 (1/4) 0.84 (5/27)
17 surv Dummy survival (disp/am) t=200 1.00 (0/14) <NA> 1.00 (0/0) 0 (4/0) <NA> 0.40 (2/2) 0.64 (6/16)
18 surv Dummy survival (disp/am) Median follow up [min ; max] 318 [120.1 ; 472] <NA> 157.15 [140.8 ; 167.6] NA [Inf ; 160] <NA> NA [Inf ; 351] 304 [120.1 ; 472]
19 surv Dummy survival (disp/am) Median survival <NA> <NA> <NA> 93.5 <NA> 145 <NA>
20 hp_date Some nonsense date Min / Max 2010-04-08 - 2010-09-03 NA / NA 2010-03-04 - 2010-05-04 2010-02-22 - 2010-04-21 NA / NA 2010-04-02 - 2010-12-02 2010-02-22 - 2010-12-02
21 hp_date Some nonsense date Med [IQR] 2010-06-30 [2010-05-31;2010-08-04] NA [NA;NA] 2010-04-20 [2010-03-04;2010-05-04] 2010-03-21 [2010-03-07;2010-04-20] NA [NA;NA] 2010-06-25 [2010-04-24;2010-09-22] 2010-05-04 [2010-04-06;2010-06-30]
22 hp_date Some nonsense date Mean (std) 2010-06-26 (1.6 months) NA (NA) 2010-04-11 (29.0 days) 2010-03-25 (24.2 days) NA (NA) 2010-07-15 (3.4 months) 2010-05-27 (2.3 months)
23 hp_date Some nonsense date N (NA) 15 (0) 0 (0) 4 (0) 8 (0) 0 (0) 5 (0) 32 (0)
24 qsec_posix Date+time Min / Max 2010-01-16 10:50:24 - 2010-01-21 06:16:48 NA / NA 2010-01-19 08:12:00 - 2010-01-23 22:36:00 2010-01-17 12:02:24 - 2010-01-20 22:36:00 NA / NA 2010-01-15 13:00:00 - 2010-01-17 22:36:00 2010-01-15 13:00:00 - 2010-01-23 22:36:00
25 qsec_posix Date+time Med [IQR] 2010-01-18 11:04:48 [2010-01-18 01:28:48;2010-01-19 01:00:00] NA [NA;NA] 2010-01-20 11:48:00 [2010-01-19 08:12:00;2010-01-21 01:00:00] 2010-01-19 15:31:12 [2010-01-18 01:28:48;2010-01-19 22:36:00] NA [NA;NA] 2010-01-16 13:00:00 [2010-01-15 15:24:00;2010-01-17 17:48:00] 2010-01-18 18:02:24 [2010-01-17 21:52:48;2010-01-19 22:36:00]
26 qsec_posix Date+time Mean (std) 2010-01-18 17:36:28 (1.3 days) NA (NA) 2010-01-21 01:36:00 (2.0 days) 2010-01-19 11:26:24 (1.2 days) NA (NA) 2010-01-16 16:21:36 (1.1 days) 2010-01-18 21:22:12 (1.8 days)
27 qsec_posix Date+time N (NA) 15 (0) 0 (0) 4 (0) 8 (0) 0 (0) 5 (0) 32 (0)
28 diff Difftime hp_date-qsec_posix (days) Min / Max 77.0 / 229.6 NA / NA 42.0 / 104.7 33.5 / 93.5 NA / NA 74.3 / 320.4 33S / 5M 20S
29 diff Difftime hp_date-qsec_posix (days) Med [IQR] 162.0 [132.9;192.1] NA [NA;NA] 88.1 [64.6;104.2] 60.7 [46.2;91.0] NA [NA;NA] 159.5 [96.1;249.5] 1M 44S [1M 16S;2M 42S]
30 diff Difftime hp_date-qsec_posix (days) Mean (std) 158.4 (48.8) NA (NA) 80.7 (30.0) 65.4 (25.0) NA (NA) 180.0 (103.9) 2M 9S (1M 10S)
31 diff Difftime hp_date-qsec_posix (days) N (NA) 15 (0) 0 (0) 4 (0) 8 (0) 0 (0) 5 (0) 32 (0)
32 cyl3 cyl3 FALSE 13 (48.15%) 0 (0%) 4 (14.81%) 5 (18.52%) 0 (0%) 5 (18.52%) 27 (100.00%)
33 cyl3 cyl3 NA 2 0 0 3 0 0 5
34 cyl3 cyl3 Total 15 (46.88%) 0 (0%) 4 (12.50%) 8 (25.00%) 0 (0%) 5 (15.62%) 32 (100.00%)
By multiple (formula)
Code
x = crosstable(mtcars3, mpg + gear ~ I(am == "auto") + vs, total = TRUE)
as.data.frame(x)
Output
.id label variable I(am == "auto")=FALSE & vs=straight I(am == "auto")=TRUE & vs=straight I(am == "auto")=FALSE & vs=vshaped I(am == "auto")=TRUE & vs=vshaped I(am == "auto")=FALSE & vs=NA I(am == "auto")=TRUE & vs=NA Total
1 mpg Miles/(US) gallon Min / Max 21.4 / 33.9 21.4 / 21.5 15.0 / 26.0 10.4 / 19.2 NA / NA 14.3 / 24.4 10.4 / 33.9
2 mpg Miles/(US) gallon Med [IQR] 30.4 [25.1;31.4] 21.4 [21.4;21.5] 20.4 [16.8;21.0] 15.2 [13.3;15.5] NA [NA;NA] 18.4 [17.5;20.1] 19.2 [15.4;22.8]
3 mpg Miles/(US) gallon Mean (std) 28.4 (4.8) 21.4 (0.1) 19.8 (4.0) 14.6 (2.9) NA (NA) 19.0 (3.3) 20.1 (6.0)
4 mpg Miles/(US) gallon N (NA) 7 (0) 2 (0) 6 (0) 9 (0) 0 (0) 8 (0) 32 (0)
5 gear Number of forward gears 3 0 (0%) 2 (13.33%) 0 (0%) 9 (60.00%) 0 (0%) 4 (26.67%) 15 (46.88%)
6 gear Number of forward gears 4 6 (50.00%) 0 (0%) 2 (16.67%) 0 (0%) 0 (0%) 4 (33.33%) 12 (37.50%)
7 gear Number of forward gears 5 1 (20.00%) 0 (0%) 4 (80.00%) 0 (0%) 0 (0%) 0 (0%) 5 (15.62%)
8 gear Number of forward gears Total 7 (21.88%) 2 (6.25%) 6 (18.75%) 9 (28.12%) 0 (0%) 8 (25.00%) 32 (100.00%)
get_percent_pattern()
Code
get_percent_pattern()
Output
$body
{n} ({p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(na = TRUE)
Output
$body
[1] "{n} ({p_row_na})"
$total_row
[1] "{n} ({p_col_na})"
$total_col
[1] "{n} ({p_row_na})"
$total_all
[1] "{n} ({p_tot_na})"
Code
get_percent_pattern(c("cells", "row", "column"))
Output
$body
{n} ({p_tot} / {p_col} / {p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(c("cells", "row", "column"), na = TRUE)
Output
$body
[1] "{n} ({p_tot_na} / {p_col_na} / {p_row_na})"
$total_row
[1] "{n} ({p_col_na})"
$total_col
[1] "{n} ({p_row_na})"
$total_all
[1] "{n} ({p_tot_na})"
Code
get_percent_pattern(margin = TRUE)
Output
$body
[1] "{n} ({p_row} / {p_col})"
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = 1)
Output
$body
{n} ({p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = c(1, 0, 2))
Output
$body
{n} ({p_tot} / {p_row} / {p_col})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = 1:2)
Output
$body
{n} ({p_row} / {p_col})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = 2:1)
Output
$body
{n} ({p_row} / {p_col})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = "row")
Output
$body
{n} ({p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
Code
get_percent_pattern(margin = c("row", "cells", "column"))
Output
$body
{n} ({p_tot} / {p_col} / {p_row})
$total_row
[1] "{n} ({p_col})"
$total_col
[1] "{n} ({p_row})"
$total_all
[1] "{n} ({p_tot})"
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