Nothing
tests/testthat/_snaps/add_overall.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables
no errors/warnings with standard use
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 32 **0**, N = 19
1 mpg 19.2 (15.4, 22.8) 17.3 (15.0, 19.2)
2 cyl <NA> <NA>
3 4 11 (34%) 3 (16%)
4 6 7 (22%) 4 (21%)
5 8 14 (44%) 12 (63%)
6 disp 196 (121, 326) 276 (196, 360)
7 hp 123 (97, 180) 175 (117, 193)
8 drat 3.70 (3.08, 3.92) 3.15 (3.07, 3.70)
9 wt 3.33 (2.58, 3.61) 3.52 (3.44, 3.84)
10 qsec 17.71 (16.89, 18.90) 17.82 (17.18, 19.17)
11 vs 14 (44%) 7 (37%)
12 gear <NA> <NA>
13 3 15 (47%) 15 (79%)
14 4 12 (38%) 4 (21%)
15 5 5 (16%) 0 (0%)
16 carb <NA> <NA>
17 1 7 (22%) 3 (16%)
18 2 10 (31%) 6 (32%)
19 3 3 (9.4%) 3 (16%)
20 4 10 (31%) 7 (37%)
21 6 1 (3.1%) 0 (0%)
22 8 1 (3.1%) 0 (0%)
**1**, N = 13
1 22.8 (21.0, 30.4)
2 <NA>
3 8 (62%)
4 3 (23%)
5 2 (15%)
6 120 (79, 160)
7 109 (66, 113)
8 4.08 (3.85, 4.22)
9 2.32 (1.94, 2.78)
10 17.02 (16.46, 18.61)
11 7 (54%)
12 <NA>
13 0 (0%)
14 8 (62%)
15 5 (38%)
16 <NA>
17 4 (31%)
18 4 (31%)
19 0 (0%)
20 3 (23%)
21 1 (7.7%)
22 1 (7.7%)
Code
res %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
2 cyl <NA> <NA>
3 4 3 (16%) 8 (62%)
4 6 4 (21%) 3 (23%)
5 8 12 (63%) 2 (15%)
6 disp 276 (196, 360) 120 (79, 160)
7 hp 175 (117, 193) 109 (66, 113)
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
11 vs 7 (37%) 7 (54%)
12 gear <NA> <NA>
13 3 15 (79%) 0 (0%)
14 4 4 (21%) 8 (62%)
15 5 0 (0%) 5 (38%)
16 carb <NA> <NA>
17 1 3 (16%) 4 (31%)
18 2 6 (32%) 4 (31%)
19 3 3 (16%) 0 (0%)
20 4 7 (37%) 3 (23%)
21 6 0 (0%) 1 (7.7%)
22 8 0 (0%) 1 (7.7%)
**Overall**, N = 32
1 19.2 (15.4, 22.8)
2 <NA>
3 11 (34%)
4 7 (22%)
5 14 (44%)
6 196 (121, 326)
7 123 (97, 180)
8 3.70 (3.08, 3.92)
9 3.33 (2.58, 3.61)
10 17.71 (16.89, 18.90)
11 14 (44%)
12 <NA>
13 15 (47%)
14 12 (38%)
15 5 (16%)
16 <NA>
17 7 (22%)
18 10 (31%)
19 3 (9.4%)
20 10 (31%)
21 1 (3.1%)
22 1 (3.1%)
Code
res %>% as.data.frame()
Output
**Characteristic** **All Species** **setosa**, N = 50
1 Sepal.Length 5.80 (5.10, 6.40) 5.00 (4.80, 5.20)
2 Sepal.Width 3.00 (2.80, 3.30) 3.40 (3.20, 3.68)
3 Petal.Length 4.35 (1.60, 5.10) 1.50 (1.40, 1.58)
4 Petal.Width 1.30 (0.30, 1.80) 0.20 (0.20, 0.30)
**versicolor**, N = 50 **virginica**, N = 50
1 5.90 (5.60, 6.30) 6.50 (6.23, 6.90)
2 2.80 (2.53, 3.00) 3.00 (2.80, 3.18)
3 4.35 (4.00, 4.60) 5.55 (5.10, 5.88)
4 1.30 (1.20, 1.50) 2.00 (1.80, 2.30)
no errors/warnings with missing data
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 228 **1**, N = 138 **2**, N = 90
1 inst 11 (3, 16) 11 (3, 15) 11 (3, 16)
2 Unknown 1 1 0
3 time 256 (167, 397) 224 (145, 369) 293 (195, 449)
4 status <NA> <NA> <NA>
5 1 63 (28%) 26 (19%) 37 (41%)
6 2 165 (72%) 112 (81%) 53 (59%)
7 age 63 (56, 69) 64 (57, 70) 61 (55, 68)
8 ph.ecog <NA> <NA> <NA>
9 0 63 (28%) 36 (26%) 27 (30%)
10 1 113 (50%) 71 (52%) 42 (47%)
11 2 50 (22%) 29 (21%) 21 (23%)
12 3 1 (0.4%) 1 (0.7%) 0 (0%)
13 Unknown 1 1 0
14 ph.karno <NA> <NA> <NA>
15 50 6 (2.6%) 4 (2.9%) 2 (2.2%)
16 60 19 (8.4%) 11 (8.0%) 8 (8.9%)
17 70 32 (14%) 20 (15%) 12 (13%)
18 80 67 (30%) 40 (29%) 27 (30%)
19 90 74 (33%) 45 (33%) 29 (32%)
20 100 29 (13%) 17 (12%) 12 (13%)
21 Unknown 1 1 0
22 pat.karno <NA> <NA> <NA>
23 30 2 (0.9%) 1 (0.7%) 1 (1.1%)
24 40 2 (0.9%) 1 (0.7%) 1 (1.1%)
25 50 4 (1.8%) 2 (1.5%) 2 (2.2%)
26 60 30 (13%) 18 (13%) 12 (13%)
27 70 41 (18%) 30 (22%) 11 (12%)
28 80 51 (23%) 32 (24%) 19 (21%)
29 90 60 (27%) 31 (23%) 29 (33%)
30 100 35 (16%) 21 (15%) 14 (16%)
31 Unknown 3 2 1
32 meal.cal 975 (635, 1,150) 1,025 (768, 1,175) 925 (588, 1,068)
33 Unknown 47 24 23
34 wt.loss 7 (0, 16) 8 (1, 19) 4 (0, 11)
35 Unknown 14 10 4
Code
res %>% as.data.frame()
Output
**Characteristic** **1**, N = 138 **2**, N = 90 **Overall**, N = 228
1 inst 11 (3, 15) 11 (3, 16) 11 (3, 16)
2 Unknown 1 0 1
3 time 224 (145, 369) 293 (195, 449) 256 (167, 397)
4 status <NA> <NA> <NA>
5 1 26 (19%) 37 (41%) 63 (28%)
6 2 112 (81%) 53 (59%) 165 (72%)
7 age 64 (57, 70) 61 (55, 68) 63 (56, 69)
8 ph.ecog <NA> <NA> <NA>
9 0 36 (26%) 27 (30%) 63 (28%)
10 1 71 (52%) 42 (47%) 113 (50%)
11 2 29 (21%) 21 (23%) 50 (22%)
12 3 1 (0.7%) 0 (0%) 1 (0.4%)
13 Unknown 1 0 1
14 ph.karno <NA> <NA> <NA>
15 50 4 (2.9%) 2 (2.2%) 6 (2.6%)
16 60 11 (8.0%) 8 (8.9%) 19 (8.4%)
17 70 20 (15%) 12 (13%) 32 (14%)
18 80 40 (29%) 27 (30%) 67 (30%)
19 90 45 (33%) 29 (32%) 74 (33%)
20 100 17 (12%) 12 (13%) 29 (13%)
21 Unknown 1 0 1
22 pat.karno <NA> <NA> <NA>
23 30 1 (0.7%) 1 (1.1%) 2 (0.9%)
24 40 1 (0.7%) 1 (1.1%) 2 (0.9%)
25 50 2 (1.5%) 2 (2.2%) 4 (1.8%)
26 60 18 (13%) 12 (13%) 30 (13%)
27 70 30 (22%) 11 (12%) 41 (18%)
28 80 32 (24%) 19 (21%) 51 (23%)
29 90 31 (23%) 29 (33%) 60 (27%)
30 100 21 (15%) 14 (16%) 35 (16%)
31 Unknown 2 1 3
32 meal.cal 1,025 (768, 1,175) 925 (588, 1,068) 975 (635, 1,150)
33 Unknown 24 23 47
34 wt.loss 8 (1, 19) 4 (0, 11) 7 (0, 16)
35 Unknown 10 4 14
no errors/warnings with standard use for continuous 2
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 32 **0**, N = 19
1 mpg <NA> <NA>
2 Median (IQR) 19.2 (15.4, 22.8) 17.3 (15.0, 19.2)
3 cyl <NA> <NA>
4 4 11 (34%) 3 (16%)
5 6 7 (22%) 4 (21%)
6 8 14 (44%) 12 (63%)
7 disp <NA> <NA>
8 Median (IQR) 196 (121, 326) 276 (196, 360)
9 hp <NA> <NA>
10 Median (IQR) 123 (97, 180) 175 (117, 193)
11 drat <NA> <NA>
12 Median (IQR) 3.70 (3.08, 3.92) 3.15 (3.07, 3.70)
13 wt <NA> <NA>
14 Median (IQR) 3.33 (2.58, 3.61) 3.52 (3.44, 3.84)
15 qsec <NA> <NA>
16 Median (IQR) 17.71 (16.89, 18.90) 17.82 (17.18, 19.17)
17 vs 14 (44%) 7 (37%)
18 gear <NA> <NA>
19 3 15 (47%) 15 (79%)
20 4 12 (38%) 4 (21%)
21 5 5 (16%) 0 (0%)
22 carb <NA> <NA>
23 1 7 (22%) 3 (16%)
24 2 10 (31%) 6 (32%)
25 3 3 (9.4%) 3 (16%)
26 4 10 (31%) 7 (37%)
27 6 1 (3.1%) 0 (0%)
28 8 1 (3.1%) 0 (0%)
**1**, N = 13
1 <NA>
2 22.8 (21.0, 30.4)
3 <NA>
4 8 (62%)
5 3 (23%)
6 2 (15%)
7 <NA>
8 120 (79, 160)
9 <NA>
10 109 (66, 113)
11 <NA>
12 4.08 (3.85, 4.22)
13 <NA>
14 2.32 (1.94, 2.78)
15 <NA>
16 17.02 (16.46, 18.61)
17 7 (54%)
18 <NA>
19 0 (0%)
20 8 (62%)
21 5 (38%)
22 <NA>
23 4 (31%)
24 4 (31%)
25 0 (0%)
26 3 (23%)
27 1 (7.7%)
28 1 (7.7%)
Code
res %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13
1 mpg <NA> <NA>
2 Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
3 cyl <NA> <NA>
4 4 3 (16%) 8 (62%)
5 6 4 (21%) 3 (23%)
6 8 12 (63%) 2 (15%)
7 disp <NA> <NA>
8 Median (IQR) 276 (196, 360) 120 (79, 160)
9 hp <NA> <NA>
10 Median (IQR) 175 (117, 193) 109 (66, 113)
11 drat <NA> <NA>
12 Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
13 wt <NA> <NA>
14 Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
15 qsec <NA> <NA>
16 Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
17 vs 7 (37%) 7 (54%)
18 gear <NA> <NA>
19 3 15 (79%) 0 (0%)
20 4 4 (21%) 8 (62%)
21 5 0 (0%) 5 (38%)
22 carb <NA> <NA>
23 1 3 (16%) 4 (31%)
24 2 6 (32%) 4 (31%)
25 3 3 (16%) 0 (0%)
26 4 7 (37%) 3 (23%)
27 6 0 (0%) 1 (7.7%)
28 8 0 (0%) 1 (7.7%)
**Overall**, N = 32
1 <NA>
2 19.2 (15.4, 22.8)
3 <NA>
4 11 (34%)
5 7 (22%)
6 14 (44%)
7 <NA>
8 196 (121, 326)
9 <NA>
10 123 (97, 180)
11 <NA>
12 3.70 (3.08, 3.92)
13 <NA>
14 3.33 (2.58, 3.61)
15 <NA>
16 17.71 (16.89, 18.90)
17 14 (44%)
18 <NA>
19 15 (47%)
20 12 (38%)
21 5 (16%)
22 <NA>
23 7 (22%)
24 10 (31%)
25 3 (9.4%)
26 10 (31%)
27 1 (3.1%)
28 1 (3.1%)
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 150 **setosa**, N = 50
1 Sepal.Length <NA> <NA>
2 Median (IQR) 5.80 (5.10, 6.40) 5.00 (4.80, 5.20)
3 Sepal.Width <NA> <NA>
4 Median (IQR) 3.00 (2.80, 3.30) 3.40 (3.20, 3.68)
5 Petal.Length <NA> <NA>
6 Median (IQR) 4.35 (1.60, 5.10) 1.50 (1.40, 1.58)
7 Petal.Width <NA> <NA>
8 Median (IQR) 1.30 (0.30, 1.80) 0.20 (0.20, 0.30)
**versicolor**, N = 50 **virginica**, N = 50
1 <NA> <NA>
2 5.90 (5.60, 6.30) 6.50 (6.23, 6.90)
3 <NA> <NA>
4 2.80 (2.53, 3.00) 3.00 (2.80, 3.18)
5 <NA> <NA>
6 4.35 (4.00, 4.60) 5.55 (5.10, 5.88)
7 <NA> <NA>
8 1.30 (1.20, 1.50) 2.00 (1.80, 2.30)
no errors/warnings with missing data for continuous 2
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 228 **1**, N = 138 **2**, N = 90
1 inst <NA> <NA> <NA>
2 Median (IQR) 11 (3, 16) 11 (3, 15) 11 (3, 16)
3 Unknown 1 1 0
4 time <NA> <NA> <NA>
5 Median (IQR) 256 (167, 397) 224 (145, 369) 293 (195, 449)
6 status <NA> <NA> <NA>
7 1 63 (28%) 26 (19%) 37 (41%)
8 2 165 (72%) 112 (81%) 53 (59%)
9 age <NA> <NA> <NA>
10 Median (IQR) 63 (56, 69) 64 (57, 70) 61 (55, 68)
11 ph.ecog <NA> <NA> <NA>
12 0 63 (28%) 36 (26%) 27 (30%)
13 1 113 (50%) 71 (52%) 42 (47%)
14 2 50 (22%) 29 (21%) 21 (23%)
15 3 1 (0.4%) 1 (0.7%) 0 (0%)
16 Unknown 1 1 0
17 ph.karno <NA> <NA> <NA>
18 50 6 (2.6%) 4 (2.9%) 2 (2.2%)
19 60 19 (8.4%) 11 (8.0%) 8 (8.9%)
20 70 32 (14%) 20 (15%) 12 (13%)
21 80 67 (30%) 40 (29%) 27 (30%)
22 90 74 (33%) 45 (33%) 29 (32%)
23 100 29 (13%) 17 (12%) 12 (13%)
24 Unknown 1 1 0
25 pat.karno <NA> <NA> <NA>
26 30 2 (0.9%) 1 (0.7%) 1 (1.1%)
27 40 2 (0.9%) 1 (0.7%) 1 (1.1%)
28 50 4 (1.8%) 2 (1.5%) 2 (2.2%)
29 60 30 (13%) 18 (13%) 12 (13%)
30 70 41 (18%) 30 (22%) 11 (12%)
31 80 51 (23%) 32 (24%) 19 (21%)
32 90 60 (27%) 31 (23%) 29 (33%)
33 100 35 (16%) 21 (15%) 14 (16%)
34 Unknown 3 2 1
35 meal.cal <NA> <NA> <NA>
36 Median (IQR) 975 (635, 1,150) 1,025 (768, 1,175) 925 (588, 1,068)
37 Unknown 47 24 23
38 wt.loss <NA> <NA> <NA>
39 Median (IQR) 7 (0, 16) 8 (1, 19) 4 (0, 11)
40 Unknown 14 10 4
Code
res %>% as.data.frame()
Output
**Characteristic** **1**, N = 138 **2**, N = 90 **Overall**, N = 228
1 inst <NA> <NA> <NA>
2 Median (IQR) 11 (3, 15) 11 (3, 16) 11 (3, 16)
3 Unknown 1 0 1
4 time <NA> <NA> <NA>
5 Median (IQR) 224 (145, 369) 293 (195, 449) 256 (167, 397)
6 status <NA> <NA> <NA>
7 1 26 (19%) 37 (41%) 63 (28%)
8 2 112 (81%) 53 (59%) 165 (72%)
9 age <NA> <NA> <NA>
10 Median (IQR) 64 (57, 70) 61 (55, 68) 63 (56, 69)
11 ph.ecog <NA> <NA> <NA>
12 0 36 (26%) 27 (30%) 63 (28%)
13 1 71 (52%) 42 (47%) 113 (50%)
14 2 29 (21%) 21 (23%) 50 (22%)
15 3 1 (0.7%) 0 (0%) 1 (0.4%)
16 Unknown 1 0 1
17 ph.karno <NA> <NA> <NA>
18 50 4 (2.9%) 2 (2.2%) 6 (2.6%)
19 60 11 (8.0%) 8 (8.9%) 19 (8.4%)
20 70 20 (15%) 12 (13%) 32 (14%)
21 80 40 (29%) 27 (30%) 67 (30%)
22 90 45 (33%) 29 (32%) 74 (33%)
23 100 17 (12%) 12 (13%) 29 (13%)
24 Unknown 1 0 1
25 pat.karno <NA> <NA> <NA>
26 30 1 (0.7%) 1 (1.1%) 2 (0.9%)
27 40 1 (0.7%) 1 (1.1%) 2 (0.9%)
28 50 2 (1.5%) 2 (2.2%) 4 (1.8%)
29 60 18 (13%) 12 (13%) 30 (13%)
30 70 30 (22%) 11 (12%) 41 (18%)
31 80 32 (24%) 19 (21%) 51 (23%)
32 90 31 (23%) 29 (33%) 60 (27%)
33 100 21 (15%) 14 (16%) 35 (16%)
34 Unknown 2 1 3
35 meal.cal <NA> <NA> <NA>
36 Median (IQR) 1,025 (768, 1,175) 925 (588, 1,068) 975 (635, 1,150)
37 Unknown 24 23 47
38 wt.loss <NA> <NA> <NA>
39 Median (IQR) 8 (1, 19) 4 (0, 11) 7 (0, 16)
40 Unknown 10 4 14
no errors/warnings with missing data in by variable
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 193 **0**, N = 132
1 Chemotherapy Treatment <NA> <NA>
2 Drug A 95 (49%) 67 (51%)
3 Drug B 98 (51%) 65 (49%)
4 Age 47 (38, 57) 46 (36, 55)
5 Unknown 10 7
6 Marker Level (ng/mL) 0.62 (0.22, 1.38) 0.59 (0.21, 1.24)
7 Unknown 10 6
8 T Stage <NA> <NA>
9 T1 52 (27%) 34 (26%)
10 T2 52 (27%) 39 (30%)
11 T3 40 (21%) 25 (19%)
12 T4 49 (25%) 34 (26%)
13 Grade <NA> <NA>
14 I 67 (35%) 46 (35%)
15 II 63 (33%) 44 (33%)
16 III 63 (33%) 42 (32%)
17 Patient Died 107 (55%) 83 (63%)
18 Months to Death/Censor 22.7 (16.1, 24.0) 20.6 (15.0, 24.0)
**1**, N = 61
1 <NA>
2 28 (46%)
3 33 (54%)
4 49 (43, 59)
5 3
6 0.98 (0.31, 1.53)
7 4
8 <NA>
9 18 (30%)
10 13 (21%)
11 15 (25%)
12 15 (25%)
13 <NA>
14 21 (34%)
15 19 (31%)
16 21 (34%)
17 24 (39%)
18 24.0 (18.4, 24.0)
add_overall-works with ordered factors
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 200 **Drug A**, N = 98
1 response <NA> <NA>
2 0 132 (68%) 67 (71%)
3 1 61 (32%) 28 (29%)
4 Unknown 7 3
**Drug B**, N = 102
1 <NA>
2 65 (66%)
3 33 (34%)
4 4
no errors/warnings with standard use for tbl_svysummary
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 200 **Drug A**, N = 98
1 Age 47 (38, 57) 46 (37, 59)
2 Unknown 11 7
3 Marker Level (ng/mL) 0.62 (0.21, 1.38) 0.82 (0.23, 1.55)
4 Unknown 10 6
5 T Stage <NA> <NA>
6 T1 53 (27%) 28 (29%)
7 T2 54 (27%) 25 (26%)
8 T3 43 (22%) 22 (22%)
9 T4 50 (25%) 23 (23%)
10 Grade <NA> <NA>
11 I 68 (34%) 35 (36%)
12 II 68 (34%) 32 (33%)
13 III 64 (32%) 31 (32%)
14 Tumor Response 61 (32%) 28 (29%)
15 Unknown 7 3
16 Patient Died 112 (56%) 52 (53%)
17 Months to Death/Censor 22.4 (15.8, 24.0) 23.4 (17.2, 24.0)
**Drug B**, N = 102
1 48 (39, 56)
2 4
3 0.51 (0.18, 1.20)
4 4
5 <NA>
6 25 (25%)
7 29 (28%)
8 21 (21%)
9 27 (26%)
10 <NA>
11 33 (32%)
12 36 (35%)
13 33 (32%)
14 33 (34%)
15 4
16 60 (59%)
17 20.9 (14.5, 24.0)
Code
res %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age 46 (37, 59) 48 (39, 56)
2 Unknown 7 4
3 Marker Level (ng/mL) 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
4 Unknown 6 4
5 T Stage <NA> <NA>
6 T1 28 (29%) 25 (25%)
7 T2 25 (26%) 29 (28%)
8 T3 22 (22%) 21 (21%)
9 T4 23 (23%) 27 (26%)
10 Grade <NA> <NA>
11 I 35 (36%) 33 (32%)
12 II 32 (33%) 36 (35%)
13 III 31 (32%) 33 (32%)
14 Tumor Response 28 (29%) 33 (34%)
15 Unknown 3 4
16 Patient Died 52 (53%) 60 (59%)
17 Months to Death/Censor 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
**Overall**, N = 200
1 47 (38, 57)
2 11
3 0.62 (0.21, 1.38)
4 10
5 <NA>
6 53 (27%)
7 54 (27%)
8 43 (22%)
9 50 (25%)
10 <NA>
11 68 (34%)
12 68 (34%)
13 64 (32%)
14 61 (32%)
15 7
16 112 (56%)
17 22.4 (15.8, 24.0)
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 2,201 **No**, N = 1,490 **Yes**, N = 711
1 Class <NA> <NA> <NA>
2 1st 325 (15%) 122 (8.2%) 203 (29%)
3 2nd 285 (13%) 167 (11%) 118 (17%)
4 3rd 706 (32%) 528 (35%) 178 (25%)
5 Crew 885 (40%) 673 (45%) 212 (30%)
6 Sex <NA> <NA> <NA>
7 Male 1,731 (79%) 1,364 (92%) 367 (52%)
8 Female 470 (21%) 126 (8.5%) 344 (48%)
9 Age <NA> <NA> <NA>
10 Child 109 (5.0%) 52 (3.5%) 57 (8.0%)
11 Adult 2,092 (95%) 1,438 (97%) 654 (92%)
12 Freq 183 (92, 438) 342 (140, 513) 79 (64, 144)
no errors/warnings with standard use for tbl_svysummary with continuous2
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 200 **Drug A**, N = 98
1 Age <NA> <NA>
2 Median (IQR) 47 (38, 57) 46 (37, 59)
3 Unknown 11 7
4 Marker Level (ng/mL) <NA> <NA>
5 Median (IQR) 0.62 (0.21, 1.38) 0.82 (0.23, 1.55)
6 Unknown 10 6
7 T Stage <NA> <NA>
8 T1 53 (27%) 28 (29%)
9 T2 54 (27%) 25 (26%)
10 T3 43 (22%) 22 (22%)
11 T4 50 (25%) 23 (23%)
12 Grade <NA> <NA>
13 I 68 (34%) 35 (36%)
14 II 68 (34%) 32 (33%)
15 III 64 (32%) 31 (32%)
16 Tumor Response 61 (32%) 28 (29%)
17 Unknown 7 3
18 Patient Died 112 (56%) 52 (53%)
19 Months to Death/Censor <NA> <NA>
20 Median (IQR) 22.4 (15.8, 24.0) 23.4 (17.2, 24.0)
**Drug B**, N = 102
1 <NA>
2 48 (39, 56)
3 4
4 <NA>
5 0.51 (0.18, 1.20)
6 4
7 <NA>
8 25 (25%)
9 29 (28%)
10 21 (21%)
11 27 (26%)
12 <NA>
13 33 (32%)
14 36 (35%)
15 33 (32%)
16 33 (34%)
17 4
18 60 (59%)
19 <NA>
20 20.9 (14.5, 24.0)
Code
res %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age <NA> <NA>
2 Median (IQR) 46 (37, 59) 48 (39, 56)
3 Unknown 7 4
4 Marker Level (ng/mL) <NA> <NA>
5 Median (IQR) 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
6 Unknown 6 4
7 T Stage <NA> <NA>
8 T1 28 (29%) 25 (25%)
9 T2 25 (26%) 29 (28%)
10 T3 22 (22%) 21 (21%)
11 T4 23 (23%) 27 (26%)
12 Grade <NA> <NA>
13 I 35 (36%) 33 (32%)
14 II 32 (33%) 36 (35%)
15 III 31 (32%) 33 (32%)
16 Tumor Response 28 (29%) 33 (34%)
17 Unknown 3 4
18 Patient Died 52 (53%) 60 (59%)
19 Months to Death/Censor <NA> <NA>
20 Median (IQR) 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
**Overall**, N = 200
1 <NA>
2 47 (38, 57)
3 11
4 <NA>
5 0.62 (0.21, 1.38)
6 10
7 <NA>
8 53 (27%)
9 54 (27%)
10 43 (22%)
11 50 (25%)
12 <NA>
13 68 (34%)
14 68 (34%)
15 64 (32%)
16 61 (32%)
17 7
18 112 (56%)
19 <NA>
20 22.4 (15.8, 24.0)
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 2,201 **No**, N = 1,490 **Yes**, N = 711
1 Class <NA> <NA> <NA>
2 1st 325 (15%) 122 (8.2%) 203 (29%)
3 2nd 285 (13%) 167 (11%) 118 (17%)
4 3rd 706 (32%) 528 (35%) 178 (25%)
5 Crew 885 (40%) 673 (45%) 212 (30%)
6 Sex <NA> <NA> <NA>
7 Male 1,731 (79%) 1,364 (92%) 367 (52%)
8 Female 470 (21%) 126 (8.5%) 344 (48%)
9 Age <NA> <NA> <NA>
10 Child 109 (5.0%) 52 (3.5%) 57 (8.0%)
11 Adult 2,092 (95%) 1,438 (97%) 654 (92%)
12 Freq <NA> <NA> <NA>
13 Median (IQR) 183 (92, 438) 342 (140, 513) 79 (64, 144)
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no errors/warnings with standard use
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 32 **0**, N = 19
1 mpg 19.2 (15.4, 22.8) 17.3 (15.0, 19.2)
2 cyl <NA> <NA>
3 4 11 (34%) 3 (16%)
4 6 7 (22%) 4 (21%)
5 8 14 (44%) 12 (63%)
6 disp 196 (121, 326) 276 (196, 360)
7 hp 123 (97, 180) 175 (117, 193)
8 drat 3.70 (3.08, 3.92) 3.15 (3.07, 3.70)
9 wt 3.33 (2.58, 3.61) 3.52 (3.44, 3.84)
10 qsec 17.71 (16.89, 18.90) 17.82 (17.18, 19.17)
11 vs 14 (44%) 7 (37%)
12 gear <NA> <NA>
13 3 15 (47%) 15 (79%)
14 4 12 (38%) 4 (21%)
15 5 5 (16%) 0 (0%)
16 carb <NA> <NA>
17 1 7 (22%) 3 (16%)
18 2 10 (31%) 6 (32%)
19 3 3 (9.4%) 3 (16%)
20 4 10 (31%) 7 (37%)
21 6 1 (3.1%) 0 (0%)
22 8 1 (3.1%) 0 (0%)
**1**, N = 13
1 22.8 (21.0, 30.4)
2 <NA>
3 8 (62%)
4 3 (23%)
5 2 (15%)
6 120 (79, 160)
7 109 (66, 113)
8 4.08 (3.85, 4.22)
9 2.32 (1.94, 2.78)
10 17.02 (16.46, 18.61)
11 7 (54%)
12 <NA>
13 0 (0%)
14 8 (62%)
15 5 (38%)
16 <NA>
17 4 (31%)
18 4 (31%)
19 0 (0%)
20 3 (23%)
21 1 (7.7%)
22 1 (7.7%)
Code
res %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
2 cyl <NA> <NA>
3 4 3 (16%) 8 (62%)
4 6 4 (21%) 3 (23%)
5 8 12 (63%) 2 (15%)
6 disp 276 (196, 360) 120 (79, 160)
7 hp 175 (117, 193) 109 (66, 113)
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
11 vs 7 (37%) 7 (54%)
12 gear <NA> <NA>
13 3 15 (79%) 0 (0%)
14 4 4 (21%) 8 (62%)
15 5 0 (0%) 5 (38%)
16 carb <NA> <NA>
17 1 3 (16%) 4 (31%)
18 2 6 (32%) 4 (31%)
19 3 3 (16%) 0 (0%)
20 4 7 (37%) 3 (23%)
21 6 0 (0%) 1 (7.7%)
22 8 0 (0%) 1 (7.7%)
**Overall**, N = 32
1 19.2 (15.4, 22.8)
2 <NA>
3 11 (34%)
4 7 (22%)
5 14 (44%)
6 196 (121, 326)
7 123 (97, 180)
8 3.70 (3.08, 3.92)
9 3.33 (2.58, 3.61)
10 17.71 (16.89, 18.90)
11 14 (44%)
12 <NA>
13 15 (47%)
14 12 (38%)
15 5 (16%)
16 <NA>
17 7 (22%)
18 10 (31%)
19 3 (9.4%)
20 10 (31%)
21 1 (3.1%)
22 1 (3.1%)
Code
res %>% as.data.frame()
Output
**Characteristic** **All Species** **setosa**, N = 50
1 Sepal.Length 5.80 (5.10, 6.40) 5.00 (4.80, 5.20)
2 Sepal.Width 3.00 (2.80, 3.30) 3.40 (3.20, 3.68)
3 Petal.Length 4.35 (1.60, 5.10) 1.50 (1.40, 1.58)
4 Petal.Width 1.30 (0.30, 1.80) 0.20 (0.20, 0.30)
**versicolor**, N = 50 **virginica**, N = 50
1 5.90 (5.60, 6.30) 6.50 (6.23, 6.90)
2 2.80 (2.53, 3.00) 3.00 (2.80, 3.18)
3 4.35 (4.00, 4.60) 5.55 (5.10, 5.88)
4 1.30 (1.20, 1.50) 2.00 (1.80, 2.30)
no errors/warnings with missing data
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 228 **1**, N = 138 **2**, N = 90
1 inst 11 (3, 16) 11 (3, 15) 11 (3, 16)
2 Unknown 1 1 0
3 time 256 (167, 397) 224 (145, 369) 293 (195, 449)
4 status <NA> <NA> <NA>
5 1 63 (28%) 26 (19%) 37 (41%)
6 2 165 (72%) 112 (81%) 53 (59%)
7 age 63 (56, 69) 64 (57, 70) 61 (55, 68)
8 ph.ecog <NA> <NA> <NA>
9 0 63 (28%) 36 (26%) 27 (30%)
10 1 113 (50%) 71 (52%) 42 (47%)
11 2 50 (22%) 29 (21%) 21 (23%)
12 3 1 (0.4%) 1 (0.7%) 0 (0%)
13 Unknown 1 1 0
14 ph.karno <NA> <NA> <NA>
15 50 6 (2.6%) 4 (2.9%) 2 (2.2%)
16 60 19 (8.4%) 11 (8.0%) 8 (8.9%)
17 70 32 (14%) 20 (15%) 12 (13%)
18 80 67 (30%) 40 (29%) 27 (30%)
19 90 74 (33%) 45 (33%) 29 (32%)
20 100 29 (13%) 17 (12%) 12 (13%)
21 Unknown 1 1 0
22 pat.karno <NA> <NA> <NA>
23 30 2 (0.9%) 1 (0.7%) 1 (1.1%)
24 40 2 (0.9%) 1 (0.7%) 1 (1.1%)
25 50 4 (1.8%) 2 (1.5%) 2 (2.2%)
26 60 30 (13%) 18 (13%) 12 (13%)
27 70 41 (18%) 30 (22%) 11 (12%)
28 80 51 (23%) 32 (24%) 19 (21%)
29 90 60 (27%) 31 (23%) 29 (33%)
30 100 35 (16%) 21 (15%) 14 (16%)
31 Unknown 3 2 1
32 meal.cal 975 (635, 1,150) 1,025 (768, 1,175) 925 (588, 1,068)
33 Unknown 47 24 23
34 wt.loss 7 (0, 16) 8 (1, 19) 4 (0, 11)
35 Unknown 14 10 4
Code
res %>% as.data.frame()
Output
**Characteristic** **1**, N = 138 **2**, N = 90 **Overall**, N = 228
1 inst 11 (3, 15) 11 (3, 16) 11 (3, 16)
2 Unknown 1 0 1
3 time 224 (145, 369) 293 (195, 449) 256 (167, 397)
4 status <NA> <NA> <NA>
5 1 26 (19%) 37 (41%) 63 (28%)
6 2 112 (81%) 53 (59%) 165 (72%)
7 age 64 (57, 70) 61 (55, 68) 63 (56, 69)
8 ph.ecog <NA> <NA> <NA>
9 0 36 (26%) 27 (30%) 63 (28%)
10 1 71 (52%) 42 (47%) 113 (50%)
11 2 29 (21%) 21 (23%) 50 (22%)
12 3 1 (0.7%) 0 (0%) 1 (0.4%)
13 Unknown 1 0 1
14 ph.karno <NA> <NA> <NA>
15 50 4 (2.9%) 2 (2.2%) 6 (2.6%)
16 60 11 (8.0%) 8 (8.9%) 19 (8.4%)
17 70 20 (15%) 12 (13%) 32 (14%)
18 80 40 (29%) 27 (30%) 67 (30%)
19 90 45 (33%) 29 (32%) 74 (33%)
20 100 17 (12%) 12 (13%) 29 (13%)
21 Unknown 1 0 1
22 pat.karno <NA> <NA> <NA>
23 30 1 (0.7%) 1 (1.1%) 2 (0.9%)
24 40 1 (0.7%) 1 (1.1%) 2 (0.9%)
25 50 2 (1.5%) 2 (2.2%) 4 (1.8%)
26 60 18 (13%) 12 (13%) 30 (13%)
27 70 30 (22%) 11 (12%) 41 (18%)
28 80 32 (24%) 19 (21%) 51 (23%)
29 90 31 (23%) 29 (33%) 60 (27%)
30 100 21 (15%) 14 (16%) 35 (16%)
31 Unknown 2 1 3
32 meal.cal 1,025 (768, 1,175) 925 (588, 1,068) 975 (635, 1,150)
33 Unknown 24 23 47
34 wt.loss 8 (1, 19) 4 (0, 11) 7 (0, 16)
35 Unknown 10 4 14
no errors/warnings with standard use for continuous 2
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 32 **0**, N = 19
1 mpg <NA> <NA>
2 Median (IQR) 19.2 (15.4, 22.8) 17.3 (15.0, 19.2)
3 cyl <NA> <NA>
4 4 11 (34%) 3 (16%)
5 6 7 (22%) 4 (21%)
6 8 14 (44%) 12 (63%)
7 disp <NA> <NA>
8 Median (IQR) 196 (121, 326) 276 (196, 360)
9 hp <NA> <NA>
10 Median (IQR) 123 (97, 180) 175 (117, 193)
11 drat <NA> <NA>
12 Median (IQR) 3.70 (3.08, 3.92) 3.15 (3.07, 3.70)
13 wt <NA> <NA>
14 Median (IQR) 3.33 (2.58, 3.61) 3.52 (3.44, 3.84)
15 qsec <NA> <NA>
16 Median (IQR) 17.71 (16.89, 18.90) 17.82 (17.18, 19.17)
17 vs 14 (44%) 7 (37%)
18 gear <NA> <NA>
19 3 15 (47%) 15 (79%)
20 4 12 (38%) 4 (21%)
21 5 5 (16%) 0 (0%)
22 carb <NA> <NA>
23 1 7 (22%) 3 (16%)
24 2 10 (31%) 6 (32%)
25 3 3 (9.4%) 3 (16%)
26 4 10 (31%) 7 (37%)
27 6 1 (3.1%) 0 (0%)
28 8 1 (3.1%) 0 (0%)
**1**, N = 13
1 <NA>
2 22.8 (21.0, 30.4)
3 <NA>
4 8 (62%)
5 3 (23%)
6 2 (15%)
7 <NA>
8 120 (79, 160)
9 <NA>
10 109 (66, 113)
11 <NA>
12 4.08 (3.85, 4.22)
13 <NA>
14 2.32 (1.94, 2.78)
15 <NA>
16 17.02 (16.46, 18.61)
17 7 (54%)
18 <NA>
19 0 (0%)
20 8 (62%)
21 5 (38%)
22 <NA>
23 4 (31%)
24 4 (31%)
25 0 (0%)
26 3 (23%)
27 1 (7.7%)
28 1 (7.7%)
Code
res %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13
1 mpg <NA> <NA>
2 Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
3 cyl <NA> <NA>
4 4 3 (16%) 8 (62%)
5 6 4 (21%) 3 (23%)
6 8 12 (63%) 2 (15%)
7 disp <NA> <NA>
8 Median (IQR) 276 (196, 360) 120 (79, 160)
9 hp <NA> <NA>
10 Median (IQR) 175 (117, 193) 109 (66, 113)
11 drat <NA> <NA>
12 Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
13 wt <NA> <NA>
14 Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
15 qsec <NA> <NA>
16 Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
17 vs 7 (37%) 7 (54%)
18 gear <NA> <NA>
19 3 15 (79%) 0 (0%)
20 4 4 (21%) 8 (62%)
21 5 0 (0%) 5 (38%)
22 carb <NA> <NA>
23 1 3 (16%) 4 (31%)
24 2 6 (32%) 4 (31%)
25 3 3 (16%) 0 (0%)
26 4 7 (37%) 3 (23%)
27 6 0 (0%) 1 (7.7%)
28 8 0 (0%) 1 (7.7%)
**Overall**, N = 32
1 <NA>
2 19.2 (15.4, 22.8)
3 <NA>
4 11 (34%)
5 7 (22%)
6 14 (44%)
7 <NA>
8 196 (121, 326)
9 <NA>
10 123 (97, 180)
11 <NA>
12 3.70 (3.08, 3.92)
13 <NA>
14 3.33 (2.58, 3.61)
15 <NA>
16 17.71 (16.89, 18.90)
17 14 (44%)
18 <NA>
19 15 (47%)
20 12 (38%)
21 5 (16%)
22 <NA>
23 7 (22%)
24 10 (31%)
25 3 (9.4%)
26 10 (31%)
27 1 (3.1%)
28 1 (3.1%)
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 150 **setosa**, N = 50
1 Sepal.Length <NA> <NA>
2 Median (IQR) 5.80 (5.10, 6.40) 5.00 (4.80, 5.20)
3 Sepal.Width <NA> <NA>
4 Median (IQR) 3.00 (2.80, 3.30) 3.40 (3.20, 3.68)
5 Petal.Length <NA> <NA>
6 Median (IQR) 4.35 (1.60, 5.10) 1.50 (1.40, 1.58)
7 Petal.Width <NA> <NA>
8 Median (IQR) 1.30 (0.30, 1.80) 0.20 (0.20, 0.30)
**versicolor**, N = 50 **virginica**, N = 50
1 <NA> <NA>
2 5.90 (5.60, 6.30) 6.50 (6.23, 6.90)
3 <NA> <NA>
4 2.80 (2.53, 3.00) 3.00 (2.80, 3.18)
5 <NA> <NA>
6 4.35 (4.00, 4.60) 5.55 (5.10, 5.88)
7 <NA> <NA>
8 1.30 (1.20, 1.50) 2.00 (1.80, 2.30)
no errors/warnings with missing data for continuous 2
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 228 **1**, N = 138 **2**, N = 90
1 inst <NA> <NA> <NA>
2 Median (IQR) 11 (3, 16) 11 (3, 15) 11 (3, 16)
3 Unknown 1 1 0
4 time <NA> <NA> <NA>
5 Median (IQR) 256 (167, 397) 224 (145, 369) 293 (195, 449)
6 status <NA> <NA> <NA>
7 1 63 (28%) 26 (19%) 37 (41%)
8 2 165 (72%) 112 (81%) 53 (59%)
9 age <NA> <NA> <NA>
10 Median (IQR) 63 (56, 69) 64 (57, 70) 61 (55, 68)
11 ph.ecog <NA> <NA> <NA>
12 0 63 (28%) 36 (26%) 27 (30%)
13 1 113 (50%) 71 (52%) 42 (47%)
14 2 50 (22%) 29 (21%) 21 (23%)
15 3 1 (0.4%) 1 (0.7%) 0 (0%)
16 Unknown 1 1 0
17 ph.karno <NA> <NA> <NA>
18 50 6 (2.6%) 4 (2.9%) 2 (2.2%)
19 60 19 (8.4%) 11 (8.0%) 8 (8.9%)
20 70 32 (14%) 20 (15%) 12 (13%)
21 80 67 (30%) 40 (29%) 27 (30%)
22 90 74 (33%) 45 (33%) 29 (32%)
23 100 29 (13%) 17 (12%) 12 (13%)
24 Unknown 1 1 0
25 pat.karno <NA> <NA> <NA>
26 30 2 (0.9%) 1 (0.7%) 1 (1.1%)
27 40 2 (0.9%) 1 (0.7%) 1 (1.1%)
28 50 4 (1.8%) 2 (1.5%) 2 (2.2%)
29 60 30 (13%) 18 (13%) 12 (13%)
30 70 41 (18%) 30 (22%) 11 (12%)
31 80 51 (23%) 32 (24%) 19 (21%)
32 90 60 (27%) 31 (23%) 29 (33%)
33 100 35 (16%) 21 (15%) 14 (16%)
34 Unknown 3 2 1
35 meal.cal <NA> <NA> <NA>
36 Median (IQR) 975 (635, 1,150) 1,025 (768, 1,175) 925 (588, 1,068)
37 Unknown 47 24 23
38 wt.loss <NA> <NA> <NA>
39 Median (IQR) 7 (0, 16) 8 (1, 19) 4 (0, 11)
40 Unknown 14 10 4
Code
res %>% as.data.frame()
Output
**Characteristic** **1**, N = 138 **2**, N = 90 **Overall**, N = 228
1 inst <NA> <NA> <NA>
2 Median (IQR) 11 (3, 15) 11 (3, 16) 11 (3, 16)
3 Unknown 1 0 1
4 time <NA> <NA> <NA>
5 Median (IQR) 224 (145, 369) 293 (195, 449) 256 (167, 397)
6 status <NA> <NA> <NA>
7 1 26 (19%) 37 (41%) 63 (28%)
8 2 112 (81%) 53 (59%) 165 (72%)
9 age <NA> <NA> <NA>
10 Median (IQR) 64 (57, 70) 61 (55, 68) 63 (56, 69)
11 ph.ecog <NA> <NA> <NA>
12 0 36 (26%) 27 (30%) 63 (28%)
13 1 71 (52%) 42 (47%) 113 (50%)
14 2 29 (21%) 21 (23%) 50 (22%)
15 3 1 (0.7%) 0 (0%) 1 (0.4%)
16 Unknown 1 0 1
17 ph.karno <NA> <NA> <NA>
18 50 4 (2.9%) 2 (2.2%) 6 (2.6%)
19 60 11 (8.0%) 8 (8.9%) 19 (8.4%)
20 70 20 (15%) 12 (13%) 32 (14%)
21 80 40 (29%) 27 (30%) 67 (30%)
22 90 45 (33%) 29 (32%) 74 (33%)
23 100 17 (12%) 12 (13%) 29 (13%)
24 Unknown 1 0 1
25 pat.karno <NA> <NA> <NA>
26 30 1 (0.7%) 1 (1.1%) 2 (0.9%)
27 40 1 (0.7%) 1 (1.1%) 2 (0.9%)
28 50 2 (1.5%) 2 (2.2%) 4 (1.8%)
29 60 18 (13%) 12 (13%) 30 (13%)
30 70 30 (22%) 11 (12%) 41 (18%)
31 80 32 (24%) 19 (21%) 51 (23%)
32 90 31 (23%) 29 (33%) 60 (27%)
33 100 21 (15%) 14 (16%) 35 (16%)
34 Unknown 2 1 3
35 meal.cal <NA> <NA> <NA>
36 Median (IQR) 1,025 (768, 1,175) 925 (588, 1,068) 975 (635, 1,150)
37 Unknown 24 23 47
38 wt.loss <NA> <NA> <NA>
39 Median (IQR) 8 (1, 19) 4 (0, 11) 7 (0, 16)
40 Unknown 10 4 14
no errors/warnings with missing data in by variable
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 193 **0**, N = 132
1 Chemotherapy Treatment <NA> <NA>
2 Drug A 95 (49%) 67 (51%)
3 Drug B 98 (51%) 65 (49%)
4 Age 47 (38, 57) 46 (36, 55)
5 Unknown 10 7
6 Marker Level (ng/mL) 0.62 (0.22, 1.38) 0.59 (0.21, 1.24)
7 Unknown 10 6
8 T Stage <NA> <NA>
9 T1 52 (27%) 34 (26%)
10 T2 52 (27%) 39 (30%)
11 T3 40 (21%) 25 (19%)
12 T4 49 (25%) 34 (26%)
13 Grade <NA> <NA>
14 I 67 (35%) 46 (35%)
15 II 63 (33%) 44 (33%)
16 III 63 (33%) 42 (32%)
17 Patient Died 107 (55%) 83 (63%)
18 Months to Death/Censor 22.7 (16.1, 24.0) 20.6 (15.0, 24.0)
**1**, N = 61
1 <NA>
2 28 (46%)
3 33 (54%)
4 49 (43, 59)
5 3
6 0.98 (0.31, 1.53)
7 4
8 <NA>
9 18 (30%)
10 13 (21%)
11 15 (25%)
12 15 (25%)
13 <NA>
14 21 (34%)
15 19 (31%)
16 21 (34%)
17 24 (39%)
18 24.0 (18.4, 24.0)
add_overall-works with ordered factors
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 200 **Drug A**, N = 98
1 response <NA> <NA>
2 0 132 (68%) 67 (71%)
3 1 61 (32%) 28 (29%)
4 Unknown 7 3
**Drug B**, N = 102
1 <NA>
2 65 (66%)
3 33 (34%)
4 4
no errors/warnings with standard use for tbl_svysummary
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 200 **Drug A**, N = 98
1 Age 47 (38, 57) 46 (37, 59)
2 Unknown 11 7
3 Marker Level (ng/mL) 0.62 (0.21, 1.38) 0.82 (0.23, 1.55)
4 Unknown 10 6
5 T Stage <NA> <NA>
6 T1 53 (27%) 28 (29%)
7 T2 54 (27%) 25 (26%)
8 T3 43 (22%) 22 (22%)
9 T4 50 (25%) 23 (23%)
10 Grade <NA> <NA>
11 I 68 (34%) 35 (36%)
12 II 68 (34%) 32 (33%)
13 III 64 (32%) 31 (32%)
14 Tumor Response 61 (32%) 28 (29%)
15 Unknown 7 3
16 Patient Died 112 (56%) 52 (53%)
17 Months to Death/Censor 22.4 (15.8, 24.0) 23.4 (17.2, 24.0)
**Drug B**, N = 102
1 48 (39, 56)
2 4
3 0.51 (0.18, 1.20)
4 4
5 <NA>
6 25 (25%)
7 29 (28%)
8 21 (21%)
9 27 (26%)
10 <NA>
11 33 (32%)
12 36 (35%)
13 33 (32%)
14 33 (34%)
15 4
16 60 (59%)
17 20.9 (14.5, 24.0)
Code
res %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age 46 (37, 59) 48 (39, 56)
2 Unknown 7 4
3 Marker Level (ng/mL) 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
4 Unknown 6 4
5 T Stage <NA> <NA>
6 T1 28 (29%) 25 (25%)
7 T2 25 (26%) 29 (28%)
8 T3 22 (22%) 21 (21%)
9 T4 23 (23%) 27 (26%)
10 Grade <NA> <NA>
11 I 35 (36%) 33 (32%)
12 II 32 (33%) 36 (35%)
13 III 31 (32%) 33 (32%)
14 Tumor Response 28 (29%) 33 (34%)
15 Unknown 3 4
16 Patient Died 52 (53%) 60 (59%)
17 Months to Death/Censor 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
**Overall**, N = 200
1 47 (38, 57)
2 11
3 0.62 (0.21, 1.38)
4 10
5 <NA>
6 53 (27%)
7 54 (27%)
8 43 (22%)
9 50 (25%)
10 <NA>
11 68 (34%)
12 68 (34%)
13 64 (32%)
14 61 (32%)
15 7
16 112 (56%)
17 22.4 (15.8, 24.0)
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 2,201 **No**, N = 1,490 **Yes**, N = 711
1 Class <NA> <NA> <NA>
2 1st 325 (15%) 122 (8.2%) 203 (29%)
3 2nd 285 (13%) 167 (11%) 118 (17%)
4 3rd 706 (32%) 528 (35%) 178 (25%)
5 Crew 885 (40%) 673 (45%) 212 (30%)
6 Sex <NA> <NA> <NA>
7 Male 1,731 (79%) 1,364 (92%) 367 (52%)
8 Female 470 (21%) 126 (8.5%) 344 (48%)
9 Age <NA> <NA> <NA>
10 Child 109 (5.0%) 52 (3.5%) 57 (8.0%)
11 Adult 2,092 (95%) 1,438 (97%) 654 (92%)
12 Freq 183 (92, 438) 342 (140, 513) 79 (64, 144)
no errors/warnings with standard use for tbl_svysummary with continuous2
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 200 **Drug A**, N = 98
1 Age <NA> <NA>
2 Median (IQR) 47 (38, 57) 46 (37, 59)
3 Unknown 11 7
4 Marker Level (ng/mL) <NA> <NA>
5 Median (IQR) 0.62 (0.21, 1.38) 0.82 (0.23, 1.55)
6 Unknown 10 6
7 T Stage <NA> <NA>
8 T1 53 (27%) 28 (29%)
9 T2 54 (27%) 25 (26%)
10 T3 43 (22%) 22 (22%)
11 T4 50 (25%) 23 (23%)
12 Grade <NA> <NA>
13 I 68 (34%) 35 (36%)
14 II 68 (34%) 32 (33%)
15 III 64 (32%) 31 (32%)
16 Tumor Response 61 (32%) 28 (29%)
17 Unknown 7 3
18 Patient Died 112 (56%) 52 (53%)
19 Months to Death/Censor <NA> <NA>
20 Median (IQR) 22.4 (15.8, 24.0) 23.4 (17.2, 24.0)
**Drug B**, N = 102
1 <NA>
2 48 (39, 56)
3 4
4 <NA>
5 0.51 (0.18, 1.20)
6 4
7 <NA>
8 25 (25%)
9 29 (28%)
10 21 (21%)
11 27 (26%)
12 <NA>
13 33 (32%)
14 36 (35%)
15 33 (32%)
16 33 (34%)
17 4
18 60 (59%)
19 <NA>
20 20.9 (14.5, 24.0)
Code
res %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age <NA> <NA>
2 Median (IQR) 46 (37, 59) 48 (39, 56)
3 Unknown 7 4
4 Marker Level (ng/mL) <NA> <NA>
5 Median (IQR) 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
6 Unknown 6 4
7 T Stage <NA> <NA>
8 T1 28 (29%) 25 (25%)
9 T2 25 (26%) 29 (28%)
10 T3 22 (22%) 21 (21%)
11 T4 23 (23%) 27 (26%)
12 Grade <NA> <NA>
13 I 35 (36%) 33 (32%)
14 II 32 (33%) 36 (35%)
15 III 31 (32%) 33 (32%)
16 Tumor Response 28 (29%) 33 (34%)
17 Unknown 3 4
18 Patient Died 52 (53%) 60 (59%)
19 Months to Death/Censor <NA> <NA>
20 Median (IQR) 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
**Overall**, N = 200
1 <NA>
2 47 (38, 57)
3 11
4 <NA>
5 0.62 (0.21, 1.38)
6 10
7 <NA>
8 53 (27%)
9 54 (27%)
10 43 (22%)
11 50 (25%)
12 <NA>
13 68 (34%)
14 68 (34%)
15 64 (32%)
16 61 (32%)
17 7
18 112 (56%)
19 <NA>
20 22.4 (15.8, 24.0)
Code
res %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 2,201 **No**, N = 1,490 **Yes**, N = 711
1 Class <NA> <NA> <NA>
2 1st 325 (15%) 122 (8.2%) 203 (29%)
3 2nd 285 (13%) 167 (11%) 118 (17%)
4 3rd 706 (32%) 528 (35%) 178 (25%)
5 Crew 885 (40%) 673 (45%) 212 (30%)
6 Sex <NA> <NA> <NA>
7 Male 1,731 (79%) 1,364 (92%) 367 (52%)
8 Female 470 (21%) 126 (8.5%) 344 (48%)
9 Age <NA> <NA> <NA>
10 Child 109 (5.0%) 52 (3.5%) 57 (8.0%)
11 Adult 2,092 (95%) 1,438 (97%) 654 (92%)
12 Freq <NA> <NA> <NA>
13 Median (IQR) 183 (92, 438) 342 (140, 513) 79 (64, 144)
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