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tests/testthat/_snaps/add_stat_label.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables
no errors/warnings with standard use
Code
tbl %>% add_stat_label() %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13
1 mpg, Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
2 cyl, n (%) <NA> <NA>
3 4 3 (16%) 8 (62%)
4 6 4 (21%) 3 (23%)
5 8 12 (63%) 2 (15%)
6 disp, Median (IQR) 276 (196, 360) 120 (79, 160)
7 hp, Median (IQR) 175 (117, 193) 109 (66, 113)
8 drat, Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
9 wt, Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
10 qsec, Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
11 vs, n (%) 7 (37%) 7 (54%)
12 gear, n (%) <NA> <NA>
13 3 15 (79%) 0 (0%)
14 4 4 (21%) 8 (62%)
15 5 0 (0%) 5 (38%)
16 carb, n (%) <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%)
Code
tbl00 %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13 **p-value**
1 mpg, Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.002
2 cyl, n (%) <NA> <NA> 0.009
3 4 3 (16%) 8 (62%) <NA>
4 6 4 (21%) 3 (23%) <NA>
5 8 12 (63%) 2 (15%) <NA>
6 disp, Median (IQR) 276 (196, 360) 120 (79, 160) <0.001
7 hp, Median (IQR) 175 (117, 193) 109 (66, 113) 0.046
8 drat, Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt, Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec, Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
11 vs, n (%) 7 (37%) 7 (54%) 0.3
12 gear, n (%) <NA> <NA> <0.001
13 3 15 (79%) 0 (0%) <NA>
14 4 4 (21%) 8 (62%) <NA>
15 5 0 (0%) 5 (38%) <NA>
16 carb, n (%) <NA> <NA> 0.3
17 1 3 (16%) 4 (31%) <NA>
18 2 6 (32%) 4 (31%) <NA>
19 3 3 (16%) 0 (0%) <NA>
20 4 7 (37%) 3 (23%) <NA>
21 6 0 (0%) 1 (7.7%) <NA>
22 8 0 (0%) 1 (7.7%) <NA>
Code
tbl %>% add_overall() %>% add_stat_label() %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 32 **0**, N = 19
1 mpg, Median (IQR) 19.2 (15.4, 22.8) 17.3 (15.0, 19.2)
2 cyl, n (%) <NA> <NA>
3 4 11 (34%) 3 (16%)
4 6 7 (22%) 4 (21%)
5 8 14 (44%) 12 (63%)
6 disp, Median (IQR) 196 (121, 326) 276 (196, 360)
7 hp, Median (IQR) 123 (97, 180) 175 (117, 193)
8 drat, Median (IQR) 3.70 (3.08, 3.92) 3.15 (3.07, 3.70)
9 wt, Median (IQR) 3.33 (2.58, 3.61) 3.52 (3.44, 3.84)
10 qsec, Median (IQR) 17.71 (16.89, 18.90) 17.82 (17.18, 19.17)
11 vs, n (%) 14 (44%) 7 (37%)
12 gear, n (%) <NA> <NA>
13 3 15 (47%) 15 (79%)
14 4 12 (38%) 4 (21%)
15 5 5 (16%) 0 (0%)
16 carb, n (%) <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
tbl %>% add_stat_label(location = "column", label = all_categorical() ~
"no. (%)") %>% as.data.frame()
Output
**Characteristic** **Statistic** **0**, N = 19 **1**, N = 13
1 mpg Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
2 cyl <NA> <NA> <NA>
3 4 no. (%) 3 (16%) 8 (62%)
4 6 no. (%) 4 (21%) 3 (23%)
5 8 no. (%) 12 (63%) 2 (15%)
6 disp Median (IQR) 276 (196, 360) 120 (79, 160)
7 hp Median (IQR) 175 (117, 193) 109 (66, 113)
8 drat Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
9 wt Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
10 qsec Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
11 vs no. (%) 7 (37%) 7 (54%)
12 gear <NA> <NA> <NA>
13 3 no. (%) 15 (79%) 0 (0%)
14 4 no. (%) 4 (21%) 8 (62%)
15 5 no. (%) 0 (0%) 5 (38%)
16 carb <NA> <NA> <NA>
17 1 no. (%) 3 (16%) 4 (31%)
18 2 no. (%) 6 (32%) 4 (31%)
19 3 no. (%) 3 (16%) 0 (0%)
20 4 no. (%) 7 (37%) 3 (23%)
21 6 no. (%) 0 (0%) 1 (7.7%)
22 8 no. (%) 0 (0%) 1 (7.7%)
Code
tbl %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age <NA> <NA>
2 Mean (SD) 47 (15) 47 (14)
3 Min - Max 6 - 78 9 - 83
4 Unknown 7 4
5 Grade, n (%) <NA> <NA>
6 I 35 (36%) 33 (32%)
7 II 32 (33%) 36 (35%)
8 III 31 (32%) 33 (32%)
no errors/warnings with standard use for continuous2
Code
tbl %>% add_stat_label() %>% 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, n (%) <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, n (%) 7 (37%) 7 (54%)
18 gear, n (%) <NA> <NA>
19 3 15 (79%) 0 (0%)
20 4 4 (21%) 8 (62%)
21 5 0 (0%) 5 (38%)
22 carb, n (%) <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%)
Code
tbl00 %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13 **p-value**
1 mpg <NA> <NA> 0.002
2 Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) <NA>
3 cyl, n (%) <NA> <NA> 0.009
4 4 3 (16%) 8 (62%) <NA>
5 6 4 (21%) 3 (23%) <NA>
6 8 12 (63%) 2 (15%) <NA>
7 disp <NA> <NA> <0.001
8 Median (IQR) 276 (196, 360) 120 (79, 160) <NA>
9 hp <NA> <NA> 0.046
10 Median (IQR) 175 (117, 193) 109 (66, 113) <NA>
11 drat <NA> <NA> <0.001
12 Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <NA>
13 wt <NA> <NA> <0.001
14 Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <NA>
15 qsec <NA> <NA> 0.3
16 Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) <NA>
17 vs, n (%) 7 (37%) 7 (54%) 0.3
18 gear, n (%) <NA> <NA> <0.001
19 3 15 (79%) 0 (0%) <NA>
20 4 4 (21%) 8 (62%) <NA>
21 5 0 (0%) 5 (38%) <NA>
22 carb, n (%) <NA> <NA> 0.3
23 1 3 (16%) 4 (31%) <NA>
24 2 6 (32%) 4 (31%) <NA>
25 3 3 (16%) 0 (0%) <NA>
26 4 7 (37%) 3 (23%) <NA>
27 6 0 (0%) 1 (7.7%) <NA>
28 8 0 (0%) 1 (7.7%) <NA>
Code
tbl %>% add_overall() %>% add_stat_label() %>% 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, n (%) <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, n (%) 14 (44%) 7 (37%)
18 gear, n (%) <NA> <NA>
19 3 15 (47%) 15 (79%)
20 4 12 (38%) 4 (21%)
21 5 5 (16%) 0 (0%)
22 carb, n (%) <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
tbl %>% add_stat_label(location = "column", label = all_categorical() ~
"no. (%)") %>% as.data.frame()
Output
**Characteristic** **Statistic** **0**, N = 19 **1**, N = 13
1 mpg <NA> <NA> <NA>
2 Median (IQR) <NA> 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
3 cyl <NA> <NA> <NA>
4 4 no. (%) 3 (16%) 8 (62%)
5 6 no. (%) 4 (21%) 3 (23%)
6 8 no. (%) 12 (63%) 2 (15%)
7 disp <NA> <NA> <NA>
8 Median (IQR) <NA> 276 (196, 360) 120 (79, 160)
9 hp <NA> <NA> <NA>
10 Median (IQR) <NA> 175 (117, 193) 109 (66, 113)
11 drat <NA> <NA> <NA>
12 Median (IQR) <NA> 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
13 wt <NA> <NA> <NA>
14 Median (IQR) <NA> 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
15 qsec <NA> <NA> <NA>
16 Median (IQR) <NA> 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
17 vs no. (%) 7 (37%) 7 (54%)
18 gear <NA> <NA> <NA>
19 3 no. (%) 15 (79%) 0 (0%)
20 4 no. (%) 4 (21%) 8 (62%)
21 5 no. (%) 0 (0%) 5 (38%)
22 carb <NA> <NA> <NA>
23 1 no. (%) 3 (16%) 4 (31%)
24 2 no. (%) 6 (32%) 4 (31%)
25 3 no. (%) 3 (16%) 0 (0%)
26 4 no. (%) 7 (37%) 3 (23%)
27 6 no. (%) 0 (0%) 1 (7.7%)
28 8 no. (%) 0 (0%) 1 (7.7%)
no errors/warnings with standard use for tbl_svysummary
Code
tbl %>% add_stat_label() %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age, Median (IQR) 46 (37, 59) 48 (39, 56)
2 Unknown 7 4
3 Marker Level (ng/mL), Median (IQR) 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
4 Unknown 6 4
5 T Stage, n (%) <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, n (%) <NA> <NA>
11 I 35 (36%) 33 (32%)
12 II 32 (33%) 36 (35%)
13 III 31 (32%) 33 (32%)
14 Tumor Response, n (%) 28 (29%) 33 (34%)
15 Unknown 3 4
16 Patient Died, n (%) 52 (53%) 60 (59%)
17 Months to Death/Censor, Median (IQR) 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
Code
tbl %>% add_stat_label(location = "column", label = all_categorical() ~
"no. (%)") %>% as.data.frame()
Output
**Characteristic** **Statistic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age Median (IQR) 46 (37, 59) 48 (39, 56)
2 Unknown n 7 4
3 Marker Level (ng/mL) Median (IQR) 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
4 Unknown n 6 4
5 T Stage <NA> <NA> <NA>
6 T1 no. (%) 28 (29%) 25 (25%)
7 T2 no. (%) 25 (26%) 29 (28%)
8 T3 no. (%) 22 (22%) 21 (21%)
9 T4 no. (%) 23 (23%) 27 (26%)
10 Grade <NA> <NA> <NA>
11 I no. (%) 35 (36%) 33 (32%)
12 II no. (%) 32 (33%) 36 (35%)
13 III no. (%) 31 (32%) 33 (32%)
14 Tumor Response no. (%) 28 (29%) 33 (34%)
15 Unknown n 3 4
16 Patient Died no. (%) 52 (53%) 60 (59%)
17 Months to Death/Censor Median (IQR) 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
no errors/warnings with standard use for tbl_svysummary with continuous2
Code
tbl %>% add_stat_label() %>% 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, n (%) <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, n (%) <NA> <NA>
13 I 35 (36%) 33 (32%)
14 II 32 (33%) 36 (35%)
15 III 31 (32%) 33 (32%)
16 Tumor Response, n (%) 28 (29%) 33 (34%)
17 Unknown 3 4
18 Patient Died, n (%) 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)
Code
tbl %>% add_stat_label(location = "column", label = all_categorical() ~
"no. (%)") %>% as.data.frame()
Output
**Characteristic** **Statistic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age <NA> <NA> <NA>
2 Median (IQR) <NA> 46 (37, 59) 48 (39, 56)
3 Unknown n 7 4
4 Marker Level (ng/mL) <NA> <NA> <NA>
5 Median (IQR) <NA> 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
6 Unknown n 6 4
7 T Stage <NA> <NA> <NA>
8 T1 no. (%) 28 (29%) 25 (25%)
9 T2 no. (%) 25 (26%) 29 (28%)
10 T3 no. (%) 22 (22%) 21 (21%)
11 T4 no. (%) 23 (23%) 27 (26%)
12 Grade <NA> <NA> <NA>
13 I no. (%) 35 (36%) 33 (32%)
14 II no. (%) 32 (33%) 36 (35%)
15 III no. (%) 31 (32%) 33 (32%)
16 Tumor Response no. (%) 28 (29%) 33 (34%)
17 Unknown n 3 4
18 Patient Died no. (%) 52 (53%) 60 (59%)
19 Months to Death/Censor <NA> <NA> <NA>
20 Median (IQR) <NA> 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
add_stat_label() with tbl_merge()
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age, Median (IQR) 46 (37, 59) 48 (39, 56)
2 Tumor Response, n (%) 28 (29%) 33 (34%)
**Drug A**, N = 98 **Drug B**, N = 102
1 46 (37, 59) 48 (39, 56)
2 28 (29%) 33 (34%)
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no errors/warnings with standard use
Code
tbl %>% add_stat_label() %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13
1 mpg, Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
2 cyl, n (%) <NA> <NA>
3 4 3 (16%) 8 (62%)
4 6 4 (21%) 3 (23%)
5 8 12 (63%) 2 (15%)
6 disp, Median (IQR) 276 (196, 360) 120 (79, 160)
7 hp, Median (IQR) 175 (117, 193) 109 (66, 113)
8 drat, Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
9 wt, Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
10 qsec, Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
11 vs, n (%) 7 (37%) 7 (54%)
12 gear, n (%) <NA> <NA>
13 3 15 (79%) 0 (0%)
14 4 4 (21%) 8 (62%)
15 5 0 (0%) 5 (38%)
16 carb, n (%) <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%)
Code
tbl00 %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13 **p-value**
1 mpg, Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.002
2 cyl, n (%) <NA> <NA> 0.009
3 4 3 (16%) 8 (62%) <NA>
4 6 4 (21%) 3 (23%) <NA>
5 8 12 (63%) 2 (15%) <NA>
6 disp, Median (IQR) 276 (196, 360) 120 (79, 160) <0.001
7 hp, Median (IQR) 175 (117, 193) 109 (66, 113) 0.046
8 drat, Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt, Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec, Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
11 vs, n (%) 7 (37%) 7 (54%) 0.3
12 gear, n (%) <NA> <NA> <0.001
13 3 15 (79%) 0 (0%) <NA>
14 4 4 (21%) 8 (62%) <NA>
15 5 0 (0%) 5 (38%) <NA>
16 carb, n (%) <NA> <NA> 0.3
17 1 3 (16%) 4 (31%) <NA>
18 2 6 (32%) 4 (31%) <NA>
19 3 3 (16%) 0 (0%) <NA>
20 4 7 (37%) 3 (23%) <NA>
21 6 0 (0%) 1 (7.7%) <NA>
22 8 0 (0%) 1 (7.7%) <NA>
Code
tbl %>% add_overall() %>% add_stat_label() %>% as.data.frame()
Output
**Characteristic** **Overall**, N = 32 **0**, N = 19
1 mpg, Median (IQR) 19.2 (15.4, 22.8) 17.3 (15.0, 19.2)
2 cyl, n (%) <NA> <NA>
3 4 11 (34%) 3 (16%)
4 6 7 (22%) 4 (21%)
5 8 14 (44%) 12 (63%)
6 disp, Median (IQR) 196 (121, 326) 276 (196, 360)
7 hp, Median (IQR) 123 (97, 180) 175 (117, 193)
8 drat, Median (IQR) 3.70 (3.08, 3.92) 3.15 (3.07, 3.70)
9 wt, Median (IQR) 3.33 (2.58, 3.61) 3.52 (3.44, 3.84)
10 qsec, Median (IQR) 17.71 (16.89, 18.90) 17.82 (17.18, 19.17)
11 vs, n (%) 14 (44%) 7 (37%)
12 gear, n (%) <NA> <NA>
13 3 15 (47%) 15 (79%)
14 4 12 (38%) 4 (21%)
15 5 5 (16%) 0 (0%)
16 carb, n (%) <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
tbl %>% add_stat_label(location = "column", label = all_categorical() ~
"no. (%)") %>% as.data.frame()
Output
**Characteristic** **Statistic** **0**, N = 19 **1**, N = 13
1 mpg Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
2 cyl <NA> <NA> <NA>
3 4 no. (%) 3 (16%) 8 (62%)
4 6 no. (%) 4 (21%) 3 (23%)
5 8 no. (%) 12 (63%) 2 (15%)
6 disp Median (IQR) 276 (196, 360) 120 (79, 160)
7 hp Median (IQR) 175 (117, 193) 109 (66, 113)
8 drat Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
9 wt Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
10 qsec Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
11 vs no. (%) 7 (37%) 7 (54%)
12 gear <NA> <NA> <NA>
13 3 no. (%) 15 (79%) 0 (0%)
14 4 no. (%) 4 (21%) 8 (62%)
15 5 no. (%) 0 (0%) 5 (38%)
16 carb <NA> <NA> <NA>
17 1 no. (%) 3 (16%) 4 (31%)
18 2 no. (%) 6 (32%) 4 (31%)
19 3 no. (%) 3 (16%) 0 (0%)
20 4 no. (%) 7 (37%) 3 (23%)
21 6 no. (%) 0 (0%) 1 (7.7%)
22 8 no. (%) 0 (0%) 1 (7.7%)
Code
tbl %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age <NA> <NA>
2 Mean (SD) 47 (15) 47 (14)
3 Min - Max 6 - 78 9 - 83
4 Unknown 7 4
5 Grade, n (%) <NA> <NA>
6 I 35 (36%) 33 (32%)
7 II 32 (33%) 36 (35%)
8 III 31 (32%) 33 (32%)
no errors/warnings with standard use for continuous2
Code
tbl %>% add_stat_label() %>% 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, n (%) <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, n (%) 7 (37%) 7 (54%)
18 gear, n (%) <NA> <NA>
19 3 15 (79%) 0 (0%)
20 4 4 (21%) 8 (62%)
21 5 0 (0%) 5 (38%)
22 carb, n (%) <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%)
Code
tbl00 %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13 **p-value**
1 mpg <NA> <NA> 0.002
2 Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) <NA>
3 cyl, n (%) <NA> <NA> 0.009
4 4 3 (16%) 8 (62%) <NA>
5 6 4 (21%) 3 (23%) <NA>
6 8 12 (63%) 2 (15%) <NA>
7 disp <NA> <NA> <0.001
8 Median (IQR) 276 (196, 360) 120 (79, 160) <NA>
9 hp <NA> <NA> 0.046
10 Median (IQR) 175 (117, 193) 109 (66, 113) <NA>
11 drat <NA> <NA> <0.001
12 Median (IQR) 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <NA>
13 wt <NA> <NA> <0.001
14 Median (IQR) 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <NA>
15 qsec <NA> <NA> 0.3
16 Median (IQR) 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) <NA>
17 vs, n (%) 7 (37%) 7 (54%) 0.3
18 gear, n (%) <NA> <NA> <0.001
19 3 15 (79%) 0 (0%) <NA>
20 4 4 (21%) 8 (62%) <NA>
21 5 0 (0%) 5 (38%) <NA>
22 carb, n (%) <NA> <NA> 0.3
23 1 3 (16%) 4 (31%) <NA>
24 2 6 (32%) 4 (31%) <NA>
25 3 3 (16%) 0 (0%) <NA>
26 4 7 (37%) 3 (23%) <NA>
27 6 0 (0%) 1 (7.7%) <NA>
28 8 0 (0%) 1 (7.7%) <NA>
Code
tbl %>% add_overall() %>% add_stat_label() %>% 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, n (%) <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, n (%) 14 (44%) 7 (37%)
18 gear, n (%) <NA> <NA>
19 3 15 (47%) 15 (79%)
20 4 12 (38%) 4 (21%)
21 5 5 (16%) 0 (0%)
22 carb, n (%) <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
tbl %>% add_stat_label(location = "column", label = all_categorical() ~
"no. (%)") %>% as.data.frame()
Output
**Characteristic** **Statistic** **0**, N = 19 **1**, N = 13
1 mpg <NA> <NA> <NA>
2 Median (IQR) <NA> 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
3 cyl <NA> <NA> <NA>
4 4 no. (%) 3 (16%) 8 (62%)
5 6 no. (%) 4 (21%) 3 (23%)
6 8 no. (%) 12 (63%) 2 (15%)
7 disp <NA> <NA> <NA>
8 Median (IQR) <NA> 276 (196, 360) 120 (79, 160)
9 hp <NA> <NA> <NA>
10 Median (IQR) <NA> 175 (117, 193) 109 (66, 113)
11 drat <NA> <NA> <NA>
12 Median (IQR) <NA> 3.15 (3.07, 3.70) 4.08 (3.85, 4.22)
13 wt <NA> <NA> <NA>
14 Median (IQR) <NA> 3.52 (3.44, 3.84) 2.32 (1.94, 2.78)
15 qsec <NA> <NA> <NA>
16 Median (IQR) <NA> 17.82 (17.18, 19.17) 17.02 (16.46, 18.61)
17 vs no. (%) 7 (37%) 7 (54%)
18 gear <NA> <NA> <NA>
19 3 no. (%) 15 (79%) 0 (0%)
20 4 no. (%) 4 (21%) 8 (62%)
21 5 no. (%) 0 (0%) 5 (38%)
22 carb <NA> <NA> <NA>
23 1 no. (%) 3 (16%) 4 (31%)
24 2 no. (%) 6 (32%) 4 (31%)
25 3 no. (%) 3 (16%) 0 (0%)
26 4 no. (%) 7 (37%) 3 (23%)
27 6 no. (%) 0 (0%) 1 (7.7%)
28 8 no. (%) 0 (0%) 1 (7.7%)
no errors/warnings with standard use for tbl_svysummary
Code
tbl %>% add_stat_label() %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age, Median (IQR) 46 (37, 59) 48 (39, 56)
2 Unknown 7 4
3 Marker Level (ng/mL), Median (IQR) 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
4 Unknown 6 4
5 T Stage, n (%) <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, n (%) <NA> <NA>
11 I 35 (36%) 33 (32%)
12 II 32 (33%) 36 (35%)
13 III 31 (32%) 33 (32%)
14 Tumor Response, n (%) 28 (29%) 33 (34%)
15 Unknown 3 4
16 Patient Died, n (%) 52 (53%) 60 (59%)
17 Months to Death/Censor, Median (IQR) 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
Code
tbl %>% add_stat_label(location = "column", label = all_categorical() ~
"no. (%)") %>% as.data.frame()
Output
**Characteristic** **Statistic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age Median (IQR) 46 (37, 59) 48 (39, 56)
2 Unknown n 7 4
3 Marker Level (ng/mL) Median (IQR) 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
4 Unknown n 6 4
5 T Stage <NA> <NA> <NA>
6 T1 no. (%) 28 (29%) 25 (25%)
7 T2 no. (%) 25 (26%) 29 (28%)
8 T3 no. (%) 22 (22%) 21 (21%)
9 T4 no. (%) 23 (23%) 27 (26%)
10 Grade <NA> <NA> <NA>
11 I no. (%) 35 (36%) 33 (32%)
12 II no. (%) 32 (33%) 36 (35%)
13 III no. (%) 31 (32%) 33 (32%)
14 Tumor Response no. (%) 28 (29%) 33 (34%)
15 Unknown n 3 4
16 Patient Died no. (%) 52 (53%) 60 (59%)
17 Months to Death/Censor Median (IQR) 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
no errors/warnings with standard use for tbl_svysummary with continuous2
Code
tbl %>% add_stat_label() %>% 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, n (%) <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, n (%) <NA> <NA>
13 I 35 (36%) 33 (32%)
14 II 32 (33%) 36 (35%)
15 III 31 (32%) 33 (32%)
16 Tumor Response, n (%) 28 (29%) 33 (34%)
17 Unknown 3 4
18 Patient Died, n (%) 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)
Code
tbl %>% add_stat_label(location = "column", label = all_categorical() ~
"no. (%)") %>% as.data.frame()
Output
**Characteristic** **Statistic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age <NA> <NA> <NA>
2 Median (IQR) <NA> 46 (37, 59) 48 (39, 56)
3 Unknown n 7 4
4 Marker Level (ng/mL) <NA> <NA> <NA>
5 Median (IQR) <NA> 0.82 (0.23, 1.55) 0.51 (0.18, 1.20)
6 Unknown n 6 4
7 T Stage <NA> <NA> <NA>
8 T1 no. (%) 28 (29%) 25 (25%)
9 T2 no. (%) 25 (26%) 29 (28%)
10 T3 no. (%) 22 (22%) 21 (21%)
11 T4 no. (%) 23 (23%) 27 (26%)
12 Grade <NA> <NA> <NA>
13 I no. (%) 35 (36%) 33 (32%)
14 II no. (%) 32 (33%) 36 (35%)
15 III no. (%) 31 (32%) 33 (32%)
16 Tumor Response no. (%) 28 (29%) 33 (34%)
17 Unknown n 3 4
18 Patient Died no. (%) 52 (53%) 60 (59%)
19 Months to Death/Censor <NA> <NA> <NA>
20 Median (IQR) <NA> 23.4 (17.2, 24.0) 20.9 (14.5, 24.0)
add_stat_label() with tbl_merge()
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age, Median (IQR) 46 (37, 59) 48 (39, 56)
2 Tumor Response, n (%) 28 (29%) 33 (34%)
**Drug A**, N = 98 **Drug B**, N = 102
1 46 (37, 59) 48 (39, 56)
2 28 (29%) 33 (34%)
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