Nothing
tests/testthat/_snaps/add_p.tbl_summary.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables
add_p creates output without error/warning
Code
tbl_summary(trial, by = grade) %>% add_p() %>% as.data.frame()
Output
**Characteristic** **I**, N = 68 **II**, N = 68 **III**, N = 64
1 Chemotherapy Treatment <NA> <NA> <NA>
2 Drug A 35 (51%) 32 (47%) 31 (48%)
3 Drug B 33 (49%) 36 (53%) 33 (52%)
4 Age 47 (37, 56) 49 (37, 57) 47 (38, 58)
5 Unknown 2 6 3
6 Marker Level (ng/mL) 1.01 (0.26, 1.61) 0.37 (0.14, 1.11) 0.62 (0.29, 1.68)
7 Unknown 2 5 3
8 T Stage <NA> <NA> <NA>
9 T1 17 (25%) 23 (34%) 13 (20%)
10 T2 18 (26%) 17 (25%) 19 (30%)
11 T3 18 (26%) 11 (16%) 14 (22%)
12 T4 15 (22%) 17 (25%) 18 (28%)
13 Tumor Response 21 (31%) 19 (30%) 21 (33%)
14 Unknown 1 5 1
15 Patient Died 33 (49%) 36 (53%) 43 (67%)
16 Months to Death/Censor 24.0 (18.2, 24.0) 22.2 (13.1, 24.0) 19.7 (16.1, 24.0)
**p-value**
1 0.9
2 <NA>
3 <NA>
4 0.8
5 <NA>
6 0.019
7 <NA>
8 0.6
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 >0.9
14 <NA>
15 0.080
16 0.060
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.002
2 cyl <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 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.046
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
Code
trial %>% tbl_summary(by = trt) %>% add_p() %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.7
2 Unknown 7 4 <NA>
3 Marker Level (ng/mL) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) 0.085
4 Unknown 6 4 <NA>
5 T Stage <NA> <NA> 0.9
6 T1 28 (29%) 25 (25%) <NA>
7 T2 25 (26%) 29 (28%) <NA>
8 T3 22 (22%) 21 (21%) <NA>
9 T4 23 (23%) 27 (26%) <NA>
10 Grade <NA> <NA> 0.9
11 I 35 (36%) 33 (32%) <NA>
12 II 32 (33%) 36 (35%) <NA>
13 III 31 (32%) 33 (32%) <NA>
14 Tumor Response 28 (29%) 33 (34%) 0.5
15 Unknown 3 4 <NA>
16 Patient Died 52 (53%) 60 (59%) 0.4
17 Months to Death/Censor 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) 0.14
Code
tbl_summary(trial, by = trt, include = -response) %>% add_p(group = response) %>%
as.data.frame()
Message
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) >0.9
2 Unknown 7 4 <NA>
3 Marker Level (ng/mL) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) 0.2
4 Unknown 6 4 <NA>
5 T Stage <NA> <NA> 0.9
6 T1 28 (29%) 25 (25%) <NA>
7 T2 25 (26%) 29 (28%) <NA>
8 T3 22 (22%) 21 (21%) <NA>
9 T4 23 (23%) 27 (26%) <NA>
10 Grade <NA> <NA> 0.8
11 I 35 (36%) 33 (32%) <NA>
12 II 32 (33%) 36 (35%) <NA>
13 III 31 (32%) 33 (32%) <NA>
14 Patient Died 52 (53%) 60 (59%) 0.3
15 Months to Death/Censor 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) 0.042
add_p creates output without error/warning for continuous2
Code
tbl_summary(trial, by = grade, type = all_continuous() ~ "continuous2") %>%
add_p() %>% as_tibble()
Output
# A tibble: 19 x 5
`**Characteristic**` `**I**, N = 68` `**II**, N = 68` `**III**, N = 64`
<chr> <chr> <chr> <chr>
1 Chemotherapy Treatment <NA> <NA> <NA>
2 Drug A 35 (51%) 32 (47%) 31 (48%)
3 Drug B 33 (49%) 36 (53%) 33 (52%)
4 Age <NA> <NA> <NA>
5 Median (IQR) 47 (37, 56) 49 (37, 57) 47 (38, 58)
6 Unknown 2 6 3
7 Marker Level (ng/mL) <NA> <NA> <NA>
8 Median (IQR) 1.01 (0.26, 1.61) 0.37 (0.14, 1.11) 0.62 (0.29, 1.68)
9 Unknown 2 5 3
10 T Stage <NA> <NA> <NA>
11 T1 17 (25%) 23 (34%) 13 (20%)
12 T2 18 (26%) 17 (25%) 19 (30%)
13 T3 18 (26%) 11 (16%) 14 (22%)
14 T4 15 (22%) 17 (25%) 18 (28%)
15 Tumor Response 21 (31%) 19 (30%) 21 (33%)
16 Unknown 1 5 1
17 Patient Died 33 (49%) 36 (53%) 43 (67%)
18 Months to Death/Censor <NA> <NA> <NA>
19 Median (IQR) 24.0 (18.2, 24.0) 22.2 (13.1, 24.0) 19.7 (16.1, 24.0)
# i 1 more variable: `**p-value**` <chr>
Code
tbl
Output
# A tibble: 28 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg <NA> <NA> 0.002
2 Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) <NA>
3 cyl <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>
# i 18 more rows
Code
trial %>% tbl_summary(by = trt, type = all_continuous() ~ "continuous2") %>%
add_p() %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age <NA> <NA> 0.7
2 Median (IQR) 46 (37, 59) 48 (39, 56) <NA>
3 Unknown 7 4 <NA>
4 Marker Level (ng/mL) <NA> <NA> 0.085
5 Median (IQR) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) <NA>
6 Unknown 6 4 <NA>
7 T Stage <NA> <NA> 0.9
8 T1 28 (29%) 25 (25%) <NA>
9 T2 25 (26%) 29 (28%) <NA>
10 T3 22 (22%) 21 (21%) <NA>
11 T4 23 (23%) 27 (26%) <NA>
12 Grade <NA> <NA> 0.9
13 I 35 (36%) 33 (32%) <NA>
14 II 32 (33%) 36 (35%) <NA>
15 III 31 (32%) 33 (32%) <NA>
16 Tumor Response 28 (29%) 33 (34%) 0.5
17 Unknown 3 4 <NA>
18 Patient Died 52 (53%) 60 (59%) 0.4
19 Months to Death/Censor <NA> <NA> 0.14
20 Median (IQR) 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) <NA>
Code
tbl_summary(trial, by = trt, include = -response, type = all_continuous() ~
"continuous2") %>% add_p(group = response) %>% as.data.frame()
Message
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age <NA> <NA> >0.9
2 Median (IQR) 46 (37, 59) 48 (39, 56) <NA>
3 Unknown 7 4 <NA>
4 Marker Level (ng/mL) <NA> <NA> 0.2
5 Median (IQR) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) <NA>
6 Unknown 6 4 <NA>
7 T Stage <NA> <NA> 0.9
8 T1 28 (29%) 25 (25%) <NA>
9 T2 25 (26%) 29 (28%) <NA>
10 T3 22 (22%) 21 (21%) <NA>
11 T4 23 (23%) 27 (26%) <NA>
12 Grade <NA> <NA> 0.8
13 I 35 (36%) 33 (32%) <NA>
14 II 32 (33%) 36 (35%) <NA>
15 III 31 (32%) 33 (32%) <NA>
16 Patient Died 52 (53%) 60 (59%) 0.3
17 Months to Death/Censor <NA> <NA> 0.042
18 Median (IQR) 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) <NA>
add_p works well
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.001
2 cyl <NA> <NA> 0.013
3 4 3 (16%) 8 (62%) <NA>
4 6 4 (21%) 3 (23%) <NA>
5 8 12 (63%) 2 (15%) <NA>
6 disp 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.2
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.001
2 cyl <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 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.046
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
add_p with custom p-value function
Code
trial[c("response", "trt")] %>% tbl_summary(by = trt) %>% add_p(test = response ~
"my_mcnemar") %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
Code
trial[c("response", "trt")] %>% tbl_summary(by = trt) %>% add_p(test = response ~
"my_mcnemar2") %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
Code
tbl_mcnemar %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
p-values are replicated within tbl_summary()
Code
tbl_test.args %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.8
2 Age 46 (37, 59) 48 (39, 56) 0.8
3 Age 46 (37, 59) 48 (39, 56) 0.7
4 Age 46 (37, 59) 48 (39, 56) 0.7
5 Age 46 (37, 59) 48 (39, 56) 0.7
6 Age 46 (37, 59) 48 (39, 56) 0.8
7 Tumor Response 28 (29%) 33 (34%) 0.6
8 Tumor Response 28 (29%) 33 (34%) 0.5
9 Tumor Response 28 (29%) 33 (34%) 0.5
10 Tumor Response 28 (29%) 33 (34%) 0.5
11 Tumor Response 28 (29%) 33 (34%) 0.3
12 Tumor Response 28 (29%) 33 (34%) <0.001
13 Tumor Response 28 (29%) 33 (34%) <0.001
Code
tbl_groups %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Grade <NA> <NA> 0.9
2 I 35 (36%) 33 (32%) <NA>
3 II 32 (33%) 36 (35%) <NA>
4 III 31 (32%) 33 (32%) <NA>
5 Grade <NA> <NA> >0.9
6 I 35 (36%) 33 (32%) <NA>
7 II 32 (33%) 36 (35%) <NA>
8 III 31 (32%) 33 (32%) <NA>
9 Tumor Response 28 (29%) 33 (34%) 0.5
10 Tumor Response 28 (29%) 33 (34%) 0.4
11 Age 46 (37, 59) 48 (39, 56) 0.6
12 Age 46 (37, 59) 48 (39, 56) 0.3
13 Age 46 (37, 59) 48 (39, 56) 0.6
14 Age 46 (37, 59) 48 (39, 56) 0.3
Groups arg and lme4
Code
tbl_groups %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.8
no error with missing data
Code
t1 %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13 **p-value**
1 has_banana <NA> <NA> <NA>
2 Yes 0 (NA%) 0 (NA%) <NA>
3 No 0 (NA%) 0 (NA%) <NA>
4 Unknown 19 13 <NA>
5 mpg 0 (NA%) 0 (NA%) <NA>
6 Unknown 19 13 <NA>
7 hp NA (NA, NA) NA (NA, NA) <NA>
8 Unknown 19 13 <NA>
add_p can be run after add_difference()
Code
tbl
Output
# A tibble: 1 x 6
label stat_1 stat_2 estimate ci p.value
<chr> <chr> <chr> <chr> <chr> <chr>
1 Age 47.011 47.449 -0.03 -0.32, 0.25 0.8
Try the gtsummary package in your browser
Any scripts or data that you put into this service are public.
gtsummary documentation built on July 26, 2023, 5:27 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
add_p creates output without error/warning
Code
tbl_summary(trial, by = grade) %>% add_p() %>% as.data.frame()
Output
**Characteristic** **I**, N = 68 **II**, N = 68 **III**, N = 64
1 Chemotherapy Treatment <NA> <NA> <NA>
2 Drug A 35 (51%) 32 (47%) 31 (48%)
3 Drug B 33 (49%) 36 (53%) 33 (52%)
4 Age 47 (37, 56) 49 (37, 57) 47 (38, 58)
5 Unknown 2 6 3
6 Marker Level (ng/mL) 1.01 (0.26, 1.61) 0.37 (0.14, 1.11) 0.62 (0.29, 1.68)
7 Unknown 2 5 3
8 T Stage <NA> <NA> <NA>
9 T1 17 (25%) 23 (34%) 13 (20%)
10 T2 18 (26%) 17 (25%) 19 (30%)
11 T3 18 (26%) 11 (16%) 14 (22%)
12 T4 15 (22%) 17 (25%) 18 (28%)
13 Tumor Response 21 (31%) 19 (30%) 21 (33%)
14 Unknown 1 5 1
15 Patient Died 33 (49%) 36 (53%) 43 (67%)
16 Months to Death/Censor 24.0 (18.2, 24.0) 22.2 (13.1, 24.0) 19.7 (16.1, 24.0)
**p-value**
1 0.9
2 <NA>
3 <NA>
4 0.8
5 <NA>
6 0.019
7 <NA>
8 0.6
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 >0.9
14 <NA>
15 0.080
16 0.060
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.002
2 cyl <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 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.046
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
Code
trial %>% tbl_summary(by = trt) %>% add_p() %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.7
2 Unknown 7 4 <NA>
3 Marker Level (ng/mL) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) 0.085
4 Unknown 6 4 <NA>
5 T Stage <NA> <NA> 0.9
6 T1 28 (29%) 25 (25%) <NA>
7 T2 25 (26%) 29 (28%) <NA>
8 T3 22 (22%) 21 (21%) <NA>
9 T4 23 (23%) 27 (26%) <NA>
10 Grade <NA> <NA> 0.9
11 I 35 (36%) 33 (32%) <NA>
12 II 32 (33%) 36 (35%) <NA>
13 III 31 (32%) 33 (32%) <NA>
14 Tumor Response 28 (29%) 33 (34%) 0.5
15 Unknown 3 4 <NA>
16 Patient Died 52 (53%) 60 (59%) 0.4
17 Months to Death/Censor 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) 0.14
Code
tbl_summary(trial, by = trt, include = -response) %>% add_p(group = response) %>%
as.data.frame()
Message
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) >0.9
2 Unknown 7 4 <NA>
3 Marker Level (ng/mL) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) 0.2
4 Unknown 6 4 <NA>
5 T Stage <NA> <NA> 0.9
6 T1 28 (29%) 25 (25%) <NA>
7 T2 25 (26%) 29 (28%) <NA>
8 T3 22 (22%) 21 (21%) <NA>
9 T4 23 (23%) 27 (26%) <NA>
10 Grade <NA> <NA> 0.8
11 I 35 (36%) 33 (32%) <NA>
12 II 32 (33%) 36 (35%) <NA>
13 III 31 (32%) 33 (32%) <NA>
14 Patient Died 52 (53%) 60 (59%) 0.3
15 Months to Death/Censor 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) 0.042
add_p creates output without error/warning for continuous2
Code
tbl_summary(trial, by = grade, type = all_continuous() ~ "continuous2") %>%
add_p() %>% as_tibble()
Output
# A tibble: 19 x 5
`**Characteristic**` `**I**, N = 68` `**II**, N = 68` `**III**, N = 64`
<chr> <chr> <chr> <chr>
1 Chemotherapy Treatment <NA> <NA> <NA>
2 Drug A 35 (51%) 32 (47%) 31 (48%)
3 Drug B 33 (49%) 36 (53%) 33 (52%)
4 Age <NA> <NA> <NA>
5 Median (IQR) 47 (37, 56) 49 (37, 57) 47 (38, 58)
6 Unknown 2 6 3
7 Marker Level (ng/mL) <NA> <NA> <NA>
8 Median (IQR) 1.01 (0.26, 1.61) 0.37 (0.14, 1.11) 0.62 (0.29, 1.68)
9 Unknown 2 5 3
10 T Stage <NA> <NA> <NA>
11 T1 17 (25%) 23 (34%) 13 (20%)
12 T2 18 (26%) 17 (25%) 19 (30%)
13 T3 18 (26%) 11 (16%) 14 (22%)
14 T4 15 (22%) 17 (25%) 18 (28%)
15 Tumor Response 21 (31%) 19 (30%) 21 (33%)
16 Unknown 1 5 1
17 Patient Died 33 (49%) 36 (53%) 43 (67%)
18 Months to Death/Censor <NA> <NA> <NA>
19 Median (IQR) 24.0 (18.2, 24.0) 22.2 (13.1, 24.0) 19.7 (16.1, 24.0)
# i 1 more variable: `**p-value**` <chr>
Code
tbl
Output
# A tibble: 28 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg <NA> <NA> 0.002
2 Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) <NA>
3 cyl <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>
# i 18 more rows
Code
trial %>% tbl_summary(by = trt, type = all_continuous() ~ "continuous2") %>%
add_p() %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age <NA> <NA> 0.7
2 Median (IQR) 46 (37, 59) 48 (39, 56) <NA>
3 Unknown 7 4 <NA>
4 Marker Level (ng/mL) <NA> <NA> 0.085
5 Median (IQR) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) <NA>
6 Unknown 6 4 <NA>
7 T Stage <NA> <NA> 0.9
8 T1 28 (29%) 25 (25%) <NA>
9 T2 25 (26%) 29 (28%) <NA>
10 T3 22 (22%) 21 (21%) <NA>
11 T4 23 (23%) 27 (26%) <NA>
12 Grade <NA> <NA> 0.9
13 I 35 (36%) 33 (32%) <NA>
14 II 32 (33%) 36 (35%) <NA>
15 III 31 (32%) 33 (32%) <NA>
16 Tumor Response 28 (29%) 33 (34%) 0.5
17 Unknown 3 4 <NA>
18 Patient Died 52 (53%) 60 (59%) 0.4
19 Months to Death/Censor <NA> <NA> 0.14
20 Median (IQR) 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) <NA>
Code
tbl_summary(trial, by = trt, include = -response, type = all_continuous() ~
"continuous2") %>% add_p(group = response) %>% as.data.frame()
Message
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age <NA> <NA> >0.9
2 Median (IQR) 46 (37, 59) 48 (39, 56) <NA>
3 Unknown 7 4 <NA>
4 Marker Level (ng/mL) <NA> <NA> 0.2
5 Median (IQR) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) <NA>
6 Unknown 6 4 <NA>
7 T Stage <NA> <NA> 0.9
8 T1 28 (29%) 25 (25%) <NA>
9 T2 25 (26%) 29 (28%) <NA>
10 T3 22 (22%) 21 (21%) <NA>
11 T4 23 (23%) 27 (26%) <NA>
12 Grade <NA> <NA> 0.8
13 I 35 (36%) 33 (32%) <NA>
14 II 32 (33%) 36 (35%) <NA>
15 III 31 (32%) 33 (32%) <NA>
16 Patient Died 52 (53%) 60 (59%) 0.3
17 Months to Death/Censor <NA> <NA> 0.042
18 Median (IQR) 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) <NA>
add_p works well
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.001
2 cyl <NA> <NA> 0.013
3 4 3 (16%) 8 (62%) <NA>
4 6 4 (21%) 3 (23%) <NA>
5 8 12 (63%) 2 (15%) <NA>
6 disp 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.2
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.001
2 cyl <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 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.046
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
add_p with custom p-value function
Code
trial[c("response", "trt")] %>% tbl_summary(by = trt) %>% add_p(test = response ~
"my_mcnemar") %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
Code
trial[c("response", "trt")] %>% tbl_summary(by = trt) %>% add_p(test = response ~
"my_mcnemar2") %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
Code
tbl_mcnemar %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
p-values are replicated within tbl_summary()
Code
tbl_test.args %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.8
2 Age 46 (37, 59) 48 (39, 56) 0.8
3 Age 46 (37, 59) 48 (39, 56) 0.7
4 Age 46 (37, 59) 48 (39, 56) 0.7
5 Age 46 (37, 59) 48 (39, 56) 0.7
6 Age 46 (37, 59) 48 (39, 56) 0.8
7 Tumor Response 28 (29%) 33 (34%) 0.6
8 Tumor Response 28 (29%) 33 (34%) 0.5
9 Tumor Response 28 (29%) 33 (34%) 0.5
10 Tumor Response 28 (29%) 33 (34%) 0.5
11 Tumor Response 28 (29%) 33 (34%) 0.3
12 Tumor Response 28 (29%) 33 (34%) <0.001
13 Tumor Response 28 (29%) 33 (34%) <0.001
Code
tbl_groups %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Grade <NA> <NA> 0.9
2 I 35 (36%) 33 (32%) <NA>
3 II 32 (33%) 36 (35%) <NA>
4 III 31 (32%) 33 (32%) <NA>
5 Grade <NA> <NA> >0.9
6 I 35 (36%) 33 (32%) <NA>
7 II 32 (33%) 36 (35%) <NA>
8 III 31 (32%) 33 (32%) <NA>
9 Tumor Response 28 (29%) 33 (34%) 0.5
10 Tumor Response 28 (29%) 33 (34%) 0.4
11 Age 46 (37, 59) 48 (39, 56) 0.6
12 Age 46 (37, 59) 48 (39, 56) 0.3
13 Age 46 (37, 59) 48 (39, 56) 0.6
14 Age 46 (37, 59) 48 (39, 56) 0.3
Groups arg and lme4
Code
tbl_groups %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.8
no error with missing data
Code
t1 %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13 **p-value**
1 has_banana <NA> <NA> <NA>
2 Yes 0 (NA%) 0 (NA%) <NA>
3 No 0 (NA%) 0 (NA%) <NA>
4 Unknown 19 13 <NA>
5 mpg 0 (NA%) 0 (NA%) <NA>
6 Unknown 19 13 <NA>
7 hp NA (NA, NA) NA (NA, NA) <NA>
8 Unknown 19 13 <NA>
add_p can be run after add_difference()
Code
tbl
Output
# A tibble: 1 x 6
label stat_1 stat_2 estimate ci p.value
<chr> <chr> <chr> <chr> <chr> <chr>
1 Age 47.011 47.449 -0.03 -0.32, 0.25 0.8
Try the gtsummary package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.