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tests/testthat/_snaps/tbl_summary.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables
tbl_summary creates output without error/warning (no by var)
Code
purrr::map(lst_tbl, as.data.frame)
Output
[[1]]
**Characteristic** **N = 32**
1 mpg 19.2 (15.4, 22.8)
2 cyl <NA>
3 8 14 (44%)
4 4 11 (34%)
5 6 7 (22%)
6 disp 196 (121, 326)
7 hp 123 (97, 180)
8 drat 3.70 (3.08, 3.92)
9 wt 3.33 (2.58, 3.61)
10 qsec 17.71 (16.89, 18.90)
11 vs 14 (44%)
12 am 13 (41%)
13 gear <NA>
14 3 15 (47%)
15 4 12 (38%)
16 5 5 (16%)
17 carb <NA>
18 2 10 (31%)
19 4 10 (31%)
20 1 7 (22%)
21 3 3 (9.4%)
22 6 1 (3.1%)
23 8 1 (3.1%)
[[2]]
**Characteristic** **N = 150**
1 Sepal.Length 5.80 (5.10, 6.40)
2 Sepal.Width 3.00 (2.80, 3.30)
3 Petal.Length 4.35 (1.60, 5.10)
4 Petal.Width 1.30 (0.30, 1.80)
5 Species <NA>
6 setosa 50 (33%)
7 versicolor 50 (33%)
8 virginica 50 (33%)
tbl_summary creates output without error/warning (with by var)
Code
tbl_summary(mtcars, by = am) %>% 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%)
tbl_summary allows for named list input
Code
tbl_summary(mtcars, by = am, label = list(mpg = "New mpg", cyl = "New cyl")) %>%
as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13
1 New mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
2 New 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%)
tbl_summary value argument works properly
Code
tbl_summary(trial, value = "grade" ~ "III") %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 Chemotherapy Treatment <NA>
2 Drug A 98 (49%)
3 Drug B 102 (51%)
4 Age 47 (38, 57)
5 Unknown 11
6 Marker Level (ng/mL) 0.64 (0.22, 1.39)
7 Unknown 10
8 T Stage <NA>
9 T1 53 (27%)
10 T2 54 (27%)
11 T3 43 (22%)
12 T4 50 (25%)
13 Grade 64 (32%)
14 Tumor Response 61 (32%)
15 Unknown 7
16 Patient Died 112 (56%)
17 Months to Death/Censor 22.4 (16.0, 24.0)
tbl_summary works in character inputs for by=
Code
tbl_summary(trial, by = all_of(my_by_variable)) %>% 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) 0.84 (0.24, 1.57) 0.52 (0.19, 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 23.5 (17.4, 24.0) 21.2 (14.6, 24.0)
Code
tbl_summary(trial, by = "trt") %>% 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) 0.84 (0.24, 1.57) 0.52 (0.19, 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 23.5 (17.4, 24.0) 21.2 (14.6, 24.0)
Code
purrr::map(c("trt", "grade", "stage"), ~ tbl_summary(trial, by = all_of(.x)) %>%
as_tibble())
Output
[[1]]
# A tibble: 17 x 3
`**Characteristic**` `**Drug A**, N = 98` `**Drug B**, N = 102`
<chr> <chr> <chr>
1 Age 46 (37, 59) 48 (39, 56)
2 Unknown 7 4
3 Marker Level (ng/mL) 0.84 (0.24, 1.57) 0.52 (0.19, 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 23.5 (17.4, 24.0) 21.2 (14.6, 24.0)
[[2]]
# A tibble: 16 x 4
`**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 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)
[[3]]
# A tibble: 15 x 5
`**Characteristic**` `**T1**, N = 53` `**T2**, N = 54` `**T3**, N = 43`
<chr> <chr> <chr> <chr>
1 Chemotherapy Treatment <NA> <NA> <NA>
2 Drug A 28 (53%) 25 (46%) 22 (51%)
3 Drug B 25 (47%) 29 (54%) 21 (49%)
4 Age 45 (37, 57) 48 (42, 55) 50 (39, 60)
5 Unknown 2 1 2
6 Marker Level (ng/mL) 0.40 (0.22, 0.98) 0.72 (0.16, 1.87) 1.06 (0.28, 1.56)
7 Unknown 4 1 4
8 Grade <NA> <NA> <NA>
9 I 17 (32%) 18 (33%) 18 (42%)
10 II 23 (43%) 17 (31%) 11 (26%)
11 III 13 (25%) 19 (35%) 14 (33%)
12 Tumor Response 18 (35%) 13 (25%) 15 (38%)
13 Unknown 1 2 3
14 Patient Died 24 (45%) 27 (50%) 22 (51%)
15 Months to Death/Censor 24.0 (18.2, 24.0) 23.9 (16.5, 24.0) 22.9 (17.0, 24.0)
# i 1 more variable: `**T4**, N = 50` <chr>
tbl_summary-testing tidyselect parsing
Code
big_test %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Chemotherapy Treatment <NA> <NA>
2 Drug A 98 (100%) 0 (0%)
3 Drug B 0 (0%) 102 (100%)
4 Patient Age 6.00 78.000 9.00 83.000
5 Marker Level (ng/mL) 0.00 3.874 0.01 3.642
6 Patient Stage 28 25
7 Grade <NA> <NA>
8 I 35 33
9 II 32 36
10 III 31 33
11 Tumor Response <NA> <NA>
12 0 67 (71%) 65 (66%)
13 1 28 (29%) 33 (34%)
14 Patient Died <NA> <NA>
15 0 46 (47%) 42 (41%)
16 1 52 (53%) 60 (59%)
17 Months to Death/Censor 3.5 24.0 5.3 24.0
18 Crazy Grade 31 (32%) 33 (32%)
tbl_summary-difftime does not cause error
Code
df_dplyr_storms %>% dplyr::mutate(date = ISOdate(year, month, day), date_diff = difftime(
dplyr::lag(date, 5), date, units = "days")) %>% tbl_summary() %>%
as.data.frame()
Output
**Characteristic** **N = 10**
1 name <NA>
2 Amy 10 (100%)
3 year <NA>
4 1975 10 (100%)
5 month <NA>
6 6 10 (100%)
7 day <NA>
8 27 4 (40%)
9 28 4 (40%)
10 29 2 (20%)
11 hour <NA>
12 0 3 (30%)
13 6 3 (30%)
14 12 2 (20%)
15 18 2 (20%)
16 lat <NA>
17 27.5 1 (10%)
18 28.5 1 (10%)
19 29.5 1 (10%)
20 30.5 1 (10%)
21 31.5 1 (10%)
22 32.4 1 (10%)
23 33.3 1 (10%)
24 34 2 (20%)
25 34.4 1 (10%)
26 long <NA>
27 -79 4 (40%)
28 -78.8 1 (10%)
29 -78.7 1 (10%)
30 -78 1 (10%)
31 -77 1 (10%)
32 -75.8 1 (10%)
33 -74.8 1 (10%)
34 status <NA>
35 disturbance 0 (0%)
36 extratropical 0 (0%)
37 hurricane 0 (0%)
38 other low 0 (0%)
39 subtropical depression 0 (0%)
40 subtropical storm 0 (0%)
41 tropical depression 8 (80%)
42 tropical storm 2 (20%)
43 tropical wave 0 (0%)
44 category NA (NA, NA)
45 Unknown 10
46 wind <NA>
47 25 7 (70%)
48 30 1 (10%)
49 35 1 (10%)
50 40 1 (10%)
51 pressure <NA>
52 1002 1 (10%)
53 1004 1 (10%)
54 1006 1 (10%)
55 1011 1 (10%)
56 1012 2 (20%)
57 1013 4 (40%)
58 tropicalstorm_force_diameter NA (NA, NA)
59 Unknown 10
60 hurricane_force_diameter NA (NA, NA)
61 Unknown 10
62 date <NA>
63 1975-06-27 12:00:00 4 (40%)
64 1975-06-28 12:00:00 4 (40%)
65 1975-06-29 12:00:00 2 (20%)
66 date_diff <NA>
67 -2 1 (20%)
68 -1 4 (80%)
69 Unknown 5
tbl_summary-all missing data does not cause error
Code
all_missing_no_by %>% as.data.frame()
Output
**Characteristic** **N = 4**
1 fct <NA>
2 lion 0 (NA%)
3 tiger 0 (NA%)
4 bear 0 (NA%)
5 Unknown 4
6 lgl 0 (NA%)
7 Unknown 4
8 chr 0 (NA%)
9 Unknown 4
10 int NA (NA, NA)
11 Unknown 4
12 dbl NA (NA, NA)
13 Unknown 4
Code
all_missing_by %>% as.data.frame()
Output
**Characteristic** **1**, N = 2 **2**, N = 2
1 fct <NA> <NA>
2 lion 0 (NA%) 0 (NA%)
3 tiger 0 (NA%) 0 (NA%)
4 bear 0 (NA%) 0 (NA%)
5 Unknown 2 2
6 lgl 0 (NA%) 0 (NA%)
7 Unknown 2 2
8 chr 0 (NA%) 0 (NA%)
9 Unknown 2 2
10 int NA (NA, NA) NA (NA, NA)
11 Unknown 2 2
12 dbl NA (NA, NA) NA (NA, NA)
13 Unknown 2 2
Code
tbl_summary(df_missing, by = my_by_var, type = vars(int, dbl) ~ "categorical") %>%
as.data.frame()
Message
! Use of `vars()` is now deprecated and support will soon be removed. Please replace calls to `vars()` with `c()`.
Variable 'int' is `NA` for all observations and cannot be summarized as
'categorical'. Using `int ~ "dichotomous"` instead.
Variable 'dbl' is `NA` for all observations and cannot be summarized as
'categorical'. Using `dbl ~ "dichotomous"` instead.
Output
**Characteristic** **1**, N = 2 **2**, N = 2
1 fct <NA> <NA>
2 lion 0 (NA%) 0 (NA%)
3 tiger 0 (NA%) 0 (NA%)
4 bear 0 (NA%) 0 (NA%)
5 Unknown 2 2
6 lgl 0 (NA%) 0 (NA%)
7 Unknown 2 2
8 chr 0 (NA%) 0 (NA%)
9 Unknown 2 2
10 int 0 (NA%) 0 (NA%)
11 Unknown 2 2
12 dbl 0 (NA%) 0 (NA%)
13 Unknown 2 2
Code
missing_fct_by %>% as.data.frame()
Output
**Characteristic** **0**, N = 132 **1**, N = 61
1 Chemotherapy Treatment <NA> <NA>
2 Drug A 67 (51%) 28 (46%)
3 Drug B 65 (49%) 33 (54%)
4 Age 46 (36, 55) 49 (43, 59)
5 Unknown 7 3
6 Marker Level (ng/mL) 0.59 (0.21, 1.24) 0.98 (0.31, 1.53)
7 Unknown 6 4
8 T Stage <NA> <NA>
9 T1 34 (26%) 18 (30%)
10 T2 39 (30%) 13 (21%)
11 T3 25 (19%) 15 (25%)
12 T4 34 (26%) 15 (25%)
13 Grade <NA> <NA>
14 I 46 (35%) 21 (34%)
15 II 44 (33%) 19 (31%)
16 III 42 (32%) 21 (34%)
17 Tumor Response 0 (0%) 61 (100%)
18 Patient Died 83 (63%) 24 (39%)
19 Months to Death/Censor 20.6 (15.0, 24.0) 24.0 (18.4, 24.0)
**(Missing)**, N = 0
1 <NA>
2 0 (NA%)
3 0 (NA%)
4 NA (NA, NA)
5 0
6 NA (NA, NA)
7 0
8 <NA>
9 0 (NA%)
10 0 (NA%)
11 0 (NA%)
12 0 (NA%)
13 <NA>
14 0 (NA%)
15 0 (NA%)
16 0 (NA%)
17 0 (NA%)
18 0 (NA%)
19 NA (NA, NA)
tbl_summary-no error when data frame with single column passed
Code
trial["trt"] %>% as.data.frame() %>% tbl_summary(label = trt ~
"TREATMENT GROUP") %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 TREATMENT GROUP <NA>
2 Drug A 98 (49%)
3 Drug B 102 (51%)
tbl_summary-no error when by variable is ordered factor
Code
trial %>% dplyr::mutate(grade = as.ordered(grade)) %>% tbl_summary(by = grade) %>%
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)
tbl_summary- works with grouped data (it ungroups it first)
Code
trial %>% dplyr::group_by(response) %>% dplyr::select(response, death, trt) %>%
tbl_summary(by = trt) %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Tumor Response 28 (29%) 33 (34%)
2 Unknown 3 4
3 Patient Died 52 (53%) 60 (59%)
tbl_summary-works with ordered factors
Code
trial %>% select(response, trt) %>% dplyr::mutate_at(vars(response, trt),
~ factor(., ordered = TRUE)) %>% tbl_summary(by = trt) %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 response <NA> <NA>
2 0 67 (71%) 65 (66%)
3 1 28 (29%) 33 (34%)
4 Unknown 3 4
tbl_summary creates output without error/warning for continuous2 (no by var)
Code
purrr::map(list(mtcars, iris), ~ tbl_summary(.x, type = all_continuous() ~
"continuous2", sort = list(all_categorical() ~ "frequency")) %>% as_tibble())
Output
[[1]]
# A tibble: 29 x 2
`**Characteristic**` `**N = 32**`
<chr> <chr>
1 mpg <NA>
2 Median (IQR) 19.2 (15.4, 22.8)
3 cyl <NA>
4 8 14 (44%)
5 4 11 (34%)
6 6 7 (22%)
7 disp <NA>
8 Median (IQR) 196 (121, 326)
9 hp <NA>
10 Median (IQR) 123 (97, 180)
# i 19 more rows
[[2]]
# A tibble: 12 x 2
`**Characteristic**` `**N = 150**`
<chr> <chr>
1 Sepal.Length <NA>
2 Median (IQR) 5.80 (5.10, 6.40)
3 Sepal.Width <NA>
4 Median (IQR) 3.00 (2.80, 3.30)
5 Petal.Length <NA>
6 Median (IQR) 4.35 (1.60, 5.10)
7 Petal.Width <NA>
8 Median (IQR) 1.30 (0.30, 1.80)
9 Species <NA>
10 setosa 50 (33%)
11 versicolor 50 (33%)
12 virginica 50 (33%)
tbl_summary creates output without error/warning for continuous2 (with by var)
Code
tbl_summary(mtcars, by = am, type = all_continuous() ~ "continuous2") %>%
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%)
tbl_summary(digits=) tests with fn inputs
Code
tbl_digits %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 Age 4.7e+01
2 Marker Level (ng/mL) 1 0.86 200.0 95.00%
3 Grade <NA>
4 I 68.0 (34.0%)
5 II 68.0 (34.0%)
6 III 64.0 (32.0%)
7 Tumor Response 61 193.0 31.61% 200.0 96.5000%
tbl_summary() continuous vars with cat summary vars only
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 Age 200
2 Unknown 11
Code
tbl2 %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age 98 102
2 Unknown 7 4
tbl_summary() works with date and date/time
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **N = 10**
1 dates 2021-02-21 to 2021-03-02
2 times 2021-02-20 20:31:34 to 2021-02-20 20:31:43
3 group 5 (50%)
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **N = 10**
1 dates February 2021 to March 2021
2 times February 2021 to February 2021
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **0**, N = 5
1 dates February 2021 to March 2021
2 times February 2021 to February 2021
**1**, N = 5
1 February 2021 to March 2021
2 February 2021 to February 2021
Code
tbl2 %>% as.data.frame()
Output
**Characteristic** **0**, N = 5
1 dates 2021-02-22 to 2021-03-02
2 times 2021-02-20 20:31:35 to 2021-02-20 20:31:43
**1**, N = 5
1 2021-02-21 to 2021-03-01
2 2021-02-20 20:31:34 to 2021-02-20 20:31:42
no error when by variable omitted from include
Code
trial %>% tbl_summary(by = trt, include = age) %>% 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
all column names are accepted
Code
tbl_summary(df, by = "variable") %>% as.data.frame()
Output
**Characteristic** **A**, N = 5 **B**, N = 5
1 value 3 (2, 4) 8 (7, 9)
Code
tbl_summary(df) %>% as.data.frame()
Output
**Characteristic** **N = 10**
1 variable <NA>
2 A 5 (50%)
3 B 5 (50%)
4 value 6 (3, 8)
Code
tbl_summary(df %>% dplyr::rename(by = variable)) %>% as.data.frame()
Output
**Characteristic** **N = 10**
1 by <NA>
2 A 5 (50%)
3 B 5 (50%)
4 value 6 (3, 8)
Code
tbl_summary(df %>% dplyr::rename(by = variable), by = "by") %>% as.data.frame()
Output
**Characteristic** **A**, N = 5 **B**, N = 5
1 value 3 (2, 4) 8 (7, 9)
no error with factor variable with all NA and no specifed levels
Code
tbl
Output
# A tibble: 2 x 3
label stat_1 stat_2
<chr> <chr> <chr>
1 has_banana 0 (NA%) 0 (NA%)
2 Unknown 98 102
no error when data frame contains named vector
Code
tbl_summary(df, type = list(everything() ~ "continuous")) %>% as.data.frame()
Output
**Characteristic** **N = 5**
1 swallowing 77 (40, 100)
2 Unknown 1
3 salivation 81 (58, 100)
4 Unknown 1
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tbl_summary creates output without error/warning (no by var)
Code
purrr::map(lst_tbl, as.data.frame)
Output
[[1]]
**Characteristic** **N = 32**
1 mpg 19.2 (15.4, 22.8)
2 cyl <NA>
3 8 14 (44%)
4 4 11 (34%)
5 6 7 (22%)
6 disp 196 (121, 326)
7 hp 123 (97, 180)
8 drat 3.70 (3.08, 3.92)
9 wt 3.33 (2.58, 3.61)
10 qsec 17.71 (16.89, 18.90)
11 vs 14 (44%)
12 am 13 (41%)
13 gear <NA>
14 3 15 (47%)
15 4 12 (38%)
16 5 5 (16%)
17 carb <NA>
18 2 10 (31%)
19 4 10 (31%)
20 1 7 (22%)
21 3 3 (9.4%)
22 6 1 (3.1%)
23 8 1 (3.1%)
[[2]]
**Characteristic** **N = 150**
1 Sepal.Length 5.80 (5.10, 6.40)
2 Sepal.Width 3.00 (2.80, 3.30)
3 Petal.Length 4.35 (1.60, 5.10)
4 Petal.Width 1.30 (0.30, 1.80)
5 Species <NA>
6 setosa 50 (33%)
7 versicolor 50 (33%)
8 virginica 50 (33%)
tbl_summary creates output without error/warning (with by var)
Code
tbl_summary(mtcars, by = am) %>% 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%)
tbl_summary allows for named list input
Code
tbl_summary(mtcars, by = am, label = list(mpg = "New mpg", cyl = "New cyl")) %>%
as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13
1 New mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4)
2 New 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%)
tbl_summary value argument works properly
Code
tbl_summary(trial, value = "grade" ~ "III") %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 Chemotherapy Treatment <NA>
2 Drug A 98 (49%)
3 Drug B 102 (51%)
4 Age 47 (38, 57)
5 Unknown 11
6 Marker Level (ng/mL) 0.64 (0.22, 1.39)
7 Unknown 10
8 T Stage <NA>
9 T1 53 (27%)
10 T2 54 (27%)
11 T3 43 (22%)
12 T4 50 (25%)
13 Grade 64 (32%)
14 Tumor Response 61 (32%)
15 Unknown 7
16 Patient Died 112 (56%)
17 Months to Death/Censor 22.4 (16.0, 24.0)
tbl_summary works in character inputs for by=
Code
tbl_summary(trial, by = all_of(my_by_variable)) %>% 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) 0.84 (0.24, 1.57) 0.52 (0.19, 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 23.5 (17.4, 24.0) 21.2 (14.6, 24.0)
Code
tbl_summary(trial, by = "trt") %>% 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) 0.84 (0.24, 1.57) 0.52 (0.19, 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 23.5 (17.4, 24.0) 21.2 (14.6, 24.0)
Code
purrr::map(c("trt", "grade", "stage"), ~ tbl_summary(trial, by = all_of(.x)) %>%
as_tibble())
Output
[[1]]
# A tibble: 17 x 3
`**Characteristic**` `**Drug A**, N = 98` `**Drug B**, N = 102`
<chr> <chr> <chr>
1 Age 46 (37, 59) 48 (39, 56)
2 Unknown 7 4
3 Marker Level (ng/mL) 0.84 (0.24, 1.57) 0.52 (0.19, 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 23.5 (17.4, 24.0) 21.2 (14.6, 24.0)
[[2]]
# A tibble: 16 x 4
`**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 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)
[[3]]
# A tibble: 15 x 5
`**Characteristic**` `**T1**, N = 53` `**T2**, N = 54` `**T3**, N = 43`
<chr> <chr> <chr> <chr>
1 Chemotherapy Treatment <NA> <NA> <NA>
2 Drug A 28 (53%) 25 (46%) 22 (51%)
3 Drug B 25 (47%) 29 (54%) 21 (49%)
4 Age 45 (37, 57) 48 (42, 55) 50 (39, 60)
5 Unknown 2 1 2
6 Marker Level (ng/mL) 0.40 (0.22, 0.98) 0.72 (0.16, 1.87) 1.06 (0.28, 1.56)
7 Unknown 4 1 4
8 Grade <NA> <NA> <NA>
9 I 17 (32%) 18 (33%) 18 (42%)
10 II 23 (43%) 17 (31%) 11 (26%)
11 III 13 (25%) 19 (35%) 14 (33%)
12 Tumor Response 18 (35%) 13 (25%) 15 (38%)
13 Unknown 1 2 3
14 Patient Died 24 (45%) 27 (50%) 22 (51%)
15 Months to Death/Censor 24.0 (18.2, 24.0) 23.9 (16.5, 24.0) 22.9 (17.0, 24.0)
# i 1 more variable: `**T4**, N = 50` <chr>
tbl_summary-testing tidyselect parsing
Code
big_test %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Chemotherapy Treatment <NA> <NA>
2 Drug A 98 (100%) 0 (0%)
3 Drug B 0 (0%) 102 (100%)
4 Patient Age 6.00 78.000 9.00 83.000
5 Marker Level (ng/mL) 0.00 3.874 0.01 3.642
6 Patient Stage 28 25
7 Grade <NA> <NA>
8 I 35 33
9 II 32 36
10 III 31 33
11 Tumor Response <NA> <NA>
12 0 67 (71%) 65 (66%)
13 1 28 (29%) 33 (34%)
14 Patient Died <NA> <NA>
15 0 46 (47%) 42 (41%)
16 1 52 (53%) 60 (59%)
17 Months to Death/Censor 3.5 24.0 5.3 24.0
18 Crazy Grade 31 (32%) 33 (32%)
tbl_summary-difftime does not cause error
Code
df_dplyr_storms %>% dplyr::mutate(date = ISOdate(year, month, day), date_diff = difftime(
dplyr::lag(date, 5), date, units = "days")) %>% tbl_summary() %>%
as.data.frame()
Output
**Characteristic** **N = 10**
1 name <NA>
2 Amy 10 (100%)
3 year <NA>
4 1975 10 (100%)
5 month <NA>
6 6 10 (100%)
7 day <NA>
8 27 4 (40%)
9 28 4 (40%)
10 29 2 (20%)
11 hour <NA>
12 0 3 (30%)
13 6 3 (30%)
14 12 2 (20%)
15 18 2 (20%)
16 lat <NA>
17 27.5 1 (10%)
18 28.5 1 (10%)
19 29.5 1 (10%)
20 30.5 1 (10%)
21 31.5 1 (10%)
22 32.4 1 (10%)
23 33.3 1 (10%)
24 34 2 (20%)
25 34.4 1 (10%)
26 long <NA>
27 -79 4 (40%)
28 -78.8 1 (10%)
29 -78.7 1 (10%)
30 -78 1 (10%)
31 -77 1 (10%)
32 -75.8 1 (10%)
33 -74.8 1 (10%)
34 status <NA>
35 disturbance 0 (0%)
36 extratropical 0 (0%)
37 hurricane 0 (0%)
38 other low 0 (0%)
39 subtropical depression 0 (0%)
40 subtropical storm 0 (0%)
41 tropical depression 8 (80%)
42 tropical storm 2 (20%)
43 tropical wave 0 (0%)
44 category NA (NA, NA)
45 Unknown 10
46 wind <NA>
47 25 7 (70%)
48 30 1 (10%)
49 35 1 (10%)
50 40 1 (10%)
51 pressure <NA>
52 1002 1 (10%)
53 1004 1 (10%)
54 1006 1 (10%)
55 1011 1 (10%)
56 1012 2 (20%)
57 1013 4 (40%)
58 tropicalstorm_force_diameter NA (NA, NA)
59 Unknown 10
60 hurricane_force_diameter NA (NA, NA)
61 Unknown 10
62 date <NA>
63 1975-06-27 12:00:00 4 (40%)
64 1975-06-28 12:00:00 4 (40%)
65 1975-06-29 12:00:00 2 (20%)
66 date_diff <NA>
67 -2 1 (20%)
68 -1 4 (80%)
69 Unknown 5
tbl_summary-all missing data does not cause error
Code
all_missing_no_by %>% as.data.frame()
Output
**Characteristic** **N = 4**
1 fct <NA>
2 lion 0 (NA%)
3 tiger 0 (NA%)
4 bear 0 (NA%)
5 Unknown 4
6 lgl 0 (NA%)
7 Unknown 4
8 chr 0 (NA%)
9 Unknown 4
10 int NA (NA, NA)
11 Unknown 4
12 dbl NA (NA, NA)
13 Unknown 4
Code
all_missing_by %>% as.data.frame()
Output
**Characteristic** **1**, N = 2 **2**, N = 2
1 fct <NA> <NA>
2 lion 0 (NA%) 0 (NA%)
3 tiger 0 (NA%) 0 (NA%)
4 bear 0 (NA%) 0 (NA%)
5 Unknown 2 2
6 lgl 0 (NA%) 0 (NA%)
7 Unknown 2 2
8 chr 0 (NA%) 0 (NA%)
9 Unknown 2 2
10 int NA (NA, NA) NA (NA, NA)
11 Unknown 2 2
12 dbl NA (NA, NA) NA (NA, NA)
13 Unknown 2 2
Code
tbl_summary(df_missing, by = my_by_var, type = vars(int, dbl) ~ "categorical") %>%
as.data.frame()
Message
! Use of `vars()` is now deprecated and support will soon be removed. Please replace calls to `vars()` with `c()`.
Variable 'int' is `NA` for all observations and cannot be summarized as
'categorical'. Using `int ~ "dichotomous"` instead.
Variable 'dbl' is `NA` for all observations and cannot be summarized as
'categorical'. Using `dbl ~ "dichotomous"` instead.
Output
**Characteristic** **1**, N = 2 **2**, N = 2
1 fct <NA> <NA>
2 lion 0 (NA%) 0 (NA%)
3 tiger 0 (NA%) 0 (NA%)
4 bear 0 (NA%) 0 (NA%)
5 Unknown 2 2
6 lgl 0 (NA%) 0 (NA%)
7 Unknown 2 2
8 chr 0 (NA%) 0 (NA%)
9 Unknown 2 2
10 int 0 (NA%) 0 (NA%)
11 Unknown 2 2
12 dbl 0 (NA%) 0 (NA%)
13 Unknown 2 2
Code
missing_fct_by %>% as.data.frame()
Output
**Characteristic** **0**, N = 132 **1**, N = 61
1 Chemotherapy Treatment <NA> <NA>
2 Drug A 67 (51%) 28 (46%)
3 Drug B 65 (49%) 33 (54%)
4 Age 46 (36, 55) 49 (43, 59)
5 Unknown 7 3
6 Marker Level (ng/mL) 0.59 (0.21, 1.24) 0.98 (0.31, 1.53)
7 Unknown 6 4
8 T Stage <NA> <NA>
9 T1 34 (26%) 18 (30%)
10 T2 39 (30%) 13 (21%)
11 T3 25 (19%) 15 (25%)
12 T4 34 (26%) 15 (25%)
13 Grade <NA> <NA>
14 I 46 (35%) 21 (34%)
15 II 44 (33%) 19 (31%)
16 III 42 (32%) 21 (34%)
17 Tumor Response 0 (0%) 61 (100%)
18 Patient Died 83 (63%) 24 (39%)
19 Months to Death/Censor 20.6 (15.0, 24.0) 24.0 (18.4, 24.0)
**(Missing)**, N = 0
1 <NA>
2 0 (NA%)
3 0 (NA%)
4 NA (NA, NA)
5 0
6 NA (NA, NA)
7 0
8 <NA>
9 0 (NA%)
10 0 (NA%)
11 0 (NA%)
12 0 (NA%)
13 <NA>
14 0 (NA%)
15 0 (NA%)
16 0 (NA%)
17 0 (NA%)
18 0 (NA%)
19 NA (NA, NA)
tbl_summary-no error when data frame with single column passed
Code
trial["trt"] %>% as.data.frame() %>% tbl_summary(label = trt ~
"TREATMENT GROUP") %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 TREATMENT GROUP <NA>
2 Drug A 98 (49%)
3 Drug B 102 (51%)
tbl_summary-no error when by variable is ordered factor
Code
trial %>% dplyr::mutate(grade = as.ordered(grade)) %>% tbl_summary(by = grade) %>%
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)
tbl_summary- works with grouped data (it ungroups it first)
Code
trial %>% dplyr::group_by(response) %>% dplyr::select(response, death, trt) %>%
tbl_summary(by = trt) %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Tumor Response 28 (29%) 33 (34%)
2 Unknown 3 4
3 Patient Died 52 (53%) 60 (59%)
tbl_summary-works with ordered factors
Code
trial %>% select(response, trt) %>% dplyr::mutate_at(vars(response, trt),
~ factor(., ordered = TRUE)) %>% tbl_summary(by = trt) %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 response <NA> <NA>
2 0 67 (71%) 65 (66%)
3 1 28 (29%) 33 (34%)
4 Unknown 3 4
tbl_summary creates output without error/warning for continuous2 (no by var)
Code
purrr::map(list(mtcars, iris), ~ tbl_summary(.x, type = all_continuous() ~
"continuous2", sort = list(all_categorical() ~ "frequency")) %>% as_tibble())
Output
[[1]]
# A tibble: 29 x 2
`**Characteristic**` `**N = 32**`
<chr> <chr>
1 mpg <NA>
2 Median (IQR) 19.2 (15.4, 22.8)
3 cyl <NA>
4 8 14 (44%)
5 4 11 (34%)
6 6 7 (22%)
7 disp <NA>
8 Median (IQR) 196 (121, 326)
9 hp <NA>
10 Median (IQR) 123 (97, 180)
# i 19 more rows
[[2]]
# A tibble: 12 x 2
`**Characteristic**` `**N = 150**`
<chr> <chr>
1 Sepal.Length <NA>
2 Median (IQR) 5.80 (5.10, 6.40)
3 Sepal.Width <NA>
4 Median (IQR) 3.00 (2.80, 3.30)
5 Petal.Length <NA>
6 Median (IQR) 4.35 (1.60, 5.10)
7 Petal.Width <NA>
8 Median (IQR) 1.30 (0.30, 1.80)
9 Species <NA>
10 setosa 50 (33%)
11 versicolor 50 (33%)
12 virginica 50 (33%)
tbl_summary creates output without error/warning for continuous2 (with by var)
Code
tbl_summary(mtcars, by = am, type = all_continuous() ~ "continuous2") %>%
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%)
tbl_summary(digits=) tests with fn inputs
Code
tbl_digits %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 Age 4.7e+01
2 Marker Level (ng/mL) 1 0.86 200.0 95.00%
3 Grade <NA>
4 I 68.0 (34.0%)
5 II 68.0 (34.0%)
6 III 64.0 (32.0%)
7 Tumor Response 61 193.0 31.61% 200.0 96.5000%
tbl_summary() continuous vars with cat summary vars only
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 Age 200
2 Unknown 11
Code
tbl2 %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102
1 Age 98 102
2 Unknown 7 4
tbl_summary() works with date and date/time
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **N = 10**
1 dates 2021-02-21 to 2021-03-02
2 times 2021-02-20 20:31:34 to 2021-02-20 20:31:43
3 group 5 (50%)
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **N = 10**
1 dates February 2021 to March 2021
2 times February 2021 to February 2021
Code
tbl1 %>% as.data.frame()
Output
**Characteristic** **0**, N = 5
1 dates February 2021 to March 2021
2 times February 2021 to February 2021
**1**, N = 5
1 February 2021 to March 2021
2 February 2021 to February 2021
Code
tbl2 %>% as.data.frame()
Output
**Characteristic** **0**, N = 5
1 dates 2021-02-22 to 2021-03-02
2 times 2021-02-20 20:31:35 to 2021-02-20 20:31:43
**1**, N = 5
1 2021-02-21 to 2021-03-01
2 2021-02-20 20:31:34 to 2021-02-20 20:31:42
no error when by variable omitted from include
Code
trial %>% tbl_summary(by = trt, include = age) %>% 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
all column names are accepted
Code
tbl_summary(df, by = "variable") %>% as.data.frame()
Output
**Characteristic** **A**, N = 5 **B**, N = 5
1 value 3 (2, 4) 8 (7, 9)
Code
tbl_summary(df) %>% as.data.frame()
Output
**Characteristic** **N = 10**
1 variable <NA>
2 A 5 (50%)
3 B 5 (50%)
4 value 6 (3, 8)
Code
tbl_summary(df %>% dplyr::rename(by = variable)) %>% as.data.frame()
Output
**Characteristic** **N = 10**
1 by <NA>
2 A 5 (50%)
3 B 5 (50%)
4 value 6 (3, 8)
Code
tbl_summary(df %>% dplyr::rename(by = variable), by = "by") %>% as.data.frame()
Output
**Characteristic** **A**, N = 5 **B**, N = 5
1 value 3 (2, 4) 8 (7, 9)
no error with factor variable with all NA and no specifed levels
Code
tbl
Output
# A tibble: 2 x 3
label stat_1 stat_2
<chr> <chr> <chr>
1 has_banana 0 (NA%) 0 (NA%)
2 Unknown 98 102
no error when data frame contains named vector
Code
tbl_summary(df, type = list(everything() ~ "continuous")) %>% as.data.frame()
Output
**Characteristic** **N = 5**
1 swallowing 77 (40, 100)
2 Unknown 1
3 salivation 81 (58, 100)
4 Unknown 1
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