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tests/testthat/_snaps/tbl_svysummary.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables
tbl_svysummary creates output without error/warning (no by var)
Code
purrr::map(list(d, dc_light), ~ tbl_svysummary(.x, sort = list(all_categorical() ~
"frequency")) %>% as_tibble())
Output
[[1]]
# A tibble: 13 x 2
`**Characteristic**` `**N = 2,201**`
<chr> <chr>
1 Class <NA>
2 1st 325 (15%)
3 2nd 285 (13%)
4 3rd 706 (32%)
5 Crew 885 (40%)
6 Sex <NA>
7 Male 1,731 (79%)
8 Female 470 (21%)
9 Age <NA>
10 Child 109 (5.0%)
11 Adult 2,092 (95%)
12 Survived 711 (32%)
13 Freq 183 (92, 438)
[[2]]
# A tibble: 6 x 2
`**Characteristic**` `**N = 6,194**`
<chr> <chr>
1 stype <NA>
2 E 4,874 (79%)
3 M 846 (14%)
4 H 474 (7.7%)
5 growth 33 (17, 53)
6 both 4,502 (73%)
tbl_svysummary creates output without error/warning (with by var)
Code
tbl_svysummary(dc_light, by = both, statistic = statistics) %>% as_tibble()
Output
# A tibble: 5 x 3
`**Characteristic**` `**No**, N = 1,692` `**Yes**, N = 4,502`
<chr> <chr> <chr>
1 stype <NA> <NA>
2 E 1,083 1,692 64 | 32 50 64 0.09 1.9 3,791 4,502 84 | 11~
3 H 237 1,692 14 | 7 50 14 0.05 0.91 237 4,502 5.3 | 7 1~
4 M 372 1,692 22 | 11 50 22 0.09 2.2 474 4,502 11 | 14 1~
5 growth 7 9 17 302 -34 47 14,927 -2 6 19 30~ 44 48 26 689 8 131 ~
tbl_svysummary allows for named list input
Code
tbl_svysummary(d, by = Survived, label = list(Class = "New Class", Sex = "New Sex")) %>%
as.data.frame()
Output
**Characteristic** **No**, N = 1,490 **Yes**, N = 711
1 New Class <NA> <NA>
2 1st 122 (8.2%) 203 (29%)
3 2nd 167 (11%) 118 (17%)
4 3rd 528 (35%) 178 (25%)
5 Crew 673 (45%) 212 (30%)
6 New Sex <NA> <NA>
7 Male 1,364 (92%) 367 (52%)
8 Female 126 (8.5%) 344 (48%)
9 Age <NA> <NA>
10 Child 52 (3.5%) 57 (8.0%)
11 Adult 1,438 (97%) 654 (92%)
12 Freq 342 (140, 513) 79 (64, 144)
tbl_svysummary value argument works properly
Code
tbl_svysummary(d, value = "Class" ~ "1st") %>% as.data.frame()
Output
**Characteristic** **N = 2,201**
1 Class 325 (15%)
2 Sex <NA>
3 Male 1,731 (79%)
4 Female 470 (21%)
5 Age <NA>
6 Child 109 (5.0%)
7 Adult 2,092 (95%)
8 Survived 711 (32%)
9 Freq 183 (92, 438)
tbl_svysummary works in character inputs for by=
Code
tbl_svysummary(d, by = all_of(my_by_variable)) %>% as.data.frame()
Output
**Characteristic** **No**, N = 1,490 **Yes**, N = 711
1 Class <NA> <NA>
2 1st 122 (8.2%) 203 (29%)
3 2nd 167 (11%) 118 (17%)
4 3rd 528 (35%) 178 (25%)
5 Crew 673 (45%) 212 (30%)
6 Sex <NA> <NA>
7 Male 1,364 (92%) 367 (52%)
8 Female 126 (8.5%) 344 (48%)
9 Age <NA> <NA>
10 Child 52 (3.5%) 57 (8.0%)
11 Adult 1,438 (97%) 654 (92%)
12 Freq 342 (140, 513) 79 (64, 144)
Code
tbl_svysummary(d, by = "Survived") %>% as.data.frame()
Output
**Characteristic** **No**, N = 1,490 **Yes**, N = 711
1 Class <NA> <NA>
2 1st 122 (8.2%) 203 (29%)
3 2nd 167 (11%) 118 (17%)
4 3rd 528 (35%) 178 (25%)
5 Crew 673 (45%) 212 (30%)
6 Sex <NA> <NA>
7 Male 1,364 (92%) 367 (52%)
8 Female 126 (8.5%) 344 (48%)
9 Age <NA> <NA>
10 Child 52 (3.5%) 57 (8.0%)
11 Adult 1,438 (97%) 654 (92%)
12 Freq 342 (140, 513) 79 (64, 144)
Code
purrr::map(c("Survived", "Class", "Sex", "Age"), ~ tbl_svysummary(d, by = all_of(
.x)) %>% as_tibble())
Output
[[1]]
# A tibble: 12 x 3
`**Characteristic**` `**No**, N = 1,490` `**Yes**, N = 711`
<chr> <chr> <chr>
1 Class <NA> <NA>
2 1st 122 (8.2%) 203 (29%)
3 2nd 167 (11%) 118 (17%)
4 3rd 528 (35%) 178 (25%)
5 Crew 673 (45%) 212 (30%)
6 Sex <NA> <NA>
7 Male 1,364 (92%) 367 (52%)
8 Female 126 (8.5%) 344 (48%)
9 Age <NA> <NA>
10 Child 52 (3.5%) 57 (8.0%)
11 Adult 1,438 (97%) 654 (92%)
12 Freq 342 (140, 513) 79 (64, 144)
[[2]]
# A tibble: 8 x 5
`**Characteristic**` `**1st**, N = 325` `**2nd**, N = 285` `**3rd**, N = 706`
<chr> <chr> <chr> <chr>
1 Sex <NA> <NA> <NA>
2 Male 180 (55%) 179 (63%) 510 (72%)
3 Female 145 (45%) 106 (37%) 196 (28%)
4 Age <NA> <NA> <NA>
5 Child 6 (1.8%) 24 (8.4%) 79 (11%)
6 Adult 319 (98%) 261 (92%) 627 (89%)
7 Survived 203 (62%) 118 (41%) 178 (25%)
8 Freq 106 (64, 127) 86 (31, 120) 115 (75, 251)
# i 1 more variable: `**Crew**, N = 885` <chr>
[[3]]
# A tibble: 10 x 3
`**Characteristic**` `**Male**, N = 1,731` `**Female**, N = 470`
<chr> <chr> <chr>
1 Class <NA> <NA>
2 1st 180 (10%) 145 (31%)
3 2nd 179 (10%) 106 (23%)
4 3rd 510 (29%) 196 (42%)
5 Crew 862 (50%) 23 (4.9%)
6 Age <NA> <NA>
7 Child 64 (3.7%) 45 (9.6%)
8 Adult 1,667 (96%) 425 (90%)
9 Survived 367 (21%) 344 (73%)
10 Freq 288 (142, 487) 80 (44, 97)
[[4]]
# A tibble: 10 x 3
`**Characteristic**` `**Child**, N = 109` `**Adult**, N = 2,092`
<chr> <chr> <chr>
1 Class <NA> <NA>
2 1st 6 (5.5%) 319 (15%)
3 2nd 24 (22%) 261 (12%)
4 3rd 79 (72%) 627 (30%)
5 Crew 0 (0%) 885 (42%)
6 Sex <NA> <NA>
7 Male 64 (59%) 1,667 (80%)
8 Female 45 (41%) 425 (20%)
9 Survived 57 (52%) 654 (31%)
10 Freq 14 (13, 21) 198 (112, 449)
tbl_svysummary-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_svysummary-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")) %>% survey::svydesign(data = .,
ids = ~1, weights = ~1) %>% tbl_svysummary() %>% as_tibble()
Output
# A tibble: 69 x 2
`**Characteristic**` `**N = 10**`
<chr> <chr>
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%)
# i 59 more rows
tbl_svysummary-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_svysummary(design_missing, by = my_by_var, type = c(int, dbl) ~
"categorical") %>% as.data.frame()
Message
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
tbl_svysummary-no error when data with single column passed
Code
trial["trt"] %>% as.data.frame() %>% survey::svydesign(data = ., ids = ~1,
weights = ~1) %>% tbl_svysummary(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_svysummary-no error when by variable is ordered factor
Code
trial %>% dplyr::mutate(grade = as.ordered(grade)) %>% survey::svydesign(data = .,
ids = ~1, weights = ~1) %>% tbl_svysummary(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) 48 (36, 57) 47 (38, 58)
5 Unknown 2 6 3
6 Marker Level (ng/mL) 0.98 (0.24, 1.58) 0.37 (0.14, 1.09) 0.61 (0.26, 1.67)
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 (17.8, 24.0) 21.6 (13.0, 24.0) 19.5 (15.8, 24.0)
tbl_summary(digits=) tests with fn inputs
Code
tbl_digits %>% as.data.frame()
Output
**Characteristic** **N = 6,194**
1 emer 0.00 49
tbl_svysummary() 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
tbl_svysummary() works with 0/1 variables
Code
survey::svydesign(data = trial, ids = ~1, weights = ~1) %>% tbl_svysummary(
include = response) %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 Tumor Response 61 (32%)
2 Unknown 7
tbl_svysummary() works with a factor having only one levem
Code
res %>% as.data.frame()
Output
**Characteristic** **N = 5**
1 fct <NA>
2 a 5 (100%)
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gtsummary documentation built on July 26, 2023, 5:27 p.m.
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tbl_svysummary creates output without error/warning (no by var)
Code
purrr::map(list(d, dc_light), ~ tbl_svysummary(.x, sort = list(all_categorical() ~
"frequency")) %>% as_tibble())
Output
[[1]]
# A tibble: 13 x 2
`**Characteristic**` `**N = 2,201**`
<chr> <chr>
1 Class <NA>
2 1st 325 (15%)
3 2nd 285 (13%)
4 3rd 706 (32%)
5 Crew 885 (40%)
6 Sex <NA>
7 Male 1,731 (79%)
8 Female 470 (21%)
9 Age <NA>
10 Child 109 (5.0%)
11 Adult 2,092 (95%)
12 Survived 711 (32%)
13 Freq 183 (92, 438)
[[2]]
# A tibble: 6 x 2
`**Characteristic**` `**N = 6,194**`
<chr> <chr>
1 stype <NA>
2 E 4,874 (79%)
3 M 846 (14%)
4 H 474 (7.7%)
5 growth 33 (17, 53)
6 both 4,502 (73%)
tbl_svysummary creates output without error/warning (with by var)
Code
tbl_svysummary(dc_light, by = both, statistic = statistics) %>% as_tibble()
Output
# A tibble: 5 x 3
`**Characteristic**` `**No**, N = 1,692` `**Yes**, N = 4,502`
<chr> <chr> <chr>
1 stype <NA> <NA>
2 E 1,083 1,692 64 | 32 50 64 0.09 1.9 3,791 4,502 84 | 11~
3 H 237 1,692 14 | 7 50 14 0.05 0.91 237 4,502 5.3 | 7 1~
4 M 372 1,692 22 | 11 50 22 0.09 2.2 474 4,502 11 | 14 1~
5 growth 7 9 17 302 -34 47 14,927 -2 6 19 30~ 44 48 26 689 8 131 ~
tbl_svysummary allows for named list input
Code
tbl_svysummary(d, by = Survived, label = list(Class = "New Class", Sex = "New Sex")) %>%
as.data.frame()
Output
**Characteristic** **No**, N = 1,490 **Yes**, N = 711
1 New Class <NA> <NA>
2 1st 122 (8.2%) 203 (29%)
3 2nd 167 (11%) 118 (17%)
4 3rd 528 (35%) 178 (25%)
5 Crew 673 (45%) 212 (30%)
6 New Sex <NA> <NA>
7 Male 1,364 (92%) 367 (52%)
8 Female 126 (8.5%) 344 (48%)
9 Age <NA> <NA>
10 Child 52 (3.5%) 57 (8.0%)
11 Adult 1,438 (97%) 654 (92%)
12 Freq 342 (140, 513) 79 (64, 144)
tbl_svysummary value argument works properly
Code
tbl_svysummary(d, value = "Class" ~ "1st") %>% as.data.frame()
Output
**Characteristic** **N = 2,201**
1 Class 325 (15%)
2 Sex <NA>
3 Male 1,731 (79%)
4 Female 470 (21%)
5 Age <NA>
6 Child 109 (5.0%)
7 Adult 2,092 (95%)
8 Survived 711 (32%)
9 Freq 183 (92, 438)
tbl_svysummary works in character inputs for by=
Code
tbl_svysummary(d, by = all_of(my_by_variable)) %>% as.data.frame()
Output
**Characteristic** **No**, N = 1,490 **Yes**, N = 711
1 Class <NA> <NA>
2 1st 122 (8.2%) 203 (29%)
3 2nd 167 (11%) 118 (17%)
4 3rd 528 (35%) 178 (25%)
5 Crew 673 (45%) 212 (30%)
6 Sex <NA> <NA>
7 Male 1,364 (92%) 367 (52%)
8 Female 126 (8.5%) 344 (48%)
9 Age <NA> <NA>
10 Child 52 (3.5%) 57 (8.0%)
11 Adult 1,438 (97%) 654 (92%)
12 Freq 342 (140, 513) 79 (64, 144)
Code
tbl_svysummary(d, by = "Survived") %>% as.data.frame()
Output
**Characteristic** **No**, N = 1,490 **Yes**, N = 711
1 Class <NA> <NA>
2 1st 122 (8.2%) 203 (29%)
3 2nd 167 (11%) 118 (17%)
4 3rd 528 (35%) 178 (25%)
5 Crew 673 (45%) 212 (30%)
6 Sex <NA> <NA>
7 Male 1,364 (92%) 367 (52%)
8 Female 126 (8.5%) 344 (48%)
9 Age <NA> <NA>
10 Child 52 (3.5%) 57 (8.0%)
11 Adult 1,438 (97%) 654 (92%)
12 Freq 342 (140, 513) 79 (64, 144)
Code
purrr::map(c("Survived", "Class", "Sex", "Age"), ~ tbl_svysummary(d, by = all_of(
.x)) %>% as_tibble())
Output
[[1]]
# A tibble: 12 x 3
`**Characteristic**` `**No**, N = 1,490` `**Yes**, N = 711`
<chr> <chr> <chr>
1 Class <NA> <NA>
2 1st 122 (8.2%) 203 (29%)
3 2nd 167 (11%) 118 (17%)
4 3rd 528 (35%) 178 (25%)
5 Crew 673 (45%) 212 (30%)
6 Sex <NA> <NA>
7 Male 1,364 (92%) 367 (52%)
8 Female 126 (8.5%) 344 (48%)
9 Age <NA> <NA>
10 Child 52 (3.5%) 57 (8.0%)
11 Adult 1,438 (97%) 654 (92%)
12 Freq 342 (140, 513) 79 (64, 144)
[[2]]
# A tibble: 8 x 5
`**Characteristic**` `**1st**, N = 325` `**2nd**, N = 285` `**3rd**, N = 706`
<chr> <chr> <chr> <chr>
1 Sex <NA> <NA> <NA>
2 Male 180 (55%) 179 (63%) 510 (72%)
3 Female 145 (45%) 106 (37%) 196 (28%)
4 Age <NA> <NA> <NA>
5 Child 6 (1.8%) 24 (8.4%) 79 (11%)
6 Adult 319 (98%) 261 (92%) 627 (89%)
7 Survived 203 (62%) 118 (41%) 178 (25%)
8 Freq 106 (64, 127) 86 (31, 120) 115 (75, 251)
# i 1 more variable: `**Crew**, N = 885` <chr>
[[3]]
# A tibble: 10 x 3
`**Characteristic**` `**Male**, N = 1,731` `**Female**, N = 470`
<chr> <chr> <chr>
1 Class <NA> <NA>
2 1st 180 (10%) 145 (31%)
3 2nd 179 (10%) 106 (23%)
4 3rd 510 (29%) 196 (42%)
5 Crew 862 (50%) 23 (4.9%)
6 Age <NA> <NA>
7 Child 64 (3.7%) 45 (9.6%)
8 Adult 1,667 (96%) 425 (90%)
9 Survived 367 (21%) 344 (73%)
10 Freq 288 (142, 487) 80 (44, 97)
[[4]]
# A tibble: 10 x 3
`**Characteristic**` `**Child**, N = 109` `**Adult**, N = 2,092`
<chr> <chr> <chr>
1 Class <NA> <NA>
2 1st 6 (5.5%) 319 (15%)
3 2nd 24 (22%) 261 (12%)
4 3rd 79 (72%) 627 (30%)
5 Crew 0 (0%) 885 (42%)
6 Sex <NA> <NA>
7 Male 64 (59%) 1,667 (80%)
8 Female 45 (41%) 425 (20%)
9 Survived 57 (52%) 654 (31%)
10 Freq 14 (13, 21) 198 (112, 449)
tbl_svysummary-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_svysummary-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")) %>% survey::svydesign(data = .,
ids = ~1, weights = ~1) %>% tbl_svysummary() %>% as_tibble()
Output
# A tibble: 69 x 2
`**Characteristic**` `**N = 10**`
<chr> <chr>
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%)
# i 59 more rows
tbl_svysummary-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_svysummary(design_missing, by = my_by_var, type = c(int, dbl) ~
"categorical") %>% as.data.frame()
Message
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
tbl_svysummary-no error when data with single column passed
Code
trial["trt"] %>% as.data.frame() %>% survey::svydesign(data = ., ids = ~1,
weights = ~1) %>% tbl_svysummary(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_svysummary-no error when by variable is ordered factor
Code
trial %>% dplyr::mutate(grade = as.ordered(grade)) %>% survey::svydesign(data = .,
ids = ~1, weights = ~1) %>% tbl_svysummary(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) 48 (36, 57) 47 (38, 58)
5 Unknown 2 6 3
6 Marker Level (ng/mL) 0.98 (0.24, 1.58) 0.37 (0.14, 1.09) 0.61 (0.26, 1.67)
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 (17.8, 24.0) 21.6 (13.0, 24.0) 19.5 (15.8, 24.0)
tbl_summary(digits=) tests with fn inputs
Code
tbl_digits %>% as.data.frame()
Output
**Characteristic** **N = 6,194**
1 emer 0.00 49
tbl_svysummary() 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
tbl_svysummary() works with 0/1 variables
Code
survey::svydesign(data = trial, ids = ~1, weights = ~1) %>% tbl_svysummary(
include = response) %>% as.data.frame()
Output
**Characteristic** **N = 200**
1 Tumor Response 61 (32%)
2 Unknown 7
tbl_svysummary() works with a factor having only one levem
Code
res %>% as.data.frame()
Output
**Characteristic** **N = 5**
1 fct <NA>
2 a 5 (100%)
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