tests/testthat/_snaps/count.md
In atorus-research/Tplyr: A Traceability Focused Grammar of Clinical Data Summary
Count layer clauses with invalid syntax give informative error
i In index: 1.
Caused by error in `value[[3L]]()`:
! group_count `where` condition `bad == code` is invalid. Filter error:
Error in `filter()`:
i In argument: `bad == code`.
Caused by error:
! object 'bad' not found
Total rows and missing counts are displayed correctly(0.1.5 Updates)
structure(list(row_label1 = c("6", "8", "Missing", "Total"),
var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L), ord_layer_1 = c(1,
2, 3, 4)), row.names = c(NA, -4L), class = c("tbl_df", "tbl",
"data.frame"))
structure(list(row_label1 = c("6", "8", "Missing", "Not Found",
"Total"), var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 0", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 0", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 0", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L, 1L), ord_layer_1 = c(1,
2, 3, 4, 5)), row.names = c(NA, -5L), class = c("tbl_df", "tbl",
"data.frame"))
structure(list(row_label1 = c("0", "Missing", "Not Found", "Total"
), var1_3 = c("15 (100.0)", " 0", " 0", " 15 [100.0]"), var1_4 = c(" 4 (33.3)",
" 8", " 0", " 12 [100.0]"), var1_5 = c(" 0 ( 0.0)", " 5", " 0",
" 5 [100.0]"), ord_layer_index = c(1L, 1L, 1L, 1L), ord_layer_1 = c(1,
5689, 5690, 9999)), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("6", "8", "Missing", "Not Found",
"Total"), var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 0", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 0", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 0", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L, 1L), ord_layer_1 = c(4,
0, 999, 1000, 9999)), row.names = c(NA, -5L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("6", "8", "Missing", "Total"),
var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L), ord_layer_1 = c(1,
2, 3, 7862)), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("0", "Missing", "Total"), var1_3 = c("15 (100.0)",
" 0", " 15 [100.0]"), var1_4 = c(" 4 (33.3)", " 8", " 12 [100.0]"
), var1_5 = c(" 0 ( 0.0)", " 5", " 5 [100.0]"), ord_layer_index = c(1L,
1L, 1L), ord_layer_1 = c(1, 3, -Inf)), row.names = c(NA, -3L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("6", "8", "Missing", "Total"),
var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L), ord_layer_1 = c(4,
0, 8, -6795)), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("6", "8", "NA", "Total"), var1_3 = c(" 2 (13.3)",
"12 (80.0)", " 1 ( 6.7)", "15 (100.0)"), var1_4 = c(" 4 (33.3)",
" 0 ( 0.0)", " 8 (66.7)", "12 (100.0)"), var1_5 = c(" 1 (20.0)",
" 2 (40.0)", " 2 (40.0)", " 5 (100.0)"), ord_layer_index = c(1L,
1L, 1L, 1L), ord_layer_1 = c(1, 2, 3, 3)), row.names = c(NA,
-4L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(row_label1 = c("2", "3", "4", "6", "8", "Missing_"
), var1_3 = c(" 0 ( 0.0)", " 0 ( 0.0)", " 0 ( 0.0)", " 2 (13.3)",
"12 (80.0)", " 1"), var1_4 = c(" 0 ( 0.0)", " 0 ( 0.0)", " 0 ( 0.0)",
" 4 (33.3)", " 0 ( 0.0)", " 8"), var1_5 = c(" 0 ( 0.0)", " 0 ( 0.0)",
" 0 ( 0.0)", " 1 (20.0)", " 2 (40.0)", " 2"), ord_layer_index = c(1L,
1L, 1L, 1L, 1L, 1L), ord_layer_1 = c(1, 2, 3, 4, 5, 6)), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
set_denom_where works as expected
structure(list(row_label1 = c("4", "8"), var1_3 = c(" 1 ( 6.7)",
"12 (80.0)"), var1_4 = c(" 8 (66.7)", " 0 ( 0.0)"), var1_5 = c(" 2 (40.0)",
" 2 (40.0)"), ord_layer_index = c(1L, 1L), ord_layer_1 = c(1,
3)), row.names = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame"
))
structure(list(row_label1 = c("4", "8"), var1_3 = c(" 1 ( 7.1)",
"12 (85.7)"), var1_4 = c(" 8 (200.0)", " 0 ( 0.0)"), var1_5 = c(" 2 (66.7)",
" 2 (66.7)"), ord_layer_index = c(1L, 1L), ord_layer_1 = c(1,
3)), row.names = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame"
))
A `denom_where` has been set with a pop_data. The `denom_where` has been ignored.You should use `set_pop_where` instead of `set_denom_where`.
structure(list(row_label1 = c("4", "8"), var1_3 = c(" 1 ( 7.7)",
"12 (92.3)"), var1_4 = c(" 8 (100.0)", " 0 ( 0.0)"), var1_5 = c(" 2 (50.0)",
" 2 (50.0)"), ord_layer_index = c(1L, 1L), ord_layer_1 = c(1,
3)), row.names = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame"
))
Nested count layers can accept text values in the first variable
i In index: 1.
Caused by error:
! Inner layers must be data driven variables
Variable names will be coersed into symbols
The first target variable has been coerced into a symbol. You should pass variable names unquoted.
The second target variable has been coerced into a symbol.You should pass variable names unquoted.
keep_levels works as expeceted
i In index: 1.
Caused by error in `value[[3L]]()`:
! group_count `where` condition `TRUE` is invalid. Filter error:
Error: level passed to `kept_levels` not found: 10 20
i In index: 1.
Caused by error in `value[[3L]]()`:
! group_count `where` condition `TRUE` is invalid. Filter error:
Error: level passed to `kept_levels` not found: nothere
nested count layers handle set_denoms_by as expected
You can not pass the second variable in `vars` as a denominator.
Code
tplyr_table(mtcars, gear, cols = vs) %>% add_layer(group_count(vars(cyl, grp)) %>%
set_denoms_by(cyl)) %>% build() %>% as.data.frame()
Output
row_label1 row_label2 var1_3_0 var1_3_1 var1_4_0 var1_4_1
1 4 4 0 ( 0.0%) 1 ( 9.1%) 0 ( 0.0%) 8 ( 72.7%)
2 4 grp.4 0 ( 0.0%) 1 ( 9.1%) 0 ( 0.0%) 3 ( 27.3%)
3 4 grp.4.5 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 5 ( 45.5%)
4 6 6 0 ( 0.0%) 2 ( 28.6%) 2 ( 28.6%) 2 ( 28.6%)
5 6 grp.6 0 ( 0.0%) 0 ( 0.0%) 1 ( 14.3%) 1 ( 14.3%)
6 6 grp.6.5 0 ( 0.0%) 2 ( 28.6%) 1 ( 14.3%) 1 ( 14.3%)
7 8 8 12 ( 85.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
8 8 grp.8 7 ( 50.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
9 8 grp.8.5 5 ( 35.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
var1_5_0 var1_5_1 ord_layer_index ord_layer_1 ord_layer_2
1 1 ( 9.1%) 1 ( 9.1%) 1 1 Inf
2 1 ( 9.1%) 0 ( 0.0%) 1 1 1
3 0 ( 0.0%) 1 ( 9.1%) 1 1 2
4 1 ( 14.3%) 0 ( 0.0%) 1 2 Inf
5 0 ( 0.0%) 0 ( 0.0%) 1 2 1
6 1 ( 14.3%) 0 ( 0.0%) 1 2 2
7 2 ( 14.3%) 0 ( 0.0%) 1 3 Inf
8 2 ( 14.3%) 0 ( 0.0%) 1 3 1
9 0 ( 0.0%) 0 ( 0.0%) 1 3 2
Code
tplyr_table(mtcars, gear, cols = vs) %>% add_layer(group_count(vars(cyl, grp))) %>%
build() %>% as.data.frame()
Output
row_label1 row_label2 var1_3_0 var1_3_1 var1_4_0 var1_4_1
1 4 4 0 ( 0.0%) 1 ( 33.3%) 0 ( 0.0%) 8 ( 80.0%)
2 4 grp.4 0 ( 0.0%) 1 ( 33.3%) 0 ( 0.0%) 3 ( 30.0%)
3 4 grp.4.5 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 5 ( 50.0%)
4 6 6 0 ( 0.0%) 2 ( 66.7%) 2 (100.0%) 2 ( 20.0%)
5 6 grp.6 0 ( 0.0%) 0 ( 0.0%) 1 ( 50.0%) 1 ( 10.0%)
6 6 grp.6.5 0 ( 0.0%) 2 ( 66.7%) 1 ( 50.0%) 1 ( 10.0%)
7 8 8 12 (100.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
8 8 grp.8 7 ( 58.3%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
9 8 grp.8.5 5 ( 41.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
var1_5_0 var1_5_1 ord_layer_index ord_layer_1 ord_layer_2
1 1 ( 25.0%) 1 (100.0%) 1 1 Inf
2 1 ( 25.0%) 0 ( 0.0%) 1 1 1
3 0 ( 0.0%) 1 (100.0%) 1 1 2
4 1 ( 25.0%) 0 ( 0.0%) 1 2 Inf
5 0 ( 0.0%) 0 ( 0.0%) 1 2 1
6 1 ( 25.0%) 0 ( 0.0%) 1 2 2
7 2 ( 50.0%) 0 ( 0.0%) 1 3 Inf
8 2 ( 50.0%) 0 ( 0.0%) 1 3 1
9 0 ( 0.0%) 0 ( 0.0%) 1 3 2
nested count can accept data if second variable is bigger than the first
Code
x
Output
row_label1 row_label2 var1_TRT1
1 Antiemetics and antinauseants Antiemetics and antinauseants 1 ( 50.0%)
2 Antiemetics and antinauseants Promethazine hydrochloride 1 ( 50.0%)
3 Psycholeptics Psycholeptics 1 ( 50.0%)
4 Psycholeptics Promethazine hydrochloride 1 ( 50.0%)
var1_TRT2 ord_layer_index ord_layer_1 ord_layer_2
1 0 ( 0.0%) 1 1 Inf
2 0 ( 0.0%) 1 1 1
3 1 (100.0%) 1 2 Inf
4 1 (100.0%) 1 2 1
set_numeric_threshold works as expected
Code
as.data.frame(build(t1))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 8 12 ( 80.0%) 0 ( 0.0%) 2 ( 40.0%) 1 0
2 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t2))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 4 1 ( 6.7%) 8 ( 66.7%) 2 ( 40.0%) 1 8
2 8 12 ( 80.0%) 0 ( 0.0%) 2 ( 40.0%) 1 0
3 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t3))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t4))
Output
[1] row_label1 ord_layer_index
<0 rows> (or 0-length row.names)
Code
as.data.frame(build(t5))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 4 1 ( 6.7%) 8 ( 66.7%) 2 ( 40.0%) 1 8
2 8 12 ( 80.0%) 0 ( 0.0%) 2 ( 40.0%) 1 0
3 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t6))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 8 12 ( 80.0%) 0 ( 0.0%) 2 ( 40.0%) 1 0
2 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t7))
Output
row_label1
1 GASTROINTESTINAL DISORDERS
2 GASTROINTESTINAL DISORDERS
3 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
4 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
5 INFECTIONS AND INFESTATIONS
6 INFECTIONS AND INFESTATIONS
7 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
8 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
9 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
row_label2 var1_Placebo
1 GASTROINTESTINAL DISORDERS 6 ( 12.8%)
2 DIARRHOEA 3 ( 6.4%)
3 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS 11 ( 23.4%)
4 APPLICATION SITE PRURITUS 4 ( 8.5%)
5 INFECTIONS AND INFESTATIONS 5 ( 10.6%)
6 UPPER RESPIRATORY TRACT INFECTION 4 ( 8.5%)
7 SKIN AND SUBCUTANEOUS TISSUE DISORDERS 7 ( 14.9%)
8 ERYTHEMA 4 ( 8.5%)
9 PRURITUS 3 ( 6.4%)
var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index
1 6 ( 7.8%) 3 ( 3.9%) 1
2 1 ( 1.3%) 2 ( 2.6%) 1
3 21 ( 27.3%) 21 ( 27.6%) 1
4 7 ( 9.1%) 5 ( 6.6%) 1
5 4 ( 5.2%) 3 ( 3.9%) 1
6 1 ( 1.3%) 1 ( 1.3%) 1
7 21 ( 27.3%) 26 ( 34.2%) 1
8 3 ( 3.9%) 2 ( 2.6%) 1
9 8 ( 10.4%) 7 ( 9.2%) 1
ord_layer_1 ord_layer_2
1 1 Inf
2 1 1
3 2 Inf
4 2 1
5 3 Inf
6 3 1
7 4 Inf
8 4 1
9 4 2
Code
as.data.frame(build(t8))
Output
row_label1
1 GASTROINTESTINAL DISORDERS
2 GASTROINTESTINAL DISORDERS
3 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
4 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
5 INFECTIONS AND INFESTATIONS
6 INFECTIONS AND INFESTATIONS
7 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
8 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
9 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
row_label2 var1_Placebo
1 GASTROINTESTINAL DISORDERS 6 ( 12.8%)
2 DIARRHOEA 3 ( 6.4%)
3 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS 11 ( 23.4%)
4 APPLICATION SITE PRURITUS 4 ( 8.5%)
5 INFECTIONS AND INFESTATIONS 5 ( 10.6%)
6 UPPER RESPIRATORY TRACT INFECTION 4 ( 8.5%)
7 SKIN AND SUBCUTANEOUS TISSUE DISORDERS 7 ( 14.9%)
8 ERYTHEMA 4 ( 8.5%)
9 PRURITUS 3 ( 6.4%)
var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index
1 6 ( 7.8%) 3 ( 3.9%) 1
2 1 ( 1.3%) 2 ( 2.6%) 1
3 21 ( 27.3%) 21 ( 27.6%) 1
4 7 ( 9.1%) 5 ( 6.6%) 1
5 4 ( 5.2%) 3 ( 3.9%) 1
6 1 ( 1.3%) 1 ( 1.3%) 1
7 21 ( 27.3%) 26 ( 34.2%) 1
8 3 ( 3.9%) 2 ( 2.6%) 1
9 8 ( 10.4%) 7 ( 9.2%) 1
ord_layer_1 ord_layer_2
1 3 Inf
2 3 2
3 21 Inf
4 21 5
5 3 Inf
6 3 1
7 26 Inf
8 26 2
9 26 7
denom and distinct_denom values work as expected
Code
as.data.frame(build(t1))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index
1 4 1/ 15 ( 6.7) 8/ 12 (66.7) 2/ 5 (40.0) 1
2 6 2/ 15 (13.3) 4/ 12 (33.3) 1/ 5 (20.0) 1
3 8 12/ 15 (80.0) 0/ 12 ( 0.0) 2/ 5 (40.0) 1
4 Missing 0 0 0 1
5 Total 15 [100.0] 12 [100.0] 5 [100.0] 1
ord_layer_1
1 8
2 4
3 0
4 0
5 12
Code
as.data.frame(build(t2))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index
1 4 1 1 1 15 2 2 8 12 1 1 2 5 1
2 6 1 1 2 15 2 2 4 12 1 1 1 5 1
3 8 1 1 12 15 0 2 0 12 1 1 2 5 1
ord_layer_1
1 1
2 2
3 3
denoms with distinct population data populates as expected
Code
as.data.frame(tab)
Output
row_label1 var1_Dosed var1_Placebo var1_Total var1_Xanomeline High Dose
1 Any Body System 93 (55.4%) 32 (37.2%) 125 (49.2%) 43 (51.2%)
var1_Xanomeline Low Dose ord_layer_index ord_layer_1
1 50 (59.5%) 1 NA
nested count layers error out when you try to add a total row
i In index: 1.
Caused by error:
! You can't include total rows in nested counts. Instead, add a seperate layer for total counts.
Tables with pop_data can accept a layer level where
Code
as.data.frame(build(t))
Output
row_label1 var1_Placebo
1 ABDOMINAL PAIN 0, [ 0] ( 0.0%) [ 0.0%]
2 AGITATION 0, [ 0] ( 0.0%) [ 0.0%]
3 ANXIETY 0, [ 0] ( 0.0%) [ 0.0%]
4 APPLICATION SITE DERMATITIS 1, [ 1] ( 1.2%) [ 2.1%]
5 APPLICATION SITE ERYTHEMA 0, [ 0] ( 0.0%) [ 0.0%]
6 APPLICATION SITE IRRITATION 1, [ 1] ( 1.2%) [ 2.1%]
7 APPLICATION SITE PAIN 0, [ 0] ( 0.0%) [ 0.0%]
8 APPLICATION SITE PRURITUS 4, [ 4] ( 4.7%) [ 8.5%]
9 APPLICATION SITE REACTION 1, [ 1] ( 1.2%) [ 2.1%]
10 APPLICATION SITE URTICARIA 0, [ 0] ( 0.0%) [ 0.0%]
11 APPLICATION SITE VESICLES 1, [ 1] ( 1.2%) [ 2.1%]
12 APPLICATION SITE WARMTH 0, [ 0] ( 0.0%) [ 0.0%]
13 ATRIAL HYPERTROPHY 1, [ 1] ( 1.2%) [ 2.1%]
14 BLISTER 0, [ 0] ( 0.0%) [ 0.0%]
15 BUNDLE BRANCH BLOCK RIGHT 1, [ 1] ( 1.2%) [ 2.1%]
16 BURNING SENSATION 0, [ 0] ( 0.0%) [ 0.0%]
17 CARDIAC FAILURE CONGESTIVE 1, [ 1] ( 1.2%) [ 2.1%]
18 CHILLS 1, [ 2] ( 1.2%) [ 4.3%]
19 COMPLEX PARTIAL SEIZURES 0, [ 0] ( 0.0%) [ 0.0%]
20 CONFUSIONAL STATE 1, [ 1] ( 1.2%) [ 2.1%]
21 CONSTIPATION 1, [ 1] ( 1.2%) [ 2.1%]
22 CYSTITIS 0, [ 0] ( 0.0%) [ 0.0%]
23 DERMATITIS CONTACT 0, [ 0] ( 0.0%) [ 0.0%]
24 DIARRHOEA 2, [ 2] ( 2.3%) [ 4.3%]
25 DIZZINESS 0, [ 0] ( 0.0%) [ 0.0%]
26 ELECTROCARDIOGRAM T WAVE INVERSION 1, [ 1] ( 1.2%) [ 2.1%]
27 EPISTAXIS 0, [ 0] ( 0.0%) [ 0.0%]
28 ERYTHEMA 3, [ 4] ( 3.5%) [ 8.5%]
29 FATIGUE 0, [ 0] ( 0.0%) [ 0.0%]
30 HALLUCINATION, VISUAL 0, [ 0] ( 0.0%) [ 0.0%]
31 HEART RATE INCREASED 1, [ 1] ( 1.2%) [ 2.1%]
32 HEART RATE IRREGULAR 1, [ 1] ( 1.2%) [ 2.1%]
33 HYPERHIDROSIS 0, [ 0] ( 0.0%) [ 0.0%]
34 HYPONATRAEMIA 1, [ 1] ( 1.2%) [ 2.1%]
35 HYPOTENSION 0, [ 0] ( 0.0%) [ 0.0%]
36 INCREASED APPETITE 1, [ 1] ( 1.2%) [ 2.1%]
37 INFLAMMATION 0, [ 0] ( 0.0%) [ 0.0%]
38 IRRITABILITY 1, [ 1] ( 1.2%) [ 2.1%]
39 MALAISE 0, [ 0] ( 0.0%) [ 0.0%]
40 MYALGIA 0, [ 0] ( 0.0%) [ 0.0%]
41 MYOCARDIAL INFARCTION 0, [ 0] ( 0.0%) [ 0.0%]
42 NAUSEA 1, [ 1] ( 1.2%) [ 2.1%]
43 OEDEMA PERIPHERAL 1, [ 1] ( 1.2%) [ 2.1%]
44 PRURITUS 3, [ 3] ( 3.5%) [ 6.4%]
45 PRURITUS GENERALISED 0, [ 0] ( 0.0%) [ 0.0%]
46 RASH 0, [ 0] ( 0.0%) [ 0.0%]
47 RASH MACULO-PAPULAR 0, [ 0] ( 0.0%) [ 0.0%]
48 RASH PRURITIC 0, [ 0] ( 0.0%) [ 0.0%]
49 SINUS BRADYCARDIA 0, [ 0] ( 0.0%) [ 0.0%]
50 SKIN EXFOLIATION 0, [ 0] ( 0.0%) [ 0.0%]
51 SKIN IRRITATION 0, [ 0] ( 0.0%) [ 0.0%]
52 SUPRAVENTRICULAR EXTRASYSTOLES 1, [ 1] ( 1.2%) [ 2.1%]
53 SYNCOPE 0, [ 0] ( 0.0%) [ 0.0%]
54 TACHYCARDIA 1, [ 1] ( 1.2%) [ 2.1%]
55 TRANSIENT ISCHAEMIC ATTACK 0, [ 0] ( 0.0%) [ 0.0%]
56 UPPER RESPIRATORY TRACT INFECTION 1, [ 1] ( 1.2%) [ 2.1%]
57 URTICARIA 0, [ 0] ( 0.0%) [ 0.0%]
58 VOMITING 0, [ 0] ( 0.0%) [ 0.0%]
59 WOUND 0, [ 0] ( 0.0%) [ 0.0%]
var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index
1 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
2 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
3 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
4 3, [ 3] ( 3.6%) [ 3.9%] 2, [ 2] ( 2.4%) [ 2.6%] 1
5 3, [ 3] ( 3.6%) [ 3.9%] 4, [ 4] ( 4.8%) [ 5.3%] 1
6 3, [ 4] ( 3.6%) [ 5.2%] 2, [ 2] ( 2.4%) [ 2.6%] 1
7 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
8 6, [ 7] ( 7.1%) [ 9.1%] 4, [ 4] ( 4.8%) [ 5.3%] 1
9 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
10 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
11 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
12 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
13 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
14 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 2] ( 1.2%) [ 2.6%] 1
15 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
16 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
17 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
18 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
19 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
20 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
21 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
22 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
23 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
24 1, [ 1] ( 1.2%) [ 1.3%] 2, [ 2] ( 2.4%) [ 2.6%] 1
25 1, [ 1] ( 1.2%) [ 1.3%] 3, [ 4] ( 3.6%) [ 5.3%] 1
26 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
27 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
28 3, [ 3] ( 3.6%) [ 3.9%] 1, [ 1] ( 1.2%) [ 1.3%] 1
29 0, [ 0] ( 0.0%) [ 0.0%] 2, [ 2] ( 2.4%) [ 2.6%] 1
30 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
31 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
32 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
33 2, [ 2] ( 2.4%) [ 2.6%] 1, [ 1] ( 1.2%) [ 1.3%] 1
34 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
35 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
36 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
37 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
38 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
39 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
40 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
41 1, [ 1] ( 1.2%) [ 1.3%] 2, [ 2] ( 2.4%) [ 2.6%] 1
42 2, [ 2] ( 2.4%) [ 2.6%] 0, [ 0] ( 0.0%) [ 0.0%] 1
43 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
44 8, [ 8] ( 9.5%) [ 10.4%] 6, [ 6] ( 7.1%) [ 7.9%] 1
45 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 2] ( 1.2%) [ 2.6%] 1
46 2, [ 2] ( 2.4%) [ 2.6%] 3, [ 4] ( 3.6%) [ 5.3%] 1
47 1, [ 2] ( 1.2%) [ 2.6%] 0, [ 0] ( 0.0%) [ 0.0%] 1
48 1, [ 1] ( 1.2%) [ 1.3%] 1, [ 1] ( 1.2%) [ 1.3%] 1
49 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
50 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
51 1, [ 1] ( 1.2%) [ 1.3%] 3, [ 3] ( 3.6%) [ 3.9%] 1
52 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
53 1, [ 1] ( 1.2%) [ 1.3%] 2, [ 2] ( 2.4%) [ 2.6%] 1
54 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
55 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
56 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
57 1, [ 2] ( 1.2%) [ 2.6%] 1, [ 2] ( 1.2%) [ 2.6%] 1
58 2, [ 2] ( 2.4%) [ 2.6%] 0, [ 0] ( 0.0%) [ 0.0%] 1
59 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
ord_layer_1
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 15
14 17
15 19
16 20
17 21
18 23
19 24
20 25
21 26
22 30
23 32
24 33
25 34
26 35
27 36
28 37
29 40
30 42
31 44
32 45
33 47
34 49
35 50
36 51
37 52
38 54
39 55
40 56
41 57
42 60
43 63
44 65
45 66
46 67
47 68
48 69
49 72
50 73
51 74
52 76
53 78
54 79
55 80
56 82
57 84
58 87
59 88
Regression test to make sure cols produce correct denom
Code
t
Output
row_label1 var1_0_F var1_0_M
1 Subjects with at least one event 19 (35.8) [53] 13 (39.4) [33]
var1_54_F var1_54_M var1_81_F var1_81_M
1 27 (54.0) [50] 23 (67.6) [34] 17 (42.5) [40] 26 (59.1) [44]
Error checking for add_missing_subjects_row()
Argument `fmt` does not inherit "f_str". Classes: character
Argument `sort_value` does not inherit "numeric". Classes: character
Argument `e` does not inherit "count_layer". Classes: tplyr_layer, desc_layer, environment
Argument `missing_subjects_row_label` must be character. Instead a class of "numeric" was passed.
length(missing_subjects_row_label) not equal to 1
Argument `e` does not inherit "count_layer". Classes: tplyr_layer, desc_layer, environment
Missing counts on nested count layers function correctly
Population data was not set separately from the target data.
Missing subject counts may be misleading in this scenario.
Did you mean to use `set_missing_count() instead?
atorus-research/Tplyr documentation built on Feb. 21, 2024, 7:36 p.m.
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Count layer clauses with invalid syntax give informative error
i In index: 1.
Caused by error in `value[[3L]]()`:
! group_count `where` condition `bad == code` is invalid. Filter error:
Error in `filter()`:
i In argument: `bad == code`.
Caused by error:
! object 'bad' not found
Total rows and missing counts are displayed correctly(0.1.5 Updates)
structure(list(row_label1 = c("6", "8", "Missing", "Total"),
var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L), ord_layer_1 = c(1,
2, 3, 4)), row.names = c(NA, -4L), class = c("tbl_df", "tbl",
"data.frame"))
structure(list(row_label1 = c("6", "8", "Missing", "Not Found",
"Total"), var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 0", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 0", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 0", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L, 1L), ord_layer_1 = c(1,
2, 3, 4, 5)), row.names = c(NA, -5L), class = c("tbl_df", "tbl",
"data.frame"))
structure(list(row_label1 = c("0", "Missing", "Not Found", "Total"
), var1_3 = c("15 (100.0)", " 0", " 0", " 15 [100.0]"), var1_4 = c(" 4 (33.3)",
" 8", " 0", " 12 [100.0]"), var1_5 = c(" 0 ( 0.0)", " 5", " 0",
" 5 [100.0]"), ord_layer_index = c(1L, 1L, 1L, 1L), ord_layer_1 = c(1,
5689, 5690, 9999)), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("6", "8", "Missing", "Not Found",
"Total"), var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 0", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 0", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 0", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L, 1L), ord_layer_1 = c(4,
0, 999, 1000, 9999)), row.names = c(NA, -5L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("6", "8", "Missing", "Total"),
var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L), ord_layer_1 = c(1,
2, 3, 7862)), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("0", "Missing", "Total"), var1_3 = c("15 (100.0)",
" 0", " 15 [100.0]"), var1_4 = c(" 4 (33.3)", " 8", " 12 [100.0]"
), var1_5 = c(" 0 ( 0.0)", " 5", " 5 [100.0]"), ord_layer_index = c(1L,
1L, 1L), ord_layer_1 = c(1, 3, -Inf)), row.names = c(NA, -3L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("6", "8", "Missing", "Total"),
var1_3 = c(" 2 (13.3)", "12 (80.0)", " 1", " 15 [100.0]"
), var1_4 = c(" 4 (33.3)", " 0 ( 0.0)", " 8", " 12 [100.0]"
), var1_5 = c(" 1 (20.0)", " 2 (40.0)", " 2", " 5 [100.0]"
), ord_layer_index = c(1L, 1L, 1L, 1L), ord_layer_1 = c(4,
0, 8, -6795)), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame"))
structure(list(row_label1 = c("6", "8", "NA", "Total"), var1_3 = c(" 2 (13.3)",
"12 (80.0)", " 1 ( 6.7)", "15 (100.0)"), var1_4 = c(" 4 (33.3)",
" 0 ( 0.0)", " 8 (66.7)", "12 (100.0)"), var1_5 = c(" 1 (20.0)",
" 2 (40.0)", " 2 (40.0)", " 5 (100.0)"), ord_layer_index = c(1L,
1L, 1L, 1L), ord_layer_1 = c(1, 2, 3, 3)), row.names = c(NA,
-4L), class = c("tbl_df", "tbl", "data.frame"))
structure(list(row_label1 = c("2", "3", "4", "6", "8", "Missing_"
), var1_3 = c(" 0 ( 0.0)", " 0 ( 0.0)", " 0 ( 0.0)", " 2 (13.3)",
"12 (80.0)", " 1"), var1_4 = c(" 0 ( 0.0)", " 0 ( 0.0)", " 0 ( 0.0)",
" 4 (33.3)", " 0 ( 0.0)", " 8"), var1_5 = c(" 0 ( 0.0)", " 0 ( 0.0)",
" 0 ( 0.0)", " 1 (20.0)", " 2 (40.0)", " 2"), ord_layer_index = c(1L,
1L, 1L, 1L, 1L, 1L), ord_layer_1 = c(1, 2, 3, 4, 5, 6)), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
set_denom_where works as expected
structure(list(row_label1 = c("4", "8"), var1_3 = c(" 1 ( 6.7)",
"12 (80.0)"), var1_4 = c(" 8 (66.7)", " 0 ( 0.0)"), var1_5 = c(" 2 (40.0)",
" 2 (40.0)"), ord_layer_index = c(1L, 1L), ord_layer_1 = c(1,
3)), row.names = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame"
))
structure(list(row_label1 = c("4", "8"), var1_3 = c(" 1 ( 7.1)",
"12 (85.7)"), var1_4 = c(" 8 (200.0)", " 0 ( 0.0)"), var1_5 = c(" 2 (66.7)",
" 2 (66.7)"), ord_layer_index = c(1L, 1L), ord_layer_1 = c(1,
3)), row.names = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame"
))
A `denom_where` has been set with a pop_data. The `denom_where` has been ignored.You should use `set_pop_where` instead of `set_denom_where`.
structure(list(row_label1 = c("4", "8"), var1_3 = c(" 1 ( 7.7)",
"12 (92.3)"), var1_4 = c(" 8 (100.0)", " 0 ( 0.0)"), var1_5 = c(" 2 (50.0)",
" 2 (50.0)"), ord_layer_index = c(1L, 1L), ord_layer_1 = c(1,
3)), row.names = c(NA, -2L), class = c("tbl_df", "tbl", "data.frame"
))
Nested count layers can accept text values in the first variable
i In index: 1.
Caused by error:
! Inner layers must be data driven variables
Variable names will be coersed into symbols
The first target variable has been coerced into a symbol. You should pass variable names unquoted.
The second target variable has been coerced into a symbol.You should pass variable names unquoted.
keep_levels works as expeceted
i In index: 1.
Caused by error in `value[[3L]]()`:
! group_count `where` condition `TRUE` is invalid. Filter error:
Error: level passed to `kept_levels` not found: 10 20
i In index: 1.
Caused by error in `value[[3L]]()`:
! group_count `where` condition `TRUE` is invalid. Filter error:
Error: level passed to `kept_levels` not found: nothere
nested count layers handle set_denoms_by as expected
You can not pass the second variable in `vars` as a denominator.
Code
tplyr_table(mtcars, gear, cols = vs) %>% add_layer(group_count(vars(cyl, grp)) %>%
set_denoms_by(cyl)) %>% build() %>% as.data.frame()
Output
row_label1 row_label2 var1_3_0 var1_3_1 var1_4_0 var1_4_1
1 4 4 0 ( 0.0%) 1 ( 9.1%) 0 ( 0.0%) 8 ( 72.7%)
2 4 grp.4 0 ( 0.0%) 1 ( 9.1%) 0 ( 0.0%) 3 ( 27.3%)
3 4 grp.4.5 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 5 ( 45.5%)
4 6 6 0 ( 0.0%) 2 ( 28.6%) 2 ( 28.6%) 2 ( 28.6%)
5 6 grp.6 0 ( 0.0%) 0 ( 0.0%) 1 ( 14.3%) 1 ( 14.3%)
6 6 grp.6.5 0 ( 0.0%) 2 ( 28.6%) 1 ( 14.3%) 1 ( 14.3%)
7 8 8 12 ( 85.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
8 8 grp.8 7 ( 50.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
9 8 grp.8.5 5 ( 35.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
var1_5_0 var1_5_1 ord_layer_index ord_layer_1 ord_layer_2
1 1 ( 9.1%) 1 ( 9.1%) 1 1 Inf
2 1 ( 9.1%) 0 ( 0.0%) 1 1 1
3 0 ( 0.0%) 1 ( 9.1%) 1 1 2
4 1 ( 14.3%) 0 ( 0.0%) 1 2 Inf
5 0 ( 0.0%) 0 ( 0.0%) 1 2 1
6 1 ( 14.3%) 0 ( 0.0%) 1 2 2
7 2 ( 14.3%) 0 ( 0.0%) 1 3 Inf
8 2 ( 14.3%) 0 ( 0.0%) 1 3 1
9 0 ( 0.0%) 0 ( 0.0%) 1 3 2
Code
tplyr_table(mtcars, gear, cols = vs) %>% add_layer(group_count(vars(cyl, grp))) %>%
build() %>% as.data.frame()
Output
row_label1 row_label2 var1_3_0 var1_3_1 var1_4_0 var1_4_1
1 4 4 0 ( 0.0%) 1 ( 33.3%) 0 ( 0.0%) 8 ( 80.0%)
2 4 grp.4 0 ( 0.0%) 1 ( 33.3%) 0 ( 0.0%) 3 ( 30.0%)
3 4 grp.4.5 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 5 ( 50.0%)
4 6 6 0 ( 0.0%) 2 ( 66.7%) 2 (100.0%) 2 ( 20.0%)
5 6 grp.6 0 ( 0.0%) 0 ( 0.0%) 1 ( 50.0%) 1 ( 10.0%)
6 6 grp.6.5 0 ( 0.0%) 2 ( 66.7%) 1 ( 50.0%) 1 ( 10.0%)
7 8 8 12 (100.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
8 8 grp.8 7 ( 58.3%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
9 8 grp.8.5 5 ( 41.7%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%)
var1_5_0 var1_5_1 ord_layer_index ord_layer_1 ord_layer_2
1 1 ( 25.0%) 1 (100.0%) 1 1 Inf
2 1 ( 25.0%) 0 ( 0.0%) 1 1 1
3 0 ( 0.0%) 1 (100.0%) 1 1 2
4 1 ( 25.0%) 0 ( 0.0%) 1 2 Inf
5 0 ( 0.0%) 0 ( 0.0%) 1 2 1
6 1 ( 25.0%) 0 ( 0.0%) 1 2 2
7 2 ( 50.0%) 0 ( 0.0%) 1 3 Inf
8 2 ( 50.0%) 0 ( 0.0%) 1 3 1
9 0 ( 0.0%) 0 ( 0.0%) 1 3 2
nested count can accept data if second variable is bigger than the first
Code
x
Output
row_label1 row_label2 var1_TRT1
1 Antiemetics and antinauseants Antiemetics and antinauseants 1 ( 50.0%)
2 Antiemetics and antinauseants Promethazine hydrochloride 1 ( 50.0%)
3 Psycholeptics Psycholeptics 1 ( 50.0%)
4 Psycholeptics Promethazine hydrochloride 1 ( 50.0%)
var1_TRT2 ord_layer_index ord_layer_1 ord_layer_2
1 0 ( 0.0%) 1 1 Inf
2 0 ( 0.0%) 1 1 1
3 1 (100.0%) 1 2 Inf
4 1 (100.0%) 1 2 1
set_numeric_threshold works as expected
Code
as.data.frame(build(t1))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 8 12 ( 80.0%) 0 ( 0.0%) 2 ( 40.0%) 1 0
2 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t2))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 4 1 ( 6.7%) 8 ( 66.7%) 2 ( 40.0%) 1 8
2 8 12 ( 80.0%) 0 ( 0.0%) 2 ( 40.0%) 1 0
3 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t3))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t4))
Output
[1] row_label1 ord_layer_index
<0 rows> (or 0-length row.names)
Code
as.data.frame(build(t5))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 4 1 ( 6.7%) 8 ( 66.7%) 2 ( 40.0%) 1 8
2 8 12 ( 80.0%) 0 ( 0.0%) 2 ( 40.0%) 1 0
3 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t6))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1
1 8 12 ( 80.0%) 0 ( 0.0%) 2 ( 40.0%) 1 0
2 Total 15 (100.0%) 12 (100.0%) 5 (100.0%) 1 12
Code
as.data.frame(build(t7))
Output
row_label1
1 GASTROINTESTINAL DISORDERS
2 GASTROINTESTINAL DISORDERS
3 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
4 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
5 INFECTIONS AND INFESTATIONS
6 INFECTIONS AND INFESTATIONS
7 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
8 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
9 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
row_label2 var1_Placebo
1 GASTROINTESTINAL DISORDERS 6 ( 12.8%)
2 DIARRHOEA 3 ( 6.4%)
3 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS 11 ( 23.4%)
4 APPLICATION SITE PRURITUS 4 ( 8.5%)
5 INFECTIONS AND INFESTATIONS 5 ( 10.6%)
6 UPPER RESPIRATORY TRACT INFECTION 4 ( 8.5%)
7 SKIN AND SUBCUTANEOUS TISSUE DISORDERS 7 ( 14.9%)
8 ERYTHEMA 4 ( 8.5%)
9 PRURITUS 3 ( 6.4%)
var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index
1 6 ( 7.8%) 3 ( 3.9%) 1
2 1 ( 1.3%) 2 ( 2.6%) 1
3 21 ( 27.3%) 21 ( 27.6%) 1
4 7 ( 9.1%) 5 ( 6.6%) 1
5 4 ( 5.2%) 3 ( 3.9%) 1
6 1 ( 1.3%) 1 ( 1.3%) 1
7 21 ( 27.3%) 26 ( 34.2%) 1
8 3 ( 3.9%) 2 ( 2.6%) 1
9 8 ( 10.4%) 7 ( 9.2%) 1
ord_layer_1 ord_layer_2
1 1 Inf
2 1 1
3 2 Inf
4 2 1
5 3 Inf
6 3 1
7 4 Inf
8 4 1
9 4 2
Code
as.data.frame(build(t8))
Output
row_label1
1 GASTROINTESTINAL DISORDERS
2 GASTROINTESTINAL DISORDERS
3 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
4 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS
5 INFECTIONS AND INFESTATIONS
6 INFECTIONS AND INFESTATIONS
7 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
8 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
9 SKIN AND SUBCUTANEOUS TISSUE DISORDERS
row_label2 var1_Placebo
1 GASTROINTESTINAL DISORDERS 6 ( 12.8%)
2 DIARRHOEA 3 ( 6.4%)
3 GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS 11 ( 23.4%)
4 APPLICATION SITE PRURITUS 4 ( 8.5%)
5 INFECTIONS AND INFESTATIONS 5 ( 10.6%)
6 UPPER RESPIRATORY TRACT INFECTION 4 ( 8.5%)
7 SKIN AND SUBCUTANEOUS TISSUE DISORDERS 7 ( 14.9%)
8 ERYTHEMA 4 ( 8.5%)
9 PRURITUS 3 ( 6.4%)
var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index
1 6 ( 7.8%) 3 ( 3.9%) 1
2 1 ( 1.3%) 2 ( 2.6%) 1
3 21 ( 27.3%) 21 ( 27.6%) 1
4 7 ( 9.1%) 5 ( 6.6%) 1
5 4 ( 5.2%) 3 ( 3.9%) 1
6 1 ( 1.3%) 1 ( 1.3%) 1
7 21 ( 27.3%) 26 ( 34.2%) 1
8 3 ( 3.9%) 2 ( 2.6%) 1
9 8 ( 10.4%) 7 ( 9.2%) 1
ord_layer_1 ord_layer_2
1 3 Inf
2 3 2
3 21 Inf
4 21 5
5 3 Inf
6 3 1
7 26 Inf
8 26 2
9 26 7
denom and distinct_denom values work as expected
Code
as.data.frame(build(t1))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index
1 4 1/ 15 ( 6.7) 8/ 12 (66.7) 2/ 5 (40.0) 1
2 6 2/ 15 (13.3) 4/ 12 (33.3) 1/ 5 (20.0) 1
3 8 12/ 15 (80.0) 0/ 12 ( 0.0) 2/ 5 (40.0) 1
4 Missing 0 0 0 1
5 Total 15 [100.0] 12 [100.0] 5 [100.0] 1
ord_layer_1
1 8
2 4
3 0
4 0
5 12
Code
as.data.frame(build(t2))
Output
row_label1 var1_3 var1_4 var1_5 ord_layer_index
1 4 1 1 1 15 2 2 8 12 1 1 2 5 1
2 6 1 1 2 15 2 2 4 12 1 1 1 5 1
3 8 1 1 12 15 0 2 0 12 1 1 2 5 1
ord_layer_1
1 1
2 2
3 3
denoms with distinct population data populates as expected
Code
as.data.frame(tab)
Output
row_label1 var1_Dosed var1_Placebo var1_Total var1_Xanomeline High Dose
1 Any Body System 93 (55.4%) 32 (37.2%) 125 (49.2%) 43 (51.2%)
var1_Xanomeline Low Dose ord_layer_index ord_layer_1
1 50 (59.5%) 1 NA
nested count layers error out when you try to add a total row
i In index: 1.
Caused by error:
! You can't include total rows in nested counts. Instead, add a seperate layer for total counts.
Tables with pop_data can accept a layer level where
Code
as.data.frame(build(t))
Output
row_label1 var1_Placebo
1 ABDOMINAL PAIN 0, [ 0] ( 0.0%) [ 0.0%]
2 AGITATION 0, [ 0] ( 0.0%) [ 0.0%]
3 ANXIETY 0, [ 0] ( 0.0%) [ 0.0%]
4 APPLICATION SITE DERMATITIS 1, [ 1] ( 1.2%) [ 2.1%]
5 APPLICATION SITE ERYTHEMA 0, [ 0] ( 0.0%) [ 0.0%]
6 APPLICATION SITE IRRITATION 1, [ 1] ( 1.2%) [ 2.1%]
7 APPLICATION SITE PAIN 0, [ 0] ( 0.0%) [ 0.0%]
8 APPLICATION SITE PRURITUS 4, [ 4] ( 4.7%) [ 8.5%]
9 APPLICATION SITE REACTION 1, [ 1] ( 1.2%) [ 2.1%]
10 APPLICATION SITE URTICARIA 0, [ 0] ( 0.0%) [ 0.0%]
11 APPLICATION SITE VESICLES 1, [ 1] ( 1.2%) [ 2.1%]
12 APPLICATION SITE WARMTH 0, [ 0] ( 0.0%) [ 0.0%]
13 ATRIAL HYPERTROPHY 1, [ 1] ( 1.2%) [ 2.1%]
14 BLISTER 0, [ 0] ( 0.0%) [ 0.0%]
15 BUNDLE BRANCH BLOCK RIGHT 1, [ 1] ( 1.2%) [ 2.1%]
16 BURNING SENSATION 0, [ 0] ( 0.0%) [ 0.0%]
17 CARDIAC FAILURE CONGESTIVE 1, [ 1] ( 1.2%) [ 2.1%]
18 CHILLS 1, [ 2] ( 1.2%) [ 4.3%]
19 COMPLEX PARTIAL SEIZURES 0, [ 0] ( 0.0%) [ 0.0%]
20 CONFUSIONAL STATE 1, [ 1] ( 1.2%) [ 2.1%]
21 CONSTIPATION 1, [ 1] ( 1.2%) [ 2.1%]
22 CYSTITIS 0, [ 0] ( 0.0%) [ 0.0%]
23 DERMATITIS CONTACT 0, [ 0] ( 0.0%) [ 0.0%]
24 DIARRHOEA 2, [ 2] ( 2.3%) [ 4.3%]
25 DIZZINESS 0, [ 0] ( 0.0%) [ 0.0%]
26 ELECTROCARDIOGRAM T WAVE INVERSION 1, [ 1] ( 1.2%) [ 2.1%]
27 EPISTAXIS 0, [ 0] ( 0.0%) [ 0.0%]
28 ERYTHEMA 3, [ 4] ( 3.5%) [ 8.5%]
29 FATIGUE 0, [ 0] ( 0.0%) [ 0.0%]
30 HALLUCINATION, VISUAL 0, [ 0] ( 0.0%) [ 0.0%]
31 HEART RATE INCREASED 1, [ 1] ( 1.2%) [ 2.1%]
32 HEART RATE IRREGULAR 1, [ 1] ( 1.2%) [ 2.1%]
33 HYPERHIDROSIS 0, [ 0] ( 0.0%) [ 0.0%]
34 HYPONATRAEMIA 1, [ 1] ( 1.2%) [ 2.1%]
35 HYPOTENSION 0, [ 0] ( 0.0%) [ 0.0%]
36 INCREASED APPETITE 1, [ 1] ( 1.2%) [ 2.1%]
37 INFLAMMATION 0, [ 0] ( 0.0%) [ 0.0%]
38 IRRITABILITY 1, [ 1] ( 1.2%) [ 2.1%]
39 MALAISE 0, [ 0] ( 0.0%) [ 0.0%]
40 MYALGIA 0, [ 0] ( 0.0%) [ 0.0%]
41 MYOCARDIAL INFARCTION 0, [ 0] ( 0.0%) [ 0.0%]
42 NAUSEA 1, [ 1] ( 1.2%) [ 2.1%]
43 OEDEMA PERIPHERAL 1, [ 1] ( 1.2%) [ 2.1%]
44 PRURITUS 3, [ 3] ( 3.5%) [ 6.4%]
45 PRURITUS GENERALISED 0, [ 0] ( 0.0%) [ 0.0%]
46 RASH 0, [ 0] ( 0.0%) [ 0.0%]
47 RASH MACULO-PAPULAR 0, [ 0] ( 0.0%) [ 0.0%]
48 RASH PRURITIC 0, [ 0] ( 0.0%) [ 0.0%]
49 SINUS BRADYCARDIA 0, [ 0] ( 0.0%) [ 0.0%]
50 SKIN EXFOLIATION 0, [ 0] ( 0.0%) [ 0.0%]
51 SKIN IRRITATION 0, [ 0] ( 0.0%) [ 0.0%]
52 SUPRAVENTRICULAR EXTRASYSTOLES 1, [ 1] ( 1.2%) [ 2.1%]
53 SYNCOPE 0, [ 0] ( 0.0%) [ 0.0%]
54 TACHYCARDIA 1, [ 1] ( 1.2%) [ 2.1%]
55 TRANSIENT ISCHAEMIC ATTACK 0, [ 0] ( 0.0%) [ 0.0%]
56 UPPER RESPIRATORY TRACT INFECTION 1, [ 1] ( 1.2%) [ 2.1%]
57 URTICARIA 0, [ 0] ( 0.0%) [ 0.0%]
58 VOMITING 0, [ 0] ( 0.0%) [ 0.0%]
59 WOUND 0, [ 0] ( 0.0%) [ 0.0%]
var1_Xanomeline High Dose var1_Xanomeline Low Dose ord_layer_index
1 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
2 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
3 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
4 3, [ 3] ( 3.6%) [ 3.9%] 2, [ 2] ( 2.4%) [ 2.6%] 1
5 3, [ 3] ( 3.6%) [ 3.9%] 4, [ 4] ( 4.8%) [ 5.3%] 1
6 3, [ 4] ( 3.6%) [ 5.2%] 2, [ 2] ( 2.4%) [ 2.6%] 1
7 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
8 6, [ 7] ( 7.1%) [ 9.1%] 4, [ 4] ( 4.8%) [ 5.3%] 1
9 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
10 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
11 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
12 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
13 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
14 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 2] ( 1.2%) [ 2.6%] 1
15 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
16 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
17 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
18 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
19 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
20 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
21 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
22 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
23 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
24 1, [ 1] ( 1.2%) [ 1.3%] 2, [ 2] ( 2.4%) [ 2.6%] 1
25 1, [ 1] ( 1.2%) [ 1.3%] 3, [ 4] ( 3.6%) [ 5.3%] 1
26 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
27 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
28 3, [ 3] ( 3.6%) [ 3.9%] 1, [ 1] ( 1.2%) [ 1.3%] 1
29 0, [ 0] ( 0.0%) [ 0.0%] 2, [ 2] ( 2.4%) [ 2.6%] 1
30 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
31 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
32 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
33 2, [ 2] ( 2.4%) [ 2.6%] 1, [ 1] ( 1.2%) [ 1.3%] 1
34 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
35 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
36 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
37 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
38 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
39 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
40 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
41 1, [ 1] ( 1.2%) [ 1.3%] 2, [ 2] ( 2.4%) [ 2.6%] 1
42 2, [ 2] ( 2.4%) [ 2.6%] 0, [ 0] ( 0.0%) [ 0.0%] 1
43 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
44 8, [ 8] ( 9.5%) [ 10.4%] 6, [ 6] ( 7.1%) [ 7.9%] 1
45 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 2] ( 1.2%) [ 2.6%] 1
46 2, [ 2] ( 2.4%) [ 2.6%] 3, [ 4] ( 3.6%) [ 5.3%] 1
47 1, [ 2] ( 1.2%) [ 2.6%] 0, [ 0] ( 0.0%) [ 0.0%] 1
48 1, [ 1] ( 1.2%) [ 1.3%] 1, [ 1] ( 1.2%) [ 1.3%] 1
49 1, [ 1] ( 1.2%) [ 1.3%] 0, [ 0] ( 0.0%) [ 0.0%] 1
50 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
51 1, [ 1] ( 1.2%) [ 1.3%] 3, [ 3] ( 3.6%) [ 3.9%] 1
52 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
53 1, [ 1] ( 1.2%) [ 1.3%] 2, [ 2] ( 2.4%) [ 2.6%] 1
54 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
55 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
56 0, [ 0] ( 0.0%) [ 0.0%] 0, [ 0] ( 0.0%) [ 0.0%] 1
57 1, [ 2] ( 1.2%) [ 2.6%] 1, [ 2] ( 1.2%) [ 2.6%] 1
58 2, [ 2] ( 2.4%) [ 2.6%] 0, [ 0] ( 0.0%) [ 0.0%] 1
59 0, [ 0] ( 0.0%) [ 0.0%] 1, [ 1] ( 1.2%) [ 1.3%] 1
ord_layer_1
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 15
14 17
15 19
16 20
17 21
18 23
19 24
20 25
21 26
22 30
23 32
24 33
25 34
26 35
27 36
28 37
29 40
30 42
31 44
32 45
33 47
34 49
35 50
36 51
37 52
38 54
39 55
40 56
41 57
42 60
43 63
44 65
45 66
46 67
47 68
48 69
49 72
50 73
51 74
52 76
53 78
54 79
55 80
56 82
57 84
58 87
59 88
Regression test to make sure cols produce correct denom
Code
t
Output
row_label1 var1_0_F var1_0_M
1 Subjects with at least one event 19 (35.8) [53] 13 (39.4) [33]
var1_54_F var1_54_M var1_81_F var1_81_M
1 27 (54.0) [50] 23 (67.6) [34] 17 (42.5) [40] 26 (59.1) [44]
Error checking for add_missing_subjects_row()
Argument `fmt` does not inherit "f_str". Classes: character
Argument `sort_value` does not inherit "numeric". Classes: character
Argument `e` does not inherit "count_layer". Classes: tplyr_layer, desc_layer, environment
Argument `missing_subjects_row_label` must be character. Instead a class of "numeric" was passed.
length(missing_subjects_row_label) not equal to 1
Argument `e` does not inherit "count_layer". Classes: tplyr_layer, desc_layer, environment
Missing counts on nested count layers function correctly
Population data was not set separately from the target data.
Missing subject counts may be misleading in this scenario.
Did you mean to use `set_missing_count() instead?
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