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tests/testthat/_snaps/analyze_vars_in_cols.md
In tern: Create Common TLGs Used in Clinical Trials
analyze_vars_in_cols works correctly
Code
res
Output
ARM
SEX n Mean SE
—————————————————————————————————
A: Drug X
F 0 NA NA
M 0 NA NA
B: Placebo
F 0 NA NA
M 0 NA NA
C: Combination
F 288 36.0 0.4
M 234 36.3 0.6
custom labels can be set with row_labels for analyze_colvars
Code
res
Output
n Mean SD SE CV (%) CV % Geometric Mean
———————————————————————————————————————————————————————————————————————————
F
some custom label 288 36.0 6.3 0.4 17.6 18.0
M
some custom label 234 36.3 8.5 0.6 23.4 23.5
Code
res
Output
n Mean SD SE CV (%) CV % Geometric Mean
——————————————————————————————————————————————————————————————————————————
F
Female Statistic 288 36.0 6.3 0.4 17.6 18.0
M
Male Statistic 234 36.3 8.5 0.6 23.4 23.5
custom labels can be set with row_labels and summarize
Code
res
Output
n Mean SD SE CV (%) CV % Geometric Mean
—————————————————————————————————————————————————————————
F 288 36.0 6.3 0.4 17.6 18.0
M 234 36.3 8.5 0.6 23.4 23.5
Code
res
Output
n Mean SD SE CV (%) CV % Geometric Mean
————————————————————————————————————————————————————————————————————————
Female Statistic 288 36.0 6.3 0.4 17.6 18.0
Male Statistic 234 36.3 8.5 0.6 23.4 23.5
summarize works with nested analyze
Code
sort_at_path(tbl, c("SEX", "*", "RACE"), scorefun(1))
Output
Sex
Ethnicity n Mean SD SE CV (%) CV % Geometric Mean
——————————————————————————————————————————————————————————————————————————————————————————————————
Female 288 36.0 6.3 0.4 17.6 18.0
asian 135 35.4 4.4 0.4 12.5 13.0
black or african american 81 35.3 6.9 0.8 19.6 21.0
white 36 38.3 7.7 1.3 20.2 20.2
american indian or alaska native 36 37.8 8.5 1.4 22.5 23.0
Male 234 36.3 8.5 0.6 23.4 23.5
asian 126 34.8 6.7 0.6 19.3 19.2
white 63 36.5 9.5 1.2 26.1 28.6
black or african american 27 45.2 9.6 1.8 21.1 20.3
american indian or alaska native 18 32.8 4.9 1.2 15.1 15.3
Code
tbl_sorted
Output
n Mean SD SE CV (%) CV % Geometric Mean
———————————————————————————————————————————————————————————————————————————————————————————
F 288 36.0 6.3 0.4 17.6 18.0
ASIAN 135 35.4 4.4 0.4 12.5 13.0
C: Combination 135 35.4 4.4 0.4 12.5 13.0
B 63 35.4 3.9 0.5 10.9 11.2
C 54 34.7 5.5 0.7 15.7 16.1
A 18 37.6 0.4 0.1 1.1 1.1
BLACK OR AFRICAN AMERICAN 81 35.3 6.9 0.8 19.6 21.0
C: Combination 81 35.3 6.9 0.8 19.6 21.0
C 36 32.8 6.8 1.1 20.9 20.7
A 27 41.1 2.4 0.5 5.9 5.8
B 18 31.5 6.1 1.4 19.5 19.9
WHITE 36 38.3 7.7 1.3 20.2 20.2
C: Combination 36 38.3 7.7 1.3 20.2 20.2
A 18 37.2 3.5 0.8 9.3 9.4
B 9 49.6 0.0 0.0 0.0 0.0
C 9 29.4 0.0 0.0 0.0 0.0
AMERICAN INDIAN OR ALASKA NATIVE 36 37.8 8.5 1.4 22.5 23.0
C: Combination 36 37.8 8.5 1.4 22.5 23.0
A 18 38.4 10.0 2.4 26.2 27.2
C 18 37.2 6.8 1.6 18.4 18.8
M 234 36.3 8.5 0.6 23.4 23.5
ASIAN 126 34.8 6.7 0.6 19.3 19.2
C: Combination 126 34.8 6.7 0.6 19.3 19.2
A 45 35.7 3.5 0.5 9.8 9.5
B 45 37.2 7.6 1.1 20.6 20.3
C 36 30.6 6.7 1.1 22.0 20.8
WHITE 63 36.5 9.5 1.2 26.1 28.6
C: Combination 63 36.5 9.5 1.2 26.1 28.6
A 36 43.0 5.7 0.9 13.2 14.1
C 18 23.6 0.6 0.2 2.7 2.7
B 9 36.1 0.0 0.0 0.0 0.0
BLACK OR AFRICAN AMERICAN 27 45.2 9.6 1.8 21.1 20.3
C: Combination 27 45.2 9.6 1.8 21.1 20.3
B 18 38.7 1.7 0.4 4.5 4.5
C 9 58.3 0.0 0.0 0.0 0.0
AMERICAN INDIAN OR ALASKA NATIVE 18 32.8 4.9 1.2 15.1 15.3
C: Combination 18 32.8 4.9 1.2 15.1 15.3
B 9 28.0 0.0 0.0 0.0 0.0
C 9 37.6 0.0 0.0 0.0 0.0
analyze_vars_in_cols works well with categorical data
Code
build_table(lyt = lyt, df = adpp %>% mutate(counter = factor("n")))
Output
STRATA1 A: Drug X B: Placebo C: Combination
SEX
—————————————————————————————————————————————————
A
F 0 0 81 (100%)
M 0 0 81 (100%)
B
F 0 0 90 (100%)
M 0 0 81 (100%)
C
F 0 0 117 (100%)
M 0 0 72 (100%)
Code
basic_table(show_colcounts = TRUE) %>% split_rows_by(var = "STRATA1",
label_pos = "topleft") %>% split_cols_by("ARM") %>% analyze(vars = "SEX",
afun = count_fraction) %>% append_topleft(" SEX") %>% build_table(adpp)
Output
STRATA1 A: Drug X B: Placebo C: Combination
SEX (N=0) (N=0) (N=522)
—————————————————————————————————————————————————
A
F 0 0 81 (16%)
M 0 0 81 (16%)
B
F 0 0 90 (17%)
M 0 0 81 (16%)
C
F 0 0 117 (22%)
M 0 0 72 (14%)
analyze_vars_in_cols works with imputation rule
Code
res
Output
n Number of BLQs Mean SD Geometric Mean Minimum Maximum
——————————————————————————————————————————————————————————————————————————————————
A: Drug X
Day 1
0 36 17 ND ND NE ND 92.4
6 18 8 ND ND 40.2 ND 97.6
12 18 4 65.6 23.3 60.8 21.2 99.2
18 18 12 ND ND 44.6 ND 99.3
Day 2
24 18 7 ND ND 42.0 ND 89.8
30 18 11 ND ND 35.6 ND 94.5
36 18 11 ND ND 36.1 ND 96.1
42 18 9 ND ND 41.8 ND 99.2
Code
res
Output
n Number of BLQs Mean SD Geometric Mean Minimum Maximum
————————————————————————————————————————————————————————————————————————————————
A: Drug X
Day 1
0 36 30 ND ND NE ND 92.4
6 18 14 ND ND 40.2 ND 97.6
12 18 12 ND ND 60.8 ND 99.2
18 18 16 ND ND 44.6 ND 99.3
Day 2
24 18 9 ND ND 42.0 ND 89.8
30 18 11 ND ND 35.6 ND 94.5
36 18 12 ND ND 36.1 ND 96.1
42 18 13 ND ND 41.8 ND 99.2
Code
res
Output
n Number of BLQs Mean SD Geometric Mean Minimum Maximum
——————————————————————————————————————————————————————————————————————————————————
A: Drug X
Day 1
0 36 17 43.4 25.6 32.8 2.3 92.4
6 18 8 46.2 24.8 40.2 17.5 97.6
12 18 4 65.6 23.3 60.8 21.2 99.2
18 18 12 ND ND ND ND 99.3
Day 2
24 18 7 51.4 25.8 42.0 7.7 89.8
30 18 11 ND ND ND ND 94.5
36 18 11 ND ND ND ND 96.1
42 18 9 54.6 28.4 41.8 1.3 99.2
analyze_vars_in_cols works with caching
Code
res
Output
n Number of BLQs Mean SD Geometric Mean Minimum Maximum
——————————————————————————————————————————————————————————————————————————————————
A: Drug X
Day 1
0 36 17 43.4 25.6 32.8 2.3 92.4
6 18 8 46.2 24.8 40.2 17.5 97.6
12 18 4 65.6 23.3 60.8 21.2 99.2
18 18 12 54.1 27.1 44.6 7.5 99.3
Day 2
24 18 7 51.4 25.8 42.0 7.7 89.8
30 18 11 48.2 31.8 35.6 3.6 94.5
36 18 11 47.0 25.6 36.1 1.3 96.1
42 18 9 54.6 28.4 41.8 1.3 99.2
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analyze_vars_in_cols works correctly
Code
res
Output
ARM
SEX n Mean SE
—————————————————————————————————
A: Drug X
F 0 NA NA
M 0 NA NA
B: Placebo
F 0 NA NA
M 0 NA NA
C: Combination
F 288 36.0 0.4
M 234 36.3 0.6
custom labels can be set with row_labels for analyze_colvars
Code
res
Output
n Mean SD SE CV (%) CV % Geometric Mean
———————————————————————————————————————————————————————————————————————————
F
some custom label 288 36.0 6.3 0.4 17.6 18.0
M
some custom label 234 36.3 8.5 0.6 23.4 23.5
Code
res
Output
n Mean SD SE CV (%) CV % Geometric Mean
——————————————————————————————————————————————————————————————————————————
F
Female Statistic 288 36.0 6.3 0.4 17.6 18.0
M
Male Statistic 234 36.3 8.5 0.6 23.4 23.5
custom labels can be set with row_labels and summarize
Code
res
Output
n Mean SD SE CV (%) CV % Geometric Mean
—————————————————————————————————————————————————————————
F 288 36.0 6.3 0.4 17.6 18.0
M 234 36.3 8.5 0.6 23.4 23.5
Code
res
Output
n Mean SD SE CV (%) CV % Geometric Mean
————————————————————————————————————————————————————————————————————————
Female Statistic 288 36.0 6.3 0.4 17.6 18.0
Male Statistic 234 36.3 8.5 0.6 23.4 23.5
summarize works with nested analyze
Code
sort_at_path(tbl, c("SEX", "*", "RACE"), scorefun(1))
Output
Sex
Ethnicity n Mean SD SE CV (%) CV % Geometric Mean
——————————————————————————————————————————————————————————————————————————————————————————————————
Female 288 36.0 6.3 0.4 17.6 18.0
asian 135 35.4 4.4 0.4 12.5 13.0
black or african american 81 35.3 6.9 0.8 19.6 21.0
white 36 38.3 7.7 1.3 20.2 20.2
american indian or alaska native 36 37.8 8.5 1.4 22.5 23.0
Male 234 36.3 8.5 0.6 23.4 23.5
asian 126 34.8 6.7 0.6 19.3 19.2
white 63 36.5 9.5 1.2 26.1 28.6
black or african american 27 45.2 9.6 1.8 21.1 20.3
american indian or alaska native 18 32.8 4.9 1.2 15.1 15.3
Code
tbl_sorted
Output
n Mean SD SE CV (%) CV % Geometric Mean
———————————————————————————————————————————————————————————————————————————————————————————
F 288 36.0 6.3 0.4 17.6 18.0
ASIAN 135 35.4 4.4 0.4 12.5 13.0
C: Combination 135 35.4 4.4 0.4 12.5 13.0
B 63 35.4 3.9 0.5 10.9 11.2
C 54 34.7 5.5 0.7 15.7 16.1
A 18 37.6 0.4 0.1 1.1 1.1
BLACK OR AFRICAN AMERICAN 81 35.3 6.9 0.8 19.6 21.0
C: Combination 81 35.3 6.9 0.8 19.6 21.0
C 36 32.8 6.8 1.1 20.9 20.7
A 27 41.1 2.4 0.5 5.9 5.8
B 18 31.5 6.1 1.4 19.5 19.9
WHITE 36 38.3 7.7 1.3 20.2 20.2
C: Combination 36 38.3 7.7 1.3 20.2 20.2
A 18 37.2 3.5 0.8 9.3 9.4
B 9 49.6 0.0 0.0 0.0 0.0
C 9 29.4 0.0 0.0 0.0 0.0
AMERICAN INDIAN OR ALASKA NATIVE 36 37.8 8.5 1.4 22.5 23.0
C: Combination 36 37.8 8.5 1.4 22.5 23.0
A 18 38.4 10.0 2.4 26.2 27.2
C 18 37.2 6.8 1.6 18.4 18.8
M 234 36.3 8.5 0.6 23.4 23.5
ASIAN 126 34.8 6.7 0.6 19.3 19.2
C: Combination 126 34.8 6.7 0.6 19.3 19.2
A 45 35.7 3.5 0.5 9.8 9.5
B 45 37.2 7.6 1.1 20.6 20.3
C 36 30.6 6.7 1.1 22.0 20.8
WHITE 63 36.5 9.5 1.2 26.1 28.6
C: Combination 63 36.5 9.5 1.2 26.1 28.6
A 36 43.0 5.7 0.9 13.2 14.1
C 18 23.6 0.6 0.2 2.7 2.7
B 9 36.1 0.0 0.0 0.0 0.0
BLACK OR AFRICAN AMERICAN 27 45.2 9.6 1.8 21.1 20.3
C: Combination 27 45.2 9.6 1.8 21.1 20.3
B 18 38.7 1.7 0.4 4.5 4.5
C 9 58.3 0.0 0.0 0.0 0.0
AMERICAN INDIAN OR ALASKA NATIVE 18 32.8 4.9 1.2 15.1 15.3
C: Combination 18 32.8 4.9 1.2 15.1 15.3
B 9 28.0 0.0 0.0 0.0 0.0
C 9 37.6 0.0 0.0 0.0 0.0
analyze_vars_in_cols works well with categorical data
Code
build_table(lyt = lyt, df = adpp %>% mutate(counter = factor("n")))
Output
STRATA1 A: Drug X B: Placebo C: Combination
SEX
—————————————————————————————————————————————————
A
F 0 0 81 (100%)
M 0 0 81 (100%)
B
F 0 0 90 (100%)
M 0 0 81 (100%)
C
F 0 0 117 (100%)
M 0 0 72 (100%)
Code
basic_table(show_colcounts = TRUE) %>% split_rows_by(var = "STRATA1",
label_pos = "topleft") %>% split_cols_by("ARM") %>% analyze(vars = "SEX",
afun = count_fraction) %>% append_topleft(" SEX") %>% build_table(adpp)
Output
STRATA1 A: Drug X B: Placebo C: Combination
SEX (N=0) (N=0) (N=522)
—————————————————————————————————————————————————
A
F 0 0 81 (16%)
M 0 0 81 (16%)
B
F 0 0 90 (17%)
M 0 0 81 (16%)
C
F 0 0 117 (22%)
M 0 0 72 (14%)
analyze_vars_in_cols works with imputation rule
Code
res
Output
n Number of BLQs Mean SD Geometric Mean Minimum Maximum
——————————————————————————————————————————————————————————————————————————————————
A: Drug X
Day 1
0 36 17 ND ND NE ND 92.4
6 18 8 ND ND 40.2 ND 97.6
12 18 4 65.6 23.3 60.8 21.2 99.2
18 18 12 ND ND 44.6 ND 99.3
Day 2
24 18 7 ND ND 42.0 ND 89.8
30 18 11 ND ND 35.6 ND 94.5
36 18 11 ND ND 36.1 ND 96.1
42 18 9 ND ND 41.8 ND 99.2
Code
res
Output
n Number of BLQs Mean SD Geometric Mean Minimum Maximum
————————————————————————————————————————————————————————————————————————————————
A: Drug X
Day 1
0 36 30 ND ND NE ND 92.4
6 18 14 ND ND 40.2 ND 97.6
12 18 12 ND ND 60.8 ND 99.2
18 18 16 ND ND 44.6 ND 99.3
Day 2
24 18 9 ND ND 42.0 ND 89.8
30 18 11 ND ND 35.6 ND 94.5
36 18 12 ND ND 36.1 ND 96.1
42 18 13 ND ND 41.8 ND 99.2
Code
res
Output
n Number of BLQs Mean SD Geometric Mean Minimum Maximum
——————————————————————————————————————————————————————————————————————————————————
A: Drug X
Day 1
0 36 17 43.4 25.6 32.8 2.3 92.4
6 18 8 46.2 24.8 40.2 17.5 97.6
12 18 4 65.6 23.3 60.8 21.2 99.2
18 18 12 ND ND ND ND 99.3
Day 2
24 18 7 51.4 25.8 42.0 7.7 89.8
30 18 11 ND ND ND ND 94.5
36 18 11 ND ND ND ND 96.1
42 18 9 54.6 28.4 41.8 1.3 99.2
analyze_vars_in_cols works with caching
Code
res
Output
n Number of BLQs Mean SD Geometric Mean Minimum Maximum
——————————————————————————————————————————————————————————————————————————————————
A: Drug X
Day 1
0 36 17 43.4 25.6 32.8 2.3 92.4
6 18 8 46.2 24.8 40.2 17.5 97.6
12 18 4 65.6 23.3 60.8 21.2 99.2
18 18 12 54.1 27.1 44.6 7.5 99.3
Day 2
24 18 7 51.4 25.8 42.0 7.7 89.8
30 18 11 48.2 31.8 35.6 3.6 94.5
36 18 11 47.0 25.6 36.1 1.3 96.1
42 18 9 54.6 28.4 41.8 1.3 99.2
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