Nothing
tests/testthat/_snaps/utils_rtables.md
In tern: Create Common TLGs Used in Clinical Trials
to_string_matrix works correctly
Code
res
Output
[,1] [,2] [,3] [,4]
[1,] "" "A: Drug X" "B: Placebo" "C: Combination"
[2,] "ASIAN" "" "" ""
[3,] " A" "" "" ""
[4,] " mean" "32.19" "33.90" "36.81"
[5,] " B" "" "" ""
[6,] " mean" "34.12" "31.62" "34.73"
[7,] " C" "" "" ""
[8,] " mean" "36.21" "33.00" "32.39"
[9,] "BLACK OR AFRICAN AMERICAN" "" "" ""
[10,] " A" "" "" ""
[11,] " mean" "31.50" "28.57" "33.62"
[12,] " B" "" "" ""
[13,] " mean" "35.60" "30.83" "33.67"
[14,] " C" "" "" ""
[15,] " mean" "35.50" "34.18" "35.00"
[16,] "WHITE" "" "" ""
[17,] " A" "" "" ""
[18,] " mean" "37.67" "31.33" "33.17"
[19,] " B" "" "" ""
[20,] " mean" "39.86" "39.00" "34.75"
[21,] " C" "" "" ""
[22,] " mean" "39.75" "44.67" "36.75"
Code
res
Output
[1] " A: Drug X B: Placebo C: Combination"
[2] "———————————————————————————————————————————————————————————————————"
[3] "ASIAN "
[4] " A "
[5] " mean 32.19 33.90 36.81 "
[6] " B "
[7] " mean 34.12 31.62 34.73 "
[8] " C "
[9] " mean 36.21 33.00 32.39 "
[10] "BLACK OR AFRICAN AMERICAN "
[11] " A "
[12] " mean 31.50 28.57 33.62 "
[13] " B "
[14] " mean 35.60 30.83 33.67 "
[15] " C "
[16] " mean 35.50 34.18 35.00 "
[17] "WHITE "
[18] " A "
[19] " mean 37.67 31.33 33.17 "
[20] " B "
[21] " mean 39.86 39.00 34.75 "
[22] " C "
[23] " mean 39.75 44.67 36.75 "
Code
print_result
Output
[1] "c("
[2] " \"\", \"A: Drug X\", \"B: Placebo\", \"C: Combination\","
[3] " \"ASIAN\", \"\", \"\", \"\","
[4] " \" A\", \"\", \"\", \"\","
[5] " \" mean\", \"32.19\", \"33.90\", \"36.81\","
[6] " \" B\", \"\", \"\", \"\","
[7] " \" mean\", \"34.12\", \"31.62\", \"34.73\","
[8] " \" C\", \"\", \"\", \"\","
[9] " \" mean\", \"36.21\", \"33.00\", \"32.39\","
[10] " \"BLACK OR AFRICAN AMERICAN\", \"\", \"\", \"\","
[11] " \" A\", \"\", \"\", \"\","
[12] " \" mean\", \"31.50\", \"28.57\", \"33.62\","
[13] " \" B\", \"\", \"\", \"\","
[14] " \" mean\", \"35.60\", \"30.83\", \"33.67\","
[15] " \" C\", \"\", \"\", \"\","
[16] " \" mean\", \"35.50\", \"34.18\", \"35.00\","
[17] " \"WHITE\", \"\", \"\", \"\","
[18] " \" A\", \"\", \"\", \"\","
[19] " \" mean\", \"37.67\", \"31.33\", \"33.17\","
[20] " \" B\", \"\", \"\", \"\","
[21] " \" mean\", \"39.86\", \"39.00\", \"34.75\","
[22] " \" C\", \"\", \"\", \"\","
[23] " \" mean\", \"39.75\", \"44.67\", \"36.75\""
[24] ")"
Code
res
Output
[1] "c("
[2] " \" A: Drug X B: Placebo C: Combination\","
[3] " \"———————————————————————————————————————————————————————————————————\","
[4] " \"ASIAN \","
[5] " \" A \","
[6] " \" mean 32.19 33.90 36.81 \","
[7] " \" B \","
[8] " \" mean 34.12 31.62 34.73 \","
[9] " \" C \","
[10] " \" mean 36.21 33.00 32.39 \","
[11] " \"BLACK OR AFRICAN AMERICAN \","
[12] " \" A \","
[13] " \" mean 31.50 28.57 33.62 \","
[14] " \" B \","
[15] " \" mean 35.60 30.83 33.67 \","
[16] " \" C \","
[17] " \" mean 35.50 34.18 35.00 \","
[18] " \"WHITE \","
[19] " \" A \","
[20] " \" mean 37.67 31.33 33.17 \","
[21] " \" B \","
[22] " \" mean 39.86 39.00 34.75 \","
[23] " \" C \","
[24] " \" mean 39.75 44.67 36.75 \""
[25] ")"
unlist_and_blank_na works as expected if not all missing
Code
res
Output
[1] 1 3 5 NA
unlist_and_blank_na works as expected if all missing
Code
res
Output
character(0)
cfun_by_flag works as expected
Code
res
Output
rcell: 1.0000
labels_or_names works correctly
Code
res
Output
a b
"a" "bla"
Code
res
Output
b
"" "b"
Code
res
Output
b
"bli" "b"
Code
res
Output
[1] "" ""
c_label_n works as expected
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 row_count 0 female (N=4)
c_label_n_alt works as expected
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 row_count 0 female (N=10)
add_rowcounts works with one row split
Code
res
Output
all obs
———————————————————
F (N=187)
M (N=169)
add_rowcounts works with multiple column and row splits
Code
res
Output
A: Drug X B: Placebo C: Combination
A B C A B C A B C
———————————————————————————————————————————————————————————————————————————————————
CHN (N=179)
F (N=94)
mean 30.92 36.91 35.36 34.33 32.89 39.75 35.33 33.40 34.75
M (N=85)
mean 36.29 38.00 39.46 30.00 32.00 32.80 34.82 33.00 31.87
USA (N=44)
F (N=24)
mean 33.00 43.00 41.33 27.50 NA 32.25 34.20 37.00 36.00
M (N=20)
mean 35.00 36.50 35.50 40.00 34.00 28.00 39.25 31.00 39.50
BRA (N=29)
F (N=15)
mean 29.00 28.75 47.00 31.00 30.50 24.00 37.00 39.00 34.00
M (N=14)
mean NA 31.00 33.00 32.33 31.67 35.00 NA 48.00 31.67
PAK (N=28)
F (N=12)
mean NA 35.00 NA 46.00 29.67 42.00 44.00 25.50 34.00
M (N=16)
mean 36.67 37.00 33.00 28.00 NA 32.00 41.00 38.50 36.33
NGA (N=24)
F (N=13)
mean 26.50 28.50 24.00 31.00 NA NA 32.00 40.00 30.50
M (N=11)
mean 32.00 37.00 42.50 NA 21.00 37.00 35.00 44.00 NA
RUS (N=20)
F (N=10)
mean 30.00 36.50 32.75 NA NA 40.00 NA 36.50 NA
M (N=10)
mean 27.00 NA 39.00 30.00 36.50 28.00 35.50 27.00 27.00
JPN (N=18)
F (N=9)
mean NA 35.00 NA NA 41.50 35.00 43.00 40.00 32.50
M (N=9)
mean 33.00 NA 26.50 29.50 27.67 NA 33.00 NA NA
GBR (N=7)
F (N=6)
mean NA 32.00 NA 29.00 NA NA 30.00 NA NA
M (N=1)
mean NA NA NA NA NA NA NA 30.00 NA
CAN (N=7)
F (N=4)
mean 32.50 43.00 NA NA 30.00 NA NA NA NA
M (N=3)
mean NA NA NA NA 38.00 NA 29.50 NA NA
add_rowcounts works with pruning
Code
res
Output
A: Drug X B: Placebo C: Combination
—————————————————————————————————————————————————————————————————————
F (N=187)
ASIAN 44 37 40
BLACK OR AFRICAN AMERICAN 18 12 13
WHITE 8 7 8
M (N=169)
ASIAN 35 31 44
BLACK OR AFRICAN AMERICAN 10 12 14
WHITE 6 7 10
Code
res
Output
A: Drug X B: Placebo C: Combination
—————————————————————————————————————————————————————————————————————
F (N=187)
ASIAN 44 37 40
BLACK OR AFRICAN AMERICAN 18 12 13
WHITE 8 7 8
add_rowcounts works with alt_counts = TRUE
Code
res
Output
A: Drug X B: Placebo C: Combination
—————————————————————————————————————————————————————————————————————
F (N=52)
ASIAN 44 37 40
BLACK OR AFRICAN AMERICAN 18 12 13
WHITE 8 7 8
M (N=48)
ASIAN 35 31 44
BLACK OR AFRICAN AMERICAN 10 12 14
WHITE 6 7 10
h_col_indices works as expected
Code
res
Output
[1] 2 3
as.rtable.data.frame works correctly
Code
res
Output
a b
———————————————
A 1.0 10.0
B 2.0 11.1
C 3.0 12.2
D 4.0 13.3
E 5.0 14.4
F 6.0 15.6
G 7.0 16.7
H 8.0 17.8
I 9.0 18.9
J 10.0 20.0
as.rtable.data.frame uses variable labels for column headers when they are available
Code
res
Output
[1] "label for a" "label for b"
h_split_param divides param values
Code
res
Output
$surv
[1] "pt_at_risk"
$surv_diff
[1] "rate_diff"
Code
res
Output
$surv
pt_at_risk event_free_rate
"xx" "xxx"
$surv_diff
NULL
afun_selected_stats works for NULL input
Code
res
Output
[1] "b"
afun_selected_stats works for character input
Code
res
Output
[1] "c"
append_varlabels works as expected
Code
res
Output
SEX A: Drug X B: Placebo C: Combination
Age (N=121) (N=106) (N=129)
—————————————————————————————————————————————————————————————————————————
F
mean 33.7142857142857 33.8392857142857 34.8852459016393
M
mean 36.5490196078431 32.1 34.2794117647059
U
mean NA NA NA
UNDIFFERENTIATED
mean NA NA NA
append_varlabels correctly concatenates multiple variable labels
Code
res
Output
SEX / Age A: Drug X B: Placebo C: Combination
—————————————————————————————————————————————————————————————————————————
F
mean 33.7142857142857 33.8392857142857 34.8852459016393
M
mean 36.5490196078431 32.1 34.2794117647059
U
mean NA NA NA
UNDIFFERENTIATED
mean NA NA NA
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tern documentation built on June 22, 2024, 10:25 a.m.
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to_string_matrix works correctly
Code
res
Output
[,1] [,2] [,3] [,4]
[1,] "" "A: Drug X" "B: Placebo" "C: Combination"
[2,] "ASIAN" "" "" ""
[3,] " A" "" "" ""
[4,] " mean" "32.19" "33.90" "36.81"
[5,] " B" "" "" ""
[6,] " mean" "34.12" "31.62" "34.73"
[7,] " C" "" "" ""
[8,] " mean" "36.21" "33.00" "32.39"
[9,] "BLACK OR AFRICAN AMERICAN" "" "" ""
[10,] " A" "" "" ""
[11,] " mean" "31.50" "28.57" "33.62"
[12,] " B" "" "" ""
[13,] " mean" "35.60" "30.83" "33.67"
[14,] " C" "" "" ""
[15,] " mean" "35.50" "34.18" "35.00"
[16,] "WHITE" "" "" ""
[17,] " A" "" "" ""
[18,] " mean" "37.67" "31.33" "33.17"
[19,] " B" "" "" ""
[20,] " mean" "39.86" "39.00" "34.75"
[21,] " C" "" "" ""
[22,] " mean" "39.75" "44.67" "36.75"
Code
res
Output
[1] " A: Drug X B: Placebo C: Combination"
[2] "———————————————————————————————————————————————————————————————————"
[3] "ASIAN "
[4] " A "
[5] " mean 32.19 33.90 36.81 "
[6] " B "
[7] " mean 34.12 31.62 34.73 "
[8] " C "
[9] " mean 36.21 33.00 32.39 "
[10] "BLACK OR AFRICAN AMERICAN "
[11] " A "
[12] " mean 31.50 28.57 33.62 "
[13] " B "
[14] " mean 35.60 30.83 33.67 "
[15] " C "
[16] " mean 35.50 34.18 35.00 "
[17] "WHITE "
[18] " A "
[19] " mean 37.67 31.33 33.17 "
[20] " B "
[21] " mean 39.86 39.00 34.75 "
[22] " C "
[23] " mean 39.75 44.67 36.75 "
Code
print_result
Output
[1] "c("
[2] " \"\", \"A: Drug X\", \"B: Placebo\", \"C: Combination\","
[3] " \"ASIAN\", \"\", \"\", \"\","
[4] " \" A\", \"\", \"\", \"\","
[5] " \" mean\", \"32.19\", \"33.90\", \"36.81\","
[6] " \" B\", \"\", \"\", \"\","
[7] " \" mean\", \"34.12\", \"31.62\", \"34.73\","
[8] " \" C\", \"\", \"\", \"\","
[9] " \" mean\", \"36.21\", \"33.00\", \"32.39\","
[10] " \"BLACK OR AFRICAN AMERICAN\", \"\", \"\", \"\","
[11] " \" A\", \"\", \"\", \"\","
[12] " \" mean\", \"31.50\", \"28.57\", \"33.62\","
[13] " \" B\", \"\", \"\", \"\","
[14] " \" mean\", \"35.60\", \"30.83\", \"33.67\","
[15] " \" C\", \"\", \"\", \"\","
[16] " \" mean\", \"35.50\", \"34.18\", \"35.00\","
[17] " \"WHITE\", \"\", \"\", \"\","
[18] " \" A\", \"\", \"\", \"\","
[19] " \" mean\", \"37.67\", \"31.33\", \"33.17\","
[20] " \" B\", \"\", \"\", \"\","
[21] " \" mean\", \"39.86\", \"39.00\", \"34.75\","
[22] " \" C\", \"\", \"\", \"\","
[23] " \" mean\", \"39.75\", \"44.67\", \"36.75\""
[24] ")"
Code
res
Output
[1] "c("
[2] " \" A: Drug X B: Placebo C: Combination\","
[3] " \"———————————————————————————————————————————————————————————————————\","
[4] " \"ASIAN \","
[5] " \" A \","
[6] " \" mean 32.19 33.90 36.81 \","
[7] " \" B \","
[8] " \" mean 34.12 31.62 34.73 \","
[9] " \" C \","
[10] " \" mean 36.21 33.00 32.39 \","
[11] " \"BLACK OR AFRICAN AMERICAN \","
[12] " \" A \","
[13] " \" mean 31.50 28.57 33.62 \","
[14] " \" B \","
[15] " \" mean 35.60 30.83 33.67 \","
[16] " \" C \","
[17] " \" mean 35.50 34.18 35.00 \","
[18] " \"WHITE \","
[19] " \" A \","
[20] " \" mean 37.67 31.33 33.17 \","
[21] " \" B \","
[22] " \" mean 39.86 39.00 34.75 \","
[23] " \" C \","
[24] " \" mean 39.75 44.67 36.75 \""
[25] ")"
unlist_and_blank_na works as expected if not all missing
Code
res
Output
[1] 1 3 5 NA
unlist_and_blank_na works as expected if all missing
Code
res
Output
character(0)
cfun_by_flag works as expected
Code
res
Output
rcell: 1.0000
labels_or_names works correctly
Code
res
Output
a b
"a" "bla"
Code
res
Output
b
"" "b"
Code
res
Output
b
"bli" "b"
Code
res
Output
[1] "" ""
c_label_n works as expected
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 row_count 0 female (N=4)
c_label_n_alt works as expected
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 row_count 0 female (N=10)
add_rowcounts works with one row split
Code
res
Output
all obs
———————————————————
F (N=187)
M (N=169)
add_rowcounts works with multiple column and row splits
Code
res
Output
A: Drug X B: Placebo C: Combination
A B C A B C A B C
———————————————————————————————————————————————————————————————————————————————————
CHN (N=179)
F (N=94)
mean 30.92 36.91 35.36 34.33 32.89 39.75 35.33 33.40 34.75
M (N=85)
mean 36.29 38.00 39.46 30.00 32.00 32.80 34.82 33.00 31.87
USA (N=44)
F (N=24)
mean 33.00 43.00 41.33 27.50 NA 32.25 34.20 37.00 36.00
M (N=20)
mean 35.00 36.50 35.50 40.00 34.00 28.00 39.25 31.00 39.50
BRA (N=29)
F (N=15)
mean 29.00 28.75 47.00 31.00 30.50 24.00 37.00 39.00 34.00
M (N=14)
mean NA 31.00 33.00 32.33 31.67 35.00 NA 48.00 31.67
PAK (N=28)
F (N=12)
mean NA 35.00 NA 46.00 29.67 42.00 44.00 25.50 34.00
M (N=16)
mean 36.67 37.00 33.00 28.00 NA 32.00 41.00 38.50 36.33
NGA (N=24)
F (N=13)
mean 26.50 28.50 24.00 31.00 NA NA 32.00 40.00 30.50
M (N=11)
mean 32.00 37.00 42.50 NA 21.00 37.00 35.00 44.00 NA
RUS (N=20)
F (N=10)
mean 30.00 36.50 32.75 NA NA 40.00 NA 36.50 NA
M (N=10)
mean 27.00 NA 39.00 30.00 36.50 28.00 35.50 27.00 27.00
JPN (N=18)
F (N=9)
mean NA 35.00 NA NA 41.50 35.00 43.00 40.00 32.50
M (N=9)
mean 33.00 NA 26.50 29.50 27.67 NA 33.00 NA NA
GBR (N=7)
F (N=6)
mean NA 32.00 NA 29.00 NA NA 30.00 NA NA
M (N=1)
mean NA NA NA NA NA NA NA 30.00 NA
CAN (N=7)
F (N=4)
mean 32.50 43.00 NA NA 30.00 NA NA NA NA
M (N=3)
mean NA NA NA NA 38.00 NA 29.50 NA NA
add_rowcounts works with pruning
Code
res
Output
A: Drug X B: Placebo C: Combination
—————————————————————————————————————————————————————————————————————
F (N=187)
ASIAN 44 37 40
BLACK OR AFRICAN AMERICAN 18 12 13
WHITE 8 7 8
M (N=169)
ASIAN 35 31 44
BLACK OR AFRICAN AMERICAN 10 12 14
WHITE 6 7 10
Code
res
Output
A: Drug X B: Placebo C: Combination
—————————————————————————————————————————————————————————————————————
F (N=187)
ASIAN 44 37 40
BLACK OR AFRICAN AMERICAN 18 12 13
WHITE 8 7 8
add_rowcounts works with alt_counts = TRUE
Code
res
Output
A: Drug X B: Placebo C: Combination
—————————————————————————————————————————————————————————————————————
F (N=52)
ASIAN 44 37 40
BLACK OR AFRICAN AMERICAN 18 12 13
WHITE 8 7 8
M (N=48)
ASIAN 35 31 44
BLACK OR AFRICAN AMERICAN 10 12 14
WHITE 6 7 10
h_col_indices works as expected
Code
res
Output
[1] 2 3
as.rtable.data.frame works correctly
Code
res
Output
a b
———————————————
A 1.0 10.0
B 2.0 11.1
C 3.0 12.2
D 4.0 13.3
E 5.0 14.4
F 6.0 15.6
G 7.0 16.7
H 8.0 17.8
I 9.0 18.9
J 10.0 20.0
as.rtable.data.frame uses variable labels for column headers when they are available
Code
res
Output
[1] "label for a" "label for b"
h_split_param divides param values
Code
res
Output
$surv
[1] "pt_at_risk"
$surv_diff
[1] "rate_diff"
Code
res
Output
$surv
pt_at_risk event_free_rate
"xx" "xxx"
$surv_diff
NULL
afun_selected_stats works for NULL input
Code
res
Output
[1] "b"
afun_selected_stats works for character input
Code
res
Output
[1] "c"
append_varlabels works as expected
Code
res
Output
SEX A: Drug X B: Placebo C: Combination
Age (N=121) (N=106) (N=129)
—————————————————————————————————————————————————————————————————————————
F
mean 33.7142857142857 33.8392857142857 34.8852459016393
M
mean 36.5490196078431 32.1 34.2794117647059
U
mean NA NA NA
UNDIFFERENTIATED
mean NA NA NA
append_varlabels correctly concatenates multiple variable labels
Code
res
Output
SEX / Age A: Drug X B: Placebo C: Combination
—————————————————————————————————————————————————————————————————————————
F
mean 33.7142857142857 33.8392857142857 34.8852459016393
M
mean 36.5490196078431 32.1 34.2794117647059
U
mean NA NA NA
UNDIFFERENTIATED
mean NA NA NA
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