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tests/testthat/_snaps/chevron_tlg-methods.md
In chevron: Standard TLGs for Clinical Trials Reporting
run works as expected for chevron_t object
Code
cat(export_as_txt(res, lpp = 100))
Output
MedDRA System Organ Class
MedDRA Preferred Term A: Drug X B: Placebo C: Combination
Grade (N=15) (N=15) (N=15)
————————————————————————————————————————————————————————————————————————————————————
- Any adverse events -
- Any Grade - 13 (86.7%) 12 (80.0%) 13 (86.7%)
Grade 1-2 5 (33.3%) 3 (20.0%) 3 (20.0%)
1 2 (13.3%) 1 (6.7%) 2 (13.3%)
2 3 (20.0%) 2 (13.3%) 1 (6.7%)
Grade 3-4 3 (20.0%) 3 (20.0%) 2 (13.3%)
3 3 (20.0%) 3 (20.0%) 2 (13.3%)
Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
cl B.2
- Overall -
- Any Grade - 11 (73.3%) 8 (53.3%) 10 (66.7%)
Grade 1-2 6 (40.0%) 2 (13.3%) 5 (33.3%)
1 6 (40.0%) 2 (13.3%) 5 (33.3%)
Grade 3-4 5 (33.3%) 6 (40.0%) 5 (33.3%)
3 5 (33.3%) 6 (40.0%) 5 (33.3%)
dcd B.2.2.3.1
- Any Grade - 8 (53.3%) 6 (40.0%) 7 (46.7%)
Grade 1-2 8 (53.3%) 6 (40.0%) 7 (46.7%)
1 8 (53.3%) 6 (40.0%) 7 (46.7%)
dcd B.2.1.2.1
- Any Grade - 5 (33.3%) 6 (40.0%) 5 (33.3%)
Grade 3-4 5 (33.3%) 6 (40.0%) 5 (33.3%)
3 5 (33.3%) 6 (40.0%) 5 (33.3%)
cl A.1
- Overall -
- Any Grade - 7 (46.7%) 6 (40.0%) 10 (66.7%)
Grade 1-2 7 (46.7%) 6 (40.0%) 10 (66.7%)
1 2 (13.3%) 0 4 (26.7%)
2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.2
- Any Grade - 5 (33.3%) 6 (40.0%) 6 (40.0%)
Grade 1-2 5 (33.3%) 6 (40.0%) 6 (40.0%)
2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1
- Any Grade - 3 (20.0%) 1 (6.7%) 6 (40.0%)
Grade 1-2 3 (20.0%) 1 (6.7%) 6 (40.0%)
1 3 (20.0%) 1 (6.7%) 6 (40.0%)
cl B.1
- Overall -
- Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
dcd B.1.1.1.1
- Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
run works as expected for chevron_t object when auto_pre = FALSE
Code
cat(export_as_txt(res, lpp = 100))
Output
A: Drug X B: Placebo C: Combination All Patients
(N=15) (N=15) (N=15) (N=45)
————————————————————————————————————————————————————————————————————————————————————————————
Age (yr)
n 15 15 15 45
Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4) 34.3 (7.3)
Median 31.0 35.0 35.0 34.0
Min - Max 24 - 40 24 - 57 24 - 49 24 - 57
Age Group
n 15 15 15 45
<65 15 (100%) 15 (100%) 15 (100%) 45 (100%)
Sex
n 15 15 15 45
F 12 (80.0%) 8 (53.3%) 10 (66.7%) 30 (66.7%)
M 3 (20.0%) 7 (46.7%) 5 (33.3%) 15 (33.3%)
Ethnicity
n 15 15 15 45
HISPANIC OR LATINO 2 (13.3%) 0 0 2 (4.4%)
NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%) 41 (91.1%)
NOT REPORTED 0 0 2 (13.3%) 2 (4.4%)
RACE
n 15 15 15 45
AMERICAN INDIAN OR ALASKA NATIVE 0 2 (13.3%) 1 (6.7%) 3 (6.7%)
ASIAN 8 (53.3%) 10 (66.7%) 8 (53.3%) 26 (57.8%)
BLACK OR AFRICAN AMERICAN 4 (26.7%) 1 (6.7%) 4 (26.7%) 9 (20.0%)
WHITE 3 (20.0%) 2 (13.3%) 2 (13.3%) 7 (15.6%)
run works as expected with argument printed
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEBODSYS"
Main args:
arm_var : "ACTARM"
row_split_var : "AEBODSYS"
lbl_overall : NULL
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEBODSYS"
prune_0 : TRUE
Code
cat(export_as_txt(tbl, lpp = 100))
Output
MedDRA System Organ Class A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
cl B.2
Total number of patients with at least one adverse event 11 (73.3%) 8 (53.3%) 10 (66.7%)
Total number of events 18 15 20
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
cl A.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
cl B.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
run works as expected with argument printed if the user argument is complicated
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEHLT"
Main args:
arm_var : "ACTARM"
row_split_var : "AEHLT"
lbl_overall : "All Patients"
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEHLT"
prune_0 : TRUE
Additional args:
not_used : structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6,
5, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1,
5.4, 5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2,
5.5, 4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1,
... (print of class <data.frame> truncated)
Code
cat(export_as_txt(tbl, lpp = 100))
Output
High Level Term A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
hlt A.1.1.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
hlt B.2.2.3
Total number of patients with at least one adverse event 8 (53.3%) 6 (40.0%) 7 (46.7%)
Total number of events 9 7 13
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
hlt B.2.1.2
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 5 (33.3%)
Total number of events 9 8 7
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
\s\n High Level Term All Patients
MedDRA Preferred Term (N=45)
—————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 38 (84.4%)
Overall total number of events 112
hlt A.1.1.1
Total number of patients with at least one adverse event 23 (51.1%)
Total number of events 35
dcd A.1.1.1.2 17 (37.8%)
dcd A.1.1.1.1 10 (22.2%)
hlt B.2.2.3
Total number of patients with at least one adverse event 21 (46.7%)
Total number of events 29
dcd B.2.2.3.1 21 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 19 (42.2%)
Total number of events 24
dcd B.1.1.1.1 19 (42.2%)
hlt B.2.1.2
Total number of patients with at least one adverse event 16 (35.6%)
Total number of events 24
dcd B.2.1.2.1 16 (35.6%)
run uses the argument passed through the ellipsis in priority
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEHLT"
Main args:
arm_var : "ARM"
row_split_var : "AEHLT"
lbl_overall : "All Patients"
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEHLT"
prune_0 : FALSE
Additional args:
another_not_used : iris
not_used : structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6,
5, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1,
5.4, 5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2,
5.5, 4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1,
... (print of class <data.frame> truncated)
Code
cat(export_as_txt(tbl, lpp = 100))
Output
High Level Term A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
hlt A.1.1.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
hlt B.2.2.3
Total number of patients with at least one adverse event 8 (53.3%) 6 (40.0%) 7 (46.7%)
Total number of events 9 7 13
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
hlt B.2.1.2
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 5 (33.3%)
Total number of events 9 8 7
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
\s\n High Level Term All Patients
MedDRA Preferred Term (N=45)
—————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 38 (84.4%)
Overall total number of events 112
hlt A.1.1.1
Total number of patients with at least one adverse event 23 (51.1%)
Total number of events 35
dcd A.1.1.1.2 17 (37.8%)
dcd A.1.1.1.1 10 (22.2%)
hlt B.2.2.3
Total number of patients with at least one adverse event 21 (46.7%)
Total number of events 29
dcd B.2.2.3.1 21 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 19 (42.2%)
Total number of events 24
dcd B.1.1.1.1 19 (42.2%)
hlt B.2.1.2
Total number of patients with at least one adverse event 16 (35.6%)
Total number of events 24
dcd B.2.1.2.1 16 (35.6%)
run works as expected with partial match argument
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEBODSYS"
Main args:
arm_var : "ARM"
row_split_var : "AEBODSYS"
lbl_overall : NULL
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEBODSYS"
prune_0 : TRUE
Code
cat(export_as_txt(tbl, lpp = 100))
Output
MedDRA System Organ Class A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
cl B.2
Total number of patients with at least one adverse event 11 (73.3%) 8 (53.3%) 10 (66.7%)
Total number of events 18 15 20
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
cl A.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
cl B.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
run displays the symbols when available
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEHLT"
Main args:
arm_var : arm_param
row_split_var : "AEHLT"
lbl_overall : "All Patients"
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEHLT"
prune_0 : FALSE
Additional args:
not_used : iris
another_not_used : "X"
Code
cat(export_as_txt(tbl, lpp = 100))
Output
High Level Term A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
hlt A.1.1.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
hlt B.2.2.3
Total number of patients with at least one adverse event 8 (53.3%) 6 (40.0%) 7 (46.7%)
Total number of events 9 7 13
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
hlt B.2.1.2
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 5 (33.3%)
Total number of events 9 8 7
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
\s\n High Level Term All Patients
MedDRA Preferred Term (N=45)
—————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 38 (84.4%)
Overall total number of events 112
hlt A.1.1.1
Total number of patients with at least one adverse event 23 (51.1%)
Total number of events 35
dcd A.1.1.1.2 17 (37.8%)
dcd A.1.1.1.1 10 (22.2%)
hlt B.2.2.3
Total number of patients with at least one adverse event 21 (46.7%)
Total number of events 29
dcd B.2.2.3.1 21 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 19 (42.2%)
Total number of events 24
dcd B.1.1.1.1 19 (42.2%)
hlt B.2.1.2
Total number of patients with at least one adverse event 16 (35.6%)
Total number of events 24
dcd B.2.1.2.1 16 (35.6%)
run print internal functions when unwrap is TRUE
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEBODSYS"
Main args:
arm_var : "ACTARM"
row_split_var : "AEBODSYS"
lbl_overall : NULL
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEBODSYS"
prune_0 : TRUE
Preprocessing function:
function (adam_db, row_split_var = "AEBODSYS", ...)
{
adam_db$adae <- adam_db$adae %>% filter(.data$ANL01FL ==
"Y") %>% mutate(AEDECOD = with_label(reformat(.data$AEDECOD,
nocoding), "MedDRA Preferred Term")) %>% mutate(across(all_of(row_split_var),
~reformat(.x, nocoding))) %>% mutate(AEBODSYS = with_label(.data$AEBODSYS,
"MedDRA System Organ Class"))
adam_db
}
Main function:
function (adam_db, arm_var = "ACTARM", row_split_var = "AEBODSYS",
lbl_overall = NULL, summary_labels = list(all = aet02_label,
TOTAL = c(nonunique = "Overall total number of events")),
...)
{
assert_all_tablenames(adam_db, "adsl", "adae")
assert_string(arm_var)
assert_character(row_split_var, null.ok = TRUE)
assert_string(lbl_overall, null.ok = TRUE)
assert_valid_variable(adam_db$adsl, c("USUBJID", arm_var),
types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, c(arm_var, row_split_var,
"AEDECOD"), types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, "USUBJID", empty_ok = TRUE,
types = list(c("character", "factor")))
assert_valid_var_pair(adam_db$adsl, adam_db$adae, arm_var)
assert_list(summary_labels, null.ok = TRUE)
assert_subset(names(summary_labels), c("all", "TOTAL", row_split_var))
assert_subset(unique(unlist(lapply(summary_labels, names))),
c("unique", "nonunique", "unique_count"))
summary_labels <- expand_list(summary_labels, c("TOTAL",
row_split_var))
lbl_overall <- render_safe(lbl_overall)
lbl_row_split <- var_labels_for(adam_db$adae, row_split_var)
lbl_aedecod <- var_labels_for(adam_db$adae, "AEDECOD")
lyt <- occurrence_lyt(arm_var = arm_var, lbl_overall = lbl_overall,
row_split_var = row_split_var, lbl_row_split = lbl_row_split,
medname_var = "AEDECOD", lbl_medname_var = lbl_aedecod,
summary_labels = summary_labels, count_by = NULL)
tbl <- build_table(lyt, adam_db$adae, alt_counts_df = adam_db$adsl)
tbl
}
Layout function:
occurrence_lyt:
function (arm_var, lbl_overall, row_split_var, lbl_row_split,
medname_var, lbl_medname_var, summary_labels, count_by)
{
split_indent <- vapply(c("TOTAL", row_split_var), function(x) {
if (length(summary_labels[[x]]) > 0L)
-1L
else 0L
}, FUN.VALUE = 0L)
split_indent[1L] <- 0L
lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by_with_overall(arm_var,
lbl_overall)
if (length(summary_labels$TOTAL) > 0) {
lyt <- lyt %>% analyze_num_patients(vars = "USUBJID",
count_by = count_by, .stats = names(summary_labels$TOTAL),
show_labels = "hidden", .labels = render_safe(summary_labels$TOTAL))
}
section_divs <- get_section_div()
for (k in seq_len(length(row_split_var))) {
lyt <- split_and_summ_num_patients(lyt = lyt, count_by = count_by,
var = row_split_var[k], label = lbl_row_split[k],
split_indent = split_indent[k], stats = names(summary_labels[[row_split_var[k]]]),
summarize_labels = render_safe(summary_labels[[row_split_var[k]]]),
section_div = section_divs[k])
}
lyt %>% count_occurrences(vars = medname_var, drop = length(row_split_var) >
0, .indent_mods = unname(tail(split_indent, 1L))) %>%
append_topleft(paste0(stringr::str_dup(" ", 2 * length(row_split_var)),
lbl_medname_var))
}
Postprocessing function:
function (tlg, row_split_var = "AEBODSYS", prune_0 = TRUE, ...)
{
tlg <- tlg %>% tlg_sort_by_vars(row_split_var, cont_n_allcols) %>%
valid_sort_at_path(path = c(get_sort_path(c(row_split_var,
"AEDECOD"))), scorefun = score_occurrences)
if (prune_0) {
tlg <- smart_prune(tlg)
}
std_postprocessing(tlg)
}
run print internal functions when unwrap is TRUE and standard chevron_tlg has no layout
Code
cat(res)
Output
Using template: mng01
Using data: syn_data
Pre args:
dataset : "adlb"
x_var : "AVISIT"
Main args:
dataset : "adlb"
x_var : "AVISIT"
y_var : "AVAL"
y_name : "PARAM"
y_unit : NULL
arm_var : "ACTARM"
center_fun : "mean"
interval_fun : "mean_ci"
jitter : 0.3
line_col : nestcolor::color_palette()
line_type : NULL
ggtheme : gg_theme_chevron()
table : c("n", center_fun, interval_fun)
Post args:
No mapped argument.
Preprocessing function:
function (adam_db, dataset, x_var = "AVISIT", ...)
{
assert_character(dataset)
dunlin::assert_all_tablenames(adam_db, dataset)
adam_db[[dataset]] <- adam_db[[dataset]] %>% filter(.data$ANL01FL ==
"Y") %>% mutate(AVISIT = reorder(.data$AVISIT, .data$AVISITN),
AVISIT = with_label(.data$AVISIT, "Visit"))
if (length(x_var) == 1 && is.numeric(adam_db[[dataset]][[x_var]])) {
adam_db
}
else {
dunlin::ls_unite(adam_db, dataset, cols = x_var, sep = "_")
}
}
Main function:
function (adam_db, dataset = "adlb", x_var = "AVISIT", y_var = "AVAL",
y_name = "PARAM", y_unit = NULL, arm_var = "ACTARM", center_fun = "mean",
interval_fun = "mean_ci", jitter = 0.3, line_col = nestcolor::color_palette(),
line_type = NULL, ggtheme = gg_theme_chevron(), table = c("n",
center_fun, interval_fun), ...)
{
center_fun_choice <- c("mean", "median")
interval_fun_choice <- c("mean_ci", "mean_sei", "mean_sdi",
"median_ci", "quantiles", "range")
assert_all_tablenames(adam_db, c(dataset, "adsl"))
assert_character(x_var)
assert_string(y_var)
assert_string(y_name)
assert_string(y_unit, null.ok = TRUE)
assert_string(arm_var)
assert_string(center_fun)
assert_string(interval_fun, null.ok = TRUE)
assert_names(center_fun, subset.of = center_fun_choice)
assert_choice(interval_fun, interval_fun_choice, null.ok = TRUE)
assert_number(jitter, lower = 0, upper = 1)
assert_class(ggtheme, "theme")
assert_character(line_col, null.ok = TRUE)
assert_character(line_type, null.ok = TRUE)
assert_valid_variable(adam_db[[dataset]], x_var)
assert_valid_variable(adam_db[[dataset]], y_var, types = list(c("numeric")))
assert_valid_variable(adam_db[[dataset]], y_unit, types = list(c("character",
"factor")))
assert_valid_variable(adam_db[[dataset]], arm_var, types = list(c("character",
"factor")), na_ok = FALSE)
assert_valid_variable(adam_db$adsl, c("USUBJID", arm_var),
types = list(c("character", "factor")))
assert_valid_variable(adam_db[[dataset]], "USUBJID", types = list(c("character",
"factor")), empty_ok = TRUE)
assert_valid_var_pair(adam_db$adsl, adam_db[[dataset]], arm_var)
assert_subset(table, c("n", center_fun_choice, interval_fun_choice))
df <- adam_db[[dataset]]
data_ls <- split(df, df$PARAM, drop = TRUE)
x_var <- paste(x_var, collapse = "_")
whiskers_fun <- if (is.null(interval_fun)) {
NULL
}
else {
switch(interval_fun, mean_ci = c("mean_ci_lwr", "mean_ci_upr"),
mean_sei = c("mean_sei_lwr", "mean_sei_upr"), mean_sdi = c("mean_sdi_lwr",
"mean_sdi_upr"), median_ci = c("median_ci_lwr",
"median_ci_upr"), quantiles = c("quantiles_0.25",
"quantile_0.75"), range = c("min", "max"))
}
y_unit <- if (is.null(y_unit))
NA
else y_unit
variables <- tern::control_lineplot_vars(x = x_var, y = y_var,
group_var = arm_var, paramcd = y_name, y_unit = y_unit,
subject_var = "USUBJID")
arm_lvl <- sort(unique(df[[arm_var]]))
col <- if (!is.null(names(line_col))) {
col_sel <- line_col[as.character(arm_lvl)]
if (anyNA(col_sel)) {
missing_col <- setdiff(arm_lvl, names(col_sel))
stop(paste("Missing color matching for", toString(missing_col)))
}
unname(col_sel)
}
else {
line_col
}
line_type <- if (!is.null(names(line_type))) {
tp <- line_type[as.character(arm_lvl)]
if (anyNA(tp)) {
missing_tp <- setdiff(arm_lvl, names(tp))
stop(paste("Missing line type matching for", toString(missing_tp)))
}
unname(tp)
}
else {
line_type
}
lapply(data_ls, tern::g_lineplot, alt_counts_df = adam_db[["adsl"]],
variables = variables, mid = center_fun, interval = interval_fun,
whiskers = whiskers_fun, position = ggplot2::position_dodge(width = jitter),
title = NULL, table = table, ggtheme = ggtheme, col = col,
linetype = line_type, subtitle_add_unit = !is.na(y_unit),
...)
}
Postprocessing function:
function (tlg, ...)
{
tlg <- report_null(tlg, ...)
tlg
}
run print internal functions when unwrap is TRUE and the chevron_tlg object is customized
Code
cat(res)
Output
Using template: custom_chevron
Using data: object of class call
Pre args:
No mapped argument.
Main args:
No mapped argument.
Post args:
No mapped argument.
Preprocessing function:
function (adam_db, ...)
adam_db
Main function:
function (adam_db, ...)
{
ggplot2::ggplot(adam_db$iris, ggplot2::aes(x = Sepal.Length,
y = Sepal.Width)) + ggplot2::geom_point()
}
Postprocessing function:
function (tlg, ...)
{
tlg <- report_null(tlg, ...)
tlg
}
run print main and postprocessing functions when unwrap is TRUE and auto_pre is FALSE
Code
cat(res)
Output
Main function:
function (adam_db, ...)
{
ggplot2::ggplot(adam_db$iris, ggplot2::aes(x = Sepal.Length,
y = Sepal.Width)) + ggplot2::geom_point()
}
Postprocessing function:
function (tlg, ...)
{
tlg <- report_null(tlg, ...)
tlg
}
script_funs works as expected in interactive mode
Code
res
Output
[1] "# Edit Preprocessing Function."
[2] "preprocess(aet04) <- "
[3] "function (adam_db, ...) "
[4] "{"
[5] " atoxgr_lvls <- c(\"1\", \"2\", \"3\", \"4\", \"5\")"
[6] " adam_db$adae <- adam_db$adae %>% filter(.data$ANL01FL == "
[7] " \"Y\") %>% mutate(AEBODSYS = with_label(reformat(.data$AEBODSYS, "
[8] " nocoding), \"MedDRA System Organ Class\"), AEDECOD = with_label(reformat(.data$AEDECOD, "
[9] " nocoding), \"MedDRA Preferred Term\"), ATOXGR = factor(.data$ATOXGR, "
[10] " levels = atoxgr_lvls))"
[11] " adam_db"
[12] "}"
[13] ""
[14] "# Create TLG"
[15] "tlg_output <- run(object = aet04, adam_db = data, verbose = TRUE, user_args = args_ls)"
script_funs generates a valid script
Code
cat(paste(res, collapse = "\n"))
Output
Using template: aet04
Using data: syn_adv
Pre args:
No mapped argument.
Main args:
arm_var : "ARM"
lbl_overall : NULL
grade_groups : NULL
Post args:
prune_0 : TRUE
Preprocessing function:
function (adam_db, ...)
{
atoxgr_lvls <- c("1", "2", "3", "4", "5")
adam_db$adae <- adam_db$adae %>% filter(.data$ANL01FL ==
"Y") %>% mutate(AEBODSYS = with_label(reformat(.data$AEBODSYS,
nocoding), "MedDRA System Organ Class"), AEDECOD = with_label(reformat(.data$AEDECOD,
nocoding), "MedDRA Preferred Term"), ATOXGR = factor(.data$ATOXGR,
levels = atoxgr_lvls))
adam_db
}
Main function:
function (adam_db, arm_var = "ACTARM", lbl_overall = NULL, grade_groups = NULL,
...)
{
assert_all_tablenames(adam_db, "adsl", "adae")
assert_string(arm_var)
assert_string(lbl_overall, null.ok = TRUE)
assert_list(grade_groups, types = "character", null.ok = TRUE)
assert_valid_variable(adam_db$adsl, c("USUBJID", arm_var),
types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, c(arm_var, "AEBODSYS",
"AEDECOD"), types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, "USUBJID", empty_ok = TRUE,
types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, "ATOXGR", na_ok = TRUE,
types = list("factor"))
assert_valid_var_pair(adam_db$adsl, adam_db$adae, arm_var)
lbl_overall <- render_safe(lbl_overall)
lbl_aebodsys <- var_labels_for(adam_db$adae, "AEBODSYS")
lbl_aedecod <- var_labels_for(adam_db$adae, "AEDECOD")
if (is.null(grade_groups)) {
grade_groups <- list(`Grade 1-2` = c("1", "2"), `Grade 3-4` = c("3",
"4"), `Grade 5` = c("5"))
}
lyt <- aet04_lyt(arm_var = arm_var, total_var = "TOTAL_VAR",
lbl_overall = lbl_overall, lbl_aebodsys = lbl_aebodsys,
lbl_aedecod = lbl_aedecod, grade_groups = grade_groups)
adam_db$adae$TOTAL_VAR <- "- Any adverse events - "
tbl <- build_table(lyt, df = adam_db$adae, alt_counts_df = adam_db$adsl)
tbl
}
Layout function:
aet04_lyt:
function (arm_var, total_var, lbl_overall, lbl_aebodsys, lbl_aedecod,
grade_groups)
{
basic_table(show_colcounts = TRUE) %>% split_cols_by_with_overall(arm_var,
lbl_overall) %>% split_rows_by(var = total_var, label_pos = "hidden",
child_labels = "visible", indent_mod = -1L) %>% summarize_num_patients(var = "USUBJID",
.stats = "unique", .labels = "- Any Grade -", .indent_mods = 7L) %>%
count_occurrences_by_grade(var = "ATOXGR", grade_groups = grade_groups,
.indent_mods = 6L) %>% split_rows_by("AEBODSYS",
child_labels = "visible", nested = FALSE, split_fun = drop_split_levels,
label_pos = "topleft", split_label = lbl_aebodsys) %>%
split_rows_by("AEDECOD", child_labels = "visible", split_fun = add_overall_level("- Overall -",
trim = TRUE), label_pos = "topleft", split_label = lbl_aedecod) %>%
summarize_num_patients(var = "USUBJID", .stats = "unique",
.labels = "- Any Grade -", .indent_mods = 6L) %>%
count_occurrences_by_grade(var = "ATOXGR", grade_groups = grade_groups,
.indent_mods = 5L) %>% append_topleft(" Grade")
}
Postprocessing function:
function (tlg, prune_0 = TRUE, ...)
{
tlg <- tlg %>% tlg_sort_by_vars(c("AEBODSYS", "AEDECOD"),
score_all_sum, decreasing = TRUE)
if (prune_0)
tlg <- trim_rows(tlg)
std_postprocessing(tlg)
}
print_list works
Code
print_list(alist(a = 1, b = b, c = xx))
Output
a : 1
b : b
c : xx
print_list works for empty list
Code
print_list(alist())
Output
No mapped argument.
NULL
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chevron documentation built on June 20, 2025, 5:08 p.m.
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run works as expected for chevron_t object
Code
cat(export_as_txt(res, lpp = 100))
Output
MedDRA System Organ Class
MedDRA Preferred Term A: Drug X B: Placebo C: Combination
Grade (N=15) (N=15) (N=15)
————————————————————————————————————————————————————————————————————————————————————
- Any adverse events -
- Any Grade - 13 (86.7%) 12 (80.0%) 13 (86.7%)
Grade 1-2 5 (33.3%) 3 (20.0%) 3 (20.0%)
1 2 (13.3%) 1 (6.7%) 2 (13.3%)
2 3 (20.0%) 2 (13.3%) 1 (6.7%)
Grade 3-4 3 (20.0%) 3 (20.0%) 2 (13.3%)
3 3 (20.0%) 3 (20.0%) 2 (13.3%)
Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
cl B.2
- Overall -
- Any Grade - 11 (73.3%) 8 (53.3%) 10 (66.7%)
Grade 1-2 6 (40.0%) 2 (13.3%) 5 (33.3%)
1 6 (40.0%) 2 (13.3%) 5 (33.3%)
Grade 3-4 5 (33.3%) 6 (40.0%) 5 (33.3%)
3 5 (33.3%) 6 (40.0%) 5 (33.3%)
dcd B.2.2.3.1
- Any Grade - 8 (53.3%) 6 (40.0%) 7 (46.7%)
Grade 1-2 8 (53.3%) 6 (40.0%) 7 (46.7%)
1 8 (53.3%) 6 (40.0%) 7 (46.7%)
dcd B.2.1.2.1
- Any Grade - 5 (33.3%) 6 (40.0%) 5 (33.3%)
Grade 3-4 5 (33.3%) 6 (40.0%) 5 (33.3%)
3 5 (33.3%) 6 (40.0%) 5 (33.3%)
cl A.1
- Overall -
- Any Grade - 7 (46.7%) 6 (40.0%) 10 (66.7%)
Grade 1-2 7 (46.7%) 6 (40.0%) 10 (66.7%)
1 2 (13.3%) 0 4 (26.7%)
2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.2
- Any Grade - 5 (33.3%) 6 (40.0%) 6 (40.0%)
Grade 1-2 5 (33.3%) 6 (40.0%) 6 (40.0%)
2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1
- Any Grade - 3 (20.0%) 1 (6.7%) 6 (40.0%)
Grade 1-2 3 (20.0%) 1 (6.7%) 6 (40.0%)
1 3 (20.0%) 1 (6.7%) 6 (40.0%)
cl B.1
- Overall -
- Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
dcd B.1.1.1.1
- Any Grade - 5 (33.3%) 6 (40.0%) 8 (53.3%)
Grade 5 5 (33.3%) 6 (40.0%) 8 (53.3%)
run works as expected for chevron_t object when auto_pre = FALSE
Code
cat(export_as_txt(res, lpp = 100))
Output
A: Drug X B: Placebo C: Combination All Patients
(N=15) (N=15) (N=15) (N=45)
————————————————————————————————————————————————————————————————————————————————————————————
Age (yr)
n 15 15 15 45
Mean (SD) 31.3 (5.3) 35.1 (9.0) 36.6 (6.4) 34.3 (7.3)
Median 31.0 35.0 35.0 34.0
Min - Max 24 - 40 24 - 57 24 - 49 24 - 57
Age Group
n 15 15 15 45
<65 15 (100%) 15 (100%) 15 (100%) 45 (100%)
Sex
n 15 15 15 45
F 12 (80.0%) 8 (53.3%) 10 (66.7%) 30 (66.7%)
M 3 (20.0%) 7 (46.7%) 5 (33.3%) 15 (33.3%)
Ethnicity
n 15 15 15 45
HISPANIC OR LATINO 2 (13.3%) 0 0 2 (4.4%)
NOT HISPANIC OR LATINO 13 (86.7%) 15 (100%) 13 (86.7%) 41 (91.1%)
NOT REPORTED 0 0 2 (13.3%) 2 (4.4%)
RACE
n 15 15 15 45
AMERICAN INDIAN OR ALASKA NATIVE 0 2 (13.3%) 1 (6.7%) 3 (6.7%)
ASIAN 8 (53.3%) 10 (66.7%) 8 (53.3%) 26 (57.8%)
BLACK OR AFRICAN AMERICAN 4 (26.7%) 1 (6.7%) 4 (26.7%) 9 (20.0%)
WHITE 3 (20.0%) 2 (13.3%) 2 (13.3%) 7 (15.6%)
run works as expected with argument printed
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEBODSYS"
Main args:
arm_var : "ACTARM"
row_split_var : "AEBODSYS"
lbl_overall : NULL
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEBODSYS"
prune_0 : TRUE
Code
cat(export_as_txt(tbl, lpp = 100))
Output
MedDRA System Organ Class A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
cl B.2
Total number of patients with at least one adverse event 11 (73.3%) 8 (53.3%) 10 (66.7%)
Total number of events 18 15 20
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
cl A.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
cl B.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
run works as expected with argument printed if the user argument is complicated
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEHLT"
Main args:
arm_var : "ACTARM"
row_split_var : "AEHLT"
lbl_overall : "All Patients"
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEHLT"
prune_0 : TRUE
Additional args:
not_used : structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6,
5, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1,
5.4, 5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2,
5.5, 4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1,
... (print of class <data.frame> truncated)
Code
cat(export_as_txt(tbl, lpp = 100))
Output
High Level Term A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
hlt A.1.1.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
hlt B.2.2.3
Total number of patients with at least one adverse event 8 (53.3%) 6 (40.0%) 7 (46.7%)
Total number of events 9 7 13
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
hlt B.2.1.2
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 5 (33.3%)
Total number of events 9 8 7
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
\s\n High Level Term All Patients
MedDRA Preferred Term (N=45)
—————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 38 (84.4%)
Overall total number of events 112
hlt A.1.1.1
Total number of patients with at least one adverse event 23 (51.1%)
Total number of events 35
dcd A.1.1.1.2 17 (37.8%)
dcd A.1.1.1.1 10 (22.2%)
hlt B.2.2.3
Total number of patients with at least one adverse event 21 (46.7%)
Total number of events 29
dcd B.2.2.3.1 21 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 19 (42.2%)
Total number of events 24
dcd B.1.1.1.1 19 (42.2%)
hlt B.2.1.2
Total number of patients with at least one adverse event 16 (35.6%)
Total number of events 24
dcd B.2.1.2.1 16 (35.6%)
run uses the argument passed through the ellipsis in priority
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEHLT"
Main args:
arm_var : "ARM"
row_split_var : "AEHLT"
lbl_overall : "All Patients"
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEHLT"
prune_0 : FALSE
Additional args:
another_not_used : iris
not_used : structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6,
5, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1,
5.4, 5.1, 4.6, 5.1, 4.8, 5, 5, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2,
5.5, 4.9, 5, 5.5, 4.9, 4.4, 5.1, 5, 4.5, 4.4, 5, 5.1, 4.8, 5.1,
... (print of class <data.frame> truncated)
Code
cat(export_as_txt(tbl, lpp = 100))
Output
High Level Term A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
hlt A.1.1.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
hlt B.2.2.3
Total number of patients with at least one adverse event 8 (53.3%) 6 (40.0%) 7 (46.7%)
Total number of events 9 7 13
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
hlt B.2.1.2
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 5 (33.3%)
Total number of events 9 8 7
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
\s\n High Level Term All Patients
MedDRA Preferred Term (N=45)
—————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 38 (84.4%)
Overall total number of events 112
hlt A.1.1.1
Total number of patients with at least one adverse event 23 (51.1%)
Total number of events 35
dcd A.1.1.1.2 17 (37.8%)
dcd A.1.1.1.1 10 (22.2%)
hlt B.2.2.3
Total number of patients with at least one adverse event 21 (46.7%)
Total number of events 29
dcd B.2.2.3.1 21 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 19 (42.2%)
Total number of events 24
dcd B.1.1.1.1 19 (42.2%)
hlt B.2.1.2
Total number of patients with at least one adverse event 16 (35.6%)
Total number of events 24
dcd B.2.1.2.1 16 (35.6%)
run works as expected with partial match argument
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEBODSYS"
Main args:
arm_var : "ARM"
row_split_var : "AEBODSYS"
lbl_overall : NULL
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEBODSYS"
prune_0 : TRUE
Code
cat(export_as_txt(tbl, lpp = 100))
Output
MedDRA System Organ Class A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
cl B.2
Total number of patients with at least one adverse event 11 (73.3%) 8 (53.3%) 10 (66.7%)
Total number of events 18 15 20
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
cl A.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
cl B.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
run displays the symbols when available
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEHLT"
Main args:
arm_var : arm_param
row_split_var : "AEHLT"
lbl_overall : "All Patients"
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEHLT"
prune_0 : FALSE
Additional args:
not_used : iris
another_not_used : "X"
Code
cat(export_as_txt(tbl, lpp = 100))
Output
High Level Term A: Drug X B: Placebo C: Combination
MedDRA Preferred Term (N=15) (N=15) (N=15)
—————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 13 (86.7%) 12 (80.0%) 13 (86.7%)
Overall total number of events 32 32 48
hlt A.1.1.1
Total number of patients with at least one adverse event 7 (46.7%) 6 (40.0%) 10 (66.7%)
Total number of events 8 11 16
dcd A.1.1.1.2 5 (33.3%) 6 (40.0%) 6 (40.0%)
dcd A.1.1.1.1 3 (20.0%) 1 (6.7%) 6 (40.0%)
hlt B.2.2.3
Total number of patients with at least one adverse event 8 (53.3%) 6 (40.0%) 7 (46.7%)
Total number of events 9 7 13
dcd B.2.2.3.1 8 (53.3%) 6 (40.0%) 7 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 8 (53.3%)
Total number of events 6 6 12
dcd B.1.1.1.1 5 (33.3%) 6 (40.0%) 8 (53.3%)
hlt B.2.1.2
Total number of patients with at least one adverse event 5 (33.3%) 6 (40.0%) 5 (33.3%)
Total number of events 9 8 7
dcd B.2.1.2.1 5 (33.3%) 6 (40.0%) 5 (33.3%)
\s\n High Level Term All Patients
MedDRA Preferred Term (N=45)
—————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event 38 (84.4%)
Overall total number of events 112
hlt A.1.1.1
Total number of patients with at least one adverse event 23 (51.1%)
Total number of events 35
dcd A.1.1.1.2 17 (37.8%)
dcd A.1.1.1.1 10 (22.2%)
hlt B.2.2.3
Total number of patients with at least one adverse event 21 (46.7%)
Total number of events 29
dcd B.2.2.3.1 21 (46.7%)
hlt B.1.1.1
Total number of patients with at least one adverse event 19 (42.2%)
Total number of events 24
dcd B.1.1.1.1 19 (42.2%)
hlt B.2.1.2
Total number of patients with at least one adverse event 16 (35.6%)
Total number of events 24
dcd B.2.1.2.1 16 (35.6%)
run print internal functions when unwrap is TRUE
Code
cat(res)
Output
Using template: aet02
Using data: syn_adv
Pre args:
row_split_var : "AEBODSYS"
Main args:
arm_var : "ACTARM"
row_split_var : "AEBODSYS"
lbl_overall : NULL
summary_labels : list(all = aet02_label, TOTAL = c(nonunique = "Overall total number of events"))
Post args:
row_split_var : "AEBODSYS"
prune_0 : TRUE
Preprocessing function:
function (adam_db, row_split_var = "AEBODSYS", ...)
{
adam_db$adae <- adam_db$adae %>% filter(.data$ANL01FL ==
"Y") %>% mutate(AEDECOD = with_label(reformat(.data$AEDECOD,
nocoding), "MedDRA Preferred Term")) %>% mutate(across(all_of(row_split_var),
~reformat(.x, nocoding))) %>% mutate(AEBODSYS = with_label(.data$AEBODSYS,
"MedDRA System Organ Class"))
adam_db
}
Main function:
function (adam_db, arm_var = "ACTARM", row_split_var = "AEBODSYS",
lbl_overall = NULL, summary_labels = list(all = aet02_label,
TOTAL = c(nonunique = "Overall total number of events")),
...)
{
assert_all_tablenames(adam_db, "adsl", "adae")
assert_string(arm_var)
assert_character(row_split_var, null.ok = TRUE)
assert_string(lbl_overall, null.ok = TRUE)
assert_valid_variable(adam_db$adsl, c("USUBJID", arm_var),
types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, c(arm_var, row_split_var,
"AEDECOD"), types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, "USUBJID", empty_ok = TRUE,
types = list(c("character", "factor")))
assert_valid_var_pair(adam_db$adsl, adam_db$adae, arm_var)
assert_list(summary_labels, null.ok = TRUE)
assert_subset(names(summary_labels), c("all", "TOTAL", row_split_var))
assert_subset(unique(unlist(lapply(summary_labels, names))),
c("unique", "nonunique", "unique_count"))
summary_labels <- expand_list(summary_labels, c("TOTAL",
row_split_var))
lbl_overall <- render_safe(lbl_overall)
lbl_row_split <- var_labels_for(adam_db$adae, row_split_var)
lbl_aedecod <- var_labels_for(adam_db$adae, "AEDECOD")
lyt <- occurrence_lyt(arm_var = arm_var, lbl_overall = lbl_overall,
row_split_var = row_split_var, lbl_row_split = lbl_row_split,
medname_var = "AEDECOD", lbl_medname_var = lbl_aedecod,
summary_labels = summary_labels, count_by = NULL)
tbl <- build_table(lyt, adam_db$adae, alt_counts_df = adam_db$adsl)
tbl
}
Layout function:
occurrence_lyt:
function (arm_var, lbl_overall, row_split_var, lbl_row_split,
medname_var, lbl_medname_var, summary_labels, count_by)
{
split_indent <- vapply(c("TOTAL", row_split_var), function(x) {
if (length(summary_labels[[x]]) > 0L)
-1L
else 0L
}, FUN.VALUE = 0L)
split_indent[1L] <- 0L
lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by_with_overall(arm_var,
lbl_overall)
if (length(summary_labels$TOTAL) > 0) {
lyt <- lyt %>% analyze_num_patients(vars = "USUBJID",
count_by = count_by, .stats = names(summary_labels$TOTAL),
show_labels = "hidden", .labels = render_safe(summary_labels$TOTAL))
}
section_divs <- get_section_div()
for (k in seq_len(length(row_split_var))) {
lyt <- split_and_summ_num_patients(lyt = lyt, count_by = count_by,
var = row_split_var[k], label = lbl_row_split[k],
split_indent = split_indent[k], stats = names(summary_labels[[row_split_var[k]]]),
summarize_labels = render_safe(summary_labels[[row_split_var[k]]]),
section_div = section_divs[k])
}
lyt %>% count_occurrences(vars = medname_var, drop = length(row_split_var) >
0, .indent_mods = unname(tail(split_indent, 1L))) %>%
append_topleft(paste0(stringr::str_dup(" ", 2 * length(row_split_var)),
lbl_medname_var))
}
Postprocessing function:
function (tlg, row_split_var = "AEBODSYS", prune_0 = TRUE, ...)
{
tlg <- tlg %>% tlg_sort_by_vars(row_split_var, cont_n_allcols) %>%
valid_sort_at_path(path = c(get_sort_path(c(row_split_var,
"AEDECOD"))), scorefun = score_occurrences)
if (prune_0) {
tlg <- smart_prune(tlg)
}
std_postprocessing(tlg)
}
run print internal functions when unwrap is TRUE and standard chevron_tlg has no layout
Code
cat(res)
Output
Using template: mng01
Using data: syn_data
Pre args:
dataset : "adlb"
x_var : "AVISIT"
Main args:
dataset : "adlb"
x_var : "AVISIT"
y_var : "AVAL"
y_name : "PARAM"
y_unit : NULL
arm_var : "ACTARM"
center_fun : "mean"
interval_fun : "mean_ci"
jitter : 0.3
line_col : nestcolor::color_palette()
line_type : NULL
ggtheme : gg_theme_chevron()
table : c("n", center_fun, interval_fun)
Post args:
No mapped argument.
Preprocessing function:
function (adam_db, dataset, x_var = "AVISIT", ...)
{
assert_character(dataset)
dunlin::assert_all_tablenames(adam_db, dataset)
adam_db[[dataset]] <- adam_db[[dataset]] %>% filter(.data$ANL01FL ==
"Y") %>% mutate(AVISIT = reorder(.data$AVISIT, .data$AVISITN),
AVISIT = with_label(.data$AVISIT, "Visit"))
if (length(x_var) == 1 && is.numeric(adam_db[[dataset]][[x_var]])) {
adam_db
}
else {
dunlin::ls_unite(adam_db, dataset, cols = x_var, sep = "_")
}
}
Main function:
function (adam_db, dataset = "adlb", x_var = "AVISIT", y_var = "AVAL",
y_name = "PARAM", y_unit = NULL, arm_var = "ACTARM", center_fun = "mean",
interval_fun = "mean_ci", jitter = 0.3, line_col = nestcolor::color_palette(),
line_type = NULL, ggtheme = gg_theme_chevron(), table = c("n",
center_fun, interval_fun), ...)
{
center_fun_choice <- c("mean", "median")
interval_fun_choice <- c("mean_ci", "mean_sei", "mean_sdi",
"median_ci", "quantiles", "range")
assert_all_tablenames(adam_db, c(dataset, "adsl"))
assert_character(x_var)
assert_string(y_var)
assert_string(y_name)
assert_string(y_unit, null.ok = TRUE)
assert_string(arm_var)
assert_string(center_fun)
assert_string(interval_fun, null.ok = TRUE)
assert_names(center_fun, subset.of = center_fun_choice)
assert_choice(interval_fun, interval_fun_choice, null.ok = TRUE)
assert_number(jitter, lower = 0, upper = 1)
assert_class(ggtheme, "theme")
assert_character(line_col, null.ok = TRUE)
assert_character(line_type, null.ok = TRUE)
assert_valid_variable(adam_db[[dataset]], x_var)
assert_valid_variable(adam_db[[dataset]], y_var, types = list(c("numeric")))
assert_valid_variable(adam_db[[dataset]], y_unit, types = list(c("character",
"factor")))
assert_valid_variable(adam_db[[dataset]], arm_var, types = list(c("character",
"factor")), na_ok = FALSE)
assert_valid_variable(adam_db$adsl, c("USUBJID", arm_var),
types = list(c("character", "factor")))
assert_valid_variable(adam_db[[dataset]], "USUBJID", types = list(c("character",
"factor")), empty_ok = TRUE)
assert_valid_var_pair(adam_db$adsl, adam_db[[dataset]], arm_var)
assert_subset(table, c("n", center_fun_choice, interval_fun_choice))
df <- adam_db[[dataset]]
data_ls <- split(df, df$PARAM, drop = TRUE)
x_var <- paste(x_var, collapse = "_")
whiskers_fun <- if (is.null(interval_fun)) {
NULL
}
else {
switch(interval_fun, mean_ci = c("mean_ci_lwr", "mean_ci_upr"),
mean_sei = c("mean_sei_lwr", "mean_sei_upr"), mean_sdi = c("mean_sdi_lwr",
"mean_sdi_upr"), median_ci = c("median_ci_lwr",
"median_ci_upr"), quantiles = c("quantiles_0.25",
"quantile_0.75"), range = c("min", "max"))
}
y_unit <- if (is.null(y_unit))
NA
else y_unit
variables <- tern::control_lineplot_vars(x = x_var, y = y_var,
group_var = arm_var, paramcd = y_name, y_unit = y_unit,
subject_var = "USUBJID")
arm_lvl <- sort(unique(df[[arm_var]]))
col <- if (!is.null(names(line_col))) {
col_sel <- line_col[as.character(arm_lvl)]
if (anyNA(col_sel)) {
missing_col <- setdiff(arm_lvl, names(col_sel))
stop(paste("Missing color matching for", toString(missing_col)))
}
unname(col_sel)
}
else {
line_col
}
line_type <- if (!is.null(names(line_type))) {
tp <- line_type[as.character(arm_lvl)]
if (anyNA(tp)) {
missing_tp <- setdiff(arm_lvl, names(tp))
stop(paste("Missing line type matching for", toString(missing_tp)))
}
unname(tp)
}
else {
line_type
}
lapply(data_ls, tern::g_lineplot, alt_counts_df = adam_db[["adsl"]],
variables = variables, mid = center_fun, interval = interval_fun,
whiskers = whiskers_fun, position = ggplot2::position_dodge(width = jitter),
title = NULL, table = table, ggtheme = ggtheme, col = col,
linetype = line_type, subtitle_add_unit = !is.na(y_unit),
...)
}
Postprocessing function:
function (tlg, ...)
{
tlg <- report_null(tlg, ...)
tlg
}
run print internal functions when unwrap is TRUE and the chevron_tlg object is customized
Code
cat(res)
Output
Using template: custom_chevron
Using data: object of class call
Pre args:
No mapped argument.
Main args:
No mapped argument.
Post args:
No mapped argument.
Preprocessing function:
function (adam_db, ...)
adam_db
Main function:
function (adam_db, ...)
{
ggplot2::ggplot(adam_db$iris, ggplot2::aes(x = Sepal.Length,
y = Sepal.Width)) + ggplot2::geom_point()
}
Postprocessing function:
function (tlg, ...)
{
tlg <- report_null(tlg, ...)
tlg
}
run print main and postprocessing functions when unwrap is TRUE and auto_pre is FALSE
Code
cat(res)
Output
Main function:
function (adam_db, ...)
{
ggplot2::ggplot(adam_db$iris, ggplot2::aes(x = Sepal.Length,
y = Sepal.Width)) + ggplot2::geom_point()
}
Postprocessing function:
function (tlg, ...)
{
tlg <- report_null(tlg, ...)
tlg
}
script_funs works as expected in interactive mode
Code
res
Output
[1] "# Edit Preprocessing Function."
[2] "preprocess(aet04) <- "
[3] "function (adam_db, ...) "
[4] "{"
[5] " atoxgr_lvls <- c(\"1\", \"2\", \"3\", \"4\", \"5\")"
[6] " adam_db$adae <- adam_db$adae %>% filter(.data$ANL01FL == "
[7] " \"Y\") %>% mutate(AEBODSYS = with_label(reformat(.data$AEBODSYS, "
[8] " nocoding), \"MedDRA System Organ Class\"), AEDECOD = with_label(reformat(.data$AEDECOD, "
[9] " nocoding), \"MedDRA Preferred Term\"), ATOXGR = factor(.data$ATOXGR, "
[10] " levels = atoxgr_lvls))"
[11] " adam_db"
[12] "}"
[13] ""
[14] "# Create TLG"
[15] "tlg_output <- run(object = aet04, adam_db = data, verbose = TRUE, user_args = args_ls)"
script_funs generates a valid script
Code
cat(paste(res, collapse = "\n"))
Output
Using template: aet04
Using data: syn_adv
Pre args:
No mapped argument.
Main args:
arm_var : "ARM"
lbl_overall : NULL
grade_groups : NULL
Post args:
prune_0 : TRUE
Preprocessing function:
function (adam_db, ...)
{
atoxgr_lvls <- c("1", "2", "3", "4", "5")
adam_db$adae <- adam_db$adae %>% filter(.data$ANL01FL ==
"Y") %>% mutate(AEBODSYS = with_label(reformat(.data$AEBODSYS,
nocoding), "MedDRA System Organ Class"), AEDECOD = with_label(reformat(.data$AEDECOD,
nocoding), "MedDRA Preferred Term"), ATOXGR = factor(.data$ATOXGR,
levels = atoxgr_lvls))
adam_db
}
Main function:
function (adam_db, arm_var = "ACTARM", lbl_overall = NULL, grade_groups = NULL,
...)
{
assert_all_tablenames(adam_db, "adsl", "adae")
assert_string(arm_var)
assert_string(lbl_overall, null.ok = TRUE)
assert_list(grade_groups, types = "character", null.ok = TRUE)
assert_valid_variable(adam_db$adsl, c("USUBJID", arm_var),
types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, c(arm_var, "AEBODSYS",
"AEDECOD"), types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, "USUBJID", empty_ok = TRUE,
types = list(c("character", "factor")))
assert_valid_variable(adam_db$adae, "ATOXGR", na_ok = TRUE,
types = list("factor"))
assert_valid_var_pair(adam_db$adsl, adam_db$adae, arm_var)
lbl_overall <- render_safe(lbl_overall)
lbl_aebodsys <- var_labels_for(adam_db$adae, "AEBODSYS")
lbl_aedecod <- var_labels_for(adam_db$adae, "AEDECOD")
if (is.null(grade_groups)) {
grade_groups <- list(`Grade 1-2` = c("1", "2"), `Grade 3-4` = c("3",
"4"), `Grade 5` = c("5"))
}
lyt <- aet04_lyt(arm_var = arm_var, total_var = "TOTAL_VAR",
lbl_overall = lbl_overall, lbl_aebodsys = lbl_aebodsys,
lbl_aedecod = lbl_aedecod, grade_groups = grade_groups)
adam_db$adae$TOTAL_VAR <- "- Any adverse events - "
tbl <- build_table(lyt, df = adam_db$adae, alt_counts_df = adam_db$adsl)
tbl
}
Layout function:
aet04_lyt:
function (arm_var, total_var, lbl_overall, lbl_aebodsys, lbl_aedecod,
grade_groups)
{
basic_table(show_colcounts = TRUE) %>% split_cols_by_with_overall(arm_var,
lbl_overall) %>% split_rows_by(var = total_var, label_pos = "hidden",
child_labels = "visible", indent_mod = -1L) %>% summarize_num_patients(var = "USUBJID",
.stats = "unique", .labels = "- Any Grade -", .indent_mods = 7L) %>%
count_occurrences_by_grade(var = "ATOXGR", grade_groups = grade_groups,
.indent_mods = 6L) %>% split_rows_by("AEBODSYS",
child_labels = "visible", nested = FALSE, split_fun = drop_split_levels,
label_pos = "topleft", split_label = lbl_aebodsys) %>%
split_rows_by("AEDECOD", child_labels = "visible", split_fun = add_overall_level("- Overall -",
trim = TRUE), label_pos = "topleft", split_label = lbl_aedecod) %>%
summarize_num_patients(var = "USUBJID", .stats = "unique",
.labels = "- Any Grade -", .indent_mods = 6L) %>%
count_occurrences_by_grade(var = "ATOXGR", grade_groups = grade_groups,
.indent_mods = 5L) %>% append_topleft(" Grade")
}
Postprocessing function:
function (tlg, prune_0 = TRUE, ...)
{
tlg <- tlg %>% tlg_sort_by_vars(c("AEBODSYS", "AEDECOD"),
score_all_sum, decreasing = TRUE)
if (prune_0)
tlg <- trim_rows(tlg)
std_postprocessing(tlg)
}
print_list works
Code
print_list(alist(a = 1, b = b, c = xx))
Output
a : 1
b : b
c : xx
print_list works for empty list
Code
print_list(alist())
Output
No mapped argument.
NULL
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