We often want to split tables based on the values of one or more
variables (e.g., lab measurement) and then paginate separately
within each of those table subsections. In rtables we
do this via page by row splits.
Row splits can be declared page by splits by setting
page_by = TRUE in the split_rows_by*() call,
as below.
When page by splits are present, page titles are generated
automatically by appending the split value (typically a factor level,
though it need not be), to the page_prefix, separated by a
:. By default, page_prefix is name of the
variable being split.
lyt2 <- basic_table(
title = "Study XXXXXXXX",
subtitles = c("subtitle YYYYYYYYYY", "subtitle2 ZZZZZZZZZ"),
main_footer = "Analysis was done using cool methods that are correct",
prov_footer = "file: /path/to/stuff/that/lives/there HASH:1ac41b242a"
) %>%
split_cols_by("ARM") %>%
split_rows_by("SEX", page_by = TRUE, page_prefix = "Patient Subset - Gender", split_fun = drop_split_levels) %>%
split_rows_by("STRATA1") %>%
analyze("AGE", mean, format = "xx.x")
tbl2 <- build_table(lyt2, DM)
cat(export_as_txt(tbl2, paginate = TRUE, page_break = "\n\n~~~~ Page Break ~~~~\n\n"))# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# A
# mean 30.9 32.9 36.0
# B
# mean 34.9 32.9 34.4
# C
# mean 35.2 36.0 34.3
# ——————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
#
#
# ~~~~ Page Break ~~~~
#
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# A
# mean 35.1 31.1 35.6
# B
# mean 36.6 32.1 34.4
# C
# mean 37.4 32.8 32.8
# ——————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242aPage by row splits can be nested, but only within other page_by splits, they cannot be nested within traditional row splits. In this case, a page title for each page by split will be present on every resulting page, as seen below:
lyt3 <- basic_table(
title = "Study XXXXXXXX",
subtitles = c("subtitle YYYYYYYYYY", "subtitle2 ZZZZZZZZZ"),
main_footer = "Analysis was done using cool methods that are correct",
prov_footer = "file: /path/to/stuff/that/lives/there HASH:1ac41b242a"
) %>%
split_cols_by("ARM") %>%
split_rows_by("SEX", page_by = TRUE, page_prefix = "Patient Subset - Gender", split_fun = drop_split_levels) %>%
split_rows_by("STRATA1", page_by = TRUE, page_prefix = "Stratification - Strata") %>%
analyze("AGE", mean, format = "xx.x")
tbl3 <- build_table(lyt3, DM)
cat(export_as_txt(tbl3, paginate = TRUE, page_break = "\n\n~~~~ Page Break ~~~~\n\n"))# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
# Stratification - Strata: A
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# mean 30.9 32.9 36.0
# ——————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
#
#
# ~~~~ Page Break ~~~~
#
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
# Stratification - Strata: B
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# mean 34.9 32.9 34.4
# ——————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
#
#
# ~~~~ Page Break ~~~~
#
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
# Stratification - Strata: C
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# mean 35.2 36.0 34.3
# ——————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
#
#
# ~~~~ Page Break ~~~~
#
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
# Stratification - Strata: A
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# mean 35.1 31.1 35.6
# ——————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
#
#
# ~~~~ Page Break ~~~~
#
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
# Stratification - Strata: B
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# mean 36.6 32.1 34.4
# ——————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
#
#
# ~~~~ Page Break ~~~~
#
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
# Stratification - Strata: C
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# mean 37.4 32.8 32.8
# ——————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242aReferential footnotes are footnotes associated with a particular component of a table: a column, a row, or a cell. They can be added during tabulation via analysis functions, but they can also be added post-hoc once a table is created.
They are rendered as a number within curly braces within the table body, row, or column labels, followed by a message associated with that number printed below the table during rendering.
afun <- function(df, .var, .spl_context) {
val <- .spl_context$value[NROW(.spl_context)]
rw_fnotes <- if (val == "C") list("This is strata level C for these patients") else list()
cl_fnotes <- if (val == "B" && df[1, "ARM", drop = TRUE] == "C: Combination") {
list("these Strata B patients got the drug combination")
} else {
list()
}
in_rows(
mean = mean(df[[.var]]),
.row_footnotes = rw_fnotes,
.cell_footnotes = cl_fnotes,
.formats = c(mean = "xx.x")
)
}
lyt <- basic_table(
title = "Study XXXXXXXX",
subtitles = c("subtitle YYYYYYYYYY", "subtitle2 ZZZZZZZZZ"),
main_footer = "Analysis was done using cool methods that are correct",
prov_footer = "file: /path/to/stuff/that/lives/there HASH:1ac41b242a"
) %>%
split_cols_by("ARM") %>%
split_rows_by("SEX", page_by = TRUE, page_prefix = "Patient Subset - Gender", split_fun = drop_split_levels) %>%
split_rows_by("STRATA1") %>%
analyze("AGE", afun, format = "xx.x")
tbl <- build_table(lyt, DM)
cat(export_as_txt(tbl, paginate = TRUE, page_break = "\n\n\n"))# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: F
#
# ——————————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————————
# A
# mean 30.9 32.9 36.0
# B
# mean 34.9 32.9 34.4 {1}
# C
# mean {2} 35.2 36.0 34.3
# ——————————————————————————————————————————————————————
#
# {1} - these Strata B patients got the drug combination
# {2} - This is strata level C for these patients
# ——————————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242a
#
#
#
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
# Patient Subset - Gender: M
#
# ——————————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————————
# A
# mean 35.1 31.1 35.6
# B
# mean 36.6 32.1 34.4 {1}
# C
# mean {2} 37.4 32.8 32.8
# ——————————————————————————————————————————————————————
#
# {1} - these Strata B patients got the drug combination
# {2} - This is strata level C for these patients
# ——————————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242aWe note that typically the type of footnote added within the analysis function would be dependent on the computations done to calculate the cell value(s), e.g., a model not converging. We simply use context information as an illustrative proxy for that.
The procedure for adding footnotes to content (summary row) rows or cells is identical to the above, when done within a content function.
In addition to inserting referential footnotes at tabulation time within our analysis functions, we can also annotate our tables with them post-hoc.
This is also the only way to add footnotes to column labels, as those cannot be controlled within an analysis or content function.
## from ?tolower example slightly modified
.simpleCap <- function(x) {
if (length(x) > 1) {
return(sapply(x, .simpleCap))
}
s <- strsplit(tolower(x), " ")[[1]]
paste(toupper(substring(s, 1, 1)), substring(s, 2), sep = "", collapse = " ")
}
adsl2 <- ex_adsl %>%
filter(SEX %in% c("M", "F") & RACE %in% (levels(RACE)[1:3])) %>%
## we trim the level names here solely due to space considerations
mutate(ethnicity = .simpleCap(gsub("(.*)OR.*", "\\1", RACE)), RACE = factor(RACE))
lyt2 <- basic_table() %>%
split_cols_by("ARM") %>%
split_cols_by("SEX", split_fun = drop_split_levels) %>%
split_rows_by("RACE", labels_var = "ethnicity", split_fun = drop_split_levels) %>%
summarize_row_groups() %>%
analyze(c("AGE", "STRATA1"))
tbl2 <- build_table(lyt2, adsl2)
tbl2# A: Drug X B: Placebo C: Combination
# F M F M F M
# ———————————————————————————————————————————————————————————————————————————————————————
# Asian 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%)
# AGE
# Mean 31.22 34.60 35.06 38.63 36.44 37.66
# STRATA1
# A 11 10 14 10 11 7
# B 11 9 15 7 11 14
# C 19 6 7 13 17 11
# Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%)
# AGE
# Mean 34.06 34.58 33.88 36.33 33.21 34.21
# STRATA1
# A 5 2 5 6 3 7
# B 6 5 3 4 4 4
# C 7 5 8 2 7 3
# White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%)
# AGE
# Mean 34.12 40.00 32.41 34.62 33.00 30.80
# STRATA1
# A 5 3 3 3 3 5
# B 5 4 8 4 5 2
# C 7 2 6 1 3 3We do this with the fnotes_at_path<- function which
accepts a row path, a column path, and a value for the full set of
footnotes for the defined locations (NULL or a
character vector).
A non-NULL row path with a NULL column path
specifies the footnote(s) should be attached to the row, while
NULL row path with non-NULL column path
indicates they go with the column. Both being non-NULL
indicates a cell (and must resolve to an individual cell).
# A: Drug X B: Placebo C: Combination
# F M F M F M
# ——————————————————————————————————————————————————————————————————————————————————————————
# Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%)
# AGE
# Mean 31.22 34.60 35.06 38.63 36.44 37.66
# STRATA1
# A 11 10 14 10 11 7
# B 11 9 15 7 11 14
# C 19 6 7 13 17 11
# Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%)
# AGE
# Mean 34.06 34.58 33.88 36.33 33.21 34.21
# STRATA1
# A 5 2 5 6 3 7
# B 6 5 3 4 4 4
# C 7 5 8 2 7 3
# White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%)
# AGE
# Mean 34.12 40.00 32.41 34.62 33.00 30.80
# STRATA1
# A 5 3 3 3 3 5
# B 5 4 8 4 5 2
# C 7 2 6 1 3 3
# ——————————————————————————————————————————————————————————————————————————————————————————
#
# {1} - hi
# {2} - there
# ——————————————————————————————————————————————————————————————————————————————————————————# A: Drug X B: Placebo {NA} C: Combination
# F M F M F M
# ——————————————————————————————————————————————————————————————————————————————————————————
# Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%)
# AGE
# Mean 31.22 34.60 35.06 38.63 36.44 37.66
# STRATA1
# A 11 10 14 10 11 7
# B 11 9 15 7 11 14
# C 19 6 7 13 17 11
# Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%)
# AGE
# Mean 34.06 34.58 33.88 36.33 33.21 34.21
# STRATA1
# A 5 2 5 6 3 7
# B 6 5 3 4 4 4
# C 7 5 8 2 7 3
# White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%)
# AGE
# Mean 34.12 40.00 32.41 34.62 33.00 30.80
# STRATA1
# A 5 3 3 3 3 5
# B 5 4 8 4 5 2
# C 7 2 6 1 3 3
# ——————————————————————————————————————————————————————————————————————————————————————————
#
# {1} - hi
# {2} - there
# {NA} - this is a placebo
# ——————————————————————————————————————————————————————————————————————————————————————————Note to step into a content row we must add that to the path, even though we didn’t need it to put a footnote on the full row.
Currently, content rows by default are named with the label
rather than name of the corresponding facet. This is reflected
in the output of, e.g., row_paths_summary.
# rowname node_class path
# ———————————————————————————————————————————————————————————————————————————
# Asian ContentRow RACE, ASIAN, @content, Asian
# AGE LabelRow RACE, ASIAN, AGE
# Mean DataRow RACE, ASIAN, AGE, Mean
# STRATA1 LabelRow RACE, ASIAN, STRATA1
# A DataRow RACE, ASIAN, STRATA1, A
# B DataRow RACE, ASIAN, STRATA1, B
# C DataRow RACE, ASIAN, STRATA1, C
# Black ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, Black
# AGE LabelRow RACE, BLACK OR AFRICAN AMERICAN, AGE
# Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, AGE, Mean
# STRATA1 LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1
# A DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A
# B DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B
# C DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C
# White ContentRow RACE, WHITE, @content, White
# AGE LabelRow RACE, WHITE, AGE
# Mean DataRow RACE, WHITE, AGE, Mean
# STRATA1 LabelRow RACE, WHITE, STRATA1
# A DataRow RACE, WHITE, STRATA1, A
# B DataRow RACE, WHITE, STRATA1, B
# C DataRow RACE, WHITE, STRATA1, CSo we can add our footnotes to the cell like so:
fnotes_at_path(
tbl2,
rowpath = c("RACE", "ASIAN", "@content", "Asian"),
colpath = c("ARM", "B: Placebo", "SEX", "F")
) <- "These asian women got placebo treatments"
tbl2# A: Drug X B: Placebo {NA} C: Combination
# F M F M F M
# ——————————————————————————————————————————————————————————————————————————————————————————————
# Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) {3} 30 (60.0%) 39 (60.9%) 32 (57.1%)
# AGE
# Mean 31.22 34.60 35.06 38.63 36.44 37.66
# STRATA1
# A 11 10 14 10 11 7
# B 11 9 15 7 11 14
# C 19 6 7 13 17 11
# Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%)
# AGE
# Mean 34.06 34.58 33.88 36.33 33.21 34.21
# STRATA1
# A 5 2 5 6 3 7
# B 6 5 3 4 4 4
# C 7 5 8 2 7 3
# White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%)
# AGE
# Mean 34.12 40.00 32.41 34.62 33.00 30.80
# STRATA1
# A 5 3 3 3 3 5
# B 5 4 8 4 5 2
# C 7 2 6 1 3 3
# ——————————————————————————————————————————————————————————————————————————————————————————————
#
# {1} - hi
# {2} - there
# {3} - These asian women got placebo treatments
# {NA} - this is a placebo
# ——————————————————————————————————————————————————————————————————————————————————————————————