In this vignette, we describe the various ways we can modify and
customize the appearance of rtables.
Loading the package:
It is possible to align the content by assigning "left",
"center" (default), and "right" to
.aligns and align arguments in
in_rows() and rcell(), respectively. It is
also possible to use decimal, dec_right, and
dec_left for decimal alignments. The first takes all
numerical values and aligns the decimal character . in
every value of the column that has align = "decimal". Also
numeric without decimal values are aligned according to an imaginary
. if specified as such. dec_left and
dec_right behave similarly, with the difference that if the
column present empty spaces at left or right, it pushes values towards
left or right taking the one value that has most decimal characters, if
right, or non-decimal values if left. For more details, please read the
related documentation page help("decimal_align").
Please consider using ?in_rows and ?rcell
for further clarifications on the two arguments, and use
formatters::list_valid_aligns() to see all available
alignment options.
In the following we show two simplified examples that use
align and .aligns, respectively.
# In rcell we use align.
lyt <- basic_table() %>%
analyze("AGE", function(x) {
in_rows(
left = rcell("l", align = "left"),
right = rcell("r", align = "right"),
center = rcell("c", align = "center")
)
})
tbl <- build_table(lyt, DM)
tbl
# all obs
# ————————————————
# left l
# right r
# center c# In in_rows, we use .aligns. This can either set the general value or the
# single values (see NB).
lyt2 <- basic_table() %>%
analyze("AGE", function(x) {
in_rows(
left = rcell("l"),
right = rcell("r"),
center = rcell("c"),
.aligns = c("right")
) # NB: .aligns = c("right", "left", "center")
})
tbl2 <- build_table(lyt2, DM)
tbl2
# all obs
# ————————————————
# left l
# right r
# center cThese concepts can be well applied to any clinical table as shown in the following, more complex, example.
lyt3 <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("SEX") %>%
analyze(c("AGE", "STRATA1"), function(x) {
if (is.numeric(x)) {
in_rows(
"mean" = rcell(mean(x)),
"sd" = rcell(sd(x)),
.formats = c("xx.x"), .aligns = "left"
)
} else if (is.factor(x)) {
rcell(length(unique(x)), align = "right")
} else {
stop("Unsupported type")
}
}, show_labels = "visible", na_str = "NE")
tbl3 <- build_table(lyt3, ex_adsl)
tbl3
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————————————
# F
# AGE
# mean 32.8 34.1 35.2
# sd 6.1 7.1 7.4
# STRATA1
# STRATA1 3 3 3
# M
# AGE
# mean 35.6 37.4 35.4
# sd 7.1 8.7 8.2
# STRATA1
# STRATA1 3 3 3
# U
# AGE
# mean 31.7 31.0 35.2
# sd 3.2 5.7 3.1
# STRATA1
# STRATA1 3 2 3
# UNDIFFERENTIATED
# AGE
# mean 28.0 NE 45.0
# sd NE NE 1.4
# STRATA1
# STRATA1 1 0 2The sequence of strings printed in the area between the column header
display and the first row label can be modified during pre-processing
using label position argument in row splits split_rows_by,
with the append_topleft function, and during
post-processing using the top_left() function. Note:
Indenting is automatically added label_pos = "topleft".
Within the layout initializer:
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("STRATA1") %>%
analyze("AGE") %>%
append_topleft("New top_left material here")
build_table(lyt, DM)
# New top_left material here A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————————————
# A
# Mean 32.53 32.30 35.76
# B
# Mean 35.46 32.42 34.39
# C
# Mean 36.34 34.45 33.54Specify label position using the split_rows function.
Notice the position of STRATA1 and SEX.
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("STRATA1", label_pos = "topleft") %>%
split_rows_by("SEX", label_pos = "topleft") %>%
analyze("AGE")
build_table(lyt, DM)
# STRATA1
# SEX A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————
# A
# F
# Mean 30.91 32.91 35.95
# M
# Mean 35.07 31.09 35.60
# U
# Mean NA NA NA
# UNDIFFERENTIATED
# Mean NA NA NA
# B
# F
# Mean 34.85 32.88 34.42
# M
# Mean 36.64 32.09 34.37
# U
# Mean NA NA NA
# UNDIFFERENTIATED
# Mean NA NA NA
# C
# F
# Mean 35.19 36.00 34.32
# M
# Mean 37.39 32.81 32.83
# U
# Mean NA NA NA
# UNDIFFERENTIATED
# Mean NA NA NAPost-processing using the top_left() function:
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("SEX") %>%
analyze(c("AGE", "STRATA1"), function(x) {
if (is.numeric(x)) {
in_rows(
"mean" = rcell(mean(x)),
"sd" = rcell(sd(x)),
.formats = c("xx.x"), .aligns = "left"
)
} else if (is.factor(x)) {
rcell(length(unique(x)), align = "right")
} else {
stop("Unsupported type")
}
}, show_labels = "visible", na_str = "NE") %>%
build_table(ex_adsl)
# Adding top-left material
top_left(lyt) <- "New top-left material here"
lyt
# New top-left material here A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————————————————————
# F
# AGE
# mean 32.8 34.1 35.2
# sd 6.1 7.1 7.4
# STRATA1
# STRATA1 3 3 3
# M
# AGE
# mean 35.6 37.4 35.4
# sd 7.1 8.7 8.2
# STRATA1
# STRATA1 3 3 3
# U
# AGE
# mean 31.7 31.0 35.2
# sd 3.2 5.7 3.1
# STRATA1
# STRATA1 3 2 3
# UNDIFFERENTIATED
# AGE
# mean 28.0 NE 45.0
# sd NE NE 1.4
# STRATA1
# STRATA1 1 0 2Table title, table body, referential footnotes and and main footers
can be inset from the left alignment of the titles and provenance footer
materials. This can be modified within the layout initializer
basic_table() using the inset argument or
during post-processing with table_inset().
Using the layout initializer:
lyt <- basic_table(inset = 5) %>%
analyze("AGE")
build_table(lyt, DM)
# all obs
# ——————————————
# Mean 34.22Using the post-processing function:
Without inset -
lyt <- basic_table() %>%
analyze("AGE")
tbl <- build_table(lyt, DM)
tbl
# all obs
# ——————————————
# Mean 34.22With an inset of 5 characters -
Below is an example with a table produced for clinical data. Compare the inset of the table and main footer between the two tables.
Without inset -
analysisfun <- function(x, ...) {
in_rows(
row1 = 5,
row2 = c(1, 2),
.row_footnotes = list(row1 = "row 1 rfn"),
.cell_footnotes = list(row2 = "row 2 cfn")
)
}
lyt <- basic_table(
title = "Title says Whaaaat", subtitles = "Oh, ok.",
main_footer = "ha HA! Footer!", prov_footer = "provenaaaaance"
) %>%
split_cols_by("ARM") %>%
analyze("AGE", afun = analysisfun)
result <- build_table(lyt, ex_adsl)
result
# Title says Whaaaat
# Oh, ok.
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# row1 {1} 5 5 5
# row2 1, 2 {2} 1, 2 {2} 1, 2 {2}
# ——————————————————————————————————————————————————
#
# {1} - row 1 rfn
# {2} - row 2 cfn
# ——————————————————————————————————————————————————
#
# ha HA! Footer!
#
# provenaaaaanceWith inset -
Notice, the inset does not apply to any title materials (main title, subtitles, page titles), or provenance footer materials. Inset settings is applied to top-left materials, referential footnotes main footer materials and any horizontal dividers.
table_inset(result) <- 5
result
# Title says Whaaaat
# Oh, ok.
#
# ——————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# row1 {1} 5 5 5
# row2 1, 2 {2} 1, 2 {2} 1, 2 {2}
# ——————————————————————————————————————————————————
#
# {1} - row 1 rfn
# {2} - row 2 cfn
# ——————————————————————————————————————————————————
#
# ha HA! Footer!
#
# provenaaaaanceA character value can be specified to modify the horizontal separation between column headers and the table. Horizontal separation applies when:
Below, we replace the default line with “=”.
tbl <- basic_table() %>%
split_cols_by("Species") %>%
add_colcounts() %>%
analyze(c("Sepal.Length", "Petal.Width"), function(x) {
in_rows(
mean_sd = c(mean(x), sd(x)),
var = var(x),
min_max = range(x),
.formats = c("xx.xx (xx.xx)", "xx.xxx", "xx.x - xx.x"),
.labels = c("Mean (sd)", "Variance", "Min - Max")
)
}) %>%
build_table(iris, hsep = "=")
tbl
# setosa versicolor virginica
# (N=50) (N=50) (N=50)
# ======================================================
# Sepal.Length
# Mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64)
# Variance 0.124 0.266 0.404
# Min - Max 4.3 - 5.8 4.9 - 7.0 4.9 - 7.9
# Petal.Width
# Mean (sd) 0.25 (0.11) 1.33 (0.20) 2.03 (0.27)
# Variance 0.011 0.039 0.075
# Min - Max 0.1 - 0.6 1.0 - 1.8 1.4 - 2.5A character value can be specified as a section divider which succeed every group defined by a split instruction. Note, a trailing divider at the end of the table is never printed.
Below, a “+” is repeated and used as a section divider.
lyt <- basic_table() %>%
split_cols_by("Species") %>%
analyze(head(names(iris), -1), afun = function(x) {
list(
"mean / sd" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
"range" = rcell(diff(range(x)), format = "xx.xx")
)
}, section_div = "+")
build_table(lyt, iris)
# setosa versicolor virginica
# ——————————————————————————————————————————————————————
# Sepal.Length
# mean / sd 5.01 (0.35) 5.94 (0.52) 6.59 (0.64)
# range 1.50 2.10 3.00
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Sepal.Width
# mean / sd 3.43 (0.38) 2.77 (0.31) 2.97 (0.32)
# range 2.10 1.40 1.60
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Petal.Length
# mean / sd 1.46 (0.17) 4.26 (0.47) 5.55 (0.55)
# range 0.90 2.10 2.40
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Petal.Width
# mean / sd 0.25 (0.11) 1.33 (0.20) 2.03 (0.27)
# range 0.50 0.80 1.10Section dividers can be set to ” ” to create a blank line.
lyt <- basic_table() %>%
split_cols_by("Species") %>%
analyze(head(names(iris), -1), afun = function(x) {
list(
"mean / sd" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
"range" = rcell(diff(range(x)), format = "xx.xx")
)
}, section_div = " ")
build_table(lyt, iris)
# setosa versicolor virginica
# ——————————————————————————————————————————————————————
# Sepal.Length
# mean / sd 5.01 (0.35) 5.94 (0.52) 6.59 (0.64)
# range 1.50 2.10 3.00
#
# Sepal.Width
# mean / sd 3.43 (0.38) 2.77 (0.31) 2.97 (0.32)
# range 2.10 1.40 1.60
#
# Petal.Length
# mean / sd 1.46 (0.17) 4.26 (0.47) 5.55 (0.55)
# range 0.90 2.10 2.40
#
# Petal.Width
# mean / sd 0.25 (0.11) 1.33 (0.20) 2.03 (0.27)
# range 0.50 0.80 1.10Separation characters can be specified for different row splits. However, only one will be printed if they “pile up” next to each other.
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_rows_by("RACE", section_div = "=") %>%
split_rows_by("STRATA1", section_div = "~") %>%
analyze("AGE", mean, var_labels = "Age", format = "xx.xx")
build_table(lyt, DM)
# A: Drug X B: Placebo C: Combination
# ———————————————————————————————————————————————————————————————————————————————————
# ASIAN
# A
# mean 32.19 33.90 36.81
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# B
# mean 34.12 31.62 34.73
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# C
# mean 36.21 33.00 32.39
# ===================================================================================
# BLACK OR AFRICAN AMERICAN
# A
# mean 31.50 28.57 33.62
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# B
# mean 35.60 30.83 33.67
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# C
# mean 35.50 34.18 35.00
# ===================================================================================
# WHITE
# A
# mean 37.67 31.33 33.17
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# B
# mean 39.86 39.00 34.75
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# C
# mean 39.75 44.67 36.75
# ===================================================================================
# AMERICAN INDIAN OR ALASKA NATIVE
# A
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# B
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# C
# mean NA NA NA
# ===================================================================================
# MULTIPLE
# A
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# B
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# C
# mean NA NA NA
# ===================================================================================
# NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER
# A
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# B
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# C
# mean NA NA NA
# ===================================================================================
# OTHER
# A
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# B
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# C
# mean NA NA NA
# ===================================================================================
# UNKNOWN
# A
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# B
# mean NA NA NA
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# C
# mean NA NA NATables by default have indenting at each level of splitting. A custom
indent value can be supplied with the indent_mod argument
within a split function to modify this default. Compare the indenting of
the tables below:
Default Indent -
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") %>%
split_rows_by("STRATA1") %>%
analyze("AGE", mean, format = "xx.x") %>%
build_table(DM)
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
#
# ——————————————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————————————
# F
# A
# mean 30.9 32.9 36.0
# B
# mean 34.9 32.9 34.4
# C
# mean 35.2 36.0 34.3
# M
# A
# mean 35.1 31.1 35.6
# B
# mean 36.6 32.1 34.4
# C
# mean 37.4 32.8 32.8
# U
# A
# mean NA NA NA
# B
# mean NA NA NA
# C
# mean NA NA NA
# UNDIFFERENTIATED
# A
# mean NA NA NA
# B
# mean NA NA NA
# C
# mean NA NA NA
# ——————————————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242aModified indent -
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", indent_mod = 3) %>%
split_rows_by("STRATA1", indent_mod = 5) %>%
analyze("AGE", mean, format = "xx.x") %>%
build_table(DM)
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
#
# ——————————————————————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————————————————————
# F
# A
# mean 30.9 32.9 36.0
# B
# mean 34.9 32.9 34.4
# C
# mean 35.2 36.0 34.3
# M
# A
# mean 35.1 31.1 35.6
# B
# mean 36.6 32.1 34.4
# C
# mean 37.4 32.8 32.8
# U
# A
# mean NA NA NA
# B
# mean NA NA NA
# C
# mean NA NA NA
# UNDIFFERENTIATED
# A
# mean NA NA NA
# B
# mean NA NA NA
# C
# mean NA NA NA
# ——————————————————————————————————————————————————————————————————
#
# Analysis was done using cool methods that are correct
#
# file: /path/to/stuff/that/lives/there HASH:1ac41b242aWith split instructions, visibility of the label for the variable
being split can be modified to visible, hidden
and topleft with the show_labels argument,
label_pos argument, and child_labels argument
where applicable. Note: this is NOT the name of the levels contained in
the variable. For analyze calls, indicates that the variable should be
visible only if multiple variables are analyzed at the same level of
nesting.
Visibility of labels for the groups generated by a split can also be
modified using the child_label argument with a split call.
The child_label argument can force labels to be visible in
addition to content rows but we cannot hide or move the content
rows.
Notice the placement of the “AGE” label in this example:
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX", split_fun = drop_split_levels, child_labels = "visible") %>%
split_rows_by("STRATA1") %>%
analyze("AGE", mean, show_labels = "default")
build_table(lyt, DM)
# A: Drug X B: Placebo C: Combination
# (N=121) (N=106) (N=129)
# —————————————————————————————————————————————————————————————————
# F
# A
# mean 30.9090909090909 32.9090909090909 35.95
# B
# mean 34.8518518518519 32.8823529411765 34.4210526315789
# C
# mean 35.1904761904762 36 34.3181818181818
# M
# A
# mean 35.0714285714286 31.0909090909091 35.6
# B
# mean 36.6428571428571 32.0869565217391 34.3684210526316
# C
# mean 37.3913043478261 32.8125 32.8333333333333When set to default, the label AGE is not repeated since
there is only one variable being analyzed at the same level of nesting.
Override this by setting the show_labels argument as
“visible”.
lyt2 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by(var = "ARM") %>%
split_rows_by("SEX", split_fun = drop_split_levels, child_labels = "hidden") %>%
split_rows_by("STRATA1") %>%
analyze("AGE", mean, show_labels = "visible")
build_table(lyt2, DM)
# A: Drug X B: Placebo C: Combination
# (N=121) (N=106) (N=129)
# —————————————————————————————————————————————————————————————————
# A
# AGE
# mean 30.9090909090909 32.9090909090909 35.95
# B
# AGE
# mean 34.8518518518519 32.8823529411765 34.4210526315789
# C
# AGE
# mean 35.1904761904762 36 34.3181818181818
# A
# AGE
# mean 35.0714285714286 31.0909090909091 35.6
# B
# AGE
# mean 36.6428571428571 32.0869565217391 34.3684210526316
# C
# AGE
# mean 37.3913043478261 32.8125 32.8333333333333Below is an example using the label_pos argument for
modifying label visibility:
Label order will mirror the order of split_rows_by
calls. If the labels of any subgroups should be hidden, the
label_pos argument should be set to hidden.
“SEX” label position is hidden -
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", split_fun = drop_split_levels, label_pos = "visible") %>%
split_rows_by("STRATA1", label_pos = "hidden") %>%
analyze("AGE", mean, format = "xx.x") %>%
build_table(DM)
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
#
# ————————————————————————————————————————————————————
# A: Drug X B: Placebo C: Combination
# ————————————————————————————————————————————————————
# SEX
# F
# A
# mean 30.9 32.9 36.0
# B
# mean 34.9 32.9 34.4
# C
# mean 35.2 36.0 34.3
# M
# 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:1ac41b242a“SEX” label position is with the top-left materials -
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", split_fun = drop_split_levels, label_pos = "topleft") %>%
split_rows_by("STRATA1", label_pos = "hidden") %>%
analyze("AGE", mean, format = "xx.x") %>%
build_table(DM)
# Study XXXXXXXX
# subtitle YYYYYYYYYY
# subtitle2 ZZZZZZZZZ
#
# ——————————————————————————————————————————————————
# SEX A: Drug X B: Placebo C: Combination
# ——————————————————————————————————————————————————
# F
# A
# mean 30.9 32.9 36.0
# B
# mean 34.9 32.9 34.4
# C
# mean 35.2 36.0 34.3
# M
# 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:1ac41b242aAn rtable can be rendered with a customized width by
setting custom rendering widths for cell contents, row labels, and
titles/footers.
This is demonstrated using the sample data and table below. In this section we aim to render this table with a reduced width since the table has very wide contents in several cells, labels, and titles/footers.
trimmed_data <- ex_adsl %>%
filter(SEX %in% c("M", "F")) %>%
filter(RACE %in% levels(RACE)[1:2])
levels(trimmed_data$ARM)[1] <- "Incredibly long column name to be wrapped"
levels(trimmed_data$ARM)[2] <- "This_column_name_should_be_split_somewhere"
wide_tbl <- basic_table(
title = "Title that is too long and also needs to be wrapped to a smaller width",
subtitles = "Subtitle that is also long and also needs to be wrapped to a smaller width",
main_footer = "Footnote that is wider than expected for this table.",
prov_footer = "Provenance footer material that is also wider than expected for this table."
) %>%
split_cols_by("ARM") %>%
split_rows_by("RACE", split_fun = drop_split_levels) %>%
analyze(
c("AGE", "EOSDY"),
na_str = "Very long cell contents to_be_wrapped_and_splitted",
inclNAs = TRUE
) %>%
build_table(trimmed_data)
wide_tbl
# Title that is too long and also needs to be wrapped to a smaller width
# Subtitle that is also long and also needs to be wrapped to a smaller width
#
# ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
# Incredibly long column name to be wrapped This_column_name_should_be_split_somewhere C: Combination
# ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
# ASIAN
# AGE
# Mean 32.50 36.68 36.99
# EOSDY
# Mean Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted
# BLACK OR AFRICAN AMERICAN
# AGE
# Mean 34.27 34.93 33.71
# EOSDY
# Mean Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted
# ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#
# Footnote that is wider than expected for this table.
#
# Provenance footer material that is also wider than expected for this table.In the following sections we will use the toString()
function to render the table in string form. This resulting string
representation is ready to be printed or written to a plain text file,
but we will use the strsplit() function in combination with
the matrix() function to preview the rendered wrapped table
in matrix form within this vignette.
The width of a rendered table can be customized by wrapping column
widths. This is done by setting custom width values via the
widths argument of the toString() function.
The length of the vector passed to the widths argument must
be equal to the total number of columns in the table, including the row
labels column, with each value of the vector corresponding to the
maximum width (in characters) allowed in each column, from left to
right.
Similarly, wrapping can be applied when exporting a table via one of
the four export_as_* functions and when implementing
pagination via the paginate_table() function from the
rtables package. In these cases, the rendered column widths
are set using the colwidths argument which takes input in
the same format as the widths argument of
toString().
For example, wide_tbl has four columns (1 row label
column and 3 content columns) which we will set the widths of below to
use in the rendered table. We set the width of the row label column to
10 characters and the widths of each of the 3 content columns to 8
characters. Any words longer than the specified width are broken and
continued on the following line. By default there are 3 spaces
separating each of the columns in the rendered table but this can be
customized via the col_gap argument to
toString() if further width customization is desired.
result_wrap_cells <- toString(wide_tbl, widths = c(10, 8, 8, 8))
matrix_wrap_cells <- matrix(strsplit(result_wrap_cells, "\n")[[1]], ncol = 1)
matrix_wrap_cells
# [,1]
# [1,] "Title that is too long and also needs to be wrapped to a smaller width"
# [2,] "Subtitle that is also long and also needs to be wrapped to a smaller width"
# [3,] ""
# [4,] "———————————————————————————————————————————"
# [5,] " Incredib This_col "
# [6,] " ly long umn_name "
# [7,] " column _should_ "
# [8,] " name be_split "
# [9,] " to be _somewhe C: Combi"
# [10,] " wrapped re nation "
# [11,] "———————————————————————————————————————————"
# [12,] "ASIAN "
# [13,] " AGE "
# [14,] " Mean 32.50 36.68 36.99 "
# [15,] " EOSDY "
# [16,] " Mean Very Very Very "
# [17,] " long long long "
# [18,] " cell cell cell "
# [19,] " contents contents contents"
# [20,] " to_be_wr to_be_wr to_be_wr"
# [21,] " apped_an apped_an apped_an"
# [22,] " d_splitt d_splitt d_splitt"
# [23,] " ed ed ed "
# [24,] "BLACK OR "
# [25,] "AFRICAN "
# [26,] "AMERICAN "
# [27,] " AGE "
# [28,] " Mean 34.27 34.93 33.71 "
# [29,] " EOSDY "
# [30,] " Mean Very Very Very "
# [31,] " long long long "
# [32,] " cell cell cell "
# [33,] " contents contents contents"
# [34,] " to_be_wr to_be_wr to_be_wr"
# [35,] " apped_an apped_an apped_an"
# [36,] " d_splitt d_splitt d_splitt"
# [37,] " ed ed ed "
# [38,] "———————————————————————————————————————————"
# [39,] ""
# [40,] "Footnote that is wider than expected for this table."
# [41,] ""
# [42,] "Provenance footer material that is also wider than expected for this table."In the resulting output we can see that the table has been correctly rendered using wrapping with a total width of 43 characters, but that the titles and footers remain wider than the rendered table.