Layouts are used to describe a table pre-data. build_table is used to
create a table using a layout and a dataset.
Usage
build_table(
lyt,
df,
alt_counts_df = NULL,
col_counts = NULL,
col_total = if (is.null(alt_counts_df)) nrow(df) else nrow(alt_counts_df),
topleft = NULL,
hsep = default_hsep(),
...
)Arguments
- lyt
layout object pre-data used for tabulation
- df
dataset (
data.frameortibble)- alt_counts_df
dataset (
data.frameortibble). Alternative full data the rtables framework will use (only) when calculating column counts.- col_counts
numeric (or
NULL). Deprecated. If non-null, column counts which override those calculated automatically during tabulation. Must specify "counts" for all resulting columns if non-NULL.NAelements will be replaced with the automatically calculated counts.- col_total
integer(1). The total observations across all columns. Defaults to
nrow(df).- topleft
character. Override values for the "top left" material to be displayed during printing.
- hsep
character(1). Set of character(s) to be repeated as the separator between the header and body of the table when rendered as text. Defaults to a connected horizontal line (unicode 2014) in locals that use a UTF charset, and to
-elsewhere (with a once per session warning).- ...
currently ignored.
Value
A TableTree or ElementaryTable object representing the
table created by performing the tabulations declared in lyt to the
data df.
Details
When alt_counts_df is specified, column counts are calculated by
applying the exact column subsetting expressions determined when applying
column splitting to the main data (df) to alt_counts_df and
counting the observations in each resulting subset.
In particular, this means that in the case of splitting based on cuts of the
data, any dynamic cuts will have been calculated based on df and
simply re-used for the count calculation.
Note
When overriding the column counts or totals care must be taken that,
e.g., length() or nrow() are not called within tabulation functions,
because those will NOT give the overridden counts. Writing/using tabulation
functions which accept .N_col and .N_total or do not rely on
column counts at all (even implicitly) is the only way to ensure overridden
counts are fully respected.
Examples
lyt <- basic_table() %>%
split_cols_by("Species") %>%
analyze("Sepal.Length", afun = function(x) {
list(
"mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
"range" = diff(range(x))
)
})
lyt
#> A Pre-data Table Layout
#>
#> Column-Split Structure:
#> Species (lvls)
#>
#> Row-Split Structure:
#> Sepal.Length (** analysis **)
#>
tbl <- build_table(lyt, iris)
tbl
#> setosa versicolor virginica
#> ———————————————————————————————————————————————————
#> mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64)
#> range 1.5 2.1 3
# analyze multiple variables
lyt2 <- basic_table() %>%
split_cols_by("Species") %>%
analyze(c("Sepal.Length", "Petal.Width"), afun = function(x) {
list(
"mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
"range" = diff(range(x))
)
})
tbl2 <- build_table(lyt2, iris)
tbl2
#> setosa versicolor virginica
#> ——————————————————————————————————————————————————————
#> Sepal.Length
#> mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64)
#> range 1.5 2.1 3
#> Petal.Width
#> mean (sd) 0.25 (0.11) 1.33 (0.20) 2.03 (0.27)
#> range 0.5 0.8 1.1
# an example more relevant for clinical trials with column counts
lyt3 <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ARM") %>%
analyze("AGE", afun = function(x) {
setNames(as.list(fivenum(x)), c("minimum", "lower-hinge", "median",
"upper-hinge", "maximum"))
})
tbl3 <- build_table(lyt3, DM)
tbl3
#> A: Drug X B: Placebo C: Combination
#> (N=121) (N=106) (N=129)
#> —————————————————————————————————————————————————————
#> minimum 20 21 22
#> lower-hinge 29 29 30
#> median 33 32 33
#> upper-hinge 39 37 38
#> maximum 60 55 53
tbl4 <- build_table(lyt3, subset(DM, AGE > 40))
tbl4
#> A: Drug X B: Placebo C: Combination
#> (N=25) (N=10) (N=21)
#> —————————————————————————————————————————————————————
#> minimum 41 41 41
#> lower-hinge 43 42 43
#> median 45 45.5 45
#> upper-hinge 49 48 47
#> maximum 60 55 53
# with column counts calculated based on different data
miniDM <- DM[sample(1:NROW(DM), 100),]
tbl5 <- build_table(lyt3, DM, alt_counts_df = miniDM)
tbl5
#> A: Drug X B: Placebo C: Combination
#> (N=37) (N=29) (N=34)
#> —————————————————————————————————————————————————————
#> minimum 20 21 22
#> lower-hinge 29 29 30
#> median 33 32 33
#> upper-hinge 39 37 38
#> maximum 60 55 53
tbl6 <- build_table(lyt3, DM, col_counts = 1:3)
tbl6
#> A: Drug X B: Placebo C: Combination
#> (N=1) (N=2) (N=3)
#> —————————————————————————————————————————————————————
#> minimum 20 21 22
#> lower-hinge 29 29 30
#> median 33 32 33
#> upper-hinge 39 37 38
#> maximum 60 55 53