Adding analyzed variables to our table layout defines the primary tabulation to be performed. We do this by
adding calls to analyze
and/or analyze_colvars()
into our layout pipeline. As with adding further splitting,
the tabulation will occur at the current/next level of nesting by default.
analyze(
lyt,
vars,
afun = simple_analysis,
var_labels = vars,
table_names = vars,
format = NULL,
na_str = NA_character_,
nested = TRUE,
inclNAs = FALSE,
extra_args = list(),
show_labels = c("default", "visible", "hidden"),
indent_mod = 0L,
section_div = NA_character_
)
A PreDataTableLayouts
object suitable for passing to further layouting functions, and to build_table()
.
(PreDataTableLayouts
)
layout object pre-data used for tabulation.
(character
)
vector of variable names.
(function
)
analysis function. Must accept x
or df
as its first parameter. Can optionally take
other parameters which will be populated by the tabulation framework. See Details in analyze()
.
(character
)
vector of labels for one or more variables.
(character
)
names for the tables representing each atomic analysis. Defaults to var
.
(string
, function
, or list
)
format associated with this split. Formats can be declared via
strings ("xx.x"
) or function. In cases such as analyze
calls, they can be character vectors or lists of
functions. See formatters::list_valid_format_labels()
for a list of all available format strings.
(string
)
string that should be displayed when the value of x
is missing. Defaults to "NA"
.
(logical
)
whether this layout instruction should be applied within the existing layout structure
if possible (TRUE
, the default) or as a new top-level element (FALSE
). Ignored if it would nest a split
underneath analyses, which is not allowed.
(logical
)
whether NA observations in the var
variable(s) should be included when performing
the analysis. Defaults to FALSE
.
(list
)
extra arguments to be passed to the tabulation function. Element position in the list
corresponds to the children of this split. Named elements in the child-specific lists are ignored if they do
not match a formal argument of the tabulation function.
(string
)
whether the variable labels corresponding to the variable(s) in vars
should be visible in the resulting table.
(numeric
)
modifier for the default indent position for the structure created by this
function (subtable, content table, or row) and all of that structure's children. Defaults to 0, which
corresponds to the unmodified default behavior.
(string
)
string which should be repeated as a section divider after the set of rows defined
by (each sub-analysis/variable) of this analyze instruction, or
NA_character_
(the default) for no section divider. This section
divider will be overridden by a split-level section divider when
both apply to the same position in the rendered output.
Gabriel Becker
When non-NULL
, format
is used to specify formats for all generated rows, and can be a character vector, a
function, or a list of functions. It will be repped out to the number of rows once this is calculated during the
tabulation process, but will be overridden by formats specified within rcell
calls in afun
.
The analysis function (afun
) should take as its first parameter either x
or df
. Whichever of these the
function accepts will change the behavior when tabulation is performed as follows:
If afun
's first parameter is x
, it will receive the corresponding subset vector of data from the relevant
column (from var
here) of the raw data being used to build the table.
If afun
's first parameter is df
, it will receive the corresponding subset data frame (i.e. all columns) of
the raw data being tabulated.
In addition to differentiation on the first argument, the analysis function can optionally accept a number of other parameters which, if and only if present in the formals, will be passed to the function by the tabulation machinery. These are listed and described in additional_fun_params.
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx")
lyt
tbl <- build_table(lyt, DM)
tbl
lyt2 <- 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")
)
})
lyt2
tbl2 <- build_table(lyt2, iris)
tbl2
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