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,
nested = TRUE,
inclNAs = FALSE,
extra_args = list(),
show_labels = c("default", "visible", "hidden"),
indent_mod = 0L
)layout object pre-data used for tabulation
character vector. Multiple variable names.
function. Analysis function, must take 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. Variable labels for 1 or more variables
character. Names for the tables representing each atomic analysis. Defaults to var.
FormatSpec. Format associated with this split. Formats can be declared via strings ("xx.x") or function. In cases such as analyze calls, they can character vectors or lists of functions.
boolean, Add this as a new top-level split (defining a new subtable directly under root). Defaults to FALSE
boolean. Should observations with NA in the var variable(s) be included when performing this analysis. Defaults to FALSE
list. Extra arguments to be passed to the tabulation function. Element position in thte 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 ttabulation function.
character(1). Should the variable labels for corresponding to the variable(s) in vars 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.
A PreDataTableLayouts object suitable for passing to further layouting functions, and to build_table.
The .spl_context data.frame gives information about the subsets of data corresponding to the
splits within-which the current analyze action is nested. Taken together, these correspond to the
path that the resulting (set of) rows the analysis function is creating, although the information is
in a slighlyt different form. Each split (which correspond to groups of rows in the resulting table) is
represented via the following columns:
The name of the split (often the variable being split in the simple case)
The string representation of the value at that split
a dataframe containing the full data (ie across all columns) corresponding to the path
defined by the combination of split and value of this row and all rows above this row
the number of observations corresponding to this row grouping (union of all columns)
These
list columns (named the same as names(col_exprs(tab))) contain logical vectors corresponding to the
subset of this row's full_parent_df corresponding to that column
List column containing logical vectors indicating the subset of that row's full_parent_df for the column currently being created by the analysis function
integer column containing the observation counts for that split
note Within analysis functions that accept .spl_context, the all_cols_n and cur_col_n columns of
the dataframe will contain the 'true' observation counts corresponding to the row-group and
row-group x column subsets of the data. These numbers will not, and currently cannot, reflect alternate
column observation counts provided by the alt_counts_df, col_counts or col_total arguments
to build_table
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 known 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. Which of these the
function accepts changes the behavior when tabulation is performed.
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 as follows:
column-wise N (column count) for the full column being tabulated within
overall N (all observation count, defined as sum of column counts) for the tabulation
row-wise N (row group count) for the group of observations being analyzed (ie with no column-based subsetting)
data.frame for observations in the row group being analyzed (ie with no column-based subsetting)
variable that is analyzed
data.frame or vector of subset corresponding to the ref_group column including subsetting
defined by row-splitting. Optional and only required/meaningful if a ref_group column has been defined
data.frame or vector of subset corresponding to the ref_group column without subsetting
defined by row-splitting. Optional and only required/meaningful if a ref_group column has been defined
boolean indicates if calculation is done for cells withing the reference column
data.frame, each row gives information about a previous/'ancestor' split state. see below
# NOT RUN {
l <- basic_table() %>%
split_cols_by("ARM") %>%
analyze("AGE", afun = list_wrap_x(summary) , format = "xx.xx")
l
build_table(l, DM)
l <- 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")
)
})
l
build_table(l, iris)
# }
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