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cross.R
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cross.R
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TOTAL_STATISTICS = c("u_cases", "w_cases", "u_responses", "w_responses", "u_cpct", "w_cpct",
"u_rpct", "w_rpct", "u_tpct", "w_tpct")
#' Cross tabulation with support of labels, weights and multiple response variables.
#'
#' \itemize{
#' \item{\code{cross_cases}}{ build a contingency table of the counts.}
#' \item{\code{cross_cpct}, \code{cross_cpct_responses}}{ build a contingency table
#' of the column percent. These functions give different results only for
#' multiple response variables. For \code{cross_cpct} base of percent is number
#' of valid cases. Case is considered as valid if it has at least one non-NA
#' value. So for multiple response variables sum of percent may be greater than
#' 100. For \code{cross_cpct_responses} base of percent is number of valid
#' responses. Multiple response variables can have several responses for single
#' case. Sum of percent of \code{cross_cpct_responses} always equals to 100\%.}
#' \item{\code{cross_rpct}}{ build a contingency table of the row percent. Base
#' for percent is number of valid cases.}
#' \item{\code{cross_tpct}}{ build a contingency table of the table percent. Base
#' for percent is number of valid cases.}
#' \item{\code{cross_*}}{ functions evaluate their arguments
#' in the context of the first argument \code{data}.}
#' \item{\code{cro_*}}{ functions use standard evaluation, e. g 'cro(mtcars$am, mtcars$vs)'.}
#' \item{\code{total}}{ auxiliary function - creates variables with 1 for valid
#' case of its argument \code{x} and NA in opposite case.}
#' }
#' You can combine tables with \link{add_rows} and \link{merge.etable}. For
#' sorting table see \link{tab_sort_asc}.
#' To provide multiple-response variables as arguments use \link{mrset} for
#' multiples with category encoding and \link{mdset} for multiples with
#' dichotomy (dummy) encoding. To compute statistics with nested
#' variables/banners use \link{nest}. For more sophisticated interface with
#' modern piping via \code{magrittr} see \link{tables}.
#'
#' @param cell_vars vector/data.frame/list. Variables on which percentage/cases
#' will be computed. Use \link{mrset}/\link{mdset} for multiple-response
#' variables.
#' @param col_vars vector/data.frame/list. Variables which breaks table by
#' columns. Use \link{mrset}/\link{mdset} for multiple-response variables.
#' @param row_vars vector/data.frame/list. Variables which breaks table by rows.
#' Use \link{mrset}/\link{mdset} for multiple-response variables.
#' @param weight numeric vector. Optional cases weights. Cases with NA's,
#' negative and zero weights are removed before calculations.
#' @param subgroup logical vector. You can specify subgroup on which table will be computed.
#' @param total_label By default "#Total". You can provide several names - each name for
#' each total statistics.
#' @param total_statistic By default it is "u_cases" (unweighted cases).
#' Possible values are "u_cases", "u_responses", "u_cpct", "u_rpct", "u_tpct",
#' "w_cases", "w_responses", "w_cpct", "w_rpct", "w_tpct". "u_" means
#' unweighted statistics and "w_" means weighted statistics.
#' @param total_row_position Position of total row in the resulting table. Can
#' be one of "below", "above", "none".
#' @param data data.frame in which context all other arguments will be evaluated
#' (for \code{cross_*}).
#' @param x vector/data.frame of class 'category'/'dichotomy'.
#' @param label character. Label for total variable.
#'
#' @return object of class 'etable'. Basically it's a data.frame but class
#' is needed for custom methods.
#' @seealso \link{tables}, \link{fre}, \link{cro_fun}.
#'
#' @examples
#' \dontrun{
#' data(mtcars)
#' mtcars = apply_labels(mtcars,
#' mpg = "Miles/(US) gallon",
#' cyl = "Number of cylinders",
#' disp = "Displacement (cu.in.)",
#' hp = "Gross horsepower",
#' drat = "Rear axle ratio",
#' wt = "Weight (1000 lbs)",
#' qsec = "1/4 mile time",
#' vs = "Engine",
#' vs = c("V-engine" = 0,
#' "Straight engine" = 1),
#' am = "Transmission",
#' am = c("Automatic" = 0,
#' "Manual"=1),
#' gear = "Number of forward gears",
#' carb = "Number of carburetors"
#' )
#'
#' cross_cases(mtcars, am, vs)
#' cro(mtcars$am, mtcars$vs) # the same result
#'
#' # column percent with multiple banners
#' cross_cpct(mtcars, cyl, list(total(), vs, am))
#'
#' # nested banner
#' cross_cpct(mtcars, cyl, list(total(), vs %nest% am))
#'
#' # stacked variables
#' cross_cases(mtcars, list(cyl, carb), list(total(), vs %nest% am))
#'
#' # nested variables
#' cross_cpct(mtcars, am %nest% cyl, list(total(), vs))
#'
#' # row variables
#' cross_cpct(mtcars, cyl, list(total(), vs), row_vars = am)
#'
#' # several totals above table
#' cross_cpct(mtcars, cyl,
#' list(total(), vs),
#' row_vars = am,
#' total_row_position = "above",
#' total_label = c("number of cases", "row %"),
#' total_statistic = c("u_cases", "u_rpct")
#' )
#'
#' # multiple-choice variable
#' # brands - multiple response question
#' # Which brands do you use during last three months?
#' set.seed(123)
#' brands = data.table(t(replicate(20,sample(c(1:5,NA),4,replace = FALSE)))) %>%
#' setNames(paste0("brand_", 1:4))
#' # score - evaluation of tested product
#' brands = brands %>%
#' let(
#' score = sample(-1:1,.N,replace = TRUE)
#' ) %>%
#' apply_labels(
#' brand_1 = "Used brands",
#' brand_1 = num_lab("
#' 1 Brand A
#' 2 Brand B
#' 3 Brand C
#' 4 Brand D
#' 5 Brand E
#' "),
#'
#' score = "Evaluation of tested brand",
#' score = num_lab("
#' -1 Dislike it
#' 0 So-so
#' 1 Like it
#' ")
#' )
#' cross_cpct(brands, mrset(brand_1 %to% brand_4), list(total(), score))
#' # responses
#' cross_cpct_responses(brands, mrset(brand_1 %to% brand_4), list(total(), score))
#' }
#' @export
cross_cases = function(data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
){
expr = substitute(cro_cases(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position)
)
calculate_internal(data, expr = expr, parent = parent.frame())
}
########################
#' @export
#' @rdname cross_cases
cross_cpct = function(data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
){
expr = substitute(cro_cpct(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position)
)
calculate_internal(data, expr = expr, parent = parent.frame())
}
#' @export
#' @rdname cross_cases
cross_rpct = function(data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
){
expr = substitute(cro_rpct(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position)
)
calculate_internal(data, expr = expr, parent = parent.frame())
}
#' @export
#' @rdname cross_cases
cross_tpct = function(data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
){
expr = substitute(cro_tpct(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position)
)
calculate_internal(data, expr = expr, parent = parent.frame())
}
#' @export
#' @rdname cross_cases
cross_cpct_responses = function(data,
cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_responses",
total_row_position = c("below", "above", "none")
){
expr = substitute(cro_cpct_responses(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position)
)
calculate_internal(data, expr = expr, parent = parent.frame())
}
########################
#' @export
#' @rdname cross_cases
cro = function(cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
){
str_cell_vars = expr_to_character(substitute(cell_vars))
str_col_vars = expr_to_character(substitute(col_vars))
str_row_vars = expr_to_character(substitute(row_vars))
cell_vars = test_for_null_and_make_list(cell_vars, str_cell_vars)
col_vars = test_for_null_and_make_list(col_vars, str_col_vars)
if(!is.null(row_vars)) row_vars = test_for_null_and_make_list(row_vars, str_row_vars)
multi_cro(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position,
stat_type = "cases"
)
}
#' @export
#' @rdname cross_cases
cro_cases = cro
#' @export
#' @rdname cross_cases
cro_cpct = function(cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
){
str_cell_vars = expr_to_character(substitute(cell_vars))
str_col_vars = expr_to_character(substitute(col_vars))
str_row_vars = expr_to_character(substitute(row_vars))
cell_vars = test_for_null_and_make_list(cell_vars, str_cell_vars)
col_vars = test_for_null_and_make_list(col_vars, str_col_vars)
if(!is.null(row_vars)) row_vars = test_for_null_and_make_list(row_vars, str_row_vars)
multi_cro(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position,
stat_type = "cpct"
)
}
#' @export
#' @rdname cross_cases
cro_rpct = function(cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
){
str_cell_vars = expr_to_character(substitute(cell_vars))
str_col_vars = expr_to_character(substitute(col_vars))
str_row_vars = expr_to_character(substitute(row_vars))
cell_vars = test_for_null_and_make_list(cell_vars, str_cell_vars)
col_vars = test_for_null_and_make_list(col_vars, str_col_vars)
if(!is.null(row_vars)) row_vars = test_for_null_and_make_list(row_vars, str_row_vars)
multi_cro(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position,
stat_type = "rpct"
)
}
#' @export
#' @rdname cross_cases
cro_tpct = function(cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_cases",
total_row_position = c("below", "above", "none")
){
str_cell_vars = expr_to_character(substitute(cell_vars))
str_col_vars = expr_to_character(substitute(col_vars))
str_row_vars = expr_to_character(substitute(row_vars))
cell_vars = test_for_null_and_make_list(cell_vars, str_cell_vars)
col_vars = test_for_null_and_make_list(col_vars, str_col_vars)
if(!is.null(row_vars)) row_vars = test_for_null_and_make_list(row_vars, str_row_vars)
multi_cro(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position,
stat_type = "tpct"
)
}
#' @export
#' @rdname cross_cases
cro_cpct_responses = function(cell_vars,
col_vars = total(),
row_vars = NULL,
weight = NULL,
subgroup = NULL,
total_label = NULL,
total_statistic = "u_responses",
total_row_position = c("below", "above", "none")
){
str_cell_vars = expr_to_character(substitute(cell_vars))
str_col_vars = expr_to_character(substitute(col_vars))
str_row_vars = expr_to_character(substitute(row_vars))
cell_vars = test_for_null_and_make_list(cell_vars, str_cell_vars)
col_vars = test_for_null_and_make_list(col_vars, str_col_vars)
if(!is.null(row_vars)) row_vars = test_for_null_and_make_list(row_vars, str_row_vars)
multi_cro(cell_vars = cell_vars,
col_vars = col_vars,
row_vars = row_vars,
weight = weight,
subgroup = subgroup,
total_label = total_label,
total_statistic = total_statistic,
total_row_position = total_row_position,
stat_type = "cpct_responses"
)
}
########################
make_datatable_for_cro = function(cell_var,
col_var,
row_var,
weight,
subgroup){
max_nrow = max(NROW(cell_var), NROW(col_var), NROW(row_var), NROW(weight))
if(is.null(subgroup)){
non_empty_rows = valid(cell_var) & valid(col_var)
} else {
non_empty_rows = valid(cell_var) & valid(col_var) & subgroup & !is.na(subgroup)
}
col_var = recycle_if_single_row(col_var, max_nrow)
cell_var = recycle_if_single_row(cell_var, max_nrow)
cell_var = unlab(cell_var)
col_var = unlab(col_var)
use_row_var = !is.null(row_var)
if(use_row_var){
non_empty_rows = valid(row_var) & non_empty_rows
row_var = recycle_if_single_row(row_var, max_nrow)
row_var = unlab(row_var)
}
use_weight = !is.null(weight)
if(use_weight) {
weight = unlab(weight)
weight = set_negative_and_na_to_zero(weight)
weight = recycle_if_single_row(weight, max_nrow)
non_empty_rows = non_empty_rows & (weight>0)
}
if(!all(non_empty_rows, na.rm = TRUE) || length(non_empty_rows)==0){
if(use_weight) weight = universal_subset(weight, non_empty_rows)
col_var = universal_subset(col_var, non_empty_rows)
cell_var = universal_subset(cell_var, non_empty_rows)
if(use_row_var) row_var = universal_subset(row_var, non_empty_rows)
}
cell_var_names = paste0("cells", seq_len(NCOL(cell_var)))
col_var_names = paste0("cols", seq_len(NCOL(col_var)))
if(use_weight){
if(use_row_var){
raw_data = data.table(cell_var,
col_var,
weight,
row_var)
setnames(raw_data, c(cell_var_names, col_var_names, "weight", "row_var"))
} else {
raw_data = data.table(cell_var,
col_var,
weight)
setnames(raw_data, c(cell_var_names, col_var_names, "weight"))
}
} else {
if(use_row_var){
raw_data = data.table(cell_var,
col_var,
row_var)
setnames(raw_data, c(cell_var_names, col_var_names, "row_var"))
} else {
raw_data = data.table(cell_var,
col_var)
setnames(raw_data, c(cell_var_names, col_var_names))
}
}
raw_data
}
extract_cell_names = function(raw_data){
grep("cell", colnames(raw_data), value = TRUE, fixed = TRUE)
}
extract_col_names = function(raw_data){
grep("col", colnames(raw_data), value = TRUE, fixed = TRUE)
}
has_row_var = function(raw_data){
"row_var" %in% colnames(raw_data)
}
has_weight = function(raw_data){
"weight" %in% colnames(raw_data)
}
### compute statistics for single cell_var and single col_var
### by now it is absolutely awfull code which likes spaghetti
### it is consequency of optimiztion which lead to four time performance
### improvement in some frequent cases
### need to rewrite
elementary_cro = function(cell_var,
col_var,
row_var,
weight,
subgroup,
total_label,
total_statistic,
total_row_position = c("below", "above", "none"),
stat_type = c("cases", "cpct", "cpct_responses", "rpct", "tpct")
){
# to pass CRAN check
row_labels = NULL
### preparations
total_row_position = match.arg(total_row_position)
unknowns = total_statistic %d% TOTAL_STATISTICS
stopif(length(unknowns)>0, "unknown total statistics - ",
paste(unknowns, collapse = ", "))
total_statistic = match.arg(total_statistic, TOTAL_STATISTICS, several.ok = TRUE)
stat_type = match.arg(stat_type)
cell_var_lab = var_lab(cell_var)
cell_val_lab = val_lab(cell_var)
col_var_lab = var_lab(col_var)
col_val_lab = val_lab(col_var)
row_var_lab = var_lab(row_var)
row_val_lab = val_lab(row_var)
raw_data = make_datatable_for_cro(cell_var = cell_var,
col_var = col_var,
row_var = row_var,
weight = weight,
subgroup = subgroup
)
col_var_names = extract_col_names(raw_data)
cell_var_names = extract_cell_names(raw_data)
use_weight = has_weight(raw_data)
use_row_var = has_row_var(raw_data)
##### cases
cases = internal_cases(raw_data,
col_var_names = col_var_names,
cell_var_names = cell_var_names,
use_weight = use_weight)
###### margins ########
# stat_type == "cpct" || ("w_cases", "u_cases", "w_tpct", "u_tpct", "w_rpct", "u_rpct")
column_margin = calculate_column_margin(raw_data,
cases,
stat_type,
total_statistic,
use_weight)
###########################
## stat_type = "rpct"
row_margin = calculate_row_margin(raw_data,
cases,
stat_type,
total_statistic,
use_weight)
### margin_responses ####
# w_responses | u_responses
response_column_margin = calculate_response_column_margin(raw_data,
cases,
stat_type,
total_statistic,
use_weight)
##############
### stat_type == "tpct" || "w_tpct", "u_tpct", "w_rpct", "u_rpct"
total_margin = calculate_total_margin(raw_data,
cases,
stat_type,
total_statistic,
use_weight)
#######
if(stat_type == "cases"){
dtable = cases
} else {
dtable = calculate_percent(cases,
margin = switch(stat_type,
cpct = column_margin[["w"]],
tpct = total_margin[["w"]],
cpct_responses = response_column_margin[["w"]],
rpct = row_margin[["w"]]
),
stat_type = stat_type)
}
################
dtable[, cell_var := set_var_lab(cell_var, cell_var_lab)]
dtable[, cell_var := set_val_lab(cell_var, cell_val_lab)]
dtable[, col_var := set_var_lab(col_var, col_var_lab)]
dtable[, col_var := set_val_lab(col_var, col_val_lab)]
if(use_row_var){
dtable[, row_var := set_var_lab(row_var, row_var_lab)]
dtable[, row_var := set_val_lab(row_var, row_val_lab)]
}
### make rectangular table
if(use_row_var){
row_var_name = "row_var"
} else {
row_var_name = NULL
}
res = long_datatable_to_table(dtable, rows = c(row_var_name, "cell_var"),
columns = "col_var", values = "value")
colnames(res)[1 + use_row_var] = "row_labels"
if(total_row_position!="none"){
total_rows = make_total_rows(
need_row_var = has_row_var(cases),
column_margin = column_margin,
total_margin = total_margin,
response_column_margin = response_column_margin,
row_margin = row_margin,
total_statistic = total_statistic,
total_label = total_label
)
total_rows[, col_var := set_var_lab(col_var, col_var_lab)]
total_rows[, col_var := set_val_lab(col_var, col_val_lab)]
if(use_row_var){
total_rows[, row_var := set_var_lab(row_var, row_var_lab)]
total_rows[, row_var := set_val_lab(row_var, row_val_lab)]
}
# total_rows[, row_labels := factor(row_labels, levels = unique(row_labels))]
total_rows = long_datatable_to_table(total_rows,
rows = c(row_var_name, "row_labels"),
columns = "col_var",
values = "total")
# total_rows = total_rows[, -"item"]
res = add_total_to_table(res, total_rows = total_rows, total_row_position = total_row_position)
}
if(use_row_var){
res[, row_labels := paste0(row_var_lab, "|", row_var, "|", cell_var_lab, "|", row_labels)]
res[, row_var:=NULL ]
} else {
res[, row_labels := paste0(cell_var_lab, "|", row_labels)]
}
setnames(res, c(colnames(res)[1] , paste0(col_var_lab, "|", colnames(res)[-1])))
res[ , row_labels := remove_unnecessary_splitters(row_labels)]
res[ , row_labels := make_items_unique(row_labels)]
setnames(res, remove_unnecessary_splitters(colnames(res)))
res = as.sheet(res)
class(res) = union("etable", class(res))
res
}
#########
margin_from_raw = function(raw_data, margin = c("columns", "rows", "total"), use_weight){
margin = match.arg(margin)
aggr_names = switch(margin,
columns = extract_col_names(raw_data),
rows = extract_cell_names(raw_data),
total = NULL
)
dtotal = internal_cases(raw_data,
col_var_names = aggr_names,
cell_var_names = NULL,
use_weight = use_weight)
if(margin=="rows"){
setnames(dtotal, "col_var", "cell_var")
}
setnames(dtotal, "value", "total")
dtotal
}
#########
margin_from_cases = function(cases, margin = c("columns", "rows", "total")){
# to pass CRAN check
value = NULL
margin = match.arg(margin)
if(has_row_var(cases)) {
row_var_name = "row_var"
} else {
row_var_name = NULL
}
margin = switch(margin,
columns = "col_var",
rows = "cell_var",
total = NULL
)
by_str = c(row_var_name, margin)
dtotal = cases[, list(total = sum(value, na.rm = TRUE)), by = by_str]
dtotal
}
# return list with weighted and unweighted column margin
# if we don't need some margins there was NULL instead of this margin
calculate_column_margin = function(raw_data, cases, stat_type, total_statistic, use_weight){
if((stat_type == "cpct") ||
any(total_statistic %in% c("w_cases", "u_cases", "w_tpct", "u_tpct", "w_rpct", "u_rpct"))
) {
need_unweighted = use_weight && any(total_statistic %in% c("u_cases", "u_tpct", "u_rpct"))
cell_var_names = extract_cell_names(raw_data)
if(!use_weight || (stat_type=="cpct") || any(total_statistic %in% c("w_cases", "w_tpct", "w_rpct"))){
if(length(cell_var_names)>1){
w_margin = margin_from_raw(raw_data = raw_data,
margin = "column",
use_weight = use_weight
)
} else {
w_margin = margin_from_cases(cases = cases,
margin = "column"
)
}
} else {
w_margin = NULL
}
if(need_unweighted){
u_margin = margin_from_raw(raw_data = raw_data,
margin = "column",
use_weight = FALSE
)
} else {
u_margin = w_margin
}
list("w" = w_margin, "u" = u_margin)
} else {
list("w" = NULL, "u" = NULL)
}
}
# return list with weighted and unweighted column margin
# if we don't need some margins there was NULL instead of this margin
calculate_row_margin = function(raw_data,
cases,
stat_type,
total_statistic,
use_weight){
margin = list(w = NULL, u = NULL)
if(stat_type %in% c("rpct")){
col_var_names = extract_col_names(raw_data)
if(length(col_var_names)>1){
margin$w = margin_from_raw(raw_data = raw_data,
margin = "row",
use_weight = use_weight
)
} else {
margin$w = margin_from_cases(cases = cases,
margin = "row"
)
}
}
margin
}
# return list with weighted and unweighted column margin
# if we don't need some margins there was NULL instead of this margin
calculate_response_column_margin = function(raw_data,
cases,
stat_type,
total_statistic,
use_weight){
# to pass CRAN check
value = NULL
if(stat_type=="cpct_responses" || any(total_statistic %in% c("u_responses", "w_responses"))){
if(has_row_var(raw_data)){
by_str = c("row_var", "col_var")
} else {
by_str = "col_var"
}
if(!use_weight || any(total_statistic %in% c("w_responses")) || stat_type=="cpct_responses" ){
w_margin = cases[, list(total = sum(value, na.rm = TRUE)), by = by_str]
} else {
w_margin = NULL
}
if(use_weight && any(total_statistic %in% c("u_responses"))){
u_cases = internal_cases(raw_data,
col_var_names = extract_col_names(raw_data),
cell_var_names = extract_cell_names(raw_data),
use_weight = FALSE)
u_margin = u_cases[, list(total = sum(value, na.rm = TRUE)), by = by_str]
} else {
u_margin = w_margin
}
list(w = w_margin, u = u_margin)
} else {
list(w = NULL, u = NULL)
}
}
calculate_total_margin = function(raw_data,
cases,
stat_type,
total_statistic,
use_weight){
if((stat_type == "tpct") ||
any(total_statistic %in% c("w_tpct", "u_tpct", "w_rpct", "u_rpct"))
) {
need_unweighted = use_weight && any(total_statistic %in% c("u_tpct", "u_rpct"))
col_var_names = extract_col_names(raw_data)
cell_var_names = extract_cell_names(raw_data)
if(!use_weight || (stat_type=="tpct") || any(total_statistic %in% c("w_tpct", "w_rpct"))){
if(length(cell_var_names)>1 || length(col_var_names)>1){
w_margin = margin_from_raw(raw_data = raw_data,
margin = "total",
use_weight = use_weight
)
} else {
w_margin = margin_from_cases(cases = cases,
margin = "total"
)
}
} else {
w_margin = NULL
}
if(need_unweighted){
u_margin = margin_from_raw(raw_data = raw_data,
margin = "total",
use_weight = FALSE
)
} else {
u_margin = w_margin
}
list("w" = w_margin, "u" = u_margin)
} else {
list("w" = NULL, "u" = NULL)
}
}
########################
# argument - list of data.tables possibly nested (no more than 2 levels)
# all data.tables will be combine in single data.table and then aggregated
# by all variables except 'value'
# names of columns may be different
rbindlist_and_aggregate = function(list_of_datatables){
value = NULL
if(length(list_of_datatables)==1){
if(is.data.table(list_of_datatables[[1]])){
return(list_of_datatables[[1]])
}
if(length(list_of_datatables[[1]])==1){
return(list_of_datatables[[1]][[1]])
}
}
if(!is.data.table(list_of_datatables[[1]])){
list_of_datatables = unlist(list_of_datatables, recursive = FALSE, use.names = FALSE)
}
res = rbindlist(list_of_datatables, use.names = FALSE, fill = FALSE)
by_str = setdiff(colnames(res), "value")
res[, list(value = sum(value, na.rm = TRUE)), by = by_str]
}
internal_cases = function(raw_data, col_var_names, cell_var_names = NULL, use_weight){
# to pass CRAN check
weight = NULL
value = NULL
col_var = NULL
cell_var = NULL
row_var = NULL
if(has_row_var(raw_data)){
row_var_name = "row_var"
} else {
row_var_name = NULL
}
if(is.null(cell_var_names)) cell_var_names = list(NULL)
if(is.null(col_var_names)) col_var_names = list(NULL)
res = lapply(cell_var_names, function(each_cell) {
res = lapply(col_var_names, function(each_col){
by_str = c(row_var_name, each_cell, each_col)
if(use_weight){
dres = raw_data[, list(value = sum(weight, na.rm = TRUE)),
by = by_str]
} else {
dres = raw_data[, list(value = .N),
by = by_str]
}
})
})
res = rbindlist_and_aggregate(res)
if(is.null(cell_var_names[[1]])) {
cell_var_names = NULL
} else {
cell_var_names = "cell_var"
}
if(is.null(col_var_names[[1]])) {
col_var_names = NULL
} else {
col_var_names = "col_var"
}
setnames(res, c(row_var_name, cell_var_names, col_var_names, "value"))
complete = complete.cases(res[,-"value"])
if(!all(complete)){
res = res[complete, ]
}
res[, value := as.double(value)]
res
}
calculate_percent = function(cases, margin, stat_type){
# to pass CRAN check
value = NULL
res = data.table::copy(cases)
if(stat_type == "tpct"){
if(has_row_var(res)){
res = margin[res, on = "row_var", nomatch = NA]
res = res[, value := value/total*100][, -"total"]
} else {
res[, value:=value/margin[[1]]*100]
}
} else {
by_vec = intersect(colnames(res), colnames(margin))
res = margin[res, on = by_vec, nomatch = NA]
res = res[, value := value/total*100][, -"total"]
}
res
}
###########################
make_total_rows = function(need_row_var,
column_margin,
total_margin,
response_column_margin,
row_margin,
total_statistic = total_statistic,
total_label = total_label){
# "u_cases", "u_responses", "u_cpct", "u_rpct", "u_tpct",
# "w_cases", "w_responses", "w_cpct", "w_rpct", "w_tpct"
# to pass CRAN check
row_labels = NULL
value = NULL
### labels
total_statistic_label = gsub("^u_", " ", total_statistic, perl = TRUE)
total_statistic_label = gsub("^w_", " wtd. ", total_statistic_label, perl = TRUE)
if(is.null(total_label)){
total_label = paste0("#Total", total_statistic_label)
} else {
if(length(total_label) < length(total_statistic)) {
total_label = paste0(total_label, total_statistic_label)
}
}
total_label = make_items_unique(total_label)
for(item in seq_along(total_label)){
total_label[item] = add_first_symbol_to_total_label(total_label[item])
}
############
total_row = lapply(seq_along(total_statistic), function(item){
curr_statistic = total_statistic[[item]]
weight = substr(curr_statistic, 1,1)
curr_statistic = gsub("^(u|w)_", "", curr_statistic, perl = TRUE)
dtotal = switch(curr_statistic,
cases = data.table::copy(column_margin[[weight]]),
responses = data.table::copy(response_column_margin[[weight]]),
cpct = {
res = data.table::copy(column_margin[[weight]])
res = res[, total:= as.double(100*(!is.na(total) | NA))]
res
},
rpct = {
res = data.table::copy(column_margin[[weight]])
setnames(res, "total", "value")
if(need_row_var){
res = total_margin[[weight]][res, on = c("row_var")]
res[, total:= value/total*100, by = "row_var"]