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dimensional_query_run_query.R
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dimensional_query_run_query.R
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#' Run query
#'
#' Once we have selected the facts, dimensions and defined the conditions on the
#' instances, we can execute the query to obtain the result.
#'
#' As an option, we can indicate if we do not want to unify the facts in the
#' case of having the same grain.
#'
#' @param dq A `dimensional_query` object.
#' @param unify_by_grain A boolean, unify facts with the same grain.
#'
#' @return A `dimensional_query` object.
#'
#' @family query functions
#'
#' @examples
#'
#' ms <- dimensional_query(ms_mrs) |>
#' select_dimension(name = "where",
#' attributes = c("city", "state")) |>
#' select_dimension(name = "when",
#' attributes = c("when_happened_year")) |>
#' select_fact(
#' name = "mrs_age",
#' measures = c("n_deaths"),
#' agg_functions = c("MAX")
#' ) |>
#' select_fact(
#' name = "mrs_cause",
#' measures = c("pneumonia_and_influenza_deaths", "other_deaths")
#' ) |>
#' filter_dimension(name = "when", when_happened_week <= "03") |>
#' filter_dimension(name = "where", city == "Boston") |>
#' run_query()
#'
#' @export
run_query <- function(dq, unify_by_grain = TRUE) {
UseMethod("run_query")
}
#' @rdname run_query
#' @export
run_query.dimensional_query <- function(dq, unify_by_grain = TRUE) {
dq <- define_selected_facts(dq)
dq <- define_selected_dimensions(dq)
dq <- filter_selected_instances(dq)
dq <- delete_unused_foreign_keys(dq)
dq <- remove_duplicate_dimension_rows(dq)
dq <- group_facts(dq)
if (unify_by_grain) {
dq <- unify_facts_by_grain (dq)
}
class(dq$output) <- class(dq$input)[1]
dq$output
}
#' Define selected facts
#'
#' Measure names are stored as the names of the columns with the aggregation
#' functions.
#'
#' @param dq A `dimensional_query` object.
#'
#' @return A `dimensional_query` object.
#'
#' @keywords internal
define_selected_facts <- function(dq) {
for (name in names(dq$fact)) {
# measure names are the names of the columns with the aggregation functions
dq$output$fact[[name]] <-
dq$input$fact[[name]][, c(attr(dq$input$fact[[name]], "foreign_keys"), names(dq$fact[[name]]))]
attr(dq$output$fact[[name]], "measures") <- names(dq$fact[[name]])
attr(dq$output$fact[[name]], "agg_functions") <- dq$fact[[name]]
}
dq
}
#' Define selected dimensions
#'
#' Include the selected dimensions and only the selected attributes in them.
#'
#' @param dq A `dimensional_query` object.
#'
#' @return A `dimensional_query` object.
#'
#' @keywords internal
define_selected_dimensions <- function(dq) {
for (name in names(dq$dimension)) {
dq$output$dimension[[name]] <- dq$input$dimension[[name]][, dq$dimension[[name]]]
}
dq
}
#' Filter selected instances
#'
#' For some dimensions the instances to include have been defined, we have the
#' value of the primary key. They are filtered for both facts and dimensions.
#'
#' @param dq A `dimensional_query` object.
#'
#' @return A `dimensional_query` object.
#'
#' @keywords internal
filter_selected_instances <- function(dq) {
for (name in names(dq$key)) {
# filter facts
for (f in names(dq$output$fact)) {
key <- sprintf("%s_key", name)
if (key %in% names(dq$output$fact[[f]])) {
dq$output$fact[[f]] <-
dq$output$fact[[f]][dq$output$fact[[f]][[key]] %in% dq$key[[name]], ]
}
}
# filter dimensions
if (name %in% names(dq$output$dimension)) {
dq$output$dimension[[name]] <-
dq$output$dimension[[name]][dq$output$dimension[[name]][[1]] %in% dq$key[[name]], ]
}
}
dq
}
#' Delete unused foreign keys
#'
#' In facts, remove foreign keys from dimensions not included in the result.
#'
#' @param dq A `dimensional_query` object.
#'
#' @return A `dimensional_query` object.
#'
#' @keywords internal
delete_unused_foreign_keys <- function(dq) {
for (name in names(dq$output$fact)) {
fk <- attr(dq$output$fact[[name]], "foreign_keys")
key_dimensions <- sprintf("%s_key", names(dq$dimension))
col <-
which(names(dq$output$fact[[name]]) %in% generics::setdiff(fk, key_dimensions))
if (length(col) > 0) {
dq$output$fact[[name]] <- dq$output$fact[[name]][,-c(col)]
}
attr(dq$output$fact[[name]], "foreign_keys") <-
generics::intersect(fk, key_dimensions)
}
dq
}
#' Remove duplicate dimension rows
#'
#' After selecting only a few columns of the dimensions, there may be rows with
#' duplicate values. We eliminate duplicates and adapt facts to the new
#' dimensions.
#'
#' @param dq A `dimensional_query` object.
#'
#' @return A `dimensional_query` object.
#'
#' @keywords internal
remove_duplicate_dimension_rows <- function(dq) {
# remove duplicate dimension rows
for (name in names(dq$dimension)) {
# remove duplicates and sort
ft <-
dplyr::arrange_all(tibble::as_tibble(unique(dq$output$dimension[[name]][, -1])))
if (nrow(ft) < nrow(dq$output$dimension[[name]])) {
# add surrogate primary key
# := variables for parameter names
# !! expands the expression into a string
ft <-
tibble::add_column(ft,!!sprintf("%s_key", name) := 1:nrow(ft), .before = 1)
for (f in names(dq$output$fact)) {
key <- sprintf("%s_key", name)
if (key %in% names(dq$output$fact[[f]])) {
dq$output$fact[[f]] <-
dereference_dimension(dq$output$fact[[f]], dq$output$dimension[[name]])
dq$output$fact[[f]] <-
reference_dimension(dq$output$fact[[f]], ft, names(ft)[-1])
}
}
class <- class(dq$output$dimension[[name]])
dq$output$dimension[[name]] <- ft
class(dq$output$dimension[[name]]) <- class
}
}
dq
}
#' Group facts
#'
#' Once the external keys have been possibly replaced, group the rows of facts.
#'
#' @param dq A `dimensional_query` object.
#'
#' @return A `dimensional_query` object.
#'
#' @keywords internal
group_facts <- function(dq) {
for (name in names(dq$output$fact)) {
dq$output$fact[[name]] <- group_table(dq$output$fact[[name]])
}
dq
}
#' Unify facts by grain
#'
#' @param dq A `dimensional_query` object.
#'
#' @return A `dimensional_query` object.
#'
#' @keywords internal
unify_facts_by_grain <- function(dq) {
fact <- NULL
unified_fact <- NULL
names_fact <- names(dq$output$fact)
for (i in seq_along(names_fact)) {
if (!(names_fact[i] %in% unified_fact)) {
fact[[names_fact[i]]] <- dq$output$fact[[names_fact[i]]]
fk_i <- attr(dq$output$fact[[names_fact[i]]], "foreign_keys")
agg <- list(fact[[names_fact[i]]])
for (j in seq_along(names_fact)[seq_along(names_fact) > i]) {
fk_j <- attr(dq$output$fact[[names_fact[j]]], "foreign_keys")
if (generics::setequal(fk_i, fk_j)) {
unified_fact <- c(unified_fact, names_fact[j])
fact2 <- dq$output$fact[[names_fact[j]]][, c(fk_i, attr(dq$output$fact[[names_fact[j]]], "measures"))]
for (m in attr(fact2, "measures")) {
m_new <- sprintf("%s_%s", names_fact[j], m)
names(fact2)[which(names(fact2) == m)] <- m_new
attr(fact2, "measures")[which(attr(fact2, "measures") == m)] <- m_new
names(attr(fact2, "agg_functions"))[which(names(attr(fact2, "agg_functions")) == m)] <- m_new
}
attr(fact[[names_fact[i]]], "measures") <-
c(attr(fact[[names_fact[i]]], "measures"), attr(fact2, "measures"))
attr(fact[[names_fact[i]]], "agg_functions") <-
c(attr(fact[[names_fact[i]]], "agg_functions"), attr(fact2, "agg_functions"))
agg <- c(agg, list(fact2))
}
}
if (length(agg) > 1) {
if (is.null(fk_i)) {
par_by = character()
} else {
par_by = fk_i
}
at <- attributes(fact[[names_fact[i]]])
fact[[names_fact[i]]] <- purrr::reduce(agg, dplyr::inner_join, by = par_by)
class(fact[[names_fact[i]]]) <- at$class
attr(fact[[names_fact[i]]], "name") <- at$name
attr(fact[[names_fact[i]]], "measures") <- at$measures
attr(fact[[names_fact[i]]], "agg_functions") <- at$agg_functions
attr(fact[[names_fact[i]]], "nrow_agg") <- at$nrow_agg
}
}
}
dq$output$fact <- fact
dq
}