R/lissyuse.r
In JosepER/lissyrtools: Tools for LISSY Jobs
Defines functions
variable_selection_for_lissyuse
load_datasets
lissyuse
Documented in
lissyuse
#' Load data easily and efficiently with lissyuse
#'
#' @description
#' `lissyuse()` enables the user to specify which variables to import, along with a set of default variables (IDs, weights, currency, year, relation, etc.). If both household-level and person-level variables are specified, lissyuse() will automatically merge the two types of files. For faster and more efficient processing, we strongly recommend selecting of a restricted set of variables in `vars` argument. Additionally, the function includes a subset argument that allows users to limit the data to a specific subgroup.
#'
#' @param data A character vector containing ISO2 country codes, and/or the country-year specific datasets in its `ccyy` format.
#' @param vars A character vector specifying the LIS/LWS variables to be loaded.
#' @param subset A logical expression defining the criteria for subsetting the data. Observations for which the expression evaluates to TRUE are included in the subset.
#' @param from A numeric value representing the year (inclusive) after which the LIS/LWS datasets should be loaded.
#' @param to A numeric value representing the year (inclusive) up to which the LIS/LWS datasets should be loaded.
#' @param lws A logical value indicating whether to load LWS data. If TRUE, LWS data is loaded; otherwise (default: FALSE), LIS data is loaded instead. Note that this does not eliminate the need to set the ‘Project’ field accordingly in the LISSY remote system.
#'
#' @return A list whose elements will be a data frame named after their respective dataset. See the naming formats in the examples below. Each data frame will contain as many columns as the selected variables, plus the default technical ones.
#' @export
#'
#' @examples
#' \dontrun{
#' library(lissyrtools)
#'
#'lis_datasets <- lissyuse(data = c("it", "de16", "us19"), vars = c("dhi", "region_c", "age", "hourstot", "status1"), subset = "!is.na(status1) & relation %in% c(1000,2000)")
#' # Checking the names of the data frames.
#'names(lis_datasets)
#'
#' # Selecting certain elemennts of the list
#'lis_datasets[["it14"]] # By their name
#'lis_datasets[1:3] # By their respective order within lis_datasets
#'
#' # Selecting all the italian datasets, while restrict them to a certain year range.
#'lis_datasets <- lissyuse( data = c("it"), vars = c("dhi", "region_c"), from = 2004, to = 2016)
#'
#'# In the previous line only household-level variables were selected.
#'# this will lead to slightly different names for the data frames.
#'names(lis_datasets)
#'
#'# The same occurs when only person-level variables were selected
#'lis_datasets <- lissyuse(data = c("it"), vars = c("age", "sex"), from = 2004, to = 2016)
#'
#'names(lis_datasets)
#'
#'
#' # ------------ LWS ------------------
#'
#'lws_datasets <- lissyuse(data = c("us", "uk17", "uk19"), vars = "dnw", from = 2015, to = 2021, lws = TRUE)
#'
#'names(lws_datasets)
#'}
lissyuse <- function(data = NULL , vars = NULL , subset = NULL , from = NULL, to = NULL , lws = FALSE) {
# 0) Define paths and location ---------------------------------------------------------
if (!exists("define_path")) {
data_to_load <- import_sample_datasets_to_lissyuse(data, lws) # local machine ----> only access to sample datasets
} else {
path_to_files <- define_path(lws)[[1]]
location <- define_path(lws)[[2]]
# 1) Argument {data} -------------------------------------
check_empty_data(data, lws)
check_length_iso2(data)
check_iso2(data, lws)
invalid_ccyy_pairs(data, lws)
# Define data to be loaded -----------------------------------
data_to_load <- load_datasets(data, lws, from, to)
}
# 2) Variable-driven selection of files bases on argument {vars} -------------------------------------
check_invalid_vars(vars, lws)
# Main function to select columns based on argument {vars}
intermediate_data_and_message <- variable_selection_for_lissyuse(data_to_load, path_to_files, vars, lws)
# allocation of its outputs
intermediate_data_to_filter <- intermediate_data_and_message$data
message <- intermediate_data_and_message$message
# 3) Filtering of rows based on the condition imposed in argument {subset} -------------------------------------
datasets_final <- if (!is.null(subset)) {
subset_expr <- rlang::parse_expr(subset)
subset_datasets(intermediate_data_to_filter, lws, subset_expr)
} else {
intermediate_data_to_filter
}
# 4) LISSY adjustment -------------------------------------
if (exists("define_path")) {
if (location == "L") {
datasets_final <- lapply(datasets_final, as.data.frame)
}
}
# 5) Attributes -------------------------
# To delete in the future perhaps
# Needed to accomodate the earliest developments in lissyrtools package
if (exists("relation", datasets_final[[1]])) {
attr(datasets_final, "level") <- "p"
attr(datasets_final, "merged_levels") <- TRUE
}
else{
attr(datasets_final, "level") <- "h"
attr(datasets_final, "merged_levels") <- FALSE
}
if (exists("inum", datasets_final[[1]])) {
attr(datasets_final, "database") <- "lws"
}
else{
attr(datasets_final, "database") <- "lis"
}
# 6) Print message on the availability of the list with the datasets and its names ----------
cat(message, "\n")
# 7) Return (instead of assigning it to the Global environment as in the past)
return(datasets_final)
}
load_datasets <- function(data = NULL, lws = FALSE, from = NULL, to = NULL) {
# Step 1: Split the `data` into series for country and dataset pairs
entire_series_for_a_country <- data[stringr::str_length(data) == 2]
ccyy_datasets <- data[stringr::str_length(data) == 4]
# Step 2: Extract dnames for entire series based on LWS or LIS
entire_series_extract_dname <- lissyrtools::datasets %>%
filter(
if (lws) {
database == "LWS" & iso2 %in% entire_series_for_a_country
} else {
database == "LIS" & iso2 %in% entire_series_for_a_country
}
) %>%
select(dname) %>%
unique() %>%
pull()
# Combine datasets for the entire series and cc/yy datasets
all_dnames <- c(entire_series_extract_dname, ccyy_datasets)
# Step 3: Filter datasets based on the combined list and the year range
data_to_load <- lissyrtools::datasets %>%
filter(
if (lws) {database == "LWS"}
else {database == "LIS"}
) %>%
filter(
if (!is.null(data)) {dname %in% all_dnames}
else {TRUE}
) %>%
filter(
if (!is.null(from) & !is.null(to)) {year >= from & year <= to}
else if (!is.null(from)) {year >= from}
else if (!is.null(to)) {year <= to}
else {year > 0}
) %>%
select(dname) %>%
unique() %>%
pull()
# Clean attributes of the final result
attributes(data_to_load) <- NULL
# Step 4: Check if the result is empty and stop if no data is found
# (probably already caught on previous checks)
if (length(data_to_load) == 0) {
stop(
glue::glue(
"No datasets matched the provided criteria. Please check the arguments provided, especially 'data'."
)
)
}
return(data_to_load)
}
variable_selection_for_lissyuse <- function(data_to_load, path_to_files, vars = NULL, lws = FALSE) {
if (exists("define_path")) {
if (lws) { # 1.1 LWS #
# 1.1.1 Select all variables, if none is specified # ---------------------------
if (is.null(vars)) {
files_h <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wh", ".dta")) )
files_p <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wp", ".dta")) )
list_with_data <- purrr::map2(files_h, files_p, ~ inner_join(.x, .y, c("hid", "inum")) %>%
select(-ends_with(".y")) %>%
rename_with(~ sub("\\.x$", "", .), ends_with(".x")))
names(list_with_data) <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n", # Proper period and paragraph space
"All variables were imported! We recommend specifying a character vector with the desired variables in the argument `vars`."
)
}
# 1.1.2 Only household-level variables were selected: load only h-level file # -----------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) > 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) == 0) {
vars <- vars[vars %in% lissyrtools::lws_household_variables] # remove any invalid variables
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wh", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_household_lws))))
names(list_with_data) <- paste0(data_to_load, "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `household-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.1.3 Only person-level variables were selected: load only p-level file # -----------------------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) == 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) > 0) {
vars <- vars[vars %in% lissyrtools::lws_person_variables]
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wp", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_person_lws))) )
names(list_with_data) <- paste0(data_to_load, "p")
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.1.4 Both household and person level varaibles were selected: load both filed and merge them # --------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) > 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) > 0) {
hvars <- vars[vars %in% lissyrtools::lws_household_variables]
pvars <- vars[vars %in% lissyrtools::lws_person_variables]
files_h <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wh", ".dta"),
col_select = unique(c(hvars, lissyrtools::key_vars_household_lws))))
files_p <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wp", ".dta"),
col_select = unique(c(pvars, lissyrtools::key_vars_person_lws))) )
list_with_data <- purrr::map2(files_h, files_p, ~ inner_join(.x, .y, by = c("hid", "inum")) %>%
select(-ends_with(".y")) %>%
rename_with(~ sub("\\.x$", "", .), ends_with(".x")))
names(list_with_data) <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.1.5 No valid lws variables supplied # ----------------
else if (sum(vars %in% lws_variables) == 0) {
stop("No valid LWS variable names specified.") # already ensured by check_invalid_vars(vars, lws)
}
# 1.1.6 # Mix between invalid variables and lws_both_hp_variables # --------------------------------
else if (sum(vars %in% lws_variables) > 0) {
vars <- vars[vars %in% lws_variables]
assertthat::assert_that(all(vars %in% lws_both_hp_variables))
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wh", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_household_lws))))
names(list_with_data) <- paste0(data_to_load, "h")
message_to_print_in_the_end <- paste0("The list contains the following data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n",
"NOTE: The selected variables are not exclusive to either household or individual-level datasets. The imported datasets have been defaulted to `household-level`."
)
}
return(list(data = list_with_data,
message = message_to_print_in_the_end
)
)
}
else { # LIS #
# 1.2.1 Select all variables, if none is specified # ------------------------------------
if (is.null(vars)) {
files_h <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ih", ".dta")) )
files_p <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ip", ".dta")) )
list_with_data <- purrr::map2(files_h, files_p, ~ inner_join(.x, .y, by = c("hid")) %>%
select(-ends_with(".y")) %>%
rename_with(~ sub("\\.x$", "", .), ends_with(".x")))
names(list_with_data) <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n", # Proper period and paragraph space
"All variables were imported! We recommend specifying a character vector with the desired variables in the argument `vars`."
)
}
# 1.2.2 Only household-level variables were selected: load only h-level file # --------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) > 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) == 0) {
vars <- vars[vars %in% lissyrtools::lis_household_variables] # remove any invalid variables
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ih", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_household_lis))))
names(list_with_data) <- paste0(data_to_load, "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `household-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.2.3 Only person-level variables were selected: load only p-level file # ------------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) == 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) > 0) {
vars <- vars[vars %in% lissyrtools::lis_person_variables] # remove any invalid variables
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ip", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_person_lis))) )
names(list_with_data) <- paste0(data_to_load, "p")
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.2.4 Both household and person level varaibles were selected: load both filed and merge them # --------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) > 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) > 0) {
hvars <- vars[vars %in% lissyrtools::lis_household_variables] # remove any invalid variables
pvars <- vars[vars %in% lissyrtools::lis_person_variables] # remove any invalid variables
files_h <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ih", ".dta"),
col_select = unique(c(hvars, lissyrtools::key_vars_household_lis))))
files_p <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ip", ".dta"),
col_select = unique(c(pvars, lissyrtools::key_vars_person_lis))))
list_with_data <- purrr::map2(files_h, files_p, ~ inner_join(.x, .y, by = c("hid")) %>%
select(-ends_with(".y")) %>%
rename_with(~ sub("\\.x$", "", .), ends_with(".x")))
names(list_with_data) <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.2.5 No valid LIS variables supplied # ----------------------------------
else if (sum(vars %in% lis_variables) == 0) {
stop("No valid LIS variable names specified.") # already ensured by check_invalid_vars(vars, lws)
}
# 1.2.6 Mix between invalid variables and lis_both_hp_variables # ---------------------------
else if (sum(vars %in% lis_variables) > 0) {
vars <- vars[vars %in% lis_variables]
assertthat::assert_that(all(vars %in% lis_both_hp_variables))
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ih", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_household_lis))))
names(list_with_data) <- paste0(data_to_load, "h")
message_to_print_in_the_end <- paste0("The list contains the following data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n",
"NOTE: The selected variables are not exclusive to either household or individual-level datasets. The imported datasets have been defaulted to `household-level`."
)
}
return(list(data = list_with_data,
message = message_to_print_in_the_end
)
)
}
}
# Variable selection for the sample datasets to be used in a local environemnt -------------------------------
else if (!exists("define_path")) {
if (lws) {
# 2.1.1 Select all variables, if none is specified # ----------------------
if (is.null(vars)) {
list_with_data <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n", # Proper period and paragraph space
"All variables were imported! We recommend specifying a character vector with the desired variables in the argument `vars`."
)
}
# 2.1.2 Only household-level variables were selected: load only h-level file # ----------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) > 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) == 0) {
vars <- vars[vars %in% lissyrtools::lws_household_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% filter(pid == 1) %>% select(unique(c(vars, key_vars_household_lws))))
names(list_with_data) <- paste0(names(list_with_data), "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `household-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.1.3 Only person-level variables were selected: load only p-level file # ----------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) == 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) > 0) {
vars <- vars[vars %in% lissyrtools::lws_person_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% select(unique(c(vars, key_vars_person_lws))))
names(list_with_data) <- paste0(names(list_with_data), "p")
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.1.4 Both household and person level varaibles were selected: load both filed and merge them # ----------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) > 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) > 0) {
hvars <- vars[vars %in% lissyrtools::lws_household_variables]
pvars <- vars[vars %in% lissyrtools::lws_person_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% select(unique(c(hvars, pvars, key_vars_household_lws ,key_vars_person_lws))))
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.1.5 No valid lws variables supplied # ----------------------
else if (sum(vars %in% lws_variables) == 0) {
stop("No valid LWS variable names specified.") # already ensured by check_invalid_vars(vars, lws)
}
# 2.1.6 # Mix between invalid variables and lws_both_hp_variables ----------------------
else if (sum(vars %in% lws_variables) > 0) {
vars <- vars[vars %in% lws_variables]
assertthat::assert_that(all(vars %in% lws_both_hp_variables))
list_with_data <- purrr::map(data_to_load, ~.x %>% filter(pid == 1) %>% select(unique(c(vars, key_vars_household_lws))))
names(list_with_data) <- paste0(names(list_with_data), "h")
message_to_print_in_the_end <- paste0("The list contains the following `sample` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n",
"NOTE: The selected variables are not exclusive to either household or individual-level datasets. The imported datasets have been defaulted to `household-level`."
)
}
return(list(data = list_with_data,
message = message_to_print_in_the_end
)
)
} else {
# 2.2.1 Select all variables, if none is specified # ----------------------
if (is.null(vars)) {
list_with_data <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n", # Proper period and paragraph space
"All variables were imported! We recommend specifying a character vector with the desired variables in the argument `vars`."
)
}
# 2.2.2 Only household-level variables were selected: load only h-level file # ----------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) > 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) == 0) {
vars <- vars[vars %in% lissyrtools::lis_household_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% filter(pid == 1) %>% select(unique(c(vars, key_vars_household_lis))))
names(list_with_data) <- paste0(names(list_with_data), "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `household-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.2.3 Only person-level variables were selected: load only p-level file # ----------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) == 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) > 0) {
vars <- vars[vars %in% lissyrtools::lis_person_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% select(unique(c(vars, key_vars_person_lis))))
names(list_with_data) <- paste0(names(list_with_data), "p")
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.2.4 Both household and person level varaibles were selected: load both filed and merge them # ----------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) > 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) > 0) {
hvars <- vars[vars %in% lissyrtools::lis_household_variables]
pvars <- vars[vars %in% lissyrtools::lis_person_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% select(unique(c(hvars, pvars, key_vars_household_lis ,key_vars_person_lis))))
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.2.5 No valid LIS variables supplied # ----------------------
else if (sum(vars %in% lis_variables) == 0) {
stop("No valid LIS variable names specified.") # already ensured by check_invalid_vars(vars, lws)
}
# 2.2.6 Mix between invalid variables and lis_both_hp_variables # ----------------------
else if (sum(vars %in% lis_variables) > 0) {
vars <- vars[vars %in% lis_variables]
assertthat::assert_that(all(vars %in% lis_both_hp_variables))
list_with_data <- purrr::map(data_to_load, ~.x %>% filter(pid == 1) %>% select(unique(c(vars, key_vars_household_lis))))
names(list_with_data) <- paste0(names(list_with_data), "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `sample` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n",
"NOTE: The selected variables are not exclusive to either household or individual-level datasets. The imported datasets have been defaulted to `household-level`."
)
}
return(list(data = list_with_data,
message = message_to_print_in_the_end
)
)
}
}
}
subset_datasets <- function(intermediate_data_to_filter, lws = FALSE, subset_expr) {
dataset_list <- intermediate_data_to_filter
# Apply filtering logic
filtered_datasets <- lapply(names(dataset_list), function(dataset_name) {
data <- dataset_list[[dataset_name]]
# Check if all variables in the subset condition exist in the dataset
missing_vars <- setdiff(all.vars(subset_expr), names(data))
if (length(missing_vars) > 0) {
message(sprintf("Skipping '%s' because it is missing variables: %s",
dataset_name, paste(missing_vars, collapse = ", ")))
return(data) # Return unfiltered dataset
}
# Check if the dataset contains rows with values matching the condition
matching_rows <- dplyr::filter(data, !!subset_expr)
if (is.null(matching_rows) || nrow(matching_rows) == 0) {
message(sprintf("Skipping filtering for '%s' because no rows match the condition.", dataset_name))
return(data) # Return unfiltered dataset
}
# Calculate the percentage of rows deleted
deleted_percentage <- (nrow(data) - nrow(matching_rows)) / nrow(data) * 100
# Log the result
message(sprintf("Applying filtering on '%s'. Rows before: %d, Rows after: %d, Rows deleted: %.2f%%",
dataset_name, nrow(data), nrow(matching_rows), deleted_percentage))
return(matching_rows)
})
names(filtered_datasets) <- names(dataset_list)
return(filtered_datasets)
}
JosepER/lissyrtools documentation built on June 12, 2025, 12:11 p.m.
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Defines functions variable_selection_for_lissyuse load_datasets lissyuse
Documented in lissyuse
#' Load data easily and efficiently with lissyuse
#'
#' @description
#' `lissyuse()` enables the user to specify which variables to import, along with a set of default variables (IDs, weights, currency, year, relation, etc.). If both household-level and person-level variables are specified, lissyuse() will automatically merge the two types of files. For faster and more efficient processing, we strongly recommend selecting of a restricted set of variables in `vars` argument. Additionally, the function includes a subset argument that allows users to limit the data to a specific subgroup.
#'
#' @param data A character vector containing ISO2 country codes, and/or the country-year specific datasets in its `ccyy` format.
#' @param vars A character vector specifying the LIS/LWS variables to be loaded.
#' @param subset A logical expression defining the criteria for subsetting the data. Observations for which the expression evaluates to TRUE are included in the subset.
#' @param from A numeric value representing the year (inclusive) after which the LIS/LWS datasets should be loaded.
#' @param to A numeric value representing the year (inclusive) up to which the LIS/LWS datasets should be loaded.
#' @param lws A logical value indicating whether to load LWS data. If TRUE, LWS data is loaded; otherwise (default: FALSE), LIS data is loaded instead. Note that this does not eliminate the need to set the ‘Project’ field accordingly in the LISSY remote system.
#'
#' @return A list whose elements will be a data frame named after their respective dataset. See the naming formats in the examples below. Each data frame will contain as many columns as the selected variables, plus the default technical ones.
#' @export
#'
#' @examples
#' \dontrun{
#' library(lissyrtools)
#'
#'lis_datasets <- lissyuse(data = c("it", "de16", "us19"), vars = c("dhi", "region_c", "age", "hourstot", "status1"), subset = "!is.na(status1) & relation %in% c(1000,2000)")
#' # Checking the names of the data frames.
#'names(lis_datasets)
#'
#' # Selecting certain elemennts of the list
#'lis_datasets[["it14"]] # By their name
#'lis_datasets[1:3] # By their respective order within lis_datasets
#'
#' # Selecting all the italian datasets, while restrict them to a certain year range.
#'lis_datasets <- lissyuse( data = c("it"), vars = c("dhi", "region_c"), from = 2004, to = 2016)
#'
#'# In the previous line only household-level variables were selected.
#'# this will lead to slightly different names for the data frames.
#'names(lis_datasets)
#'
#'# The same occurs when only person-level variables were selected
#'lis_datasets <- lissyuse(data = c("it"), vars = c("age", "sex"), from = 2004, to = 2016)
#'
#'names(lis_datasets)
#'
#'
#' # ------------ LWS ------------------
#'
#'lws_datasets <- lissyuse(data = c("us", "uk17", "uk19"), vars = "dnw", from = 2015, to = 2021, lws = TRUE)
#'
#'names(lws_datasets)
#'}
lissyuse <- function(data = NULL , vars = NULL , subset = NULL , from = NULL, to = NULL , lws = FALSE) {
# 0) Define paths and location ---------------------------------------------------------
if (!exists("define_path")) {
data_to_load <- import_sample_datasets_to_lissyuse(data, lws) # local machine ----> only access to sample datasets
} else {
path_to_files <- define_path(lws)[[1]]
location <- define_path(lws)[[2]]
# 1) Argument {data} -------------------------------------
check_empty_data(data, lws)
check_length_iso2(data)
check_iso2(data, lws)
invalid_ccyy_pairs(data, lws)
# Define data to be loaded -----------------------------------
data_to_load <- load_datasets(data, lws, from, to)
}
# 2) Variable-driven selection of files bases on argument {vars} -------------------------------------
check_invalid_vars(vars, lws)
# Main function to select columns based on argument {vars}
intermediate_data_and_message <- variable_selection_for_lissyuse(data_to_load, path_to_files, vars, lws)
# allocation of its outputs
intermediate_data_to_filter <- intermediate_data_and_message$data
message <- intermediate_data_and_message$message
# 3) Filtering of rows based on the condition imposed in argument {subset} -------------------------------------
datasets_final <- if (!is.null(subset)) {
subset_expr <- rlang::parse_expr(subset)
subset_datasets(intermediate_data_to_filter, lws, subset_expr)
} else {
intermediate_data_to_filter
}
# 4) LISSY adjustment -------------------------------------
if (exists("define_path")) {
if (location == "L") {
datasets_final <- lapply(datasets_final, as.data.frame)
}
}
# 5) Attributes -------------------------
# To delete in the future perhaps
# Needed to accomodate the earliest developments in lissyrtools package
if (exists("relation", datasets_final[[1]])) {
attr(datasets_final, "level") <- "p"
attr(datasets_final, "merged_levels") <- TRUE
}
else{
attr(datasets_final, "level") <- "h"
attr(datasets_final, "merged_levels") <- FALSE
}
if (exists("inum", datasets_final[[1]])) {
attr(datasets_final, "database") <- "lws"
}
else{
attr(datasets_final, "database") <- "lis"
}
# 6) Print message on the availability of the list with the datasets and its names ----------
cat(message, "\n")
# 7) Return (instead of assigning it to the Global environment as in the past)
return(datasets_final)
}
load_datasets <- function(data = NULL, lws = FALSE, from = NULL, to = NULL) {
# Step 1: Split the `data` into series for country and dataset pairs
entire_series_for_a_country <- data[stringr::str_length(data) == 2]
ccyy_datasets <- data[stringr::str_length(data) == 4]
# Step 2: Extract dnames for entire series based on LWS or LIS
entire_series_extract_dname <- lissyrtools::datasets %>%
filter(
if (lws) {
database == "LWS" & iso2 %in% entire_series_for_a_country
} else {
database == "LIS" & iso2 %in% entire_series_for_a_country
}
) %>%
select(dname) %>%
unique() %>%
pull()
# Combine datasets for the entire series and cc/yy datasets
all_dnames <- c(entire_series_extract_dname, ccyy_datasets)
# Step 3: Filter datasets based on the combined list and the year range
data_to_load <- lissyrtools::datasets %>%
filter(
if (lws) {database == "LWS"}
else {database == "LIS"}
) %>%
filter(
if (!is.null(data)) {dname %in% all_dnames}
else {TRUE}
) %>%
filter(
if (!is.null(from) & !is.null(to)) {year >= from & year <= to}
else if (!is.null(from)) {year >= from}
else if (!is.null(to)) {year <= to}
else {year > 0}
) %>%
select(dname) %>%
unique() %>%
pull()
# Clean attributes of the final result
attributes(data_to_load) <- NULL
# Step 4: Check if the result is empty and stop if no data is found
# (probably already caught on previous checks)
if (length(data_to_load) == 0) {
stop(
glue::glue(
"No datasets matched the provided criteria. Please check the arguments provided, especially 'data'."
)
)
}
return(data_to_load)
}
variable_selection_for_lissyuse <- function(data_to_load, path_to_files, vars = NULL, lws = FALSE) {
if (exists("define_path")) {
if (lws) { # 1.1 LWS #
# 1.1.1 Select all variables, if none is specified # ---------------------------
if (is.null(vars)) {
files_h <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wh", ".dta")) )
files_p <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wp", ".dta")) )
list_with_data <- purrr::map2(files_h, files_p, ~ inner_join(.x, .y, c("hid", "inum")) %>%
select(-ends_with(".y")) %>%
rename_with(~ sub("\\.x$", "", .), ends_with(".x")))
names(list_with_data) <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n", # Proper period and paragraph space
"All variables were imported! We recommend specifying a character vector with the desired variables in the argument `vars`."
)
}
# 1.1.2 Only household-level variables were selected: load only h-level file # -----------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) > 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) == 0) {
vars <- vars[vars %in% lissyrtools::lws_household_variables] # remove any invalid variables
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wh", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_household_lws))))
names(list_with_data) <- paste0(data_to_load, "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `household-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.1.3 Only person-level variables were selected: load only p-level file # -----------------------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) == 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) > 0) {
vars <- vars[vars %in% lissyrtools::lws_person_variables]
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wp", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_person_lws))) )
names(list_with_data) <- paste0(data_to_load, "p")
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.1.4 Both household and person level varaibles were selected: load both filed and merge them # --------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) > 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) > 0) {
hvars <- vars[vars %in% lissyrtools::lws_household_variables]
pvars <- vars[vars %in% lissyrtools::lws_person_variables]
files_h <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wh", ".dta"),
col_select = unique(c(hvars, lissyrtools::key_vars_household_lws))))
files_p <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wp", ".dta"),
col_select = unique(c(pvars, lissyrtools::key_vars_person_lws))) )
list_with_data <- purrr::map2(files_h, files_p, ~ inner_join(.x, .y, by = c("hid", "inum")) %>%
select(-ends_with(".y")) %>%
rename_with(~ sub("\\.x$", "", .), ends_with(".x")))
names(list_with_data) <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.1.5 No valid lws variables supplied # ----------------
else if (sum(vars %in% lws_variables) == 0) {
stop("No valid LWS variable names specified.") # already ensured by check_invalid_vars(vars, lws)
}
# 1.1.6 # Mix between invalid variables and lws_both_hp_variables # --------------------------------
else if (sum(vars %in% lws_variables) > 0) {
vars <- vars[vars %in% lws_variables]
assertthat::assert_that(all(vars %in% lws_both_hp_variables))
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "wh", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_household_lws))))
names(list_with_data) <- paste0(data_to_load, "h")
message_to_print_in_the_end <- paste0("The list contains the following data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n",
"NOTE: The selected variables are not exclusive to either household or individual-level datasets. The imported datasets have been defaulted to `household-level`."
)
}
return(list(data = list_with_data,
message = message_to_print_in_the_end
)
)
}
else { # LIS #
# 1.2.1 Select all variables, if none is specified # ------------------------------------
if (is.null(vars)) {
files_h <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ih", ".dta")) )
files_p <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ip", ".dta")) )
list_with_data <- purrr::map2(files_h, files_p, ~ inner_join(.x, .y, by = c("hid")) %>%
select(-ends_with(".y")) %>%
rename_with(~ sub("\\.x$", "", .), ends_with(".x")))
names(list_with_data) <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n", # Proper period and paragraph space
"All variables were imported! We recommend specifying a character vector with the desired variables in the argument `vars`."
)
}
# 1.2.2 Only household-level variables were selected: load only h-level file # --------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) > 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) == 0) {
vars <- vars[vars %in% lissyrtools::lis_household_variables] # remove any invalid variables
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ih", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_household_lis))))
names(list_with_data) <- paste0(data_to_load, "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `household-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.2.3 Only person-level variables were selected: load only p-level file # ------------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) == 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) > 0) {
vars <- vars[vars %in% lissyrtools::lis_person_variables] # remove any invalid variables
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ip", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_person_lis))) )
names(list_with_data) <- paste0(data_to_load, "p")
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.2.4 Both household and person level varaibles were selected: load both filed and merge them # --------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) > 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) > 0) {
hvars <- vars[vars %in% lissyrtools::lis_household_variables] # remove any invalid variables
pvars <- vars[vars %in% lissyrtools::lis_person_variables] # remove any invalid variables
files_h <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ih", ".dta"),
col_select = unique(c(hvars, lissyrtools::key_vars_household_lis))))
files_p <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ip", ".dta"),
col_select = unique(c(pvars, lissyrtools::key_vars_person_lis))))
list_with_data <- purrr::map2(files_h, files_p, ~ inner_join(.x, .y, by = c("hid")) %>%
select(-ends_with(".y")) %>%
rename_with(~ sub("\\.x$", "", .), ends_with(".x")))
names(list_with_data) <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 1.2.5 No valid LIS variables supplied # ----------------------------------
else if (sum(vars %in% lis_variables) == 0) {
stop("No valid LIS variable names specified.") # already ensured by check_invalid_vars(vars, lws)
}
# 1.2.6 Mix between invalid variables and lis_both_hp_variables # ---------------------------
else if (sum(vars %in% lis_variables) > 0) {
vars <- vars[vars %in% lis_variables]
assertthat::assert_that(all(vars %in% lis_both_hp_variables))
list_with_data <- purrr::map(data_to_load,
~haven::read_dta(file = paste0(path_to_files, .x, "ih", ".dta"),
col_select = unique(c(vars, lissyrtools::key_vars_household_lis))))
names(list_with_data) <- paste0(data_to_load, "h")
message_to_print_in_the_end <- paste0("The list contains the following data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n",
"NOTE: The selected variables are not exclusive to either household or individual-level datasets. The imported datasets have been defaulted to `household-level`."
)
}
return(list(data = list_with_data,
message = message_to_print_in_the_end
)
)
}
}
# Variable selection for the sample datasets to be used in a local environemnt -------------------------------
else if (!exists("define_path")) {
if (lws) {
# 2.1.1 Select all variables, if none is specified # ----------------------
if (is.null(vars)) {
list_with_data <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n", # Proper period and paragraph space
"All variables were imported! We recommend specifying a character vector with the desired variables in the argument `vars`."
)
}
# 2.1.2 Only household-level variables were selected: load only h-level file # ----------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) > 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) == 0) {
vars <- vars[vars %in% lissyrtools::lws_household_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% filter(pid == 1) %>% select(unique(c(vars, key_vars_household_lws))))
names(list_with_data) <- paste0(names(list_with_data), "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `household-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.1.3 Only person-level variables were selected: load only p-level file # ----------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) == 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) > 0) {
vars <- vars[vars %in% lissyrtools::lws_person_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% select(unique(c(vars, key_vars_person_lws))))
names(list_with_data) <- paste0(names(list_with_data), "p")
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.1.4 Both household and person level varaibles were selected: load both filed and merge them # ----------------------
else if (sum(vars %in% setdiff(lws_household_variables, lws_person_variables)) > 0 & sum(vars %in% setdiff(lws_person_variables, lws_household_variables)) > 0) {
hvars <- vars[vars %in% lissyrtools::lws_household_variables]
pvars <- vars[vars %in% lissyrtools::lws_person_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% select(unique(c(hvars, pvars, key_vars_household_lws ,key_vars_person_lws))))
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.1.5 No valid lws variables supplied # ----------------------
else if (sum(vars %in% lws_variables) == 0) {
stop("No valid LWS variable names specified.") # already ensured by check_invalid_vars(vars, lws)
}
# 2.1.6 # Mix between invalid variables and lws_both_hp_variables ----------------------
else if (sum(vars %in% lws_variables) > 0) {
vars <- vars[vars %in% lws_variables]
assertthat::assert_that(all(vars %in% lws_both_hp_variables))
list_with_data <- purrr::map(data_to_load, ~.x %>% filter(pid == 1) %>% select(unique(c(vars, key_vars_household_lws))))
names(list_with_data) <- paste0(names(list_with_data), "h")
message_to_print_in_the_end <- paste0("The list contains the following `sample` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n",
"NOTE: The selected variables are not exclusive to either household or individual-level datasets. The imported datasets have been defaulted to `household-level`."
)
}
return(list(data = list_with_data,
message = message_to_print_in_the_end
)
)
} else {
# 2.2.1 Select all variables, if none is specified # ----------------------
if (is.null(vars)) {
list_with_data <- data_to_load
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n", # Proper period and paragraph space
"All variables were imported! We recommend specifying a character vector with the desired variables in the argument `vars`."
)
}
# 2.2.2 Only household-level variables were selected: load only h-level file # ----------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) > 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) == 0) {
vars <- vars[vars %in% lissyrtools::lis_household_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% filter(pid == 1) %>% select(unique(c(vars, key_vars_household_lis))))
names(list_with_data) <- paste0(names(list_with_data), "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `household-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.2.3 Only person-level variables were selected: load only p-level file # ----------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) == 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) > 0) {
vars <- vars[vars %in% lissyrtools::lis_person_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% select(unique(c(vars, key_vars_person_lis))))
names(list_with_data) <- paste0(names(list_with_data), "p")
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.2.4 Both household and person level varaibles were selected: load both filed and merge them # ----------------------
else if (sum(vars %in% setdiff(lis_household_variables, lis_person_variables)) > 0 & sum(vars %in% setdiff(lis_person_variables, lis_household_variables)) > 0) {
hvars <- vars[vars %in% lissyrtools::lis_household_variables]
pvars <- vars[vars %in% lissyrtools::lis_person_variables]
list_with_data <- purrr::map(data_to_load, ~.x %>% select(unique(c(hvars, pvars, key_vars_household_lis ,key_vars_person_lis))))
message_to_print_in_the_end <- paste0(
"The list contains the following `person-level sample` data frames: ",
paste(names(list_with_data), collapse = ", "), ".")
}
# 2.2.5 No valid LIS variables supplied # ----------------------
else if (sum(vars %in% lis_variables) == 0) {
stop("No valid LIS variable names specified.") # already ensured by check_invalid_vars(vars, lws)
}
# 2.2.6 Mix between invalid variables and lis_both_hp_variables # ----------------------
else if (sum(vars %in% lis_variables) > 0) {
vars <- vars[vars %in% lis_variables]
assertthat::assert_that(all(vars %in% lis_both_hp_variables))
list_with_data <- purrr::map(data_to_load, ~.x %>% filter(pid == 1) %>% select(unique(c(vars, key_vars_household_lis))))
names(list_with_data) <- paste0(names(list_with_data), "h")
message_to_print_in_the_end <- paste0(
"The list contains the following `sample` data frames: ",
paste(names(list_with_data), collapse = ", "),
".\n",
"NOTE: The selected variables are not exclusive to either household or individual-level datasets. The imported datasets have been defaulted to `household-level`."
)
}
return(list(data = list_with_data,
message = message_to_print_in_the_end
)
)
}
}
}
subset_datasets <- function(intermediate_data_to_filter, lws = FALSE, subset_expr) {
dataset_list <- intermediate_data_to_filter
# Apply filtering logic
filtered_datasets <- lapply(names(dataset_list), function(dataset_name) {
data <- dataset_list[[dataset_name]]
# Check if all variables in the subset condition exist in the dataset
missing_vars <- setdiff(all.vars(subset_expr), names(data))
if (length(missing_vars) > 0) {
message(sprintf("Skipping '%s' because it is missing variables: %s",
dataset_name, paste(missing_vars, collapse = ", ")))
return(data) # Return unfiltered dataset
}
# Check if the dataset contains rows with values matching the condition
matching_rows <- dplyr::filter(data, !!subset_expr)
if (is.null(matching_rows) || nrow(matching_rows) == 0) {
message(sprintf("Skipping filtering for '%s' because no rows match the condition.", dataset_name))
return(data) # Return unfiltered dataset
}
# Calculate the percentage of rows deleted
deleted_percentage <- (nrow(data) - nrow(matching_rows)) / nrow(data) * 100
# Log the result
message(sprintf("Applying filtering on '%s'. Rows before: %d, Rows after: %d, Rows deleted: %.2f%%",
dataset_name, nrow(data), nrow(matching_rows), deleted_percentage))
return(matching_rows)
})
names(filtered_datasets) <- names(dataset_list)
return(filtered_datasets)
}
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