improve documentation

R/compute_fgt_new.R

@@ -1,7 +1,7 @@
 # OLD APPROACH WITH MEAN --------------
 
-# Efficient FGT calculation for a data.table and vector of poverty lines
-#' Title
+#' Efficient FGT calculation for a data.table and vector of poverty lines
+#'
 #'
 #' @param dt data frame with `welfare` and `weight` columns
 #' @param welfare character: welfare variable name
@@ -76,8 +76,6 @@ compute_fgt_dt <- function(dt, welfare, weight, povlines, mean_and_med = FALSE)
 
 }
 
-
-
 #' Efficient FGT calculation for vectors (No data.table)
 #'
 #' @param w character: welfare variable name
@@ -125,7 +123,6 @@ compute_fgt <- function(w, wt, povlines) {
     }
   }
 
-
   data.table(
       povline          = povlines,
       headcount        = res[, 1],
@@ -166,8 +163,6 @@ DT_fgt_by_rl <- \(x, y, nx, povline) {
   )]
 }
 
-
-
 #' jkoin reporting level and lt list into one data.table
 #'
 #' @rdname map_fgt
@@ -196,11 +191,10 @@ map_lt_to_dt <- \(lt, l_rl_rows, povline) {
     rbindlist(fill = TRUE)
 }
 
-
 #' map over list of data.tables and indices to compute FGT by reporting_level
 #'
 #' @param lt list of data.tables with welfare and weight data
-#' @param l_rl_rows list of indeces
+#' @param l_rl_rows list of indices
 #'
 #' @return data.table with all measured
 #' @keywords internal
@@ -273,8 +267,6 @@ load_data_list <- \(metadata) {
 
 }
 
-
-
 pov_from_DT <- function(DT, povline, g, cores = 1) {
   w       <- DT$welfare
   wt      <- DT$weight
@@ -322,10 +314,6 @@ pov_from_DT <- function(DT, povline, g, cores = 1) {
   out
 }
 
-
-
-
-
 # pov_from_DT2 <- function(DT, povline, g) {
 #   fgt0 <- numeric(length(povline))
 #   fgt1 <- numeric(length(povline))

R/duckdb_func.R

@@ -2,7 +2,7 @@
 #'
 #' @inheritParams subset_lkup
 #'
-#' @return Dataframe
+#' @return list with 3 elements data_present_in_master, modified `lkup` value and `povline`
 #' @export
 return_if_exists <- function(slkup,
                              povline,
@@ -200,7 +200,7 @@ return_if_exists <- function(slkup,
 #' @param dat Dataframe to be appended
 #' @param cache_file_path path where cache file is saved
 #'
-#' @return number of rows updated
+#' @return a number i.e no. of rows updated
 #' @export
 #'
 update_master_file <- function(dat,
@@ -291,7 +291,7 @@ update_master_file <- function(dat,
   "))
 
   duckdb::dbDisconnect(write_con)
-  
+
   if (nr > 0 && verbose)  message(glue("{target_file} is updated."))
 
   return(nr)
@@ -362,7 +362,7 @@ reset_cache <- function(pass = Sys.getenv('PIP_CACHE_LOCAL_KEY'),
     DBI::dbExecute(write_con, "DELETE from fg_master_file")
   }
   duckdb::dbDisconnect(write_con)
-  
+
 }
 
 create_duckdb_file <- function(cache_file_path) {
@@ -388,17 +388,14 @@ create_duckdb_file <- function(cache_file_path) {
                  watts     DOUBLE
   )")
   DBI::dbDisconnect(con)
-
-}
-
-
 
+}
 
 #' Load Intermediate cache data
 #'
 #' @inheritParams return_if_exists
 #'
-#' @return data frame
+#' @return cached data frame
 #' @export
 load_inter_cache <- function(lkup = NULL,
                              cache_file_path = NULL,
@@ -423,8 +420,6 @@ load_inter_cache <- function(lkup = NULL,
   # connection object if it is not closed More details here
   # https://app.clickup.com/t/868cdpe3q
   duckdb::dbDisconnect(con)
-
-
   setDT(master_file)
 }
 

R/fg_pip.R

@@ -192,7 +192,6 @@ fg_pip <- function(country,
 
 }
 
-
 #' Remove duplicated rows created during the interpolation process
 #'
 #' @param df data.table: Table of results created in `fg_pip()`
@@ -239,8 +238,6 @@ fg_remove_duplicates <- function(df,
     # Ensure that out does not have duplicates
     df <- unique(df)
   }
-
-
   return(df)
 }
 
@@ -277,11 +274,6 @@ fg_assign_nas_values_to_dup_cols <- function(df,
   return(df)
 }
 
-
-
-
-
-
 #' Create full list for fg data load, not including country-years in cache
 #'
 #' @param metadata data table from subset_lkup()$lkup
@@ -292,6 +284,3 @@ create_full_list <- function(metadata) {
     funique()
 
 }
-
-
-

R/pip.R

@@ -1,5 +1,3 @@
-
-
 #' Compute PIP statistics
 #'
 #' Compute the main PIP poverty and inequality statistics.
@@ -97,10 +95,3 @@ pip <- function(country         = "ALL",
   out
 
 }
-
-
-
-
-
-
-

R/pip_grp_logic.R

@@ -2,10 +2,6 @@
 #' @rdname pip_agg
 #'
 #' @return data.table
-#' @examples
-#' \dontrun{
-#' # Create lkups
-#' }
 #' @export
 pip_grp_logic <- function(country         = "ALL",
                           year            = "ALL",
@@ -243,20 +239,14 @@ pip_grp_logic <- function(country         = "ALL",
   } else {
     ret <- de
   }
-
-
   # add new estimate type
-
   ret <- estimate_type_var(ret,lkup)
-
-
   # Censor regional values ----------- We are not censoring at this stage
   # anymore because we need to show al the years in the homre page, including
   # nowcast. we are now filtering at the UI and wrappers levels
   # if (censor) {
   #   ret <- censor_rows(ret, lkup[["censored"]], type = "regions")
   # }
-
   data.table::setcolorder(ret, names_grp)
 
   # Select columns
@@ -266,8 +256,6 @@ pip_grp_logic <- function(country         = "ALL",
 
   #Order rows by country code and reporting year
   setorder(ret, region_code , reporting_year)
-
-
   #   ____________________________________________________________________
   #   Return                                                         ####
   return(ret)

R/rg_pip.R

@@ -3,7 +3,7 @@
 #' Compute the main PIP poverty and inequality statistics for survey years.
 #'
 #' @inheritParams pip
-#' @return data.frame
+#' @return list of 2 data.frames, main_data and data_in_cache
 #' @keywords internal
 rg_pip <- function(country,
                    year,
@@ -78,7 +78,7 @@ rg_pip <- function(country,
     res <- lapply(lt, process_dt, povline = povline)
   }
   rm(lt)
-  
+
 
   res <- rbindlist(res, fill = TRUE)
 
@@ -100,9 +100,5 @@ rg_pip <- function(country,
 
   setnames(out, "povline", "poverty_line")
 
-
   return(list(main_data = out, data_in_cache = data_present_in_master))
 }
-
-
-