More consistent naming of data sets

NEWS.md

@@ -1,16 +1,16 @@
 # fpp3 (development version)
   * Many more data sets added: 
       - aus_births
-      - aus_cigtob
       - aus_fertility
       - aus_inbound
       - aus_mortality
-      - aus_nom	
+      - aus_migration
       - aus_outbound
+      - aus_tobacco
       - aus_vehicle_sales	
-      - daily_walkers	
+      - melb_walkers	
+      - nsw_offences	
       - ny_childcare	
-      - offences	
       - otexts_views
 
 # fpp3 (0.5)

R/data.R

@@ -254,14 +254,13 @@ NULL
 #' @keywords datasets
 #'
 #' @examples
-#' library(tsibble)
 #' aus_fertility
 #'
 NULL
 
 #' Monthly offences in NSW
 #'
-#' \code{offences} is a monthly `tsibble` with one measured variable:
+#' \code{nsw_offences} is a monthly `tsibble` with one measured variable:
 #' \tabular{ll}{
 #'     Count: \tab Number of offences reported \cr
 #' }
@@ -276,14 +275,13 @@ NULL
 #' @source NSW Bureau of Crime Statistics and Research. <https://www.bocsar.nsw.gov.au/Pages/bocsar_datasets/Offence.aspx>
 #'
 #'
-#' @name offences
+#' @name nsw_offences
 #' @docType data
 #' @format Time series of class `tsibble`
 #' @keywords datasets
 #'
 #' @examples
-#' library(tsibble)
-#' offences
+#' nsw_offences
 #'
 NULL
 
@@ -295,14 +293,13 @@ NULL
 #' @source Melbourne Open Data Portal. <https://data.melbourne.vic.gov.au/>
 #'
 #'
-#' @name daily_walkers
+#' @name melb_walkers
 #' @docType data
 #' @format Time series of class `tsibble`
 #' @keywords datasets
 #'
 #' @examples
-#' library(tsibble)
-#' daily_walkers
+#' autoplot(melb_walkers)
 #'
 NULL
 
@@ -330,7 +327,6 @@ NULL
 #' @keywords datasets
 #'
 #' @examples
-#' library(tsibble)
 #' aus_inbound
 #'
 NULL
@@ -359,7 +355,6 @@ NULL
 #' @keywords datasets
 #'
 #' @examples
-#' library(tsibble)
 #' aus_outbound
 #'
 NULL
@@ -451,7 +446,7 @@ NULL
 #'
 #' Net Overseas Migration (NOM) to Australia.
 #'
-#' \code{aus_nom} is a quarterly `tsibble` with one measured variable:
+#' \code{aus_migration} is a quarterly `tsibble` with one measured variable:
 #' \tabular{ll}{
 #'     \code{NOM}:  \tab The net gain or loss of population through immigration
 #'     to Australia and emigration from Australia\cr
@@ -471,13 +466,13 @@ NULL
 #'
 #' @source Australian Bureau of Statistics. <https://www.abs.gov.au/statistics/people/population/national-state-and-territory-population/dec-2023>. Cat No. 310102.
 #'
-#' @name aus_nom
+#' @name aus_migration
 #' @docType data
 #' @format Time series of class `tsibble`
 #' @keywords datasets
 #' @examples
 #'
-#' aus_nom
+#' aus_migration
 #'
 NULL
 
@@ -486,7 +481,7 @@ NULL
 #' The total household expenditure for cigarette and tobacco consumption (CTC)
 #' in Australia.
 #'
-#' \code{aus_cigtob} is a quarterly `tsibble` with one value:
+#' \code{aus_tobacco} is a quarterly `tsibble` with one value:
 #' \tabular{ll}{
 #'     \code{Expenditure}:  \tab The total expenditure\cr
 #' }
@@ -503,15 +498,15 @@ NULL
 #'     \code{State}: The state or territory.\cr
 #' }
 #'
-#' @source Australian Bureau of Statistics
+#' @source Australian Bureau of Statistics. <https://www.abs.gov.au/statistics/economy/national-accounts/australian-national-accounts-national-income-expenditure-and-product/mar-2024>
 #'
-#' @name aus_cigtob
+#' @name aus_tobacco
 #' @docType data
 #' @format Time series of class `tsibble`
 #' @keywords datasets
 #' @examples
 #'
-#' aus_cigtob
+#' aus_tobacco |> autoplot(Expenditure) + scale_y_log10()
 #'
 NULL
 

_pkgdown.yml

@@ -1,4 +1,4 @@
-url:  https://pkg.robjhyndman.com/fpp3package/
+url:  https://pkg.robjhyndman.com/fpp3package
 template:
   bootstrap: 5
   theme: tango
@@ -33,21 +33,21 @@ reference:
       - aus_accommodation
       - aus_arrivals
       - aus_births
-      - aus_cigtob
       - aus_fertility
       - aus_inbound
+      - aus_migration
       - aus_mortality
-      - aus_nom
       - aus_outbound
+      - aus_tobacco
       - aus_vehicle_sales
       - bank_calls
       - boston_marathon
       - canadian_gas
-      - daily_walkers
       - guinea_rice
       - insurance
+      - melb_walkers
+      - nsw_offences
       - ny_childcare
-      - offences
       - otexts_views
       - prices
       - souvenirs

data-raw/Exams/AUS_CigTob_Exp/AUS_CigTob_Exp.R

@@ -1,12 +1,17 @@
 library(fpp3)
 
-# Data download from?????
+# Data from
+# https://www.abs.gov.au/statistics/economy/national-accounts/australian-national-accounts-national-income-expenditure-and-product/mar-2024
+# We downloaded the data files for each state separately
+# (For example "Table 27. State Final Demand, Detailed Components: Victoria"
+# for Victoria), and merged the respective cigarettes and tobacco expenditure
+# data into a single data set.
 
-aus_cigtob <- readxl::read_xlsx(path ="data-raw/Exams/AUS_CigTob_Exp/CigTob_Updated.xlsx") |>
+aus_tobacco <- readxl::read_xlsx(path ="data-raw/Exams/AUS_CigTob_Exp/CigTob_Updated.xlsx") |>
   mutate(Quarter = yearquarter(Quarter)) |>
   pivot_longer(VIC:ACT, names_to = "State", values_to = "Expenditure") |>
   mutate(Expenditure = Expenditure / 1e3) |>
   as_tsibble(index = Quarter, key = State)
 
-#aus_cigtob |> autoplot(Expenditure) + scale_y_log10()
-usethis::use_data(aus_cigtob, overwrite=TRUE)
+#aus_tobacco |> autoplot(Expenditure) + scale_y_log10()
+usethis::use_data(aus_tobacco, overwrite=TRUE)

data-raw/Exams/AUS_NOM/AUS_NOM.R

@@ -2,12 +2,12 @@ library(fpp3)
 
 # Data downloaded from https://www.abs.gov.au/statistics/people/population/national-state-and-territory-population/
 # on 30 May 2024
-aus_nom <- readxl::read_xlsx(path = here::here("data-raw/Exams/AUS_NOM/migration_updated.xlsx"))[1:10] |>
+aus_migration <- readxl::read_xlsx(path = here::here("data-raw/Exams/AUS_NOM/migration_updated.xlsx"))[1:10] |>
   mutate(Quarter = yearquarter(Date)) |>
   pivot_longer(NSW:AUS, names_to = "State", values_to = "NOM") |>
   select(Quarter, State, NOM) |>
   as_tsibble(index = Quarter, key = State) |>
   filter(State != "AUS")
 
-#aus_nom |> autoplot()
-usethis::use_data(aus_nom, overwrite=TRUE)
+#aus_migration |> autoplot()
+usethis::use_data(aus_migration, overwrite=TRUE)

data-raw/Exams/Melb_Walk/melb_walk.R

@@ -5,7 +5,7 @@ start <- as.Date("2019-01-01")
 end <- as.Date("2024-05-29")
 pedestrian <- melb_walk(from = start, to = end, na.rm = FALSE, session = NULL)
 
-daily_walkers <- pedestrian |>
+melb_walkers <- pedestrian |>
   mutate(Count = as.numeric(Count)) |>
   group_by(Date, Sensor) |>
   summarise(Count = sum(Count, na.rm=TRUE)) |>
@@ -14,21 +14,21 @@ daily_walkers <- pedestrian |>
   as_tsibble(index = Date, key = Sensor)
 
 # Remove censors with large numbers of missing values
-nmiss <- daily_walkers |>
+nmiss <- melb_walkers |>
   as_tibble() |>
   group_by(Sensor) |>
   summarise(nmiss = sum(is.na(Count))) |>
   filter(nmiss < 10)
 
 # Interpolate missing values
-daily_walkers <- daily_walkers |>
+melb_walkers <- melb_walkers |>
   filter(Sensor %in% nmiss$Sensor) |>
   mutate(Count = zoo::na.approx(Count))
 
-daily_walkers <- daily_walkers |>
+melb_walkers <- melb_walkers |>
   as_tibble() |>
   group_by(Date) |>
   summarise(Count = mean(Count)/1e3) |>
   as_tsibble(index = Date)
 
-usethis::use_data(daily_walkers, overwrite=TRUE)
+usethis::use_data(melb_walkers, overwrite=TRUE)

data-raw/Exams/NSW_Offence/offence.R

@@ -2,9 +2,9 @@ library(fpp3)
 
 # Data downloaded from https://www.bocsar.nsw.gov.au/Pages/bocsar_datasets/Offence.aspx
 
-offences <- readr::read_csv("data-raw/Exams/NSW_Offence/RCI_offencebymonth.csv")
+nsw_offences <- readr::read_csv("data-raw/Exams/NSW_Offence/RCI_offencebymonth.csv")
 
-offences <- offences |>
+nsw_offences <- nsw_offences |>
   pivot_longer(-(1:3), names_to = "Month", values_to = "Count") |>
   rename(Type = `Offence category`) |>
   group_by(Type, Month) |>
@@ -15,5 +15,5 @@ offences <- offences |>
   ungroup() |>
   tsibble(index = Month, key = Type)
 
-usethis::use_data(offences, overwrite=TRUE)
+usethis::use_data(nsw_offences, overwrite=TRUE)