Nothing
R/data.R
In tsibbledata: Diverse Datasets for 'tsibble'
#' Monthly Medicare Australia prescription data
#'
#' \code{PBS} is a monthly `tsibble` with two values:
#' \tabular{ll}{
#' Scripts: \tab Total number of scripts\cr
#' Cost: \tab Cost of the scripts in $AUD\cr
#' }
#'
#' The data is disaggregated using four keys:
#' \tabular{ll}{
#' Concession: \tab Concessional scripts are given to pensioners, unemployed, dependents, and other card holders \cr
#' Type: \tab Co-payments are made until an individual's script expenditure hits a threshold ($290.00 for concession, $1141.80 otherwise). Safety net subsidies are provided to individuals exceeding this amount. \cr
#' ATC1: \tab Anatomical Therapeutic Chemical index (level 1)\cr
#' ATC2: \tab Anatomical Therapeutic Chemical index (level 2)\cr
#' }
#'
#' @source
#' Medicare Australia
#'
#' @name PBS
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' PBS
#'
NULL
#' Fastest running times for Olympic races
#'
#' \code{olympic_running} is a quadrennial `tsibble` with one value:
#' \tabular{ll}{
#' Time: \tab Fastest running time for the event (seconds)\cr
#' }
#'
#' The event is identified using two keys:
#' \tabular{ll}{
#' Length: \tab The length of the race (meters)\cr
#' Sex: \tab The sex of the event\cr
#' }
#'
#' The data contains missing values in 1916, 1940 and 1944 due to the World Wars.
#'
#' @source
#' <https://olympics.com/en/sports/athletics/>
#'
#' @name olympic_running
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' olympic_running
#'
#' if(requireNamespace("ggplot2")){
#' library(ggplot2)
#' olympic_running %>% as_tibble %>%
#' ggplot(aes(x=Year, y = Time, colour = Sex)) +
#' geom_line() +
#' facet_wrap(~ Length, scales = "free_y")
#' }
NULL
#' Australian retail trade turnover
#'
#' \code{aus_retail} is a monthly `tsibble` with one value:
#' \tabular{ll}{
#' Turnover: \tab Retail turnover in $Million AUD\cr
#' }
#'
#' Each series is uniquely identified using two keys:
#' \tabular{ll}{
#' State: \tab The Australian state (or territory)\cr
#' Industry: \tab The industry of retail trade\cr
#' }
#'
#' @source
#' Australian Bureau of Statistics, catalogue number 8501.0, table 11.
#'
#' @name aus_retail
#' @format Time series of class `tsibble`
#' @keywords datasets
#'
#' @examples
#' library(tsibble)
#' aus_retail
#'
NULL
#' Passenger numbers on Ansett airline flights
#'
#' The data features a major pilots' industrial dispute which results in some
#' weeks having zero passengers. There were also at least two changes in the
#' definitions of passenger classes.
#'
#' \code{ansett} is a weekly `tsibble` with one value:
#' \tabular{ll}{
#' Passengers: \tab Total air passengers travelling with Ansett\cr
#' }
#'
#' Each series is uniquely identified using two keys:
#' \tabular{ll}{
#' Airports: \tab The airports that passengers are travelling between (both directions)\cr
#' Class: \tab The class of the ticket.\cr
#' }
#'
#' @source
#' Ansett Airlines (which no longer exists).
#'
#' @name ansett
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' ansett
#'
NULL
#' NYC Citi Bike trips
#'
#' A sample from NYC Citi Bike usage of 10 bikes throughout 2018. The data
#' includes event data on each trip, including the trip's start and end times
#' and locations. The customer's gender, birth year and bike usage type is
#' also available.
#'
#' \code{nyc_bikes} is a `tsibble` containing event data, the events include
#' these details:
#' \tabular{ll}{
#' start_time: \tab The time and date when the trip was started.\cr
#' stop_time: \tab The time and date when the trip was ended.\cr
#' start_station:\tab A unique identifier for the starting bike station.\cr
#' start_lat: \tab The latitude of the starting bike station.\cr
#' start_long: \tab The longitude of the starting bike station.\cr
#' end_station: \tab A unique identifier for the destination bike station.\cr
#' end_lat: \tab The latitutde of the destination bike station.\cr
#' end_long: \tab The longitude of the destination bike station.\cr
#' type: \tab The type of trip. A "Customer" has purchased either a 24-hour or 3-day pass, and a "Subscriber" has purchased an annual subscription.\cr
#' birth_year \tab The bike rider's year of birth.\cr
#' gender: \tab The gender of the bike rider.\cr
#' }
#'
#' Each series is uniquely identified by one key:
#' \tabular{ll}{
#' bike_id: \tab A unique identifier for the bike.\cr
#' }
#'
#' @source
#' Citi Bike NYC, <https://www.citibikenyc.com/system-data>
#'
#' @name nyc_bikes
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' nyc_bikes
NULL
#' GAFA stock prices
#'
#' Historical stock prices from 2014-2018 for Google, Amazon, Facebook and Apple.
#' All prices are in $USD.
#'
#' \code{gafa_stock} is a `tsibble` containing data on irregular trading days:
#' \tabular{ll}{
#' Open: \tab The opening price for the stock.\cr
#' High: \tab The stock's highest trading price.\cr
#' Low: \tab The stock's lowest trading price.\cr
#' Close: \tab The closing price for the stock.\cr
#' Adj_Close: \tab The adjusted closing price for the stock.\cr
#' Volume: \tab The amount of stock traded.\cr
#' }
#'
#' Each stock is uniquely identified by one key:
#' \tabular{ll}{
#' Symbol: \tab The ticker symbol for the stock.\cr
#' }
#'
#' @source
#' Yahoo Finance historical data
#'
#' @name gafa_stock
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' gafa_stock
NULL
#' Pelt trading records
#'
#' Hudson Bay Company trading records for Snowshoe Hare and Canadian Lynx furs
#' from 1845 to 1935. This data contains trade records for all areas of the company.
#'
#' \code{pelt} is an annual `tsibble` with two values:
#' \tabular{ll}{
#' Hare: \tab The number of Snowshoe Hare pelts traded.\cr
#' Lynx: \tab The number of Canadian Lynx pelts traded.\cr
#' }
#'
#' @source
#' Hudson Bay Company
#'
#' @name pelt
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' pelt
NULL
#' Global economic indicators
#'
#' Economic indicators featured by the World Bank from 1960 to 2017.
#'
#' \code{global_economy} is an annual `tsibble` with six values:
#' \tabular{ll}{
#' GDP: \tab Gross domestic product (in $USD February 2019).\cr
#' Growth: \tab Annual percentage growth in GDP.\cr
#' CPI: \tab Consumer price index (base year 2010).\cr
#' Imports: \tab Imports of goods and services (% of GDP).\cr
#' Exports: \tab Exports of goods and services (% of GDP).\cr
#' Population:\tab Total population.
#' }
#'
#' Each series is uniquely identified by one key:
#' \tabular{ll}{
#' Country: \tab The country or region of the series.\cr
#' }
#'
#' @source
#' The World Bank, <http://datatopics.worldbank.org/world-development-indicators/>
#'
#' @name global_economy
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' global_economy
NULL
#' Australian livestock slaughter
#'
#' Meat production in Australia for human consumption
#'
#' \code{aus_livestock} is a monthly `tsibble` with one value:
#' \tabular{ll}{
#' Count: \tab Number of animals slaughtered.\cr
#' }
#'
#' Each series is uniquely identified using two keys:
#' \tabular{ll}{
#' Animal: \tab The animal slaughtered.\cr
#' State: \tab The Australian state (or territory).\cr
#' }
#'
#' @source
#' Australian Bureau of Statistics, catalogue number 7218.0.55.001 tables 1 to 7.
#'
#' @name aus_livestock
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' aus_livestock
NULL
#' Half-hourly electricity demand for Victoria, Australia
#'
#' \code{vic_elec} is a half-hourly `tsibble` with three values:
#' \tabular{ll}{
#' Demand: \tab Total electricity demand in MWh.\cr
#' Temperature: \tab Temperature of Melbourne (BOM site 086071).\cr
#' Holiday: \tab Indicator for if that day is a public holiday.\cr
#' }
#'
#' This data is for operational demand, which is the demand met by local
#' scheduled generating units, semi-scheduled generating units, and
#' non-scheduled intermittent generating units of aggregate capacity larger
#' than 30 MWh, and by generation imports to the region. The operational demand
#' excludes the demand met by non-scheduled non-intermittent generating units,
#' non-scheduled intermittent generating units of aggregate capacity smaller
#' than 30 MWh, exempt generation (e.g. rooftop solar, gas tri-generation, very
#' small wind farms, etc), and demand of local scheduled loads. It also
#' excludes some very large industrial users (such as mines or smelters).
#'
#' @name vic_elec
#' @docType data
#' @format Time series of class `tsibble`.
#'
#' @source
#' Australian Energy Market Operator.
#'
#' @keywords datasets
#' @examples
#' library(tsibble)
#' vic_elec
NULL
#' Quarterly production of selected commodities in Australia.
#'
#' Quarterly estimates of selected indicators of manufacturing production in Australia.
#'
#' \code{aus_production} is a half-hourly `tsibble` with six values:
#' \tabular{ll}{
#' Beer: \tab Beer production in megalitres.\cr
#' Tobacco: \tab Tobacco and cigarette production in tonnes.\cr
#' Bricks: \tab Clay brick production in millions of bricks.\cr
#' Cement: \tab Portland cement production in thousands of tonnes.\cr
#' Electricity:\tab Electricity production in gigawatt hours.\cr
#' Gas: \tab Gas production in petajoules.\cr
#' }
#'
#' @name aus_production
#' @docType data
#' @format Time series of class `tsibble`.
#' @source
#' Australian Bureau of Statistics, catalogue number 8301.0.55.001 table 1.
#'
#' @keywords datasets
#' @examples
#' library(tsibble)
#' aus_production
#'
NULL
#' Household budget characteristics
#'
#' Annual indicators of household budgets for Australia, Japan, Canada and USA
#' from 1995-2016.
#'
#' \code{hh_budget} is an annual `tsibble` with six values:
#' \tabular{ll}{
#' Debt: \tab Debt as a percentage of net disposable income.\cr
#' DI: \tab Annual growth rate of disposable income.\cr
#' Expenditure: \tab Annual growth rate of expenditure.\cr
#' Savings: \tab Savings as a percentage of household disposable income.\cr
#' Wealth: \tab Wealth as a percentage of net disposable income.\cr
#' Unemployment: \tab Percentage of unemployed in the labour force.\cr
#' }
#'
#' Each country is uniquely identified by one key:
#' \tabular{ll}{
#' Country: \tab The country of the series.\cr
#' }
#'
#' @name hh_budget
#' @docType data
#' @format Time series of class `tsibble`.
#' @source
#' The Organisation for Economic Co-operation and Development (<https://data.oecd.org/>)
#'
#' @keywords datasets
#' @examples
#' library(tsibble)
#' hh_budget
#'
NULL
Try the tsibbledata package in your browser
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tsibbledata documentation built on Sept. 1, 2022, 5:09 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' Monthly Medicare Australia prescription data
#'
#' \code{PBS} is a monthly `tsibble` with two values:
#' \tabular{ll}{
#' Scripts: \tab Total number of scripts\cr
#' Cost: \tab Cost of the scripts in $AUD\cr
#' }
#'
#' The data is disaggregated using four keys:
#' \tabular{ll}{
#' Concession: \tab Concessional scripts are given to pensioners, unemployed, dependents, and other card holders \cr
#' Type: \tab Co-payments are made until an individual's script expenditure hits a threshold ($290.00 for concession, $1141.80 otherwise). Safety net subsidies are provided to individuals exceeding this amount. \cr
#' ATC1: \tab Anatomical Therapeutic Chemical index (level 1)\cr
#' ATC2: \tab Anatomical Therapeutic Chemical index (level 2)\cr
#' }
#'
#' @source
#' Medicare Australia
#'
#' @name PBS
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' PBS
#'
NULL
#' Fastest running times for Olympic races
#'
#' \code{olympic_running} is a quadrennial `tsibble` with one value:
#' \tabular{ll}{
#' Time: \tab Fastest running time for the event (seconds)\cr
#' }
#'
#' The event is identified using two keys:
#' \tabular{ll}{
#' Length: \tab The length of the race (meters)\cr
#' Sex: \tab The sex of the event\cr
#' }
#'
#' The data contains missing values in 1916, 1940 and 1944 due to the World Wars.
#'
#' @source
#' <https://olympics.com/en/sports/athletics/>
#'
#' @name olympic_running
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' olympic_running
#'
#' if(requireNamespace("ggplot2")){
#' library(ggplot2)
#' olympic_running %>% as_tibble %>%
#' ggplot(aes(x=Year, y = Time, colour = Sex)) +
#' geom_line() +
#' facet_wrap(~ Length, scales = "free_y")
#' }
NULL
#' Australian retail trade turnover
#'
#' \code{aus_retail} is a monthly `tsibble` with one value:
#' \tabular{ll}{
#' Turnover: \tab Retail turnover in $Million AUD\cr
#' }
#'
#' Each series is uniquely identified using two keys:
#' \tabular{ll}{
#' State: \tab The Australian state (or territory)\cr
#' Industry: \tab The industry of retail trade\cr
#' }
#'
#' @source
#' Australian Bureau of Statistics, catalogue number 8501.0, table 11.
#'
#' @name aus_retail
#' @format Time series of class `tsibble`
#' @keywords datasets
#'
#' @examples
#' library(tsibble)
#' aus_retail
#'
NULL
#' Passenger numbers on Ansett airline flights
#'
#' The data features a major pilots' industrial dispute which results in some
#' weeks having zero passengers. There were also at least two changes in the
#' definitions of passenger classes.
#'
#' \code{ansett} is a weekly `tsibble` with one value:
#' \tabular{ll}{
#' Passengers: \tab Total air passengers travelling with Ansett\cr
#' }
#'
#' Each series is uniquely identified using two keys:
#' \tabular{ll}{
#' Airports: \tab The airports that passengers are travelling between (both directions)\cr
#' Class: \tab The class of the ticket.\cr
#' }
#'
#' @source
#' Ansett Airlines (which no longer exists).
#'
#' @name ansett
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' ansett
#'
NULL
#' NYC Citi Bike trips
#'
#' A sample from NYC Citi Bike usage of 10 bikes throughout 2018. The data
#' includes event data on each trip, including the trip's start and end times
#' and locations. The customer's gender, birth year and bike usage type is
#' also available.
#'
#' \code{nyc_bikes} is a `tsibble` containing event data, the events include
#' these details:
#' \tabular{ll}{
#' start_time: \tab The time and date when the trip was started.\cr
#' stop_time: \tab The time and date when the trip was ended.\cr
#' start_station:\tab A unique identifier for the starting bike station.\cr
#' start_lat: \tab The latitude of the starting bike station.\cr
#' start_long: \tab The longitude of the starting bike station.\cr
#' end_station: \tab A unique identifier for the destination bike station.\cr
#' end_lat: \tab The latitutde of the destination bike station.\cr
#' end_long: \tab The longitude of the destination bike station.\cr
#' type: \tab The type of trip. A "Customer" has purchased either a 24-hour or 3-day pass, and a "Subscriber" has purchased an annual subscription.\cr
#' birth_year \tab The bike rider's year of birth.\cr
#' gender: \tab The gender of the bike rider.\cr
#' }
#'
#' Each series is uniquely identified by one key:
#' \tabular{ll}{
#' bike_id: \tab A unique identifier for the bike.\cr
#' }
#'
#' @source
#' Citi Bike NYC, <https://www.citibikenyc.com/system-data>
#'
#' @name nyc_bikes
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' nyc_bikes
NULL
#' GAFA stock prices
#'
#' Historical stock prices from 2014-2018 for Google, Amazon, Facebook and Apple.
#' All prices are in $USD.
#'
#' \code{gafa_stock} is a `tsibble` containing data on irregular trading days:
#' \tabular{ll}{
#' Open: \tab The opening price for the stock.\cr
#' High: \tab The stock's highest trading price.\cr
#' Low: \tab The stock's lowest trading price.\cr
#' Close: \tab The closing price for the stock.\cr
#' Adj_Close: \tab The adjusted closing price for the stock.\cr
#' Volume: \tab The amount of stock traded.\cr
#' }
#'
#' Each stock is uniquely identified by one key:
#' \tabular{ll}{
#' Symbol: \tab The ticker symbol for the stock.\cr
#' }
#'
#' @source
#' Yahoo Finance historical data
#'
#' @name gafa_stock
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' gafa_stock
NULL
#' Pelt trading records
#'
#' Hudson Bay Company trading records for Snowshoe Hare and Canadian Lynx furs
#' from 1845 to 1935. This data contains trade records for all areas of the company.
#'
#' \code{pelt} is an annual `tsibble` with two values:
#' \tabular{ll}{
#' Hare: \tab The number of Snowshoe Hare pelts traded.\cr
#' Lynx: \tab The number of Canadian Lynx pelts traded.\cr
#' }
#'
#' @source
#' Hudson Bay Company
#'
#' @name pelt
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' pelt
NULL
#' Global economic indicators
#'
#' Economic indicators featured by the World Bank from 1960 to 2017.
#'
#' \code{global_economy} is an annual `tsibble` with six values:
#' \tabular{ll}{
#' GDP: \tab Gross domestic product (in $USD February 2019).\cr
#' Growth: \tab Annual percentage growth in GDP.\cr
#' CPI: \tab Consumer price index (base year 2010).\cr
#' Imports: \tab Imports of goods and services (% of GDP).\cr
#' Exports: \tab Exports of goods and services (% of GDP).\cr
#' Population:\tab Total population.
#' }
#'
#' Each series is uniquely identified by one key:
#' \tabular{ll}{
#' Country: \tab The country or region of the series.\cr
#' }
#'
#' @source
#' The World Bank, <http://datatopics.worldbank.org/world-development-indicators/>
#'
#' @name global_economy
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' global_economy
NULL
#' Australian livestock slaughter
#'
#' Meat production in Australia for human consumption
#'
#' \code{aus_livestock} is a monthly `tsibble` with one value:
#' \tabular{ll}{
#' Count: \tab Number of animals slaughtered.\cr
#' }
#'
#' Each series is uniquely identified using two keys:
#' \tabular{ll}{
#' Animal: \tab The animal slaughtered.\cr
#' State: \tab The Australian state (or territory).\cr
#' }
#'
#' @source
#' Australian Bureau of Statistics, catalogue number 7218.0.55.001 tables 1 to 7.
#'
#' @name aus_livestock
#' @format Time series of class `tsibble`
#' @keywords datasets
#' @examples
#' library(tsibble)
#' aus_livestock
NULL
#' Half-hourly electricity demand for Victoria, Australia
#'
#' \code{vic_elec} is a half-hourly `tsibble` with three values:
#' \tabular{ll}{
#' Demand: \tab Total electricity demand in MWh.\cr
#' Temperature: \tab Temperature of Melbourne (BOM site 086071).\cr
#' Holiday: \tab Indicator for if that day is a public holiday.\cr
#' }
#'
#' This data is for operational demand, which is the demand met by local
#' scheduled generating units, semi-scheduled generating units, and
#' non-scheduled intermittent generating units of aggregate capacity larger
#' than 30 MWh, and by generation imports to the region. The operational demand
#' excludes the demand met by non-scheduled non-intermittent generating units,
#' non-scheduled intermittent generating units of aggregate capacity smaller
#' than 30 MWh, exempt generation (e.g. rooftop solar, gas tri-generation, very
#' small wind farms, etc), and demand of local scheduled loads. It also
#' excludes some very large industrial users (such as mines or smelters).
#'
#' @name vic_elec
#' @docType data
#' @format Time series of class `tsibble`.
#'
#' @source
#' Australian Energy Market Operator.
#'
#' @keywords datasets
#' @examples
#' library(tsibble)
#' vic_elec
NULL
#' Quarterly production of selected commodities in Australia.
#'
#' Quarterly estimates of selected indicators of manufacturing production in Australia.
#'
#' \code{aus_production} is a half-hourly `tsibble` with six values:
#' \tabular{ll}{
#' Beer: \tab Beer production in megalitres.\cr
#' Tobacco: \tab Tobacco and cigarette production in tonnes.\cr
#' Bricks: \tab Clay brick production in millions of bricks.\cr
#' Cement: \tab Portland cement production in thousands of tonnes.\cr
#' Electricity:\tab Electricity production in gigawatt hours.\cr
#' Gas: \tab Gas production in petajoules.\cr
#' }
#'
#' @name aus_production
#' @docType data
#' @format Time series of class `tsibble`.
#' @source
#' Australian Bureau of Statistics, catalogue number 8301.0.55.001 table 1.
#'
#' @keywords datasets
#' @examples
#' library(tsibble)
#' aus_production
#'
NULL
#' Household budget characteristics
#'
#' Annual indicators of household budgets for Australia, Japan, Canada and USA
#' from 1995-2016.
#'
#' \code{hh_budget} is an annual `tsibble` with six values:
#' \tabular{ll}{
#' Debt: \tab Debt as a percentage of net disposable income.\cr
#' DI: \tab Annual growth rate of disposable income.\cr
#' Expenditure: \tab Annual growth rate of expenditure.\cr
#' Savings: \tab Savings as a percentage of household disposable income.\cr
#' Wealth: \tab Wealth as a percentage of net disposable income.\cr
#' Unemployment: \tab Percentage of unemployed in the labour force.\cr
#' }
#'
#' Each country is uniquely identified by one key:
#' \tabular{ll}{
#' Country: \tab The country of the series.\cr
#' }
#'
#' @name hh_budget
#' @docType data
#' @format Time series of class `tsibble`.
#' @source
#' The Organisation for Economic Co-operation and Development (<https://data.oecd.org/>)
#'
#' @keywords datasets
#' @examples
#' library(tsibble)
#' hh_budget
#'
NULL
Try the tsibbledata package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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