R/datasets.R

#' Yearly populations of countries from 1960 to 2017
#'
#' A dataset that presents yearly, total populations of countries. Total
#' population is based on counts of all residents regardless of legal status or
#' citizenship. Country identifiers include the English-language country names,
#' and the 2- and 3-letter ISO 3166-1 country codes. Each row contains a
#' `population` value for a given `year` (from 1960 to 2017). Any `NA` values
#' for `populations` indicate the non-existence of the country during that year.
#'
#' @format A tibble with 12470 rows and 5 variables:
#' \describe{
#' \item{country_name}{Name of the country}
#' \item{country_code_2}{The 2-letter ISO 3166-1 country code}
#' \item{country_code_3}{The 3-letter ISO 3166-1 country code}
#' \item{year}{The year for the population estimate}
#' \item{population}{The population estimate, midway through the year}
#' }
#'
#' @source <https://data.worldbank.org/indicator/SP.POP.TOTL>
#'
#' @examples
#' # Here is a glimpse at the data
#' # available in `countrypops`
#' dplyr::glimpse(countrypops)
#'
#' @family Datasets
#' @section Function ID:
#' 11-1
#'
"countrypops"

#' Twice hourly solar zenith angles by month & latitude
#'
#' This dataset contains solar zenith angles (in degrees, with the range of
#' 0-90) every half hour from 04:00 to 12:00, true solar time. This set of
#' values is calculated on the first of every month for 4 different northern
#' hemisphere latitudes. For determination of afternoon values, the presented
#' tabulated values are symmetric about noon.
#'
#' The solar zenith angle (SZA) is one measure that helps to describe the sun's
#' path across the sky. It's defined as the angle of the sun relative to a line
#' perpendicular to the earth's surface. It is useful to calculate the SZA in
#' relation to the true solar time. True solar time relates to the position of
#' the sun with respect to the observer, which is different depending on the
#' exact longitude. For example, two hours before the sun crosses the meridian
#' (the highest point it would reach that day) corresponds to a true solar time
#' of 10 a.m. The SZA has a strong dependence on the observer's latitude. For
#' example, at a latitude of 50 degrees N at the start of January, the noontime
#' SZA is 73.0 but a different observer at 20 degrees N would measure the
#' noontime SZA to be 43.0 degrees.
#'
#' @format A tibble with 816 rows and 4 variables:
#' \describe{
#'   \item{latitude}{The latitude in decimal degrees for the observations}
#'   \item{month}{The measurement month; all calculations where conducted
#'   for the first day of each month}
#'   \item{tst}{The true solar time at the given `latitude` and date
#'   (first of `month`) for which the solar zenith angle is calculated}
#'   \item{sza}{The solar zenith angle in degrees, where `NA`s
#'   indicate that sunrise hadn't yet occurred by the `tst` value}
#' }
#'
#' @source Calculated Actinic Fluxes (290 - 700 nm) for Air Pollution
#' Photochemistry Applications (Peterson, 1976), available at:
#' <https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=9100JA26.txt>.
#'
#' @examples
#' # Here is a glimpse at the data
#' # available in `sza`
#' dplyr::glimpse(sza)
#'
#' @family Datasets
#' @section Function ID:
#' 11-2
#'
"sza"

#' Deluxe automobiles from the 2014-2017 period
#'
#' Expensive and fast cars. Not your father's `mtcars`. Each row describes a car
#' of a certain make, model, year, and trim. Basic specifications such as
#' horsepower, torque, EPA MPG ratings, type of drivetrain, and transmission
#' characteristics are provided. The country of origin for the car manufacturer
#' is also given.
#'
#' All of the `gtcars` have something else in common (aside from the high asking
#' prices): they are all grand tourer vehicles. These are proper GT cars that
#' blend pure driving thrills with a level of comfort that is more expected from
#' a fine limousine (e.g., a Rolls-Royce Phantom EWB). You'll find that, with
#' these cars, comfort is emphasized over all-out performance. Nevertheless, the
#' driving experience should also mean motoring at speed, doing so in style and
#' safety.
#'
#' @format A tibble with 47 rows and 15 variables:
#' \describe{
#'   \item{mfr}{The name of the car manufacturer}
#'   \item{model}{The car's model name}
#'   \item{year}{The car's model year}
#'   \item{trim}{A short description of the car model's trim}
#'   \item{bdy_style}{An identifier of the car's body style, which is either
#'   `coupe`, `convertible`, `sedan`, or `hatchback`}
#'   \item{hp, hp_rpm}{The car's horsepower and the associated RPM level}
#'   \item{trq, trq_rpm}{The car's torque and the associated RPM level}
#'   \item{mpg_c, mpg_h}{The miles per gallon fuel efficiency rating for city
#'   and highway driving}
#'   \item{drivetrain}{The car's drivetrain which, for this dataset is either
#'   `rwd` (Rear Wheel Drive) or `awd` (All Wheel Drive)}
#'   \item{trsmn}{The codified transmission type, where the number part is
#'   the number of gears; the car could have automatic transmission
#'   (`a`), manual transmission (`m`), an option to switch between
#'   both types (`am`), or, direct drive (`dd`)}
#'   \item{ctry_origin}{The country name for where the vehicle manufacturer
#'   is headquartered}
#' }
#'
#' @examples
#' # Here is a glimpse at the data
#' # available in `gtcars`
#' dplyr::glimpse(gtcars)
#'
#' @family Datasets
#' @section Function ID:
#' 11-3
#'
"gtcars"

#' Daily S&P 500 Index data from 1950 to 2015
#'
#' This dataset provides daily price indicators for the S&P 500 index from the
#' beginning of 1950 to the end of 2015. The index includes 500 leading
#' companies and captures about 80\% coverage of available market capitalization.
#'
#' @format A tibble with 16607 rows and 7 variables:
#' \describe{
#' \item{date}{The date expressed as `Date` values}
#' \item{open, high, low, close}{The day's opening, high, low, and closing
#' prices in USD; the `close` price is adjusted for splits}
#' \item{volume}{the number of trades for the given `date`}
#' \item{adj_close}{The close price adjusted for both dividends and splits}
#' }
#'
#' @examples
#' # Here is a glimpse at the data
#' # available in `sp500`
#' dplyr::glimpse(sp500)
#'
#' @family Datasets
#' @section Function ID:
#' 11-4
#'
"sp500"

#' A year of pizza sales from a pizza place
#'
#' A synthetic dataset that describes pizza sales for a pizza place somewhere in
#' the US. While the contents are artificial, the ingredients used to make the
#' pizzas are far from it. There are 32 different pizzas that fall into 4
#' different categories: `classic` (classic pizzas: 'You probably had one like
#' it before, but never like this!'), `chicken` (pizzas with chicken as a major
#' ingredient: 'Try the Southwest Chicken Pizza! You'll love it!'), `supreme`
#' (pizzas that try a little harder: 'My Soppressata pizza uses only the finest
#' salami from my personal salumist!'), and, `veggie` (pizzas without any meats
#' whatsoever: 'My Five Cheese pizza has so many cheeses, I can only offer it in
#' Large Size!').
#'
#' Each pizza in the dataset is identified by a short `name`. The following
#' listings provide the full names of each pizza and their main ingredients.
#'
#' Classic Pizzas:
#' \itemize{
#' \item `classic_dlx`: The Classic Deluxe Pizza (Pepperoni, Mushrooms, Red
#' Onions, Red Peppers, Bacon)
#' \item `big_meat`: The Big Meat Pizza (Bacon, Pepperoni, Italian Sausage,
#' Chorizo Sausage)
#' \item `pepperoni`: The Pepperoni Pizza (Mozzarella Cheese, Pepperoni)
#' \item `hawaiian`: The Hawaiian Pizza (Sliced Ham, Pineapple, Mozzarella
#' Cheese)
#' \item `pep_msh_pep`: The Pepperoni, Mushroom, and Peppers Pizza
#' (Pepperoni, Mushrooms, and Green Peppers)
#' \item `ital_cpcllo`: The Italian Capocollo Pizza (Capocollo, Red
#' Peppers, Tomatoes, Goat Cheese, Garlic, Oregano)
#' \item `napolitana`: The Napolitana Pizza (Tomatoes, Anchovies, Green
#' Olives, Red Onions, Garlic)
#' \item `the_greek`: The Greek Pizza (Kalamata Olives, Feta Cheese,
#' Tomatoes, Garlic, Beef Chuck Roast, Red Onions)
#' }
#'
#' Chicken Pizzas:
#' \itemize{
#' \item `thai_ckn`: The Thai Chicken Pizza (Chicken, Pineapple, Tomatoes,
#' Red Peppers, Thai Sweet Chilli Sauce)
#' \item `bbq_ckn`: The Barbecue Chicken Pizza (Barbecued Chicken, Red
#' Peppers, Green Peppers, Tomatoes, Red Onions, Barbecue Sauce)
#' \item `southw_ckn`: The Southwest Chicken Pizza (Chicken, Tomatoes, Red
#' Peppers, Red Onions, Jalapeno Peppers, Corn, Cilantro, Chipotle Sauce)
#' \item `cali_ckn`: The California Chicken Pizza (Chicken, Artichoke,
#' Spinach, Garlic, Jalapeno Peppers, Fontina Cheese, Gouda Cheese)
#' \item `ckn_pesto`: The Chicken Pesto Pizza (Chicken, Tomatoes, Red
#' Peppers, Spinach, Garlic, Pesto Sauce)
#' \item `ckn_alfredo`: The Chicken Alfredo Pizza (Chicken, Red Onions,
#' Red Peppers, Mushrooms, Asiago Cheese, Alfredo Sauce)
#' }
#'
#' Supreme Pizzas:
#' \itemize{
#' \item `brie_carre`: The Brie Carre Pizza (Brie Carre Cheese, Prosciutto,
#'  Caramelized Onions, Pears, Thyme, Garlic)
#' \item `calabrese`: The Calabrese Pizza (‘Nduja Salami, Pancetta,
#' Tomatoes, Red Onions, Friggitello Peppers, Garlic)
#' \item `soppressata`: The Soppressata Pizza (Soppressata Salami, Fontina
#' Cheese, Mozzarella Cheese, Mushrooms, Garlic)
#' \item `sicilian`: The Sicilian Pizza (Coarse Sicilian Salami, Tomatoes,
#' Green Olives, Luganega Sausage, Onions, Garlic)
#' \item `ital_supr`: The Italian Supreme Pizza (Calabrese Salami,
#' Capocollo, Tomatoes, Red Onions, Green Olives, Garlic)
#' \item `peppr_salami`: The Pepper Salami Pizza (Genoa Salami, Capocollo,
#' Pepperoni, Tomatoes, Asiago Cheese, Garlic)
#' \item `prsc_argla`: The Prosciutto and Arugula Pizza (Prosciutto di San
#' Daniele, Arugula, Mozzarella Cheese)
#' \item `spinach_supr`: The Spinach Supreme Pizza (Spinach, Red Onions,
#' Pepperoni, Tomatoes, Artichokes, Kalamata Olives, Garlic, Asiago Cheese)
#' \item `spicy_ital`: The Spicy Italian Pizza (Capocollo, Tomatoes, Goat
#' Cheese, Artichokes, Peperoncini verdi, Garlic)
#' }
#'
#' Vegetable Pizzas
#' \itemize{
#' \item `mexicana`: The Mexicana Pizza (Tomatoes, Red Peppers, Jalapeno
#' Peppers, Red Onions, Cilantro, Corn, Chipotle Sauce, Garlic)
#' \item `four_cheese`: The Four Cheese Pizza (Ricotta Cheese, Gorgonzola
#' Piccante Cheese, Mozzarella Cheese, Parmigiano Reggiano Cheese, Garlic)
#' \item `five_cheese`: The Five Cheese Pizza (Mozzarella Cheese, Provolone
#' Cheese, Smoked Gouda Cheese, Romano Cheese, Blue Cheese, Garlic)
#' \item `spin_pesto`: The Spinach Pesto Pizza (Spinach, Artichokes,
#' Tomatoes, Sun-dried Tomatoes, Garlic, Pesto Sauce)
#' \item `veggie_veg`: The Vegetables + Vegetables Pizza (Mushrooms,
#' Tomatoes, Red Peppers, Green Peppers, Red Onions, Zucchini, Spinach, Garlic)
#' \item `green_garden`: The Green Garden Pizza (Spinach, Mushrooms,
#' Tomatoes, Green Olives, Feta Cheese)
#' \item `mediterraneo`: The Mediterranean Pizza (Spinach, Artichokes,
#' Kalamata Olives, Sun-dried Tomatoes, Feta Cheese, Plum Tomatoes, Red Onions)
#' \item `spinach_fet`: The Spinach and Feta Pizza (Spinach, Mushrooms,
#' Red Onions, Feta Cheese, Garlic)
#' \item `ital_veggie`: The Italian Vegetables Pizza (Eggplant, Artichokes,
#' Tomatoes, Zucchini, Red Peppers, Garlic, Pesto Sauce)
#' }
#'
#' @format A tibble with 49574 rows and 7 variables:
#' \describe{
#'   \item{id}{The ID for the order, which consists of one or more pizzas
#'   at a give `date` and `time`}
#'   \item{date}{A character representation of the `order` date,
#'   expressed in the ISO 8601 date format (YYYY-MM-DD)}
#'   \item{time}{A character representation of the `order` time,
#'   expressed as a 24-hour time the ISO 8601 extended time format
#'   (hh:mm:ss)}
#'   \item{name}{The short name for the pizza}
#'   \item{size}{The size of the pizza, which can either be `S`,
#'   `M`, `L`, `XL` (rare!), or `XXL` (even rarer!);
#'   most pizzas are available in the `S`, `M`, and `L` sizes
#'   but exceptions apply}
#'   \item{type}{The category or type of pizza, which can either be
#'   `classic`, `chicken`, `supreme`, or `veggie`}
#'   \item{price}{The price of the pizza and the amount that it sold for
#'   (in USD)}
#' }
#'
#' @examples
#' # Here is a glimpse at the data
#' # available in `pizzaplace`
#' dplyr::glimpse(pizzaplace)
#'
#' @family Datasets
#' @section Function ID:
#' 11-5
#'
"pizzaplace"

#' A toy example tibble for testing with gt: exibble
#'
#' This tibble contains data of a few different classes, which makes it
#' well-suited for quick experimentation with the functions in this package. It
#' contains only eight rows with numeric, character, and factor columns. The
#' last 4 rows contain `NA` values in the majority of this tibble's columns (1
#' missing value per column). The `date`, `time`, and `datetime` columns are
#' character-based dates/times in the familiar ISO 8601 format. The `row` and
#' `group` columns provide for unique rownames and two groups (`grp_a` and
#' `grp_b`) for experimenting with the [gt()] function's `rowname_col` and
#' `groupname_col` arguments.
#'
#' @format A tibble with 8 rows and 9 variables:
#' \describe{
#'   \item{num}{a numeric column ordered with increasingly larger values}
#'   \item{char}{a character column composed of names of fruits from `a` to
#'   `h`}
#'   \item{fctr}{a factor column with numbers from 1 to 8, written out}
#'   \item{date, time, datetime}{character columns with dates, times, and
#'   datetimes}
#'   \item{currency}{a numeric column that is useful for testing currency-based
#'   formatting}
#'   \item{row}{a character column in the format `row_X` which can be
#'   useful for testing with row captions in a table stub}
#'   \item{group}{a character column with four `grp_a` values and four
#'   `grp_b` values which can be useful for testing tables that contain
#'   row groups}
#' }
#'
#' @examples
#' # Here is a glimpse at the data
#' # available in `exibble`
#' dplyr::glimpse(exibble)
#'
#' @family Datasets
#' @section Function ID:
#' 11-6
#'
"exibble"

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gt documentation built on Aug. 6, 2020, 1:07 a.m.