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* updating a few datasets * fixed the time in the paralympic dataset * fixing the variable names * temperature data, 1950 and 2022 * Update paralympic data prep and example * Document paralympic data * Document NYC marathon data * Update 2022 durham pm25 data + align 2011 data * Update pipe * Update pipe * Run document * Add type variable to docs * Fix data prep issues and add pkgs ex depends on * Move US temp data to usdata pkg * Ungroup data in example * Add new datasets to pkgdown site --------- Co-authored-by: Mine Çetinkaya-Rundel <cetinkaya.mine@gmail.com>
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| Original file line number | Diff line number | Diff line change |
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| #' Race time for Olympic and Paralympic 1500m. | ||
| #' | ||
| #' Compiled gold medal times for the 1500m race in the Olympic Games and the | ||
| #' Paralympic Games. The times given for contestants competing in | ||
| #' the Paralympic Games are for athletes with different visual impairments; | ||
| #' T11 indicates fully blind (with an option to race with a guide-runner) | ||
| #' with T12 and T13 as lower levels of visual impairment. | ||
| #' | ||
| #' | ||
| #' @name paralympic_1500 | ||
| #' @docType data | ||
| #' @format A data frame with 83 rows and 10 variables. | ||
| #' \describe{ | ||
| #' \item{year}{Year the games took place.} | ||
| #' \item{city}{City of the games.} | ||
| #' \item{country_of_games}{Country of the games.} | ||
| #' \item{division}{Division: `Men` or `Women`.} | ||
| #' \item{type}{Type.} | ||
| #' \item{name}{Name of the athlete.} | ||
| #' \item{country_of_athlete}{Country of athlete.} | ||
| #' \item{time}{Time of gold medal race, in m:s.} | ||
| #' \item{time_min}{Time of gold medal race, in decimal minutes (min + sec/60).} | ||
| #' } | ||
| #' @source [https://www.paralympic.org/](https://www.paralympic.org/) and [https://en.wikipedia.org/wiki/1500_metres_at_the_Olympics](https://en.wikipedia.org/wiki/1500_metres_at_the_Olympics). | ||
| #' @keywords datasets | ||
| #' @examples | ||
| #' | ||
| #' library(ggplot2) | ||
| #' library(dplyr) | ||
| #' | ||
| #' paralympic_1500 |> | ||
| #' mutate( | ||
| #' sight_level = case_when( | ||
| #' type == "T11" ~ "total impairment", | ||
| #' type == "T12" ~ "some impairment", | ||
| #' type == "T13" ~ "some impairment", | ||
| #' type == "Olympic" ~ "no impairment" | ||
| #' ) | ||
| #' ) |> | ||
| #' filter(division == "Men", year > 1920) |> | ||
| #' filter(type == "Olympic" | type == "T11") |> | ||
| #' ggplot(aes(x = year, y = time_min, color = sight_level, shape = sight_level)) + | ||
| #' geom_point() + | ||
| #' scale_x_continuous(breaks = seq(1924, 2020, by = 8)) + | ||
| #' labs( | ||
| #' title = "Men's Olympic and Paralympic 1500m race times", | ||
| #' x = "Year", | ||
| #' y = "Time of Race (minutes)", | ||
| #' color = "Sight level", | ||
| #' shape = "Sight level" | ||
| #' ) | ||
| #' | ||
| "paralympic_1500" |
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| #' Air quality for Durham, NC | ||
| #' | ||
| #' Daily air quality is measured by the air quality index (AQI) reported by the | ||
| #' Environmental Protection Agency in 2022. | ||
| #' | ||
| #' | ||
| #' @name pm25_2022_durham | ||
| #' @docType data | ||
| #' @format A data frame with 356 observations on the following 20 variables. | ||
| #' \describe{ | ||
| #' \item{date}{Date.} | ||
| #' \item{aqs_site_id}{The numeric site ID.} | ||
| #' \item{poc}{A numeric vector, the Parameter Occurance Code.} | ||
| #' \item{daily_mean_pm2_5_concentration}{A numeric vector with the average daily concentration of fine particulates, or particulate matter 2.5.} | ||
| #' \item{units}{A character vector with value \code{ug/m3 LC}.} | ||
| #' \item{daily_aqi_value}{A numeric vector with the daily air quality index.} | ||
| #' \item{daily_obs_count}{A numeric vector.} | ||
| #' \item{percent_complete}{A numeric vector.} | ||
| #' \item{aqs_parameter_code}{A numeric vector.} | ||
| #' \item{aqs_parameter_desc}{A factor vector with level \code{PM2.5 - Local Conditions}.} | ||
| #' \item{cbsa_code}{A numeric vector.} | ||
| #' \item{cbsa_name}{A character vector with value \code{Durham-Chapel Hill, NC}.} | ||
| #' \item{state_code}{A numeric vector.} | ||
| #' \item{state}{A character vector with value \code{North Carolina}.} | ||
| #' \item{county_code}{A numeric vector.} | ||
| #' \item{county}{A character vector with value \code{Durham}.} | ||
| #' \item{site_latitude}{A numeric vector of the latitude.} | ||
| #' \item{site_longitude}{A numeric vector of the longitude.} | ||
| #' \item{site_name}{A character vector with value \code{Durham Armory}.} | ||
| #' } | ||
| #' @source US Environmental Protection Agency, AirData, 2022. | ||
| #' \url{http://www3.epa.gov/airdata/ad_data_daily.html} | ||
| #' @keywords datasets | ||
| #' @examples | ||
| #' | ||
| #' library(ggplot2) | ||
| #' | ||
| #' ggplot(pm25_2022_durham, aes(x = date, y = daily_mean_pm2_5_concentration, group = 1)) + | ||
| #' geom_line() | ||
| "pm25_2022_durham" |
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| # load packages ---------------------------------------------------------------- | ||
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| library(tidyverse) | ||
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| # load data -------------------------------------------------------------------- | ||
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| paralympic_1500_raw <- read_csv(here::here("data-raw/paralympic_1500/paralympic_1500.csv")) | ||
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| # cleaning --------------------------------------------------------------------- | ||
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| paralympic_1500 <- paralympic_1500_raw |> | ||
| mutate(time_min = time) |> | ||
| mutate(time = paste(min, sec, sep = ":")) |> | ||
| select(-min, -sec) | ||
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| # save data -------------------------------------------------------------------- | ||
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| usethis::use_data(paralympic_1500, overwrite = TRUE) |
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