R/document_data.R

In paternogbc/examples: Worldwide datasets for teaching data science and data visualization

#' @importFrom tibble tibble
NULL

#' Worldwide datasets for teaching data science.
#'
#' An implementation of sensitivity analysis for phylogenetic comparative
#' An umbrella of social and environmental datasets worldwide for
#' teaching data science, R programming, basic statistics and data visualization in R.
#' The package is an umbrella of usefull datasets to be used for teaching data analysis and
#' statistics.
#' @name examples-package
#' @author Gustavo Brant Paterno
#' @section Bug reports:
#'
#' \url{https://github.com/paternogbc/examples/issues}.
#' @section How to contribute?:
#'
#' You can find instructions on how to contribute to \bold{examples} at
#' this link: \url{https://github.com/paternogbc/examples}
#'
#' @details
#'
#' This package is build on the work of many that gathered this incredible datasets.
#' @docType package
NULL

#' Malaria deaths worldwide.
#'
#' Malaria deaths by age across the world and time.
#'
#' @format A data frame with five variables:
#' \itemize{
#' \item{\strong{country}} {The name of the country}
#' \item{\strong{code}} {The code of the country}
#' \item{\strong{year}} {The year of deaths}
#' \item{\strong{age_group}} {The age category}
#' \item{\strong{deaths}} {Number of malaria deaths per 100,000 individuals}
#' }
#'
#' @details
#' For further details, see \url{https://ourworldindata.org/malaria}
#'
#' @source
#' Data downloaded from \strong{TidyTuesday Project} Github repository:
#' \url{https://github.com/rfordatascience/tidytuesday}
#'
#' @references
#'
#' Max Roser and Hannah Ritchie (2019) - "Malaria". Published online at OurWorldInData.org.
#' Retrieved from: \url{https://ourworldindata.org/malaria} [Online Resource]
"malaria"

#' Global warming
#'
#' Global Annual Mean Surface Air Temperature Change
#'
#' @format A data frame with three variables:
#' \itemize{
#' \item{\strong{Source}} {The source of data: \strong{(1)} GISS Surface Temperature
#' (GISTEMP) analysis and \strong{(2)} the global component of Climate at a Glance (GCAG).}
#' \item{\strong{year}} {The year of temperature measures.}
#' \item{\strong{month}} {The month of temperature measures.}
#' \item{\strong{day}} {The day of temperature measures.}
#' \item{\strong{temp}} {Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies
#' (deviations from a corresponding period, see details)}
#' }
#'
#' @details
#'
#' For \strong{GISTEMP}: Temperature Anomalies are deviations from 1951-1980
#' means (see \url{https://data.giss.nasa.gov/gistemp})
#'
#' For \strong{GCAG}: Temperature Anomalies are deviations with respect to the
#'  20th century average (see \url{http://www.ncdc.noaa.gov/cag/data-info/global})
#'
#' @source
#' Data downloaded from \url{https://datahub.io/core/global-temp#readme}
#'
#' @references
#
#' GISTEMP: NASA Goddard Institute for Space Studies (GISS) Surface
#' Temperature Analysis, Global Land-Ocean Temperature Index.
#'
#' NOAA National Climatic Data Center (NCDC), global component of Climate
#' at a Glance (GCAG).
#'
#' @import ggplot2
#' @examples
#' #load packages
#' library(ggplot2)
#' library(ggplot2)
#' library(dplyr, warn.conflicts = F)
#'
#'# Select source and average by year
#'df <-
#'  warming %>%
#'  dplyr::filter(source == "GISTEMP") %>%
#'  dplyr::group_by(year) %>%
#'  summarise(temp = mean(temp, na.rm = T))
#'
#'# Plot trend in global temperature
#'ggplot(df, aes(y = temp, x = year, color = temp)) +
#'  geom_hline(yintercept = 0,
#'             lty = "dashed",
#'             alpha = .8) +
#'  geom_line(size = 1,
#'            alpha = .5,
#'            show.legend = F) +
#'  geom_point(size = 2, show.legend = F) +
#'  scale_color_gradient(low = "steelblue", high = "red") +
#'  scale_x_continuous(breaks = seq(1880, 2020, 10)) +
#'  scale_y_continuous(breaks = seq(-.5, 1, .25)) +
#'  theme_bw(base_size = 12) +
#'  theme(panel.grid = element_blank(),
#'        title = element_text(size = 12)) +
#'  ylab("Diferença de Temperatura (°C)") + xlab("Ano") +
#'  labs(title = "Mudança Na Temperatura Média Global",
#'       subtitle = "Diferença para o período entre 1950 e 1980",
#'       caption = "Fonte: https://data.giss.nasa.gov/gistemp")
"warming"