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removed unhcr_popstats_2017 dataset because of annoying note

This commit is contained in:
pvictor 2021-11-29 20:04:50 +01:00
parent 4f4142211b
commit dddd472c1f
5 changed files with 51 additions and 88 deletions
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27
R/data.R
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@ -1,20 +1,3 @@
#' UNHCR data for 2017
#'
#' The dataset contains data about UNHCR's populations of concern for the year 2017.
#'
#' @format A data frame with 11237 observations and the following 6 variables:
#' \describe{
#' \item{\code{country_origin}}{Country of origin of population}
#' \item{\code{country_residence}}{Country / territory of asylum/residence of population}
#' \item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
#' Returned IDPs, Stateless persons, Others of concern.}
#' \item{\code{value}}{Number of people concerned}
#' \item{\code{continent_residence}}{Continent of origin of population}
#' \item{\code{continent_origin}}{Continent of residence of population}
#' }
#' @source UNHCR (The UN Refugee Agency) (\url{https://www.unhcr.org/})
"unhcr_popstats_2017"
#' UNHCR data by continent of origin
#'
@ -24,7 +7,7 @@
#' \describe{
#' \item{\code{year}}{Year concerned.}
#' \item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
#' Returned IDPs, Stateless persons, Others of concern.}
#' Returned IDPs, Stateless persons, Others of concern.}
#' \item{\code{continent_origin}}{Continent of residence of population.}
#' \item{\code{n}}{Number of people concerned.}
#' }
@ -40,7 +23,7 @@
#' @format A data frame with 120 observations and the following 3 variables:
#' \describe{
#' \item{\code{date}}{date.}
#' \item{\code{type}}{Type of data : realized or forecast.}
#' \item{\code{type}}{Type of data : realized or forecast.}
#' \item{\code{value}}{Value in giga-watt per hour.}
#' }
#' @source Rte (Electricity Transmission Network in France) (\url{https://data.rte-france.com/})
@ -53,7 +36,7 @@
#' @format A data frame with 60 observations and the following 5 variables:
#' \describe{
#' \item{\code{datetime}}{Timestamp.}
#' \item{\code{open}}{Open value.}
#' \item{\code{open}}{Open value.}
#' \item{\code{high}}{Highest value.}
#' \item{\code{low}}{Lowest value.}
#' \item{\code{close}}{Close value.}
@ -63,14 +46,14 @@
#' @title Paris Climate
#'
#'
#' @description Average temperature and precipitation in Paris for the period 1971-2000.
#'
#'
#' @format A data frame with 12 observations and the following 3 variables:
#' \describe{
#' \item{\code{month}}{Month}
#' \item{\code{temperature}}{Temperature (in degree celsius).}
#' \item{\code{temperature}}{Temperature (in degree celsius).}
#' \item{\code{precipitation}}{Precipitation (in mm).}
#' }
#' @source Wikipedia (\url{https://fr.wikipedia.org/wiki/Climat_de_Paris})

BIN
data/unhcr_popstats_2017.rda
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42
examples/add-lines.R
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@ -2,34 +2,38 @@
library(apexcharter)
# On a column chart
apex(
data = table(unhcr_popstats_2017$continent_residence),
aes(Var1, Freq),
"column"
) %>%
add_hline(value = 2100)
unhcr_ts %>%
subset(year == 2017 & population_type == "Asylum-seekers") %>%
apex(
aes(continent_origin, n),
"column"
) %>%
add_hline(value = 5e5)
# On a scatter chart
apex(
data = iris,
aes(Sepal.Length, Sepal.Width),
data = cars,
aes(speed, dist),
"scatter"
) %>%
add_hline(value = mean(iris$Sepal.Width)) %>%
add_vline(value = mean(iris$Sepal.Length))
) %>%
add_hline(value = mean(cars$dist)) %>%
add_vline(value = mean(cars$speed))
# With labels
apex(
data = iris,
aes(Sepal.Length, Sepal.Width),
data = cars,
aes(speed, dist),
"scatter"
) %>%
) %>%
add_hline(
value = mean(iris$Sepal.Width),
label = "Mean of Sepal.Width"
) %>%
value = mean(cars$dist),
label = "Mean of dist"
) %>%
add_vline(
value = mean(iris$Sepal.Length),
label = "Mean of Sepal.Length"
value = mean(cars$speed),
label = label(
text = "Mean of speed",
borderColor = "red"
)
)

42
man/add-vh-lines.Rd
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@ -38,35 +38,39 @@ Add horizontal or vertical line
library(apexcharter)
# On a column chart
apex(
data = table(unhcr_popstats_2017$continent_residence),
aes(Var1, Freq),
"column"
) \%>\%
add_hline(value = 2100)
unhcr_ts \%>\%
subset(year == 2017 & population_type == "Asylum-seekers") \%>\%
apex(
aes(continent_origin, n),
"column"
) \%>\%
add_hline(value = 5e5)
# On a scatter chart
apex(
data = iris,
aes(Sepal.Length, Sepal.Width),
data = cars,
aes(speed, dist),
"scatter"
) \%>\%
add_hline(value = mean(iris$Sepal.Width)) \%>\%
add_vline(value = mean(iris$Sepal.Length))
) \%>\%
add_hline(value = mean(cars$dist)) \%>\%
add_vline(value = mean(cars$speed))
# With labels
apex(
data = iris,
aes(Sepal.Length, Sepal.Width),
data = cars,
aes(speed, dist),
"scatter"
) \%>\%
) \%>\%
add_hline(
value = mean(iris$Sepal.Width),
label = "Mean of Sepal.Width"
) \%>\%
value = mean(cars$dist),
label = "Mean of dist"
) \%>\%
add_vline(
value = mean(iris$Sepal.Length),
label = "Mean of Sepal.Length"
value = mean(cars$speed),
label = label(
text = "Mean of speed",
borderColor = "red"
)
)
}

28
man/unhcr_popstats_2017.Rd
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@ -1,28 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{unhcr_popstats_2017}
\alias{unhcr_popstats_2017}
\title{UNHCR data for 2017}
\format{
A data frame with 11237 observations and the following 6 variables:
\describe{
\item{\code{country_origin}}{Country of origin of population}
\item{\code{country_residence}}{Country / territory of asylum/residence of population}
\item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
Returned IDPs, Stateless persons, Others of concern.}
\item{\code{value}}{Number of people concerned}
\item{\code{continent_residence}}{Continent of origin of population}
\item{\code{continent_origin}}{Continent of residence of population}
}
}
\source{
UNHCR (The UN Refugee Agency) (\url{https://www.unhcr.org/})
}
\usage{
unhcr_popstats_2017
}
\description{
The dataset contains data about UNHCR's populations of concern for the year 2017.
}
\keyword{datasets}