2019-02-14 09:22:57 +01:00
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#' UNHCR data for 2017
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#'
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#' The dataset contains data about UNHCR's populations of concern for the year 2017.
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#'
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2020-04-02 17:43:24 +02:00
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#' @format A data frame with 11237 observations and the following 6 variables:
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2019-02-14 09:22:57 +01:00
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#' \describe{
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#' \item{\code{country_origin}}{Country of origin of population}
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#' \item{\code{country_residence}}{Country / territory of asylum/residence of population}
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#' \item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
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#' Returned IDPs, Stateless persons, Others of concern.}
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#' \item{\code{value}}{Number of people concerned}
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#' \item{\code{continent_residence}}{Continent of origin of population}
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#' \item{\code{continent_origin}}{Continent of residence of population}
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#' }
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2019-11-27 23:17:23 +01:00
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#' @source UNHCR (The UN Refugee Agency) (\url{https://www.unhcr.org/})
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2019-02-14 09:22:57 +01:00
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"unhcr_popstats_2017"
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2019-09-05 10:48:34 +02:00
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#' UNHCR data by continent of origin
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#'
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2019-11-27 23:17:23 +01:00
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#' The dataset contains data about UNHCR's populations of concern summarised by continent of origin.
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2019-09-05 10:48:34 +02:00
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#'
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2020-04-02 17:43:24 +02:00
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#' @format A data frame with 913 observations and the following 4 variables:
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2019-09-05 10:48:34 +02:00
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#' \describe{
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#' \item{\code{year}}{Year concerned.}
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#' \item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
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#' Returned IDPs, Stateless persons, Others of concern.}
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#' \item{\code{continent_origin}}{Continent of residence of population.}
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#' \item{\code{n}}{Number of people concerned.}
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#' }
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2019-11-27 23:17:23 +01:00
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#' @source UNHCR (The UN Refugee Agency) (\url{https://www.unhcr.org/})
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2019-09-05 10:48:34 +02:00
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"unhcr_ts"
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2020-04-02 17:43:24 +02:00
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#' Electricity consumption and forecasting
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#'
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#' Electricity consumption per day in France for january and february of year 2020.
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#'
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#' @format A data frame with 120 observations and the following 3 variables:
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#' \describe{
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#' \item{\code{date}}{date.}
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2021-04-28 09:01:33 +02:00
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#' \item{\code{type}}{Type of data : realized or forecast.}
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2020-04-02 17:43:24 +02:00
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#' \item{\code{value}}{Value in giga-watt per hour.}
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#' }
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#' @source Rte (Electricity Transmission Network in France) (\url{https://data.rte-france.com/})
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"consumption"
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2020-06-13 13:04:10 +02:00
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#' Candlestick demo data
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#'
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#'
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#' @format A data frame with 60 observations and the following 5 variables:
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#' \describe{
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#' \item{\code{datetime}}{Timestamp.}
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#' \item{\code{open}}{Open value.}
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#' \item{\code{high}}{Highest value.}
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#' \item{\code{low}}{Lowest value.}
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#' \item{\code{close}}{Close value.}
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#' }
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#' @source Apexcharts (\url{https://apexcharts.com/javascript-chart-demos/candlestick-charts/basic/})
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"candles"
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2020-04-02 17:43:24 +02:00
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2020-07-26 10:45:25 +02:00
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#' @title Paris Climate
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#'
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#' @description Average temperature and precipitation in Paris for the period 1971-2000.
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#'
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#'
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#' @format A data frame with 12 observations and the following 3 variables:
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#' \describe{
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#' \item{\code{month}}{Month}
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#' \item{\code{temperature}}{Temperature (in degree celsius).}
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#' \item{\code{precipitation}}{Precipitation (in mm).}
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#' }
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#' @source Wikipedia (\url{https://fr.wikipedia.org/wiki/Climat_de_Paris})
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"climate_paris"
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