added consumption dataset

R/data.R

@@ -2,7 +2,7 @@
 #'
 #' The dataset contains data about UNHCR's populations of concern for the year 2017.
 #'
-#' @format A data frame with 11237 observations on the following 6 variables.
+#' @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}
@@ -20,7 +20,7 @@
 #'
 #' The dataset contains data about UNHCR's populations of concern summarised by continent of origin.
 #'
-#' @format A data frame with 913 observations on the following 4 variables.
+#' @format A data frame with 913 observations and the following 4 variables:
 #'   \describe{
 #'     \item{\code{year}}{Year concerned.}
 #'     \item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
@@ -31,3 +31,20 @@
 #' @source UNHCR (The UN Refugee Agency) (\url{https://www.unhcr.org/})
 "unhcr_ts"
 
+
+
+#' Electricity consumption and forecasting
+#'
+#' Electricity consumption per day in France for january and february of year 2020.
+#'
+#' @format A data frame with 120 observations and the following 3 variables:
+#'   \describe{
+#'     \item{\code{date}}{date.}
+#'     \item{\code{type}}{Type of data : realised or forecast.}    
+#'     \item{\code{value}}{Value in giga-watt per hour.}
+#'   }
+#' @source Rte (Electricity Transmission Network in France) (\url{https://data.rte-france.com/})
+"consumption"
+
+
+

data-raw/elec-data.R

@@ -0,0 +1,50 @@
+## code to prepare `elec-data` dataset goes here
+
+
+
+# Packages ----------------------------------------------------------------
+
+library(data.table)
+library(lubridate)
+library(rte.data)
+library(apexcharter)
+
+
+
+
+# Consumption & forecast --------------------------------------------------
+
+consumption <- get_consumption(
+  resource = "short_term", 
+  type = c("REALISED", "D-1"), 
+  start_date = "2020-01-01",
+  end_date = "2020-03-01"
+)
+
+apex(consumption, aes(start_date, value, group = type), "line")
+
+
+consumption <- consumption[, list(value = round(sum(value) / 4000)), by = list(date = as_date(start_date), type)]
+consumption[type == "REALISED", type := "Realised"]
+consumption[type == "D-1", type := "Forecast D-1"]
+
+apex(consumption, aes(date, value, group = type), "line")
+
+consumption <- as.data.frame(consumption)
+usethis::use_data(consumption, overwrite = TRUE)
+
+
+
+
+
+# Actual generation -------------------------------------------------------
+
+actual_generation <- get_actual_generation(
+  resource = "actual_generations_per_production_type", 
+  start_date = "2017-06-12",
+  end_date = "2017-06-13"
+)
+
+
+
+

man/consumption.Rd

@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{consumption}
+\alias{consumption}
+\title{Electricity consumption and forecasting}
+\format{
+A data frame with 120 observations and the following 3 variables:
+\describe{
+\item{\code{date}}{date.}
+\item{\code{type}}{Type of data : realised or forecast.}
+\item{\code{value}}{Value in giga-watt per hour.}
+}
+}
+\source{
+Rte (Electricity Transmission Network in France) (\url{https://data.rte-france.com/})
+}
+\usage{
+consumption
+}
+\description{
+Electricity consumption per day in France for january and february of year 2020.
+}
+\keyword{datasets}

man/unhcr_popstats_2017.Rd

@@ -5,7 +5,7 @@
 \alias{unhcr_popstats_2017}
 \title{UNHCR data for 2017}
 \format{
-A data frame with 11237 observations on the following 6 variables.
+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}

man/unhcr_ts.Rd

@@ -5,7 +5,7 @@
 \alias{unhcr_ts}
 \title{UNHCR data by continent of origin}
 \format{
-A data frame with 913 observations on the following 4 variables.
+A data frame with 913 observations and the following 4 variables:
 \describe{
 \item{\code{year}}{Year concerned.}
 \item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,