R/rosstat.R
In rOpenGov/rustfare: rustfare: toolbox for R language for open welfare data from Russian Federation
# This file is part of the rustfare program (https://github.com/muuankarski/rustfare)
# Copyright (C) 2012-2013 Markus Kainu <markuskainu@gmail.com>. All rights reserved.
# This program is open source software; you can redistribute it and/or modify
# it under the terms of the FreeBSD License (keep this notice):
# http://en.wikipedia.org/wiki/BSD_licenses
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#' List the currently available
#' indicators from Rosstat Regional Statistics
#'
#' RosstatIndicator loads data frame that lists
#' currently available indicators from Rosstat Regional Statistics
#'
#' @param urli is the url address to the data
#'
#' @return data frame with indicator name, url, levels of aggregation and metadata in Russian and in English
#'
#' @export
#' @examples # available.indicators <- IndicatorRosstat()
#' @author Markus Kainu <markuskainu(at)gmail.com>
IndicatorRosstat <- function(urli = "http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData") {
load(url(urli))
RosstatIndicatorData
}
#' Scrape the raw html-table data
#'
#' Scrape the raw html-table data from Rosstat Regional Statistics
#'
#' @param indicator Character string. Select indicator from the \code{indicator} column
#' of data frame returned by function \code{RosstatIndicator}.
#'
#' @return data frame with raw data from html-table
#'
#' @export
#' @examples # raw.html.data <- ScrapeData("")
#' @author Markus Kainu <markuskainu(at)gmail.com>
ScrapeData <- function(indicator="mortality_rate"){
library(XML)
load(url("http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData"))
condition <- indicator
urldata <- subset(RosstatIndicatorData, indicator == condition)
url <- as.character(urldata[1,2])
x <- readHTMLTable(url, header = TRUE, dec=",")
x=x[[1]]
x
}
#' Scrape the raw html-table data (skipping the 1st row)
#'
#' Scrape the raw html-table data from Rosstat Regional Statistics
#' for tables with rowspan used on first row.
#'
#' @param indicator Character string. Select indicator from the \code{indicator} column
#' of data frame returned by function \code{RosstatIndicator}.
#'
#' @return data frame with raw data from html-table
#'
#' @export
#' @examples # raw.html.data <- ScrapeData_skip1("")
#' @author Markus Kainu <markuskainu(at)gmail.com>
ScrapeData_skip1 <- function(indicator){
library(XML)
load(url("http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData"))
condition <- indicator
urldata <- subset(RosstatIndicatorData, indicator == condition)
url <- as.character(urldata[1,2])
x <- readHTMLTable(url, header = TRUE, dec=",", skip.rows=1)
x=x[[1]]
x
}
#' Download data from Rosstat Regional Statistics
#'
#' Download data based on indicator and aggregation level
#' from Rosstat Regional Statistics
#'
#' @param indicator Character string. Select indicator from the \code{indicator} column
#' of data frame returned by function \code{RosstatIndicator}. More than one indicator is NOT allowed.
#' @param level Character string. Defines the administrative level of aggregation
#' \itemize{
#' \item \code{federal} at the federal level
#' \item \code{federal_district} at the federal district level (8 regions)
#' \item \code{region} at the regional level (83 regions)
#' } More than one level is NOT allowed.
#'
#' @return data frame with 7 columns
#' \itemize{
#' \item \code{region} Region name in Russian
#' \item \code{region_en} Region name in English
#' \item \code{year} Year of observation
#' \item \code{value} Value of the indicator
#' \item \code{level} Aggregation level
#' \item \code{id_shape} id number for mergin with shapefile from GADM
#' \item \code{indicator} name of the indicator
#' }
#'
#' @export
#' @examples # dat <- GetRosstat("life_expectancy_men","federal_district")
#' @author Markus Kainu <markuskainu(at)gmail.com>
GetRosstat <- function(indicator = "mortality_rate", level = "federal_district"){
library(XML)
library(reshape2)
library(stringr)
# extract columns with TOTAL values
# if (indicator %in% c("sports_halls",
# "swimming_pools",
# "planar_sports_facilities_(playgrounds_and_fields)",
# "stadiums_with_stands_for_1500_seats_or_more",
# "life_expectancy_total",
# "life_expectancy_men",
# "life_expectancy_women",
# "population_total",
# "population_urban",
# "population_rural")) {
# load(url("http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData"))
# condition <- indicator
# urldata <- subset(RosstatIndicatorData, indicator == condition)
# url <- as.character(urldata[1,2])
# x <- readHTMLTable(url, header = TRUE, dec=",")
# x=x[[1]]
# x
# }
# else {
# load(url("http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData"))
# condition <- indicator
# urldata <- subset(RosstatIndicatorData, indicator == condition)
# url <- as.character(urldata[1,2])
# x <- readHTMLTable(url, header = TRUE, dec="," , skip.rows=1
# )
# x=x[[1]]
# x
# }
##########################################
# extracting the right columns from non-harmonized html-tables
if (indicator == "average_percapita_income") {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1,3:14)]
names(y) <- c("region","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "gross_regional_product") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x
names(y) <- c("region","x2000","x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
######################################
# Population
if (indicator == "population_total") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1:4)]
names(y) <- c("region","x1989","x2002","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "population_urban") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,5:7)]
names(y) <- c("region","x1989","x2002","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "population_rural") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,8:10)]
names(y) <- c("region","x1989","x2002","x2010")
y.long <- melt(y, id.vars="region")
}
######################################
# Life expectancy
if (indicator == "life_expectancy_total") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,2,5,8)]
names(y) <- c("region","x2000","x2005","x2009")
y.long <- melt(y, id.vars=c("region"))
}
if (indicator == "life_expectancy_men") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,3,6,9)]
names(y) <- c("region","x2000","x2005","x2009")
y.long <- melt(y, id.vars=c("region"))
}
if (indicator == "life_expectancy_women") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,4,7,10)]
names(y) <- c("region","x2000","x2005","x2009")
y.long <- melt(y, id.vars=c("region"))
}
######################################
# Demography
if (indicator %in% c("mortality_rate",
"infant_mortality_rate",
"crude_birth_rate")) {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1:14)]
names(y) <- c("region","x1990","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator %in% c("average_size_of_pensions",
"average_nominal_monthly_salary")) {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1,3:14)]
names(y) <- c("region","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010",
"x2011")
y.long <- melt(y, id.vars="region")
}
######################################
## Culture & sports
if (indicator == "number_of_theater_goers_per_1000_population") {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1:14)]
names(y) <- c("region","x1990","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "number_of_visits_to_museums_per_1000_population") {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1:14)]
names(y) <- c("region","x1990","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "publication_of_newspapers_per_1000_people") {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1:14)]
names(y) <- c("region","x1990","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "stadiums_with_stands_for_1500_seats_or_more") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,2:5)]
names(y) <- c("region","x1995",
"x2005",
"x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "planar_sports_facilities_(playgrounds_and_fields)") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,6:9)]
names(y) <- c("region","x1995",
"x2005",
"x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "sports_halls") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,10:13)]
names(y) <- c("region","x1995",
"x2005",
"x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "swimming_pools") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,14:17)]
names(y) <- c("region","x1995",
"x2005",
"x2009","x2010")
y.long <- melt(y, id.vars="region")
}
######################################
######################################
# Operations for all datasets
# remove cases with missing values
y.long <- y.long[!is.na(y.long$value),]
# change variable into numeric years (x2009 <- 2009)
y.long$variable <- str_replace_all(y.long$variable, "x","")
# change comma to dot in value
y.long$value <- str_replace_all(y.long$value, ",",".")
# make both variables into numerical
y.long$value <- factor(y.long$value)
y.long$variable <- factor(y.long$variable)
y.long$value <- suppressWarnings(as.numeric(levels(y.long$value))[y.long$value])
y.long$variable <- as.numeric(levels(y.long$variable))[y.long$variable]
y.long$list <- str_extract_all(y.long$region, "[а-яА-я]+")
y.long$one <- sapply(y.long$list, function(x){as.character(x[1])})
y.long$two <- sapply(y.long$list, function(x){as.character(x[2])})
y.long$three <- sapply(y.long$list, function(x){as.character(x[3])})
y.long$four <- sapply(y.long$list, function(x){as.character(x[4])})
y.long$five <- sapply(y.long$list, function(x){as.character(x[5])})
y.long$six <- sapply(y.long$list, function(x){as.character(x[6])})
y.long$x <- paste(y.long$one,
y.long$two,
y.long$three,
y.long$four,
y.long$five,
y.long$six,
sep="")
y.long$only_txt <- str_replace_all(y.long$x,
"NA",
"")
y.long$name_begin <- str_sub(y.long$only_txt, 1, 7)
y.long$name_end <- str_sub(y.long$only_txt, -5)
y.long$id_name <- tolower(paste(y.long$name_begin,
y.long$name_end,
sep=""))
y.long$year <- y.long$variable
regiodata <- subset(y.long, select=c("region",
"year",
"value",
"id_name"))
# translation
if (level == "federal") {
load(url("http://research.muuankarski.org/rustfare/data/RosstatRegionKey.RData"))
key <- subset(RosstatRegionKey, level == "federal")
regiodata <- merge(regiodata,key,
by="id_name")
regiodata$indicator <- indicator
subset(regiodata, select=c("region","region_en",
"year","value","level",
"id_shape","indicator"))
}
if (level == "federal_district") {
load(url("http://research.muuankarski.org/rustfare/data/RosstatRegionKey.RData"))
key <- subset(RosstatRegionKey, level == "federal_district")
regiodata <- merge(regiodata,key,
by="id_name")
regiodata$indicator <- indicator
subset(regiodata, select=c("region","region_en",
"year","value","level",
"id_shape","indicator"))
}
if (level == "region") {
load(url("http://research.muuankarski.org/rustfare/data/RosstatRegionKey.RData"))
key <- subset(RosstatRegionKey, level == "region")
regiodata <- merge(regiodata,key,
by="id_name")
regiodata$indicator <- indicator
subset(regiodata, select=c("region","region_en",
"year","value","level",
"id_shape","indicator"))
} else {
regiodata
}
}
rOpenGov/rustfare documentation built on May 26, 2019, 8:50 p.m.
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# This file is part of the rustfare program (https://github.com/muuankarski/rustfare)
# Copyright (C) 2012-2013 Markus Kainu <markuskainu@gmail.com>. All rights reserved.
# This program is open source software; you can redistribute it and/or modify
# it under the terms of the FreeBSD License (keep this notice):
# http://en.wikipedia.org/wiki/BSD_licenses
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#' List the currently available
#' indicators from Rosstat Regional Statistics
#'
#' RosstatIndicator loads data frame that lists
#' currently available indicators from Rosstat Regional Statistics
#'
#' @param urli is the url address to the data
#'
#' @return data frame with indicator name, url, levels of aggregation and metadata in Russian and in English
#'
#' @export
#' @examples # available.indicators <- IndicatorRosstat()
#' @author Markus Kainu <markuskainu(at)gmail.com>
IndicatorRosstat <- function(urli = "http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData") {
load(url(urli))
RosstatIndicatorData
}
#' Scrape the raw html-table data
#'
#' Scrape the raw html-table data from Rosstat Regional Statistics
#'
#' @param indicator Character string. Select indicator from the \code{indicator} column
#' of data frame returned by function \code{RosstatIndicator}.
#'
#' @return data frame with raw data from html-table
#'
#' @export
#' @examples # raw.html.data <- ScrapeData("")
#' @author Markus Kainu <markuskainu(at)gmail.com>
ScrapeData <- function(indicator="mortality_rate"){
library(XML)
load(url("http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData"))
condition <- indicator
urldata <- subset(RosstatIndicatorData, indicator == condition)
url <- as.character(urldata[1,2])
x <- readHTMLTable(url, header = TRUE, dec=",")
x=x[[1]]
x
}
#' Scrape the raw html-table data (skipping the 1st row)
#'
#' Scrape the raw html-table data from Rosstat Regional Statistics
#' for tables with rowspan used on first row.
#'
#' @param indicator Character string. Select indicator from the \code{indicator} column
#' of data frame returned by function \code{RosstatIndicator}.
#'
#' @return data frame with raw data from html-table
#'
#' @export
#' @examples # raw.html.data <- ScrapeData_skip1("")
#' @author Markus Kainu <markuskainu(at)gmail.com>
ScrapeData_skip1 <- function(indicator){
library(XML)
load(url("http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData"))
condition <- indicator
urldata <- subset(RosstatIndicatorData, indicator == condition)
url <- as.character(urldata[1,2])
x <- readHTMLTable(url, header = TRUE, dec=",", skip.rows=1)
x=x[[1]]
x
}
#' Download data from Rosstat Regional Statistics
#'
#' Download data based on indicator and aggregation level
#' from Rosstat Regional Statistics
#'
#' @param indicator Character string. Select indicator from the \code{indicator} column
#' of data frame returned by function \code{RosstatIndicator}. More than one indicator is NOT allowed.
#' @param level Character string. Defines the administrative level of aggregation
#' \itemize{
#' \item \code{federal} at the federal level
#' \item \code{federal_district} at the federal district level (8 regions)
#' \item \code{region} at the regional level (83 regions)
#' } More than one level is NOT allowed.
#'
#' @return data frame with 7 columns
#' \itemize{
#' \item \code{region} Region name in Russian
#' \item \code{region_en} Region name in English
#' \item \code{year} Year of observation
#' \item \code{value} Value of the indicator
#' \item \code{level} Aggregation level
#' \item \code{id_shape} id number for mergin with shapefile from GADM
#' \item \code{indicator} name of the indicator
#' }
#'
#' @export
#' @examples # dat <- GetRosstat("life_expectancy_men","federal_district")
#' @author Markus Kainu <markuskainu(at)gmail.com>
GetRosstat <- function(indicator = "mortality_rate", level = "federal_district"){
library(XML)
library(reshape2)
library(stringr)
# extract columns with TOTAL values
# if (indicator %in% c("sports_halls",
# "swimming_pools",
# "planar_sports_facilities_(playgrounds_and_fields)",
# "stadiums_with_stands_for_1500_seats_or_more",
# "life_expectancy_total",
# "life_expectancy_men",
# "life_expectancy_women",
# "population_total",
# "population_urban",
# "population_rural")) {
# load(url("http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData"))
# condition <- indicator
# urldata <- subset(RosstatIndicatorData, indicator == condition)
# url <- as.character(urldata[1,2])
# x <- readHTMLTable(url, header = TRUE, dec=",")
# x=x[[1]]
# x
# }
# else {
# load(url("http://research.muuankarski.org/rustfare/data/RosstatIndicatorData.RData"))
# condition <- indicator
# urldata <- subset(RosstatIndicatorData, indicator == condition)
# url <- as.character(urldata[1,2])
# x <- readHTMLTable(url, header = TRUE, dec="," , skip.rows=1
# )
# x=x[[1]]
# x
# }
##########################################
# extracting the right columns from non-harmonized html-tables
if (indicator == "average_percapita_income") {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1,3:14)]
names(y) <- c("region","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "gross_regional_product") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x
names(y) <- c("region","x2000","x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
######################################
# Population
if (indicator == "population_total") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1:4)]
names(y) <- c("region","x1989","x2002","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "population_urban") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,5:7)]
names(y) <- c("region","x1989","x2002","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "population_rural") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,8:10)]
names(y) <- c("region","x1989","x2002","x2010")
y.long <- melt(y, id.vars="region")
}
######################################
# Life expectancy
if (indicator == "life_expectancy_total") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,2,5,8)]
names(y) <- c("region","x2000","x2005","x2009")
y.long <- melt(y, id.vars=c("region"))
}
if (indicator == "life_expectancy_men") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,3,6,9)]
names(y) <- c("region","x2000","x2005","x2009")
y.long <- melt(y, id.vars=c("region"))
}
if (indicator == "life_expectancy_women") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,4,7,10)]
names(y) <- c("region","x2000","x2005","x2009")
y.long <- melt(y, id.vars=c("region"))
}
######################################
# Demography
if (indicator %in% c("mortality_rate",
"infant_mortality_rate",
"crude_birth_rate")) {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1:14)]
names(y) <- c("region","x1990","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator %in% c("average_size_of_pensions",
"average_nominal_monthly_salary")) {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1,3:14)]
names(y) <- c("region","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010",
"x2011")
y.long <- melt(y, id.vars="region")
}
######################################
## Culture & sports
if (indicator == "number_of_theater_goers_per_1000_population") {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1:14)]
names(y) <- c("region","x1990","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "number_of_visits_to_museums_per_1000_population") {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1:14)]
names(y) <- c("region","x1990","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "publication_of_newspapers_per_1000_people") {
x <- suppressWarnings(ScrapeData(indicator))
y <- x[,c(1:14)]
names(y) <- c("region","x1990","x1995","x2000","x2001",
"x2002","x2003","x2004","x2005",
"x2006","x2007","x2008","x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "stadiums_with_stands_for_1500_seats_or_more") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,2:5)]
names(y) <- c("region","x1995",
"x2005",
"x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "planar_sports_facilities_(playgrounds_and_fields)") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,6:9)]
names(y) <- c("region","x1995",
"x2005",
"x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "sports_halls") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,10:13)]
names(y) <- c("region","x1995",
"x2005",
"x2009","x2010")
y.long <- melt(y, id.vars="region")
}
if (indicator == "swimming_pools") {
x <- suppressWarnings(ScrapeData_skip1(indicator))
y <- x[,c(1,14:17)]
names(y) <- c("region","x1995",
"x2005",
"x2009","x2010")
y.long <- melt(y, id.vars="region")
}
######################################
######################################
# Operations for all datasets
# remove cases with missing values
y.long <- y.long[!is.na(y.long$value),]
# change variable into numeric years (x2009 <- 2009)
y.long$variable <- str_replace_all(y.long$variable, "x","")
# change comma to dot in value
y.long$value <- str_replace_all(y.long$value, ",",".")
# make both variables into numerical
y.long$value <- factor(y.long$value)
y.long$variable <- factor(y.long$variable)
y.long$value <- suppressWarnings(as.numeric(levels(y.long$value))[y.long$value])
y.long$variable <- as.numeric(levels(y.long$variable))[y.long$variable]
y.long$list <- str_extract_all(y.long$region, "[а-яА-я]+")
y.long$one <- sapply(y.long$list, function(x){as.character(x[1])})
y.long$two <- sapply(y.long$list, function(x){as.character(x[2])})
y.long$three <- sapply(y.long$list, function(x){as.character(x[3])})
y.long$four <- sapply(y.long$list, function(x){as.character(x[4])})
y.long$five <- sapply(y.long$list, function(x){as.character(x[5])})
y.long$six <- sapply(y.long$list, function(x){as.character(x[6])})
y.long$x <- paste(y.long$one,
y.long$two,
y.long$three,
y.long$four,
y.long$five,
y.long$six,
sep="")
y.long$only_txt <- str_replace_all(y.long$x,
"NA",
"")
y.long$name_begin <- str_sub(y.long$only_txt, 1, 7)
y.long$name_end <- str_sub(y.long$only_txt, -5)
y.long$id_name <- tolower(paste(y.long$name_begin,
y.long$name_end,
sep=""))
y.long$year <- y.long$variable
regiodata <- subset(y.long, select=c("region",
"year",
"value",
"id_name"))
# translation
if (level == "federal") {
load(url("http://research.muuankarski.org/rustfare/data/RosstatRegionKey.RData"))
key <- subset(RosstatRegionKey, level == "federal")
regiodata <- merge(regiodata,key,
by="id_name")
regiodata$indicator <- indicator
subset(regiodata, select=c("region","region_en",
"year","value","level",
"id_shape","indicator"))
}
if (level == "federal_district") {
load(url("http://research.muuankarski.org/rustfare/data/RosstatRegionKey.RData"))
key <- subset(RosstatRegionKey, level == "federal_district")
regiodata <- merge(regiodata,key,
by="id_name")
regiodata$indicator <- indicator
subset(regiodata, select=c("region","region_en",
"year","value","level",
"id_shape","indicator"))
}
if (level == "region") {
load(url("http://research.muuankarski.org/rustfare/data/RosstatRegionKey.RData"))
key <- subset(RosstatRegionKey, level == "region")
regiodata <- merge(regiodata,key,
by="id_name")
regiodata$indicator <- indicator
subset(regiodata, select=c("region","region_en",
"year","value","level",
"id_shape","indicator"))
} else {
regiodata
}
}
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