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################################################################################
#
# ikashnitsky.github.io 2017-11-07
# Data acquisition in R - Part 2/4
# https://ikashnitsky.github.io/2017/data-acquisition-two
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com
#
################################################################################
# load required packages
library(tidyverse) # data manipulation and viz
# Eurostat ---------------------------------------------------------------------
library(tidyverse)
library(eurostat)
library(lubridate)
library(viridis)
search_eurostat("life expectancy")
# download the selected dataset
e0 <- get_eurostat("demo_mlexpec")
e0 %>%
filter(! sex == "T",
age == "Y65",
geo %in% c("DE", "FR", "IT", "RU", "ES", "UK")) %>%
ggplot(aes(x = time %>% year(), y = values, color = sex))+
geom_path()+
facet_wrap(~ geo, ncol = 3)+
labs(y = "Life expectancy at age 65", x = NULL)+
theme_minimal(base_family = "mono")
ggsave("eurostat.png", width = 8, height = 5)
# World Bank -------------------------------------------------------------------
library(tidyverse)
library(wbstats)
# search for a dataset of interest
wbsearch("fertility") %>% View
# fetch the selected dataset
df_wb <- wb(indicator = "SH.MMR.RISK.ZS", startdate = 2000, enddate = 2015)
# have look at the data for one year
df_wb %>% filter(date == 2015) %>% View
df_wb %>%
filter(iso2c %in% c("V4", "V1", "1W")) %>%
ggplot(aes(x = date %>% as.numeric(), y = value, color = country))+
geom_path(size = 1)+
scale_color_brewer(NULL, palette = "Dark2")+
labs(x = NULL, y = NULL, title = "Lifetime risk of maternal death (%)")+
theme_minimal(base_family = "mono")+
theme(panel.grid.minor = element_blank(),
legend.position = c(.8, .9))
ggsave("worldbank.png", width = 8, height = 5)
# OECD -------------------------------------------------------------------------
library(tidyverse)
library(viridis)
library(OECD)
search_dataset("unemployment") %>% View
df_oecd <- get_dataset("AVD_DUR")
names(df_oecd) <- names(df_oecd) %>% tolower()
df_oecd %>%
filter(country %in% c("EU16", "EU28", "USA"), sex == "MEN", ! age == "1524") %>%
ggplot(aes(obstime, age, fill = obsvalue))+
geom_tile()+
scale_fill_viridis("Months", option = "B")+
scale_x_discrete(breaks = seq(1970, 2015, 5) %>% paste)+
facet_wrap(~ country, ncol = 1)+
labs(x = NULL, y = "Age groups",
title = "Average duration of unemployment in months, males")+
theme_minimal(base_family = "mono")
ggsave("oecd.png", width = 8, height = 5)
# WID --------------------------------------------------------------------------
library(tidyverse)
#install.packages("devtools")
devtools::install_github("WIDworld/wid-r-tool")
library(wid)
?wid_series_type
?wid_concepts
df_wid <- download_wid(
indicators = "shweal", # Shares of personal wealth
areas = c("FR", "GB"), # In France an Italy
perc = c("p90p100", "p99p100") # Top 1% and top 10%
)
df_wid %>%
ggplot(aes(x = year, y = value, color = country)) +
geom_path()+
labs(title = "Top 1% and top 10% personal wealth shares in France and Great Britain",
y = "top share")+
facet_wrap(~ percentile)+
theme_minimal(base_family = "mono")
ggsave("wid.png", width = 8, height = 5)
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