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library(tidyverse) | |
library(eurostat) | |
library(ggthemes) | |
eurostat <- get_eurostat("ilc_di12",filters=list(geo=c("AT","DE")),type="code",time_format="num") | |
eurostat$time <- as.factor(eurostat$time) | |
eurostat %>% filter(time %in% 2005:2016) %>% | |
ggplot(aes(x=time,y=values,group=geo,colour=factor(geo,labels=c("Österreich","Deutschland")))) + | |
geom_line(size=1.2) + geom_point(size=1.2) + xlab(" ") + | |
ylab("Gini-Koeffizient") + labs(color = " ") + theme_fivethirtyeight() + |
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# Adapted from https://jschoeley.github.io/2018/06/30/bubble-grid-map.html | |
library(eurostat) | |
library(tidyverse) | |
library(lubridate) | |
library(sf) | |
library(lwgeom) | |
library(msthemes) | |
# download eurostat data for nuts 2 regions |
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library(tidyverse) | |
library(eurostat) | |
library(ggmap) | |
library(msthemes) | |
# Get Eurostat data and calculate diff 2010-2017 | |
data <- get_eurostat("prc_hpi_a",filters=list(typpurch="TOTAL",unit="INX_A_AVG",time=c(2010,2017)),type="code",time_format="num") %>% | |
select(geo,time,values) %>% group_by(geo) %>% mutate(inc=(values-lag(values))/lag(values)*100) %>% filter(!is.na(inc)) %>% select(geo,inc) | |
# Create categories for changes in house prices |
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library(eurostat) | |
library(tidyverse) | |
library(stringr) | |
library(lubridate) | |
library(ggridges) | |
library(viridis) | |
library(ggthemes) | |
# Only EU countries | |
cty = eu_countries$code |
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library(eurostat) | |
library(tidyverse) | |
library(RColorBrewer) | |
library(ggthemes) | |
data <- get_eurostat("ilc_ats12",filters=list(hhtyp="TOTAL",incgrp="TOTAL",unit="PC_POP"),type="label",lang="de") %>% | |
select(geo,lev_diff,values) %>% spread(lev_diff,values) %>% | |
mutate(leicht=`Leicht `+`Relativ leicht `+`Sehr leicht`,schwierig=Groß+Mässig+Einige) %>% | |
filter(!geo %in% c("Europäische Union (derzeitige Zusammensetzung)", "Europäische Union (vor dem Beitritt Kroatiens)", | |
"Euroraum (19 Länder)", "Euroraum (18 Länder)")) %>% |
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library(eurostat) | |
library(tidyverse) | |
library(forcats) | |
data <- get_eurostat("lfso_16move4j",filters=list(age="Y20-34",wstatus="UNE",sex="T",isced11="TOTAL",workstud="TOTAL"),type="code") %>% | |
select(geo,mobility,values) %>% spread(mobility,values) | |
cube <- data %>% group_by(geo) %>% mutate(pop=ifelse(is.na(NRP),TOTAL,TOTAL-NRP), | |
known=sum(MOV_EU_FOR,MOV_EXT_EU,MOV_NAT,NMOV,na.rm=TRUE), | |
unknown=pop-known, |
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library(tidyverse) | |
library(ggthemes) | |
# load data | |
overtime <- read_csv2("overtime.csv") %>% mutate(highest=pmax(Männer,Frauen)) %>% arrange(highest) %>% | |
mutate(Wirtschaftszweig = factor(Wirtschaftszweig, levels = .$Wirtschaftszweig)) %>% | |
gather(Geschlecht,value,Männer:Frauen) | |
# labels | |
right_label <- overtime %>% |
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library(eurostat) | |
library(tidyverse) | |
library(ggmap) | |
data <- get_eurostat("ilc_ats07",filters=list(deg_urb="TOTAL",hhtyp="TOTAL",incgrp="TOTAL",lastTimePeriod=1),type="code",time_format = "num") %>% | |
select(geo,lev_diff,values) %>% spread(lev_diff,values) %>% mutate(ease=EASY+FEASY+VEASY,difficult=GRT+MOD+SOME) | |
map <- get_eurostat_geospatial(output_class = "df", resolution = 10,update_cache = TRUE) %>% filter(STAT_LEVL_==0) | |
mapdat <- data %>% filter(!geo %in% c("EU28","EU27","EA19","EA18")) %>% | |
mutate(cat = cut(difficult,breaks = seq(0, 100, by = 20))) %>% select(geo,cat) %>% |
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library(eurostat) | |
library(tidyverse) | |
library(RColorBrewer) | |
library(ggthemes) | |
# Vector with capital cities only | |
caps <- c(paste0(c("AT","BE","BG","CY","CZ","DE","DK","EE","EK","EL","ES","FI","FR","HR","HU","IE","IS", | |
"IT","LT","LU","LV","MT","NO","PL","PT","RO","SE","SI","SK","TR"), '001C1'),"NL002C1","FI001C2","UK001K2") | |
# Load data from Eurostat Perception Survey |
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library(maptools) | |
library(ggplot2) | |
library(readr) | |
library(dplyr) | |
# Download SHP file from http://data.statistik.gv.at/web/meta.jsp?dataset=OGDEXT_POLBEZ_1 | |
# load map | |
map=readShapeSpatial("shp2018/STATISTIK_AUSTRIA_POLBEZ_20180101",verbose=TRUE) | |
class(map) | |
names(map@data) |
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