mschnetzer

library(tidyverse)
library(waffle)
library(viridis)

data <- data_frame(
  labs = c("Betreuung/Pflege","Keine Vollzeit gewünscht","Andere persönliche Gründe","Keine Vollzeit gefunden",
           "Aus-/Fortbildung","Krankheit","Sonstige Gründe"),
  male = c(4.6,18.8,14.4,17.3,29.9,5.9,9.2),
  female = c(38.6,19.2,16.54,10.5,8.9,2.5,3.7)
)

mschnetzer

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)

mschnetzer

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

mschnetzer

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) %>%

mschnetzer

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 %>%

mschnetzer

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,

mschnetzer

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() +

mschnetzer

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)")) %>%

mschnetzer

library(eurostat)
library(tidyverse)
library(stringr)
library(lubridate)
library(ggridges)
library(viridis)
library(ggthemes)

# Only EU countries
cty = eu_countries$code

mschnetzer

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