mschnetzer

# 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

mschnetzer

library(tidyverse)
library(msthemes) # https://github.com/mschnetzer/msthemes

# Get data from http://www.zamg.ac.at/histalp/dataset/station/csv.php
data <- read_csv2("HISTALP_AT_WIE_T01_1760_2025.csv", skip = 13)

data <- data %>% mutate_at(vars("jan-dec"), list(temp = ~ .*0.1)) %>% 
                 select(year, temp) %>% filter(year %in% c(1775:2019)) %>% 
                 mutate(abw = log(temp/mean(temp)))
data$draw <- 1

mschnetzer

library(tidyverse)
library(msthemes)
library(eurostat)

# Download data from UNECE: http://bit.ly/2wtn9Sp
data <- read.csv("marriage.csv",skip=1,na.strings=c("","..","NA"),dec=".")
data <- data %>% gather(year,value, -Sex, -Country) 
data$year <- as.numeric(substring(data$year, 2))

# Change in EU countries where data is available

mschnetzer

library(worldtilegrid)
library(plyr)
library(tidyverse)
library(countrycode)
library(viridis)
library(msthemes)

# Get GDP per capita data from UNdata http://bit.ly/2N4hUlV
data <- read.csv("undata.csv",dec=".") %>% rename(Country=Country.or.Area) %>% select(-Value.Footnotes)

mschnetzer

library(tidyverse)
library(eurostat)

sci <- get_eurostat("hrst_st_nocc",filters=list(geo=eu_countries$code,unit="THS",isco08="TOTAL",category="SE",age="Y25-64",lastTimePeriod=1),type="code",time_format = "num") %>%
  select(geo,values) %>% rename(sci=values)
pop <- get_eurostat("demo_gind",filters=list(geo=eu_countries$code,indic_de="JAN",lastTimePeriod=1),type="code",time_format = "num") %>%
  select(geo,values) %>% rename(pop=values)

# Scientists are measures in Thousands
data <- left_join(sci,pop,by="geo") %>% mutate(scipop = round(sci*1000/pop*1000,0)) %>%

mschnetzer

# Download XLS, merge sheets and export as csv: http://www.statistik.at/web_de/statistiken/wirtschaft/preise/immobilien_durchschnittspreise/index.html
immodat <- read_csv2("baugrundstueckspreise_2017.csv",skip = 3) %>%
  select(iso=B.Nr., preis=starts_with("Euro")) %>% add_row(iso=900,preis=1000) %>% 
  filter(!is.na(iso), !is.na(preis)) %>% mutate(preis=round(preis,-1)) %>%
  mutate(preis=cut(preis,breaks=c(0,50,100,200,500,3000),
                   labels=c("<50€","50-100€","100-200€","200-500€",">500€")))

source("plotmap.R")

plotbezirke(dataset=immodat,fillvar="preis",wienbezirke=F,colpal=msc_palette[5:1],

mschnetzer

library(tidyverse)
library(msmaps)
library(sf)

# Load data
pop <- read_csv2("population.csv") %>% filter(iso<901) %>% select(iso,pop)
quarter <- pop %>% summarise(sum(pop)/4) %>% as.numeric()
quarter <- quarter*c(0:4)

# Load Geodata

mschnetzer

library(tidyverse)
library(msmaps)
library(eurostat)

data <- get_eurostat("ilc_mded02",filters=list(hhtyp="TOTAL",incgrp="TOTAL",time=c("2016","2017")),type="code",time_format="num") %>% 
  mutate(geo=recode(geo,"UK"="GB","EL"="GR")) %>% select(geo,time,values) %>% 
  group_by(geo) %>% fill(values) %>% filter(time==2017)

mapdata <- data %>%
  mutate(cat = cut(values, breaks = c(0,15,20,25,30,35))) %>% select(geo,cat) %>%

mschnetzer

#### Code was adapted from https://github.com/ryantimpe/ChartOfTheDay ###
#### H/T to @ryantimpe!

library(tidyverse)
library(rvest)
library(png)
library(msthemes)

df <- tibble(geo=as.factor(c("Wien","Vorarlberg","Salzburg","Steiermark","Niederösterreich","Tirol","Oberösterreich","Kärnten","Burgenland")),
             public=c(92,66,61,58,57,55,50,42,36),

mschnetzer

library(tidyverse)
library(rdbnomics)
library(scales)
library(ggrepel)
library(msthemes)

unemp <- rdb(ids='OECD/MEI/AUT.LMUNRRTT.STSA.Q') 
unemp$draw <- 1
unemp <- unemp %>% mutate(label = case_when(
                          value == max(value) ~ 1,