mschnetzer

library(tidyverse)
library(lubridate)
library(msthemes)
library(patchwork)

# Get Covid data by age here: https://www.data.gv.at/katalog/dataset/3765ed62-0f9d-49ad-83b0-1405ed833108
# And vaccination data here: https://www.ages.at/themen/krankheitserreger/coronavirus/

cov <- read_csv2("CovidFaelle_Altersgruppe.csv") %>%
  mutate(Time = as.Date(Time, "%d.%m.%Y"))

mschnetzer

# This code is adapted from original code by @cnicault https://github.com/cnicault/tidytuesday/tree/master/2021/2021-21-ask_a_manager

library(tidyverse)
library(readxl)
library(scales)
library(ggbump)
library(glue)
library(showtext)
library(msthemes)

mschnetzer

library(tidyverse)
library(lubridate)
library(msthemes)
library(ragg)
library(showtext)

# get data from https://www.oenb.at/isaweb/report.do?lang=DE&report=7.17
unemp <- vroom::vroom("unemployment.csv")

font_add_google("Abel", "abel")

mschnetzer

library(tidyverse)
library(sf)
library(gganimate)
library(transformr)
library(lubridate)
library(msthemes) # https://github.com/mschnetzer/msthemes

# Import online map
bezirke <- st_read("https://raw.githubusercontent.com/ginseng666/GeoJSON-TopoJSON-Austria/master/2017/simplified-99.9/bezirke_999_geo.json") %>%
  mutate(iso = as.numeric(iso))

mschnetzer

library(tidyverse)
library(survey)
library(msthemes)
library(gganimate)
library(Hmisc)

# Load HFCS and Non-core data
load("hfcs2017AT.rda")
load("non-core-at.rda")

mschnetzer

library(tidyverse)
library(lubridate)
library(msthemes)
library(gganimate)

unemp <- read_csv("unemployment.csv")

df <- unemp %>% filter(period == "month", indicator == "Unemployment registered, total, M-end NSA") %>% 
  select(year,month,values) %>% 
  mutate(values = values*1000) %>% 

mschnetzer

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

beds <- get_eurostat("hlth_rs_bds", filters = list(unit = "P_HTHAB", facility = "HBEDT", time = 2000:2017), time_format = "num")

abs <- beds %>% filter(geo %in% c("AT","DE","FR","ES","IT","SE","UK"), time == 2017) %>% 
  ggplot(aes(x = reorder(geo, values), y = values)) +
  geom_bar(stat = "identity", aes(fill=geo)) +

mschnetzer

library(tidyverse)
library(readxl)
library(countrycode)
library(msthemes)
library(patchwork)

ineq <- read_xlsx("HFCS - Statistical tables - wave 2017 - March 2020.xlsx",
                  sheet = "J4 Net wealth inequality ind", skip = 2) %>%
  filter(...2 %in% c("Top 10% share", "Gini coefficient")) %>%
  select(-c(...1,...3,...4,`euro\r\narea`), measure = ...2) %>%

mschnetzer

library(tidyverse)
library(rvest)
library(msthemes)
library(gganimate)
library(magick)
library(sf)
library(viridis)


url <- "https://de.wikipedia.org/wiki/COVID-19-Pandemie_in_%C3%96sterreich"

mschnetzer

library(tidyverse)
library(msthemes)
library(readxl)
library(patchwork)
library(eurostat)

# Download data from http://statistik.at/web_de/statistiken/wirtschaft/oeffentliche_finanzen_und_steuern/steuerstatistiken/oeko-steuern/index.html
data <- read_xlsx("oeko-steuern_1995_bis_2018.xlsx",skip = 1, n_max = 5) %>% filter(!is.na(`1995`))
taxdat <- data %>% gather(year, tax, 2:25) %>% mutate_at(vars(tax), ~./1000)