mschnetzer

library(tidyverse)
library(readxl)
library(gt)
library(gtExtras)
library(gtUtils)
library(countrycode)
library(colorspace)

# Download https://images.transparencycdn.org/images/CPI2024-Results-and-trends.xlsx
data <- read_xlsx("CPI2024-Results-and-trends.xlsx", 

mschnetzer

library(tidyverse)
library(eurostat)

rawdat <- get_eurostat("tipsnf10", filters = list(geo = "AT"), time = "date", type = "label", lang = "de")

gdpdat <- get_eurostat("nama_10_gdp", filters = list(geo = "AT", unit = "CP_MEUR", na_item = "B1GQ"), time = "date", type = "label", lang = "de")

findat <- rawdat |> 
  mutate(values = ifelse(sector == "Volkswirtschaft", values*-1, values),
         sector = case_match(sector,

mschnetzer

library(tidyverse)
library(ggsankey)
library(ggtext)
library(colorspace)

# Daten von https://www.bmi.gv.at/412/Europawahlen/Europawahl_2024/files/endgueltiges_Ergebnis_Europawahl_26062024.xlsx
raw <- readxl::read_xlsx("endgueltiges_Ergebnis_Europawahl_26062024.xlsx")

# Daten auswählen, neue Variablen generieren, in Long-Format bringen
data <- raw |> filter(Gebietsname == "Österreich") |> 

mschnetzer

library(tidyverse)
library(eurostat)
library(ggflags)
library(countrycode)
library(geomtextpath)
library(ggtext)

data <- get_eurostat("icw_tax_03")

plotdat <- data |> 

mschnetzer

library(tidyverse)
library(oenb)
library(colorspace)
library(futurevisions)

# Load data on quarterly profits in the banking sector
data <- oenb_data(id = "3104", pos = "VDBKICBD66EL210") |> 
  select(period, value) |> 
  as_tibble()

mschnetzer

library(tidyverse)
library(countrycode)
library(rdbnomics)
library(ggbump)
library(MetBrewer)
library(scales)

allcty <- codelist |> filter(!is.na(eu28)) |> pull(iso3c) |> tolower()

gfcf_private <- rdb(provider_code = "AMECO", dataset_code = "UIGP", 

mschnetzer

library(tidyverse)
library(ggstream)
library(pdftools)
library(wesanderson)
library(ggpp)

raw <- pdf_text(pdf = "https://www.statistik.at/fileadmin/pages/77/Pressemappe_2023.pdf")

pkw <- unlist(strsplit(raw[15], split = "\n")) |> 
  str_trim(side = "left") |> 

mschnetzer

# GRAFIK: Anteil Kinder mit Mittagessen in der Betreuungseinrichtung

library(tidyverse)
library(readODS)
library(sf)
library(ggtext)
library(patchwork)

bez <- geojsonsf::geojson_sf("https://raw.githubusercontent.com/ginseng666/GeoJSON-TopoJSON-Austria/master/2021/simplified-99.9/bezirke_999_geo.json")

mschnetzer

library(tidyverse)
library(readODS)

# Daten unter https://www.statistik.at/fileadmin/pages/298/Durchschnittliche_Zeitverwendung_2021-22.ods

longdata <- 
  read_ods("Durchschnittliche_Zeitverwendung_2021-22.ods", 
           sheet = "Tabelle_17", range = "A5:J129", col_names = F, 
           col_types = "ctcttcttct") |> 
  janitor::clean_names() |> 

mschnetzer

library(tidyverse)
library(ggforce)
library(geomtextpath)

# Download data from here: https://www.riaa.com/u-s-sales-database/
vinyl <- read_csv2("vinyl.csv") |> 
  select(year = Year, value = `Value (Actual)`)

vinyl |> 
  ggplot(aes(x = year, y = 1)) +