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(eurostat)
library(countrycode)
library(gt)
library(gtExtras)

data <- get_eurostat("prc_hicp_manr", filters = list(coicop = "CP00"))

ea.avg <- data |> filter(geo == "EA") |> slice_max(time, n=1) |> pull(values)

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)) + 

mschnetzer

library(tidyverse)
library(gghighlight)
library(ggtext)

# Data download: https://ads.nipr.ac.jp/vishop/#/extent
raw <- read.csv("VISHOP_EXTENT_GRAPH.csv")

dat <- raw |> 
  select(month, day = date, X1989:X2023) |> 
  pivot_longer(cols = X1989:X2023, names_to = "year", values_to = "ice", 

mschnetzer

librarian::shelf(tidyverse, pdftools, geojsonsf, sf, ggtext, patchwork)

# PDF herunterladen und Seite 3 extrahieren
rawpdf <- pdf_text("https://www.parlament.gv.at/dokument/XXVII/AB/14482/imfname_1572157.pdf")
rawpdf <- rawpdf[3]

# Rohdaten in Zeilen umwandeln und Datenbereich auswählen
lines <- strsplit(rawpdf, '\n') |> unlist() |> str_trim(side = "left")
short <- lines[c(11:24,30:36,42:63)]

mschnetzer

library(tidyverse)
library(rvest)
library(cropcircles)
library(magick)
library(gt)
library(gtExtras)

data <- read_html("https://sport.orf.at/stories/3111293/") |> 
  html_elements("section") |> 
  html_table() |>