mschnetzer

library(tidyverse)
library(waffle)
library(msthemes)
library(MetBrewer)
library(showtext)

df <- tribble(
  ~Geschlecht, ~Status, ~"<1", ~"1", ~"2", ~"3-5", ~"6-9", ~"10-14",
  "Männer", "Vollzeit", 84.4, 83.2, 82.5, 83.7, 85.2, 83.5,
  "Frauen", "Vollzeit", 8.6, 6, 11.9, 16.4, 20.1, 30.7,

mschnetzer

library(tidyverse)
library(lubridate)
library(httr)
library(jsonlite)
library(msthemes)
library(geojsonsf)
library(sf)

# Get data: https://dataset.api.hub.zamg.ac.at/app/station-new/historical/klima-v1-1d?anonymous=true or use API like here

mschnetzer

librarian::shelf(tidyverse, sf, ows4R, httr, msthemes, osmdata, RColorBrewer, ggtext, showtext)

# Add Google fonts
font_add_google(name = "Bodoni Moda", family = "bodoni")
font_add_google(name = "Roboto Slab", family = "roboto-slab")
font_add_google(name = "Roboto Slab", family = "roboto-slab-thin", regular.wt = 300, bold.wt = 500)
showtext_auto()

# Define map boundaries
bb <- c(16.3151, 48.1856, 16.4087, 48.2279)

mschnetzer

librarian::shelf(tidyverse, pdftools, msthemes, ggtext, showtext)

# Add Google fonts
font_add_google("Alfa Slab One", "alfa")
font_add_google("Roboto Mono", "space", regular.wt = 200)
showtext_opts(dpi = 320)
showtext_auto(enable = TRUE)

# Download PDF: 
# https://www.oegb.at/content/dam/oegb/downloads/der-ögb/Streikstatistik_2021.pdf

mschnetzer

librarian::shelf(tidyverse, janitor, ggridges, ggtext, glue, colorspace, MetBrewer, msthemes, ggforce)

rawdata <- read.csv2("https://data.statistik.gv.at/data/OGD_vpi58ii_VPI_1958_ii_1.csv")

inflation <- rawdata |> 
  mutate(datum = ym(str_extract(C.VPIZR.0, "(\\d)+"))) |>  
  select(datum, Index = F.VPIMZBM) |> 
  drop_na() |> 
  mutate(Inflation = (Index - lag(Index,12))/lag(Index,12)*100,
         decade = factor(floor(year(datum)/10)*10))

mschnetzer

librarian::shelf(tidyverse, janitor, ggstream, ggtext, colorspace, MetBrewer, msthemes)

rawdata <- read.csv2("https://data.statistik.gv.at/data/OGD_vpi15_VPI_2015_1.csv")
coicop <- read.csv2("https://data.statistik.gv.at/data/OGD_vpi15_VPI_2015_1_C-VPI5-0.csv")

inflation <- rawdata %>%
  mutate(datum = ym(str_extract(C.VPIZR.0, "(\\d)+"))) %>%
  left_join(coicop %>% select(code, name), by = c("C.VPI5.0"="code")) %>%
  select(datum, Coicop = name, Beitrag = F.VPIEFVJM) %>%
  drop_na() %>%

mschnetzer

librarian::shelf(tidyverse, msthemes, janitor, lubridate, ggstream, MetBrewer, ggtext)

# Data from statcube.at
raw <- readxl::read_xlsx("table_2022-07-27_23-59-52.xlsx", na = "-", sheet = "Data Sheet 0", range = "B11:AU57")

inflation <- raw %>%
  clean_names() %>%
  mutate(Wohnen = x04_wohnung_wasser_energie - x04_5_aufwand_fur_energie) %>%
  select("Datum" = "x1", 
         "Inflation" = "gesamtindex_nach_coicop",

mschnetzer

library(tidyverse)
library(lubridate)
library(msthemes)
library(geojsonsf)
library(sf)

# Get data: https://dataset.api.hub.zamg.ac.at/app/station-new/historical/klima-v1-1d?anonymous=true

tempdat <- read.csv("TAG Datensatz_19500101_20211231.csv") %>% 
  mutate(name = case_when(

mschnetzer

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

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

temp <- raw %>% select(year:dec) %>%
  pivot_longer(names_to = "month", values_to = "value", cols = jan:dec) %>%

mschnetzer

library(tidyverse)
library(rvest)
library(stringi)
library(janitor)
library(countrycode)
library(MetBrewer)
library(msthemes)
library(patchwork)

url <- "https://en.wikipedia.org/wiki/List_of_weather_records#Highest_temperatures_ever_recorded"