mschnetzer

library(tidyverse)
library(eurostat)
library(grid)
library(png)
library(msthemes)

# Load data
data <- get_eurostat("avia_paoc", filters = list(schedule="TOT",time = c("2007","2017"),tra_cov="TOTAL",tra_meas="PAS_CRD",unit="PAS"), time_format = "num")

# Calculate growth 2007-2017

mschnetzer

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

# Load HFCS and Non-core data
load("hfcs2014AT.rda")
load("Noncore AT/non-core-at.rda")

# Calculate estimated decile with mean from multiple imputation data

mschnetzer

library(tidyverse)
library(pdftools)
library(msmaps)
library(countrycode)

# Link to source document
url <- "https://www.socialistsanddemocrats.eu/sites/default/files/2019-01/the_european_tax_gap_en_190123.pdf"

rawpdf <- pdf_text(url)
rawpdf <- rawpdf[9] # Table of interest is on page 9

mschnetzer

library(tidyverse)
library(msthemes)
library(RColorBrewer)
library(gganimate)

# Get data from http://www.zamg.ac.at/histalp/dataset/station/csv.php
data <- read_csv2("HISTALP_AT_WIE_T01_1760_2020.csv",skip = 13)

data <- data %>% mutate_at(vars("jan-dec"),funs(temp=.*0.1)) %>% select(year,temp) %>% filter(year %in% c(1775:2018)) 
data$draw <- 1

mschnetzer

library(tidyverse)
library(eurostat)
library(countrycode)
library(msthemes)
library(lubridate)
library(png)
library(jpeg)
library(grid)

# Load tax rate data (https://ec.europa.eu/taxation_customs/sites/taxation/files/taxation_trends_report_2018_statutory_rates.xlsx)

mschnetzer

library(tidyverse)
library(grid)
library(png)
library(msthemes)
library(gganimate)

gun <- readPNG("gun.png")

# Download data from https://wonder.cdc.gov/controller/saved/D76/D48F344
gundf <- read.table(file = "Firearm deaths by intent, 1999-2017.txt", header = T, fill=T, sep="\t") %>% filter(Notes=="Total", !is.na(Year)) %>% select(Year,Deaths)

mschnetzer

library(tidyverse)
library(rdbnomics)
library(gganimate)
library(msthemes)
library(png)
library(grid)

wsdata <- rdb("AMECO","ALCD2",dimensions=list(geo = c("aut","deu","d-w","ita","fra","esp"))) %>% 
  select(geo,original_period,value) %>% na.omit %>% 
  mutate(year= as.Date(original_period,"%Y")) %>% 

mschnetzer

library(tidyverse)
library(pdftools)
library(msthemes)

# Link to source document
url <- "https://itep.org/wp-content/uploads/notadime-rgb.pdf"

rawpdf <- pdf_text(url)

# Split lines at \n and delete the whitespaces at the beginning

mschnetzer

library(tidytext)
library(tm)
library(tidyverse)
library(ggforce)
library(msthemes)
library(rvest)

strache <- read_html("https://neuwal.com/transkript/20190518-pressekonferenz-heinz-christian-strache.php") %>%
  html_nodes(".antwort") %>%
  html_text()

mschnetzer

library(tidyverse)
library(msthemes)
library(waffle)

faclevel <- c("Berufliche Tätigkeiten","Haushaltsführung","Kinderbetreuung","Betreuung Erwachsener","Freiwilligenarbeit")

# Data from http://www.statistik.at/wcm/idc/idcplg?IdcService=GET_PDF_FILE&RevisionSelectionMethod=LatestReleased&dDocName=052108
tibble(labels=factor(faclevel,levels=faclevel),
       'Männer' = c(111.0,49.7,7.7,0.6,5.5),
       'Frauen'=c(71.4,99.2,17.5,1.1,5.2)) -> df