johnburnmurdoch

install.packages("needs")
library(needs)
needs(sjlabelled, tidyverse, haven, magrittr, ggrepel, sf)

# Load in data from all waves
USoc_indresp_1 <- read_dta("/path to wave 1/", encoding = "latin1")
USoc_indresp_2 <- read_dta("/path to wave 2/", encoding = "latin1")
etc

# Join all waves together, keeping the most recent driving licence record for each respondent, and their vote in the last election

johnburnmurdoch

johnburnmurdoch

# Install and load required packages
install.packages("needs")
library(needs)
needs(tidyverse, magrittr, animation, pdftools, png, scales)

# Function that extracts data from Google Mobility PDFs
process_google_mobility <- function(country_code, start_date, end_date){
  
  # Convert first page of PDF into high-res PNG
  pdf_convert(paste0("https://www.gstatic.com/covid19/mobility/",end_date,"_",country_code,"_Mobility_Report_en.pdf"), format = "png", pages = 1, dpi = 300, filenames = "IMG1.png")

johnburnmurdoch

read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv") %>%
  gather(date, cases, 5:ncol(.)) %>%
  mutate(date = as.Date(date, "%m/%d/%y")) %>%
  group_by(country = `Country/Region`, date) %>%
  summarise(cases = sum(cases)) %>%
  filter(country != "Others" & country != "Mainland China") %>%
  bind_rows(
    tibble(country = "Republic of Korea", date = as.Date("2020-03-11"), cases = 7755)
  ) %>%
  group_by(country) %>%

johnburnmurdoch

install.packages("needs")
library(needs)
needs(tidyverse, magrittr, rvest, sf, raster, rgdal)

ireland_constits <- c("https://en.wikipedia.org/wiki/Carlow%E2%80%93Kilkenny_(D%C3%A1il_constituency)") %>%
  read_html() %>%
  html_nodes('div[aria-labelledby="Current_Dáil_constituencies"] tr:nth-child(2) ul li a') %>%
  html_attr("href") %>%
  tail(-1) %>%
  c("/wiki/Carlow%E2%80%93Kilkenny_(D%C3%A1il_constituency)", .)

johnburnmurdoch

install.packages("needs")
library(needs)
needs(tidyverse, magrittr, rvest, zoo, scales)

WHO_sars_links <- read_html("https://www.who.int/csr/sars/country/en/") %>%
  html_nodes("ul.auto_archive") %>%
  magrittr::extract(1) %>%
  html_nodes("li a") %>%
  map_dfr(~{
    link <- .x %>% html_attr("href") %>% paste0("https://www.who.int",.)

johnburnmurdoch

johnburnmurdoch

needs(tidyverse, magrittr, png)

# Create a folder for storing the charts
dir.create("BFB_images")

# Loop though constituency codes in England and Wales, downloading the chart for each on from BfB
for(pcon in c(B4B_MRP$westminster_constituency[B4B_MRP$region != "Scotland"])){
  tryCatch(download.file(paste0("https://www.getvoting.org/charts/",pcon,"_1.png"), destfile = paste0("BFB_images/", pcon, ".png")), error = function(e){message("No chart")})
}

johnburnmurdoch

needs(sjlabelled, tidyverse, haven, magrittr)

# Load wave 8
USoc_indresp_8 <- read_dta("~/Downloads/UKDA-6614-stata/stata11_se/ukhls_w8/h_indresp.dta", encoding = "latin1")

# Load all other waves
USoc_indresp_1 <- read_dta("~/Downloads/UKDA-6614-stata/stata11_se/ukhls_w1/a_indresp.dta", encoding = "latin1")
USoc_indresp_2 <- read_dta("~/Downloads/UKDA-6614-stata/stata11_se/ukhls_w2/b_indresp.dta", encoding = "latin1")
USoc_indresp_3 <- read_dta("~/Downloads/UKDA-6614-stata/stata11_se/ukhls_w3/c_indresp.dta", encoding = "latin1")
USoc_indresp_4 <- read_dta("~/Downloads/UKDA-6614-stata/stata11_se/ukhls_w4/d_indresp.dta", encoding = "latin1")

johnburnmurdoch

install.packages("needs")
library(needs)
needs(tidyverse, magrittr)

# I’m using an example dataset of net national income per capita, from the World Bank (https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.CD?most_recent_value_desc=true)
head(dataset)

# Here’s a basic histogram using the ggplot defaults to show the distribution of NNI per capita across the world:
ggplot(dataset, aes(value)) +
  geom_histogram()