johnburnmurdoch

johnburnmurdoch

# Prepare world data

# First up, we need to load the built-up area data that we’re going to be plotting. We download this from the European Commission’s Global Human Settlement Data portal [https://ghsl.jrc.ec.europa.eu/datasets.php] — specifically using the links from this page [http://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_BUILT_LDSMT_GLOBE_R2015B/]. We want the 250m-resolution rasters for 1975 and 2015 (GHS_BUILT_LDS1975_GLOBE_R2016A_54009_250 and GHS_BUILT_LDS2014_GLOBE_R2016A_54009_250).

# Once you’ve downloaded these (they’re BIG, so might take a little while...), we can save ourselves a lot of hassle later on by re-projecting them into the same co-ordinate space as the other data we’re going to be using. Specifically we want to change their units from metres to lat/lon. We do this by:
# 1) Unzipping the archive, and then
# 2) Running the following script on the command-line:

# gdalwarp -t_srs EPSG:4326 -tr 0.01 0.01 path/to/your/built-up-area.tif path/to/your/built-up-area_reprojected.

johnburnmurdoch

name,group,year,value,subGroup,city_id,lastValue,lat,lon
Agra,India,1575,200,India,Agra - India,200,27.18333,78.01667
Agra,India,1576,212,India,Agra - India,200,27.18333,78.01667
Agra,India,1577,224,India,Agra - India,212,27.18333,78.01667
Agra,India,1578,236,India,Agra - India,224,27.18333,78.01667
Agra,India,1579,248,India,Agra - India,236,27.18333,78.01667
Agra,India,1580,260,India,Agra - India,248,27.18333,78.01667
Agra,India,1581,272,India,Agra - India,260,27.18333,78.01667
Agra,India,1582,284,India,Agra - India,272,27.18333,78.01667
Agra,India,1583,296,India,Agra - India,284,27.18333,78.01667

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

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johnburnmurdoch

license: mit
height: 620
scrolling: no
border: no

johnburnmurdoch

needs(tidyverse, magrittr, scales)

jose.porto <- read_csv("http://api.clubelo.com/porto")
jose.chelsea <- read_csv("http://api.clubelo.com/chelsea")
jose.inter <- read_csv("http://api.clubelo.com/inter")
jose.real <- read_csv("http://api.clubelo.com/realmadrid")
jose.mufc <- read_csv("http://api.clubelo.com/manunited")

jose.all <- bind_rows(
  jose.porto %>% filter(From >= as.Date("2002-01-22") & To <= as.Date("2004-06-30")),

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

# 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")

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