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johnburnmurdoch / index.html
Last active February 4, 2019 14:48
Animating 50,000 points with WebGL
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@johnburnmurdoch
johnburnmurdoch / cape_town.R
Last active March 9, 2019 17:37
#### All thanks to the wonderful Tyler Morgan-Wall for creating the fantastic Rayshader package ####
devtools::install_github("tylermorganwall/rayshader")
install.packages("needs")
needs(tidyverse, rayshader, magrittr, raster)
# Cape Town DEM downloaded from the City of Cape Town Open Data Portal: http://web1.capetown.gov.za/web1/OpenDataPortal/DatasetDetail?DatasetName=Digital%20elevation%20model
unzip("~/Downloads/10m_Grid_GeoTiff.zip")
CT <- raster("10m_Grid_GeoTiff/10m_BA.tif")
# plot the raster to make sure everything looks okay, and to get an idea of what area it covers
plot(CT)
@johnburnmurdoch
johnburnmurdoch / urbanisation_mountains.R
Created February 18, 2019 19:18
# 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
johnburnmurdoch / INTERBRAND_brand_values_2000_2018.csv
Created February 22, 2019 18:29
value name year
NA 3M 2000
NA 3M 2001
NA 3M 2002
NA 3M 2003
NA 3M 2004
NA 3M 2005
NA 3M 2006
NA 3M 2007
NA 3M 2008
@johnburnmurdoch
johnburnmurdoch / federer-ATP-100.R
Created March 2, 2019 21:16
# Load the packages we’re going to be using:
# Alongside the usual stuff like tidyverse and magrittr, we’ll be using rvest for some web-scraping, jsonline to parse some JSON, and extrafont to load some nice custom fonts
needs(tidyverse, magrittr, rvest, jsonlite, extrafont)
# Before we go on, two things to note:
# First, on web scraping:
# You should always check the terms of the site you are extracting data from, to make sure scraping (often referred to as `crawling`) is not prohibited. One way to do this is to visit the website’s `robots.txt` page, and ensure that a) there is nothing explicitly stating that crawlers are not permitted, and b) ideally, the site simply states that all user agents are permitted (indicated by a line saying `User-Agect: *`). Both of those are the case for our use-case today (see https://www.ultimatetennisstatistics.com/robots.txt).
# And second, about those custom fonts:
@johnburnmurdoch
johnburnmurdoch / INTERBRAND_brand_values_2000_2018_decimalised.csv
Last active April 29, 2019 16:26
We can't make this file beautiful and searchable because it's too large.
name,value,year,lastValue
3M,NA,2000,NA
3M,NA,2000.1,NA
3M,NA,2000.2,NA
3M,NA,2000.3,NA
3M,NA,2000.4,NA
3M,NA,2000.5,NA
3M,NA,2000.6,NA
3M,NA,2000.7,NA
3M,NA,2000.8,NA
@johnburnmurdoch
johnburnmurdoch / city_populations
Last active November 26, 2023 17:46
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
johnburnmurdoch / ne_10m_simplified.json
Created March 17, 2019 18:40
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@johnburnmurdoch
johnburnmurdoch / currency.csv
Last active March 28, 2019 10:18
currency.csv
value time
1.34944 2017-12-31 23:00:00
1.35889 2018-01-01 23:00:00
1.35142 2018-01-02 23:00:00
1.35504 2018-01-03 23:00:00
1.3564 2018-01-04 23:00:00
1.3564 2018-01-05 23:00:00
1.3564 2018-01-06 23:00:00
1.35662 2018-01-07 23:00:00
1.35395 2018-01-08 23:00:00
@johnburnmurdoch
johnburnmurdoch / landsat8.txt
Created April 6, 2019 22:22
LANDSAT 8
1 39084U 13008A 19096.49276745 .00000042 00000-0 19423-4 0 9994
2 39084 98.1930 167.4492 0001375 87.8678 272.2685 14.57117477326927