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#===============================================================================
# 2019-07-19-- ikashnitsky.github.io
# Reproduce Figure 2 from http://doi.org/10.1007/s10708-018-9953-5
# Ilya Kashnitsky, [email protected]
#===============================================================================
library(tidyverse)
library(hrbrthemes); import_roboto_condensed()
# the data as tribble
@eliocamp
eliocamp / tweet_storm.R
Last active December 3, 2024 23:01
Example of posting a twitter thread from R
---
title: "Tweet thread"
author: "Elio Campitelli"
output: github_document
---
```{r}
knitr::opts_chunk$set(dev = "png",
tweet_this = TRUE)
@uribo
uribo / population_and_flood_damage_biscale.R
Last active June 4, 2019 10:15
東京23区1kmメッシュにおける人口総数と想定浸水深の可視化
library(sf)
library(ensurer)
library(assertr)
library(dplyr)
library(fgdr)
library(jpmesh)
library(readr)
library(ggplot2)
library(cowplot)
library(biscale)
@tjmahr
tjmahr / chess-moves.md
Created May 28, 2019 16:27
chess moves
library(tidyverse)
theme_set(theme_minimal())

mt <- read.csv(url('https://gist.githubusercontent.com/ashtonanderson/cfbf51e08747f60472ee2132b0d35efb/raw/80acd2ad7c0fba4e85c053e61e9e5457137e00ee/moveno_piecetype_counts'))

mt$piece_type <- factor(
  mt$piece_type, 
  levels = c("P","N","B","Q","R","O","K"),
  labels = c(
# 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.
@vankesteren
vankesteren / lantaarnpaal_utrecht.R
Last active June 29, 2019 05:24
Creating a map with all the lampposts in Utrecht
library(tidyverse)
lights_dat <- read_csv("https://ckan.dataplatform.nl/dataset/83402c68-1c05-4aa5-ab28-2e99d2bc2261/resource/dc10e0ac-351a-49b6-b3db-d0152c29dc02/download/paal-20180906.csv")
pp <-
lights_dat %>%
filter(latitude > 50) %>%
ggplot(aes(x = longitude, y = latitude)) +
geom_point(alpha = 0.03, fill = "#FAFAAB", stroke = 0, pch = 21, size = 1.6) +
geom_point(alpha = 0.8, fill = "#FAFAAB", stroke = 0, pch = 21, size = 0.2) +
@johnburnmurdoch
johnburnmurdoch / mourinho_clubelo.R
Created December 18, 2018 17:57
Scripts for downloading and visualising data from clubelo.com showing José Mourinho’s third season problem. A hand-finished version of this chart appears in this Financial Times story: https://www.ft.com/content/56acdd82-02d2-11e9-99df-6183d3002ee1
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")),
@schochastics
schochastics / age_vs_value.R
Created August 24, 2018 21:21
scrape mean age and market values for European Football leagues
library(tidyverse)
library(rvest)
library(ggimage)
library(lubridate)
#get first 25 leagues in Europe ----
url <- "https://www.transfermarkt.de/wettbewerbe/europa"
doc <- read_html(url)
leagues <- doc %>% html_nodes(".hauptlink a") %>% html_attr("href")
library(tidycensus)
library(mapdeck)
library(tidyverse)
token <- "your mapbox token"
hv <- get_acs(geography = "tract",
variables = "B25077_001",
state = "CA",
geometry = TRUE) %>%
@gadenbuie
gadenbuie / join-animations-with-gganimate.R
Last active January 11, 2022 15:48
Animated dplyr joins with gganimate
# Animated dplyr joins with gganimate
# * Garrick Aden-Buie
# * garrickadenbuie.com
# * MIT License: https://opensource.org/licenses/MIT
# Note: I used Fira Sans and Fira Mono fonts.
# Use search and replace to use a different font if Fira is not available.
library(tidyverse)
library(gganimate)