johnburnmurdoch

# The packages we'll be using
packages <- c("rvest","dplyr","tidyr","pipeR","ggplot2","stringr","data.table")

# From those packages, which ones are not yet installed?
newPackages <- packages[!(packages %in% as.character(installed.packages()[,"Package"]))]

# If any weren't already installed, install them now
if(length(newPackages)) install.packages(newPackages)

# Now make sure all necessary packages are loaded

hrbrmstr

library(tidyverse)

gpi <- jsonlite::fromJSON("http://staging-maps.visionofhumanity.org/json_feeds/gpi.json?v=2.6.12&vv=2.6.12")

map(gpi, "csv") %>% 
  map_df(~read_csv(sprintf("http://staging-maps.visionofhumanity.org/csv/%s", .x))) %>% 
  docxtractr::mcga() -> gpi_df

glimpse(gpi_df)
## Observations: 1,618

walkerke

library(rnaturalearth)
library(sf)
library(plotly)
library(crosstalk)
library(viridis)

ng <- ne_states(country = "Nigeria", returnclass = "sf") %>%
  select(Name = name) 

# Source: https://dhsprogram.com/pubs/pdf/FR293/FR293.pdf

Gedevan-Aleksizde

require(tidyverse)
require(ggthemes)
require(rjson)
require(jsonlite)

# 参考
# https://www.data.jma.go.jp/gmd/risk/obsdl/index.php
# https://www.data.jma.go.jp/gmd/risk/obsdl/top/help3.html#hukajoho
# https://twitter.com/mehori/status/1020644999703089152

gadenbuie

# 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)

walkerke

library(tidycensus)
library(mapdeck)
library(tidyverse)

token <- "your mapbox token"

hv <- get_acs(geography = "tract", 
              variables = "B25077_001", 
              state = "CA", 
              geometry = TRUE) %>%

schochastics

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")

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")),

vankesteren

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

# 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.