Zev Ross zross

Eurostat, World Bank and others: https://ikashnitsky.github.io/2017/data-acquisition-two/
Star Wars data: in dplyr, http://dplyr.tidyverse.org/reference/starwars.html
Baby names data: babynames package, https://cran.r-project.org/web/packages/babynames/index.html
Movies data: https://cran.r-project.org/web/packages/ggplot2movies/index.html ggplot2movies package
Game of Thrones screen time: https://github.com/Preetish/GoT_screen_time
Open Bike Data: https://github.com/ropensci/bikedata
Tons of data through 538: https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html
Public health data England: fingertipsR, https://cran.r-project.org/web/packages/fingertipsR/
Financial data via Quandl: https://www.quandl.com/tools/r
Cyclones: https://github.com/ropensci/rrricanesdat

Suggestions from Twitter for more "elegant" web mapping applications

Specifically I'm looking for web mapping applications that include multiple layers and don't look like an ArcGIS clone -- perhaps something that looks like it was designed by a designer rather than a GIS tech :) This is a list of responses from Twitter, many of which do look GIS-y so more suggestions are welcome...

	[{
	"id": 1,
	"sex_code": 1,
	"mortality_death_age": [{
	"mortality_death_age_id": 1,
	"death_age": "death_age_00",
	"death_num": "995"
	}, {
	"mortality_death_age_id": 2,
	"death_age": "death_age_01",

	[
	{
	"name": "Germany",
	"series": [
	{
	"name": "2010",
	"value": 7300000
	},
	{
	"name": "2011",

	[{
	"name": "death_age_95",
	"series": [{
	"name": "Females",
	"value": -6517
	}, {
	"name": "Males",
	"value": 1891
	}]
	}, {

	[{
	"results": [{
	"name": "death_age_95",
	"series": [{
	"name": "Females",
	"value": 6517
	}, {
	"name": "Males",
	"value": 1891
	}]

	# Grab the list of spatial packages from the R Task View (Bivand)
	# and get the count of monthly downloads

	library(rvest)
	library(dplyr)

	spatial_task_view <- read_html("https://cran.r-project.org/web/views/Spatial.html")
	res <- spatial_task_view %>%
	html_nodes(xpath = "/html/body/ul[1]") %>%
	html_children() %>%

	# An example of spatial analysis with R that involves grabbing data
	# from a PostGIS database, joining to tabular data, projecting
	# simplifying and creating an interactive map.

	# This data is publicly available but requires some processing
	# if you want the county boundaries you can find them here:

	# cftp://ftp2.census.gov/geo/tiger/TIGER2016/COUNTY/

	# The tabular data requires some processing but the raw data

	# The idea I'm trying to test is if I can nest a parallel
	# for loop inside another parallel for loop.

	# The following does open up 4 cores with the first loop
	# and opens an additional 3 cores with the second. I see
	# seven running at one time, the four are turned off then
	# 3 are turned off.

	# I'm not sure if this is truly 7 parallel cores or if it's
	# 4 then 3 sequentially.

	body{
	color:red;
	}