If you're one of those people that dreads long plane flights, this map by Matt Strimas-Mackey will help you find routes to avoid. It shows Wikipedia's list of the top 30 scheduled commercial flights by distance (with code-share duplicates removed), represented as a map showing the routes colour-coded by the time spent in the air. Don't be distracted by...
The Numerical Template Toolbox (NT2)
collection of header-only C++ libraries that make it
possible to explicitly request the use of SIMD instructions
when possible, while falling back to regular scalar
operations when not. NT2 itself is powered
by Boost, alongside two proposed
Boost libraries – Boost.Dispatch, which provides a
mechanism for efficient tag-based dispatch for functions,
and Boost.SIMD, which provides a framework for the
Today’s guest post is by Julia Silge. After reading Julia’s analysis of religions in America (“This is the Place, Apparently“) I invited her to teach my readers how to map information about US Religious Adherence by County in R. Julia can be found blogging here or on Twitter. I took Ari’s free email course for
I have a guest post up today at Ari Lamstein’s blog where I show some more fun things that can be done with the Religious Congregations and Membership Study at the ARDA that I used to look at Utah. I looked in some detail at Iowa ahead of their caucus in a few days, in light of...
Guest post by Krishna Prasad As a business analyst using R, one has often stumbled across situations for visually representing the data on a map. Here are some common scenarios: · Represent the most densely populated cities on a Map · Showing the cheapest places to live in · Where can one buy a home with the cheapest home Insurance...
Below are 10 charts made in R or Python by Plotly users on weather, maps and geography. 1. Doctor Who? – Timelords weather forecast for different cities Chart Link: https://plot.ly/~wthrmn/30/timelords-weather-forecast-1152016/Chart Author: @ wthrmnTouched up in: Plotly online editorCode: Python code R Code 2. Its raining ! – US Precipitation (June 2015) Chart Link: https://plot.ly/~RPlotBot/334/us-precipitation-06-30-2015-source-noaa/Chart Author:
A new version of choroplethr (v3.4.0) is now on CRAN. It allows you to combine Administrative Level 1 choropleths with reference maps. For reference, this functionality has been present for US maps for a while now (1, 2). This update just extends that functionality to the Administrative Level 1 mapping function, admin1_choropleth. To do this,
Hexamaps are gaining in popularity. Most notably has been the versions, where the map of the USA has been made into a hexamap. But people have also made maps of Europe using hexagons. The idea is that one unit is one hexagon. So in case of the US, each state is one hexagon. In the... Read more »
A new version of ChoroplethrAdmin1 is now on CRAN, and it dramatically speeds up making Administrative Level 1 maps in choroplethr. “Administrative Level 1” is just a generic term for “1st subnational division”. In the US this is called a State, in Canada it’s called a Province, and so on. Overall, this package contains Admin
Just because we all like numbers doesn’t mean we can’t have some fun. Here’s to wishing to everyone a very Happy New Year ! # install.packages("jpeg") library(jpeg) library(plotly) # Download a jpeg file from imgur URL <- "http://i.imgur.com/FWsFq6r.jpg" file <- tempfile() download.file(URL, file, mode = "wb") # Read in JPEG file j <- readJPEG(file) j