This is a small post about upgrading to gdal 2.1.1 on a Mac using homebrew.
Assumptions You have homebrew installed and setup. You already have gdal 1.x installed via homebrew’s default ‘gdal’ formula. Install gdal 2.1.1 Unlink gdal 1.x usingbrew unlink gdal
Tap into osgeo4mac brew tap osgeo/osgeo4mac && brew tap --repair
brew install gdal2 --with-armadillo \ --with-complete --with-libkml --with-unsupported Link gdal2brew link --force gdal2
This is the fourth blog entry in my series ‘Leaflet-Diary’. In my last post Chapter 2: Projections and then some, I talked projections, easy button support and upgrading leaflet to 0.7.7. This one is about phase III, making the Leaflet package extensible and allowing for users to integrate many more Leaflet plugins.
TL;DR Version Some bugs were squashed. Leaflet package is now extensible. You can write your own R package that augment core leaflet functionality by incorporating various plugins.
This is the third blog entry in my series ‘Leaflet-Diary’. In my last post Chapter 1: Plugins galore, I talked about upgrading existing plugins and adding any missing functionality to those plugins. This one is about phase II, where I’ve added two extremely powerful features, custom projection support and custom buttons/toolbars and also upgraded Leaflet JS to 0.7.7.
This is the second blog entry in my series ‘Leaflet-Diary’, the first entry was nothing more than me announcing to the world that I’m contracting with RStudio for adding new features to the Leaflet package.
Sometime in Sep./Oct. 2015 I was working on a research effort that required building some web and print maps. Not a whole lot of GIS analysis but simply plotting some data on map for better representation. I had joined the project mid-way and the first editions of the maps were screen shots of Google MapsTM. Needless to say the were aesthetically hideous and not interactive. So I set about to find a better alternative to mapping and stumbled on leaflet for R by RStudio.
This is my review of Northwestern University’s Masters in Predictive Analytics (MSPA) online degree. I enrolled in MSAP in the Summer of 2013 and finished in the Summer of 2016. Normally it shouldn’t take this long to finish this program, but I took a break after the first Q1/015 and resumed in Q1/2016. This blog post is a retrospective analysis of the program, what I got out of it, and what it meant to me.
Recently I got hold of some regional spending forecast data. I quickly plotted it using ggplot2, and here’s the first version of it.
Figure 1: First Attempt The data is from 2014 and the values from 2015 to 2019 are the forecasted values. For now don’t worry about the validity of this data or the lack of margin of error in the forecasted values. Lets just concentrate on the problems with the visual elements of this chart.
My Cartography mentor Bob Rudis pointed me to a blog post visualizing Russian Air Strikes in Syria and commanded me to redo the static maps to something more interactive and easier to explore.
TL;DR Version Interactive Map at Rpubs created using Leaflet after scraping data using RSelenium+ PhantomJS + dplyr. You can use the LayerSelector at the Top Right to toggle various Base Tiles. Clicking on any Marker will show details about that Air Strike.
My Last post showed you how to install R inside a SmartOS zone. This post is about installing the shiny server in the said zone. While setting up R was relatively straight forward, for setting up Shiny server I had to patch some C++ code to make shiny server work on solaris. Which means you don’t have to, just follow along.
First install R in a zone as shown in my earlier post.
Recently I converted a spare beefy laptop (8 cores, 16 GB RAM, 750GB HD) to a SmartOS hypervisor. I wanted to play with some bare metal hypervisor / container stuff and ESXi was just not cutting it. I’m not a Solaris nerd, but I know enough Unix to find may way around in Linux/*BSDs/Solaris/HP-UX, so it was not a big pain. In fact ZFS is really nice.
Anyway, this post is about setting up R in a zone.