25 Jan 2019
In this post, I will share with you my experience in the creation and visualization of coauthorship networks with R. We are going to focus on a particular type of network centered around one scholar (me in this example). The nodes of the network will be my coauthors (people with whom I published at least one paper) and the link between two coauthors will be proportional to the number of papers they cosigned (if any). We will first scrap data from Google Scholar using the R package scholar to build the network and then rely on the package networkD3 to visualize it.
01 May 2018
Shiny is an amazing R package that makes it easy to build interactive web applications. Combined with RStudio it becomes a powerful tool allowing you to design, test and finally deploy your application on the web. In addition, you can freely host your app on the shinyapps server. I discovered Shiny one year ago, while working on a method to generate weights of ordered weighted averaging aggregation operators. I usually put all my scripts online, but in this work I wanted a more user-friendly way of sharing information. This is why I got interested into Shiny and developed my first web application. This application is very basic but it gave me the opportunity to learn more about Shiny and how I can use it as researcher to reach a broader audience by developing vizualization tools along with my academic research articles. In this post, I’ll present you a few things I learned while developping my first interactive maps with Shiny.
17 May 2017
I recently discovered Selenium, a very useful tool to automate browsers navigation. Selenium allows to write scripts to automatically perform actions on a web browser: visit a page, click on a link, fill in a form… and retrieve the results of these actions. In this post, I am going to show you how to use Selenium from Python to automatically send messages to a list of Flickr contact through a contact form from your Flickr account.
27 Jun 2016
Lately, I have been facing several times the same problem while trying to spatially aggregate milion of spatial points over a spatial distribution of thousands of (not overlapping) polygons: How to efficiently identify the polygon in which every point is located? Indeed, as the number of elements increases R starts to become less and less efficient. In this post, I am going to show you how to efficiently perform operations between geometries by interfacing with PostGIS from R.
15 Feb 2016
In this post I’m going to show you how to display temporal trajectories on Google Earth using the python package
simplekml. This package enables us to generate KML videos that we can then plot
and record on Google Earth. The python script kmlMovie.py described in this post is available on my