About this post We are creating maps of data showing changes over a span of time for different countries and pointing at all kinds of cities. That basically means that we need to map any region of the world with R. Today there are all kinds of packages and techniques to do that. I will share the strategy I used with ggplot2 and maps packages, using support of Open Street Map to obtain the coordinates of cities and finally making it interactive with shiny.
Andrew Couch has a nice video about deploying a shiny app using docker. He goes from the very basics, that asume no knowledge of docker whatsoever, which is the position of many R users like myself. I’ve been working in some shiny app lately, and although I’ve never needed docker so far, I decided to start learning it because I can already foresee the future when it won’t be the case.
A few weeks ago I opened an account on Digital Ocean to start my own cloud server. Not long after that I took a workshop on Shiny and, although it was too technical with nothing new for me, I learn a couple of things unrelated to R. The speaker was talking about the importance of making your portfolio showing your apps instead of sharing the link to your code as most of us do.
Easy Emacs To start using R, or almost anything else in Emacs you basically need to know 3 things: 1) How to move in Emacs, meaning understanding what is what and learning a few key commands; 2) What is the configuration file and how to use it and 3) How to use packages to extend Emacs. In the first half of this post I will try to show how easy it is to cover these 3 points even for people who are inexperienced in programming.
Scope of this post This is the second part of the series to create a map of any region of the world with R.
We are creating maps of data showing changes over a span of time for different countries and pointing at all kinds of cities. That basically means that we need to map any region of the world with R. Today there are all kinds of packages and techniques to do that.
A couple of years ago I was interested in the efficiency of R when it comes to time processing and management of memory and I read a few blog posts about this topic, particularly pointing at the fact that R hasn’t been designed to be a very efficient language, especially when it comes to big data processing, and this could be its doom at some point in the future. By that time I also read a great article or blog post regarding the complexity of using the tidyverse family of packages in R, especially with the task of teaching R to beginners.
Scope of this post When you prepare for a job interview one of the questions they always tell you to prepare is “What are you most proud of?”. Personally I’ve never been asked that question in a job interview but it kept me thinking. Some years ago I developed the R code for the creation of maps of infrastructure for a Political Sciences project, and I can say that this is one of the projects I’m most proud of.
Welcome to R minitutorials of R White Dwarf Since the beginning of this year I’ve been forced to abandon completely the blog for countless and rather abstract personal reasons that include personal health, family matters and changes in my daily activities including volunteer work as well as main job. As part of the last, I finally got hired for a position as R developer, which brings great joy to me.
I am happy and excited as I have just deployed my first shiny app on the web. You can find it running at shiny.rwhitedwarf.com (NOTE: I don’t have ssl certificate so, your browser might tell you that is not secure, but you can trust me that there’s no risk). I have created a few shiny apps in the past but I never deployed one, especially in an owned domain.
The app can create a map of all cities listed in a table for a given country.
This post is part of our series on functions in R. You can see our previous post if you want to understand the basics but it is not strictly necessary. Here we will go into detail about for loops and if statements in R, two key elements of any function. We are going to define a process, map it in a step-by-step approach and wrape it in a function that can repeat it automatically.