Two months into the R language

It has been some time since I started diving into the fantastic world of #R. I got into my suit, put the oxygen bottle on my back, fell into the water and started swimming downwards. My first sensation must have been the same as any amateur swimmer who goes deeper than 2m (probably the deepest I have gone underwater) – I looked left, looked right, looked down and thought “there is no end to this”.

Well, I guess I was right with this first assumption. I have been around R for only two months now and it seams limitless, from the jungles of packages and queries to the back and front-end possibilities is gives us. I am starting to understand why everyone says this is one of the main languages to learn if you want, like me, to set your foot in the #datascience world.

More recently I have been working around open source data APIs, how to reach them, store, analyze and treat this data, from the code in R, its integration with SQL and JSON and some front-end possibilities.

After some research and play time with the codes, I have picked myself up, built some courage and went through to create a real-time app with this data.

From the info I gathered, I opted to create a front-end over the Lisbon’s shared bicycle system information. In this app you will see a map with the location of every bike share dock, showing its name, street, number of bicycles and number of vacant slots in the dock. I have also put an update button and a slider. Since the background information is being updated every minute, by choosing a number (x) on the slider and pressing the update button, the map will be updated, showing the docks that have x bicycles in the dock at the moment you pressed the button. Through the usage of one impressive front-end package called “Shiny” (https://shiny.rstudio.com/), I was able not only to create this html app but also to share it online.

I must say that I was totally amazed from what I could do with no more than 70 lines of code and using only four R packages (from data manipulation like “dplyr” to image and front-end creating like “shiny”). As an example on the simplicity of the code, I have pasted in the end of this article, the code that creates the map.

Here is the link to the html app:

https://manuelfelix.shinyapps.io/app1/

As a conclusion, for anyone who likes to work with data and is fascinated by programming languages, this is definitely a world diving into and one I am thrilled to keep swimming downwards to new depths.