A lot of data analysts will find themselves doing repetitive manual tasks on a data set every day/week/month in Excel, then copying and pasting their updated pivot tables and charts into Word or PowerPoint reports for their stakeholders. If this sounds like your job description, you may want to consider switching to a programming language like R. Writing scripts will allow you to automate the majority of these processes; from importing your data all the way through to emailing your boss the final report.
Background Last June I did a blog post about building dot-denisty maps in R using UK Census data. It has proven to be a fairly popular post, most likely due to the maps looking like something you’re more likely to see in the Tate Modern… Not only do these maps looks beautiful, but there is a strong argument that they do a better job of representing data compared to the more common choropleth methods of filling geographical regions with one colour based on one variable.
Getting Shiny out into the wild Shiny has really changed game in terms of analytical web-application development. Anyone with a solid grasp of R programming and some basic HTML + CSS knowledge can get production quality apps and dashboards up and running in days rather than months, and be in complete control of the process yourself. Furthermore, because it’s all open-source software, you have total ownership of the product you build - unlike many expensive off-the-shelf GUI solutions.
I Know What You Vizzed Last Summer tl;dr click the image to launch the app I guess I’m of that school of thought, I don’t mind my mobile tracking me. As long as I don’t go breaking the law, or tweeting an ill-advised truth about a politician, it’s unlikely that anyone will be typing the Google Location of my front room into a cruise missile control unit. But I confess a stirring of nerves when I decided to map my own Google Location data using R’s Leaflet package.
Twitter Analytics There has been a surge in a lot of great twitter analytics recently in the #rstats world, in part due to Michael Kearney’s excellent rtweet package. It makes wrangling with the twitter API like water off a duck’s back (twitter, bird, duck? sorry). You almost feel like some sort of Zuckerberg-esque data tycoon when you realise the amount of data you can access with a few lines of code.
TL;DR - check the tracker out here. As a recent cryptocurrency ‘Investor’ (0.13 ETH baby) I wanted to build a light tracker tool that could help me keep up with the mad market volatility in a more personalised manner. Of course, there’s no better tool for this task than the open-source programming language R, and the multitude of packages built for it that allows programmers with very modest levels of front-end development and sys-admin knowledge to go from nada to fully deployed, production quality web-application in a few hours.
The Death of Flash SAP XCelsius was the Ford Fiesta of the BI industry. Inexpensive to run, simple to implement. Businesses could get dashboards up and running in days or hours. We’ve helped clients design over 1,000 XCelsius dashboards over time, so we know how practical and adaptable they were. But then the industry went and killed Flash. Old XCelsius dashboards, which worked perfectly well last month and posed no threat to anyone’s IT security, are now downloaded and treated as high-risk by browsers, or blocked outright.
Happy New Year to one and all! As data scientists/analysts/researchers/programmers/anything else on that crazy data science Venn diagram, I’m assuming all of our new years resolutions involve visualising our data with more sophistication and finesse. So with that in mind, I thought it was high time for a post about the joys of modularizing your shiny app code. New Year, new improved workflows with emphasis on efficiency & reproducibility, amiright?
Synopsis Market Research is great at compiling the right data, but not so good at making it easy to use. This isn’t about “storytelling”. It’s about the data itself, and clarity on what delivering it actually means. Getting the data out of siloes like tabs and SPSS where it cannot adequately be mined, and out into the big wide world as Tidy Data.
A Dive Into Some Global Flooding Data I always like to keep a look out for interesting open data sets. One great resource for such things is Jeremy Singer-Vine’s Data is Plural weekly newsletter that brings together a collection of “useful, curious datasets” for us all to enjoy and wrangle with. One that cropped up last week was The Dartmouth Flood Observatory’s Global Archive of Large Flood Events.