Ok. Here’s an ugly secret of that data world: lots of your work will be prep work. Of course, any maker, artist, or craftsman has the same issue: chefs have their mise en place. Carpenters spend a heck of a lot of time measuring vs. cutting. Etcetera. So, you just need to be prepared that […]
An important principle in analyzing data is “overview first, zoom and filter, then details on demand” (quote: Ben Shneiderman) In practice, this typically means starting at a high level with a single chart, and then “zooming into” the data by replicating that chart for specific subsets of the dataset. And, even more valuable is being […]
Histograms are an excellent tool for examining numerical data distributions, and they are remarkably easy to build with R’s ggplot2 package.
Along with the scatterplot and line chart, I consider the bar chart to be one of the “big 3″ visualizations: the 3 foundational visualizations that you need to master.
And just like the code for those two plots…
Last week, I was talking to a guy who’s learning analytics, coaching him on what skills to learn next and helping him plan a career path. He’s a smart guy with an analytical background and minor coding experience, but he’s new to R.
Towards the end of the conversation, I asked him, “what’s the biggest challenge you have right now, learning analytics.”
His response? “The code is intimidating.”
Creating a line chart in R using ggplot is relatively easy. If you know how to think about visualizing your data, and know the right tools ….
How to create a bubble chart in r using the ggplot2 library. Here we provide code and a tutorial that explains how the code works, step-by-step.
Scatterplot Overview The scatterplot plots points on grid. Uses Use the scatterplot to visualize the relationship between two quantitative variables. (see Wikipedia) Code: Scatterplot in R Below, I’m going to show you some simple code to create a scatterplot in R using the ggplot2 package. To do this, you’ll need to have R and ggplot2 […]
As I noted in an earlier post, the powerful thing about data visualization (and analytics in general) is that it reveals insights that otherwise remain hidden. The objective with visualization is to see more clearly (and typically, we’re aiming to see problems more clearly so we can imagine solutions). As a case in point, I’ve […]
I love maps. If done right, they can be compelling, beautiful, and informative; everything a good visualization should be. Moreover, everyone understands them. Granted, you still need a legend and a little explanation, but when someone looks at a map of the United States, they immediately “just get it” and know that they’re looking at […]