Recently, I started looking into data sets to compete in Go Code Colorado (check it out if you live in CO). The problem with such diversity in data sets is finding a way to quickly visualize the data and do exploratory analysis. While tools like Tableau make data visualization extremely easy, the data isn’t always properly formatted to be easily consumed. Here’s are a few tips to help speed up your exploratory data analysis!
We’ll use data from two sources to aid with this example:
A little while ago I did a brief tutorial of the Google Vision API using RoogleVision created by Mark Edmonson. I couldn’t find anything similar to that in R for the Microsoft Cognitive Services API so I thought I would give it a shot. I whipped this example together quickly to give it a proof-of-concept but I could certainly see myself building an R package to support this (unless someone can point to one – and please do if one exists)!
A quick example, sending this image retrieved the location of the human face and created a caption! Here’s my dog lined up next to his doppelganger:
The disastrous impact of recent hurricanes, Harvey and Irma, generated a large influx of data within the online community. I was curious about the history of hurricanes and tropical storms so I found a data set on data.world and started some basic Exploratory data analysis (EDA).
EDA is crucial to starting any project. Through EDA you can start to identify errors & inconsistencies in your data, find interesting patterns, see correlations and start to develop hypotheses to test. For most people, basic spreadsheets and charts are handy and provide a great place to start. They are an easy-to-use method to manipulate and visualize your data quickly. Data scientists may cringe at the idea of using a graphical user interface (GUI) to kick-off the EDA process but those tools are very effective and efficient when used properly. However, if you’re reading this, you’re probably trying to take EDA to the next level. The best way to learn is to get your hands dirty, let’s get started.
After doing my post last month on OpenCV and face detection, I started looking into other algorithms used for pattern detection in images. As it turns out, Google has done a phenomenal job with their Vision API. It’s absolutely incredible the amount of information it can spit back to you by simply sending it a picture.
Also, it’s 100% free! I believe that includes 1000 images per month. Amazing!
About two weeks ago I created an extremely rough version of an R Shiny Application surrounding Medicare data. Right after publishing the blog post, I received a lot of input for improvement and help from others.
I was traveling for two weeks and had very little time to do any work on it. After creating a GitHub Repository for it, the user Ginberg played a huge role in cleaning it up and adding a lot more functionality. I found it incredible that a complete stranger to me would put in such effort to something like this. In fact, he isn’t even a resident of the USA – so Medicare probably isn’t on his radar as often as it is for some of us. Fantastic generosity!
Ultimately, I will be looking to keep this project alive and grow it to fully utilize a lot more of the Medicare data available. The infections data set was very simple and easy to use, so I started off with it but there are a lot more tables listed on data.gov. The purpose of this application is to allow people who don’t want to spend time digging through tables to utilize the information available. This isn’t necessarily just for people seeking care to make a decision but this could perhaps be utilized for others doing research regarding hospitals in the US.
The R Shiny App allows you to filter by location and infection information. These are important in helping to actually find information on what you care about.
Three key tabs were created by (@Ginberg):
Sorting hospitals by infection score
Maps of hospitals in the area
Data table of hospital data
Sorting hospital data by score:
This is a tricky plot because “score” is different for each type of metric
Higher “scores” aren’t necessarily bad because they can be swayed by more heavily populated areas (or density)