Hello R community. if you’re up for some fun tinkering with a Shiny App please join me on a new project. I would love to see some collaboration in designing a Shiny Application which will help people make a decision about a healthcare provider. I have only just begun on this project but would to work with others.
This is just a quick look at the data, the roughest shiny app you’ve ever seen can be located on my shinyapps.io page
The first goal is to help people find a provider based off of City and State (or perhaps zipcode and latitude/longitude). This can take the form of a list, map, etc. I would also like people to be able to glean some information about the place they are going in comparison to the surrounding locations.
I was only able to put a an hour or so into this (and that was months ago) but have decided that it would be fun to start collaborating with anyone who is interested. Please make any pull requests and I’ll get to them!
Call it gentrification, supply-and-demand, call it whatever you’d like… the fact is, rent prices have gone up in Colorado in the last decade. Chip Oglesby – GitHub – did a nice analysis on the data provided by colorado.gov.
What Type of Data Visualization Do You Choose (if any)?
Determining whether or not you need a visualization is step one. While it seems silly, this is probably something everyone (including myself) should be doing more often. A lot of times, it seems like a great way to showcase the amount of work you have been doing, but winds up being completely ineffective and could potentially harm what you’re doing. Once you determine that you actually need to visualize your data, you should have a rough idea of the options to look at. This post will explain and demonstrate some of the common types of charts and plots.
While it will be important to focus on theory, I want to explain the ggplot2 package because I will be using it throughout the rest of this series. Knowing how it works will keep the focus on the results rather than the code. It’s an incredibly powerful package and once you wrap your head around what it’s doing, your life will change for the better! There are a lot of tools out there which provide better charts, graphs and ease of use (i.e. plot.ly, d3.js, Qlik, Tableau), but ggplot2 is still a fantastic resource and I use it all of the time.
Introduction to Data Visualization – Theory, R & ggplot2
The topic of data visualization is very popular in the data science community. The market size for visualization products is valued at $4 Billion and is projected to reach $7 Billion by the end of 2022 according to Mordor Intelligence. While we have seen amazing advances in the technology to display information, the understanding of how, why, and when to use visualization techniques has not kept up. Unfortunately, people are often taught how to make a chart before even thinking about whether or not it’s appropriate.
In short, are you adding value to your work or are you simply adding this to make it seem less boring? Let’s take a look at some examples before going through the Stoltzmaniac Data Visualization Philosophy.