Category Archives: Data Visualization

data-visualization

Rent Prices and TrelliscopeJS

Rent Prices are Soaring in Most of Colorado

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.

Chip’s analysis can be seen here.

His analysis states, “Efficiency apartments in Fort Collins/Loveland saw the largest increase in rent between 1996 and 2015. During this 19 year period, rent rose 226.5% from $239.26 to $781.18.”

One of his charts for median prices for Fort Collins/Loveland is very telling:

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Data Visualization – Part 3

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.

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Data Visualization – Part 2


A Quick Overview of the ggplot2 Package in R

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.

In case you missed it, here’s a link to Data Visualization – Part 1

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Data Visualization – Part 1


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.

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Random Forest Classification of Mushrooms

There is a plethora of classification algorithms available to people who have a bit of coding experience and a set of data. A common machine learning method is the random forest, which is a good place to start.

This is a use case in R of the randomForest package used on a data set from UCI’s Machine Learning Data Repository.

Are These Mushrooms Edible? Continue reading