data 608 assignment 3

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DATA 608: Assignment #1

Michael munguia.

Principles of Data Visualization and Introduction to ggplot2

I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in:

And lets preview this data:

Having looked at the first few rows of the data, we see it’s ordered by company ranking. Let’s have a look at the bottom few rows too. I’ll also load tidyverse to work with ggplot2 and the rest of that suite of libraries.

Before thinking about visualization, I think it’s useful to have a sense of what’s going on with the text data - particularly if I think these will serve as labels, legends, etc. downstream.

Not only do we get a preview of some important insights (NYC is clear and away the leading city whereas California is the leading state - and interesting contrast), but we now know that we likely don’t have a single categorical variable that consists of just a handful of labels. Certainly, intuition would tell us not to expect a handful of values in Name , City or State .

We can confirm this and have a look at the count of values present in Industry and note that we have a total of 25.

There was also only one variable with NA values, at a count of just 12, so we can evaluate those below.

We can also take a look at what states are impacted by these NA values:

Create a graph that shows the distribution of companies in the dataset by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (ie taller than wide), which should further guide your layout choices.

Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.

We know that there are no missing data elements for this state from inspecting NA values earlier, but there are certainly outliers. I feel like imputing average values or simply dropping those data points would be misleading, so I’m going to handle outliers by comparing average and median. Knowing that the median is robust against outliers, that should be an anchor in surveying this data visually. An average value should stand out and immediately inform me what the variability looks like, rather than being hidden away by dropping the original value/imputing something new.

Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.