Using RPubs To Publish Work

I need to apologize to anyone who is actually reading my blog.  I had every intention of publishing a follow up post about Greece -- comparing its 10-year bond rates with those of the other "troubled" EU countries of Spain, Italy, and Portugal -- but life got in the way a bit.

This post will be a brief explanation of my current course.  I'm currently in the Reproducible Research course in the Data Science specialization certificate offered through Coursera.

One of the assignments in this course has me working with a particularly messy dataset.  After downloading the data, cleaning it up, and performing some exploratory analysis, I published my findings to RPubs.com.

Here is a link to my report.  It's nothing fancy, but I'm proud of it. 

This was created using the knitr package that integrates nicely with RStudio.  It allows you to create markdown documents that are easily viewable in HTML, as well as publishing them to RPubs, which is maintained by the folks who created and maintain RStudio.

knitr can also create documents in .doc and .pdf format as well.  I recommend trying it if you're in a data analysis role in any compacity.

For now, I'm going to leave off here.

Update 11 December 2015: Due to RPubs documents being public, and not wanting to tempt others who are taking the Data Science specialization through Coursera to copy my work, I have removed the report from RPubs.  I apologize for the inconvenience.

First Post

I'm trying to blog in order to exercise my R skills and as an outlet for my own interest in data analysis... or data science, since that's the new "buzz term".

I'm using R (currently, version 3.2.1) and RStudio (currently, version 0.99.441) on an "aging" Acer Aspire TimelineX laptop running Linux Mint 17 'Qiana' for its operating system (OS).

So, for my first post, I'm analysing data that I had previously looked at using Stata12.  Since migrating to R, I'm finding that the graphical output is much better.  Please note that the following graphs are using the base graphics package.

My first plot is Weekly U.S. Retail Gasoline Prices:

Followed by Weekly U.S. Total Stocks:

These are exploratory graphs.  I am just viewing the data.  The price data will probably show a strong correlation with oil prices (which I will show in a later post).  The stock quantities graph looked interesting, so I created a more detailed graph using the 'ggplot2' graphics package:

There was an interesting decrease in the quantities of gasoline stocks that began around 1992 through 1996.  Quantities of gasoline stocks did not show an increase until 2007.  This may be worth exploring further.