Tableau is a great tool for visualization, although there are some learning curves. Charts like the ones I have below are on a high level intuitive to create because you can drag and drop your data as well as the x and y axis classifications and the information will appear before your eyes. Then you can choose between a myriad of plots to best represent your objective. Below I have total checkouts of the San Francisco Public Library by age classification and the total by each home branch. I wanted to compare these because of the large uptick of checkouts in 2015, I was searching for something in the dataset that might explain why so many more books were being checked out in just a year. For this assumption I hypothesized that it was a localized uptick. While the Main branch in pink did have the largest uptick overall, all the other branches seem to trend in the same way where there is exponential growth between 2015 and 2016.

In further exploration I decided to look at the demographics of the patrons checking out books. Age was the only identifier of the dataset and as seen above, adults were the most represented classification. This is not surprising, but I thought it would still be helpful to see the amount of people classified as adults by comparing the age ranges used. Juvenile, young adult, and seniors, represent the margins of the age ranges and therefore there are less of them overall. Adulthood covering the ages of 18-65, make it the most encompassing classification. The accompanying line graphs show the trend of adults checking out books at the Main Library and all libraries excluding the Main Library. In an effort to reinforce that these are only showing the adults in each age range I have made the key a gradient of blues to match the key for adults in the bar chart.

In order to more effectively show only the data that pertains to my purposes I used the filters in tableau. You are easily able to filter each unique value from a column. For the line graphs throughout I have filtered out the years before 2009 because there was steady rate of checkouts that showed little difference year to year. The most interesting points in this data happen from 2012 onward, but I did not want to completely get rid of all the years before that because the growth in 2012 is more significant when compared to the steady state of checkouts from the years before. Other filters used were Age Ranges and Patron Type Definition.

The final assumption I made in attempt to figure out why there was such a spike in library checkouts was increased membership. In the charts below I have shown the breakdown of patronage sign up by year since 2003, I am using the pie chart to act as a key for the accompanying line graphs in order to reduce redundant information, there could be a better way of doing this, but with limited tableau knowledge I have fit these charts together in premade containers tableau provides.

The most surprising part of looking at this data was finding out that the uptick in checkouts were due most heavily by patrons who signed up in 2003. This made me pause and wonder how accurate this part of the dataset was. We can see from the pie chart that the amount of patrons from 2003 is large, but similar to recent years of 2015 and 2014, and we don’t see the same jump in usage. This ultimately leaves me with more questions about the data overall and no real hypothesis for why the number of checkouts has changed so dramatically.

I enjoyed using tableau as a way to easily visualize data and quickly check my assumptions. This is not practical data analysis as a whole, but an interesting way to quickly see what you have without need for other specific software knowledge. And with increased knowledge of how to use the platform more complex images can be produced. But with basic information and intuitive design, you can easily display important factors from your data. There are some drawbacks though, not all datasets will work well with tableau’s platform and found that there is a particular way datasets need to be organized for tableau to read the information easily.