The task today was more analytical in nature and there was a heavy emphasis on asking questions. We had survey data from 1992-2019 . Below are the questions I wanted to initially answer, before the inevitable data difficulties!

• Did the majority of respondents smoke menthol cigarettes?
• How were the ages of first smoke related to frequency of smoking?
• What was the average smokes per day for each age bracket
• How many respondents started within the last year?

Sadly I didn't answer any of these initial question in the end deciding to look more closely at whether everyday smokers would quit.

Further to this there was a guidebook which yielded a few more questions to answer;

• What is the current cigarette smoking status and amount smoked;
• What has the use of menthol cigarettes (since 2003) been like;
• What is the smoking history, quit attempts, and intention to quit?
• What are the levels of nicotine dependence (since 2003);
• What is the cost of cigarettes and purchase location (since 2003);
• What are medical/dental advice to quit;
• General cigar, pipe, and smokeless tobacco use;
• Future harm reduction and other emerging products (since 2003);
• General workplace and home smoking restrictions;
• What are attitudes toward smoke-free policies in public places?
  

Having said all this, today was a departure from what we have previously done. Namely the introduction of statistical files. Below is the appearance of the data

I did a quick bit of googling, trawled the Tableau Community forums and posted on Convo yet wasn’t able to find a quick fix to the import of SAS files into Tableau. So my first challenge was naturally getting usable data, after much experimentation I finally found a solution attached in the workflow below.

It should be noted that we were using survey data from 1992-2019, naturally there were some hiccups in the generation of the data table, but ultimately it was overcome due to the data dictionary and the fact each column was considered a separate character. This alongside the formula tool joining the values yielded some useful answers to questions.

I have included a screenshot of the empty data tables, these questions ought to have been answered by some of the records but given their similarity to the previous questions I believe they have been harmonised into one column.

Having narrowed down the data significantly I really focused on three questions which could be sliced by Year/Month, Region, State, Age, Sex and Family Income.
Yielding the result below;

So to recap the Alteryx Workflow

1. Import the .dat file and use fixed width
2. Regex on each character using the ‘.’
3. Filter the character which had been concatenated to produce a 2018/19 and 1992-2015 dataset that could be unioned
4. Went through some onerous formula to merge values together
5. Unioned the data and removed all the excess columns caching along the way
6. Remove the last columns which were empty
7. Used string values to replace the numbers so it is intelligible when visualising
   

Now to Tableau

Due to the aforementioned questions I was able to look at the everyday smokers and try to judge their intention to quit. The answer sadly is a resounding NO. Having said this the number of respondents has been decreasing steadily which would suggest that there is a smaller sample size and given the variables of wealth and age suggest a likelihood that everyday smokers are becoming less common.

Challenges today were;

1. The statistical file and resulting work leading to more narrow questions of the data
2. Finding useful insights in Tableau that could be easily communicated
3. The quantity of data was significant so narrowing down and finding the right and useful data was challenging

If I had more time I would have liked to try turning the three pillars into a sankey to show the split in a nicer way and perhaps have brought in some more comparisons across the dataset.

In conclusion, I learnt how to manipulate statistical files, utilised RegEx in a manner I am satisfied with and narrowed down on some core questions from an expansive dataset. In future I want to focus on bringing more comparisons and perhaps producing the sankey.