About

For this lab, we will be using Tableau to learn some basic concepts in visual analytics to identify data outliers, seasonality effects, relationships and impacts. There is no R coding in this lab session.

Setup

This worksheet will be used to capture your images from Tableau and to share your observations. To refresh your memory, an example of capturing and including an image is included at the end of this sheet for your reference. You will need to log onto Tableau and Connect/Import the Excel file EuroStore.xls found in the data folder under the ‘bsad-lab10’ folder.

Remember to always set your working directory to the source file location. Go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Read carefully the below and follow the instructions to complete the tasks and answer any questions. Submit your work to RPubs as detailed in previous notes.

Note

For your assignment you may be using different data sets than what is included here. Always read carefully the instructions on Sakai. For clarity, tasks/questions to be completed/answered are highlighted in red color (visible in preview) and numbered according to their particular placement in the task section. Quite often you will need to add your own code chunk.

Execute all code chunks, preview, publish, and submit link on Sakai.

Task 1: Data Outliers and Seasonality Effect

First get familiar with the data and what each column represent. A description of the data can be found in the Excel file in a seperate sheet called ‘Desc’. Refer to early lab exercise on how to use Tableau. Also check the quick Tableau get started guide published on Sakai. Note that this time you will be reading an Excel file of type xls (unlike csv) into Tableau. Once the file is read into Tableau you will need to select the Data sheet for your work.

In a new Tableau sheet

Drag the measure Sales and drop into Rows. Similarly drag the dimension Week and drop into Columns.

##### 1A) Plot Sales (Rows) versus Week (Columns). Include a snapshot here. Analyse the data source and explain the behavior you observe.

After Analyzing the graph and the excel sheet, it is evident that, average sales in c-stores takes a steep decrease from week 22 to week 26. Based on this 27k decrease, it can be concluded that it is because weeks 23, 24, and 25 only have one value for sales as oppsoed to the other weeks which have two. Furthermore, the major dip is a result of not having two sales values to add together for that particular week.

Pull the drop-down menu by clicking the down arrow visible once you hover with the mouse over the variable name in the Rows or Columns field.

##### 1B) Switch from SUM(Sales) to Average AVG(Sales). Doubleclick on the Sales axis and change scale to be more representative of the data range. This should provide a better view of the data. Include a snapshot here. Explain, both qualitatively and quantitatively, how the switch to average impacted the previously observed behavior in 1A).

It is clear from the graph that when you switch the data from sum to average, the major decrease in sales disappears. This is due to the fact that now we are looking at an average sales of the weeks, meaning that the two values for each week is added then divded by two as opposed to the sum when you just add them together. Because weeks 23, 24 and 25 only have one value to consider, they are closer to the average value of the other weeks which in turn, eliminates the dip in sales.

##### 1C) Drag the measure Temp to Color found in Marks. Change SUM(Temp) to AVG(Temp). Edit the colors in the newly created color legend to be more representative of hot and cold temperatures. Include a snapshot here. Explain the combined behavior of Sales, Week, and Temp.

It is evident from this data that when the temperature was warmer, average sales increased. That can be concluded from the fact that when more people were exposed to TV and Radio ads during weeks 25 to 35 and becuase it was warmer out, more people left their homes to go to the c-store, inceasing sales.

Task 2: Relationships and Impacts

In a seperate Tableau sheet

##### 2A) Drop the measure Sales into Rows and the measure TV into Columns. Switch both measures from SUM() to Dimension using the drop-down menu. The plot should now look like a scatter plot. Include a snapshot here. Explain the overall behavior of Sales versus TV. Ignore the values at zero. Can you identify an upper threshold for the amount of investment in TV ads beyond which any purchase of additional TV ads would have little or no impact on Sales?

When looking at the data, it is evident that the more more money you put into Tv ads, the more sales are made. However, the upper thereshold comes at 95. After that, there should be no more money put into TV ads as the impact on sales remains constant after that. In other words, there is no increase.

##### 2B) Drag the measure Radio to Size found in Marks. Include a snapshot of the plot here. Explain what impact the addition of Radio ads to TV ads is having on Sales.

The Addition of Radio ads to TV ads had no more of an impact on sales than TV, In other words, the addition did not make sales go up more. The addition of radio was also consistant with the trend noted in the previous question, 2B.

In a separate Tableau sheet

##### 2C) Create a scatter plot of Sales versus Fuel Volume. Explain behavior. No need for a snapshot here.

After looking at the scatter plot, it is clear that as fuel volume increased, sales did as well.

##### 2D) Drag the measure Temp to Color found in Marks. Follow 1C) to edit the color legend for Temp. Explain the added impact of temperature on Sales. No need for a snapshot here.

Once the temperatuere was added with the color shown as red (meaning higher temp) it is demonstrated that the warmer it is, the higher the sales.

##### 2E) Drag the measure Holiday to Label found in Marks. Include a snapshot here. Explain the new combined impact of Temp, Holiday, and Fuel Volume on Sales.

From analyzing the above scatter plot, it is clear that, when the temperature is warm, and there is at least one public holiday, the fuel volume is high and the sales increase.

In a separate Tableau sheet

##### 2F) Create a Tree Map to show the combined effect of Sales, Temp, Holiday, and Fuel Volume. Use the Show Me menu to select a treemap. Make sure Sales is represented by Size, Temp by Color, Holiday by Label, and Fuel Volume by Label. A sample treemap view is shown below. You may want to view in Presentation mode for more clarity. Consider using a Quick Filter on Holiday and Temp to isolate and better assess the impact of each variable. To create a filter simply hover with the mouse over the measure and select Show Filter from the drop-down menu. Include a snapshot here.

##### 2G) Write a small coincise paragraph (data story) summarizing your findings on how you think the different measures work together or independently, and under what conditions, to affect Sales.

It was very interesting to see how each varible effects sales throguhout the compeltion of the precedding scatter plots. It is aparent that higher temperatures, incresing fuel volume and the instance of a public holiday all have a postivie impact on sales and as each of those measures increased, the sales did as well.