About

In this section, we will be using Tableau to learn concepts on data outliers, seasonality effect, and the 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. 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 file EuroStore.xls found in 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. Tasks/questions to be completed/answered are highlighted in larger bolded fonts and numbered according to their particular placement in the task section.

### mydata = read.csv("data/EuroStore.csv")
### head(mydata)

PART A: DATA OUTLIERS AND SEASONALITY EFFECT

First get familiar with the data and what each columns represent. A description of the data is provided in a seperate sheet called ‘Desc’ in the same Excel file. Refer to Lab05 for early exercise using Tableau.

In a new Tableau sheet

TASK 1: Plot Sales (Rows) versus Week (Columns). Include a snapshot here, e.g. #####’ Analyse the data source and explain in clear words the behavior you observe.

ANSWER TASK 1:

Beginning with Week 22 and ending with Week 25 there is a sharp decrease in sales. This is because the dataset contains duplicates for all of the weeks expect weeks 22-25. Therefore, the sales number is much lower, about half that of the other weeks as sales are only counted once, not twice as with all the other 52 weeks.

TASK 2: Switch from SUM(Sales) to Average AVG(Sales). Change the Sales scale to be more reflective of the data. Include a snapshot here. Explain the new behavior relative to 1A).

ANSWER TASK 2:

The sales data is represented as a AVG now instead of SUM therefore the values do not drop during weeks 22-25. This change accounts for the duplicate weeks and shows that sales do not decrease during weeks 22-25 as displayed in Task 1.

TASK 3: Add Temp to the Color scale found in Marks. Change SUM(Temp) to AVG(Temp). Edit the color legend to be more reflective of hot and cold temperatures. Include a snapshot here. Explain the combined behavior of sales and temperature.

ANSWER TASK 3:

Adding temperature displays how the temperature impacts sales. When the temperature is higher the average sales are less consistent and bounce around a lot more as displayed by the constantly increasing and decreasing red sections. When the temperature is lower the sales are more consistent and make a small plateau as displayed by the green smoother sections.

PART B: RELATIONSHIPS AND IMPACTS

In a seperate Tableau sheet

TASK 4: Plot Sales (Rows) versus TV (Columns). Switch both measures from SUM() to Dimension. The plot should look more like a scatter plot. Include a snapshot here. Explain the behavior of Sales versus TV. How much you think is the upper limit amount that should be invested in TV ads?

ASNWER TASK 4:

Based off this plot, there are many weeks where there is no investment in TV ads yet there are still high sales amounts. TV ads have a relatively low correlation with sales and therefore a large amount should not be invested in TV ads. The upper limit should be 100. There are few sales numbers above 100 and they are very dispersed. It would be best to keep the value invested below 100.

TASK 5: Overlay Radio to the previous plot using the Size. scale found in Marks. Include a snapshot here. Explain how the additional Radio ads to Tv ads is impacting Sales.

ANSWER TASK 5:

From this plot it is observed that radio ads are more beneficial to sales than tv ads. Adding radio ads increases sales.

In a separate Tableau sheet

TASK 6: Plot Sales versus Fuel Volume. Switch both measures from SUM() to Dimension. Explain behavior.

ANSWER TASK 6:

There is a slight positive correlation between Sales and Fuel Volume. Generally, as Sales increase, fuel volume does as well.

TASK 7: Overlay Temperature using the Color scale. Follow TASK 3 for temperature settings. Explain the new combined behavior and the impact of temperature.

ANSWER TASK 7:

Sales correlates with temperature. As the temperature increases, so do sales as portrayed in the plot. When the temperature is cooler, there are generally fewer sales.

TASK 8: Overlay Holiday using the Label scale. Include a snapshot here. Explain the new combined behavior and the impact of Holiday.

ASNWER TASK 8:

When we overlay Holiday, we can see that Holidays have a large impact on Sales. Many of the days with highest sales are days that are also holidays.

In a separate sheet

TASK 9: Use a Tree Map to best show the combined effect of Sales, Fuel Volume, Temp, and Holiday. A sample view is shown below. Consider using the Quick Filter on Holiday and Temp to isolate and better view the impact of each. You can have more than one filter at a time. Include a snapshot here.
TASK 10: Write a small paragraph summarizing your final conclusions on what you think most affect Sales and under what conditions.

ASNWER TASK 10:

I believe temperature has the greatest impact on sales for this gas station. This is because for every different task, upon adding temperature, it was evident that the warmer the temperature the more sales were made. TV Ads and Radio Ads do not have a large impact on sales. The second largest variable that has an impact on sales is holidays. The highest selling days were generally warm holidays.