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.

Task 1: 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

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

Sales are generally rising until weeks 20-25 where there is a large drop in sales. After that there is a sharp rise in sales and more of a normal distribution thereafter.

1B) 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).
img1_path <- "imgs/AVG Sales vs Week.png"
knitr::include_graphics(img1_path)

The drop in avergae sales is not as great during the weeks 20-25. It is not as drastic when taking account the average vs. the sum of sales. The average sales decrease greatly during weeks 30-35.

1C) 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.
img1_path <- "imgs/Temp and sales.png"
knitr::include_graphics(img1_path)

Sales reach their highest with higher temperatures.As the weather gets colder sales begin to drop.

Task 2: Relationships and Impacts

In a seperate Tableau sheet

2A) 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?
img1_path <- "imgs/TV and sales.png"
knitr::include_graphics(img1_path)

The upper limit that should be invested in TV is 100 because after that point sales to not seem to increase with more money in the budget.

2B) 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.
img1_path <- "imgs/TV, Sales, and Radio.png"
knitr::include_graphics(img1_path)

If there are more radio ads we need less TV ads to reach our desired sales numbers.

In a separate Tableau sheet

2C) Plot Sales versus Fuel Volume. Explain behavior.

There is a general positive correlation between Sales revenue and the fuel volume. Typically an icrease in fuel volume leads to an increase in sales. However, there are some outliers in the data.

2D) Overlay Temperature using the Color scale. Follow 1C) for temperature settings. Explain the new combined behavior and the impact of temperature.

The scatter plot shows that generally an increase in temperature is associated with more fuel volume and thus greater sales.

2E) Overlay Holiday using the Label scale. Include a snapshot here. Explain the new combined behavior and the impact of Holiday.
img1_path <- "imgs/Fuel Volume vs. Sales.png"
knitr::include_graphics(img1_path)

The graph shows that sales are generally higher when there are holidays.

In a separate sheet

2F) 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.
img1_path <- "imgs/My tree map.png"
knitr::include_graphics(img1_path)

2G) Write a small paragraph summarizing your final conclusions on what you think most affect Sales and under what conditions.

Sales are most affected by temperature and holidays. There is a strong positive correlation between higher temperatures and average sales. There is also a positive correlation with holidays and temperature which also correlates strongly with sales.