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.
img1_path <- "imgs/SalesByWeek.png"
knitr::include_graphics(img1_path)

This plot shows the amount of dollar sales that occur each week throughout the year. It displays a significant drop in sales in the middle of the year, between Week 22 and Week 26.

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/AvgSales.png"
knitr::include_graphics(img1_path)

This plot shows the average sales that occur each week throughout the year. It displays the highest average sales between Week 28 and Week 33. Compared to the first graph, average sales do not display the huge decline in the middle of the year in comparison to the rest of the weeks.

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.png"
knitr::include_graphics(img1_path)

This shows that Average Sales are relatively high when temperature is high, which can mean that the two variables have a correlation in the data.

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/SalesTV.png"
knitr::include_graphics(img1_path)

This graph shows that reaching more target audience with TV results in an increase in sales. However, there is a peak in target audience reach at about 100. After that, sales start to slowly decrease and more is spent on TV than Sales can make up for, so it should not go past that amount.

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/Radio.png"
knitr::include_graphics(img1_path)

Adding Radio to the graph shows a trend that is similar to the previous one with TV and Sales. Spending more on radio ads seems to correlate with higher sales, but there is a limit like there was with TV. Sales seem to peak after about 200 Radio, so beyond that, there is not enough evidence to support an increase in Sales.

In a separate Tableau sheet

2C) Plot Sales versus Fuel Volume. Explain behavior.
img1_path <- "imgs/SalesFuelVol.png"
knitr::include_graphics(img1_path)

Sales and Fuel Volume have a slightly positive correlation, but it is a weak correlation since the points are very scattered.

2D) Overlay Temperature using the Color scale. Follow 1C) for temperature settings. Explain the new combined behavior and the impact of temperature.
img1_path <- "imgs/FuelTemp.png"
knitr::include_graphics(img1_path)

Higher temperatures are corelated with higher sales and higher fuel volume. This could be explained due to more people driving when the temperature is nicer.

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

A majority of the days with higher sales are holidays. These higher sales are also correlated with higher temperatures. This means that more people travel and use fuel on holidays, but even more so when the temperature is high.

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/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.

The highest amount of Sales occur during warm weather holidays with high fuel sales. This is displayed by the tree map.