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

There is a drop in sales between Weeks 22 and 25, where sales drops down to 20K-25K. Otherwise, the sales stay in the range of 40,000 to 50,000.

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/screenshot2.png"
knitr::include_graphics(img1_path)

The data is better to represent by using average sales because the raw data is more than one year of sales data included. The average sales per week range between about 20K-27K each week. the drop in sales that we saw earlier is greatly reduced by this chart. Although there is a downward trend in Week 30 until week 36.

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/screenshot3.png"
knitr::include_graphics(img1_path)

The graph shows clearly that sale sincrease in the summertime and decrease in the winter. This means when the temperature is hotter, sales are higher than when its cold.

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/screenshot4.png"
knitr::include_graphics(img1_path)

The scatter plot shows correlation between sales and tv advertising spending. The graph shows that if there is no spending on TV advertising, there is still forseeable sales. In this scatterplot, there is very little, or no correlation between sales and TV advertising. The upper limit is about 90K.

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/screenshot5.png"
knitr::include_graphics(img1_path)

Adding the additional radio ads to TV ads in the scatter plot shows that there is a similar relationship between sales vs. TV ads and sales vs. radio ads. Neither of the two relationships reflect perfect correlation. Radio ads have a larger affect in sales than tv ads.

In a separate Tableau sheet

2C) Plot Sales versus Fuel Volume. Explain behavior.
img1_path <- "imgs/screenshot6.png"
knitr::include_graphics(img1_path)

The sales increases as the fuel volume increases. Sales and fuel volume have a positive correlation.

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/screenshot7.png"
knitr::include_graphics(img1_path)

As temperature rises, so does fuel volume and sales. So they all rise at the same time.

2E) Overlay Holiday using the Label scale. Include a snapshot here. Explain the new combined behavior and the impact of Holiday.
img1_path <- "imgs/screenshot8.png"
knitr::include_graphics(img1_path)

The holidays here happen frequently during the summer. During the holiday/summer time, the fuel volume increases and sales increases too.

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/lastscreenshot.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,holiday, and fuel volume. However, it also affected by radio ads by a reasonable amount. Sales increase during the summer months, where there are more holidays and a stronger correlation between sales and fuel volume.