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)
##   Week Sales   TV Radio Fuel.Volume Fuel.Price Temp Holiday
## 1   26 24864 74.5  66.5       61825     104.24 27.9       1
## 2   27 23809 74.5  66.5       62617     103.97 27.7       1
## 3   28 24476 90.0  75.0       60227     107.48 29.1       1
## 4   29 25279 90.0  75.0       63273     111.75 30.0       1
## 5   30 26263 90.0  75.0       65196     109.08 29.3       1
## 6   31 24299 90.0  75.0       64789     105.36 28.1       1

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. (To save the image go to Worksheet, select Export, Image, Save.) Analyse the data source and explain in clear words the behavior you observe.

ANSWER TASK 1:

There is a sharp drop in sales from weeks 23-25. In these weeks there is missing data. This excel sheet tracks 2 years of data at a time, however from weeks 23-25 there is only one week of recorded sales. This could be why there is a distortion in the data.

TASK 2: Switch from SUM(Sales) to Average AVG(Sales) by clicking the arrow on the right of Sum(Sales), select Measure and Average. Change the Sales scale to be more reflective of the data (double click on the Avg. Sales axis, under Range select Fixed, change the Fixed Start and Fixed End then close the window). Include a snapshot here. Explain the new behavior relative to Task 1).

ANSWER TASK 2:

In comparison with task 1, we notice that this graph better represents the sales from the 2 years. Because the sales are averaged and the scale is adjusted to show a more dynamic view, the graph is more clear as compared to task 1 which was flat. When we averaged the sales, the weeks with only one revenue recorded fit the graph better. From the scaled view, sales are highest in weeks 20-32.

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 (by clicking on Color, Edit Color, select relevant color, Applu, OK). Include a snapshot here. Explain the combined behavior of sales and temperature.

ANSWER TASK 3:

By adding the temperature color to the graph, we notice that as the temperature increases, the sales increase as well. The middle of the year (around summer time) is the highest sales and the red portion of the graph. While the colder months are closer to blue in color and are lower in sales.

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:

From the graph we can notice that the more we spend on TV ads, the greater increase in sales. This appears to be only true until 90 GRP units. There is minimal increases past this mark, which proves the costs do not outweight the benefits past 90.

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:

By adding radio ads, we can see the relationship is similar to that of TV ads. When spending more on ads for TV and radio, sales increase. But it is noticed that this benefit only exists until 90 GRP units.

In a separate Tableau sheet

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

ANSWER TASK 6:

Fuel Volume and Sales is positively correlated and that increases when more fuel is sold per week. Correlation doest not always imply causation, however.

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:

We can notice that sales in volume go up during warmer temperatures, while when it is colder sales go down.

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:

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:

Sales are greatly affected by temperatures and advertisement. I would heavily advertise in the summer months when there is great demand for business to bring in more customers. As seen by the treemap, holidays are a nonfactor. The business should not worry about holiday affecting the sales and focus on TV, radio ads along with a correlation in warmer temperatures for an optimal sales revenue.