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
## 1       Sales
## 2          TV
## 3       Radio
## 4 Fuel Volume
## 5 Fuel Priice
## 6        Temp
##                                                                                                                                               Running.count.of.the.week
## 1                                                                                                       Average c-stores sales (in Euros) per week per c-store location
## 2 Total volume of target audience reached in GRP units. One GRP equals one percent of target audience reached one time during week. If more than 100 reached repeatedly
## 3                                                                                                                                                      Total radio GRPs
## 4                                                                                                         Average volume of fuel sold per faciltiy per week (in liters)
## 5                                                                                                                            Average price of fuel (in cents per liter)
## 6                                                                                                                                            Average temperature (in C)

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:

The sales ranged from about 40K to 50K from week 1 to week 22. Between weeks 20 and 25 (at about week 23), we see a large drop in sales. Then the sales increase sharply at about week 26. There is a gradual decline in sales from week 30 to about 37. The sales then range from 40K to 50K for weeks 37 to 55.

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:

This graph is different because it shows the average sales per week, not the total sales per week. This graph is similar to the graph from task 1, in the way that both graphs depict strong decreases in sales from week 22 to week 26.

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:

This graph, like the one is task 2, shows he average sales. This graphs differs from graph 2 in the fact that it depicts the average temperature of the weeks. Looking at the average temperatures, along with the average sales you are able to see that on average there are higher sales when the temperature is higher.

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 this graph, we can see that the majority of sales happen with less than 100 being spent on TV ads. There are a lot of sales made even when there is 0 being spent on TV ads.

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:

This graph seems to show that additional radio ads does not greatly impact 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:

From this graph, we can see that there is a positive correlation between Fuel Volume and Sale. That tells us that the value of fuel volume increasing leads to the value of sales to increase 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:

Through this graphs dipction, the impact of temperature on fuel and sales is visible. The higher the temperature, the higher volume and sales are (and vice versa).

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:

This snapshot shows that sales and fuel volume are postively correlated with Holiday. When there is a high fuel volume and high sales, it is more likely that there is holiday. Futhermore, when the temperature is higher there seems to be more 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:

After analyzing this data, I have found that the highest affect on sales comes from temperature and holidays. There minor affects by TV and radio ads. The strong positive correlation between high temperature, a holiday and high sales leads me to thing that the most profitable time for sales (highest sales) will be in the warm summer months.