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. Analyse the data source and explain in clear words the behavior you observe.

##ANSWER TASK 1: Sales are pretty volatile from week to week but have a general upward trend from week 1-20, and then sales drastically fall through weeks 21-25, and then go back up during week 26, but then are volatile again with a general downward trend.

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: On average, sales are volatile but have a general upward trend until around week 33 when average sales plummet and then are volatile the rest of the weeks, but not moving in a general upward or downward fashion.

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: Average sales increase with the temperature; sales are higher when the temperature outside is warmer and go down when the whether gets cooler.

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: The trend between TV and Sales is a slightly positive trend. You can see that there are more sales with more TV adds, however, it is not a perfect relationship. I believe the upper limit for TV ad investment should be held around 100; at this point, we can see that TV ads had a positive impact on sales, however, after 100, the pattern does not hold up as well.

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: The additional radio ads add a bit of value to sales, however, not a significant amount. I would advise to lower the radio ads investment.

In a separate Tableau sheet

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

##ANSWER TASK 6: There is a general positive relationships between fuel volume and sales.

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 see that there is a positive relationship between fuel volume and sales as well as temperature and sales and temperature and fuel volume. The warmer the temperature outside, the higher the sales and the higher the fuel volume. This makes sense when considering people are driving more and going to more places during warmer months than colder months.

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: There are more sales during holidays, even when the temperature is lower. This explains the outliers of the sales on colder days - those appear to be 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:

Weather and holidays seem to be a major influencers on average sales. The pattern I have observed is the increase in purchase of gas when the weather is warmer and around the holidays; meaning even when if the weather is cold on a holiday, sales will be higher than average on a similarly cold non-holiday day. Also, in previous graphs, we saw that radio ads had very little affect on increasing sales, and tv ads have a slight positive impact on sales, but not enough to max-out the budget. In conclusion, it appears that sales cannot be affected by the managers of the gas station since the variables which have the most impact on sales - weather and holidays - cannot be manipulated by any person.