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: The sales mostly remain around 40-50k with slight ups and downs in the first 22 weeks. Sales experience a slightl increase overall in weeks 0 to 22. then in week 23 till 24 sales experience a huge downfall from about 50k sales to around 23k sales. This level stabalizes in the next two weeks increasing slightly before returning to normal levels on week 26 at about 52k sales. From there there is bounces up and down slightly but experiences a slight decrease overall from week 26 to the 53rd and final week.

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 3: When we input temperature as the color we are able to make more conclusions about the data and have a more holistic view of the data and its trends. As the temperature begins to warm up at the start of the year we see a rise in sales silmaltuious with increasing temperature. The temperature peaks at the same point as slaes peak. From there it decreases as the temperature decreases. We could articulate that temperature and sales have a positive correlation by introducing temperature into the graph.

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 plot look a lot like a scatter plot. It represents and displays the relationship between the sales of TV’s and TV’s advertising budget. It seems as if the correlation is rather low after looking at the scatter plot. This leads me to conclude that because the majority of the sales occur under a 100k TV advertising budget, 100k could offer a good upper limit for the data. Just because you increase the TV ad budget does not always mean you will see sales increase because the two are not strongly correlated.

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: Contrary to the TV advertising, this time we see a much stronger correlation. As a result, the higher the budget for Radio advertising becomes, the higher the sales of radios will be.

In a separate Tableau sheet

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

ANSWER TASK 6: Although the data is rather scattered it appears that for the most part Sales and fuel volume have a positive correlation between the two sets of data. The majority of the time as sales increases so does the fuel volume.

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: By adding temperature into the picture we are now able to see the relationship temperature plays in both sales levels and amount of fuel volume. As you can see in the graph warmer average temperatures tend to go hand in hand with high sales numbers and high fuel volume levels. In other words, warmer temperatures tend to encourage higher levels of sales and fuel volume while cooler temperatures usually result in fewer sales and a lower fuel volume. Adding temperature adds a new dynamic to the data set that allows viewers to make more connections and develop relationships between the data points than they could in the previous graph.

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: Much like warmer temperatures holidays can be typically associated with higher sales and fuel volumes. People tend to spend more money and travel for the holidays so it is no surprise this results in higher fuel levels and larger sales than in the cooler temperature and non-holiday data points. Holidays tend to occur when the weather is warmer, making travel easier on individuals which further encourages travel resulting in higher sales and fuel volume.

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: All three factors (temperature, fuel volume, and holiday) have an impact on sales levels. For the most part as these other factors experience increases the sales category also experiences increases. Despite these factors having a positive correlation with the amount of sales there are outliers within each of these factors. For example, during a holiday sales levels tend to be higher than when it is not a holiday, that being said there are various data points that are sales of holidays that are less than the sales on a non-holiday. Sales perform best when it is a holiday, the temperature is warmer, and the fuel levels are higher. This data allows us to conclude that warm weather, and holidays lead to increased travel resulting in higher fuel levels, ultimately leading to higher sales. As any of these factor began to decrease so does the sales level.