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)

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: With minute fluctuations, sales increased gradually until the 20th week. After which there is a drop of a large magnitude. But between the 25th nd 30th week, there is a dramatic increase in sales that puts it back on the position it was in before the 20th 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 2: While sales are fluctuating, it reaches an all time high during the thirtieth week after which it begins to gradually decrease. Relative to task 1 there is no sharp increase or deacrese, its changes are gradual.

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: Similar to task 2, the increase and decrease hre is not as sharp as task 1. Here the colour is added to account for hot and cold tempratures. As we can see, the hot tempratures experience higher sales which falls eventually with respect to the temprature

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: This depicts that there is no relationship between Sales and TV and moreover, justification of ivestements into TV is difficult as there are not enough sales to depict so. $220,000 is invested while it only yields $100,000.

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: In compariosn to TV ads, the increase in sales pertains more to the Radio advertisements. But, more seems to be spent on TV rather than Radio. Similar to task 4, sales typically occurs below $100,000. Both radio and TV ads are similar where they both have a positive relationship with sales where more ads equals more 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: Fuel volume seems to have a postive relationship with sales as well. In the scatter plot depicted, we can see that by drawing a regression line, we can easily see that more fuel contributes to higher 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: This scatter plot takes into account the weather as well. Which is why we can how warmer weather sells more fuel. this subequently contributes to increased sales and all have a positive realtionship with each other.

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 this task we alsa take into account the weeks that had holidays by indicating the number “1”next to it. Typically a holiday is depicted below to have higher sales establishing that there is a positive relationship between sales and holidays as well.

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: Summarizimg all our studies, we get that sales is most affected by the tempratures and whether or not it is a holiday. While fuel does not have much imapct on sales, it has a positive relationship with higher tempratures and holidays.This could be explained through the example of how more people are likely to go on long drives and road trips during the holidays. This also explains why cold tempratures ahve no effect on these variables.