About

For this lab, we will be using Tableau to learn some basic concepts in visual analytics to identify data outliers, seasonality effects, 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. To refresh your memory, an 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 Excel file EuroStore.xls found in the data folder under 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. For clarity, tasks/questions to be completed/answered are highlighted in red color (visible in preview) and numbered according to their particular placement in the task section. Quite often you will need to add your own code chunk.

Execute all code chunks, preview, publish, and submit link on Sakai.

Task 1: Data Outliers and Seasonality Effect

First get familiar with the data and what each column represent. A description of the data can be found in the Excel file in a seperate sheet called ‘Desc’. Refer to early lab exercise on how to use Tableau. Also check the quick Tableau get started guide published on Sakai. Note that this time you will be reading an Excel file of type xls (unlike csv) into Tableau. Once the file is read into Tableau you will need to select the Data sheet for your work.

In a new Tableau sheet

Drag the measure Sales and drop into Rows. Similarly drag the dimension Week and drop into Columns.

##### 1A) Plot Sales (Rows) versus Week (Columns). Include a snapshot here. Analyse the data source and explain the behavior you observe. thats a snapshot throughout a year theres a very predictable rise and fall and between weeks 2 till 22 and 35 till 52 theres a huge dip in sales between 22 and 27 Pull the drop-down menu by clicking the down arrow visible once you hover with the mouse over the variable name in the Rows or Columns field.

##### 1B) Switch from SUM(Sales) to Average AVG(Sales). Doubleclick on the Sales axis and change scale to be more representative of the data range. This should provide a better view of the data. Include a snapshot here. Explain, both qualitatively and quantitatively, how the switch to average impacted the previously observed behavior in 1A). changing the sum and scale show a much better view of the data and compared to the first one you can tell how the average of the sales dont drop as much as the sum of sales in the middle of the year ##### 1C) Drag the measure Temp to Color found in Marks. Change SUM(Temp) to AVG(Temp). Edit the colors in the newly created color legend to be more representative of hot and cold temperatures. Include a snapshot here. Explain the combined behavior of Sales, Week, and Temp. the hotter it got the more sales happned due to more people prob driving

Task 2: Relationships and Impacts

In a seperate Tableau sheet

##### 2A) Drop the measure Sales into Rows and the measure TV into Columns. Switch both measures from SUM() to Dimension using the drop-down menu. The plot should now look like a scatter plot. Include a snapshot here. Explain the overall behavior of Sales versus TV. Ignore the values at zero. Can you identify an upper threshold for the amount of investment in TV ads beyond which any purchase of additional TV ads would have little or no impact on Sales? spending on tv will only increase sales till 100K then it will stay the same orevendropat very high spening amounts ##### 2B) Drag the measure Radio to Size found in Marks. Include a snapshot of the plot here. Explain what impact the addition of Radio ads to TV ads is having on Sales. this shows a strong presance of radio only at the very high amounts but not nessesarily making an impact to sales In a separate Tableau sheet

##### 2C) Create a scatter plot of Sales versus Fuel Volume. Explain behavior. No need for a snapshot here. the more fuel sold the more sales happned ##### 2D) Drag the measure Temp to Color found in Marks. Follow 1C) to edit the color legend for Temp. Explain the added impact of temperature on Sales. No need for a snapshot here. the hotter the day the more people needed fuel the more sales on average ##### 2E) Drag the measure Holiday to Label found in Marks. Include a snapshot here. Explain the new combined impact of Temp, Holiday, and Fuel Volume on Sales. this shows that when theres a holiday and when its generaly warmer there are alot more people driving causing more people to buy fuel and thus stop at the store and purchase things increacing sales In a separate Tableau sheet

##### 2F) Create a Tree Map to show the combined effect of Sales, Temp, Holiday, and Fuel Volume. Use the Show Me menu to select a treemap. Make sure Sales is represented by Size, Temp by Color, Holiday by Label, and Fuel Volume by Label. A sample treemap view is shown below. You may want to view in Presentation mode for more clarity. Consider using a Quick Filter on Holiday and Temp to isolate and better assess the impact of each variable. To create a filter simply hover with the mouse over the measure and select Show Filter from the drop-down menu. Include a snapshot here.

##### 2G) Write a small coincise paragraph (data story) summarizing your findings on how you think the different measures work together or independently, and under what conditions, to affect Sales. THe most people travel during vacations during the hotter time of the year so closer to the middle. Spending alot of money during colder times of the year dosnt really affect sales as much as they seem like they should.