excel
Playing with SAS Visual Analytics
I got a hold of a free trial software of SAS Visual Analytics. So far it is a pretty fun business intelligence reporting tool. Over all it is easy to use as well. Below are a few graphs that you can make out of SAS Visual Analytics.
Created a chart that shows the Product Cost of Sale by Facility Continent
Created a box plot that shows the Product Cost of Sale by Product Line.
Created an automatic chart that shows the Unit Capacity by Transaction Date where the date format is in “Year, Quarter”.
Created a tree map visualization that shows Product Sale by Facility Country.
Created a 3 different graphs using product sale for each country in South America.
Created 2 different graphs of product sale of “Kiosk” product line in Vancouver and in Toronto from 2010 till 2012.
Create a graph showing the two months of 2012 the cost of sale was lowest in Atlanta.
Creating visualization 1 we decided to use categories of Facility Continent, Facility Country, and Product Line. The measures Product Sale, Product Cost of Sale, Product Price (Target), and Product Material Cost.
This bar chart graph allows us to check each Continent of the Product Sale and Product Cost. As you can easily see that there is a high volume of sales in North America.
This graph allows us to dig into North America in United States and lets us drill down deeper into sales into each State. Also we have split brand by Novelty and Toy. As we can see Toy has a high sale volume than Novelty in the overall sale from each State.
This line chart allows us to see quarterly sales in North America, United States region. There is a trend line can be easily determine by showing 4th quarter increases in product sale while 1st quarter seems to be down time on product sales.
This descriptive chart of product sale and product Cost by North America, United States region in a table format that list detail of each product sale and product cost. Easily allow users to see line items of product sale and product cost for product line.
This bar chart allows us to compare material cost, product cost, product sale, and potential price target for North America, United States region. Also to expand on the product sale to certain product line to see what the volume for each category. Figurine and Game seem to have the high volume of sales.
All in all SAS Visual Analytics let’s marketing target peak quarters and allows finance to plan forecasting for next year. Also allowing cost and sales to be broken down from Continent to Regions to even States, it really lets us drill down into the detail of data.
Position with MEXL add-on Excel
This is my chart using an old data from PDA users. Decided to run a position graph on what consumers rate PDAs. As you can see Compaq has the highest average from attributes while Connector has the lowest. My goal is to help market Connector PDA.
Using the data I have made a position map of what consumers think of each product. I’ve broken these into 3 clusters. Connector PDA has the lowest mean but has greatest connectivity. It would be best to try to market Connector PDA with it’s best attribute which is connectivity.
Pros about positioning you find benefits of the products and the weakness of the products. Disadvantage of this graph is that it may be misleading and its a snap shot of the current data you have on hand.
MLB players’ performance and salary
Software
Software used are Microsoft Excel, Tableau, and Notepad.
Source
Found my salary of MLB players through http://www.baseballplayersalaries.com/ and performance statistics through http://mlb.mlb.com/stats/ for 2014 season.
Data Extraction
Extracting the data with a simple copy and paste to a notepad.
Process
This process was easy since the data was easy to find on MLB’s website. It was an easy task of copy and paste to a Notepad and then uploaded to Microsoft Excel. This point I just had to clean the data which was simple. The next step was adding their salaries to Microsoft Excel which was a simple task of looking players up from http://www.baseballplayersalaries.com/ . After the data has been cleaned it was then uploading it to Tableau. Tableau allows you to drag and drop attributes you want use. I use scatter plot graph for my visual on this project. It allows you to also see clusters of players’ performance relative attributes I wanted to see.
Analyzing the Data
The visualization of my first graph using Tableau’s scatter plot has 1B representing position first base players. The x-axis has HR as home run and the y-axis Avg as avg hit per ball. The plots are circled with different sizes which determine their salary. The bigger the plots the bigger the salary of the player. Also the plots are different color to represent different players.The great part of using Tableau’s scatter plot is that it allows you to visualize the performance of a player by certain attributes and see if they are performing anywhere near what they are getting paid for. Also it can help show clusters of where most players stats are around.
As you can see for the first graph there is about 4-5 different clusters depending how you cluster the players together. The graph also lets you see outliers (a plot that is completely no where near the others) for example Jose Abreu of 36 home runs and a batting average of 0.317. Also you can see some players salaries aren’t as big, but still perform as well as high paid salary players for example Anthony Rizzo.
The same explanation is for the other graphs with different position of players of 2B (second base), 3B(third base) and so on.
The great thing about Tableau under each graph you make they explain the attributes you use and the detail of everything that needs to be explained about the graph produced from Tableau.
Baseball Team Salary and World Series
Software
Software used is Microsoft Excel.
Source
Finding Major League Baseball salary through http://www.usatoday.com/sports/mlb/salaries/2014/team/all/ .
Data Extraction
Extracting the data with a simple copy and paste to a notepad.
Process
The process of this was a simple copy and paste to a notepad. After that I upload the file to an Excel and cleaned the data and sorting the data to my preference. The next step was sorting the data by “Salary” largest to smallest for each year. After that filling the team with proper visual analytics colors.
Analyzing the Data
The visualization shows MLB team salaries from each year from 2004 to 2014. Each column represents the lowest (bottom) to the highest (top) paid salaries. All the World Series Championships winners (highlighted orange) are aligned horizontally to easily compare relative team salaries.
I’ve also wanted to show a simple bar chart from 2004-2006 graph made with excel to compare the different visualization techniques.
Both charts have its’ pros and cons. If I wanted something to pop out with a quick glance the first graph would be better, but if I wanted detail data the second graph would be a better choice.
The question we have to ask “Does a higher salary equate to a better chance or more World Series Championships? “
vizualanalytics 