Surbhi Shankar
01012632
I have used two different datasets to represent two graphs (data.csv and football.csv).
The data that I selected was huge and hence I have considered only required data.
Demonstration of channel types and channels:
Graph 1 - This is a scatterplot which represents the football data. This is a plot of Passing rate against Pass Completions. This graph clearly shows points on the X-Y graph with the corresponding values. Different colors are used to distinguish between Conferences. Hence, this graph demonstrates magnitude as position and identity as color hue. Also, we have given different sizes to the bubbles in the scatterplot depending on the values of passing completions. This demonstrates magnitude as area. It is easy for us to know which conference has the highest number of pass completions and also what passing rate does it hold.
Graph 2 - This is a grouped bar graph which represents data about selected states and their population record varying on a range of years from 2010 to 2011. Here the data about each state is grouped and hence it is a demonstration of identity as spatial region. Also, there has been use of colors which demonstrate identity as color hue.
Following are the graphs generated using tableau.
[note: colors here may be different from D3 reprsentation.]
![R image] (http://s2.postimg.org/mttl992k9/Graph_1.png "R image")
![R image] (http://s11.postimg.org/493qult03/Graph_2.png "R image")
Sources for datasets:
http://www.sports-reference.com/cfb/years/2014-passing.html
http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=PEP_2015_PEPANNRES&prodType=table
References:
/weiglemc/bdc0474429e6567bc320
/enjalot/211bd42857358a60a936/example/
http://blockbuilder.org/
https://bost.ocks.org/mike/