Abhishek Polavarapu abhishekpolavarapu
Abhishek Polavarapu.
Working through the examples from Scott Murray's tutorial at http://alignedleft.com/tutorials/d3/
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Image and code from http://www.harding.edu/fmccown/r/
Embedding an image in Markdown:
1.
setwd("C:/Users/apolavar.CS/Documents/Rdocs")
mydata=read.csv("passing-stats-2014.csv")
mydata
myvars <- c("Passing.Yards", "Passing.TD", "Rate", "Rushing.Yards", "Rushing.TD")
newdata <- mydata[myvars]
pairs(newdata)
Abhishek Polavarapu
1.This bar chart gives the information regarding the passsing yards per player and each conference is assigned a colour.
2.
Name: Abhishek Polavarapu.
3 things that i learnt from tutorials.
a. Learned about the various transition effects.
b. I have noticed that if the scaling is done it doesn't have effect on the transitions.
c. Also the transitions can be applied to each individual elements.
I have taken a simple dataset which contains positive and negative value.This graph plots the positive values from dataset towards right side and negative values towards left side.All the positive values are represented in blue color and negative values are given red colors.
Channel used: Magnitude channel.(magnitude as position)
Marks : Bars.
The discriminability is maintained here as the common scale is used for representing the both type of values and magnitude of bar changes with change in position along the axis
The dataset for this graph contains name, Interaction score and team to which the player belongs to. I have plotted a bubble chart in which the size of bubble varies with the interaction value for each player.Also the team members who belongs to a particular team are assigned to one color.
Channels used : Identity channe(color hue), magnitude channel (area)
Marks : Bubbles.
The discriminability is maintained here by representing each team by particular color which satisfies the colour hue property and the size of each player varies with change in the interaction value. As a result the area size property is satisfied.
I have created simple dataset which consists of 4 types of data such as data1,data2,data3,data4 for various years. I used the punchccard chart over hear to represent the data for the respective years.And size of each bubble varies with value associated to it.
Channels used: Identity channel (spatial region)
Marks : bubbles.
Rule: no unjustified 3d- perspective distortion and occulusion hides information.
I have taken a sample dataset and represented them in barcharts.
The first chart is a bad chart because the bars which are shorter and present at a farther distance are not visible(occulusion hides information) and the bars at the farther distance appears to be smaller in size (perspective distortion)
The second chart is a good one beacause we are not facing any problems that we come across in 3D representation.
BAD CHART:
The states are divided into various groups and each group is assigned with a particular color. The nodes which has less number of connections are moving more freely when compared which has links. There are more number of states that belong to south atlantic region.
My implementation is more similar to the Mike bostocks and I took the json dataset from Mr.Hung Do