meysamabl

Resource used: https://flowingdata.com/2014/10/15/linked-small-multiples/

We are using small multiples to compare number of men and women participants in each Olympic event. When we sort the small multiples by women, we can see that the number of women participating the Olympic event is growing sequentially over time.

meysamabl

Read me file for VI9

meysamabl

ReadMe file for VI8

meysamabl

Insight gained: you can see the state adjacency as well as find the path distance between each node(state).

meysamabl

Insight gained: You can easily recognize the connected states and how they are divided into regions by encoding colors.

meysamabl

meysamabl

meysamabl

Name: Meysam Abolghasemi

Three things that I have learnt:

1. How to generate scatterplot with scale and x and y axis.
   
2. How to animate the objects using d3 transition.
   
3. How to create path in SVG.

meysamabl

Name: Meysam Abolghasemi

Part 1 - Tableau

Comments about working with Tableau:

1. It is much easier to work with Tableau compared to R and D3.
2. Although Tableau has all the functionalities pre-defined for you, but it is obvious you are limited to these function and you don't have the flexibility to implement very complex idioms.

Graph 1: bar chart of passing yards per player (best displayed as a horizontal bar chart), with conference mapped to color

meysamabl

Q1) scatterplot matrix of passing yards, passing TDs, passer rating, rushing yards, and rushing TDs

# read data from csv file
originalData <- read.csv("path/to/passing-stats-2014.csv")

# getting the subset of the data
q1Data <- subset(originalData, select= c(Passing.Yards, Passing.TD, Rate, Rushing.Yards, Rushing.TD))

# generate Scatterplot
pairs(q1Data)