Statistics 4868/6610 Data Visualization
Prof. Eric A. Suess
1/13/2016
Topics today.
Question: So what is the underlying design of the websites we have looked at?
Answer: High level overview goes into more and more detail until you can change the views yourself.
If you are going to work with data and the web, you are likely to run into data in JSON and XML formats.
So if you going into writing apps, …
Recently there was an interview study conducted by some faculty at Stanford and UC Berkely to examine the differences in companies between different kinds of data analysis.
Question: Which of the following three archetypes are you?
Read the Section 6 Future Trends.
Enterprise Data Analysis and Visualization: An Interview Study by Kandel, Paepcke, Hellerstein, Heer
Related
An interesting related TEDx talk Data hacking - data science for entrepeneurs | Kevin Novak | Uber
PBS kids cyberchase in the show they are Using Data
Is the Data Hacker now the Data Scientist?
Short answer, Stanford
Polaris: A System for Query, Analysis and Visualization of Multidimensional Relational Databases by Stolte, Tang, Hanrahan
So what is github? You might have heard of it. It is a very useful cloud based platform for sharing code.
Some useful tools mentioned in the book. Shared on github.
An audio podcast that discusses visualization.
An audio podcast about R
Local online TV with a nice show about learning to code.
Microsoft TV
When I listen to music or podcasts I often listen using
The aim of Sonic Visualiser is to be the first program you reach for when want to study a musical recording rather than simply listen to it.
Find data
Manage data
Visualization process
Okay, you have your data. Now it's time to get visual!
attach(mtcars)
# Filled Density Plot
d <- density(mtcars$mpg)
plot(d, main="Kernel Density of Miles Per Gallon")
polygon(d, col="red", border="blue")
# Simple Scatterplot
plot(wt, mpg, main="Scatterplot Example",
xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19)
# Basic Scatterplot Matrix
pairs(~mpg+disp+drat+wt,data=mtcars,
main="Simple Scatterplot Matrix")
plot(wt, mpg, main="Scatterplot Example",
xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19)
# Add fit lines
abline(lm(mpg~wt), col="red") # regression line (y~x)
lines(lowess(wt,mpg), col="blue") # lowess line (x,y)
From the Quick-R website.
3D Scatterplots.
# 3D Scatterplot
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt,disp,mpg, main="3D Scatterplot")