ilanman

This is a simple visualization of customer landings to a website due to advertisement. Project forward the time series prior to advertisement, then calculate the difference between the actual landings to the projected. See blog for more details.

Looking for suggestions to improve code and/or visual design.

ilanman

The US government released medicare data in early 2013. This is an analysis of that data across the US. It compares the cost of treatments per hospital and state vs. total medicare coverage and shows where there is greatest discrepancy.

ilanman

library(ggplot2)
library(scales)
library(reshape)

## Get the data from here: https://archive.ics.uci.edu/ml/machine-learning-databases/00275/
data <- read.csv('day.csv')

data$workingday <- as.factor(data$workingday)
data$weathersit <- as.factor(data$weathersit)
data$weekday <- as.factor(data$weekday)

ilanman

(function() {

// Inspired by http://informationandvisualization.de/blog/box-plot
d3.box = function() {
  var width = 1,
      height = 1,
      duration = 0,
      domain = null,
      value = Number,
      whiskers = boxWhiskers,

ilanman

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ilanman

This is my first foray in d3. It's a force directed graph representing correlations amongst college students' vices. Thicker bands represent stronger correlations, and larger nodes represent larger proportions of student partaking in that vice. More info found on my blog.

ilanman

This chart compares congressional workload over the years to the appropriations they are given. There is also the option of looking at the U.S. CPI over the same time to highlight the discrepancy. I am using d3-tip for my tooltips. More context around this chart can be found on my blog post.

ilanman