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install.packages("devtools")  # so we can install from github
library("devtools")
install_github("ropensci/plotly")  # plotly is part of ropensci
library(plotly)

py <- plotly(username="r_user_guide", key="mw5isa4yqp")  # open plotly connection

library(ggplot2)
d <- 1:9
prob = log10(1 + (1/d))

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We've paid these famous graphs our sincerest form of compliment: trying to re-make them for the web. This post is inspired by <a href="http://www.edwardtufte.com/tufte/">Edward Tufte</a>. The plots were made with our free online product--you can press "play with this data" and start editing your own copy online. We have <a href="https://plot.ly/learn">tutorials</a> for using our web product and <a href="https://plot.ly/api">APIs</a>; contact us if you're interested in a trial of <a href="https://plot.ly/product/enterprise/">plotly on-premise</a>. 
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<h3>March on Moscow</h3>
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<a href="http://www.edwardtufte.com/tufte/minard-obit">Charles Minard</a>'s graph of the March on Moscow shows the dwindling size of Napoleon's army. The broad tan line shows the army's size on the March from Poland to Moscow. The lower, thinner, dark line shows the army's size on the retreat. The width of the lines shows the army size, which started over 400,000 strong and dwindled to 10,000. The bottom lines show t

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msund

Imagine exploring graphs like you would explore terrain on Google Maps. You would want to zoom, toggle, see data when you hover your mouse, and filter. For viewers, this is more engaging, informative, and fun. For exploration, it's more precise and efficient. Unfortunately, creating interactive plots can be difficult, time-consuming, and a technical burden.
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<a href="https://plot.ly/">Plotly</a> converts ggplot2 plots into interactive, online plots rendered with D3.js, a JavaScript visualizaiton library. Letting you publish interactive ggplot2 plots with one line of code is the goal behind our project. Plotly is free and online, or can be hosted (or <a href="https://plot.ly/product/enterprise/">host Plotly</a> on your servers). 
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Let's make a histogram, bar chart, and boxplot. You can copy and paste this code into your R terminal to make an <a href="http://stackoverflow.com/questions/6957549/overlaying-histograms-with-ggplot2-in-r">overlad histogram</a>. <a href="https://plot.ly/gg

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