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<!DOCTYPE html>
<html>
<head>
<title>Visualize Data in R</title>
<meta charset="utf-8">
<meta name="description" content="Visualize Data in R">
<meta name="author" content="Miguel Vazquez-Prada Baillet , miguelvb[at]yahoo.com">
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<slides class="layout-widescreen">
<!-- LOGO SLIDE -->
<slide class="title-slide segue nobackground">
<hgroup class="auto-fadein">
<h1>Visualize Data in R</h1>
<h2>graph types and interactivity to explore data</h2>
<p>Miguel Vazquez-Prada Baillet , miguelvb[at]yahoo.com<br/>Centre for Epidemiology and Screening , KU</p>
</hgroup>
<a href="https://github.com/miguelvb/visualDataPresentation/zipball/gh-pages" class="example">
Download
</a>
<article></article>
</slide>
<!-- SLIDES -->
<slide class="" id="slide-1" style="background:#FFFFCC;">
<article data-timings="">
<style>
</style>
<p><a href="https://github.com/miguelvb/visualDataPresentation"><img style="position: absolute; top: 0; right: 0; border: 0;" src="https://s3.amazonaws.com/github/ribbons/forkme_right_darkblue_121621.png" alt="Fork me on GitHub"></a></p>
<pre><code>## Warning: there is no package called 'ffbase'
</code></pre>
<h2>Visualize Data</h2>
<p><br/></p>
<blockquote>
<p><strong>See it</strong> , that is the question... </p>
</blockquote>
<p>Being able to "see" our data is one of the best techniques we can have to understand, communicate and analize them.<br>
<br/><br/></p>
<p><strong>R</strong> gives the opportunity to create many graphical representations. </p>
<p><br/></p>
<p>In the last years there has been a <em>revolution</em> in data visualization, that bring new possibilities to display and interact with data. </p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-2" style="background:#FFFFCC;">
<hgroup>
<h2>Why display data</h2>
</hgroup>
<article data-timings="">
<p><br/></p>
<ol class = "build incremental">
<li>To communicate. </li>
<li>To explore. </li>
<li>To analyze. </li>
<li>To discover. </li>
</ol>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-3" style="background:;">
<hgroup>
<h3>The usual suspects</h3>
</hgroup>
<article data-timings="">
<p><center> <img src="figures/plots.png" alt="types of plots"> </center></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-4" style="background:#FFFFCC;">
<hgroup>
<h2>GGPLOT 2</h2>
</hgroup>
<article data-timings="">
<blockquote>
<p>The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots.
It is unlike most other graphics packages because it has a deep underlying grammar</p>
</blockquote>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-5" style="background:;">
<article data-timings="">
<p>Example:<br>
<strong>Proportion of Danish women aged 50-69 years invited to mammography screening</strong></p>
<pre><code class="r">x <- data.frame( years = factor(1990:2010, levels = 1990:2010) ,
percent = c(0,4,8,8,14,rep(18,9), 23,24,18,19,48,89,100));
txt.perc <- function(x, d){ paste( format ( x, digits = 1, nsmall = d), "%", sep = "") }
titl <-'Proportion of Danish women aged 50-69 years \ninvited to mammography screening';
pl <- ggplot(x,aes(x=years, y=percent, fill = percent)) +
geom_bar( width =0.9) + ylab('percent') + xlab('') +
geom_text(data=x,aes(x=years,y=percent,label=txt.perc(percent, 0)),vjust=-0.5, size = 4) +
geom_text(aes(x=nrow(x)/2,y=90,label= titl),vjust=-0.5) +
theme_bw() +
scale_y_continuous( breaks=seq(0, 100, 5) , limits = c(0, 110), expand = c(0,0) ) +
theme(
axis.text.x= element_text( size= 12 ) ,
legend.position = "none" ,
panel.margin = unit(0,"null")
)
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-6" style="background:;">
<article data-timings="">
<p><center><img src="figures/percent.mammogrphy.png" ></center></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-7" style="background:;">
<hgroup>
<h2>The usual suspects and more in R : ggplot2</h2>
</hgroup>
<article data-timings="">
<p>Map example : </p>
<pre><code class="r">m0 <- ggplot(data=MapDf)
m1 <- m0 +
geom_path(aes(x=long, y=lat, group=group), color='gray') +
coord_equal() +
xlim(8,13) +
labs( title = "Cancer Diagnoses in Denmark")
m2 <- m1 +
geom_polygon( aes(x=long, y=lat, group=group, fill=id), alpha = 0.5 )
map <- m2 +
geom_text(data = centroids, x=centroids$long, y=centroids$lat, label = centroids$label, size = 2) +
coord_map(project="globular") +
theme(legend.position = "none")
map
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-8" style="background:;">
<hgroup>
<h4>Denmark, cancer cases per region.</h4>
</hgroup>
<article data-timings="">
<p><img src="figures/denmark_cases_2.png" alt="types of plots"></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-9" style="background:;">
<hgroup>
<h2>More ggplot2 : heat map example</h2>
</hgroup>
<article data-timings="">
<pre><code class="r"># Make the heatmap :
load("data/month_year")
c.scale <- scale_fill_gradient(low = "white",
high = "steelblue",
limits=c(min(month_year$freq),
max(month_year$freq)))
p1 <- ggplot(month_year, aes(month, year)) +
geom_tile(aes(fill = freq), color="white")
p2 <- p1 + c.scale
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-10" style="background:;">
<article data-timings="">
<pre><code class="r">p2
</code></pre>
<p><img src="assets/fig/ggplot3.png" alt="plot of chunk ggplot3"> </p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="notso" id="slide-11" style="background:url(assets/img/bike_ggplot.png);">
<hgroup>
<h2>Not so usual plots and diagrams</h2>
</hgroup>
<article data-timings="">
<p>London Cycle Hire and Pollution, made with R and ggplot</p>
<p><em>see the post in <a href="http://spatialanalysis.co.uk/2012/02/london-cycle-hire-pollution/">spatialy</a></em> </p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-12" style="background:#E6E6FA;">
<hgroup>
<h2>Today's visualizations</h2>
</hgroup>
<article data-timings="">
<h3>To visualize</h3>
<ul class = "build incremental">
<li>large data </li>
<li>complex data </li>
<li>interactions</li>
<li>data explorations </li>
</ul>
<h3>We use :</h3>
<ul class = "build incremental">
<li>new plots, diagrams, etc. </li>
<li>interactivity </li>
<li>real-time graphing</li>
<li>animations </li>
<li>3D</li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-13" style="background:;">
<article data-timings="">
<p><center><a href="https://github.com/mbostock/d3/wiki/Gallery"><img src="assets/img/newplots_1.png" ></a></center></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-14" style="background:;">
<article data-timings="">
<p><center><a href="https://github.com/mbostock/d3/wiki/Gallery"><img src="assets/img/newplots_2.png" ></a></center></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-15" style="background:#E6E6FA;">
<hgroup>
<h2>But, How To in R !!??</h2>
</hgroup>
<article data-timings="">
<p><strong>R</strong> is a great tool to make modern visualizations. </p>
<p>Due to its growing enthusiastic community, many packages and tutorials have been made to visualize data. </p>
<p>Here, we will use mainly these packages : </p>
<ul>
<li><a href="http://slidify.org">slidify</a> is an extremly powerful tool to convert any <strong>Rmd</strong> (R markdown) document to a beautiful and custom html5 presentation. This presentation is done with it. <a href="http://ramnathv.github.io/slidifyExamples/">slidify examples</a></li>
<li><a href="https://github.com/ramnathv/rCharts">rCharts</a>. rCharts is an R package to create, customize and publish interactive javascript visualizations from R using a familiar lattice style plotting interface.</li>
<li><a href="https://github.com/taiyun/corrplot">Corrplot</a>. Package corrplot is for visualizing a correlation matrix and
confidence interval. It also contains some algorithms to do matrix reordering.</li>
<li><a href="http://decastillo.github.io/googleVis_Tutorial/">GoogleVis</a></li>
</ul>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-16" style="background:#E6E6FA;">
<hgroup>
<h2>R , d3.js HTML5 and javascript</h2>
</hgroup>
<article data-timings="">
<p>To diplay and run the visualizations, one common procedure is use <code>HTML5</code> and <code>javascript</code>, to allow web access and manipulation. </p>
<p>Combination of <code>rCharts</code>, <code>googleVis</code>, <code>ggplot2</code> , <code>d3.js</code> and custom programming will transform R in one of the best platforms to make <code>complex-interactive-animated</code> visualizations. </p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="segue" id="slide-17" style="background:indigo;">
<hgroup>
<h2>EXAMPLES</h2>
</hgroup>
<article data-timings="">
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-18" style="background:#FFFFCC;">
<hgroup>
<h2>Time plots, zooms....</h2>
</hgroup>
<article data-timings="">
<p>Let us code an interactive graph: </p>
<pre><code class="r">require(rCharts)
load("data/time_data")
n1 <- nPlot(
N~dateint,
data =dtb ,
group = "id", # even though only one series need to specify group
type = "lineWithFocusChart"
)
n1$xAxis(
tickFormat=
"#!function(d) { return d3.time.format('%Y %b')(new Date( d )); }!#"
)
n1$x2Axis(
tickFormat=
"#!function(d) {return d3.time.format('%Y')(new Date( d ));}!#"
)
n1$set( width = 700, height = 500)
</code></pre>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-19" style="background:;">
<hgroup>
<h2>Time plot of movements of screened women</h2>
</hgroup>
<article data-timings="">
<div id = 'chart_time' class = 'rChart nvd3'></div>
<script type='text/javascript'>
$(document).ready(function(){
drawchart_time()
});
function drawchart_time(){
var opts = {
"dom": "chart_time",
"width": 700,
"height": 500,
"x": "dateint",
"y": "N",
"group": "id",
"type": "lineWithFocusChart",
"id": "chart_time"
},
data = [
{
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