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<?xml version="1.0" encoding="utf-8" ?>
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<meta name="author" content="DPI R Bootcamp" />
<title>Tutorial 2: Getting Data In</title>
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</head>
<body>
<div class="slide titlepage">
<h1 class="title">Tutorial 2: Getting Data In</h1>
<p class="author">
DPI R Bootcamp
</p>
<p class="date">Jared Knowles</p>
</div>
<div class="section slide level1" id="overview">
<h1>Overview</h1>
<ul>
<li>Loading Packages</li>
<li>Understand data types</li>
<li>Load a CSV file</li>
<li>Organize an analysis project</li>
<li>Query a database</li>
</ul>
<p align="center">
<img src="data:image/png;base64,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" height="81" width="138">
</p>
</div>
<div class="section slide level1" id="a-quick-note-on-r-packages">
<h1>A quick note on R <code>packages</code></h1>
<ul>
<li><code>packages</code> are essentially free and open source add-ons for R</li>
<li>There are over 3,000 packages available for R that add all sorts of functionality</li>
<li>A few examples (from the mundane to the crazy)</li>
<li>Additional graphics capabilities from the <code>ggplot2</code> package</li>
<li>Advanced regression techniques from the <code>lme4</code> package for mixed effects models</li>
<li>3d graphics from the <code>scatterplot3d</code> package (also <code>webGL</code>)</li>
<li>GIS analytics and mapping functionality with <code>sp</code></li>
<li>Text mining analytics with <code>tm</code></li>
<li>Predictive modeling frameworks with <code>caret</code></li>
<li>Interfaces to other programming languages like Python, Java, and C and C++</li>
<li>A web server: <code>Rserve</code></li>
<li>And Minesweeper from the <code>fun</code> package</li>
</ul>
</div>
<div class="section slide level1" id="i-can-haz-packages">
<h1>I can haz packages?</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># You can find and install packages within R</span>
<span class="kw">install.packages</span>(<span class="st">"foo"</span>) <span class="co"># Name must be in quotes</span>
<span class="kw">install.packages</span>(<span class="kw">c</span>(<span class="st">"foo"</span>, <span class="st">"foo1"</span>, <span class="st">"foo2"</span>))
<span class="co"># Packages get updated FREQUENTLY</span>
<span class="kw">update.packages</span>() <span class="co"># Gonna update them all</span></code></pre>
<ul>
<li>Note, on Windows Vista and later R either needs to be run as an administrator to install packages, or you have to fiddle with where the packages are installed</li>
<li>Packages are stored in something called the <strong>library</strong> which is just a collection of packages</li>
<li>Sometimes folks call packages <strong>libraries</strong></li>
<li>Loading a package couldn't be easier <code>library(ggplot2)</code> and you're done!</li>
</ul>
</div>
<div class="section slide level1" id="finding-packages">
<h1>Finding Packages</h1>
<ul>
<li>Official packages are found on CRAN (Comprehensive R A Network)</li>
<li>Unofficial packages or <strong>beta</strong> versions of packages are found on <a href="http://r-forge.r-project.org/">RForge</a> and <a href="http://www.github.com">GitHub</a></li>
<li>To find out what packages are out there that do a specific function, try:</li>
<li>Google "doing X in R package"</li>
<li>Look at CRAN taskviews</li>
<li><a href="http://cran.r-project.org/web/views/">CRAN taskviews</a> are great to find a bunch of packages related to a problem you are trying to solve</li>
</ul>
</div>
<div class="section slide level1" id="some-must-have-packages">
<h1>Some Must Have Packages</h1>
<p><code>plyr</code> <code>ggplot2</code> <code>lme4</code> <code>sp</code> <code>knitr</code></p>
<ul>
<li>These packages come with data, code, functions, and utilities that you can access</li>
<li>If you want to learn about them, check them out in the RStudio Packages tab, which gives an index of their help files and handy links to their help documentation</li>
<li>Some packages also have <code>vignettes</code> which walk you through use cases for the package</li>
<li><code>caret</code> has a great example</li>
</ul>
</div>
<div class="section slide level1" id="data-management">
<h1>Data Management</h1>
<ul>
<li>Data management in R used to be managed by the <code>ls()</code> command</li>
<li>Go ahead, type it.</li>
<li>Now you can look at the Workspace tab in RStudio and have a complete list of the data in R's memory that is accessible to you</li>
<li>All data objects have names</li>
<li>All object names are unique (not strictly so, but let's not violate this)</li>
<li>To reference items within an object type we need to give it an address like in <code>mydata$thingIwant</code> or <code>mydata@thingIwant</code></li>
<li>The <code>$</code> and <code>@</code> distinction depends on whether this is an S3 or an S4 class</li>
<li>R will warn you if you do it wrong, so just remember when <code>$</code> doesn't work, use <code>@</code></li>
</ul>
</div>
<div class="section slide level1" id="the-working-directory">
<h1>The Working Directory</h1>
<ul>
<li>The working directory, lovingly denoted by <code>wd</code> is both your friend and enemy</li>
<li>R needs to know where it is in your file system to be able to access data and write output</li>
<li>Check this by typing <code>getwd()</code></li>
<li>R work is best done in a selfcontained directory, like <code>C:/Users/My Documents/My Project/</code> which is then set as the working directory</li>
<li>How to set the working directory? The <code>setwd()</code> command: <code>setwd("PATH/TO/MY PROJECT/")</code></li>
</ul>
</div>
<div class="section slide level1" id="preliminaries">
<h1>Preliminaries</h1>
<ul>
<li>Get and set the working directory</li>
<li>Understand file system paths</li>
<li>Understand relative and full paths</li>
<li>Find out where things are</li>
<li>To start, we'll download the Bootcamp files and open up "Tutorial 2.R"</li>
<li>We'll notice that in the Bootcamp folder we have a number of subfolders for things like <code>data</code> and <code>handouts</code></li>
<li>When you do an R project, it really helps to put it into a self-contained folder so you can reference everything you need easily within the file system</li>
</ul>
</div>
<div class="section slide level1" id="rstudio-shortcuts">
<h1>RStudio Shortcuts</h1>
<ul>
<li>RStudio makes this so much easier!</li>
<li>RStudio can set the working directory to the <strong>Files</strong> pane in the bottom right, or to the <strong>Source</strong> file in the top left</li>
<li>The source file is the most likely path, but sometimes we need to access data in a separate folder and store results in another</li>
<li>So RStudio shortcuts can't help there</li>
</ul>
</div>
<div class="section slide level1" id="rstudio-projects">
<h1>RStudio Projects</h1>
<ul>
<li>A great feature of RStudio is that it allows us to create a <strong>project</strong> which allows us to quickly jump from folder to folder, store data, and keep open tasks and scripts</li>
<li>If you have multiple R tasks at once (how lucky!) you have to use projects</li>
<li>Let's walkthrough creating a Project in RStudio and understand it a little bit</li>
<li><strong>DEMO</strong></li>
</ul>
</div>
<div class="section slide level1" id="manipulating-project-paths">
<h1>Manipulating Project Paths</h1>
<ul>
<li>Paths are strings that tell R where to find files on your computer</li>
<li>R uses both full and relative paths</li>
<li>When we want to call files from different directories or write to different directories, we can use relative paths within the project</li>
<li>Relative paths look like <code>/data</code> or <code>~data</code> which means, look for the <code>data</code> folder in our current working directory</li>
<li>Full paths work differently, they specify the exact location on the hard drive of the machine like <code>C:/Path/To/My/Data</code> or <code>usr/home/jaredrocks</code></li>
</ul>
</div>
<div class="section slide level1" id="when-to-use-a-full-path">
<h1>When to use a full path</h1>
<ul>
<li>Never</li>
<li>It breaks the ability to pass a project directory to a coauthor/collaborator</li>
<li>But when you must...</li>
<li>When you have something on a whole different drive or network store</li>
<li>When you don't think you'll migrate your code to a new operating system or a different machine/network environment</li>
<li>When the project is simple</li>
</ul>
</div>
<div class="section slide level1" id="ground-rules">
<h1>Ground Rules</h1>
<ul>
<li>Get used to plain text input files</li>
<li>R can handle other formats, but your error rate increases as does the tweaking necessary</li>
<li>R has a limited set of special characters (symbols) you cannot use in your data input to be translated correctly</li>
<li>These symbols are reserved and will be interpreted in strange ways if you include them in your plain text data file</li>
<li>Most of them are fairly obvious operators, see <a href="http://www.stat.auckland.ac.nz/~paul/ItDT/HTML/node75.html">Paul Murrell's excellent summary</a></li>
</ul>
</div>
<div class="section slide level1" id="missing-data-symbols">
<h1>Missing Data Symbols</h1>
<ul>
<li>Missing data has the symbols <code>NA</code> or <code>NaN</code> or <code>NULL</code> depending on the context.</li>
<li>Consider:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">a <- <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>) <span class="co"># a is a vector with three elements</span>
<span class="co"># Ask R for element 4</span>
<span class="kw">print</span>(a[<span class="dv">4</span>])</code></pre>
<pre><code>## [1] NA</code></pre>
<ul>
<li>But what is the difference between <code>NA</code> and <code>NULL</code>?</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">a <- <span class="kw">c</span>(a, <span class="ot">NULL</span>) <span class="co"># Append NULL onto a</span>
<span class="kw">print</span>(a)</code></pre>
<pre><code>## [1] 1 2 3</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Notice no change</span>
a <- <span class="kw">c</span>(a, <span class="ot">NA</span>)
<span class="kw">print</span>(a)</code></pre>
<pre><code>## [1] 1 2 3 NA</code></pre>
<ul>
<li><code>NA</code> can hold a place, <code>NULL</code> cannot</li>
</ul>
</div>
<div class="section slide level1" id="what-the-heck-is-not-a-number">
<h1>What the heck is Not a Number?</h1>
<ul>
<li><code>NaN</code> is even more special, and only holds things like imaginary numbers</li>
<li><code>NaN</code> stands for "Not a Number"</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">b <- <span class="dv">1</span>
b <- <span class="kw">sqrt</span>(-b)</code></pre>
<pre><code>## Warning: NaNs produced</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(b)</code></pre>
<pre><code>## [1] NaN</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">pi/<span class="dv">0</span></code></pre>
<pre><code>## [1] Inf</code></pre>
<ul>
<li><strong>Inf</strong> is a special case as well representing an infinite value</li>
<li>Just for fun <code>sin(Inf)</code> = <code>NaN</code></li>
</ul>
</div>
<div class="section slide level1" id="beginning-analysis">
<h1>Beginning Analysis</h1>
<ul>
<li>Now let's set up our analysis project</li>
<li>It is best to keep projects discrete in directories</li>
<li>Create a few subdirectories</li>
<li>Data</li>
<li>Functions / R src</li>
<li>Figures / Plots</li>
<li>Cleaned Data</li>
<li>We've already done this in the Bootcamp folder itself!</li>
<li>See the <code>ProjectTemplate</code> package for a more detailed philosophy about organizing projects</li>
</ul>
</div>
<div class="section slide level1" id="organization-of-a-project-is-key">
<h1>Organization of a project is key</h1>
<ul>
<li>Separate data from scripts</li>
<li>Separate automatic scripts from interactive scripts</li>
<li>Put figures apart from both of these</li>
<li><strong>Always keep your raw data</strong></li>
</ul>
</div>
<div class="section slide level1" id="create-our-project">
<h1>Create our Project</h1>
<ul>
<li>Open the "bootcamp" folder on your machine</li>
<li>Underneath this make a a "figure" folder to go with the "data" folder</li>
<li>Create an R script in the "bootcamp" folder called "myscriptname.R"</li>
<li>Put all the data files in the "data" folder</li>
</ul>
</div>
<div class="section slide level1" id="read-in-data">
<h1>Read in Data</h1>
<ul>
<li>Reading in data is one of the trickiest issues for R</li>
<li>This is because R is incredibly flexible and can handle data in almost any form including <code>.csv</code> <code>.dta</code> <code>.sas</code> <code>.spss</code> <code>.dat</code> and even <code>.xls</code> and <code>.xlsx</code> with some care</li>
<li>So we have to carefully specify the data types to R so it can understand what form the data needs to take</li>
<li>Compared to C this is great!</li>
</ul>
</div>
<div class="section slide level1" id="csv-is-our-friend">
<h1>CSV is Our Friend</h1>
<ul>
<li>The easiest data type is .csv though Excel files can be read as well</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Set working directory to the tutorial directory In RStudio can do this</span>
<span class="co"># in 'Tools' tab</span>
<span class="kw">setwd</span>(<span class="st">"~/GitHub/r_tutorial_ed"</span>)
<span class="co"># Load some data</span>
df <- <span class="kw">read.csv</span>(<span class="st">"data/smalldata.csv"</span>)
<span class="co"># Note if we don't assign data to 'df' R just prints contents of table</span></code></pre>
</div>
<div class="section slide level1" id="lets-check-what-we-got">
<h1>Let's Check What We Got</h1>
<pre><code>## 'data.frame': 2700 obs. of 6 variables:
## $ schoolavg: int 1 1 1 1 1 1 1 1 1 1 ...
## $ schoollow: int 0 0 0 0 0 0 0 0 0 0 ...
## $ readSS : num 357 264 370 347 373 ...
## $ mathSS : num 387 303 365 344 441 ...
## $ proflvl : Factor w/ 4 levels "advanced","basic",..: 2 3 2 2 2 4 4 4 3 2 ...
## $ race : Factor w/ 5 levels "A","B","H","I",..: 2 2 2 2 2 2 2 2 2 2 ...</code></pre>
</div>
<div class="section slide level1" id="always-check-your-data">
<h1>Always Check Your Data</h1>
<ul>
<li>A few great commands:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">dim</span>(df)</code></pre>
<pre><code>## [1] 2700 32</code></pre>
<ul>
<li><code>summary</code></li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(df[, <span class="dv">1</span>:<span class="dv">5</span>])</code></pre>
<pre><code>## X school stuid grade
## Min. : 44 Min. : 1 Min. : 205 Min. :3.00
## 1st Qu.: 108677 1st Qu.: 195 1st Qu.: 44205 1st Qu.:4.00
## Median : 458596 Median : 436 Median : 88205 Median :5.00
## Mean : 557918 Mean : 460 Mean : 99229 Mean :5.44
## 3rd Qu.: 972291 3rd Qu.: 717 3rd Qu.:132205 3rd Qu.:7.00
## Max. :1499992 Max. :1000 Max. :324953 Max. :8.00
## schid
## Min. : 6.0
## 1st Qu.: 15.0
## Median : 55.5
## Mean : 52.0
## 3rd Qu.: 75.0
## Max. :105.0</code></pre>
</div>
<div class="section slide level1" id="checking-your-data-ii">
<h1>Checking your data II</h1>
<ul>
<li><code>names</code></li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">names</span>(df)</code></pre>
<pre><code>## [1] "X" "school" "stuid" "grade" "schid"
## [6] "dist" "white" "black" "hisp" "indian"
## [11] "asian" "econ" "female" "ell" "disab"
## [16] "sch_fay" "dist_fay" "luck" "ability" "measerr"
## [21] "teachq" "year" "attday" "schoolscore" "district"
## [26] "schoolhigh" "schoolavg" "schoollow" "readSS" "mathSS"
## [31] "proflvl" "race"</code></pre>
<ul>
<li><code>attributes</code> and <code>class</code></li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">names</span>(<span class="kw">attributes</span>(df))</code></pre>
<pre><code>## [1] "names" "row.names" "class"</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">class</span>(df)</code></pre>
<pre><code>## [1] "data.frame"</code></pre>
<ul>
<li><code>str</code> which lists all data elements in an object and their type</li>
</ul>
</div>
<div class="section slide level1" id="data-warehouses-oracle-sql-and-rodbc">
<h1>Data Warehouses, Oracle, SQL and RODBC</h1>
<ul>
<li>Do you have data in a warehouse?</li>
<li>RODBC can help</li>
<li>You can query the data directly and bring it into R, saving time and hassle</li>
<li>Makes your work reproducible, always start with a clean slate of data</li>
<li>At DPI this can allow us to pull data directly from LDS or other databases using SQL queries</li>
</ul>
</div>
<div class="section slide level1" id="an-example-from-dpi">
<h1>An Example From DPI</h1>
<ul>
<li>The basics of the RODBC package are easy to understand</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(RODBC) <span class="co"># interface driver for R</span>
channel <- <span class="kw">odbcConnect</span>(<span class="st">"Mydatabase.location"</span>, <span class="dt">uid =</span> <span class="st">"useR"</span>, <span class="dt">pwd =</span> <span class="st">"secret"</span>)
<span class="co"># establish connection we can do multiple connections in the same R</span>
<span class="co"># session</span>
<span class="co"># </span>
<span class="co"># WARNING: credentials stored in plain text unless you do some magic</span>
table_list <- <span class="kw">sqltables</span>(channel, <span class="dt">schema =</span> <span class="st">"My_DB"</span>)
<span class="co"># Get a list of tables in the connection</span>
<span class="kw">colnames</span>(<span class="kw">sqlFetch</span>(channel, <span class="st">"My_DB.TABLE_NAME"</span>, <span class="dt">max =</span> <span class="dv">1</span>))
<span class="co"># get the column names of a table</span>
datapull <- <span class="kw">sqlQuery</span>(channel, <span class="st">"SELECT DATA1, DATA2, DATA3 FROM My_DB.TABLE_NAME"</span>)
<span class="co"># execute some SQLquery, can paste any SQLquery as a string into this</span>
<span class="co"># space</span></code></pre>
</div>
<div class="section slide level1" id="missing-data">
<h1>Missing Data</h1>
<ul>
<li>Let's add some missing data to our dataframe so we can see how missing data works</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">random <- <span class="kw">sample</span>(<span class="kw">unique</span>(df$stuid), <span class="dv">100</span>)
random2 <- <span class="kw">sample</span>(<span class="kw">unique</span>(df$stuid), <span class="dv">120</span>)
messdf <- df
messdf$readSS[messdf$stuid %in% random] <- <span class="ot">NA</span>
messdf$mathSS[messdf$stuid %in% random2] <- <span class="ot">NA</span></code></pre>
<ul>
<li>What is this code doing?</li>
<li>How can we find out?</li>
<li>Don't try this at home!</li>
</ul>
</div>
<div class="section slide level1" id="checking-for-missing-data">
<h1>Checking for Missing Data</h1>
<ul>
<li>The <code>summary</code> function helps identify missing data</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(messdf[, <span class="kw">c</span>(<span class="st">"stuid"</span>, <span class="st">"readSS"</span>, <span class="st">"mathSS"</span>)])</code></pre>
<pre><code>## stuid readSS mathSS
## Min. : 205 Min. :252 Min. :210
## 1st Qu.: 44205 1st Qu.:431 1st Qu.:418
## Median : 88205 Median :497 Median :480
## Mean : 99229 Mean :497 Mean :484
## 3rd Qu.:132205 3rd Qu.:564 3rd Qu.:543
## Max. :324953 Max. :833 Max. :828
## NA's :223 NA's :288</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">nrow</span>(messdf[!<span class="kw">complete.cases</span>(messdf), ]) <span class="co"># number of rows with missing data</span></code></pre>
<pre><code>## [1] 494</code></pre>
<ul>
<li>To get rid of missing data, we can copy our data with all missing cases dropped using the <code>na.omit</code> function</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">cleandf <- <span class="kw">na.omit</span>(messdf)
<span class="kw">nrow</span>(cleandf)</code></pre>
<pre><code>## [1] 2206</code></pre>
</div>
<div class="section slide level1" id="now-we-have-the-data">
<h1>Now we have the data</h1>
<ul>
<li>What next?</li>
<li>We need to do some basic diagnostics on our data to understand the look and feel of it before we proceed</li>
<li>Here are a few examples of scripts we could run to understand our data object</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">dim</span>(messdf)</code></pre>
<pre><code>## [1] 2700 32</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">str</span>(messdf[, <span class="dv">18</span>:<span class="dv">26</span>])</code></pre>
<pre><code>## 'data.frame': 2700 obs. of 9 variables:
## $ luck : int 0 1 0 1 0 0 1 0 0 0 ...
## $ ability : num 87.9 97.8 104.5 111.7 81.9 ...
## $ measerr : num 11.13 6.82 -7.86 -17.57 52.98 ...
## $ teachq : num 39.0902 0.0985 39.5389 24.1161 56.6806 ...
## $ year : int 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
## $ attday : int 180 180 160 168 156 157 169 180 170 152 ...
## $ schoolscore: num 29.2 56 56 56 56 ...
## $ district : int 3 3 3 3 3 3 3 3 3 3 ...
## $ schoolhigh : int 0 0 0 0 0 0 0 0 0 0 ...</code></pre>
</div>
<div class="section slide level1" id="looking-at-data-structure">
<h1>Looking at data structure</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">names</span>(messdf)</code></pre>
<pre><code>## [1] "X" "school" "stuid" "grade" "schid"
## [6] "dist" "white" "black" "hisp" "indian"
## [11] "asian" "econ" "female" "ell" "disab"
## [16] "sch_fay" "dist_fay" "luck" "ability" "measerr"
## [21] "teachq" "year" "attday" "schoolscore" "district"
## [26] "schoolhigh" "schoolavg" "schoollow" "readSS" "mathSS"
## [31] "proflvl" "race"</code></pre>
<ul>
<li>It looks like we have a number of <code>id</code> variables, this is useful and it is good to check if these variables have multiple rows per id or not and we do this using <code>length</code> and <code>unique</code></li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">length</span>(<span class="kw">unique</span>(messdf$stuid))</code></pre>
<pre><code>## [1] 1200</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">length</span>(<span class="kw">unique</span>(messdf$schid))</code></pre>
<pre><code>## [1] 6</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">length</span>(<span class="kw">unique</span>(messdf$dist))</code></pre>
<pre><code>## [1] 3</code></pre>
</div>
<div class="section slide level1" id="checking-for-coding">
<h1>Checking for Coding</h1>
<ul>
<li>Data is coded using numeric or character representations of attributes - commonly things are coded using a 1 and 0 scheme or an A,B,C scheme</li>
<li>With R we can check how our variables are coded very easily</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">unique</span>(messdf$grade)</code></pre>
<pre><code>## [1] 3 4 5 6 7 8</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">unique</span>(messdf$econ)</code></pre>
<pre><code>## [1] 0 1</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">unique</span>(messdf$race)</code></pre>
<pre><code>## [1] B H I W A
## Levels: A B H I W</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">unique</span>(messdf$disab)</code></pre>
<pre><code>## [1] 0 1</code></pre>
<ul>
<li>Which are factors?</li>
<li>Which are not?</li>
</ul>
</div>
<div class="section slide level1" id="next-steps">
<h1>Next Steps</h1>
<ul>
<li>In the next section we will learn to aggregate, explore, reshape, and recode data</li>
<li>Questions?</li>
</ul>
</div>
<div class="section slide level1" id="exercises">
<h1>Exercises</h1>
<ol style="list-style-type: decimal">
<li><p>Read in the CSV file from the T drive or the project folder</p></li>
<li><p>Read in the R data file from the T drive or the project folder</p></li>
<li><p>Read in the sample datafile. Find the <code>readSS</code> (reading scale score) for student 205 in grade 4.</p></li>
<li><p>Create a list of two attributes for each district in the <code>df</code> datafile.</p></li>
<li><p>Think about your own data warehouse environment. Could R interface with it? How?</p></li>
</ol>
</div>
<div class="section slide level1" id="other-references">
<h1>Other References</h1>
<ul>
<li><a href="http://www.ats.ucla.edu/stat/r/pages/raw_data.htm">UCLA Academic Technology Services: Reading in Raw Data</a></li>
<li><a href="http://www.statmethods.net/input/importingdata.html">Quick-R: Data Import</a></li>
<li><a href="http://www.twotorials.com/">Video Tutorials</a></li>
<li><a href="http://cran.r-project.org/doc/manuals/R-data.html">Long CRAN Manual on Data Import/Export</a></li>
</ul>
</div>
<div class="section slide level1" id="session-info">
<h1>Session Info</h1>
<p>It is good to include the session info, e.g. this document is produced with <strong>knitr</strong> version 0.9.6. Here is my session info:</p>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(<span class="kw">sessionInfo</span>(), <span class="dt">locale =</span> <span class="ot">FALSE</span>)</code></pre>
<pre><code>## R version 2.15.2 (2012-10-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] sandwich_2.2-9 quantreg_4.94 SparseM_0.96 gridExtra_0.9.1
## [5] mgcv_1.7-22 eeptools_0.1 mapproj_1.2-0 maps_2.3-0
## [9] proto_0.3-10 plyr_1.8 stringr_0.6.2 ggplot2_0.9.3
## [13] lmtest_0.9-30 zoo_1.7-9 knitr_0.9.6
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.2-0 dichromat_1.2-4 digest_0.6.0
## [4] evaluate_0.4.3 formatR_0.7 gtable_0.1.2
## [7] labeling_0.1 lattice_0.20-10 MASS_7.3-22
## [10] Matrix_1.0-10 munsell_0.4 nlme_3.1-106
## [13] RColorBrewer_1.0-5 reshape2_1.2.2 scales_0.2.3
## [16] tools_2.15.2</code></pre>
</div>
<div class="section slide level1" id="attribution-and-license">
<h1>Attribution and License</h1>
<p xmlns:dct="http://purl.org/dc/terms/">
<a rel="license" href="http://creativecommons.org/publicdomain/mark/1.0/"> <img src="data:image/png;base64,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" style="border-style: none;" alt="Public Domain Mark" /> </a> <br /> This work (<span property="dct:title">R Tutorial for Education</span>, by <a href="www.jaredknowles.com" rel="dct:creator"><span property="dct:title">Jared E. Knowles</span></a>), in service of the <a href="http://www.dpi.wi.gov" rel="dct:publisher"><span property="dct:title">Wisconsin Department of Public Instruction</span></a>, is free of known copyright restrictions.
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