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Tutorial3_DataSort.html
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</head>
<body>
<div class="slide titlepage">
<h1 class="title">Tutorial 3: Manipulating Data in R</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>
<p>In this lesson we hope to learn:</p>
<ul class="incremental">
<li>Aggregating data</li>
<li>Organizing our data</li>
<li>Manipulating vectors</li>
<li>Dealing with missing data</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="again-read-in-our-dataset">
<h1>Again, read in our dataset</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Set working directory to the tutorial directory In RStudio can do</span>
<span class="co"># this in 'Tools' tab</span>
<span class="kw">setwd</span>(<span class="st">"~/GitHub/r_tutorial_ed"</span>)
<span class="co"># Load some data</span>
<span class="kw">load</span>(<span class="st">"data/smalldata.rda"</span>)
<span class="co"># Note if we don't assign data to 'df' R just prints contents of</span>
<span class="co"># table</span></code></pre>
</div>
<div class="section slide level1" id="aggregation">
<h1>Aggregation</h1>
<ul class="incremental">
<li>Sometimes we need to do some basic checking for the number of observations or types of observations in our dataset</li>
<li>To do this quickly and easily - the <code>table</code> function is our friend</li>
<li>Let's look at our observations by year and grade</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">table</span>(df$grade, df$year)</code></pre>
<pre><code>
2000 2001 2002
3 200 100 200
4 100 200 100
5 200 100 200
6 100 200 100
7 200 100 200
8 100 200 100</code></pre>
<ul class="incremental">
<li>The first command gives the rows, the second gives the columns</li>
<li>Ugly, but effective</li>
</ul>
</div>
<div class="section slide level1" id="aggregation-can-be-more-complex">
<h1>Aggregation can be more complex</h1>
<ul class="incremental">
<li>Let's aggregate by race and year</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">table</span>(df$year, df$race)</code></pre>
<pre><code>
A B H I W
2000 16 370 93 7 414
2001 16 370 93 7 414
2002 16 370 93 7 414</code></pre>
<ul class="incremental">
<li>Race is consistent across years, interesting</li>
<li>What if we want to only look at 3rd graders that year?</li>
</ul>
</div>
<div class="section slide level1" id="more-complicated-still">
<h1>More complicated still</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">with</span>(df[df$grade == <span class="dv">3</span>, ], {
<span class="kw">table</span>(year, race)
})</code></pre>
<pre><code> race
year A B H I W
2000 4 78 22 4 92
2001 1 44 8 2 45
2002 0 74 20 1 105</code></pre>
<ul class="incremental">
<li><code>with</code> specifies a data object to work on, in this case all elements of <code>df</code> where <code>grade==3</code></li>
<li><code>table</code> is the same command as above, but since we specified the data object in the <code>with</code> statement, we don't need the <code>df$</code> in front of the variables of interest</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">df2 <- <span class="kw">subset</span>(df, grade == <span class="dv">3</span>)
<span class="kw">table</span>(df2$year, df2$race)</code></pre>
<pre><code>
A B H I W
2000 4 78 22 4 92
2001 1 44 8 2 45
2002 0 74 20 1 105</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">rm</span>(df2)</code></pre>
</div>
<div class="section slide level1" id="quick-exercise">
<h1>Quick exercise</h1>
<ul class="incremental">
<li>Can you find the number of black students in each grade in each year?</li>
<li>hint: <strong><code>with(df[df$___==___,]...)</code></strong></li>
<li>How many in year 2002, grade 6?</li>
<li>48</li>
<li>How many in 2001, grade 7?</li>
<li>39</li>
</ul>
</div>
<div class="section slide level1" id="answer">
<h1>Answer</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">with</span>(df[df$race==<span class="st">"B"</span>,],{<span class="kw">table</span>(year,grade)})</code></pre>
<pre><code> grade
year 3 4 5 6 7 8
2000 78 48 87 39 74 44
2001 44 78 48 87 39 74
2002 74 44 78 48 87 39</code></pre>
<ul class="incremental">
<li>Quick question, how can we understand the three types of closures we have in this function: <strong>()</strong> <strong>[]</strong> and <strong>{}</strong></li>
</ul>
</div>
<div class="section slide level1" id="tables-cont.">
<h1>Tables cont.</h1>
<ul class="incremental">
<li>This is really powerful for looking at the descriptive dimensions of the data, we can ask questions like:</li>
<li>How many students are at each proficiency level each year?</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">table</span>(df$year, df$proflvl)</code></pre>
<pre><code>
advanced basic below basic proficient
2000 56 313 143 388
2001 229 183 64 424
2002 503 27 3 367</code></pre>
<ul class="incremental">
<li>How many students are at each proficiency level by race?</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">table</span>(df$race, df$proflvl)</code></pre>
<pre><code>
advanced basic below basic proficient
A 19 7 3 19
B 160 302 162 486
H 54 76 33 116
I 7 4 1 9
W 548 134 11 549</code></pre>
</div>
<div class="section slide level1" id="proportional-tables">
<h1>Proportional Tables</h1>
<ul class="incremental">
<li>What if we aren't interested in counts?</li>
<li>R makes it really easy to calculate proportions</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">prop.table</span>(<span class="kw">table</span>(df$race, df$proflvl))</code></pre>
<pre><code>
advanced basic below basic proficient
A 0.0070370 0.0025926 0.0011111 0.0070370
B 0.0592593 0.1118519 0.0600000 0.1800000
H 0.0200000 0.0281481 0.0122222 0.0429630
I 0.0025926 0.0014815 0.0003704 0.0033333
W 0.2029630 0.0496296 0.0040741 0.2033333</code></pre>
<ul class="incremental">
<li>Hmmm, this is goofy. This tells us the proportion of each cell out of the total. Also, the digits are distracting. How can we fix this?</li>
</ul>
</div>
<div class="section slide level1" id="try-number-2">
<h1>Try number 2</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">round</span>(<span class="kw">prop.table</span>(<span class="kw">table</span>(df$race, df$proflvl), <span class="dv">1</span>), <span class="dt">digits =</span> <span class="dv">3</span>)</code></pre>
<pre><code>
advanced basic below basic proficient
A 0.396 0.146 0.062 0.396
B 0.144 0.272 0.146 0.438
H 0.194 0.272 0.118 0.416
I 0.333 0.190 0.048 0.429
W 0.441 0.108 0.009 0.442</code></pre>
<ul class="incremental">
<li>The <code>1</code> tells R we want proportions rowise, a <code>2</code> goes columnwise</li>
<li><code>round</code> tells R to cut off some digits for us</li>
<li>Proportions are just that, not in percentage terms (we need to multiply by 100 for this)</li>
<li>Can you make this table express percentages instead of proportions? How might that code look?</li>
<li>A few more problems arise - this pools all observations, including students across years</li>
<li>To avoid these, we need to aggregate the data somehow</li>
</ul>
</div>
<div class="section slide level1" id="checking-understanding">
<h1>Checking Understanding</h1>
<ul class="incremental">
<li>We have seen how to chain functions together</li>
<li>We have also seen how to examine a dataframe by looking at the observations in it</li>
<li>We are now going to move on to aggregating data so we can look at unique cases when we have more than one observation for each unit</li>
</ul>
</div>
<div class="section slide level1" id="aggregating-data">
<h1>Aggregating Data</h1>
<ul class="incremental">
<li>One of the most common questions you need to answer is to compute aggregates of data</li>
<li>R has an <code>aggregate</code> function that can be used and helps us avoid the clustering problems above</li>
<li>This works great for simple aggregation like scale score by race, we just need a <code>formula</code> (think I want variable X <strong>by</strong> grouping factor Y) and the statistic we want to compute</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Reading Scores by Race</span>
<span class="kw">aggregate</span>(readSS ~ race, <span class="dt">FUN =</span> mean, <span class="dt">data =</span> df)</code></pre>
<pre><code> race readSS
1 A 508.7
2 B 460.2
3 H 473.2
4 I 485.2
5 W 533.2</code></pre>
</div>
<div class="section slide level1" id="aggregate-ii">
<h1>Aggregate (II)</h1>
<ul class="incremental">
<li><code>aggregate</code> can take us a little further, we can use aggregate multiple variables at a time</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">aggregate</span>(<span class="kw">cbind</span>(readSS, mathSS) ~ race, <span class="dt">data =</span> df, mean)</code></pre>
<pre><code> race readSS mathSS
1 A 508.7 477.9
2 B 460.2 442.5
3 H 473.2 442.7
4 I 485.2 455.9
5 W 533.2 529.8</code></pre>
<ul class="incremental">
<li>We can add multiple grouping varialbes using the <code>formula</code> syntax</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(<span class="kw">aggregate</span>(<span class="kw">cbind</span>(readSS, mathSS) ~ race + grade, <span class="dt">data =</span> df, mean),
<span class="dv">8</span>)</code></pre>
<pre><code> race grade readSS mathSS
1 A 3 397.8 454.8
2 B 3 409.8 371.6
3 H 3 417.7 364.2
4 I 3 407.6 449.3
5 W 3 481.1 450.7
6 A 4 456.0 438.2
7 B 4 426.9 408.1
8 H 4 418.8 404.6</code></pre>
</div>
<div class="section slide level1" id="crosstabs">
<h1>Crosstabs</h1>
<ul class="incremental">
<li>We can build a systematic cross-tab now</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">ag <- <span class="kw">aggregate</span>(readSS ~ race + grade, <span class="dt">data =</span> df, mean)
<span class="kw">xtabs</span>(readSS ~ ., <span class="dt">data =</span> ag)</code></pre>
<pre><code> grade
race 3 4 5 6 7 8
A 397.8 456.0 479.1 539.5 600.4 605.3
B 409.8 426.9 447.6 470.9 492.3 523.5
H 417.7 418.8 481.2 489.1 500.3 534.2
I 407.6 531.1 547.6 0.0 405.5 518.0
W 481.1 498.5 517.1 546.6 565.2 596.1</code></pre>
<ul class="incremental">
<li>And prettier output</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ftable</span>(<span class="kw">xtabs</span>(readSS ~ ., <span class="dt">data =</span> ag))</code></pre>
<pre><code> grade 3 4 5 6 7 8
race
A 397.8 456.0 479.1 539.5 600.4 605.3
B 409.8 426.9 447.6 470.9 492.3 523.5
H 417.7 418.8 481.2 489.1 500.3 534.2
I 407.6 531.1 547.6 0.0 405.5 518.0
W 481.1 498.5 517.1 546.6 565.2 596.1</code></pre>
</div>
<div class="section slide level1" id="check-your-work">
<h1>Check your work</h1>
<ul class="incremental">
<li><p>What is the mean reading score for 6th grade students with disabilities?</p></li>
<li><p><strong>481.83</strong></p></li>
<li><p>How many points is this from non-disabled students?</p></li>
<li><p><strong>29.877</strong></p></li>
</ul>
</div>
<div class="section slide level1" id="answer-ii">
<h1>Answer II</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">aggregate</span>(<span class="kw">cbind</span>(readSS, mathSS) ~ disab + grade, <span class="dt">data =</span> df, mean)</code></pre>
<pre><code> disab grade readSS mathSS
1 0 3 449.9 418.3
2 1 3 421.1 376.3
3 0 4 464.0 454.2
4 1 4 438.2 425.1
5 0 5 484.9 470.2
6 1 5 475.1 431.0
7 0 6 511.7 507.9
8 1 6 481.8 476.9
9 0 7 532.0 532.0
10 1 7 516.1 474.3
11 0 8 567.6 567.7
12 1 8 518.8 534.1</code></pre>
</div>
<div class="section slide level1" id="school-means">
<h1>School Means</h1>
<ul class="incremental">
<li>Consider the case we want to turn our student level data into school level data</li>
<li>Who hasn't had to do this?!?</li>
<li>In <code>aggregate</code> we do:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">z <- <span class="kw">aggregate</span>(readSS ~ dist, <span class="dt">FUN =</span> mean, <span class="dt">data =</span> df)
z</code></pre>
<pre><code> dist readSS
1 205 496.5
2 402 500.5
3 495 491.6</code></pre>
<ul class="incremental">
<li>But I want more! I want to aggregate multiple variables. I want to do it across multiple groups. I want the output to be a dataframe I can work on.</li>
<li>Thank you <code>plyr</code></li>
</ul>
</div>
<div class="section slide level1" id="aggregate-isnt-enough">
<h1>Aggregate Isn't Enough</h1>
<ul class="incremental">
<li><code>aggregate</code> is cool, but it isn't very flexible</li>
<li>We can only use aggregate output as a table, which we have to convert to a data frame</li>
<li>There is a better way; the <code>plyr</code> package</li>
<li><code>plyr</code> is a set of routines/logical structure for transforming, summarizing, reshaping, and reorganizing data objects of one type in R into another type (or the same type)</li>
<li>We will focus here on summarizing and aggregating a data frame, but later in the bootcamp we'll apply functions to lists and turn lists into data frames as well</li>
<li>This is cool!</li>
</ul>
</div>
<div class="section slide level1" id="the-logic-of-plyr">
<h1>The Logic of plyr</h1>
<ul class="incremental">
<li>In R this is known as "split, apply, and combine"</li>
<li>Why? First, we <strong>split</strong> the data into groups by some factor or logical operator</li>
<li>Then we <strong>apply</strong> some function or another to that group (i.e. count the unique values of a variable, take the mean of a variable, etc.)</li>
<li>Then we <strong>combine</strong> the data back together</li>
<li>This has some advantages - unlike other methods, the data does not have to be ordered by our ID variable for this to work</li>
<li>The disadvantage is that this method is computationally expensive, even in R, and requires copying our data frame using up RAM</li>
</ul>
</div>
<div class="section slide level1" id="an-aside-about-split-apply-combine">
<h1>An Aside about Split-Apply-Combine</h1>
<ul class="incremental">
<li>The <code>plyr</code> package has a number of utilities to help us split-apply-combine across data types for both input and output</li>
<li>In R we can't just use <code>for</code> loops to iterate over groups of students, because in R <code>for</code> loops are <a href="http://stackoverflow.com/questions/7142767/why-are-loops-slow-in-r">slow, inefficient, and impractical</a></li>
<li><code>plyr</code> to the rescue, while not as fast as a compiled language, it is pretty dang good!</li>
<li>And still readable</li>
</ul>
<p align="center">
<img src="data:image/png;base64,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" height="200" width="650">
</p>
</div>
<div class="section slide level1" id="the-logic-of-plyr-1">
<h1>The logic of plyr</h1>
<ul class="incremental">
<li>This shows how the dataframe is broken up into pieces and each piece then gets whatever functions, summaries, or transformations we apply to it</li>
</ul>
<p align="center">
<img 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" height="300" width="650">
</p>
</div>
<div class="section slide level1" id="how-plyr-works-on-dataframes">
<h1>How plyr works on dataframes</h1>
<ul class="incremental">
<li>And this shows the output <code>ddply</code> has before it combines it back for us when we do the call <code>ddply(df,.(sex,age),"nrow")</code></li>
</ul>
<p align="center">
<img 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" height="260" width="650">
</p>
</div>
<div class="section slide level1" id="using-plyr">
<h1>Using plyr</h1>
<ul class="incremental">
<li><code>plyr</code> has a straightforward syntax</li>
<li>All <code>plyr</code> functions are in the format <strong>XX</strong>ply. The two X's specify what the input file we are applying a function to is, and then what way we would like it outputted.</li>
<li>In <code>plyr</code> d = dataframe, l= list, m=matrix, and a=array. By far the most common usage is <code>ddply</code></li>
<li>From a dataframe, to a dataframe.</li>
<li>We will see more of <code>plyr</code> in Tutorial 4 as well</li>
</ul>
</div>
<div class="section slide level1" id="plyr-in-action">
<h1>plyr in Action</h1>
<pre class="sourceCode r"><code class="sourceCode r"> <span class="kw">library</span>(plyr)
myag<-<span class="kw">ddply</span>(df, .(dist,grade),summarize,
<span class="dt">mean_read=</span><span class="kw">mean</span>(readSS,<span class="dt">na.rm=</span>T),
<span class="dt">mean_math=</span><span class="kw">mean</span>(mathSS,<span class="dt">na.rm=</span>T),
<span class="dt">sd_read=</span><span class="kw">sd</span>(readSS,<span class="dt">na.rm=</span>T),
<span class="dt">sd_math=</span><span class="kw">sd</span>(mathSS,<span class="dt">na.rm=</span>T),
<span class="dt">count_read=</span><span class="kw">length</span>(readSS),
<span class="dt">count_math=</span><span class="kw">length</span>(mathSS))</code></pre>
<ul class="incremental">
<li>This looks complex, but it only has a few components.</li>
<li>The first argument is the dataframe we are working on, the next argument is the level of identification we want to aggregate to</li>
<li><code>summarize</code> tells <code>ddply</code> what we are doing to the data frame</li>
<li>Then we make a list of new variable names, and how to calculate them on each of the subsets in our large data frame</li>
<li>That's it!</li>
</ul>
</div>
<div class="section slide level1" id="results">
<h1>Results</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(myag)</code></pre>
<pre><code> dist grade mean_read mean_math sd_read sd_math count_read
1 205 3 451.7 406.1 93.52 72.45 200
2 205 4 438.9 459.9 77.76 79.10 100
3 205 5 487.9 462.6 85.30 75.10 200
4 205 6 514.7 526.8 76.83 66.04 100
5 205 7 530.0 521.5 84.82 74.85 200
6 205 8 575.5 581.2 79.58 83.45 100
count_math
1 200
2 100
3 200
4 100
5 200
6 100</code></pre>
</div>
<div class="section slide level1" id="more-plyr">
<h1>More plyr</h1>
<ul class="incremental">
<li>This is great, we can quickly build a summary dataset from individual records</li>
<li>A few advanced tricks. How do we build counts and percentages into our dataset?</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">myag<-<span class="kw">ddply</span>(df, .(dist,grade),summarize,
<span class="dt">mean_read=</span><span class="kw">mean</span>(readSS,<span class="dt">na.rm=</span>T),
<span class="dt">mean_math=</span><span class="kw">mean</span>(mathSS,<span class="dt">na.rm=</span>T),
<span class="dt">sd_read=</span><span class="kw">sd</span>(readSS,<span class="dt">na.rm=</span>T),
<span class="dt">sd_math=</span><span class="kw">sd</span>(mathSS,<span class="dt">na.rm=</span>T),
<span class="dt">count_read=</span><span class="kw">length</span>(readSS),
<span class="dt">count_math=</span><span class="kw">length</span>(mathSS),
<span class="dt">count_black=</span><span class="kw">length</span>(race[race==<span class="st">'B'</span>]),
<span class="dt">per_black=</span><span class="kw">length</span>(race[race==<span class="st">'B'</span>])/<span class="kw">length</span>(readSS))
<span class="kw">summary</span>(myag[,<span class="dv">7</span>:<span class="dv">10</span>]) </code></pre>
<pre><code> count_read count_math count_black per_black
Min. :100 Min. :100 Min. :36.0 Min. :0.360
1st Qu.:100 1st Qu.:100 1st Qu.:41.2 1st Qu.:0.386
Median :150 Median :150 Median :60.5 Median :0.412
Mean :150 Mean :150 Mean :61.7 Mean :0.411
3rd Qu.:200 3rd Qu.:200 3rd Qu.:82.0 3rd Qu.:0.439
Max. :200 Max. :200 Max. :92.0 Max. :0.480 </code></pre>
</div>
<div class="section slide level1" id="note-for-sql-junkies">
<h1>Note for SQL Junkies</h1>
<ul class="incremental">
<li>There is an alternate package to plyr called <code>data.table</code> which is really handy</li>
<li>It allows SQL like querying of R data frames</li>
<li>It is incredibly fast</li>
<li>It will be incorporated into the next <code>plyr</code> version</li>
<li>You can <a href="http://datatable.r-forge.r-project.org/">read up on it online</a></li>
</ul>
</div>
<div class="section slide level1" id="quick-exercises-in-ddply">
<h1>Quick Exercises in ddply</h1>
<ul class="incremental">
<li>What if we want to compare how districts do on educating ELL students?</li>
<li><p>What district ID has the highest mean score for 4th grade ELL students on reading? Math?</p></li>
<li>66 in reading, 105 in math</li>
<li><p>How many students are in these classes?</p></li>
<li><p>12 and 7 respectively</p></li>
</ul>
</div>
<div class="section slide level1" id="answer-iii">
<h1>Answer III</h1>
<pre class="sourceCode r"><code class="sourceCode r">myag2<-<span class="kw">ddply</span>(df, .(dist,grade,ell),summarize,
<span class="dt">mean_read=</span><span class="kw">mean</span>(readSS,<span class="dt">na.rm=</span>T),
<span class="dt">mean_math=</span><span class="kw">mean</span>(mathSS,<span class="dt">na.rm=</span>T),
<span class="dt">sd_read=</span><span class="kw">sd</span>(readSS,<span class="dt">na.rm=</span>T),
<span class="dt">sd_math=</span><span class="kw">sd</span>(mathSS,<span class="dt">na.rm=</span>T),
<span class="dt">count_read=</span><span class="kw">length</span>(readSS),
<span class="dt">count_math=</span><span class="kw">length</span>(mathSS),
<span class="dt">count_black=</span><span class="kw">length</span>(race[race==<span class="st">'B'</span>]),
<span class="dt">per_black=</span><span class="kw">length</span>(race[race==<span class="st">'B'</span>])/<span class="kw">length</span>(readSS))
<span class="kw">subset</span>(myag2,ell==<span class="dv">1</span>&grade==<span class="dv">4</span>) </code></pre>
<pre><code> dist grade ell mean_read mean_math sd_read sd_math count_read
4 205 4 1 403.0 392.9 64.52 39.09 16
16 402 4 1 443.1 388.7 79.52 53.28 29
28 495 4 1 408.8 431.9 77.47 70.77 13
count_math count_black per_black
4 16 2 0.12500
16 29 6 0.20690
28 13 1 0.07692</code></pre>
</div>
<div class="section slide level1" id="sorting">
<h1>Sorting</h1>
<ul class="incremental">
<li>A key way to explore data in tabular form is to sort data</li>
<li>Sorting data in R can be dangerous as you can reorder the vectors of a dataframe</li>
<li>We use the <code>order</code> function to sort data</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">df.badsort <- <span class="kw">order</span>(df$readSS, df$mathSS)
<span class="kw">head</span>(df.badsort)</code></pre>
<pre><code>[1] 106 1026 2 56 122 118</code></pre>
<ul class="incremental">
<li>Why is this wrong? What is R giving us?</li>
<li>Rownames...</li>
</ul>
</div>
<div class="section slide level1" id="correct-example">
<h1>Correct Example</h1>
<ul class="incremental">
<li>To fix it, we need to tell R to reorder the dataframe using the rownames in the order we want</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">df.sort <- df[<span class="kw">order</span>(df$readSS, df$mathSS, df$attday), ]
<span class="kw">head</span>(df[, <span class="kw">c</span>(<span class="dv">3</span>, <span class="dv">23</span>, <span class="dv">29</span>, <span class="dv">30</span>)])</code></pre>
<pre><code> stuid attday readSS mathSS
1 149995 180 357.3 387.3
2 13495 180 263.9 302.6
3 106495 160 369.7 365.5
4 45205 168 346.6 344.5
5 142705 156 373.1 441.2
6 14995 157 436.8 463.4</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(df.sort[, <span class="kw">c</span>(<span class="dv">3</span>, <span class="dv">23</span>, <span class="dv">29</span>, <span class="dv">30</span>)])</code></pre>
<pre><code> stuid attday readSS mathSS
106 106705 160 251.5 277.0
1026 80995 176 263.2 377.8
2 13495 180 263.9 302.6
56 122402 180 264.3 271.7
122 79705 168 266.4 318.7
118 40495 173 266.9 275.0</code></pre>
</div>
<div class="section slide level1" id="lets-clean-it-up-a-bit-more">
<h1>Let's clean it up a bit more</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(df[<span class="kw">with</span>(df, <span class="kw">order</span>(-readSS, -attday)), <span class="kw">c</span>(<span class="dv">3</span>, <span class="dv">23</span>, <span class="dv">29</span>, <span class="dv">30</span>)])</code></pre>
<pre><code> stuid attday readSS mathSS
1631 145205 137 833.2 828.4
1462 107705 180 773.3 746.6
2252 122902 180 744.0 621.6
2341 44902 175 741.7 676.3
1482 134705 180 739.2 705.4
1630 14495 162 738.9 758.2</code></pre>
<ul class="incremental">
<li>Here we find the high performing students, note that the <code>-</code> denotes we want descending order, R's default is ascending order</li>
<li>This is easy to correct</li>
</ul>
</div>
<div class="section slide level1" id="about-sorting">
<h1>About sorting</h1>
<ul class="incremental">
<li>Sorting works differently on some data types like matrices</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">M <- <span class="kw">matrix</span>(<span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">6</span>, <span class="dv">4</span>, <span class="dv">5</span>), <span class="dv">4</span>, <span class="dv">2</span>, <span class="dt">byrow =</span> <span class="ot">FALSE</span>, <span class="dt">dimnames =</span> <span class="kw">list</span>(<span class="ot">NULL</span>,
<span class="kw">c</span>(<span class="st">"a"</span>, <span class="st">"b"</span>)))
M[<span class="kw">order</span>(M[, <span class="st">"a"</span>], -M[, <span class="st">"b"</span>]), ]</code></pre>
<pre><code> a b
[1,] 1 3
[2,] 2 6
[3,] 2 5
[4,] 2 4</code></pre>
</div>
<div class="section slide level1" id="about-sorting-1">
<h1>About Sorting</h1>
<ul class="incremental">
<li>Tables are familiar</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">mytab <- <span class="kw">table</span>(df$grade, df$year)
mytab[<span class="kw">order</span>(mytab[, <span class="dv">1</span>]), ]</code></pre>
<pre><code>
2000 2001 2002
4 100 200 100
6 100 200 100
8 100 200 100
3 200 100 200
5 200 100 200
7 200 100 200</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">mytab[<span class="kw">order</span>(mytab[, <span class="dv">2</span>]), ]</code></pre>
<pre><code>
2000 2001 2002
3 200 100 200
5 200 100 200
7 200 100 200
4 100 200 100
6 100 200 100
8 100 200 100</code></pre>
</div>
<div class="section slide level1" id="filtering-data">
<h1>Filtering Data</h1>
<ul class="incremental">
<li>Filtering data is an incredibly powerful feature and we have already seen it used to do some interesting things</li>
<li>Filtering data in R is loaded with trouble though, because the filtering arguments must be very carefully specified</li>
<li>Filtering is like a mini-sort, and we've done it already</li>
<li>Always, always, always check your work</li>
<li>And remember, this is the place the NAs do the most damage</li>
<li>Let's look at some examples</li>
</ul>
</div>
<div class="section slide level1" id="basic-filtering-a-column">
<h1>Basic Filtering a Column</h1>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Gives all rows that meet this requirement</span>
df[df$readSS > <span class="dv">800</span>, ]</code></pre>
<pre><code> X school stuid grade schid dist white black hisp indian
1631 1281061 852 145205 8 15 205 1 0 0 0
asian econ female ell disab sch_fay dist_fay luck ability
1631 0 0 1 0 0 0 0 0 108.3
measerr teachq year attday schoolscore district schoolhigh
1631 6.325 155.7 2001 137 227.7 19 0
schoolavg schoollow readSS mathSS proflvl race
1631 1 0 833.2 828.4 advanced W</code></pre>
<pre class="sourceCode r"><code class="sourceCode r">df$grade[df$mathSS > <span class="dv">800</span>]</code></pre>
<pre><code>[1] 8</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Gives all values of grade that meet this requirement</span></code></pre>
<ul class="incremental">
<li>Before the brackets we specify what we want returned, and within the brackets we present the logical expression to evaluate</li>
<li>Behind the scenes R does a logical test and gets the row numbers that match the logical expression</li>
<li>It then combines them back with the object in front of the brackets to return the values</li>
<li>This seems basic enough, let's filter on multiple dimensions</li>
</ul>
</div>
<div class="section slide level1" id="multiple-filters">
<h1>Multiple filters</h1>
<pre class="sourceCode r"><code class="sourceCode r">df$grade[df$black == <span class="dv">1</span> & df$readSS > <span class="dv">650</span>]</code></pre>
<pre><code> [1] 8 7 8 6 6 7 8 7 8 8 8 4</code></pre>
<ul class="incremental">
<li>The <strong>&</strong> operator tells R we want rows where <strong>both</strong> of these are true</li>
<li>How would we tell R we wanted rows where <strong>either</strong> were true?</li>
<li>What happens if we type <code>df$black=1</code> or <code>black==1</code>?</li>
<li>Why won't this work?</li>
</ul>
</div>
<div class="section slide level1" id="using-filters-to-assign-values">
<h1>Using filters to assign values</h1>
<ul class="incremental">
<li>We can also use filters to assign values as well</li>
<li>This is how you recode variables and create new ones</li>
<li>Let's create a variable <code>spread</code> indicating whether a district has high or low spread among its student scores</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">myag$spread <- <span class="ot">NA</span> <span class="co"># create variable</span>
myag$spread[myag$sd_read < <span class="dv">75</span>] <- <span class="st">"low"</span>
myag$spread[myag$sd_read > <span class="dv">75</span>] <- <span class="st">"high"</span>
myag$spread <- <span class="kw">as.factor</span>(myag$spread)
<span class="kw">summary</span>(myag$spread)</code></pre>
<pre><code>high low
15 3 </code></pre>
<ul class="incremental">
<li>How did we define <strong>spread</strong> in this block of code?</li>
</ul>
</div>
<div class="section slide level1" id="how-does-it-work">
<h1>How does it work?</h1>
<ul class="incremental">
<li>The previous block of code is a useful way to learn how to recode variables</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">myag$spread <- <span class="ot">NA</span> <span class="co"># create variable</span>
myag$spread[myag$sd_read < <span class="dv">75</span>] <- <span class="st">"low"</span>
myag$spread[myag$sd_read > <span class="dv">75</span>] <- <span class="st">"high"</span>
myag$spread <- <span class="kw">as.factor</span>(myag$spread)</code></pre>
<ul class="incremental">
<li>Create a new variable in <code>myag</code> called <code>schoolperf</code> for <code>mean_math</code> scores with the following coding scheme:</li>
</ul>
<table>
<thead>
<tr class="header">
<th align="left">Grade</th>
<th align="left">Score Range</th>
<th align="right">Code</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">3</td>
<td align="left">>425</td>
<td align="right">"Hi"</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">>450</td>
<td align="right">"Hi"</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">>475</td>
<td align="right">"Hi"</td>
</tr>
<tr class="even">
<td align="left">6</td>
<td align="left">>500</td>
<td align="right">"Hi"</td>
</tr>
<tr class="odd">
<td align="left">7</td>
<td align="left">>525</td>
<td align="right">"Hi"</td>
</tr>
<tr class="even">
<td align="left">8</td>
<td align="left">>575</td>
<td align="right">"Hi"</td>
</tr>
</tbody>
</table>
<ul class="incremental">
<li>All other values are coded as "lo"</li>
<li>How many "high" and "lo" observations do we have?</li>
<li>By <code>dist</code>?</li>
</ul>
</div>
<div class="section slide level1" id="results-1">
<h1>Results</h1>
<pre class="sourceCode r"><code class="sourceCode r">myag$schoolperf <- <span class="st">"lo"</span>
myag$schoolperf[myag$grade == <span class="dv">3</span> & myag$mean_math > <span class="dv">425</span>] <- <span class="st">"hi"</span>
myag$schoolperf[myag$grade == <span class="dv">4</span> & myag$mean_math > <span class="dv">450</span>] <- <span class="st">"hi"</span>
myag$schoolperf[myag$grade == <span class="dv">5</span> & myag$mean_math > <span class="dv">475</span>] <- <span class="st">"hi"</span>
myag$schoolperf[myag$grade == <span class="dv">6</span> & myag$mean_math > <span class="dv">500</span>] <- <span class="st">"hi"</span>
myag$schoolperf[myag$grade == <span class="dv">7</span> & myag$mean_math > <span class="dv">525</span>] <- <span class="st">"hi"</span>
myag$schoolperf[myag$grade == <span class="dv">8</span> & myag$mean_math > <span class="dv">575</span>] <- <span class="st">"hi"</span>
myag$schoolperf <- <span class="kw">as.factor</span>(myag$schoolperf)
<span class="kw">summary</span>(myag$schoolperf)</code></pre>
<pre><code>hi lo
9 9 </code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">table</span>(myag$dist, myag$schoolperf)</code></pre>
<pre><code>
hi lo
205 3 3
402 3 3
495 3 3</code></pre>
</div>
<div class="section slide level1" id="lets-replace-data">
<h1>Let's replace data</h1>
<ul class="incremental">
<li>For district 6 let's negate the grade 3 scores by replacing them with missing data</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">myag$mean_read[myag$dist == <span class="dv">6</span> & myag$grade == <span class="dv">3</span>] <- <span class="ot">NA</span>
<span class="kw">head</span>(myag[, <span class="dv">1</span>:<span class="dv">4</span>], <span class="dv">2</span>)</code></pre>
<pre><code> dist grade mean_read mean_math
1 205 3 451.7 406.1
2 205 4 438.9 459.9</code></pre>
<ul class="incremental">
<li>Let's replace one data element with another</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">myag$mean_read[myag$dist == <span class="dv">6</span> & myag$grade == <span class="dv">3</span>] <- myag$mean_read[myag$dist ==
<span class="dv">6</span> & myag$grade == <span class="dv">4</span>]
<span class="kw">head</span>(myag[, <span class="dv">1</span>:<span class="dv">4</span>], <span class="dv">2</span>)</code></pre>
<pre><code> dist grade mean_read mean_math
1 205 3 451.7 406.1
2 205 4 438.9 459.9</code></pre>
<ul class="incremental">
<li>Voila</li>
</ul>
</div>
<div class="section slide level1" id="why-do-nas-matter-so-much">
<h1>Why do NAs matter so much?</h1>
<ul class="incremental">
<li>Let's consider the case above but insert some NA values for all 3rd grade tests</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">myag$mean_read[myag$grade == <span class="dv">3</span>] <- <span class="ot">NA</span>
<span class="kw">head</span>(myag[<span class="kw">order</span>(myag$grade), <span class="dv">1</span>:<span class="dv">4</span>])</code></pre>
<pre><code> dist grade mean_read mean_math
1 205 3 NA 406.1
7 402 3 NA 431.9
13 495 3 NA 405.5
2 205 4 438.9 459.9
8 402 4 474.9 432.8
14 495 4 447.8 469.1</code></pre>
</div>
<div class="section slide level1" id="nas-ii">
<h1>NAs II</h1>
<ul class="incremental">
<li>Now let's calculate a few statistics:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mean</span>(myag$mean_math)</code></pre>
<pre><code>[1] 490.7</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mean</span>(myag$mean_read)</code></pre>
<pre><code>[1] NA</code></pre>
<ul class="incremental">
<li>Remember, NA values propogate, so R assumes an NA value could take literally any value, and as such it is impossible to know the <code>mean</code> of a vector with NA</li>
<li>We can override this though:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mean</span>(myag$mean_math, <span class="dt">na.rm =</span> T)</code></pre>
<pre><code>[1] 490.7</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">mean</span>(myag$mean_read, <span class="dt">na.rm =</span> T)</code></pre>
<pre><code>[1] 507.5</code></pre>
</div>
<div class="section slide level1" id="beyond-the-mean">
<h1>Beyond the Mean</h1>
<ul class="incremental">
<li>But for other problems it is tricky</li>
<li>What if we want to know the number of rows that have a <code>mean_read</code> of less than 500?</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">length</span>(myag$dist[myag$mean_read < <span class="dv">500</span>])</code></pre>
<pre><code>[1] 10</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(myag$mean_read[myag$mean_read < <span class="dv">500</span>])</code></pre>
<pre><code>[1] NA 438.9 487.9 NA 474.9 472.5</code></pre>
<ul class="incremental">
<li>And what if we want to add the standard deviation to these vectors?</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">badvar <- myag$mean_read + myag$sd_read
<span class="kw">summary</span>(badvar)</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
517 566 592 587 614 655 3 </code></pre>
</div>
<div class="section slide level1" id="so-we-need-to-filter-nas-explicitly">
<h1>So we need to filter NAs explicitly</h1>
<ul class="incremental">
<li>Consider the case where two sets of variables have different missing elements</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">myag$sd_read[myag$count_read < <span class="dv">100</span> & myag$mean_read < <span class="dv">550</span>] <- <span class="ot">NA</span>
<span class="kw">length</span>(myag$mean_read[myag$mean_read < <span class="dv">550</span>])</code></pre>
<pre><code>[1] 16</code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">length</span>(myag$mean_read[myag$mean_read < <span class="dv">550</span> & !<span class="kw">is.na</span>(myag$mean_read)])</code></pre>
<pre><code>[1] 13</code></pre>
<ul class="incremental">
<li>What is <code>!is.na()</code> ?</li>
<li><code>is.na()</code> is a helpful function to identify TRUE if a value is missing</li>
<li><code>!</code> is the reverse operator</li>
<li>We are asking R if this value is not a missing value, and to only give us non-missing values back</li>
</ul>
</div>
<div class="section slide level1" id="merging-data">
<h1>Merging Data</h1>
<ul class="incremental">
<li>It is unlikely all the data we will want resides in a single dataset and often we have to combine data from several sources</li>
<li>R makes this easy, but that simplicity comes at a cost - it can be easy to make mistakes if you don't specify things carefully</li>
<li>Let's merge attributes about a student's school with the student row data</li>
<li>We might want to do that if we want to evaluate the performance of students in different school climates, and school climate was measured in part by the mean performance</li>
</ul>
</div>
<div class="section slide level1" id="merging-data-ii">
<h1>Merging Data II</h1>
<ul class="incremental">
<li>We have two data objects <code>df</code> which has multiple rows per student and <code>myag</code> which has multiple rows per school</li>
<li>What are the variables that <strong>link</strong> these two together?</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">names</span>(myag)</code></pre>
<pre><code> [1] "dist" "grade" "mean_read" "mean_math"
[5] "sd_read" "sd_math" "count_read" "count_math"
[9] "count_black" "per_black" "spread" "schoolperf" </code></pre>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">names</span>(df[, <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">4</span>, <span class="dv">6</span>)])</code></pre>
<pre><code>[1] "school" "stuid" "grade" "dist" </code></pre>
<ul class="incremental">
<li>It looks like <code>dist</code> and <code>grade</code> are in common. Is this ok?</li>
<li>Why might we want to consider re-aggregating with <code>year</code> as well?</li>
<li>For this example we won't just yet</li>
</ul>
</div>
<div class="section slide level1" id="merge-options">
<h1>Merge Options</h1>
<ul class="incremental">
<li>We have a few options with <code>merge</code> we want to consider with <code>?merge</code></li>
<li>In the simple case we let <code>merge</code> <strong>automagically</strong> combine the data</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">simple_merge <- <span class="kw">merge</span>(df, myag)
<span class="kw">names</span>(simple_merge)</code></pre>
<pre><code> [1] "grade" "dist" "X" "school"
[5] "stuid" "schid" "white" "black"
[9] "hisp" "indian" "asian" "econ"
[13] "female" "ell" "disab" "sch_fay"
[17] "dist_fay" "luck" "ability" "measerr"
[21] "teachq" "year" "attday" "schoolscore"
[25] "district" "schoolhigh" "schoolavg" "schoollow"
[29] "readSS" "mathSS" "proflvl" "race"
[33] "mean_read" "mean_math" "sd_read" "sd_math"
[37] "count_read" "count_math" "count_black" "per_black"
[41] "spread" "schoolperf" </code></pre>
<ul class="incremental">
<li>It looks like it did a good job</li>
</ul>
</div>
<div class="section slide level1" id="merge-options-1">
<h1>Merge Options</h1>
<ul class="incremental">
<li>In complicated cases, merge has some important options we should review</li>
<li>First is the simple sounding 'by' argument:</li>
<li><code>simple_merge(df1,df2,by=c("id1","id2"))</code></li>
<li>We can also specify <code>simple_merge(df1,df2,by.x=c("id1","id2"),by.y=c("id1_a","id2_a"))</code></li>
<li>This allows us to have different names for our ID variables</li>
<li>Now, what if we have two different sized objects and not all matches between them?</li>
<li><code>notsosimple_merge(df1,df2,all.x=TRUE,all.y=TRUE)</code></li>
<li>We can tell R whether we want to keep all of the <code>x</code> observations (df1), all the <code>y</code> observations (df2) or neither, or both</li>
</ul>
</div>
<div class="section slide level1" id="reshaping-data">
<h1>Reshaping Data</h1>
<ul class="incremental">
<li>Reshaping data is a slightly different issue than aggregating data</li>
<li>Let's review the two data types: long and wide</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(df[, <span class="dv">1</span>:<span class="dv">10</span>], <span class="dv">3</span>)</code></pre>
<pre><code> X school stuid grade schid dist white black hisp indian
1 44 1 149995 3 105 495 0 1 0 0
2 53 1 13495 3 45 495 0 1 0 0
3 116 1 106495 3 45 495 0 1 0 0</code></pre>
<ul class="incremental">
<li>Now let's look at wide:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">head</span>(widedf[, <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">28</span>:<span class="dv">40</span>)], <span class="dv">3</span>)</code></pre>
<pre><code> stuid readSS.2000 mathSS.2000 proflvl.2000 race.2000 X.2001
1 149995 357.3 387.3 basic B 441000
2 13495 263.9 302.6 below basic B 531000
3 106495 369.7 365.5 basic B 1161000
school.2001 grade.2001 schid.2001 dist.2001 white.2001 black.2001
1 1 4 105 495 0 1
2 1 4 45 495 0 1
3 1 4 45 495 0 1
hisp.2001 indian.2001
1 0 0
2 0 0
3 0 0</code></pre>
<ul class="incremental">
<li>How did we reshape this data?</li>
</ul>
</div>
<div class="section slide level1" id="wide-data-v.-long-data">
<h1>Wide Data v. Long Data</h1>
<ul class="incremental">
<li>The great debate</li>
<li>Most econometrics, panel, and time series datasets come wide and so these seem familiar</li>
<li>R for most cases prefers long data, including for most graphing and analysis functions</li>
<li>So we have to learn both</li>
</ul>
</div>
<div class="section slide level1" id="the-reshape-function">
<h1>The reshape Function</h1>
<ul class="incremental">
<li><code>reshape</code> is the way to move from wide to long</li>
<li>The data stays the same, but the shape of it changes</li>
<li>The long data had dimensions: 2700, 32</li>
<li>The wide data has dimensions: 1200, 91</li>
<li>How do we get to these numbers?</li>
<li>The rows in the wide dataframe represent unique students</li>
</ul>
</div>
<div class="section slide level1" id="deconstructing-reshape">
<h1>Deconstructing reshape</h1>
<pre class="sourceCode r"><code class="sourceCode r">widedf <- <span class="kw">reshape</span>(df, <span class="dt">timevar =</span> <span class="st">"year"</span>, <span class="dt">idvar =</span> <span class="st">"stuid"</span>, <span class="dt">direction =</span> <span class="st">"wide"</span>)</code></pre>
<ul class="incremental">
<li><code>idvar</code> represents the unit we want to represent a single row, in this case each unique student gets a single row</li>
<li>In this simple case <code>timevar</code> is the variable that differenaties between two rows with the same student ID</li>
<li>Note that <code>timevar</code> needn't always represent time!</li>
<li><code>direction</code> tells R we are going to move to wide data</li>
<li>As written all data will move, but using the <code>varying</code> argument we can tell R explicitly which items we want to move wide</li>
</ul>
</div>
<div class="section slide level1" id="what-about-wide-to-long">
<h1>What about Wide to Long?</h1>
<ul class="incremental">
<li>We often need to do this to plot data in R</li>
<li>Luckily the <code>reshape</code> function works well in both directions</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">longdf <- <span class="kw">reshape</span>(widedf, <span class="dt">idvar =</span> <span class="st">"stuid"</span>, <span class="dt">timevar =</span> <span class="st">"year"</span>, <span class="dt">varying =</span> <span class="kw">names</span>(widedf[,
<span class="dv">2</span>:<span class="dv">91</span>]), <span class="dt">direction =</span> <span class="st">"long"</span>, <span class="dt">sep =</span> <span class="st">"."</span>)</code></pre>
<ul class="incremental">
<li>If our data is formatted nicely, R can do the guessing and identify the years for us by parsing the dataframe names</li>
</ul>
</div>
<div class="section slide level1" id="subsetting-data">
<h1>Subsetting Data</h1>
<ul class="incremental">
<li>We have already seen a lot of subsetting examples above, which is what filtering is, but R provides some great shortcuts to this</li>
<li>Let's look at the <code>subset</code> function to get only 4th grade scores</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">g4 <- <span class="kw">subset</span>(df, grade == <span class="dv">4</span>)
<span class="kw">dim</span>(g4)</code></pre>
<pre><code>[1] 400 32</code></pre>
<ul class="incremental">
<li>This is equivalent to:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r">g4_b <- df[df$grade == <span class="dv">4</span>, ]</code></pre>
<ul class="incremental">
<li>These two elements are the same:</li>
</ul>
<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">identical</span>(g4, g4_b)</code></pre>
<pre><code>[1] TRUE</code></pre>
</div>
<div class="section slide level1" id="thats-it">
<h1>That's it</h1>
<ul class="incremental">
<li>Now you can filter, subset, sort, recode, and aggregate data!</li>
<li>Let's look at a few exercises to test these skills</li>
<li>Once these skills are mastered, we can begin to understand how to automate R to clean data with known errors, and to recode data in R so it is ready to be used for analysis</li>
<li>Then we can really take off!</li>
</ul>
</div>
<div class="section slide level1" id="exercises">
<h1>Exercises</h1>
<ol class="incremental" style="list-style-type: decimal">
<li><p>Say we are unhappy about attributing the school/grade mean score across years to student-year observations like we did in this lesson. Let's fix it by <strong>first</strong> aggregating our student data frame to a school/grade/year data frame, and <strong>second</strong> by merging that new data frame with our student level data.</p></li>
<li><p>Sort the student-level data frame on <code>attday</code> and <code>ability</code> in descending order.</p></li>
<li><p>Find the highest proportion of black students in any school/grade/year combination.</p></li>
</ol>
</div>
<div class="section slide level1" id="other-references">
<h1>Other References</h1>
<ul class="incremental">
<li><a href="http://www.statmethods.net/management/index.html">Quick-R: Data Management</a></li>
<li><a href="http://www.ats.ucla.edu/stat/r/faq/default.htm">UCLA ATS: R FAQ on Data Management</a></li>