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<title>Predicting the manner in which exercises are done</title>
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<h1>Predicting the manner in which exercises are done</h1>
<p>Load the data</p>
<pre><code class="r">tr <- read.csv("pml-training.csv")
</code></pre>
<p>Fix numerics imported as character values, dropping the first 7 columns
, which don't represent sensor measurements, but things like time-stamps, user names
, and various other metadata about each data sample.</p>
<pre><code class="r">tr.fix <- tr[, 8:160]
tr.fix[, 1:152] <- sapply(tr.fix[, 1:152], paste)
suppressWarnings(tr.fix[, 1:152] <- sapply(tr.fix[, 1:152], as.numeric))
</code></pre>
<p>Drop columns with NAs.</p>
<pre><code class="r">na.sums <- apply(tr.fix, 2, function(x) sum(is.na(x)))
rows <- nrow(tr.fix)
tr.fix.nona <- tr.fix[, na.sums == 0]
</code></pre>
<p>Fit a random forests model to the data.</p>
<pre><code class="r">suppressWarnings(require(randomForest))
set.seed(1975)
fit <- randomForest(classe ~ ., data = tr.fix.nona, na.action = na.roughfix)
</code></pre>
<p>Let's take a look at the model.</p>
<pre><code class="r">fit
</code></pre>
<pre><code>##
## Call:
## randomForest(formula = classe ~ ., data = tr.fix.nona, na.action = na.roughfix)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 7
##
## OOB estimate of error rate: 0.25%
## Confusion matrix:
## A B C D E class.error
## A 5577 2 0 0 1 0.0005376
## B 10 3784 3 0 0 0.0034238
## C 0 10 3412 0 0 0.0029223
## D 0 0 16 3198 2 0.0055970
## E 0 0 1 5 3601 0.0016634
</code></pre>
<p>The OOB estimate represents the expected out of sample error: <strong>0.25%</strong> </p>
<p>Now let's move to the test data and predict the manner the exercises were done. </p>
<p>Read and fix the data, leave NAs.</p>
<pre><code class="r">tst <- read.csv("pml-testing.csv")
tst.fix <- tst[, 8:159]
tst.fix[, 1:152] <- sapply(tst.fix[, 1:152], paste)
suppressWarnings(tst.fix[, 1:152] <- sapply(tst.fix[, 1:152], as.numeric))
</code></pre>
<p>And finally - predict the test sample classes:</p>
<pre><code class="r">answers <- predict(fit, newdata = tst.fix)
answers
</code></pre>
<pre><code>## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## B A B A A E D B A A B C B A E E A B B B
## Levels: A B C D E
</code></pre>
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