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<h1>Reproducible Research: Peer Assessment 1</h1>
<h1>by Chris Keen</h1>
<p>This is the R Markdown document I am submitting for Peer Assessment 1. </p>
<h2>Loading and preprocessing the data</h2>
<p>I used the unzip() function to unzip the file. Then created the table 'ActivityData' from the read.csv function.</p>
<pre><code class="r">unzip("activity.zip")
ActivityData <- read.csv("activity.csv")
head(ActivityData)
</code></pre>
<pre><code>## steps date interval
## 1 NA 2012-10-01 0
## 2 NA 2012-10-01 5
## 3 NA 2012-10-01 10
## 4 NA 2012-10-01 15
## 5 NA 2012-10-01 20
## 6 NA 2012-10-01 25
</code></pre>
<pre><code class="r">str(ActivityData)
</code></pre>
<pre><code>## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
</code></pre>
<pre><code class="r">dim(ActivityData)
</code></pre>
<pre><code>## [1] 17568 3
</code></pre>
<pre><code class="r">summary(ActivityData)
</code></pre>
<pre><code>## steps date interval
## Min. : 0.0 2012-10-01: 288 Min. : 0
## 1st Qu.: 0.0 2012-10-02: 288 1st Qu.: 589
## Median : 0.0 2012-10-03: 288 Median :1178
## Mean : 37.4 2012-10-04: 288 Mean :1178
## 3rd Qu.: 12.0 2012-10-05: 288 3rd Qu.:1766
## Max. :806.0 2012-10-06: 288 Max. :2355
## NA's :2304 (Other) :15840
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>I used tapply to find the total number(sum) of steps taken each day.</p>
<pre><code class="r">sumAD <- with(ActivityData, tapply(steps, date, sum, na.rm=TRUE))
</code></pre>
<p>Then I created a histogram using the results.</p>
<pre><code class="r">hist(sumAD, ylim=range(0:30), main="Daily Total Steps Taken",
xlab="Total of Steps")
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAGwCAMAAABy/YdMAAAAflBMVEX9/v0AAAAAADkAAGUAOTkAOWUAOY8AZrU5AAA5ADk5AGU5OQA5OY85ZrU5j9plAABlADllAGVlOQBlZjlltf2POQCPOTmPOWWPjzmPtY+P27WP29qP2/21ZgC1tWW124+1/tq1/v3ajzna/rXa/v39tWX924/9/rX9/tr9/v0BcV8+AAAAKnRSTlP//////////////////////////////////////////////////////wAyqPBcAAAACXBIWXMAAAsSAAALEgHS3X78AAANWElEQVR4nO2dD3vauh1Gr+kWcruUtBu0u2u4W7jh3/f/grNkG+yggAWmr6XfOc/T0GL7raKDZBks8dseTPKbugCgAfFGQbxREG8UxBsF8UZBvFEQbxTEGwXxRkG8URBvFMQbBfFGQbxREG8UxBsF8UZBvFEQbxTEGwXxRkG8URBvFMQbJV3x2+fCMW8/8+nV/WnttFv4nYrp8anlvHPAMezhrbs1zPvEd/9hMiQvvpi9e/aC+GUREF9lTbtbwyBeTVXj66L8uS5FTF6aFv+/hX+qeUFsHn1TXvnXiNc2bR9w2McfWRldVT3JbvHpz8Lll9urx3ZiK6NMnQeOGjWpi3dttGqvXp3v6ldl/S9LJxWVpmXVTCvx7QPqfXxLr18Wy7onqRu32696bCe2M9x/+P6oaaDIYyJ58auqc26kuz+bx1Lwocl5TZtH3zjLH01n3hkQeFnu726rP8AFlM/OnOB53Wns24mtjP8+zvbvjloXnSNGSB7iXfM7it8tHv56PrQ4b2TtW6DvCrz44wH1XsvKvNu6rk7ik5fq1bMqZr51v+/qDxmTf7jI1lH+NYb4e3Hs6t0ZuC1+v5r88XgY8wXEtw/YH/ert56Krw7onjyOGdWLonsU4u/HcXBXqWmJL6UcLIW6+vYBfp9VM6I/dPWOQ1fv/3Uc8Psd2hnV3w9HIf6uHC/nqsbWEl8aO9Z7Z3Dn/zJtH1DvUzRd/WFw9/B2GNwdNrcST/7T41GIvyutN3DKOp+t3Oi+cblsDao7l3Pe8cNb64Bmp6or91u9w2qo9mc1mG82txPbGf7i8XgU4lWsLr4P04fWpUF+ZCl+IGOIT4xVMUiDRzxkCOKNgnijIN4oiDcK4o2CeKMg3iiINwrijYJ4oyDeKIg3CuKNgnijIN4oiDcK4o2CeKMg3iiINwrijYJ4oyDeKIg3ymXx1VTR8S/qAlFcFL9bVNOR1mOf/glRXBS//fbaeYQ8oMUb5fI5vl6AgHN8XjCqN8o14ouGwUsDv4xel3OzZbCrR3zC9BrcLd0SYk8ngzvEJ0yvy7n1LHg5h/iE6X05R4vPiz6Xc878inN8XtxyOYf4hEG8URBvFMQbBfFGQbxREG8UxBsF8UZBvFEQbxTEG8WC+GJQ1L/NQJgQP9owIYhXhglBvDJMCOKVYUIQrwwTgnhlmBDEK8OEIF4ZJgTxyjAhiFeGCUG8MkwI4pVhQhCvDBOCeGWYEMQrw4QgXhkmBPHKMCGIV4YJQbwyTAjilWFCEK8ME9Jr8SO3uGVgRdNU6gDxAfqI9wtebb6cbEqlDhAfoI94v+BVwsudIT7AZfHPk58/XpNe7gzxAXoM7naLYrpfJ7zcGeIDMKpXhgmxsHo14gP0Wr168sLg7i5hQnqtZbtbzBB/jzAhPb+MaDlF/B3ChPRdvXr1t8+IHzxMSJ/Vq2fuIbB8dSp1gPgAXM4pw4QgXhkmBPHKMCGIV4YJQbwyTAjilWFCEK8ME4J4ZZgQxCvDhCBeGSYE8cowIYhXhglBvDJMCOKVYUIQrwwTgnhlmBDEK8OEIF4ZJgTxyjAhiFeGCUG8MkwI4pVhQhCvDBOCeGWYEMQrw4QgXhkmBPHKMCGIV4YJQbwyTAjilWFCEK8ME4J4ZZgQxCvDhCBeGSak13JnDla2vEOYkL6LH+3Xp+uWp1IHiA/Qc7kzFji8S5gQWrwyTEif5c44x98rTAijemWYEFavVoYJYfVqZZgQVq9Whglh9WplmBBWr1aGCWH1amWYEC7nlGFCEK8ME4J4ZZgQxCvDhCBeGSYE8cowIYhXhglBvDJMCOKVYUIQrwwTgnhlmBDEK8OEVOK3z9Mrjk2lDhAfoGnx68LfZxNFKnWA+ACtrn63KIp5zLGp1AHiAzTiqzvrArfZnCGVOkB8gOYcfzpd4jKp1AHiAzCqV4YJqcWvy7P7KnZ0l0odID5A3dV/dc43p/dTniWVOkB8gEp8dSttYF7kWVKpA8QHqLt6PzMyMC/yLKnUAeIDMLhThglBvDJMyGFU/8Ec+HOkUgeID9C8gRP1Xm1NKnWA+AC1+Ki3ahtSqQPEB6i7+uXsimNTqQPEB2i6es7xijAhjOqVYUIQrwwTUovfLYqHv77yIc0vDhPSvFc/2zy98V79rw4TcricK8XHXtSlUgeID9Bu8atQi3evBjfoD2xLpQ4QH+B4jg+69eJ9T7D5crIplTpAfIA+y52VnQELHN4lTMhl8c+Tnz9ci39i9erBw4T0eOeuPA1M92tWr75DmJB2i19FvmGfSh0gPkBbfN/LOVavzoC2+FB3fo5U6gDxATrn+Mi7MVKpA8QH6DGqr/t11rIdPkzI5U/n3GL1YVKpA8QH6HT14Qu67Uef2qVSB4gPULf41bT5EUEqdYD4AO2bLfl07leHCTl8Orenxf/6MCHtT+diF0BKpQ4QH4B77pRhQhCvDBPCzZbKMCHcbKkME8LNlsowIRdvtjxDKnWA+AAXb7Y8Qyp1gPgAjOqVYUKYH68ME1Kf47/HrlzteFcHxaDc/qt9VM4RhQkZcH78aOt3tAVTMuA5frT1O9qCKUG8MkyIE3/d0A7xSdOID8yQugjiEwbxyjAhiFeGCfHiz91jewbEJ8xoR/UjfTMI8XtafNIgXhkmBPHKMCGIV4YJQbwyTAjilWFCEK8ME4J4ZZgQxCvDhCBeGSYE8cowIYhXhgm5LH7z+NEnd4hPmIviqy+aDn7VNOITps+y5Z3HFohPGFq8MkzI5XP8x5MtEJ8wjOqVYUKuEf/BLU2jrd/RFkwJLV4ZJgTxyjAhAy5bPtr6HW3BlAy4bPlo63e0BVPSo6vvu2z5aOt3tAVTwjleGSYE8cowIYhXhglBvDJMCOKVYUIQrwwTgnhlmBDEK8OEIF4ZJgTxyjAhiFeGCUG8MkwI4pVhQhCvDBOCeGWYEMQrw4QgXhkmBPHKMCGIV4YJQbwyTAjilWFCEK8ME4J4ZZgQxCvDhCBeGSYE8cowIYhXhglBvDJMCOKVYUIQrwwTgvjYsNF+MVociM8mLA7EZxMWB+KzCYsD8dmExYH4bMLiQHw2YXEgPpuwOHqtV+8WOTxdtRzx4wqLo494/x0Fmy8nmxA/qrA4+ojfPL11vqGC1atHGRZHj7VsJz9/uBb/xDdUjDssjh6Du92imO7XfEPF2MPiYFSfTVgciM8mLA7EZxMWB+KzCYsD8dmExYH4bMLiQHw2YXEgPpuwOBCfTVgciM8mLA7EZxMWB+KzCYsD8dmExYH4bMLiQHw2YXEgPpuwOBCfTVgciM8mLA7EZxMWB+KzCYsD8dmExYH4bMLiQHw2YXEgPpuwOBAvDdOtpIR4o2GINxqGeKNhiDcahnijYYg3GoZ4o2GINxqGeKNhiDcahnijYYg3GoZ4o2GINxqGeKNhiDcadln85tF/ys/KlnmFXRS/W8z94/p03XLEJxzWa7369uN+/+Hq1aBlWPFnWjwkzOVzvPt6iiJ4joeEuWVUDwmDeKMg3iiINwrijYJ4oyDeKIg3yoDixW9Ygkz8cFF8SHP3MMQbDUO80TDEGw1DvNEwxBsNQ7zRMN7AMQrijYJ4oyDeKIg3CuKNgnijIN4oiDcK4o0ylPjtc3Hr5LpV4Sdq1Undhzg2n1/fB1wf58OGKZubcT4fqmR12LUlG0i8m1q5mt6WsZy3kroPcaxdTQRzrojzYcOUbfv1Zb/5/WWYktVhV5dsIPFuErVvGtez+/7SSuo+RAUtJ3+URwRz4uOqsGHKtnYulvNhSlaHXV2ygcRvnt78a/AG/KzceZPUfYgtTflLB3OuiXNhw5XtoyJdG3Z1yQYS72bP3yje9Vvl67dO6j7ERpWugjnXxPlX0VBl2y1mw5XMhV1dsvG0eM9yPsoWP1TZts+z/WAl82FXl2w853jPB2fAyJTNcOf4jvhbwzaPbiQ2UMmqsKtLNtiofnbrqN71T7sfr3VS9yES90sHc66Ja84bt5etVjVMyeqwq0s2ruv4ycsAF953uo6/vWwrP99lPkzJmrBrS8Y7d0ZBvFEQbxTEGwXxRkG8URBvFMQbBfFGQbxREG8UxBsF8UZBvFEQbxTEGwXxRkG8URBvFMQbxYL43cLfmNjcfni88bhzC3K5l7tP3U1N+TTMveKjxoL4fVfxB+Krf2yf3QzUhzfE50FjtWzM/kc1x7i22zztblOu7sz++p/Dnvvtt3/7Leu8vm3TlPiln7jmppq4OcafX2vxh6f3rsOvzgjuX0s/LWH7/PC2/uRnp9w6EXxMWBLv3LmZZXU7/1aL7z7tWvbEvyzcq6Pc5jr/3feXIWYGjglL4jfVTHf/96XruCvX7afrvX93//IzkCeV8eXcrUAxyci9JfHHpu1a8aGr77R4v96Am35aTT3cV0tP1MsP5PRV6pbEH0/m9Zz3wDnej+rrl0H1/PZ56h6cdMQnR2tUX47fPr2uiuLv/5y3R/WtIb7r0t1O1UB/+/Vfvo9fMqq3RW6juhrEXwLxkBOINwrijYJ4oyDeKIg3CuKNgnijIN4oiDcK4o2CeKMg3iiINwrijYJ4o/wfgTXMTvxddgIAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-3"/> </p>
<p>Finally, I calculated the Mean and Median for the total steps taken each day.</p>
<pre><code class="r">mnADsum <- mean(sumAD)
medADsum <- median(sumAD)
print(paste("ActivityData Mean: ", mnADsum))
</code></pre>
<pre><code>## [1] "ActivityData Mean: 9354.22950819672"
</code></pre>
<pre><code class="r">print(paste("ActivityData Median: ", medADsum))
</code></pre>
<pre><code>## [1] "ActivityData Median: 10395"
</code></pre>
<h2>What is the average daily activity pattern?</h2>
<p>I calculated the Mean of steps for each interval across all the days.</p>
<pre><code class="r">mnADintervals <- with(ActivityData, tapply(steps, interval, mean, na.rm=TRUE))
</code></pre>
<p>Next, I created a time series plot of the average number of steps taken at each 5-minute interval each day.</p>
<pre><code class="r">plot(mnADintervals, type="l", xlab="5-Minute Intervals(24hr clock)",
ylab="Avg Number of Steps", main="Avg. Number of Steps per Interval",
xaxt="n")
axis(side=1, at=seq(0,288, 12), labels=c(0:24))
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAAsVBMVEX9/v0AAAAAADkAAGUAOTkAOWUAOY8AZo8AZrU5AAA5ADk5AGU5OQA5OWU5OY85Zo85ZrU5j485j9plAABlADllAGVlOQBlOTllOY9lZgBlZjllZmVlZrVltbVltf2POQCPOTmPOWWPZgCPZo+Pj2WPtY+P29qP2/21ZgC1Zjm1tWW124+1/rW1/tq1/v3ajznaj2XatWXa24/a/rXa/tra/v39tWX924/9/rX9/tr9/v0luDTuAAAAO3RSTlP/////////////////////////////////////////////////////////////////////////////AKHEOx4AAAAJcEhZcwAACxIAAAsSAdLdfvwAABXsSURBVHic7Z0Le6S2FYYru3XsONkm42yaSzvebZt42k09bRLPhf//w4ougASSECCE4Hzvk+yY29EZvSMhGGD+UACS/GHpBMAyQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXii5Cn++szuvStcntier3b76tjescDg/MBuXqpw7O6t/OOwH5qqkZRRakCs1hYpyVN8qcRfI6WpUtRE8Qe2r6OV3GtzRmFqDIkF8S2O7EdebQfR7vm/ZRdw+8uTaJQCrmon/ArHl3LRiX1WrvR6ZOWW5cx/qzZcTsvO4eaHqn3zWXxjplYpP2d3b6WD/zxL/fUmPAg3U34Oqw9iM7MbmWusll/bsfhKX9Tv51TOLreBeJOy9j49lHV04mIuTzcvohr/+KCLv/1eStfEV6hqF1oP4g9Ds5x1b4gXrVPJMjbhq8gOQa7qiyzFa9Fbse7+V72fKiLEm5wf7oXPsp28CPvnh3LqyAzxnx52LfG3r9yg+qDsRA3ztqw6hmrUcH4QTa38p+6MhRaugM/RN9mJwcRZ+8SZM83ISvxOfmDbse6L5v1Ua0O8ybGsM/5/+c9OVOCJV9vF6OpvXw83P5vi71XVc/FKo+oG+BzVz8tYIry2Fz5I87KsehMepExBtM+mq5czLZGr0mWmrVhiper9yH0VxLeoukvh+jdeN1bxl6fPuuLv3kaJV929XbzYx6sBQr94lY5VfPV+RECIbyOrRfa9N/8sK76wdfV8htyd78UiU7ze1XMaPZau/liN6OvuWW6ienUxpVbu9v8+8Z2V1PuRnyaIb8GrRdW0OtSuB3dVTYlXPtR/VeOnjnhjCCbnqvCHqj+pxVcNkC+61zZRf9SLi+7gTo/cFm/Ekiup93NiaPFdRBtWzVAd0fE2Jg7nDPEFH8+JveXful19dTh3kJo08epwTj/S5mr58vJVKZWDMhWkWlwUhS+yKb4VS62k3k85e3fkA1GI7+ekdfUpsJ4FCjs1tALWIV4e94pmmg6Iz4CT2i2nBOLBBoF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnihTxDOQMzOKn7AtmBuIJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJQlF8tomlBOKJMlm89rjBYaGXI9vEUjJV/PVZPgzu1H3WWLb1m21iKZkq/vL+1XgND70c2SaWErR4okzex6uHC2IfvzIwqicKxBOF5OFctpklhOTgLtvMEjLD4VzgldvLkW9mCSHY4lm2maWE4OEcxHMIjuohngPxRIlxOMd/SGdF5+ohnhNjcHd93kH82ohzOHe4h/iVEelw7vjHR4hfFREO58Svfh27x3O5Vi/EczCqJwrEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJQlJ8tqklBOKJAvFEgXii0BPPimxTSwnEE4Wq+EyTSwfEE4WoeNw3CfFEgXiiQDxRiIrPNbl0QDxRaIrPNrl0QDxRIJ4oEE8UiCcKxBMF4olCVXyu2SUD4okC8USBeKJAPFEgnijUxDOIl0A8USCeKBBPFIgnCsQThZh4BvEKiCcKOfFVVjlmlxKIJwrEEwXiiQLxRIF4olATX0C8BOKJAvFEoSWeFRCvgHiiQDxRIJ4oEE8UiCcKxBMF4okC8USZLP78wDi3r0NDLwHE10wVf33ei9fT3dvA0EvAHH8TZKr4y/tX4zU89BJAfA1aPFEm7+MvT+vax1v/Jgi5Ub31b4JAPFFiDO54b9/dxedYsxBfE0G8GNCfvxoaegkgviaC+PO7N+NwjlVMzy42EF8zWfzTzc8feYt/h8O5VTF9cHd9ZvfFCYdzKwOjeqJAPFEgnigQT5Tpo3p17NYd3WVYsxBfM7nFX59340IvAcTXTO/qL9+8jAq9BBBfg308UUiJZ84JekA8USCeKBBPFIgnCsQTBeKJAvFEgXii0BWfYX4pgXiihIk/3r0dGdtHDb0AEN8QJP7yzUv53/mxe0HlhNALAPENYeLfv5ZtHuK3RGBXz25eTujqtwQGd0SBeKKEib8+M8bu44ZeAIhvCBIvL6g8DjSfX8VCfEPoqL6wPt9oSugFgPiGwFH9fYEWvy3CWrz7ronxoRcA4hswqicKxBMl+HDu7jfXHTMjQy8AxDeEHs6d371ZHl45JfQCMO8kLUIP50rxWzucyzDBhAxo8Ue0+A0x4JTtQO8Z1ivENxAe1WeYYEIIn7LNMMGEBIivz9thH78hBrT4yKEXAOIbsI8nSoj4y9M9/7GpQd/Q9IdeAIhvCBF/2Imfntna17IZJpiQkMFduYfnl1ZjVL8lAsXzs3Zbu64+wwQTEtTV78VFdwd09RsibHBXHsLzEV7U0AsA8Q04nCMKxBMF4okSOKqfIfQCQHxDkPhfcK5+cwQdzo25qj7Hem1nlF+G6aD87VyGGaaD8uAuwwzTQfmauwwzTMeQ26S3dgVOhhmmg/I1dxlmmA60eKJgH08UjOqJAvFEgXiiQDxRcMqWKGGHcx8GPgwjJPQCQHxDWIt/2ua3cxmmmAzS+/gMU0wGbfEZ5piK4DN323vqlX0OFULP1TufenV+cO3/86tUiG8IPZxzPfWK303JsXwq8qtUiG8Y0OKt385VnwbLpyK/SrVklF+SiZj67dzKW3yGSSZi8qjefYyfX51CfAPxw7kMk0zEgK5+c3fLOmalKnpZJl96te7DOYh30HuxJQZ3o4telrCu/uD8FSrL4RyriJRiPJYTn19lhNw06ftFmrW3+ERZDhU/f1aUDuesCUG8FSX+tInv4yE+vIT69+Pjh04PxIeXsKlr7iA+vITqlyZ38UOnB+LDS6jEO/fxniE/xGulrFO8bx8vz+qNCJ0eiA8vIWAf7/xUbE/8+He0UvHYxw9ft7XhOsVv5Lr66eLHvqW1ip8ldHogPrwEiDfXHfme2MDaGF3QkCK8oKs31yUmXjJ0hLc58UP1jd8yL/Grf+oVxBtFeNHFn6h39fTEq338Pmro9EC8UYQXjOrNVSF+euj0QLxRhJfea+7Gh04PxBtFeNFa/BH7eIriL09Df1MY4o0yVir+yIZ/PwfxehkDxc9+JX7g4dzg5t4fOj0rEM+GbjCaEPGnEc29P3R61iV+5vrDqH7Q9qTEzxQ6PRDfLckFxJurQvz00OlZjXj937mA+EHbQ/z00OlZmfh5KzDwOH7MsH4d4gekSU989UQM+0NNR4ZOT/7iWWbiq2fgDPs5cYjXi1il+OqpV7++Q4sfW7BzS31BbuLlT8jfX54GfTEL8UYRrk3zFj9D6PREED/uTQ0TzyA+MlmKZ13x1ZzlxV+ehv6ubEDo9EB8Oyc3VYvnDy5d/bNsJ4r3dtijt8xcPGebN1RkIZ4ZEzmJR4uPId62cdbisY+vVxzzpupjNMvGrOiKH5zZGOiM6l1Xri4tXl+Qpfjjyi+9Wot41t5wHgLFnxi7GfpYU4g3SggSb2a5tPhyZHfzchh4H01+4h1XqqcU3906Z/HyuzmIn088y1O8uA5jD/Hzi2dFXuI5h1Xv4+XhMcR3knKijeqvz+sd1cujpKXF2zJYgfjYoVMC8Y6knGxKvHtZcBCInx46JTOIH/DuKq0Qn5xsxat9EMTPROtUqLksOIixNsSPDZ2SXMR3tqpGnfYjPoifCsS7knKxHfHObCDexsrEO1t1zuKb7CA+dpF5iLdsVcW0i5+zCiE+NE2m/TtgswLiE7Bu8WIFiI9ZZBbibVt1xY/KbQQkxNtPk/dsZF+tEc9si/sKgPh5cH/l7nlCaFiezHiB+PGh5yB78e3NID4OS4oPK8AmvtDEj0ltFJsS77nWAuKHhYb41loQPzn0DEB8OFTETz6cc4lv1LniOMdrDOIjYa18FlV88wGIIZ4ZcyF+fIlu8b6twmIbr6PFa5P94mesw22LV009rnhmFDVFPIP4OCwo3lPKRsXzh6TYn2+ch3jfVVf1SkGx1SuziTefaOCNvxHx12d5M6Xl+cZLiG+PpOTXM8HiPSs6xbP6X1Liqx+Vt/y4/ELiW8PmSOK1wziI5+TX4lckftWjevUjBjns42Xlzy3eHNxVkyPEs2LV4keHjo9FfKcTcGxmbOBdCeLdIStGJTSBkeKNztdV+37x5tPqnOHNSdaeuSrxfFDHe3vLExCXEa+fQzcapX879Zfr3F9nfzCDeNvmWYsXA/rzV0NDx0eKr40sIT5A3nbEn/kPluRwONcyUovvS2SceL04n3jnNVorF/908/NH3uK7P1ezuPiq0w8W7zgCmCTePWfd4vmRPLu3PtI8F/G9iZji7ab6xMsljo3tc9Yufmzo+DD9Reu1lxTv6EDavQPET2E28cwn3tiphIpvf3XUKz56ZUJ8kPi2pIniWWfkYR8YQnxIaaZn7QzSEPHWJ1MFi+9ubBVftXlzPWuaoe9hMJsUz7otyr+pvqVNPOus3ive6H5smUJ8HFrihxQ+UHy1Q64O/qzi2xfhWjM1Y0L8yNJmFN9d3xDfaecQnwxmtrZB3xDpIyibALd4ZhPPtG3sA7Yx4mPX5ibFD6ynHvG2AZsannXEN/t+j3jH8B3iRxW2iPhOV19UnY1HvKMbgPhxhU0Wz4aKtwzpNPHGKj0BC4gfX9iGxVfdS1Qgfrz4ejKKeHvGEN9XWKvmholX1Vs34dbizvoe8ealWRA/N3mKD88C4kcXNv7RgKwaoEN8zWrFD9+26TMWEe9YtZIO8c7CMhFfNOKHZNQrPnZ1bkj8pG2N0/Sstbiz/gziPfNlZhDvKCyleOaZ1MQPysE9H+K9ZU0oTt5N3bp3tbBPFb3ii2qkGKMCIL63rMXE2769g/hEZCg+zvtn9V6DWXOZEtgHxIeItyczq/jJwSG+sItvwi0qXkmHeHdZU8QXpnhZ16z+u7M6xE8IHZVpZcmqbYkvIH6e0FGJIb5oiVfTEB85dFRyFB+HlnhWz54c1wvE20Iz72Qa8VNLgPjCKr7qq13XQvsKh/h0TBZfuMTbIkP8lNBRiSy+umA+F/GFLt78BIyP64WUeH1OXdt5ip/+RQDEF9aDr24nEF74POLrzyHERysL4juQFa/mj4o7g/gmLIP4uGVZxY8LG1W8cc5GDPAhPmpRUcXHfOtM+0Me2TWDzgnlQLwnRM7infunoWHtQPyoUDOKLyA+blEBx+vhsRKJn1IOxHtCZC9+whuH+BmYVXyhu4f4rIh+o5uM2ojXz+eMj+YD4scwl3jjMgyIT1tUCDOlw9pTEE9DfLuQSWcIIX4G0qTDID4z7xCfitzEp6E+dTfq7UP8aoF4ouIFo0/cQvy6gXiijD6ah/h1A/FEgXiiQDxRSIsn7H38+TuIXzn1XX5Dt/Mvhvjc6YoPqw6IXzkd8YHncyB+7Rjiw0/kQfzakd/TaJdhQjwltMswIZ4SrPkX4knBzH/C1ncD8WtBcx5UHxsQD+8cn3hbDa1PfNDbood2VNfWH/LkrjZ5imftGUD/rqYj3lZFaxVf9WztX5ikS0e8Nrl28cp2/Yjxob/xtWlaP7DRnMNbWjyzzh3y+age+8Pq54GM/GZqk3TFt/5ore6P1i/+/MA4t699ofUOSFukt1hm+UvrwJrnPTHX55gyZu0Z4i1VNVX89XkvXk93b32hux11YfrTP7P1yefqN0OMhwHUzR3YqCuVMWd3P1X85f2r8apKY4x1dr71PCmTVWlp6+ub6jGaLV0/FQV0ur+n07XhjxCzxYOcmLyPvzwF7uNBVqzqcA7EA+KJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USZUzzImfnEtz4GmM4sAS8QP9f08gl4gfi5ppdPwAvEzzW9fAJeIH6u6eUT8ALxc00vn4AXiJ9revkEvMQTD1YFxBMF4okC8USBeKJAPFEgnigQTxSIJwrEEyWS+MsT695GrcEfq7H3Rqhux3YtZTcv/viW23mbxY+v3hzFck+OYrknR7Hcl2NVgD1JVbIzQbXcmWC9wF+JBnHE8wKP9+7ll29eivPnPnPF0fvBOOxtN+gb8Y/u5Sde4Z4cxXJPjiclzJWjXO7JsS7AmqQq2ZmgWu5MsFngr0SDOOL54zJUq7By4u/n4Evq/OW3nsXa4zjsW79786xzuPmpzM2do1zuzlEud+fYxPcm4ExSlexMUC13Jlgv8FeiSRzx4j19423R/uXXDz/7eqnzu797u/qeFi/q05djVd/e5Z4cZXxPjnwFb5LlQm8lqgXe5T2VaBJHPO/iesRfn3eepcedd/d0ftgLcU56xhi83n051vtwR45iuSdHId6XY98gg5fsS1Bl5kxQLOipRJNULf7y5PNebu8X3xOf7+FOntFdWIt356i294v35ShWcCcpSvYEUJk5E6y2Ty6+bx8vmoOHo7gQ3P3RuHznF9/X45y9+/h60O3MkS/35Sji+3L0dzmyZHeCKjNngnJBXyWaxBrV77yj+h7vMkTPqN7X1Ye0eF+OVVft296Xo1juy9Hb4lXJzgTDvHsT7JLmOF5+GL1J+XMu4/uO08vjJe/gL+Q43pdjkHhfjmIFV5JVya4E1XJngs2C9OLB2oB4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE8UiCcKxBMF4okC8USBeKJAPFEgnihrFs8vMpSXN54fxF1Et6+dy5ONGc2E+1pwfclxZ96PaNuqPe/6gV9P6b37IwvWLL65j+z8+Z/fistf+rwMFF/aM+9HDBfPPzN5s2Lxqoo558cfSkE/PvIWz+9hU5LOj7/wS57r657rpZdm9vnLb/lFzWUw2bjFrQ+svv3VuB/x/Pi9WOXLb2/lnTfs5kXcSyELENNloOvz3VvfbZ6Ls2Lx4meuZaM/P/57X/z3Jyn+QdyGJsWLvv9Q3ahgLpWzzw97/gk6v/uNN26xAZdWLqncNfcjVhs/1J3/6e5XGamcLad///By4K3de29wBqxYPO+CVas/P3766vrxkxTfGFceS3FSom22uJdip7pmOa3uc1L7ae1+RC10UXXy6oPC73YV09cPX4hQuff1KxYvOOwPjN2fH//zj9/+enaI5z2DuIXFNlve7/g7/wAdeA8v76bi68unHOj3I7bEiw+Gmua7CjF9ff76O/4K8fMie9Sy6v/1z51L/HttSNeZLa39VI7jnvaaVd6jc5HG/YhhLf5FOIf42eAt8vqxcnASjbdRKx5DcFvt42XrNT8Wh3pvX4rdVfc18lDlyic5PjPvRzTFyzmfjH182fV8eBH7H+zj5+NY34RYK2/U8qVfv3+9Povh+81Lsxp/aWbLO6T5EVu5wZ++3YuPRD2qN+9HNMU7R/VVL5A1axY/N1POwuTe00O8j/H2cOYOZArEEwXiiQLxRIF4okA8USCeKBBPFIgnCsQTBeKJAvFEgXiiQDxRIJ4oEE+U/wOUFbTfF4FnjQAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-6"/> </p>
<p>Finally, I searched for the interval with the maximum number of steps on average for each day.</p>
<pre><code class="r">maxADint <- max(mnADintervals)
names(mnADintervals)[which(mnADintervals == maxADint)]
</code></pre>
<pre><code>## [1] "835"
</code></pre>
<h2>Imputing missing values</h2>
<p>Calculate the number of 'NA' values in the dataset.</p>
<pre><code class="r">sumADna <- sum(is.na(ActivityData))
print(sumADna)
</code></pre>
<pre><code>## [1] 2304
</code></pre>
<p>I copied the ActivityData data frame to a new data frame, completeAD, in order to replace the 'NA' values. Then used a for loop to replace the 'NA' values with the means for each of the 5-minute intervals. </p>
<pre><code class="r">completeAD <- ActivityData
for (i in which(sapply(completeAD, is.numeric))) {
completeAD[is.na(completeAD[, i]), i] <-
with(completeAD, tapply(steps, interval, mean, na.rm=TRUE))
}
</code></pre>
<p>I used tapply to find the total number(sum) of steps taken each day for the new dataset 'completeAD'.</p>
<pre><code class="r">compADsteps <- with(completeAD, tapply(steps, date, sum, na.rm=TRUE))
</code></pre>
<p>Here is a comparison of the two datasets 'ActivityData', which has the NA values included and 'completeAD', which has the NA values replaced with the means for each of the 5-minute intervals.<br/>
The first comparison is in the form of histograms</p>
<pre><code class="r">par(mfcol=c(1,2))
hist(sumAD, ylim=range(0:40),
main="Daily Total Steps Taken\n (w/ NA's)",
xlab="Total of Steps")
hist(compADsteps, ylim=range(0:40),
main="Daily Total Steps Taken\n (NA's replaced)",
xlab="Total of Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p>
<p>Finally, I created a data frame with the Mean and Median for each data set.</p>
<pre><code class="r">mncompAD <- mean(compADsteps)
medcompAD <- median(compADsteps)
bothMN <- c(mnADsum, mncompAD)
bothMED <- c(medADsum, medcompAD)
difMNMED <- c((mncompAD-mnADsum), (medcompAD-medADsum))
MNMEDdf <- data.frame(bothMN, bothMED)
MNMEDdf <- rbind(MNMEDdf, difMNMED)
rownames(MNMEDdf) <- c("w/ NA's", "NA's replaced", "Difference")
colnames(MNMEDdf) <- c("Mean", "Median")
print(MNMEDdf)
</code></pre>
<pre><code>## Mean Median
## w/ NA's 9354 10395.0
## NA's replaced 10766 10766.2
## Difference 1412 371.2
</code></pre>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>Created a new column in completeAD dataset for two level factor “weekday” and “weekend”.</p>
<pre><code class="r">completeAD$date <- as.Date(completeAD$date)
completeAD$weekdays <- as.factor(weekdays(completeAD$date))
levels(completeAD$weekdays) <- c("weekday", "weekday", "weekday", "weekday",
"weekday", "weekend", "weekend")
str(completeAD)
</code></pre>
<pre><code>## 'data.frame': 17568 obs. of 4 variables:
## $ steps : num 1.717 0.3396 0.1321 0.1509 0.0755 ...
## $ date : Date, format: "2012-10-01" "2012-10-01" ...
## $ interval: num 0 5 10 15 20 25 30 35 40 45 ...
## $ weekdays: Factor w/ 2 levels "weekday","weekend": 1 1 1 1 1 1 1 1 1 1 ...
</code></pre>
<p>Then I calculated the Mean of steps for each interval across the two factors.
Here I split the dataset 'completeAD' into two separate datasets; 'weekdayDF' and 'weekendDF'. </p>
<pre><code class="r">weekdayDF <- subset(completeAD, completeAD$weekdays == "weekday")
weekendDF <- subset(completeAD, completeAD$weekdays == "weekend")
</code></pre>
<p>This is the mean of steps for each interval for the “weekday” factor.</p>
<pre><code class="r">mnWeekdayInts <- with(weekdayDF, tapply(steps, interval, mean, na.rm=TRUE))
</code></pre>
<p>This is the mean of steps for each interval for the “weekend” factor.</p>
<pre><code class="r">mnWeekendInts <- with(weekendDF, tapply(steps, interval, mean, na.rm=TRUE))
</code></pre>
<p>Finally, I created two time plots to show comparison between the “Weekday” and “Weekend” Mean of steps for each interval.</p>
<pre><code class="r">par(mfrow = c(2,1))
plot(mnWeekdayInts, type="l", xlab="5-Minute Intervals(24hr clock)",
ylab="Avg Number of Steps", main="Avg. Number of Steps per Interval\n Weekdays",
xaxt="n")
axis(side=1, at=seq(0,288, 12), labels=c(0:24))
plot(mnWeekendInts, type="l", xlab="5-Minute Intervals(24hr clock)",
ylab="Avg Number of Steps", main="Avg. Number of Steps per Interval\n Weekends",
xaxt="n")
axis(side=1, at=seq(0,288, 12), labels=c(0:24))
</code></pre>
<p><img 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" 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