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<h1>Reproducible Research: Peer Assessment 1</h1>
<p>Read the data into R Studio and view the summary of it.</p>
<pre><code class="r">setwd("C:/Users/u6027576/Documents/Coursera/reproducibleResearch/RepData_PeerAssessment1")
</code></pre>
<pre><code class="r">activity <- read.csv("activity.csv")
summary(activity)
</code></pre>
<pre><code>## steps date interval
## Min. : 0.00 2012-10-01: 288 Min. : 0.0
## 1st Qu.: 0.00 2012-10-02: 288 1st Qu.: 588.8
## Median : 0.00 2012-10-03: 288 Median :1177.5
## Mean : 37.38 2012-10-04: 288 Mean :1177.5
## 3rd Qu.: 12.00 2012-10-05: 288 3rd Qu.:1766.2
## Max. :806.00 2012-10-06: 288 Max. :2355.0
## NA's :2304 (Other) :15840
</code></pre>
<p>Remove the rows in the data frame where the number of steps is not given (NA) or just calculate the means of all.</p>
<h1>What is mean total number of steps taken per day?</h1>
<p>!Optional: remove data entries NA from the data set before processing</p>
<pre><code>## Warning: package 'ggplot2' was built under R version 3.1.3
</code></pre>
<pre><code class="r">activityNAR <- na.omit(activity)
## Sum
activitySum <- tapply(activity$steps, activity$date, sum)
df.actSum <- data.frame(date=names(activitySum), sum.steps=activitySum)
## Create histogram using ggplot
ggplot(df.actSum,aes(x=sum.steps)) +
geom_histogram(fill = "red", alpha = 0.4)
</code></pre>
<pre><code>## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAAtFBMVEUAAAAAADoAAGYAOpAAZrY6AAA6AGY6kJA6kNtmAABmADpmAGZmZjpmtv9/f39/f5V/f6t/lcF/q9aQOgCQOjqQkDqQkGaQ2/+Vf3+Vq9aVweurf6urlZWrlcGr1v+2ZgC2///BlX/BlZXBlavBq8HBwdbB6//Wq3/Wq5XW///bkDrb///l5eXrwZXr///viYny8vL3kZH/mZn/tmb/1qv/25D/68H//7b//9b//9v//+v////09js0AAAACXBIWXMAAAsSAAALEgHS3X78AAAMlklEQVR4nO3dD18TyR0GcO5PWz1rVGyr5RT/XAuHhyDIFSTv/301mwTYwGYmzEycLPt9Pgq5yebJb/ebTaICtzWWQWar9gBSJ+AHGvADDfiBBvxAA36gAT/QgB9owA804Aca8AMN+IEG/EBzT/jDSKIbtPJl9U3vUbue1vrDZteCT2mtPyz4Kq31hwVfpbX+sOCrtNYfFnyV1vrDgq/SWn9Y8FVa6w8Lvkpr/WHBV2mtPyz4Kq31hwVfpbX+sOCrtNYfFnyV1vrDgq/SWn9Y8FVa6w8Lvkpr/WHBV2mtPyz4Kq31hwVfpbX+sOBLtR4fnx5fpWAt+LTxsmvBgw8HPHjw4LNrwaeNl10LHnw44MGDB59dCz5tvOxa8ODDAQ8efDRfIolukJZ11J6cnJ5cpWjxhh4DZ/w8znjw4MFn14JPGy+7Fjz4cMCDBw8+uxZ82njZteDBhwMePHjw2bXg08bLrgUPPhzw4MGDz64FnzZedi148OGABw8efHYt+LTxsmvBgw8HPHjw4LNrwaeNl10LHnw44MGDB59dCz5tvOxa8ODDAQ8ePPjsWvBp42XXggcfDnjw4MFn14JPGy+7Fjz4cMCDBw8+uxZ82njZteDBhwMePHjw2bXg08bLrgUPPhzw4MGDz64FnzZedi148OGABw8efHYt+LTxsmvBgw8HPHjwrXweTbLTXNofjZ7tgV85PYef5PI/R83Hj3s3SwWnq30swS/N71PxizevR9vNheYpILB133LaSu1ZKiQAf7Y9+/T8YPx5d75W8GFZ+yRyxi/L7zdP8Wc74FdO3+Ev3h5dozvjBwT/v3/P9ffn7+7Br5a+w3em4HS1jyV48ODBZ9eCTxsvuxY8+HDAgwcPPrsWfNp42bXgwYcDHjx48Nm14NPGy64FDz4c8ODBg8+uBZ82XnYtePDhgAcPHnx2Lfi08bJrwYMPBzx48OCza8GnjZddCx58OODBgwefXQs+bbzsWvDgwwEPHjz47FrwaeNl14IHHw548ODBZ9eCTxsvuxY8+HDAgwcPPrsWfNp42bXgwYcDHjx48Nm14NPGy64FDz4c8ODBg8+uBZ82XnYtePDhgAcPHnx27QOF/xJJdIO0rKP25OT05CpFizf0GDjj53HGgwcPPrsWfNp42bXgwYcDHjx48Nm14NPGy64FDz4c8ODBg8+uBZ82XnYtePDhgAcPHnx2Lfi08bJrwYMPBzx48OCza8GnjZddCx58OODBgwefXQs+bbzsWvDgwwEPHjz47FrwaeNl14IHHw548ODBZ9eCTxsvuxY8+HDAgwcPPrsWfNp42bXgwYcDHjx48Nm14NPGy64FDz4c8ODBg8+uBZ82XnYtePDhgAcPHnx2Lfi08bJrwYMPBzx48OCza8GnjZddCx58OODBg29nfzR6tje7MPsMfrX0HP7y41z77MXR5Bf4ldNz+Is3r0fbzYXPO+OLXw8mF0aTLH1+6F9OW9n01jVkKfzZ84Px593JhcmHy98O5qsFH5a1T6L1nPHHrdpyrU2+55u7s51x64wHv1Jr3+Eb9OkZ7zX+PrX9h2/e1U/O9bdH3tXfp/YBwHem4HTgwUcDHnyxVvCtnD95f/0RfEptL+HPH2/N8tMf4BNrewkfPNfBr9jaS/hoCk4HfpPgv06f6n/0Gp9a21P488dPnfFZtX2F9xqfWdtT+PG7R+CzansKP/8Dndf45NqewkdTcDrw4KMB76m+WCv4O/m0/B1ewenAbxy8f6RJr+01/FdP9cm1PYWfv8Yv/+u7gtOB3yD4aApOBx58NODXDf/tZfDrMMDHW3sJ/+1l8ye5T74CJ7m2p/C+5i63tqfwzvjc2p7Ce43Pre0rfCwFpwMPPhrw6/9iy6fjTz+8Ap9a21P4818a8z//6l19am1P4b+9bP6a/qt39cm1PYWf/SvN8n+cAx9v7Sd8LAWnAw8+GvDgi7WCBw8efG4t+MTxsmvBgw8HPHjw4HNrwSeOl12bBL807drO9but4JPGy64FDz4c8ODBgwcPHjx48AWnAw8+GvDgi7WCBw8+mi+RRDdIyzpqT05OT6JZvEH3+q3Wm9qy8+YeA2f8PM548ODBgwcPHjz4gtOBBx8NePDFWsGDBw8ePHjw4MEXnA48+GjAgy/WCh48ePDgwYMHD77gdODBRwMefLFW8ODBgwcPHjx48AWnAw8+GvDgi7WCBw8ePHjw4MGDLzjd94Jf5rUS/HVOVnhwzO8NfMp42bXgwc8Cfn578Ffr4MGDBw8ePHjw4ItNBx58NODB37sV/Pz24K/WwYMHDx48ePDgwRebDjz4aMCDv3cr+PntwV+tgwcP/jqXH0ajF0fNpf3R6Nke+KHAn21PyHebR8DHvZvVgtOB30z4Jp8b+Is3r0fbzX+NJgltvaE5bWXZelZWureNSwj+bOp99vxg9ghoUvBh6Yzf1DN+f/vmIbADfijwlx92b9Cd8cOB329e0ncu3h41l65OePAPH747BacDDz4a8ODv3Qp+fnvwV+vgwYMHDx48ePDgi00HHnw04MHfuxX8/Pbgr9bBgwcPHjx48ODBF5sOPPhowG86/OnSI7LieJ1pEy2sd9zZl9tXrB3+dsCnjNcZ8IfgF9Y77gw8+LtZvDF48NGADwc8ePDxHQMfDvhD8AvrHXcGHvzdLN4YPPhowIcDHjz4+I6BDwf8IfiF9Y47Aw/+bhZvDB58NODDAQ8efHzHwIcD/hD8wnrHnYEHfzeLNwYPPhrw4YAHDz6+Y+DDAX8IfmG9487Ag7+bxRuDBx8N+HDAgwcf3zHw4YA/BL+w3nFn4MHfzeKNwa+eL5GcnrQS23jlnJx2ly69s5MlWdzmdNlmebmuLbb/00SPfez2zvirGzvjwUcDPhzw4MHHdwx8OOAPwS+sd9wZePB3s3hj8OCjAR8OePDg4zs2MPhlx7Jro1vHcjl8jtAKKQm/0Bo7otc3OO0obd9taxl8OODBRwM+HPDgwUf2/xh81/bdxxL80oPUWgYfDnjw0YAPBzx48JH9PwbftX33sQS/9CC1lsGHAx58NODDAQ8efGT/j8F3bd99LMEvPUitZfDhgAcfDfhwwIMHH9n/Y/Bd23cfS/BLD1JrGXw44MFHAz4c8ODBR/b/GHzX9t3HEvzSg9RaBh8OePDRgA8HPHjwkf0/Bt+1ffexBL/0ILWWwYcDHnw04MMBDx58ZP+PwXdt330swS89SK1l8OGABx8N+HDAgwcf2f9j8F3bdx9L8EsPUmsZfDjgwUcDPhzw/YffHz3bW7wA/n61kf0/3kz4sxdHk1/tC+DvWRvZ/+PNhP+8M7749aB9YTTJ0ucH6VmWw++OL387aF9oEnts3uOHqt/nh7Xn/qz27Nb6w363n1d/94wHX7x2E+HTXuN7dSx7Nez3+z9UTN/MX7w9ute7+l4dy14Nu+H/a5JeHcteDQu+Smv9YcFXaa0/LPgqrfWHBV+ltf6w4Ku01h8WfJXW+sOCr9Jaf1jwVVrrDwu+Smv9YcFXaa0/LPgqrfWHBV+ltf6w4Ku01h8WfJXW+sNWho9lTV+Ut57aXg1buhb8QGvBD7QW/EBrC8NLXwJ+oAE/0IAfaMAPNEXh2996UaKtqZt1tj/mZPrNYHcrc4vntWXnvfwwGr04WsO005SEX/hmq+xcfty77mz/yuk8Gz0/6KjMLZ7WFp/3bHtivFt+2llKwi98e2V2Lt68Hm3PO9sfMyov/9t83+/dysziWe0a5m2+Vbn4tPMUhW9/Q3V2ziZn0WS/p53tj1mlU/g7ldnFza3XMe/kpF/DtNNs7hnf5Gyn8EN9LWf8+Bqi8Lz72+N1TDvN5r7Gn+00zyGFX9ymp2b5V81pbel5Lz/sjrveNGzea3z5d/U7pd/OTk/N8u+T57Vl591vfvbMTh/e1UuPAn6gAT/QgB9owA804G/nz7++rz3C9wj42wH/IPN1a+vH91Pcye9v//jX1tajydKjm+vOH199GJ8/+efWD6+ubvPAMjD48yfvx59+voZ/Obn4l5/nJ3nrunePmkvnj3/64+vkUTBbf2AZGvwvr5pP12f8q3Hze7Z6c11zacJ9/vhpc/V8/YFlYPCTE7x59u6Ev7lu8ky/Nbk4XX73dL7+wDI0+Em+/vRHN/z1dc2T+3j2FNBcPVuvNu96MjD4BnDyuyH99OMt+Pl189f4ZqvHPzef5uu1Ry+cgcGP383eoX/a2vrbkzb8lHt63beX03f1PzTLf58+x7/zrn5YeZjv6uYBvzzg5eEF/EADfqABP9CAH2jADzTgBxrwA83/AZbD05m3DkT5AAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>Calculating the mean steps per day:</p>
<pre><code class="r">activityMeans <- tapply(activityNAR$steps, activityNAR$date, sum)
AvStepsPerDay <- mean(activityMeans, na.rm=TRUE)
AvStepsPerDay
</code></pre>
<pre><code>## [1] 10766.19
</code></pre>
<p>Calculating the median steps per day:</p>
<pre><code class="r">MedianStepsPerDay <- median(activityMeans, na.rm=TRUE)
MedianStepsPerDay
</code></pre>
<pre><code>## [1] 10765
</code></pre>
<p>The subject took a mean of 10766.19 and a median of 10765 steps per day.</p>
<h1>What is the average daily activity pattern?</h1>
<pre><code class="r">TimeSeries <- tapply(activityNAR$steps, activityNAR$interval, mean)
df.timeSeries <- data.frame(Time=as.numeric(as.character(names(TimeSeries))), mean.steps=TimeSeries)
dtt <- sprintf("%04d", df.timeSeries$Time)
df.timeSeries$Time2 <- strptime(dtt, format="%H%M")
df.timeSeries$IntervalMinutes <- (df.timeSeries$Time %% 100) + (df.timeSeries$Time %/% 100)*60
q <- ggplot() +
geom_line(data = df.timeSeries, aes(x = IntervalMinutes, y = mean.steps, group=1), colour="#000099") +labs(x = "Time in minutes", y = "Mean average of steps", title = "Average steps through the day")
q
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-8"/> </p>
<p>Alternatively: time could be added to axis</p>
<pre><code class="r">p <- ggplot() +
geom_line(data = df.timeSeries, aes(x = Time2, y = mean.steps, group=1), color="red") +labs(x = "Time", y = "Mean average of steps", title = "Average steps through the day")
p
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/> </p>
<p>Which 5-minute interval contains the highest average for steps?</p>
<pre><code class="r">maxStepAv <- max(df.timeSeries$mean.steps)
maxStepAvIndex <- grep(pattern=maxStepAv, df.timeSeries$mean.steps)
fiveMInterval <- df.timeSeries$Time[maxStepAvIndex]
df2 <- data.frame(Interval=fiveMInterval, Steps=maxStepAv)
df2
</code></pre>
<pre><code>## Interval Steps
## 1 835 206.1698
</code></pre>
<p>The maximum average steps per period occur at interval 835, with an average of 206 steps per 5 minutes.</p>
<h1>Imputing missing values</h1>
<p>Calculating rows containing NA. False values on complete cases are the rows which contain NA.</p>
<p>False cases represent rows missing step entries.</p>
<pre><code class="r">table(complete.cases(activity))
</code></pre>
<pre><code>##
## FALSE TRUE
## 2304 15264
</code></pre>
<p>Loop over time intervals where step data is NA and replace the value with the average step value for that interval.</p>
<pre><code class="r">intervals <- unique(activity$interval)
a <- which(is.na(activity$steps))
for (i in intervals){
b <- which(activity$interval== i)
empties <- intersect(a, b)
intervalPosition <- which(df.timeSeries$Time == i)
intMean <- df.timeSeries$mean.steps[intervalPosition]
activity$steps[empties] <- intMean
}
</code></pre>
<p>Complete cases is run again to show all cases are complete. No false cases means that all NAs have been replaced with mean data for that interval.</p>
<pre><code class="r">A <- complete.cases(activity)
table(A)
</code></pre>
<pre><code>## A
## TRUE
## 17568
</code></pre>
<p>Making the histograms with the new data</p>
<pre><code class="r">activitySum <- tapply(activity$steps, activity$date, sum)
df.actSum <- data.frame(date=names(activitySum), sum.steps=activitySum)
##plot
ggplot(df.actSum,aes(x=sum.steps)) +
geom_histogram(fill = "red", alpha = 0.4)
</code></pre>
<pre><code>## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-14"/> </p>
<pre><code class="r">## Mean
activityMeans <- tapply(activityNAR$steps, activityNAR$date, sum)
AvStepsPerDay <- mean(activityMeans, na.rm=TRUE)
AvStepsPerDay
</code></pre>
<pre><code>## [1] 10766.19
</code></pre>
<p>After imputing the data with the method, the number of daily steps shows a fractional difference between the mean and median. From these observations, it seems that the impact of imputing missing values on the total number of daily steps is negligible.</p>
<h1>Are there differences in activity patterns between weekdays and weekends?</h1>
<p>When creating the column, Monday, Tuesday, Wednesday, Thursday, Friday are weekday and Saturday and Sunday are Weekends.</p>
<pre><code class="r">activity$day <- weekdays(as.Date(activity$date, "%Y-%m-%d"))
weekend <- c('Saturday', 'Sunday')
activity$weekday <- factor((activity$day %in% weekend), levels=c(TRUE, FALSE), labels=c('weekend', 'weekday'))
</code></pre>
<p>To plot weekdays Vs. weekends</p>
<pre><code class="r">DayEnd <- split(activity, activity$weekday)
weekendData <- data.frame(DayEnd[[1]])
weekdayData <- data.frame(DayEnd[[2]])
TimeSeries <- tapply(weekendData$steps, weekendData$interval, mean)
TimeSeries2 <- tapply(weekdayData$steps, weekdayData$interval, mean)
#weekend df
df.timeSeries <- data.frame(Time=as.numeric(as.character(names(TimeSeries))), mean.steps=TimeSeries, weekday="weekend")
df.timeSeries$IntervalMinutes <- (df.timeSeries$Time %% 100) + (df.timeSeries$Time %/% 100)*60
#weekday df
df.timeSeries2 <- data.frame(Time=as.numeric(as.character(names(TimeSeries2))), mean.steps=TimeSeries2, weekday="weekday")
df.timeSeries2$IntervalMinutes <- (df.timeSeries2$Time %% 100) + (df.timeSeries2$Time %/% 100)*60
df.full <- rbind(df.timeSeries2, df.timeSeries)
## q plot panel of weekend and weekday
qplot(IntervalMinutes, mean.steps, data=df.full, geom="line", color=weekday) + facet_grid(~weekday, scales='free', space='free') +labs(x = "Time in minutes", y = "Mean average of steps", title = "Average steps through the day")
</code></pre>
<p><img src="data:image/png;base64,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" 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