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<p>File <em>activity.csv</em> was obtained from URL <a href="https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip">https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip</a>
Download and unzip the file.</p>
<p><em>Necessary packages</em></p>
<pre><code class="r">library("dplyr")
library("ggplot2")
</code></pre>
<p>##Loading and preprocessing the data
To reproduce my results you have top set working directory. It should contain file activity.csv obtained from the URL above. I use D:\Eugene\R\RR. Please make necessary adjustments.</p>
<pre><code class="r">setwd("D:/Eugene/R/RR")
activity <- read.csv("activity.csv")
#converting second column to Date
activity[,2] <- as.Date(activity[,2])
# making interval more descriptive
activity[,3] <- paste(substr(sprintf("%00004d",activity[,3]),1,2),':',substr(sprintf("%00004d",activity[,3]),3,4),sep="")
# help column - minutes from midnight
activity[,4] <- strtoi(substr(activity[,3],1,2),base=10)*60 + strtoi(substr(activity[,3],4,5),base=10)
names(activity)[4] <- "minutes"
</code></pre>
<p>##What is mean total number of steps taken per day?</p>
<p>Let us calculate the total number of steps taken per day</p>
<pre><code class="r">total_steps <- activity %>%
group_by(date) %>%
summarize(total_steps = sum(steps, na.rm=TRUE))
total_steps
</code></pre>
<pre><code>## Source: local data frame [61 x 2]
##
## date total_steps
## 1 2012-10-01 0
## 2 2012-10-02 126
## 3 2012-10-03 11352
## 4 2012-10-04 12116
## 5 2012-10-05 13294
## 6 2012-10-06 15420
## 7 2012-10-07 11015
## 8 2012-10-08 0
## 9 2012-10-09 12811
## 10 2012-10-10 9900
## .. ... ...
</code></pre>
<p>Now let us draw a histogram of the total number of steps taken each day.</p>
<pre><code class="r"># We should make vector from dataframe column
steps_hist <- total_steps[['total_steps']]
# Now the histogram
hist(steps_hist, right=FALSE, breaks=seq(0,22000,by=2000), xlim=c(0,25000),xlab="Steps per day", main="", col="red", xaxt='n', yaxt='n')
axis(side=1, at=seq(0,22000,2000),labels=seq(0,22000,2000))
axis(side=2, at=seq(0,16,2),labels=seq(0,16,2))
abline(v=mean(steps_hist), col="yellow")
abline(v=median(steps_hist), col="green")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>Mean value is marked yellow and the median is green.</p>
<p>Let us calculate the mean and the median of the total number of steps taken per day.</p>
<pre><code class="r">mean(steps_hist)
</code></pre>
<pre><code>## [1] 9354.23
</code></pre>
<pre><code class="r">median(steps_hist)
</code></pre>
<pre><code>## [1] 10395
</code></pre>
<p>##What is the average daily activity pattern?
Here is a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis).</p>
<pre><code class="r">mean_steps <- activity %>%
group_by(minutes) %>%
summarize(mean_steps = mean(steps, na.rm=TRUE))
plot(mean_steps,type="l", main="Average number of steps during a day", ylab="Steps taken", xlab="Hours", xlim=c(0,1440),xaxt="n")
axis(side=1, at=seq(0,1440,60),labels=seq(0,24,1))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-6"/> </p>
<p>Let us determine 5-minute interval, on average across all the days in the dataset, containing the maximum number of steps.</p>
<pre><code class="r">mean_steps_sorted <- arrange(mean_steps,desc(mean_steps))
paste(mean_steps_sorted[1,1] %/% 60,"hours",mean_steps_sorted[1,1] %% 60,"minutes -",round(mean_steps_sorted[1,2],2),"steps")
</code></pre>
<pre><code>## [1] "8 hours 35 minutes - 206.17 steps"
</code></pre>
<p>As we can see, the interval containig maximum number of steps is 515 minutes from midnight, which corresponds to 8:35 - 8:40</p>
<p>##Imputing missing values</p>
<p>There are plenty of missing values in the dataset.</p>
<pre><code class="r">NA_values_count <- length(activity[is.na(activity[,1]),1])
NA_values_count
</code></pre>
<pre><code>## [1] 2304
</code></pre>
<p>Now we need to fill in missing data (NA). Let us suppose that all days are almost equal. When there is no data for the interval, the rounded mean value for the same interval through all days will be used.
Let us create a new dataset that is equal to the original dataset but with the missing data filled in.</p>
<pre><code class="r">activity_processed <- activity
#mean_steps is shorter and will be reused
#merge would be much safer
activity_processed[,5] <- round(mean_steps[,2])
#now we replace missing values with averages
activity_processed[is.na(activity_processed[,1]),1] <- activity_processed[is.na(activity_processed[,1]),5]
#and drop the last column - do not need it anymore
activity_processed <- activity_processed[-5]
#now will redo the same steps as before
total_steps_processed <- activity_processed %>%
group_by(date) %>%
summarize(total_steps = sum(steps, na.rm=TRUE))
</code></pre>
<p>Here is a histogram of the total number of steps taken each day with the missing data replaced.</p>
<pre><code class="r">steps_hist_processed <- total_steps_processed[['total_steps']]
hist(steps_hist_processed, right=FALSE, breaks=seq(0,22000,by=2000), xlim=c(0,25000),xlab="Steps per day", main="", col="red", xaxt='n', yaxt='n')
axis(side=1, at=seq(0,22000,2000),labels=seq(0,22000,2000))
axis(side=2, at=seq(0,24,2),labels=seq(0,24,2))
abline(v=mean(steps_hist_processed), col="yellow")
abline(v=median(steps_hist_processed), col="green")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p>
<pre><code class="r">mean(steps_hist_processed)
</code></pre>
<pre><code>## [1] 10765.64
</code></pre>
<pre><code class="r">median(steps_hist_processed)
</code></pre>
<pre><code>## [1] 10762
</code></pre>
<p>Mean and median are almost the same now and both have grown. As we can see, imputing missing data had some impact on our average. Some zeroes became positive numbers, and average numbers grew.
Even more impact we can see at the histogram. The bucket 12000-14000 now contains 24 days!</p>
<p>##Are there differences in activity patterns between weekdays and weekends?</p>
<p>Let us create a new factor variable in the dataset with two levels – “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.</p>
<pre><code class="r">activity_processed[format(activity_processed[,2], "%u") %in% c(6, 7),5] <- 'weekend'
activity_processed[format(activity_processed[,2], "%u") %in% 1:5,5] <- 'weekday'
names(activity_processed)[5] <- "type"
</code></pre>
<p>Here is a panel plot containing a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).</p>
<pre><code class="r">mean_steps_week <- activity_processed %>%
group_by(type, minutes) %>%
summarize(mean_steps = mean(steps, na.rm=TRUE))
m<-qplot(x=minutes,y=mean_steps,data=mean_steps_week,xlim=c(0,1440), facets=type~.,ylab="Steps taken",xlab="Hours")
m<-m + scale_x_continuous(breaks=seq(0,1440,60),labels=seq(0,24,1))+geom_line(colour="black",size=0)
</code></pre>
<pre><code>## Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
</code></pre>
<pre><code class="r">print(m)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-12"/> </p>
<p>As we can see, patterns are slightly different.On weekdays there is considerably more steps at the morning (when all go to their working places) and considerably less at the late evening. Looks like men stroll more at weekends.</p>
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