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title author date output
Peer Graded Assignment_1
Anna Litvinenko
8/23/2017
html_document

1. Reading data

Cleaning the workspace and loading the libraries

remove(list=ls())
library(ggplot2)
library(dplyr)

Loading data assuming the zipped data was saved to the working directory

unzip("activity.zip", exdir = getwd())
activity <- read.csv("activity.csv", na.strings = "NA")

Converting dates into date format

activity$date <- as.Date(activity$date)

2. Histogram of the total number of steps taken each day

Aggregating the total number of steps by date and plotting with ggplot2

total.steps.day <- summarise(group_by(activity, date), steps=sum(steps))
ggplot(data=total.steps.day, aes(x=date, y=steps))+geom_bar(stat="identity")+
        ggtitle("Total Number Of Steps Taken Each Day")+xlab("Date")+
        ylab("Number of steps")+theme(axis.text.x=element_text(angle = 90, hjust = 0))+
        scale_x_date(date_labels="%d %b %y",date_breaks  ="5 days")
## Warning: Removed 8 rows containing missing values (position_stack).

![plot of chunk hist_total steps](figure/hist_total steps-1.png)

3. Mean and median number of steps taken each day

mean(total.steps.day$steps, na.rm = TRUE)
## [1] 10766.19

median(total.steps.day$steps, na.rm = TRUE)
## [1] 10765

4. Time series plot of the average number of steps taken

Aggregating the average number of steps by interval and plotting with ggplot2

average.steps.interval <- summarise(group_by(activity, interval), steps=mean(
        steps, na.rm = TRUE))
ggplot(data=average.steps.interval, aes(x=interval, y=steps))+geom_line()+
        ggtitle("Average Number Of Steps Taken Daily")+xlab("5-minute Interval")+
        ylab("Number of steps")+theme(axis.text.x=element_text(angle = 90, hjust = 0))+
        scale_x_continuous(breaks = pretty(average.steps.interval$interval, n=20))

![plot of chunk timeser_average steps](figure/timeser_average steps-1.png)

5. The 5-minute interval that, on average, contains the maximum number of steps

average.steps.interval[average.steps.interval[,"steps"]==max(average.steps.interval$steps, na.rm=TRUE),]
## # A tibble: 1 x 2
##   interval    steps
##      <int>    <dbl>
## 1      835 206.1698

6. Code to describe and show a strategy for imputing missing data

Taking a look at missing vales

sum(is.na(activity))
## [1] 2304

NAs <- subset(activity, is.na(activity))
NAs <- transform(NAs, count=1)
table(NAs$date, NAs$count)
##             
##                1
##   2012-10-01 288
##   2012-10-08 288
##   2012-11-01 288
##   2012-11-04 288
##   2012-11-09 288
##   2012-11-10 288
##   2012-11-14 288
##   2012-11-30 288

8 days are completely lost. These NAs will be substituted by the mean number of steps for a particular 5-minute interval across all days

activity.complete <- activity
for(i in 1:nrow(activity.complete)){
        if(is.na(activity.complete$steps[i])==TRUE){
                        activity.complete$steps[i] <- average.steps.interval$steps[match(activity.complete$interval[i], average.steps.interval$interval)]
        }
}

Checking if the NAs are gone

sum(is.na(activity.complete))
## [1] 0

Calculating new mean and median number for complete data

total.steps.day.complete <- summarise(group_by(activity.complete, date), steps=sum(steps))
mean(total.steps.day.complete$steps)
## [1] 10766.19

median(total.steps.day.complete$steps)
## [1] 10766.19

7. Histogram of the total number of steps taken each day after missing values are imputed

Using imputed data, aggregating the total number of steps by date and plotting with ggplot2

total.steps.day.complete <- summarise(group_by(activity.complete, date), steps=sum(steps))
ggplot(data=total.steps.day.complete, aes(x=date, y=steps))+geom_bar(stat="identity")+
        ggtitle("Total Number Of Steps Taken Each Day_Complete Data")+xlab("Date")+
        ylab("Number of steps")+theme(axis.text.x=element_text(angle = 90, hjust = 0))+
        scale_x_date(date_labels="%d %b %y",date_breaks  ="5 days")

![plot of chunk hist_total steps_imputed](figure/hist_total steps_imputed-1.png)

8. Panel plot comparing the average number of steps taken per 5-minute interval across weekdays and weekends

Recoding dates to "weekday" and "weekend"

activity.complete$daytype <- weekdays(activity.complete$date)
activity.complete$day <- ifelse(activity.complete$daytype %in% c("Saturday", "Sunday"), "weekend", "weekday")

Aggregating the average number of steps by weekday(end) and plotting with ggplot2

average.steps.interval.day <- summarise(group_by(activity.complete, interval, day), steps=mean(
        steps, na.rm = TRUE))
ggplot(data=average.steps.interval.day, aes(x=interval, y=steps))+geom_line()+
        ggtitle("Average Number Of Steps Taken Daily")+xlab("5-minute Interval")+
        ylab("Number of steps")+theme(axis.text.x=element_text(angle = 90, hjust = 0))+
        scale_x_continuous(breaks = pretty(average.steps.interval$interval, n=20))+
        facet_grid(day~.)

![plot of chunk panelplot_average steps by weekday(end)](figure/panelplot_average steps by weekday(end)-1.png)