Reproducible Research: Peer Assessment 1

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.5.1

library(scales)
library(Hmisc)

## Warning: package 'Hmisc' was built under R version 3.5.1

## Loading required package: lattice

## Loading required package: survival

## Loading required package: Formula

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:base':
## 
##     format.pval, units

Loading and preprocessing the data

if(!file.exists('activity.csv')){
    unzip('activity.zip')
}
activity <- read.csv('activity.csv')

What is mean total number of steps taken per day?

1. the total steps per day

TotalStepsPerDay<- tapply(activity$steps,activity$date, sum, na.rm=T)

2. histogram for the total number of steps taken each day

qplot(TotalStepsPerDay,xlab='Total steps per day', ylab='Frequency', binwidth=500,col="red")

3. the mean and median of the steps for each day

MeanStepsPerDay= mean(TotalStepsPerDay)
MedianStepsPerDay= median(TotalStepsPerDay)

What is the average daily activity pattern?

1. mean steps per day ##### it must be aggregated by the interval because day might have been occured more than one time

AverageStepsPerDay<- aggregate(x=list(steps=activity$steps),by=list(interval=activity$interval),
                               FUN=mean, na.rm=TRUE)

2. the time series plot

ggplot(data=AverageStepsPerDay, aes(x=interval, y=steps)) +
    geom_line() +
    xlab("5-minute interval") +
    ylab("average number of steps taken")

3. the maximum number of steps by the 5-minute interval

MaxNumberOfSteps <- which.max(AverageStepsPerDay$steps)
timeOfMaxSteps <-  gsub("([0-9]{1,2})([0-9]{2})", "\\1:\\2", AverageStepsPerDay[MaxNumberOfSteps,'interval'])

Imputing missing values

1. the total number of missing values

NumberOfMissingValues<- length(which(is.na(activity$steps)))

2. my strategy would be filling the missing values spaces with the mean of the steps
3. creating a new dataset with replaced missing values by the mean number of steps

activityDataWithoutNA <- activity
activityDataWithoutNA$steps <- impute(activity$steps, fun=mean)

4. histogram for the total number of steps taken each day but with no missing values

TotalStepsPerDayWithoutNA <- tapply(activityDataWithoutNA$steps, activityDataWithoutNA$date, sum)
qplot(TotalStepsPerDayWithoutNA, xlab='Total steps per day (without NA)', ylab='Frequency', binwidth=500,col="red")

#### the mean and median of the steps for each day

stepsByDayMeanWithoutNA<- mean(TotalStepsPerDayWithoutNA)
stepsByDayMedianWithoutNA <- median(TotalStepsPerDayWithoutNA)

we notice that filling the empty spaces increases trhe values of mean and median

Are there differences in activity patterns between weekdays and weekends?

1. factorizing days between weekday or weekind by mutating a new column

activityDataWithoutNA$DayType <-  ifelse(as.POSIXlt(activityDataWithoutNA$date)$wday %in% c(0,6), 'weekend', 'weekday')

2. Makeing a panel plot containing a time series plot

AverageStepsPerDayWithoutNA <- aggregate(steps ~ interval + DayType, data=activityDataWithoutNA, mean)
ggplot(AverageStepsPerDayWithoutNA, aes(interval, steps)) + 
    geom_line() + 
    facet_grid(DayType ~ .) +
    xlab("5-minute interval") + 
    ylab("avarage number of steps")