Analysis of Steps taken by Persons

1.Loading and Preprocessing of data

library(knitr)

## Warning: package 'knitr' was built under R version 4.0.5

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.5

library(plyr)

## Warning: package 'plyr' was built under R version 4.0.5

data<-read.csv("activity.csv")
activitydata<-data[complete.cases(data),]

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

filterdata<-aggregate(activitydata$steps, by=list(activitydata$date), sum)
names(filterdata)<-c("date","steps")
ggplot(data=filterdata, aes(x= steps, col="purple"))+ geom_histogram(binwidth = 1000)+ labs(title = "Total steps taken per day",x="Steps", y="Frequency")

3.Mean and median number of steps taken each day

meansteps<-mean(filterdata$steps)
meansteps

## [1] 10766.19

mediansteps<-median(filterdata$steps)
mediansteps

## [1] 10765

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

timeseries<-aggregate(activitydata$steps, by=list(activitydata$interval), mean)
names(timeseries)<-c("Interval","Steps")
ggplot(data=timeseries, aes(x= Interval, y=Steps)) +geom_line()

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

maxStepInterval<-timeseries[which.max(timeseries$Steps),]
maxStepInterval

##     Interval    Steps
## 104      835 206.1698

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

totalNAs<-sum(is.na(data$steps))
totalNAs

## [1] 2304

index<-which(is.na(data$steps))
newData<-data
for (i in index)
{
      newData$steps[i]<- timeseries$Steps[which(timeseries$Interval== newData$interval[i])]
}

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

newFilterData<-aggregate(newData$steps , by=list(newData$date), sum)
names(newFilterData)<-c("date","steps")
ggplot(data=newFilterData, aes(x= steps, col="purple"))+ geom_histogram(binwidth = 1000)+ labs(title = "Total steps taken per day",x="Steps", y="Frequency")

8.Mean and Median after filling missing values

Newmeansteps<-mean(newFilterData$steps)
Newmeansteps

## [1] 10766.19

Newmediansteps<-median(newFilterData$steps)
Newmediansteps

## [1] 10766.19

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

days<-as.factor(weekdays(as.Date(activitydata$date)))
DayType<-revalue(days, c("Monday"="Weekday","Tuesday"="Weekday","Wednesday"="Weekday","Thursday"="Weekday","Friday"="Weekday","Saturday"="Weekend","Sunday"="Weekend"))
activitydata<-cbind(activitydata, DayType)
Newtimeseries<-aggregate(activitydata$steps, by=list(activitydata$interval, activitydata$DayType), mean)
names(Newtimeseries)<-c("Interval","DayType","Steps")
ggplot(data=Newtimeseries, aes(x= Interval, y=Steps)) +geom_line(aes(colour=DayType)) +facet_grid(DayType~.)