Reproducible Research: Project 1

library(lattice)

Loading and preprocessing the data

actdata <- read.csv("activity.csv")
actdata$date <- as.Date(actdata$date,"%Y-%m-%d")
head(actdata)

##   steps       date interval
## 1    NA 2012-10-01        0
## 2    NA 2012-10-01        5
## 3    NA 2012-10-01       10
## 4    NA 2012-10-01       15
## 5    NA 2012-10-01       20
## 6    NA 2012-10-01       25

What is mean total number of steps taken per day?

To Calculate Mean Compute total number of steps per day

totsteps <- tapply(actdata$steps, actdata$date,sum)

Plot histogram of total number of steps per day

hist(totsteps,col="blue",xlab="Total Steps per Day", 
      ylab="Frequency", main="Histogram of Total Steps taken per day")

Compute Mean total steps taken per day

mean(totsteps,na.rm=TRUE)

## [1] 10766.19

What is the average daily activity pattern?

Compute mean of steps over all days by time interval

meansteps <- tapply(actdata$steps,actdata$interval,
                                 mean,na.rm=TRUE)

Timeseries plot of of the 5-minute interval and the average number of steps taken, averaged across all days

plot(row.names(meansteps),meansteps,type="l",
     xlab="Time Intervals (5-minute)", 
     ylab="Mean number of steps taken (all Days)", 
     main="Average Steps Taken at 5 minute Intervals",
     col="blue")

Find the time interval that contains maximum average number of steps over all days

interval_num <- which.max(meansteps)
interval_max_steps <- names(interval_num)
interval_max_steps

## [1] "835"

We foundout that ** 835** minute or ** 104th ** 5 minute interval contains the maximum number of steps on average across all the days.

Imputing missing values

Compute the number of NA values in the activity dataset.

num_na_values <- sum(is.na(actdata))
num_na_values 

## [1] 2304

Fill in missing values using the average interval value across all days

na_indices <-  which(is.na(actdata))
imputed_values <- meansteps[as.character(actdata[na_indices,3])]
names(imputed_values) <- na_indices
for (i in na_indices) {
    actdata$steps[i] = imputed_values[as.character(i)]
}
sum(is.na(actdata)) 

## [1] 0

totsteps <- tapply(actdata$steps, actdata$date,sum)
hist(totsteps,col="red",xlab="Total Steps per Day", 
      ylab="Frequency", main="Histogram of Total Steps taken per day")

Are there differences in activity patterns between weekdays and weekends?

1. 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.
2. Make 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). See the README file in the GitHub repository to see an example of what this plot should look like using simulated data.

days <- weekdays(actdata$date)
actdata$day_type <- ifelse(days == "Saturday" | days == "Sunday", 
                                "Weekend", "Weekday")
meansteps <- aggregate(actdata$steps,
                                    by=list(actdata$interval,
                                            actdata$day_type),mean)
names(meansteps) <- c("interval","day_type","steps")
xyplot(steps~interval | day_type, meansteps,type="l",
       layout=c(1,2),xlab="Interval",ylab = "Number of steps")

Mean, Median, Max and min of the steps across all intervals and days by Weekdays/Weekends

tapply(meansteps$steps,meansteps$day_type,
       function (x) { c(MINIMUM=min(x),MEAN=mean(x),
                        MEDIAN=median(x),MAXIMUM=max(x))})

## $Weekday
##   MINIMUM      MEAN    MEDIAN   MAXIMUM 
##   0.00000  35.61058  25.80314 230.37820 
## 
## $Weekend
##   MINIMUM      MEAN    MEDIAN   MAXIMUM 
##   0.00000  42.36640  32.33962 166.63915