Part 1

Loading and preprocessing the data

In this Section, the activity data is loaded. If we run the command str(activitydat), it can be seen that, the date variable is stored as a factor. So, one of the preprocessing steps is to convert the date variable into Date data type.

activitydat <- read.csv("activity.csv")
activitydat$date <- as.Date(activitydat$date)
str(activitydat)

## 'data.frame':    17568 obs. of  3 variables:
##  $ steps   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ date    : Date, format: "2012-10-01" "2012-10-01" ...
##  $ interval: int  0 5 10 15 20 25 30 35 40 45 ...

Part 2

What is mean total number of steps taken per day?

In this Section, the total number of steps for each day is calculated using the aggregate function. A sample of the aggregation is shown below. Then, a histogram of the number of steps taken each day is plotted. Finally, the mean and median of the total number of steps is calculated.

steps_day <- aggregate(steps ~ date, sum, data = activitydat)
head(steps_day)

##         date steps
## 1 2012-10-02   126
## 2 2012-10-03 11352
## 3 2012-10-04 12116
## 4 2012-10-05 13294
## 5 2012-10-06 15420
## 6 2012-10-07 11015

hist(steps_day$steps, 
     xlab = "Steps taken Each day",
     ylab = "Number of Days",
     main = "Histogram of Steps taken Each Day")

mean(steps_day$steps)

## [1] 10766.19

median(steps_day$steps)

## [1] 10765

Part 3

What is the average daily activity pattern?

In this Section, the daily activity pattern averaged over the 5 minute interval is studied via a time series plot. The interval containing the maximum number of steps is also found.

pattern <- aggregate(steps ~ interval, mean, data = activitydat)
library(ggplot2)
qplot(interval,steps,data = pattern, 
      geom = "line", 
      xlab = "5 Minute Interval",
      ylab = "Average Number of steps",
      main = "Daily Activity Pattern")

pattern[(pattern$steps == max(pattern$steps)),]

##     interval    steps
## 104      835 206.1698

Part 4

Imputing missing values

Calculating the Number of missing values in the data set.

NumMissing <- which(is.na(activitydat))

The Number of missing values in the dataset is 2304

Imputing Missing Values using Mean
The Missing values are imputed using the mean number of steps for each day and interval.

imputedat <- activitydat
for (i in 1 : nrow(imputedat))
{
  if(is.na(imputedat$steps[i]))
  {
    ind <- which((pattern$interval == imputedat$interval[i]))
    imputedat$steps[i] <- pattern$steps[ind]
  }
}

New data set with imputed values.

head(imputedat)

##       steps       date interval
## 1 1.7169811 2012-10-01        0
## 2 0.3396226 2012-10-01        5
## 3 0.1320755 2012-10-01       10
## 4 0.1509434 2012-10-01       15
## 5 0.0754717 2012-10-01       20
## 6 2.0943396 2012-10-01       25

Histogram and report of Mean and Median

imp_steps_day <- aggregate(steps ~ date, sum, data = imputedat)
head(imputedat)

##       steps       date interval
## 1 1.7169811 2012-10-01        0
## 2 0.3396226 2012-10-01        5
## 3 0.1320755 2012-10-01       10
## 4 0.1509434 2012-10-01       15
## 5 0.0754717 2012-10-01       20
## 6 2.0943396 2012-10-01       25

hist(imp_steps_day$steps, 
     xlab = "Steps taken Each day", 
     ylab = "Number of Days", 
     main = "Histogram of Steps taken Each Day - After Imputing")

mean(imp_steps_day$steps)

## [1] 10766.19

median(imp_steps_day$steps)

## [1] 10766.19

Part 5

Are there differences in activity patterns between weekdays and weekends?

Creating a new factor variable - day In this section a new variable day is created.

library(timeDate)
for (i in 1: nrow(imputedat)) 
  {
  if(isWeekend(imputedat$date[i])) 
    imputedat$day[i] <- "Weekend" 
  else 
    imputedat$day[i] <- "Weekday" 
  }

imputedat$day <- as.factor(imputedat$day)

Creating a Panel Plot for Weekdays and Weekends

patternwk <- aggregate(steps ~ interval+day, mean, data = imputedat)
qplot(interval,steps,data = patternwk, 
      geom = "line", 
      xlab = "5 Minute Interval",
      ylab = "Average Number of steps",
      main = "Daily Activity Pattern", facets = (day ~ .)
      )

Reproducible Research: Peer Assessment 1

Srividya