Course Project 1

Introduction

It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the “quantified self” movement - a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.

This assignment makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day.

The data for this assignment can be downloaded from the course web site:

• Dataset: Activity monitoring data

The variables included in this dataset are:

• steps: Number of steps taking in a 5-minute interval (missing values are coded as NA)
• date: The date on which the measurement was taken in YYYY-MM-DD format
• interval:Identifier for the 5-minute interval in which measurement was taken

The dataset is stored in a comma-separated-value (CSV) file and there are a total of 17,568 observations in this dataset.

Assignment

Package to use

library(ggplot2)
library(dplyr)

Loading and preprocessing the data

Show any code that is needed to

1. Load the data (i.e. read.csv())

activity = read.csv('activity.csv', header = T)

What is mean total number of steps taken per day?

For this part of the assignment, you can ignore the missing values in the dataset.

1. Calculate the total number of steps taken per day

sum(activity$steps, na.rm = TRUE)

## [1] 570608

2. Make a histogram of the total number of steps taken each day

step_date <- aggregate(steps~date, data=activity, FUN=sum, na.rm=TRUE)
ggplot(step_date, aes( x = steps )) + 
      geom_histogram(binwidth=3000, fill="#9F7EC4", color="#FAA3F4", alpha=0.7) + 
      ggtitle("Number of steps taken each day")

3. Calculate and report the mean and median of the total number of steps taken per day

mean(step_date$steps) #mean

## [1] 10766.19

median(step_date$steps) #median

## [1] 10765

What is the average daily activity pattern?

1. Make a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)

mean_interval <- aggregate(steps~interval, data=activity, FUN=mean, na.rm=TRUE)
ggplot(mean_interval, aes( x = interval, y = steps  )) +
      geom_line(color="#9F7EC4") +
      ggtitle("Average number of steps taken of the 5-minute interval")

2. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?

max_step <- max(mean_interval$steps)
mean_interval$prueba <- ifelse(max_step == mean_interval$steps, 1, NA)
mean_interval <- mean_interval[!is.na(mean_interval$prueba),]
mean_interval$interval 

## [1] 835

Imputing missing values

Note that there are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.

1. Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs).

activity_na <- activity
activity_na$na <- ifelse(is.na(activity_na) , 1, 0)
sum(activity_na$na)

## [1] 2304

2. Devise a strategy for filling in all of the missing values in the dataset.

We will use the mean per interval

3. Create a new dataset that is equal to the original dataset but with the missing data filled in.

activity_p <- activity
mean_interval <- aggregate(steps~interval, data=activity, FUN=mean, na.rm=TRUE)
for (i in 1:17568){
if(is.na(activity$steps[i])){
      mean_interval_d <- mean_interval
      mean_interval_d <- filter(mean_interval_d, mean_interval_d$interval == activity$interval[i])
      activity_p$steps[i] <-  mean_interval_d$steps[1]
}
}
head(activity_p)

##       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

4. Make a histogram of the total number of steps taken each day

step_date_p <- aggregate(steps~date, data=activity_p, FUN=sum)
ggplot(step_date_p, aes( x = steps )) + 
      geom_histogram(binwidth=3000, fill="#9F7EC4", color="#FAA3F4", alpha=0.7) + 
      ggtitle("Number of steps taken each day")

5. Calculate and report the mean and median total number of steps taken per day.

mean(step_date_p$steps) #mean

## [1] 10766.19

median(step_date_p$steps) #median

## [1] 10766.19

Values are similar to when we omit NAs

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.

activity_p$day <- weekdays(as.Date(activity_p$date))
activity_p$week <- ifelse(activity_p$day == "s攼㸱bado" | activity_p$day == "domingo","weekend","weekday")
head(activity_p)

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

2. Make a panel plot containing a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).

activity_p_weekday <- filter(activity_p, week == "weekday")
activity_p_weekend <- filter(activity_p, week == "weekend")
mean_interval_p_weekday <- aggregate(steps~interval, data=activity_p_weekday, FUN=mean, na.rm=TRUE)
mean_interval_p_weekend <- aggregate(steps~interval, data=activity_p_weekend, FUN=mean, na.rm=TRUE)
mean_interval_p_weekday$day <- "weekday"
mean_interval_p_weekend$day <- "weekend"
mean_interval_p_w <- rbind(mean_interval_p_weekday,mean_interval_p_weekend)
ggplot(mean_interval_p_w, aes( x = interval, y = steps  )) +
      geom_line(aes(color = day)) +
      ggtitle("Average number of steps taken of the 5-minute interval") + facet_grid(. ~ day)