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.

Data

The variables included in this dataset are:

Steps: Number of steps taking in a 5-minute interval.

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.

Questions

  1. What is mean total number of steps taken per day?

  2. What is the average daily activity pattern?

  3. Are there differences in activity patterns between weekdays and weekends?

Loading the data

Sys.setlocale("LC_TIME", "English")
## [1] "English_United States.1252"
my_data <- read.csv("activity.csv", sep = ",", header = TRUE)

Calculate the total number of steps taken per day.

library(ggplot2)
library(lubridate)
my_data$date <- as.Date(my_data$date)
my_data$day <- day(my_data$date)
my_subset <- na.omit(my_data)
tst <- aggregate(my_subset$steps ~ my_subset$date, FUN = sum)
tst[,2]
##  [1]   126 11352 12116 13294 15420 11015 12811  9900 10304 17382 12426
## [12] 15098 10139 15084 13452 10056 11829 10395  8821 13460  8918  8355
## [23]  2492  6778 10119 11458  5018  9819 15414 10600 10571 10439  8334
## [34] 12883  3219 12608 10765  7336    41  5441 14339 15110  8841  4472
## [45] 12787 20427 21194 14478 11834 11162 13646 10183  7047

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

ggplot(my_subset, aes(x=date, y=steps)) + 
  geom_histogram(stat="identity", binwidth = 30) + 
  ggtitle("Total number of steps taken per day (separated by month)") + 
  xlab("Date") + 
  ylab("Steps")

ggplot(my_subset, aes(x=day, y=steps)) + 
  geom_histogram(stat="identity") + 
  ggtitle("Total number of steps taken per day") + 
  xlab("Day") + 
  ylab("Steps")

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

mean(tst[,2])
## [1] 10766.19
median(tst[,2])
## [1] 10765

What is the average daily activity pattern? Make a time series plot of the 5-minute interval and the average number of steps taken, averaged across all days.

avgSteps <- aggregate(my_subset$steps ~ my_subset$interval, FUN = mean)
names(avgSteps) <- c("interval", "steps")

ggplot(avgSteps, aes(x=avgSteps[,1], y=avgSteps[,2])) + 
  geom_line() + 
  xlab("Interval") + 
  ylab("Avg Steps")

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

avgSteps[avgSteps[,2] == max(avgSteps[,2]),]
##     interval    steps
## 104      835 206.1698

Imputing missing values

sum(is.na(my_data))
## [1] 2304

Devise a strategy for filling in all of the missing values in the dataset. Create a new dataset that is equal to the original dataset but with the missing data filled in.

new_data <- my_data
for(i in 1:nrow(new_data)){
  if(is.na(new_data$steps[i])){
    new_data$steps[i] <- mean(new_data$steps, na.rm = TRUE)
  }
}
sum(is.na(new_data))
## [1] 0

Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?

ggplot(new_data, aes(x=date, y=steps)) + 
  geom_histogram(stat="identity") + 
  ggtitle("Total number of steps taken per day (separated by month)") + 
  xlab("Date") + 
  ylab("Steps")

ggplot(new_data, aes(x=day, y=steps)) + 
  geom_histogram(stat="identity") + 
  ggtitle("Total number of steps taken per day") + 
  xlab("Day") + 
  ylab("Steps")

a <- aggregate(new_data$steps ~ new_data$date, FUN = sum)
mean(a[,2])#no change
## [1] 10766.19
median(a[,2])#small change
## [1] 10766.19

Are there differences in activity patterns between weekdays and weekends?

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.

new_data$weekday <- ifelse(weekdays(new_data$date) == "Saturday" | weekdays(new_data$date) == "Sunday", "weekend", "weekday")
table(new_data$weekday)
## 
## weekday weekend 
##   12960    4608

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

agg <- aggregate(new_data$steps, list(new_data$interval, new_data$weekday), FUN = mean)
names(agg) <- c("steps", "weekday", "interval")

ggplot(agg, aes(x=steps, y=interval)) + 
  geom_line() + 
  xlab("Interval") + 
  ylab("Avg Steps") + 
  facet_grid(.~weekday)