Introduction

Rpub Link: Click Here

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

The variables included in this dataset are:

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

Assignment

This assignment will be described in multiple parts. You will need to write a report that answers the questions detailed below. Ultimately, you will need to complete the entire assignment in a single R markdown document that can be processed by knitr and be transformed into an HTML file.

Throughout your report make sure you always include the code that you used to generate the output you present. When writing code chunks in the R markdown document, always use echo = TRUE so that someone else will be able to read the code. This assignment will be evaluated via peer assessment so it is essential that your peer evaluators be able to review the code for your analysis.

For the plotting aspects of this assignment, feel free to use any plotting system in R (i.e., base, lattice, ggplot2)

Fork/clone the GitHub repository created for this assignment. You will submit this assignment by pushing your completed files into your forked repository on GitHub. The assignment submission will consist of the URL to your GitHub repository and the SHA-1 commit ID for your repository state.

NOTE: The GitHub repository also contains the dataset for the assignment so you do not have to download the data separately.

Qns 1: Loading and preprocessing the data

Show any code that is needed to

  1. Load the data (i.e. read.csv())

  2. Process/transform the data (if necessary) into a format suitable for your analysis

# Load all required library
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date
library(ggplot2)
library(httpuv)
## Warning: package 'httpuv' was built under R version 3.4.3
# Load file and have a quick look
setwd("C:/Users/TAT/Desktop/Coursera/Lectures/5) Reproducible Research/Assignment 1")
activity <- read.csv("activity.csv", header=TRUE, na.strings = "NA")

# Clean up date class
activity$date <- ymd(activity$date)

# Remove NA
activity1 <- na.omit(activity)

# Make new variables 
#activity$monthly <- as.numeric(format(activity$date, "%m"))
#activity$daily <- as.numeric(format(activity$date, "%d"))

# Quick check
summary(activity1)
##      steps             date               interval     
##  Min.   :  0.00   Min.   :2012-10-02   Min.   :   0.0  
##  1st Qu.:  0.00   1st Qu.:2012-10-16   1st Qu.: 588.8  
##  Median :  0.00   Median :2012-10-29   Median :1177.5  
##  Mean   : 37.38   Mean   :2012-10-30   Mean   :1177.5  
##  3rd Qu.: 12.00   3rd Qu.:2012-11-16   3rd Qu.:1766.2  
##  Max.   :806.00   Max.   :2012-11-29   Max.   :2355.0
str(activity1)
## 'data.frame':    15264 obs. of  3 variables:
##  $ steps   : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ date    : Date, format: "2012-10-02" "2012-10-02" ...
##  $ interval: int  0 5 10 15 20 25 30 35 40 45 ...
##  - attr(*, "na.action")=Class 'omit'  Named int [1:2304] 1 2 3 4 5 6 7 8 9 10 ...
##   .. ..- attr(*, "names")= chr [1:2304] "1" "2" "3" "4" ...
head(activity1)
##     steps       date interval
## 289     0 2012-10-02        0
## 290     0 2012-10-02        5
## 291     0 2012-10-02       10
## 292     0 2012-10-02       15
## 293     0 2012-10-02       20
## 294     0 2012-10-02       25
tail(activity1)
##       steps       date interval
## 17275     0 2012-11-29     2330
## 17276     0 2012-11-29     2335
## 17277     0 2012-11-29     2340
## 17278     0 2012-11-29     2345
## 17279     0 2012-11-29     2350
## 17280     0 2012-11-29     2355

Qns 2: 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. Make a histogram of the total number of steps taken each day
# Summarize data for ggplot
activity2 <- summarize(group_by(activity1,date),daily.step=sum(steps))
mean.activity <- as.integer(mean(activity2$daily.step))
median.activity <- as.integer(median(activity2$daily.step))

# Plot histogram
plot.steps.day <- ggplot(activity2, aes(x=daily.step)) + 
  geom_histogram(binwidth = 1000, aes(y=..count.., fill=..count..)) + 
  geom_vline(xintercept=mean.activity, colour="red", linetype="dashed", size=1) +
  geom_vline(xintercept=median.activity, colour="green" , linetype="dotted", size=1) +
  labs(title="Histogram of Number of Steps taken each day", y="Frequency", x="Daily Steps") 
plot.steps.day

  1. Calculate and report the mean and median total number of steps taken per day
# Mean total number of steps taken per day
mean.activity
## [1] 10766
# Median total number of steps taken per day
median.activity
## [1] 10765

Mean total number of steps taken per day is 10766.

Median total number of steps taken per day is 10765.

Qns 3: What is the average daily activity pattern?

  1. Make 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 days (y-axis)
# Prepare data for ggplot
activity3 <- activity1 %>% group_by(interval) %>% summarize(mean.step=mean(steps))

# Plot average number of steps by 5-minute interval
plot.step.interval <- ggplot(activity3, aes(x=interval,y=mean.step)) + 
  geom_line(color="red") + 
  labs(title="Average Number of Steps Taken vs 5-min Interval", y="Average Number of Steps", x="5-min Interval Times Series")
plot.step.interval

  1. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
optimal <- which.max(activity3$mean.step)
optimal.step <- activity3$interval[optimal]
sprintf("Maximum number of steps is coming from %gth 5-min interval", optimal.step)
## [1] "Maximum number of steps is coming from 835th 5-min interval"

Qns 4: 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)
# Total number of missing values in the dataset
sum(is.na(activity))
## [1] 2304

Ans: Total number of missing values in the dataset is 2304.

  1. Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc.
impute.activity <- activity
impute.activity$steps[is.na(impute.activity$steps)] <- mean(impute.activity$steps,na.rm=TRUE)
impute.activity$steps <- as.numeric(impute.activity$steps)
impute.activity$interval <- as.numeric(impute.activity$interval)
colSums(is.na(impute.activity))
##    steps     date interval 
##        0        0        0
  1. Create a new dataset that is equal to the original dataset but with the missing data filled in.
summary(impute.activity)
##      steps             date               interval     
##  Min.   :  0.00   Min.   :2012-10-01   Min.   :   0.0  
##  1st Qu.:  0.00   1st Qu.:2012-10-16   1st Qu.: 588.8  
##  Median :  0.00   Median :2012-10-31   Median :1177.5  
##  Mean   : 37.38   Mean   :2012-10-31   Mean   :1177.5  
##  3rd Qu.: 37.38   3rd Qu.:2012-11-15   3rd Qu.:1766.2  
##  Max.   :806.00   Max.   :2012-11-30   Max.   :2355.0
  1. 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?
# Summarize data by date
impute.activity2 <- summarize(group_by(impute.activity,date),daily.step=sum(steps))

mean.impute   <- as.integer(mean(impute.activity2$daily.step))
mean.impute
## [1] 10766
median.impute <- as.integer(median(impute.activity2$daily.step))
median.impute
## [1] 10766
# Plot histogram
plot.steps.day <- ggplot(impute.activity2, aes(x=daily.step)) + 
  geom_histogram(binwidth = 1000, aes(y=..count.., fill=..count..)) + 
  geom_vline(xintercept=mean.impute, colour="red", linetype="dashed", size=1) +
  geom_vline(xintercept=median.impute, colour="green" , linetype="dotted", size=1) +
  labs(title="Histogram of Number of Steps taken each day (impute)", y="Frequency", x="Daily Steps")
plot.steps.day

Ans: Mean total number of steps taken per day (after impute) is 10766.

Ans: Median total number of steps taken per day (after impute) is 10766.

Observation: After imputation, the mean and median become the same. Imputing with mean values make the histogram more normally distributed.

Qns 5: Are there differences in activity patterns between weekdays and weekends?

For this part the weekdays() function may be of some help here. Use the dataset with the filled-in missing values for this part.

  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.
impute.activity$day <- ifelse(weekdays(impute.activity$date) %in% c("Saturday","Sunday"), "weekday", "weekend")
  1. 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). The plot should look something like the following, which was created using simulated data:
# Preparing data for ggplot
impute.df <- impute.activity %>% group_by(interval,day) %>% summarise(mean.step=mean(steps))

# Plot Average steps across weekday/weekend vs 5-min interval Time Series
plot.weekday.interval <- ggplot(impute.df, aes(x=interval, y=mean.step, color=day)) + 
  facet_grid(day~.) +
  geom_line() + 
  labs(title="Average Number of Steps Taken vs 5-min Interval on Weekday/Weekend", y="Average Number of Steps", x="5-min Interval Times Series")
plot.weekday.interval