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.

Dataset

Activity monitoring data [52K]
The dataset is stored in a comma-separated-value (CSV) file and there are a total of 17,568 observations in this dataset.The variables included in this dataset are:

  • steps: Number of steps taking in a 5-minute interval (missing values are coded as )
  • 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

Question to be answered:

1. 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.

  • Calculate the total number of steps taken per day
  • If you do not understand the difference between a histogram and a barplot, research the difference between them. Make a histogram of the total number of steps taken each day
  • Calculate and report the mean and median of the total number of steps taken per day

2. What is the average daily activity pattern?

  • Make a time series plot (i.e. ) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
  • Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?

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

  • Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with s)
  • 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.
  • Create a new dataset that is equal to the original dataset but with the missing data filled in.
  • 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?

4. Are there differences in activity patterns between weekdays and weekends?
For this part the function may be of some help here. Use the dataset with the filled-in missing values for this part.

  • 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.
  • Make a panel plot containing a time series plot (i.e. ) 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.

CODE

Setup to show every code chunks

knitr::opts_chunk$set(echo = TRUE)

Loading and preprocessing the data

Look at data summary and few first lines

data=read.csv("activity.csv")
data$date=as.Date(as.character(data$date))
summary(data)
##      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.: 12.00   3rd Qu.:2012-11-15   3rd Qu.:1766.2  
##  Max.   :806.00   Max.   :2012-11-30   Max.   :2355.0  
##  NA's   :2304
head(data)
##   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

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

a. Total steps taken on each day

byStep=aggregate(data$steps,by=list(data$date),sum)
names(byStep)=c("Date","TotalSteps")
head(byStep)
##         Date TotalSteps
## 1 2012-10-01         NA
## 2 2012-10-02        126
## 3 2012-10-03      11352
## 4 2012-10-04      12116
## 5 2012-10-05      13294
## 6 2012-10-06      15420

b. Histogram of the total number of steps taken each day

hist(byStep$TotalSteps,breaks = 25,col = "steelblue",border = "white",
     main = "Total number of Steps per Day", xlab = "Total Steps",ylim = c(0,20))

c. Mean and median of the total number of steps taken per day

mean=round(mean(byStep$TotalSteps,na.rm=TRUE),2)
median=round(median(byStep$TotalSteps,na.rm = TRUE),2)
  • Mean of the total number of steps taken per day is: 10766.19
  • Median of the total number of steps taken per day is: 10765

2. What is the average daily activity pattern?

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)

byInterval=aggregate(na.omit(data)$steps,by=list(na.omit(data)$interval),mean)
names(byInterval)=c("Interval","Mean.Steps")

plot(x=byInterval$Interval,y=byInterval$Mean.Steps,type = "l",col =10,lwd=2,main ="Average of Steps accross all Days",xlab="Interval",ylab = "Step")

b. The 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps

byInterval[byInterval$Mean.Steps==max(byInterval$Mean.Steps),]
##     Interval Mean.Steps
## 104      835   206.1698

3. Imputing missing values

a. Total number of missing values in the dataset (i.e. the total number of rows with NAs)

sapply(X=data,FUN = function(x) sum(is.na(x)))
##    steps     date interval 
##     2304        0        0

b. All the NAs will be replaced by the mean of steps taken on its interval

c. New dataset that is equal to the original dataset but with the missing data filled in.

 complete.data=data
for (i in c(which(is.na(data$steps)))) {
  complete.data$steps[i]=byInterval[which(byInterval$Interval==data$interval[i]),2]
}
 sapply(X=complete.data,FUN = function(x) sum(is.na(x)))
##    steps     date interval 
##        0        0        0

d. Histogram of the total number of steps taken each day

byStep.new=aggregate(complete.data$steps,by=list(complete.data$date),sum)
names(byStep.new)=c("Date","TotalSteps")
hist(byStep.new$TotalSteps,breaks = 25,col = "steelblue",border = "white",
     main = "Total number of Steps per Day", xlab = "Total Steps",
     ylim = c(0,20))

e. Any differences in mean and median between original data and data with NAs replaced?

Compare between histogram

newmean=round(mean(byStep.new$TotalSteps),2)
newmedian=round(median(byStep.new$TotalSteps),2)
par(mfrow=c(1,2))
hist(byStep$TotalSteps,breaks = 25,col = "steelblue",border = "white",
     main=NULL,xlab = "Total Steps per day before fill NAs",ylim = c(0,20))
hist(byStep.new$TotalSteps,breaks = 25,col = "steelblue",border = "white",
     main=NULL,xlab = "Total Steps per day after fill NAs",
     ylim = c(0,20))

  • New Mean of the total number of steps taken per day is: 10766.19
  • New median of the total number of steps taken per day is: 10766.19
  • The mean between those two data is the same which is 10766.19. However, the median rise from 10765 to 10766.19

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

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=complete.data
new.data$day.of.week=ifelse(weekdays(as.Date(new.data$date)) %in% c("Saturday","Sunday"),
                     "weekend","weekday")
new.data=aggregate(new.data$steps,by=list(new.data$interval,new.data$day.of.week),mean)
names(new.data)=c("Interval","day.of.week","Mean.Steps")
head(new.data)
##   Interval day.of.week Mean.Steps
## 1        0     weekday 2.25115304
## 2        5     weekday 0.44528302
## 3       10     weekday 0.17316562
## 4       15     weekday 0.19790356
## 5       20     weekday 0.09895178
## 6       25     weekday 1.59035639

b. 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)

library(ggplot2)
ggplot(new.data,aes(x=Interval,y=Mean.Steps,color=day.of.week))+
  geom_line()+
  facet_grid(day.of.week~.)+
  labs(title="Means of Steps by Interval", x="Interval",y="Mean Steps")