Repruducible Research Course Project 1

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.

Loading and preprocessing the data

setwd("/Users/junwen/Documents/Coursera/Data Sciences/Reproducible Research")
df <- read.csv("activity.csv")
summary(df)

##      steps                date          interval     
##  Min.   :  0.00   2012-10-01:  288   Min.   :   0.0  
##  1st Qu.:  0.00   2012-10-02:  288   1st Qu.: 588.8  
##  Median :  0.00   2012-10-03:  288   Median :1177.5  
##  Mean   : 37.38   2012-10-04:  288   Mean   :1177.5  
##  3rd Qu.: 12.00   2012-10-05:  288   3rd Qu.:1766.2  
##  Max.   :806.00   2012-10-06:  288   Max.   :2355.0  
##  NA's   :2304     (Other)   :15840

What is mean total number of steps taken per day?

df.total_step <- aggregate(steps~date, df, sum, na.rm = TRUE)
hist(df.total_step$steps, 
     main="Histogram for Total Number of Steps", 
     xlab="total number of steps taken per day")

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

mean(df.total_step$steps)

## [1] 10766.19

median(df.total_step$steps)

## [1] 10765

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)

df.average_step <- aggregate(steps~interval, df, mean, na.rm = TRUE)
plot(steps~interval, data = df.average_step, type = "l")

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

df.average_step[which.max(df.average_step$steps), ]$interval

## [1] 835

Imputing missing values:

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

sum(is.na(df$steps))

## [1] 2304

2. Devise a strategy for filling in all of the missing values in the dataset: use the median for that 5-minute interval.

df.filled <- df
filtered <- is.na(df$steps)
by_interval <- tapply(df$steps, df$interval, median, na.rm=TRUE)
df.filled$steps[filtered] <- by_interval[as.character(df$interval[filtered])]

3. 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.

total_step <- aggregate(steps~date, df.filled, sum, na.rm = TRUE)
hist(total_step$steps, 
     main="Histogram for Total Number of Steps after Imputing", 
     xlab="total number of steps taken per day")

mean(total_step$steps)

## [1] 9503.869

median(total_step$steps)

## [1] 10395

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.

df$date <- as.Date(strptime(df$date, format="%Y-%m-%d"))
df$datetype <- sapply(df$date, function(x) {
                            if (weekdays(x) == "Saturday" | weekdays(x) =="Sunday") 
                              {y <- "Weekend"} else 
                              {y <- "Weekday"}
                              y
                            })
head(df)

##   steps       date interval datetype
## 1    NA 2012-10-01        0  Weekday
## 2    NA 2012-10-01        5  Weekday
## 3    NA 2012-10-01       10  Weekday
## 4    NA 2012-10-01       15  Weekday
## 5    NA 2012-10-01       20  Weekday
## 6    NA 2012-10-01       25  Weekday

2. 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).

library(ggplot2)
by_date <- aggregate(steps~interval + datetype, df, mean, na.rm = TRUE)
plot<- ggplot(by_date, aes(x = interval , y = steps, color = datetype)) +
       geom_line() +
       labs(title = "Average Daily Steps by Datetype", x = "Interval", y = "Average number of Steps") +
       facet_wrap(~datetype, ncol = 1, nrow=2)
print(plot)