Loading and preprocessing the data

Show any code that is needed to

  1. Load the data
filename <- "activity.zip"
if(!file.exists(filename)){
        fileURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
        download.file(fileURL, filename)
}
if(!file.exists("activity.csv")){
        unzip(filename)
}
act <- read.csv("activity.csv", skipNul = TRUE)

What is mean total number of steps taken per day?

  1. Calculate the total number of steps taken per day
ttlStepsEachDay <- tapply(act$steps, act$date, sum, na.rm = TRUE)
ttlStepsEachDay
## 2012-10-01 2012-10-02 2012-10-03 2012-10-04 2012-10-05 2012-10-06 
##          0        126      11352      12116      13294      15420 
## 2012-10-07 2012-10-08 2012-10-09 2012-10-10 2012-10-11 2012-10-12 
##      11015          0      12811       9900      10304      17382 
## 2012-10-13 2012-10-14 2012-10-15 2012-10-16 2012-10-17 2012-10-18 
##      12426      15098      10139      15084      13452      10056 
## 2012-10-19 2012-10-20 2012-10-21 2012-10-22 2012-10-23 2012-10-24 
##      11829      10395       8821      13460       8918       8355 
## 2012-10-25 2012-10-26 2012-10-27 2012-10-28 2012-10-29 2012-10-30 
##       2492       6778      10119      11458       5018       9819 
## 2012-10-31 2012-11-01 2012-11-02 2012-11-03 2012-11-04 2012-11-05 
##      15414          0      10600      10571          0      10439 
## 2012-11-06 2012-11-07 2012-11-08 2012-11-09 2012-11-10 2012-11-11 
##       8334      12883       3219          0          0      12608 
## 2012-11-12 2012-11-13 2012-11-14 2012-11-15 2012-11-16 2012-11-17 
##      10765       7336          0         41       5441      14339 
## 2012-11-18 2012-11-19 2012-11-20 2012-11-21 2012-11-22 2012-11-23 
##      15110       8841       4472      12787      20427      21194 
## 2012-11-24 2012-11-25 2012-11-26 2012-11-27 2012-11-28 2012-11-29 
##      14478      11834      11162      13646      10183       7047 
## 2012-11-30 
##          0
  1. Make a histogram of the total number of steps taken each day
hist(ttlStepsEachDay)

  1. Calculate and report the mean and median of the total number of steps taken per day
mean(ttlStepsEachDay, na.rm = TRUE)
## [1] 9354.23
median(ttlStepsEachDay, na.rm = TRUE)
## [1] 10395

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)
x <- unique(act$interval)
y <- tapply(act$steps, act$interval, mean, na.rm = TRUE)
plot(x,y, type = "l", xlab = "5-minute interval", 
     ylab = "average number of steps taken")

  1. Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
subset(act, steps == max(act$steps, na.rm = TRUE))
##       steps       date interval
## 16492   806 2012-11-27      615

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).
table(is.na(act$steps))
## 
## FALSE  TRUE 
## 15264  2304

  1. Devise a strategy for filling in all of the missing values in the dataset. I’ll use the mean of the interval which we already have saved as “y”

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

act2 <- act
for(i in 1:nrow(act2)){
        if(is.na(act2[i,1])){
                act2[i,1] <- y[as.character(act2[i,3])]
        }        
}

4.a. 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.

ttlStepsEachDay2 <- tapply(act2$steps, act2$date, sum)
hist(ttlStepsEachDay2)

mean(ttlStepsEachDay2, na.rm = TRUE)
## [1] 10766.19
median(ttlStepsEachDay2, na.rm = TRUE)
## [1] 10766.19

4.b. Do these values differ from the estimates from the first part of the assignment? There appears to be no change

4.c. What is the impact of imputing missing data on the estimates of the total daily number of steps? There doesn’t seem to be any impact.

Are there differences in activity patterns between weekdays and weekends?

library(lubridate)
## Warning: package 'lubridate' was built under R version 3.3.3
## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date
dates <- act[,2]
dates2 <- ymd(dates)
dayofweek <- weekdays(dates2)
dayofweek <- gsub("Monday", "Weekday", dayofweek)
dayofweek <- gsub("Tuesday", "Weekday", dayofweek)
dayofweek <- gsub("Wednesday", "Weekday", dayofweek)
dayofweek <- gsub("Thursday", "Weekday", dayofweek)
dayofweek <- gsub("Friday", "Weekday", dayofweek)
dayofweek <- gsub("Saturday", "Weekend", dayofweek)
dayofweek <- gsub("Sunday", "Weekend", dayofweek)
act <- cbind(act, dayofweek)
avgStepsEachDayofWeek <- tapply(act$steps, act$dayofweek, mean, na.rm = TRUE)
xyplot(tapply(act$steps, act$interval, mean, na.rm = TRUE) ~ unique(act$interval) | unique(act$dayofweek), data = act, layout = c(1,2), type = "l", ylab = "Number of Steps", xlab = "interval")