Project-01.md

Processing and interpreting the personal activity monitoring Data

Introduction

Loading and preprocessing the data; Read the data in the csv and do the right transformations

Sys.setlocale(category = "LC_ALL", locale = "C")

## [1] "C"

setwd("C:/Work/bibliography/courses/2014/Data Specialization/Reproducible Research/Project-1")
ActivityData <- read.table("./activity.csv", sep = ",", header = TRUE, na.strings = NA)
ActivityData$date <- as.Date(ActivityData$date)
head(ActivityData, n = 10)

##    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
## 7     NA 2012-10-01       30
## 8     NA 2012-10-01       35
## 9     NA 2012-10-01       40
## 10    NA 2012-10-01       45

What is mean total number of steps taken per day?

We do the following:

1. Summarize the steps per day by using the plyr package.

2. Plot the histogram of the sums

3. Calculate and plot the mean and the median

library(plyr)
df <- ddply(ActivityData, .(date), summarize, sumSteps = sum(steps, na.rm = TRUE))
hist(df$sumSteps, col = "green", breaks = 10, xlab = "Number of steps per day", 
    main = "Histogram of the total number of steps taken each day")
stepsMean <- mean(df$sumSteps, na.rm = TRUE)
stepsMedian <- median(df$sumSteps, na.rm = TRUE)
abline(v = mean(df$sumSteps, na.rm = TRUE), col = "red", lwd = 4)
abline(v = median(df$sumSteps, na.rm = TRUE), col = "magenta", lwd = 4)

The mean value of the total number of steps taken per day is 9354.2295 and the median value of the total number of steps taken per day is 10395.

What is the average daily activity pattern?

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)

library(plyr)
library(ggplot2)
df2 <- ddply(ActivityData, .(interval), summarize, avgSteps = mean(steps, na.rm = TRUE))
g <- ggplot(df2, aes(x = df2$interval, y = df2$avgSteps))
g + geom_line() + theme(panel.background = element_rect(colour = "pink")) + 
    labs(x = "Interval") + labs(y = "Average Steps per interval") + labs(title = "Time series plot of the 5-minute interval and the avg num of steps")

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

df2 <- ddply(ActivityData, .(interval), summarize, avgSteps = mean(steps, na.rm = TRUE))
maxAvgSteps <- max(df2$avgSteps, na.rm = TRUE)
tmp <- df2[df2$avgSteps == maxAvgSteps, ]
maxInterval <- tmp$interval
maxInterval

## [1] 835

The 835 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps.

Imputing missing values

totalNumOfNAs <- sum(is.na(ActivityData$steps))

There are 2304 missing values in the dataset (i.e. the total number of rows with NAs).

Filling in all of the missing values in the dataset with the avg of steps in the same interval

library(plyr)
df3 <- ddply(ActivityData, .(interval), summarize, avgSteps = mean(steps, na.rm = TRUE))
newActivityData <- ActivityData
for (row in 1:length(newActivityData$steps)) {
    if (is.na(newActivityData$steps[row])) {
        ndate <- newActivityData$interval[row]
        tmp <- df3[df3$interval == ndate, ]
        result <- tmp$avgSteps
        newActivityData$steps[row] = result
    }
}
df3 <- ddply(newActivityData, .(date), summarize, avgSteps = sum(steps, na.rm = TRUE))
hist(df3$avgSteps, col = "green", breaks = 10, xlab = "Number of steps per day", 
    main = "Histogram of the total number of steps taken each day")
nstepsMean <- mean(df3$avgSteps, na.rm = TRUE)
nstepsMedian <- median(df3$avgSteps, na.rm = TRUE)
abline(v = mean(df3$avgSteps, na.rm = TRUE), col = "red", lwd = 4)
abline(v = median(df3$avgSteps, na.rm = TRUE), col = "magenta", lwd = 4)

###What is mean total number of steps taken per day with the missing values filled in? The mean value of the total number of steps taken per day is 1.0766 × 10⁴ and the median value of the total number of steps taken per day is 1.0766 × 10⁴.

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.

for (row in 1:length(newActivityData$date)) {
    if (weekdays(as.Date(newActivityData$date[row])) %in% c("Saturday", "Sunday")) {
        newActivityData$typeOfDay[row] = "Weekend"
    } else {
        newActivityData$typeOfDay[row] = "Weekday"
    }
}
newActivityData$typeOfDay <- as.factor(newActivityData$typeOfDay)

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)
df4 <- ddply(newActivityData, .(interval, typeOfDay), summarize, avgSteps = sum(steps, 
    na.rm = TRUE))
colnames(df4) = c("interval", "typeOfDay", "avgSteps")
g <- ggplot(df4, aes(x = interval, y = avgSteps))
g + geom_line() + facet_grid(typeOfDay ~ .) + theme(panel.background = element_rect(colour = "pink")) + 
    labs(x = "Interval") + labs(y = "Average Steps per interval") + labs(title = "Time series plot of the 5-minute interval and the avg num of steps")