Human Activity Recognition Using Smartphones Dataset

Analyze the acitivity dataset

Vaibhav Bhatnagar

Agenda

  1. What is mean total number of steps taken per day
  2. What is the average daily activity pattern
  3. Imputing missing values

Load required libraries

library(reshape2)
library(lattice)

Loading and preprocessing the data

data <- read.csv("activity.csv", colClasses = c("numeric", "Date", "numeric"))

What is mean total number of steps taken per day?

melted <- melt(data, id.vars="date", measure.var = "steps" )
totalSteps <- dcast(melted, date ~ variable, sum)

hist(totalSteps$steps, xlab= "Steps", main = paste("Histogram of" , "Steps per Day"))

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

Mean
meanSteps <- dcast(melted, date ~ variable, mean)

Median

medianSteps <- dcast(melted, date ~ variable, median, fill=0)

What is the average daily activity pattern?

dataNoNA <- na.omit(data)
meltInterval <- melt(dataNoNA, id.vars="interval", measure.var = "steps" )
totalStepsInterval <- dcast(meltInterval, interval ~ variable, sum)
plot(totalStepsInterval$interval,totalStepsInterval$steps, type="l", xlab = "Interval", ylab = "Steps")
title(main = "Nmber of Steps per 5 min Interval")

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Question 3 - Imputing missing values

Poplulate the NA with the mean for that 5-minute interval

sum(is.na(data$steps))

## [1] 2304

for(i in 1:nrow(data)){
    temp <- 0
    if(is.na(data[i,1])){
        temp <- mean(subset(data, data$interval == data[i,3])[,1], na.rm=T)
        data[i,1] <- temp
    }
}

continue....

Histogram of the total number of steps taken each day

meltedClean <- melt(data, id.vars="date", measure.var = "steps" )
totalStepsclean <- dcast(meltedClean, date ~ variable, sum)

hist(totalStepsclean$steps, xlab= "Steps", main = paste("Histogram of" , "Steps per Day"))

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Mean

meanStepsClean <- dcast(meltedClean, date ~ variable, mean)

Median

medianStepsClean <- dcast(meltedClean, date ~ variable, median)