Getting and Cleaning Data Course Project

The R script and tidy data set included in this repository are derived from the Human Activity Recognition Using Smartphones Dataset (Version 1.0). The original data files represent experiments carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, the experiment captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments were video-recorded to label the data manually. The obtained dataset was then randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. A fuller description of the original data is available at the link below:

• http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

The specific data files used for this project are available at this link:

• https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

The run_analysis.R script reads in the relevant data files list below; merges the training and test data sets; extracts the measurements on the mean and standard deviation for each featured measurement (listed in the features.txt file); and finally creates an independent tidy data set with the average of each measured variable from the combined training and test sets for each activity and each subject.

Original dataset files:

• 'features_info.txt': Shows information about the variables used on the feature vector.
• 'features.txt': List of all features.
• 'activity_labels.txt': Links the class labels with their activity name.
• 'train/X_train.txt': Training set.
• 'train/y_train.txt': Training labels.
• 'test/X_test.txt': Test set.
• 'test/y_test.txt': Test labels.
• 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
• 'test/subject_test.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

R Script file (r_analysis.R):

## Requires packages, install if necessary
## install.packages("plyr");install.packages("dplyr"); install.packages("reshape2")
## library(plyr); library(dplyr);library("reshape2")

## Activity factor labels, and measured features for both training and test sets.
aclabel <- read.table("./activity_labels.txt", header=FALSE, sep="", col.names=c("level","label"))
features <- read.table("./features.txt", header=FALSE, sep="",  col.names=c("index","varnames"))

## Merge test and training data tables
xtest <- read.table("./test/X_test.txt", sep="", header=FALSE, col.names=features$varnames) 
xtrain <- read.table("./train/X_train.txt", sep="", header=FALSE, col.names=features$varnames)
alldata <- rbind(xtest,xtrain)
rm(xtest); rm(xtrain)

## Extract mean and std measurements  
mean <-select(alldata, contains(".mean.."))
std <-select(alldata, contains(".std.."))
mean_std <- cbind(mean,std)  
rm(alldata);rm(mean);rm(std)

## Add activity variable for measurements - match order for rbind(xtest,xtrain)
ytest <- read.table("./test/y_test.txt", header=FALSE, sep="",  col.names=c("activity"))
ytrain <- read.table("./train/y_train.txt", header=FALSE, sep="",  col.names=c("activity"))
allacts <- rbind(ytest, ytrain)
mean_std <- cbind(allacts, mean_std)
rm(ytest);rm(ytrain);rm(allacts)

## Add subject & measurements - match order for rbind(xtest,xtrain)
subtest <- read.table("./test/subject_test.txt", sep="", header=FALSE, col.names=c("subject"))  
subtrain <- read.table("./train/subject_train.txt", sep="", header=FALSE, col.names=c("subject"))
allsubjects <- rbind(subtest,subtrain)
mean_std <- cbind(allsubjects, mean_std)
rm(subtest);rm(subtrain);rm(allsubjects);

## Order by subject and activities
mean_std <- mean_std[order(mean_std$subject, mean_std$activity),]

## Set vars for creating tidy data set
numCols = ncol(mean_std)
varNames <- colnames(mean_std) 
sub_acts <- mean_std[,1:2]
tidyData <- unique(sub_acts)

for (i in 3:68) {
  df <- cbind(sub_acts, mean_std[,i])
  names(df)[3] <- "V3"
  temp <- ddply(df, .(activity, subject), summarize, ave=ave(V3))
  temp <- unique(temp)
  tidyData <- cbind(tidyData, temp$ave)
  names(tidyData)[i] <- varNames[i]               
}
tidyData$activity = factor(tidyData$activity, levels=aclabel$level, labels=aclabel$label)
write.table(tidyData, file = "tidy_data.txt", row.names = FALSE)

Data Dictionary for tidy_data.txt file:

• Subject: integers from 1 to 30
• Activity: factors
  • 1 WALKING
  • 2 WALKING_UPSTAIRS
  • 3 WALKING_DOWNSTAIRS
  • 4 SITTING
  • 5 STANDING
  • 6 LAYING
• Variables for the mean and standard deviation for each featured measurement. Note( the chars '-','('. and ')' in original variable names were replaced with '.':
  • tBodyAcc.mean...X
  • tBodyAcc.mean...Y
  • tBodyAcc.mean...Z
  • tGravityAcc.mean...X
  • tGravityAcc.mean...Y
  • GravityAcc.mean...Z
  • tBodyAccJerk.mean...X
  • tBodyAccJerk.mean...Y
  • tBodyAccJerk.mean...Z
  • tBodyGyro.mean...X
  • tBodyGyro.mean...Y
  • tBodyGyro.mean...Z
  • tBodyGyroJerk.mean...X
  • tBodyGyroJerk.mean...Y
  • tBodyGyroJerk.mean...Z
  • tBodyAccMag.mean..
  • tGravityAccMag.mean..
  • tBodyAccJerkMag.mean..
  • tBodyGyroMag.mean..
  • tBodyGyroJerkMag.mean..
  • fBodyAcc.mean...X
  • fBodyAcc.mean...Y
  • fBodyAcc.mean...Z
  • fBodyAccJerk.mean...X
  • fBodyAccJerk.mean...Y
  • fBodyAccJerk.mean...Z
  • fBodyGyro.mean...X
  • fBodyGyro.mean...Y
  • fBodyGyro.mean...Z
  • fBodyAccMag.mean..
  • fBodyBodyAccJerkMag.mean..
  • fBodyBodyGyroMag.mean..
  • fBodyBodyGyroJerkMag.mean..
  • tBodyAcc.std...X
  • tBodyAcc.std...Y
  • tBodyAcc.std...Z
  • tGravityAcc.std...X
  • tGravityAcc.std...Y
  • tGravityAcc.std...Z
  • tBodyAccJerk.std...X
  • tBodyAccJerk.std...Y
  • tBodyAccJerk.std...Z
  • tBodyGyro.std...X
  • tBodyGyro.std...Y
  • tBodyGyro.std...Z
  • tBodyGyroJerk.std...X
  • tBodyGyroJerk.std...Y
  • tBodyGyroJerk.std...Z
  • tBodyAccMag.std..
  • tGravityAccMag.std..
  • tBodyAccJerkMag.std..
  • tBodyGyroMag.std..
  • tBodyGyroJerkMag.std..
  • fBodyAcc.std...X
  • fBodyAcc.std...Y
  • fBodyAcc.std...Z
  • fBodyAccJerk.std...X
  • fBodyAccJerk.std...Y
  • fBodyAccJerk.std...Z
  • fBodyGyro.std...X
  • fBodyGyro.std...Y
  • fBodyGyro.std...Z
  • fBodyAccMag.std..
  • fBodyBodyAccJerkMag.std..
  • fBodyBodyGyroMag.std..
  • fBodyBodyGyroJerkMag.std..