run_analysis.R

# Here are the data for the project:
# https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip 
#
# This R script does the following:
#    Merges the training and the test sets to create one data set.
#    Extracts only the measurements on the mean and standard deviation for each measurement. 
#    Uses descriptive activity names to name the activities in the data set
#    Appropriately labels the data set with descriptive variable names. 
#    Creates a second, independent tidy data set with the average of each variable for each activity and each subject. 

# Merges the training and the test sets to create one data set.
train <- read.table("train/X_train.txt")
test <- read.table("test/X_test.txt")
X <- rbind(train, test)

train <- read.table("train/subject_train.txt")
test <- read.table("test/subject_test.txt")
Subject <- rbind(train, test)

train <- read.table("train/y_train.txt")
test <- read.table("test/y_test.txt")
Y <- rbind(train, test)

# Extracts only the measurements on the mean and standard deviation for each measurement.
features <- read.table("features.txt")
indices_of_mean_std_features <- grep("-mean\\(\\)|-std\\(\\)", features[, 2])
X <- X[, indices_of_mean_std_features]
names(X) <- features[indices_of_mean_std_features, 2]
names(X) <- gsub("\\(|\\)", "", names(X))
names(X) <- tolower(names(X))  

# Uses descriptive activity names to name the activities in the data set
activities <- read.table("activity_labels.txt")
activities[, 2] = gsub("_", "", tolower(as.character(activities[, 2])))
Y[,1] = activities[Y[,1], 2]
names(Y) <- "activity"

# Appropriately labels the data set with descriptive variable names.
names(Subject) <- "subject"
cleaned <- cbind(Subject, Y, X)
write.table(cleaned, "merged_data.txt")

# Creates a second, independent tidy data set with the average of each variable for each activity and each subject.
uniqueSubjects = unique(Subject)[,1]
numSubjects = length(unique(Subject)[,1])
numActivities = length(activities[,1])
numCols = dim(cleaned)[2]
result = cleaned[1:(numSubjects*numActivities), ]

row = 1
for (s in 1:numSubjects) {
	for (a in 1:numActivities) {
		result[row, 1] = uniqueSubjects[s]
		result[row, 2] = activities[a, 2]
		tmp <- cleaned[cleaned$subject==s & cleaned$activity==activities[a, 2], ]
		result[row, 3:numCols] <- colMeans(tmp[, 3:numCols])
		row = row+1
	}
}
write.table(result, "data_set_with_average.txt")