This is my own project for Coursera "Getting and Cleaning Data" course. The code is written in R; the source code is run_analysis.R.

This script does the following:

• Load the data.table package
• Create a "data" directory in order to download the raw data that is found on the following link: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
• Unzip the file using the function unzip()
• List all the files of the dataset
• Read the data (UCI HAR Dataset/train/X_train.txt, UCI HAR Dataset/train/y_train.txt, UCI HAR Dataset/train/subject_train.txt, UCI HAR Dataset/test/X_test.txt, UCI HAR Dataset/test/y_test.txt, UCI HAR Dataset/test/subject_test.txt) and store them in trainX, trainY, trainSubject, testX, testY, testSubject.
• Check if data was loaded correctly (View())

• Check the dimension of each variable
• Concatenate by columns the test data(testX, testY, testSubject)
• Concatenate by columns the train data(trainX, trainY, trainSubject)
• Merge train and test data using rbind()
• Check if data is merged correctly (View())

• Load feature data into R
• Extract mean and standard deviation measurements using the function grep(). Does not include "mean frequency".
• Store the data in finalData
• Name the columns on the finalData table(subject, activty and features names)
• Check if finalData is correctly

• Read the activity label data (UCI HAR Dataset/activity_labels.txt) and store it in a variable
• Switch the numbers name by its correspondent label
• Check if finalData is correctly

• Subsitute the variable names by using the function gsub(). The names will be as following:

"()" to ""

"^t" to “Time"

"^f" to “Frequency”

"Acc" to "Accelerometer"

"Gyro" to "Gyroscope"

"Mag" to "Magnitude"

"BodyBody" to "Body"

"-mean" to "Mean"

"-std" to "Standard Deviation"

• Check if everything is labeled correctly (View())

• Load the dplyr package
• Create a new dataset by calculating the mean value of each variable for each activity and subject.
• Generate a final file using the function write.table

output file is: average_tidyData.txt (180 observations - 30 subjects x 6 activities - of 66 variables)

This file explains the specific details of the tidy data file.