GettingCleaningDataAssessment3: An R repository from warniiiz

Introduction

Purpose of this repository

The purpose of this project is to demonstrate my ability to collect, work with, and clean a data set. The goal is to prepare a tidy data that can be used for later analysis.

I am required to submit:

a tidy data set as described below,
a link to a Github repository with your script for performing the analysis, and
a code book that describes the variables, the data, and any transformations or work that you performed to clean up the data called CodeBook.md.

I should also include a README.md (actually the file you are currently reading) in the repo with your scripts. This file explains how all of the scripts work and how they are connected.

Data theme

One of the most exciting areas in all of data science right now is wearable computing. Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Here are the data for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

Note that these data are also available on this repository, in the UCI HAR Dataset/ directory.

Please read the CodeBook.md for more information about the raw data and all the transformations I did.

Summary of the assessment goal

I should create one R script called run_analysis.R that 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.
From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

Details about this work

Files list

Here are all the files you can find in this repository:

CodeBook.md describes the variables, the data, and the work that I performed to clean up the raw data and get a tidy dataset
run_analysis.R contains all the R code I used in order to obtain the tidy dataset from the raw data
tidy.csv is the final tidy dataset I obtained from the raw data, using the above R script
the raw dataset has been stored in UCI HAR Dataset/ directory

How the R script works

Please find below a description of the run_analysis.R file.

Merges the training and the test sets to create one data set

Note that we consider that the dataset has been stored in the UCI HAR Dataset/ directory. We will first load all the different files into R environment, and then merge all the dataset in a unique one.

read.table is used to load the data from text files to R environment. Train and test data are loaded from respectively train and test directories. Each directories containing 3 files : the first one for the data, the second one for the activities and the third one for the subjects.

dTestData      <- read.table("./UCI HAR Dataset/test/X_test.txt", header=FALSE)
dTestLabels    <- read.table("./UCI HAR Dataset/test/y_test.txt", header=FALSE)
dTestSubjects  <- read.table("./UCI HAR Dataset/test/subject_test.txt", header=FALSE)
dTrainData     <- read.table("./UCI HAR Dataset/train/X_train.txt", header=FALSE)
dTrainLabels   <- read.table("./UCI HAR Dataset/train/y_train.txt", header=FALSE)
dTrainSubjects <- read.table("./UCI HAR Dataset/train/subject_train.txt", header=FALSE)

Then dAllData is a merge of all the above 6 datasets (column binding between activities, subjects and raw data ; and then row binding between train and test datasets).

dAllData <- rbind(
                cbind(dTestLabels, dTestSubjects, dTestData), 
                cbind(dTrainLabels, dTrainSubjects, dTrainData)
            )

Extracts only the measurements on the mean and standard deviation for each measurement

Functions mean() and sd() are used with sapply() on dAllData dataset in order to extract the requested measurements.

dAllDataMean contains the mean for each measurement
dAllDataSd contains the standard deviation for each measurement

dAllDataMean <- sapply(dAllData, mean, na.rm=TRUE)
dAllDataSd   <- sapply(dAllData, sd,   na.rm=TRUE)

Uses descriptive activity names to name the activities in the data set

The activity names are loaded from the activity_labels.txt file (in the raw data directory). The figures coming from of the dTestLabels and dTrainLabels datasets are replaced by the corresponding names:

activityNames <- read.table("./UCI HAR Dataset/activity_labels.txt", header=FALSE, colClasses="character")
dAllData$V1   <- factor(dAllData$V1, levels=activities$V1, labels=activities$V2)

Appropriately labels the data set with descriptive variable names

The labels of each measurement are loaded from the features.txt file (in the raw data directory) and applied to the corresopnding column names of the dAllData dataset. The first 2 columns (which contains Activity and Subject values) are also manually named.

features <- read.table("./UCI HAR Dataset/features.txt", header=FALSE, colClasses="character")
colnames(dAllData) <- c("Activity", "Subject", features$V2)

From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

The function aggregate has been used to split the data into subsets, compute summary statistics for each (in our case it computes the mean value for each subset), and return the result in a convenient form. Note that the first 2 columns are the Activity and the Subject, so it make no sense to calculate the mean on these columns. The subset are defined with the argument by. As asked, I grouped the data by activity and by subject. dTidyData is the resulting dataset.

dTidyData <- aggregate(dAllData[, 3:563], by=list(dAllData$Activity, dAllData$Subject), mean)
colnames(dTidyData)[1:2] <- c("GroupByActivity", "GroupBySubject")

Saves Tidy data set under "./tidy_data_set.csv"

The last activity has not been explicitely asked for this assessment. The goal is to save the resulting datasets in 2 files :

./tidy_data_set.csv : contains the final tidy dataset;
./raw_data_set.csv : contains the raw dataset with proper labels and activity names.

write.table(dTidyData, file="./tidy_data_set.csv", sep=";", row.names = FALSE, col.names = TRUE)
write.table(dAllData, file="./raw_data_set.csv", sep=";", row.names = FALSE, col.names = TRUE)

warniiiz/GettingCleaningDataAssessment3