run_analysis.R

# Getting and Cleaning Data Project - John Hopkins Coursera
# Author: Nuno Roberto

# The submitted data set is tidy.
# The Github repo contains the required scripts.
# GitHub contains a code book that modifies and updates the available codebooks with the data to indicate all the variables and summaries calculated, along with units, and any other relevant information.
# The README that explains the analysis files is clear and understandable.
# The work submitted for this project is the work of the student who submitted it.

# Load Packages and get the Data
packages <- c("data.table", "reshape2")
sapply(packages, require, character.only=TRUE, quietly=TRUE)
path <- getwd()
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(url, file.path(path, "getdata_projectfiles_UCI HAR Dataset.zip"))
unzip(zipfile = "getdata_projectfiles_UCI HAR Dataset.zip")

# Load activity labels + features
activityLabels <- fread(file.path(path, "UCI HAR Dataset/activity_labels.txt"),
                        col.names = c("classLabels", "activityName"))

features <- fread(file.path(path, "UCI HAR Dataset/features.txt"),
                  col.names = c("index", "featureNames"))

featuresWanted <- grep("(mean|std)\\(\\)", features[, featureNames])
measurements <- features[featuresWanted, featureNames]
measurements <- gsub('[()]', '', measurements)

# Load train datasets
train <- fread(file.path(path, "UCI HAR Dataset/train/X_train.txt"))[, featuresWanted, with = FALSE]
data.table::setnames(train, colnames(train), measurements)

trainActivities <- fread(file.path(path, "UCI HAR Dataset/train/Y_train.txt"),
                         col.names = c("Activity"))

trainSubjects <- fread(file.path(path, "UCI HAR Dataset/train/subject_train.txt"),
                       col.names = c("SubjectNum"))

train <- cbind(trainSubjects, trainActivities, train)

# Load test datasets
test <- fread(file.path(path, "UCI HAR Dataset/test/X_test.txt"))[, featuresWanted, with = FALSE]
data.table::setnames(test, colnames(test), measurements)

testActivities <- fread(file.path(path, "UCI HAR Dataset/test/Y_test.txt"),
                        col.names = c("Activity"))

testSubjects <- fread(file.path(path, "UCI HAR Dataset/test/subject_test.txt"),
                      col.names = c("SubjectNum"))

test <- cbind(testSubjects, testActivities, test)

# merge datasets
combined <- rbind(train, test)

# Convert classLabels to activityName basically. More explicit. 
combined[["Activity"]] <- factor(combined[, Activity],
                                 levels = activityLabels[["classLabels"]],
                                 labels = activityLabels[["activityName"]])

combined[["SubjectNum"]] <- as.factor(combined[, SubjectNum])
combined <- reshape2::melt(data = combined, id = c("SubjectNum", "Activity"))
combined <- reshape2::dcast(data = combined, SubjectNum + Activity ~ variable, fun.aggregate = mean)

data.table::fwrite(x = combined, file = "tidyData.txt", quote = FALSE)