analysis

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

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

# 3. Uses descriptive activity names to name the activities in the data set

# 4. Appropriately labels the data set with descriptive variable names.

# 5. 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.



# 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, "dataFiles.zip"))

unzip(zipfile = "dataFiles.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)