DataScienceCourseProject

Coursera Getting and Cleaning Data Course Project

When executed, this script will download the data collected from the accelerometers from the Samsung Galaxy S smartphone generated in the Human Activity Recognition Using Smartphones Data Set

Once downloaded, the script will do the following:

Merge the training and the test sets to create one data set
Extract the measurements on the mean and standard deviation for each measurement
Use descriptive activity names to name the activities in the data set
Appropriately label the data set with descriptive variable name
Create a new, independent tidy data set with the average of each variable for each activity and each subject - New data set name is TidyData.txt

Set working directory (left out for security)

datafileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(datafileUrl, destfile="ProjectData.zip", method="curl")

unzip(zipfile="ProjectData.zip")

list.files(dataPath, recursive=TRUE)
dataPath <- "UCI HAR Dataset"

Navigate to working directory to test data and create datasets

setwd(file.path(dataPath, "test"))
list.files()
##[1] "Inertial Signals" "subject_test.txt" "X_test.txt"          "y_test.txt"      

data_subject_test<-read.table("subject_test.txt", header=FALSE)
data_features_test<-read.table("x_test.txt", header=FALSE)
data_activity_test<-read.table("y_test.txt", header=FALSE)

Navigate to working directory to train data and create datasets

setwd("../train")
list.files()
##[1] "Inertial Signals"  "subject_train.txt" "X_train.txt"       "y_train.txt"      

data_subject_train <- read.table("subject_train.txt", header=FALSE)
data_features_train <- read.table("X_train.txt", header=FALSE)
data_activity_train <- read.table("y_train.txt", header=FALSE)

Merge data sets to create 1 data set

Combine dataframes with like rows

dataFeatures <- rbind(data_features_train, data_features_test)
dataSubject <- rbind(data_subject_train, data_subject_test)
dataActivity <- rbind(data_activity_train, data_activity_test)

Name columns appropriately

setwd("../")
dataFeaturesNames <- read.table("features.txt", header=FALSE)
names(dataFeatures) <- dataFeaturesNames$V2
names(dataSubject) <- c("Subject")
names(dataActivity) <- c("Activity")

dataSet <- cbind(dataSubject, dataActivity, dataFeatures)

Get subset of data column names for std() or mean()

Use grep() to find matching column names http://www.freeformatter.com/regex-tester.html Learn the hard way that R uses \ rather than \ to escape

dataSubsetNames <- dataFeaturesNames$V2[grep("mean\\(\\)|std\\(\\)", dataFeaturesNames$V2)]
dataSubsetNames <- as.character(dataSubsetNames)
dataSubsetNames <- c(dataSubsetNames, "Subject", "Activity")

dataSubset <- subset(dataSet,select=dataSubsetNames)

Update descriptive activity names to rename columns

prefix t = Time (noted in features_info.txt) prefix f = Frequency (noted in features_info.txt) Acc = Acceleration (noted in features_info.txt) Gyro = Gyroscope (noted in features_info.txt) Jerk = Leave as is Mag = Magnitude (noted in features_info.txt)

names(dataSubset) <- gsub("^t", "Time", names(dataSubset))
names(dataSubset) <- gsub("^f", "Frequency", names(dataSubset))
names(dataSubset) <- gsub("Acc", "Acceleration", names(dataSubset))
names(dataSubset) <- gsub("Gyro", "Gyroscope", names(dataSubset))
names(dataSubset) <- gsub("Mag", "Magnitude", names(dataSubset))

Create an average of values for each subject and activity

tidyData <- aggregate(.~ Subject * Activity, data=dataSubset, FUN=mean)

Save data to txt file

write.table(tidyData,"TidyData.txt", row.name=FALSE)

brendo234/DataScienceCourseProject