Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. [email protected] www.smartlab.ws

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, I captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. See 'features_info.txt' for more details.

• The corresponding mean / average of each subject in the study by each activity

• 'README.md' This file

• 'CodeBook.md' describes the variables, the data, and any transformations or work that I performed to clean up the data

• 'cleandata.csv' The clean dataset itself

• 'run_analysis.R' The R code to make this tidy dataset

#Read train dataset train <- read.table("./train/X_train.txt") #Get train labels train.labels <- read.table("./train/y_train.txt")

#Read test dataset test <- read.table("./test/X_test.txt") #Get test labels test.labels <- read.table("./test/y_test.txt")

data.full <- rbind(train,test)

#Get features name features <- read.table("features.txt") #Logical vector to get only features that have the characters "Mean, mean or std" good<- grepl("[Mm]ean|std",features$V2) #Extract the corresponding features features.extracted <- features[good,] #Get data subset with corresponding variables name stored in the first element of the features.extracted data frame data <- subset(data.full,select- features.extracted[[1]])

#Merge code labels from train and test dataset to have the full dataset. Remember that it contains only numbers labels <- rbind(train.labels,test.labels) #Get the labels as vector mylabels<- labels[,1]

#Now get activity label. Dont convert to factor labels.activity <- read.table("activity_labels.txt",as.is-TRUE)

#Convert labels to factors mylabels<- factor(mylabels,levels-c(1,2,3,4,5,6),labels-labels.activity$V2) #Now bind the activity labels column to the dataset. Now I have descriptive activity names

data <- cbind(data,mylabels)

#Get the column names in features.extracted$V2 then add them to the data columns names #Last column is "activity" which were added in step 3 colnames(data)<- c(as.character(features.extracted$V2),"activity") #Now display the data to see if every requirements met: head(data)

train.subjects <- read.table("./train/subject_train.txt")

test.subjects <- read.table("./test/subject_test.txt")

subjects <- rbind(train.subjects,test.subjects)

data <- cbind(subjects,data)

colnames(data)[1]<- "subjects" #Creates a data set with the average of each variable for each activity and each subject. aggdata<- aggregate(data,by-list(Group.subjects-data$subjects,Group.activity-data$activity),FUN-mean,na.rm-TRUE) #Remember that aggdata have variables that keep the same values: Group.subjects - subjects,

#Remove those two variable from the dataframe drops<- c("subjects","activity") df <- aggdata[,!(names(aggdata) %in% drops)] #Now write the tidy dataset to a csv file! write.csv(file-"cleandata.csv",x-df)