Getting_Cleaning_Data_Course_Project

##This script creates a tidy data set that summarizes some of the data ##from the Human Activity Recognition Using Smartphones Data Set found at: #http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones #This script performs the following operations:

0. Reads in training, test, features, activities, activity names, and subject

text files.

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.

setwd("C:/Users/Public/Documents/Dave/JHU_DataScience/GettingAndCleaningData/Course Project") library(dplyr) #read in and transpose features.txt to use as column names features <- read.table("features.txt") str(features) featuresonly <- select(features, V2) str(featuresonly) variablebad <- t(featuresonly) #fix variable names so they are unique and legitimate in R varnamesgood <- make.names(variablebad, unique = TRUE) ##test data section followed by train data section ##if I had time to figure out how to do this in a loop, I would... #read in test data and rename variables using variablelst test_data <- read.table("X_test.txt") str(test_data) head(test_data, n=1) names(test_data) <- varnamesgood head(test_data, n=1) #just get mean variables test_data_mean <- select(test_data, contains("mean..")) test_data_mean <- select(test_data_mean, -contains("gravityMean")) #just get std variables test_data_std <- select(test_data, contains("std..")) #column bind mean and std variables test_mean_std <- cbind(test_data_mean, test_data_std) #read in subject codes for test data froom subject_test.txt subjects <- read.table("subject_test.txt") head(subjects) names(subjects) <- "subjects" str(subjects) #read in activities for test data activities <- read.table("Y_test.txt") str(activities) names(activities) <- "activities" str(activities) #column bind test data with subjects and activities data test_data_all <- cbind(subjects, activities, test_mean_std) head(test_data_all, n=1) str(test_data_all)

#read in train data and rename variables using variablelst train_data <- read.table("X_train.txt") str(train_data) head(train_data, n=1) names(train_data) <- varnamesgood head(train_data, n=1) #just get mean variables train_data_mean <- select(train_data, contains("mean..")) train_data_mean <- select(train_data_mean, -contains("gravityMean")) #just get std variables train_data_std <- select(train_data, contains("std..")) #column bind mean and std variables train_mean_std <- cbind(train_data_mean, train_data_std)

#read in subject codes for train data froom subject_train.txt subjects <- read.table("subject_train.txt") head(subjects) names(subjects) <- "subjects" str(subjects) #read in activities for train data activities <- read.table("Y_train.txt") str(activities) names(activities) <- "activities" str(activities) #column bind train data with subjects and activities data train_data_all <- cbind(subjects, activities, train_mean_std) head(train_data_all, n=1) str(train_data_all) #now row bind test and train data into a single data set data_all <- rbind(test_data_all, train_data_all) #clean up variable names names(data_all) <- gsub(".","",names(data_all), fixed = TRUE) names(data_all) <- gsub("meanX","Xmean",names(data_all), fixed = TRUE) names(data_all) <- gsub("meanY","Ymean",names(data_all), fixed = TRUE) names(data_all) <- gsub("meanZ","Zmean",names(data_all), fixed = TRUE) names(data_all) <- gsub("stdX","Xstd",names(data_all), fixed = TRUE) names(data_all) <- gsub("stdY","Ystd",names(data_all), fixed = TRUE) names(data_all) <- gsub("stdZ","Zstd",names(data_all), fixed = TRUE)

by_subs_acts <- group_by(data_all, subjects, activities) summ_data_all <- summarise_each(by_subs_acts, funs(mean), -(subjects:activities) ) activity_labels <- read.table("activity_labels.txt") summ_data_active <- merge(summ_data_all, activity_labels, by.x = "activities", by.y = "V1") summ_data_active <- rename(summ_data_active, activity_desc = V2) summ_data_active <- summ_data_active[,c(2,1,69,3:68)] summ_data_active <- arrange(summ_data_active, subjects, activities) write.table(summ_data_active, file = "summ_activity_recog_smartphones.txt", row.names = FALSE)