getdata009CourseAssignment Course Assignment for getdata-009 Getting and Cleaning Data course
title: "README.md - getdata009 - Course Assignment"
author: "amungi"
date: "Sunday, November 23, 2014"
output: html_document getdata009 - Course Assignment You should create one R script called run_analysis.R 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. This script does not set the working directory, and does not download or extract the dataset files The working directory for your R environment is already set before invoking this script. The dataset zip file is already downloaded to your local machine
The dataset zip file is extracted / unzipped to create the base dataset directory "UCI HAR Dataset"
The base dataset directory "UCI HAR Dataset" is located within the working directory The relative locations of sub-folders and files within the directory "UCI HAR Dataset" are the same as the original zip file Step 1: Merge the training and the test sets to create one data set. Step 1.1 Load required libraries - stringr, dplyr, tidyr, reshape2 Step 1.2: Read all the relevant dataset files into data frames
Files do not contain headers
Use read.table so that multiple whitespaces in the files can be easily handled during reading Step 1.2.1: Read activity labels, with 6 observations The two columns in the data file are Activity ID and Activity Name Step 1.2.2: Read the list of all features, with 561 observations The two columns in the data file are Feature ID and Feature Name Step 1.2.2.1 : Clean the Feature Names Convert each Feature Name into a syntactically valid name Replace "..." with "." in each Feature Name Replace ".." with "." in each Feature Name If a feature name ends with a "." then remove the ending "." Step 1.2.2.1 is Complete : Features data (features_df) is now clean Step 1.2.3 : Read the training dataset, with 7352 observations Step 1.2.3.1 : Read X_train.txt Add column names as per the Feature Names Step 1.2.3.2 : Read y_train.txt Add column name = "Activity_ID", since the values refer to activity labels Step 1.2.3.3 : Read subject_train.txt Add column name = "Subject_ID", since it denotes the subject who performed the activity Step 1.2.4: Read the test dataset, with 2947 observations Step 1.2.4.1 : Read X_test.txt Add column names as per the Feature Names Step 1.2.4.2 : Read y_test.txt Add column name = "Activity_ID", since the values refer to activity labels Step 1.2.4.3 : Read subject_test.txt Add column name = "Subject_ID", since it denotes the subject who performed the activity Step 1.3 : Merge the training and test data sets into one merged dataset with 10299 observations Step 1.3.1 : Create a new merged dataset by taking the training dataset and adding the rows from the test dataset Step 1.3.1.1 : Merge training set (Xtrain) and test set (Xtest), in that order Step 1.3.1.2 : Merge training activities (ytrain) and test activities (ytest), in that order Step 1.3.1.3 : Merge training subjects (subjecttrain) and test subjects (subjecttest), in that order Step 1.3.2: Create a new Merged dataset and combine - merged_y_df and merged_X_df, in that order Step 1.3.3: Combine - merged_subject_df and merged_dataset, in that order, to add the subject ID to the merged dataset Step 1 Complete: Training and test sets (merged_X_df, merged_y_df, merged_subject_df) are merged into one merged dataset Step 2 : Extract only the measurements on the mean and standard deviation for each measurement Returns a dataframe of 86 variables containing measurements on the mean ("mean") and standard deviations ("std") Step 3 : Use descriptive activity names to name the activities in the data set from Step 2 Add a new Activity column to the merged_mean_std_measure_df, and initialize it to the Activity_ID Replace the Activity IDs in the new Activity column by lookup of the Activity Names from activitylabels_df Remove the Activity_ID column from merged_mean_std_measure_df Step 4 : Appropriately label the data set with descriptive variable names Based on the feature name cleanup done in Step 1.2.2.1 above, the variable names are clean and follow a naming convention which makes it easy to determine what each variable is. Additional descriptions are provided in the accompanying CodeBook. Step 5 : Create a second, independent tidy data set with the average of each variable for each activity and each subject. Melt the data merged_mean_std_measure_df By: Subject and Activity Variables containing mean and standard deviation measures are the "selected features" Tidy data set tidy_avg_data is calculated as the average of each variable (selected feature) for each activity and each subject The tidy data set is written to a file using write.table() and excluding row names