getdata009CourseAssignment

title: "README.md - getdata009 - Course Assignment" author: "amungi" date: "Sunday, November 23, 2014" output: html_document

getdata009 - Course Assignment

Assignment Instructions:

You should create one R script called run_analysis.R

that does the following.

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.

Solution:

Assumptions:

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

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

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 2 Complete

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 3 Complete

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 4 Complete

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