Introduction

This repository contains the scripts created as part of the "Getting and Cleaning Data" course assignment. It contains an R script run_analysis.r that does the following (list copied from the assignment page):

• Merges the training and the test sets to create one data set
• Extracts only the measurements on the mean and standard deviation for each measurement
• Uses descriptive activity names to name the activities in the data set
• Appropriately labels the data set with descriptive names
• Creates a second, independent tidy data set with the average of each variable for each activity and each subject

Script Execution

• The script downloads a data set from Smartphone Data.
• The training files are in the train directory. A training data frame is created from the three files in that directory: subject_train.txt, y_train.txt, X_train.txt
• The test files are in the test directory. The three files pertaining to the test data subject_test.txt, y_test.txt, X_test.txt are combined into a test data frame.
• The subject_ files contain the subject variable data. The y_ files contain data about the activity variable. The X_ files contain the measurement variables data.
• The training and testing data frames are combined to form a single frame

Exploratory Analysis

Data Details

• There are 7352 observations in each of the files and no missing data in any of them
• The features.txt file contains the measurement variable names. These are used as the column names for the 561 measurement variables
• The subject data is assigned the subject column name and the activity data gets the activitycode name

Data Cleanup

• Since we are interested in only the mean and standard deviation measurements, we retrieve a subset of the original data frame with only the following columns:
  • Subject
  • Activity
  • All columns that have "mean()" or "std()" in their names
• The column names are made tidy by removing any special characters in them and are lower-cased

Result Computation

• The aggregate built-in function is used to calculate the average for each activity/suject on each variable
• The activity column with numeric values is replaced with a corresponding column that contains descriptive labels for those values
• The result is written out to a Results.txt file using the write.table function and a code book skeleton is created using the prompt function