README.md

The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set.

Source

Site

Source Data

Repository Files:

• README.md
• CodeBook.md
• run_analysis.R

Assumptions

• Ignored raw data in "/Inertial Signals" as data files available in "/test" and "/train" folders produce an acceptable result. Total 563 variables with 561 features + Subject and Activity. Result 10299 obs. of 563 variables
• The features required are mean() and std() which will result in 66 Variables. Total 68 Variables including Subject and Activity. Result 10299 obs. of 68 variables
• "Uses descriptive activity names to name the activities in the data set", rename column names for the features
• "Appropriately labels the data set with descriptive activity names.", replace activity code to activity names as data values
• Create tidy dataset with the average of each variable for each activity (6) and each subject (30). Result 30 Subjects x 6 activities = 180 obs. of 68 variables

Requirements

• Download "getdata-projectfiles-UCI HAR Dataset.zip" from the link above.
• Unzip "getdata-projectfiles-UCI HAR Dataset.zip"
• Place "run_analysis.R" R script in "UCI HAR Dataset" folder where "test" and "train" folders reside.

run_analysis.R

Merges the training and the test sets to create one data set.

• Used read.table() and cbind() to create test and train data frames.
• Used read.table() and colnames() to rename column names
• Used rbind() to merge test and train data frames.

Extracts only the measurements on the mean and standard deviation for each measurement.

• Used grep() to select only Subject, Activity, mean() and std() columns.
• Used data frame selecting (Keeping) Variables.

Uses descriptive activity names to name the activities in the data set

• Used gsub() to remove -, ( and )
• Used gsub() to replace column names mean to Mean and std and Std
• Used colnames() to rename column names

Appropriately labels the data set with descriptive activity names.

• Used read.table() to add a data frame to be used as activity name values
• Used match() to replace the activity code with activity name values
• Used gsub() to replace underscores to spaces

Creates a second, independent tidy data set with the average of each variable for each activity and each subject.

• Load reshape2 library.
• Used order() to have a better sorting view of the data
• Used melt() to prepare the measures for aggregation for Subject and Activity. Removed row names.
• Used dcast() to get the average values using mean() on the measures based on Subject and Activity
• Used write.table() to save the tidy dataset as tidydata.txt

CodeBook.md

Code book that describes the variables, the data, and any transformations or work that you performed to clean up the data