About this repo

Link: https://github.com/galenyip/getdata-project

Background

This repo is for the course project of Coursera Course "R Programing - Getting and Cleaning Data" provided by Johns Hopkins University.

It consist of 3 files:

• README.MD (this file)
• run_analysis.R (the script that used to genrate output for the project)
• CodeBook.MD (describes the variables, the data, and any transformations or work that you performed to clean up the data)

All the R code to process the data is in run_analysis.R . The file basically having 5 parts, which addressing the 5 requirements of the project. It is with comment at the beginning of each part to briefly describe the objective and how to achieve that. The code is run in sequence to produce the output.

Note: The run_analysis.R and other data files should be put in the same folder when executing. The name of the data files are listed below for reference:

• activity_labels.txt
• features.txt
• subject_test.txt
• X_test.txt
• y_test.txt
• subject_train.txt
• X_train.txt
• y_train.txt

Part 1: Merges the training and the test sets to create one data set.

Solution: the test and train data were merge using rbind(). Then combining subject, y (activity) and x (sensor values) into a single Data Set with cbind().

Part 2: Extracts only the measurements on the mean and standard deviation for each measurement.

Solution: using the grepl() function to get all the column name with pattern mean() and std(). And treat it as filter to obtain the data with desired column only.

Part 3: Uses descriptive activity names to name the activities in the data set

Solution: To replace the 1-6 activity code with activity name provided in activity_labels.txt

Part 4: Appropriately labels the data set with descriptive variable names.

Solution: using the name in features.txt (already supplemented in Step 1) but remove '()' and '-' from the string using the gsub() function. Also replace 'mean' with 'Mean' and 'std' with 'Std'.

Part 5: From the data set in part 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

Solution: Summarize (avg) the data with the aggregate() function and group by subject and activity name. Then export the result using write.table()