This repo consists of the following as a part of the Course project for the "Getting and Cleaning Data" Coursera course:
- README.md provides a description of the contents of this repo
- run_analysis.R provides the R script for this assignment
- Codebook provides a description of the data that is generated using run_analysis.R
As a part of this project, data collected using Samsung sensors (accelerometer and gyroscope) on 30 voluteers are acquired and tidied. The original source of the data is "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
The script run_analysis.R assumes that a folder entitled "UCI HAR Dataset" is stored in the working directory. The "UCI HAR Dataset" must contain
- 'features.txt': List of all features.
- 'activity_labels.txt': Links the class labels with their activity name.
- 'train/X_train.txt': Training set.
- 'train/y_train.txt': Training labels.
- 'test/X_test.txt': Test set.
- 'test/y_test.txt': Test labels.
- 'train/subject_train.txt': Subject labels for training set.
- 'test/subject_test.txt': Subject labels for test data set.
run_analysis.R reads these text files as R objects. It then merges the test and training set data. Only mean and standard deviation data are selected. Then this data is grouped by a subject ID that ranges from 1 to 30 as well as descriptive activity labels. Labels for variables in the data set are acquired from features.txt. Further descriptions on the variable names is provided in the Codebook. The script also evaluates the means and standard deviations of the selected data grouped by subject ID and activity. It then generates a tidy data text file that meets the principles of tidy data in the following ways
- Each variable forms a column
- Each observation for a given subject doing a specific activity forms a row
- The table stores data of the same type i.e. sensor measurements