lukgad / gettingandcleaningdatacp

Repository for course project in Getting and Cleaning Data course from Data Specialization

Getting And Cleaning Data - Cleaning and Tyding Human Activity Recognition Using Smartphones Data Set

Warning - this readme file is written using Github Flavoured Markdown - some formatting maybe loss if viewing using different markdown parser.

This reposity contains tools to extract average of feature variable from training and test data sets in UCI Machine Learning Reposity: Human Activity Recognition Using Smartphones Data Set.

Original data set is present here: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones .

This script:

• extracts data from two data sets: training and test data sets.
• combines subject, activity labels and measurments for training and data sets. It results in two complete data sets: one for training data and one for test data.
• combines training and test data into one data sets
• replaces column names with readable ones using source data from feature.txt file.
• replaces numeric values in ACTIVITY column with activity names taken from activity_labels.txt file.
• select only mean and std measurments into data frame
• resulting data frame is used to create a tidy data set
• data is cleaned, only complete cases are taken
• values are arragned ascending by ACTIVITY and SUBJECT
• scripts then calculates mean values for every measurment each activty and each subject

To run this script you need:

• install R packages: dplyr and sqldf
• clone this repository and checkout master branch
• source script run_analysis.R
• run function runAnalysis(directoryName)
• argument directoryName is character string. It expects a directory name of UCI Machine Learning data in your working directory. It should contain this structure:

   ├── activity_labels.txt
   ├── features_info.txt
   ├── features.txt
   ├── README.txt
   ├── test
   │   ├── Inertial Signals
   │   │   ├── body_acc_x_test.txt
   │   │   ├── body_acc_y_test.txt
   │   │   ├── body_acc_z_test.txt
   │   │   ├── body_gyro_x_test.txt
   │   │   ├── body_gyro_y_test.txt
   │   │   ├── body_gyro_z_test.txt
   │   │   ├── total_acc_x_test.txt
   │   │   ├── total_acc_y_test.txt
   │   │   └── total_acc_z_test.txt
   │   ├── subject_test.txt
   │   ├── X_test.txt
   │   └── y_test.txt
   └── train
       ├── Inertial Signals
       │   ├── body_acc_x_train.txt
       │   ├── body_acc_y_train.txt
       │   ├── body_acc_z_train.txt
       │   ├── body_gyro_x_train.txt
       │   ├── body_gyro_y_train.txt
       │   ├── body_gyro_z_train.txt
       │   ├── total_acc_x_train.txt
       │   ├── total_acc_y_train.txt
       │   └── total_acc_z_train.txt
       ├── subject_train.txt
       ├── X_train.txt
       └── y_train.txt

Default value for this argument is UCI HAR Dataset. If your directory name is the same, you don't need to provide it.

• the result of this function is data frame. All columns are described in CodeBook.md