thuyngh08 / getting-and-cleaning-data-course-project Goto Github PK

The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis.

The dataset for this assignment is data collected from the accelerometers from the Samsung Galaxy S smartphone, featuring one of the most exciting areas in all of data science right now is wearable computing that many big cooperation such as Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users.

A full description is available at the site where the data was obtained:

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Download data of this project from the following address:

https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

Create one R script called run_analysis.R that does the following.

• 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 variable names.
• From the data set in step 4, creates a second, independent tidy datasets with the average of each variable for each activity and each subject.

The folder includes two sub-folders that containing data from 2 groups. The sub-folder 'train' contains data for all subjects randomly chosen into training group. The sub-folder 'test' contains data for all subjects chosen into test group

To make a dataset for training or test group, we have to combine 3 files by column join:

• X_...: containing data collected on 561 attributes
• Y_...: activity for each observation (each row)
• subject_...: the subject id of each observation

After obtaining 2 dataset for test and train group, we could merge two datasets into one dataset called data_full by binding rows. I chose rbind() over binds_row() as it kept all columns of the dataset

Get the name of all the measurements (variables) by reading features.txt into R. The dataset has 2 columns. V1 is the number and V2 is the corresponding measurment names. Choose only the measurements that has 'mean' or 'std' in its value and save as a new dataframe called mean_std_name. There are 79 measurements satisfied this criteria

Use this dataframe to select the variables in data_full that measure on the mean and standard deviation. Save this dataset to new dataframe called 'mean_std_data_frame'

Get the corresponding descriptive activity name by loading activity_labels.txt into R. The dataframe has 2 columns, V1: activity code and V2: activity name. Use both two variables to label the 'Activity' variable, making it a factor variable with two attributes, levels (code) and labels(name).

Create a data_label dataframe, then assign the variable name for 'mean_std_data_frame' using it.

Convert the dataset from wide (82 columns) form to long form (4 columns) with id variables are 'Subject' and 'Activity'. Aggregate the long form dataset to get the average value of each variables for each activity and subject and create a dataset with the average of each variable for each activity and subject.