This project is part of the Coursera Data Science Track course Getting & Cleaning Data. The goal of this project is to demonstrate the ability to collect, work with, and clean a data set from the Human Activity Recognition Using Smartphones dataset.
The project focuses on creating an R script called 'run_analysis.R' that performs the following tasks:
- Merges the training and 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.
- Creates a second, independent tidy data set with the average of each variable for each activity and each subject.
- Downloads the dataset and extracts it.
- Loads activity labels and features information.
- Extracts mean and standard deviation measurements from the features.
- Loads training and test datasets.
- Merges training and test datasets into a single dataset.
- Converts activity labels to descriptive activity names.
- Simplifies measurement names for better clarity.
- Labels the dataset columns with meaningful variable names.
- Creates a tidy dataset with the average values of each variable for each subject and activity pair.
- Saves the tidy dataset to a file called 'tidyData.txt'.
Jesus Andres Alvarez Alvarado.