datasciencecoursera

Getting-and-Cleaning-Data-Project

Overview

This project aims to demonstrate the ability to collect, work with, and clean a data set related to wearable computing. The data used in this project are collected from accelerometers in the Samsung Galaxy S smartphone. The goal is to prepare tidy data that can be used for later analysis.

Instructions

1. Tidy Data Set - Submission Requirements:

Please upload the tidy data set created in step 5 of the instructions. The data set should be submitted as a TXT file created with write.table() using row.name=FALSE.

2. Github Repository with Code:

Please submit a link to a Github repository containing the code for performing the analysis. The repository should include:

• run_analysis.R: R script in the main directory that can be executed if the Samsung data is in your working directory. The output should be the tidy data set submitted for part 1.

• README.md: Explanation of how the script works and how the code is organized.

• CodeBook.md: Code book describing the variables, the data, and any transformations performed.

3. CodeBook

The CodeBook.md file details the variables, data, and transformations performed to clean up the data.

Project Steps

1. Data Download and Unzip:

   • Data files are downloaded and unzipped using download.file() and unzip() in R.

2. Read Files:

   • Activity, subject, and features files are read using read.table().

3. Merge Datasets:

   • Datasets are merged using rbind().

4. Label Features:

   • Feature names are labeled using descriptive variable names with names().

5. Libraries and Comments:

   • Libraries plyr and knitr are called using library().
   • Key steps are commented for clarity.

6. Create Tidy Data Set 1:

   • Tidy data set is created by combining subjects, data labels, and features.
   • Only std and mean features are subset from the list.
   • Features are renamed for readability.
   • Data is labeled with descriptive activity names.
   • Merged data is written to a file.

7. Create Tidy Data Set 2:

   • Averages of measurements for each activity and subject are calculated.
   • Second tidy data set is written to a file.

Data Source

The dataset used for this project is sourced from the Human Activity Recognition Using Smartphones.

Notes

• Ensure an active internet connection to download the dataset.
• The script assumes the necessary R packages (dplyr, tidyr, etc.) are installed.

Contact

Feel free to contact me if you have any questions or concerns. Good luck with your analysis!