minervas / gcdcourseproject

This is my Course Project for the Getting and Cleaning Data Coursera Course

GCDCourseProject

This is my Course Project for the Getting and Cleaning Data Coursera Course

Data

Data for this project was sourced from this dataset: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones See CodeBook.md for more info on the data.

Directions for this project

Here are the directions for this project

• You should 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 data set with the average of each variable for each activity and each subject.

Running this project:

In order to successfully reproduce this project, make sure you have the dataset mentioned above downloaded and unzipped (without any modifications to directory names) into the same directory as the run_analysis.R file when you execute run_analysis.R. You must also have the R dplyr library installed. For your convenience, I have included some commented lines near the top of the run_analysis.R that will download and unzip the data as well as install and load the dplyr library. Execution of run_analysis.R is a bit slow, so be patient. This script should print out some text related to it's progress.

How the run_analysis.R works

Here are the steps involved in run_analysis.R (See comments in the script itself for more details.)

• load dplyr

• read in training data

• read in testing data

• stack training and testing data

• select features with either mean or std in their name (It was unclear from the project directions whether I was supposed to take only features with names ending with std or mean or with std or mean anywhere in the feature name. So I opted for the latter.)

• tidy activity labels

• join datatables

• create final aggregated dataset via group by and summerize

• write data to file

Return value

My final tidy dataset (Each variable is in one column, Each different observation of that variable is in a different row) produced with run_analysis.R named tidyData_means should be available as a data.frame upon successful execution. (run_analysis.R also writes tidyData_means.txt to the current working directory.)