The run_analysis.R script contained in this repo is created as part of the 'Getting and Cleaning Data' course on Coursera. It's based on the data provided by a project which performed several measurements using a Samsung Galaxy SII smartphone on 30 volunteers performing different activities. For a full-background on the project, you can check they're website at:

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

The data required can be downloaded from:

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

Please, make sure to place your working directory into the main directory of the downloaded files. To be sure that you're on the right place, a dir/ls command (depending on your OS) should list the 'test' and 'train' folders, aswell as the 'activities_labels.txt' and 'features.txt' files. Also, please, make sure that the library 'reshape2' is installed.

Running the script can take some time, depending on your hardware, principally due to read operations on the downloaded data. For such tasks, there's a pair of print commands for each set of files ('test' and 'train') that will indicate in your console/terminal when the tasks have begun and when they're completed.

The output of the run_analysis.R script is placed in the initial working directory under the name 'tidyDataMeans.txt', which can be read as a dataframe in R using read.table with header = TRUE. The table generated will contain two id variables (labeled 'Activity' and 'Subject') and several measurement variables, which represent the mean of all instances of each measure's mean and standard deviation for each subject doing each activity.

For a complete explanation on each variable, please, check the Codebook.md file placed in this repository.