data description: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones data url: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip

Data Set Characteristics: Multivariate, Time-Series

Number of Instances: 10299

Area: Computer

Attribute Characteristics: N/A

Number of Attributes: 561

Date Donated: 2012-12-10

Associated Tasks: Classification, Clustering

Missing Values?: N/A

Number of Web Hits: 95106

Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto.

Smartlab - Non Linear Complex Systems Laboratory

DITEN - Università degli Studi di Genova, Genoa I-16145, Italy.

activityrecognition '@' smartlab.ws

www.smartlab.ws

1. Unzip the dataset to the R working directory. Unzipping the dataset create a directory named "UCI HAR Dataset" into the working directory.
2. Install the following packages: "reshape2" and "plyr".
3. From the R environment and with the target directory present in the working directory, execute the command: source("run_analysis.R")
4. Output of the execution of code is generation of the tab-delimited text file "tidydata.txt" in the working directory.

• activity - read and store info from file "activity_labels.txt".
• testactivity and trainactivity - read and store info about activity codes from files in each train and test groups, namely "Y-train.txt" and "Y-test.txt" respectively.
• test and train - read and store info about the measurements that were collected for test and train groups.
• features - read and store info about the feature names which are labeled V1 through V561 in the test and train data files. Features are used to decode the measurement names.

• The generated tidydata set is stored in a tab-delimited text file "tidydata.txt" in the working directory.
• The file tidydata.txt has 180 rows (30 subjects * 6 activities).
• The file tidydata.txt has 68 columns. Column 1 is "subject", column 2 is "activity" and the remaining 66 columns are the selected variables (mean and std).