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Getting-and-Cleaning-Data-project

Getting and Cleaning Data week three course project

"run_analysis.R" script reads data from the "Human Activity Recognition Using Smartphones Dataset Version 1.0" and produces a new - tidy - dataset which may be used for further analysis.

Informations about how the data were collected are available here: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

The original dataset included the following data files:

  • 'features.txt': List of all features.

  • 'activity_labels.txt': List of class labels and their activity name.

  • 'train/X_train.txt': Training set.

  • 'train/y_train.txt': Training labels.

  • 'train/subject_train.txt': ID's of subjects in the training data

  • 'test/X_test.txt': Test set.

  • 'test/y_test.txt': Test labels.

  • 'test/subject_test.txt': ID's of subjects in the training data

A brief description of the script:

The run_analysis.R script merges data from a number of .txt files and produces a tidy data set which may be used for further analysis.

  • Loads the useful "reshape2" package;

  • Reads all required .txt files (3 test files, 3 training files, 1 activity labels file, 1 feature files);

  • Renames the columns of training and test datasets with the appropriate feature's name;

  • Merge the three test data and the three training data, and then combine them in one data.frame ("FullData");

  • Create a dataset using the "grep" function with only columns with mean() and std() values, including only the "activityNumber", the "subjectNumber" and the mean() and std() columns ("std_mean_Data");

  • Merge the descriptive activity names with the mean/std values dataset, to get one dataset with descriptive activity names ("Descr_std_mean_Data");

  • Converte mean values of all the included features into a table, ordered by the activityDescription and the subjectNumber ("Descr_melt")

  • Write down the file without row names.

The tidy dataset is available here: https://s3.amazonaws.com/coursera-uploads/user-79bc3638751665e5f2de754d/973502/asst-3/d63a33b0177111e5bd26d991f7ae653f.txt (A description of "tidyData.txt" file may be found in the "CodeBook.md" file)

Acknowledgements:

Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012

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