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CleaningDataCourseProject

Course project in Getting and Cleaning Data

Repo Content:

  1. README.MD - this document
  2. run_analysis.R - Script to generate tidy data sets
  3. TidyData1.txt - Data set for task 1-4 of the course project
  4. TidyData2.txt - Data set for task 5 of the course project
  5. codebook.MD - description of the data sets

Data Source: Human Activity Recognition Using Smartphones Dataset Version 1.0

Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. activityrecognition@smartlab.ws www.smartlab.ws

script name: run_analysis.R

requires libraries:

  • utils
  • reshape2

script content:

  1. Downloading data file in ZIP format to local work directory output: data.zip
  2. Unziping the data file
  3. Reading the test and train data sets into dataframes output: subject_test, X_test, y_test, subject_train, X_train, y_train features, activity_labels
  4. Data sets for within both test and train are combined (subject, activity, measurements) output: test.data and train.data
  5. Both test and train data sets are combined output: all.data
  6. Column headers are applied to combined data set ouput: all.data having column header
  7. Selecting column index for column headers containing mean() and std() in the header text. First two columns
    Subject and Activity are added. output: column.index
  8. Extracted data set with measurements only on mean and standard deviation output: extracted.data
  9. Replacing activity code with descriptive activity from activity_labels output: extracted.data 10.Write results to TidyData1.txt 11.Melt and decast data on the measures. ID are Subject and Activity. Calculate mean of all measures. output: mean.data 12.Write results to TidyData2.txt