This functions collection developed for UCI HAR Dataset processing. See following publication for dataset detailes:

[1] 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

Main processing function:

• 'run_anaysis': Returns data.frame (for results' data set detailes see 'CodeBook.md') with cleaned data from UCI HAR Dataset. Function makes following:
  • gets data from test and train sets
  • decode labels to each set
  • preprocesses headers to make them readable
  • merges datasets with each other
  • adds subjects and readable labels
  • extracts only Mean and STD columns
  • creates a second, independent tidy data set with the average of each variable for each activity and each subject

Extra functions:

• 'get_data_from_file': Returns data.frame with data, getted from txt file specified in path parametr.

• 'features_clean': Prepares dataset headers by removing '-',',','(',')' characters from each feature-name upcasing parts of names to make them unified and readable (e.g., mean -> Mean, std -> STD)

• 'clean_feat_name': Cleans a string passed in a ch parametr as follows: removes '-',',','(',')' characters replaces parts of string according to a list: mean() -> Mean std() -> STD mad() -> MAD max() -> Max min() -> Min sma() -> SMA energy() -> Energy iqr() -> IQR entropy() -> Entropy arCoeff() -> ArCoeff correlation() -> Correlation maxInds() -> MaxInds meanFreq() -> MeanFreq skewness() -> Skewness kurtosis() -> Kurtosis bandsEnergy() -> BandsEnergy angle() -> Angle

• 'data_labels_clean': Creates a vector with decoded data labels. parameters: df - vector of coded labels labels - data.frame with code and label Example: 1 -> WALKING 2 -> WALKING_UPSTAIRS 3 -> WALKING_DOWNSTAIRS 4 -> SITTING 5 -> STANDING 6 -> LAYING