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R function: runanalysis.R
run_analysis.R is an R program that downloads data from the UCI HAR Dataset and combines the separate data files into two tidy datasets: the first tidy dataset is named "TidyDataSet.txt" and contains all the available data from the individual HAR datasets, while the second tidy dataset is named "TidyDataSetMeanStdDev.txt" and contains the mean and standard deviation values for the data.
Note: The run_analysis.R function assumes that the UCI HAR Dataset directory structure resides in the working directory. If the files have been moved from the UCI directory structure, the code won't find the data.
The run_analysis() function reads the UCI HAR Dataset, combines the data into one tidy file, saves that file in the working directory with the name "TidyDataSet.txt". The function then calculates the mean and standard deviation of each measurement for each activity for each individual and saves that dataset in the working directory with the name "TidyDataSetMeanStdDev.txt".
The function gets the header and format data and saves the data into data frames named activity_labels and features, respectively. These data frames are for reference only and are not used further in the function.
The function then sets up some data frames to hold data temporarily and downloads the "test" and "train"" data using the read.fwf() function. The data frames are:
The function then combines all the data into one tidy data frame named DataTotal using the cbind() function and sets the labels for each column in the data frame per the above data frame names using the colnames() function.
The DataTotal data frame is then sorted by Subject and Y_Data, which isn't necessary but is the coder's preference.
The data frame DataTotal is then written to a file with the name "TidyDataSet.txt" using the function write.table().
A new data frame named DataMeanStdDev is created to hold the mean and standard deviation data.
The function loops over the subjects and activities in the total dataframe, calculates mean and standard deviation for each measurement, and adds the data to the DataMeanStdDev data frame. The functions mean() and sd() are used to calculate the means and standard deviations.
The function then sets the column names for the DataMeanStdDev data frame to the following labels:
"DataSet", "Subject", "Activity", "Body_Acc_X_Mean", "Body_Acc_X_StdDev", "Body_Acc_Y_Mean", "Body_Acc_Y_StdDev", "Body_Acc_Z_Mean", "Body_Acc_Z_StdDev", "Body_Gyro_X_Mean", "Body_Gyro_X_StdDev", "Body_Gyro_Y_Mean", "Body_Gyro_Y_StdDev", "Body_Gyro_Z_Mean", "Body_Gyro_Z_StdDev", "Total_Acc_X_Mean", "Total_Acc_X_StdDev", "Total_Acc_Y_Mean", "Total_Acc_Y_StdDev", "Total_Acc_Z_Mean", "Total_Acc_Z_StdDev"
The data frame DataMeanStdDev is then written to a file with the name "TidyDataSetMeanStdDev.txt" using the function write.table().
[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
Use of the datasets referenced in run_analysis.R function must be acknowledged by referencing the following publication [1]
[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
This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.
Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.