GettingAndCleaningDataCourseProject

The repo contains

• the R script run_analysis.R
• the codebook codebook.md

The R script processes raw data (more precisely: observed subjects, their activity and pre-processed smartphone sensor signals) from experiments on "Human Activity Recognition Using Smartphones", published in [1]. It generates 2 tidy data sets: har and avg, see codebook.md for details.

The raw data had been split into 2 subsets and separated into several files:

• features.txt provides the names of the 561 variables calculated from the sensor signals
• activity_labels.txt provides labels for the activity ids used in the actual data

The actual data for the "train" subset is stored in respective folder "train" with files

• subject_train.txt
• X_train.txt
• y_train.txt.

These 3 files have the same number of lines. While subject_train.txt provides a subject id per line and y_train.txt provides an activity id per line, X_train.txt provides an 561 numeric values per row which correspond to the variable names in features.txt.

The actual data for the "test" subset is stored in respective folder "test" with the same file structure.

The R script reads those files from the working directory and generates a tidy dataset called har, which is merged from the "train" and "test" subsets. From the 561 variables only those on mean and standard deviation are selected.

Based on the har data set it generates a tidy data set avg. In this dataset the average of each of the variables selected for the har data set is provided for each observed subject and activity.

The R script uses the data.table package. First the variable names and the activity mapping is read. The creation of tidy data sets for each subset is delegated to the function load_data defined at the beginning of the script. The merging is then achieved by a simple rbind.

The raw data files do not have headers. Therefore the col.names parameter of the fread function is used to assign descriptive variable names - particularly for the 561 X-variables.

The function load_data reads the 3 files for the specified subset into data tables. The tidy data set is created using a cbind of 3 items:

• the subject "vector"
• the activity "vector", created using the initally loaded mapping for activity ids
• the subset of the X-variables, selected using a regular expression on the variable names

Finally, the avg data set is created chaining a melt and a dcast operation, the latter applying the mean on the value column from the melt operation. The prefix "avg_" is added to the variable names in order to make the data set tidy.

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References:

[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