run_analysis.R is an R script designed to fullfil the requirements of
the Getting and Cleaning Data Programming Project. The script reads raw
data collected from the accelerometers of Samsung Galaxy S smartphones,
and outputs a tidy dataset representing the means of a subset of the
original variables aggregated over the Subject and Activityvariables.
The original data was made available in the UCI Human Activity Recognition Using Smartphones Data Set.
The requirements of the script were specified as follows:
- Merges the training and the test sets to create one data set.
- Extracts only the measurements on the mean and standard deviation for each measurement.
- Uses descriptive activity names to name the activities in the data set
- Appropriately labels the data set with descriptive variable names.
- From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
Each of these steps is clearly commented in the run_analysis.R script.
The script assumes the downloaded data is available in a folder whose
name is given in the rawDataDirName variable. The summary output data
table is written to a folder specified by the outputDataDirName
variable.
The script operates by first reading the features.txt file, which
contains descriptions of the variables of the test and training X
datasets, and then creating a logical vector of dataset variables to
retain. The three test data sets are read in, the X measurements,
the Subject identifiers and the Activity codes. The X dataset is
subsetted, keeping only the columns (variables) to be retained. The
three data sets are then column bound together. The process is repeated
for the training data sets, and the resulting frames are row bound. The
numeric Activity code values are replaced by their descriptive values.
A second summary dataset is created representing the means of the
retained X variables broken down by Subject and Activity. The summary
data set is written to disk.