For this assignment, students were asked to write an R script that:
- 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.
Reference: https://class.coursera.org/getdata-031/human_grading/view/courses/975115/assessments/3/submissions
In addition, students were asked to submit a codebook, named CodeBook.md, as well as this README.md document.
* run_analysis.R - the R script that generates the required output in tidy data format * CodeBook.md - the codebook describing the variables in the tidy data set * README.md - this document The original data consisted of several files, some containing data, and others containing descriptions of the data. Each file is described in detail in the README.txt file of the source data. Steps associated with completing this assignment were as follows: The script merges the training and the test sets to create one data set by building a dataframe from the training data, and building a separate dataframe from the test data. Each of these two data frames combines the data from three files (the values from the measurments of the activities, the subject performing the activity, and the type of activity). The script then merges the to dataframes into one dataframe. The script extracts the variables related to mean and standard deviation for each measurement. As noted in several discussion forums (see, for example, the the point made by class TA David Hood in the section on "what columns are measurements on the mean and standard deviation" found [here](https://class.coursera.org/getdata-031/forum/thread?thread_id=28) ), the instructions were not clear as to which variable names actually referred to mean or standard deviation. I chose to simply subset the dataframe to those variables that contained either the string "mean()" or "std()". Some variable names that contained the string "mean" or "std" (for example, "angle(X,gravityMean)") were excluded based on this approach. The assignment instructions ask students to use descriptive activity names to name the activities in the data set. To effectuate this, the script replaces the activities, identified by number in the y.txt files in the source data, with the activity names found in activity_labels.txt. The assignment insructions ask students to appropriately label the data set with descriptive variable names. To some degree, this script addresses this in Step 1. When the script built the test and training data frames, it named the last two variables that were created by adding columns from the y_(train/test) and subject_(train/test) files. I took the position that otherwise, all variables appear to have had descriptive names in their orignal form (meaning, as they appeared in the X_[train/test] files). I took the position that any attempt to modify the given variable names would make them, in many cases, exceedinlgy long and complex, without adding much in the way of comprehensibility, and may even make them less accurate since I did not have a understanding of the domain of this project (accellerometer readings). The codebook I am submitting explains the variable names in more detail. From the data set in step 4, the script creates a second, independent tidy data set with the average of each variable for each activity and each subject. The script creates a long form of tidy data, by melting all variables except the identifying variables. The data is long, as described [here](https://class.coursera.org/getdata-031/forum/thread?thread_id=225). It was not clear that melting is really required, per the assignement instructions, which say that wide form is permitted here. Without more information about the use to which this data is being put, we cannot determine whether this melting step is required to make this data tidy. Run the file run_analysis.R, with the working directory in your R environment set to the "UCI HAR Dataset" directory that is unzipped from the zip of the source file, to generate the tidy data set described above. The tidy data set is stored in a file called finalResult.txt that is written to the working directory.