run_analysis.R is the only file needed to:

1. Download the data
2. Process the data
3. Create the tidy data set.

There two other files included per assignment objectives:

1. CodeBook.md: Meets assignment objective #3: GitHub contains a code book that modifies and updates the available codebooks with the data to indicate all the variables and summaries calculated, along with units, and any other relevant information.
2. tidyDataSet.csv: Meets assignment objective #5 (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.)

This section details how the code accomplishes the objectives of the assignment.

The code breaks down the work required to accomplish the objectives of the assignment into 8 primary steps.

1. Download the data
2. Read the test and training data
3. Merge the test and training data together to create one data set (assignment objective #1)
4. Extract only the measurements on the mean and standard deviation for each measurement (assignment objective #2)
5. Clean up column names and appropriately label the data set with descriptive variable names (assignment objective #4)
6. Create a second, independent tidy data set with the average of each variable for each activity and each subject (assignment objective #5).
7. Write the tidy data to a .csv

The steps above are each accomplished with 1 or more primary functions. Each primary function may use 1 or more helper functions.

The sections below detail the code that accomplishes each step.

This is accomplished with function downloadData().

• Uses download.file() to download the zip file containing the data
• Uses unzip() to extract the data files necessary for the assignment objectives

Output: A directory "data" created in the users working directory, containing the data files necessary for the project objectives

Reads the test and training data into their own data frames, testActivityData and trainingActivityData. There are 2 functions for this:

1. getTestActivityData()
2. getTrainingActivityData()

Each function is the same, the only difference is the files used for each step. Here is a summary:

1. Read the activity data, either "./data/X_test.txt" for the test data set, or "./data/X_train.txt" for the training data set. This is done via a helper function which wraps read.table(). The data is read into a data frame, either testData or trainingData

2. Set the column headers on the data frame created in step 1 by reading the "./data/features.txt" file

• Call helper function getColumnNames() which uses read.table() to read the second colume of data from "./data/features.txt"
• Use make.names() to make each column name unique because there are some duplicated values in "./data/features.txt"

3. Calls helper function addActivityLabels(). This meets objective #4 ("Uses descriptive activity names to name the activities in the data set")

• Uses read.table() to read the data from "./data/activity_labels.txt" into a dataframe called activityLabels with 2 columns: activityId and activityDescription
• Uses read.table to read either "./data/y_test.txt" for the test data, or "./data/y_train.txt" for the training data - to read the activity id's. Creates a data frame, activities, with a single column, activityId
• Uses left_join() from dplyr to join activities and activityLabels into a single data frame, activities. The 2 dataframes are joined on activityID.
• Uses dplyr select() to select just the friendly activity description in the activityDescription column +Uses dplyr bind_cols() to join the data frames containing the activity data and the data frame containing the friendly activity description

Output: 2 data frames (testActivityData and trainingActivityData) with the training and test data - including a column of descriptive activity names and with unique column names.

This step merges the 2 data frames created in step 2.

Output: a single data frame called combinedData which contains both the training and test data.

1. Uses dplyr bind_rows() to bind the testActivityData and trainingActivityData data frames into a single data frame called combined data.

Uses a function removeExtraneousColumns() which:

1. Creates a character vector containing the names of columns to keep called initialColumnNames

• Reads in the values ("subject","activityDescription","dataSet") into meanColumnNames
• Uses grep() to read in any values containing "std" or "mean" into standardDeviationColumnNames
• Combines initialColumnNames, meanColumnNames and standardDeviationColumnNames into a vector called columnsToKeep

2. Removes any ny columns containging "angle" because as per line 49 of 'features_info.txt' these measures actually measure the angle between to vectors as opposed to being a measure of mean or standard deviation
3. Remove any columns containing "meanFreq" because as per line 45 of 'features_info.txt' these are actually "weighted average of the frequency components to obtain a mean frequency" and not a meausre of the mean for a single measurement as per assignment instructions
4. Use dplyr select() to keep only the columns in the columnsToKeep vector from the activityData data frame

Output: modifies the dataframe named combinedData, with only the measures measurements on the mean and standard deviation for each measurement (assignment objective #2)

Calls a function called cleanUpColumnNames() which:

1. Replace repeated '.' characters in the column names with a single '.' character
2. Removes trailing '.' periods in column names

Output: a modified combinedData data frame with no trailing or repeated '.' characters in the column headers

Calls a function createAverageTidyDataSet() which

1. Uses melt() from the reshape2 library to take the data from the combinedData data frame and make it "narrow" - with one row per measure - for each subject and activity description combination

2. Calls a helper functions getTidyDataBySubject() and getTidyDataByActivity() which +cast the meletd data from step 1 into 1 row per each subject + variable combination - calcuating the mean for each variable +add a column called "grouped by" to indicate which attribute from source the data is grouped by - either subject or activityDescription +For subject data only: convert subject column from integer to character so it binds with the tidy data by activity data frame, where subject is a factor +Calls a helper function moveColumnToBeginningOfDataFrame() which moves the new "groupedBy" column to the first column in the data frame. NOTE: adapted from code in this thread: http://stackoverflow.com/a/3370115/7416441 +Renames the subject column to groupedByValue to indicate what subject or activityDescription each row relates to

Output: a data frame, tidyData, which is a second, independent tidy data set with the average of each variable for each activity and each subject. (assignment objective #5)

Calls write.csv() to write the tidyData to a .csv file (assignment objective #5)