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README.md

Readme.md

The tidy data in tidy_data_set.txt can be read into R with the following code:

read.table("tidy_data_set.txt", header=TRUE, colClasses=c('factor', 'factor', rep('numeric', 66)))

Overview

codebook.md describes the specific details of variables, values, and units in the tidy data set.

The tidy_data_set.txt file in this directory is a tidy subset of the data provided in the Human Activity Recognition Using Smartphones Data Set. The source data is available from http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones# and it's also included in the UCI HAR Dataset directory in this repo.

tidy_data_set.txt includes the combined test and training data sets from the following files:

  • [UCI HAR Dataset/test/subject_test.txt](./UCI HAR Dataset/test/subject_test.txt)
  • [UCI HAR Dataset/test/X_test.txt](./UCI HAR Dataset/test/X_test.txt)
  • [UCI HAR Dataset/test/y_test.txt](./UCI HAR Dataset/test/y_test.txt)
  • [UCI HAR Dataset/train/subject_train.txt](./UCI HAR Dataset/train/subject_train.txt)
  • [UCI HAR Dataset/train/X_train.txt](./UCI HAR Dataset/train/X_train.txt)
  • [UCI HAR Dataset/train/y_train.txt](./UCI HAR Dataset/train/y_train.txt)

There are some interesting threads on the course discussion board about wide vs. narrow formats for tidy data. I chose to use the wide format, aligning to these principles:

  1. Each column represents a variable or measure or characteristic.
  2. Each variable is in one column.
  3. Each observation of the variable is in a different row.

Hence the final tidy data set is 180 rows x 68 columns.

codebook.md describes the tidy data set.

Description of run_analysis.R

run_analysis.R takes source data from the UCI Har Dataset directory, imports it into R, and transforms it into a tidy data subset.

The script performs the following operations to import, clean, and transform the data set. These steps are also indicated in comments throughout the .R file.

  1. Read the files from the test and training data and merge the training and test sets to create one data set.
    1. Combine the values from the subject_test and subject_train files to create a single TestSubject column that identifies the study participant.
    2. Combine the values from the Y_test and Y_train data to create a single Activity column that indicates that activity class (for instance, walking or sitting).
    3. Combine the values from the X_test and X_train files to create additional variable columns, one column for each measurement and calculation included in the data set (561 variable columns total, in the initial combined data set; 563 columns including the TestSubject and Activity columns).
    4. Clean up the column names to remove hyphens and parentheses and replace them with periods.
  2. Extract only the measurements on the mean and standard deviation for each measurement.
    1. Use the dplyr select function to create a subset of the data that only includes columns that have ".mean." and ".std." in their column names.
    2. It's not required for the subset, but at this point the script also converts the test subject and activity columns to factors, to facilitate correct calculations later.
  3. Use descriptive activity names to name the activities in the data set.
    1. Use the mapvalues function to map the numeric activity values to descriptive names like "Walking" and "Standing."
  4. Appropriately label the data set with descriptive variable names.
    1. Use the stringr_replace_all function from the stringr library to do a number of find and replace operations on the column names. The details of the resulting descriptive names are included in codebook.md.
  5. From the data set in step 4, create a second, independent tidy data set with the average of each variable for each activity and each subject.
    1. Use split/apply/combine logic. First, split the data by the subject and activity factors using the split method.
    2. Next, use lapply to iterate over each item in the resulting list, and use apply to apply the mean method to calculate the average of each column.
    3. The output of lapply is a list, so combine it back to a data frame.
    4. Use strsplit to break the subject and activity factors back into separate sets, and use cbind to properly bind them as the first columns in the resulting data set.

Verifying the calculations in run_analysis.R

I love the way that R commands can simplify calculations over data frames and lists into just a few lines of code, but since I'm not an experience R programmer I had concerns about whether my calculations were producing correct results. I verified the results in the Data Verification section I added to the R script. This section selects two subsets of data for individual combinations of subjects and activities, calculates the mean for each subset, and compares the result to the result for the same variables in the tidy data set.

Special instructions for running run_analysis.R

  • The script assumes that the data source files are in the a directory called UCI HAR Dataset that's in the current working directory. It assumes that the directory structure and file names and locations with UCI Har Dataset have not been changed since they were extracted from the source .zip file.
  • The references to file locations in run_analysis.R are written to work with the Windows file system. You'll need to modify the file paths in the read.table calls to run on Mac/Linux systems.