Coursera Getting and Cleaning Data Course Project
The files and information in the repo has been created as a submission for the course project in Coursera's 'Getting and Cleaning Data' course and is hosted here on Github for peer review.
As students we were provided with a copy of the Human Activity Recognition Using Smartphones Dataset produced in the study by Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio and Luca Oneto.
The data set information from the README.txt, in the compressed dataset archive, provides this introductory description of the source of the data.
The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.
The file 'run_analysis.R' in the root of this repository is a R script which performs the required tasks of merging the sets, reducing the number of features/columns to those that are a calculated mean or standard deviation, applies appropriate labels to the dataset, replaces the integer references for the various activities with the activity names and finally outputs a tidy data set consisting of the average of each variable for each activity performed by each subject. A detailed explanation of how each of these steps was achieved by this script is provided by the following table.
| Line(s) | Description of task implimentation in script |
|---|---|
| 9~16 | Check if the initial provided data set archive is present in the current working directory. If not it is then downloaded from the coursera CDN. |
| 17~23 | Check if the data set has been extracted from the archive into the working and if not then extract the files from the ZIP archive. |
| 25~43 | Accomplishes project task #1 |
| Loads and merges the data set components from the training and testing portions in the files 'subject_train.txt', 'X_train.txt' and 'y_train.txt' (for the training portion) and 'subject_test.txt', 'X_test.txt' and 'y_test.txt' (for the testing portion). Each is column binded to the accumulating data frame for traing and testing, and these two data frames are then row binded to complete the initial data set.
4560 | Accomplishes project task #4
| Labels the columns in our dataset with descriptive variable names. Variable names are read from the file 'features.txt' and the names of the first and last columns ("Subject" and "Activity") are appended to the start and end of the vector of variable names. Any characters in the variable names which may cause syntax error in R are removed from each name and dashes and commas are replaced by underscores to preserve readability. To further the descriptive nature of these variable names 't', 'f' and 'anglet' at the start of the variable names are replaced with the more descriptive 68 | Accomplishes project task #2
| A logical vector of the columns containing the variables we wish to have in our final tidy dataset is created by searching the vector of column names for those containing the strings 'Subject', 'Activity', 'mean' or 'std'. The data frame is then subset keeping only the columns/variables as specified by the logical vector 'neededCols'.
70'time_', 'freq_' and 'angle_time_'. The new variable names are then applied to the columns of our data frame.
6276 | Accomplishes project task #3
| The activity labels and names are read from the file 'activity_labels.txt' and the values in the 'Activity' column of our dataset are mapped to their corresponding character strings as factors.
7885 | Accomplishes project task #5
| The data frame is melted and then cast into our final tidy data frame containing the average value for each variable for each activity performed by each subject. This final data frame is then written to a file 'tidyData.txt' in the working directory using the write.csv() function and can be read back into R using the following command (assuming the 'tidyData.txt' file is in the working directory): read.csv("tidyData.txt")
87~90 | Cleans up the environment by removing all vectors and data frames that are no longer needed, and then prints out a final message informing the user of the description of the two data frames data (the initial processed data frame) and tidyData (the final tidy data set).
The following R packages are required by the run_analysis.R script:
- plyr
- utils
- reshape2