Tidy_data: An R repository from paolovaona

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#ReadMe

The assignment of Getting and Cleaning Data is to create one R script called run_analysis.R that does the following:

1. Merges the training and the test sets to create one data set.
2. Extracts only the measurements on the mean and standard deviation for each measurement.
3. Uses descriptive activity names to name the activities in the data set
4. Appropriately labels the data set with descriptive variable names.
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.

##Input Data
Data come from the paper:
*Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012*

The experiments have been carried out with a group of 30 volunteers.

Each person performed six activities:

1. WALKING
2. WALKING_UPSTAIRS
3. WALKING_DOWNSTAIRS
4. SITTING
5. STANDING
6. LAYING

During the activities were recorder 3-axial linear acceleration(XYZ acc) and 3-axial angular velocity(XYZ ang).

The obtained dataset has been randomly partitioned into two sets:

* **Training** 21 person
* **Test** 9 person

XYZ acc and XYZ ang were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window)

From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

For each record it is provided:

- Triaxial acceleration from the accelerometer (total acceleration) and the estimated
body acceleration.
- Triaxial Angular velocity from the gyroscope.
- A 561-feature vector with time and frequency domain variables.
- Its activity label.
- An identifier of the subject who carried out the experiment.

The dataset includes the following files:

- 'README.txt'

- 'features_info.txt': Shows information about the variables used on the feature vector.

- 'features.txt': List of all features.

- 'activity_labels.txt': Links the class labels with their activity name.

- 'train/X_train.txt': Training set.

- 'train/y_train.txt': Training labels.

- 'test/X_test.txt': Test set.

- 'test/y_test.txt': Test labels.

- subject: Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

- Inertial Signals: **not needed for the assignment**

The units of the measurements are the following:

* Total Acceleration [g]
* Body Acceleration, where g is subtracted [m/s^2]
* Angular velocity [ rad/sec]

##Merge Train and Test sets

The function **stat_data <- function(name,stat)** with input the name of the folders(train and test) do the following:

1. Read from the folder UCI HAR Dataset the activity name and the features. These values are labeld to be reused.

2. A **for cycle** run for the two folder and read the subject list, the feature vector and the lables of the activity.

3. Assign and create 3 new rows at the beginning of the data.table with the information of the set, the information of the activity and the information of the subject that performed the activity.

4.Merge the two data.tables with rbind.

The total readed variables are 561 to which have to be added the 3 new created variables ID_set,ID_vol and ID_act. The total is then 564 variables.
The rows of the test set are 2947, while the rows of the train set are 7352. The total is then 10299 rows.

##Mean and std extraction

Only the varibles which contains means and standard deviation are extracted.These are characterized by the following labels:

* **mean()** suffix.
* **std()** suffix.
* **meanFreq()** suffix
* **Mean** for the angle variables, since mean and average are synonimous.

Total variables that have to be extracted are then 33 for mean(), 33 for std() and 7 for Mean and 13 for meanFreq() for a total of 86 variables. Adding the 3 ID variables a total of 89 variables is obtained.

##Name the activity in the data set
To do this the a new column is added to the dataset.
To add it the command mutate is used. In this way for each record it is possible to obtain from the variable **activity_ext** the extended name of the activity according to the description provided in the original data set.

##Name the variables with descriptive names
The current names assigned to the different variables already describe the content of the variable. The shrinked names have been extended( e.g. t has been changed to time), using gsub.
For any doubt please refer to codebook.

##Average of each variable for each activity and each subject.
To subset the data set it has been chose to be used ddply function to summarize for each volunteer and activity the mean of all the features.
The result is a wide tidy data set. The columns are 88, equal to 86 observation plus activity and volunteer id variables.
The rows are equal to 180, which is correct since there are 6 activity for 30 volunteers, 6x30=180.

paolovaona/Tidy_data