Create CodeBook.md

Author: ramperher

CodeBook.md

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+### Introduction
+
+This Markdown file describes the variables, the data, and any transformations or work performed 
+to clean up the original data in order to transform it into the final dataset.
+
+Note that the file [run_analysis.R](https://github.com/ramperher/ProgrammingAssignment3/blob/master/run_analysis.R)
+has already had all the steps followed in this project completely explained, but they will be 
+reflected here for better understanding.
+
+### Tasks
+
+**1. Merge the training and the test sets to create one data set**
+
+* Download raw data and extract all files. It also provides information about the mean of each file 
+and variable from the dataset (so we will not explain that here).
+* Load train/test data frames using read.table-cbind functions over all the files involved, which are:
+  * Subject who performed the activity (from subject_train/test.txt).
+  * Activity (from y_train/test.txt).
+  * Measures (from X_train/test.txt).
+* Merge train and test data frame using rbind. We will obtain a data frame with the same number of
+variables as train and test data frames, while the number of observations will be the sum of the two.
+
+**2. Extract only the measurements on the mean and standard deviation for each measurement**
+
+* Read features.txt with read.table, which have the names for measures in X_train/text.txt, and 
+transform them to a character vector.
+* Look for the position of names which contains "mean()" or "std()" and add them 2 in order to choose 
+the correct columns in our data frame (remember that first and second column in the data frame are the 
+subject and the activity). For this purpose, we can use grep function with a regular expression.
+* Update the data frame choosing the columns found before, in addition to the first and second column 
+from the data frame (subject and activity). Now, our data frame has 68 variables (subject, activity, 33 
+mean variables and another 33 std variables).
+
+**3. Use descriptive activity names to name the activities in the data set**
+
+* Read activity_labels.txt, which have the names for every activity, and transform them to a character 
+vector, as we did with features.txt.
+* Transform df second column (activity) into factor, using the previous character vector as levels.
+
+**4. Appropriately label the data set with descriptive variable names**
+
+* With colnames function, we will put names to all variables in the dataset.
+  * First and second column will be called "subject" and "activity", respectively.
+  * The rest of columns will use the names obtained in the task 2.
+
+**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**
+
+* Here we will need dplyr package with group_by/summarise_each functions.
+  * Firstly, group by subject and activity.
+  * After it, use summarise_each to compute the mean of the rest of variables.
+* For better understanding, we will rename the measure columns use the prefix "MEAN-".
+* Save the tidy data frame into a file called "tidy_df.txt" with write.table function.
+
+### Result
+
+The result is the tidy data frame called [tidy_df.txt](https://github.com/ramperher/ProgrammingAssignment3/blob/master/tidy_df.txt),
+which presents the following structure:
+
+* `subject` - (integer) subject who performed the activity for each window sample. Its 
+range is from 1 to 30.
+* `activity` - (factor) activity performed by the subject. The possible values are WALKING, 
+WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING and LAYING.
+* `MEAN-<variable>` - (numeric) mean of <variable> for every pair of subject-activity. The
+variables involved are all the mean and std variables from the original dataset. Its meaning can 
+be found in the documentation of the original dataset.