- This is my final coursera project in : Getting and Cleaning Data .
- I include in my repository the run_analysis.R script where i made the project following the steps descrives in the CodeBOOK.md file
1. Merges the training and the test sets to create one data set.
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Create a x_data variable where can put x_train and x_test objects using rbind().
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Create a y_data variable where can put y_train and y_test objects using rbind().
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Create a subject variable where can put subject_train and subject_test objects using rbind().
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Create a Marged variable where can put subject and x_data objects using cbind().
2. Extracts only the measurements on the mean and standard deviation for each measurement.
- Select only the colums subject and code with mean and std , for each variable.
3. Uses descriptive activity names to name the activities in the data set
- Remplace code for activities variable
4. Appropriately labels the data set with descriptive variable names.
- In this step we only have to change the names of the variables, using the gsub() function ( ?gsub)
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
- Save all in tidy_dataset.txt.