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This document explains run_analysis.R.

run_analysis.R does the follow things:

• Read the training and test data, merging subject, x, y and activity labels into all.dataset.
• Create a new dataset only contains the subjects, activities, and selected features.
• Create a tidy dataset with the average of each variable for each activity and each subject.

1. Load the activity label into activity.labels, column names are

• id: activity id
• activity: activity name

2. Load the features into features, column names are

• id: feature id
• feature: feature name

3. Create feature_labels.txt whit seleted features, column names are

• id: seleted feature id
• feature: factor for the x describes bellow, which transformed from features in features.txt by replacing - to . and remove ()

4. Load the selected features into feature.selected
5. Read the training and test dataset and create train.data and test.data by merging subject, x and y. Notice that, the column names of train.x and test.x are set to the feature id. So that, it can be easily replace to the filted by the seleted features.
6. Create all.dataset by merging train.data, test.data and activity.labels. all.dataset is a data.frame with 10299 rows and 68 columns(subject, activity, activity.id, and 561 features).

dataset is a subset of all.dataset with subject, activity and 66 selected features. The selected freature's column name is set to the feature.selected$feature.

The tidy dataset is created by the function melt() and dcast(), to compute the mean of features by each subject and each activity.