####Getting and Cleaning Data

##Introduction

• The script run_analysis.Rperforms the 5 steps described in the course project's definition.
• Initially, i can make sure whether required packages are available in R with a help of require function.
• First, all the similar data is merged using the rbind() function. By similar, we address those files having the same number of columns and referring to the same entities.
• Then, only those columns with the mean and standard deviation measures are taken from the whole dataset. After extracting these columns, they are given the correct names, taken from features.txt.
• As activity data is addressed with values 1:6, we take the activity names and IDs from activity_labels.txt and they are substituted in the dataset.
• On the whole dataset, those columns with vague column names are corrected.
• Finally, I have generated a new dataset with all the average measures for each subject and activity type (30 subjects * 6 activities = 180 rows). The output file is called tidy_data.txt, and uploaded to this repository.