README

Vishal Sharma
Saturday, December 20, 2014

Getting and Cleaning Data Assignment

Environment Preparation for run_Analysis.R

* Clone this repository
* Download the data set and extract. It should result in a UCI HAR Dataset folder that has all the files in the required structure.
* Copy all the files/folders in the current working directory those are in UCI HAR Dataset folder
* Create run_analysis.R in current working directory
* Run Rscript <path to>/run_analysis.R
* The tidy dataset should get created in the current directory as tidyDataFile
* Code book for the tidy dataset is available here

Process: Algorithmic Steps for run_Analysis.R

For both the test and train datasets, produce an interim dataset

* Get the data of activities by Reading the y data set files
* Get the data of subjects by Reading  the Subject data set files
* Extract the mean and standard deviation features (listed in CodeBook.md, section 'Extracted Features') by Reading    the Feature data file
* Get the data of measures by Reading the X data set files and subset this data for the features derived in the above step
* Add the subjectid and activity id in the above data

Join the test and train interim datasets.

* With the above step-1 desired data filtered from the test data and train data 
* Merge these two data sets using rbind
* Make the column names nicer by replacing the string mean with Mean and std with Std
* Final data set named as data in run_Analysis.R

Read the activity Name from the Activity data set file and join with the data set formed in step2
```
 * Final data set named as data_labeled in run_Analysis.R
```

Create a tidy data set that has the average of each variable for each activity and each subject.

 * Primary coloums are - ActivityID", "ActivityName", "SubjectID , Rest of the coloums are measured coloums.
 * Using melt and dcast functions clean dataset is prerpated.
 * Final data set named as melted_data in run_Analysis.R

Write the clean dataset in a file named as tidyDataFile.txt as rowno false as suggested in the instructions.

Tidy Data FIle:

Name of the file is given as tidyDataFile.txt.For Each Activity 30 subjects are captured. There are total 6 activites so total number of rows in the Tidy data file is 180. Each row contains mean or standard deviation from the original dataset against a specific activity and subject.

Function Description for run_Analysis.R

Please refer run_Analysis.R

Assumptions

The training and test data are available in folders named train and test respectively. For each of these data sets:

Measurements are present in X_.txt file
Subject information is present in subject_.txt file
Activity codes are present in y_.txt file
All activity codes and their labels are in a file named activity_labels.txt.
Names of all measurements taken are present in file features.txt ordered and indexed as they appear in the X_.txt files.
All columns representing means contain ...mean() in them.
All columns representing standard deviations contain ...std() in them.

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

Relevenat function in run_Analysis.R as per Assignment

Q1 : Merges the training and the test sets to create one data set.

      # Refer Function: mergeData

Extracts only the measurements on the mean and standard deviation for each measurement

      # Refer Function: readData

Q3: Uses descriptive activity names to name the activities in the data set

      # Refer Function: applyActivityLabel [ Refer ActivityName column in tidy.txt]

Q4: Appropriately labels the data set with descriptive variable names.

      # Refer Function: mergeData

Q5: 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.

       # Refer Function:  getTidyData