Course Project for Getting and Cleaning Data containing R script, README.md and codebook.md
Now, I explain the R script step by step
1.Merges the training and the test sets to create one data set.
- a. Reading test and train data using read.table() function.
- b. Combining both row-wise using rbind() function. This is not complete dataset.
2.Extracts only the measurements on the mean and standard deviation for each measurement.
- a. Reading feature data using read.table() function
- b. Selecting only those feature indices from feature data which containing mean and std names using grepl() function. Then combine those indices followed by sorting them in ascending order.
- c. Selecting only those variables/columns having same indices as of feature indices calculated in step 2.b.
3.Uses descriptive activity names to name the activities in the data set.
- a. Reading subject test and Y test data using read.table() function.
- b. Combine both using column-wise using cbind() function.
- c. Reading subject train and Y train data using read.table() function.
- d. Combine both using column-wise using cbind() function.
- e. Combine data computed in step 3.b and 3.d row-wise using rbind() function.
- f. Changing column names "SubjectIDs" and "Activities" respectively for data computed in 3.e.
- g. combining data computed in step 3.f and 2.c column-wise using cbind() function. This is complete dataset.
- h. Assigning descriptive activity names to the "activities" column in the complete dataset.
4.Appropriately labels the data set with descriptive variable names.
- a. Capturing variable names from complete dataset computed in step 3.g. Extracting indices from variable names using sub() function.
- b. Replacing features corresponding to indices computed in step 4.a from feature data to complete dataset's variables by for loop.
- c. Giving descriptive names to complete dataset's variables computed in step 4.b using sub(), gsub() function by for loop.
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.
- a. Creating tidy dataset with the average of each variable for each activity and each subject from complete dataset computed in step 4.c by using aggregate function.