title | author | date | output | ||||
---|---|---|---|---|---|---|---|
README -- Getting and Cleaning Data Course Project |
Veeresh Taranalli |
July 25, 2015 |
|
##Steps to create the tidy data file
- Download the UCI HAR dataset in your working directory.
- Download the R script "run_analysis.R" from GitHub in your working directory.
- Run the R script "run_analysis.R" to obtain the tidy data file named "UCI_HAR_Tidy_Dataset_Coursera_Project.txt" in your working directory.
- Your working directory structure should look like as shown below
- UCI HAR Dataset (Directory/Folder)
- run_analysis.R (R script file)
- readme.md (markdown file)
- codebook.md (markdown file)
- UCI_HAR_Tidy_Dataset_Coursera_Project.txt (text file containing the tidy dataset)
##Steps performed by the "run_analysis.R" script for cleaning the data
- Read data files from the provided UCI HAR Dataset.
- Process the train and test feature data frames to remove NAs and make it tidy.
- Bind the the train and test data frames together to create a merged dataframe useful for further processing and tidying.
- Remove intermediate dataframes from global environment.
- Assign feature names to column variables.
- Assign descriptive activity labels for the Activity variable (column).
- Select subset of only mean and standard deviation measurements for every feature measurement.
- Clean up the variable (column) labels.
- Create a tbl_df for the tidy dataset.
- Compute average of the existing variables grouped by SubjectID and Activity variables.
- Remove duplicate rows and order rows by SubjectID.
- Convert to long form of data by gathering all columns as a 'Feature' variable.
- Clean up the feature names.
- Separate the Feature and Measurement Type into two variables.
- Write tidy dataset to a file.