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Data Wrangling with R


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Table of Contents

1. Preface 2. Introduction a. The Role of Data Wrangling i. Introduction to R 1. Open Source 2. Flexibility 3. Community ii. R Basics 1. Assignment & Evaluation 2. Vectorization 3. Getting help 4. Workspace 5. Working with packages 6. Style guide
3. Working with Different Types of Data in R a. Dealing with Numbers i. Integer vs. Double ii. Generating sequence of non-random numbers iii. Generating sequence of random numbers iv. Setting the seed for reproducible random numbers v. Comparing numeric values vi. Rounding numbers b. Dealing with Character Strings i. Character string basics ii. String manipulation with base R iii. String manipulation with stringr iv. Set operatons for character strings c. Dealing with Regular Expressions i. Regex Syntax ii. Regex Functions iii. Additional resources d. Dealing with Factors i. Creating, converting & inspecting factors ii. Ordering levels iii. Revalue levels iv. Dropping levels e. Dealing with Dates i. Getting current date & time ii. Converting strings to dates iii. Extract & manipulate parts of dates iv. Creating date sequences v. Calculations with dates vi. Dealing with time zones & daylight savings vii. Additional resources
<4. Managing Data Structures in R a. Data Structure Basics i. Identifying the Structure ii. Attributes b. Managing Vectors i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting c. Managing Lists i. Creating iii. Adding attributes iv. Subsetting d. Managing Matrices i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting e. Managing Data Frames i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting f. Dealing with Missing Values i. Testing for missing values ii. Recoding missing values iii. Excluding missing values
5. Importing, Scraping, and Exporting Data with R a. Importing Data i. Reading data from text files ii. Reading data from Excel files iii. Load data from saved R object file iv. Additional resources b. Scraping Data i. Importing tabular and Excel files stored online ii. Scraping HTML text iii. Scraping HTML table data iv. Working with APIs v. Additional Resources c. Exporting Data i. Writing data to text files ii. Writing data to Excel files iii. Saving data as an R object file iv. Additional resources
6. Creating Efficient & Readable Code in R a. Functions i. Function Components ii. Arguments iii. Scoping Rules iv. Lazy Evaluation v. Returning Multiple Outputs from a Function vi. Dealing with Invalid Parameters vii. Saving and Sourcing Functions viii. Additional Resources b. Loop Control Statements i. Basic control statements (i.e. if, for, while, etc.) ii. Apply family iii. Other useful "loop-like" functions iv. Additional Resources c. Simplify Your Code with %>% i. Pipe (%>%) Operator ii. Additional Functions iii. Additional Pipe Operators iv. Additional Resources
7. Shaping & Transforming Your Data with R a. Reshaping Your Data with tidyr i. Making wide data long ii. Making long data wide iii. Splitting a single column into multiple columns iv. Combining multiple columns into a single column v. Additional tidyr functions vi. Sequencing your tidyr operations vii. Additional resources b. Transforming Your Data with dplyr i. Selecting variables of interest ii. Filtering rows iii. Grouping data by categorical variables iv. Performing summary statistics on variables v. Arranging variables by value vi. Joining datasets vii. Creating new variables viii. Additional resources

About the Author

Brad Boehmke, Ph.D., is an Operations Research Analyst at Headquarters Air Force Materiel Command, Studies and Analyses Division. He is also Assistant Professor in the Operational Sciences Department at the Air Force Institute of Technology. Dr. Boehmke's research interests are in the areas of cost analysis, economic modeling, decision analysis, and developing applied modeling applications through the R statistical language.

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