💻Intro to Programming in R Unit 20 – Advanced R Topics and Applications

Key Concepts and Terminology

• R is a programming language and environment for statistical computing and graphics
• Vectors are one-dimensional arrays that can hold numeric, character, or logical values
• Lists are ordered collections of objects that can be of different types (numeric, character, logical, or even other lists)
• Data frames are two-dimensional data structures with rows and columns, similar to a spreadsheet
• Factors are used to represent categorical variables with a fixed set of possible values (levels)
• Matrices are two-dimensional arrays that can only hold elements of the same data type
• Arrays are multi-dimensional data structures that can hold elements of the same data type
• Functions are reusable blocks of code that perform a specific task and can accept input arguments and return output values

Advanced Data Structures in R

• Tibbles are modern reimagining of data frames that provide a more consistent and stricter behavior
  • Tibbles never change the type of the inputs (e.g., strings to factors) and never change the names of variables
  • Tibbles have a more compact print method that shows only the first 10 rows and all the columns that fit on the screen
• Data tables are an extension of data frames that provide fast and memory-efficient operations for large datasets
  • Data tables use a special syntax (e.g.,

    DT[i, j, by]

    ) for subsetting, grouping, and modifying data
  • Data tables support fast aggregation, joins, and reshaping operations
• Sparse matrices are matrices where most of the elements are zero, and only non-zero values are stored to save memory
  • R provides the

    Matrix

    package for working with sparse matrices
  • Sparse matrices are useful for representing large, high-dimensional data (e.g., text data, recommendation systems)
• Nested data frames are data frames that contain other data frames or lists as columns
  • Nested data frames are useful for representing hierarchical or grouped data structures
  • The

    tidyr

    package provides functions for working with nested data frames (e.g.,

    nest()

    ,

    unnest()

    )

Functional Programming Techniques

• Functional programming is a programming paradigm that treats computation as the evaluation of mathematical functions and avoids changing state and mutable data
• Pure functions are functions that always produce the same output for the same input and have no side effects
  • Pure functions make code more predictable, testable, and easier to reason about
• Higher-order functions are functions that take other functions as arguments or return functions as results
  • Examples of higher-order functions in R include

    lapply()

    ,

    sapply()

    ,

    map()

    , and

    reduce()

• Anonymous functions (lambda functions) are functions without a name that can be defined inline and passed as arguments to other functions
  • Anonymous functions are defined using the

    function()

    keyword in R
• Closures are functions that capture and retain access to variables from their surrounding environment
  • Closures are useful for creating functions with customizable behavior based on external parameters
• Recursion is a technique where a function calls itself to solve a problem by breaking it down into smaller subproblems
  • Recursive functions need a base case to stop the recursion and prevent infinite loops

Efficient Data Manipulation and Analysis

• The

  dplyr

  package provides a grammar of data manipulation with functions for filtering, selecting, arranging, mutating, and summarizing data

  • filter()

    selects rows based on a condition

  • select()

    selects columns by name

  • arrange()

    sorts rows by one or more columns

  • mutate()

    creates new columns or modifies existing ones

  • summarize()

    reduces multiple values to a single summary value
• The

  tidyr

  package provides functions for tidying and reshaping data

  • gather()

    converts wide data to long format

  • spread()

    converts long data to wide format

  • separate()

    splits a single column into multiple columns

  • unite()

    combines multiple columns into a single column
• Piping (

  %>%

  ) is a technique for chaining multiple functions together, where the output of one function becomes the input of the next function
  • Piping improves code readability and avoids nested function calls
• Grouping and aggregation are techniques for performing operations on subsets of data based on one or more grouping variables
  • The

    group_by()

    function from

    dplyr

    is used to group data by one or more variables
  • Aggregation functions (e.g.,

    sum()

    ,

    mean()

    ,

    max()

    ) are used to calculate summary statistics for each group
• Window functions (e.g.,

  lag()

  ,

  lead()

  ,

  rank()

  ) are used to perform calculations across a group of rows that are related to the current row
  • Window functions are useful for calculating running totals, rankings, or differences between rows

Creating Custom Functions and Packages

• Custom functions allow you to encapsulate reusable code and improve the modularity and maintainability of your projects
  • Functions are defined using the

    function()

    keyword, followed by the function name, input arguments, and the function body
  • Functions can have default argument values, variable-length argument lists, and named arguments
• Function documentation is important for describing what a function does, what arguments it takes, and what value it returns
  • Roxygen comments (starting with

    #'

    ) are used to document functions in R
  • Roxygen comments are parsed to generate help files and NAMESPACE declarations
• Package development allows you to create reusable and shareable collections of functions, data, and documentation
  • Packages have a standardized structure with specific directories (e.g.,

    R/

    ,

    man/

    ,

    tests/

    ,

    data/

    )
  • The

    devtools

    package provides functions for creating, building, and testing packages
• Unit testing is the practice of writing tests to verify the correctness of individual functions or units of code
  • The

    testthat

    package provides a framework for writing and running unit tests in R
  • Tests are organized into test files and test cases, and they use expectations to assert the expected behavior of functions
• Continuous integration (CI) is the practice of automatically building, testing, and deploying code changes
  • CI ensures that code changes are regularly tested and integrated into the main development branch
  • Popular CI platforms for R packages include Travis CI and GitHub Actions

Data Visualization with Advanced R Libraries

• The

  ggplot2

  package is a powerful and flexible framework for creating statistical graphics in R

  • ggplot2

    uses a grammar of graphics that separates the data, aesthetics, geometries, and other plot components
  • Plots are built up in layers, with each layer representing a different aspect of the visualization (e.g., points, lines, bars, labels)
• Faceting is a technique for creating multiple subplots based on one or more categorical variables
  • The

    facet_wrap()

    function creates subplots arranged in a grid based on a single variable
  • The

    facet_grid()

    function creates subplots arranged in a grid based on two variables (one for rows and one for columns)
• Customizing plot aesthetics (e.g., colors, shapes, sizes) allows you to create visually appealing and informative graphics

  • ggplot2

    provides a variety of scales (e.g.,

    scale_color_manual()

    ,

    scale_shape_manual()

    ) for mapping data values to visual properties
  • Themes (e.g.,

    theme_bw()

    ,

    theme_minimal()

    ) control the overall appearance of the plot (background, gridlines, fonts)
• Interactive visualizations allow users to explore and interact with data through actions like hovering, clicking, or selecting
  • The

    plotly

    package creates interactive web-based visualizations from

    ggplot2

    plots or using its own API
  • The

    shiny

    package allows you to create interactive web applications with R, including interactive visualizations and dashboards
• Geospatial data visualization involves plotting data on maps or in a spatial context
  • The

    leaflet

    package provides an R interface to the Leaflet JavaScript library for creating interactive maps
  • The

    sf

    package provides a standardized way to work with spatial vector data in R and integrates well with

    ggplot2

    for spatial visualization

Statistical Modeling and Machine Learning in R

• Linear regression is a statistical method for modeling the relationship between a dependent variable and one or more independent variables
  • The

    lm()

    function is used to fit linear regression models in R
  • Assumptions of linear regression include linearity, independence, normality, and homoscedasticity
• Logistic regression is a statistical method for modeling binary outcomes (e.g., success/failure, yes/no) based on one or more predictor variables
  • The

    glm()

    function with

    family = binomial

    is used to fit logistic regression models in R
  • Logistic regression estimates the probability of the outcome based on the predictor variables
• Decision trees are machine learning models that predict outcomes by learning a series of if-then rules based on the input features
  • The

    rpart

    package implements recursive partitioning for building decision trees in R
  • Decision trees are easy to interpret but can be prone to overfitting if not properly pruned or regularized
• Random forests are ensemble machine learning models that combine multiple decision trees to improve prediction accuracy and reduce overfitting
  • The

    randomForest

    package implements random forests in R
  • Random forests introduce randomness by using a random subset of features for each tree and a random subset of observations for each tree (bagging)
• Clustering is an unsupervised machine learning technique for grouping similar observations together based on their features
  • The

    kmeans()

    function implements k-means clustering in R, which partitions observations into a specified number of clusters
  • Hierarchical clustering (

    hclust()

    ) builds a tree-like structure of nested clusters based on the similarity between observations
• Model evaluation and selection involve assessing the performance of machine learning models and choosing the best model for a given task
  • Cross-validation (e.g., k-fold, leave-one-out) is used to estimate the performance of models on unseen data
  • Metrics like accuracy, precision, recall, and F1 score are used to evaluate the performance of classification models
  • The

    caret

    package provides a unified interface for training and evaluating machine learning models in R

Best Practices and Performance Optimization

• Writing clean and readable code is important for collaboration, maintenance, and debugging
  • Use consistent indentation, naming conventions, and code formatting
  • Break complex tasks into smaller, reusable functions
  • Comment your code to explain its purpose, inputs, and outputs
• Version control systems (e.g., Git) allow you to track changes to your code, collaborate with others, and revert to previous versions if needed
  • Use informative commit messages to describe the changes made in each commit
  • Use branches to work on new features or bug fixes without affecting the main codebase
• Profiling and benchmarking are techniques for identifying performance bottlenecks and comparing the speed of different code implementations
  • The

    profvis

    package provides a visual interface for profiling R code and identifying slow parts of your program
  • The

    microbenchmark

    package allows you to compare the execution time of multiple expressions or functions
• Parallel computing allows you to speed up computations by distributing tasks across multiple cores or machines
  • The

    parallel

    package provides functions for running parallel computations in R
  • The

    foreach

    package allows you to write parallel loops that can be run on multiple cores or distributed across a cluster
• Memory management is important for working with large datasets and avoiding out-of-memory errors
  • Use appropriate data structures (e.g., data tables, sparse matrices) for efficient memory usage
  • Remove unnecessary objects and use

    rm()

    to free up memory
  • Use

    gc()

    to trigger garbage collection and reclaim unused memory
• Debugging techniques help you identify and fix errors in your code
  • Use

    print()

    or

    cat()

    statements to output intermediate values and check the flow of your program
  • Use

    browser()

    or

    debug()

    to interactively debug your code and step through execution line by line
  • Use

    traceback()

    to print the call stack and identify the location of an error
  • Use

    try()

    and

    tryCatch()

    to handle errors gracefully and prevent your program from crashing