Discover and read the best of Twitter Threads about #advancedr
Most recents (10)
1/ 💼 R in Production: Deploying and Maintaining R Applications 🏭 Learn how to deploy, monitor, and maintain R applications in production environments for robust, real-world solutions. #rstats #AdvancedR #DataScience 
2/ 🌐 Web Apps: Deploy interactive web applications with Shiny:
•Shiny Server or Shiny Server Pro for self-hosted solutions
•RStudio Connect for an integrated platform
•shinyapps.io for hosting on RStudio's servers
#rstats #AdvancedR #DataScience
•Shiny Server or Shiny Server Pro for self-hosted solutions
•RStudio Connect for an integrated platform
•shinyapps.io for hosting on RStudio's servers
#rstats #AdvancedR #DataScience
3/ 📦 R APIs: Create and deploy RESTful APIs using R with:
•plumber for building, testing, and deploying APIs
•OpenCPU for creating scalable, stateless APIs
•RStudio Connect for hosting and managing your APIs
#rstats #AdvancedR #DataScience
•plumber for building, testing, and deploying APIs
•OpenCPU for creating scalable, stateless APIs
•RStudio Connect for hosting and managing your APIs
#rstats #AdvancedR #DataScience
1/ 🌐 Web Scraping and Text Mining in R: Unlocking Insights 🔍 Learn advanced web scraping techniques and text mining tools to extract valuable insights from online data. #rstats #AdvancedR #TextMining #DataScience
2/ 🕸️ Web Scraping: Extract data from websites using powerful R tools:
•rvest for HTML scraping and parsing
•httr for managing HTTP requests
•xml2 for handling XML and XPath queries
•RSelenium for scraping dynamic web content
#rstats #datascience #AdvancedR
•rvest for HTML scraping and parsing
•httr for managing HTTP requests
•xml2 for handling XML and XPath queries
•RSelenium for scraping dynamic web content
#rstats #datascience #AdvancedR
3/🧪 Advanced Web Scraping Techniques: Go beyond basic scraping with:
•Setting up custom headers and cookies with httr
•Handling pagination and infinite scrolling
•Throttling requests to avoid getting blocked
•Using proxy servers to bypass restrictions
#rstats #AdvancedR
•Setting up custom headers and cookies with httr
•Handling pagination and infinite scrolling
•Throttling requests to avoid getting blocked
•Using proxy servers to bypass restrictions
#rstats #AdvancedR
1/ 🎛️ Advanced Functional Programming in R: purrr and beyond 🚀 Discover the power of functional programming techniques in R to write cleaner, more efficient code. #rstats #AdvancedR #FunctionalProgramming #DataScience
2/ 🐾 purrr Basics: Transform your code using purrr's functional tools:
•map() functions for element-wise operations
•reduce() for cumulative operations
•pmap() for multiple input iteration
•safely() to handle errors gracefully
#rstats #datascience #AdvancedR
•map() functions for element-wise operations
•reduce() for cumulative operations
•pmap() for multiple input iteration
•safely() to handle errors gracefully
#rstats #datascience #AdvancedR
3/ 🔄 Loop Alternatives: Replace traditional loops with functional equivalents:
•map() instead of for loops
•map_dbl(), map_chr(), map_lgl(), and map_int() for type-specific output
•map2() and pmap() for iterating over multiple objects
#rstats #datascience #AdvancedR
•map() instead of for loops
•map_dbl(), map_chr(), map_lgl(), and map_int() for type-specific output
•map2() and pmap() for iterating over multiple objects
#rstats #datascience #AdvancedR
1/ 📚 Advanced R Package Development: Best Practices and Tips 🛠️ Explore the process of creating, documenting, and testing your own R packages like a pro! #rstats #AdvancedR #PackageDevelopment #DataScience
2/ 🏗️ Package Structure: Organize your package effectively by:
⚫️Using usethis::create_package() to set up the structure
⚫️Organizing functions in R/ directory
⚫️Placing data in data/ or extdata/ directories
⚫️Adding vignettes and README for documentation
#rstats #datascience
⚫️Using usethis::create_package() to set up the structure
⚫️Organizing functions in R/ directory
⚫️Placing data in data/ or extdata/ directories
⚫️Adding vignettes and README for documentation
#rstats #datascience
3/ 📝 Documentation: Make your package user-friendly by:
⚫️Writing clear, concise function descriptions
⚫️Using roxygen2 for function documentation
⚫️Creating package-level documentation with pkgdown
⚫️Writing vignettes to demonstrate usage
#rstats #datascience #AdvancedR
⚫️Writing clear, concise function descriptions
⚫️Using roxygen2 for function documentation
⚫️Creating package-level documentation with pkgdown
⚫️Writing vignettes to demonstrate usage
#rstats #datascience #AdvancedR
1/ 🧪 Advanced Shiny App Development: Tips for Creating Engaging, Scalable, and Robust Apps 🌐 Dive into this thread to learn how to build powerful, interactive web apps with Shiny! We'll cover advanced features, optimization, and deployment. #rstats #datascience #ShinyApps
2/ 🎛️ Advanced Features: Enhance your Shiny apps with:
•Custom input controls and UI elements
•Dynamic UI generation with renderUI()
•Incorporating JavaScript and CSS for added interactivity and styling
•Integrating with APIs for external data
#rstats #datascience
•Custom input controls and UI elements
•Dynamic UI generation with renderUI()
•Incorporating JavaScript and CSS for added interactivity and styling
•Integrating with APIs for external data
#rstats #datascience
3/ 🚀 Optimization: Improve app's performance and responsiveness by:
•Using reactive programming for efficient data manipulation
•Profiling and benchmarking with profvis
•Caching data and results to reduce computation time
•Employing async and future for nonblocking tasks
•Using reactive programming for efficient data manipulation
•Profiling and benchmarking with profvis
•Caching data and results to reduce computation time
•Employing async and future for nonblocking tasks
1/ 📊 Advanced Data Visualization in R: ggplot2 and Beyond 🎨 Take your data storytelling to the next level! In this thread, we'll explore advanced techniques for creating beautiful, informative, and interactive visualizations in R. #rstats #AdvancedR #DataViz #DataScience
2/ 🎨 ggplot2: Enhance your ggplot2 graphics with:
•Custom themes and color palettes
•Faceting for small multiples
•Adding annotations and labels with ggrepel
•Incorporating custom geoms and stats
•ggplotly for interactive ggplot2 graphs
#Rstats #DataScience #DataViz
•Custom themes and color palettes
•Faceting for small multiples
•Adding annotations and labels with ggrepel
•Incorporating custom geoms and stats
•ggplotly for interactive ggplot2 graphs
#Rstats #DataScience #DataViz
3/ 🌐 Interactive Visualization: Create engaging, interactive visuals using:
•plotly for interactive charts and graphs
•highcharter for Highcharts-based visualizations
•leaflet for interactive maps
•dygraphs for time series data
#Rstats #DataScience #DataViz #AdvancedR
•plotly for interactive charts and graphs
•highcharter for Highcharts-based visualizations
•leaflet for interactive maps
•dygraphs for time series data
#Rstats #DataScience #DataViz #AdvancedR
1/ 💡 Advanced R Markdown: Tips, Tricks, and Best Practices 📝 Learn how to create impressive, dynamic, and reproducible documents with R Markdown. We'll cover advanced formatting, interactive elements, and custom templates. Follow along! #rstats #AdvancedR #RMarkdown
2/ 📐 Advanced Formatting: Enhance your R Markdown documents with:
•LaTeX for mathematical notation
•HTML/CSS for custom styling
•Tables with kableExtra or gt for enhanced visuals
•Incorporating citations with BibTeX
•Including external images or files
#rstats
•LaTeX for mathematical notation
•HTML/CSS for custom styling
•Tables with kableExtra or gt for enhanced visuals
•Incorporating citations with BibTeX
•Including external images or files
#rstats
3/ 💫 Interactive Elements: Bring your documents to life with:
•Shiny for reactive, interactive content
•plotly for interactive graphs and charts
•DT for searchable, sortable tables
•crosstalk to link interactive widgets
•leaflet for interactive maps
#rstats
•Shiny for reactive, interactive content
•plotly for interactive graphs and charts
•DT for searchable, sortable tables
•crosstalk to link interactive widgets
•leaflet for interactive maps
#rstats
1/ 🏗️ Mastering R's Object-Oriented Programming: S3, S4, and R6 Systems 🤖 Let's unravel the mysteries of R's object-oriented systems, and learn how to create flexible and reusable code! Join the thread for insights and examples. #rstats #OOP #datascience
2/ 🔎 S3 System: The most widely used and simplest OOP system in R. Create classes using "class()" and define generic functions with "UseMethod()". S3 is informal, making it both easy to use and prone to errors. #S3 #OOP #RStats #DataScience
3/ 🛠️ Example - S3: Define a simple "Person" class and a "greet()" generic function.
#S3 #OOP #RStats #DataScience
#S3 #OOP #RStats #DataScience
1/ 🌟 R's Hidden Gems: Lesser-Known Functions and Packages You Need to Know! 🎉 Discover powerful functions and packages that can help you become an R power user. Let's dive in and learn together! #rstats #AdvancedR #DataScience
2/ 🧙♂️ Functions: R has many built-in functions that often go unnoticed. Here are a few:
•with(): Apply expressions to a data frame without using the $ operator
•switch(): Simplify conditional expressions
•do.call(): Call a function with arguments in a list
•with(): Apply expressions to a data frame without using the $ operator
•switch(): Simplify conditional expressions
•do.call(): Call a function with arguments in a list
3/ 📦 janitor: Clean and preprocess your data with ease. Functions like clean_names() and remove_empty() make your data tidier and more manageable. Say goodbye to messy data frames! #rstats #datascience
1/ 🔥 Speeding Up Your R Code: Profiling, Benchmarking, and Parallelization 🚀 Ever felt like your R code could run faster? In this thread, we'll explore advanced techniques to optimize and parallelize your code. Stay tuned! #RStats #AdvancedR #DataScience
2/ 🕵️♂️ Profiling: Before optimizing, you need to know where your code bottlenecks are. Use R's built-in tools like Rprof() and summaryRprof() or the profvis package to visualize and analyze your code's performance. #profiling #RStats #DataScience
3/ ⏱️ Benchmarking: Compare different implementations of a function or solution using the microbenchmark or bench package. This will help you choose the most efficient approach. #benchmarking #RStats #DataScience
