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
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
4/ 🎨 Custom Templates: Stand out with your own R Markdown templates:
•Create your own HTML, PDF, or Word templates
•Use YAML header options for customization
•Use rmarkdown::render() for programmatic rendering
•Share templates with others via packages #rstats
5/ 📊 Parameterized Reports: Create reusable, customizable R Markdown documents by:
•Using params in the YAML header
•Passing parameters via rmarkdown::render()
•Employing conditional statements in your code #rstats
6/ 🔄 Reproducible Workflows: Improve your reproducibility with:
•Cache code chunks for faster document rendering
•Use knitr::spin() for literate programming
•Employ RStudio projects for organized and shareable workspaces #rstats
7/ 🚀 Output Formats: Expand your reach by outputting to multiple formats:
•Use bookdown for books, thesis, or large documents
•Create presentations with xaringan or ioslides
•Generate websites with blogdown or distill #rstats
8/ 🌐 Hosting and Sharing: Publish your R Markdown documents on:
•GitHub Pages for static websites
•RStudio Connect for Shiny apps and interactive content
•RPubs for simple sharing of R Markdown output #rstats
9/ 📚 Resources: Dive deeper into advanced R Markdown with these books and articles:
•"R Markdown: The Definitive Guide" by Yihui Xie et al.
•"Dynamic Documents with R and knitr" by Yihui Xie
•"R Markdown Cookbook" by Yihui Xie et al. #rstats
10/ 🎉 Mastering advanced R Markdown techniques allows you to create impressive, dynamic, and reproducible documents. Explore these tips, tricks, and best practices to level up your R Markdown game! #rstats#AdvancedR#RMarkdown#DataScience
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1/🧵✨Occam's razor is a principle that states that the simplest explanation is often the best one. But did you know that it can also be applied to statistics? Let's dive into how Occam's razor helps us make better decisions in data analysis. #OccamsRazor#Statistics#DataScience
2/ 📏 Occam's razor is based on the idea of "parsimony" - the preference for simpler solutions. In statistics, this means choosing models that are less complex but still accurate in predicting outcomes. #Simplicity#DataScience
3/ 📊 Overfitting is a common problem in statistics, where a model becomes too complex and captures noise rather than the underlying trend. Occam's razor helps us avoid overfitting by prioritizing simpler models with fewer parameters. #Overfitting#ModelSelection#DataScience
Hello #Rstats community! Today, we're going to explore the Law of Large Numbers (LLN), a fundamental concept in probability theory, and how to demonstrate it using R. Get ready for some code! 🚀
LLN states that as the number of trials (n) in a random experiment increases, the average of the outcomes converges to the expected value. In other words, the more we repeat an experiment, the closer we get to the true probability.
Imagine flipping a fair coin. The probability of getting heads (H) is 0.5. As we increase the number of flips, the proportion of H should approach 0.5. Let's see this in action with R!
1/🧵 Welcome to this thread on the Central Limit Theorem (CLT), a key concept in statistics! We'll cover what the CLT is, why it's essential, and how to demonstrate it using R. Grab a cup of coffee and let's dive in! ☕️ #statistics#datascience#rstats
2/📚 The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size (n) increases, given that the population has a finite mean and variance. It's a cornerstone of inferential statistics! #CLT#DataScience#RStats
3/🔑 Why is the CLT important? It allows us to make inferences about population parameters using sample data. Since many statistical tests assume normality, CLT gives us the foundation to apply those tests even when the underlying population is not normally distributed. #RStats
[1/11] 🚀 Level Up Your R Machine Learning Skills with These Lesser-Known #RPackages! In this thread, we'll explore 10 hidden gems that can help you optimize your #MachineLearning workflows in R. Let's dive in! 🌊 #rstats#datascience
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
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
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
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