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Apr 20 • 10 tweets • 6 min read Twitter logo

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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

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

4/ 🧰 glue: Say hello to a more convenient way of working with strings! The glue package makes it easy to create and format strings with embedded R expressions. #rstats #datascience

5/ 🌐 httr: Ever needed to work with APIs in R? The httr package makes it a breeze to send HTTP requests and handle the response, making it a powerful tool for web scraping and API interactions. #rstats #datascience

6/ 📈 ggplot2 extensions: Extend ggplot2's capabilities with packages like ggrepel, gganimate, and ggforce. These add-ons can help you create more informative and visually appealing plots. #ggplot2 #DataViz
#rstats #datascience

7/ 🎛️ config: Manage your project configurations with ease. The config package allows you to set up and switch between different environments (e.g., development, production), streamlining your workflow. #rstats #datascience

8/ 📊 skimr: Get quick and informative summaries of your data with the skimr package. It generates easy-to-read summaries that can help you understand your data better and faster. #rstats #datascience

9/ 📚 Resources: Want to explore even more hidden gems in R? Check out these resources:
• "R Cookbook" by James Long
• "R Graphics Cookbook" by Winston Chang
#rstats #datascience

10/ 🎉 Mastering these lesser-known functions and packages can boost your R skills and help you become an R power user. Keep exploring, and happy coding! #rstats #AdvancedR #datascience

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Apr 23

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

Read 6 tweets

Twitter author

Apr 22

🧵1/10 - Law of Large Numbers (LLN) in R 📈

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! 🚀

#Probability #Statistics #DataScience

🧵2/10 - What is LLN? 🧐

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.

#RStats #DataScience

🧵3/10 - Coin Flip Example 🪙

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!

#RStats #DataScience

Read 11 tweets

Twitter author

Apr 22

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

Read 12 tweets

Twitter author

Apr 22

[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

[2/11] 📊 caretEnsemble: Model ensembling with caret - Combine multiple models with ease and boost your model performance using this powerful package. #rstats #datascience #machinelearning
🔗 cran.r-project.org/web/packages/c…

[3/11] 📈 finalfit: Create regression model tables - Quickly generate publication-ready tables for regression models with #finalfit. Simplify reporting and communication of your results! #rstats #datascience #machinelearning
🔗 cran.r-project.org/web/packages/f…

Read 12 tweets

Twitter author

Apr 22

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

Read 10 tweets

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Apr 22

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

Read 10 tweets

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