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Apr 20 10 tweets 7 min read Twitter logo Read on Twitter
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 Image
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
4/ 💡 Code Optimization: R's Just-In-Time (JIT) compilation can improve the performance of your code. Enable JIT with the compiler package to potentially speed up your functions. Also, consider efficient data structures like data.table. #codeOptimization #RStats #DataScience
5/ 🚄 Parallelization: Utilize the full power of your computer by running R code in parallel. Use packages like parallel, foreach, and future to speed up your computations. Be mindful of potential pitfalls, like race conditions and memory limitations. #RStats #DataScience
6/ 🖥️ Using Rcpp: For computationally intensive tasks, consider using Rcpp to write C++ code that can be called from R. This can significantly speed up your code, but make sure to weigh the benefits against the added complexity. #Rcpp #RStats #DataScience
7/ 🧹 Garbage Collection: R's memory management can be a bottleneck for performance. Use gc() to trigger garbage collection and free up memory, especially in long-running scripts or before/after memory-intensive operations. #garbageCollection #RStats #DataScience
8/ 📦 Package Optimization: Keep your packages up-to-date and consider using alternative, faster packages for specific tasks. For example, use data.table instead of dplyr for large data manipulation or fst instead of readr for fast I/O. #RStats #DataScience
9/ 📚 Resources: Want to dive deeper into R performance optimization? Check out these books:

"Efficient R Programming" by Gillespie and Lovelace
"R High Performance Programming" by Lim and Tjhi
"Rcpp for everyone" by Masaki E. Tsuda

#RStats #DataScience
10/ 🎉 Speeding up your R code involves profiling, benchmarking, code optimization, parallelization, using Rcpp, garbage collection, and package optimization. Apply these techniques and watch your code run faster than ever! #RStats #AdvancedR #DataScience

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More from @

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
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 Image
🧵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
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 Source: https://www.digital...
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
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 Source: https://arkiana.com...
[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
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 Source: https://anderfernan...
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
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 Source: https://www.linkedi...
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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