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Apr 22 • 10 tweets • 8 min read Twitter logo

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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

4/ 📚 Text Processing: Clean and preprocess text data using:
•stringr for string manipulation
•tidyr for reshaping and cleaning text data
•tm (Text Mining) package for managing text corpora
•quanteda for advanced text processing
#rstats #AdvancedR #datascience

5/ 🤖 Natural Language Processing: Analyze text data with powerful NLP techniques using:
•tidytext for text analysis in the tidyverse
•sentimentr for sentiment analysis
•spacyr for part-of-speech tagging and dependency parsing
•topicmodels for topic modeling
#rstats

6/ 💡 Advanced Text Mining Techniques: Explore sophisticated text mining methods like:
•Word embeddings with word2vec or GloVe
•Text classification with RTextTools or caret
•Network analysis with igraph or tidygraph
#rstats #AdvancedR #datascience

7/ 📊 Visualizing Text Data: Create insightful visualizations with:
•ggplot2 for word frequency plots and bar charts
•wordcloud or wordcloud2 for visually appealing word clouds
•ggraph for network visualizations
#rstats #AdvancedR #datascience

8/ 🚀 Case Studies: Apply web scraping and text mining techniques to real-world problems, such as:
•Social media sentiment analysis
•Web content summarization
•Trend and keyword analysis
•Recommender systems
#rstats #AdvancedR #datascience

9/ 📚 Resources: Learn more about advanced web scraping and text mining in R with these books:
•"R Web Scraping Quick Start Guide" by Olgun Aydin
•"Text Mining with R" by Julia Silge and David Robinson
#rstats #AdvancedR #datascience

10/ 🎉 In conclusion, mastering advanced web scraping and text mining techniques in R can help you unlock valuable insights from online data. Keep exploring these methods to elevate your R skills and data analysis capabilities! #rstats #AdvancedR #TextMining #DataScience

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Apr 23

[1/9] 🎲 Let's talk about the difference between probability and likelihood in #statistics. These two terms are often confused, but understanding their distinction is key for making sense of data analysis! #Rstats #DataScience

[2/9]💡Probability is a measure of how likely a specific outcome is in a random process. It quantifies the degree of certainty we have about the occurrence of an event. It ranges from 0 (impossible) to 1 (certain). The sum of probabilities for all possible outcomes is always 1.

[3/9] 📊 Likelihood, on the other hand, is a measure of how probable a particular set of observed data is, given a specific set of parameters for a statistical model. Likelihood is not a probability, but it shares the same mathematical properties (i.e., it's always non-negative).

Read 10 tweets

Twitter author

Apr 23

1/🧵🔍 Making sense of Principal Component Analysis (PCA), Eigenvectors & Eigenvalues: A simple guide to understanding PCA and its implementation in R! Follow this thread to learn more! #RStats #DataScience #PCA

2/📚PCA is a dimensionality reduction technique that helps us to find patterns in high-dimensional data by projecting it onto a lower-dimensional space. It's often used for data visualization, noise filtering, & finding variables that explain the most variance. #DataScience

3/🎯 The goal of PCA is to identify linear combinations of original variables (principal components) that capture the maximum variance in the data, with each principal component being orthogonal to the others. #RStats #DataScience

Read 10 tweets

Twitter author

Apr 23

[1/10] 🚀 Advanced R Debugging: Debugging & error handling are essential skills for every R programmer. In this thread, we'll explore powerful tools & techniques like traceback(), browser(), & conditional breakpoints to make debugging in R a breeze. #rstats #datascience

[2/10] 📝 traceback(): When your code throws an error, use traceback() to get a detailed call stack. This function helps you identify the exact location of the error in your code, making it easier to pinpoint the issue. #rstats #debugging #datascience

[3/10] 🔍 browser(): With browser(), you can pause the execution of your code & step through it one line at a time. This interactive debugging tool allows you to inspect the values of variables and expressions, which can be a game-changer when diagnosing complex issues. #rstats

Read 10 tweets

Twitter author

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

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