In my latest blog post, I cover how to find specific strings in data columns using the str_detect function from the stringr package and base R functions. You'll see practical examples with both grepl for identifying matches and gregexpr for counting occurrences.
Learn efficient ways to collapse text by group in R! Explore base R's aggregate(), dplyr's group_by() and summarise(), and data.table's grouping. Mastering these techniques enhances data preprocessing skills. Try these examples with your datasets to optimize workflows. Happy coding! 📊💻
👍 In R, you can easily extract specific columns from a data frame by their numerical positions. For instance, to grab the second column from a data frame df, you can use df[, 2].
🙅♂️ You can also exclude columns by using negative indexing, such as df[, -2] to exclude the second column.
Today I am writing on the AIC functions available in my hashtag#R hashtag#Package TidyDensity.
There are many of them, with many more on the way. Some of them are a little temperamental but not to worry it will all be addressed.
My approach is different then that of fitdistrplus which is an amazing package. I am trying to forgo the necessity of supplying a start list where it may at times be required.
Want a simple form of #MCMC analysis in #R well, I got you covered.
My #R#Package TidyDensity has a function called tidy_mcmc_sampling() that is pretty straight forward. It takes a raw vector and performs the calculation you give it over a default of 2k samples.
Exciting news for R users! TidyDensity's latest update introduces util_chisquare_param_estimate(), leveraging MLE to estimate Chi-square distribution parameters like dof and ncp.
Generate a dataset with rchisq() and use util_chisquare_param_estimate() to analyze it, even without knowing the underlying distribution. Visualize results with tidy_combined_autoplot().
📣 Exciting news, everyone! 🌟 Make sure to head over to this weeks blog "What's new in R 4.4.0?" by Russ Hyde, and dive into the world of the latest R release📊🔬💻
Discover some of the amazing new features that this version has to offer! 🔍 🔭 🚀
Master data manipulation in R by dropping unnecessary columns from data frames using simple methods like the $ operator, subset() function, and dplyr package's select() function.
Try these techniques on your own datasets for efficient data cleaning and analysis!
Today's topic is: Identifying Common Rows Between Data Frames in R
In data analysis, comparing datasets is crucial. A common task is checking if rows from one data frame exist in another. I have had to do this myself many times.
Today I discuss the following:
1️⃣ The merge() Function
2️⃣ The %in% Operator
For a step-by-step guide and examples, check out the full blog post.
I had previously discussed how to drop those pesky NA records from your data.frame but now, what if you actually want to inspect them? That is what I cover in today's post.
Need to Find Rows with a Specific Value (Anywhere!) in R?
Ever have a large R data table where you need rows containing a specific value, but you're not sure which column it's in? We've all been there! Here's a quick guide to tackle this using both dplyr and base R functionalities.
Estimating the degrees of freedom 'k' and the non-centrality 'ncp' parameters of the chi-square distribution from just a vector of numbers? I think I am there. Here is a post the work I did over the last couple of days:
@ramikrispin I think this is it. The Mega Test Scrip creates 1000 different combinations of the rchisq() data and runs it all using different approachs
I decided to make a blog post out of a problem I worked on a day or two ago and thankfully I was also pointed to another solution from @embiggenData which worked well too.