Learn how to handle rows in R containing specific strings using base R's grep() and dplyr's filter() with str_detect(). Select or drop rows efficiently and enhance your data manipulation skills. Give it a try with your datasets for better data cleaning and organization.
🚀 Elevate Your R Programming Skills: Removing Elements from Vectors
Want to level up your R programming game? Let's talk about removing specific elements from vectors! It's a fundamental skill.
But here's the real fun: try it yourself! Experiment with your own data and see which method resonates with you. To get yourself familiar with what's happening, you have to experiment.
The newest version of my #R#package TidyDensity really took off for me. Now wait until the next release which introduces 39 new functions. #R#RStats#RProgramming
If you work with text data in R, the gregexpr() function is essential for pattern matching. It finds all occurrences of a pattern within a string. Key parameters include pattern, text, ignore.case, perl, fixed, and useBytes. You can match characters, ignore case, use advanced regex, and search fixed strings.
Want to check duplicate values across columns of a data.frame? Well you can do that in a basic way with TidyDensity and the check_duplicate_rows() function, or you can go through todays blog post for some other ideas with #BaseR#dplyr and #datatable
Discover essential techniques to check for column existence in R data frames!
Use %in% with names() or colnames(), explore dynamic checks with exists() and within(), or identify patterns with grepl(). Experiment with these methods in your projects.
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.
In base R, we can filter rows where a column is between two values using bracket notation or the subset() function along with logical operators like >=, <=, &, and !. The key is creating a logical test that checks if values are within our desired range.
For example, to filter rows where the column "value" is between 5 and 8
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().