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.
This VBA macro saves an Excel worksheet as a PDF. It sets and sorts the data, creates a temporary sheet with headers, formats and aligns the data, and adjusts column widths. The macro defines the PDF path, deletes any existing file, sets page orientation and footer, exports the sheet to PDF, deletes the temporary sheet, and shows a message with the PDF path. Modify to your needs if you find it useful.
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
DNFB for medicine is discharged not final billed and is used a lot at hospitals as it represents the AR not yet billable. It is important to keep this to a minimum of days for efficiency and ability to get paid. It affects cashflow, so the lower the better. I wrote a quick diddy that sifted 147mllion rows in 2 seconds to give me my average dnfb by AR snapshot month
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.