🚀 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.
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
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.
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.
Level up your data wrangling! Learn how to add index columns in R – both base & tidyverse Choose your weapon & customize! Ready to try? Create your own data frame & experiment! Share your creations & challenges!
Wrangling dates in R got you pulling your hair? ⏱️ Time travel to mastery with these 3 powerful tools:
Base R's seq.Date: Your daily/weekly/monthly hero.
lubridate's seq: Filter magic for specific weekdays. Analyze those Tuesdays!
timetk's tk_make_timeseries: Define complex sequences in a simple table. Easy time travel!
Drowning in daily data? Conquer weekly analysis with R's strftime() magic! Extract ISO week numbers & group your data like a pro. Ready to level up? Explore "U" for Sunday starts & packages for more grouping power. Challenge: calc weekly averages, peak sales, etc. Share your data wrangling wins in the comments!
Imagine you have a bunch of data points and you want to know how many belong to different categories. This is where grouped counting comes in. We've got three fantastic methods for you to explore, each with its own flair: aggregate(), dplyr, and data.table.