🚀 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
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!
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.
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.
We’re thrilled to announce dplyr powered by DuckDB: duckplyr 🎉
A collaboration between the dplyr project team at Posit, cynkra, and DuckDB, duckplyr is a powerful new option that marries the user-friendly dplyr syntax with the execution capabilities of DuckDB.
One could probably write a fairly popular & successful #rstats pkg that does nothing but wrap #dplyr join functions and implements all the sundy bells & whistles from FRs that pop up over & over.
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!
No disrespect to Wes McKinney (I don’t like #pandas, but I personally could have never done something like that myself), but there’s literally 0 reason (apart from running legacy code) to use #pandas now when there’s #polars on #Python. With #RStats, #dplyr is still the GOAT
As a little teaser for my upcoming #rstats#dplyr online course, I'll be releasing a free video series on related topics on the Statistics Globe YouTube channel during the next few days!
@jrosell - The most underrated #rstats package is #sqldf because it allows you to just write #SQL instead of using a double handful of #dplyr functions. And if you have a database connection, there also you can just write SQL instead of using #dbplyr. Sqldf massively simplifies data wrangling relative to base R or tidyverse functions.
Phew, had a really productive but exhausting #Rstats day today. It's a report that works with #quarto and #knitr and I created something like a "create_graph()" function, because the graphs a very similar and it saves a lot of copy paste.
I really want to make one thing clear: Without #Rstudio and #ggplot and #dplyr and all things #R I could not do my job. Neither Excel, nor Stata, nor SPSS could help in that specific way. I wouldn't get anything of the non-data tasks done...
Yesterday I learned at the #EuroScipy2023#IbisData tutorial that Ibis now offers an implementation of the across function first introduced in #dplyr to conveniently and concisely apply transformations on a set of columns defined by selectors (e.g. based on column data types or name patterns).
This is especially convenient to implement scalable, in-DB feature engineering for machine learning models.
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.
Sawzall, inspired heavily by dplyr and the relational algebra. Sawzall builds on top of Alex Harsanyi’s data-frame package, but provides a set of operations that are designed to compose and avoid mutating the original data-set, leading to a natural style of data manipulation following the idea of "do this, then that".