Lmst

🔍 Need to reshape rows into columns in R? This complete guide to transposing data frames will help you see your data in a whole new way. Try the examples and let us know how it works for you! 💡

👉 https://www.spsanderson.com/steveondata/posts/2025-01-02/

#R #RStats #Programming #TidyR #Tips #Blog

A code snippet displayed in a dark-themed code editor. The code demonstrates a basic transposition in R, where a data frame is transposed using the t() function and converted back to a data frame with as.data.frame(). The code reads: transposed_df <- as.data.frame(t(original_df)).

💡 Master the art of column combination in R with our beginner-friendly guide!

Learn how to use Base R and tidyr to merge columns effortlessly. Check it out and share your experience!

#R #RStats #Programming #Data #Tips #tidyr #paste #sprintf 🛠️

Post: https://www.spsanderson.com/steveondata/posts/2024-10-08/

🚀 New to R?

Learn how to combine two columns into one using Base R and tidyr!

Check out our step-by-step guide with examples to make your data manipulation a breeze. Dive in and share your thoughts!

#R #RStats #Programming #Data #Tips #tidyr #paste #sprintf 📊

Post: https://www.spsanderson.com/steveondata/posts/2024-10-08/

🖥️ Ready to transform your data frames?

Discover the art of stacking columns in R with my comprehensive guide.

Dive into practical examples and tips at https://www.spsanderson.com/steveondata/posts/2024-09-30/

Let's make data manipulation easy!

#R #RStats #Programming #DataScience #Code #dplyr #tidyr #reshape2 #melt #stack #datatable

📊 New to R? Learn how to stack data frame columns like a pro!

Check out my latest guide: "Mastering Data Manipulation in R" at https://www.spsanderson.com/steveondata/posts/2024-09-30/

Perfect for beginners looking to enhance their data skills.

#R #RStats #Programming #Data #LearnR #tidyr #datatable #melt #reshape2

It's a 72nd day of my #100DaysOfCoding with #R and #RStats. I met #tidyr and #purrr.

@edyhsgr Potentially controversial, but I agree!

I was annoyed when the code demos for a particular uni course relied on #tidyR, because I felt that I hadn't learned enough #baseR yet.

So I stuck to base for my big assignment, only bringing in other packages where needed. It's not that I dislike tidy; it's just that I want to be in control of the packages - I don't want the packages to be in control of me.

Any #rstats users here that use #tidyr et al. for clustering and ordination?

I'm trying to wrap my head around how I should manage a workflow like:

data -> distance matrix -> clustering or ordination

*without* the benefit of row names to link the original data to the resulting cluster leaf or ordination point.

I also want to acknowledge that gcplyr is built on foundations laid by the #tidyr package for data tidying and the #dplyr package for data manipulation, the latter of which is the inspiration for its name: a grammar of data manipulation for growth curve data
11/12

One of the outcomes of my work over the last four years is the #tabshiftr R-package.

We all love #tidyr but downloading data from all over the internet reveals that "rectangular messy" is not the same as "disorganised messy" (check out also these pretty cool visuals by Julia Lowndes and @allison_horst on tidy data!). With tabshiftr, I am able to reorganise almost all types of tables with reproducible code, even if they are not rectangular yet.

https://luckinet.github.io/tabshiftr/