Publications by INWT-Blog-RBloggers

shinyMatrix – Matrix Input for Shiny Apps

13.02.2020

In this post we’d like to introduce you to our new R package shinyMatrix. It provides you with an editable matrix input field for shiny apps. Use Cases for Matrices in Shiny We have been developing shiny apps for quite some years now. A problem we stumbled upon multiple times in this process was, that there is no easy approach to define m...

3127 sym R (628 sym/1 pcs) 4 img

Code performance in R: Which part of the code is slow?

26.04.2021

This is the first part of our series about code performance in R. Let’s assume you have written some code, it’s working, it computes the results you need, but it is really slow. If you don’t want to get slowed down in your work, you have no other choice than improving the code’s performance. But how to start? The best approach is to fin...

3021 sym R (1226 sym/5 pcs) 2 img

Code performance in R: How to make code faster

05.05.2021

This is the second part of our series about code performance in R. It contains a lot of approaches to reduce the time your code needs to run. It’s useful to know those ideas before starting to write new code, but it also helps to optimize existing code. If you have already written some code you want to speed up, but don’t know which part of...

4907 sym R (2437 sym/4 pcs)

Code performance in R: Parallelization

30.06.2021

This is the third part of our series about code performance in R. In the first part, I introduced methods to measure which part of a given code is slow. The second part lists general techniques to make R code faster. In this part you are going to see how to take advantage from parallelization in R. What is parallelization? In many cases, your c...

5315 sym R (519 sym/2 pcs)

Code performance in R: Working with large datasets

05.08.2021

This is the fourth part of our series about code performance in R. In the first part, I introduced methods to measure which part of a given code is slow. The second part lists general techniques to make R code faster. The third part deals with parallelization. In this part we are going to have a look at the challenges that come with large datas...

4040 sym R (1047 sym/2 pcs)