Articles by INWT-Blog-RBloggers

Code performance in R: Working with large datasets

August 5, 2021 | INWT-Blog-RBloggers

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 pa... [Read more...]

Code performance in R: Parallelization

June 30, 2021 | INWT-Blog-RBloggers

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 ... [Read more...]

Code performance in R: How to make code faster

May 5, 2021 | INWT-Blog-RBloggers

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 ... [Read more...]

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

April 26, 2021 | INWT-Blog-RBloggers

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, y...

shinyMatrix – Matrix Input for Shiny Apps

February 13, 2020 | INWT-Blog-RBloggers

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 ...

Data Visualization in R vs. Python

December 23, 2019 | INWT-Blog-RBloggers

A decisive step in the data science process is communicating the results of your analysis. As a data scientist, you are often tasked with presenting these results to people with little or no statistical background, making it important to be able to present the content clearly and understandably. It is ...

Debugging in R: How to Easily and Efficiently Conquer Errors in Your Code

November 19, 2019 | INWT-Blog-RBloggers

When you write code, you’re sure to run into problems from time to time. Debugging is the process of finding errors in your code to figure out why it’s behaving in unexpected ways. This typically involves: Runni...

Multi-Armed Bandits as an A/B Testing Solution

September 26, 2019 | INWT-Blog-RBloggers

These days, most people are familiar with the concept of A/B testing. This is one of the most common ways to make advertising decisions, particularly in online marketing. In an A/B test, the customer base is divided into two or more groups, each of which is served a ...

Best Practice: Development of Robust Shiny Dashboards as R Packages

September 9, 2019 | INWT-Blog-RBloggers

This blog article is dedicated to creating dashboards. Dashboards are an excellent interactive tool for visualizing raw data, aggregated information, and analytical results. When developing software solutions with R, we at INWT use the shiny package by RStudio. With shiny you can create apps that act as a standalone web ...

What’s the Best Statistical Software? A Comparison of R, Python, SAS, SPSS and STATA

July 25, 2019 | INWT-Blog-RBloggers

Common statistics program packages differ considerably in terms of their strengths, weaknesses, and handling. The decision as to which system is the best fit should be made with care. Changing to a new system can result in high costs for ... [Read more...]

Shiny Modules

July 16, 2019 | INWT-Blog-RBloggers

Tidiness is half the life .. this is a German saying that you might not necessarily have to live. While it becomes essential in programming, at least in my opinion. Because when you do not invest a little time into the order and structure of your projects, the time you spend ...

rsync as R package

May 21, 2019 | INWT-Blog-RBloggers

In this article we present our R package rsync, which serves as an interface between R and the popular Linux command line tool rsync. Originally rsync is an open source tool for efficiently synchronizing files. Published by Paul Mackerras and Andrew Tridgell under the GNU General Public License, it allows ...

Using Modules in R

May 7, 2019 | INWT-Blog-RBloggers

When a code base grows we may think of using several files first and then source them. Functions, of course, are rightfully advocated to new R users, and are the essential building block. Packages are then, already, the next level of abstraction we have to offer. With the modules package ...

ggCorpIdent: Stylize ggplot2 Graphics in Your Corporate Design

March 25, 2019 | INWT-Blog-RBloggers

This is the add-on to our recently published R Markdown template for business reports. Since we’re working with ggplot2 on a daily basis and use it in nearly every our projects, we designed a ggplot2 theme in our corporate design. That is, it uses our font, coporate colors and ...

R Markdown Template for Business Reports

January 30, 2019 | INWT-Blog-RBloggers

In this post I'd like to introduce the R Markdown template for business reports by INWTlab. It's been my aim to have a nice and clean template that is easy to customize in colors, cover and logo. I know there are quite a few templates available, but I was missing ...

Cluster Analysis – Part 2: Hands On

November 21, 2018 | INWT-Blog-RBloggers

library(broom) library(cluster) library(dplyr) library(ggplot2) library(ggdendro) In the first part of this blog series, we examined the theoretical foundations of cluster analysis. In the following article we put the theory into practice using R. For the analysis in R, we will use the variables mpg (fuel ...

Cluster Analysis – Part 1: Introduction

November 6, 2018 | INWT-Blog-RBloggers

What is Cluster Analysis? Cluster analysis is a collective term for various algorithms to find group structures in data. The groups are called clusters and are usually not known a priori. In contrast, classification procedures assign the observations to already known groups (e.g., buyers and non-buyers). A classification is ...

Optimize your R Code using Memoization

October 11, 2018 | INWT-Blog-RBloggers

This article describes how you can apply a programming technique, called Memoization, to speed up your R code and solve performance bottlenecks. Wikipedia says: In computing, […] memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached ... [Read more...]

Introducing the Kernel Heaping Package III

September 25, 2018 | INWT-Blog-RBloggers

In the second part of this blog series, I showed how to compute spatial kernel density estimates based on area-level data. The Kernelheaping package also supports boundary-corrected kernel density estimation, which allows us to exclude certain areas, where we know that the density must be zero. One example is estimating ...

Do GPU-based Basic Linear Algebra Subprograms (BLAS) improve the performance of standard modeling techniques in R?

August 6, 2018 | INWT-Blog-RBloggers

Introduction The speed or run-time of models in R can be a critical factor, especially considering the size and complexity of modern datasets. The number of data points as well as the number of features can easily be in the millions...

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