Publications by INWT-Blog-RBloggers
R Markdown Template for Business Reports
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 one to be used in a corporate environment. That is, I want to have a logo inclu...
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ggCorpIdent: Stylize ggplot2 Graphics in Your Corporate Design
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 our logo in the background of the plot. We ourselves find this a ve...
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Using Modules in R
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 I want to provide something in between: local namespace de...
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rsync as R package
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 users of Unix systems to synchronize loc...
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Shiny Modules
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 debugging will multiply. If you developed a shiny app before, you had t...
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What’s the Best Statistical Software? A Comparison of R, Python, SAS, SPSS and STATA
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 things like new licenses and re-training. This article introduces and contrasts the market leaders – R, Pyt...
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Best Practice: Development of Robust Shiny Dashboards as R Packages
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 page, or interactive elem...
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Multi-Armed Bandits as an A/B Testing Solution
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 different version of whatever is being tested (such as a special offer, or the l...
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Debugging in R: How to Easily and Efficiently Conquer Errors in Your Code
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: Running the code Stopping the code where something suspicious is taking place Looking at the code step-by-step from this point on to e...
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Data Visualization in R vs. Python
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 often also useful to begin a data science pr...
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