Do you need a custom R package or Shiny app? I can build it for you

Before the main content: I am creating an R Community on Google Groups. You can join the group using this form.

I build custom R packages and Shiny/Tabler apps, and I’m currently taking on new clients. You can find my profile on Fiverr.

Before starting my second master’s and my PhD, I ran a statistics consulting practice for nearly ten years. Most of that work fell into three buckets: designing and implementing SQL databases to streamline data analysis, building tailored R packages to simplify data access and reporting for teams, and building Shiny dashboards to summarise information through plots and KPIs.

With the rise of AI, this is a slightly harder pitch to make than it was a couple of years ago: sketching out a function or a small app is easier than ever. Where I think I still add real value is in the parts AI doesn’t do well on its own – turning a set of requirements into a maintainable package, designing something that your team will actually enjoy using, and making sure the whole thing is tested, documented, and installable by people who aren’t R experts. Besides it, I put a focus on writing everything with a minimal approach, meaning that I make an active effort in keeping the code logic simple and thinking about its long-term maintenance.

I’m also in the final year of my PhD, and between a still-unresolved pending payment from my previous university and a side project – a small guitar pedal business – that I started to cope with that financial emergency but that is not very profitable nor aligned with what I study.

I’m looking to pick up this kind of work again alongside my research. I also have a mobility disability (I get around with a cane, due to arthritis), which has made the usual part-time options like coffee shops or restaurants impractical, so consulting work I can do from a laptop is genuinely the best fit for me right now.

If your organization uses R, there are clear benefits to having an internal R package, whether you have a single R user or dozens. A package built around your organization’s specific needs opens up easier data access, shared functions for transformation and analysis, and a consistent look and feel across reports and dashboards.

Getting that first internal package off the ground can still feel daunting: what functions belong in it, how colleagues will install it and get updates, and how to keep quality consistent as more people contribute. This is exactly the kind of problem I like helping with – planning the package around your team’s actual workflow, fitting it into your existing infrastructure, and building out the core functions for data access, analysis, and reporting so the project has a solid foundation from day one.

Something similar can be said about dashboards, and I can help you to build something informative that keep quality consistent as more users help to improve it.

If any of this sounds useful, feel free to reach out through Fiverr or take a look at the packages above to get a sense of my work.

Below is a sample of the packages I’ve built over the years.

Data visualization

• d3po: A set of opinionated templates for quick data visualization using D3.js and R. It is fully compatible with RMarkdown and Shiny, and it is available under the Apache 2.0 license for use in commercial and non-commercial projects.
• tabler: A fully open-source alternative to Shiny worth considering if you need a multi-session, multi-user dashboard.

International Trade

• tradestatistics: Open trade Statistics API wrapper and utility program.
• wbstats: An R package for searching and downloading data from the World Bank API.

Econometrics

• capybara: Fast and memory efficient fitting of linear models with high-dimensional fixed effects.
• gravity: Estimation methods for gravity models.

R and C++ bindings

• cpp4r: Header-Only ‘C++’ and ‘R’ interface

Linear algebra

• armadillo4r: Provides function declarations and inline function definitions that facilitate communication between R and the Armadillo C++ library for linear algebra and scientific computing.

REDATAM format

• Open REDATAM (C++): Open Redatam is an open source software for extracting raw information from REDATAM databases. It was created to recover information of REDATAM databases for statistical analysis using standard tools such as SPSS, STATA, R, etc. It currently has both R and Python wrappers.