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Service

R development and automation

You redo the same manipulations every month, or you have inherited a script nobody dares touch. I turn those analyses into tools that rerun in one command and give the same result every time.

Discuss your project
01

Who it is for

This service is for those whose analysis already exists, but costs too much to run, to fix or to hand over.

  • Teams rebuilding the same report by hand every month
  • Researchers needing reproducible analyses to publish
  • Organisations that inherited undocumented code
  • R users wanting to make their scripts faster and safer
  • Labs wanting to distribute an internal tool
  • Method authors wanting to publish as a package
02

The problem it solves

An analysis done by hand in a spreadsheet is correct exactly once: the day it was done. The following month a column has moved, a formula was not copied down, and the discrepancy goes unnoticed. The real cost is not the time spent, it is the lost confidence in the numbers.

The opposite symptom is the inherited script: it works, nobody knows quite how, and every change is a gamble. Eventually the team routes around the tool rather than fixing it.

In both cases the remedy is the same: make the processing explicit, tested and repeatable.

  • The monthly report takes two days of copy-paste
  • Nobody can reproduce last quarter's figures
  • A script works on one machine and not on another
  • An upstream correction forces you to redo everything
  • Your R code is slow enough to get in the way
  • You want to publish a tool, but not your code as it stands
03

What you get

The goal is for the tool to outlive you: readable, documented and modifiable by someone other than its author.

  • Structured, commented, version-controlled R scripts
  • Quarto or R Markdown reports that regenerate themselves
  • An R Shiny application to explore your data without coding
  • An installable, documented and tested R package
  • Taking over and rebuilding existing code
  • A handover session with your teams
04

How it works

  1. Audit

    A review of what exists: what works, what is fragile, what can go.

  2. Target

    We define together what the tool must do, and above all what it need not do.

  3. Development

    Built in deliverable stages, so you can test early rather than at the end.

  4. Handover

    Documentation, walkthrough and, if needed, training on maintenance.

05

Concrete examples

Examples of typical work.

Automated monthly report

A Quarto document that reads the current data and regenerates text, tables and figures in one command.

Shiny dashboard

A web interface where your teams filter and explore the data without writing a line of code.

Internal R package

In-house functions scattered across several scripts, gathered into a documented and tested package.

Taking over legacy code

A script that became unreadable, rebuilt, verified against unchanged results and made modifiable again.

Speeding up a computation

A calculation that took hours brought down to minutes through vectorisation and parallelisation.

Reproducible analysis

A project where every figure and every number in the paper regenerates from the raw data.

Frequently asked questions

Does someone on our side need to know R?
No. A Shiny application or an automated report is used without writing any code. If you want to extend the tool yourselves afterwards, though, you need at least one person comfortable with R — which is exactly what the training covers.
Why R rather than Python or a BI tool?
Because it is the tool I know best and the one best suited to work with a heavy statistical component. If your need is really about software engineering or data infrastructure, I will say so rather than stretching R beyond what it is good at.
Can you take over code written by someone else?
Yes, that is a frequent request. I start by checking that I reproduce the existing results exactly before changing anything — otherwise it becomes impossible to tell a fix from a regression.
Where will the Shiny application be hosted?
Depending on your constraints: on your own servers, on a Shiny hosting service, or locally if the data must not leave the building. We discuss it at scoping, because it drives some technical choices.
Do I own the code?
Entirely. Everything developed during the assignment is transferred to you, and to you only. You are free to modify it, distribute it or hand it to someone else.

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Study design and sample size

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Consulting and decision support

From dormant data to evidence-based decisions

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Training

Making your teams self-sufficient in R and data analysis

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A dataset to analyse, a method to validate?

Describe your need in a few lines, or book a first no-commitment call. I will tell you straight if I am the right person for it.

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