Rverse Analytics

Biostatistics and R consulting studio: statistical analysis, clinical trial TLFs, CDISC/ADaM programming and SAS-to-R migration for CROs and sponsors — plus Shiny applications and reproducible reporting, shipped as code you own.

Biostatistics & R Consulting Studio

Data analysis, engineered in R.

From clinical trial TLFs and CDISC-shaped datasets to Shiny applications and automated reports — statistical work with a biostatistics backbone, double-programmed where it matters, and shipped as code you own.

Book a free scoping call Services for CROs & pharma

42video tutorials
17calculators, R-verified
16cheat-sheet PDFs
48practical R guides

Clinical trials & regulated work

For CROs & sponsors

Overflow statistical programming, independent biostatistics and a validated path off SAS — the FDA has received and reviewed fully R-based submissions through the R Consortium pilots, and we work with the same pharmaverse toolchain the industry built.

TLF production

Demography, AE and shift tables, KM curves and forest plots from your mock shells — see the shell-to-output gallery.

CDISC, ADaM & QC

ADaM-style derivations with admiral, traceability by construction, and double-programming QC evidence your SOPs require.

SAS → R migration

Pilot study, parallel run, documented equivalence — a PROC-by-PROC map instead of a leap of faith.

All services for CROs & pharma

What we do

Analysis, applications and automation

Statistical Analysis

From study design to publication-ready results: hypothesis testing, regression modelling and power analysis, all scripted and reproducible.

Shiny Applications

Interactive dashboards and internal tools that turn static spreadsheets into living decision-support systems.

Reproducible Reporting

Automated Quarto and R Markdown pipelines: one command from raw data to polished Word, PDF or HTML deliverables.

See all five services

Why us

Rigour you can rerun

R-native, end to end. The analysis, the figures and the report come from one scripted pipeline — no translating between tools.

Reproducible by default. Every deliverable regenerates from raw data with a single command, so results always match the numbers.

Biostatistics backbone. Methodology forged in clinical research, and code you own outright after handover — even our free calculators are unit-tested against R itself.

Submission-aware. CDISC-shaped datasets, version-pinned environments and double-programmed QC — deliverables built to slot into a regulated quality system.

Explore the industries we serve, browse case studies, or read the frequently asked questions.

Free tools

Statistics calculators, free to use

No sign-up, nothing uploaded — fast browser-based calculators built on the same engine we use in client work, verified against R.

Sample size & power

How many participants do you need? Exact noncentral-t, matched to R.

Which test should I use?

Answer a few questions, get the right test and the R function to run it.

Correlation calculator

Pearson r, r², significance and a scatter plot from paired data.

See all free calculators

Learn interactively

Free courses that run in your browser

Edit real code and see the result instantly — no install, nothing to set up. From R fundamentals to reproducible reporting.

Learn R

Run R live in the browser: vectors, data frames, plotting and your first statistics.

Learn Markdown

Master Markdown syntax with a live editor on every page — plus a standalone previewer.

Learn Quarto

Turn text and R code into reproducible HTML, PDF and Word reports — with runnable lessons.

Or see them all at a glance in the interactive demo gallery

Our stack

One coherent ecosystem

R logo tidyverse ggplot2 Shiny Quarto lme4 · survival

The R logo is © The R Foundation, used under CC-BY-SA 4.0.

R, live on this page

Every figure on this site is rendered by R at build time — no screenshots, no stock images. Here is the code and its output:

library(ggplot2)

set.seed(42)
d <- data.frame(
  dose     = rep(c(2, 4, 8, 16, 32), each = 30),
  response = unlist(lapply(c(2, 4, 8, 16, 32),
                           function(x) rnorm(30, 20 + 8 * log2(x), 4)))
)

ggplot(d, aes(factor(dose), response)) +
  geom_boxplot(fill = "#2f6fed", alpha = 0.15, colour = "#1b2a4a",
               outlier.shape = NA) +
  geom_jitter(width = 0.15, alpha = 0.5, colour = "#17a2b8", size = 1.6) +
  labs(
    title = "Dose–response relationship, simulated trial data",
    x = "Dose (mg)", y = "Response score"
  ) +
  theme_minimal(base_family = "sans") +
  theme(
    plot.title = element_text(face = "bold", colour = "#1b2a4a"),
    panel.grid.minor = element_blank()
  )

From the blog

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Tell us the study, the deliverable list and the timeline — we reply within two business days with an honest read and a fixed quote. Ask for a free 20-minute scoping call in your message. Get in touch →