Rverse Analytics

R consulting studio for statistical analysis, Shiny applications and reproducible reporting — biostatistics-grade rigour, shipped as code you own.

Boutique R Studio

Data analysis, engineered in R.

We deliver statistical analysis, interactive Shiny applications and fully reproducible reports — built on a rigorous biostatistics backbone and shipped as code you own.

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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.

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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.

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.

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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()
  )

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