Blog

Notes on R, statistics and reproducible workflows.

Your Table 1, in one line, with gtsummary

clinical

biostatistics

gtsummary

reporting

Baseline characteristics tables are the most-copied, most-error-prone table in clinical papers. gtsummary generates a publication-ready one — with the right summary and test per variable — from a single call.

Jul 9, 2026

2 min

What to know before hiring an R consultant

consulting

reproducibility

workflow

The questions that separate a one-off script from work your team can actually own: scope, reproducibility, code ownership and handover.

Jul 8, 2026

3 min

Cohen’s d and effect size in R

statistics

effect-size

r-tutorial

Why a p-value isn’t enough, and how to compute and interpret Cohen’s d effect size in R — the standardized measure of how big a difference between two groups really is.

Jul 7, 2026

2 min

Sensitivity, specificity, PPV and NPV in R — and why prevalence changes everything

clinical

biostatistics

diagnostics

Diagnostic accuracy from a 2×2 table in base R: sensitivity, specificity, predictive values and likelihood ratios — plus the reason the same test looks brilliant in one clinic and useless in another.

Jul 6, 2026

3 min

Linear regression in R: how to read the output

statistics

regression

r-tutorial

Fit a linear regression in R with lm() and interpret every part of the summary — coefficients, p-values, R-squared and residuals — plus a tidy table with broom.

Jul 5, 2026

2 min

R vs Python for statistical consulting: an honest take

consulting

workflow

Both are excellent. For inference-heavy, publication-bound statistical work, R still has an edge — and here is the specific, non-tribal reason why.

Jul 4, 2026

2 min

Logistic regression in R: odds ratios and ROC/AUC, done right

clinical

biostatistics

regression

pROC

A binary clinical outcome needs two things: adjusted odds ratios that answer ‘what matters?’ and an ROC/AUC that answers ‘how well does the model discriminate?’. Here is the clean R workflow for both.

Jul 3, 2026

2 min

Chi-square test of independence in R

statistics

categorical-data

r-tutorial

Test whether two categorical variables are associated using the chi-square test in R — building the contingency table, running chisq.test, and knowing when to switch to Fisher’s exact test.

Jul 2, 2026

2 min

Why Welch’s t-test should be your default

statistics

simulation

A short simulation showing what unequal variances do to the classic Student’s t-test — and why the Welch correction costs you almost nothing.

Jul 1, 2026

2 min

Cox proportional hazards in R — and the assumption you must check

clinical

biostatistics

survival

A hazard ratio is only meaningful if the proportional-hazards assumption holds. Fit a Cox model, report hazard ratios, then test the assumption with cox.zph() — the step too many analyses skip.

Jun 30, 2026

2 min

One-way ANOVA in R with post-hoc tests

statistics

anova

r-tutorial

Run a one-way ANOVA in R, then use Tukey’s HSD to find which groups actually differ — with the multiple-comparison correction that keeps your error rate honest.

Jun 28, 2026

1 min

Power analysis before data collection — in base R

statistics

study-design

You don’t need special software to plan a sample size. power.t.test() and a power curve answer the question in ten lines of R.

Jun 25, 2026

3 min

Checking normality in R: Shapiro–Wilk and Q–Q plots

statistics

assumptions

r-tutorial

How to check the normality assumption in R the right way — combining the Shapiro–Wilk test with a Q–Q plot, and why the plot usually matters more than the p-value.

Jun 24, 2026

2 min

Analysing energy consumption time series in R

energy

time-series

forecasting

Metering data is a time series with structure hiding in plain sight. In R you can decompose electricity load into trend, weekly seasonality and noise — and read a weekday load profile straight off the data.

Jun 22, 2026

3 min

t-test vs ANOVA: when to use which (with R)

statistics

hypothesis-testing

anova

A t-test compares two groups; ANOVA compares three or more. See in R why they’re the same test for two groups, and why you shouldn’t run many t-tests instead of one ANOVA.

Jun 20, 2026

2 min

One template, fifty reports: parameterised Quarto in practice

quarto

reporting

workflow

How we turn a single Quarto document into a batch of per-site or per-client reports with one line of R.

Jun 18, 2026

2 min

Pearson vs Spearman correlation: which one, and when (in R)

statistics

correlation

r-tutorial

Pearson measures linear association; Spearman measures monotonic association on ranks. A clear R example shows exactly when they disagree — and which to trust.

Jun 15, 2026

2 min

Confidence intervals explained, with an R simulation

statistics

confidence-intervals

estimation

What a 95% confidence interval really means — demonstrated by simulating repeated samples in R — plus how to compute intervals for a mean and a proportion.

Jun 12, 2026

2 min

How to interpret a p-value (correctly), with R

statistics

hypothesis-testing

p-values

What a p-value is, what it isn’t, and a simulation in R that shows why p-values are uniform under the null hypothesis — the intuition most explanations skip.

Jun 10, 2026

2 min

Write your ggplot2 theme once, use it everywhere

ggplot2

workflow

A ten-line theme function is the cheapest branding investment an analytics team can make. Here is ours, and how to set it as a session default.

Jun 8, 2026

3 min

Sample size calculation in R: t-tests, proportions and correlations

power-analysis

study-design

sample-size

How to compute the sample size you need in R with power.t.test and power.prop.test — the four quantities that trade off, worked examples, and a power curve you can adapt.

Jun 5, 2026

2 min

Publication-quality Kaplan–Meier curves with survival + ggplot2

survival-analysis

ggplot2

biostatistics

The survfit object has everything you need; ggplot2 does the rest. A dependency-light recipe for survival curves you control completely.

May 28, 2026

2 min

Same correlation, wildly different data: always plot first

statistics

ggplot2

data-quality

Anscombe’s quartet in tidy form — four datasets with identical summary statistics and completely different stories. A 50-year-old lesson that still gets ignored.

May 12, 2026

3 min

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