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R Stats Companion

Run statistical tests and build models in R with plain English guidance.

A custom GPT by @rstatsguru for data science & analytics tasks. Available in the ChatGPT GPT Store with a Plus, Team, or Enterprise subscription.

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R Stats Companion is a custom GPT built by @rstatsguru for run statistical tests and build models in r with plain english guidance. It is available in the ChatGPT GPT Store under the Data Science & Analytics category and requires a ChatGPT Plus subscription to access.

About this GPT

R Stats Companion is part of the Data Science & Analytics category in OpenAI's GPT Store. Custom GPTs are specialized versions of ChatGPT that have been configured with specific instructions, knowledge bases, and capabilities by their creators. This GPT was designed by @rstatsguru to help users with run statistical tests and build models in r with plain english guidance.

Unlike prompting a general-purpose ChatGPT, this GPT comes pre-configured with the context, tone, and expertise needed for data science & analytics-related tasks. This means you spend less time explaining what you need and more time getting useful results.

To use this GPT, you need an active ChatGPT Plus ($20/month), Team, or Enterprise subscription. Once subscribed, you can find it by searching for "R Stats Companion" in the GPT Store or browsing the Data Science & Analytics category.

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Data Science & AnalyticsBy @rstatsguruChatGPT GPT Store

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FAQ

Common questions about R Stats Companion and how to use it effectively.

01

I have only ever used GUI statistics tools like SPSS or JMP. Can you help me make the jump?

It bridges the GUI-to-code gap by mapping your familiar SPSS workflows to their R equivalents with side-by-side comparisons. 'In SPSS you would click Analyze > Compare Means > Independent Samples T-Test and select these variables — in R, that is t.test(outcome ~ group, data = df).' Each translation includes not just the code but an explanation of what the R function is doing that the GUI was hiding from you. The transition is framed as gaining transparency rather than losing convenience.

02

How does it handle Bayesian analysis, or is it purely frequentist?

It covers Bayesian approaches alongside frequentist ones and helps you choose between them based on your analytical goals. It writes code using rstanarm and brms for Bayesian regression, provides guidance on prior specification (including default weakly informative priors and how to customise them), and explains posterior interpretation in plain English — 'given the data, there is a 94% probability that the true effect size is between 0.3 and 1.2.' It does not evangelise for either paradigm but helps you use the right tool for your question.

03

Can it help with power analysis before I collect data?

It generates power-analysis code using the pwr package that answers 'how many participants do I need?' for common tests — t-tests, ANOVA, chi-square, correlation, and regression — given your expected effect size, desired power, and alpha level. It also runs sensitivity analyses showing how required sample size changes as your assumptions change, and it flags when your expected effect size is unrealistically large relative to published research in your domain.

04

How does it handle missing data — listwise deletion, imputation, full-information methods?

It provides a structured missing-data workflow: first, visualise and quantify the missingness pattern to determine if data is MCAR, MAR, or MNAR. Then, based on the pattern and proportion of missing data, it recommends an approach — multiple imputation with mice for MAR data, full-information maximum likelihood for SEM/longitudinal models, or sensitivity analyses when data may be MNAR. It also explains why listwise deletion is almost never the right answer and shows the bias it would introduce in your specific situation.

05

Can it help with reproducible research — not just the analysis but the entire workflow?

It produces complete, self-contained R scripts or Quarto documents that another researcher could run on the same data and get identical results. This includes setting a random seed at the top, documenting package versions, using relative file paths with here() from the here package, writing sessionInfo() output to a log file, and structuring the script so that it can be re-run from scratch. The GPT treats reproducibility as a professional obligation, not a nice-to-have, and the output reflects that standard.

06

What if my data violates the assumptions of the test I wanted to run?

It checks assumptions as a matter of course and provides alternative approaches when assumptions fail. If your residuals are not normally distributed, it suggests transformations, robust regression, or non-parametric alternatives rather than pressing ahead with the invalid test. If variances are unequal, it recommends Welch's correction or a heteroscedasticity-consistent covariance matrix. Each assumption violation gets a specific remedy rather than a generic 'your assumptions are violated, good luck' message.

07

Can it help me prepare publication-quality tables and figures?

It generates code for publication-ready output using gtsummary for regression tables, flextable for custom formatted tables, and ggplot2 with theme customisations for journal-specific formatting requirements. It can produce APA-style tables with proper decimal alignment, confidence intervals in brackets, and significance stars mapped to specific p-value thresholds. The output is designed to go from R directly to your manuscript with minimal manual formatting in Word.

08

How do I know when I am ready to stop using this GPT and work independently?

The GPT itself helps you recognise that moment. It tracks the patterns in your questions — when you stop asking 'how do I run a t-test?' and start asking 'should I use a Welch correction here given the variance ratio?', you are ready for more independence. It gradually shifts from providing code to asking Socratic questions that guide you to write the code yourself. The goal is to make itself progressively less necessary, and it treats your growing autonomy as a success metric.