Write, debug and analyze R
Write, explain, debug, review, and optimize R code for data analysis, statistics, research, and application development. Clean and transform data with tidyverse or data.table, create ggplot2 and interactive visualizations, run statistical and machine-learning analyses, and build Shiny apps, Quarto reports, and R packages. More
Skills 34 in the ChatGPT listing
Advanced statistics Handles advanced statistics in R: survival and time-to-event analysis, Bayesian models with brms and rstanarm, structural equation modeling with lavaan, factor analysis, PCA, cluster analysis, generalized additive models with mgcv, meta-analysis with metafor, and bootstrapping. Code conversion Converts code to and from R: Python pandas, NumPy and scikit-learn to R and back, SAS, SPSS and Stata syntax to R, Excel formulas and pivot tables to R, SQL to dplyr, base R to tidyverse, and plyr or reshape2 to modern packages. Data cleaning Cleans messy data in R: missing values, duplicates, inconsistent text, dates and times with lubridate, strings and regex with stringr, factors with forcats, outliers, column names with janitor, data validation, and recoding variables. Data import export Imports and exports data in R: CSV, Excel, SPSS, Stata, SAS, JSON, XML, Parquet, Feather, Google Sheets, databases and APIs using readr, readxl, writexl, openxlsx, haven, jsonlite, arrow and DBI. Data wrangling Transforms and reshapes data in R with dplyr, tidyr, data.table and base R: filter, select, mutate, summarise, group_by, joins, pivot_longer, pivot_wider, separate, nest, across, case_when, window functions and row-wise operations. Databases big data Connects R to databases and big data: DBI, odbc, RPostgres, RMariaDB, RSQLite, duckdb, dbplyr to write dplyr that runs as SQL, arrow for Parquet and larger-than-memory data, sparklyr for Spark, and secure connection handling. Exploratory data analysis Runs exploratory data analysis and descriptive statistics in R: summaries, distributions, correlations, group comparisons, missing-data patterns, outliers and automated EDA reports with skimr, DataExplorer, GGally and gtsummary. Functional programming oop Explains and writes advanced R: functions, closures, environments, scoping, the apply family, purrr map functions, functional programming, tidy evaluation, metaprogramming, and object-oriented programming with S3, S4, R6 and S7. Ggplot2 visualization Creates publication-quality charts in R with ggplot2: scatter, line, bar, histogram, boxplot, violin, heatmap, facets, themes, colors, labels, annotations, scales, patchwork layouts and ggsave export for papers, reports and slides. Hypothesis testing Chooses and runs the right statistical test in R: t-tests, ANOVA, chi-square, Fisher, Mann-Whitney, Wilcoxon, Kruskal-Wallis, correlation tests, post hoc tests, assumption checks, effect sizes, power analysis and multiple comparison correction, with plain-language interpretation. Interactive visualization Builds interactive charts, maps and tables in R with plotly, ggplotly, highcharter, echarts4r, leaflet, DT and reactable, and exports them as HTML widgets for dashboards, Quarto and Shiny. Machine learning Builds machine learning models in R with tidymodels, caret, ranger, xgboost and glmnet: classification, regression, feature engineering with recipes, cross-validation, hyperparameter tuning, model evaluation, variable importance and explainability. R code explainer Explains any R code line by line in plain language: what each function, pipe, argument and operator does, how data flows, what the output means, and how to adapt it. R code review Reviews R code like a senior developer: correctness, readability, tidyverse style guide, reproducibility, performance, error handling, security of credentials, and refactoring into functions. R code writer Writes complete, runnable R code and scripts from plain-English requests: functions, data processing pipelines, loops, simulations, automation scripts and full analysis scripts in base R, tidyverse or data.table. R debugger Debugs R errors and warnings: object not found, could not find function, subscript out of bounds, non-numeric argument, arguments imply differing number of rows, package installation failures, ggplot2 and dplyr errors, and wrong results. R help Shows what R Programming Expert can do, with a menu of capabilities and example requests, and helps users get started, share data with dput or reprex, and pick the right skill for their R question. R interview prep Prepares users for R programming, data analyst, statistician and data scientist interviews: common R interview questions with answers, live coding challenges, statistics questions, take-home project guidance and mock interviews with feedback. R package development Builds R packages from scratch with devtools, usethis, roxygen2 and testthat: package structure, DESCRIPTION and NAMESPACE, documentation, unit tests, vignettes, pkgdown websites, GitHub Actions checks and CRAN submission. R performance Speeds up slow R code: vectorization, avoiding growing objects in loops, profiling with profvis and bench, data.table and dtplyr, arrow and duckdb for big data, memory management, parallel processing with future and furrr, and Rcpp for hot spots. R programming expert Always use this skill for every message while R Programming Expert is active, including greetings, thanks, short replies, off-topic messages and every follow-up. R setup reproducibility Sets up and manages R environments: installing R, RStudio and Positron, packages from CRAN, GitHub and Bioconductor, fixing install errors, RStudio projects, renv for reproducible package versions, Git and GitHub from RStudio, targets pipelines and Docker. R tutor Teaches R programming step by step for beginners to advanced learners: personalized learning paths, clear explanations, practice exercises with solutions, mini projects, quizzes, and help with R homework and assignments by explaining the concepts. Recommended tools Recommends the best tools, packages and resources for any R task: the right R packages by task, IDEs (RStudio, Positron, VS Code), learning resources, documentation, communities, and research tools such as SciSpace for literature work. Regression modeling Builds and interprets regression models in R: linear regression with lm, logistic and Poisson regression with glm, interactions, polynomial terms, mixed-effects models with lme4, model diagnostics, model comparison, marginal effects and publication-ready regression tables. Reports quarto rmarkdown Creates reproducible reports, papers, slides, dashboards and websites with Quarto and R Markdown: YAML headers, code chunk options, parameterized reports, tables with gt, kableExtra and flextable, citations, cross-references, and output to HTML, PDF and Word. Research statistics reporting Turns R analyses into research-ready writing: APA and journal-style reporting of statistical results, methods and results sections, publication-quality tables with gtsummary and gt, citing R and packages, reproducibility statements, and connecting analyses to published literature. Scientific data analysis Analyzes scientific and research data in R: Bioconductor and genomics data structures, ecology with vegan, psychometrics, survey data with the survey package, experimental design, dose-response curves, and lab data workflows, with reproducible methods ready for papers. Scispace guide Explains how R users, students and researchers can use SciSpace alongside R: finding papers that use a statistical method, understanding methods and results in research PDFs, extracting data from papers for analysis in R, literature reviews and citation-backed writing. Shiny apps Builds interactive Shiny web apps and dashboards in R: UI and server logic, reactivity, inputs and outputs, bslib and shinydashboard layouts, modules, file upload and download, performance, debugging and deployment to shinyapps.io, Posit Connect or Docker. Spatial analysis Performs spatial and GIS analysis in R: sf for vector data, terra for rasters, reading shapefiles and GeoJSON, projections and CRS, spatial joins, buffers, distance calculations, choropleth maps with ggplot2 and tmap, and interactive maps with leaflet and mapview. Text mining Analyzes text data in R with tidytext, quanteda, stringr and tm: tokenizing, stop words, word frequencies, TF-IDF, n-grams, sentiment analysis, topic modeling with LDA and STM, word clouds and text classification. Time series forecasting Analyzes and forecasts time series in R: ts, xts, zoo, tsibble and fable, ARIMA, ETS, seasonal decomposition, stationarity tests, prophet, cross-validation of forecasts, accuracy metrics and forecast plots. Web scraping apis Collects web data in R responsibly: rvest for HTML scraping, CSS selectors and tables, httr2 for REST APIs with authentication, pagination and rate limits, JSON parsing, and building plumber APIs. 