R is the standard language for statistical computing, data analysis, and academic research across statistics, biology, epidemiology, psychology, social science, and economics. Our R specialists deliver correct, reproducible R code with proper statistical interpretation — not just outputs, but conclusions that make scientific sense.
| Statistics & Analysis | Domain Applications | Tools & Frameworks |
|---|---|---|
| Descriptive statistics and EDA | Bioinformatics (DESeq2, edgeR, Bioconductor) | tidyverse (dplyr, tidyr, purrr) |
| Hypothesis testing (t-tests, ANOVA, chi-squared) | Epidemiology (survival analysis, logistic regression) | ggplot2 data visualisation |
| Linear and multiple regression | Econometrics (time series, panel data) | R Markdown and Quarto reporting |
| Generalised linear models (logistic, Poisson, negative binomial) | Psychology (factor analysis, SEM, mixed effects) | Shiny interactive apps |
| Mixed effects models (lme4, nlme) | Ecology (species distribution modelling, vegan) | caret and tidymodels for ML |
| Bayesian analysis (rstan, brms, MCMC) | Spatial statistics (sf, sp, raster) | Data wrangling and cleaning |
R assignments in statistics and data science are rarely pure coding tasks. Most require:
set.seed() before any random process; R version and package versions documented or sessionInfo() output includedAlways check model assumptions before interpreting regression output. A linear regression that violates homoscedasticity (fan-shaped residual plot) or normality of residuals produces unreliable standard errors and p-values. The assumption checks — residual vs. fitted plot, Q-Q plot, scale-location plot, Cook's distance — are often worth as many marks as the model output itself.
Statistical analysis, ggplot2 visualisation, bioinformatics, econometrics, and R Markdown reports — correct code and proper interpretation.
Yes. R Markdown (.Rmd) and Quarto (.qmd) documents that combine code, output, and narrative text — knitted to PDF, HTML, or Word — are a standard deliverable format for R assignments. We deliver the source document (which you can re-run and verify) along with the compiled output. APA-format tables using papaja or kableExtra are also handled.
Yes. Bioconductor packages (DESeq2, edgeR, limma for differential expression; Seurat for scRNA-seq; VariantAnnotation for genomics), GSEA/pathway analysis, and phylogenetics in R (ape, phangorn) are handled by our bioinformatics specialists. These assignments require both correct R implementation and biological interpretation of results.
Yes. Time series analysis (ARIMA, VAR, GARCH using forecast, vars, rugarch), panel data models (plm package), instrumental variables (ivreg), and spatial econometrics (spdep) are covered. Econometrics assignments require both the correct R implementation and the economic interpretation of coefficients, diagnostics, and policy implications.