# How to Structure an R Project for Bioinformatics By the end of this article you will be able to build a complete, version-controlled R project for a genomics or omics analysis. You will know where raw data, processed data, functions, configuration, reports, and outputs belong. You will pin every package version so the project reruns years later, and you will wire the whole thing into a dependency-aware pipeline so that changing one input rebuilds only what changed. You will also know when R alone stops being enough and how to hand your R stage off to a workflow engine such as Nextflow or Snakemake. You need the following installed before you start: - R 4.3 or newer and RStudio Desktop or Posit Cloud - Git, available on your PATH - The R packages `usethis`, `renv`, and `targets` (installation is covered in step 1) - Bioconductor, if your analysis uses genomics packages (covered in a dedicated section) - Roughly 1 GB of free disk space for the package library cache Every command in the walkthrough is real and runnable. Expected output is shown so you can check each step. ## Why project structure decides whether your analysis is reproducible Bioinformatics analyses are chains of interdependent steps. Raw sequencing reads pass through quality control, alignment, quantification, and [statistical modeling](/blog/guides/understanding-model-assumptions-in-statistical-analysis), and the output of each step feeds the next. Reviews of bioinformatics pipeline practice describe this explicitly as a workflow problem, where some outputs form inputs for other steps and reproducibility depends on managing those dependencies deliberately rather than implicitly [1]. An unstructured R project fails in predictable ways. Scripts named `analysis_final_v3_real.R` live in the same folder as raw FASTQ summaries. One script reads `../data/counts.csv` while another reads `~/Desktop/counts.csv`. Six months later, neither the original analyst nor a collaborator can tell which script produced which figure. Researchers who study computational reproducibility have a name for part of this problem: workflow decay, the drift that happens when analyses depend on a too-specific computing platform and undocumented steps that nobody can reconstruct later [2]. The fix is structural, not motivational. A large empirical review of bioinformatics pipeline development concluded that scripting languages, including R, are sufficient for small single-user analyses but are not enough on their own for large, shareable, maintainable pipelines, and that explicit structure plus workflow tooling is what makes pipelines reusable [3]. The structure in this article gives you that foundation inside a single project, in a format that later plugs into larger workflow systems without a rewrite. There is also a collaboration argument. Multi-institution consortia such as H3ABioNet, which built variant calling, 16S rDNA, GWAS, and imputation workflows across 27 institutions in 17 African countries, found that portable, reproducible, easy-to-use workflows were the central infrastructure challenge, and that standardized structure was what let heterogeneous computing environments run the same analysis [1]. Even a two-person collaboration benefits from the same discipline: one shared directory layout, one dependency lock file, one entry point. ## The template at a glance Here is the r project structure bioinformatics template this article builds. It works for RNA-seq, methylation, metagenomics, proteomics, and single-cell projects alike. Paths are shown as a plain list so you can create them mechanically. ``` rnaseq-diet-2026/ R/ functions and pipeline steps, one file per theme config/ parameters, sample metadata, paths data/ raw/ immutable input files, never edited processed/ generated intermediates, safe to delete and rebuild output/ figures/ publication figures tables/ result tables for export reports/ Quarto or R Markdown documents for humans renv/ renv infrastructure, mostly auto-generated renv.lock exact package versions, committed to Git DESCRIPTION package dependencies and project metadata _targets.R pipeline definition .gitignore tells Git what to skip README.md how to reproduce this project ``` The rule behind the layout is simple. `data/raw` is read-only. Everything in `data/processed` and `output` is generated by code, so deleting it is safe. Human-readable reasoning lives in `reports`, machine-readable results live in `output`, and reusable logic lives in `R`. This separation mirrors the staged design used in published bioinformatics protocols, which split [preprocessing](/blog/data-analysis/what-is-data-preprocessing), primary computation, and downstream [statistical analysis](/blog/guides/statistical-analysis) into distinct stages with explicit reporting of intermediate outputs between them [4]. ## Step-by-step walkthrough ![Five setup steps in order: create the project, make directories, turn on Git, configure gitignore, then pin packages with renv.](/article-images/illustrations/r-project-structure-bioinformatics-project-setup-order.webp) ### Step 1. Create the project with usethis Install the three infrastructure packages and create the project. Run this in an R console. ```r install.packages(c("usethis", "renv", "targets")) usethis::create_project("~/projects/rnaseq-diet-2026") ``` Expected output: ``` ✔ Setting active project to '/home/analyst/projects/rnaseq-diet-2026' ✔ Creating 'R/' ✔ Writing 'DESCRIPTION' ✔ Writing 'R/Hello.R' ✔ Modifying '.gitignore' ✔ Opening new project 'rnaseq-diet-2026' in RStudio ``` RStudio restarts inside the new project. The working directory is now the project root, which matters because every path in your code should be relative to that root. Delete the placeholder `R/Hello.R` now. Why `usethis::create_project()` instead of clicking through RStudio's New Project dialog? The function produces the same result, works identically on Windows, macOS, and Linux, and is scriptable, so you can document your own setup in a bootstrap script. ### Step 2. Build the directory skeleton From the R console inside the project: ```r dirs <- c( "config", "data/raw", "data/processed", "output/figures", "output/tables", "reports" ) invisible(lapply(dirs, dir.create, recursive = TRUE)) ``` Empty directories are invisible to Git, so add a placeholder file to each one you want tracked: ```r file.create(file.path(dirs, ".gitkeep")) ``` Put your actual raw inputs in `data/raw` now. For the running example used throughout this walkthrough, that is a gene-level count matrix and a sample metadata table: ```r # Example only, replace with your real inputs # data/raw/counts.tsv genes x samples integer counts # config/sample_metadata.tsv sample_id, condition, batch, lib_size ``` ### Step 3. Turn on Git before you write code ```r usethis::use_git() usethis::git_sitrep() ``` `git_sitrep()` reports your configured user name and email and warns if they are missing. Fix any warnings before the first commit, because Git stamps every commit with that identity. Expected output from `use_git()`: ``` ✔ Adding Git commit of current state ✔ Initializing Git repository ``` Make the first commit through the RStudio Git pane or the terminal: ```bash git add -A git commit -m "Project skeleton" ``` ### Step 4. Configure .gitignore the bioinformatics way Open `.gitignore` and make it contain at least this: ``` .Rhistory .RData .Rproj.user/ # generated outputs are rebuildable, do not track them data/processed/ output/ *.html # large raw inputs may be too big for Git, see FAQ data/raw/* !data/raw/.gitkeep ``` The logic: track everything needed to rebuild the analysis, ignore everything the analysis generates. Raw data sits in an awkward middle. Small metadata tables belong in Git. A 40 GB BAM file does not, and the FAQ section covers the standard options for that case. Large consortia face the same question at scale, and the AACR Project GENIE consortium documented how much explicit effort goes into agreeing how data are gathered, harmonized, and shared across institutions, decisions that start with conventions exactly like these [5]. ### Step 5. Pin your package versions with renv This is the step that separates a reproducible project from a folder of scripts. `renv` records the exact version of every package your project uses and can reinstall that exact set later. ```r renv::init() ``` Expected output: ``` - Discovering package dependencies ... Done! - Copying packages into the project library ... Done! The following package(s) have been updated: - renv [1.0.7] - * Lockfile written to 'renv.lock'. ``` Then write real code for a while, and when you add a package with `install.packages()` or `BiocManager::install()`, record it: ```r renv::snapshot() ``` Expected output: ``` The following package(s) will be updated in the lockfile: # CRAN ------------------------------ - cli [3.6.3] - usethis [3.1.0] # Bioconductor ---------------------- - DESeq2 [1.46.0] - Lockfile written to 'renv.lock'. ``` Commit `renv.lock` to Git. Anyone can now rebuild your environment: ```r renv::restore() ``` The lockfile records package name, version, source, and a cryptographic hash of each package, so `restore()` installs byte-identical versions. This addresses workflow decay directly, because the standardized infrastructure survives even when the original analyst has moved on to a different computer [2]. ### Step 6. Declare dependencies in DESCRIPTION Even for an analysis project and not a package, a `DESCRIPTION` file is the machine-readable list of what your project needs. Open it and reduce it to this form: ``` Package: rnaseqdiet Title: Diet effects on mouse liver transcriptome Version: 0.0.1 Description: Differential expression analysis of the 2026 diet study. Depends: R (>= 4.3.0) Imports: DESeq2, dplyr, ggplot2, readr, targets Config/PkgType: analysis ``` The `Imports` field documents intent, `renv.lock` documents exact versions, and the two together answer different questions from future readers. `Depends: R (>= 4.3.0)` sets the minimum R version, which matters because Bioconductor releases are tied to specific R versions, a point covered in the Bioconductor section below. ### Step 7. Put logic in R/, parameters in config/ A well-structured project has almost no interactive code in scripts. Instead, each analytical step is a function in `R/`, and a small config file holds the values you would otherwise hardcode. Create `R/qc_counts.R`: ```r #' Filter low-count genes from a count matrix #' #' @param counts Integer count matrix, genes in rows, samples in columns. #' @param min_count Minimum count required in at least one sample. #' @return The filtered count matrix. filter_low_counts <- function(counts, min_count = 10) { stopifnot(is.matrix(counts), is.numeric(counts)) keep <- rowSums(counts) >= min_count message(sprintf("Kept %d of %d genes", sum(keep), nrow(counts))) counts[keep, , drop = FALSE] } ``` Create `config/params.R`: ```r project_params <- list( counts_file = "data/raw/counts.tsv", metadata_file = "config/sample_metadata.tsv", min_count = 10, contrast = c("condition", "highfat", "control"), fdr_cutoff = 0.05 ) ``` Now the analysis script contains no magic numbers. Changing the FDR threshold means editing one config line, and the provenance of every threshold is visible in version control. Published pipeline protocols emphasize exactly this kind of harmonized parameterization across stages as a reproducibility mechanism [4]. A naming convention helps as projects grow. The MeTime package for longitudinal metabolomics, an open-source R package built around a central S4 container that stores datasets, metadata, and analysis outputs, organizes its functions by prefix, with data transformations under `mod_*`, calculations under `calc_*`, and meta-analyses under `meta_*`, so the workflow stays transparent and easy to modify [6]. Adopting the same idea in your own `R/` folder, for example `read_*`, `qc_*`, `model_*`, `plot_*`, gives collaborators a predictable map. ### Step 8. Wire it together with targets Standalone scripts run top to bottom, even when nothing upstream changed. The `targets` package makes the pipeline dependency-aware: each step is a target, and `tar_make()` rebuilds only the targets whose inputs changed. Create `_targets.R` in the project root: ```r library(targets) library(tarchetypes) source("R/qc_counts.R") source("config/params.R") list( tar_target(counts_file, project_params$counts_file, format = "file"), tar_target(meta, read.delim(project_params$metadata_file)), tar_target(counts_raw, as.matrix(read.delim(counts_file, row.names = 1))), tar_target(counts, filter_low_counts(counts_raw, project_params$min_count)), tar_target(dds, DESeq2::DESeqDataSetFromMatrix( countData = round(counts), colData = meta, design = ~ batch + condition )), tar_target(res, DESeq2::results(DESeq2::DESeq(dds), contrast = project_params$contrast)), tar_target(sig_table, as.data.frame( subset(res, padj < project_params$fdr_cutoff) )), tar_render(reports/de_report.qmd) ) ``` Run the pipeline: ```r tar_make() ``` Expected output: ``` ▶ dispatched target counts_file ▶ dispatched target meta ▶ dispatched target counts_raw ▶ dispatched target counts ● completed target counts [1.2s] ● completed target dds [4.8s] ● completed target res [9.3s] ● completed target sig_table [0.1s] ● completed pipeline [16.7s] ``` Run `tar_make()` again immediately and you get this instead: ``` ✔ skipped target counts_file ✔ skipped target meta ✔ skipped target counts_raw ✔ skipped target counts ✔ skipped pipeline [0.2s] ``` That skip list is the payoff of structure. The pipeline knows nothing changed, so a full rerun takes under a second. Edit one value in `config/params.R` and only the downstream targets rebuild. The three-stage shape here, inputs, primary computation, downstream statistics and reporting, matches the stage structure used in published best-practice protocols for sequencing analysis, including their explicit quality control checkpoints between stages [4]. Check what the pipeline knows at any time: ```r tar_visnetwork() # interactive dependency graph tar_manifest() # printed list of every target and its command ``` ### Step 9. Write reports humans can read Quarto documents in `reports/` combine prose, code, and results. A minimal `reports/de_report.qmd` starts like this: ```` --- title: "Differential expression: high-fat vs control" params: date: 2026-11-11 --- ```{r} tar_load(sig_table) tar_load(res) ``` ## Volcano plot ```{r} #| label: fig-volcano #| fig-width: 6 #| fig-height: 5 plot(-log10(res$padj), res$log2FoldChange) ``` ```` Render it from the pipeline or by hand: ```r quarto::quarto_render("reports/de_report.qmd") ``` The output is a self-contained HTML file that records the code, the package versions from your `sessionInfo()` block, and the numbers behind every claim. Automatically generated HTML and PDF reports are the reporting mechanism used by reproducible R packages designed for omics workflows, and retaining intermediate results and provenance alongside them is what makes iterative exploration safe [6]. ### Step 10. Write the README and record the session ```r usethis::use_readme_md() ``` Edit `README.md` to contain at minimum: one sentence on what the project does, the exact commands to reproduce it (`renv::restore()`, then `tar_make()`), and a pointer to the report files. Then create `R/session_info.R` and source it at the end of every report: ```r report_session_info <- function() { cat("R version:", R.version.string, "\n") print(sessionInfo()) } ``` `sessionInfo()` captures the R version, locale, and attached packages of the run that produced a given report. It is weaker than `renv.lock`, because it does not capture package hashes, but it belongs in every rendered report as a per-run fingerprint. Commit everything and push: ```bash git add -A git commit -m "Pipeline, config, report, lockfile" git push ``` ## The Bioconductor layer Much of R for bioinformatics runs on Bioconductor, the open-source project for genomic data analysis packages. Three conventions matter for project structure. First, install and check through `BiocManager`: ```r if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install(c("DESeq2", "SummarizedExperiment", "GenomicRanges")) BiocManager::valid() ``` `BiocManager::valid()` compares your installed package versions against the versions for your Bioconductor release and reports any that are out of date. `renv` and `BiocManager` work together, because `renv::snapshot()` detects Bioconductor packages and records them under a `Bioconductor` section in the lockfile with their repository and version. Second, respect the release-to-R-version binding. Bioconductor publishes two releases per year, and each release requires a minimum R version, so a lockfile built under Bioconductor 3.19 will not restore cleanly under an R version that predates it. Record both in your README. Third, use the standard container classes instead of ad hoc data frames where you can. A `SummarizedExperiment` object holds the count matrix together with per-gene and per-sample metadata in one validated object, so the counts and the metadata cannot drift apart. Packages built on this principle show its value at scale. MeTime stores multiple datasets, metadata, and analysis outputs in a single S4 container and retains provenance across every transformation, which is what allows whole workflows to remain transparent and easy to modify [6]. In your own project, the practical version of this rule is simple: never keep a counts matrix in one variable and its sample metadata in an unordered separate one. If you are following a Bioconductor tutorial, map its steps into this structure rather than running them as loose scripts. The tutorial's loading step becomes a target in `_targets.R`, its modeling step becomes a function in `R/`, and its figures become report chunks in `reports/`. ## When R alone is not enough: the workflow engine boundary ![A decision path checking for mixed languages, cluster scheduling, container portability and step count before moving to Nextflow or Snakemake.](/article-images/illustrations/r-project-structure-bioinformatics-workflow-engine-boundary.webp) R projects scale beautifully to hundreds of samples. They scale poorly to pipeline logic that involves external command-line tools, cluster scheduling, or multi-language steps. Know the boundary. A published example shows the pattern clearly. The tcga-data-nf workflow downloads, preprocesses, and analyzes thousands of TCGA samples to infer regulatory networks, with the pipeline logic in Nextflow and the R-specific data management, mapping, and filtering isolated in a standalone companion R package called NetworkDataCompanion [7]. The R package is a clean, testable unit inside a larger workflow. Similarly, the GCAC system for virtual screening model building combines R statistical packages and literate programming tools inside the Galaxy workflow system, using the workflow engine to guarantee reproducibility and automated configuration around R-based steps [8]. The practical rule for your project: keep R for everything that is data manipulation, statistics, and visualization, and hand off orchestration when you hit any of these conditions: - the pipeline mixes tools from different languages, for example FastQC, BWA, and Picard alongside R [4] - you need to run on a high-performance computing cluster or across heterogeneous environments [1] - you need container-level portability guarantees across institutions [1] - the number of steps makes a single `_targets.R` file hard to reason about The good news is that the structure in this article converts cleanly. A `targets` pipeline already has named, dependency-tracked steps, which map one-to-one onto processes in Nextflow or rules in Snakemake. Your `R/` functions become the payload of those processes, your `config/` becomes the parameter sheet, and your `renv.lock` travels inside the container image. Reviews of workflow reuse in bioinformatics found that modularity and clear interfaces are what determine whether a pipeline actually gets reused, which is precisely what this structure produces [3]. ## Choosing and checking your structure The table below summarizes the records that make a structured R project verifiable, what each one captures, and what it proves to a future reader. | File or artifact | What it records | When it updates | What it guarantees | |, - |, - |, - |, - | | `renv.lock` | Exact package versions, sources, and hashes | When you run `renv::snapshot()` | An identical environment can be rebuilt anywhere | | `DESCRIPTION` | Declared dependencies and minimum R version | When you add a package | Intent, readable without opening the lockfile | | `config/params.R` | Every threshold, path, and contrast used | When you change a setting | No magic numbers hidden in scripts | | `_targets.R` | The full step-by-step analysis definition | When the pipeline changes | A single entry point that rebuilds outputs | | `sessionInfo()` output | R version and attached packages for one run | Every rendered report | Which session produced each report | | `data/raw/` (read-only) | The immutable inputs | Never, by convention | Results trace to unchanged data | | Git history | Every change to code and config | Every commit | When and why any decision was made | To check that your structure actually works, run the rebuild test on a clean machine or at minimum a fresh R library: 1. Clone the repository. 2. Run `renv::restore()` and confirm it completes with no errors. 3. Run `tar_make()` from scratch after deleting `data/processed` and `output`. 4. Confirm the rendered report reproduces the committed figures. If all four pass, the project is reproducible in the strict sense. H3ABioNet applied an analogous standard across its four production workflows, testing them across heterogeneous computing environments precisely because reproducibility claims mean little until someone else has run them [1]. ## Common Mistakes and Limitations - Keeping raw data and analysis code in the same directory level. If `data/raw` sits inside a folder a collaborator might rsync or Git-track by accident, the first 40 GB commit will hurt. Keep raw inputs outside Git or ignored, as shown in step 4. - Absolute paths anywhere in the code. `read.csv("/Users/alice/data/counts.tsv")` runs on exactly one computer. Every path in a structured project is relative to the project root, and the working directory is guaranteed by opening the `.Rproj` file. - Using only `sessionInfo()` for reproducibility. It records attached packages but not versions of packages loaded indirectly or package hashes, so two sessions with identical `sessionInfo()` output can still behave differently. Use `renv.lock` as the primary record and `sessionInfo()` as the per-run addendum. - Editing files in `data/raw`. The moment a raw input is touched by hand, its provenance chain breaks and no amount of structure recovers it. If a raw file needs correction, transform it in code into `data/processed`. - Treating a notebook as the whole analysis. A Quarto or R Markdown document is a report, not a pipeline. If the notebook is the only artifact, nothing enforces the order of execution or detects stale outputs. Keep the computation in `targets` and let reports consume pipeline outputs, the division used in the walkthrough above and in published protocol designs that separate computation from explicit downstream reporting [4]. - Ignoring the scalability boundary. A 5,000-line R script handling terabyte-scale inputs and cluster scheduling is the failure mode that pipeline reviews identify when scripting alone is stretched beyond its scale [3]. Plan the workflow-engine handoff before you hit it, not after. - Hardcoding thresholds in scripts instead of config. When the FDR cutoff lives in five scripts, someone will change it in three of them. The config file plus `targets` dependency tracking makes the change propagate predictably. - Skipping Bioconductor version discipline. Installing Bioconductor packages from mixed releases produces subtly wrong results that are nearly impossible to diagnose. Check with `BiocManager::valid()` and record the release in the README. - One honest limitation of any structure: it records decisions, it does not make them. Individual analyses still require domain judgment about model design, batch effects, and biological interpretation, and that judgment lives in the prose of your reports rather than in the directory layout. ## Frequently Asked Questions ### What does a good r project structure bioinformatics example look like? It separates immutable raw data, generated intermediates, reusable functions, configuration, and human reports into fixed directories, with `renv.lock` pinning package versions and a `_targets.R` file defining the pipeline. The full example in this article, built around a differential expression project, shows every directory and file in its expected place. ### Is there a standard r project structure bioinformatics format across labs? No single enforced standard exists, but the layout in this article matches the conventions used by published bioinformatics workflows and R packaging practice, and it converts cleanly into Nextflow, Snakemake, or Galaxy structures, which is what makes it interoperable with larger pipeline efforts [3]. ### Should I turn my analysis into a proper R package? For analyses you will rerun, share, or extend, yes. A package gives you namespaces, tests, and documentation for free, and the `DESCRIPTION` file you already wrote becomes the package manifest. Packages designed this way, such as the companion package in the TCGA regulatory network workflow, survive as reusable units inside larger pipelines [7]. ### Where do large files like FASTQ and BAM files go in the project? Keep them in `data/raw` but exclude them from Git with `.gitignore` entries, and record their locations, checksums, and download commands in the README. For heavy preprocessing, the pattern used in published workflows is to run alignment and methylation extraction outside R and hand R only the compact downstream inputs [4]. ### Do I need renv if I already use Docker or Conda? Use both levels. Conda and Docker pin the system layer, including R itself and external tools, while `renv.lock` pins the exact R package versions and their hashes inside that system. Consortia running identical analyses on heterogeneous computing environments rely on this combined portability [1]. ### When should I move my R analysis into Snakemake or Nextflow? Move when the pipeline mixes command-line tools with R, needs cluster scheduling, or has grown too many interdependent steps to track reliably in a single project. Reviews of pipeline practice identify exactly this scale threshold where workflow systems become necessary for maintainability and reuse [3]. ### How does this structure relate to a Bioconductor tutorial? Map each tutorial step into the structure: data loading becomes a target, analysis functions go in `R/`, and figures go in a Quarto report in `reports/`. Install tutorial packages with `BiocManager`, verify versions with `BiocManager::valid()`, and let `renv::snapshot()` capture them in the lockfile. ### How do I hand the project to a collaborator so they can rerun it? Push the repository with `renv.lock` committed, and put three commands in the README: clone, `renv::restore()`, and `tar_make()`. If your collaborator uses a different operating system or cluster, wrap the pipeline in a container or a workflow engine so the environment travels with the code, the approach used for cross-institution workflows [1]. ## Sources 1. [Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics.](https://pubmed.ncbi.nlm.nih.gov/30486782/) 2. [An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64.](https://pubmed.ncbi.nlm.nih.gov/26618079/) 3. [Developing and reusing bioinformatics data analysis pipelines using scientific workflow systems.](https://pubmed.ncbi.nlm.nih.gov/36968012/) 4. [A Guide to Enzymatic Targeted Methylation Sequencing Bioinformatics Workflow: Best Practices.](https://pubmed.ncbi.nlm.nih.gov/42848382/) 5. [American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange: From Inception to First Data Release and Beyond-Lessons Learned and Member Institutions' Perspectives.](https://pubmed.ncbi.nlm.nih.gov/30652542/) 6. [MeTime: an R package for reproducible longitudinal metabolomics data analysis.](https://pubmed.ncbi.nlm.nih.gov/42730207/) 7. [Reproducible processing of TCGA regulatory networks.](https://pubmed.ncbi.nlm.nih.gov/41114645/) 8. [GCAC: galaxy workflow system for predictive model building for virtual screening.](https://pubmed.ncbi.nlm.nih.gov/30717669/)