RESEARCH SUPPORT
RStudio support for
R Markdown and Quarto reports
For researchers whose models already run in RStudio: the report layer is built as a parameterized R Markdown or Quarto document, so every table, figure and quoted estimate comes from the code and renders again on a coauthor's computer.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What Does RStudio Support Involve?
RStudio support at OnlineSPSS is help with the reporting layer of a research analysis: models fitted in RStudio become a parameterized R Markdown or Quarto report that renders the same tables, figures and quoted estimates each time, in your journal or institutional template. It is research-analysis support, not Posit product support, installation or licensing. RStudio help of this kind covers R Markdown reports and Quarto reports for research, built so that nothing in the report is copied by hand, with a record of package versions kept in the project folder. Such projects are built and checked by in-house PhD-level statisticians at OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, and the research questions behind them are set by you.
WHEN TO ENGAGE
When a Folder of Scripts Stops Being an Analysis
Reproducibility problems surface late, when a collaborator cannot run the code or a journal asks for it, and by then the history of the analysis may be hard to reconstruct.
Journal Template Required A journal or graduate school sets its own Word or LaTeX template, and the tables, captions and figure sizes have to come out of the code already in that format instead of being reformatted by hand.
Numbers Pasted Into Word Tables and estimates are copied from the console into a manuscript, so a late change to the data or the model means every figure in the text has to be rechecked by hand.
A Report to Update Each Cycle An annual or monthly report repeats on new data, and the analysis has to rerun with a changed parameter and return the same layout without anyone editing the document again.
Comments Returned by Coauthors Coauthors comment and edit in Word while the numbers come from code, so the workflow has to carry their changes back into the source document without overwriting the generated results.
PROJECT ANATOMY
Building a Reproducible RStudio Project
A reproducible analysis gives the same report from the same files on another computer, which depends on structure, recorded versions and documents generated from code.
Folder Layout
An RStudio project keeps raw data, code and outputs in separate folders with relative paths, so the whole folder can be moved or shared without anything breaking.
Package Versions
The packages and their versions are recorded, for example with renv, so that a later update cannot silently change what a model returns when the project is opened again.
Analysis Scripts
Cleaning, modeling and checking run in a stated order from the raw files, with seeds fixed wherever resampling is used, as part of Quantitative Data Analysis.
R Markdown Documents
Results are inserted from the fitted objects, so a changed model updates the table, the figure and the sentence quoting the estimate together instead of leaving them out of step.
Quarto Option
Quarto renders the same kind of document to Word, PDF or HTML and also runs Python, which suits a team or a journal template that spans both languages.
Review and Handover
The project is rerun from a clean session before delivery, and an independent check of the code is available through Independent Statistical Review.
WHY RENDERS GO WRONG
Where a Rendered Report Goes Wrong
A report can render without an error and still print a fixed, stale or hidden result, so every render is read and compared before it is shared.
Parameters Not Passed Through A parameterized report is rendered for one site or year while a table or sentence still reads a fixed value, so other versions repeat the first one's numbers until each render is compared.
Figure Dimensions Left to Defaults Figure size and resolution are left to the defaults, so a plot that looked right on screen arrives cropped or unreadable in the Word or PDF output, and chunk options fix the dimensions the journal requires.
Cached Chunks Gone Stale Caching shortens rendering but can leave an old result in the report after the data or an upstream step changed, so caching is used sparingly and always checked.
Errors Hidden by Chunk Options Chunks set to continue after an error, or to hide warnings and messages, let a report render even though a step failed, so these options are kept for chunks whose output is already known, and the render log is read before the report is shared.
Every Step Inside One Document Cleaning, modeling and reporting all sit in one long document, so every render reruns slow steps and a failure is hard to locate, and heavy steps move into scripts whose saved results the report reads.
Rounded Numbers Retyped Results are rounded and typed into the text by hand, so the report and the model drift apart, whereas inline code reads each value from the fitted object itself.
DELIVERABLES
The Project Folder and the Rendered Report
The folder is organized so that rendering it again is a single step, and the report is only one of the files the project produces.
Organized Project Folder Raw data, scripts, outputs and a README in a layout a coauthor can open in RStudio, with the order in which the files are run stated plainly.
Version Lockfile A record of the R version and every package version used, which allows the working environment to be restored on another computer.
Report Source File An R Markdown or Quarto source that generates the tables, figures and quoted estimates, with the journal or institutional format already applied.
Parameterized Template For a report that repeats on new data, a template with parameters such as the year or the site, so each run returns the same structure.
Rendered Outputs The report as Word, PDF or HTML, with figures exported at the resolution required and tables formatted with their confidence intervals.
Clean-Session Test Log A record that the project was rerun from an empty session and gave the same results, with any system requirements and packages noted.
ILLUSTRATIVE EXAMPLE
A Yearly Visitor Report That Rebuilds Itself
An illustration of how a yearly report moves from hand-copied tables to a project that regenerates itself when the data change.
ILLUSTRATIVE EXAMPLE · STATE PARK VISITOR SURVEY RESEARCH · UNITED STATES
A research team at a state parks agency produced a yearly visitor survey report on satisfaction and crowding by park region, and each year an analyst rebuilt the tables by hand from a spreadsheet and a set of loose scripts.
The analysis was moved into an RStudio project with a fixed folder layout, a recorded package environment and a parameterized Quarto report that took the year and the park region as parameters. The complication appeared on the first full run: one small region had so few responses that several tables showed empty cells and intervals that could not be computed. The template was given a rule, agreed with the team, that suppresses estimates below a stated number of responses and prints a note in their place, and the report was rendered again from a clean session for every region. The team could then render the report for any region in the same structure, with proportions shown beside their confidence intervals.
Outcome: The team received the project folder, the version lockfile, the parameterized report template, the rendered report with proportions and their 95% confidence intervals, and a README that explains how to rerun it.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
SOFTWARE
Where RStudio Meets the Rest of a Team's Tools
A project often has to coexist with a coauthor's language or a department's standard package, and the report can draw on both when it is built with that in mind.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCH RESPONSIBILITY
A Rendered Report Reproduces a Calculation, Not a Claim
The question an analysis answers and the way its findings are argued come from the researcher, and the project only makes the numbers behind them reproducible.
Agreed Statistical Support OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, defined in a scope that is confirmed before any work begins.
No Intellectual Contribution OnlineSPSS does not claim any intellectual contribution to your article, report or thesis. Authorship remains the researcher's.
Your Research Decisions The research question, the choice of what to report, the interpretation of the estimates, the discussion and the conclusions remain yours, with overall responsibility.
Confidential Handling Research data and project details are handled confidentially, project information is not published, and an NDA is available where required.
FREQUENTLY ASKED QUESTIONS
Questions About RStudio Projects and Reports
Before a project is shared, three choices need an answer: the document system, the journal template and the place where sensitive data are kept.
What do R Markdown reports from OnlineSPSS include?
R Markdown reports at OnlineSPSS include the organization of the R analysis as an RStudio project, the statistical models inside it, and a reproducible R Markdown document, or Quarto report, rendered from the results. Some projects need the full build, and others only the report layer on top of existing code. The scope depends on what is already in place.
Should the report use R Markdown or Quarto?
Either can serve. R Markdown is long established and widely supported, and existing documents keep rendering. Quarto is the newer system from the same developers, with one syntax across R, Python and other languages and richer output formats. The deciding factors are the journal or institutional template, the languages involved and what the coauthors already use.
Can an R Markdown report be rendered into my journal's Word template?
Often yes. R Markdown can render to Word using a reference document that carries the styles, and Quarto offers the same idea, so headings, fonts and caption styles follow the template while the numbers still come from code. Multi-column layouts, tracked changes and final typesetting remain with the journal or the authors, and template features differ by version.
Should the dataset be stored inside the project folder?
It depends on the data. Open or anonymized files can sit in the folder so the project is complete, whereas sensitive data are usually kept in an approved location that the code reads from, with a synthetic or masked example file included for testing. The data protection terms of the study decide, not convenience.
How do you protect my research data and confidentiality?
Your data and project details are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as your current R scripts, the data files, a draft of the report and any journal code policy, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
From Loose Scripts to
a Report You Can Render Again
Reproducibility is simplest to build in while an analysis is still taking shape, yet an existing folder of scripts can be reorganized at any stage.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014