Your R script or Markdown file, the dataset if you can share it, the complete error message copied from the console, and one line on what you have already tried. That way the session goes on the problem rather than setup.
Everyone’s code runs except yours, the error mentions an object you are certain exists, and Stack Overflow answers a slightly different question. Whether you need R programming help for one assignment or a full dissertation, our RStudio tutors sit with you in your own script and work out what R is actually complaining about.
Anyone can hand you working code. That is useless in a viva. Our RStudio tutoring works through your own script until you can read the error, fix it and explain the fix yourself. Live, one to one, in your project.
Tutors Who Write R for Their Own Work
Our R tutors come from statistics, epidemiology and data science backgrounds. They have hit every cryptic error you are hitting and already know what it usually means.
Your Project, Your Console
Share your screen and we debug live inside RStudio. You keep the working script and, more importantly, the reasoning that lets you fix the next error yourself.
Tidyverse or Base R, Your Choice
Some departments teach base R, others go tidyverse first. Tell us what your module uses and the session follows it, rather than a style your marker will not recognise.
R looks different in every department. A psychology lab runs mixed models, an ecology module lives in ggplot2, a business analytics course wants clean dashboards. We match you with an RStudio tutor who uses R in your field, so the session starts with your script, not a generic tutorial.
t-tests, ANOVA, mixed models and reliability checks for lab reports and dissertation chapters.
GLMs, community analysis and publication-ready plots for field and lab data.
Survival models, logistic regression and summary tables for clinical and population studies.
Panel regressions, forecasting and tidy data pipelines for analytics coursework and capstones.
Pick the stage where your script stopped cooperating. Everything below is something students bring to a session, from a file that will not import to a plot that renders completely blank.
Use RStudio projects and relative paths so your script runs on any machine, instead of breaking the moment a folder moves.
here() · .RprojFix the install failures, version clashes and permission errors that block packages on locked-down university machines.
install.packages · libraryRead CSV, Excel, SPSS and Stata files into R without mangling dates, dropping leading zeros or turning numbers into text.
read_csv · read_excel · havenWork out why a column is a character or factor when you expected a number, which is behind a surprising share of R errors.
str() · as.numeric · factorHandle NA properly, and understand why a single missing value can turn an entire mean into NA without warning you.
is.na · na.rm = TRUEUse the console, environment pane and help files well enough that you can answer small questions without leaving RStudio.
?function · View()Cut your data down to the rows and columns you need, and understand why a filter returned zero rows when you expected hundreds.
filter · select · sliceBuild derived columns, recode categories and apply conditional logic without resorting to a long chain of nested statements.
mutate · case_whenProduce counts, means and other summaries by group, and get the tidy summary table your results section actually needs.
group_by · summariseMerge tables on a key and notice when a join silently duplicated rows or dropped the cases that failed to match.
left_join · inner_joinMove between formats for repeated measures or plotting, which is the step most students find least intuitive in R.
pivot_longer · pivot_widerChain steps together so your script reads as a sequence of operations rather than a stack of nested function calls.
%>% · |>Choose and run the right comparison for your design, whether that is two groups, several, paired or independent.
t.test · aov · wilcox.testFit linear and logistic models, specify them correctly with factors and interactions, and read the summary output properly.
lm · glm · summary()Test normality, variance and model fit, and know what to do when a diagnostic plot clearly shows a problem.
plot(model) · shapiro.testHandle repeated measures and nested data when a standard regression cannot represent your study design.
lmer · lme4Get from a console printout to a sentence about effect size and uncertainty that a marker or supervisor can follow.
confint · broom::tidyDecode object not found, undefined columns selected and non-numeric argument, and find what each is really pointing at.
Understand how data, aesthetics and geoms fit together, so you can build a plot deliberately rather than by trial and error.
ggplot · aes · geom_Fix the classic causes, usually a missing aesthetic mapping, wrong data shape or a layer added with a plus in the wrong place.
Control labels, scales, colours and themes so your figures meet your department's presentation standards.
labs · scale_ · themeSave plots at the right size and resolution for a thesis or journal, instead of screenshotting the RStudio plot pane.
ggsaveCombine code, output and writing in one reproducible document, and control which chunks run and what readers see.
knitr · chunk optionsWork out why a document runs fine in the console but refuses to knit, which is almost always an environment problem.
Not sure which stage your problem sits at? Send us your script and the full error message, and we will tell you before you book.
Your session can focus on one urgent question or form part of a longer learning plan. Common areas of support include:
Create an account on our platform powered by Teachworks LMS by sharing your academic needs, availability, and learning preferences.
Receive your secure login credentials and access your personalized student portal to manage your sessions, tutors, and learning progress.
Book your session, complete your secure payment via Stripe, and join your live, interactive lesson to begin your learning journey.
Leave with clearer priorities, suggested practice and the option to book further sessions.
Stack Overflow answers a question that is almost yours. Online courses use tidy example data. Here is where a private R tutor saves the most time.
| What you need | Stack Overflow and YouTube | Online R courses | Private RStudio tutor |
|---|---|---|---|
| Fix the error in your own script | Similar question, different data | Not covered | Debugged live in your RStudio |
| Clean a messy real dataset | Piece together answers | Clean example data | Worked through on your file |
| Choose and check the right model | Conflicting advice | General rules only | Matched to your research question |
| Knit a report that meets the brief | Trial and error | Generic templates | Formatted to your module's requirements |
| Explain your code in a viva or review | No | No | Practice explaining every line |
Ideal for students needing help with basic or entry-level college material.
Tailored support for core and elective undergraduate courses.
Designed for master's level coursework and capstone projects.
Elite academic coaching for doctoral-level work and publications.
Tell us what you are analysing, which line breaks and when you are free. Paste the error message and attach your script, data and brief if you can. We will read it before we reply and match you with a tutor who works in your area.
No guaranteed grades. No completed assignments. Educational tutoring only.
What Our Students Say
Feedback from students across the UK and US we have supported with RStudio, R programming, statistical analysis and research projects.
Online RStudio tutoring starts at $60 per hour for Foundation Year students, $75 for undergraduates, $85 for graduate students and $100 for PhD and research work. You pay per session, with no package required.
Yes. Your tutor works through your R assignment with you, explains the code and output, and checks your approach so you can complete it yourself. We do not complete graded work for you.
Yes. Many students book one session to fix a specific error, a failed knit or a blank plot before a deadline. You can book more sessions later if you need them.
Our RStudio tutoring covers R setup and projects, importing and cleaning data, statistical modelling, ggplot2 visualisation, debugging errors and R Markdown reporting. Sessions run in your own script and dataset rather than generic textbook examples.
Send us your script, the full error message and a note on what you are trying to produce. We read it before replying, match you with an R tutor from the right field, and book a live session at a time that suits you.
Both, and the distinction matters. RStudio is the interface, R is the language doing the work. Most students arrive with an RStudio problem that turns out to be an R problem, so sessions cover the language, the environment and the packages together.
Whichever your course uses. Tell us when you book. If your lecturer marks base R and you have been learning dplyr from YouTube, that mismatch is worth sorting out early, and we can help you work in both.
That is one of the most common reasons students book. A join that duplicated rows, a factor R is reading as a number, or NA values quietly excluded from a mean will all produce output that looks plausible and is wrong. We trace it with you so you can catch it next time.
Yes. R dissertation support usually covers choosing an analysis approach, justifying it in your methodology, running it correctly and presenting the results. The analysis stays your own work, guided by a tutor who explains the reasoning.
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