What our R programming help covers ¶
If you are moving from SPSS, most of what you know carries over. R simply makes every step explicit.
R is a free, open-source programming language for statistics and data analysis, usually run in the RStudio interface. Our R help is one-to-one support for using it on your own research: importing and cleaning data, fitting and checking models, making figures, writing reproducible scripts and fixing errors. A consultant works through your code with you and explains each step.
You can use us as a consultant on a live thesis or paper, or as a tutor while you learn R. Either way, you write or approve the code and keep the script. For design and test-choice questions that apply to any software, see our statistics help and consulting service. If you are learning statistics and R together for a course, our statistics tutoring covers both.
R for data analysis with the tidyverse ¶
The tidyverse is a family of packages that make data cleaning readable. We help you build pipelines with readr, dplyr and tidyr that you can follow months later.
Example script
library(tidyverse)survey <- read_csv("survey.csv") |> mutate(across(c(q3, q7), \(x) 6 - x)) |> mutate(anxiety = rowMeans(pick(q1:q8), na.rm = TRUE)) |> drop_na(group)survey |> group_by(group) |> summarise(m = mean(anxiety), sd = sd(anxiety), n = n())
This reverse-scores two items on a 1 to 5 scale, computes a scale mean and summarizes it by group. We also help with joins (left_join()), reshaping with pivot_longer() and pivot_wider(), factors and dates, and importing SPSS or Stata files with haven.
R Programming Help
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Statistical models in R: lm, glm and lme4 ¶
R uses formula notation to describe models: y ~ x1 + x2 adds predictors, x1 * x2 adds both main effects and their interaction, and (1 | participant) adds a random intercept in lme4.
| Model | R function | Typical use |
|---|---|---|
| Linear regression, ANOVA | lm(score ~ condition + age, data = df) | Continuous outcome, independent observations |
| Logistic regression | glm(passed ~ hours, family = binomial, data = df) | Binary outcome |
| Poisson regression | glm(visits ~ age, family = poisson, data = df) | Count outcome |
| Linear mixed model | lmer(rt ~ session + (1 | participant), data = df) | Repeated measures, nested or clustered data |
| Generalized mixed model | glmer(correct ~ session + (1 | participant), family = binomial, data = df) | Binary repeated measures |
We go beyond summary(): confint() for intervals, car::Anova() for Type II or III tests, emmeans for estimated marginal means and contrasts, broom for tidy coefficient tables, and diagnostic plots of residuals. Our guide to the regression equation shows how coefficients from lm() map onto a written model.
lme4 deliberately reports no p values for fixed effects. Load lmerTest for Satterthwaite or Kenward–Roger tests, and say which you used.
Figures with ggplot2 ¶
ggplot2 builds figures in layers: data, aesthetic mappings, geoms, scales and themes. We help you produce publication-quality plots and export them at the resolution your journal requires with ggsave().
- Group means with confidence intervals using
stat_summary() - Raincloud, box and violin plots to show distributions honestly
- Interaction plots and predicted values from fitted models
- Faceting with
facet_wrap(), accessible color palettes and APA-friendly themes
A good figure starts from a clear question: what comparison should the reader see first? We help you choose the plot that answers it, label axes with units, order groups meaningfully and keep the code in your script, so the figure regenerates exactly when the data change. If your journal sets size or font limits, we build those into a reusable theme.
Reproducible scripts, R Markdown and Quarto ¶
Restart R and run your script from the top before you submit. If it fails, a reviewer's attempt will too.
Reproducibility is one of the main reasons to use R. We help you set up an RStudio Project with a clear folder structure, file paths built with the here package, a fixed random seed with set.seed() for simulations and bootstraps, and package versions recorded with renv.
With R Markdown or Quarto you can combine text, code and results in one document and render it to Word, PDF or HTML. Numbers in your results section are pulled straight from the analysis, so a change to the data updates every figure and statistic. We help with inline code, chunk options, cross-referenced tables and figures, and APA-style output.
Debugging R code ¶
Error messages in R are short and often cryptic. We explain what they mean and teach you a method for fixing them:
- could not find function: the package is not loaded, or the name is misspelled.
- object not found: a typo, a column referenced outside its data frame, or code run out of order.
- Unexpected NA results: missing values propagating through
mean()orsum()withoutna.rm = TRUE. - Model warnings: singular fits or convergence failures in lme4, and what they say about your random-effects structure.
We use traceback(), browser() and small reproducible examples built with the reprex package, so you learn to isolate the next problem yourself.
How R help works, and our integrity policy ¶
- Send your materials: your script or project folder, anonymized data and the error or question you are stuck on.
- Get a quote: we confirm the scope first. See pricing.
- Work together: live sessions in RStudio or a commented script returned to you, with written results feedback in Track Changes and margin comments.
- Ask follow-up questions about any comment after delivery.
Turnaround runs from 24 hours to 7 days. Files are handled confidentially, an NDA is available on request, and we delete files when you ask. We do not complete graded assignments or exams, or write code for you to submit as your own. Please check your institution's rules on outside help; our academic integrity policy explains what we do.