Clean, commented R scripts
Every step from importing raw data to the final table is written as tidy, commented R code, so you, your guide or a reviewer can follow and re-run the full analysis.
Data & analytics
Statistical Analysis in R
Reproducible statistical analysis in R, with clean scripts and reports you can re-run anytime.
We analyse research and business data in R and RStudio, from regression and ANOVA to mixed models, time series and survival analysis. You receive publication-ready ggplot2 figures, an R Markdown or Quarto report and the full scripts behind every result.
- PhD scholars whose guide or university prefers R
- Researchers revising a paper after reviewer comments
- Medical, nursing and life science studies with survival or repeated-measures data
Overview
Why it matters
R is the statistics language used by many researchers, journals and universities, and for good reason. It handles everything from a simple t-test to complex mixed-effects and survival models, it produces publication-quality graphs, and every step is written down as code. That last point matters most. When your guide, a reviewer or you yourself want to check how a number was produced, an R script shows the exact path from raw data to final table, which point-and-click software cannot always do.
We work in RStudio using the tidyverse for data cleaning and ggplot2 for figures. Depending on your design, we run linear and logistic regression, ANOVA and ANCOVA, linear mixed models for repeated measures or nested data, time series models such as ARIMA for monthly or yearly data, and survival analysis with Kaplan-Meier curves and Cox regression. We check model assumptions, report effect sizes and confidence intervals, and explain the results in plain language alongside the statistical output.
Everything is delivered as a reproducible report built with R Markdown or Quarto, with code, output and explanation together in one document, plus the clean scripts and data files. If your data changes or a reviewer asks for one more analysis, the scripts can be re-run in minutes. For academic work, we analyse only your own data and never change values to reach a desired result. You get a fixed written quote after a short call, and the explanation session is included.
Data & analytics
What you get with us
Regression and ANOVA
Linear, logistic and multiple regression, ANOVA, ANCOVA and post-hoc tests, with assumption checks, effect sizes and confidence intervals reported the way journals expect.
Mixed and multilevel models
Linear and generalised mixed models for repeated measures, students within schools or patients within hospitals, using lme4 and related packages, with results explained step by step.
Time series and forecasting
Trend, seasonality and ARIMA-type models for monthly sales, rainfall, prices or admissions data, with forecast intervals that show the real uncertainty in each prediction.
Survival analysis
Kaplan-Meier curves, log-rank tests and Cox proportional hazards models for medical, nursing and reliability studies, with hazard ratios and assumption checks clearly reported.
R Markdown and Quarto reports
Code, output, figures and explanation combined in one reproducible Word, PDF or HTML report, so every number in your chapter or paper can be traced to its source.
Step by step
How we work
- 1
Discuss design
We go through your objectives, hypotheses, study design and variables, and confirm which analyses suit them.
- 2
Data check & quote
We review your dataset, flag issues, propose an analysis plan and send a fixed written quote with timeline.
- 3
Clean & explore
We import and tidy the data in R, run descriptive summaries and exploratory plots, and share them for your review.
- 4
Model & report
We fit the agreed models, check assumptions, and prepare ggplot2 figures and a reproducible R Markdown or Quarto report.
- 5
Explain & hand over
We walk you through the scripts and results on a call and hand over all code, data and output files.
Checklist
What's included
- Commented R scripts covering cleaning, analysis and figures
- Cleaned dataset with a variable description sheet
- R Markdown or Quarto report in Word, PDF or HTML
- Publication-ready ggplot2 figures in high resolution
- Results tables formatted in APA or journal style
- Assumption checks and diagnostic plots
- Plain-language interpretation of each result
- Explanation call and one round of clarifications
FAQ
Questions people ask
Still unsure? Message us on WhatsApp and we will answer the same day.
I do not know R. Will I be able to use the scripts?
Yes. The scripts are commented line by line, and we show you how to open them in RStudio and run them. Even if you never edit the code, the report explains every result in plain language.
Why choose R over SPSS?
R handles advanced models such as mixed models and survival analysis well, produces high-quality figures, and keeps a complete record of every step. If your university or guide prefers SPSS, we can do the analysis there instead.
Can you re-run the analysis if a reviewer asks for changes?
Yes. Because the analysis is scripted, changes such as adding a covariate or excluding a group are quick. Small revisions linked to the original analysis are usually included; new analyses are quoted separately.
Will you change my data to get significant results?
No. We never fabricate, alter or selectively remove data to reach a desired result. Any outliers or exclusions are decided on clear rules, documented in the script and reported in your write-up.
In which format will I get the figures?
Figures are exported as high-resolution PNG, TIFF or PDF, sized and labelled to your journal's or university's requirements. We can match a specific font or colour scheme if needed.
Can you help me learn R as well?
Yes. Along with the analysis, we can walk you through the key parts of the code. For structured learning, our data science training covers R and Python from the basics.
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Ready to get started?
Share your objectives and a sample of your data, and we will propose an R analysis plan and fixed written quote after a short call.