Shodhyantri Engineering Services
Research · Analysis · Editing · Publication Support
Reproducible R scripts, tidy data pipelines and publication-ready graphs for your thesis or paper, with every step explained so you can defend it.
Tell us about your r programming analysis need. We reply within one working day.
R programming data analysis gives scholars something point-and-click software cannot: a written record of every step from raw data to final table. Shodhyantri helps PhD, M.Tech, MBA and Master's scholars plan, run and check their analysis in R and RStudio. We build clean scripts around your research questions, test assumptions properly, and produce tables and graphs that match your university or journal style.
Many scholars start R with a borrowed script and a long list of error messages. We fix that. You share your data, objectives and hypotheses; we agree an analysis plan with you, write commented code, and walk you through the output. The interpretation, the argument and the thesis remain yours. Our role is to make sure the numbers are correct, reproducible and clearly reported, and that you understand them well enough to answer your examiner.
R is a free, open-source language for statistics and graphics, maintained by the R Core Team and extended through thousands of packages on CRAN. RStudio (now from Posit) is the most common editor for it. Because analysis in R is written as code, anyone can rerun it and get the same result, which suits theses, journal reviewers and data-sharing requirements.
A typical thesis analysis in R moves through importing data, cleaning and recoding, descriptive statistics, assumption checks, the main models, diagnostics and reporting. Packages such as tidyverse, psych, lme4, lavaan, survival and ggplot2 cover most social science, health, agriculture and engineering needs.
Every recode, filter and model sits in a script, so you or a reviewer can rerun the analysis months later and get identical results.
We choose models from your objectives and data type, not from habit, and explain why a mixed model or GLM fits better.
Normality, homoscedasticity, multicollinearity and influential cases are tested and reported, so examiners see the model was not run blindly.
ggplot2 graphs are built to your journal's size, font and colour rules and exported as high-resolution TIFF, PNG or PDF.
Scripts carry plain comments for each block, so you can follow the logic, adapt it later and show it if asked.
We hold a walkthrough session on the output so you can interpret coefficients, p-values and effect sizes in your own words.
Ten stages, each with a clear output. Nothing moves to the next stage until you have reviewed the last one.
— You review every stage first, then decide —
Row counts, duplicates, impossible values and coding errors are checked against your questionnaire or codebook.
Each chosen test is checked against the measurement level, design and sample size of your study.
Residual plots, variance checks and multicollinearity diagnostics are reviewed before results are accepted.
The full script is run in a clean R session to confirm results reproduce without hidden objects.
Numbers in tables and text are matched line by line against the raw R console output.
Tables, decimals, italics for statistics and figure captions are aligned with APA or your journal's rules.
Every stage is shared with notes explaining what was changed and why. You and your supervisor check it, and the next stage starts only after your go-ahead.
| Common problem | How Shodhyantri helps |
|---|---|
| Running scripts copied from the internet without understanding what each line does. | We write code for your variables and design, and comment each block so you know what it does. |
| Treating Likert items as factors or as numbers without a stated rationale. | We decide item and composite handling with you and record the choice in the methodology and script. |
| Deleting rows with missing values silently, shrinking the sample without explanation. | Missingness is reported, its pattern examined, and a defensible method such as listwise deletion or imputation chosen. |
| Reporting p-values alone with no effect sizes or confidence intervals. | Every main result is reported with an effect size and confidence interval, as APA and most journals now expect. |
| Saving work in the global environment and losing track of which object is current. | We use RStudio projects and scripts that rebuild everything from raw data, so the environment never needs saving. |
| Exporting graphs at screen resolution that blur when printed in the thesis. | Figures are exported at 300 dpi or higher with set dimensions, fonts and colours suitable for print. |
Store the original file read-only and do all cleaning in code, so you can always trace back to what was collected.
Run sessionInfo() at the end and save it. Package updates can change defaults and occasionally change results.
Short, lower-case names without spaces, such as job_sat_total, make scripts easier to read and reduce typing errors.
Histograms, boxplots and scatterplots often reveal coding errors and skewness that a summary table hides.
Bootstrapping, imputation and cross-validation use random numbers. set.seed() makes those results repeatable for examiners.
Most R errors name the function and the problem. Reading the first line carefully saves hours of guessing.
Cite R, its version and key packages in your methodology chapter. Use citation() to get correct references.
Separate scripts for cleaning, descriptives and each objective keep the analysis easy to review and revise.
Your work is handled by people who read your field's literature, not by a single generalist.
A PhD-qualified specialist from your field who checks the research logic, gap and contribution.
Handles statistics, models and qualitative coding, and documents every analytical decision.
Improves structure, flow, clarity and academic tone without changing your meaning.
Builds and validates MATLAB, Python, ANSYS or COMSOL work where your study needs it.
Checks grammar, consistency, numbering, citations and the small errors examiners notice.
Applies your university or journal template, reference style and front matter exactly.
No. Many scholars come with no R experience. We set up the RStudio project, write commented scripts and explain the output. If you want to learn, we can add training sessions so you can rerun and modify the analysis yourself before your viva.
Yes. We import your SPSS file with labels, reproduce the analysis in R and compare results. Small differences sometimes appear because of different defaults, such as sums of squares type in ANOVA, and we explain each one so you can report it correctly.
No. We prepare the analysis, formatted tables, figures and a walkthrough of what each result means. You write the interpretation and discussion in your own words. We can then review your draft for statistical accuracy and correct reporting of test values.
Descriptive and inferential tests, regression families, mixed effects and multilevel models, factor analysis, SEM with lavaan, survival analysis, time series, meta-analysis with metafor, and machine learning basics. If your design needs something specialised, we confirm feasibility before starting.
No, and no honest analyst can. Results depend on your data, and acceptance depends on journal peer review. We guarantee correct methods, honest reporting and reproducible code. Non-significant findings are valid findings, and we help you report them clearly.
Yes. Your dataset and scripts are used only for your project and are not shared or reused. If your data contains personal identifiers, we suggest removing names and contact details before sending, which is also good practice under most ethics approvals.
Yes. You receive the full commented script and RStudio project. Many scholars place key code in an appendix or a public repository such as OSF or GitHub, which supports transparency and is increasingly requested by journals.
We provide research guidance, statistical analysis, editing, formatting and training support. All research content remains the scholar's own work, in line with UGC and university academic integrity norms. We do not guarantee publication, acceptance, indexing or academic outcomes: journals decide through peer review and universities award degrees.
Share your topic, draft or data. We will tell you honestly what it needs, and what it does not, before you pay anything.