Utilize Lotas' RMarkdown intelligent generation feature to create dynamic documents that meet academic specifications in four steps:
Standard Workflow
- Template Selection: Selection of the "Academic paper" template at startup (with APA/MLA formatting presets)
- Data import: Drag-and-drop import of study data, automatic generation of data dictionaries
- Analytical Modularity: Describe the analysis method in natural language (e.g., "ANOVA test for between-group differences"), and the system generates a code block with explanatory comments.
- Integration of results: Automatically puts the key results in the form of
kableFormat insertion into documents and generation of draft "Interpretation of results" paragraphs
Featured Functions
- Standardization of variable naming: Automatically places the
var1etc. into semanticized names (e.g.blood_pressure) - citation management: Identification
lm()etc., automatically add the corresponding methodology literature references when the function is used. - Labeling of charts: Automatically generate journal-compliant Caption for ggplot2 outputs
quality control
This will be checked using the "Academic Review" function:
1) p-value labeling integrity
2) Error bar display normality
3) Accuracy of sample size description
4) Multi-check calibration tips
Final output in PDF/Word format via RStudio's Knitr with one click.
This answer comes from the articleRao (Lotas): AI code editor to accelerate RStudio workflowsThe
May not be reproduced without permission:AI productivity tools " How to quickly achieve reproducible data analysis reports in academic essay writing?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil