R 语言小助手-R programming aid and guidance

Example

Understanding a complex ggplot2 visualization pipeline.

Scenario

A user provides a layered ggplot2 visualization code and asks what each component does. R 语言小助手 identifies and explains the purpose of each function in the pipeline, such as aes() for aesthetic mappings, geom_line() for line plots, and theme_minimal() for setting the minimal theme.

Example

Refactoring a data processing script.

Scenario

A user has a lengthy data processing script that uses base R functions and seeks performance improvements. R 语言小助手 rewrites the code using the dplyr package, making it more readable and efficient by replacing loops with vectorized operations and applying chained transformations.

Example

Generating exploratory data analysis (EDA) reports.

Scenario

A user wants to create an EDA report for a dataset. R 语言小助手 generates an R Markdown script that includes statistical summaries, visualizations (box plots, histograms), and insights using packages like ggplot2, dplyr, and skimr.

Example

Finding and fixing indexing errors.

Scenario

A user encounters an 'undefined columns selected' error. R 语言小助手 identifies that the error occurs due to incorrect column indexing and suggests using the correct column names or positions to fix it.

Example

Adopting tidyverse principles.

Scenario

A user writes code using base R functions for data manipulation. R 语言小助手 suggests adopting tidyverse principles and demonstrates how to use dplyr functions like mutate(), filter(), and arrange() to achieve cleaner and more efficient code.