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Ravel MVP Walkthrough

Source: vignettes/ravel-mvp.Rmd
ravel-mvp.Rmd

What Ravel is

Ravel is an RStudio-native analytics copilot for R users. The MVP focuses on:

  • context-aware chat orchestration
  • provider abstraction
  • safe code staging and execution
  • model interpretation helpers
  • Quarto drafting helpers

Provider support 

ravel::ravel_list_providers()
#> # A tibble: 4 × 4
#>   provider  label          auth_modes default_model           
#>   <chr>     <chr>          <I<list>>  <chr>                   
#> 1 openai    OpenAI         <chr [2]>  gpt-5.3-codex           
#> 2 copilot   GitHub Copilot <chr [2]>  copilot-cli             
#> 3 gemini    Gemini         <chr [2]>  gemini-2.5-pro          
#> 4 anthropic Anthropic      <chr [1]>  claude-sonnet-4-20250514

The provider layer keeps auth and capability boundaries explicit. OpenAI, Gemini, and Anthropic can run over official HTTP APIs. GitHub Copilot is kept behind the official gh copilot CLI surface when available.

Analysis-aware helpers 

fit <- lm(mpg ~ wt * am, data = mtcars)
ravel::ravel_summarize_model(fit)
#> $name
#> [1] "fit"
#> 
#> $class
#> [1] "lm"
#> 
#> $formula
#> [1] "mpg ~ wt * am"
#> 
#> $fit
#> $fit$n
#> [1] 32
#> 
#> $fit$aic
#> [1] 157.476
#> 
#> $fit$bic
#> [1] 164.8046
#> 
#> $fit$r_squared
#> [1] 0.8330375
#> 
#> $fit$adj_r_squared
#> [1] 0.8151486
#> 
#> $fit$sigma
#> [1] 2.591247
#> 
#> 
#> $coefficients
#> # A tibble: 4 × 5
#>   term        estimate std_error statistic  p_value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)    31.4      3.02      10.4  4.00e-11
#> 2 wt             -3.79     0.786     -4.82 4.55e- 5
#> 3 am             14.9      4.26       3.49 1.62e- 3
#> 4 wt:am          -5.30     1.44      -3.67 1.02e- 3
cat(ravel::ravel_interpret_model(fit))
#> Model type: lm.
#> Formula: mpg ~ wt * am.
#> The model explains about 83.3% of the observed variance (R-squared = 0.833).
#> Term `am` has a positive association (estimate = 14.878) with statistical evidence at the 0.05 level.
#> Term `wt:am` has a negative association (estimate = -5.298) with statistical evidence at the 0.05 level.
#> Term `wt` has a negative association (estimate = -3.786) with statistical evidence at the 0.05 level.
#> Interaction terms are present (wt:am), so main effects should be interpreted conditionally.
ravel::ravel_suggest_diagnostics(fit)
#> [1] "Inspect residuals versus fitted values for non-linearity and heteroskedasticity."   
#> [2] "Review a Q-Q plot of residuals for heavy tails or strong departures from normality."
#> [3] "Check leverage and Cook's distance for influential observations."                   
#> [4] "Consider multicollinearity diagnostics if predictors are strongly related."

Safe execution 

action <- ravel::ravel_preview_code("summary(mtcars$mpg)")
approved <- ravel::ravel_approve_action(action)
ravel::ravel_run_code(approved)
#> $success
#> [1] TRUE
#> 
#> $output
#> [1] "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. "
#> [2] "  10.40   15.43   19.20   20.09   22.80   33.90 "
#> 
#> $warnings
#> character(0)
#> 
#> $messages
#> character(0)
#> 
#> $value
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   10.40   15.43   19.20   20.09   22.80   33.90 
#> 
#> $error
#> NULL

Quarto drafting 

cat(ravel::ravel_draft_quarto_section("diagnostics", model = fit))
#> ## Diagnostics
#> 
#> ```{r diagnostics}
#> # Produce diagnostic plots or checks here
#> ```
#> 
#> Model type: lm.
#> Formula: mpg ~ wt * am.
#> The model explains about 83.3% of the observed variance (R-squared = 0.833).
#> Term `am` has a positive association (estimate = 14.878) with statistical evidence at the 0.05 level.
#> Term `wt:am` has a negative association (estimate = -5.298) with statistical evidence at the 0.05 level.
#> Term `wt` has a negative association (estimate = -3.786) with statistical evidence at the 0.05 level.
#> Interaction terms are present (wt:am), so main effects should be interpreted conditionally.
#> 
#> Suggested checks:
#> - Inspect residuals versus fitted values for non-linearity and heteroskedasticity.
#> - Review a Q-Q plot of residuals for heavy tails or strong departures from normality.
#> - Check leverage and Cook's distance for influential observations.
#> - Consider multicollinearity diagnostics if predictors are strongly related.

RStudio addin

After installation, launch the chat gadget with:

Use the settings gadget to configure default providers or store API keys: