Ravel is an RStudio-native analytics copilot for R users. The MVP focuses on:
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-4-20250514The 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.
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
#>
#>
#> $coefficients
#> # A tibble: 4 × 5
#> term estimate std_error statistic p_value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 31.4 3.02 10.4 4.00e-11
#> 2 wt -3.79 0.786 -4.82 4.55e- 5
#> 3 am 14.9 4.26 3.49 1.62e- 3
#> 4 wt:am -5.30 1.44 -3.67 1.02e- 3
cat(ravel::ravel_interpret_model(fit))
#> Model type: lm.
#> Formula: mpg ~ wt * am.
#> The model explains about 83.3% of the observed variance (R-squared = 0.833).
#> Term `am` has a positive association (estimate = 14.878) with statistical evidence at the 0.05 level.
#> Term `wt:am` has a negative association (estimate = -5.298) with statistical evidence at the 0.05 level.
#> Term `wt` has a negative association (estimate = -3.786) with statistical evidence at the 0.05 level.
#> Interaction terms are present (wt:am), so main effects should be interpreted conditionally.ravel::ravel_suggest_diagnostics(fit)
#> [1] "Inspect residuals versus fitted values for non-linearity and heteroskedasticity."
#> [2] "Review a Q-Q plot of residuals for heavy tails or strong departures from normality."
#> [3] "Check leverage and Cook's distance for influential observations."
#> [4] "Consider multicollinearity diagnostics if predictors are strongly related."action <- ravel::ravel_preview_code("summary(mtcars$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#> ## 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.After installation, launch the chat gadget with:
Use the settings gadget to configure default providers or store API keys:
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