fix: R CMD CHECK and lint errors

- Render roxygen documentation
- Export the new functions
- Fix lint errors (line length, mainly)
- Prevent problem with empty range interpolation (seq_len)
- Use explicit exports for external functions (stats)
- Use .data to prevent unbound variable warnings

Author: adamblake

NAMESPACE

@@ -18,6 +18,8 @@ export(fVal)
 export(fit_stats)
 export(fitstats)
 export(gf_model)
+export(gf_resid)
+export(gf_squaresid)
 export(lower)
 export(middle)
 export(p)

R/gf_model.R

@@ -190,7 +190,7 @@ gf_model <- function(object, model, ...) {
   grid[info$model$outcome] <- stats::predict(info$model$fit, newdata = grid)
   info$layer$args$data <- grid
 
-  return(do.call(info$layer$plotter, info$layer$args))
+  do.call(info$layer$plotter, info$layer$args)
 }
 
 fortify_layer <- function(object, ...) {

R/gf_resid_gf_squaresid.R

@@ -1,17 +1,19 @@
 #' Add Residual Lines to a Plot
 #'
-#' This function adds vertical lines representing residuals from a linear model to a ggformula plot. 
+#' This function adds vertical lines representing residuals from a linear model to a ggformula plot.
 #' The residuals are drawn from the observed data points to the predicted values from the model.
 #'
 #' @param plot A ggformula plot object, typically created with `gf_point()`.
 #' @param model A fitted linear model object created using `lm()`.
 #' @param linewidth A numeric value specifying the width of the residual lines. Default is `0.2`.
-#' @param ... Additional aesthetics passed to `geom_segment()`, such as `color`, `alpha`, `linetype`.
+#' @param ... Additional aesthetics passed to `geom_segment()`, such as `color`, `alpha`,
+#'   `linetype`.
 #'
 #' @return A ggplot object with residual lines added.
 #'
 #' @export
 #' @examples
+#' Height_model <- lm(Thumb ~ Height, data = Fingers)
 #' gf_point(Thumb ~ Height, data = Fingers) %>%
 #'   gf_model(Height_model) %>%
 #'   gf_resid(Height_model, color = "red", alpha = 0.5)
@@ -22,9 +24,9 @@ gf_resid <- function(plot, model, linewidth = 0.2, ...) {
 
   # Get model predictions and residuals and assign them to the model data
   model_data <- model$model
-  model_data$prediction <- predict(model)
-  model_data$residual <- resid(model)
-  
+  model_data$prediction <- stats::predict(model)
+  model_data$residual <- stats::resid(model)
+
   # Access the x and y coordinates used in the plot
   plot_data <- ggplot_build(plot)$data[[1]]
   x_loc <- plot_data$x
@@ -33,73 +35,77 @@ gf_resid <- function(plot, model, linewidth = 0.2, ...) {
   # Ensures same jitter as the x and y coord from plot
   set.seed(rand_int)
   plot +
-    geom_segment(aes(
-      x = x_loc,
-      y = model_data$prediction,
-      xend = x_loc,
-      yend = y_loc
-    ),
-    inherit.aes = TRUE,
-    linewidth = linewidth,
-    ...
+    geom_segment(
+      aes(
+        x = x_loc,
+        y = model_data$prediction,
+        xend = x_loc,
+        yend = y_loc
+      ),
+      inherit.aes = TRUE,
+      linewidth = linewidth,
+      ...
     )
 }
 
 #' Add Squared Residual Visualization to a Plot
 #'
-#' This function adds squared residual representations to a ggformula plot, illustrating squared error as a polygon.
-#' The function dynamically adjusts the aspect ratio to ensure proper scaling of squares.
+#' This function adds squared residual representations to a ggformula plot, illustrating
+#' squared error as a polygon. The function dynamically adjusts the aspect ratio to ensure
+#' proper scaling of squares.
 #'
 #' @param plot A ggformula plot object, typically created with `gf_point()`.
 #' @param model A fitted linear model object created using `lm()`.
 #' @param aspect A numeric value controlling the square's aspect ratio. Default is `4/6`.
-#' @param alpha A numeric value specifying the transparency level of the polygon fill. Default is `0.1`.
+#' @param alpha A numeric value specifying the transparency of the square's fill. Default is `0.1`.
 #' @param ... Additional aesthetics passed to `geom_polygon()`, such as `color` and `fill`.
 #'
 #' @return A ggplot object with squared residuals added.
 #'
 #' @export
 #' @examples
+#' Height_model <- lm(Thumb ~ Height, data = Fingers)
 #' gf_point(Thumb ~ Height, data = Fingers) %>%
 #'   gf_model(Height_model) %>%
 #'   gf_squaresid(Height_model, color = "blue", alpha = 0.5)
-gf_squaresid <- function(plot, model, aspect = 4/6, alpha = 0.1, ...) {
+gf_squaresid <- function(plot, model, aspect = 4 / 6, alpha = 0.1, ...) {
   # Handles random jitter
   rand_int <- sample(1:100, 1)
   set.seed(rand_int)
-  
+
   # Get model predictions and residuals and assign them to the model data
   model_data <- model$model
-  model_data$prediction <- predict(model)
-  model_data$residual <- resid(model)
-  
+  model_data$prediction <- stats::predict(model)
+  model_data$residual <- stats::resid(model)
+
   # Access the x and y coordinates used in the plot
   plot_data <- ggplot_build(plot)$data[[1]]
   model_data$x_loc <- plot_data$x
   model_data$y_loc <- plot_data$y
-  
+
   # Access the range of x and y used in the panel
   plot_layout <- ggplot_build(plot)$layout
   panel_params <- plot_layout$panel_params[[1]]
   x_range <- panel_params$x.range
   y_range <- panel_params$y.range
-  
+
   # Compute ratio for proper aspect scaling
   range_ratio <- (x_range[2] - x_range[1]) / (y_range[2] - y_range[1])
   model_data$dir <- ifelse(model_data$x_loc > mean(x_range), -1, 1)
-  model_data$adj_side <- model_data$x_loc + model_data$dir * abs(model_data$residual * aspect * range_ratio)
-  
+  side_length <- abs(model_data$residual) * aspect * range_ratio
+  model_data$adj_side <- model_data$x_loc + model_data$dir * side_length
+
   # Create a dataframe for plotting polygons
-  squares_data <- do.call(rbind, lapply(1:nrow(model_data), function(i) {
+  squares_data <- do.call(rbind, lapply(seq_len(nrow(model_data)), function(i) {
     resid_side <- model_data$x_loc[i]
     top <- model_data$prediction[i]
     bottom <- model_data$y_loc[i]
     opp_side <- model_data$adj_side[i]
-    
+
     data.frame(
       x = c(resid_side, opp_side, opp_side, resid_side),
       y = c(bottom, bottom, top, top),
-      id = i  # Unique identifier for each square
+      id = i # Unique identifier for each square
     )
   }))
 
@@ -108,7 +114,7 @@ gf_squaresid <- function(plot, model, aspect = 4/6, alpha = 0.1, ...) {
   plot +
     geom_polygon(
       data = squares_data,
-      aes(x = x, y = y, group = id),
+      aes(x = .data$x, y = .data$y, group = .data$id),
       inherit.aes = FALSE,
       alpha = alpha,
       ...