Collect roxygen import tags in a single location

NAMESPACE

@@ -196,4 +196,3 @@ export(theme_modern)
 export(theme_radar)
 export(theme_radar_dark)
 import(ggplot2)
-importFrom(ggplot2,.data)

R/geom_violindot.R

@@ -17,7 +17,7 @@
 #' ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
 #'   geom_violindot() +
 #'   theme_modern()
-#' @import ggplot2
+#'
 #' @export
 geom_violindot <- function(mapping = NULL,
                            data = NULL,

R/geom_violinhalf.R

@@ -31,7 +31,7 @@
 #'   geom_violinhalf(flip = c(1, 3)) +
 #'   theme_modern() +
 #'   scale_fill_material_d()
-#' @import ggplot2
+#'
 #' @export
 geom_violinhalf <- function(mapping = NULL,
                             data = NULL,
@@ -68,7 +68,7 @@ geom_violinhalf <- function(mapping = NULL,
 #' @rdname ggplot2-ggproto
 #' @format NULL
 #' @usage NULL
-#' @import ggplot2
+#'
 #' @keywords internal
 GeomViolinHalf <- ggproto("GeomViolinHalf", Geom,
   extra_params = c("na.rm", "flip"),

R/plot.bayesfactor_parameters.R

@@ -15,7 +15,7 @@
 #'
 #' @return A ggplot2-object.
 #'
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_bayesfactor_parameters <- function(x,
                                             size_point = 2,

R/plot.binned_residuals.R

@@ -1,4 +1,4 @@
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_binned_residuals <- function(x,
                                       size_line = 0.7,

R/plot.check_collinearity.R

@@ -13,7 +13,7 @@
 #' result <- check_collinearity(m)
 #' result
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_check_collinearity <- function(x,
                                         data = NULL,
@@ -119,7 +119,7 @@
       color = NA,
       alpha = 0.15
     ) +
     {
       if (!is.null(ci_data)) {
         list(
           ggplot2::geom_linerange(

R/plot.check_homogeneity.R

@@ -12,7 +12,7 @@
 #' result <- check_homogeneity(model)
 #' result
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_check_homogeneity <- function(x, data = NULL, ...) {
   if (is.null(data)) {

R/plot.check_model.R

@@ -9,14 +9,14 @@
 #'
 #' @return A ggplot2-object.
 #'
 #' @seealso See also the vignette about [`check_model()`](https://easystats.github.io/performance/articles/check_model.html).
 #'
 #' @examplesIf require("performance") && require("patchwork")
 #' model <- lm(qsec ~ drat + wt, data = mtcars)
 #' plot(check_model(model))
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_check_model <- function(x,
                                  style = theme_lucid,
                                  colors = NULL,
                                  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
@@ -40,7 +40,7 @@
   overdisp_type <- attr(x, "overdisp_type")
   plot_type <- attr(x, "type")
 
   if (missing(type) && !is.null(plot_type) && plot_type %in% c("density", "discrete_dots", "discrete_interval", "discrete_both")) {
     type <- plot_type
   } else {
     type <- match.arg(type)

R/plot.compare_performance.R

@@ -40,10 +40,10 @@
   dataplot$values[dataplot$values > 1] <- 1
 
   attr(dataplot, "info") <- list(
     "xlab" = "",
     "ylab" = "",
     "title" = "Comparison of Model Indices",
     "legend_color" = "Models"
   )
 
   class(dataplot) <- c("data_plot", "see_compare_performance", "data.frame")
@@ -72,7 +72,7 @@
 #' result <- compare_performance(lm1, lm2, lm3)
 #' result
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_compare_performance <- function(x, size_line = 1, ...) {
   # We may think of plotting the "performance scores" as bar plots,

R/plot.estimate_contrasts.R

@@ -16,9 +16,9 @@
   dataplot <- .data_contrasts_and_means(x, means, x_name = x_name, y_name = y_name)
 
   attr(dataplot, "info") <- list(
     "xlab" = x_name,
     "ylab" = y_name,
     "title" = paste0("Estimated ", y_name, "s and Contrasts")
   )
 
   class(dataplot) <- c("data_plot", "see_estimate_contrasts", class(dataplot))
@@ -76,7 +76,7 @@
 #' means <- estimate_means(model)
 #' plot(contrasts, means)
 #' }
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_estimate_contrasts <- function(x, data = NULL, ...) {
   if (!inherits(x, "data_plot")) {

R/plot.estimate_density.R

@@ -96,7 +96,7 @@ data_plot.estimate_density <- function(x,
 #' m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
 #' result <- estimate_density(m)
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_estimate_density <- function(x,
                                       stack = TRUE,
@@ -244,7 +244,7 @@ plot.see_estimate_density <- function(x,
 data_plot.estimate_density_df <- data_plot.estimate_density
 
 
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_estimate_density_df <- function(x,
                                          stack = TRUE,

R/plot.hdi.R

@@ -202,7 +202,7 @@ data_plot.bayestestR_eti <- data_plot.hdi
 #' result <- hdi(m)
 #' result
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_hdi <- function(x,
                          data = NULL,

R/plot.n_factors.R

@@ -84,7 +84,7 @@ data_plot.n_clusters <- data_plot.n_factors
 #' result <- n_factors(mtcars, type = "PCA")
 #' result
 #' plot(result, type = "line")
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_n_factors <- function(x,
                                data = NULL,

R/plot.p_direction.R

@@ -157,7 +157,7 @@ data_plot.p_direction <- function(x, data = NULL, show_intercept = FALSE, ...) {
 #' m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
 #' result <- p_direction(m)
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_p_direction <- function(x,
                                  data = NULL,

R/plot.p_significance.R

@@ -169,7 +169,7 @@ data_plot.p_significance <- function(x,
 #' m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
 #' result <- p_significance(m)
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_p_significance <- function(x,
                                     data = NULL,

R/plot.parameters_pca.R

@@ -73,7 +73,7 @@ data_plot.parameters_efa <- data_plot.parameters_pca
 #' result <- principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
 #' result
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_parameters_pca <- function(x,
                                     type = c("bar", "line"),

R/plot.parameters_sem.R

@@ -86,7 +86,7 @@ data_plot.parameters_sem <- function(x,
 #' @param threshold_coefficient Numeric, threshold at which value coefficients will be displayed.
 #' @param threshold_p Numeric, threshold at which value p-values will be displayed.
 #' @param ci Logical, whether confidence intervals should be added to the plot.
-#' @importFrom ggplot2 .data
+#'
 #' @rdname plot.see_parameters_model
 #' @export
 plot.see_parameters_sem <- function(x,

R/plot.point_estimates.R

@@ -98,7 +98,7 @@ data_plot.map_estimate <- data_plot.point_estimate
 #' result <- point_estimate(m, centrality = "median")
 #' result
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_point_estimate <- function(x,
                                     data = NULL,

R/plot.rope.R

@@ -83,7 +83,7 @@ data_plot.rope <- function(x, data = NULL, show_intercept = FALSE, ...) {
 #' result <- rope(m)
 #' result
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_rope <- function(x,
                           data = NULL,

R/plot.si.R

@@ -20,7 +20,7 @@
 #' result <- si(m)
 #' result
 #' plot(result)
-#' @importFrom ggplot2 .data
+#'
 #' @export
 plot.see_si <- function(x,
                         si_color = "#0171D3",

R/see-package.R

@@ -17,6 +17,13 @@
 #' @keywords internal
 "_PACKAGE"
 
+## see namespace: start
+##
+#' @import ggplot2
+#'
+## see namespace: end
+NULL
+
 # Suppress R CMD check note
 # Namespace in Imports field not imported from: PKG
 #   All declared Imports should be used.