Clean up some lints

R/geom_binomdensity.R

@@ -46,7 +46,7 @@
   insight::check_if_installed(c("ggplot2", "ggdist"))
 
   # Sanitize y (e.g., if levels with no values, etc.)
-  if (is.factor(data[[y]]) && length(levels(data[[y]])) > 2L) {
+  if (is.factor(data[[y]]) && nlevels(data[[y]]) > 2L) {
     data[[y]] <- droplevels(data[[y]])
   }
 
@@ -91,7 +91,7 @@
   if (length(scale) == 1 && is.character(scale) && scale %in% c("density", "proportion", "auto")) {
     # Density instead of proportion
     if (scale == "density") {
       prop <- sapply(split(data, data[[y]]), function(df) {
         max(stats::density(df[[x]], na.rm = TRUE)$y) * nrow(df)
       })
       prop <- prop / sum(prop)

R/plot.equivalence_test.R

@@ -105,12 +105,7 @@
   tmp <- merge(tmp, cp, by = "predictor")
   tmp$predictor <- factor(tmp$predictor, levels = rev(unique(tmp$predictor)))
 
-  has_multiple_panels <-
-    (!"Effects" %in% names(tmp) || length(unique(tmp$Effects)) <= 1L) &&
-      (!"Component" %in% names(tmp) || length(unique(tmp$Component)) <= 1L)
-
-  # check if we have multiple panels
-  if (has_multiple_panels) {
+  if (.has_multiple_panels(tmp)) {
     n_columns <- NULL
   }
 
@@ -121,19 +116,19 @@
 
   # check for user defined arguments
 
   fill.color <- c("#CD423F", "#018F77", "#FCDA3B")
   if (length(unique(tmp$HDI)) > 1L) {
     x.title <- "Highest Density Region of Posterior Samples"
   } else {
     x.title <- sprintf("%g%% Highest Density Region of Posterior Samples", 100 * x$CI[1])
   }
   legend.title <- "Decision on H0"
 
   fill.color <- fill.color[sort(unique(match(x$ROPE_Equivalence, c("Accepted", "Rejected", "Undecided"))))]
 
   add.args <- lapply(match.call(expand.dots = FALSE)$`...`, function(x) x)
   if ("colors" %in% names(add.args)) fill.color <- eval(add.args[["colors"]])
   if ("x.title" %in% names(add.args)) x.title <- eval(add.args[["x.title"]])
   if ("legend.title" %in% names(add.args)) legend.title <- eval(add.args[["legend.title"]])
   if ("labels" %in% names(add.args)) labels <- eval(add.args[["labels"]])
 

R/plot.estimate_density.R

@@ -123,11 +123,15 @@ plot.see_estimate_density <- function(x,
 
 
   if (!inherits(x, "data_plot")) {
-    x <- data_plot(x, data = model, centrality = centrality, ci = ci, ...)
+    x <- data_plot(x,
+      data = model,
+      centrality = centrality,
+      ci = ci,
+      ...
+    )
   }
 
-  if ((!"Effects" %in% names(x) || length(unique(x$Effects)) <= 1) &&
-    (!"Component" %in% names(x) || length(unique(x$Component)) <= 1)) {
+  if (.has_multiple_panels(x)) {
     n_columns <- NULL
   }
 

R/plot.p_direction.R

@@ -174,9 +174,7 @@ plot.see_p_direction <- function(x,
     x <- data_plot(x, data = data, show_intercept = show_intercept)
   }
 
-  # check if we have multiple panels
-  if ((!"Effects" %in% names(x) || length(unique(x$Effects)) <= 1) &&
-    (!"Component" %in% names(x) || length(unique(x$Component)) <= 1)) {
+  if (.has_multiple_panels(x)) {
     n_columns <- NULL
   }
 

R/plot.p_significance.R

@@ -186,9 +186,7 @@ plot.see_p_significance <- function(x,
     x <- data_plot(x, data = data, show_intercept = show_intercept)
   }
 
-  # check if we have multiple panels
-  if ((!"Effects" %in% names(x) || length(unique(x$Effects)) <= 1L) &&
-    (!"Component" %in% names(x) || length(unique(x$Component)) <= 1L)) {
+  if (.has_multiple_panels(x)) {
     n_columns <- NULL
   }
 

R/plot.rope.R

@@ -96,9 +96,7 @@ plot.see_rope <- function(x,
     x <- data_plot(x, data = data, show_intercept = show_intercept)
   }
 
-  # check if we have multiple panels
-  if ((!"Effects" %in% names(x) || length(unique(x$Effects)) <= 1) &&
-    (!"Component" %in% names(x) || length(unique(x$Component)) <= 1)) {
+  if (.has_multiple_panels(x)) {
     n_columns <- NULL
   }
 

R/plots.R

@@ -65,7 +65,7 @@ plots <- function(...,
 
   # Add tags
   if (!is.null(tags)) {
-    if (length(tags) == 1) {
+    if (length(tags) == 1L) {
       if (isTRUE(tags)) {
         tags <- "A"
       } else if (isFALSE(tags) || is.na(tags)) {
@@ -100,13 +100,15 @@ plots <- function(...,
     msg_display1 <- "\n- To fix this issue, please make the window larger."
     msg_display3 <- "\n- If this still doesn't resolve your problems, you may check whether your apps are rescaled. On Windows, this can be done in the display settings (Start > Settings > System > Display, \"Scale and layout\"). Reduce the scaling and try again."
     msg_display4 <- "\n- Finally, you can try to decrease the base font-size of your theme before plotting. Load `library(ggplot2)` and run: `theme_set(theme_classic(base_size = 6))`"
+
     if (Sys.getenv("RSTUDIO") == "1") {
       msg <- "The RStudio 'Plots' window is too small to show this set of plots."
       msg_display2 <- "\n- If this doesn't help, try to reset your zoom settings. In RStudio, go to Menu \"View > Actual Size\" and then retry."
     } else {
       msg <- "The viewport is too small to show this set of plots."
       msg_display2 <- "\n- If this doesn't help, try to reset the zoom settings of your IDE and then retry."
     }
+
     msg <- paste(msg, "You may try one of the following steps to resolve this problem.")
     insight::format_error(msg, msg_display1, msg_display2, msg_display3, msg_display4)
   }

R/utils.R

@@ -27,7 +27,10 @@
   as.numeric(as.character(x))
 }
 
-
+.has_multiple_panels <- function(x) {
+  (!"Effects" %in% names(x) || length(unique(x$Effects)) <= 1L) &&
+    (!"Component" %in% names(x) || length(unique(x$Component)) <= 1L)
+}
 
 .clean_parameter_names <- function(params, grid = FALSE) {
   params <- unique(params)
@@ -91,23 +94,25 @@
 .fix_facet_names <- function(x) {
   if ("Component" %in% names(x)) {
     x$Component <- as.character(x$Component)
-    if (!"Effects" %in% names(x)) {
-      x$Component[x$Component == "conditional"] <- "Conditional"
-      x$Component[x$Component == "zero_inflated"] <- "Zero-Inflated"
-      x$Component[x$Component == "dispersion"] <- "Dispersion"
-      x$Component[x$Component == "simplex"] <- "Monotonic Effects"
-    } else {
+    if ("Effects" %in% names(x)) {
       x$Component[x$Component == "conditional"] <- "(Conditional)"
       x$Component[x$Component == "zero_inflated"] <- "(Zero-Inflated)"
       x$Component[x$Component == "dispersion"] <- "(Dispersion)"
       x$Component[x$Component == "simplex"] <- "(Monotonic Effects)"
+    } else {
+      x$Component[x$Component == "conditional"] <- "Conditional"
+      x$Component[x$Component == "zero_inflated"] <- "Zero-Inflated"
+      x$Component[x$Component == "dispersion"] <- "Dispersion"
+      x$Component[x$Component == "simplex"] <- "Monotonic Effects"
     }
   }
+
   if ("Effects" %in% names(x)) {
     x$Effects <- as.character(x$Effects)
     x$Effects[x$Effects == "fixed"] <- "Fixed Effects"
     x$Effects[x$Effects == "random"] <- "Random Effects"
   }
+
   x
 }
 

tests/testthat/test-plot.estimate_contrasts.R

@@ -4,6 +4,6 @@ test_that("`plot.see_estimate_contrasts()` works", {
 
   model <- stan_glm(Sepal.Width ~ Species, data = iris, refresh = 0)
   contrasts <- modelbased::estimate_contrasts(model, contrast = "Species")
-  means <- modelbased::estimate_means(model, at = c("Species"))
+  means <- modelbased::estimate_means(model, at = "Species")
   expect_s3_class(plot(contrasts, means), "gg")
 })

vignettes/parameters.Rmd

@@ -326,21 +326,27 @@ plot(result)
 
 ## Bayesian Meta-Analysis using brms
 
-```{r}
-# We download the model to save computation time. Here is the code
-# to refit the exact model used below...
+We download the model to save computation time, but here is the code to refit the exact model used below:
 
+```{r, eval=FALSE}
 # Data from
 # https://github.com/MathiasHarrer/Doing-Meta-Analysis-in-R/blob/master/_data/Meta_Analysis_Data.RData
-# set.seed(123)
-# priors <- c(prior(normal(0,1), class = Intercept),
-#             prior(cauchy(0,0.5), class = sd))
-#
-# model <-
-#    brm(TE|se(seTE) ~ 1 + (1|Author),
-#    data = Meta_Analysis_Data,
-#    prior = priors,
-#    iter = 4000)
+set.seed(123)
+priors <- c(
+  prior(normal(0, 1), class = Intercept),
+  prior(cauchy(0, 0.5), class = sd)
+)
+
+model <- brm(
+  TE | se(seTE) ~ 1 + (1 | Author),
+  data = Meta_Analysis_Data,
+  prior = priors,
+  iter = 4000
+)
+```
+
+
+```{r}
 library(brms)
 model <- insight::download_model("brms_meta_1")
 result <- model_parameters(model)