Use cli instead of crayon. (fix spacing

DESCRIPTION

@@ -18,7 +18,6 @@ License: MIT + file LICENSE
 Encoding: UTF-8
 Imports:
   ggplot2 (>= 3.5.0),
-  crayon,
   cli,
   generics,
   jtools (>= 2.0.3),

NAMESPACE

@@ -37,11 +37,6 @@ import(jtools)
 import(rlang)
 importFrom(cli,cat_rule)
 importFrom(cli,rule)
-importFrom(crayon,bold)
-importFrom(crayon,inverse)
-importFrom(crayon,italic)
-importFrom(crayon,red)
-importFrom(crayon,underline)
 importFrom(generics,glance)
 importFrom(generics,tidy)
 importFrom(stats,aggregate)

R/interact_plot.R

@@ -443,11 +443,14 @@ interact_plot <- function(model, pred, modx, modx.values = NULL, mod2 = NULL,
   # Check for factor predictor and send to plot_cat() if so
   if (!is.numeric(d[[pred]])) {
     # Warn users that this is kinda janky
-    msg_wrap("Detected factor predictor. Plotting with cat_plot() instead.
-              Consult cat_plot() documentation (?cat_plot) for full details
-              on how to specify models with categorical predictors. If you 
-              experience errors or unexpected results, try using cat_plot() 
-              directly.")
+    cli::cli_inform(c(
+      x = "Detected factor predictor.",
+      i = "Plotting with cat_plot() instead.",
+      i = "See {.help interactions::cat_plot} for full details on 
+         how to specify models with categorical predictors.",
+      i = "If you  experience errors or unexpected results, try using 
+           cat_plot() directly."
+    ))
     # Gather arguments for plot_cat()
     args <- list(predictions = pm, pred = pred, modx = modx, mod2 = mod2,
                   data = d, modx.values = modxvals2, mod2.values = mod2vals2,

R/johnson_neyman.R

@@ -603,17 +603,16 @@ johnson_neyman <- function(model, pred, modx, vmat = NULL, alpha = 0.05,
 }
 
 #' @export
-#' @importFrom crayon bold inverse underline
 
 print.johnson_neyman <- function(x, ...) {
 
   atts <- attributes(x)
 
   # Describe whether sig values are inside/outside the interval
   if (atts$inside == FALSE) {
-    inout <- inverse("OUTSIDE")
+    inout <- cli::style_inverse("OUTSIDE")
   } else {
-    inout <- inverse("INSIDE")
+    inout <- cli::style_inverse("INSIDE")
   }
 
   b_format <- num_print(x$bounds, atts$digits)
@@ -622,13 +621,16 @@ print.johnson_neyman <- function(x, ...) {
   pmsg <- paste("p <", alpha)
 
   # Print the output
-  cat(bold(underline("JOHNSON-NEYMAN INTERVAL")), "\n\n")
+  cli::cat_line(
+    cli::style_bold(cli::style_underline("JOHNSON-NEYMAN INTERVAL")),
+    "\n"
+  )
   if (all(is.finite(x$bounds))) {
     cat_wrap("When ", atts$modx, " is ", inout, " the interval [",
              b_format[1], ", ", b_format[2], "], the slope of ", atts$pred,
              " is ", pmsg, ".", brk = "\n\n")
-    cat_wrap(italic("Note: The range of observed values of", atts$modx,
-        "is "), "[", m_range[1], ", ", m_range[2], "]", brk = "\n\n")
+    cat_wrap(cli::style_italic("Note: The range of observed values of ", atts$modx,
+        " is "), "[", m_range[1], ", ", m_range[2], "]", brk = "\n\n")
   } else {
     cat_wrap("The Johnson-Neyman interval could not be found.
         Is the p value for your interaction term below

R/simple_margins.R

@@ -247,7 +247,6 @@ sim_margins <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
 
 #' @export
 #' @importFrom cli cat_rule rule
-#' @importFrom crayon red bold italic
 
 print.sim_margins <- function(x, ...) {
 
@@ -308,7 +307,10 @@ print.sim_margins <- function(x, ...) {
     }
 
     # Clearly label simple slopes
-    cat(bold(underline("SIMPLE MARGINS")), "\n\n")
+    cli::cat_line(
+      cli::style_bold(cli::style_underline("SIMPLE MARGINS")),
+      "\n"
+    )
 
     for (i in seq_along(x$modx.values)) {
 
@@ -329,8 +331,8 @@ print.sim_margins <- function(x, ...) {
         modx_label <- paste0(m[i, x$modx])
       }
 
-      cat(italic(paste0("Average marginal effect of ", x$pred, " when ",
-                        x$modx, " = ", modx_label, ": \n\n")))
+      cli::cat_line(cli::style_italic(paste0("Average marginal effect of ", x$pred, " when ",
+                        x$modx, " = ", modx_label, ": \n")))
       print(md_table(slopes, digits = x$digits, format = "pandoc",
                      row.names = FALSE, sig.digits = FALSE))
 

R/simple_slopes.R

@@ -764,7 +764,6 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
 
 #' @export
 #' @importFrom cli cat_rule rule
-#' @importFrom crayon red bold italic
 
 print.sim_slopes <- function(x, ...) {
 
@@ -848,7 +847,10 @@ print.sim_slopes <- function(x, ...) {
     }
 
     # Clearly label simple slopes
-    cat(bold(underline("SIMPLE SLOPES ANALYSIS")), "\n\n")
+    cli::cat_line(
+      cli::style_bold(cli::style_underline("SIMPLE SLOPES ANALYSIS")),
+      "\n"
+    )
 
     for (i in seq_along(x$modx.values)) {
 
@@ -871,7 +873,7 @@ print.sim_slopes <- function(x, ...) {
       # Print conditional intercept
       if (x$cond.int == TRUE | fac_pred == TRUE) {
         pred_lab <- if (fac_pred) {slopes[["Coef."]]} else {x$pred}
-        cat(italic(paste0("When ", x$modx, " = ", modx_label, ": \n\n")))
+        cli::cat_line(cli::style_italic(paste0("When ", x$modx, " = ", modx_label, ": \n")))
         if (x$cond.int) {
           ints <- m$ints[i,2:ncol(m$slopes)]
           slopes <- as.data.frame(rbind(slopes, ints))
@@ -884,8 +886,8 @@ print.sim_slopes <- function(x, ...) {
         print(md_table(slopes %not% "Coef.", digits = x$digits,
                        format = "pandoc", sig.digits = FALSE))
       } else {
-        cat(italic(paste0("Slope of ", x$pred, " when ", x$modx, " = ",
-                          modx_label, ": \n\n")))
+        cli::cat_line(cli::style_italic(paste0("Slope of ", x$pred, " when ", x$modx, " = ",
+                          modx_label, ": \n")))
         print(md_table(slopes, digits = x$digits, format = "pandoc",
                        row.names = FALSE, sig.digits = FALSE))
       }