diff --git a/R/int_utils.R b/R/int_utils.R
index ed5bf9e..20a8861 100644
--- a/R/int_utils.R
+++ b/R/int_utils.R
@@ -422,7 +422,7 @@ center_ss_non_survey <- function(d, weights, facvars = NULL, fvars, pred,
     # Dealing with two-level factors that aren't part
     # of an interaction/focal pred
     for (v in fv2) {
-      if (is.factor(d[[v]]) & length(unique(d[[v]])) == 2) {
+      if (is.factor(d[[v]]) && length(unique(d[[v]])) == 2) {
 
         facvars <- c(facvars, v)
 
diff --git a/R/johnson_neyman.R b/R/johnson_neyman.R
index 6c94815..514aff4 100644
--- a/R/johnson_neyman.R
+++ b/R/johnson_neyman.R
@@ -190,7 +190,7 @@ johnson_neyman <- function(model, pred, modx, vmat = NULL, alpha = 0.05,
   # Construct interaction term
   ## Create helper function to use either fixef() or coef() depending on input
   get_coef <- function(mod) {
-    if (inherits(mod, "merMod") | inherits(mod, "brmsfit")) {
+    if (inherits(mod, "merMod") || inherits(mod, "brmsfit")) {
       coef <- lme4::fixef(model)
       if (inherits(mod, "brmsfit")) {
         coefs <- coef[,1, drop = TRUE]
@@ -307,7 +307,7 @@ johnson_neyman <- function(model, pred, modx, vmat = NULL, alpha = 0.05,
     test <- 0
     i <- 1 + length(marginal_effects)
 
-    while (test == 0 & i > 1) {
+    while (test == 0 && i > 1) {
 
       i <- i - 1
       test <- min(ps[ps_o][1:i] <= multipliers[i] * (alpha * 2))
@@ -557,7 +557,7 @@ johnson_neyman <- function(model, pred, modx, vmat = NULL, alpha = 0.05,
       ggplot2::geom_segment(ggplot2::aes(x = modrangeo[1], xend = modrangeo[2],
                                        y = 0, yend = 0,
                                        linetype = "Range of\nobserved\ndata"),
-                          lineend = "square", size = 1.25)
+                          lineend = "square", linewidth = 1.25)
   }
 
     # Adding this scale allows me to have consistent ordering
diff --git a/R/simple_slopes.R b/R/simple_slopes.R
index eb846d9..7db31ad 100644
--- a/R/simple_slopes.R
+++ b/R/simple_slopes.R
@@ -240,6 +240,7 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
     of model. If you are using a package like glmmTMB or other mixed modeling 
     packages, install and load the broom.mixed package and try again. Make sure
     you have the broom package installed and loaded otherwise."))
+
     pred_names <- pred_names %just% tidied$term
     if (length(pred_names) == 0) {
       stop_wrap("Could not find the focal predictor in the model. If it was
@@ -283,14 +284,14 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
                         modx.labels = modx.labels,
                         any.mod2 = !is.null(mod2), sims = TRUE)
 
-  if ((pred_factor | !is.numeric(d[[modx]])) & johnson_neyman == TRUE) {
+  if ((pred_factor || !is.numeric(d[[modx]])) && johnson_neyman == TRUE) {
         warn_wrap("Johnson-Neyman intervals are not available for factor
                    predictors or moderators.", call. = FALSE)
         johnson_neyman <- FALSE
   }
 
   # Now specify def or not (for labeling w/ print method)
-  if (is.character(modx.values) | is.null(modx.values) | !is.null(modx.labels)) {
+  if (is.character(modx.values) || is.null(modx.values) || !is.null(modx.labels)) {
 
     ss <- structure(ss, def = TRUE)
 
@@ -326,7 +327,7 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
     }
 
     # Now specify def or not
-    if (is.character(mod2.values) | is.null(mod2.values) | !is.null(mod2.labels)) {
+    if (is.character(mod2.values) || is.null(mod2.values) || !is.null(mod2.labels)) {
 
       ss <- structure(ss, def2 = TRUE)
 
@@ -411,7 +412,7 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
 
   }
 
-  # Looping through (perhaps non-existent)
+  # Looping through (perhaps non-existent) second moderator values
   for (j in seq_len(mod2val_len)) {
 
     # We don't want to do the J-N interval with the 1st moderator adjusted,
@@ -456,7 +457,7 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
     }
 
     # Getting SEs, robust or otherwise
-    if (robust != FALSE & is.null(v.cov)) {
+    if (robust != FALSE && is.null(v.cov)) {
       # For J-N
       covmat <- get_robust_se(newmod, robust, cluster, dt)$vcov
     } else if (is.null(v.cov)) {
@@ -545,7 +546,7 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
       } else {
         covmat <- NULL
       }
-      # Use j_summ to get the coefficients
+      # Use summ to get the coefficients
       if (has_summ) {
         sum <- summ(newmod, robust = robust, model.fit = FALSE,
                     confint = TRUE, ci.width = ci.width, vifs = FALSE,
@@ -554,8 +555,6 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
       } else {
         sum <- generics::tidy(newmod, conf.int = TRUE, conf.level = ci.width)
       }
-
-
     } else {
       if (is.null(v.cov)) {
         # For J-N
@@ -587,7 +586,6 @@ sim_slopes <- function(model, pred, modx, mod2 = NULL, modx.values = NULL,
       intp <- summat[summat$term == "(Intercept)", ]
     }
 
-
     # Have to account for variable amount of rows needed due to factor 
     # predictors
     rows <- split(1:nrow(retmat),