diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index de5a6e8..22eb609 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 0.2.0
-Date: 2024-10-23 01:14:59 UTC
-SHA: 7d534be302fc0f099dcd42a4983b0b49d0bf3ca2
+Date: 2024-10-23 12:54:05 UTC
+SHA: 50e5591427422b48f58d03028ae9285bda4a1e21
diff --git a/DESCRIPTION b/DESCRIPTION
index 2fe5d78..196a602 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -34,5 +34,6 @@ Suggests:
     knitr,
     rmarkdown,
     stats,
-    ggplot2
+    ggplot2,
+    tidyselect
 VignetteBuilder: knitr
diff --git a/R/auto-rw30.R b/R/auto-rw30.R
index af1cef9..947a612 100644
--- a/R/auto-rw30.R
+++ b/R/auto-rw30.R
@@ -18,24 +18,13 @@
 #' `num_steps`, `mu`, and `sd` are attached to the tibble.
 #'
 #' @examples
-#' library(ggplot2)
-#'
 #' # Generate random walks and print the result
 #' set.seed(123)
 #' rw30()
 #'
 #' set.seed(123)
 #' rw30() |>
-#'  ggplot(aes(x = x, y = y, color = walk_number, group = walk_number)) +
-#'  geom_line() +
-#'  theme_minimal() +
-#'  theme(legend.position = "none") +
-#'  labs(
-#'    title = "30 Random Walks",
-#'    x = "Step",
-#'    y = "Value",
-#'    color = "Walk Number"
-#'  )
+#'  visualize_walks()
 #'
 #' @name rw30
 NULL
diff --git a/R/gen-brown-motion-geometric.R b/R/gen-brown-motion-geometric.R
index 49324c2..8e79672 100644
--- a/R/gen-brown-motion-geometric.R
+++ b/R/gen-brown-motion-geometric.R
@@ -43,18 +43,12 @@
 #' of class matrix will be returned.
 #'
 #' @examples
-#' library(ggplot2)
-#'
 #' set.seed(123)
 #' geometric_brownian_motion()
 #'
 #' set.seed(123)
-#' geometric_brownian_motion() |>
-#'   ggplot(aes(x = x, y = y, group = walk_number, color = walk_number)) +
-#'   geom_line() +
-#'   labs(title = "Geometric Brownian Motion", x = "Time", y = "Value") +
-#'   theme_minimal() +
-#'   theme(legend.position = "none")
+#' geometric_brownian_motion(.num_walks = 5) |>
+#'   visualize_walks()
 #'
 #' @return
 #' A tibble/matrix
diff --git a/R/gen-brown-motion.R b/R/gen-brown-motion.R
index aa3b326..a351f94 100644
--- a/R/gen-brown-motion.R
+++ b/R/gen-brown-motion.R
@@ -32,18 +32,12 @@
 #' of class matrix will be returned.
 #'
 #' @examples
-#' library(ggplot2)
-#'
 #' set.seed(123)
 #' brownian_motion()
 #'
 #' set.seed(123)
-#' brownian_motion() |>
-#'   ggplot(aes(x = x, y = y, group = walk_number, color = walk_number)) +
-#'   geom_line() +
-#'   labs(title = "Brownian Motion", x = "Time", y = "Value") +
-#'   theme_minimal() +
-#'   theme(legend.position = "none")
+#' brownian_motion(.num_walks = 5) |>
+#'   visualize_walks()
 #'
 #' @return
 #' A tibble/matrix
diff --git a/R/gen-discrete-walk.R b/R/gen-discrete-walk.R
index 9a161f5..13ee1ca 100644
--- a/R/gen-discrete-walk.R
+++ b/R/gen-discrete-walk.R
@@ -33,12 +33,9 @@
 #' discrete_walk()
 #'
 #' set.seed(123)
-#' discrete_walk(.num_walks = 30, .n = 250, .upper_probability = 0.55) |>
-#' ggplot(aes(x = x, y = cum_sum)) +
-#'  geom_line(aes(group = walk_number), alpha = .618, color = "steelblue") +
-#'  theme_minimal() +
-#'  theme(legend.position = "none") +
-#'  geom_smooth(method = "lm", se = FALSE)
+#' discrete_walk(.num_walks = 10, .n = 250, .upper_probability = 0.51,
+#' .initial_value = 100) |>
+#' visualize_walks()
 #'
 #' @return
 #' A tibble containing the simulated walks, with columns for the walk number,
diff --git a/R/gen-random-normal-walk-drift.R b/R/gen-random-normal-walk-drift.R
index 6a497ae..1502044 100644
--- a/R/gen-random-normal-walk-drift.R
+++ b/R/gen-random-normal-walk-drift.R
@@ -24,16 +24,10 @@
 #' @param .initial_value A numeric value indicating the initial value of the walks. Default is 0.
 #'
 #' @examples
-#' library(ggplot2)
-#'
 #' set.seed(123)
-#' walks <- random_normal_drift_walk(.num_walks = 10, .n = 50, .mu = 0, .sd = 1.2,
-#'                                   .drift = 0.05)
-#' ggplot(walks, aes(x = x, y = y, group = walk_number, color = walk_number)) +
-#'   geom_line() +
-#'   labs(title = "Random Walks with Drift", x = "Time", y = "Value") +
-#'   theme_minimal() +
-#'   theme(legend.position = "none")
+#' random_normal_drift_walk(.num_walks = 10, .n = 50, .mu = 0, .sd = 1.2,
+#'                                   .drift = 0.05, .initial_value = 100) |>
+#'                                   visualize_walks()
 #'
 #' @return A tibble in long format with columns `walk_number`, `x` (step index),
 #' and `y` (walk value). The tibble has attributes for the number of walks,
diff --git a/R/gen-random-normal-walk.R b/R/gen-random-normal-walk.R
index 994e94d..643067c 100644
--- a/R/gen-random-normal-walk.R
+++ b/R/gen-random-normal-walk.R
@@ -53,12 +53,8 @@
 #' random_normal_walk(.num_walks = 10, .n = 50, .samp = FALSE)
 #'
 #' set.seed(123)
-#' random_normal_walk(.num_walks = 2, .n = 100) |>
-#'   ggplot(aes(x = x, y = y, group = walk_number, color = walk_number)) +
-#'   geom_line() +
-#'   labs(title = "Random Normal Walk", x = "Time", y = "Value") +
-#'   theme_minimal() +
-#'   theme(legend.position = "none")
+#' random_normal_walk(.num_walks = 2, .n = 100, .initial_value = 100) |>
+#'   visualize_walks()
 #'
 #' @name random_normal_walk
 NULL
diff --git a/R/helpers.R b/R/helpers.R
index d8ec5e6..1468b3c 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -239,7 +239,7 @@ std_cum_sum_augment <- function(.data,
 
   calls <- purrr::set_names(calls, newname)
 
-  ret <- tibble::as_tibble(dplyr::mutate(.data, !!!calls))
+  ret <- dplyr::as_tibble(dplyr::mutate(.data, !!!calls))
 
   return(ret)
 }
@@ -307,7 +307,7 @@ std_cum_prod_augment <- function(.data,
 
   calls <- purrr::set_names(calls, newname)
 
-  ret <- tibble::as_tibble(dplyr::mutate(.data, !!!calls))
+  ret <- dplyr::as_tibble(dplyr::mutate(.data, !!!calls))
 
   return(ret)
 }
@@ -375,7 +375,7 @@ std_cum_min_augment <- function(.data,
 
   calls <- purrr::set_names(calls, newname)
 
-  ret <- tibble::as_tibble(dplyr::mutate(.data, !!!calls))
+  ret <- dplyr::as_tibble(dplyr::mutate(.data, !!!calls))
 
   return(ret)
 }
@@ -443,7 +443,7 @@ std_cum_max_augment <- function(.data,
 
   calls <- purrr::set_names(calls, newname)
 
-  ret <- tibble::as_tibble(dplyr::mutate(.data, !!!calls))
+  ret <- dplyr::as_tibble(dplyr::mutate(.data, !!!calls))
 
   return(ret)
 }
@@ -511,7 +511,7 @@ std_cum_mean_augment <- function(.data,
 
   calls <- purrr::set_names(calls, newname)
 
-  ret <- tibble::as_tibble(dplyr::mutate(.data, !!!calls))
+  ret <- dplyr::as_tibble(dplyr::mutate(.data, !!!calls))
 
   return(ret)
 }
diff --git a/R/plt-visualize-walks.R b/R/plt-visualize-walks.R
index 94f47eb..846209b 100644
--- a/R/plt-visualize-walks.R
+++ b/R/plt-visualize-walks.R
@@ -26,7 +26,7 @@
 #' liking.
 #'
 #' You can also choose whether you want the visualization to be interactive or
-#' not by setting `.interactive` to TRUE. The function uses the {ggiraph}
+#' not by setting `.interactive` to TRUE. The function uses the `ggiraph`
 #' package for making the patches interactive.
 #'
 #' If you want to visualize only one of the attributes, you can choose use one of
@@ -61,17 +61,17 @@
 #'
 #' # Use the function with an input that has alternatives for y
 #' set.seed(123)
-#' random_normal_walk() |>
+#' random_normal_walk(.num_walks = 5, .initial_value = 100) |>
 #'  visualize_walks()
 #'
 #' # Use the function to create interactive visualizations
 #' set.seed(123)
-#' random_normal_walk() |>
+#' random_normal_walk(.num_walks = 5, .initial_value = 100) |>
 #'  visualize_walks(.interactive = TRUE)
 #'
 #' # Use .pluck to pick just one visualization
 #' set.seed(123)
-#' random_normal_walk() |>
+#' random_normal_walk(.num_walks = 5, .initial_value = 100) |>
 #'  visualize_walks(.pluck = "cum_sum")
 #'
 #' @name visualize_walks
diff --git a/man/brownian_motion.Rd b/man/brownian_motion.Rd
index 2e602f7..442af29 100644
--- a/man/brownian_motion.Rd
+++ b/man/brownian_motion.Rd
@@ -51,18 +51,12 @@ walk processes in general. It is a useful tool in probability theory and
 statistical analysis.
 }
 \examples{
-library(ggplot2)
-
 set.seed(123)
 brownian_motion()
 
 set.seed(123)
-brownian_motion() |>
-  ggplot(aes(x = x, y = y, group = walk_number, color = walk_number)) +
-  geom_line() +
-  labs(title = "Brownian Motion", x = "Time", y = "Value") +
-  theme_minimal() +
-  theme(legend.position = "none")
+brownian_motion(.num_walks = 5) |>
+  visualize_walks()
 
 }
 \seealso{
diff --git a/man/discrete_walk.Rd b/man/discrete_walk.Rd
index bdb8292..11adb8d 100644
--- a/man/discrete_walk.Rd
+++ b/man/discrete_walk.Rd
@@ -52,12 +52,9 @@ set.seed(123)
 discrete_walk()
 
 set.seed(123)
-discrete_walk(.num_walks = 30, .n = 250, .upper_probability = 0.55) |>
-ggplot(aes(x = x, y = cum_sum)) +
- geom_line(aes(group = walk_number), alpha = .618, color = "steelblue") +
- theme_minimal() +
- theme(legend.position = "none") +
- geom_smooth(method = "lm", se = FALSE)
+discrete_walk(.num_walks = 10, .n = 250, .upper_probability = 0.51,
+.initial_value = 100) |>
+visualize_walks()
 
 }
 \seealso{
diff --git a/man/geometric_brownian_motion.Rd b/man/geometric_brownian_motion.Rd
index ab40322..1a1b2e9 100644
--- a/man/geometric_brownian_motion.Rd
+++ b/man/geometric_brownian_motion.Rd
@@ -66,18 +66,12 @@ of a GBM process. It can be used to analyze the behavior of financial
 assets and to make informed investment decisions.
 }
 \examples{
-library(ggplot2)
-
 set.seed(123)
 geometric_brownian_motion()
 
 set.seed(123)
-geometric_brownian_motion() |>
-  ggplot(aes(x = x, y = y, group = walk_number, color = walk_number)) +
-  geom_line() +
-  labs(title = "Geometric Brownian Motion", x = "Time", y = "Value") +
-  theme_minimal() +
-  theme(legend.position = "none")
+geometric_brownian_motion(.num_walks = 5) |>
+  visualize_walks()
 
 }
 \seealso{
diff --git a/man/random_normal_drift_walk.Rd b/man/random_normal_drift_walk.Rd
index 9c306fe..4d7ad39 100644
--- a/man/random_normal_drift_walk.Rd
+++ b/man/random_normal_drift_walk.Rd
@@ -44,16 +44,10 @@ Each walk is generated using a normal distribution for the steps, with an
 additional drift term added to each step.
 }
 \examples{
-library(ggplot2)
-
 set.seed(123)
-walks <- random_normal_drift_walk(.num_walks = 10, .n = 50, .mu = 0, .sd = 1.2,
-                                  .drift = 0.05)
-ggplot(walks, aes(x = x, y = y, group = walk_number, color = walk_number)) +
-  geom_line() +
-  labs(title = "Random Walks with Drift", x = "Time", y = "Value") +
-  theme_minimal() +
-  theme(legend.position = "none")
+random_normal_drift_walk(.num_walks = 10, .n = 50, .mu = 0, .sd = 1.2,
+                                  .drift = 0.05, .initial_value = 100) |>
+                                  visualize_walks()
 
 }
 \seealso{
diff --git a/man/random_normal_walk.Rd b/man/random_normal_walk.Rd
index b9dc7a3..dd12eb2 100644
--- a/man/random_normal_walk.Rd
+++ b/man/random_normal_walk.Rd
@@ -73,12 +73,8 @@ set.seed(123)
 random_normal_walk(.num_walks = 10, .n = 50, .samp = FALSE)
 
 set.seed(123)
-random_normal_walk(.num_walks = 2, .n = 100) |>
-  ggplot(aes(x = x, y = y, group = walk_number, color = walk_number)) +
-  geom_line() +
-  labs(title = "Random Normal Walk", x = "Time", y = "Value") +
-  theme_minimal() +
-  theme(legend.position = "none")
+random_normal_walk(.num_walks = 2, .n = 100, .initial_value = 100) |>
+  visualize_walks()
 
 }
 \seealso{
diff --git a/man/rw30.Rd b/man/rw30.Rd
index 8fbf24b..d56bd6e 100644
--- a/man/rw30.Rd
+++ b/man/rw30.Rd
@@ -21,24 +21,13 @@ Each walk is generated independently and stored in a tibble. The resulting
 tibble is then pivoted into a long format for easier analysis.
 }
 \examples{
-library(ggplot2)
-
 # Generate random walks and print the result
 set.seed(123)
 rw30()
 
 set.seed(123)
 rw30() |>
- ggplot(aes(x = x, y = y, color = walk_number, group = walk_number)) +
- geom_line() +
- theme_minimal() +
- theme(legend.position = "none") +
- labs(
-   title = "30 Random Walks",
-   x = "Step",
-   y = "Value",
-   color = "Walk Number"
- )
+ visualize_walks()
 
 }
 \author{
diff --git a/man/visualize_walks.Rd b/man/visualize_walks.Rd
index 6872482..18d70ed 100644
--- a/man/visualize_walks.Rd
+++ b/man/visualize_walks.Rd
@@ -52,7 +52,7 @@ but you can set \code{.alpha} if the default value of 0.7 isn't to your
 liking.
 
 You can also choose whether you want the visualization to be interactive or
-not by setting \code{.interactive} to TRUE. The function uses the {ggiraph}
+not by setting \code{.interactive} to TRUE. The function uses the \code{ggiraph}
 package for making the patches interactive.
 
 If you want to visualize only one of the attributes, you can choose use one of
@@ -72,17 +72,17 @@ rw30() |>
 
 # Use the function with an input that has alternatives for y
 set.seed(123)
-random_normal_walk() |>
+random_normal_walk(.num_walks = 5, .initial_value = 100) |>
  visualize_walks()
 
 # Use the function to create interactive visualizations
 set.seed(123)
-random_normal_walk() |>
+random_normal_walk(.num_walks = 5, .initial_value = 100) |>
  visualize_walks(.interactive = TRUE)
 
 # Use .pluck to pick just one visualization
 set.seed(123)
-random_normal_walk() |>
+random_normal_walk(.num_walks = 5, .initial_value = 100) |>
  visualize_walks(.pluck = "cum_sum")
 
 }