diff --git a/R/plot_fastest.R b/R/plot_fastest.R
index 1718e04e..8776516c 100644
--- a/R/plot_fastest.R
+++ b/R/plot_fastest.R
@@ -66,7 +66,7 @@ plot_fastest <- function(season = get_current_season(), round = 1, session = 'R'
   lim_min <- min(min(driver_data$x, na.rm = T), min(driver_data$y, na.rm = T))
   lim_max <- max(max(driver_data$x, na.rm = T), max(driver_data$y, na.rm = T))
   if(color == 'gear'){
-    ggplot2::ggplot(driver_data, ggplot2::aes(.data$x, .data$y, color = as.factor(.data$n_gear), group = NA)) +
+    ggplot2::ggplot(driver_data, ggplot2::aes(.data$x, .data$y, color = as.factor(.data$n_gear), group = 1)) +
       ggplot2::geom_path(linewidth = 4, lineend = 'round') +
       ggplot2::scale_color_manual(name = 'Gear',
                                   values = c("#BC3C29", "#0072B5", "#E18727", "#20854E", "#7876B1", "#6F99AD", "#FFDC91", "#EE4C97"),
@@ -78,7 +78,7 @@ plot_fastest <- function(season = get_current_season(), round = 1, session = 'R'
                     subtitle = glue::glue('{driver} Fastest Lap | {lap_time}',driver = driver, lap_time = lap_time),
                     caption = 'Generated by {f1dataR} package')
   } else if(color == 'speed'){
-    ggplot2::ggplot(driver_data, ggplot2::aes(.data$x, .data$y, color = .data$speed, group = NA)) +
+    ggplot2::ggplot(driver_data, ggplot2::aes(.data$x, .data$y, color = .data$speed, group = 1)) +
       ggplot2::geom_path(linewidth = 4, lineend = 'round') +
       ggplot2::scale_color_gradient(low = 'white', high = 'red')+
       theme_dark_f1() +
diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
index ae851e32..3336a423 100644
--- a/vignettes/introduction.Rmd
+++ b/vignettes/introduction.Rmd
@@ -22,12 +22,15 @@ dir.create(file.path(getwd(), "vignettes","vig_build_tmp"), recursive = TRUE)
 withr::local_options(f1dataR.cache = file.path(getwd(), "vignettes","vig_build_tmp"))
 ```
 
+## Basics
+
 f1dataR serves as a tool to get neatly organized Formula 1 data into your R environment. Here we will go over the basic functions to understand how the package works.
 
 
 
 The most sought-after aspect of F1 data is telemetry data. 
 Let's get Leclerc's fastest lap from the first race of 2022:
+
 ```{r telem}
 library(f1dataR)
 
@@ -36,7 +39,7 @@ load_driver_telemetry(2022, 1, driver = 'LEC', fastest_only = T)
 
 Now let's use ggplot2 to visualize some of the data we have
 
-```{r simple_plot}
+```{r simple_plot, warning=FALSE, message=FALSE}
 library(dplyr)
 library(ggplot2)
 
@@ -65,7 +68,9 @@ ggplot(data, aes(distance, throttle, color = driver_code))+
 
 ```
 
-Now lets visualize speed and use the f1 dark theme for ggplot included in the package
+## Integrated plotting
+
+There are a couple of functions in the package that help with plotting. The first one is `theme_dark_f1()` that simply applies a theme similar to the official F1 graphics. We can apply it to our previous data.
 
 ```{r use_theme}
 ggplot(data, aes(distance, speed, color = driver_code))+
@@ -81,13 +86,13 @@ ggplot(data, aes(distance, speed, color = driver_code))+
 ```
 
 
-We can also use one of the plotting functions included in the package.
+Another built-in function is `plot_fastest()` that can plot the speed or gear changes throughout the fastest lap for a driver/race.
 
 ```{r use_builtin}
 plot_fastest(2022, 1, 'R', 'PER')
 ```
 
-
+## Combining several functions
 
 Now let's look at a more complete analysis. We want to visualize how lap time change over time (tyre age) for Pérez with every compound used in the Spanish GP.