Turning off some legends in a ggplot

A Guide to Turning Off Legends in ggplot

🎨 Have you ever struggled with removing certain legends in a ggplot? 📊 Don't worry, you're not alone! It's a common issue that many data visualization enthusiasts face. In this blog post, we'll tackle this problem head-on and provide you with easy solutions to turn off specific legends in your ggplot. Let's get started! 🚀

The Dilemma: Multiple Legends in a ggplot

📈 Suppose you have a ggplot with more than one legend, just like in the code snippet below:

mov <- subset(movies, length != "")
p0 <- ggplot(mov, aes(year, rating, colour = length, shape = mpaa)) +
  geom_point()

🌟 Now, let's say you want to turn off the display of all legends. You can achieve this easily with the following code:

p1 <- p0 + theme(legend.position = "none")

🎉Voila! No more legends distracting you from your beautiful visualization. But what if you only want to turn off a specific legend, like the color or shape legend? 🤔

Solution 1: Turning Off the Shape Legend

🔲 To turn off the shape legend, you can pass the show_guide = FALSE argument to the geom_point function, just like in the code snippet below:

p2 <- ggplot(mov, aes(year, rating, colour = length, shape = mpaa)) +
  geom_point(show_guide = FALSE)

💥 And just like magic, the shape legend disappears, leaving you with a cleaner and more focused visualization.

The Challenge: Turning Off the Color Legend

🎨 However, when it comes to turning off the color legend, things get a bit trickier. The show_guide = FALSE argument doesn't allow us to specify which legend it should apply to. Frustrating, right? 😫

❌ Attempting to use show_guide with scales or aesthetics, like in the examples below, sadly results in errors or unexpected behavior:

p3 <- ggplot(mov, aes(year, rating, colour = length, shape = mpaa)) +
  scale_colour_discrete(show_guide = FALSE) +
  geom_point()
# Error in discrete_scale

p4 <- ggplot(mov, aes(year, rating, shape = mpaa)) +
  aes(colour = length, show_guide = FALSE) +
  geom_point()
# Draws both legends

Solution 2: The guides Function

🗺️ Fortunately, we have another option to control legends in ggplot2 version 0.9.2 and newer: the guides function. Let's take a closer look at how we can use it.

🔖 The goal is to specify which legends should be displayed. Ideally, we could do something like the code snippet below:

p0 + guides(
  colour = guide_legend(show = FALSE) 
)

🚫 However, the guide_legend function unfortunately doesn't have a show argument, preventing us from achieving our desired result.

🌟 But don't give up just yet! We can still reach our goal by utilizing a workaround. Here's the step-by-step solution:

1. Extract the desired legend(s) from the plot.

2. Use the override.aes argument in the guides function to specify the desired legend(s) to display.

# Step 1: Extract the color legend and create a blank legend
p5 <- p0 + guides(colour = guide_legend())

# Step 2: Override the aesthetics (color) in the blank legend to make it disappear
p6 <- p5 + guides(colour = guide_legend(override.aes = list(colour = "white")))

# Voila! The color legend is turned off!

🎉 Hooray! You've successfully turned off the color legend in your ggplot using the guides function. You can apply similar steps to turn off other legends as well.

Time to Take Control of Your Legends 🎮

🔥 And there you have it! Two easy solutions to turn off specific legends in your ggplot. No more wrestling with unwanted legends and cluttered visualizations. It's time to take control and unleash the full potential of your data! 💪

🌐 If you found this guide helpful, make sure to share it with your fellow data enthusiasts. Let's spread the knowledge and make data visualization even more engaging and insightful! ✨

💌 We'd love to hear your thoughts and experiences with turning off legends in ggplot. Have you encountered any other challenges? Share your insights and join the conversation by leaving a comment below. 🗨️

✨ Remember, the magic of data visualization lies in the details. So, let's keep exploring, experimenting, and creating stunning visual representations of our data. Happy plotting! 📊✨