A world of geom

ggplot2 builds charts through layers using geom_ functions. Here is a list of the different available geoms. Click one to see an example using it.

geom_bar geom_bin geom_boxplot geom_density geom_error geom_hex geom_hist geom_hline geom_jitter geom_label geom_line geom_point geom_polygon geom_rect geom_ribbon geom_rug geom_segment geom_smooth geom_text geom_tile geom_violin geom_vline

Re-ordering with ggplot2

When working with categorical variables (= factors), a common struggle is to manage the order of entities on the plot.

Post #267 is dedicated to reordering. It describes 3 different way to arrange groups in a ggplot2 chart:

Using the forcats package
With dplyr
With the reorder() function of base R

Read post

Tidyverse

Here’s the official ggplot2 cheatsheet created by Posit. It covers all the key concepts of the library.

I've also compiled it with the most useful R and data visualization cheatsheets into a single PDF you can download:

ggplot2

A cheatsheet for quickly recalling the key functions and arguments of the ggplot2 library.

ggplot2 title

The ggtitle() function allows to add a title to the chart. The following post will guide you through its usage, showing how to control title main features: position, font, color, text and more.

Basic

Learn how to add a title with ggtitle, default options.

Appearance

Learn how to change title font, size, color, text and more.

Position

Check how to change title position.

Use custom fonts with ggplot2

If you don't want your plot to look like any others, you'll definitely be interested in using custom fonts for your title and labels! This is totally possible thanks to 2 main packages: ragg and showtext. The blog-post below should help you using any font in minutes.

Custom font

Learn how to add a custom font using the ragg or the showtext library in a ggplot2 chart.

Small multiples: facet_wrap() and facet_grid()

Small multiples is a very powerful dataviz technique. It split the chart window in many small similar charts: each represents a specific group of a categorical variable. The following post describes the main use cases using facet_wrap() and facet_grid() and should get you started quickly.

Facet_wrap

Facet wrap allows to build small multiples using one categorical variable.

Facet_grid

Same idea, but using 2 categorical variables for the faceting.

Customized

An advanced examples to make sure you know how to make your small multiple pretty.

Strip features

Customize the general layout with the strip option.

Mixing charts

How to combine several charts together with ggplot2.

A set of pre-built themes

It is possible to customize any part of a ggplot2 chart thanks to the theme() function. Fortunately, heaps of pre-built themes are available, allowing to get a good style with one more line of code only. Here is a glimpse of the available themes. See code

→ ggplot2

default theme_bw() theme_minimal() theme_classic() theme_gray()

→ ggthemes

theme_excel() theme_economist() theme_fivethirtyeight() theme_tufte() theme_gdocs() theme_wsj() theme_calc() theme_hc()

→ other

theme_article() theme_pubclean() theme_bigstatsr() theme_ipsum()

Plants Vs Zombies 6.1.11 Best <2026>

What's your favorite feature in the new update? Share your thoughts, strategies, and reactions in the comments below!

So, what are you waiting for? Dive into the update and experience the fresh excitement of Plants vs. Zombies!

Hey there, PvZ enthusiasts! Are you ready for some exciting news? The popular tower defense game, Plants vs. Zombies, has just received a significant update - version 6.1.11! In this blog post, we'll dive into the juicy details of what's new, what's changed, and what's got everyone buzzing.

The Plants vs. Zombies 6.1.11 update is a game-changer! With new features, gameplay changes, and improvements, there's something for everyone to enjoy. Whether you're a seasoned pro or a newcomer to the world of PvZ, this update is sure to breathe new life into the game.

Related chart types

Ggplot2

Animation

Interactivity

Caveats

Data art