Overview

{ggcharts} provides a high-level {ggplot2} interface for creating common charts. Its aim is both simple and ambitious: to get you from your data visualization idea to an actual plot faster. How so? By taking care of a lot of data preprocessing, obscure {ggplot2} details and plot styling for you. The resulting plots are ggplot objects and can be further customized using any {ggplot2} function.

Installation

The package is available from CRAN.

install.packages("ggcharts")

Alternatively, you can install the latest development version from GitHub.

if (!"remotes" %in% installed.packages()) {
  install.packages("remotes")
}
remotes::install_github("thomas-neitmann/ggcharts", upgrade = "never")

If you get an error when trying to install from GitHub, run this code and then try to install once again.

Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS = "true")

If the installation still fails please open an issue.

Why ggcharts?

Thanks to {ggplot2} you can create beautiful plots in R. However, it can often take quite a bit of effort to get from a data visualization idea to an actual plot. As an example, let’s say you want to create a faceted bar chart displaying the top 10 within each facet ordered from highest to lowest. What sounds simple is actually pretty hard to achieve. Have a look:

library(dplyr)
library(ggplot2)
library(ggcharts)
data("biomedicalrevenue")

biomedicalrevenue %>%
  filter(year %in% c(2012, 2015, 2018)) %>%
  group_by(year) %>%
  top_n(10, revenue) %>%
  ungroup() %>%
  mutate(company = tidytext::reorder_within(company, revenue, year)) %>%
  ggplot(aes(company, revenue)) +
  geom_col() +
  coord_flip() +
  tidytext::scale_x_reordered() +
  facet_wrap(vars(year), scales = "free_y")

That’s a lot of code! And you likely never heard of some of the functions involved. With {ggcharts} you can create the same plot (actually an even better looking one) in almost a single line of code.

biomedicalrevenue %>%
  filter(year %in% c(2012, 2015, 2018)) %>%
  bar_chart(x = company, y = revenue, facet = year, top_n = 10)