{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.


The package is available from CRAN.


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

if (!"remotes" %in% installed.packages()) {
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.


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:


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)