ggcharts 
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, letโs 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")
Thatโs 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)
Gallery
Charts
data("revenue_wide")
line_chart(data = revenue_wide, x = year, y = Roche:Bayer) +
labs(x = "Year", y = "Revenue (Billion USD)")
biomedicalrevenue %>%
filter(year == 2018) %>%
lollipop_chart(x = company, y = revenue, threshold = 30) +
labs(
x = NULL,
y = "Revenue",
title = "Biomedical Companies with Revenue > $30Bn."
) +
scale_y_continuous(
labels = function(x) paste0("$", x, "Bn."),
expand = expansion(mult = c(0, .05))
)
data("popeurope")
dumbbell_chart(
data = popeurope,
x = country,
y1 = pop1952,
y2 = pop2007,
top_n = 10,
point_colors = c("lightgray", "#494F5C")
) +
labs(
x = NULL,
y = "Population",
title = "Europe's Largest Countries by Population in 2007"
) +
scale_y_continuous(
limits = c(0, NA),
labels = function(x) paste(x, "Mn.")
)
data(mtcars)
mtcars_z <- dplyr::transmute(
.data = mtcars,
model = row.names(mtcars),
hpz = scale(hp)
)
diverging_bar_chart(data = mtcars_z, x = model, y = hpz)
diverging_lollipop_chart(
data = mtcars_z,
x = model,
y = hpz,
lollipop_colors = c("#006400", "#b32134"),
text_color = c("#006400", "#b32134")
)
data("popch")
pyramid_chart(data = popch, x = age, y = pop, group = sex)
Themes
ggcharts_set_theme("theme_hermit")
bar_chart(data = diamonds, x = cut)
ggcharts_set_theme("theme_ng")
bar_chart(data = diamonds, x = cut)
ggcharts_set_theme("theme_nightblue")
bar_chart(data = diamonds, x = cut)