12 ggplot extensions for snazzier R graphics | Cult Tech

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ggplot2 is just not solely the preferred information visualization bundle for the R language, additionally it is an ecosystem. Quite a few add-on packages give ggplot extra energy to do every little thing from extra simply altering axis labels to mechanically producing statistical data and customizing . . . virtually every little thing.

Listed below are a dozen nice ggplot2 extensions it’s best to find out about, plus a couple of extra goodies on the finish.

Create your individual geoms: ggpackets

As soon as you have added a number of layers and changes to a ggplot plot, how do you save that work so it is easy to reuse? A method is to transform your code right into a perform. One other is to transform it to an RStudio code snippet. However the ggpackets bundle has a extra ggplot-friendly means: Create your individual customized geom! It is so simple as storing it in a variable utilizing the ggpacket() perform.

The next pattern code creates a bar chart from the Boston snowfall information and has a number of strains of customizations that I want to reuse with different information. The primary block of code is the preliminary graph:

snowfall2000s <- import("https://gist.githubusercontent.com/smach/5544e1818a76a2cf95826b78a80fc7d5/uncooked/8fd7cfd8fa7b23cba5c13520f5f06580f4d9241c/boston_snowfall.2000s.csv")
ggplot(snowfall2000s, aes(x = Winter, y = Whole)) +
  geom_col(colour = "black", fill="#0072B2") +
  theme_minimal() +
  theme(panel.border = element_blank(), panel.grid.main = element_blank(),
        panel.grid.minor = element_blank(), axis.line =
          element_line(color = "grey"),
        plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5)
  ) +
  ylab("") + xlab("")

Here is methods to convert that to a customized geom referred to as my_geom_col:

my_geom_col <- ggpacket() +
  geom_col(colour = "black", fill="#0072B2") +
  theme_minimal() +
  theme(panel.border = element_blank(), panel.grid.main = element_blank(),
        panel.grid.minor = element_blank(), axis.line =
          element_line(color = "grey"),
        plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5)
  ) +
  ylab("") + xlab("")

Notice that I saved every little thing besides the unique graphic first ggplot() line of code to the customized geom.

That is how easy it’s to make use of that new geom:

ggplot(snowfall2000s, aes(x = Winter, y = Whole)) +
bar chart with blue bars Sharon Maclis

Graph created with a customized ggpackets geom.

ggpackets is by Doug Kelkhoff and is out there from CRAN.

Simpler ggplot2 code: ggblanket and others

ggplot2 is extremely highly effective and customizable, however typically that comes at a price of complexity. A number of packages purpose to optimize ggplot2 to make widespread information visualizations less complicated or extra intuitive.

When you are likely to neglect which geoms to make use of for what, I like to recommend you give ggblanket a strive. One among my favourite issues concerning the bundle is that it merges column and fill aesthetics right into a single column aesthetic, so I now not want to recollect whether or not to make use of one. scale_fill_ both scale_colour_ perform.

One other advantage of ggblanket: its geomes as gg_col() both gg_point() embrace customization choices inside the capabilities themselves as a substitute of requiring separate layers. And meaning I solely want to take a look at a assist file to see issues like pal is to outline a colour palette and y_title units the y-axis title, as a substitute of looking out assist recordsdata for a number of separate capabilities. ggblanket might not make it any simpler for me keep in mind all these choices, however they’re simpler to discover.

Here is methods to generate a histogram from the Palmer’s penguin dataset with ggblanket (instance taken from the bundle web site):

penguins |>
  gg_histogram(x = body_mass_g, col = species)
Histogram with 3 colors and a legend Sharon Maclis

Histogram created with ggblanket.

The end result remains to be a ggplot object, which implies you possibly can proceed to customise it by including layers with common ggplot2 code.

ggblanket is by David Hodge and is out there from CRAN.

A number of different packages attempt to simplify ggplot2 and in addition change its defaults, together with ggcharts. Its simplified capabilities use syntax like

column_chart(snowfall2000s, x = Winter, y = Whole)

That single line of code offers a reasonably respectable default, plus auto-ordered slashes (you possibly can simply override that).

Bar chart with blue bars sorted by ascending values Sharon Maclis

The bar chart created with ggcharts mechanically types the bars by values.

Take a look at InfoWorld’s ggcharts tutorial or the video under for extra particulars.

Easy textual content customization: ggeasy

ggeasy doesn’t have an effect on the “core” a part of your information show, i.e. bar/dot/line sizes, colours, orders, and so on. As a substitute, it is all about customizing the textual content across the charts, akin to labels and axis formatting. All ggeasy capabilities begin with easy_ so it is, sure, simple to seek out them utilizing RStudio’s autocomplete.

Must middle a plot title? easy_center_title(). Do you need to rotate the x-axis labels by 90 levels? easy_rotate_labels(which = "x").

Be taught extra concerning the bundle within the InfoWorld ggeasy tutorial or within the video under.

ggeasy is by Jonathan Carroll et al and is out there from CRAN.

Spotlight parts in your plots: gghighlight

Generally you need to draw consideration to particular information factors on a chart. You may actually try this with simply ggplot, however gghighlight goals to make it simpler. Simply add the gghighlight() perform along with a situation. For instance, if winters with snow totals larger than 85 inches are necessary to the story I am telling, I may use gghighlight(Whole > 85):

ggplot(snowfall2000s, aes(x = Winter, y = Whole)) +
  my_geom_col() +
  gghighlight(Whole > 85)
Bar chart with 2 blue bars highlighted and the rest grey. Sharon Maclis

Chart with totals larger than 85 highlighted with gghighlight.

Or if I need to name particular years, like 2011-12 and 2014-15, I can set them as my gghighlight() situation:

ggplot(snowfall2000s, aes(x = Winter, y = Whole)) +
  my_geom_col() +
  gghighlight(Winter %in% c('2011-12', '2014-15'))

gghighlight is by Hiroaki Yutani and is out there from CRAN.

Add themes or colour palettes: ggthemes and others

The ggplot2 ecosystem consists of a lot of packages so as to add themes and colour palettes. You in all probability will not want all of them, however you would possibly need to flick thru them to seek out ones which have themes or palettes that attraction to you.

After putting in certainly one of these packages, you possibly can usually use a brand new theme or colour palette in the identical means that you’d use a built-in ggplot2 theme or palette. Here is an instance with the solarized theme and colorblind palette from the ggthemes bundle:

ggplot(penguins, aes(x = bill_length_mm, y = body_mass_g, colour = species)) +
  geom_point() +
  ggthemes::theme_solarized() +
Scatter chart with pale yellow background Sharon Maclis

Scatterplot utilizing a colorblind palette and a solarized theme from the ggthemes bundle.

ggthemes is by Jeffrey B. Arnold et al and is out there from CRAN.

Different theme packs and palettes to contemplate:

ggsci is a group of ggplot2 colour palettes “impressed by scientific journals, information visualization libraries, science fiction motion pictures and TV reveals” akin to scale_fill_lancet() Y scale_color_startrek().

hrbrthemes is a well-liked theme pack that focuses on typography.

ggthemr is a bit much less well-known than the others, however it has loads of themes to select from, plus a GitHub repository that makes it simple to seek out themes and see what they seem like.

bbplot has just one theme, bbc_style()the BBC’s ready-to-publish type, in addition to a second characteristic to save lots of a plot for publication, finalise_plot().

paletteer is a metapackage that mixes palettes from dozens of separate R palette packages into one with a single constant interface. And that interface consists of capabilities particularly for ggplot to make use of, with syntax like scale_color_paletteer_d("nord::aurora"). Right here nord it’s the authentic palette pack Title, aurora is the particular palette identify, and the _d implies that this palette is for discrete (not steady) values. palette generally is a bit overwhelming at first, however you may virtually actually discover a palette that appeals to you.

Notice that you need to use none R colour palette with ggplot, even when you do not have ggplot-specific colour scaling capabilities, with ggplot’s handbook scaling capabilities and colour palette values, akin to scale_color_manual(values=c("#486030", "#c03018", "#f0a800")).

Add colour and different kinds to ggplot2 textual content: ggtext

The ggtext bundle makes use of Markdown-like syntax so as to add kinds and colours to textual content inside a chart. For instance, underscores round textual content add italics, and two asterisks round textual content create a daring type. For this to work appropriately with ggtext, the bundle element_markdown() The perform should even be added to a ggplot theme. The syntax is so as to add the suitable markdown type to the textual content Y Then add element_markdown() to theme componentlike this to italicize a subtitle:

ggplot(snowfall2000s, aes(x = Winter, y = Whole)) +
  my_geom_col() +
  labs(title = "Annual Boston Snowfall", subtitle = "_2000 to 2016_") +
    plot.subtitle = element_markdown()

ggtext is by Claus O. Wilke and is out there from CRAN.

Convey uncertainty: ggdist

ggdist provides geoms to visualise information distribution and uncertainty, producing graphs like rain cloud plots and logit plots with new geoms like stat_slab() Y stat_dotsinterval(). Right here is an instance from the ggdist web site:

set.seed(12345) # for reproducibility
  abc = c("a", "b", "b", "c"),
  worth = rnorm(200, c(1, 8, 8, 3), c(1, 1.5, 1.5, 1))
) %>%
  ggplot(aes(y = abc, x = worth, fill = abc)) +
  stat_slab(aes(thickness = stat(pdf*n)), scale = 0.7) +
  stat_dotsinterval(facet = "backside", scale = 0.7, slab_size = NA) +
  scale_fill_brewer(palette = "Set2")
Three rain cloud graphics, each a different color Sharon Maclis

Rain cloud plot generated with the ggdist bundle.

Go to the ggdist web site for full particulars and extra examples. ggidst is by Matthew Kay and is out there from CRAN.

Add interactivity to ggplot2: plotly and ggiraph

In case your charts are going to the online, it’s your decision them to be interactive, providing options like turning collection on and off and displaying underlying information if you hover over some extent, line, or bar. Each plotly and ggiraph flip ggplots into interactive HTML widgets.

plotly, an R wrapper for the plotly.js JavaScript library, is extraordinarily simple to make use of. All you do is put your closing ggplot contained in the bundle ggplotly() perform, and the perform returns an interactive model of its plot. For instance:

ggplot(snowfall2000s, aes(x = Winter, y = Whole)) +
  geom_col() +
  labs(title = "Annual Boston Snowfall", subtitle = "2000 to 2016")

plotly works with different extensions, together with ggpackets and gghighlights. plotly’s plots do not at all times embrace every little thing that seems in a static model (as of this writing, it did not acknowledge ggplot2’s subtitles, for instance). However the bundle is difficult to beat for quick interactivity.

Notice that the plotly library additionally has a perform unrelated to ggplot, plot_ly()which makes use of syntax just like that of ggplot qplot():

plot_ly(snowfall2000s, x = ~Winter, y = ~Whole, kind = "bar")

I hope the article roughly 12 ggplot extensions for snazzier R graphics provides notion to you and is helpful for tallying to your data

12 ggplot extensions for snazzier R graphics