Plots and images

The plot_tt() function can embed images and plots in a tinytable. We can insert images by specifying their paths and positions (i/j).

library(tinytable)
options(tinytable_tt_digits = 3)
options(tinytable_theme_placement_latex_float = "H")
x <- mtcars[1:4, 1:5]

Inserting images in tables

To insert images in a table, we use the plot_tt() function. The path_img values must be relative to the main document saved by save_tt() or to the Quarto (or Rmarkdown) document in which the code is executed.

dat <- data.frame(
  Species = c("Spider", "Squirrel"),
  Image = ""
)

img <- c(
  "figures/spider.png",
  "figures/squirrel.png"
)

tt(dat) |>
  plot_tt(j = 2, images = img, height = 3)

Species	Image
Spider
Squirrel

In HTML tables, it is possible to insert tables directly from a web address, but not in LaTeX.

dat <- data.frame("R" = "")
img <- "https://cran.r-project.org/Rlogo.svg"
tt(dat) |>
  plot_tt(i = 1, j = 1, images = img, height = 7) |>
  style_tt(j = 1, align = "c")

R

Inline plots

We can draw inline plots three ways, with

Built-in templates for histograms, density plots, and bar plots
Custom plots using base R plots.
Custom plots using ggplot2.

To draw custom plots, one simply has to define a custom function, whose structure we illustrate below.

Built-in plots

There are several types of inline plots available by default. For example,

plot_data <- list(mtcars$mpg, mtcars$hp, mtcars$qsec)

dat <- data.frame(
  Variables = c("mpg", "hp", "qsec"),
  Histogram = "",
  Density = "",
  Bar = "",
  Line = ""
)

# random data for sparklines
lines <- lapply(1:3, \(x) data.frame(x = 1:10, y = rnorm(10)))

tt(dat) |>
  plot_tt(j = 2, fun = "histogram", data = plot_data) |>
  plot_tt(j = 3, fun = "density", data = plot_data, color = "darkgreen") |>
  plot_tt(j = 4, fun = "bar", data = list(2, 3, 6), color = "orange") |>
  plot_tt(j = 5, fun = "line", data = lines, color = "blue") |>
  style_tt(j = 2:5, align = "c")

Variables	Histogram	Density	Bar	Line
mpg
hp
qsec

Custom plots: Base `R`

Important: Custom functions must have ... as an argument.

To create a custom inline plot using Base R plotting functions, we create a function that returns another function. tinytable will then call that second function internally to generate the plot.

This is easier than it sounds! For example:

f <- function(d, ...) {
  function() hist(d, axes = FALSE, ann = FALSE, col = "lightblue")
}

plot_data <- list(mtcars$mpg, mtcars$hp, mtcars$qsec)

dat <- data.frame(Variables = c("mpg", "hp", "qsec"), Histogram = "")

tt(dat) |>
  plot_tt(j = 2, fun = f, data = plot_data)

Variables	Histogram
mpg
hp
qsec

Custom plots: `ggplot2`

Important: Custom functions must have ... as an argument.

To create a custom inline plot using ggplot2, we create a function that returns a ggplot object:

library(ggplot2)

f <- function(d, color = "black", ...) {
  d <- data.frame(x = d)
  ggplot(d, aes(x = x)) +
    geom_histogram(bins = 30, color = color, fill = color) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    theme_void()
}

plot_data <- list(mtcars$mpg, mtcars$hp, mtcars$qsec)

tt(dat) |>
  plot_tt(j = 2, fun = f, data = plot_data, color = "pink")

Variables	Histogram
mpg
hp
qsec

We can insert arbitrarily complex plots by customizing the ggplot2 call:

penguins <- read.csv(
  "https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv",
  na.strings = ""
) |> na.omit()

# split data by species
dat <- split(penguins, penguins$species)
body <- lapply(dat, \(x) x$body_mass_g)
flip <- lapply(dat, \(x) x$flipper_length_mm)

# create nearly empty table
tab <- data.frame(
  "Species" = names(dat),
  "Body Mass" = "",
  "Flipper Length" = "",
  "Body vs. Flipper" = "",
  check.names = FALSE
)

# custom ggplot2 function to create inline plot
f <- function(d, ...) {
  ggplot(d, aes(x = flipper_length_mm, y = body_mass_g, color = sex)) +
    geom_point(size = .2) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_continuous(expand = c(0, 0)) +
    scale_color_manual(values = c("#E69F00", "#56B4E9")) +
    theme_void() +
    theme(legend.position = "none")
}

# `tinytable` calls
tt(tab) |>
  plot_tt(j = 2, fun = "histogram", data = body, height = 2) |>
  plot_tt(j = 3, fun = "density", data = flip, height = 2) |>
  plot_tt(j = 4, fun = f, data = dat, height = 2) |>
  style_tt(j = 2:4, align = "c")

Species	Body Mass	Flipper Length	Body vs. Flipper
Adelie
Chinstrap
Gentoo

Fontawesome

We can use the fontawesome package to include fancy icons in HTML tables:

library(fontawesome)
tmp <- mtcars[1:4, 1:4]
tmp[1, 1] <- paste(fa("r-project"), "for statistics")
tt(tmp)

mpg	cyl	disp	hp
for statistics	6	160	110
21	6	160	110
22.8	4	108	93
21.4	6	258	110