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_latex_placement = "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

We can also combine text and images using the sprintf argument and %s placeholder:

tt(head(iris)) |>
    plot_tt(1, 1,
        images = "figures/spider.png",
        sprintf = "Boris: %s",
        height = 5) |>
    style_tt(i = 1, j = 1, alignv = "m")

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
Boris:	3.5	1.4	0.2	setosa
4.9	3	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa

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.

Bar plots

Bar plots can be created with single or dual colors. With two colors, the first color is the bar and the second is the background:

dat <- data.frame(
  Metric = c("Sales", "Conversion", "Growth", "Efficiency"),
  Value = c(75, 45, 92, 38),
  Percentage = c(0.75, 0.45, 0.92, 0.38)
)

tt(dat) |>
  plot_tt(j = 2, fun = "bar", data = as.list(dat$Value), color = "darkorange") |>
  plot_tt(j = 3, fun = "bar", data = as.list(dat$Percentage),
          color = c("steelblue", "lightgrey"), xlim = c(0, 1))

Metric	Value	Percentage
Sales
Conversion
Growth
Efficiency

Other plot types

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

dat <- data.frame(
  Variables = c("mpg", "hp", "qsec"),
  Histogram = "",
  Density = "",
  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 = "line", data = lines, color = "blue") |>
  style_tt(j = 2:4, align = "c")

Variables	Histogram	Density	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(alignv = "m") |>
  style_tt(j = 2:4, align = "c")

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