Draft

3D Interactive Charts

Using the plotly package

Published

June 25, 2026

Modified

June 27, 2026

1 Setting up R Packages

library(tidyverse) # Manage data
library(plotly) # For 2D and 3D interactive plots

Plot Fonts and Theme

Show the Code
library(systemfonts)
library(showtext)
library(ggrepel)
library(marquee)
## Clean the slate
systemfonts::clear_local_fonts()
systemfonts::clear_registry()
##
showtext_opts(dpi = 96) # set DPI for showtext
sysfonts::font_add(
  family = "Alegreya",
  regular = "../../../../../../fonts/Alegreya-Regular.ttf",
  bold = "../../../../../../fonts/Alegreya-Bold.ttf",
  italic = "../../../../../../fonts/Alegreya-Italic.ttf",
  bolditalic = "../../../../../../fonts/Alegreya-BoldItalic.ttf"
)

sysfonts::font_add(
  family = "Roboto Condensed",
  regular = "../../../../../../fonts/RobotoCondensed-Regular.ttf",
  bold = "../../../../../../fonts/RobotoCondensed-Bold.ttf",
  italic = "../../../../../../fonts/RobotoCondensed-Italic.ttf",
  bolditalic = "../../../../../../fonts/RobotoCondensed-BoldItalic.ttf"
)
showtext_auto(enable = TRUE) # enable showtext
##
theme_custom <- function() {
  theme_bw(base_size = 10) +

    theme_sub_axis(
      title = element_text(
        family = "Roboto Condensed",
        size = 8
      ),
      text = element_text(
        family = "Roboto Condensed",
        size = 6
      )
    ) +

    theme_sub_legend(
      text = element_text(
        family = "Roboto Condensed",
        size = 6
      ),
      title = element_text(
        family = "Alegreya",
        size = 8
      )
    ) +

    theme_sub_plot(
      title = element_text(
        family = "Alegreya",
        size = 14, face = "bold"
      ),
      title.position = "plot",
      subtitle = element_text(
        family = "Alegreya",
        size = 10
      ),
      caption = element_text(
        family = "Alegreya",
        size = 6
      ),
      caption.position = "plot"
    )
}

## Use available fonts in ggplot text geoms too!
ggplot2::update_geom_defaults(geom = "text", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "label", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))

ggplot2::update_geom_defaults(geom = "marquee", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "text_repel", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "label_repel", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))

## Set the theme
ggplot2::theme_set(new = theme_custom())

## tinytable options
options("tinytable_tt_digits" = 2)
options("tinytable_format_num_fmt" = "significant_cell")
options(tinytable_html_mathjax = TRUE)


## Set defaults for flextable
flextable::set_flextable_defaults(font.family = "Roboto Condensed")

2 Introduction

The plotly package in R, created by Carson Sievert allows the plotly.js JavaScript library to be used in R, using R syntax. It offers a wide variety of 2D and 3D interactive charts.

We will today see how to make our charts interactive, with popups and labels. And we can zoom in and out too.

3D charts are generally not a good idea, the literature says. but sometimes, they are exactly the thing that is needed, especially in teaching and learning. So we will look at those too. Both of these aspects using a new package called plotly.

For form’s sake, we will proceed as we have always done, looking at a dataset (however familiar), writing up its Data Dictionary, and then making plots.

3 Examine the Data

We will use the penguins dataset built into R itself. Your should try other datasets too!

Here is a glimpse of the data:

data(penguins, package = "datasets") # Load the penguins dataset)
glimpse(penguins)
Rows: 344
Columns: 8
$ species     <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Ad…
$ island      <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Tor…
$ bill_len    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, 42.0, …
$ bill_dep    <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, 20.2, …
$ flipper_len <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186, 180,…
$ body_mass   <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, …
$ sex         <fct> male, female, female, NA, female, male, female, male, NA, …
$ year        <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

3.1 Data Dictionary

NoteQuantitative Data
  • bill_len - length of the penguin’s bill in millimeters (continuous)
  • bill_dep - depth of the penguin’s bill in millimeters (continuous)
  • flipper_len - length of the penguin’s flipper in millimeters (continuous)
  • body_mass - mass of the penguin in grams (continuous)
  • year - year of observation (discrete, integer)
NoteQualitative Data
  • species - the species of penguin (categorical)
  • island - the island where the penguin was observed (categorical)

4 Our first interactive chart with {plotly}

plotly uses the tidyverse syntax and supports use of the pipe as well ( both the tidyverse pipe and the R-native pipe). So let’s do this:

penguins %>%
  drop_na() %>%
  plot_ly(
    x = ~flipper_len, y = ~body_mass,
    color = ~species, # colour by categorical variable
    type = "scatter",
    mode = "markers", # lines", "markers", "lines+markers", "lines+markers+text", "none"
    colors = "Accent" # Palettes from RColorBrewer
  )
Figure 1: Interactive 2D Scatter Plot

Note the automatic toolbar at the top-left of the chart; this allows all sorts of interactivity. Each point in the chart pops up a set of values for the two variables, and the popup itself is coloured based on the grouping variable.

All these aspects we will customize presently, once we have understood the basics of the plotly API.

5 The {plotly} API defined

If we type help(plot_ly) in our Console, we obtain the following parameters for the plot_ly() command:

  • data: The Data frame driving the chart

  • type: chart type (e.g. “scatter”, “bar”, “box”…)

  • name: trace name. Acts like grouping variable

  • color: Colour for marker ( i.e. symbol glyph), fill, text…

  • colors: Colour palette

  • alpha: Colour saturation,

  • stroke: Stroke colour when Markers have separate colour and fill attributes

  • strokes: Strokes palette

  • alpha_stroke: Stroke colour saturation

  • size: Controls mapping of data to size/width of marker/text/…

  • sizes: c(10, 100): Scale for mapping size to variable

  • span: Controls mapping of data to stroke-size

  • spans: c(1, 20): Scale for mapping span to variable

  • symbol: Discrete values for type of symbol used

  • symbols: Controls mapping of symbol to variable (think pch)

  • linetype: “solid”, “dot”, “dash”, “longdash”, “dashdot”, or “longdashdot”

  • linetypes: Controls mapping of linetype to variable

  • split: Grouping variable

  • frame: Variable values for chart animation ( yes!)

  • width: width in pixels

  • height: height in pixels

  • source: Used with shiny apps. ( Yet another R course!!) )

This function is rather like ggplot() that initiates a plot and passes on global chart variables to the subsequent layers of the chart. However, with type specified, it is capable of building a chart on its own, unlike with ggplot2.

In order to learn the different chart types, we will not specify type in theplot_ly()` command, and use specific chart oriented commands to achieve our purposes. This will become clear in the following.

6 Basic 2D Chart Types in {plotly}

Let us rapidly look at plotly code to generate a set of basic familiar charts.

6.1 Scatter Revisited

Show the Code
penguins <- penguins %>% drop_na()
model <- stats::lm(body_mass ~ flipper_len,
  data = penguins
)

penguins %>%
  mutate(island = as_factor(island), species = as_factor(species)) %>%
  plot_ly() %>%
  add_trace(
    x = ~flipper_len, y = ~body_mass,
    name = ~species,
    symbol = ~species,
    size = ~ as.integer(species), # Note!!
    sizes = c(5, 25),
    type = "scatter", mode = "markers"
  ) %>%
  add_lines(
    x = ~flipper_len,
    y = ~ stats::fitted(model),
    name = "Linear Regression Line", showlegend = T
  ) %>%
  ## See <https://plotly.com/r/reference/layout/>
  layout(
    font = list(family = "Roboto Condensed"),
    title = list(
      text = "Penguins, with Linear Model",
      subtitle = list(text = "Scatter with Colour with Size")
    ),
    legend = list(title = list(text = "Species")),
    margin = c(5, 20, 5, 20),
    plot_bgcolor = "#DCE2F0",
    paper_bgcolor = "#7b9acc",
    modebar = list(bgcolor = "white", activecolor = "black"),
    xaxis = list(title = "Flipper Length", gridcolor = "white"),
    yaxis = list(title = "Body Mass in grams", gridcolor = "white")
  )
Figure 2: Scatter Plot with Size and Colour Mapping

6.2 Line

Show the Code
apple <- read_csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv")
apple
Figure 3: Economic Trends
Show the Code
apple %>%
  mutate(AAPL_x = as.Date(AAPL_x)) %>%
  plot_ly(
    x = ~AAPL_x,
    y = ~AAPL_y, type = "scatter",
    mode = "lines",
    name = "Share Prices (in USD)"
  ) %>%
  layout(
    title = "Apple Share Prices over time (2014)",
    plot_bgcolor = "#e5ecf6",
    xaxis = list(
      title = "Time",
      zerolinecolor = "#ffff",
      zerolinewidth = 2,
      gridcolor = "ffff"
    ),
    yaxis = list(
      title = "Apple Stock Price",
      zerolinecolor = "#ffff",
      zerolinewidth = 2,
      gridcolor = "ffff"
    ),
    showlegend = TRUE,
    margin = c(5, 5, 5, 5)
  )
Figure 4: Economic Trends

6.3 Bars and Histograms

Show the Code
penguins %>%
  drop_na() %>%
  dplyr::count(species, name = "count") %>%
  plot_ly() %>%
  add_bars(x = ~species, y = ~count, color = ~species)
penguins %>%
  drop_na() %>%
  # dplyr::count(species, name = "count") %>%
  plot_ly() %>%
  add_histogram(x = ~species, color = ~species)
(a) Pre-computed Counts
(b) Chart-Computed Counts
Figure 5: Plotly Bar Chart
Show the Code
# number of diamonds by cut and clarity (n)
cc <- count(diamonds, cut, clarity)
cc
Figure 6: Stacked Bar Chart
Show the Code
# number of diamonds by cut (nn)
cc2 <- left_join(cc, count(cc, cut, wt = n, name = "nn"))
cc2
Figure 7: Stacked Bar Chart
Show the Code
cc2 %>%
  mutate(prop = n / nn) %>%
  plot_ly(x = ~cut, y = ~prop, color = ~clarity) %>%
  add_bars() %>%
  layout(barmode = "stack")
Figure 8: Stacked Bar Chart

6.4 Histogram and Density

plot_ly(diamonds, x = ~cut, color = ~clarity) %>%
  add_histogram() %>%
  layout(
    title = "Diamond Prices",
    autosize = TRUE,
    margin = list(l = 50, r = 20, b = 20, t = 40, pad = 20)
  )
Figure 9: Histograms

6.5 Box Plots

p <- plot_ly(diamonds,
  y = ~price, color = I("black"),
  alpha = 0.1, boxpoints = "suspectedoutliers"
)
p1 <- p %>% add_boxplot(x = "Overall")
p2 <- p %>% add_boxplot(x = ~cut, color = ~cut, fillcolor = ~cut)
subplot(
  p1, p2,
  shareY = TRUE,
  widths = c(0.2, 0.8), margin = 0.05
) %>% hide_legend()
Figure 10: Boxplots for Diamond Prices (two subplots)

6.6 Combo Chart

6.7 Maps

7 Basic of 3D charts in {plotly}

7.1 3D Scatter

Show the Code
plot_ly(penguins, x = ~flipper_len, y = ~bill_len, z = ~body_mass) %>%
  add_markers(color = ~species) %>%
  layout(
    scene = list(
      xaxis = list(title = "Flipper Length"),
      yaxis = list(title = "Bill Length"),
      zaxis = list(title = "Body Mass")
    )
  )
Figure 11: 3D Scatter Plot

7.2 3D Surface

Show the Code
x0 <- 1
y0 <- -0.5
sigma <- 1

x <- seq(-3, 3, length.out = 80)
y <- seq(-3, 3, length.out = 80)
grid <- expand.grid(x = x, y = y)

grid$z <- exp(-(((grid$x - x0)^2 + (grid$y - y0)^2) / (2 * sigma^2)))

z_matrix <- matrix(grid$z, nrow = length(x), ncol = length(y))

plot_ly(
  x = ~x,
  y = ~y,
  z = ~z_matrix,
  type = "surface",
  colorscale = "Viridis"
) %>%
  layout(
    title = list(
      text = "3D Gaussian RBF Surface<br><span style='font-size: 14px;'>Centered at the [1,-0.5]</span>"
    ),
    scene = list(
      xaxis = list(title = "x"),
      yaxis = list(title = "y"),
      zaxis = list(title = "phi(r)")
    ),
    autosize = TRUE,
    margin = list(l = 20, r = 20, b = 40, t = 40, pad = 40)
  )
Figure 12: 3D Gaussian RBF Surface

7.3 3D Combo Chart

And what exactly do you intend here, Arvind?

8 Customizing the {plotly} chart

8.1 Themes

8.2 Facets

8.3 Toolbar

8.4 Custom Popups

8.5 Animations

9 Conclusion

10 References

  1. Plotly R Open Source Graphing Library https://plotly.com/r/
  2. Carson Sievert. Interactive web-based data visualization with R, plotly, and shiny. https://plotly-r.com/
  3. Plotly Figure Reference. https://plotly.com/r/reference/index/
R Package Citations
Package Version Citation
plotly 4.12.0 Sievert (2020)
Sievert, Carson. 2020. Interactive Web-Based Data Visualization with r, Plotly, and Shiny. Chapman; Hall/CRC. https://plotly-r.com.
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