ggplot2

Misc

Packages
- {ggreveal} - Reveal a ggplot Incrementally (e.g. groups, facets)
- {thematic} - Enables automatic styling of R plots in Shiny, R Markdown, and RStudio
- {hrbrthemes} - hrbrmstr’s collection of themes
- {{brand_yml}} - Create reports, apps, dashboards, plots and more that match your company’s brand guidelines with a single _brand.yml file
  - For Quarto dashboards, websites, reports, etc.
  - For Python shiny, seaborn, matplotlib, etc.
  - For R shiny, ggplot2, plot, RMarkdown
- {ggblanket} - Similar ggplot ui but with simpler structure. Stuff that would require multiple geoms are replaced with one geom and basic arguments. Compatible with popular ggplot extensions
Resources
- Docs, Aesthetic Specifications, A Complete Guide to Scales
- ggplot2: Elegant Graphics for Data Analysis (Wickam, Navarro, Pederson)
- Modern Data Visualization with R
- R Graphics Cookbook
Don’t use stat calculating geoms and set axis limits with scale_y_continuous
- See examples of the behavior in this thread
Defaults for any {ggplot2} geom using the default_aes field (i.e. GeomBlah$default_aes )

Basic Function

viz_monthly <- 
  function(df, y_var, threshhold = NULL) {

    ggplot(df,
           aes(x = .data[["day"]],
               y = .data[[y_var]])) +
      geom_line() +
      geom_hline(yintercept = threshhold, 
                 color = "red", 
                 linetype = 2) +
      scale_x_continuous(breaks = seq(1, 29, by = 7)) +
      theme_minimal()

}

I’ve written a couple basic ones in the wild without tidyeval, but uses conditionals for theming: Fun1, Fun2

Storing geom settings in a dataframe (source)

df <- data.frame(x = seq(from = 1, to = 20, by = 1),
                 y = rnorm(20, mean = 50, sd = 4),
                 limits = "c(0,30)")

ggplot(data = df, aes(x = x, y = y)) +
  geom_line() +
  scale_y_continuous(limits= eval(parse(text=df$limits)))

FIltering Data in Geoms (source)

mtcars |> 
  rownames_to_column() |> 
  ggplot(aes(mpg, hp)) +
  geom_point() +
  geom_label(
    aes(label = rowname),
    data = ~slice_max(.x,
                      hp,
                      n = 2),
    nudge_x = 3
  )

Dynamic Axis Limits (source)

ggplot(...) +
  ... +
  scale_x_continuous(
    breaks = seq(2021, 2023, 1),
    limits = c(min(df$year) - .1, max(df$year) + .1),
    position = "top",
    labels = c("Baseline", "2022", "2023")
  )

Themes

Docs
- Theme elements
Theme Elements Cheatsheet (source)
Use the same font family to all geom_text elements
```
update_geom_defaults(
  geom = "text",
  aes(family = "Open Sans")
)
```
- geom_text is not impacted by theme settings, so setting this option keeps you from having to manually add the font family to each instance.

Setting color defaults (source)

# Discrete with a list
options(ggplot2.discrete.colour = list_of_colors)

# Continuous with a function
options(
  ggplot2.continuous.colour = \(...){
    scico::scale_color_scico(
      palette = "batlow", 
      ...
    )
  }
)

# Ordinal with a function
options(
  ggplot2.ordinal.colour = \(...){
    scale_color_viridis_d(
      option = "G", 
      direction = -1, 
      ...
    )
  }
)

If you have a particular set of palettes you like to use for projects, these options can be put into a R file and loaded via source e.g. source(here::here("_defaults.R"))

Basic Syntax

theme_psc <- function() {
  theme_void(base_family = "Open Sans") +
    theme()
}

Transparent Background

p +
  theme(
    panel.background = element_rect(fill='transparent'), #transparent panel bg
    plot.background = element_rect(fill='transparent', color=NA), #transparent plot bg
    panel.grid.major = element_blank(), #remove major gridlines
    panel.grid.minor = element_blank(), #remove minor gridlines
    legend.background = element_rect(fill='transparent'), #transparent legend bg
    legend.box.background = element_rect(fill='transparent') #transparent legend panel
  )
ggsave('myplot.png', p, bg='transparent')

theme_notebook

theme_notebook <- function(...) {
  theme_minimal() %+replace%
    theme(
      panel.background = element_rect(fill='#FFFDF9FF'),
      panel.grid.minor = element_blank(),
      plot.background = element_rect(fill='#FFFDF9FF', color=NA),
      legend.background = element_rect(fill='#FFFDF9FF'),
      legend.box.background = element_rect(fill='#FFFDF9FF'),
      ...
    )
}

Cedric Sherer (article)

theme_set(theme_minimal(base_size = 15, base_family = "Anybody"))
theme_update(
  axis.title.x = element_text(margin = margin(12, 0, 0, 0), color = "grey30"),
  axis.title.y = element_text(margin = margin(0, 12, 0, 0), color = "grey30"),
  panel.grid.minor = element_blank(),
  panel.border = element_rect(color = "grey45", fill = NA, linewidth = 1.5),
  panel.spacing = unit(.9, "lines"),
  strip.text = element_text(size = rel(1)),
  plot.title = element_text(size = rel(1.4), face = "bold", hjust = .5),
  plot.title.position = "plot"
)

Eric Bickel

ggplot2::theme_set(
  theme_bw(base_size = 12) +
    theme(
          plot.title = element_text(face = 'bold', hjust = 0),
          text = element_text(colour = '#4e5c65'),
          panel.background = element_rect('#ffffff'),
          strip.background = element_rect('#ffffff', colour = 'white'),
          plot.background = element_rect('#ffffff'),
          panel.border = element_rect(colour = '#ffffff'),
          panel.grid.major.x = element_blank(),
          panel.grid.major.y = element_blank(),
          panel.grid.minor.y = element_blank(),
          legend.background = element_rect('#ffffff'),
          legend.title = element_blank(),
          legend.position = 'right',
          legend.direction = 'vertical',
          legend.key = element_blank(),
          strip.text = element_text(face = 'bold', size = 10),
          axis.text = element_text(face = 'bold', size = 9),
          axis.title = element_blank(),
          axis.ticks = element_blank()
    )

Bespoke

Comparison Plot (source)

Example

Code

library(tidyverse)
library(scales)

comparison_plot <- 
  function(
    df,
    highlight_town,
    value_type,
    highlight_color
  ) {

    plot <- 
      df |>
        ggplot(
          aes(
            x = value,
            y = 1
          )
        ) +
        # Light gray lines for all towns
        geom_point(
          shape = 124,
          color = "gray80"
        ) +
        # Line for town to highlight
        geom_point(
          data = df |> filter(location == highlight_town),
          shape = 124,
          color = highlight_color,
          size = 10
        )

    if (value_type == "percent") {
        final_plot <- plot +
          scale_x_continuous(
            labels = percent_format(accuracy = 1)
          )
    }

    if (value_type == "number") {
      final_plot <- plot +
        scale_x_continuous(
          labels = comma_format(accuracy = 1)
        )
    }
  }

comparison_plot(
  df = single_family_homes,
  highlight_town = "Hartford",
  value_type = "percent",
  highlight_color = psc_blue
) +
  theme_whatever()


big_number_plot <- 
  function(value, 
           text,
           value_color
  ) {
    ggplot() +
      # Add value
      geom_text(
        aes(
          x = 1,
          y = 1,
          label = value
        ),
        color = value_color,
        fontface = "bold",
        size = 20,
        hjust = 0
      ) +
      # Add text
      geom_text(
        aes(
          x = 1,
          y = 2,
          label = str_to_upper(text)
        ),
        color = "gray70",
        size = 7,
        hjust = 0
      )
}

big_number_plot(
  value = "19%",
  text = "Single-Family Homes as\nPercent of All Homes",
  value_color = psc_blue
) +
  theme_whatever() # should start with theme_void