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
  • ggplot2 extension cookbook - step-by-step, how-to extension examples (mostly Stat/geom_*)

• 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()
  }

• Use Utility FIles

  • utils/base-theme.R (source)

    ## R/themes/base_theme.R
    
    #' Get default theme colors
    #' @param palette Optional custom color palette
    #' @return List of theme colors
    get_theme_colors <- function(palette = NULL) {
        list(
            background = "#f5f5f2",
            title      = "gray20",
            subtitle   = "gray30",
            text       = "gray30",
            caption    = "gray40",
            palette    = palette  # This can be overridden weekly
        )
    }
    
    #' Create base theme with consistent elements
    #' @param colors List of colors from get_theme_colors()
    #' @return ggplot theme object
    create_base_theme <- function(colors = get_theme_colors()) {
        # Only include the truly fixed elements that don't change weekly
        theme_minimal(base_size = 14, base_family = fonts$text) +
            theme(
                plot.title.position   = "plot",
                plot.caption.position = "plot",
                legend.position       = "plot",
    
                # Background elements
                plot.background  = element_rect(fill = colors$background, color = colors$background),
                panel.background = element_rect(fill = colors$background, color = colors$background),
    
                # Common margins
                plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
    
                # Text elements that stay consistent
                axis.text  = element_text(
                    family = fonts$text,
                    size   = rel(0.79),
                    color  = colors$text
                ),
                axis.title = element_text(
                    family = fonts$text,
                    size   = rel(0.93),
                    face   = "bold",
                    color  = colors$text
                )
    
                # Note: specific theme elements should be added in the scripts
            )
    }
    
    #' Extend base theme with customizations
    #' @param base_theme Existing theme to extend
    #' @param new_theme_elements List of theme elements specific to this project
    #' @return Modified ggplot theme
    extend_base_theme <- function(base_theme, new_theme_elements) {
        base_theme + new_theme_elements
    }

  • script.R (source)

    source(here::here("utils/base_theme.R"))
    
    # Get base colors with custom palette
    colors <- 
      get_theme_colors(palette = c("#f7fbff", "#9ecae1", "#2171b5", "#084594"))
    
    base_theme <- create_base_theme(colors)
    
    project_theme <- extend_base_theme(
        base_theme,
        theme(
            # project-specific modifications
            axis.line.x           = element_line(color = "#252525", linewidth = .2),
    
            panel.spacing.x       = unit(2, 'lines'),
            panel.spacing.y       = unit(1, 'lines'),
            panel.grid.major.x    = element_blank(),
            panel.grid.major.y    = element_line(color = alpha(colors[5], 0.2), linewidth = 0.2),
            panel.grid.minor      = element_blank(),
        )
    )
    
    theme_set(project_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