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
    • Has 49 dependencies. A zero-dependency theme_ipsom_rc (roboto condensed font) can be had in {tinythemes}
  • {{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 and geom_label elements

  update_geom_defaults("text", list(family = "Public Sans"))
  update_geom_defaults("label", list(family = "Public Sans"))

  • geom_text (and probably geom_label) is not impacted by theme settings, so setting this option keeps you from having to manually add the font family to each instance.
  • There’s also an update_stat_defaults

• 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() +
      theme(
        plot.title = element_text(family = "Anybody",
                                  face = "bold",
                                  size = 18),
        plot.subtitle = element_text(family = "Anybody", 
                                     size = 14),
        axis.title = element_text(family = "Anybody", 
                                  size = 12),
        axis.text = element_text(family = "Anybody", 
                                 size = 10),
        panel.background = element_rect(fill='#FFFDF9FF'),
        panel.grid.minor = element_blank(),
        plot.background = element_rect(fill='#FFFDF9FF', 
                                       color = '#FFFDF9FF'),
        legend.background = element_rect(color = '#FFFDF9FF', 
                                         fill='#FFFDF9FF'),
        legend.box.background = element_rect(fill='#FFFDF9FF', 
                                             color = '#FFFDF9FF'),
        ...
      )
  }

• Andrew Heiss (source)

  theme_public <- function() {
    theme_minimal(base_family = "Public Sans") +
      theme(
        panel.grid.minor = element_blank(),
        plot.title = element_text(family = "Public Sans", 
                                  face = "bold", 
                                  size = rel(1.25)),
        plot.subtitle = element_text(family = "Public Sans Light", 
                                     face = "plain"),
        plot.caption = element_text(family = "Public Sans Light", 
                                    face = "plain"),
        axis.title = element_text(family = "Public Sans Semibold", 
                                  size = rel(0.8)),
        axis.title.x = element_text(hjust = 0),
        axis.title.y = element_text(hjust = 1),
        strip.text = element_text(
          family = "Public Sans Semibold", 
          face = "plain",
          size = rel(0.8), 
          hjust = 0
        ),
        strip.background = element_rect(fill = "grey90", 
                                        color = NA),
        legend.title = element_text(family = "Public Sans Semibold", 
                                    size = rel(0.8)),
        legend.text = element_text(size = rel(0.8)),
        legend.position = "bottom",
        legend.justification = "left",
        legend.title.position = "top",
        legend.margin = margin(l = 0, 
                               t = 0)
      )
  }

  • Don’t think he’s particular about this font. He was just working with government survey data and that’s the font they use.

• 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

• Smooth Line Chart (source)
  

  Code

  df = data.frame(x = -1:10, y = c(-3,2,1,2,5,3,6,7,3,6,10,11))
  
  smooth_spline_x = \(formula, data) {
    X = model.matrix(formula[-2], data)
    # this is dumb but good enough for a hack
    X %*% rep(1, ncol(X))
  }
  
  smooth_spline = \(formula, data, ..., weights = NULL) {
    x = smooth_spline_x(formula, data)
    y = eval(formula[[2]], data, environment(formula))
    structure(
      splinefun(x, y, ...), 
      class = c("smooth_spline", "function"),
      formula = formula
    )
  }
  
  predict.smooth_spline = \(object, newdata, ..., weights = NULL, se.fit = FALSE) {
    fit = object(smooth_spline_x(attr(object, "formula"), newdata))
    if (se.fit) {
      list(fit = data.frame(fit = fit, lwr = 0, upr = 0), se.fit = NA_real_)
    } else {
      fit
    }
  }
  
  df |>
    ggplot(aes(x, y)) +
    stat_smooth(formula = y ~ x, method = smooth_spline) +
    geom_point()

• Comet (source)
  

  Code

  library(tidyverse)
  library(jsonlite)
  library(ggtext)
  library(ggforce)
  library(extrafont)
  
  # Custom ggplot2 theme
  theme_f5 <- function () { 
    theme_minimal(base_size = 9, base_family="Roboto") %+replace% 
      theme(
        plot.background = element_rect(fill = 'floralwhite', color = "floralwhite"), 
        panel.grid.minor = element_blank(), 
        plot.subtitle = element_text(color = 'gray65', hjust = 0, margin=margin(0,0,10,0))
      )
  }
  
  # function for getting team data from pbpstats.com
  get_data <- function(season) {
  
    url <- paste0("https://api.pbpstats.com/get-totals/nba?Season=", season, "&SeasonType=Regular%2BSeason&StartType=All&GroupBy=Season&Type=Team")
  
    x <- fromJSON(url)
  
    # select variables we care about
    df <- x$multi_row_table_data %>% 
      select(TeamAbbreviation, OffPoss, PenaltyOffPoss) %>% 
      mutate(bonus_pct = PenaltyOffPoss / OffPoss, 
             season = season)
  
    return(df)
  
  }
  
  # get data from 2023-24 and 2024-25
  dat <- map_df(c("2023-24", "2024-25"), get_data)
  
  
  df <- dat %>% 
    # select relevant variables
    select(TeamAbbreviation, bonus_pct, season) %>% 
    # pivot data wide
    pivot_wider(names_from = season, values_from = bonus_pct) %>% 
    # rename columns
    rename(abbr = TeamAbbreviation, 
           bonus_pct_24 = `2023-24`, 
           bonus_pct_25 = `2024-25`) %>% 
    # create an indicator variable that tells us if a team is spending more or less time in bonus
    mutate(more_less = ifelse(bonus_pct_25 - bonus_pct_24 >= 0, "More Often", "Less Often"), 
           # convert abbr to a factor and order data by bonus time in 2024-25
           abbr = as.factor(abbr), 
           abbr = fct_reorder(abbr, bonus_pct_25))
  
  
  p <- df %>%
    ggplot() +
    geom_link(
      aes(x = bonus_pct_24, xend = bonus_pct_25,
          y = abbr, yend = abbr,
          color = more_less, linewidth = after_stat(index)), n = 1000) +
    geom_point(
      aes(x = bonus_pct_25, y = abbr, color = more_less),
      shape = 21,
      fill = "white",
      size = 3.5
    ) +
    scale_color_manual(values = c("#E64B35FF", "#00A087FF")) +
    scale_linewidth(range = c(.01, 4)) +
    scale_x_continuous(limits = c(.175, .325), breaks = seq(0, .35, .025), labels = scales::percent_format(.1)) + 
    coord_cartesian(clip = 'off') +
    theme_f5() +
    theme(legend.position = 'none',
          plot.title = element_markdown(size = 11, face = 'bold', family = 'Roboto'), 
          plot.title.position = 'plot') +
    labs(
      title = "Which Teams Are Spending 
               <span style='color:#00A087FF'>**More**</span> or 
               <span style='color:#E64B35FF'>**Less**</span> Time In the Bonus vs. Last Season?", 
      subtitle = "Teams sorted by percentage of possessions spent in the bonus in 2024-25",
      x = "Percentage of Possessions in Bonus",
      y = "")
  
  ggsave(plot = p, "bonus_pct_change.png", h = 6, w = 6, dpi = 1000, device = grDevices::png)