The Acidic Preference of Edible Plants

Visualization of preferred pH ranges for plants, ordered by temperature class and median pH preference

#TidyTuesday
Author

Aditya Dahiya

Published

February 4, 2026

About the Data

The Edible Plant Database is a valuable resource that emerged from the GROW Observatory, a European Citizen Science project focused on growing food, soil moisture sensing, and land monitoring. This comprehensive database contains detailed information on 146 edible plant species, including their ideal growing conditions, germination requirements, and time to harvest. The dataset was curated by Nicola Rennie as part of the #TidyTuesday project, a weekly data challenge in the R community. Each entry in the database includes taxonomic and common names, cultivation class, sunlight and water requirements, preferred pH levels, nutrient needs, soil type, growing season, temperature requirements for both germination and growth, time to germination and harvest, nutritional information including energy values, and potential sensitivities or challenges the plant may face. Designed to answer practical questions like “What can I plant now?” and “What can I plant that will yield a crop on a future date?”, this dataset serves as an excellent resource for gardeners, researchers, and data scientists alike to explore relationships between growing conditions and plant characteristics across different geographical locations and seasons.

Figure 1: This visualization reveals the pH preferences of 140 edible plant species from the GROW Observatory database. Each horizontal line represents one plant’s preferred pH range, colored by temperature hardiness. The clustering around pH 6-7 demonstrates that most edible plants thrive in slightly acidic to neutral soil, regardless of their temperature tolerance—a crucial insight for gardeners planning diverse plantings.

How I Made This Graphic

Loading required libraries

Code 
pacman::p_load(
  tidyverse, # All things tidy

  scales, # Nice Scales for ggplot2
  fontawesome, # Icons display in ggplot2
  ggtext, # Markdown text support for ggplot2
  showtext, # Display fonts in ggplot2
  colorspace, # Lighten and Darken colours
  sf, # Spatial Features

  patchwork,  # Composing Plots
  packcircles, # for hierarchichal packing circles
  colorspace, # Modify and play with colours, extract dominant colours
  magick  # Playing with images
)

edible_plants <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-02-03/edible_plants.csv')

Visualization Parameters

Code 
# Font for titles
font_add_google("Saira",
  family = "title_font"
)

# Font for the caption
font_add_google("Saira Condensed",
  family = "body_font"
)

# Font for plot text
font_add_google("Saira Extra Condensed",
  family = "caption_font"
)

showtext_auto()

# A base Colour
bg_col <- "grey95"
seecolor::print_color(bg_col)

# Colour for highlighted text
text_hil <- "grey20"
seecolor::print_color(text_hil)

# Colour for the text
text_col <- "grey10"
seecolor::print_color(text_col)

# Define Base Text Size
bts <- 120

# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")
plot_caption <- paste0(
  "**Data:**  GROW Observatory Edible Plant Database",
  " |  **Code:** ",
  social_caption_1,
  " |  **Graphics:** ",
  social_caption_2
)
rm(
  github, github_username, xtwitter,
  xtwitter_username, social_caption_1,
  social_caption_2
)

plot_title <- "tidy_edible_plants"

plot_subtitle <- "ttidy_edible_plants" |> 
  str_wrap(110)

Exploratory Data Analysis and Wrangling

Code 
bts <- 90

edible_plants |> 
  summarytools::dfSummary() |> 
  summarytools::view()


df1 <- edible_plants |> 
  mutate(
    ph = (preferred_ph_upper + preferred_ph_lower) / 2
  ) |> 
  mutate(
    # First, standardize capitalization
    water = str_to_title(water),
    # Then convert to ordered factor
    water = factor(
      water,
      levels = c("Very Low", "Low", "Medium", "High", "Very High"),
      ordered = TRUE
    ),
    
    # Convert to ordered factor
    temperature_class = factor(
      temperature_class,
      levels = c("Very tender", "Tender", "Half hardy", 
                 "Hardy", "Very hardy", "Very hard"),
      ordered = TRUE
    ),
    temperature_class = fct_rev(temperature_class),
    preferred_ph_lower = preferred_ph_lower + runif(n(), min = -0.2, max = 0.2),
    preferred_ph_upper = preferred_ph_upper + runif(n(), min = -0.2, max = 0.2)
  ) |> 
  select(
    water,
    temperature_class,
    cultivation, 
    ph, 
    preferred_ph_lower,
    preferred_ph_upper,
    sunlight
  )

df1 |> 
  count(temperature_class)

The Plot

Code 
# Define Base Text Size for the plot
bts <- 90

# Improved version with better readability and features
g <- df1 |>
  arrange(temperature_class, ph) |> 
  mutate(row_n = row_number()) |> 
  ggplot(aes(y = row_n)) +
  # Use a single geom_segment with geom_point overlay for cleaner code
  geom_segment(
    aes(
      x = preferred_ph_lower,
      xend = preferred_ph_upper,
      colour = temperature_class
    ),
    linewidth = 2.4,  # Make lines slightly thicker
    alpha = 0.7       # Add transparency to see overlaps
  ) +
  geom_point(
    aes(x = preferred_ph_lower),
    size = 3,
    shape = 21,
    fill = "white",
    stroke = 0.2,
    alpha = 0.5
  ) +
  geom_point(
    aes(x = preferred_ph_upper),
    size = 3,
    shape = 21,
    fill = "white",
    stroke = 0.2,
    alph = 0.5
  ) +
  paletteer::scale_colour_paletteer_d("beyonce::X26") +
  # Improved labels and titles
  labs(
    title = "Preferred pH Ranges for Edible Plants",
    subtitle = "Ordered by temperature class and median pH",
    x = "Preferred pH Range",
    y = "Plant Index",
    colour = "Temperature\nClass",
    caption = plot_caption
  ) +
  # Extend x-axis slightly for better framing
  scale_x_continuous(
    breaks = seq(4, 8, 0.5),
    limits = c(4, 8.5),
    expand = expansion(0)
  ) +
  scale_y_reverse() +
  coord_cartesian(
    clip = "off"
  ) +
  theme_minimal(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    text = element_text(colour = "grey20"),
    legend.position = "inside",
    legend.position.inside = c(0, 0.7),
    legend.justification = c(0, 1),
    legend.direction = "vertical",
    legend.text = element_text(
      margin = margin(3,3,3,3, "mm"),
      size = bts * 1.1
    ),
    legend.title = element_text(
      margin = margin(0,0,0,0, "mm"),
      lineheight = 0.3,
      size = bts * 1.3,
      face = "bold"
    ),
    legend.margin = margin(0,0,0,0, "mm"),
    axis.ticks.x.bottom = element_blank(),
    axis.ticks.length.x.bottom = unit(0, "mm"),
    panel.grid = element_blank(),
    axis.line.x = element_line(
      arrow = arrow(
        ends = "both",
        length = unit(5, "mm")
      ),
      linewidth = 0.5
    ),
    
    
    plot.title = element_text(
      margin = margin(10, 0, 2, 0, "mm"),
      hjust = 0.5,
      size = bts * 2,
      face = "bold",
      colour = text_hil
    ),
    plot.subtitle = element_text(
      margin = margin(0, 0, 5, 0, "mm"),
      hjust = 0.5,
      size = bts * 1.5,
      colour = text_hil
    ),
    plot.caption = element_textbox(
      hjust = 0.5,
      family = "caption_font",
      size = bts * 0.8,
      colour = text_hil
    ),
    axis.text.x = element_text(
      margin = margin(1,1,1,1, "mm")
    ),
    axis.title.x = element_text(
      margin = margin(1,1,1,1, "mm")
    ),
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    plot.background = element_rect(
      fill = bg_col, 
      color = NA
      )
  )

# Save the plot
ggsave(
  filename = here::here(
    "data_vizs",
    "tidy_edible_plants.png"
  ),
  plot = g,
  width = 400,
  height = 500,
  units = "mm",
  bg = bg_col
)

Savings the thumbnail for the webpage

Code 
# Saving a thumbnail

library(magick)

# Saving a thumbnail for the webpage
image_read(
  here::here(
    "data_vizs",
    "tidy_edible_plants.png"
    )
  ) |>
  image_resize(geometry = "x400") |>
  image_write(
    here::here(
      "data_vizs",
      "thumbnails",
      "tidy_edible_plants.png"
    )
  )

Session Info

Code 
pacman::p_load(
  tidyverse, # All things tidy

  scales, # Nice Scales for ggplot2
  fontawesome, # Icons display in ggplot2
  ggtext, # Markdown text support for ggplot2
  showtext, # Display fonts in ggplot2
  colorspace, # Lighten and Darken colours
  sf, # Spatial Features

  patchwork,  # Composing Plots
  packcircles # for hierarchichal packing circles
)

sessioninfo::session_info()$packages |>
  as_tibble() |>
  
  # The attached column is TRUE for packages that were 
  # explicitly loaded with library()
  dplyr::filter(attached == TRUE) |>
  dplyr::select(package,
    version = loadedversion,
    date, source
  ) |>
  dplyr::arrange(package) |>
  janitor::clean_names(
    case = "title"
  ) |>
  gt::gt() |>
  gt::opt_interactive(
    use_search = TRUE
  ) |>
  gtExtras::gt_theme_espn()
Table 1: R Packages and their versions used in the creation of this page and graphics

Links