Lisa’s Vegetable Garden Data

Exploring Lisa Lendway’s vegetable garden from summer 2020 and summer 2021, from her {gardenR} package.

Lisa’s Vegetable Garden Data

The data for this analysis is sourced from the gardenR package , which contains records from Lisa Lendway’s vegetable garden, collected during the summers of 2020 and 2021. This dataset, first introduced in 2021 and updated with 2021 data on January 29, 2022, was utilized in Lisa Lendway’s Introduction to Data Science course at Macalester College to teach various data science concepts. The circular graph created from this data illustrates the average weights of 12 different tomato varieties harvested in 2020. The analysis reveals that the Amish Paste Tomato variety is the heaviest, while the Grape Tomatoes are the smallest. This visual representation provides a clear comparison of the tomato varieties based on their average weights.

This circular plot displays the average weights of 12 different tomato varieties harvested in 2020 from Lisa’s vegetable garden. The Amish Paste Tomato stands out as the heaviest variety, while the Grape Tomatoes are the smallest. The visualization provides a clear comparison of each variety’s average weight.

How I made this graphic?

Loading required libraries, data import & creating custom functions

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support for ggplot2
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # Lighten and Darken colours
library(patchwork)            # Combining plots

# Extras and Annotations
library(magick)               # Image processing
library(ggfittext)            # Fitting labels inside bar plots
 
# Load Data
harvest_2020 <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-05-28/harvest_2020.csv')

Exploratory Data Analysis & Data Wrangling

bg_col <- "#fcd7d4"

harvest_2020 |> visdat::vis_dat()

harvest_2020 |> summarytools::dfSummary() |> summarytools::view()

df <- harvest_2020 |> 
  filter(vegetable == "tomatoes") |> 
  group_by(vegetable, variety) |>
  summarise(
    avg_wt = mean(weight, na.rm = TRUE),
    n = n()
  ) |> 
  ungroup() |> 
  mutate(
    id = row_number(),
    file_name = paste0(
      "temp1_tomato_", id, ".png"
    )
  )

library(magick)

for (i in 1:12) {
  im <- magick::image_read(
  paste0("temp_tomato_", i, ".jpg")
)
  
  # Technique Credits: https://stackoverflow.com/questions/64597525/r-magick-square-crop-and-circular-mask
  # get height, width and crop longer side to match shorter side
  ii <- image_info(im)
  ii_min <- min(ii$width, ii$height)
  im1 <- image_crop(
    im, 
    geometry = paste0(ii_min, "x", ii_min, "+0+0"), 
    repage = TRUE
    )
  
  # create a new image with white background and black circle
  fig <- image_draw(image_blank(ii_min, ii_min))
  symbols(ii_min / 2, ii_min / 2, 
          circles = (ii_min / 2) - 3, 
          bg = "black", inches = FALSE, add = TRUE)
  dev.off()
  
  # create an image composite using both images
  im2 <- magick::image_composite(im1, fig, operator = "copyopacity")
  im2 <- image_resize(im2, "x400")
  # set background as white
  
  
  image_write(
    image = magick::image_background(im2, bg_col),
    path = paste0("temp1_tomato_", i,".png"),
    format = "png"
    )
}

Visualization Parameters

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

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

# Font for plot text
font_add_google("Saira",
  family = "body_font"
) 

bts <- 80

showtext_auto()

# Credits for coffeee palette
mypal <- paletteer::paletteer_d("RColorBrewer::Reds")
bg_col <- mypal[2]
text_col <-  mypal[9]
text_hil <-  mypal[7]
line_col <- mypal[4]

# 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>")

Annotation Text for the Plot

plot_title <- "Hefty Harvest:\nTomato Weights from Lily's Garden"

plot_caption <- paste0(
  "**Data:** {gardenR} Lisa's Vegetable Garden Data", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )

plot_subtitle <- str_wrap("The average weights of 12 tomato varieties from Lisa's 2020 garden: Amish Paste Tomato is the heaviest and Grape Tomatoes are the lightest.", 80)

The Base Plot

g <- df |> 
  ggplot(
  mapping = aes(
    x = reorder(variety, avg_wt),
    y = avg_wt
    )
  ) +
  ggimage::geom_image(
    aes(image = file_name),
    size = 0.05
  ) +
  geom_text(
    aes(
    label = paste0(
      variety, " (", round(avg_wt, 0), " gm)"
      )
    ),
    hjust = "outward",
    vjust = "outward",
    family = "caption_font",
    lineheight = 0.5,
    size = bts/4, 
    colour = text_col
  ) +
  # annotate(
  #   geom = "text",
  #   label = plot_subtitle,
  #   x = 6, y = 1000,
  #   hjust = 0.5,
  #   colour = text_hil,
  #   lineheight = 0.35,
  #   size = bts / 2,
  #   family = "caption_font"
  # ) +
  scale_y_continuous(
    limits = c(200, 1000),
    labels = label_number(suffix = " gm"),
    expand = expansion(0),
    breaks = seq(200, 1000, 200)
  ) +
  scale_x_discrete(
    expand = expansion(c(0, 0.1))
  ) +
  coord_radial(
    theta = "x",
    # start = -90 * pi / 180,
    # end = +90 * pi / 180,
    clip = "off",
    r_axis_inside = TRUE
  ) +
  labs(
    title = plot_title,
    caption = plot_caption,
    subtitle = plot_subtitle
  ) +
  theme_minimal(
    base_family = "caption_font",
    base_size = bts
  ) +
  theme(
    axis.title = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_line(
      colour = line_col,
      linewidth = 0.5,
      linetype = 3
    ),
    panel.grid.minor.y = element_line(
      colour = line_col,
      linewidth = 0.5
    ),
    axis.text.x = element_blank(),
    axis.text.y = element_text(colour = text_hil),
    panel.border = element_blank(),
    plot.title = element_text(
      hjust = 0.5,
      family = "title_font",
      margin = margin(5,0,5,0, "mm"),
      colour = text_hil,
      size = 2 * bts,
      lineheight = 0.3
    ),
    plot.subtitle = element_text(
      margin = margin(0,0,20,0,"mm"),
      lineheight = 0.35,
      colour = text_hil,
      hjust = 0.5,
      size = 1.5 * bts
    ),
    plot.caption = element_textbox(
      hjust = 0.5,
      family = "caption_font",
      margin = margin(10,0,0,0, "mm"),
      colour = text_hil
    ),
    plot.background = element_rect(
      fill = bg_col, colour = bg_col
    ),
    panel.background = element_rect(
      fill = bg_col, colour = bg_col
    )
  )

ggsave(
  filename = here::here("data_vizs", "tidy_lisa_garden.png"),
  plot = g,
  width = 400,    # Best Twitter Aspect Ratio = 4:5
  height = 400,   
  units = "mm",
  bg = bg_col
)

Adding annotations to the plot

g <- p1 +
  inset_element(
    p = inset1,
    on_top = TRUE,
    ignore_tag = TRUE,
    clip = FALSE,
    align_to = "panel",
    left = 0.32, right = 0.7,
    bottom = 0.3, top = 0.7
  ) +
  inset_element(
    p = p_subtitle,
    on_top = TRUE,
    ignore_tag = TRUE,
    clip = FALSE,
    align_to = "panel",
    left = -1,  right = 1,
    top = 1.3, bottom = 0.7
  ) +
  
  # Add stacked horizontal Bar Charts
  inset_element(
    p = p2,
    on_top = TRUE,
    clip = FALSE,
    align_to = "panel",
    left = 0.45, right = 1.08,
    top = 0.27,
    bottom = -0.3
  ) +
  
  inset_element(
    p = p3,
    on_top = TRUE,
    clip = FALSE,
    align_to = "panel",
    left = -0.08, right = 0.55,
    top = 0.27,
    bottom = -0.3
  ) +
  
  plot_annotation(
    theme = theme(
      plot.background = element_rect(
        fill = bg_col,
        colour = bg_col
      )
    )
  )

Savings the graphics

ggsave(
  filename = here::here("data_vizs", "tidy_lisa_garden.png"),
  plot = g,
  width = 400,    # Best Twitter Aspect Ratio = 4:5
  height = 500,   
  units = "mm",
  bg = bg_col
)

# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", "tidy_lisa_garden.png")) |> 
  image_resize(geometry = "400") |> 
  image_write(here::here("data_vizs", "thumbnails", "tidy_lisa_garden.png"))



unlink(paste0("temp_tomato_", 1:12, ".jpg"))

unlink(paste0("temp1_tomato_", 1:12, ".png"))