Five Major U.S. Fresh Vegetable Crops (2000-2022)

A streamgraph showing harvested acres across different vegetables

SWDchallenge
Data Visualization
R Programming
2025
An exploration of USDA agricultural data using streamgraphs to visualize production trends across five major vegetable crops, highlighting the dominance of sweet corn and tomatoes in U.S. fresh market production from 2000 to 2022.
Author

Steven Ponce

Published

February 1, 2025

Modified

August 18, 2025

Figure 1: A streamgraph visualization showing harvested acres of five major U.S. fresh vegetable crops from 2000 to 2022. The graph reveals layers of production with Sweet Corn and Tomatoes dominating at over 60K acres each by 2020. Smaller production areas are shown for Squash, Spinach, and Potatoes. The visualization uses color-coding and connecting lines with dots to identify each vegetable type. An upward trend in overall production is notable after 2010.

Steps to Create this Graphic

1. Load Packages & Setup

Show code 
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  tidyverse,         # Easily Install and Load the 'Tidyverse'
  ggtext,            # Improved Text Rendering Support for 'ggplot2'
  showtext,          # Using Fonts More Easily in R Graphs
  scales,            # Scale Functions for Visualization
  glue,              # Interpreted String Literals
  here,              # A Simpler Way to Find Your Files
  janitor,           # Simple Tools for Examining and Cleaning Dirty Data
  skimr,             # Compact and Flexible Summaries of Data
  camcorder,         # Record Your Plot History
  ggstream           # Create Streamplots in 'ggplot2'
) 

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code 
vegatables_raw <- read_csv(
  here::here("data/NASS - 52F3230D-BDBF-3D09-902C-7125CCE63C9F.csv")
  ) |> 
  clean_names()

3. Examine the Data

Show code 
glimpse(vegatables_raw)
skim(vegatables_raw)

4. Tidy Data

Show code 
vegatables_clean <- vegatables_raw |>
  # Select only the relevant columns 
  select(year, commodity, value) |>
  # Handle special codes 
  filter(
    value != "(D)",  # Withheld to avoid disclosing data
    value != "(Z)",  # Less than half unit
    value != "(S)",  # Insufficient reports
    value != "(NA)", # Not available
    value != "(X)"   # Not applicable
  ) |>
  mutate(
    value = as.numeric(value),
    # Format commodity names
    commodity = case_when(
      commodity == "SWEET CORN" ~ "Sweet Corn",
      commodity == "POTATOES" ~ "Potatoes",
      commodity == "TOMATOES" ~ "Tomatoes",
      TRUE ~ str_to_title(commodity)
    )
  ) |>
  # Remove any remaining NA values 
  filter(!is.na(value)) |>
  # Group and summarize
  group_by(year, commodity) |>
  summarise(total_acres = sum(value, na.rm = TRUE), .groups = 'drop') |> 
  ungroup()


# Tibble for manual label positions
label_positions <- tibble(
  commodity = c("Potatoes", "Spinach", "Squash", "Sweet Corn", "Tomatoes"),
  # X positions for vertical alignment
  x_position = c(2002, 2005, 2008, 2011, 2013),
  # Label positions - extending beyond the streams
  y_position = c(25000, 35000, 45000, 60000, -60000),  
  # Stream connection points - where the lines should touch the streams
  y_start = c(19000, 10000, 8000, 8000, -30000)    
)

5. Visualization Parameters

Show code 
### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "Potatoes"   = "#C4A484",   
  "Spinach"    = "#165B33",     
  "Squash"     = "#FFB01F",      
  "Sweet Corn" = "#F7E03D",   
  "Tomatoes"   = "#E41B17"    
  )
)

### |-  titles and caption ----
title_text   <- str_glue("Five Major U.S. Fresh Vegetable Crops (2000-2022)") 
subtitle_text <- str_glue("A streamgraph showing harvested acres across different vegetables")

# Create caption
caption_text <- create_swd_caption(
    year = 2025,
    month = "Feb",
    source_text = "Data Source: USDA Agricultural Statistics Service"
  )


# |- fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling 
    plot.title = element_text(face = "bold", size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis formatting
    axis.title   = element_text(color = colors$text, face = "bold", size = rel(0.72)),
    axis.text    = element_text(color = colors$text, size = rel(0.9)),
    axis.line.x  = element_line(color = "#252525", linewidth = .3),
    axis.ticks.x = element_line(color = colors$text),  

    # Grid customization
    panel.grid.minor   = element_blank(),
    panel.grid.major   = element_blank(),
    panel.grid.major.y = element_line(color = "grey85", linewidth = .4),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
    
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code 
p <- ggplot(vegatables_clean, 
       aes(x = year, 
           y = total_acres, 
           fill = commodity, 
           group = commodity)) +
  geom_stream(
    type = "mirror",
    bw = 0.85,
    extra_span = 0.2
  ) +
  # Add vertical connecting lines
  geom_segment(
    data = label_positions,
    aes(
      x = x_position,
      y = y_start,
      xend = x_position,
      yend = y_position
    ),
    color = colors$text,
    linewidth = 0.3,
    linetype = "solid"
  ) +
  # Add points at stream intersections
  geom_point(
    data = label_positions,
    aes(
      x = x_position,
      y = y_start
    ),
    color = colors$text,
    size = 1.5
  ) +
  # Add labels
  geom_text(
    data = label_positions,
    aes(
      x = x_position,
      y = y_position,
      label = commodity
    ),
    size = 4.5,
    fontface = "bold",
    color = colors$text,
    vjust = ifelse(label_positions$y_position < 0, 1.2, -0.2)  
  ) +
  # Add trend annotation
  annotate(
    "text", 
    x = 1999, 
    y = -53000, 
    label = str_glue("Overall vegetable production grew\n
                     significantly after 2010,dominated\n
                     by sweet corn and tomatoes with\n
                     over 60K acres each"),
    lineheight = 0.55,
    size = 4,
    fontface = "italic",
    hjust = 0
  ) +
  # Scales
  scale_fill_manual(values = colors$palette) +
  scale_x_continuous(
    breaks = seq(2000, 2025, 5),
    expand = c(0.02, 0)
  ) +
  scale_y_continuous(
    labels = scales::label_number(scale = 1/1000, suffix = "K"),
    expand = c(0.02, 0),
    position = "right",
    sec.axis = dup_axis(  # Add secondary axis for better title placement
      name = NULL,
      labels = NULL
    )
  ) +
  # Labs
  labs(
    x = "Year",
    y = NULL,
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
  ) +
  # Add custom y-axis title using annotate
  annotate(
    "text",
    x = 1998,  
    y = 0,     
    label = "Acres Harvested\n(Thousands)",
    angle = 90,
    fontface = "bold", 
    size = 3.5,
    vjust = 0.5,
    hjust = 0.5,
    color = "gray30"
  ) + 
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.7),
      family = fonts$title,
      face   = "bold",
      color  = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.95),
      family = fonts$subtitle,
      color  = colors$subtitle,
      lineheight = 1.1,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.65),
      family = fonts$caption,
      color  = colors$caption,
      lineheight = 1.1,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 15, b = 5)
    ),
  )

7. Save

Show code 
### |-  plot image ----  

source(here::here("R/image_utils.R"))
save_plot(
  p, type = 'swd', year = 2025, month = 02, 
  width = 8, height = 8
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] ggstream_0.1.0  camcorder_0.1.0 skimr_2.1.5     janitor_2.2.0  
 [5] here_1.0.1      glue_1.8.0      scales_1.3.0    showtext_0.9-7 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2    
[17] readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
[21] tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3      pillar_1.9.0     
[25] magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37    
[29] stringi_1.8.4     labeling_0.4.3    rsvg_2.6.1        rprojroot_2.0.4  
[33] fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1  cli_3.6.3        
[37] magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4        withr_3.0.2      
[41] bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29    bit_4.5.0        
[45] ragg_1.3.3        hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[49] markdown_1.13     rlang_1.1.4       gridtext_0.1.5    Rcpp_1.0.13-1    
[53] xml2_1.3.6        renv_1.0.3        svglite_2.1.3     rstudioapi_0.17.1
[57] vroom_1.6.5       jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in swd_2025_02.qmd. For the full repository, click here.

10. References

Data Sources:

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