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

A streamgraph showing harvested acres across different vegetables

SWDchallenge

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

October 7, 2026

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

Expand for 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

Expand for GitHub Repo

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

10. References

Expand for References

Data Sources:

USDA National Agricultural Statistics Service: USDA National Agricultural Statistics Service
USDA National Agricultural Statistics Service (Quick Stats): USDA National Agricultural Statistics Service (Quick Stats

--- title: "Five Major U.S. Fresh Vegetable Crops (2000-2022)" subtitle: "A streamgraph showing harvested acres across different vegetables" description: "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" date: "2025-02-01" date-modified: last-modified categories: ["SWDchallenge", "2025"] tags: [ "streamgraph", "agricultural data", "USDA statistics", "ggplot2", "data storytelling", "vegetable production", "time series", "area charts", "crop analysis", "tidyverse" ] image: "thumbnails/swd_2025_02.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/SWD Challenge/2025/swd_2025_02.html" # description: "Discover how U.S. fresh vegetable production evolved over two decades through an innovative streamgraph visualization, revealing significant growth in sweet corn and tomato cultivation after 2010." # linkedin: true # twitter: true # email: true --- ![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.](swd_2025_02.png){#fig-1} ### <mark> __Steps to Create this Graphic__ </mark> #### 1. Load Packages & Setup ```{r} #| label: load 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 ```{r} #| label: read vegatables_raw <- read_csv( here::here("data/NASS - 52F3230D-BDBF-3D09-902C-7125CCE63C9F.csv") ) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(vegatables_raw) skim(vegatables_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy 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 ```{r} #| label: params ### |- 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 ```{r} #| label: plot 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 ```{r} #| label: save ### |- 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 ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`swd_2025_02.qmd`](https://github.com/poncest/personal-website/tree/master/data_visualizations/SWD%20Challenge/2025/02_Feb/swd_2025_02.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References Data Sources: - USDA National Agricultural Statistics Service: [`USDA National Agricultural Statistics Service`](https://www.nass.usda.gov/Data_and_Statistics/) - USDA National Agricultural Statistics Service (Quick Stats): [`USDA National Agricultural Statistics Service (Quick Stats`](https://quickstats.nass.usda.gov/results/3A29A7C1-6D8D-347A-908F-89E54126430F) :::