🏞️ National Parks Colors Visualization Gallery

Beautiful data visualizations using color palettes inspired by America’s National Parks

← Back to Palette Showcase

Introduction

This gallery demonstrates the nationalparkscolors package with real-world data visualization examples. Each palette is designed to bring the natural beauty of America’s National Parks to your data stories.

Quick Installation

devtools::install_github("username/nationalparkscolors")
library(nationalparkscolors)

Scatter Plots

Classic: Iris Dataset with Acadia Palette

The Acadia palette captures the coastal beauty of Maine with ocean blues and granite greys.

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 3, alpha = 0.8) +
  scale_color_manual(values = natparks_palette("Acadia")) +
  labs(
    title = "Iris Species Measurements",
    subtitle = "Using the Acadia National Park palette",
    x = "Sepal Length (cm)",
    y = "Sepal Width (cm)"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    legend.position = "bottom"
  )

Cars Dataset with Glacier Palette

The Glacier palette brings glacial lake colors to automotive data.

ggplot(mtcars, aes(x = wt, y = mpg, color = hp, size = disp)) +
  geom_point(alpha = 0.7) +
  scale_color_gradientn(colors = natparks_palette("Glacier", type = "continuous")) +
  scale_size_continuous(range = c(3, 10)) +
  labs(
    title = "Vehicle Performance Characteristics",
    subtitle = "Using the Glacier National Park palette (continuous)",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon",
    color = "Horsepower",
    size = "Displacement"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    legend.position = "right"
  )

Bar Charts

Simple Bar Chart with Yellowstone Palette

The Yellowstone palette showcases the park’s geothermal diversity.

# Create sample data
park_visitors <- data.frame(
  Park = c("Yellowstone", "Grand Canyon", "Yosemite", "Zion", "Acadia"),
  Visitors = c(4.86, 5.97, 3.67, 4.69, 3.97),
  stringsAsFactors = FALSE
)

ggplot(park_visitors, aes(x = reorder(Park, Visitors), y = Visitors, fill = Park)) +
  geom_col(show.legend = FALSE, width = 0.7) +
  scale_fill_manual(values = natparks_palette("Yellowstone")) +
  coord_flip() +
  labs(
    title = "National Park Visitors (2023)",
    subtitle = "Millions of visitors - Using Yellowstone palette",
    x = NULL,
    y = "Visitors (Millions)"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    panel.grid.major.y = element_blank()
  )

Grouped Bar Chart with Grand Teton Palette

The Grand Teton palette evokes alpine meadows and mountain peaks.

# Sample quarterly data
quarterly_data <- data.frame(
  Quarter = rep(c("Q1", "Q2", "Q3", "Q4"), each = 3),
  Category = rep(c("Product A", "Product B", "Product C"), 4),
  Sales = c(23, 45, 32, 28, 52, 38, 35, 58, 42, 40, 65, 48)
)

ggplot(quarterly_data, aes(x = Quarter, y = Sales, fill = Category)) +
  geom_col(position = "dodge", width = 0.7) +
  scale_fill_manual(values = natparks_palette("GrandTeton")) +
  labs(
    title = "Quarterly Sales by Product Category",
    subtitle = "Using the Grand Teton National Park palette",
    x = "Quarter",
    y = "Sales (thousands)",
    fill = "Product"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    legend.position = "bottom"
  )

Stacked Bar Chart with Smokies Palette

The Smokies palette reflects the misty mountains and diverse forests.

ggplot(quarterly_data, aes(x = Quarter, y = Sales, fill = Category)) +
  geom_col(position = "stack", width = 0.7) +
  scale_fill_manual(values = natparks_palette("Smokies")) +
  labs(
    title = "Total Quarterly Sales Composition",
    subtitle = "Using the Great Smoky Mountains palette",
    x = "Quarter",
    y = "Total Sales (thousands)",
    fill = "Product"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    legend.position = "bottom"
  )

Line Charts

Time Series with Crater Lake Palette

The Crater Lake palette showcases the lake’s famous deep blue colors.

# Use economics dataset
economics_subset <- economics %>%
  filter(date >= as.Date("2000-01-01"))

ggplot(economics_subset, aes(x = date, y = unemploy / 1000)) +
  geom_line(color = natparks_palette("CraterLake")[1], size = 1.2) +
  geom_area(fill = natparks_palette("CraterLake")[1], alpha = 0.3) +
  labs(
    title = "US Unemployment Over Time",
    subtitle = "Using Crater Lake palette for a water-like effect",
    x = "Year",
    y = "Unemployed (Millions)"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    panel.grid.minor = element_blank()
  )

Multiple Lines with Zion Palette

The Zion palette features red rock canyon colors.

# Reshape economics data for multiple lines
econ_long <- economics %>%
  filter(date >= as.Date("2010-01-01")) %>%
  select(date, unemploy, pop) %>%
  mutate(
    unemploy = unemploy / 1000,
    pop = pop / 1000
  ) %>%
  pivot_longer(cols = c(unemploy, pop), names_to = "metric", values_to = "value")

ggplot(econ_long, aes(x = date, y = value, color = metric)) +
  geom_line(size = 1.2) +
  scale_color_manual(
    values = natparks_palette("Zion")[c(1, 3)],
    labels = c("Population (000s)", "Unemployed (000s)")
  ) +
  facet_wrap(~metric, scales = "free_y", ncol = 1) +
  labs(
    title = "US Economic Indicators",
    subtitle = "Using the Zion National Park palette",
    x = "Year",
    y = "Value"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    legend.position = "none",
    strip.text = element_text(face = "bold", size = 12)
  )

Density Plots

Single Density with Yosemite Palette

The Yosemite palette captures granite cliffs and waterfall mist.

ggplot(diamonds, aes(x = price)) +
  geom_density(fill = natparks_palette("Yosemite")[3], 
               color = natparks_palette("Yosemite")[4], 
               alpha = 0.7, 
               size = 1) +
  scale_x_continuous(labels = scales::dollar_format()) +
  labs(
    title = "Distribution of Diamond Prices",
    subtitle = "Using the Yosemite National Park palette",
    x = "Price",
    y = "Density"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18)
  )

Multiple Densities with Bryce Canyon Palette

The Bryce Canyon palette reflects the park’s famous hoodoos and rock formations.

ggplot(diamonds, aes(x = price, fill = cut)) +
  geom_density(alpha = 0.6) +
  scale_fill_manual(values = natparks_palette("BryceCanyon")) +
  scale_x_continuous(labels = scales::dollar_format(), limits = c(0, 20000)) +
  labs(
    title = "Diamond Price Distribution by Cut Quality",
    subtitle = "Using the Bryce Canyon National Park palette",
    x = "Price",
    y = "Density",
    fill = "Cut Quality"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    legend.position = "bottom"
  )

Box Plots and Violin Plots

Box Plot with Grand Canyon Palette

The Grand Canyon palette showcases layered sedimentary rocks.

ggplot(mpg, aes(x = class, y = hwy, fill = class)) +
  geom_boxplot(show.legend = FALSE, alpha = 0.8) +
  scale_fill_manual(values = rep(natparks_palette("GrandCanyon"), 2)) +
  coord_flip() +
  labs(
    title = "Highway Fuel Efficiency by Vehicle Class",
    subtitle = "Using the Grand Canyon National Park palette",
    x = "Vehicle Class",
    y = "Highway MPG"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    panel.grid.major.y = element_blank()
  )

Violin Plot with Sequoia Palette

The Sequoia palette represents giant sequoia trees and Sierra Nevada mountains.

ggplot(diamonds %>% filter(cut %in% c("Fair", "Good", "Very Good", "Premium", "Ideal")), 
       aes(x = cut, y = carat, fill = cut)) +
  geom_violin(show.legend = FALSE, alpha = 0.8) +
  scale_fill_manual(values = natparks_palette("Sequoia")) +
  labs(
    title = "Diamond Carat Distribution by Cut Quality",
    subtitle = "Using the Sequoia National Park palette",
    x = "Cut Quality",
    y = "Carat"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18)
  )

Heatmaps

Correlation Heatmap with Death Valley Palette

The Death Valley palette captures extreme desert colors.

# Create correlation matrix
mtcars_cor <- cor(mtcars[, 1:7])
mtcars_cor_df <- as.data.frame(as.table(mtcars_cor))
names(mtcars_cor_df) <- c("Var1", "Var2", "Correlation")

ggplot(mtcars_cor_df, aes(x = Var1, y = Var2, fill = Correlation)) +
  geom_tile(color = "white") +
  scale_fill_gradientn(
    colors = natparks_palette("DeathValley", 100, type = "continuous"),
    limits = c(-1, 1)
  ) +
  geom_text(aes(label = round(Correlation, 2)), size = 3) +
  labs(
    title = "Vehicle Characteristics Correlation Matrix",
    subtitle = "Using the Death Valley National Park palette",
    x = NULL,
    y = NULL
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid = element_blank()
  )

Tile Heatmap with Olympic Palette

The Olympic palette reflects rainforest and coastal environments.

# Create sample monthly data
months <- month.abb
years <- 2020:2024
heatmap_data <- expand.grid(Month = months, Year = years)
set.seed(42)
heatmap_data$Value <- runif(nrow(heatmap_data), 10, 100)
heatmap_data$Month <- factor(heatmap_data$Month, levels = month.abb)

ggplot(heatmap_data, aes(x = Year, y = Month, fill = Value)) +
  geom_tile(color = "white", size = 0.5) +
  scale_fill_gradientn(colors = natparks_palette("Olympic", 100, type = "continuous")) +
  scale_x_continuous(breaks = 2020:2024) +
  labs(
    title = "Monthly Performance Heatmap",
    subtitle = "Using the Olympic National Park palette",
    x = "Year",
    y = "Month",
    fill = "Value"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    panel.grid = element_blank()
  )

Faceted Plots

Facet Wrap with Arches Palette

The Arches palette features red sandstone formations.

ggplot(mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 2, alpha = 0.7) +
  facet_wrap(~year, ncol = 2) +
  scale_color_manual(values = rep(natparks_palette("Arches"), 2)) +
  labs(
    title = "Engine Displacement vs Highway MPG",
    subtitle = "Comparing 1999 and 2008 models - Using Arches palette",
    x = "Engine Displacement (L)",
    y = "Highway MPG",
    color = "Vehicle Class"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    legend.position = "bottom",
    strip.text = element_text(face = "bold", size = 12)
  )

Facet Grid with Shenandoah Palette

The Shenandoah palette captures Blue Ridge Mountains and autumn colors.

ggplot(diamonds %>% 
         filter(cut %in% c("Fair", "Good", "Ideal")) %>%
         sample_n(1000), 
       aes(x = carat, y = price, color = color)) +
  geom_point(alpha = 0.6, size = 1.5) +
  facet_grid(cut ~ clarity) +
  scale_color_manual(values = rep(natparks_palette("Shenandoah"), 2)) +
  scale_y_continuous(labels = scales::dollar_format()) +
  labs(
    title = "Diamond Prices by Cut and Clarity",
    subtitle = "Using the Shenandoah National Park palette",
    x = "Carat",
    y = "Price",
    color = "Color Grade"
  ) +
  theme_minimal(base_size = 11) +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    legend.position = "bottom",
    strip.text = element_text(face = "bold", size = 10)
  )

Maps

US State Map with RockyMountain Palette

The Rocky Mountain palette features alpine tundra and snow-capped peaks.

# Get US state map data
us_states <- map_data("state")
us_states$region <- tools::toTitleCase(us_states$region)

# Create sample state data
state_data <- data.frame(
  region = unique(us_states$region),
  value = runif(length(unique(us_states$region)), 0, 100)
)

# Merge with map data
us_states <- left_join(us_states, state_data, by = "region")

ggplot(us_states, aes(x = long, y = lat, group = group, fill = value)) +
  geom_polygon(color = "white", size = 0.2) +
  scale_fill_gradientn(
    colors = natparks_palette("RockyMountain", 100, type = "continuous"),
    name = "Value"
  ) +
  coord_map("albers", lat0 = 39, lat1 = 45) +
  labs(
    title = "United States - Sample Data Visualization",
    subtitle = "Using the Rocky Mountain National Park palette"
  ) +
  theme_void(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5),
    legend.position = "right"
  )

Advanced Examples

Ridgeline Plot with Denali Palette

The Denali palette reflects Alaska’s tundra and mountain landscapes.

# Prepare data
lincoln_temps <- data.frame(
  Month = rep(month.name, each = 30),
  Temperature = c(
    rnorm(30, 25, 5),  # Jan
    rnorm(30, 30, 5),  # Feb
    rnorm(30, 40, 5),  # Mar
    rnorm(30, 55, 5),  # Apr
    rnorm(30, 65, 5),  # May
    rnorm(30, 75, 5),  # Jun
    rnorm(30, 80, 5),  # Jul
    rnorm(30, 78, 5),  # Aug
    rnorm(30, 70, 5),  # Sep
    rnorm(30, 58, 5),  # Oct
    rnorm(30, 42, 5),  # Nov
    rnorm(30, 28, 5)   # Dec
  )
)
lincoln_temps$Month <- factor(lincoln_temps$Month, levels = rev(month.name))

ggplot(lincoln_temps, aes(x = Temperature, y = Month, fill = Month)) +
  ggridges::geom_density_ridges(alpha = 0.8, scale = 3, show.legend = FALSE) +
  scale_fill_manual(values = rep(natparks_palette("Denali"), 3)) +
  labs(
    title = "Temperature Distribution by Month",
    subtitle = "Using the Denali National Park palette",
    x = "Temperature (°F)",
    y = NULL
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18)
  )

Lollipop Chart with Joshua Tree Palette

The Joshua Tree palette captures desert landscapes and night skies.

# Create sample data
park_scores <- data.frame(
  Park = c("Yellowstone", "Yosemite", "Grand Canyon", "Zion", "Glacier",
           "Acadia", "Denali", "Olympic", "Arches", "Bryce Canyon"),
  Score = c(9.2, 9.5, 9.7, 9.1, 9.4, 8.8, 9.0, 8.9, 9.3, 9.6)
) %>%
  arrange(Score)

park_scores$Park <- factor(park_scores$Park, levels = park_scores$Park)

ggplot(park_scores, aes(x = Park, y = Score)) +
  geom_segment(aes(x = Park, xend = Park, y = 0, yend = Score),
               color = natparks_palette("JoshuaTree")[1], size = 1.5) +
  geom_point(color = natparks_palette("JoshuaTree")[5], size = 5) +
  coord_flip() +
  ylim(0, 10) +
  labs(
    title = "National Park Visitor Satisfaction Scores",
    subtitle = "Using the Joshua Tree National Park palette",
    x = NULL,
    y = "Satisfaction Score (out of 10)"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank()
  )

Waffle Chart Concept with Everglades Palette

The Everglades palette represents wetlands and sawgrass marshes.

# Create sample categorical data
survey_data <- data.frame(
  Response = c("Strongly Agree", "Agree", "Neutral", "Disagree", "Strongly Disagree"),
  Count = c(45, 30, 15, 7, 3)
)

ggplot(survey_data, aes(x = "", y = Count, fill = Response)) +
  geom_col(width = 1) +
  scale_fill_manual(values = natparks_palette("Everglades")) +
  coord_polar(theta = "y") +
  labs(
    title = "Survey Response Distribution",
    subtitle = "Using the Everglades National Park palette",
    fill = "Response"
  ) +
  theme_void(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5),
    legend.position = "right"
  )

Combining Multiple Palettes

Creative Comparison with Badlands Palette

The Badlands palette features layered rock formations and prairie.

# Create faceted comparison
comparison_data <- data.frame(
  x = rep(1:10, 4),
  y = c(
    cumsum(rnorm(10, 0.5, 0.3)),
    cumsum(rnorm(10, 0.3, 0.2)),
    cumsum(rnorm(10, 0.7, 0.4)),
    cumsum(rnorm(10, 0.4, 0.3))
  ),
  Category = rep(c("A", "B", "C", "D"), each = 10)
)

ggplot(comparison_data, aes(x = x, y = y, color = Category, fill = Category)) +
  geom_line(size = 1.2) +
  geom_ribbon(aes(ymin = 0, ymax = y), alpha = 0.3) +
  scale_color_manual(values = natparks_palette("Badlands")[1:4]) +
  scale_fill_manual(values = natparks_palette("Badlands")[1:4]) +
  facet_wrap(~Category, ncol = 2, scales = "free_y") +
  labs(
    title = "Growth Trajectories by Category",
    subtitle = "Using the Badlands National Park palette",
    x = "Time Period",
    y = "Cumulative Value"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 18),
    legend.position = "none",
    strip.text = element_text(face = "bold", size = 12)
  )

Tips for Effective Visualization

Best Practices

  1. Match palette to data context: Use ocean/water palettes (Crater Lake, Acadia) for water-related data
  2. Consider color count: Most palettes have 5 colors; use type = "continuous" for more
  3. Test accessibility: Ensure sufficient contrast for all viewers
  4. Stay consistent: Use the same palette family for related visualizations
  5. Let data shine: Don’t let colors overwhelm the message

Palette Selection Guide

  • Categorical data (3-5 categories): Use discrete palettes as-is
  • Continuous data: Add type = "continuous" for smooth gradients
  • Diverging data: Choose palettes with warm-to-cool transitions
  • Maps: Use single-color gradients (e.g., Crater Lake blues)
  • Time series: Consider palettes with calm, flowing colors

Explore More

View All Palettes →

Created with the nationalparkscolors package

Inspired by the natural beauty of America’s National Parks

Session Information

sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22631)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] mapproj_1.2.12            maps_3.4.2.1              tidyr_1.3.0              
## [4] dplyr_1.0.10              ggplot2_3.4.1             rmarkdown_2.20           
## [7] nationalparkscolors_0.1.0
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1  xfun_0.39         bslib_0.4.2       remotes_2.4.2     purrr_1.0.1      
##  [6] colorspace_2.0-3  vctrs_0.5.2       generics_0.1.3    testthat_3.1.6    usethis_2.1.5    
## [11] htmltools_0.5.4   yaml_2.3.6        rlang_1.0.6       pkgbuild_1.3.1    jquerylib_0.1.4  
## [16] pillar_1.11.1     glue_1.8.0        withr_2.5.0       DBI_1.2.3         sessioninfo_1.2.2
## [21] plyr_1.8.7        lifecycle_1.0.3   munsell_0.5.0     gtable_0.3.1      devtools_2.4.3   
## [26] memoise_2.0.1     evaluate_0.20     labeling_0.4.2    knitr_1.42        callr_3.7.3      
## [31] fastmap_1.1.0     ps_1.6.0          curl_7.0.0        highr_0.11        Rcpp_1.0.9       
## [36] scales_1.2.1      cachem_1.0.6      desc_1.4.1        pkgload_1.2.4     jsonlite_1.8.4   
## [41] farver_2.1.0      fs_1.6.6          brio_1.1.3        digest_0.6.31     processx_3.8.0   
## [46] rprojroot_2.0.3   grid_4.1.3        cli_3.6.0         tools_4.1.3       magrittr_2.0.4   
## [51] sass_0.4.10       tibble_3.3.1      crayon_1.5.2      pkgconfig_2.0.3   ellipsis_0.3.2   
## [56] rsconnect_0.8.29  prettyunits_1.1.1 ggridges_0.5.3    assertthat_0.2.1  rstudioapi_0.14  
## [61] R6_2.5.1          compiler_4.1.3