library(ggplot2)
library(dplyr)
library(readr)

1 Why ggplot2?

R has two main plotting systems: base R (plot(), hist(), barplot()) and ggplot2. We use ggplot2 because it follows a consistent grammar — once you learn the grammar, you can build any visualization by combining a small set of building blocks.

The “gg” in ggplot2 stands for Grammar of Graphics, a framework where every plot is described by the same set of components.

In this tutorial, we learn the grammar using real FIA (Forest Inventory and Analysis) data — the same dataset you will use throughout this course to investigate tree species distributions, community composition, and biogeographic patterns across the eastern United States.

2 The Grammar: Seven Building Blocks

Every ggplot2 plot is built from these components:

Component What it does Required?
Data The data frame to plot Yes
Aesthetics (aes()) Maps columns to visual properties (x, y, color, size, …) Yes
Geom The type of mark to draw (points, bars, lines, …) Yes
Facets Split into sub-panels No
Stats Statistical transformations (counts, means, smoothers) No (many geoms have defaults)
Scales Control how data values map to visual values (axis limits, color palettes) No (sensible defaults)
Theme Non-data visual elements (fonts, gridlines, background) No (sensible defaults)

You build a plot by adding layers with the + operator:

ggplot(data, aes(x = ..., y = ...)) +
  geom_...() +
  labs() +
  theme_...()

3 Loading the FIA Data

We will use the eastern US FIA dataset containing ~2.8 million tree records across 31 states. Let’s load and prepare the data.

# Load tree data and species reference table
tree <- read_csv("fia_eastern31_recent.csv", show_col_types = FALSE)
ref_species <- read_csv("REF_SPECIES_trimmed.csv", show_col_types = FALSE)

# Join to get common names
tree <- tree %>% left_join(ref_species, by = "SPCD")

# Filter to live trees only
live <- tree %>% filter(STATUSCD == 1)

# For quick examples, we'll also create a Tennessee subset
tn <- live %>% filter(STATE_ABBR == "TN")
# Look at the data
head(tn, 5)
## # A tibble: 5 × 48
##   TREE_CN  PLT_CN INVYR STATECD COUNTYCD  PLOT  SUBP  SPCD SPGRPCD   DIA    HT
##     <dbl>   <dbl> <dbl>   <dbl>    <dbl> <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
## 1 7.34e14 5.04e14  2017      47        1     1     1   611      34  13.3   101
## 2 7.34e14 5.04e14  2017      47        1     1     1   611      34   9.2    62
## 3 7.34e14 5.04e14  2017      47        1     1     1   621      39  11.8    87
## 4 7.34e14 5.04e14  2017      47        1     1     1   611      34  10.3    74
## 5 7.34e14 5.04e14  2017      47        1     1     1   611      34   5.9    62
## # ℹ 37 more variables: ACTUALHT <dbl>, STATUSCD <dbl>, CR <dbl>, CCLCD <dbl>,
## #   TPA_UNADJ <dbl>, DRYBIO_AG <dbl>, CARBON_AG <dbl>, COND_STATUS_CD <dbl>,
## #   FORTYPCD <dbl>, SITECLCD <dbl>, STDAGE <dbl>, OWNCD <dbl>, OWNGRPCD <dbl>,
## #   SLOPE <dbl>, ASPECT <dbl>, DSTRBCD1 <dbl>, BALIVE <dbl>, LAT <dbl>,
## #   LON <dbl>, ELEV <dbl>, ECOSUBCD <chr>, MEASYEAR <dbl>, TPA <dbl>,
## #   EXPALL <dbl>, EXPCURR <dbl>, GRIDID <dbl>, centroidLat <dbl>,
## #   centroidLon <dbl>, STATE_ABBR <chr>, COMMON_NAME <chr>, GENUS <chr>, …

Key columns we will use:

Column Description
DIA Diameter at breast height (inches)
HT Total tree height (feet)
COMMON_NAME Species common name (from REF_SPECIES)
SFTWD_HRDWD Softwood (“S”) or Hardwood (“H”)
STATE_ABBR State abbreviation
centroidLat, centroidLon Grid cell center coordinates
ELEV Elevation (feet)
BALIVE Live-tree basal area per acre (sq ft)

4 Your First Plot

ggplot(data = tn, aes(x = DIA, y = HT)) +
  geom_point()

What happened here:

  • ggplot(data = tn, ...) — told ggplot which data frame to use (Tennessee trees)
  • aes(x = DIA, y = HT) — mapped diameter to the x-axis, tree height to the y-axis
  • geom_point() — drew each tree as a point

You can already see an ecological pattern: diameter and height are positively correlated, but the relationship isn’t linear — height plateaus at larger diameters. This is a fundamental growth pattern in trees.

5 Aesthetics: Mapping Data to Visual Properties

The aes() function maps columns in your data to visual properties of the plot. Common aesthetics include:

Aesthetic Controls
x Position on x-axis
y Position on y-axis
color Color of points, lines, outlines
fill Fill color of bars, areas
size Size of points
shape Shape of points
alpha Transparency (0 = invisible, 1 = opaque)
linetype Line style (solid, dashed, dotted)

5.1 Mapping color to a variable

ggplot(tn, aes(x = DIA, y = HT, color = SFTWD_HRDWD)) +
  geom_point(size = 1.5, alpha = 0.3) +
  labs(color = "Wood Type")

Each wood type gets a different color automatically. ggplot2 added a legend because the color carries information. Notice that softwoods (conifers like pines) tend to be taller for a given diameter than hardwoods — they invest more in vertical growth.

5.2 Setting vs. Mapping

There is an important difference:

  • Mapping (inside aes()): links a visual property to a column — it varies with the data
  • Setting (outside aes()): applies a fixed value to all points
# MAPPING: color varies by wood type
ggplot(tn, aes(x = DIA, y = HT, color = SFTWD_HRDWD)) +
  geom_point(alpha = 0.3)

# SETTING: all points are steelblue
ggplot(tn, aes(x = DIA, y = HT)) +
  geom_point(color = "steelblue", alpha = 0.3, size = 1.5)

Common mistake: Putting a fixed color inside aes():

# WRONG — ggplot treats "blue" as a data value, not a color
ggplot(tn, aes(x = DIA, y = HT, color = "blue")) +
  geom_point()

6 Geoms: The Shapes on Your Plot

Each geom_*() function draws a different type of mark. Here are the ones you will use most often.

6.1 geom_histogram() — Distributions of one variable

ggplot(tn, aes(x = DIA)) +
  geom_histogram(binwidth = 2, fill = "steelblue", color = "white") +
  labs(title = "Diameter Distribution of Trees in Tennessee",
       x = "DBH (inches)", y = "Count")

Key arguments: binwidth controls the width of each bar. Smaller = more detail, larger = smoother.

Notice the shape: a strong right skew, with many small trees and few large ones. This is the classic reverse-J diameter distribution — a hallmark of an uneven-aged, self-replacing forest. Most trees are young and small; only a few survive to become large.

6.2 geom_density() — Smooth distribution curves

# Compare diameter distributions: softwoods vs. hardwoods
ggplot(tn, aes(x = DIA, fill = SFTWD_HRDWD)) +
  geom_density(alpha = 0.4) +
  labs(title = "Diameter Distribution: Softwoods vs. Hardwoods (TN)",
       x = "DBH (inches)", fill = "Wood Type")

alpha controls transparency — essential when overlapping multiple curves.

6.3 geom_point() — Scatter plots

ggplot(tn, aes(x = DIA, y = HT)) +
  geom_point(alpha = 0.2, color = "darkgreen", size = 0.8) +
  labs(title = "Tree Height vs. Diameter in Tennessee",
       x = "DBH (inches)", y = "Height (ft)")

With ~200,000 points, alpha (transparency) is critical. Without it, the plot would be a solid blob. Low alpha values let you see density — where are the MOST trees concentrated?

6.4 geom_smooth() — Trend lines

ggplot(tn, aes(x = DIA, y = HT)) +
  geom_point(alpha = 0.1, size = 0.5) +
  geom_smooth(method = "loess", color = "red", se = TRUE) +
  labs(title = "Height-Diameter Relationship (TN)",
       x = "DBH (inches)", y = "Height (ft)")

  • method = "loess" fits a local regression (flexible curve). Other options: "lm" for a straight line.
  • se = TRUE shows the confidence band around the line.

6.5 geom_col() — Bar charts from pre-computed values

Use geom_col() when you have already calculated the values (e.g., means, counts). Use geom_bar() when you want ggplot to count for you.

# Count stems per species and get top 10
top10_tn <- tn %>%
  count(COMMON_NAME, sort = TRUE) %>%
  slice_head(n = 10)

ggplot(top10_tn, aes(x = reorder(COMMON_NAME, n), y = n)) +
  geom_col(fill = "forestgreen") +
  coord_flip() +
  labs(title = "Top 10 Most Abundant Tree Species in Tennessee",
       x = NULL, y = "Number of Stems")

  • reorder(COMMON_NAME, n) orders the bars by count instead of alphabetically
  • coord_flip() rotates the chart to make species names readable

6.6 geom_boxplot() — Comparing distributions across groups

# Compare diameter distributions of 5 common TN species
common5 <- c("yellow-poplar", "red maple", "white oak", "Virginia pine", "chestnut oak")

tn %>%
  filter(COMMON_NAME %in% common5) %>%
  ggplot(aes(x = COMMON_NAME, y = DIA, fill = COMMON_NAME)) +
  geom_boxplot(show.legend = FALSE) +
  labs(title = "Diameter Distribution of 5 Common TN Species",
       x = NULL, y = "DBH (inches)") +
  theme_minimal()

6.7 geom_line() — Connecting points in order

# Calculate mean diameter by latitude (1-degree bins)
lat_dia <- live %>%
  mutate(lat_bin = round(centroidLat)) %>%
  group_by(lat_bin) %>%
  summarise(mean_DIA = mean(DIA, na.rm = TRUE), .groups = "drop")

ggplot(lat_dia, aes(x = lat_bin, y = mean_DIA)) +
  geom_line(color = "darkblue", linewidth = 1) +
  geom_point(color = "darkblue", size = 2) +
  labs(title = "Mean Tree Diameter Along the Latitudinal Gradient",
       x = "Latitude (°N)", y = "Mean DBH (inches)")

This reveals a biogeographic pattern: mean tree diameter varies with latitude. Think about why — does it reflect species composition, growing conditions, or forest management practices?

7 Layering Multiple Geoms

ggplot2’s power comes from stacking layers. Each + adds a new layer on top:

tn %>%
  filter(COMMON_NAME %in% common5) %>%
  ggplot(aes(x = DIA, y = HT, color = COMMON_NAME)) +
  geom_point(alpha = 0.2, size = 1) +                              # layer 1: points
  geom_smooth(method = "lm", se = FALSE, linewidth = 1) +           # layer 2: trend lines
  labs(
    title = "Height-Diameter Relationships by Species",
    x = "DBH (inches)",
    y = "Height (ft)",
    color = "Species"
  ) +
  theme_minimal(base_size = 13)

Layers are drawn in order — later layers appear on top. This is why we put geom_smooth() after geom_point().

Notice how the height-diameter relationship differs among species: Virginia pine (a softwood) grows taller for a given diameter, while oaks are shorter and wider. This reflects fundamental differences in growth strategy — pines invest in height to compete for light, while oaks invest in crown width and structural stability.

8 Adding Text and Labels

8.1 Annotating bars with geom_text()

ggplot(top10_tn, aes(x = reorder(COMMON_NAME, n), y = n)) +
  geom_col(fill = "steelblue") +
  geom_text(aes(label = format(n, big.mark = ",")), hjust = -0.1, size = 3.2) +
  coord_flip() +
  labs(title = "Top 10 Tree Species in Tennessee",
       x = NULL, y = "Number of Stems")

  • aes(label = ...) tells geom_text() what to write
  • hjust = -0.1 nudges the label slightly to the right of the bar end
  • format(n, big.mark = ",") adds comma separators for readability

8.2 Plot titles and axis labels with labs()

ggplot(tn, aes(x = DIA, y = HT)) +
  geom_point(alpha = 0.1) +
  labs(
    title = "Height-Diameter Relationship",
    subtitle = "Live trees in Tennessee from FIA data",
    x = "Diameter at Breast Height (inches)",
    y = "Total Height (feet)",
    caption = "Source: USDA Forest Service FIA Database"
  )

9 Faceting: Small Multiples

Faceting splits your data into panels based on a categorical variable. This is extremely useful for comparing groups.

9.1 facet_wrap() — wrap panels in rows

tn %>%
  filter(COMMON_NAME %in% common5) %>%
  ggplot(aes(x = DIA)) +
  geom_histogram(binwidth = 2, fill = "forestgreen", color = "white") +
  facet_wrap(~ COMMON_NAME) +
  labs(title = "Diameter Distributions by Species (TN)",
       x = "DBH (inches)", y = "Count") +
  theme_minimal()

facet_wrap(~ COMMON_NAME) creates one panel per species. You can control the layout with ncol = 2 or nrow = 1.

Compare the shapes: some species (like red maple) have many small stems — they are prolific seedlings and understory trees. Others (like white oak) have a broader distribution with more large individuals — they are long-lived canopy dominants.

9.2 facet_grid() — grid of two variables

tn %>%
  filter(COMMON_NAME %in% c("red maple", "white oak", "Virginia pine")) %>%
  ggplot(aes(x = DIA, y = HT)) +
  geom_point(alpha = 0.2, size = 0.5) +
  facet_grid(SFTWD_HRDWD ~ COMMON_NAME) +
  labs(title = "Height vs. Diameter by Species and Wood Type") +
  theme_minimal()

facet_grid(rows ~ columns) creates a matrix of panels. Here we split by wood type (rows) and species (columns). Notice that Virginia pine only appears in the “S” (softwood) row, while red maple and white oak appear only in “H” (hardwood) — each species belongs to one wood type.

10 Scales: Controlling How Data Maps to Visuals

10.1 Color scales

ggplot(tn, aes(x = DIA, y = HT, color = ELEV)) +
  geom_point(alpha = 0.3, size = 1) +
  scale_color_viridis_c(option = "viridis", name = "Elevation (ft)") +
  labs(title = "Tree Size Colored by Elevation (TN)",
       x = "DBH (inches)", y = "Height (ft)") +
  theme_minimal()

Common color scales:

  • scale_color_viridis_c() — great for continuous data, colorblind-friendly
  • scale_color_brewer(palette = "Set2") — good for categorical data
  • scale_color_manual(values = c("red", "blue")) — pick your own

10.2 Axis scales

ggplot(tn, aes(x = DIA, y = HT)) +
  geom_point(alpha = 0.1) +
  scale_x_continuous(breaks = seq(0, 50, by = 10), limits = c(0, 50)) +
  scale_y_continuous(breaks = seq(0, 120, by = 20)) +
  labs(title = "Custom Axis Breaks and Limits",
       x = "DBH (inches)", y = "Height (ft)")

11 Themes: Controlling the Look

Themes control non-data elements: background, gridlines, fonts, legend position.

p <- ggplot(tn, aes(x = DIA, y = HT)) + geom_point(alpha = 0.1, size = 0.5)

library(gridExtra)
grid.arrange(
  p + ggtitle("Default (theme_grey)"),
  p + theme_minimal() + ggtitle("theme_minimal()"),
  p + theme_bw() + ggtitle("theme_bw()"),
  p + theme_classic() + ggtitle("theme_classic()"),
  ncol = 2
)

You can also customize individual elements with theme():

tn %>%
  filter(COMMON_NAME %in% common5) %>%
  ggplot(aes(x = DIA, y = HT, color = COMMON_NAME)) +
  geom_point(alpha = 0.3, size = 1) +
  labs(title = "Custom Theme Example", color = "Species") +
  theme_minimal(base_size = 14) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold", hjust = 0.5),
    panel.grid.minor = element_blank()
  )

12 The Complete ggplot2 Template

Here is the general pattern. Every ggplot2 call follows this structure:

ggplot(data = <DATA>, aes(<MAPPINGS>)) +
  <GEOM_FUNCTION>(aes(<ADDITIONAL MAPPINGS>), <FIXED SETTINGS>) +
  facet_...() +          # optional
  scale_...() +          # optional
  labs(...) +            # optional but recommended
  theme_...()            # optional

Once you internalize this pattern, building any visualization becomes a matter of choosing the right geom and aesthetics.

13 Ecological Example: Bringing It All Together

Let’s build a plot step by step using the full eastern US dataset. We will map species richness — the number of unique tree species — across all 31 states.

13.1 Step 1: Compute species richness per grid cell

richness <- live %>%
  group_by(GRIDID, centroidLon, centroidLat) %>%
  summarise(n_species = n_distinct(COMMON_NAME), .groups = "drop")

head(richness)
## # A tibble: 6 × 4
##   GRIDID centroidLon centroidLat n_species
##    <dbl>       <dbl>       <dbl>     <int>
## 1    190       -81.8        24.7         3
## 2    191       -81.6        24.6         3
## 3    192       -81.4        24.6         4
## 4    423       -81.4        24.8         1
## 5    425       -81.0        24.7         2
## 6    889       -80.6        25.0         5

13.2 Step 2: Basic scatter map

ggplot(richness, aes(x = centroidLon, y = centroidLat)) +
  geom_point(aes(color = n_species), size = 0.8)

The shape of the eastern US is already visible — but the default color scale doesn’t highlight the pattern well.

13.3 Step 3: Better colors and aspect ratio

ggplot(richness, aes(x = centroidLon, y = centroidLat)) +
  geom_point(aes(color = n_species), size = 0.8) +
  scale_color_viridis_c(option = "C") +
  coord_quickmap()

coord_quickmap() adjusts the aspect ratio so the map doesn’t look squashed. viridis option "C" (“plasma”) makes the richness gradient pop.

13.4 Step 4: Polish with labels, theme, and legend

ggplot(richness, aes(x = centroidLon, y = centroidLat)) +
  geom_point(aes(color = n_species), size = 0.8, alpha = 0.8) +
  scale_color_viridis_c(option = "C", name = "# Species") +
  coord_quickmap() +
  labs(
    title = "Tree Species Richness Across the Eastern United States",
    subtitle = "Each point is a FIA grid cell; color = number of unique species",
    x = "Longitude",
    y = "Latitude",
    caption = "Source: USDA FIA Database"
  ) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "right")

Each step added one new idea. This is how you should build your plots: start simple, then layer on complexity.

The richness map reveals the latitudinal diversity gradient — one of the most fundamental patterns in biogeography. Species richness peaks in the southern Appalachians (~33–36°N) due to glacial refugia, topographic heterogeneity, and favorable moisture conditions.

14 Practice Exercises

Use the FIA data you loaded above. The live data frame has the full eastern US; tn has Tennessee only.

14.1 Exercise 1: Histogram of Tree Heights

Create a histogram of tree heights (HT) for Tennessee. Use a bin width of 5 feet and fill the bars with a color of your choice. Add appropriate title and axis labels.

14.1.1 Hint

Use geom_histogram(binwidth = 5, fill = "your_color", color = "white").

14.1.2 Solution

ggplot(tn, aes(x = HT)) +
  geom_histogram(binwidth = 5, fill = "coral", color = "white") +
  labs(title = "Distribution of Tree Heights in Tennessee",
       x = "Height (ft)", y = "Count") +
  theme_minimal()

14.2 Exercise 2: Boxplot by Species

Create boxplots comparing tree diameter (DIA) across the top 5 most common species in Tennessee. Flip the coordinates for readability.

14.2.1 Hint

First find the top 5 species with count(). Then filter and pipe into ggplot. Use coord_flip().

14.2.2 Solution

top5_sp <- tn %>% count(COMMON_NAME, sort = TRUE) %>% slice_head(n = 5) %>% pull(COMMON_NAME)

tn %>%
  filter(COMMON_NAME %in% top5_sp) %>%
  ggplot(aes(x = reorder(COMMON_NAME, DIA, FUN = median), y = DIA, fill = COMMON_NAME)) +
  geom_boxplot(show.legend = FALSE) +
  coord_flip() +
  labs(title = "Diameter Distribution of Top 5 Species (TN)",
       x = NULL, y = "DBH (inches)") +
  theme_minimal()

14.3 Exercise 3: Scatter with Facets

Create a scatter plot of DIA vs. HT, faceted by SFTWD_HRDWD (softwood vs. hardwood). Add a loess trend line to each panel. Use the Tennessee data.

14.3.1 Hint

Use facet_wrap(~ SFTWD_HRDWD) and geom_smooth(method = "loess").

14.3.2 Solution

ggplot(tn, aes(x = DIA, y = HT)) +
  geom_point(alpha = 0.1, size = 0.5, color = "darkblue") +
  geom_smooth(method = "loess", color = "red", se = FALSE) +
  facet_wrap(~ SFTWD_HRDWD) +
  labs(title = "Height vs. Diameter: Softwoods vs. Hardwoods",
       x = "DBH (inches)", y = "Height (ft)") +
  theme_minimal()

14.4 Exercise 4: Bar Chart with Labels

Calculate the mean diameter by species for the top 10 species in Tennessee. Create a horizontal bar chart ordered by value, with the mean diameter displayed next to each bar.

14.4.1 Hint

Combine group_by(), summarise(), reorder(), geom_col(), geom_text(), and coord_flip().

14.4.2 Solution

top10_sp <- tn %>% count(COMMON_NAME, sort = TRUE) %>% slice_head(n = 10) %>% pull(COMMON_NAME)

sp_dia <- tn %>%
  filter(COMMON_NAME %in% top10_sp) %>%
  group_by(COMMON_NAME) %>%
  summarise(mean_DIA = mean(DIA, na.rm = TRUE))

ggplot(sp_dia, aes(x = reorder(COMMON_NAME, mean_DIA), y = mean_DIA)) +
  geom_col(fill = "darkgreen") +
  geom_text(aes(label = round(mean_DIA, 1)), hjust = -0.2, size = 3.5) +
  coord_flip() +
  labs(title = "Mean Diameter of Top 10 Species (TN)",
       x = NULL, y = "Mean DBH (inches)") +
  theme_minimal()

14.5 Exercise 5: Density Plot by Species

Create overlapping density plots of diameter (DIA) for three species that represent different ecological strategies: red maple (shade-tolerant generalist), yellow-poplar (fast-growing gap specialist), and Virginia pine (early-successional conifer). Set transparency so all curves are visible. Which species tends to have the largest trees?

14.5.1 Hint

Use geom_density() with fill = COMMON_NAME and alpha = 0.4.

14.5.2 Solution

three_sp <- c("red maple", "yellow-poplar", "Virginia pine")

tn %>%
  filter(COMMON_NAME %in% three_sp) %>%
  ggplot(aes(x = DIA, fill = COMMON_NAME)) +
  geom_density(alpha = 0.4) +
  labs(title = "Diameter Distribution by Species (TN)",
       x = "DBH (inches)", y = "Density", fill = "Species") +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom")

# Yellow-poplar tends to have the largest diameters — it is a fast-growing
# canopy tree that can reach impressive sizes in rich cove forests.

14.6 Exercise 6: Species Distribution Map (Challenge)

Using the full eastern US data (live), create a map showing the geographic distribution of loblolly pine. For each grid cell where loblolly pine occurs, plot a point with color mapped to the number of loblolly pine stems in that cell. Use coord_quickmap() for proper aspect ratio and scale_color_viridis_c() for color.

14.6.1 Hint

Filter live to loblolly pine, then group_by(GRIDID, centroidLon, centroidLat) and count. Use geom_point() with aes(color = n).

14.6.2 Solution

loblolly_grid <- live %>%
  filter(COMMON_NAME == "loblolly pine") %>%
  group_by(GRIDID, centroidLon, centroidLat) %>%
  summarise(n = n(), .groups = "drop")

ggplot(loblolly_grid, aes(x = centroidLon, y = centroidLat, color = n)) +
  geom_point(size = 1.2, alpha = 0.7) +
  scale_color_viridis_c(option = "D", name = "# Stems") +
  coord_quickmap() +
  labs(title = "Geographic Distribution of Loblolly Pine",
       subtitle = "Color intensity = abundance (stem count per grid cell)",
       x = "Longitude", y = "Latitude") +
  theme_minimal(base_size = 13)

15 Quick Reference Table

Geom Use For Key Arguments
geom_histogram() Distribution of one variable binwidth, fill, color
geom_density() Smooth distribution curves alpha, fill
geom_point() Scatter plots, maps alpha, size, color, shape
geom_smooth() Trend lines method, se, linewidth
geom_col() Bar charts (pre-computed values) fill
geom_bar() Bar charts (ggplot counts for you) fill
geom_boxplot() Compare distributions across groups fill
geom_line() Connect points in order linewidth, linetype
geom_text() Add text labels to a plot label, hjust, vjust, size
Helper What It Does
aes() Map data columns to visual properties
labs() Set title, subtitle, axis labels, caption
coord_flip() Swap x and y axes
coord_quickmap() Aspect ratio for geographic maps
facet_wrap() Split into panels by one variable
facet_grid() Split into panels by two variables
scale_color_viridis_c() Colorblind-friendly continuous color scale
scale_color_brewer() Colorblind-friendly discrete color scale
theme_minimal() Clean theme with minimal gridlines
reorder(x, y) Reorder a factor by values of another variable

Next step: In Lab 2, you will apply these ggplot2 skills to explore the full FIA dataset — visualizing diameter distributions, species abundance patterns, and geographic gradients across the eastern US.

End of Tutorial — ggplot2 Grammar of Graphics with FIA Data