Set up R console:

library(dplyr)
library(ggplot2)

acacia <- read.csv("http://www.esapubs.org/archive/ecol/E095/064/ACACIA_DREPANOLOBIUM_SURVEY.txt", sep="\t")

Get familiarized with metadata - Acacia drepanolobium Surveys

Basics

• ggplot() arguments:
  • default dataset
  • set of mappings
    • ‘Aesthetics’ from variables
  • ggplot(acacia, aes(x = CIRC, y = AXIS1))
• Add components of figures with layers
  • geom_point()
• Simple scatter plot showing branch circumference and canopy width

ggplot(acacia, aes(x = CIRC, y = AXIS1)) + 
  geom_point()

• Rescale variables with mapping

ggplot(acacia, aes(x = CIRC, y = log10(AXIS1))) +
  geom_point()

• Rescale variables with layer
  • scale_continuous()

ggplot(acacia, aes(x = CIRC, y = AXIS1)) +
  geom_point() +
  scale_y_log10()

• Labels and theme

ggplot(acacia, aes(x = CIRC, y = AXIS1)) +
  geom_point(size = 3, color = "red") +
  scale_y_log10() +
  labs(x = "Circumference [cm]", y = "Canopy Width [m]",
       title = "Acacia Survey at UHURU") +
  annotation_logticks(sides = "l") +
  theme_bw()

Do Exercise 2 - Mass vs Metabolism.

Grouping

• Group on a single graph

ggplot(acacia, aes(x = CIRC, y = AXIS1, color = ANT)) +
  geom_point()

• Facet specification

ggplot(acacia, aes(x = CIRC, y = AXIS1)) +
  geom_point() +
  facet_wrap(~ANT)

Do Tasks 1-4 in Exercise 3 - Adult vs Newborn Size.

Layers

• Usage
  • ggplot() sets defaults for layers
  • Combine layers with ggplot() using +
  • Must have at least one layer to plot
  • Add additional layers, as necessary
    • Order matters
• Combining different kinds of layers

ant_acacia <- filter(acacia, ANT %in% c("CM", "CS", "TP"))
ggplot(ant_acacia, aes(x = CIRC, y = AXIS1)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~ANT)

• Combining different data sources

ggplot(acacia, aes(x = CIRC, y = AXIS1)) +
  geom_point() +
  geom_point(data = acacia, aes(x = CIRC, y = AXIS2), color = "red") +
  labs(x = "Circumference [cm]", y = "Canopy Width [m]")

• Each layer will default to ggplot() mappings unless modified
  • So, we don’t have to specify the arguments that are the same

ggplot(acacia, aes(x = CIRC, y = AXIS1)) +
  geom_point() +
  geom_point(aes(y = AXIS2), color = "red") +
  labs(x = "Circumference [cm]", y = "Canopy Width [m]")

Do Task 5 in Exercise 3 - Adult vs Newborn Size.

Statistical transformations

• Geoms include statistical transformations
• So far we’ve seen
  • identity: the raw form of the data or no transformation
  • smooth: model line (e.g., loess, lm)
• Transformations also exist to make things like histograms, bar plots, etc.

• Occur as defaults in associated Geoms

• To look at the abundances of different ant associations in the dataset, use a bar plot
  • geom_bar()

ggplot(acacia, aes(x = ANT)) + 
  geom_bar()

• Uses the transformation stat_count()
  • Counts the number of rows for each species
• To look at the distribution of circumferences in the dataset use a histogram
  • geom_histogram()

ggplot(acacia, aes(x = CIRC)) +
  geom_histogram()

• Uses stat_bins() for data transformation
  • Splits circumferences into bins and counts rows in each bin
• Uses bins argument to split data into groups
  • Defaults to bins = 30 if not specified in function call
• These can be combined with all of the other ggplot2 features we’ve learned

ggplot(acacia, aes(x = CIRC, fill = ANT)) +
  geom_histogram(bins = 15) +
  scale_x_log10() +
  annotation_logticks(sides = "b") +
  facet_wrap(~TREATMENT) +
  labs(x = "Circumference", y = "Number of Individuals") +
  theme_bw(base_size = 16)

Additional information

• Geometric object
  • geom_point()
  • geom_line()
• Statistical visualization
  • geom_smooth()
  • geom_bar()
  • geom_histogram()
  • geom_boxplot()
• Dataset and aesthetic adjustments
  • scale_continuous()
  • scale_manual()
  • lims()
  • labs()
  • guide_legend()
  • theme(), theme_bw(), theme_classic()
• Grouping related data
  • Same plot
    • Assign grouping variables as default or layer aes()
      • group
      • color
      • shape
  • Multiple plots
    • facet_grid()
    • facet_wrap()