Get familiarized with metadata - Acacia drepanolobium Surveys

ggplot

• Very popular plotting package
• Good plots quickly
• Declarative - describe what you want not how to build it
• Contrasts w/Imperative - how to build it step by step
• Install ggplot2 using install.packages

Data

• Data on acacia size in an experiment in Kenya excluding large herbivores
• Download both UHURU datasets into new project
• Data is tab separated
• Includes information on if the plant is dead in the HEIGHT column

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

• Show data and talk through treatments, sizes, and ants

Basics

library(ggplot2)

• ggplot() creates a base ggplot object that we can then add things to
• Like a blank canvas
• Optional arguments for information to be shared across different components of the plot including
  • default dataset - what data are we working with
  • set of mappings or ‘Aesthetics’ that describe which columns are used for different aspects of the plot

ggplot(data = acacia, mapping = aes(x = CIRC, y = HEIGHT))

• This doesn’t create a figure, it’s just a blank canvas and some information on default values for data and mapping columns to pieces of the plot
• Add components of figures with layers
• Scatter plot showing branch circumference and height

ggplot(data = acacia, mapping = aes(x = CIRC, y = HEIGHT)) +
  geom_point()

• To change things about the layer pass arguments to the geom

ggplot(data = acacia, mapping = aes(x = CIRC, y = HEIGHT)) +
  geom_point(size = 3, color = "blue", alpha = 0.5)

• Add Labels (documentation for your graphs!)

ggplot(data = acacia, mapping = aes(x = CIRC, y = HEIGHT)) +
  geom_point(size = 3, color = "blue", alpha = 0.5) +
  labs(x = "Circumference [cm]", y = "Height [m]",
       title = "Acacia Survey at UHURU")

Do Task 1 in Acacia and ants.

Grouping

• Group on a single graph
• Look at influence of experimental treatment

ggplot(acacia, aes(x = CIRC, y = HEIGHT, color = TREATMENT)) +
  geom_point(size = 3, alpha = 0.5)

• Facet specification

ggplot(acacia, aes(x = CIRC, y = HEIGHT)) +
  geom_point(size = 3, alpha = 0.5) +
  facet_wrap(~TREATMENT)

• Where are all the acacia in the open plots? (eaten?)

Do Tasks 2-3 in Acacia and ants.

Rescaling axes

ggplot(data = acacia, mapping = aes(x = CIRC, y = HEIGHT)) +
  geom_point(size = 3, color = "blue", alpha = 0.5) +
  scale_y_log10() +
  scale_x_log10()

• Not changing the data itself, just the presentation of it

Assign Mass vs Metabolism and Tasks 1-4 in Adult vs Newborn Size.

Layers

• We’ve seen that ggplot makes graphs by combining information on
  • Data
  • Mapping of parts of that data to aspects of the plot
  • A geometric object to represent the data

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

• Many kinds of geometric object (type geom_ and show completions)

• Usage
  • ggplot() sets defaults for layers
  • Can combine multiple layers using +
    • Order matters
• Combine different kinds of layers
• Add a linear model

ggplot(acacia, aes(x = CIRC, y = HEIGHT)) +
  geom_point() +
  geom_smooth(method = "lm")

• Both the geom_point layer and the geom_smooth layer use the defaults form ggplot

• Both use acacia for data and x = CIRC, y = HEIGHT for the aesthetic

• Do this by treatment

ggplot(acacia, aes(x = CIRC, y = HEIGHT, color = TREATMENT)) +
  geom_point() +
  geom_smooth(method = "lm")

• One set of points and one model for each treatment

Do Task 4 in Acacia and ants.

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 number of acacia in each treatment use a bar plot
  • geom_bar()

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

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

ggplot(acacia, aes(x = CIRC)) +
  geom_histogram(fill = "red")

• Uses stat_bins() for data transformation
  • Splits circumferences into bins and counts rows in each bin
• Set number of bins or binwidth

ggplot(acacia, aes(x = CIRC)) +
  geom_histogram(fill = "red", bins = 15)

ggplot(acacia, aes(x = CIRC)) +
  geom_histogram(fill = "red", binwidth = 5)

• These can be combined with all of the other ggplot2 features we’ve learned

Do Tasks 1-2 in Acacia and ants histograms.

Changing values across layers

• We can also plot data from different columns or even data frames on the same graph
• To do this we need to better understand how layers and defaults work
• So far we’ve put all of the information on data and aesthetic mapping into ggplot()

ggplot(data = acacia, mapping = aes(x = CIRC, y = HEIGHT)) +
  geom_point()

• This sets the default data frame and aesthetic, which is then used by geom_point()
• Alternatively instead of setting the default we could just give these values directly to geom_point()

ggplot() +
  geom_point(data = acacia,
             mapping = aes(x = CIRC, y = HEIGHT,
                           color = TREATMENT))

• We can see that this information is no longer shared with other geoms since it is no longer the default

ggplot() +
  geom_point(data = acacia,
             mapping = aes(x = CIRC, y = HEIGHT)) +
                           color = TREATMENT))
  geom_smooth()

• Can use this combine different aesthetics
• Make a single model across all treatments while still coloring points

ggplot() +
  geom_point(data = acacia,
             mapping = aes(x = CIRC, y = HEIGHT,
                           color = TREATMENT)) +
  geom_smooth(data = acacia,
              mapping = aes(x = CIRC, y = HEIGHT))

• color is only set in the aesthethic for the point layer

• So the smooth layer is made across all x and y values

• Check if this makes sense to everyone

• This same sort of change can be used to plot different columns on the same plot by changing the values of x or y

Do Task 3 in Acacia and ants histograms.

Grammar of graphics

• Geometric object(s)
  • Data
  • Mapping
  • Statistical transformation
  • Position (allows you to shift objects, e.g., spread out overlapping data points)
• Facets
• Coordinates (coordinate systems other than cartesian, also allows zooming)
• In combination uniquely describes any plot

Saving plots as new files

ggsave(“acacia_by_treatment.jpg”)

• Lots of optional arguments
  • Location
  • Type
  • Size

ggsave(“figures/acacia_by_treatment.pdf”, height = 5, width = 5)

Assign the rest of the exercises.

Combining different datasets (time allowing)

• We can use this to plot data from different sources together
• Add tree size data for context

• Layers are plotted in the order they are added

• Use the readr package to read in this data
• It has a lot of issues and readr fixes many of them automatically

library(readr)
trees <- read_tsv("data/TREE_SURVEYS.txt")
ggplot() +
  geom_point(data = trees,
             aes(x = CIRC, y = HEIGHT),
             color = "gray") +
  geom_point(data = acacia,
             aes(x = CIRC, y = HEIGHT),
             color = "red") +
  labs(x = "Circumference [cm]", y = "Height [m]")

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

ggplot(mapping = aes(x = CIRC, y = HEIGHT)) +
  geom_point(data = trees, color = "gray") +
  geom_point(data = acacia, color = "red") +
  labs(x = "Circumference [cm]", y = "Height [m]")