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
- 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
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()
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 transformationsmooth
: 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
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
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
- Defaults to
- 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
- Statistical visualization
- 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
- Assign grouping variables as default or layer
- Multiple plots
- Same plot