Set up R console:

library(dplyr)
library(ggplot2)

surveys <- read.csv("surveys.csv")

ts_data <- surveys %>%
             group_by(species_id, year) %>%
             summarize(count = n(), biomass = sum(weight))

dm_ts_data <- filter(ts_data, species_id == "DM")
do_ts_data <- filter(ts_data, species_id == "DO")

Basics

• ggplot() arguments:
  • default dataset
  • set of mappings
    • ‘Aesthetics’ from variables
  • ggplot(dm_ts_data, aes(x = year, y = count))
• Simple line plot

ggplot(dm_ts_data, aes(x = year, y = count)) +
  geom_line()

• Rescale variables
  • One scale for each variable used
  • Scale common across layers

ggplot(dm_ts_data, aes(x = year, y = count)) +
  geom_line() +
  scale_y_log10()

• Labels and theme

ggplot(dm_ts_data, aes(x = year, y = count)) +
  geom_line(size = 1, color = "red") +Population
  scale_y_log10() +
  xlab("Year") +
  ylab("Number of Individuals") +
  ggtitle("Dipodomys Merriami Time-Series at Portal") +
  theme_bw()

Do Exercise 2 - Mass vs Metabolism.

Grouping

• Group on a single graph

ggplot(ts_data, aes(x = year, y = count, color = species_id)) +
  geom_line()

• Facet specification

ggplot(ts_data, aes(x = year, y = count)) +
  geom_line() +
  facet_wrap(~species_id)

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

ggplot(ts_data, aes(x = year, y = count)) +
  geom_line() +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~species_id)

• Combining different data sources

ggplot(dm_ts_data, aes(x = year, y = count)) +
  geom_line() +
  geom_line(data = do_ts_data, aes(x = year, y = count), 
	color = "red")

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 transoformation
  • 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 species in the dataset use a bar plot

surveys <- read.csv("surveys.csv")
surveys <- filter(surveys, weight > 0)
ggplot(surveys, aes(x = species_id))
  + geom_bar()

• Uses the transformation count to count the number of rows for each species

• To look at the distribution of weights in the dataset use a histogram

ggplot(surveys, aes(x = weight)) +
  geom_histogram()

• Uses bin transform: split weights into bins and count rows in each bin
• These can be combined with all of other things we’ve learned

ggplot(surveys, aes(x = weight, fill = species_id)) +
  geom_histogram() +
  scale_x_log10() +
  facet_wrap(~plot_id) +
  xlab("Mass (g)") +
  ylab("Number of Individuals") +
  theme_bw(base_size = 16)

Additional information

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