Graphing using ggplot

• Get familiarized with metadata - Acacia drepanolobium Surveys
• UHURU Acacia Experiment Data
• UHURU Tree Survey Data

Introduction to the UHURU dataset

Data

• For the next set of lessons we’ll be working with data on acacia size from an experiment in Kenya
• The experiment is designed to understand the influence of herbivores on vegetation by excluding different sized herbivores
• There are 3 different treatments:
  • The top-left image shows Megaherbivore exclosures, which use wires 2m high to keep elephants
  • The top-right image shows Mesoherbivore exclosures, which use fenses starting 1/3m off the ground to exclude things like impalla
  • The bottom-left image shows full exclosures, which use fenses all the way to the ground to keep out all mammalian herbivores
  • And the bottom-right image shows control plots

• So far we’ve been working with datasets that are separated by commas
• Click on surveys.csv to show csv format
• But if we look at this new dataset it looks different
• • Click on Acacia Dataset to Open in Text Editor
• This data is tab separated, so we’ll need to treat it differently when we import it
• To do this we manually set the character separating each column using the optional sep argument
• So we’ll call our data frame acacia and assign it the output from read.csv
• We still give it the name of the file in quotes as the first argument
• Then we add a comma and sep = "\t"
• \t is who we indicate a Tab character in programming

acacia <- read.csv("ACACIA_DREPANOLOBIUM_SURVEY.txt", sep="\t")

• We can also see that it includes information on whether or not the plant is dead in the HEIGHT column
• Is that good data structure?
• If you said “No”, you’re right, information on if the tree is dead should be stored in a separate column
• For now, we’ll just treat the “dead” entries as null values
• We can do this by using another optional argument na.strings
• So let’s modify our read.csv statement by adding na.strings = c("dead").
• This will replace the string "dead" with NA
• It gets passed as a vector because this allows multiple different values to be set as nulls

acacia <- read.csv("ACACIA_DREPANOLOBIUM_SURVEY.txt", sep="\t", na.strings = c("dead"))

• If we open the resulting table we can see that it includes information on:
  • the time and location of the sampling
  • the experimental treatment
  • the size of each Acacia including a height, the canopy diameter measured in the direction (or axis) or the largest diameter and the diameter of the axis perpendicular to that, and the circumference of the shrub
  • information on the number of flowers, buds, and fruits
  • And finally information on the species of ant associated with the shrub because there is a very interesting ant-acacia mutualism where the Acacia special structures that serve as houses for the ants and the ants swarm herbivores that try to eat the acacia

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

Basics

• We load the ggplot2 package just like we loaded dplyr

library(ggplot2)

• We’ll also load the UHURU like we discussed in the video on the dataset

acacia <- read.csv("ACACIA_DREPANOLOBIUM_SURVEY.txt", sep="\t", na.strings = c("dead"))

• To build a plot using ggplot we start with the ggplot() function

ggplot()

• ggplot() creates a base ggplot object that we can then add things to

• Like a blank canvas

• We can also add optional arguments for information to be shared across different components of the plot
• The two main arguments we typically use here are
• data - which is the name of the data frame we are working with, so acacia
• mapping - which describes which columns of the data are used for different aspects of the plot
• We create a mapping by using the aes function, which stands for “aesthetic”, and then linking columns to pieces of the plot
• We’ll start with telling ggplot what value should be on the x and y axes
• Let’s plot the relationship betwen the circumference of an acacia and its height

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

• This still 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
• We can add data to the plot using layers
• We do this by adding a + after the the ggplot function and then adding something called a geom, which stands for geometry
• To make a scatter plot we use geom_point

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

• It is standard to hit Enter after the plus so that each layer shows up on its own line

• To change things about the layer we can pass additional arguments to the geom
• We can do things like change
  • the size of the points, we’ll set it to 3
  • the color of the points, we’ll set it to "blue"
  • the transparency of the points, which is called alpha, we’ll set it to 0.5

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

• To add labels (like documentation for your graphs!) we use the labs function

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")

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

Do Tasks 1-2 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 3-4 in Acacia and ants.

Statistical transformations

• 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)

• Each geom includes a statistical transformations
• So far we’ve only seen
  • identity: the raw form of the data or no transformation
• Transformations 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.

Position

• geom’s also come with a default position
• In many cases the position is "identity", which just means the object is plotted in the position determined by the data, but not always
• Let’s remake our histogram, but color the bars by the treatment

ggplot(acacia, aes(x = CIRC, color = TREATMENT)) +
  geom_histogram(binwidth = 5)

• You can see that the total height of the bars stayed the same
• That’s because ggplot has just colored the pieces of each bar that correspond to each treatment
• It does this because the default position for geom_histogram is "stacked", which stacks the bars on top of one another and therefore makes a stacked histogram.
• If we want separate overlapping histograms then we need to change the position

ggplot(acacia, aes(x = CIRC, color = TREATMENT)) +
  geom_histogram(binwidth = 5, position = "identity")

• And then add some transparency so we can see all of the histograms

ggplot(acacia, aes(x = CIRC, color = TREATMENT)) +
  geom_histogram(binwidth = 5, position = "identity", alpha = 0.5)

Layers

• So far we’ve only plotted one layer or geom at a time, but we can combine multiple layers in a single plot
  • ggplot() sets defaults for all layers
  • Can combine multiple layers using +
  • The first geom is plotted first and then additional geoms are layered on top
• 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

• geom_smooth uses the statistical transformation stat_smooth() to produce a smoothed representation of the data

• Do this by treatment

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

• Because the color aesthetic is the default it is inherited by geom_smooth
• One set of points and one model for each treatment

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() +
  geom_smooth(method = "lm")

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

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

• We can see that this information is no longer shared with other geoms since it is no longer the default, so we’ve asked for a smooth of nothing and so no smoother is shown

• 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
• If we wanted to plot two different columns as Y we could change the value of y in the aesthetic

• If we wanted to use data from two different data frames we could change the value of data

• We can also keep all of the values that are shared as defaults if we want to

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

Do Task 3 in Acacia and ants histograms.

Grammar of graphics

• Uniquely describe any plot based on a defined set of information
• Leland Wilkinson
• 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)

Saving plots as new files

ggsave("acacia_by_treatment.jpg")

• The type of the file is determined by the file extension

ggsave("acacia_by_treatment.pdf")

• Lots of optional arguments including size

ggsave("acacia_by_treatment.pdf", height = 5, width = 5)

Assign the rest of the exercises.