Data Carpentry for Biologists

Data Visualization

Assignment

Learning Objectives

Following this assignment students should be able to:

  • understand the basic plot function of ggplot2
  • import ‘messy’ data with missing values and extra lines
  • execute and visualize a regression analysis

Reading

Lecture Notes

Setup

install.packages(c('dplyr', 'ggplot2', 'readr', 'tidyr'))
download.file("https://ndownloader.figshare.com/files/5629542",
              "ACACIA_DREPANOLOBIUM_SURVEY.txt")
download.file("https://ndownloader.figshare.com/files/5629536",
              "TREE_SURVEYS.txt")
download.file("https://esapubs.org/archive/ecol/E084/093/Mammal_lifehistories_v2.txt",
              "Mammal_lifehistories_v2.txt")

Lecture Notes

Place this code at the start of the assignment to load all the required packages.

library(dplyr)
library(ggplot2)
library(readr)

Exercises

  1. Acacia and Ants (10 pts)

    An experiment in Kenya has been exploring the influence of large herbivores on plants.

    Check to see if ACACIA_DREPANOLOBIUM_SURVEY.txt is in your workspace. If not, download it. Read it into R using the following command:

    acacia <- read_tsv("ACACIA_DREPANOLOBIUM_SURVEY.txt", na = c("dead"))
    1. Make a scatter plot with CIRC on the x axis and AXIS1 (the maximum canopy width) on the y axis. Label the x axis “Circumference” and the y axis “Canopy Diameter”.
    2. The same plot as (1), but with points colored based on the ANT column (the species of ant symbiont living with the acacia)
    3. The same plot as (2), but instead of different colors show different species of ant (values of ANT) each in a separate subplot.
    Expected outputs for Acacia and Ants

  2. Mass vs Metabolism (10 pts)

    The relationship between the body size of an organism and its metabolic rate is one of the most well studied and still most controversial areas of organismal physiology. We want to graph this relationship in the Artiodactyla using a subset of data from a large compilation of body size data (Savage et al. 2004). You can copy and paste this data frame into your program:

    size_mr_data <- data.frame(
  body_mass = c(32000, 37800, 347000, 4200, 196500, 100000,
    4290, 32000, 65000, 69125, 9600, 133300, 150000, 407000,
    115000, 67000,325000, 21500, 58588, 65320, 85000, 135000,
    20500, 1613, 1618),
  metabolic_rate = c(49.984, 51.981, 306.770, 10.075, 230.073,
    148.949, 11.966, 46.414, 123.287, 106.663, 20.619, 180.150,
    200.830, 224.779, 148.940, 112.430, 286.847, 46.347,
    142.863, 106.670, 119.660, 104.150, 33.165, 4.900, 4.865),
  family = c("Antilocapridae", "Antilocapridae", "Bovidae",
    "Bovidae", "Bovidae", "Bovidae", "Bovidae", "Bovidae",
    "Bovidae", "Bovidae", "Bovidae", "Bovidae", "Bovidae",
    "Camelidae", "Camelidae", "Canidae", "Cervidae",
    "Cervidae", "Cervidae", "Cervidae", "Cervidae", "Suidae",
    "Tayassuidae", "Tragulidae", "Tragulidae"))

    Make the following plots with appropriate axis labels:

    1. A plot of body mass vs. metabolic rate
    2. The same plot as (1) but with the point size set to 3.
    3. The same plot as (2), but with the different families indicated using color.
    4. The same plot as (2), but with the different families each in their own subplot.
    Expected outputs for Mass vs Metabolism

  3. Acacia and Ants Data Manipulation (10 pts)

    An experiment in Kenya has been exploring the influence of large herbivores on plants.

    Check to see if TREE_SURVEYS.txt is in your workspace. If not, download TREE_SURVEYS.txt. Use read_tsv from the readr package to read in the data using the following command:

    trees <- read_tsv("TREE_SURVEYS.txt",
                  col_types = list(HEIGHT = col_double(),
                                   AXIS_2 = col_double()))
    1. Update the trees data frame with a new column named canopy_area that contains the estimated canopy area calculated as the value in the AXIS_1 column times the value in the AXIS_2 column. Show output of the trees data frame with just the SURVEY, YEAR, SITE, and canopy_area columns.
    2. Make a scatter plot with canopy_area on the x axis and HEIGHT on the y axis. Color the points by TREATMENT and plot the points for each value in the SPECIES column in a separate subplot. Label the x axis “Canopy Area (m)” and the y axis “Height (m)”. Make the point size 2.
    3. That’s a big outlier in the plot from (2). 50 by 50 meters is a little too big for a real Acacia, so filter the data to remove any values for AXIS_1 and AXIS_2 that are over 20 and update the data frame. Then remake the graph.
    4. Using the data without the outlier (i.e., the data generated in (3)), find out how the abundance of each species has been changing through time. Use group_by, summarize, and n to make a data frame with YEAR, SPECIES, and an abundance column that has the number of individuals in each species in each year. Print out this data frame.
    5. Using the data frame generated in (4), make a line plot with points (by using geom_line in addition to geom_point) with YEAR on the x axis and abundance on the y axis with one subplot per species. To let you seen each trend clearly let the scale for the y axis vary among plots by adding scales = "free_y" as an optional argument to facet_wrap.
    Expected outputs for Acacia and Ants Data Manipulation

  4. Lifespan vs Gestation Time (20 pts)

    Longer lived organisms typically invest more in their offspring. We want to explore the form of this relationship by looking at the relationship between lifespan and gestation period in mammals.

    Check to see if Mammal_lifehistories_v2.txt is in your working directory. If not download it from the web. This is tab delimited data, so you’ll want to use read_tsv().

    Missing data in this file is specified by -999 and -999.00. Tell R that these are null values using the optional read_tsv() argument, na = c("-999", "-999.00"). This will stop them from being plotted.

    Some of the column names have parentheses in them. E.g., mass(g). To work with column names like this we enclose them in back ticks. E.g., `mass(g)` Back ticks are typically on the same key as the ~ and look like a slanted single quotation mark.

    1. Graph lifespan (max. life(mo)) vs. gestation period(gestation(mo)). Label the axes with clearer labels than the column names.
    2. This looks like a pretty regular pattern, so you wonder if it varies among different groups. Graph lifespan vs. gestation periodwith the data points colored by order. Label the axes.
    3. Coloring the points was useful, but there are a lot of points and it’s kind of hard to see what’s going on with all of the orders. Use facet_wrap to create a subplot for each order.
    4. Since different orders have different average sizes it can be hard to see the relationship for some orders. Let the axes vary across different facets by setting the options scales argument to "free"
    5. Now let’s visualize the relationships between the variables using a simple linear model. Create a new graph like your faceted plot, but using geom_smooth to fit a linear model to each order. You can do this using the optional argument method = "lm" in geom_smooth.
    6. Challenge (optional): Some of the orders don’t have enough data points to fit a meaningful linear model. Use group_by and summarize and your data frame to create a new data frame with counts of the number of species (i.e., rows) in each order. Join this data frame (using inner_join) to your main data frame and use the new species counts to filter the data frame to only keep orders with at least 20 species. Then remake the graph from (5) with this filtered data. Note that there won’t be 20 points for all orders because some orders are missing values for some columns.
    Expected outputs for Lifespan vs Gestation Time

  5. Acacia and Ants Histograms (20 pts)

    An experiment in Kenya has been exploring the influence of large herbivores on plants.

    Check to see if ACACIA_DREPANOLOBIUM_SURVEY.txt is in your workspace. If not, download it. Read it into R using the following command:

    acacia <- read_tsv("data/ACACIA_DREPANOLOBIUM_SURVEY.txt", na = c("dead"))
    1. Make a bar plot of the number of acacia with each mutualist ant species (using the ANT column).
    2. Make a histogram of the height of acacia (using the HEIGHT column). Label the x axis “Height (m)” and the y axis “Number of Acacia”.
    3. Make a non-stacked histogram of the height of acacia (using the HEIGHT column) colored by the TREATMENT. Set the transparency (using alpha) to 0.5 so that you can see all of the bars. Label the x-axis “Heigth (m)” and the y-axis “Count of Acacia”. Set the binwidth to 0.5.
    Expected outputs for Acacia and Ants Histograms

  6. Acacia and Ants Layers (20 pts)

    An experiment in Kenya has been exploring the influence of large herbivores on plants.

    Check to see if ACACIA_DREPANOLOBIUM_SURVEY.txt is in your workspace. If not, download it. Read it into R using the following command:

    acacia <- read_tsv("data/ACACIA_DREPANOLOBIUM_SURVEY.txt", na = c("dead"))

    1. Make a scatter plot with CIRC on the x axis and AXIS1 (the maximum canopy width) on the y axis. Add a simple model of the data by adding geom_smooth. Label the x axis “Circumference” and the y axis “Canopy Diameter”.

    2. The same plot as (1), but use a linear model (method = "lm") and show different species of ant (values of ANT) in separate subplots. Once this works, you can, as an optional challenge, try to automatically include only plot subplots (i.e., ant species) with at least 5 data points. Note: results are shown for the basic exercise, not the optional challenge.

    3. Make a plot that shows histograms of both AXIS1 and AXIS2. Due to the way the data is structured you’ll need to add a 2nd geom_histogram() layer that specifies a new aesthetic. To make it possible to see both sets of bars you’ll need to make them transparent with the optional argument alpha = 0.3. Set the color for AXIS1 to “red” and AXIS2 to “black” using the fill argument. Label the x axis “Canopy Diameter(m)” and the y axis “Number of Acacia”.

    4. Use facet_wrap() to make the same plot as (3) but with one subplot for each treatment. Set the number of bins in the histogram to 10.

    Expected outputs for Acacia and Ants Layers

  7. Check That Your Code Runs (10 pts)

    Sometimes you think you’re code runs, but it only actually works because of something else you did previously. To make sure it actually runs you should save your work and then run it in a clean environment.

    Follow these steps in RStudio to make sure your code really runs:

    1. Restart R (see above) by clicking Session in the menu bar and selecting Restart R:

    Screenshot showing clicking session from the menu bar and selecting Restart R

    2. If the Environment tab isn’t empty click on the broom icon to clear it:

    Screenshot showing the Environment tab with the cursor hovering over the broom icon

    The Environment tab should now say “Environment Is Empty”:

    Screenshot showing the Environment tab with only the words Environment Is Empty

    3. Rerun your entire homework assignment using “Source with Echo” to make sure it runs from start to finish and produces the expected results.

    Screenshot showing the RStudio Source with Echo item hovered in the Source dropdown

    4. Make sure that you saved your code with the name assignment somewhere in the file name. You should see the file in the Files tab and the name of the file should be black (not red with an * in the tab at the top of the text editor):

    Screenshot showing the Files tab with the cursor hovering over the assignment file

    Screenshot showing the file name in the editor tab and it is black and there is no *

    5. Make sure that your code will run on other computers

    • No setwd() (use RStudio Projects instead)
    • Use / not \ for paths
    Expected outputs for Check That Your Code Runs

  8. Graphing Data From Multiple Tables (Challenge - optional)

    An experiment in Kenya has been exploring the influence of large herbivores on plants.

    Check to see if ACACIA_DREPANOLOBIUM_SURVEY.txt and TREE_SURVEYS.txt is in your workspace. If not, download ACACIA_DREPANOLOBIUM_SURVEY.txt and TREE_SURVEYS.txt Install the readr package and use read_tsv to read in the data using the following commands:

    library(readr)
acacia <- read_tsv("ACACIA_DREPANOLOBIUM_SURVEY.txt",
                   na = c("dead"))
trees <- read_tsv("TREE_SURVEYS.txt",
                  col_types = list(HEIGHT = col_double(),
                                   AXIS_2 = col_double()))

    We want to compare the circumference to height relationship in acacia on different treatments in the context of the same relationship for trees in the region. These data are stored in the two tables above. Make a graph with the relationship between CIRC and HEIGHT for the trees as gray points in the background and the same relationship for acacia as red points plotted on top of the tree points. There should be one subplot for each treatment. Include linear models for both sets of data. Provide clear labels for the axes.

    Expected outputs for Graphing Data From Multiple Tables

Assignment submission & checklist