Data Carpentry -for- Ecologists

Teaching the tools to get computers to do cool science

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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

Exercises

  1. -- Basic Queries Review --

    This is a follow-up to Introduction to Databases.

    Download the sql-practice.sqlite database. Open it in SQLite Manager and write a query for each of the following:

    When writing the solution to each review problem, take a second to think carefully about which fields are actually relevant to the problem. Write your query so that only these relevant fields are selected for the final output. In other words, if you are going to do some form of analysis with the data you select, which fields do you actually need? (HINT: none of these problems should have SELECT * in the answer.)

    1. Write a query that lists all the males in order by weight, starting with the heaviest.

    2. Write a query that lists all males with a weight greater than 10 and all females with a weight less than 10.

    3. Write a query that displays the number of females at each site. Name the field with the number of females something meaningful.

  2. -- Mass vs Metabolism --

    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))

    Now make three plots with appropriate axis labels:

    1. A graph of body mass vs. metabolic rate
    2. A graph of log(body mass) vs. log(metabolic rate) (You can do this transformation inside the call to aes())
    3. A graph of body mass vs. metabolic rate, with logarithmically scaled axes (this is different from number 2), and the point size set to 5.

    Think about what the shape of these graphs tells you about the form of the relationship between mass and metabolic rate.

    [click here for output] [click here for output] [click here for output]

  3. -- Adult vs Newborn Size --

    It makes sense that larger organisms have larger offspring, but what the mathematical form of this relationship should be is unclear. Let’s look at the problem empirically for mammals.

    Download some mammal life history data from the web. You can do this either directly in the program using read.csv() or download the file to your computer using your browser, save it in the data subdirectory, and import it from there. It is tab delimited so you’ll want to use sep = "\t" as an optional argument when calling read.csv(). The \t is how we indicate a tab character to R (and most other programming languages).

    When you import the data there are some extra blank lines at the end of this file. Get rid of them by using the optional read.csv() argument nrows = 1440 to select the valid 1440 rows.

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

    1. Graph adult mass vs. newborn mass. Label the axes with clearer labels than the column names.
    2. It looks like there’s a regular pattern here, but it’s definitely not linear. Let’s see if log-transformation straightens it out. Graph adult mass vs. newborn mass, with both axes scaled logarithmically. Label the axes.
    3. This looks like a pretty regular pattern, so you wonder if it varies among different groups. Graph adult mass vs. newborn mass, with both axes scaled logarithmically, and the data points colored by order. Label the axes.
    4. 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 subplot for each order.
    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.
    [click here for output] [click here for output] [click here for output] [click here for output] [click here for output]

  4. -- Sexual Dimorphism Exploration --

    You are interested in understanding whether sexual size dimorphism is a general pattern in birds.

    Download and import a large publicly available dataset of bird size measures created by Lislevand et al. 2007.

    Import the data into R. It is tab delimited so you’ll want to use sep = "\t" as an optional argument when calling read.csv(). The \t is how we indicate a tab character to R (and most other programming languages).

    Using ggplot:

    1. Create a histogram of female masses (they are in the F_mass column). Change the x axis label to "Female Mass(g)".
    2. A few really large masses dominate the histogram so create a log10 scaled version. Change the x axis label to "Female Mass(g)" and the color of the bars to blue (using the fill = "blue" argument).
    3. Now let’s add the data for male birds as well. Create a single graph with histograms of both female and male body mass. 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.
    4. These distributions seem about the same, but this is all birds together so it might be difficult to see any patterns. Use facet_wrap() to make one subplot for each family.
    5. Make the same graph as in the last task, but for wing size instead of mass. Do you notice anything strange? If so, you may have gotten caught by the use of non-standard null values. If you already noticed and fixed this, Nice Work! If not, you can use the optional na.strings = c(-999, -999.0) argument in read.csv() to tell R what value(s) indicated nulls in a dataset.
    [click here for output] [click here for output] [click here for output] [click here for output] [click here for output]

  5. -- Sexual Dimorphism Data Manipulation --

    This is a follow up to Sexual Dimorophism Exploration.

    Having done some basic visualization of the Lislevand et al. 2007 dataset of bird size measures you realize that you’ll need to do some data manipulation to really get at the questions you want to answer.

    1. In Sexual Dimorophism Exploration you created a plot of the histograms of female and male masses by family. This resulted in a lot of plots, but many of them had low sample sizes.

      The following code creates a data frame with a column of Family IDs and a column of the number of species in the associated family that have non-null masses for both males and females.

       large_n_families <- data %>%
   filter(!is.na(M_mass), !is.na(F_mass)) %>%
   group_by(Family) %>%
   summarize(num_species = n())

      Modify this code so that the resulting data frame only includes families with more than 25 species, and add a comment to the top of the block of code describing what it does.

      Now join this with your original data to get the subset of your data with more than 25 species in each family. inner_join() only keeps rows where the joining field(s) occur in both tables, so since you’ve already removed families without a lot of species from large_n_families, they will be removed from the resulting data frame.

      Now, remake your original graph using only the data on families with greater than 25 species.

    2. Sexual size dimorphism doesn’t seem to show up clearly when visually comparing the distributions of male and female masses across species. Maybe the differences among species are too large relative to the differences between sexes to see what is happening; so, you decide to calculate the difference between male and female masses for each species and look at the distribution of those values for all species in the data.

      Use mutate() to create a new column which is the relative size difference between female and male masses

      (F_mass - M_mass) / F_mass

      and then make a single histogram that shows all of the species-level differences. Add a vertical line at 0 difference for reference.

    3. Combine the two other tasks to produce histograms of the relative size difference for each family, only including families with more than 25 species.

    [click here for output] [click here for output] [click here for output]

Check out the solution