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.
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