Data Science for Biologists

Preparing Biologists for the Data Era

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Graphing

Acacia and Ants Data Manipulation


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

Download the data on Trees for the experiment into a data subdirectory. There are a number of problematic entries in this data so use the readr package to import it:

library(readr)
trees <- read_tsv("data/TREE_SURVEYS.txt")
  1. Add a new column to the trees data frame 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. Print out the SURVEY, YEAR, SITE, and canopy_area columns from data frame.
  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. 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. 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.
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