Acacia and Ants Data Manipulation (Graphing)
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
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()))
- Update the
treesdata frame with a new column named
canopy_areathat contains the estimated canopy area calculated as the value in the
AXIS_1column times the value in the
AXIS_2column. Show output of the
treesdata frame with just the
- Make a scatter plot with
canopy_areaon the x axis and
HEIGHTon the y axis. Color the points by
TREATMENTand plot the points for each value in the
SPECIEScolumn in a separate subplot. Label the x axis “Canopy Area (m)” and the y axis “Height (m)”. Make the point size 2.
- 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_2that are over 20 and update the data frame. Then remake the graph.
- 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.
nto make a data frame with
SPECIES, and an
abundancecolumn that has the number of individuals in each species in each year. Print out this data frame.
- Using the data frame generated in (4),
make a line plot with points (by using
geom_linein addition to
YEARon the x axis and
abundanceon 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