Removing outliers

1. Download the file TREE_SURVEYS.txt and save it to your “data-raw” folder
2. Read the file with the function read_tsv from the package readr and assign it to an object called trees:

   trees <- read_tsv("TREE_SURVEYS.txt",
                  col_types = list(HEIGHT = col_double(),
                                   AXIS_2 = col_double()))
   

3. Use the $ to add a new column to the trees data frame that is named canopy_area and contains the estimated canopy area calculated as the value in the AXIS_1 column times the value in the AXIS_2 column.
4. Create a subset the trees data frame with just the SURVEY, YEAR, SITE, and canopy_area columns.
5. Make a scatter plot with canopy_area on the x axis and HEIGHT on the y axis. Color the points by TREATMENT and create a subplot per species using the function facet_wrap(). This will plot the points for each variable 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.
6. That’s a big outlier in the plot from (2). 50 by 50 meters is a little too big for a real acacia tree, 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.