# 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 `TREE_SURVEYS.txt`. Use `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()))
``````
1. Update the `trees` data frame with a new column 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. Show output of the `trees` data frame with just the `SURVEY`, `YEAR`, `SITE`, and `canopy_area` columns.
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. 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. 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. Using the data frame generated in (4), 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`.
Expected outputs for Acacia and Ants Data Manipulation: 1 2 3 4 5