### Learning Objectives

Following this assignment students should be able to:

• understand the basic plot function of `ggplot2`
• import ‘messy’ data with missing values and extra lines
• execute and visualize a regression analysis

### Exercises

1. #### Bird Banding Multiple Vectors (50 pts)

The number of birds banded at a series of sampling sites has been counted by your field crew and entered into the following vector. Counts are entered in order and sites are numbered starting at one. There is also information on the number of trees at each site. Cut and paste the vector into your assignment and then answer the following questions by using code and printing the result to the screen.

``````number_of_birds <- c(28, 32, 1, 0, 10, 22, 30, NA, 145, 27,
36, 25, 9, 38, 21, 12, 122, 87, 36, 3, 0, 5, 55, 62, 98, 32,
900, 33, 14, 39, 56, 81, 29, 38, 1, 0, 143, 37, 98, 77, 92,
83, 34, 98, 40, 45, 51, 17, 22, 37, 48, NA, 91, 73, 54, 46,
102, 273, 600, 10, 11)
number_of_trees <- c(10, 12, 2, 3, 10, 8, 19, 19, 14, 3,
4, 5, 8, 4, 8, 1, 12, 10, 3, 1, 2, 3, 5, 6, 8, 2,
90, 3, 4, 3, 6, 8, NA, 4, 0, 1, 14, 3, 10, NA, 9,
8, 4, 8, 4, 4, 5, 1, 2, 3, 5, 4, 10, 7, 5, 8,
10, 30, 26, 1, 6)
``````
1. How many sites are there?
2. How many birds were counted at the 26th site?
3. What is the largest number of birds counted?
4. What is the average number of birds seen at a site?
5. What is the total number of trees counted across all of the sites?
6. What is the smallest number of trees counted?
7. Produce a vector with the number of birds counted on sites with at least 10 trees.
8. Produce a vector with the number of trees counted on sites with at least 10 trees.
9. Combine the `number_of_birds` and `number_of_trees` vectors into a dataframe that also includes a year column with the year 2012 in every row and site column containing the numbers 1 through 61.
Expected outputs for Bird Banding Multiple Vectors: 1
2. #### Portal Data Review (50 pts)

If `surveys.csv`, `species.csv`, and `plots.csv` are not available in your workspace download them:

Load them into R using `read.csv()`.

1. Create a data frame with only data for the `species_id` `DO`, with the columns `year`, `month`, `day`, `species_id`, and `weight`.
2. Create a data frame with only data for species IDs `PP` and `PB` and for years starting in 1995, with the columns `year`, `species_id`, and `hindfoot_length`, with no null values for `hindfoot_length`.
3. Create a data frame with the average `hindfoot_length` for each `species_id` in each `year` with no null values.
4. Create a data frame with the `year`, `genus`, `species`, `weight` and `plot_type` for all cases where the `genus` is `"Dipodomys"`.
5. Make a scatter plot with `weight` on the x-axis and `hindfoot_length` on the y-axis. Use a `log10` scale on the x-axis. Color the points by `species_id`. Include good axis labels.
6. Make a histogram of weights with a separate subplot for each `species_id`. Do not include species with no weights. Set the `scales` argument to `"free_y"` so that the y-axes can vary. Include good axis labels.
7. (Challenge) Make a plot with histograms of the weights of three species, `PP`, `PB`, and `DM`, colored by `species_id`, with a different facet (i.e., subplot) for each of three `plot_type`’s `Control`, `Long-term Krat Exclosure`, and `Short-term Krat Exclosure`. Include good axis labels and a title for the plot. Export the plot to a `png` file.
Expected outputs for Portal Data Review: 1 2 3 4

Assignment submission & checklist