Data Carpentry for Biologists

Introduction to R and RStudio

Assignment

Learning Objectives

Following this assignment students should be able to:

  • execute simple math in the R console
  • assign and manipulate variables
  • use built-in functions for math and stats
  • understand the assignment and execute flow of an R script
  • understand the vector and data frame object structures
  • assign, subset, and manipulate data in a vector
  • execute vector algebra
  • import data frames and interact with columns as vectors

Reading

Topics

  • R & RStudio
  • Expressions & Variables
  • Types
  • Errors
  • Vectors & Data Frames
  • Importing Data
  • Readable Code

Readings

Introduction to RStudio (choose one)

Introduction to R

Starting with Data

Lecture Notes

  1. Course Introduction
  2. Introduction
  3. Vectors
  4. Checking that your code runs
  5. Using Large Language Models to Learn

Exercises

  1. Basic Expressions (10 pts)

    Write the following calculations in the text editor.

    1. 2 - 10
    2. 3 * 5
    3. 9 / 2
    4. 5 - 3 * 2
    5. (5 - 3) * 2
    6. 4 ^ 2
    7. 8 / 2 ^ 2

    Run them by either clicking the Run button in the top-right corner of the editor or press Ctrl+Enter (Windows & Linux) or Command+Enter (Mac) to run code and print the results in the console.

    If no code is highlighted/selected this will run the line the cursor is on. If you highlighted/selected a block of code it will run that entire group of lines.

    You can also run the entire script by clicking the arrow next to Source and selecting Source with Echo or by using Ctrl+Shift+Enter (Windows & Linux) or Command+Shift+Enter (Mac).

    To tell someone reading the code what this section of the code is about, add a comment line that says ‘Exercise 1’ before the code that answers the exercise. Comments in R are added by adding the # sign. Anything after a # sign on the same line is ignored when the program is run. So, the start of your program should look something like:

    # Exercise 1

#1.1
2 - 10

#1.2
3 * 5
    Expected outputs for Basic Expressions

  2. Basic Variables (10 pts)

    Here is a small program that converts a mass in kilograms to a mass in grams and then prints out the resulting value.

    mass_kg <- 2.62
mass_g <- mass_kg * 1000
mass_g

    Create similar code to convert a mass in pounds to a mass kilograms.

    • Create a variable to store a body mass in pounds. Assign this variable a value of 3.5 (an appropriate mass for a Sylvilagus audubonii).
    • Convert the variable from body mass in pounds to body mass in kilograms (by dividing it by 2.2046), and assign it to a new variable.
    • Print the value of the new variable to the screen.
    Expected outputs for Basic Variables

  3. More Variables (10 pts)

    Calculate a total biomass in grams for 3 white-throated woodrats (Neotoma albigula) and then convert it to kilograms. The total biomass is three times the average size of a single individual. An average individual weighs 250 grams.

    • Add a new section to your R script starting with a comment.
    • Create a variable grams and assign it the mass of a single Neotoma albigula (250).
    • Create a variable number and assign it the number of individuals (3).
    • Create a variable biomass and assign it a value by multiplying the grams and number variables together.
    • Convert the value of biomass into kilograms (there are 1000 grams in a kilogram so divide by 1000) and assign this value to a new variable.
    • Print the final answer to the screen.
    Expected outputs for More Variables

  4. Built-in Functions (10 pts)

    A built-in function is one that you don’t need to install and load a package to use. Some examples include:

    • abs() returns the absolute value of a number (e.g., abs(-2))
    • round(), rounds a number (the first argument) to a given number of decimal places (the second argument) (e.g., round(12.1123, 2))
    • sqrt(), takes the square root of a number (e.g., sqrt(4))
    • tolower(), makes a string all lower case (e.g., tolower("HELLO"))
    • toupper(), makes a string all upper case (e.g., toupper("hello"))

    Use these built-in functions to print the following items:

    1. The absolute value of -15.5.
    2. 4.483847 rounded to one decimal place.
    3. 3.8 rounded to the nearest integer. You don’t have to specify the number of decimal places in this case if you don’t want to, because round() will default to using 0 if the second argument is not provided. Look at help(round) or ?round to see how this is indicated.
    4. "species" in all capital letters.
    5. "SPECIES" in all lower case letters.
    6. Assign the value of the square root of 2.6 to a variable. Then round the variable you’ve created to 2 decimal places and assign it to another variable. Print out the rounded value.

    Optional Challenge: Do the same thing as task 6 (immediately above), but instead of creating the intermediate variable, perform both the square root and the round on a single line by putting the sqrt() call inside the round() call.

    Expected outputs for Built-in Functions

  5. Modify the Code (10 pts)

    The following code estimates the total net primary productivity (NPP) per day for two sites. It does this by multiplying the grams of carbon produced in a single square meter per day by the total area of the site. It then prints the daily NPP for each site.

    site1_g_carbon_m2_day <- 5
site2_g_carbon_m2_day <- 2.3
site1_area_m2 <- 200
site2_area_m2 <- 450
site1_npp_day <- site1_g_carbon_m2_day * site1_area_m2
site2_npp_day <- site2_g_carbon_m2_day * site2_area_m2
site1_npp_day
site2_npp_day

    Copy this code into your assignment and then add additional lines of code to do the following steps and print them out after the daily NPP values (the ones currently printed by the code):

    1. The sum of the total daily NPP for the two sites.
    2. The difference between the daily NPP for the two sites. We only want an absolute difference, so use abs() function to make sure the number is positive.
    3. The total NPP over a year for the two sites combined (the sum of the total daily NPP values from part (1) multiplied by 365).
    Expected outputs for Modify the Code

  6. Basic Vectors (10 pts)

    Cut and paste the following vector into your assignment and then use code to print the requested values related to the vector.

    numbers <- c(5, 2, 26, 8, 16)
    1. The number of items in the numbers vector (using the length function)
    2. The third item in the numbers vector (using [])
    3. The smallest number in the numbers vector (using the min function)
    4. The largest number in the numbers vector (using the max function)
    5. The average of the numbers in the numbers vector (using the mean function)
    6. The first, second and third numbers in the numbers vector (using [])
    7. The sum of the values in the numbers vector (using the sum function)
    Expected outputs for Basic Vectors

  7. Nulls in Vectors (15 pts)

    Cut and paste the following vector into your assignment. Then use code to print the requested values related to the vector. You’ll need to use na.rm = TRUE to ignore the null values.

    numbers <- c(7, 6, 22, 5, NA, 42)
    1. The smallest number in the numbers vector
    2. The largest number in the numbers vector
    3. The average of the numbers in the numbers
    4. The sum of the values in the numbers vector
    Expected outputs for Nulls in Vectors

  8. Shrub Volume Vectors (15 pts)

    You have data on the length, width, and height of 10 individuals of the yew Taxus baccata stored in the following vectors:

    length <- c(2.2, 2.1, 2.7, 3.0, 3.1, 2.5, 1.9, 1.1, 3.5, 2.9)
width <- c(1.3, 2.2, 1.5, 4.5, 3.1, NA, 1.8, 0.5, 2.0, 2.7)
height <- c(9.6, 7.6, 2.2, 1.5, 4.0, 3.0, 4.5, 2.3, 7.5, 3.2)

    Copy these vectors into an R script and then determine the following:

    1. The volume of each shrub (length × width × height). Storing this in a variable will make some of the next problems easier.
    2. The sum of the volume of all of the shrubs (using the sum function).
    3. A vector of the height of shrubs with lengths > 2.5.
    4. A vector of the height of shrubs with heights > 5.
    5. A vector of the heights of the first 5 shrubs (using []).
    6. A vector of the volumes of the first 3 shrubs (using []).

    Optional Challenge: A vector of the volumes of the last 5 shrubs with the code written so that it will return the last 5 values regardless of the length of the vector (i.e., it will give the last 5 values if there are 10, 20, or 50 individuals).

    Expected outputs for Shrub Volume Vectors

  9. Check That Your Code Runs (10 pts)

    Sometimes you think you’re code runs, but it only actually works because of something else you did previously. To make sure it actually runs you should save your work and then run it in a clean environment.

    Follow these steps in RStudio to make sure your code really runs:

    1. Restart R (see above) by clicking Session in the menu bar and selecting Restart R:

    Screenshot showing clicking session from the menu bar and selecting Restart R

    2. If the Environment tab isn’t empty click on the broom icon to clear it:

    Screenshot showing the Environment tab with the cursor hovering over the broom icon

    The Environment tab should now say “Environment Is Empty”:

    Screenshot showing the Environment tab with only the words Environment Is Empty

    3. Rerun your entire homework assignment using “Source with Echo” to make sure it runs from start to finish and produces the expected results.

    Screenshot showing the RStudio Source with Echo item hovered in the Source dropdown

    4. Make sure that you saved your code with the name assignment somewhere in the file name. You should see the file in the Files tab and the name of the file should be black (not red with an * in the tab at the top of the text editor):

    Screenshot showing the Files tab with the cursor hovering over the assignment file

    Screenshot showing the file name in the editor tab and it is black and there is no *

    5. Make sure that your code will run on other computers

    • No setwd() (use RStudio Projects instead)
    • Use / not \ for paths
    Expected outputs for Check That Your Code Runs

  10. Biomass vectors (Challenge - optional)

    You have data on the diameter of all of the trees in three sites and each site includes a mix of managmeent stratagies. Tree masses can be estimated using diameter based on the equation mass = 0.124 * diameter ^ 2.53. The biomass is the sum of the masses of all the trees at a site. Copy the vectors below into your R script.

    site <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3)
diameter <- c(10.5, 14.2, 13.7, 15.3, 12.8, 9.8, 11.4, 10.9,
               12.1, 13.5, 14.7, 8.5, 9.2, 10.1, 11.3, 12.4,
               13.6, 14.8)
management <- c("active", "active", "passive", "passive",
                "active", "passive", "active", "active",
                "passive", "passive", "active", "active",
                "passive", "active", "passive", "active",
                "passive", "active")

    Using only the tools we covered in class and the information above, answer the following questions:

    1. What is the mass of the largest tree, which site is it in, and what is its management status?
    2. Which site has the greatest total biomass and what is that biomass?
    3. What is the average mass of a tree in each site?
    4. What is the difference in total biomass for each site between the “active” management and “passive” management strategies?
    Expected outputs for Biomass vectors

Assignment submission & checklist