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)
- Reading: Before We Start (Data Carpentry Lesson)
- Video (15 min): An opinionated tour of RStudio (R-Ladies Sydney)
Introduction to R
Starting with Data
Lecture Notes
Exercises
Basic Expressions (10 pts)
Think about what value each of the following expressions will return? Check your answers using the R Console by typing each expression into the console on the line marked
>
and pressing enter.- 2 - 10
- 3 * 5
- 9 / 2
- 5 - 3 * 2
- (5 - 3) * 2
- 4 ^ 2
- 8 / 2 ^ 2
Did any of the results surprise you? If so, then might have run in to some order of operations confusion. The order of operators for math in R are the same as for mathematics more generally.
Now turn this set of expressions into a program that you can save by using an R script. For each expression add one line to the script. Run the script in the console to display the answer to the screen. If you are using RStudio, you can click the
Run
button in the top-right corner of the editor or use Ctrl+Enter (Windows & Linux) or Command+Enter (Mac) to run the line or selection of code directly from your script. You can run the entire script by clicking the arrow next toSource
and selectingSource 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:[click here for output]# Exercise 1 2-10
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
Modify this code to create a variable that stores a body mass in pounds and assign it a value of 3.5 (about the right size for a Desert Cottontail Rabbit – Sylvilagus audubonii). Convert this value to kilograms. There are approximately 2.2046 lbs in a kilogram, so divide the variable storing the weight in pounds by 2.2046 and store this value in a new variable for storing mass in kilograms. Print the value of the new variable to the screen.
[click here for output]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. - Create a variable
number
and assign it the number of individuals. - Create a variable
biomass
and assign it a value by multiplying thegrams
andnumber
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.
Are the variable names
grams
,number
, andbiomass
the best choice? If we came back to the code for this assignment in two weeks would we be able to remember what these variables were referring to and therefore what was going on in the code? The variable namebiomass
is also kind of long. If we had to type it many times it would be faster just to typeb
. We could also use really descriptive alternatives likeindividual_mass_in_grams
. Or we would compromise and abbreviate this or leave out some of the words to make it shorter (e.g.,indiv_mass_g
).Think about good variable names and then rename the variables in your program to what you find most useful. Make sure your code still runs properly after you’ve changed the names.
[click here for output]Built-in Functions (10 pts)
A built-in function is one that you don’t need to install and load a package to use. To learn how to use a function you can use the
help()
function or theHelp
tab in RStudio.help()
takes one parameter, the name of the function that you want information about (e.g.,help(abs)
) or type the name into the search box on theHelp
tab. Familiarize yourself with the built-in functionsabs()
,round()
,sqrt()
,tolower()
, andtoupper()
. Use these built-in functions to print the following items:- The absolute value of -15.5.
- 4.483847 rounded to one decimal place. The function
round()
takes two arguments, the number to be rounded and the number of decimal places. - 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 using0
if the second argument is not provided. Look athelp(round)
or?round
to see how this is indicated. "species"
in all capital letters."SPECIES"
in all lower case letters.- 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.
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
[click here for output]sqrt()
call inside theround()
call.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
Modify the code to produce the following items and print them out after the daily NPP values (the ones currently printed by the code):
- The sum of the total daily NPP for the two sites combined.
- 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.
- The total NPP over a year for the two sites combined (the sum of the total daily NPP values multiplied by 365).
Code Shuffle (10 pts)
We are interested in understanding the monthly variation in precipitation in Gainesville, FL. We’ll use some data from the NOAA National Climatic Data Center.
Each row of the data file is a year (from 1961-2013) and each column is a month (January - December).
Rearrange the following program so that it:
- Imports the data
- Calculates the average precipitation in each month across years
- Plots the monthly averages as a simple line plot
Finally, add a comment above the code that describes what it does. The comment character in R is
#
.It’s OK if you don’t know exactly how the details of the program work at this point, you just need to figure out the right order of the lines based on when variables are defined and when they are used.
[click here for output]plot(monthly_mean_ppt, type = "l", xlab = "Month", ylab = "Mean Precipitation") monthly_mean_ppt <- colMeans(ppt_data) ppt_data <- read.csv("https://datacarpentry.org/semester-biology/data/gainesville-precip.csv", header = FALSE)
Bird Banding (15 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. Cut and paste the vector into your assignment and then answer the following questions by printing them to the screen. Some R functions that will come in handy include
length()
,max()
,min()
,sum()
, andmean()
.number_of_birds <- c(28, 32, 1, 0, 10, 22, 30, 19, 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, 38, 91, 73, 54, 46, 102, 273, 600, 10, 11)
- How many sites are there?
- How many birds were counted at site 42?
- What is the total number of birds counted across all of the sites?
- What is the smallest number of birds counted?
- What is the largest number of birds counted?
- What is the average number of birds seen at a site?
- How many birds were counted at the last site? Have the computer choose the last site automatically in some way, not by manually entering its position. Do you know a function that will give you the position of the last value? (since positions start at 1 the position of the last value in a vector is the same as its length).
Shrub Volume Vectors (10 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:
- The volume of each shrub (i.e., the length times the width times the height). storing this in a variable will make some of the next problems easier
- The total volume of all of the shrubs.
- A vector of the height of shrubs with lengths greater than 2.5.
- A vector of the height of shrubs with heights greater than 5.
- A vector of the heights of the first 5 shrubs.
- A vector of the volumes of the first 3 shrubs.
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 their are 10, 20, or 50 individuals).
[click here for output]Shrub Volume Data Frame (15 pts)
This is a follow up to Shrub Volume Vectors.
One of your collaborators has posted a comma-delimited text file online for you to analyze. The file contains dimensions of a series of shrubs (shrubID, length, width, height) and they need you to determine their volumes (
length * width * height
). You could do this using a spreadsheet, but the project that you are working on is going to be generating lots of these files so you decide to write a program to automate the process.Download the data, use
read.csv()
to import it into R, and use it to produce the following information:- A vector of shrub lengths
- A vector of the volume of each of the shrubs
- A data frame with just the shrubID and height columns
- A data frame with the second row of the full data frame
- A data frame with the first 5 rows of the full data frame