Data Carpentry for Biologists

Repeating Things 2

Assignment

Learning Objectives

Following this assignment students should be able to:

  • practice basic syntax and usage of for loops
  • use for loops to automate function operations
  • understand how to decompose complex problems

Reading

Lecture Notes

Setup

install.packages(c('dplyr', 'ggplot2', 'readr', 'tidyr'))
download.file("https://datacarpentry.org/semester-biology/data/dinosaur_lengths.csv",
  "dinosaur_lengths.csv")
download.file("https://datacarpentry.org/semester-biology/data/individual_collar_data.zip",
  "individual_collar_data.zip")
download.file("https://datacarpentry.github.io/semester-biology/data/temperature_sensor_data.zip",
  "temperature_sensor_data.zip")

Lecture Notes

Loops

Place this code at the start of the assignment to load all the required packages.

library(dplyr)
library(ggplot2)

Exercises

  1. Basic For Loops (20 pts)

    1. The code below prints the numbers 1 through 5 one line at a time. Modify it to print each of these numbers multiplied by 3.

    numbers <- c(1, 2, 3, 4, 5)
for (number in numbers){
  print(number)
}

    2. Write a for loop that loops over the following vector and prints out the mass in kilograms (mass_kg = 2.2 * mass_lb)

    mass_lbs <- c(2.2, 3.5, 9.6, 1.2)

    3. Complete the code below so that it prints out the name of each bird one line at a time.

    birds = c('robin', 'woodpecker', 'blue jay', 'sparrow')
for (i in 1:length(_________)){
  print(birds[__])
}

    4. Complete the code below so that it stores one area for each radius.

    radius <- c(1.3, 2.1, 3.5)
areas <- vector(_____ = "numeric", length = ______)
for (__ in 1:length(________)){
  areas[__] <- pi * radius[i] ^ 2
}
areas

    5. Write a for loop that loops over the following vector and stores the height in meters (height_m = height_ft / 3.28) in a new vector. After the for loop make sure to display the vector to the console by running the vectors name on its own line.

    height_ft <- c(5.1, 6.3, 5.7, 5.4)

    6. Complete the code below to calculate an area for each pair of lengths and widths, store the areas in a vector, and after they are all calculated print them out:

    lengths = c(1.1, 2.2, 1.6)
widths = c(3.5, 2.4, 2.8)
areas <- vector(length = __________)
for (i in _____) {
  areas[__] <- lengths[__] * widths[__]
}
areas
    Expected outputs for Basic For Loops

  2. Size Estimates By Name Loop (20 pts)

    If dinosaur_lengths.csv is not already in your working directory download a copy of the data on dinosaur lengths with species names. Load it into R.

    The following function estimates a dinosaur’s mass based on its length and name of its taxonomic group:

    get_mass_from_length_by_name <- function(length, name){
  if (name == "Stegosauria"){
    mass = 10.95 * length ^ 2.64
  }
  else if (name == "Theropoda"){
    mass = 0.73 * length ^ 3.63
  }
  else if (name == "Sauropoda"){
    mass = 214.44 * length ^ 1.46
  }
  else {
    mass = NA
  }
  return(mass)
}

    Update this function so that instead of returning NA when none of the species names matches it returns mass = 25.37 * length ^ 2.49 instead.

    1. Use this function and a for loop to calculate the estimated mass for each dinosaur in dinosaur_lengths, store the masses in a vector, and after all of the calculations are complete show the first few items in the vector using head().
    2. Add the results in the vector back to the original data frame and display first few rows of the new data frame using head().
    3. Calculate the mean mass for each species using dplyr, using the data from you created in (2).
    Expected outputs for Size Estimates By Name Loop

  3. Temperature Sensors (20 pts)

    You have deployed multiple temperature sensors at different locations to monitor daily temperature patterns. Each sensor records temperature readings every hour for a 24-hour period. The data from each sensor is stored in a separate CSV file with the naming pattern sensor-X-temp.csv where X is the sensor number.

    • If temperature_sensor_data.zip is not already in your working directory download the zip file using download.file()
    • Unzip it using unzip()
    • Obtain a list of all of the files with file names matching the pattern "sensor-" (using list.files())

    Each file contains two columns:

    • hour: Hour of the day (0-23)
    • temperature: Temperature reading in Celsius

    1. Use a loop to load each sensor data file and calculate the mean temperature for the sensor. Store the results in a vector called mean_temps. After the loop display the completed vector.

    2. Create a copy of your code from (1) and modify it to also find the maximum temperature recorded by each sensor and the temperature range (difference between maximum and minimum temperature) for each sensor. Store these values in vectors called max_temps, and temp_ranges. After the loop display the completed vectors.

    3. Create an empty data frame to store all your results and then write a loop to determine the following values for each file and store them in the data frame:

    • sensor_file: The filename of the sensor data
    • mean_temp: Mean temperature for that sensor
    • max_temp: Maximum temperature recorded
    • min_temp: Minimum temperature recorded
    • temp_range: Temperature range (max - min)

    4. Challenge (optional) Extend your analysis to find the hour when each sensor recorded its highest temperature. Add a column called peak_hour to your results data frame and display the data frame.

    Expected outputs for Temperature Sensors

  4. DNA or RNA Iteration (30 pts)

    This is a follow-up to DNA or RNA.

    Write a function, dna_or_rna(sequence), that determines if a sequence of base pairs is DNA, RNA, or if it is not possible to tell given the sequence provided. Since all the function will know about the material is the sequence the only way to tell the difference between DNA and RNA is that RNA has the base Uracil ("u") instead of the base Thymine ("t"). Have the function return one of three outputs: "DNA", "RNA", or "UNKNOWN".

    Copy and paste the following sequence data into your script:

    sequences = c("ttgaatgccttacaactgatcattacacaggcggcatgaagcaaaaatatactgtgaaccaatgcaggcg", "gauuauuccccacaaagggagugggauuaggagcugcaucauuuacaagagcagaauguuucaaaugcau", "gaaagcaagaaaaggcaggcgaggaagggaagaagggggggaaacc", "guuuccuacaguauuugaugagaaugagaguuuacuccuggaagauaauauuagaauguuuacaacugcaccugaucagguggauaaggaagaugaagacu", "gauaaggaagaugaagacuuucaggaaucuaauaaaaugcacuccaugaauggauucauguaugggaaucagccggguc")
    1. Use the function you wrote and a for loop to create a vector of sequence types for the values in sequences
    2. Use the function and a for loop to create a data frame that includes a column of sequences and a column of their types
    3. Use the function and sapply to create a vector of sequence types for the values in sequences
    4. Use the function, and dplyr to create a data frame that inclues a column of sequences and a column of their types

    Optional: For a little extra challenge make your function work with both upper and lower case letters, or even strings with mixed capitalization

    Expected outputs for DNA or RNA Iteration

  5. 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

  6. Multi-file Analysis (Challenge - optional)

    You have a satellite collars on a number of different individuals and want to be able to quickly look at all of their recent movements at once. The data is posted daily to a url as a zip file that contains one csv file for each individual: http://www.datacarpentry.org/semester-biology/data/individual_collar_data.zip Start your solution by:

    • If individual_collar_data.zip is not already in your working directory download the zip file using download.file()
    • Unzip it using unzip()
    • Obtain a list of all of the files with file names matching the pattern "collar-data-.*.txt" (using list.files())

    1. Use a loop to load each of these files into R and make a line plot (using geom_path()) for each file with long on the x axis and lat on the y axis. Graphs, like other types of output, won’t display inside a loop unless you explicitly display them, so you need put your ggplot() command inside a print() statement. Include the name of the file in the graph as the graph title using labs().

    2. Add code to the loop to calculate the minimum and maximum latitude in the file, and store these values, along with the name of the file, in a data frame. Show the data frame as output.

    If you’re interested in seeing another application of for loops, check out the code used to simulate the data for this exercise using for loops.

    Expected outputs for Multi-file Analysis

  7. Cocili Data Exploration (Challenge - optional)

    Understanding the spatial distribution of ecological phenomena is central to the study of natural systems. A group of scientists has collected a dataset on the size, location, and species identify of all of the trees in a 4 ha site in Panama call “Cocoli”.

    Download the Cocoli Data and explore the following spatial properties.

    1. Make a single plot showing the location of each tree for all species with more than 100 individuals. Each species should be in its own subplot (i.e., facet). Label the subplots with the genus and species names, not the species code. Scale the size of the point by its stem diameter (use dbh1) so that larger trees display as larger points. Have the code save the plot in a figures folder in your project.
    2. Basal area is a common measure in forest management and ecology. It is the sum of the cross-sectional areas of all of the trees occuring in some area and can be calculated as the sum of 0.00007854 * DBH^2 over all of the trees. To look at how basal area varies across the site divide the site into 100 m^2 sample regions (10 x 10 m cells) and determining the total basal area in each region. I.e., take all of the trees in a grid cell where x is between 0 and 10 and y is between 0 and 10 and determine their basal area. Do the same thing for x between 0 and 10 and y between 10 and 20, and so on. You can do this using two “nested” for loops to subset the data and calculate the basal area in that region. Make a plot that shows how the basal area varies spatially. Since the calculation is for a square region, plot it that way using geom_tile() with the center of the tile at the center of the region where basal area was calculated. Have the code save the plot in a figures folder in your project.
    Expected outputs for Cocili Data Exploration

  8. Length of Floods (Challenge - optional)

    You are interested in studying the influence of the timing and length of small scale flood events on an ecosystem. To do this you need to determine when floods occurred and how long they lasted based on stream gauge data.

    Download the stream guage data for USGS stream gauge site 02236000 on the St. Johns River in Florida. Find the continuous pieces of the time-series where the stream level is above the flood threshold of 2.26 feet and store the information on the start date and length of each flood in a data frame.

    Expected outputs for Length of Floods

Assignment submission & checklist