Labs are designed to reinforce the code/lessons covered that week and provide you a chance to practice working with R. Labs are to be completed in your local project (on your computer) and uploaded to Canvas by the beginning of class (1pm) the following Thursday. That said, these due dates are largely suggestive as a way to help you prioritize and stay caught up as a group – if you need, or want, more time, take it. At the end of the quarter, we will simply look over the tasks you have completed in concert with the reflection you submit.


Labs

Lab Week 1

This lab will help you get oriented with R, RStudio, and your first programming concepts. The goal is to practice the fundamentals covered in the the introduction to R and RStudio lecture.

Setup Instructions:

  1. Open RStudio

  2. Create a new R script by going to File -> New File -> R Script or pressing Ctrl/Cmd+Shift+N

  3. Save your script as lastname_lab_week_1.R in an appropriate location (with your own last name)

  4. As you work through the problems below, write your code in the script and run it using Ctrl/Cmd+Enter (try to practice not highlighting and clicking “run”)

Part 1: Basic Arithmetic and Order of Operations

  1. Use parentheses to edit 15 + 7 * 3 force R to return 66 instead of 36.

  2. Calculate the square root of 144.

  3. Calculate 2 raised to the power of 8.

Part 2: Objects and Assignment

  1. Create an object called current_year and assign a value of 2025 to it using the <- operator.

  2. Calculate how many years it has been since 1977 using the current_year object.

Part 3: Mathematical Functions

  1. Calculate the natural logarithm of 10 using the log() function.

  2. Calculate e raised to the power of 2 using the exp() function.

Part 4: Logical Comparisons

  1. Test whether 10 is equal to 5 + 5 using the == operator.

  2. Test whether 7 is greater than 10.

  3. Test whether 15 is greater than or equal to 15.

  4. Create an object called x with the value 25, then test whether x is not equal to 30.

Part 5: Exploring Functions and Getting Help

  1. Use the help function to learn about the round() function by typing ?round in the console.

  2. Round the number 3.14159 to 2 decimal places using the round() function.

  3. Calculate the absolute value of -15 using the abs() function.

Part 6: Working with Scripts

  1. Add comments to your script using # to explain what each section does.

  2. Save your script and make sure you can run the entire script from top to bottom without errors.

Lab Week 2

In this lab we are going to practice subsetting and manipulating vectors.

First, open a new script in your workgin directory and save it to your scripts folder. Call this new script lastname_week_2_lab (with your last name).

Copy and paste the chunk of code below into your new lastname_week_2_lab script and run it. This chunk of code will create the vector you will use in your lab today. Check in your environment to see what it looks like. What do you think each line of code is doing?

set.seed(15)
hw2 <- runif(50, 4, 50)
hw2 <- replace(hw2, c(4,12,22,27), NA)
hw2
##  [1] 31.697246 12.972021 48.457102        NA 20.885307 49.487524 41.498897
##  [8] 15.682545 35.612619 42.245735  8.814791        NA 27.418158 36.504914
## [15] 43.666428 42.722117 24.582411 48.374680 10.494605 39.728776 40.971460
## [22]        NA 20.447903  6.668049 30.024323 34.314318        NA 10.825658
## [29] 46.676823 25.913006 26.933701 15.810164 26.616794  9.403891 27.589087
## [36] 34.262403  9.591257 27.733004 17.877330 38.975078 46.102046 25.041810
## [43] 46.369401 15.919465 19.813791 23.741937 19.192818 38.630297 42.819312
## [50]  4.500130

  1. Take your hw2 vector and removed all the NAs then select all the numbers between 14 and 38 inclusive, call this vector prob1.

  2. Multiply each number in the prob1 vector by 3 to create a new vector called times3. Then add 10 to each number in your times3 vector to create a new vector called plus10.

  3. Select every other number in your plus10 vector by selecting the first number, not the second, the third, not the fourth, etc. If you’ve worked through these three problems in order, you should now have a vector that is 12 numbers long that looks exactly like this one:

final
##  [1] 105.09174  57.04763  92.25447  83.74723 100.07297  87.73902  57.43049
##  [8]  92.76726  93.19901  85.12543  69.44137  67.57845

Finally, save your script and upload your file to Canvas.

Lab Week 3

Lab this week will be playing with the surveys data we worked on in class. Create a new script within your scripts folder called lastname_week_3_lab.R (with your own last name).

Load the surveys data frame with the read.csv() function. Create a new data frame called surveys_base with only the species_id, the weight, and the plot_type columns. Have this data frame only be the first 5,000 rows. Convert both species_id and plot_type to factors. Remove all rows where there is an NA in the weight column. Explore these variables and try to explain why a factor is different from a character. Why might we want to use factors? Can you think of any examples?

CHALLENGE: Create a second data frame called challenge_base that only consists of individuals from your surveys_base data frame with weights greater than 150g.


Lab Week 4

This week the lab will review data manipulation in the tidyverse.

  1. Create a tibble named surveys from the portal_data_joined.csv file, found at https://ucd-rdavis.github.io/R-DAVIS/data/portal_data_joined.csv.

  2. Subset surveys using Tidyverse methods to keep rows with weight between 30 and 60, and print out the first 6 rows.

  3. Create a new tibble showing the maximum weight for each species + sex combination and name it biggest_critters. Sort the tibble to take a look at the biggest and smallest species + sex combinations. HINT: it’s easier to calculate max if there are no NAs in the dataframe…

  4. Try to figure out where the NA weights are concentrated in the data- is there a particular species, taxa, plot, or whatever, where there are lots of NA values? There isn’t necessarily a right or wrong answer here, but manipulate surveys a few different ways to explore this. Maybe use tally and arrange here.

  5. Take surveys, remove the rows where weight is NA and add a column that contains the average weight of each species+sex combination to the full surveys dataframe. Then get rid of all the columns except for species, sex, weight, and your new average weight column. Save this tibble as surveys_avg_weight.

  6. Take surveys_avg_weight and add a new column called above_average that contains logical values stating whether or not a row’s weight is above average for its species+sex combination (recall the new column we made for this tibble).

Lab Week 5

This week’s questions will have us practicing pivots and conditional statements.

  1. Create a tibble named surveys from the portal_data_joined.csv file, found at https://ucd-rdavis.github.io/R-DAVIS/data/portal_data_joined.csv. Then manipulate surveys to create a new dataframe called surveys_wide with a column for genus and a column named after every plot type, with each of these columns containing the mean hindfoot length of animals in that plot type and genus. So every row has a genus and then a mean hindfoot length value for every plot type. The dataframe should be sorted by values in the Control plot type column. This question will involve quite a few of the functions you’ve used so far, and it may be useful to sketch out the steps to get to the final result.

  2. Using the original surveys dataframe, use the two different functions we laid out for conditional statements, ifelse() and case_when(), to calculate a new weight category variable called weight_cat. For this variable, define the rodent weight into three categories, where “small” is less than or equal to the 1st quartile of weight distribution, “medium” is between (but not inclusive) the 1st and 3rd quartile, and “large” is any weight greater than or equal to the 3rd quartile. (Hint: the summary() function on a column summarizes the distribution). For ifelse() and case_when(), compare what happens to the weight values of NA, depending on how you specify your arguments.

BONUS: How might you soft code the values (i.e. not type them in manually) of the 1st and 3rd quartile into your conditional statements in question 2?

Lab Week 7

For our week seven lab, we are going to be practicing the skills we learned with ggplot during class. You will be happy to know that we are going to be using a brand new data set called gapminder. This data set is looking at statistics for a few different counties including population, GDP per capita, and life expectancy. Download the data using the code below.

library(tidyverse)

gapminder <- read_csv("https://ucd-rdavis.github.io/R-DAVIS/data/gapminder.csv") #ONLY change the "data" part of this path if necessary
## Rows: 1704 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, continent
## dbl (4): year, pop, lifeExp, gdpPercap
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Part A: Basic ggplot Skills

  1. First calculates mean life expectancy on each continent. Then create a plot that shows how life expectancy has changed over time in each continent. Try to do this all in one step using pipes! (aka, try not to create intermediate dataframes)

  2. Look at the following code and answer the following questions. What do you think the scale_x_log10() line of code is achieving? What about the geom_smooth() line of code?

Challenge! Modify the above code to size the points in proportion to the population of the country. Hint: Are you translating data to a visual feature of the plot?

Hint: There’s no cost to tinkering! Try some code out and see what happens with or without particular elements.

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
    geom_point(aes(color = continent), size = .25) + 
    scale_x_log10() +
    geom_smooth(method = 'lm', color = 'black', linetype = 'dashed') +
    theme_bw()
## `geom_smooth()` using formula = 'y ~ x'

  1. Create a boxplot that shows the life expectency for Brazil, China, El Salvador, Niger, and the United States, with the data points in the backgroud using geom_jitter. Label the X and Y axis with “Country” and “Life Expectancy” and title the plot “Life Expectancy of Five Countries”.

Part B: Advanced ggplot Skills - Graph Recreation

For the second part of this lab, we’re going to be working on 2 critical ggplot skills: recreating a graph from a dataset and googling stuff.

Our goal will be to make this final graph using the gapminder dataset:

The x axis labels are all scrunched up because we can’t make the image bigger on the webpage, but if you make it and then zoom it bigger in RStudio it looks much better.

We’ll touch on some intermediate steps here, since it might take quite a few steps to get from start to finish. Here are some things to note:

  1. To get the population difference between 2002 and 2007 for each country, it would probably be easiest to have a country in each row and a column for 2002 population and a column for 2007 population.

  2. Notice the order of countries within each facet. You’ll have to look up how to order them in this way.

  3. Also look at how the axes are different for each facet. Try looking through ?facet_wrap to see if you can figure this one out.

  4. The color scale is different from the default- feel free to try out other color scales, just don’t use the defaults!

  5. The theme here is different from the default in a few ways, again, feel free to play around with other non-default themes.

  6. The axis labels are rotated! Here’s a hint: angle = 45, hjust = 1. It’s up to you (and Google) to figure out where this code goes!

  7. Is there a legend on this plot?

This lesson should illustrate a key reality of making plots in R, one that applies as much to experts as beginners: 10% of your effort gets the plot 90% right, and 90% of the effort is getting the plot perfect. ggplot is incredibly powerful for exploratory analysis, as you can get a good plot with only a few lines of code. It’s also extremely flexible, allowing you to tweak nearly everything about a plot to get a highly polished final product, but these little tweaks can take a lot of time to figure out!

So if you spend most of your time on this lesson googling stuff, you’re not alone!

Lab Week 8

Let’s look at some real data from Mauna Loa to try to format and plot. These meteorological data from Mauna Loa were collected every minute for the year 2001. This dataset has 459,769 observations for 9 different metrics of wind, humidity, barometric pressure, air temperature, and precipitation. You can read the CSV directly from the R-DAVIS Github: mloa <- read_csv("https://ucd-rdavis.github.io/R-DAVIS/data/mauna_loa_met_2001_minute.csv")

Use the README file associated with the Mauna Loa dataset to determine in what time zone the data are reported, and how missing values are reported in each column. With the mloa data.frame, remove observations with missing values in rel_humid, temp_C_2m, and windSpeed_m_s. Generate a column called “datetime” using the year, month, day, hour24, and min columns. Next, create a column called “datetimeLocal” that converts the datetime column to Pacific/Honolulu time (HINT: look at the lubridate function called with_tz()). Then, use dplyr to calculate the mean hourly temperature each month using the temp_C_2m column and the datetimeLocal columns. (HINT: Look at the lubridate functions called month() and hour()). Finally, make a ggplot scatterplot of the mean monthly temperature, with points colored by local hour.

Lab Week 9

In this assignment, you’ll use the iteration skills we built in the course to apply functions to an entire dataset.

Let’s load the surveys dataset:

surveys <- read.csv("https://ucd-rdavis.github.io/R-DAVIS/data/portal_data_joined.csv")
  1. Using a for loop, print to the console the longest species name of each taxon. Hint: the function nchar() gets you the number of characters in a string.

Next let’s load the Mauna Loa dataset from last week.

mloa <- read_csv("https://ucd-rdavis.github.io/R-DAVIS/data/mauna_loa_met_2001_minute.csv")
## Rows: 459769 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): filename, siteID
## dbl (14): year, month, day, hour24, min, windDir, windSpeed_m_s, windSteady,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

  1. Use the map function from purrr to print the max of each of the following columns: “windDir”,“windSpeed_m_s”,“baro_hPa”,“temp_C_2m”,“temp_C_10m”,“temp_C_towertop”,“rel_humid”,and “precip_intens_mm_hr”.

  2. Make a function called C_to_F that converts Celsius to Fahrenheit. Hint: first you need to multiply the Celsius temperature by 1.8, then add 32. Make three new columns called “temp_F_2m”, “temp_F_10m”, and “temp_F_towertop” by applying this function to columns “temp_C_2m”, “temp_C_10m”, and “temp_C_towertop”. Bonus: can you do this by using map_df? Don’t forget to name your new columns “temp_F…” and not “temp_C…”!

Challenge: Use lapply to create a new column of the surveys dataframe that includes the genus and species name together as one string.