--- title: "Lab 8: Functions + Fish" author: "Your name here!" format: gfm editor: source execute: echo: true warning: false --- The goal of this lab is learn more about exploring missing data and writing modular code and functions. ```{r} #| label: setup #load packages #read in data ``` ## The Data This lab's data concerns mark-recapture data on four species of trout from the Blackfoot River outside of Helena, Montana. These four species are **rainbow trout (RBT)**, **westslope cutthroat trout (WCT)**, **bull trout**, and **brown trout**. Mark-recapture is a common method used by ecologists to estimate a population's size when it is impossible to conduct a census (count every animal). This method works by *tagging* animals with a tracking device so that scientists can track their movement and presence. ## Data Exploration The measurements of each captured fish were taken by a biologist on a raft in the river. The lack of a laboratory setting opens the door to the possibility of measurement errors. **1. Let's look for missing values in the dataset. Output ONE table that answers BOTH of the following questions:** + **How many observations have missing values?** + **What variable(s) have missing values present?** Hint: You should use `across()`! ```{r} #| label: find-missing-values ``` **2. Using `map_int()`, produce a nicely formatted table of the number of missing values for each variable in the `fish` data that displays the same information as 1** ```{r} #| label: map-missing-values-of-fish ``` **3. Create ONE thoughtful visualization that explores the frequency of missing values across the different years, sections, and trips.** ```{r} #| label: visual-of-missing-values-over-time ``` ## Rescaling the Data If I wanted to rescale every quantitative variable in my dataset so that they only have values between 0 and 1, I could use this formula: $$y_{scaled} = \frac{y_i - min\{y_1, y_2,..., y_n\}}{max\{y_1, y_2,..., y_n\} - min\{y_1, y_2,..., y_n\}}$$ I might write the following `R` code to carry out the rescaling procedure for the `length` and `weight` columns of the `BlackfoorFish` data: ```{r} #| echo: true #| eval: false fish <- fish |> mutate(length = (length - min(length, na.rm = TRUE)) / (max(length, na.rm = TRUE) - min(length, na.rm = TRUE)), weight = (weight - min(weight, na.rm = TRUE)) / (max(weight, na.rm = TRUE) - min(length, na.rm = TRUE))) ``` This process of duplicating an action multiple times can make it difficult to understand the intent of the process. *Additionally, it can make it very difficult to spot mistakes.* **4. What is the mistake I made in the above rescaling code?** When you find yourself copy-pasting lines of code, it's time to write a function, instead! **5. Transform the repeated process above into a `rescale_01()` function. Your function should...** + **... take a single vector as input.** + **... return the rescaled vector.** ```{r} #| label: write-rescale-function ``` **6. Let's incorporate some input validation into your function. Modify your previous code so that the function stops if ...** + **... the input vector is not numeric.** + **... the length of the input vector is not greater than 1.** Do not create a new code chunk here -- simply add these stops to your function above! ## Test Your Function **7. Run the code below to test your function. Verify that the maximum of your rescaled vector is 1 and the minimum is 0!** ```{r} #| label: verify-rescale-function x <- c(1:25, NA) rescaled <- rescale_01(x) min(rescaled, na.rm = TRUE) max(rescaled, na.rm = TRUE) ``` Next, let's test the function on the `length` column of the `BlackfootFish` data. **8. The code below makes a histogram of the original values of `length`. Add a plot of the rescaled values of `length`. Output your plots side-by-side, so the reader can confirm the only aspect that has changed is the scale.** ```{r} #| label: compare-original-with-rescaled-lengths fish |> ggplot(aes(x = length)) + geom_histogram(binwidth = 45) + labs(x = "Original Values of Fish Length (mm)") + scale_y_continuous(limits = c(0,4000)) # Code for Q8 plot. ``` ## Challenge: Use Variables within a Dataset Suppose you would like for your `rescale()` function to perform operations on a **variable within a dataset**. Ideally, your function would take in a data frame and a variable name as inputs and return a data frame where the variable has been rescaled. **9. Create a `rescale_column()` function that accepts two arguments:** + **a dataframe** + **the name(s) of the variable(s) to be rescaled** **The body of the function should call the original `rescale_01()` function you wrote previously. Your solution MUST use one of the `rlang` options from class.** ```{r} #| label: rescale-data-frame-function ``` **10. Use your `rescale_column()` function to rescale *both* the `length` and `weight` columns.** Note: I expect that you carry out this process by calling the `rescale_column()` function only ONE time! ```{r} #| label: rescale-two-columns ```