--- title: "Lab 1: Looking Under the Hood of R" --- ```{r} #| label: setup #| include: false knitr::opts_chunk$set( echo = TRUE, message = FALSE, warning = FALSE, fig.width = 6, fig.height = 3.6, fig.align = "center", dpi = 120, error = TRUE, fig.path = "_figs/lab-1-looking-under-the-hood/" ) library(tidyverse) ``` Predict, then run. The point is to catch R in the act. Work in an **R script**, not only in the console. Save the predict-run work as `lab01.R`. Section G is a short Quarto document. Do **1.1** through **1.5** first. You will need `tidyverse` installed as in note 1.2, and you will import `students.csv` as in note 1.3. Note **1.5** is for section G. Download [students.csv](files/students.csv) and put it in your working directory (`getwd()` should see it), or use a relative path from note 1.3. For every item marked **Predict**, do the following in order: 1. Write your prediction in a comment, *before* you run the code. 2. Run the code. 3. Write a short explanation of *why* R did that, using the rules from the notes. "R printed that" is not an explanation. The notes give you the rules. The lab asks you to apply them *before* you see the output. Some results will surprise you. That is the point. You should not need material that is not in 1.1–1.5. Do not look up the answer until you have a written prediction. --- ## A. What `c()` actually builds R's atomic vectors are not "lists of values of any type." They are typed objects. `c()` is willing to rewrite your values in order to keep that true. ### A1. Predict ```{r} #| label: chunk-1 typeof(c(1, TRUE)) typeof(c(1L, TRUE)) typeof(c(1, "2", TRUE)) ``` Why are the three answers not the same? What did `TRUE` become in each case? ### A2. Predict ```{r} #| label: chunk-2 TRUE + TRUE + TRUE c(TRUE, FALSE, TRUE) * 2 ``` If `TRUE` is a logical value, why is arithmetic legal? ### A3. Predict ```{r} #| label: chunk-3 1 == 1L typeof(1) typeof(1L) is.numeric(1L) ``` `1` and `1L` compare as equal. Are they the same type? What does `is.numeric()` say about an integer? ### A4. Predict ```{r} #| label: chunk-4 f <- factor(c("10", "2", "2")) as.numeric(f) as.numeric(as.character(f)) ``` Note 1.4 treats factors as categorical data with levels. What is `as.numeric()` looking at when the object is a factor? --- ## B. Three kinds of nothing `NA`, `NaN`, and `NULL` are not three names for the same idea. ### B1. Predict ```{r} #| label: chunk-5 x <- c(NA, NaN, Inf, NULL) x length(x) typeof(x) ``` You passed four things to `c()`. Why does `x` not have length 4? ### B2. Predict ```{r} #| label: chunk-6 NA == NA NaN == NaN NULL == NULL ``` Then: ```{r} #| label: chunk-7 is.na(NA) is.nan(NaN) is.null(NULL) ``` Why is `==` a poor way to test for these values? Which `is.*()` function matches which kind of nothing? ### B3. Predict ```{r} #| label: chunk-8 is.na(NaN) is.nan(NA) typeof(NA) typeof(c(NA, 1)) typeof(c(NA, "a")) ``` `NA` looks like one object. Use the coercion hierarchy from note 1.4. What happened to `NA` when it entered those two vectors? --- ## C. Indexing is a language `[` is not "get the nth thing." It is a function with rules for integers, negatives, logicals, and names. ### C1. Predict ```{r} #| label: chunk-9 x <- 10:13 x[c(1, 1, 1)] x[-c(1, 1)] ``` Positive indices select. Negative indices drop. Why does repeating `1` mean two different things in these two lines? ### C2. Predict ```{r} #| label: chunk-10 x <- c(10, 20, 30, 40) x[c(TRUE, FALSE)] x[c(TRUE, FALSE, TRUE)] ``` Note 1.4 says R recycles the shorter vector in arithmetic. What rule is R using here, and where does the extra `TRUE` go? ### C3. Predict ```{r} #| label: chunk-11 record <- list( info = list(name = "Ada", year = 1L), scores = c(90, 85) ) record[1] record[[1]] record$info$name record[["info"]][1] record[["info"]][[1]] ``` Note 1.4 distinguishes `[`, `[[`, and `$`. `info` is a list inside a list. What does each line return, and which results are still lists? ### C4. Predict ```{r} #| label: chunk-12 students <- data.frame(name = c("Ada", "Bob"), grade = c(91, 74)) typeof(students) is.list(students) is.data.frame(students) class(students[, "grade"]) class(students[, "grade", drop = FALSE]) class(students["grade"]) class(students[["grade"]]) ``` `typeof()` and `is.list()` are from note 1.4. Why can a data frame be a list and a data frame at the same time? Which extractions are still data frames, and what is `drop` for? ### C5. Predict ```{r} #| label: chunk-13 m <- matrix(1:6, nrow = 2) m m[2, 1] ``` Fill in the matrix on paper before you run this, using `matrix(1:6, nrow = 2)` from note 1.4. In what order does R fill the matrix, and why is `m[2, 1]` equal to 2 rather than 4? --- ## D. Packages, names, and what R is searching A function name is not a unique identity. It is a name R looks up on a search path. ### D1. Explain, then try only the quoted form for installation Why does this fail (do not keep running it): ```{r} #| label: chunk-14 #| eval: false install.packages(ggplot2) ``` while this is the intended form: ```{r} #| label: chunk-15 #| eval: false install.packages("ggplot2") ``` and yet both of these can succeed: ```{r} #| label: chunk-16 library(ggplot2) library("ggplot2") ``` What is `library()` doing with an unquoted name that `install.packages()` will not do? ### D2. Predict, in a session where you have not yet loaded dplyr ```{r} #| label: chunk-17 students <- data.frame(name = c("Ada", "Bob"), grade = c(91, 74)) filter filter(students, grade > 80) ``` Then load dplyr and run the same two lines again. ```{r} #| label: chunk-18 library(dplyr) filter filter(students, grade > 80) ``` Note 1.2 covers `library()`, `search()`, and `dplyr::filter()`. There is already a `filter()` in Base R / stats. What changed after `library(dplyr)`? How can two functions share a name? How do you call the older one anyway? ### D3. Predict ```{r} #| label: chunk-19 x <- c(10, 20, 30) x |> mean() x %>% mean() ``` Run this *before* loading tidyverse. Why does one pipe exist in Base R and the other does not? After `library(tidyverse)`, run both lines again. ### D4. Predict ```{r} #| label: chunk-20 search() library(ggplot2) search() ``` Where did `ggplot2` go on the search path, and why does that position matter when two packages contain the same function name? --- ## E. Arithmetic that is not always the arithmetic you expect ### E1. Predict ```{r} #| label: chunk-21 seq(from = 1, to = 3) + c(10, 20, 30, 40, 50, 60) seq(1, 6, 2) * c(TRUE, FALSE) ``` Note 1.4 covers `seq()`, named arguments, and recycling. Write the recycled vectors out by hand. When does R warn, and when does it stay silent? What did `TRUE` and `FALSE` become? ### E2. Predict ```{r} #| label: chunk-22 1 / 0 0 / 0 TRUE / FALSE Inf - Inf NA + 1 NULL + 1 ``` Which of these are `Inf`, `NaN`, `NA`, and which is an error? What coercion makes `TRUE / FALSE` legal? --- ## F. Files, data frames, and shape Place `students.csv` in your working directory. ### F1. Run and compare ```{r} #| label: chunk-23 #| eval: false file.exists("students.csv") file.exists(file.path(".", "students.csv")) base_students <- read.csv("students.csv") tidy_students <- readr::read_csv("students.csv") class(base_students) class(tidy_students) str(base_students) str(tidy_students) ``` Note 1.3: `.` is the current directory, and `file.path()` builds a path. Why are the two existence checks the same? Both read the same file. Why are the objects not the same class? What extra classes does `read_csv()` attach, and what is a tibble claiming to be besides a data frame? ### F2. Predict ```{r} #| label: chunk-24 base_students$grade * 0.01 base_students[, "grade"] base_students["grade"] ``` One of these uses the vectorized arithmetic from note 1.4. Which extraction still has two dimensions? --- ## G. A document, not a script A script is the right tool for A–F. Note 1.5 is the right tool for a short writeup. Create `lab01_practice.qmd` as in note 1.5: YAML with a title, your name, and `format: html`. Put this chunk in it: ```{r} #| echo: true students <- read.csv("students.csv") typeof(students) is.list(students) ``` Render it. Then set `#| echo: false` and render again. What disappeared from the HTML, and why? Write one sentence in the Quarto file (ordinary prose, not a comment) answering that. The `typeof()` / `is.list()` lines are the same idea as C4. --- ## H. Source drills These are the original Week 1 lab items. Work in the Quarto file from G, or in a second `.qmd`. Number them. 1. Sum the even numbers from 10 to 1002. Use `seq()` and `sum()`. 2. `c("Hello", "world!", "How", "are", "you?")`, then one string with underscores between the words (`paste()` and `collapse`). 3. What happens with `"1" + 2`? 4. Comment that line out and rerun. 5. Sum of the multiples of 3 or 5 that are less than 1000. Build `1:999`, then subset with `%%` and `|`. 6. Render the document to HTML (Quarto) or knit an `.Rmd` to PDF. --- ## What to submit Submit `lab01.R` and `lab01_practice.qmd` with: - a prediction comment above every **Predict** block - the code - a short explanation comment below the output of each block - for G, the Quarto file you ended with (`#| echo: false`) and the sentence about what disappeared If a result still feels impossible after you have explained it, write down the question you now have. That question is part of the lab.