class: center, top, title-slide # CSSS508, Week 1 ## RStudio and RMarkdown ### Chuck Lanfear ### Mar 31, 2020<br>Updated: Mar 31, 2021 --- # Course Goals * Develop intermediate data management and visualization skills in R -- * Learn basic programming -- * Introduce reproducible research practices -- * Prepare you for statistics and CSSS courses --- # Who is this guy? **Chuck Lanfear** * Instructor (*not professor*) -- * 7th (and last) Year Sociology PhD student -- * Research: -- + Quantitative Sociology -- + Computational Social Science -- + Experimental Criminology -- * Translation: -- + I write code every day -- + I am a turbo-nerd -- + I think programming is incredibly important --- # Who is that guy? **Breon Haskett** * Rhymes with Neon -- * 4th Year Sociology PhD Student -- * Research -- + Labor, Collective Action, Pay Setting Institutions -- + Econometrics, Counterfactual Reasoning, Computational Data Collection -- + Reproducible Research -- * About Me -- + Also, I write code every day -- + A nerd, but only between the hours of 9 - 6 -- + Social Science programming is magical --- # Logistics Location: * Lecture: [Zoom (848-704-242)](https://washington.zoom.us/j/848704242) on Wednesdays, 3:30-5:20 * Recommended Lab: [Zoom (848-704-242)](https://washington.zoom.us/j/848704242) on Mondays, 3:30-5:20 * Office Hours: * Breon: Thursdays, 11 - 12 * Chuck: By appointment Materials: http://clanfear.github.io/CSSS508 Grading: * Final grade: C/NC, 60% to get Credit * **Homework** most weeks (75% of grade), combination of reading and programming * **Peer Grading** of homeworks (25% of grade) * Both handed in via Canvas. --- # Ugh, peer grading? Yes, because: -- * You will write your reports better knowing others will see them -- * You learn alternate approaches to the same problem -- ### Format: * Randomly assigned peers, due before the following Wednesday * Follow the [grading rubric](https://clanfear.github.io/CSSS508/docs/peer_review.html) * Leave constructive comments--more than just a "good job!" + On assignments 1-3 I expect *a few sentences at least*. + Assignments 4+ may follow labs and need less feedback if perfect. * **Email us** if you want more feedback --- # Materials All course materials are on the course website. This includes: * These slides and the code used to generate them. * An R script for the slides to follow along in class. * PDFs of slides if you like those. * Homework templates (HW 4+). * Video recordings of the lectures and labs. * Useful links to other resources. If you find something on the website doesn't work, please Slack or email me. --- # Lab Lab is optional but strongly recommended. They will be recorded this term. -- ### Labs 1-3 I will provide general technical support and answer any R related questions you have, whether for the homework or not. -- ### Labs 4+ In the latter two-thirds of the course, we will *walk through homeworks together*. This provides students guidance through the more complex homeworks. This gives students the option of (1) working independently on homeworks for maximum learning, (2) reserving homeworks for lab if they are too challenging or time consuming, or (3) a bit of both. --- # Using the Mailing List Don't ask like this: > tried lm(y~x) but it iddn't work wat do .footnote[ Note: If you're not enrolled but want to be on the mailing list, email me! ] -- Instead, ask like this: <blockquote> <pre> y <- seq(1:10) + rnorm(10) x <- seq(0:10) model <- lm(y ~ x) </pre> Running the block above gives me the following error, anyone know why? <pre> Error in model.frame.default(formula = y ~ x, drop.unused.levels = TRUE) : variable lengths differ (found for 'x') </pre> </blockquote> --- # Slack Channel This course uses a [Slack Channel](https://uwcsss508.slack.com/) for additional communication. You will receive a link to join via the email list. Use it like the mailing list to ask questions, particularly for *short questions*. Use the mailing list for *long questions* or those requiring you to attach a file. I encourage you to answer each others' questions! .footnote[ Note: If you're not enrolled but want to be Slack, email me! ] --- # A Note on Slide Formatting **Bold** usually indicates an important vocabulary term. Remember these! -- *Italics* indicate emphasis but also are used to point out things you must click with a mouse, for example: "Please click *File > Print*" -- `Code` represents R code you type into the editor or console or keystrokes used to perform actions, for example: "Press `Ctrl-P` to open the print dialogue." -- Code chunks that span the page represent *actual R code embedded in the slides*. ```r *# Sometimes important stuff is highlighted! 7 * 49 ``` ``` ## [1] 343 ``` The lines preceded by `##` represent the output, or result, of running the code in the code chunk. We'll talk about this more later! --- # Lecture Plan 1. RStudio and R Markdown -- 2. Visualizing Data -- 3. Manipulating and Summarizing Data -- 4. Understanding R Data Structures -- 5. Importing, Exporting, Cleaning Data -- 6. Using Loops -- 7. Writing Functions -- 8. Working with Text Data -- 9. Working with Geographical Data -- 10. Reproducibility and Model Results --- class: inverse # R and RStudio --- # Why R? R is a programming language built for statistical computing. If one already knows Stata or similar software, why use R? -- * R is *free*, so you don't need a terminal server. -- * R has a *very* large community. -- * R can handle virtually any data format. -- * R makes replication easy. -- * R is a *language* so it can do *everything*. -- * R skills transfer to other languages like Python and Julia. --- # R Studio R Studio is a "front-end" or integrated development environment (IDE) for R that can make your life *easier*. -- We'll show RStudio can... -- * Organize your code, output, and plots -- * Auto-complete code and highlight syntax -- * Help view data and objects -- * Enable easy integration of R code into documents with **R Markdown** -- It can also... * Manage `git` repositories * Run interactive tutorials * Handle other languages like C++, Python, SQL, HTML, and shell scripting --- # Selling you on R Markdown The ability to create R Markdown files is a powerful advantage of R: -- * Document analyses by combining text, code, and output -- + No copying and pasting into Word -- + Easy for collaborators to understand -- + Show as little or as much code as you want -- * Produce many different document types as output -- + PDF documents + HTML webpages and reports + Word and PowerPoint documents + Presentations (like these slides) -- * Works with LaTeX and HTML for math and more formatting control -- We'll get back to this shortly! --- # Getting Started Open up RStudio now and choose *File > New File > R Script*. Then, let's get oriented with the interface: * *Top Left*: Code **editor** pane, data viewer (browse with tabs) * *Bottom Left*: **Console** for running code (`>` prompt) * *Top Right*: List of objects in **environment**, code **history** tab. * *Bottom Right*: Tabs for browsing files, viewing plots, managing packages, and viewing help files. You can change the layout in *Preferences > Pane Layout* --- # Editing and Running Code There are several ways to run R code in RStudio: -- * Highlight lines in the **editor** window and click *Run* at the top or hit `Ctrl+Enter` or `⌘+Enter` to run them all. -- * With your **caret**<sup>1</sup> on a line you want to run, hit `Ctrl+Enter` or `⌘+Enter`. Note your caret moves to the next line, so you can run code sequentially with repeated presses. .footnote[ This thing is the caret: .blink_me[`|`] ] -- * Type individual lines in the **console** and press `Enter`. -- * In R Markdown documents, click within a code chunk and click the green arrow to run the chunk. The button beside that runs *all prior chunks*. -- The console will show the lines you ran followed by any printed output. --- # Incomplete Code If you mess up (e.g. leave off a parenthesis), R might show a `+` sign prompting you to finish the command: ```r > (11-2 + ``` Finish the command or hit `Esc` to get out of this. --- # R as a Calculator In the **console**, type `123 + 456 + 789` and hit `Enter`. -- ```r 123 + 456 + 789 ``` ``` ## [1] 1368 ``` -- The `[1]` in the output indicates the numeric **index** of the first element on that line. -- Now in your blank R document in the **editor**, try typing the line `sqrt(400)` and either clicking *Run* or hitting `Ctrl+Enter` or `⌘+Enter`. -- ```r sqrt(400) ``` ``` ## [1] 20 ``` --- # Functions and Help `sqrt()` is an example of a **function** in R. If we didn't have a good guess as to what `sqrt()` will do, we can type `?sqrt` in the console and look at the **Help** panel on the right. ```r ?sqrt ``` **Arguments** are the *inputs* to a function. In this case, the only argument to `sqrt()` is `x` which can be a number or a vector of numbers. Help files provide documentation on how to use functions and what functions produce. --- # Creating Objects R stores everything as an **object**, including data, functions, models, and output. -- Creating an object can be done using the **assignment operator**: `<-` -- ```r new.object <- 144 ``` -- **Operators** like `<-` are functions that look like symbols but typically sit between their arguments (e.g. numbers or objects) instead of having them inside `()` like in `sqrt(x)`<sup>1</sup>. .footnote[[1] We can actually call operators like other functions by stuffing them between backticks: <code class="r">``` \`+\`(x,y) ```</code>] -- We do math with operators, e.g., `x + y`. `+` is the addition operator! --- # Calling Objects You can display or "call" an object simply by using its name. ```r new.object ``` ``` ## [1] 144 ``` -- Object names can contain `_` and `.` in them but cannot *begin* with numbers. Try to be consistent in naming objects. RStudio auto-complete means *long names are better than vague ones*! *Good names save confusion later.<sup>1</sup>* .footnote[[1] "There are only two hard things in Computer Science: cache invalidation and naming things." - Phil Karlton] --- # Using Objects An object's **name** represents the information stored in that **object**, so you can treat the object's name as if it were the values stored inside. -- ```r new.object + 10 ``` ``` ## [1] 154 ``` ```r new.object + new.object ``` ``` ## [1] 288 ``` ```r sqrt(new.object) ``` ``` ## [1] 12 ``` --- # Creating Vectors A **vector** is a series of **elements**, such as numbers. -- You can create a vector and store it as an object in the same way. To do this, use the function `c()` which stands for "combine" or "concatenate". -- ```r new.object <- c(4, 9, 16, 25, 36) new.object ``` ``` ## [1] 4 9 16 25 36 ``` -- If you name an object the same name as an existing object, *it will overwrite it*. -- You can provide a vector as an argument for many functions. -- ```r sqrt(new.object) ``` ``` ## [1] 2 3 4 5 6 ``` --- # More Complex Objects The same principles can be used to create more complex objects like **matrices**, **arrays**, **lists**, and **dataframes** (lists which look like matrices but can hold multiple data types at once). Most data sets you will work with will be read into R and stored as a **dataframe**, so this course will mainly focus on manipulating and visualizing these objects. Before we get into these, let's revisit R Markdown. --- class: inverse # R Markdown --- # R Markdown Documents Let's try making an R Markdown file: 1. Choose *File > New File > R Markdown...* 2. Make sure *HTML Output* is selected and click OK 3. Save the file somewhere, call it `my_first_rmd.Rmd` 4. Click the *Knit HTML* button 5. Watch the progress in the R Markdown pane, then gaze upon your result! You may also open up the file in your computer's browser if you so desire, using the *Open in Browser* button at the top of the preview window. --- # R Markdown Headers The header of an .Rmd file is a [YAML](http://yaml.org/) (YAML Ain't Markup Language<sup>1</sup>) code block, and everything else is part of the main document. .footnote[[1] Nerds love recursive acronyms. ] -- ```{} --- title: "Untitled" author: "Charles Lanfear" date: "March 28, 2018" output: html_document --- ``` -- To mess with global formatting, [you can modify the header](http://rmarkdown.rstudio.com/html_document_format.html)<sup>2</sup>. .footnote[[2] Be careful though, YAML is space-sensitive; indents matter!] ```{} output: html_document: theme: readable ``` --- # R Markdown Syntax .pull-left[ ## Output **bold/strong emphasis** *italic/normal emphasis* # Header ## Subheader ### Subsubheader > Block quote from > famous person ] .pull-right[ ## Syntax <pre> **bold/strong emphasis** *italic/normal emphasis* # Header ## Subheader ### Subsubheader > Block quote from > famous person </pre> ] --- # More R Markdown Syntax .pull-left[ ## Output 1. Ordered lists 1. Are real easy 1. Even with sublists 1. Or when lazy with numbering * Unordered lists * Are also real easy + Also even with sublists [URLs are trivial](http://www.uw.edu)  ] .pull-right[ ## Syntax <pre> 1. Ordered lists 1. Are real easy 1. Even with sublists 1. Or when lazy with numbering * Unordered lists * Are also real easy + Also even with sublists [URLs are trivial](http://www.uw.edu)  </pre> ] --- # Formulae and Syntax .pull-left[ ## Output You can put some math `\(y= \left( \frac{2}{3} \right)^2\)` right up in there. `$$\frac{1}{n} \sum_{i=1}^{n} x_i = \bar{x}_n$$` Or a sentence with `code-looking font`. Or a block of code: <pre> y <- 1:5 z <- y^2 <pre> ] .pull-right[ ## Syntax <pre> You can put some math $y= \left( \frac{2}{3} \right)^2$ right up in there `$$\frac{1}{n} \sum_{i=1}^{n} x_i = \bar{x}_n$$` Or a sentence with `code-looking font`. Or a block of code: ``` y <- 1:5 z <- y^2 ``` </pre> ] --- # R Markdown Tinkering R Markdown docs can be modified in many ways. Visit these links for more information. * [Ways to modify the overall document appearance](http://rmarkdown.rstudio.com/html_document_format.html) * [Ways to format parts of your document](http://rmarkdown.rstudio.com/authoring_basics.html) * [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/) --- # Formatting Caveats To keep R Markdown dead-simple, it lacks some features you might occasionally want to use. Your options for fancier documents are: * Templates * Use HTML with CSS for custom formatting<sup>1</sup> * Use LaTeX and .Rnw files instead of .Rmd<sup>2</sup> For day-to-day use, plain vanilla R Markdown does the job. For handouts, memos, and homeworks, default R Markdown PDFs look surprisingly good! .footnote[ [1] These slides were created using [Xaringan](https://github.com/yihui/xaringan), a blend of RMarkdown and CSS. [2] Here be dragons! LaTeX is powerful but exacts a terrible price. ] --- # R Code in R Markdown Inside RMarkdown, lines of R code are called **chunks**. Code is sandwiched between sets of three backticks and `{r}`. This chunk of code... ```{r} summary(cars) ``` Produces this output in your document: ```r summary(cars) ``` ``` ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00 ``` --- # Chunk Options Chunks have options that control what happens with their code, such as: * `echo=FALSE`: Keeps R code from being shown in the document * `eval=FALSE`: Shows R code in the document without running it * `include=FALSE`: Hides all output but still runs code (good for `setup` chunks where you load packages!) * `results='hide'`: Hides R's (non-plot) output from the document * `cache=TRUE`: Saves results of running that chunk so if it takes a while, you won't have to re-run it each time you re-knit the document * `fig.height=5, fig.width=5`: modify the dimensions of any plots that are generated in the chunk (units are in inches) Some of these can be modified using the gear-shaped *Modify Chunk Options* button in each chunk. [There are a *lot* of other options, however](https://yihui.name/knitr/options/). --- # Playing with Chunk Options Try adding or changing the chunk options (separated by commas) for the two chunks in `my_first_Rmd.Rmd` and re-knitting to check what happens. You can also name your chunks by putting something after the `r` before the chunk options. ```{r summarize_cars, echo=FALSE} summary(cars) ``` After you name your chunks, look what happens in the dropdown on the bottom left of your editor pane. Naming chunks allows you to browse through an RMarkdown document by named chunks. You can also browse by sections named using headers and subheaders. --- # In-Line R code Sometimes we want to insert a value directly into our text. We do that using code in single backticks starting off with `r`. -- Four score and seven years ago is the same as `r 4*20 + 7` years. -- Four score and seven years ago is the same as 87 years. -- Maybe we've saved a variable in a chunk we want to reference in the text: ```r x <- sqrt(77) # <- is how we assign objects ``` -- The value of `x` rounded to the nearest two decimals is `r round(x, 2)`. -- The value of `x` rounded to the nearest two decimals is 8.77. --- # This is Amazing! Having R dump values directly into your document protects you from silly mistakes: -- * Never wonder "how did I come up with this quantity?" ever again: Just look at your formula in your .Rmd file! -- * Consistency! No "find/replace" mishaps; reference a variable in-line throughout your document without manually updating if the calculation changes (e.g. reporting sample sizes). -- * You are more likely to make a typo in a "hard-coded" number than you are to write R code that somehow runs but gives you the wrong thing. --- # Example: Keeping Dates In your YAML header, make the date come from R's `Sys.time()` function by changing: date: "March 30, 2016" to: date: "`r Sys.time()`" Fancier option: Use this instead to take today's date and make it read nicely:<sup>1</sup> date: "`r format(Sys.Date(), format='%B %d, %Y')`" .footnote[[1] `format(Sys.Date(), format='%B %d, %Y')` says "format system date as month name (`%B`), day-of-month (`%d`), and four-digit year (`%Y`): March 31, 2021. See `?strptime` for these format codes.] --- class: inverse # Data Frames --- # What's Up with `cars`? In the sample R Markdown document you are working on, we can load the built-in data `cars`, which loads as a dataframe, a type of object mentioned earlier. Then, we can look at it in a couple different ways. -- `data(cars)` loads this dataframe into the **Global Environment** (as a *promise*<sup>1</sup>). .pull-right[.footnote[[1] Promises are *unevaluated arguments*. Read more about R's [lazy evaluation here](http://adv-r.had.co.nz/Functions.html).]] -- `View(cars)` pops up a **Viewer** pane ("interactive" use only, don't put in R Markdown document!) or... -- ```r head(cars, 5) # prints first 5 rows, see tail() too ``` ``` ## speed dist ## 1 4 2 ## 2 4 10 ## 3 7 4 ## 4 7 22 ## 5 8 16 ``` --- # Tell Me More About `cars` `str()` displays the structure of an object: ```r str(cars) # str[ucture] ``` ``` ## 'data.frame': 50 obs. of 2 variables: ## $ speed: num 4 4 7 7 8 9 10 10 10 11 ... ## $ dist : num 2 10 4 22 16 10 18 26 34 17 ... ``` -- `summary()` displays summary information<sup>1</sup>: ```r summary(cars) ``` ``` ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.00 ## Mean :15.4 Mean : 42.98 ## 3rd Qu.:19.0 3rd Qu.: 56.00 ## Max. :25.0 Max. :120.00 ``` .pull-right[.footnote[[1] [Note R is **object-oriented**](https://adv-r.hadley.nz/oo.html): `summary()` provides different information for different types of objects!]] --- # Ugly Pictures of `cars` `hist()` generates a histogram of a vector. Note you can access a vector that is a column of a dataframe using `$`, the **extract operator**. .pull-left[ ```r hist(cars$speed) # Histogram ``` <img src="CSSS508_Week1_RStudio_and_RMarkdown_files/figure-html/unnamed-chunk-8-1.png" height="330px" /> ] .pull-right[ ```r hist(cars$dist) ``` <img src="CSSS508_Week1_RStudio_and_RMarkdown_files/figure-html/unnamed-chunk-9-1.png" height="330px" /> ] --- ## Drawing Slightly Less Ugly Pictures ```r hist(cars$dist, xlab = "Distance (ft)", # X axis label main = "Observed stopping distances of cars") # Title ``` <img src="CSSS508_Week1_RStudio_and_RMarkdown_files/figure-html/unnamed-chunk-10-1.png" height="400px" /> --- # Math with `cars` If you put an assignment such as `x <- y` in parentheses `()`, R will print the output of the assignment out for you in your document. Otherwise, it won't show the value. -- ```r ( dist_mean <- mean(cars$dist) ) ``` ``` ## [1] 42.98 ``` ```r ( speed_mean <- mean(cars$speed) ) ``` ``` ## [1] 15.4 ``` --- # Drawing Still Ugly Pictures .small[ ```r *plot(dist ~ speed, data = cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)", main = "Speeds and stopping distances of cars", pch = 16) # Point size abline(h = dist_mean, col = "firebrick") abline(v = speed_mean, col = "cornflowerblue") ``` <img src="CSSS508_Week1_RStudio_and_RMarkdown_files/figure-html/unnamed-chunk-12-1.png" height="330px" /> ] .pull-right[ .footnote[Note that `dist ~ speed` is a **formula** of the type `y ~ x`. The first element (`dist`) gets plotted on the y-axis and the second (`speed`) goes on the x-axis. Regression formulae follow this convention as well!] ] --- # `swiss` Time Let's switch gears to the `swiss` data frame built in to R. -- First, use `?swiss` to see what things mean. -- Then, load it using `data(swiss)` -- Add chunks to your R Markdown document inspecting swiss, defining variables, doing some exploratory plots using hist or plot. You might experiment with [colors](http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf) and [shapes](http://www.cookbook-r.com/Graphs/Shapes_and_line_types/). --- ## Looking at `swiss` ```r *pairs(swiss, pch = 8, col = "violet", main = "Pairwise comparisons of Swiss variables") ``` <img src="CSSS508_Week1_RStudio_and_RMarkdown_files/figure-html/unnamed-chunk-13-1.png" height="340px" /> .pull-right30[ .footnote[`pairs()` is a pairwise scatterplot function. Good for a quick look at small datasets, but mostly useless for larger data.] ] --- # Installing Packages Let's make a table that looks a little less code-y in the output. To do this, we'll want to install a **package** called `pander`. Packages contain premade functions and/or data we can use. R's strength is its wide variety of packages! In the console: `install.packages("pander")`. -- * Note that unlike the `library()` command, *the name of a package to be installed must be in quotes*. This is because the name here is a search term (text, not an object!) while for `library()` it is an actual R object. -- * Once you install a package, you don't need to re-install it until you update R. *Consequently, you should not include* `install.packages()` *in any markdown document or R script!* --- # Making Tables ```r library(pander) # loads pander, do once in your session *pander(summary(swiss), style = "rmarkdown", split.tables = 120) ``` .small[ | Fertility | Agriculture | Examination | Education | Catholic | Infant.Mortality | |:-------------:|:-------------:|:-------------:|:-------------:|:---------------:|:----------------:| | Min. :35.00 | Min. : 1.20 | Min. : 3.00 | Min. : 1.00 | Min. : 2.150 | Min. :10.80 | | 1st Qu.:64.70 | 1st Qu.:35.90 | 1st Qu.:12.00 | 1st Qu.: 6.00 | 1st Qu.: 5.195 | 1st Qu.:18.15 | | Median :70.40 | Median :54.10 | Median :16.00 | Median : 8.00 | Median : 15.140 | Median :20.00 | | Mean :70.14 | Mean :50.66 | Mean :16.49 | Mean :10.98 | Mean : 41.144 | Mean :19.94 | | 3rd Qu.:78.45 | 3rd Qu.:67.65 | 3rd Qu.:22.00 | 3rd Qu.:12.00 | 3rd Qu.: 93.125 | 3rd Qu.:21.70 | | Max. :92.50 | Max. :89.70 | Max. :37.00 | Max. :53.00 | Max. :100.000 | Max. :26.60 | ] .footnote[Note that we put the `summary(swiss)` function call inside the `pander()` call. This is called *nesting functions* and is very common. I'll introduce a method next week to avoid confusion from nesting too many functions inside each other.] --- ## Data Look a Little Nicer This Way ```r pander(head(swiss, 5), style = "rmarkdown", split.tables = 120) ``` .small[ | | Fertility | Agriculture | Examination | Education | Catholic | Infant.Mortality | |:----------------:|:---------:|:-----------:|:-----------:|:---------:|:--------:|:----------------:| | **Courtelary** | 80.2 | 17 | 15 | 12 | 9.96 | 22.2 | | **Delemont** | 83.1 | 45.1 | 6 | 9 | 84.84 | 22.2 | | **Franches-Mnt** | 92.5 | 39.7 | 5 | 5 | 93.4 | 20.2 | | **Moutier** | 85.8 | 36.5 | 12 | 7 | 33.77 | 20.3 | | **Neuveville** | 76.9 | 43.5 | 17 | 15 | 5.16 | 20.6 | ] .footnote[`split.tables = 120` tells `pander` to break a table into multiple tables if it will be over 120 characters wide. Adjust this to get widths *just right*.] --- class: inverse # Homework Write up a .Rmd file showing some exploratory analyses of the Swiss fertility data. Upload both the `.Rmd` file and the `.html` file to Canvas. You must upload *both* for credit. Mix in-line R calculations, tables, R output, and plots with text describing the relationships you see. Include *at least* one plot and one table. You are encouraged to include more! You must use in-line R calculations/references at least once (e.g. functions like `nrow()`, `mean()`, `sd()`, `cor()`, `median()`, `min()`) and *may not hard-code any numbers referenced in your text*. Your document should be pleasant for a peer to look at, with some organization using sections or lists, and all plots labeled clearly. Use chunk options `echo` and `results` to limit the code/output you show in the .html. Discussion of specific values should be summarized in sentences in your text---*not as printed code and output*---and rounded so as not to be absurdly precise (e.g. round `x` with `round(x, 2)`). --- # Grading Rubric 0 - Didn't turn anything in. -- 1 - Turned in but low effort, ignoring many directions. -- 2 - Decent effort, followed directions with some minor issues. -- 3 - Nailed it!