Welcome to the tidyverse

class: center middle main-title section-title-4

# Welcome to the tidyverse

.class-info[

**Week 2**

AEM 2850 / 5850 : R for Business Analytics 
Cornell Dyson 
Fall 2025

Acknowledgements: 


[Grant McDermott](https://github.com/uo-ec607/lectures),
[Allison Horst](https://github.com/allisonhorst/stats-illustrations)

]

---

# Announcements

If you are new to the class, please review canvas and the [course site](https://aem2850.toddgerarden.com)

**Prelim dates:** Oct 2 and Dec 4 at 7:30pm

**Other reminders:**
- Bookmark [the schedule](https://aem2850.toddgerarden.com/schedule) and visit often
- Submit assignments via canvas by Monday at 11:59pm going forward

---

# Office hours

**TA office hours:**
- Mondays 2:00pm - 6:00pm in Warren 370
- Fridays 1:00pm - 2:00pm in Warren B05

**My office hours:**
- Tuesdays 11:30 - 12:30 in Warren 464
- Thursday afternoons by appointment: [aem2850.youcanbook.me](https://aem2850.youcanbook.me)

---

# Questions before we get started?

---

# Plan for today

[Generative AI policy](#gai) and [Prologue](#prologue)

[Tidyverse basics](#basics)
  - [data frames](#tibbles) and [pipes](#pipes)

[Data transformation with dplyr](#dplyr)
  - [select](#select)
  - [filter](#filter)
  - [arrange](#arrange)
  - [mutate](#mutate)
  - [summarize](#summarize)
  - [other goodies](#goodies)

---
class: inverse, center, middle
name: gai

# Generative AI policy

---

# Generative AI policy

Generative AI (GenAI) tools are very powerful assistants for widely used programming languages like R

You may use GenAI tools in this course **unless explicitly forbidden** for a specific assignment

However, be aware of the limitations of GenAI tools:
  - GenAI tools can be misleading
  - GenAI tools often propose code that does not function
  - GenAI code can be difficult to debug if you do not understand the fundamentals

---

# Generative AI tools can be misleading

.center[
<figure>
 <img src="img/01/gpt-5-deception-chart.png.webp" alt="GPT-5 release chart from OpenAI" title="GPT-5 release chart from OpenAI" width="88%">
</figure>
]

???

Source: https://www.pcgamer.com/software/ai/openais-performance-charts-in-the-gpt-5-launch-video-are-such-a-mess-you-have-to-think-gpt-5-itself-probably-made-them-and-the-companys-attempted-fixes-raise-even-more-questions/

---

# Generative AI example

Does anyone know where this quote is from?

> A-B-C. A-always, B-be, C-closing. Always be closing! Always be closing!!
> 
> A-I-D-A. Attention, interest, decision, action. Attention -- do I have your attention? Interest -- are you interested? I know you are because it's fuck or walk. You close or you hit the bricks! Decision -- have you made your decision for Christ?!! And action.
> 
> A-I-D-A; get out there!! You got the prospects comin' in; you think they came in to get out of the rain? Guy doesn't walk on the lot unless he wants to buy. Sitting out there waiting to give you their money! Are you gonna take it?

---

# Generative AI example

---

# Generative AI example

---

# Which answer is better?

.pull-left[
<img src="img/02/gggr-abc1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="img/02/gggr-abc2.png" width="100%" style="display: block; margin: auto;" />
]

I **strongly** encourage you to focus on learning essential syntax and then, later, consider using these tools as helpers (or not at all), rather than relying on them as replacements for your own knowledge and function

---
class: inverse, center, middle
name: prologue

# Prologue

---

# How we feel about the course, in a few words

> I'm excited to learn more about R!

> I think the pace is just right!

> Slightly nervous

---

# Where we're from

How can we use your 89 survey responses to construct our origin story without having to read them one by one?

- **Caveat:** This is based on submissions as of 13:25 Monday

We can use R for this!

I won't go through details now

By the end of the course you should be able to do this kind of thing easily

---

# Are we all from New York?

What share of us are from the state of New York?

``` r
# step 1: do a bunch of stuff that's omitted for clarity
# step 2: show us the answer!
ny_share
```

```
## # A tibble: 1 × 2
## origin percent
## <chr> <dbl>
## 1 new york 32
```

---

# Are we all from New York?

---

# Are we all from the U.S.?

---
class: inverse, center, middle
name: basics

# Tidyverse basics

---

# R "dialects"

Last week we *briefly* introduced several R programming concepts:
  - logical expressions
  - assignment
  - object types
  - [object names, namespace conflicts]
  - [indexing]

We did this using base R
  - **pro:** comes in the box, close parallels to other programming languages
  - **con:** can be confusing for new programmers (esp. indexing operations)

---

# R "dialects"

.more-left[
This course will primarily use the tidyverse

You can think of it as a particular "dialect" of R

Why we're using the tidyverse:
- Intuitive if you're new to programming
- Consistent philosophy and syntax
- Great documentation and community support
- For data wrangling and plotting, it's 🔥

But not representative of all R programming
]

.less-right[
<img src="img/02/r_first_then.png" width="100%" style="display: block; margin: auto;" />
]

---

# Let's load the tidyverse meta-package

``` r
library(tidyverse)
```

```
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
```

We just loaded a bunch of packages: **ggplot2**, **dplyr**, **tidyr**, **tibble**, etc.

We also see some **namespace conflicts**

---

# Tidyverse packages

When you install the tidyverse, you actually get a lot more packages:

``` r
tidyverse_packages()
```

```
##  [1] "broom"         "conflicted"    "cli"           "dbplyr"       
##  [5] "dplyr"         "dtplyr"        "forcats"       "ggplot2"      
##  [9] "googledrive"   "googlesheets4" "haven"         "hms"          
## [13] "httr"          "jsonlite"      "lubridate"     "magrittr"     
## [17] "modelr"        "pillar"        "purrr"         "ragg"         
## [21] "readr"         "readxl"        "reprex"        "rlang"        
## [25] "rstudioapi"    "rvest"         "stringr"       "tibble"       
## [29] "tidyr"         "xml2"          "tidyverse"
```

These other packages have to be loaded separately

---

# Tidyverse packages

Today we'll focus on [**dplyr**](https://dplyr.tidyverse.org/)

But first let's talk about data frames and pipes

---
name: tibbles

# Review: What are objects?

There are many different *types* (or *classes*) of objects

What are some examples from last week?

Here are some objects that we'll be working with regularly:
- vectors
- matrices
- data frames
- lists
- functions

---

# Data frames

The most important object we'll work with is the **data frame**

.pull-left[

``` r
# Create a small data frame called "d"
d <- tibble(x = c("a", "b", "c"), y = 7:9)
d
```

```
## # A tibble: 3 × 2
## x y
## <chr> <int>
## 1 a 7
## 2 b 8
## 3 c 9
```
]

.pull-right.small[
Think of it as a spreadsheet

Tibbles are the tidyverse term for data frame

*We'll treat "tibbles" and "data frames" as synonymous*
]

This is essentially just a table with columns named `x` and `y`

Each row is an observation telling us the values of both `x` and `y`

---
name: pipes

# Pipes

**Generic programming question:** Does anyone know what pipes do?

Pipes allow us to pass information through a sequence of operations

In this class, we will use R's native pipe: `|>`

**Keyboard shortcut:** use `Ctrl/Cmd+Shift+m` to insert the pipe (with spaces)

You might also see the `magrittr` pipe, denoted `%>%`

They are identical for simple cases (but have some differences)

---

# Pipe usage

To see why using pipes is so powerful, consider this contrived example based on what we did this morning:

1. Wake up
2. Get out of bed
4. Get dressed
5. Drink coffee
6. Go to class

---

# A day without pipes

You could code this through a series of assignment operations:

``` r
me <- wake_up(me)
me <- get_out_of_bed(me)
me <- get_dressed(me)
me <- drink(me, "coffee")
me <- go(me, "class")
```

Or by putting all these operations in a single line of code:

``` r
me <- go(drink(get_dressed(get_out_of_bed(wake_up(me))), "coffee"), "class")
```

Neither is ideal

---

# Pipes are the best of both worlds

We can do everything at once, in order:

Emphasis is on verbs (e.g., `wake_up`, `get_out_of_bed`, etc.), not nouns (i.e., `me`)

---

# Pipes: Best practices

Write linear code

Spread code out for readability: use one line per verb

Don't use pipes...
- when you need more than 10 (use intermediate objects instead)
- for multiple inputs or outputs
- for non-linear relationships

**Reminder:** `|>` and `%>%` can both be used, though they have some important differences for more advanced work. We will us `|>` in this class.

---
class: inverse, center, middle
name: dplyr

# dplyr

---

# What is dplyr?

---

# What is dplyr?

The dplyr package provides a **grammar of data manipulation**

dplyr's functions are **verbs** that correspond to things we want to **do**

dplyr functions all have these things in common:
1. their first argument is a data frame
2. their other arguments describe what you want to do
3. their output is a new data frame

**1** + **3** + **pipes** allow us to combine simple steps to achieve complex results

---

# dplyr has five key verbs we will use

1. `select`: select columns (i.e., variables)

2. `filter`: subset rows based on their values

3. `arrange`: reorder rows based on their values

4. `mutate`: create new columns

5. `summarize`: collapse multiple rows into a single summary value

---

# dplyr verbs operate on different things

- Rows:
  - `filter`: subset rows based on their values
  - `arrange`: reorder rows based on their values
- Columns:
  - `select`: select columns (i.e., variables)
  - `mutate`: create new columns
- Groups of rows:
  - `summarize`: collapse multiple rows into a single summary value

Let's study these commands together using the `starwars` data frame that comes pre-packaged with dplyr

---
name: select

# 1) dplyr::select

Recall what the `starwars` data frame looks like:

``` r
starwars
```

```
## # A tibble: 87 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 
## 1 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 2 C-3PO 167 75 <NA> gold yellow 112 none mascu…
## 3 R2-D2 96 32 <NA> white, bl… red 33 none mascu…
## 4 Darth V… 202 136 none white yellow 41.9 male mascu…
## 5 Leia Or… 150 49 brown light brown 19 fema… femin…
## 6 Owen La… 178 120 brown, gr… light blue 52 male mascu…
## 7 Beru Wh… 165 75 brown light blue 47 fema… femin…
## 8 R5-D4 97 32 <NA> white, red red NA none mascu…
## 9 Biggs D… 183 84 black light brown 24 male mascu…
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
## # ℹ 77 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>
```

---

# 1) dplyr::select

`select` allows us to select columns / variables:

``` r
starwars |> select(name)
```

```
*## # A tibble: 87 × 1
## name 
## <chr> 
## 1 Luke Skywalker 
## 2 C-3PO 
## 3 R2-D2 
## 4 Darth Vader 
## 5 Leia Organa 
## 6 Owen Lars 
## 7 Beru Whitesun Lars
## 8 R5-D4 
## 9 Biggs Darklighter 
## 10 Obi-Wan Kenobi 
## # ℹ 77 more rows
```

---

# 1) dplyr::select

You can use commas to select multiple columns, deselect a column using "-"

.pull-left[

``` r
vitals <- starwars |> 
 select(name, species, mass, height)
vitals
```

```
## # A tibble: 87 × 4
## name species mass height
## <chr> <chr> <dbl> <int>
## 1 Luke Skywalker Human 77 172
## 2 C-3PO Droid 75 167
## 3 R2-D2 Droid 32 96
## 4 Darth Vader Human 136 202
## 5 Leia Organa Human 49 150
## 6 Owen Lars Human 120 178
## 7 Beru Whitesun Lars Human 75 165
## 8 R5-D4 Droid 32 97
## 9 Biggs Darklighter Human 84 183
## 10 Obi-Wan Kenobi Human 77 182
## # ℹ 77 more rows
```
]

.pull-right[

``` r
new_vitals <- vitals |> 
 select(-height)
new_vitals
```

```
## # A tibble: 87 × 3
## name species mass
## <chr> <chr> <dbl>
## 1 Luke Skywalker Human 77
## 2 C-3PO Droid 75
## 3 R2-D2 Droid 32
## 4 Darth Vader Human 136
## 5 Leia Organa Human 49
## 6 Owen Lars Human 120
## 7 Beru Whitesun Lars Human 75
## 8 R5-D4 Droid 32
## 9 Biggs Darklighter Human 84
## 10 Obi-Wan Kenobi Human 77
## # ℹ 77 more rows
```
]

---

# 1) dplyr::rename

If you just want to rename columns without subsetting them, use `rename`:

``` r
starwars |>
  rename(alias = name, crib = homeworld)
```

```
## # A tibble: 87 × 14
## alias height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 
## 1 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 2 C-3PO 167 75 <NA> gold yellow 112 none mascu…
## 3 R2-D2 96 32 <NA> white, bl… red 33 none mascu…
## 4 Darth V… 202 136 none white yellow 41.9 male mascu…
## 5 Leia Or… 150 49 brown light brown 19 fema… femin…
## 6 Owen La… 178 120 brown, gr… light blue 52 male mascu…
## 7 Beru Wh… 165 75 brown light blue 47 fema… femin…
## 8 R5-D4 97 32 <NA> white, red red NA none mascu…
## 9 Biggs D… 183 84 black light brown 24 male mascu…
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
## # ℹ 77 more rows
## # ℹ 5 more variables: crib <chr>, species <chr>, films <list>, vehicles <list>,
## # starships <list>
```

---
class: inverse, center, middle
name: example

# Let's work through an example on Posit Cloud

---
name: filter

# 2) dplyr::filter

---

# 2) dplyr::filter

`filter` allows us to focus on certain rows / observations

For example, you may only be interested in droids:

``` r
starwars |> 
  filter(species == "Droid") 
```

```
## # A tibble: 6 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender 
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 
## 1 C-3PO 167 75 <NA> gold yellow 112 none masculi…
## 2 R2-D2 96 32 <NA> white, blue red 33 none masculi…
## 3 R5-D4 97 32 <NA> white, red red NA none masculi…
## 4 IG-88 200 140 none metal red 15 none masculi…
## 5 R4-P17 96 NA none silver, red red, blue NA none feminine
## 6 BB8 NA NA none none black NA none masculi…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>
```

---

# 2) dplyr::filter

Or perhaps you want to subset the humans that are taller than I am:

``` r
starwars |> 
  select(name, height, species) |> 
  filter( 
    species == "Human", 
    height >= 194
  )
```

Can you name one? *Hint: there's only one*

```
## # A tibble: 1 × 3
## name height species
## <chr> <int> <chr> 
*## 1 Darth Vader 202 Human
```

---

# 2) dplyr::filter

A useful `filter` use case is identifying/removing missing data using `is.na`:

``` r
starwars |> 
  filter(is.na(height))
```

```
## # A tibble: 6 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 
## 1 Arvel Cr… NA NA brown fair brown NA male mascu…
## 2 Finn NA NA black dark dark NA male mascu…
## 3 Rey NA NA brown light hazel NA fema… femin…
## 4 Poe Dame… NA NA brown light brown NA male mascu…
## 5 BB8 NA NA none none black NA none mascu…
## 6 Captain … NA NA none none unknown NA fema… femin…
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>
```

---

# 2) dplyr::filter

How could you build on this to remove rows with missing data?

``` r
starwars |> 
  filter(!is.na(height)) # use ! for negation
```

```
## # A tibble: 81 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 
## 1 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 2 C-3PO 167 75 <NA> gold yellow 112 none mascu…
## 3 R2-D2 96 32 <NA> white, bl… red 33 none mascu…
## 4 Darth V… 202 136 none white yellow 41.9 male mascu…
## 5 Leia Or… 150 49 brown light brown 19 fema… femin…
## 6 Owen La… 178 120 brown, gr… light blue 52 male mascu…
## 7 Beru Wh… 165 75 brown light blue 47 fema… femin…
## 8 R5-D4 97 32 <NA> white, red red NA none mascu…
## 9 Biggs D… 183 84 black light brown 24 male mascu…
## 10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
## # ℹ 71 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>
```

---
name: arrange

# 3) dplyr::arrange

`arrange` sorts the data frame based on the variable(s) you supply:

``` r
starwars |> 
  arrange(birth_year)
```

```
## # A tibble: 87 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 
## 1 Wicket … 88 20 brown brown brown 8 male mascu…
## 2 IG-88 200 140 none metal red 15 none mascu…
## 3 Luke Sk… 172 77 blond fair blue 19 male mascu…
## 4 Leia Or… 150 49 brown light brown 19 fema… femin…
## 5 Wedge A… 170 77 brown fair hazel 21 male mascu…
## 6 Plo Koon 188 80 none orange black 22 male mascu…
## 7 Biggs D… 183 84 black light brown 24 male mascu…
## 8 Han Solo 180 80 brown fair brown 29 male mascu…
## 9 Lando C… 177 79 black dark brown 31 male mascu…
## 10 Boba Fe… 183 78.2 black fair brown 31.5 male mascu…
## # ℹ 77 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>
```

---

# 3) dplyr::arrange

We can also arrange items in descending order using `arrange(desc())`:

``` r
starwars |> 
  arrange(desc(birth_year))
```

```
## # A tibble: 87 × 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 
## 1 Yoda 66 17 white green brown 896 male mascu…
## 2 Jabba D… 175 1358 <NA> green-tan… orange 600 herm… mascu…
## 3 Chewbac… 228 112 brown unknown blue 200 male mascu…
## 4 C-3PO 167 75 <NA> gold yellow 112 none mascu…
## 5 Dooku 193 80 white fair brown 102 male mascu…
## 6 Qui-Gon… 193 89 brown fair blue 92 male mascu…
## 7 Ki-Adi-… 198 82 white pale yellow 92 male mascu…
## 8 Finis V… 170 NA blond fair blue 91 male mascu…
## 9 Palpati… 170 75 grey pale yellow 82 male mascu…
## 10 Cliegg … 183 NA brown fair blue 82 male mascu…
## # ℹ 77 more rows
## # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>
```

---
name: mutate

# 4) dplyr::mutate

---

# 4) dplyr::mutate

You can create new columns from scratch or by transforming existing columns:

``` r
starwars |> 
  select(name, height) |>
  mutate(height_in_inches = height / 2.54)
```

```
## # A tibble: 87 × 3
## name height height_in_inches
## <chr> <int> <dbl>
## 1 Luke Skywalker 172 67.7
## 2 C-3PO 167 65.7
## 3 R2-D2 96 37.8
## 4 Darth Vader 202 79.5
## 5 Leia Organa 150 59.1
## 6 Owen Lars 178 70.1
## 7 Beru Whitesun Lars 165 65.0
## 8 R5-D4 97 38.2
## 9 Biggs Darklighter 183 72.0
## 10 Obi-Wan Kenobi 182 71.7
## # ℹ 77 more rows
```

---

# 4) dplyr::mutate

You can create new columns from scratch or by transforming existing columns:

``` r
starwars |> 
  select(name, height) |>
  mutate(height_in_inches = height / 2.54) |>
  mutate(height_in_feet = height_in_inches / 12)
```

```
## # A tibble: 87 × 4
## name height height_in_inches height_in_feet
## <chr> <int> <dbl> <dbl>
## 1 Luke Skywalker 172 67.7 5.64
## 2 C-3PO 167 65.7 5.48
## 3 R2-D2 96 37.8 3.15
## 4 Darth Vader 202 79.5 6.63
## 5 Leia Organa 150 59.1 4.92
## 6 Owen Lars 178 70.1 5.84
## 7 Beru Whitesun Lars 165 65.0 5.41
## 8 R5-D4 97 38.2 3.18
## 9 Biggs Darklighter 183 72.0 6.00
## 10 Obi-Wan Kenobi 182 71.7 5.97
## # ℹ 77 more rows
```

---

# 4) dplyr::mutate

`mutate` creates variables in order, so we can chain them together in a single call:

``` r
starwars |> 
  select(name, height) |>
  mutate(height_in_inches = height / 2.54, # separate with a comma
         height_in_feet = height_in_inches / 12)
```

---

# 4) dplyr::mutate

Boolean, logical, and conditional operators all work well with `mutate`

For example:

``` r
starwars |> 
  filter(name=="Luke Skywalker" | name=="Anakin Skywalker") |> 
  select(name, height) |>
  mutate(tall = height > 180)
```

How many rows do you think this will return? How many columns?

```
## # A tibble: 2 × 3
## name height tall 
## <chr> <int> <lgl>
## 1 Luke Skywalker 172 FALSE
## 2 Anakin Skywalker 188 TRUE
```

---
name: summarize

# 5) dplyr::summarize

Often we want to get summary statistics or *collapse* our data

`summarize` provides an easy way to do this

**Example:** "What are the mininum, maximum, and average prices of `diamonds`?"

``` r
diamonds |> 
  summarize(
    min = min(price),      # calculate the min price
    max = max(price),      # calculate the max price
    average = mean(price)  # calculate the mean price
  )
```

```
## # A tibble: 1 × 3
## min max average
## <int> <int> <dbl>
## 1 326 18823 3933.
```

---

# 5) dplyr::summarize

Including `na.rm = TRUE` keeps NAs from propagating to the end result:

.pull-left[

``` r
# probably not what we want
starwars |> 
  summarize(mh = mean(height))
```

```
## # A tibble: 1 × 1
## mh
## <dbl>
## 1 NA
```
]

.pull-right[

``` r
# better
starwars |> 
  summarize(mh = mean(height, na.rm = TRUE))
```

```
## # A tibble: 1 × 1
## mh
## <dbl>
## 1 175.
```
]

Is this a feature or a bug?

It depends on the context!

---

# Bonus: dplyr::group_by

`summarize` is particularly useful in combination with `group_by`:

``` r
starwars |> 
  group_by(species) |> # for each species
  summarize(mean_height = mean(height, na.rm = TRUE)) # calculate the mean height
```

```
## # A tibble: 38 × 2
## species mean_height
## <chr> <dbl>
## 1 Aleena 79 
## 2 Besalisk 198 
## 3 Cerean 198 
## 4 Chagrian 196 
## 5 Clawdite 168 
## 6 Droid 131.
## 7 Dug 112 
## 8 Ewok 88 
## 9 Geonosian 183 
## 10 Gungan 209.
## # ℹ 28 more rows
```

---

# Bonus: dplyr::ungroup

Groups are persistent (sort of), `ungroup` removes them

---
class: inverse, center, middle
name: goodies

# Let's work through an example on Posit Cloud

(the remaining slides contain other dplyr goodies for your reference)

---

# Other dplyr goodies: distinct

`distinct` to isolate unique observations:

``` r
starwars |> distinct(species)
```

```
## # A tibble: 38 × 1
## species 
## <chr> 
## 1 Human 
## 2 Droid 
## 3 Wookiee 
## 4 Rodian 
## 5 Hutt 
## 6 <NA> 
## 7 Yoda's species
## 8 Trandoshan 
## 9 Mon Calamari 
## 10 Ewok 
## # ℹ 28 more rows
```

---

# Other dplyr goodies: count

`count` to get the number of observations:
.pull-left[

``` r
starwars |> 
  count(species)
```

```
## # A tibble: 38 × 2
## species n
## <chr> <int>
## 1 Aleena 1
## 2 Besalisk 1
## 3 Cerean 1
## 4 Chagrian 1
## 5 Clawdite 1
## 6 Droid 6
## 7 Dug 1
## 8 Ewok 1
## 9 Geonosian 1
## 10 Gungan 3
## # ℹ 28 more rows
```
]

.pull-right[

``` r
starwars |> 
  group_by(species) |> summarize(num = n())
```

```
## # A tibble: 38 × 2
## species num
## <chr> <int>
## 1 Aleena 1
## 2 Besalisk 1
## 3 Cerean 1
## 4 Chagrian 1
## 5 Clawdite 1
## 6 Droid 6
## 7 Dug 1
## 8 Ewok 1
## 9 Geonosian 1
## 10 Gungan 3
## # ℹ 28 more rows
```
]

---

# Other dplyr goodies: window functions

[Window functions](https://cran.r-project.org/web/packages/dplyr/vignettes/window-functions.html) make it easy to get leads and lags, percentiles, cumulative sums, etc.
- These are often used in conjunction with grouped mutates
- See `vignette("window-functions")`

The final set of dplyr "goodies" worth mentioning are the family of join operations, but we'll come back to those later