R Objects and Functions

class: center, middle, inverse

## R Objects and Functions

### Kelly McConville

.large[Math 241 | Week 6 | Spring 2021]

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## Announcements/Reminders

* Lab 4 due Thursday.

* Mini-Project 1 due Thursday (Mar 5th) at 8:30am
    + Presentations during class.
    + Add slides [here](https://docs.google.com/presentation/d/1Vn_YwO9hqDhDELJ-TqLiHyoLFZ4vroA-FX1aLem3c9U/edit?usp=sharing).
        + Please add the title page (with the package name) ASAP as I will grab those names tomorrow for the voting forms.

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## Looking Ahead...

* After you have finished Mini-Project 1, make sure to provide feedback [here](https://docs.google.com/forms/d/e/1FAIpQLSfaQarOzh3X_O9znuxjEV7koy8nangCDFRP0Ck35nR1W-fzQQ/viewform?usp=sf_link).

* Will receive Mini-Project 2 next week.

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## Looking Behind...
  
What have we done so far?
  
* Data viz principles and construction with `ggplot2`

* Reproducible examples with `reprex`

* Data wrangling with `dplyr` and `tidyr`

* R (data) packages with `usethis`, `devtools`, and `roxygen2`

* Version control and project collaboration with `git` and GitHub

* Ingesting data with `readr`

* Web data with API wrappers, `httr` for talking to APIs directly, and `rvest` for scraping web data

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## Goals for Today

* Strengthening our R programming skills

* Exploring R objects

* Learning how to create functions
    + Motivation
    + Basic components
    + Testing, testing, testing
    + Input decisions
    + Generalizing

---

## Let's Go Back to Square One

* We talked about `list()`s in R last time.

```r
groceries <- list()

groceries$new_seasons <- c("apples", "chocolate", "kale", "garlic")
groceries$safeway <- c("vinegar", "soap")
groceries$salt_n_straw <- c("almond_brittle", "double_fold_vanilla", 
                            "tanzanian_tin_roof")
groceries$budget <- data.frame(stores = c("new_seasons", "safeway", 
                                          "salt_n_straw"), 
                               fund = c(100, 25, 200))
```

---

## Let's Go Back to Square One

* Square one: `vector()`s
    + `c()` simple way to create a vector of length greater than 1.

```r
x <- 5
is.vector(x)
```

```
## [1] TRUE
```

```r
y <- c(5, 1, 7)
is.vector(y)
```

```
## [1] TRUE
```

---

## Flavors of Vectors

```r
str(x)
```

```
##  num 5
```

```r
str(y)
```

```
##  num [1:3] 5 1 7
```

```r
z <- c("cat", "dog", "mouse", "snail")
str(z)
```

```
##  chr [1:4] "cat" "dog" "mouse" "snail"
```

```r
a <- c(TRUE, FALSE, FALSE)
str(a)
```

```
##  logi [1:3] TRUE FALSE FALSE
```

---

## Flavors of Vectors

```r
b <- c(x, z)
str(b)
```

```
##  chr [1:5] "5" "cat" "dog" "mouse" "snail"
```

```r
z <- as.factor(z)
str(z)
```

```
##  Factor w/ 4 levels "cat","dog","mouse",..: 1 2 3 4
```

```r
levels(z)
```

```
## [1] "cat"   "dog"   "mouse" "snail"
```

---

## Flavors of Vectors

**Logical**: TRUEs and FALSEs

**Numeric**: numbers, integers, double-precision floating point numbers

**Character**: strings (contains 1 or more character)

**Factors**: strings with order

---

## Vectorized

* R is built to work with vectors.

* Many operations are **vectorized**: will happen component-wise when given a vector as input.

```r
y + 4
```

```
## [1]  9  5 11
```

```r
y * 2
```

```
## [1] 10  2 14
```

```r
rnorm(n = 3, mean = c(-5, 0, 5))
```

```
## [1] -5.31  0.79  5.54
```

---

* But we need to be careful!

```r
dat <- data.frame(x = c(1, 2, 2, 4, 1, 3), y = c(8, 7, 6, 5, 8, 6))
dat
```

```
##   x y
## 1 1 8
## 2 2 7
## 3 2 6
## 4 4 5
## 5 1 8
## 6 3 6
```

* Want the rows where `x` equals 1 or 2
    + What happened?

```r
library(tidyverse)
dat %>%
  filter(x == c(1, 2))
```

```
##   x y
## 1 1 8
## 2 2 7
## 3 1 8
```

---

## Recycling

* R recycles vectors if they are not the necessary length.
    + Notice we got NO error but that wasn't what we wanted.

```r
library(tidyverse)
dat %>%
  filter(x == c(1, 2))
```

```
##   x y
## 1 1 8
## 2 2 7
## 3 1 8
```

---

## Recycling

* R recycles vectors if they are not the necessary length.
    + Notice we got NO error but that wasn't what we wanted.

```r
library(tidyverse)
dat %>%
  filter(x %in% c(1, 2))
```

```
##   x y
## 1 1 8
## 2 2 7
## 3 2 6
## 4 1 8
```

---

## Indexing a Vector

```r
x[1]
```

```
## [1] 5
```

```r
y[-1]
```

```
## [1] 1 7
```

```r
b[c(2, 4)]
```

```
## [1] "cat"   "mouse"
```

```r
y[a]
```

```
## [1] 5
```

---

## Now Back to Lists

* `list()`s are just super vectors

```r
groceries <- list()

---

## Now Back to Lists

```r
groceries
```

```
## $new_seasons
## [1] "apples"    "chocolate" "kale"      "garlic"   
## 
## $safeway
## [1] "vinegar" "soap"   
## 
## $salt_n_straw
## [1] "almond_brittle"      "double_fold_vanilla" "tanzanian_tin_roof" 
## 
## $budget
##         stores fund
## 1  new_seasons  100
## 2      safeway   25
## 3 salt_n_straw  200
```

---