Introduction to R

class: center, middle, inverse, title-slide

.title[
# Introduction to R
]
.author[
### Mikhail Dozmorov
]
.institute[
### Virginia Commonwealth University
]
.date[
### 2025-08-20
]

---

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## R expressions, function calls, and objects

* According to John Chambers (creator of S, R’s precursor):
  * *Everything that exists in R is an* **object**  
  * *Everything that happens in R is a* **call to a function**

* In R, you work with:
  * **Objects** – data in various forms that you store and manipulate  
  * **Functions** – methods that operate on these objects to compute, visualize, or modify them

---

## Functions

- A function is a set of statements organized to perform a specific task:
  * **Name** – e.g., `summary()`, `mean()`
  * **Arguments** – values passed to functions
  * **Code** – type function name without `()` to inspect, e.g., `cor`
  * **Return value** – result from the function

```r
# `read.csv` imports a CSV file
read.csv(file = "scores.csv")
```

* R has many built-in functions  
* You can define your own functions

---

## Assignment

* Use the assignment operator `<-` to save function outputs

```r
scores <- read.csv(file = "scores.csv")
```

* Use saved object in other functions:

```r
summary(scores)
```

### Tips:

* Shortcut: `Alt + -` (Windows), `Option + -` (Mac)  
* You can also use `=`, but `<-` is preferred

---

## Getting help

- `?function_name` — help for **loaded** functions

- `??function_name` — search across all installed packages

- `apropos("partial_name")` — list of related function names

- `library(sos)` + `findFn("cosine", maxPages=2)` — broad search

- 🔍 Use search engines for tricky questions!

---

## Running functions

### Two ways to run code:

1. **Console** — type and hit Enter  
2. **Script** — edit and execute full `.R` files

* An R script:
  * Saves, edits, shares, and reproduces your analysis
  * Is a plain text file containing your code

---

## Packages

- Functions live in **packages**  
  * `read.csv()` is from `utils` (a base R package)

- R ships with ~30 **base** packages  
  * Thousands more are user-contributed (e.g., `ggplot2`)

- Install a package once → Load it every time you use it

---

## Package repositories

- **CRAN** – Comprehensive R Archive Network  
  * > 22,500 packages

- **Bioconductor** – genomics-focused  
  * > 3,600 packages

- **GitHub** – developer-friendly, bleeding-edge packages

.small[
Statistics as of August 2025  
<https://cran.r-project.org/>  
<https://www.bioconductor.org/>  
<https://github.com/>  
]

---

## Installing packages

- From CRAN: `install.packages("BiocManager")`

- From tarball: `install.packages("pkg.tar.gz", repos = NULL)`

- Command line: `R CMD INSTALL pkg.tar.gz`

- From GitHub: `remotes::install_github("tidyverse/ggplot2")`

- Bioconductor: `BiocManager::install()`

---

## Loading packages

- Use `library()` - Preferred for most use cases
  ```r
  library(readxl)
  ```

- Conditional loading with `require()`:
  ```r
  if (!require(ggplot2)) {
    install.packages("ggplot2")
  }
  ```
  
| Function    | Use Case                         | Behavior on Failure        |
| ----------- | -------------------------------- | -------------------------- |
| `library()` | Regular use in scripts/notebooks | Stops execution with error |
| `require()` | Conditional loading in functions | Returns FALSE              |

---

## Useful Ways of Getting Data into R

* **CSV / Delimited files**  
  * Use the `readr` package
    * `read_csv()`, `read_delim()` — fast and consistent
    
* **Excel files**  
  * `readxl`: read-only support for `.xls` and `.xlsx`  
  * `writexl`: write `.xlsx` files (no Java required)  
 
* **Fixed-width files**  
  * Use base R `read.fwf()` or `readr::read_fwf()`

* **Large/tabular files**  
  * Use `data.table::fread()` — extremely fast, auto-detects delimiter, supports compression