013-parallel-processing.qmd

---
title: "Parallel processing in R"
author: "Jeff Oliver"
date: "`r format(Sys.time(), '%d %B, %Y')`"
---

```{r library-check, echo = FALSE, results = "hide"}
#| output: false

libs <- c("randomForest", "dplyr")
libs_missing <- libs[!(libs %in% installed.packages()[, "Package"])]
if (length(libs_missing) > 0) {
  for (l in libs_missing) {
    install.packages(l)
  }
}
```

[INTRODUCTORY SENTENCE]

#### Learning objectives
1. Use `for` loop syntax to repeat operations
2. Apply built-in functions for more efficient repetition
3. Implement processes in parallel to reduce computation time

## [DESCRIPTION OR MOTIVATION; 2-4 sentences that would be used for an announcement]

***

## Getting started

***

## Repeating things

```{r}
# Sampling
# https://www.census.gov/quickfacts/fact/table/US/LFE046217
prop.women <- 0.582

random.draw <- sample(x = c("female", "male"),
                      size = 500,
                      replace = TRUE,
                      prob = c(prop.women, 1 - prop.women))

table(random.draw)

prop.latinx <- 0.183

random.draw <- sample(x = c("latinx", "not"),
                      size = 500,
                      replace = TRUE,
                      prob = c(prop.latinx, 1 - prop.latinx))

table(random.draw)

# Observed Fortune 500 U.S. CEOs
f500.women <- 33
f500.latinx <- 11

# For loop, 1000 replicates to build distribution
```

***

## Repeating things faster
```{r}
# use sapply or lapply instead of for loop
```

***

## Don't wait in line
```{r}
# replace s/lapply with parS/Lapply
```

***
## Additional resources

+ [resource one](url-one)
+ [resource two](url-two)
+ A [PDF version](https://jcoliver.github.io/learn-r/lesson-name.pdf) of this lesson