Functional programming and iteration with purrr

# Functional programming and iteration with purrr
### Malcolm Barrett
### 2019-09-26

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### Interactive app: [malcolmbarrett.shinyapps.io/purrr_exercises](https://malcolmbarrett.shinyapps.io/purrr_exercises)
### Or try `exercises.Rmd` in the repo: [github.com/malcolmbarrett/lawrug_purrr](https://github.com/malcolmbarrett/lawrug_purrr)

---

background-image: url(http://hexb.in/hexagons/purrr.png)
background-position: 90% 26%

# purrr: A functional programming toolkit for R

## Complete and consistent set of tools for working with functions and vectors

---
class: inverse, middle

# Problems we want to solve:
1. Making code clear
2. Making code safe
3. Working with lists and data frames

---

# Lists, vectors, and data.frames (or tibbles)

```r
c(a = "hello", b = 1)
```

```
##       a       b 
## "hello"     "1"
```

---

# lists can contain any object

```r
list(a = "hello", b = 1, c = mean)
```

```
## $a
## [1] "hello"
## 
## $b
## [1] 1
## 
## $c
## function (x, ...) 
## UseMethod("mean")
## <bytecode: 0x7fe91ec2c958>
## <environment: namespace:base>
```

---

#  data frames are lists

```r
x <- list(a = "hello", b = 1)
as.data.frame(x)
```

```
##       a b
## 1 hello 1
```

---

#  data frames are lists

```r
library(gapminder)
head(gapminder$pop)
```

```
## [1]  8425333  9240934 10267083 11537966 13079460 14880372
```

---

#  data frames are lists

```r
gapminder[1:6, "pop"]
```

---

#  data frames are lists

```r
gapminder[1:6, "pop"]
```

```
## # A tibble: 6 x 1
##        pop
##      <int>
## 1  8425333
## 2  9240934
## 3 10267083
## 4 11537966
## 5 13079460
## 6 14880372
```

---

#  data frames are lists

```r
head(gapminder[["pop"]])
```

```
## [1]  8425333  9240934 10267083 11537966 13079460 14880372
```

---

#  vectorized functions don't work on lists

```r
sum(rnorm(10))
```

---

#  vectorized functions don't work on lists

```r
sum(rnorm(10))
```

```
## [1] -3.831574
```

---

#  vectorized functions don't work on lists

```r
sum(rnorm(10))
```

```
## [1] -3.831574
```

```r
sum(list(x = rnorm(10), y = rnorm(10), z = rnorm(10)))
```

---

#  vectorized functions don't work on lists

```r
sum(rnorm(10))
```

```
## [1] -3.831574
```

```r
sum(list(x = rnorm(10), y = rnorm(10), z = rnorm(10)))
```

```
## Error in sum(list(x = rnorm(10), y = rnorm(10), z = rnorm(10))): invalid 'type' (list) of argument
```

---

background-image: url(http://hexb.in/hexagons/purrr.png)
background-position: 95% 2%

# map(.x, .f)

## .x: a vector, list, or data frame

## .f: a function

## Returns a list

---

# Using map()

```r
library(purrr)
x_list <- list(x = rnorm(10), y = rnorm(10), z = rnorm(10))

map(x_list, mean)
```

---

# Using map()

```r
library(purrr)
*x_list <- list(x = rnorm(10), y = rnorm(10), z = rnorm(10))

map(x_list, mean)
```

---

# Using map()

```r
library(purrr)
x_list <- list(x = rnorm(10), y = rnorm(10), z = rnorm(10))

*map(x_list, mean)
```

---

# Using map()

```r
library(purrr)
x_list <- list(x = rnorm(10), y = rnorm(10), z = rnorm(10))

map(x_list, mean)
```

```
## $x
## [1] -0.6097971
## 
## $y
## [1] -0.2788647
## 
## $z
## [1] 0.6165922
```

---

---

---

---

## using `map()` with data frames

```r
gapminder %>% 
  dplyr::select_if(is.numeric) %>% 
  map(sd)
```

---

## using `map()` with data frames

```r
*gapminder %>%
* dplyr::select_if(is.numeric) %>%
  map(sd)
```

---

## using `map()` with data frames

```r
gapminder %>% 
  dplyr::select_if(is.numeric) %>% 
* map(sd)
```

---

## using `map()` with data frames

```r
gapminder %>% 
  dplyr::select_if(is.numeric) %>% 
  map(sd)
```

```
## $year
## [1] 17.26533
## 
## $lifeExp
## [1] 12.91711
## 
## $pop
## [1] 106157897
## 
## $gdpPercap
## [1] 9857.455
```

---

#  Review: writing functions

```r
x <- x^2
x <- scale(x)
x <- max(x)
```

---

#  Review: writing functions

```r
x <- x^2
x <- scale(x)
x <- max(x)

y <- x^2
y <- scale(y)
y <- max(y)

z <- z^2
z <- scale(x)
z <- max(z)
```

---

#  Review: writing functions

```r
x <- x^2
x <- scale(x)
x <- max(x)

*y <- x^2
y <- scale(y)
y <- max(y)

z <- z^2
*z <- scale(x)
z <- max(z)
```

---

#  Review: writing functions

```r
*x <- x^3
x <- scale(x)
x <- max(x)

*y <- x^2
y <- scale(y)
y <- max(y)

*z <- z^2
z <- scale(x)
z <- max(z)
```

---

#  Review: writing functions

```r
.f <- function(x) {
  x <- x^3
  x <- scale(x)
  
  max(x)
}

.f(x)
.f(y)
.f(z)
```

---

# **If you copy and paste your code three times, it's time to write a function**

---

class: inverse
# Three ways to pass functions to `map()`
1. pass directly to `map()`
2. use an anonymous function
3. use ~

---

---

---

---

## Annonymous functions

```r
map(gapminder, ~length(unique(.x)))
```

---

## Annonymous functions

```
## $country
## [1] 142
## 
## $continent
## [1] 5
## 
## $year
## [1] 12
## 
## $lifeExp
## [1] 1626
## 
## $pop
## [1] 1704
## 
## $gdpPercap
## [1] 1704
```

---

# Returning types

---

# Returning types

```r
map_int(gapminder, ~length(unique(.x)))
```

---

# Returning types

```r
map_int(gapminder, ~length(unique(.x)))
```

```
##   country continent      year   lifeExp       pop gdpPercap 
##       142         5        12      1626      1704      1704
```

---

# map2(.x, .y, .f)

## .x, .y: a vector, list, or data frame

## .f: a function

## Returns a list

---

---

---

---

# map2()

```r
means <- c(-3, 4, 2, 2.3)
sds <- c(.3, 4, 2, 1)
  
map2_dbl(means, sds, rnorm, n = 1)
```

---

# map2()

```r
means <- c(-3, 4, 2, 2.3)
sds <- c(.3, 4, 2, 1)
  
*map2_dbl(means, sds, rnorm, n = 1)
```

---

# map2()

```r
means <- c(-3, 4, 2, 2.3)
sds <- c(.3, 4, 2, 1)
  
map2_dbl(means, sds, rnorm, n = 1)
```

```
## [1] -2.997932  2.178125  1.266952  2.948287
```

---

---

# Other mapping functions

## **pmap()** and friends: take n lists or data frame with argument names
---

# Other mapping functions

## ~~pmap() and friends: take n lists or data frame with argument names~~
## **walk()** and friends: for side effects like plotting; returns input invisibly
---

# Other mapping functions

## ~~pmap() and friends: take n lists or data frame with argument names~~
## ~~walk() and friends: for side effects like plotting; returns input invisibly~~
## **imap()** and friends: includes counter `i`
---

# Other mapping functions

## ~~pmap() and friends: take n lists or data frame with argument names~~
## ~~walk() and friends: for side effects like plotting; returns input invisibly~~
## ~~imap() and friends: includes counter `i`~~
## **map_if()**, **map_at()**: Apply only to certain elements

---

---

# Base R

---

# Benefits of purrr
1. Consistent 
2. Type-safe
3. ~f(.x)

---

# Efficient Loops

---

## Loops vs functional programming

```r
x <- rnorm(10)
y <- map(x, mean)
```

```r
x <- rnorm(10)
y <- vector("list", length(x))
for (i in seq_along(x)) {
  y[[i]] <- mean(x[[i]])
}
```

---

## Loops vs functional programming

```r
x <- rnorm(10)
y <- map(x, mean)
```

```r
x <- rnorm(10) 
*y <- vector("list", length(x))
for (i in seq_along(x)) {
  y[[i]] <- mean(x[[i]]) 
}
```

---

## Loops vs functional programming

```r
x <- rnorm(10)
y <- map(x, mean)
```

```r
x <- rnorm(10)
y <- vector("list", length(x)) 
*for (i in seq_along(x)) {
  y[[i]] <- mean(x[[i]]) 
}
```

---

## Loops vs functional programming

```r
x <- rnorm(10)
y <- map(x, mean)
```

```r
x <- rnorm(10)
y <- vector("list", length(x)) 
for (i in seq_along(x)) { 
* y[[i]] <- mean(x[[i]])
}
```

---

# **Of course someone has to write loops. It doesn’t have to be you.**
# **--Jenny Bryan**

---

# Working with lists and nested data

---

# Working with lists and nested data

---

# Adverbs: Modify function behavior

---

# Learn more!
## [Jenny Bryan's purrr tutorial](https://jennybc.github.io/purrr-tutorial/): A detailed introduction to purrr. Free online.
## [R for Data Science](http://r4ds.had.co.nz/): A comprehensive but friendly introduction to the tidyverse. Free online.
## [RStudio Primers](https://rstudio.cloud/learn/primers): Free, interactive courses, including purrr.

---

### *Thanks for coming!*
#### *map(kittens, wag_tail)*

### <i class="fab  fa-github "></i> [malcolmbarrett](https://github.com/malcolmbarrett/)
### <i class="fab  fa-twitter "></i> [@malco_barrett](https://twitter.com/malco_barrett)

Slides created via the R package [xaringan](https://github.com/yihui/xaringan).