Parallel Looping & Variants

class: center, middle, inverse, title-slide

# Parallel Looping & Variants
### Daniel Anderson
### Week 5

---

layout: true

</span>
  </div>

---

# Agenda
* Discuss `map2_*` and `pmap_*` (parallel iterations)

* `walk()` and friends

* `modify()`

* `safely()`

* `reduce()`

---
# Learning objectives
* Understand the differences between `map`, `map2`, and `pmap`

* Know when to apply `walk` instead of `map`, and why it may be useful

* Understand the similarities and differences between `map` and `modify`

* Diagnose errors with `safely` and understand other situations where it may
be helpful

* Collapsing/reducing lists with `purrr::reduce()` or `base::Reduce()`

---
background-image:url(https://d33wubrfki0l68.cloudfront.net/7a545699ff7069a98329fcfbe6e42b734507eb16/211a5/diagrams/functionals/map2-arg.png)
background-size:contain
# `map2`

---
class: inverse-blue middle

# A few Examples
Basic simulations - iterating over two vectors

Plots by month, changing the title

---
# Simulation
* Simulate data from a normal distribution

+ Vary `$n$` from 5 to 150 by increments of 5
	
--
	
	+ For each `$n$`, vary `$\mu$` from -2 to 2 by increments of 0.25

--
### How do we get all combinations

--
`expand.grid`

---
# Example `expand.grid`
### Bonus: It turns it into a data frame!

```r
ints <- 1:3
lets <- c("a", "b", "c")
expand.grid(ints, lets)
```

```
##   Var1 Var2
## 1    1    a
## 2    2    a
## 3    3    a
## 4    1    b
## 5    2    b
## 6    3    b
## 7    1    c
## 8    2    c
## 9    3    c
```

---
# Set conditions

Please follow along

```r
conditions <- expand.grid(
  n = seq(5, 150, 5),
  mu = seq(-2, 2, 0.25)
)
```

.pull-left[

```r
head(conditions)
```

```
##    n mu
## 1  5 -2
## 2 10 -2
## 3 15 -2
## 4 20 -2
## 5 25 -2
## 6 30 -2
```
]

.pull-right[

```r
tail(conditions)
```

```
##       n mu
## 505 125  2
## 506 130  2
## 507 135  2
## 508 140  2
## 509 145  2
## 510 150  2
```
]

---
# Simulate!

```r
sim1 <- map2(conditions$n, conditions$mu, ~{
    rnorm(n = .x, mean = .y, sd = 10)
})
str(sim1)
```

```
## List of 510
##  $ : num [1:5] -2.135 10.466 11.083 -0.149 18.545
##  $ : num [1:10] -8.72 -13.05 1.54 6.27 -7.47 ...
##  $ : num [1:15] 11.1 -16.15 -10.05 4.72 10.5 ...
##  $ : num [1:20] 0.227 8.498 -20.939 -22.962 -0.948 ...
##  $ : num [1:25] 15.86 -7.01 -5.43 -1.81 -5.86 ...
##  $ : num [1:30] -1.54 5.95 -0.14 13.83 6.79 ...
##  $ : num [1:35] -0.588 6.627 -12.193 2.372 -13.161 ...
##  $ : num [1:40] -5.34 7.05 -2.53 12.09 4.39 ...
##  $ : num [1:45] -7.43 -1.1 -9.65 -8.73 -1.87 ...
##  $ : num [1:50] -11.61 -5.59 -2.1 -17.48 -6.56 ...
##  $ : num [1:55] -8.05 -2.02 13.7 -14.15 -6.81 ...
##  $ : num [1:60] -5.527 -12.283 -13.94 6.904 -0.334 ...
##  $ : num [1:65] -6.2518 -1.5114 -0.0274 3.5003 -9.0024 ...
##  $ : num [1:70] 15.294 -8.09 0.538 -7.834 -7.617 ...
##  $ : num [1:75] -13.55 0.984 -0.64 -11.444 -3.83 ...
##  $ : num [1:80] 11.25 8.55 8.89 -19.8 5.67 ...
##  $ : num [1:85] -2.35 -5.68 16.43 4.82 12.68 ...
##  $ : num [1:90] -19.095 13.054 -15.354 -0.498 -7.509 ...
##  $ : num [1:95] 9.06 2.6 -17.42 12.68 -13.58 ...
##  $ : num [1:100] 14.78 -2.65 14.25 -5.68 1.08 ...
##  $ : num [1:105] -1.44 -12.11 12.62 -3.27 2.44 ...
##  $ : num [1:110] -16.27 7.98 -5.97 -2.8 -14.41 ...
##  $ : num [1:115] 9.54 16.85 -7.4 -11.38 -1.09 ...
##  $ : num [1:120] 5.31 6.976 5.66 -6.599 -0.844 ...
##  $ : num [1:125] 4.66 -12.18 11.78 -6.08 -2.37 ...
##  $ : num [1:130] 1.419 1.797 -0.556 2.156 -14.721 ...
##  $ : num [1:135] -0.7536 0.0431 -11.0482 -21.7445 4.5122 ...
##  $ : num [1:140] -5.091 -0.662 8.399 9.693 -2.69 ...
##  $ : num [1:145] -4.47 12.38 -9.18 -18.92 -25.53 ...
##  $ : num [1:150] -12.57 6.89 -3.88 -13.99 -9.94 ...
##  $ : num [1:5] 13.25 8.59 -19.07 -2.13 -4.46
##  $ : num [1:10] -11.118 -0.335 8.384 10.266 -10.506 ...
##  $ : num [1:15] -13.99 -9.18 -3.12 2.55 -16.86 ...
##  $ : num [1:20] 0.486 15.647 9.483 10.967 -5.976 ...
##  $ : num [1:25] -5.69 6.26 4.24 -5.56 23.39 ...
##  $ : num [1:30] -14.364 -0.686 -9.779 3.741 -3.284 ...
##  $ : num [1:35] -21.15 16.41 -5.03 -5.13 -10.06 ...
##  $ : num [1:40] 4.42 3.28 4.11 -5.49 -12.07 ...
##  $ : num [1:45] -6.59 15.53 21.61 9.07 -3.76 ...
##  $ : num [1:50] -0.0784 9.1196 -19.4697 -7.6971 4.5796 ...
##  $ : num [1:55] 0.378 -0.779 1.344 -5.242 -9.46 ...
##  $ : num [1:60] -2.39 -13.4 4.41 -11.93 3.71 ...
##  $ : num [1:65] 19.48 -14.76 -5.83 9.05 18.81 ...
##  $ : num [1:70] 26.3 -2.95 -5.87 -5.42 4.98 ...
##  $ : num [1:75] 21.731 -4.778 2.62 0.653 -1.628 ...
##  $ : num [1:80] 12 8.24 2.81 12.06 2.06 ...
##  $ : num [1:85] -1.122 0.697 -17.395 3.086 -8.11 ...
##  $ : num [1:90] -12.17 -3.49 6.28 -4.22 10.07 ...
##  $ : num [1:95] -11.992 -2.839 -0.541 -9.832 5.252 ...
##  $ : num [1:100] -10.18 -5.09 8.5 4.57 6.16 ...
##  $ : num [1:105] -8.77 12.529 -5.545 -0.231 3.304 ...
##  $ : num [1:110] -4.64 5.71 2.79 -4.5 -7.36 ...
##  $ : num [1:115] -5.25 -18.23 -5.65 2.56 -5.28 ...
##  $ : num [1:120] 3.254 2.861 -3.972 -15.111 -0.886 ...
##  $ : num [1:125] 2.09 -6.01 6.15 4.5 -4.73 ...
##  $ : num [1:130] -5.05 -9.92 6.77 -11.94 -7.53 ...
##  $ : num [1:135] 3.399 -0.886 -15.508 13.685 -10.01 ...
##  $ : num [1:140] 9.36 9.33 6.97 -5.94 23.07 ...
##  $ : num [1:145] -3.5 -5.47 2.14 6.78 0.89 ...
##  $ : num [1:150] -4.93 -6.83 -1.33 -4.82 -10.52 ...
##  $ : num [1:5] 3.4 -4.65 -6.8 -13.84 7
##  $ : num [1:10] 8.878 -12.646 0.865 -6.316 -6.799 ...
##  $ : num [1:15] 0.199 -16.621 -6.265 5.45 -13.263 ...
##  $ : num [1:20] 8.3333 16.9791 -12.3281 13.4383 -0.0692 ...
##  $ : num [1:25] -6.42 4.34 -6.42 15.24 4.83 ...
##  $ : num [1:30] 2.28 -15.83 -9.63 -9.63 -3.59 ...
##  $ : num [1:35] -2.99 -1.61 -5.34 14.5 25.4 ...
##  $ : num [1:40] -6.3019 1.1355 2.9021 0.0776 -10.4499 ...
##  $ : num [1:45] -5.75 -3.33 16.42 13.11 -8.82 ...
##  $ : num [1:50] -7.48 -11.52 17.08 -4.13 -17.37 ...
##  $ : num [1:55] -11 1.06 3.13 3.25 -15.81 ...
##  $ : num [1:60] -7.699 -8.987 -9.756 -4.217 0.813 ...
##  $ : num [1:65] -19.05 -29.28 -4.01 18.1 -3.92 ...
##  $ : num [1:70] -0.89 -6.65 -2.4 -10.28 -5.57 ...
##  $ : num [1:75] 0.164 15.412 1.634 -10.484 3.918 ...
##  $ : num [1:80] 6.47 2.44 3.81 -5.79 17.68 ...
##  $ : num [1:85] -0.828 12.719 6.7 4.086 -3.096 ...
##  $ : num [1:90] -7.042 15.799 -13.421 4.396 -0.536 ...
##  $ : num [1:95] -6.5 -2.18 13.79 3.09 -21.64 ...
##  $ : num [1:100] -17.38 1.917 -0.516 1.215 -8.002 ...
##  $ : num [1:105] -2.232 -0.515 17.965 -18.168 8.545 ...
##  $ : num [1:110] 13.96 0.428 -1.474 -2.781 5.572 ...
##  $ : num [1:115] -9.82 -2.89 3.26 -1.94 -17.93 ...
##  $ : num [1:120] -20.51 2.18 -13.14 7.23 -13.92 ...
##  $ : num [1:125] -5.66 -11.54 -7.89 -2.98 -12.02 ...
##  $ : num [1:130] -2.61 -18.4 8.95 12.24 -13.42 ...
##  $ : num [1:135] -7.1 -3.62 -14.76 -2.02 17.41 ...
##  $ : num [1:140] 7.12 9.5 5.29 4.47 7.26 ...
##  $ : num [1:145] -0.477 -15.662 -2.699 0.856 -22.186 ...
##  $ : num [1:150] 7.025 4.514 0.778 -5.803 9.802 ...
##  $ : num [1:5] -8.17 -12.21 -1.71 2.84 -6.24
##  $ : num [1:10] 6.98 3.8 -5.07 -3.84 4.11 ...
##  $ : num [1:15] 19.37 -5.85 2.29 -3.47 10.2 ...
##  $ : num [1:20] -2.4492 -5.8553 15.8816 0.1707 -0.0201 ...
##  $ : num [1:25] -8.48 -6.87 -4.92 18.76 -6.2 ...
##  $ : num [1:30] 11.588 -18.958 1.722 -0.312 10.077 ...
##  $ : num [1:35] -16.25 -2.3 -4.82 -9.78 -7.88 ...
##  $ : num [1:40] 1.89 1.28 -2.77 -4.49 1.48 ...
##  $ : num [1:45] 10.27 18.17 2.79 -5.57 -2.35 ...
##   [list output truncated]
```

---
# More powerful
### Add it as a list column!

```r
sim2 <- conditions %>%
  as_tibble() %>% # Not required, but definitely helpful
  mutate(sim = map2(n, mu, ~rnorm(n = .x, mean = .y, sd = 10))) 
sim2
```

```
## # A tibble: 510 × 3
##        n    mu sim       
##    <dbl> <dbl> <list>    
##  1     5    -2 <dbl [5]> 
##  2    10    -2 <dbl [10]>
##  3    15    -2 <dbl [15]>
##  4    20    -2 <dbl [20]>
##  5    25    -2 <dbl [25]>
##  6    30    -2 <dbl [30]>
##  7    35    -2 <dbl [35]>
##  8    40    -2 <dbl [40]>
##  9    45    -2 <dbl [45]>
## 10    50    -2 <dbl [50]>
## # … with 500 more rows
```

---
# Unnest

```r
conditions %>%
  as_tibble() %>% 
  mutate(sim = map2(n, mu, ~rnorm(.x, .y, sd = 10))) %>%
  unnest(sim)
```

```
## # A tibble: 39,525 × 3
##        n    mu        sim
##    <dbl> <dbl>      <dbl>
##  1     5    -2  -7.886723
##  2     5    -2   5.402129
##  3     5    -2  -9.258305
##  4     5    -2  -5.295245
##  5     5    -2 -17.72833 
##  6    10    -2  13.15974 
##  7    10    -2 -17.04609 
##  8    10    -2   3.520510
##  9    10    -2 -18.55369 
## 10    10    -2   5.565162
## # … with 39,515 more rows
```

---
# Challenge

Can you replicate what we just did, but using a `rowwise()` approach?

```r
conditions %>% 
  rowwise() %>% 
  mutate(sim = list(rnorm(n, mu, sd = 10))) %>% 
  unnest(sim)
```

```
## # A tibble: 39,525 × 3
##        n    mu         sim
##    <dbl> <dbl>       <dbl>
##  1     5    -2   3.591224 
##  2     5    -2 -18.28598  
##  3     5    -2   4.355475 
##  4     5    -2  10.54270  
##  5     5    -2   7.748755 
##  6    10    -2   4.939168 
##  7    10    -2   1.026145 
##  8    10    -2 -13.14307  
##  9    10    -2   0.8444179
## 10    10    -2  -8.977694 
## # … with 39,515 more rows
```

---
class: inverse-orange middle
# Vary the sd too?

--
### `pmap`
Which we'll get to soon

---
class: inverse-blue middle
# Varying the title of a plot

---
# The data
Please follow along

```r
library(fivethirtyeight)
pulitzer
```

```
## # A tibble: 50 × 7
##    newspaper             circ2004 circ2013 pctchg_circ num_finals1990_2003
##    <chr>                    <dbl>    <dbl>       <int>               <int>
##  1 USA Today              2192098  1674306         -24                   1
##  2 Wall Street Journal    2101017  2378827          13                  30
##  3 New York Times         1119027  1865318          67                  55
##  4 Los Angeles Times       983727   653868         -34                  44
##  5 Washington Post         760034   474767         -38                  52
##  6 New York Daily News     712671   516165         -28                   4
##  7 New York Post           642844   500521         -22                   0
##  8 Chicago Tribune         603315   414930         -31                  23
##  9 San Jose Mercury News   558874   583998           4                   4
## 10 Newsday                 553117   377744         -32                  12
## # … with 40 more rows, and 2 more variables: num_finals2004_2014 <int>,
## #   num_finals1990_2014 <int>
```

---
# Prep data

```r
pulitzer<- pulitzer %>%
  select(newspaper, starts_with("num")) %>%
  pivot_longer(
    -newspaper,
    names_to = "year_range",
    values_to = "n",
    names_prefix = "num_finals"
  ) %>% 
  mutate(year_range = str_replace_all(year_range, "_", "-")) %>%
  filter(year_range != "1990-2014")

head(pulitzer)
```

```
## # A tibble: 6 × 3
##   newspaper           year_range     n
##   <chr>               <chr>      <int>
## 1 USA Today           1990-2003      1
## 2 USA Today           2004-2014      1
## 3 Wall Street Journal 1990-2003     30
## 4 Wall Street Journal 2004-2014     20
## 5 New York Times      1990-2003     55
## 6 New York Times      2004-2014     62
```

---
# One plot

```r
wsj <- pulitzer %>%
	filter(newspaper == "Wall Street Journal")

ggplot(wsj, aes(n, year_range)) +
  geom_col(aes(fill = n)) +
  scale_fill_distiller(
    type = "seq",
    limits = c(0, max(pulitzer$n)),
    palette = "BuPu",
    direction = 1
  ) +
  scale_x_continuous(
    limits = c(0, max(pulitzer$n)),
    expand = c(0, 0)
  ) +
  guides(fill = "none") +
  labs(
    title = "Pulitzer Prize winners: Wall Street Journal",
    x = "Total number of winners",
    y = ""
  ) 
```

---
class: middle

![](w5_files/figure-html/unnamed-chunk-5-1.png)

---
# Nest data

```r
by_newspaper <- pulitzer %>%
	group_by(newspaper) %>%
	nest()

by_newspaper
```

```
## # A tibble: 50 × 2
## # Groups:   newspaper [50]
##    newspaper             data            
##    <chr>                 <list>          
##  1 USA Today             <tibble [2 × 2]>
##  2 Wall Street Journal   <tibble [2 × 2]>
##  3 New York Times        <tibble [2 × 2]>
##  4 Los Angeles Times     <tibble [2 × 2]>
##  5 Washington Post       <tibble [2 × 2]>
##  6 New York Daily News   <tibble [2 × 2]>
##  7 New York Post         <tibble [2 × 2]>
##  8 Chicago Tribune       <tibble [2 × 2]>
##  9 San Jose Mercury News <tibble [2 × 2]>
## 10 Newsday               <tibble [2 × 2]>
## # … with 40 more rows
```

---
# Produce all plots
### You try first!
Don't worry about the correct title yet, if you don't want

---

```r
by_newspaper %>%
	mutate(
	  plot = map(
	    data, ~{
	      ggplot(aes(n, year_range)) +
	        geom_col(aes(fill = n)) +
          scale_fill_distiller(
            type = "seq",
            limits = c(0, max(pulitzer$n)),
            palette = "BuPu",
            direction = 1
          ) +
          scale_x_continuous(
            limits = c(0, max(pulitzer$n)),
            expand = c(0, 0)
          ) +
        guides(fill = "none") +
        labs(
          title = "Pulitzer Prize winners",
          x = "Total number of winners",
          y = ""
        )
      }
    )
  )
```

---
# Add title

```r
library(glue)

p <- by_newspaper %>%
	mutate(
	  plot = map2(
*    data, newspaper, ~{
	      ggplot(.x, aes(n, year_range)) +
	        geom_col(aes(fill = n)) +
          scale_fill_distiller(
            type = "seq",
            limits = c(0, max(pulitzer$n)),
            palette = "BuPu",
            direction = 1
          ) +
          scale_x_continuous(
            limits = c(0, max(pulitzer$n)),
            expand = c(0, 0)
          ) +
          guides(fill = "none") +
          labs(
*           title = glue("Pulitzer Prize winners: {.y}"),
            x = "Total number of winners",
            y = ""
          )
        }
	    )
    )
```

---

```r
p
```

```
## # A tibble: 50 × 3
## # Groups:   newspaper [50]
##    newspaper             data             plot  
##    <chr>                 <list>           <list>
##  1 USA Today             <tibble [2 × 2]> <gg>  
##  2 Wall Street Journal   <tibble [2 × 2]> <gg>  
##  3 New York Times        <tibble [2 × 2]> <gg>  
##  4 Los Angeles Times     <tibble [2 × 2]> <gg>  
##  5 Washington Post       <tibble [2 × 2]> <gg>  
##  6 New York Daily News   <tibble [2 × 2]> <gg>  
##  7 New York Post         <tibble [2 × 2]> <gg>  
##  8 Chicago Tribune       <tibble [2 × 2]> <gg>  
##  9 San Jose Mercury News <tibble [2 × 2]> <gg>  
## 10 Newsday               <tibble [2 × 2]> <gg>  
## # … with 40 more rows
```

---
# Look at a couple plots

.pull-left[

```r
p$plot[[1]]
```

![](w5_files/figure-html/p1-1.png)

```r
p$plot[[3]]
```

![](w5_files/figure-html/p1-2.png)
]

.pull-right[

```r
p$plot[[2]]
```

![](w5_files/figure-html/p2-1.png)

```r
p$plot[[4]]
```

![](w5_files/figure-html/p2-2.png)
]

---
# Challenge
(You can probably guess where this is going)

## Can you reproduce the prior plots using a `rowwise()` approach?

---

```r
pulitzer %>%
*nest_by(newspaper) %>%
	mutate(
	  plot = list(
*    ggplot(data, aes(n, year_range)) +
        geom_col(aes(fill = n)) +
        scale_fill_distiller(
          type = "seq",
          limits = c(0, max(pulitzer$n)),
          palette = "BuPu",
          direction = 1
        ) +
        scale_x_continuous(
          limits = c(0, max(pulitzer$n)), 
          expand = c(0, 0)
        ) +
        guides(fill = "none") +
        labs(
*         title = glue("Pulitzer Prize winners: {newspaper}"),
          x = "Total number of winners",
          y = ""
        )
    )
  )
```

```
## # A tibble: 50 × 3
## # Rowwise:  newspaper
##    newspaper                                  data plot  
##    <chr>                        <list<tibble[,2]>> <list>
##  1 Arizona Republic                        [2 × 2] <gg>  
##  2 Atlanta Journal Constitution            [2 × 2] <gg>  
##  3 Baltimore Sun                           [2 × 2] <gg>  
##  4 Boston Globe                            [2 × 2] <gg>  
##  5 Boston Herald                           [2 × 2] <gg>  
##  6 Charlotte Observer                      [2 × 2] <gg>  
##  7 Chicago Sun-Times                       [2 × 2] <gg>  
##  8 Chicago Tribune                         [2 × 2] <gg>  
##  9 Cleveland Plain Dealer                  [2 × 2] <gg>  
## 10 Columbus Dispatch                       [2 × 2] <gg>  
## # … with 40 more rows
```

---
class: inverse-red middle
# Iterating over `$n$` vectors
### `pmap`

---
background-image:url(https://d33wubrfki0l68.cloudfront.net/e426c5755e2e65bdcc073d387775db79791f32fd/92902/diagrams/functionals/pmap.png)
background-size:contain
# `pmap`

---
# Simulation
* Simulate data from a normal distribution

+ Vary `$n$` from 5 to 150 by increments of 5

--
	+ For each `$n$`, vary `$\mu$` from -2 to 2 by increments of 0.25

--
	+ For each `$\sigma$` from 1 to 3 by increments of 0.1

---

```r
full_conditions <- expand.grid(
  n = seq(5, 150, 5),
  mu = seq(-2, 2, 0.25),
  sd = seq(1, 3, .1)
)
```

.pull-left[

```r
head(full_conditions)
```

```
##    n mu sd
## 1  5 -2  1
## 2 10 -2  1
## 3 15 -2  1
## 4 20 -2  1
## 5 25 -2  1
## 6 30 -2  1
```
]

.pull-right[

```r
tail(full_conditions)
```

```
##         n mu sd
## 10705 125  2  3
## 10706 130  2  3
## 10707 135  2  3
## 10708 140  2  3
## 10709 145  2  3
## 10710 150  2  3
```
]

---
# Full Simulation

```r
fsim <- pmap(
  list(
    number = full_conditions$n,
    average = full_conditions$mu,
    stdev = full_conditions$sd
  ), 
  function(number, average, stdev) {
    rnorm(n = number, mean = average, sd = stdev)
  }
)
str(fsim)
```

```
## List of 10710
##  $ : num [1:5] -2.899 -1.854 0.271 -0.689 -3.361
##  $ : num [1:10] -1.043 -1.656 0.428 -2.755 -1.118 ...
##  $ : num [1:15] -3.508 -2.114 -0.82 -0.952 -1.021 ...
##  $ : num [1:20] -2.335 -2.774 -3.432 -1.401 -0.857 ...
##  $ : num [1:25] -2.08 -3.98 -1.8 -1.5 -1.45 ...
##  $ : num [1:30] -2.51 -2.65 -2.96 -1.85 -2.05 ...
##  $ : num [1:35] -3.936 -2.175 -0.987 -1.441 -0.568 ...
##  $ : num [1:40] -1.847 -0.312 0.187 -2.501 -2.88 ...
##  $ : num [1:45] -3.67 -1.9 -1.29 -3.51 -2.12 ...
##  $ : num [1:50] -1.693 -2.443 -4.54 -0.818 -1.517 ...
##  $ : num [1:55] -1.68 -4.4 -3.82 -1.47 -2.61 ...
##  $ : num [1:60] 0.111 -1.336 -2.077 -1.28 -1.098 ...
##  $ : num [1:65] -2.57 -2.01 -2.6 -1.93 -3.57 ...
##  $ : num [1:70] -0.888 -3.818 -1.17 -1.062 -0.615 ...
##  $ : num [1:75] -1.93 -1.69 -2.49 -1.13 -2.92 ...
##  $ : num [1:80] -3.464 -1.279 -0.958 -1.72 -1.575 ...
##  $ : num [1:85] -0.235 -1.901 -2.282 -3.016 -2.692 ...
##  $ : num [1:90] -1.49 -3.75 -3.51 -2.38 -1.19 ...
##  $ : num [1:95] -1.92 -2.59 -1.97 -2.54 -1.82 ...
##  $ : num [1:100] -0.915 -1.657 -2.834 -2.784 -2.876 ...
##  $ : num [1:105] -1.02 -3.92 -1.86 -1.89 -1.92 ...
##  $ : num [1:110] -1.7 -1.4 -2.15 -1.03 -1.51 ...
##  $ : num [1:115] -3.29 -2.86 -0.29 -3.74 -3.79 ...
##  $ : num [1:120] -3.991 -1.785 -1.635 -0.372 -1.775 ...
##  $ : num [1:125] -2.1581 -2.0733 -2.6558 0.0774 -2.9995 ...
##  $ : num [1:130] -2.298 -2.409 -2.41 -1.198 -0.691 ...
##  $ : num [1:135] -1.762 -2.498 -3.638 0.187 -3.589 ...
##  $ : num [1:140] -2.79 -2.32 -1.93 -2.14 -2.62 ...
##  $ : num [1:145] -2.029 -1.438 -0.935 -2.294 -1.775 ...
##  $ : num [1:150] -2.341 -1.317 -0.486 -1.993 -1.027 ...
##  $ : num [1:5] -0.439 -1.029 -0.324 0.648 -1.748
##  $ : num [1:10] -0.81 -1.341 -0.677 -2.591 -1.508 ...
##  $ : num [1:15] -2.277 -1.561 -0.902 -1.997 -0.446 ...
##  $ : num [1:20] -1.65 -3.1 -1.45 -2.44 -2.55 ...
##  $ : num [1:25] -2.957 -0.817 -2.161 -0.57 -1.95 ...
##  $ : num [1:30] -1.49 -2.08 -1.29 -1.68 -2.38 ...
##  $ : num [1:35] 0.444 -0.777 -2.673 -0.183 -0.725 ...
##  $ : num [1:40] -1.081 -2.549 -1.554 -3.196 -0.832 ...
##  $ : num [1:45] -2.012 -1.392 -2.308 -0.475 -2.15 ...
##  $ : num [1:50] -1.363 -1.725 -0.641 -0.827 -1.44 ...
##  $ : num [1:55] -2.497 -0.046 -2.543 -1.318 -2.705 ...
##  $ : num [1:60] -1.94 -3.78 -3.16 -1.98 -2.56 ...
##  $ : num [1:65] -2.649 -0.67 -2.373 -1.521 -0.956 ...
##  $ : num [1:70] -2.454 -1.281 -0.769 -1.251 -2.615 ...
##  $ : num [1:75] -2.702 -2.445 -1.748 -2.282 -0.503 ...
##  $ : num [1:80] -3.073 -1.053 -0.248 -1.504 -3.599 ...
##  $ : num [1:85] -2.389 -1.972 -0.844 -1.913 -0.138 ...
##  $ : num [1:90] -1.532 -2.292 -1.113 -2.462 -0.612 ...
##  $ : num [1:95] 0.101 -1.125 -2.077 -2.517 -2.894 ...
##  $ : num [1:100] -2.501 -1.813 -2.453 -0.454 -2.196 ...
##  $ : num [1:105] -3.11 -2.16 -3.21 -1.57 -2.29 ...
##  $ : num [1:110] 0.0221 -0.3542 -1.1348 -1.8837 -2.7147 ...
##  $ : num [1:115] -2.944 -2.555 -1.606 -3.158 -0.431 ...
##  $ : num [1:120] -3.05 -1.34 -2.16 -2.17 -2.23 ...
##  $ : num [1:125] -3.92 -2.43 -2.87 -2.02 -1.96 ...
##  $ : num [1:130] -1.9806 -1.3754 -0.0422 -1.2931 0.5103 ...
##  $ : num [1:135] -1.85 -2.443 -0.864 -2.5 -1.318 ...
##  $ : num [1:140] -3.061 -1.322 -1.618 -0.574 -3.674 ...
##  $ : num [1:145] -2.95 -1.93 -1.13 -1.3 -2.48 ...
##  $ : num [1:150] -1.879 -1.544 -0.634 -0.146 -1.209 ...
##  $ : num [1:5] -0.755 -2.371 -1.138 -0.608 0.11
##  $ : num [1:10] -1.48 -2.519 -1.476 -1.587 -0.442 ...
##  $ : num [1:15] -0.621 -1.931 -1.431 0.59 -2.148 ...
##  $ : num [1:20] -2.474 -1.043 -1.929 -0.483 -0.677 ...
##  $ : num [1:25] -1.712 -1.314 -2.904 -0.746 -1.364 ...
##  $ : num [1:30] -1.506 -2.865 -1.779 -2.084 0.218 ...
##  $ : num [1:35] -0.0888 -2.8476 -1.1306 -1.6184 -1.5593 ...
##  $ : num [1:40] -0.924 1.252 -0.826 -1.645 -0.794 ...
##  $ : num [1:45] -1.49 -2.398 -0.421 -1.686 -0.751 ...
##  $ : num [1:50] -1.051 -0.604 -2.562 -1.946 -0.858 ...
##  $ : num [1:55] -2.609 0.859 -0.123 -0.503 -2.222 ...
##  $ : num [1:60] -0.721 -1.289 -1.366 0.449 -1.459 ...
##  $ : num [1:65] -1.187 -0.731 -0.655 -2.309 -1.116 ...
##  $ : num [1:70] -1.705 -0.509 -0.645 -0.598 -0.365 ...
##  $ : num [1:75] -0.165 -1.722 -1.035 -0.533 -1.258 ...
##  $ : num [1:80] -2.599 0.137 -2.794 -0.989 -1.523 ...
##  $ : num [1:85] -2.09 -2.47 -1.7 -1.36 -2.08 ...
##  $ : num [1:90] -0.614 -0.692 -2.174 -2.297 -1.265 ...
##  $ : num [1:95] -1.27 -0.53 -1.19 -2.7 -1.21 ...
##  $ : num [1:100] -2.03 -1.4 -3.53 -1.35 -3 ...
##  $ : num [1:105] -2.239 -0.786 -1.178 -1.509 -1.011 ...
##  $ : num [1:110] -0.574 -0.255 -3.816 -0.768 -1.251 ...
##  $ : num [1:115] 0.0849 -0.8844 -2.4786 -2.2744 -1.8509 ...
##  $ : num [1:120] -0.343 -2.457 -2.097 -3.502 -2.733 ...
##  $ : num [1:125] -3.766 -1.309 -2.378 -2.602 0.848 ...
##  $ : num [1:130] -0.759 -3.489 0.05 1.63 -4.049 ...
##  $ : num [1:135] -3.9458 -0.4551 0.1423 -0.698 -0.0371 ...
##  $ : num [1:140] -0.971 -2.578 -0.67 -0.472 -2.357 ...
##  $ : num [1:145] -2.14 -1.8 -2.83 -1.71 -2.34 ...
##  $ : num [1:150] -0.195 -0.738 -1.85 -0.62 -1.114 ...
##  $ : num [1:5] -2.424 -1.366 -0.552 -1.418 -1.151
##  $ : num [1:10] -1.613 -1.826 -0.595 -1.839 0.209 ...
##  $ : num [1:15] -2.93 -1.74 -1.44 -2.2 -1.38 ...
##  $ : num [1:20] -1.04 -1.37 -2.55 -1.79 -2.94 ...
##  $ : num [1:25] -0.7343 -1.7964 0.2689 -0.6538 0.0879 ...
##  $ : num [1:30] -0.997 -0.948 -1.613 -1.011 -1.937 ...
##  $ : num [1:35] -3.033 -0.224 -2.761 -1.277 -1.088 ...
##  $ : num [1:40] -1.118 0.115 -0.618 -0.619 -1.53 ...
##  $ : num [1:45] -0.318 -0.992 -0.196 -2.175 -0.504 ...
##   [list output truncated]
```

---
# Alternative spec

```r
fsim <- pmap(
  list(
    full_conditions$n,
    full_conditions$mu,
    full_conditions$sd
  ),
  ~rnorm(n = ..1, mean = ..2, sd = ..3)
)
str(fsim)
```

```
## List of 10710
##  $ : num [1:5] -3.392 -0.9933 -2.2558 0.0526 -3.939
##  $ : num [1:10] -3.24 -2.35 -2.15 -2.74 -2.23 ...
##  $ : num [1:15] -1.926 -2.1026 -0.0394 -1.9635 -1.8651 ...
##  $ : num [1:20] -3.177 -2.868 -2.569 -0.733 -0.395 ...
##  $ : num [1:25] -0.988 -1.228 -0.716 -2.742 -1.866 ...
##  $ : num [1:30] -3.62 -1.87 -3.83 -1.87 -3.8 ...
##  $ : num [1:35] -1.623 -1.417 -1.914 -4.289 -0.918 ...
##  $ : num [1:40] -1.278 -1.693 -1.257 -0.321 -1.72 ...
##  $ : num [1:45] 0.7488 0.0512 -1.9767 -2.7075 -2.1823 ...
##  $ : num [1:50] -2.21 -1.88 -2.45 -3.29 -2.66 ...
##  $ : num [1:55] -3.786 -1.132 -1.259 -0.535 -1.946 ...
##  $ : num [1:60] -2.972 -2.073 -2.069 -0.997 -3.302 ...
##  $ : num [1:65] -1.38 -1.56 -3.17 -1.45 -4.86 ...
##  $ : num [1:70] -3.05 -2.26 -1.57 -2.77 -1.95 ...
##  $ : num [1:75] -1.713 -3.604 -2.198 -0.497 -2.063 ...
##  $ : num [1:80] -3.66 -1.33 -2.38 -2.23 -1.59 ...
##  $ : num [1:85] -1.806 -0.85 -0.685 -2.287 -2.127 ...
##  $ : num [1:90] -1.952 -3.123 -1.189 0.465 -2.299 ...
##  $ : num [1:95] -3.05 -1.35 -2.09 -2.65 -1.94 ...
##  $ : num [1:100] -1.592 -1.421 -1.796 -0.967 -1.348 ...
##  $ : num [1:105] -0.618 -2.875 -3.457 -3.695 -3.547 ...
##  $ : num [1:110] -1.3 -2.01 -2.9 -2.54 -4.08 ...
##  $ : num [1:115] -3.65 -1.27 -2.19 -2.18 -4.18 ...
##  $ : num [1:120] -3.6812 -1.4201 -0.7788 0.0428 -1.3453 ...
##  $ : num [1:125] -3.361 -0.919 -1.744 -1.21 -0.925 ...
##  $ : num [1:130] -1.32 -1.04 -2.61 -3.2 -2.57 ...
##  $ : num [1:135] -2.5 -1.49 -3.16 -1.11 -1.96 ...
##  $ : num [1:140] -2.21 -2.7 -2.91 -1.77 -1.03 ...
##  $ : num [1:145] -2.524 -0.335 -2.964 -2.305 -2.409 ...
##  $ : num [1:150] -1.977 -0.296 -0.799 -2.052 -1.649 ...
##  $ : num [1:5] -1.44 -1.79 -1.2 -2.14 -2.21
##  $ : num [1:10] -1.208 -1.917 -0.693 -2.271 -3.184 ...
##  $ : num [1:15] -2.794 -2.852 -0.838 -2.227 0.829 ...
##  $ : num [1:20] -3.094 -1.653 -0.407 -1.516 -1.854 ...
##  $ : num [1:25] -3.1792 0.0503 -1.46 -2.3736 -1.208 ...
##  $ : num [1:30] -0.345 -3.195 -1.637 -0.839 -1.329 ...
##  $ : num [1:35] -1.63 -1.95 -1.18 -3.84 -2.96 ...
##  $ : num [1:40] -1.48 0.11 -0.252 -1.887 0.639 ...
##  $ : num [1:45] -2.022 -1.631 -0.956 -0.545 -2.216 ...
##  $ : num [1:50] -2.129 -1.019 -0.248 -2.113 -2.491 ...
##  $ : num [1:55] -2.5323 0.0926 -1.103 -0.7597 -1.282 ...
##  $ : num [1:60] -2.25 -1.92 -1.01 -1.9 -2.89 ...
##  $ : num [1:65] -1.28 -0.361 -3.539 -1.221 -1.969 ...
##  $ : num [1:70] -1.772 -0.605 -2.142 -0.841 -2.011 ...
##  $ : num [1:75] -1.671 -1.638 -1.456 -0.584 -1.285 ...
##  $ : num [1:80] -0.519 -3.402 -1.545 -1.815 -1.021 ...
##  $ : num [1:85] -1.861 -2.997 0.904 -3.716 -2.798 ...
##  $ : num [1:90] -2.86 -1.54 -1.49 -1.37 -2.32 ...
##  $ : num [1:95] -2.91 -1.85 -4.96 -2.08 -2.47 ...
##  $ : num [1:100] -2.24 -0.384 -2.815 -1.314 -0.898 ...
##  $ : num [1:105] -1.5 -1.52 -1.79 -1.23 -2.85 ...
##  $ : num [1:110] -0.199 -1.538 -2.178 -2.888 -0.99 ...
##  $ : num [1:115] -0.428 -1.553 -2.157 -2.47 -2.429 ...
##  $ : num [1:120] 1.445 -1.915 0.103 -2.584 -2.88 ...
##  $ : num [1:125] -1.756 -2.689 -2.043 -1.676 -0.454 ...
##  $ : num [1:130] -1.68 -2.38 -1.46 -2.39 -1.42 ...
##  $ : num [1:135] -1.946 -0.747 -0.493 -2.931 -1.555 ...
##  $ : num [1:140] -0.159 -2.58 -2.861 -2.188 -0.117 ...
##  $ : num [1:145] -1.31 -1.92 -1.56 -1.36 -2.28 ...
##  $ : num [1:150] -1.19 -2.03 -3.39 -2.76 -2.17 ...
##  $ : num [1:5] -1.755 -0.144 -1.142 -1.182 -0.973
##  $ : num [1:10] -0.058 -1.808 -1.246 -0.48 -1.446 ...
##  $ : num [1:15] -3.24 -1.07 -3.14 -2.23 -2.05 ...
##  $ : num [1:20] -2.285 -1.284 -2.366 -2.14 -0.777 ...
##  $ : num [1:25] -2.7 -2.42 -3.54 -2.58 -1.68 ...
##  $ : num [1:30] -1.331 -1.573 -1.796 -0.855 -2.431 ...
##  $ : num [1:35] -0.94 -1.457 -0.947 -3.221 -0.638 ...
##  $ : num [1:40] -1.842 -2.925 -0.187 -1.049 -1.066 ...
##  $ : num [1:45] 1.073 -3.284 -0.997 -1.295 -2.392 ...
##  $ : num [1:50] -2.934 0.483 -0.458 0.616 -0.465 ...
##  $ : num [1:55] -1.26 -2.682 -1.536 -0.923 -0.653 ...
##  $ : num [1:60] -2.905 -1.504 -3.405 -0.807 -1.185 ...
##  $ : num [1:65] -2.626 -2.5 -1.837 -0.205 -3.061 ...
##  $ : num [1:70] -2.541 0.113 -1.637 -1.397 -0.749 ...
##  $ : num [1:75] -1.15 -1.38 -2.01 -2.28 -1.6 ...
##  $ : num [1:80] -0.271 -2.957 -1.095 -0.255 0.186 ...
##  $ : num [1:85] -2.169 -1.762 -2.371 -2.084 -0.762 ...
##  $ : num [1:90] -1.2019 -3.7622 -2.6474 -0.0599 -2.1553 ...
##  $ : num [1:95] -1.316 -1.797 -1.518 -0.539 0.496 ...
##  $ : num [1:100] -2.65 -2 -1.31 -2.24 -2.1 ...
##  $ : num [1:105] -3.51 -1.9 -1.42 -3.59 -3.35 ...
##  $ : num [1:110] -2.267 -2.803 -0.447 -2.074 -2.612 ...
##  $ : num [1:115] -2.04 -1.89 -1.85 -1.22 -3.31 ...
##  $ : num [1:120] -1.55 -0.677 -1.538 1.082 -1.294 ...
##  $ : num [1:125] -0.1396 -0.5911 -1.481 -2.5253 -0.0471 ...
##  $ : num [1:130] -0.951 -0.901 -0.713 -1.634 -0.859 ...
##  $ : num [1:135] -2.73 -3.72 -2.23 -2.36 -2.11 ...
##  $ : num [1:140] -2.487 -1.0681 -0.0279 -1.9474 -0.6085 ...
##  $ : num [1:145] -0.231 -2.073 -1.83 -2.429 -1.515 ...
##  $ : num [1:150] 0.457 -1.126 0.306 -2.274 -1.24 ...
##  $ : num [1:5] -0.331 -1.507 -3.153 -0.757 -3.085
##  $ : num [1:10] -1.451 -2.849 -1.588 -0.859 0.993 ...
##  $ : num [1:15] -2.511 -1.289 -2.724 -0.506 0.896 ...
##  $ : num [1:20] -3.386 -0.231 -2.41 -4.034 -1.106 ...
##  $ : num [1:25] -2.16 -0.614 0.462 -1.392 -1.012 ...
##  $ : num [1:30] -1.389 -0.523 -0.267 -0.41 -2.295 ...
##  $ : num [1:35] -0.509 -1.428 -1.303 -1.684 -1.458 ...
##  $ : num [1:40] -1.561 0.118 -2.219 -1.718 -0.434 ...
##  $ : num [1:45] -0.606 -1.276 -3.395 -1.145 -2.504 ...
##   [list output truncated]
```

---
# Simpler
### Maybe a little too clever
* A data frame is a list so...

```r
fsim <- pmap(
  full_conditions,
  ~rnorm(n = ..1, mean = ..2, sd = ..3)
)
str(fsim)
```

```
## List of 10710
##  $ : num [1:5] -2.483 -0.966 -1.195 -3.669 -0.783
##  $ : num [1:10] -3.01 -1.753 -2.453 -0.996 -1.681 ...
##  $ : num [1:15] -3.51 -3.02 -2.73 -3.52 -2.46 ...
##  $ : num [1:20] -3.01 -2.1 -2.31 -1.5 -0.67 ...
##  $ : num [1:25] -1.418 -3.201 -1.511 0.791 -3.146 ...
##  $ : num [1:30] -0.652 -1.063 -2.132 -1.504 -1.339 ...
##  $ : num [1:35] -3.207 -1.448 -2.173 -2.473 -0.681 ...
##  $ : num [1:40] -3.41 -1.69 -4.06 -3.56 -2.22 ...
##  $ : num [1:45] -3.17 -1.91 -1.43 -3.02 -2.35 ...
##  $ : num [1:50] -2.65 -3.61 -2.39 -1.36 -1.04 ...
##  $ : num [1:55] -2.62 -1.63 -2.51 -2 -2.6 ...
##  $ : num [1:60] -3.83 -2.84 -2.93 -1.56 -2.7 ...
##  $ : num [1:65] -1.0858 -3 -2.0788 0.0333 -2.8598 ...
##  $ : num [1:70] -1.91 -2.29 -2.09 -2.36 -2.76 ...
##  $ : num [1:75] -0.903 -4.198 -1.353 -1.853 -3.146 ...
##  $ : num [1:80] -2.82 -1.66 -2.23 -2.56 -1.73 ...
##  $ : num [1:85] -1.38 -2.484 -2.869 -3.29 -0.604 ...
##  $ : num [1:90] -1.34 -2.65 -2 -1.45 -1.61 ...
##  $ : num [1:95] -2.15 -1.31 -1.58 -2.11 -2.06 ...
##  $ : num [1:100] -3.367 -0.512 -2.598 -2.212 -4.086 ...
##  $ : num [1:105] -0.144 -0.531 -2.808 -1.923 -2.798 ...
##  $ : num [1:110] -1.92 -3.04 -3.57 -1.04 -3.64 ...
##  $ : num [1:115] -1.959 -0.664 -2.037 -2.64 -3.506 ...
##  $ : num [1:120] -1.4242 -3.6394 0.0901 -0.9878 -3.4518 ...
##  $ : num [1:125] -0.4124 0.0652 -2.0728 -2.5879 -1.2572 ...
##  $ : num [1:130] -1.73 -1.1 -2.54 -1.75 -2.43 ...
##  $ : num [1:135] -3.037 0.758 -3.231 -1.685 -2.654 ...
##  $ : num [1:140] -2.51 -1.38 -2.37 -3.38 -3.79 ...
##  $ : num [1:145] -2.04 -1.93 -2.6 -3.45 -2.38 ...
##  $ : num [1:150] -1.89 -1.67 -2.82 -3.75 -1.16 ...
##  $ : num [1:5] -2.123 -1.03 -0.132 -2.666 -2.68
##  $ : num [1:10] -2.37 -3.44 -1.01 -2.5 -1.65 ...
##  $ : num [1:15] -0.644 -2.291 -1.436 -1.098 -1.4 ...
##  $ : num [1:20] -1.38 -2.38 -1.21 -1.94 -2.12 ...
##  $ : num [1:25] -2.7613 -2.0795 -0.3011 -0.0335 -2.3788 ...
##  $ : num [1:30] -0.302 -2.515 -1.307 -2.251 -2.374 ...
##  $ : num [1:35] -1.255 -3.153 -0.839 -1.495 -2.032 ...
##  $ : num [1:40] -0.601 -1.763 -1.842 -0.172 -0.766 ...
##  $ : num [1:45] -2.293 -0.181 0.824 -1.397 -3.079 ...
##  $ : num [1:50] -2.94 -2.37 -2.18 -1.56 -2.2 ...
##  $ : num [1:55] -2.232 -0.997 -1.977 -2.326 -2.458 ...
##  $ : num [1:60] -1.055 -2.254 -0.833 -1.18 -1.043 ...
##  $ : num [1:65] -2.96 -1.5 -2.46 -2.14 -2.84 ...
##  $ : num [1:70] 0.94 -0.713 -2.682 -2.092 -1.614 ...
##  $ : num [1:75] -1.806 -1.756 -3.262 -0.767 -1.556 ...
##  $ : num [1:80] -1.495 -2.852 -1.99 -0.24 -0.343 ...
##  $ : num [1:85] -1.75 -0.965 -0.194 -0.576 -1.734 ...
##  $ : num [1:90] -1.71 -1.13 -1.85 -2.72 -1.81 ...
##  $ : num [1:95] -2.432 -1.533 -2.132 -4.217 -0.904 ...
##  $ : num [1:100] -1.716 -0.384 -1.252 -2.841 -2.544 ...
##  $ : num [1:105] -1.11 -2.88 -1.67 -2.48 -1.29 ...
##  $ : num [1:110] 0.815 -2.386 0.376 -1.338 -2.056 ...
##  $ : num [1:115] -0.937 -0.926 -2.475 -1.613 -1.559 ...
##  $ : num [1:120] -0.111 -1.958 -3.896 -1.69 -0.475 ...
##  $ : num [1:125] -2.072 -2.736 -1.115 -2.031 -0.787 ...
##  $ : num [1:130] 0.0353 -1.6196 -3.5122 -1.7421 -1.1754 ...
##  $ : num [1:135] -1.577 -1.956 -0.128 -0.582 -1.9 ...
##  $ : num [1:140] -2.278 -1.32 -1.607 -1.771 -0.969 ...
##  $ : num [1:145] -1.0544 -2.6704 -2.4971 0.0864 -1.2751 ...
##  $ : num [1:150] -2.187 0.888 0.393 -2.528 -2.041 ...
##  $ : num [1:5] -1.272 -0.69 -0.406 -2.852 -1.571
##  $ : num [1:10] -0.751 -3.165 -1.587 -1.577 -1.436 ...
##  $ : num [1:15] -2.21 -1.18 -1.08 -1.1 -2.09 ...
##  $ : num [1:20] -2.604 -1.13 -1.189 -0.248 -1.348 ...
##  $ : num [1:25] -2.191 -1.821 -0.81 -0.224 -1.624 ...
##  $ : num [1:30] -1.686 -0.974 -2.65 -0.6 -0.3 ...
##  $ : num [1:35] -2.69 -2.763 -1.149 -0.568 -1.606 ...
##  $ : num [1:40] -1.97 -1.82 -2.35 -1.36 -2.66 ...
##  $ : num [1:45] -1.05 -1.25 -2.49 -2.08 -2.02 ...
##  $ : num [1:50] -1.662 -2.085 -2.097 -2.392 0.679 ...
##  $ : num [1:55] -0.299 -1.191 -1.685 -2.122 -1.134 ...
##  $ : num [1:60] -0.855 -1.275 -1.451 -2.034 -2.878 ...
##  $ : num [1:65] -0.239 0.171 -1.282 -1.097 0.428 ...
##  $ : num [1:70] -1.956 -0.806 -1.187 -1.751 -3.6 ...
##  $ : num [1:75] 0.12 -2.59 -2.3 -1.28 -1.65 ...
##  $ : num [1:80] -0.986 -1.648 -0.997 -1.535 -0.459 ...
##  $ : num [1:85] -1.583 -1.975 -0.728 -1.058 -1.773 ...
##  $ : num [1:90] -0.0379 -0.5056 -2.181 -2.4305 -1.6892 ...
##  $ : num [1:95] -1.68 -2.994 -1.427 -0.961 -1.526 ...
##  $ : num [1:100] -1.05 -2.78 -0.82 -2.84 -1.04 ...
##  $ : num [1:105] -2.075 -2.64 -0.456 -2.199 -1.442 ...
##  $ : num [1:110] -1.93 -1.75 -2.65 -2.03 -2.44 ...
##  $ : num [1:115] -0.511 -0.668 -0.715 -0.494 -0.854 ...
##  $ : num [1:120] -1.248 -2.938 -0.263 -0.434 -2.303 ...
##  $ : num [1:125] -1.454 -1.68 0.159 -1.013 -1.026 ...
##  $ : num [1:130] -0.109 -0.339 -0.806 -0.189 -1.976 ...
##  $ : num [1:135] -0.766 -2.496 -1.339 -0.876 -1.522 ...
##  $ : num [1:140] -2.132 -3.443 -1.559 -2.438 -0.533 ...
##  $ : num [1:145] -0.305 -2.488 0.503 -2.006 -1.59 ...
##  $ : num [1:150] -1.906 -3.717 -2.481 -0.332 -1.673 ...
##  $ : num [1:5] 0.0907 -2.4147 -0.2001 -0.3707 -3.2347
##  $ : num [1:10] -2.37 -0.236 -0.542 -1.363 -1.766 ...
##  $ : num [1:15] -1.275 -1.384 -0.375 -1.371 -3.41 ...
##  $ : num [1:20] -1.077 -1.299 -1.341 -0.526 -2.133 ...
##  $ : num [1:25] -0.893 -2.269 -2.76 -1.079 -2.815 ...
##  $ : num [1:30] -1.252 -2.188 -0.463 -0.432 -3.33 ...
##  $ : num [1:35] -1.94 -1.86 -1.07 -2.79 -0.39 ...
##  $ : num [1:40] -0.698 -0.343 -1.36 -0.426 -0.569 ...
##  $ : num [1:45] -0.282 -0.528 -1.024 -1.173 -2.988 ...
##   [list output truncated]
```

---
# List column version

```r
full_conditions %>%
	as_tibble() %>%
	mutate(sim = pmap(list(n, mu, sd), ~rnorm(..1, ..2, ..3)))
```

```
## # A tibble: 10,710 × 4
##        n    mu    sd sim       
##    <dbl> <dbl> <dbl> <list>    
##  1     5    -2     1 <dbl [5]> 
##  2    10    -2     1 <dbl [10]>
##  3    15    -2     1 <dbl [15]>
##  4    20    -2     1 <dbl [20]>
##  5    25    -2     1 <dbl [25]>
##  6    30    -2     1 <dbl [30]>
##  7    35    -2     1 <dbl [35]>
##  8    40    -2     1 <dbl [40]>
##  9    45    -2     1 <dbl [45]>
## 10    50    -2     1 <dbl [50]>
## # … with 10,700 more rows
```

---
# Unnest

```r
full_conditions %>%
	as_tibble() %>%
	mutate(sim = pmap(
	    list(n, mu, sd), ~rnorm(..1, ..2, ..3)
	  )
  ) %>%
	unnest(sim)
```

```
## # A tibble: 830,025 × 4
##        n    mu    sd        sim
##    <dbl> <dbl> <dbl>      <dbl>
##  1     5    -2     1 -1.496691 
##  2     5    -2     1 -3.955064 
##  3     5    -2     1 -2.074510 
##  4     5    -2     1 -3.654000 
##  5     5    -2     1 -2.049551 
##  6    10    -2     1 -2.332229 
##  7    10    -2     1 -3.707039 
##  8    10    -2     1 -2.192668 
##  9    10    -2     1 -2.232620 
## 10    10    -2     1 -0.4610839
## # … with 830,015 more rows
```

---
# Replicate with `nest_by()`
### You try first

```r
full_conditions %>% 
  rowwise() %>% 
  mutate(sim = list(rnorm(n, mu, sd))) %>% 
  unnest(sim)
```

```
## # A tibble: 830,025 × 4
##        n    mu    sd        sim
##    <dbl> <dbl> <dbl>      <dbl>
##  1     5    -2     1 -0.8569708
##  2     5    -2     1 -2.321645 
##  3     5    -2     1 -1.759086 
##  4     5    -2     1 -2.139428 
##  5     5    -2     1 -2.602792 
##  6    10    -2     1 -0.9650236
##  7    10    -2     1 -2.013695 
##  8    10    -2     1 -2.970106 
##  9    10    -2     1 -2.655783 
## 10    10    -2     1 -1.272286 
## # … with 830,015 more rows
```

---
class: inverse-blue middle
# Plot
Add a caption stating the total number of Pulitzer prize winners across years

---
# Add column for total

```r
pulitzer <- pulitzer %>%
	group_by(newspaper) %>%
	mutate(tot = sum(n))
pulitzer
```

```
## # A tibble: 100 × 4
## # Groups:   newspaper [50]
##    newspaper           year_range     n   tot
##    <chr>               <chr>      <int> <int>
##  1 USA Today           1990-2003      1     2
##  2 USA Today           2004-2014      1     2
##  3 Wall Street Journal 1990-2003     30    50
##  4 Wall Street Journal 2004-2014     20    50
##  5 New York Times      1990-2003     55   117
##  6 New York Times      2004-2014     62   117
##  7 Los Angeles Times   1990-2003     44    85
##  8 Los Angeles Times   2004-2014     41    85
##  9 Washington Post     1990-2003     52   100
## 10 Washington Post     2004-2014     48   100
## # … with 90 more rows
```

---
# Easiest way (imo)
Create a column to represent exactly the label you want.

```r
#install.packages("english")
library(english)
pulitzer <- pulitzer %>%
	mutate(
	  label = glue(
	    "{str_to_title(as.english(tot))} Total Pulitzer Awards"
    )
)
```

---

```r
select(pulitzer, newspaper, label)
```

```
## # A tibble: 100 × 2
## # Groups:   newspaper [50]
##    newspaper           label                                      
##    <chr>               <glue>                                     
##  1 USA Today           Two Total Pulitzer Awards                  
##  2 USA Today           Two Total Pulitzer Awards                  
##  3 Wall Street Journal Fifty Total Pulitzer Awards                
##  4 Wall Street Journal Fifty Total Pulitzer Awards                
##  5 New York Times      One Hundred Seventeen Total Pulitzer Awards
##  6 New York Times      One Hundred Seventeen Total Pulitzer Awards
##  7 Los Angeles Times   Eighty-Five Total Pulitzer Awards          
##  8 Los Angeles Times   Eighty-Five Total Pulitzer Awards          
##  9 Washington Post     One Hundred Total Pulitzer Awards          
## 10 Washington Post     One Hundred Total Pulitzer Awards          
## # … with 90 more rows
```

---
# Produce one plot

```r
wsj2 <- pulitzer %>%
	filter(newspaper == "Wall Street Journal")

ggplot(wsj2, aes(n, year_range)) +
  geom_col(aes(fill = n)) +
  scale_fill_distiller(
    type = "seq",
    limits = c(0, max(pulitzer$n)),
    palette = "BuPu",
    direction = 1
  ) +
  scale_x_continuous(
    limits = c(0, max(pulitzer$n)),
    expand = c(0, 0)
  ) +
  guides(fill = "none") +
  labs(
    title = glue("Pulitzer Prize winners: Wall Street Journal"),
    x = "Total number of winners",
    y = "",
    caption = unique(wsj2$label)
  )
```

---
class: middle

![](w5_files/figure-html/plot-cap1-eval-1.png)

---
# Produce all plots
### Nest first

```r
by_newspaper_label <- pulitzer %>%
	group_by(newspaper, label) %>%
	nest()

by_newspaper_label
```

```
## # A tibble: 50 × 3
## # Groups:   newspaper, label [50]
##    newspaper             label                                       data            
##    <chr>                 <glue>                                      <list>          
##  1 USA Today             Two Total Pulitzer Awards                   <tibble [2 × 3]>
##  2 Wall Street Journal   Fifty Total Pulitzer Awards                 <tibble [2 × 3]>
##  3 New York Times        One Hundred Seventeen Total Pulitzer Awards <tibble [2 × 3]>
##  4 Los Angeles Times     Eighty-Five Total Pulitzer Awards           <tibble [2 × 3]>
##  5 Washington Post       One Hundred Total Pulitzer Awards           <tibble [2 × 3]>
##  6 New York Daily News   Six Total Pulitzer Awards                   <tibble [2 × 3]>
##  7 New York Post         Zero Total Pulitzer Awards                  <tibble [2 × 3]>
##  8 Chicago Tribune       Thirty-Eight Total Pulitzer Awards          <tibble [2 × 3]>
##  9 San Jose Mercury News Six Total Pulitzer Awards                   <tibble [2 × 3]>
## 10 Newsday               Eighteen Total Pulitzer Awards              <tibble [2 × 3]>
## # … with 40 more rows
```

---
# Produce plots

```r
final_plots <- by_newspaper_label %>%
	mutate(plots = pmap(list(newspaper, label, data), ~{
*   ggplot(..3, aes(n, year_range)) +
      geom_col(aes(fill = n)) +
      scale_fill_distiller(
        type = "seq",
        limits = c(0, max(pulitzer$n)),
        palette = "BuPu",
        direction = 1
      ) +
	    scale_x_continuous(
	      limits = c(0, max(pulitzer$n)),
	      expand = c(0, 0)
      ) +
	    guides(fill = "none") +
	    labs(
*      title = glue("Pulitzer Prize winners: {..1}"),
	      x = "Total number of winners",
	      y = "",
*      caption = ..2
	    )
	  })
)
```

---
# Look at a couple plots

.pull-left[

```r
final_plots$plots[[1]]
```

![](w5_files/figure-html/p3-1.png)

```r
final_plots$plots[[3]]
```

![](w5_files/figure-html/p3-2.png)
]

.pull-right[

```r
final_plots$plots[[2]]
```

![](w5_files/figure-html/p4-1.png)

```r
final_plots$plots[[4]]
```

![](w5_files/figure-html/p4-2.png)
]

---
# Replicate with `nest_by()`
You try first

---

```r
final_plots2 <- pulitzer %>%
  ungroup() %>% 
  nest_by(newspaper, label) %>% 
	mutate(
	  plots = list(
	    ggplot(data, aes(n, year_range)) + 
	      geom_col(aes(fill = n)) +
        scale_fill_distiller(
          type = "seq",
          limits = c(0, max(pulitzer$n)),
          palette = "BuPu",
          direction = 1
        ) +
	      scale_x_continuous(
	        limits = c(0, max(pulitzer$n)), 
	        expand = c(0, 0)
        ) +
	      guides(fill = "none") +
	      labs(
	        title = glue("Pulitzer Prize winners: {newspaper}"),
	        x = "Total number of winners",
	        y = "",
	        caption = label
        )
	  )
)
```

---

```r
final_plots2
```

```
## # A tibble: 50 × 4
## # Rowwise:  newspaper, label
##    newspaper                    label                                        data plots
##    <chr>                        <glue>                             <list<tibble[> <lis>
##  1 Arizona Republic             Seven Total Pulitzer Awards               [2 × 3] <gg> 
##  2 Atlanta Journal Constitution Six Total Pulitzer Awards                 [2 × 3] <gg> 
##  3 Baltimore Sun                Thirteen Total Pulitzer Awards            [2 × 3] <gg> 
##  4 Boston Globe                 Forty-One Total Pulitzer Awards           [2 × 3] <gg> 
##  5 Boston Herald                Zero Total Pulitzer Awards                [2 × 3] <gg> 
##  6 Charlotte Observer           Four Total Pulitzer Awards                [2 × 3] <gg> 
##  7 Chicago Sun-Times            Two Total Pulitzer Awards                 [2 × 3] <gg> 
##  8 Chicago Tribune              Thirty-Eight Total Pulitzer Awards        [2 × 3] <gg> 
##  9 Cleveland Plain Dealer       Eleven Total Pulitzer Awards              [2 × 3] <gg> 
## 10 Columbus Dispatch            One Total Pulitzer Awards                 [2 × 3] <gg> 
## # … with 40 more rows
```

---
# Save all plots

We'll have to iterate across at least two things: (a) file path/names, and (b)
the plots themselves

--
We can do this with the `map()` family, but instead we'll use a different function, which we'll talk about more momentarily.

--
As an aside, what are the **steps** we would need to take to do this?

--
Could we use a `nest_by()` solution?

---
# Try with `nest_by()`

You try first:
* Create a vector of file paths
* "loop" through the file paths and the plots to save them

---
# Example

### Create a directory

```r
fs::dir_create(here::here("plots", "pulitzers"))
```

--
### Create file paths

```r
files <- str_replace_all(
  tolower(final_plots$newspaper),
  " ",
  "-"
)
paths <- here::here("plots", "pulitzers", glue("{files}.png"))
paths
```

```
##  [1] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/usa-today.png"                   
##  [2] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/wall-street-journal.png"         
##  [3] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/new-york-times.png"              
##  [4] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/los-angeles-times.png"           
##  [5] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/washington-post.png"             
##  [6] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/new-york-daily-news.png"         
##  [7] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/new-york-post.png"               
##  [8] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/chicago-tribune.png"             
##  [9] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/san-jose-mercury-news.png"       
## [10] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/newsday.png"                     
## [11] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/houston-chronicle.png"           
## [12] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/dallas-morning-news.png"         
## [13] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/san-francisco-chronicle.png"     
## [14] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/arizona-republic.png"            
## [15] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/chicago-sun-times.png"           
## [16] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/boston-globe.png"                
## [17] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/atlanta-journal-constitution.png"
## [18] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/newark-star-ledger.png"          
## [19] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/detroit-free-press.png"          
## [20] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/minneapolis-star-tribune.png"    
## [21] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/philadelphia-inquirer.png"       
## [22] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/cleveland-plain-dealer.png"      
## [23] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/san-diego-union-tribune.png"     
## [24] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/tampa-bay-times.png"             
## [25] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/denver-post.png"                 
## [26] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/rocky-mountain-news.png"         
## [27] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/oregonian.png"                   
## [28] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/miami-herald.png"                
## [29] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/orange-county-register.png"      
## [30] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/sacramento-bee.png"              
## [31] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/st.-louis-post-dispatch.png"     
## [32] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/baltimore-sun.png"               
## [33] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/kansas-city-star.png"            
## [34] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/detroit-news.png"                
## [35] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/orlando-sentinel.png"            
## [36] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/south-florida-sun-sentinel.png"  
## [37] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/new-orleans-times-picayune.png"  
## [38] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/columbus-dispatch.png"           
## [39] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/indianapolis-star.png"           
## [40] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/san-antonio-express-news.png"    
## [41] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/pittsburgh-post-gazette.png"     
## [42] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/milwaukee-journal-sentinel.png"  
## [43] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/tampa-tribune.png"               
## [44] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/fort-woth-star-telegram.png"     
## [45] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/boston-herald.png"               
## [46] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/seattle-times.png"               
## [47] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/charlotte-observer.png"          
## [48] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/daily-oklahoman.png"             
## [49] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/louisville-courier-journal.png"  
## [50] "/Users/daniel/Teaching/data_sci_specialization/2021-22/c3-fp-2022/plots/pulitzers/investor's-buisiness-daily.png"
```

---
# Add paths to data frame

```r
final_plots %>% 
* ungroup() %>%
  mutate(path = paths) %>% 
  select(plots, path)
```

```
## # A tibble: 50 × 2
##    plots 
##    <list>
##  1 <gg>  
##  2 <gg>  
##  3 <gg>  
##  4 <gg>  
##  5 <gg>  
##  6 <gg>  
##  7 <gg>  
##  8 <gg>  
##  9 <gg>  
## 10 <gg>  
## # … with 40 more rows, and 1 more variable: path <chr>
```

---
# Save

```r
final_plots %>% 
* ungroup() %>%
  mutate(path = paths) %>% 
* rowwise() %>%
  summarize(
    ggsave(
      path, 
      plots, 
      width = 9.5, 
      height = 6.5,
      dpi = 500
    )
  )
```

```
## # A tibble: 50 × 1
## # … with 40 more rows, and 1 more variable:
## #   `ggsave(path, plots, width = 9.5, height = 6.5, dpi = 500)` <chr>
```

---
# Wrap-up
* Parallel iterations greatly increase the things you can do - iterating through at least two things simultaneously is pretty common

--
* The `nest_by()` approach can regularly get you the same result as `group_by() %>% nest() %>% mutate() %>% map()`

+ Caveat - must be in a data frame, which means working with list columns
  
--
* My view - it's still worth learning both. Looping with **{purrr}** is super flexible and often safer than base versions (type safe). Doesn't have to be used within a data frame.

---
class: inverse-blue middle
# Break

---
class: inverse-red middle
# Looping variants

---
# Agenda
* `walk()` and friends

* `modify()`

* `safely()`

* `reduce()`

---
# Reminder
## Learning Objectives (for this part)

* Know when to apply `walk` instead of `map`, and why it may be useful

* Understand the parallels and differences between `map` and `modify`

* Diagnose errors with `safely` and understand other situations where it may
be helpful

* Collapsing/reducing lists with `purrr::reduce()` or `base::Reduce()`

---
# Setup
Let's go back to our plotting example:

## Saving
* We saw last time that we could use `nest_by()`
  + Required a bit of awkwardness with adding the paths to the data frame
  
--
* Instead, we'll do it again but with the `walk()` family

---
# Why `walk()`?

> Walk is an alternative to map that you use when you want to call a function for its side effects, rather than for its return value. You typically do this because you want to render output to the screen or save files to disk - the important thing is the action, not the return value.

\-[r4ds](https://r4ds.had.co.nz/iteration.html#walk)

---
class: inverse-red middle
# More practical
If you use `walk()`, nothing will get printed to the screen. This is particularly helpful for RMarkdown files.

---
# Example
Please do the following
* Create a new RMarkdown document
* Paste the code you have for creating the plots in a code chunk there (along with the library loading, data cleaning, etc.)

---
# Create a directory

We already did this, but in case we hadn't...

```r
fs::dir_create(here::here("plots", "pulitzers"))
```

--
### Create file paths

```r
newspapers <- str_replace_all(
  tolower(final_plots$newspaper),
  " ",
  "-"
)
paths <- here::here(
  "plots",
  "pulitzers",
  glue("{newspapers}.png")
)
```

---
# Challenge
* Use a `map()` family function to loop through `paths` and `final_plots$plots` to save all plots.

* Render (knit) your file. What do you notice?

---
# `walk()`
Just like `map()`, we have parallel variants of `walk()`, including, `walk2()`, and `pwalk()`

These work just like `map()` but don't print to the screen

Try replacing your prior code with a `walk()` version.

How does the rendered output change?

---
# Save plots

```r
walk2(paths, final_plots$plots, ggsave,
      width = 9.5, 
      height = 6.5,
      dpi = 500)
```

---
class: inverse-red middle
# modify

---
class: middle

> Unlike `map()` and its variants which always return a fixed object type (list for `map()`, integer vector for `map_int()`, etc), the `modify()` family always returns the same type as the input object.

---
# `map` vs `modify`
### map

```r
map(mtcars, ~as.numeric(scale(.x)))
```

```
## $mpg
##  [1]  0.15088482  0.15088482  0.44954345  0.21725341 -0.23073453 -0.33028740
##  [7] -0.96078893  0.71501778  0.44954345 -0.14777380 -0.38006384 -0.61235388
## [13] -0.46302456 -0.81145962 -1.60788262 -1.60788262 -0.89442035  2.04238943
## [19]  1.71054652  2.29127162  0.23384555 -0.76168319 -0.81145962 -1.12671039
## [25] -0.14777380  1.19619000  0.98049211  1.71054652 -0.71190675 -0.06481307
## [31] -0.84464392  0.21725341
## 
## $cyl
##  [1] -0.1049878 -0.1049878 -1.2248578 -0.1049878  1.0148821 -0.1049878  1.0148821
##  [8] -1.2248578 -1.2248578 -0.1049878 -0.1049878  1.0148821  1.0148821  1.0148821
## [15]  1.0148821  1.0148821  1.0148821 -1.2248578 -1.2248578 -1.2248578 -1.2248578
## [22]  1.0148821  1.0148821  1.0148821  1.0148821 -1.2248578 -1.2248578 -1.2248578
## [29]  1.0148821 -0.1049878  1.0148821 -1.2248578
## 
## $disp
##  [1] -0.57061982 -0.57061982 -0.99018209  0.22009369  1.04308123 -0.04616698
##  [7]  1.04308123 -0.67793094 -0.72553512 -0.50929918 -0.50929918  0.36371309
## [13]  0.36371309  0.36371309  1.94675381  1.84993175  1.68856165 -1.22658929
## [19] -1.25079481 -1.28790993 -0.89255318  0.70420401  0.59124494  0.96239618
## [25]  1.36582144 -1.22416874 -0.89093948 -1.09426581  0.97046468 -0.69164740
## [31]  0.56703942 -0.88529152
## 
## $hp
##  [1] -0.53509284 -0.53509284 -0.78304046 -0.53509284  0.41294217 -0.60801861
##  [7]  1.43390296 -1.23518023 -0.75387015 -0.34548584 -0.34548584  0.48586794
## [13]  0.48586794  0.48586794  0.85049680  0.99634834  1.21512565 -1.17683962
## [19] -1.38103178 -1.19142477 -0.72469984  0.04831332  0.04831332  1.43390296
## [25]  0.41294217 -1.17683962 -0.81221077 -0.49133738  1.71102089  0.41294217
## [31]  2.74656682 -0.54967799
## 
## $drat
##  [1]  0.56751369  0.56751369  0.47399959 -0.96611753 -0.83519779 -1.56460776
##  [7] -0.72298087  0.17475447  0.60491932  0.60491932  0.60491932 -0.98482035
## [13] -0.98482035 -0.98482035 -1.24665983 -1.11574009 -0.68557523  0.90416444
## [19]  2.49390411  1.16600392  0.19345729 -1.56460776 -0.83519779  0.24956575
## [25] -0.96611753  0.90416444  1.55876313  0.32437703  1.16600392  0.04383473
## [31] -0.10578782  0.96027290
## 
## $wt
##  [1] -0.610399567 -0.349785269 -0.917004624 -0.002299538  0.227654255  0.248094592
##  [7]  0.360516446 -0.027849959 -0.068730634  0.227654255  0.227654255  0.871524874
## [13]  0.524039143  0.575139986  2.077504765  2.255335698  2.174596366 -1.039646647
## [19] -1.637526508 -1.412682800 -0.768812180  0.309415603  0.222544170  0.636460997
## [25]  0.641571082 -1.310481114 -1.100967659 -1.741772228 -0.048290296 -0.457097039
## [31]  0.360516446 -0.446876870
## 
## $qsec
##  [1] -0.77716515 -0.46378082  0.42600682  0.89048716 -0.46378082  1.32698675
##  [7] -1.12412636  1.20387148  2.82675459  0.25252621  0.58829513 -0.25112717
## [13] -0.13920420  0.08464175  0.07344945 -0.01608893 -0.23993487  0.90727560
## [19]  0.37564148  1.14790999  1.20946763 -0.54772305 -0.30708866 -1.36476075
## [25] -0.44699237  0.58829513 -0.64285758 -0.53093460 -1.87401028 -1.31439542
## [31] -1.81804880  0.42041067
## 
## $vs
##  [1] -0.8680278 -0.8680278  1.1160357  1.1160357 -0.8680278  1.1160357 -0.8680278
##  [8]  1.1160357  1.1160357  1.1160357  1.1160357 -0.8680278 -0.8680278 -0.8680278
## [15] -0.8680278 -0.8680278 -0.8680278  1.1160357  1.1160357  1.1160357  1.1160357
## [22] -0.8680278 -0.8680278 -0.8680278 -0.8680278  1.1160357 -0.8680278  1.1160357
## [29] -0.8680278 -0.8680278 -0.8680278  1.1160357
## 
## $am
##  [1]  1.1899014  1.1899014  1.1899014 -0.8141431 -0.8141431 -0.8141431 -0.8141431
##  [8] -0.8141431 -0.8141431 -0.8141431 -0.8141431 -0.8141431 -0.8141431 -0.8141431
## [15] -0.8141431 -0.8141431 -0.8141431  1.1899014  1.1899014  1.1899014 -0.8141431
## [22] -0.8141431 -0.8141431 -0.8141431 -0.8141431  1.1899014  1.1899014  1.1899014
## [29]  1.1899014  1.1899014  1.1899014  1.1899014
## 
## $gear
##  [1]  0.4235542  0.4235542  0.4235542 -0.9318192 -0.9318192 -0.9318192 -0.9318192
##  [8]  0.4235542  0.4235542  0.4235542  0.4235542 -0.9318192 -0.9318192 -0.9318192
## [15] -0.9318192 -0.9318192 -0.9318192  0.4235542  0.4235542  0.4235542 -0.9318192
## [22] -0.9318192 -0.9318192 -0.9318192 -0.9318192  0.4235542  1.7789276  1.7789276
## [29]  1.7789276  1.7789276  1.7789276  0.4235542
## 
## $carb
##  [1]  0.7352031  0.7352031 -1.1221521 -1.1221521 -0.5030337 -1.1221521  0.7352031
##  [8] -0.5030337 -0.5030337  0.7352031  0.7352031  0.1160847  0.1160847  0.1160847
## [15]  0.7352031  0.7352031  0.7352031 -1.1221521 -0.5030337 -1.1221521 -1.1221521
## [22] -0.5030337 -0.5030337  0.7352031 -0.5030337 -1.1221521 -0.5030337 -0.5030337
## [29]  0.7352031  1.9734398  3.2116766 -0.5030337
```

---
### modify

```r
modify(mtcars, ~as.numeric(scale(.x)))
```

```
##                             mpg        cyl        disp          hp        drat
## Mazda RX4            0.15088482 -0.1049878 -0.57061982 -0.53509284  0.56751369
## Mazda RX4 Wag        0.15088482 -0.1049878 -0.57061982 -0.53509284  0.56751369
## Datsun 710           0.44954345 -1.2248578 -0.99018209 -0.78304046  0.47399959
## Hornet 4 Drive       0.21725341 -0.1049878  0.22009369 -0.53509284 -0.96611753
## Hornet Sportabout   -0.23073453  1.0148821  1.04308123  0.41294217 -0.83519779
## Valiant             -0.33028740 -0.1049878 -0.04616698 -0.60801861 -1.56460776
## Duster 360          -0.96078893  1.0148821  1.04308123  1.43390296 -0.72298087
## Merc 240D            0.71501778 -1.2248578 -0.67793094 -1.23518023  0.17475447
## Merc 230             0.44954345 -1.2248578 -0.72553512 -0.75387015  0.60491932
## Merc 280            -0.14777380 -0.1049878 -0.50929918 -0.34548584  0.60491932
## Merc 280C           -0.38006384 -0.1049878 -0.50929918 -0.34548584  0.60491932
## Merc 450SE          -0.61235388  1.0148821  0.36371309  0.48586794 -0.98482035
## Merc 450SL          -0.46302456  1.0148821  0.36371309  0.48586794 -0.98482035
## Merc 450SLC         -0.81145962  1.0148821  0.36371309  0.48586794 -0.98482035
## Cadillac Fleetwood  -1.60788262  1.0148821  1.94675381  0.85049680 -1.24665983
## Lincoln Continental -1.60788262  1.0148821  1.84993175  0.99634834 -1.11574009
## Chrysler Imperial   -0.89442035  1.0148821  1.68856165  1.21512565 -0.68557523
## Fiat 128             2.04238943 -1.2248578 -1.22658929 -1.17683962  0.90416444
## Honda Civic          1.71054652 -1.2248578 -1.25079481 -1.38103178  2.49390411
## Toyota Corolla       2.29127162 -1.2248578 -1.28790993 -1.19142477  1.16600392
## Toyota Corona        0.23384555 -1.2248578 -0.89255318 -0.72469984  0.19345729
## Dodge Challenger    -0.76168319  1.0148821  0.70420401  0.04831332 -1.56460776
## AMC Javelin         -0.81145962  1.0148821  0.59124494  0.04831332 -0.83519779
## Camaro Z28          -1.12671039  1.0148821  0.96239618  1.43390296  0.24956575
## Pontiac Firebird    -0.14777380  1.0148821  1.36582144  0.41294217 -0.96611753
## Fiat X1-9            1.19619000 -1.2248578 -1.22416874 -1.17683962  0.90416444
## Porsche 914-2        0.98049211 -1.2248578 -0.89093948 -0.81221077  1.55876313
## Lotus Europa         1.71054652 -1.2248578 -1.09426581 -0.49133738  0.32437703
## Ford Pantera L      -0.71190675  1.0148821  0.97046468  1.71102089  1.16600392
## Ferrari Dino        -0.06481307 -0.1049878 -0.69164740  0.41294217  0.04383473
## Maserati Bora       -0.84464392  1.0148821  0.56703942  2.74656682 -0.10578782
## Volvo 142E           0.21725341 -1.2248578 -0.88529152 -0.54967799  0.96027290
##                               wt        qsec         vs         am       gear
## Mazda RX4           -0.610399567 -0.77716515 -0.8680278  1.1899014  0.4235542
## Mazda RX4 Wag       -0.349785269 -0.46378082 -0.8680278  1.1899014  0.4235542
## Datsun 710          -0.917004624  0.42600682  1.1160357  1.1899014  0.4235542
## Hornet 4 Drive      -0.002299538  0.89048716  1.1160357 -0.8141431 -0.9318192
## Hornet Sportabout    0.227654255 -0.46378082 -0.8680278 -0.8141431 -0.9318192
## Valiant              0.248094592  1.32698675  1.1160357 -0.8141431 -0.9318192
## Duster 360           0.360516446 -1.12412636 -0.8680278 -0.8141431 -0.9318192
## Merc 240D           -0.027849959  1.20387148  1.1160357 -0.8141431  0.4235542
## Merc 230            -0.068730634  2.82675459  1.1160357 -0.8141431  0.4235542
## Merc 280             0.227654255  0.25252621  1.1160357 -0.8141431  0.4235542
## Merc 280C            0.227654255  0.58829513  1.1160357 -0.8141431  0.4235542
## Merc 450SE           0.871524874 -0.25112717 -0.8680278 -0.8141431 -0.9318192
## Merc 450SL           0.524039143 -0.13920420 -0.8680278 -0.8141431 -0.9318192
## Merc 450SLC          0.575139986  0.08464175 -0.8680278 -0.8141431 -0.9318192
## Cadillac Fleetwood   2.077504765  0.07344945 -0.8680278 -0.8141431 -0.9318192
## Lincoln Continental  2.255335698 -0.01608893 -0.8680278 -0.8141431 -0.9318192
## Chrysler Imperial    2.174596366 -0.23993487 -0.8680278 -0.8141431 -0.9318192
## Fiat 128            -1.039646647  0.90727560  1.1160357  1.1899014  0.4235542
## Honda Civic         -1.637526508  0.37564148  1.1160357  1.1899014  0.4235542
## Toyota Corolla      -1.412682800  1.14790999  1.1160357  1.1899014  0.4235542
## Toyota Corona       -0.768812180  1.20946763  1.1160357 -0.8141431 -0.9318192
## Dodge Challenger     0.309415603 -0.54772305 -0.8680278 -0.8141431 -0.9318192
## AMC Javelin          0.222544170 -0.30708866 -0.8680278 -0.8141431 -0.9318192
## Camaro Z28           0.636460997 -1.36476075 -0.8680278 -0.8141431 -0.9318192
## Pontiac Firebird     0.641571082 -0.44699237 -0.8680278 -0.8141431 -0.9318192
## Fiat X1-9           -1.310481114  0.58829513  1.1160357  1.1899014  0.4235542
## Porsche 914-2       -1.100967659 -0.64285758 -0.8680278  1.1899014  1.7789276
## Lotus Europa        -1.741772228 -0.53093460  1.1160357  1.1899014  1.7789276
## Ford Pantera L      -0.048290296 -1.87401028 -0.8680278  1.1899014  1.7789276
## Ferrari Dino        -0.457097039 -1.31439542 -0.8680278  1.1899014  1.7789276
## Maserati Bora        0.360516446 -1.81804880 -0.8680278  1.1899014  1.7789276
## Volvo 142E          -0.446876870  0.42041067  1.1160357  1.1899014  0.4235542
##                           carb
## Mazda RX4            0.7352031
## Mazda RX4 Wag        0.7352031
## Datsun 710          -1.1221521
## Hornet 4 Drive      -1.1221521
## Hornet Sportabout   -0.5030337
## Valiant             -1.1221521
## Duster 360           0.7352031
## Merc 240D           -0.5030337
## Merc 230            -0.5030337
## Merc 280             0.7352031
## Merc 280C            0.7352031
## Merc 450SE           0.1160847
## Merc 450SL           0.1160847
## Merc 450SLC          0.1160847
## Cadillac Fleetwood   0.7352031
## Lincoln Continental  0.7352031
## Chrysler Imperial    0.7352031
## Fiat 128            -1.1221521
## Honda Civic         -0.5030337
## Toyota Corolla      -1.1221521
## Toyota Corona       -1.1221521
## Dodge Challenger    -0.5030337
## AMC Javelin         -0.5030337
## Camaro Z28           0.7352031
## Pontiac Firebird    -0.5030337
## Fiat X1-9           -1.1221521
## Porsche 914-2       -0.5030337
## Lotus Europa        -0.5030337
## Ford Pantera L       0.7352031
## Ferrari Dino         1.9734398
## Maserati Bora        3.2116766
## Volvo 142E          -0.5030337
```

---

```r
map2(LETTERS[1:3], letters[1:3], paste0)
```

```
## [[1]]
## [1] "Aa"
## 
## [[2]]
## [1] "Bb"
## 
## [[3]]
## [1] "Cc"
```

```r
modify2(LETTERS[1:3], letters[1:3], paste0)
```

```
## [1] "Aa" "Bb" "Cc"
```

---
class: inverse-red middle
# safely

---
# Errors during iterations

Sometimes a loop will work for most cases, but return an error on a few

--
Often, you want to return the output you can

--
Alternatively, you might want to diagnose *where* the error is occurring

--
`purrr::safely()`

---
# Example

```r
by_cyl <- mpg %>% 
  group_by(cyl) %>% 
  nest() 
by_cyl
```

```
## # A tibble: 4 × 2
## # Groups:   cyl [4]
##     cyl data              
##   <int> <list>            
## 1     4 <tibble [81 × 10]>
## 2     6 <tibble [79 × 10]>
## 3     8 <tibble [70 × 10]>
## 4     5 <tibble [4 × 10]>
```

Please run the code above

---
# Try to fit a model

(please follow along)

Notice the error message is *super* helpful! (this is new)

```r
by_cyl %>% 
  mutate(mod = map(data, ~lm(hwy ~ displ + drv, data = .x)))
```

```
## Error in `mutate()`:
## ! Problem while computing `mod = map(data, ~lm(hwy ~ displ +
##   drv, data = .x))`.
## ℹ The error occurred in group 2: cyl = 5.
## Caused by error in `contrasts<-`:
## ! contrasts can be applied only to factors with 2 or more levels
```

---
# Safe return

* First, define safe function - note that this will work for .ital[.b[any]] function

```r
safe_lm <- safely(lm)
```

* Next, loop the safe function, instead of the standard function

```r
safe_models <- by_cyl %>% 
  mutate(safe_mod = map(data, ~safe_lm(hwy ~ displ + drv, data = .x)))
safe_models
```

```
## # A tibble: 4 × 3
## # Groups:   cyl [4]
##     cyl data               safe_mod        
##   <int> <list>             <list>          
## 1     4 <tibble [81 × 10]> <named list [2]>
## 2     6 <tibble [79 × 10]> <named list [2]>
## 3     8 <tibble [70 × 10]> <named list [2]>
## 4     5 <tibble [4 × 10]>  <named list [2]>
```

---
# What's returned?

```r
safe_models$safe_mod[[1]]
```

```
## $result
## 
## Call:
## .f(formula = ..1, data = ..2)
## 
## Coefficients:
## (Intercept)        displ         drvf  
##      37.370       -5.289        3.882  
## 
## 
## $error
## NULL
```

```r
safe_models$safe_mod[[4]]
```

```
## $result
## NULL
## 
## $error
## <simpleError in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels>
```

---
# Inspecting
I often use `safely()` to help me de-bug. Why is it failing *there* (but note the new error messages help with this too).

--
First - create a new variable to filter for results with errors

```r
safe_models %>% 
  mutate(error = map_lgl(safe_mod, ~!is.null(.x$error)))
```

```
## # A tibble: 4 × 4
## # Groups:   cyl [4]
##     cyl data               safe_mod         error
##   <int> <list>             <list>           <lgl>
## 1     4 <tibble [81 × 10]> <named list [2]> FALSE
## 2     6 <tibble [79 × 10]> <named list [2]> FALSE
## 3     8 <tibble [70 × 10]> <named list [2]> FALSE
## 4     5 <tibble [4 × 10]>  <named list [2]> TRUE
```

---
# Inspecting the data

```r
safe_models %>% 
  mutate(error = map_lgl(safe_mod, ~!is.null(.x$error))) %>% 
  filter(isTRUE(error)) %>% 
  select(cyl, data) %>% 
  unnest(data)
```

```
## # A tibble: 4 × 11
## # Groups:   cyl [1]
##     cyl manufacturer model      displ  year trans      drv     cty   hwy fl   
##   <int> <chr>        <chr>      <dbl> <int> <chr>      <chr> <int> <int> <chr>
## 1     5 volkswagen   jetta        2.5  2008 auto(s6)   f        21    29 r    
## 2     5 volkswagen   jetta        2.5  2008 manual(m5) f        21    29 r    
## 3     5 volkswagen   new beetle   2.5  2008 manual(m5) f        20    28 r    
## 4     5 volkswagen   new beetle   2.5  2008 auto(s6)   f        20    29 r    
## # … with 1 more variable: class <chr>
```

The `displ` and `drv` variables are constant, so no relation can be estimated.

---
# Pull results that worked

```r
safe_models %>% 
  mutate(results = map(safe_mod, "result"))
```

```
## # A tibble: 4 × 4
## # Groups:   cyl [4]
##     cyl data               safe_mod         results
##   <int> <list>             <list>           <list> 
## 1     4 <tibble [81 × 10]> <named list [2]> <lm>   
## 2     6 <tibble [79 × 10]> <named list [2]> <lm>   
## 3     8 <tibble [70 × 10]> <named list [2]> <lm>   
## 4     5 <tibble [4 × 10]>  <named list [2]> <NULL>
```

--
Now we can `broom::tidy()` or whatevs

---

Notice that there is no `cyl == 5`.

```r
safe_models %>% 
  mutate(results = map(safe_mod, "result"),
         tidied = map(results, broom::tidy)) %>% 
  select(cyl, tidied) %>% 
  unnest(tidied)
```

```
## # A tibble: 11 × 6
## # Groups:   cyl [3]
##      cyl term          estimate std.error  statistic      p.value
##    <int> <chr>            <dbl>     <dbl>      <dbl>        <dbl>
##  1     4 (Intercept) 37.37023   3.537572  10.56381   1.052943e-16
##  2     4 displ       -5.288562  1.436068  -3.682668  4.235795e- 4
##  3     4 drvf         3.882134  0.9971876  3.893083  2.073699e- 4
##  4     6 (Intercept) 27.96536   2.347630  11.91217   5.718039e-19
##  5     6 displ       -2.333261  0.6373304 -3.660991  4.651570e- 4
##  6     6 drvf         4.570840  0.6012367  7.602397  6.789988e-11
##  7     6 drvr         6.384355  1.229277   5.193585  1.713129e- 6
##  8     8 (Intercept) 14.82265   2.887289   5.133759  2.708515e- 6
##  9     8 displ        0.3060487 0.5719058  0.5351383 5.943528e- 1
## 10     8 drvf         8.555294  2.679129   3.193311  2.156229e- 3
## 11     8 drvr         3.709336  0.7319048  5.068058  3.473594e- 6
```

---
# When else might we use this?

Any sort of web scraping - pages change and URLs don't always work

---
# Example

```r
library(rvest)
links <- list(
  "https://en.wikipedia.org/wiki/FC_Barcelona",
  "https://nosuchpage",
  "https://en.wikipedia.org/wiki/Rome"
)
pages <- map(links, ~{
  Sys.sleep(0.1)
  read_html(.x)
})
```

```
## Error in open.connection(x, "rb"): Timeout was reached: [nosuchpage] Failed to connect to nosuchpage port 443: Operation timed out
```

---
# The problem
I can't connect to https://nosuchpage because it doesn't exist

--
.center[.blue[.realbig[BUT]]]

--
That also means I can't get *any* of my links because *one* page errored (imagine it was 1 in 1,000 instead of 1 in 3)

--
## `safely()` to the rescue

---
# Safe version

```r
safe_read_html <- safely(read_html)
pages <- map(links, ~{
  Sys.sleep(0.1)
  safe_read_html(.x)
})
str(pages)
```

```
## List of 3
##  $ :List of 2
##   ..$ result:List of 2
##   .. ..$ node:<externalptr> 
##   .. ..$ doc :<externalptr> 
##   .. ..- attr(*, "class")= chr [1:2] "xml_document" "xml_node"
##   ..$ error : NULL
##  $ :List of 2
##   ..$ result: NULL
##   ..$ error :List of 2
##   .. ..$ message: chr "Failed to connect to nosuchpage port 443: Connection refused"
##   .. ..$ call   : language open.connection(x, "rb")
##   .. ..- attr(*, "class")= chr [1:3] "simpleError" "error" "condition"
##  $ :List of 2
##   ..$ result:List of 2
##   .. ..$ node:<externalptr> 
##   .. ..$ doc :<externalptr> 
##   .. ..- attr(*, "class")= chr [1:2] "xml_document" "xml_node"
##   ..$ error : NULL
```

---
# Non-results
In a real example, we'd probably want to double-check the pages where we got no results

```r
errors <- map_lgl(pages, ~!is.null(.x$error))
links[errors]
```

```
## [[1]]
## [1] "https://nosuchpage"
```

---
class: inverse-red middle
# reduce

---
# Reducing a list
The `map()` family of functions will always return a vector the same length as the input

--
`reduce()` will collapse or reduce the list to a single element

---
# Example

```r
l <- list(
  c(1, 3),
  c(1, 5, 7, 9),
  3,
  c(4, 8, 12, 2)
)

reduce(l, sum)
```

```
## [1] 55
```

---
# Compare to `map()`

```r
map(l, sum)
```

```
## [[1]]
## [1] 4
## 
## [[2]]
## [1] 22
## 
## [[3]]
## [1] 3
## 
## [[4]]
## [1] 26
```

---
# What's going on?
The code `reduce(l, sum)` is the same as

```r
sum(l[[4]], sum(l[[3]], sum(l[[1]], l[[2]])))
```

```
## [1] 55
```

Or slidghlty differently

```r
first_sum <- sum(l[[1]], l[[2]])
second_sum <- sum(first_sum, l[[3]])
final_sum <- sum(second_sum, l[[4]])
final_sum
```

```
## [1] 55
```

---
# Why might you use this?
What if you had a list of data frames like this

```r
l_df <- list(
  tibble(id = 1:3, score = rnorm(3)),
  tibble(id = 1:5, treatment = rbinom(5, 1, .5)),
  tibble(id = c(1, 3, 5, 7), other_thing = rnorm(4))
)
```

We can join these all together with a single loop - we want the output to be of length 1!

---

```r
reduce(l_df, full_join)
```

```
## # A tibble: 6 × 4
##      id      score treatment other_thing
##   <dbl>      <dbl>     <int>       <dbl>
## 1     1  0.2548487         0   0.8905293
## 2     2  1.272697          0  NA        
## 3     3 -0.7417612         1   0.5923346
## 4     4 NA                 1  NA        
## 5     5 NA                 0   0.4740725
## 6     7 NA                NA  -0.1488833
```

---
Note - you have to be careful on directionality

```r
reduce(l_df, left_join)
```

```
## # A tibble: 3 × 4
##      id      score treatment other_thing
##   <dbl>      <dbl>     <int>       <dbl>
## 1     1  0.2548487         0   0.8905293
## 2     2  1.272697          0  NA        
## 3     3 -0.7417612         1   0.5923346
```

```r
reduce(l_df, right_join)
```

```
## # A tibble: 4 × 4
##      id      score treatment other_thing
##   <dbl>      <dbl>     <int>       <dbl>
## 1     1  0.2548487         0   0.8905293
## 2     3 -0.7417612         1   0.5923346
## 3     5 NA                 0   0.4740725
## 4     7 NA                NA  -0.1488833
```

---
# Another example
You probably just want to `bind_rows()`

```r
l_df2 <- list(
  tibble(id = 1:3, scid = 1, score = rnorm(3)),
  tibble(id = 1:5, scid = 2, score = rnorm(5)),
  tibble(id = c(1, 3, 5, 7), scid = 3, score = rnorm(4))
)
reduce(l_df2, bind_rows)
```

```
## # A tibble: 12 × 3
##       id  scid      score
##    <dbl> <dbl>      <dbl>
##  1     1     1  1.636123 
##  2     2     1  0.9520929
##  3     3     1 -1.635054 
##  4     1     2 -1.595450 
##  5     2     2  0.3252145
##  6     3     2  0.9735523
##  7     4     2 -0.9043736
##  8     5     2 -0.4567605
##  9     1     3 -1.208666 
## 10     3     3 -2.079515 
## 11     5     3 -0.7194680
## 12     7     3  1.207981
```

---
# Non-loop version

Luckily, the prior slide has become obsolete, because `bind_rows()` will do the list reduction for us.

```r
bind_rows(l_df2)
```

---
# Another example

This is a poor example, but there are use cases like this

```r
library(palmerpenguins)
map(penguins, as.character) %>% 
  reduce(paste)
```

```
##   [1] "Adelie Torgersen 39.1 18.7 181 3750 male 2007"  
##   [2] "Adelie Torgersen 39.5 17.4 186 3800 female 2007"
##   [3] "Adelie Torgersen 40.3 18 195 3250 female 2007"  
##   [4] "Adelie Torgersen NA NA NA NA NA 2007"           
##   [5] "Adelie Torgersen 36.7 19.3 193 3450 female 2007"
##   [6] "Adelie Torgersen 39.3 20.6 190 3650 male 2007"  
##   [7] "Adelie Torgersen 38.9 17.8 181 3625 female 2007"
##   [8] "Adelie Torgersen 39.2 19.6 195 4675 male 2007"  
##   [9] "Adelie Torgersen 34.1 18.1 193 3475 NA 2007"    
##  [10] "Adelie Torgersen 42 20.2 190 4250 NA 2007"      
##  [11] "Adelie Torgersen 37.8 17.1 186 3300 NA 2007"    
##  [12] "Adelie Torgersen 37.8 17.3 180 3700 NA 2007"    
##  [13] "Adelie Torgersen 41.1 17.6 182 3200 female 2007"
##  [14] "Adelie Torgersen 38.6 21.2 191 3800 male 2007"  
##  [15] "Adelie Torgersen 34.6 21.1 198 4400 male 2007"  
##  [16] "Adelie Torgersen 36.6 17.8 185 3700 female 2007"
##  [17] "Adelie Torgersen 38.7 19 195 3450 female 2007"  
##  [18] "Adelie Torgersen 42.5 20.7 197 4500 male 2007"  
##  [19] "Adelie Torgersen 34.4 18.4 184 3325 female 2007"
##  [20] "Adelie Torgersen 46 21.5 194 4200 male 2007"    
##  [21] "Adelie Biscoe 37.8 18.3 174 3400 female 2007"   
##  [22] "Adelie Biscoe 37.7 18.7 180 3600 male 2007"     
##  [23] "Adelie Biscoe 35.9 19.2 189 3800 female 2007"   
##  [24] "Adelie Biscoe 38.2 18.1 185 3950 male 2007"     
##  [25] "Adelie Biscoe 38.8 17.2 180 3800 male 2007"     
##  [26] "Adelie Biscoe 35.3 18.9 187 3800 female 2007"   
##  [27] "Adelie Biscoe 40.6 18.6 183 3550 male 2007"     
##  [28] "Adelie Biscoe 40.5 17.9 187 3200 female 2007"   
##  [29] "Adelie Biscoe 37.9 18.6 172 3150 female 2007"   
##  [30] "Adelie Biscoe 40.5 18.9 180 3950 male 2007"     
##  [31] "Adelie Dream 39.5 16.7 178 3250 female 2007"    
##  [32] "Adelie Dream 37.2 18.1 178 3900 male 2007"      
##  [33] "Adelie Dream 39.5 17.8 188 3300 female 2007"    
##  [34] "Adelie Dream 40.9 18.9 184 3900 male 2007"      
##  [35] "Adelie Dream 36.4 17 195 3325 female 2007"      
##  [36] "Adelie Dream 39.2 21.1 196 4150 male 2007"      
##  [37] "Adelie Dream 38.8 20 190 3950 male 2007"        
##  [38] "Adelie Dream 42.2 18.5 180 3550 female 2007"    
##  [39] "Adelie Dream 37.6 19.3 181 3300 female 2007"    
##  [40] "Adelie Dream 39.8 19.1 184 4650 male 2007"      
##  [41] "Adelie Dream 36.5 18 182 3150 female 2007"      
##  [42] "Adelie Dream 40.8 18.4 195 3900 male 2007"      
##  [43] "Adelie Dream 36 18.5 186 3100 female 2007"      
##  [44] "Adelie Dream 44.1 19.7 196 4400 male 2007"      
##  [45] "Adelie Dream 37 16.9 185 3000 female 2007"      
##  [46] "Adelie Dream 39.6 18.8 190 4600 male 2007"      
##  [47] "Adelie Dream 41.1 19 182 3425 male 2007"        
##  [48] "Adelie Dream 37.5 18.9 179 2975 NA 2007"        
##  [49] "Adelie Dream 36 17.9 190 3450 female 2007"      
##  [50] "Adelie Dream 42.3 21.2 191 4150 male 2007"      
##  [51] "Adelie Biscoe 39.6 17.7 186 3500 female 2008"   
##  [52] "Adelie Biscoe 40.1 18.9 188 4300 male 2008"     
##  [53] "Adelie Biscoe 35 17.9 190 3450 female 2008"     
##  [54] "Adelie Biscoe 42 19.5 200 4050 male 2008"       
##  [55] "Adelie Biscoe 34.5 18.1 187 2900 female 2008"   
##  [56] "Adelie Biscoe 41.4 18.6 191 3700 male 2008"     
##  [57] "Adelie Biscoe 39 17.5 186 3550 female 2008"     
##  [58] "Adelie Biscoe 40.6 18.8 193 3800 male 2008"     
##  [59] "Adelie Biscoe 36.5 16.6 181 2850 female 2008"   
##  [60] "Adelie Biscoe 37.6 19.1 194 3750 male 2008"     
##  [61] "Adelie Biscoe 35.7 16.9 185 3150 female 2008"   
##  [62] "Adelie Biscoe 41.3 21.1 195 4400 male 2008"     
##  [63] "Adelie Biscoe 37.6 17 185 3600 female 2008"     
##  [64] "Adelie Biscoe 41.1 18.2 192 4050 male 2008"     
##  [65] "Adelie Biscoe 36.4 17.1 184 2850 female 2008"   
##  [66] "Adelie Biscoe 41.6 18 192 3950 male 2008"       
##  [67] "Adelie Biscoe 35.5 16.2 195 3350 female 2008"   
##  [68] "Adelie Biscoe 41.1 19.1 188 4100 male 2008"     
##  [69] "Adelie Torgersen 35.9 16.6 190 3050 female 2008"
##  [70] "Adelie Torgersen 41.8 19.4 198 4450 male 2008"  
##  [71] "Adelie Torgersen 33.5 19 190 3600 female 2008"  
##  [72] "Adelie Torgersen 39.7 18.4 190 3900 male 2008"  
##  [73] "Adelie Torgersen 39.6 17.2 196 3550 female 2008"
##  [74] "Adelie Torgersen 45.8 18.9 197 4150 male 2008"  
##  [75] "Adelie Torgersen 35.5 17.5 190 3700 female 2008"
##  [76] "Adelie Torgersen 42.8 18.5 195 4250 male 2008"  
##  [77] "Adelie Torgersen 40.9 16.8 191 3700 female 2008"
##  [78] "Adelie Torgersen 37.2 19.4 184 3900 male 2008"  
##  [79] "Adelie Torgersen 36.2 16.1 187 3550 female 2008"
##  [80] "Adelie Torgersen 42.1 19.1 195 4000 male 2008"  
##  [81] "Adelie Torgersen 34.6 17.2 189 3200 female 2008"
##  [82] "Adelie Torgersen 42.9 17.6 196 4700 male 2008"  
##  [83] "Adelie Torgersen 36.7 18.8 187 3800 female 2008"
##  [84] "Adelie Torgersen 35.1 19.4 193 4200 male 2008"  
##  [85] "Adelie Dream 37.3 17.8 191 3350 female 2008"    
##  [86] "Adelie Dream 41.3 20.3 194 3550 male 2008"      
##  [87] "Adelie Dream 36.3 19.5 190 3800 male 2008"      
##  [88] "Adelie Dream 36.9 18.6 189 3500 female 2008"    
##  [89] "Adelie Dream 38.3 19.2 189 3950 male 2008"      
##  [90] "Adelie Dream 38.9 18.8 190 3600 female 2008"    
##  [91] "Adelie Dream 35.7 18 202 3550 female 2008"      
##  [92] "Adelie Dream 41.1 18.1 205 4300 male 2008"      
##  [93] "Adelie Dream 34 17.1 185 3400 female 2008"      
##  [94] "Adelie Dream 39.6 18.1 186 4450 male 2008"      
##  [95] "Adelie Dream 36.2 17.3 187 3300 female 2008"    
##  [96] "Adelie Dream 40.8 18.9 208 4300 male 2008"      
##  [97] "Adelie Dream 38.1 18.6 190 3700 female 2008"    
##  [98] "Adelie Dream 40.3 18.5 196 4350 male 2008"      
##  [99] "Adelie Dream 33.1 16.1 178 2900 female 2008"    
## [100] "Adelie Dream 43.2 18.5 192 4100 male 2008"      
## [101] "Adelie Biscoe 35 17.9 192 3725 female 2009"     
## [102] "Adelie Biscoe 41 20 203 4725 male 2009"         
## [103] "Adelie Biscoe 37.7 16 183 3075 female 2009"     
## [104] "Adelie Biscoe 37.8 20 190 4250 male 2009"       
## [105] "Adelie Biscoe 37.9 18.6 193 2925 female 2009"   
## [106] "Adelie Biscoe 39.7 18.9 184 3550 male 2009"     
## [107] "Adelie Biscoe 38.6 17.2 199 3750 female 2009"   
## [108] "Adelie Biscoe 38.2 20 190 3900 male 2009"       
## [109] "Adelie Biscoe 38.1 17 181 3175 female 2009"     
## [110] "Adelie Biscoe 43.2 19 197 4775 male 2009"       
## [111] "Adelie Biscoe 38.1 16.5 198 3825 female 2009"   
## [112] "Adelie Biscoe 45.6 20.3 191 4600 male 2009"     
## [113] "Adelie Biscoe 39.7 17.7 193 3200 female 2009"   
## [114] "Adelie Biscoe 42.2 19.5 197 4275 male 2009"     
## [115] "Adelie Biscoe 39.6 20.7 191 3900 female 2009"   
## [116] "Adelie Biscoe 42.7 18.3 196 4075 male 2009"     
## [117] "Adelie Torgersen 38.6 17 188 2900 female 2009"  
## [118] "Adelie Torgersen 37.3 20.5 199 3775 male 2009"  
## [119] "Adelie Torgersen 35.7 17 189 3350 female 2009"  
## [120] "Adelie Torgersen 41.1 18.6 189 3325 male 2009"  
## [121] "Adelie Torgersen 36.2 17.2 187 3150 female 2009"
## [122] "Adelie Torgersen 37.7 19.8 198 3500 male 2009"  
## [123] "Adelie Torgersen 40.2 17 176 3450 female 2009"  
## [124] "Adelie Torgersen 41.4 18.5 202 3875 male 2009"  
## [125] "Adelie Torgersen 35.2 15.9 186 3050 female 2009"
## [126] "Adelie Torgersen 40.6 19 199 4000 male 2009"    
## [127] "Adelie Torgersen 38.8 17.6 191 3275 female 2009"
## [128] "Adelie Torgersen 41.5 18.3 195 4300 male 2009"  
## [129] "Adelie Torgersen 39 17.1 191 3050 female 2009"  
## [130] "Adelie Torgersen 44.1 18 210 4000 male 2009"    
## [131] "Adelie Torgersen 38.5 17.9 190 3325 female 2009"
## [132] "Adelie Torgersen 43.1 19.2 197 3500 male 2009"  
## [133] "Adelie Dream 36.8 18.5 193 3500 female 2009"    
## [134] "Adelie Dream 37.5 18.5 199 4475 male 2009"      
## [135] "Adelie Dream 38.1 17.6 187 3425 female 2009"    
## [136] "Adelie Dream 41.1 17.5 190 3900 male 2009"      
## [137] "Adelie Dream 35.6 17.5 191 3175 female 2009"    
## [138] "Adelie Dream 40.2 20.1 200 3975 male 2009"      
## [139] "Adelie Dream 37 16.5 185 3400 female 2009"      
## [140] "Adelie Dream 39.7 17.9 193 4250 male 2009"      
## [141] "Adelie Dream 40.2 17.1 193 3400 female 2009"    
## [142] "Adelie Dream 40.6 17.2 187 3475 male 2009"      
## [143] "Adelie Dream 32.1 15.5 188 3050 female 2009"    
## [144] "Adelie Dream 40.7 17 190 3725 male 2009"        
## [145] "Adelie Dream 37.3 16.8 192 3000 female 2009"    
## [146] "Adelie Dream 39 18.7 185 3650 male 2009"        
## [147] "Adelie Dream 39.2 18.6 190 4250 male 2009"      
## [148] "Adelie Dream 36.6 18.4 184 3475 female 2009"    
## [149] "Adelie Dream 36 17.8 195 3450 female 2009"      
## [150] "Adelie Dream 37.8 18.1 193 3750 male 2009"      
## [151] "Adelie Dream 36 17.1 187 3700 female 2009"      
## [152] "Adelie Dream 41.5 18.5 201 4000 male 2009"      
## [153] "Gentoo Biscoe 46.1 13.2 211 4500 female 2007"   
## [154] "Gentoo Biscoe 50 16.3 230 5700 male 2007"       
## [155] "Gentoo Biscoe 48.7 14.1 210 4450 female 2007"   
## [156] "Gentoo Biscoe 50 15.2 218 5700 male 2007"       
## [157] "Gentoo Biscoe 47.6 14.5 215 5400 male 2007"     
## [158] "Gentoo Biscoe 46.5 13.5 210 4550 female 2007"   
## [159] "Gentoo Biscoe 45.4 14.6 211 4800 female 2007"   
## [160] "Gentoo Biscoe 46.7 15.3 219 5200 male 2007"     
## [161] "Gentoo Biscoe 43.3 13.4 209 4400 female 2007"   
## [162] "Gentoo Biscoe 46.8 15.4 215 5150 male 2007"     
## [163] "Gentoo Biscoe 40.9 13.7 214 4650 female 2007"   
## [164] "Gentoo Biscoe 49 16.1 216 5550 male 2007"       
## [165] "Gentoo Biscoe 45.5 13.7 214 4650 female 2007"   
## [166] "Gentoo Biscoe 48.4 14.6 213 5850 male 2007"     
## [167] "Gentoo Biscoe 45.8 14.6 210 4200 female 2007"   
## [168] "Gentoo Biscoe 49.3 15.7 217 5850 male 2007"     
## [169] "Gentoo Biscoe 42 13.5 210 4150 female 2007"     
## [170] "Gentoo Biscoe 49.2 15.2 221 6300 male 2007"     
## [171] "Gentoo Biscoe 46.2 14.5 209 4800 female 2007"   
## [172] "Gentoo Biscoe 48.7 15.1 222 5350 male 2007"     
## [173] "Gentoo Biscoe 50.2 14.3 218 5700 male 2007"     
## [174] "Gentoo Biscoe 45.1 14.5 215 5000 female 2007"   
## [175] "Gentoo Biscoe 46.5 14.5 213 4400 female 2007"   
## [176] "Gentoo Biscoe 46.3 15.8 215 5050 male 2007"     
## [177] "Gentoo Biscoe 42.9 13.1 215 5000 female 2007"   
## [178] "Gentoo Biscoe 46.1 15.1 215 5100 male 2007"     
## [179] "Gentoo Biscoe 44.5 14.3 216 4100 NA 2007"       
## [180] "Gentoo Biscoe 47.8 15 215 5650 male 2007"       
## [181] "Gentoo Biscoe 48.2 14.3 210 4600 female 2007"   
## [182] "Gentoo Biscoe 50 15.3 220 5550 male 2007"       
## [183] "Gentoo Biscoe 47.3 15.3 222 5250 male 2007"     
## [184] "Gentoo Biscoe 42.8 14.2 209 4700 female 2007"   
## [185] "Gentoo Biscoe 45.1 14.5 207 5050 female 2007"   
## [186] "Gentoo Biscoe 59.6 17 230 6050 male 2007"       
## [187] "Gentoo Biscoe 49.1 14.8 220 5150 female 2008"   
## [188] "Gentoo Biscoe 48.4 16.3 220 5400 male 2008"     
## [189] "Gentoo Biscoe 42.6 13.7 213 4950 female 2008"   
## [190] "Gentoo Biscoe 44.4 17.3 219 5250 male 2008"     
## [191] "Gentoo Biscoe 44 13.6 208 4350 female 2008"     
## [192] "Gentoo Biscoe 48.7 15.7 208 5350 male 2008"     
## [193] "Gentoo Biscoe 42.7 13.7 208 3950 female 2008"   
## [194] "Gentoo Biscoe 49.6 16 225 5700 male 2008"       
## [195] "Gentoo Biscoe 45.3 13.7 210 4300 female 2008"   
## [196] "Gentoo Biscoe 49.6 15 216 4750 male 2008"       
## [197] "Gentoo Biscoe 50.5 15.9 222 5550 male 2008"     
## [198] "Gentoo Biscoe 43.6 13.9 217 4900 female 2008"   
## [199] "Gentoo Biscoe 45.5 13.9 210 4200 female 2008"   
## [200] "Gentoo Biscoe 50.5 15.9 225 5400 male 2008"     
## [201] "Gentoo Biscoe 44.9 13.3 213 5100 female 2008"   
## [202] "Gentoo Biscoe 45.2 15.8 215 5300 male 2008"     
## [203] "Gentoo Biscoe 46.6 14.2 210 4850 female 2008"   
## [204] "Gentoo Biscoe 48.5 14.1 220 5300 male 2008"     
## [205] "Gentoo Biscoe 45.1 14.4 210 4400 female 2008"   
## [206] "Gentoo Biscoe 50.1 15 225 5000 male 2008"       
## [207] "Gentoo Biscoe 46.5 14.4 217 4900 female 2008"   
## [208] "Gentoo Biscoe 45 15.4 220 5050 male 2008"       
## [209] "Gentoo Biscoe 43.8 13.9 208 4300 female 2008"   
## [210] "Gentoo Biscoe 45.5 15 220 5000 male 2008"       
## [211] "Gentoo Biscoe 43.2 14.5 208 4450 female 2008"   
## [212] "Gentoo Biscoe 50.4 15.3 224 5550 male 2008"     
## [213] "Gentoo Biscoe 45.3 13.8 208 4200 female 2008"   
## [214] "Gentoo Biscoe 46.2 14.9 221 5300 male 2008"     
## [215] "Gentoo Biscoe 45.7 13.9 214 4400 female 2008"   
## [216] "Gentoo Biscoe 54.3 15.7 231 5650 male 2008"     
## [217] "Gentoo Biscoe 45.8 14.2 219 4700 female 2008"   
## [218] "Gentoo Biscoe 49.8 16.8 230 5700 male 2008"     
## [219] "Gentoo Biscoe 46.2 14.4 214 4650 NA 2008"       
## [220] "Gentoo Biscoe 49.5 16.2 229 5800 male 2008"     
## [221] "Gentoo Biscoe 43.5 14.2 220 4700 female 2008"   
## [222] "Gentoo Biscoe 50.7 15 223 5550 male 2008"       
## [223] "Gentoo Biscoe 47.7 15 216 4750 female 2008"     
## [224] "Gentoo Biscoe 46.4 15.6 221 5000 male 2008"     
## [225] "Gentoo Biscoe 48.2 15.6 221 5100 male 2008"     
## [226] "Gentoo Biscoe 46.5 14.8 217 5200 female 2008"   
## [227] "Gentoo Biscoe 46.4 15 216 4700 female 2008"     
## [228] "Gentoo Biscoe 48.6 16 230 5800 male 2008"       
## [229] "Gentoo Biscoe 47.5 14.2 209 4600 female 2008"   
## [230] "Gentoo Biscoe 51.1 16.3 220 6000 male 2008"     
## [231] "Gentoo Biscoe 45.2 13.8 215 4750 female 2008"   
## [232] "Gentoo Biscoe 45.2 16.4 223 5950 male 2008"     
## [233] "Gentoo Biscoe 49.1 14.5 212 4625 female 2009"   
## [234] "Gentoo Biscoe 52.5 15.6 221 5450 male 2009"     
## [235] "Gentoo Biscoe 47.4 14.6 212 4725 female 2009"   
## [236] "Gentoo Biscoe 50 15.9 224 5350 male 2009"       
## [237] "Gentoo Biscoe 44.9 13.8 212 4750 female 2009"   
## [238] "Gentoo Biscoe 50.8 17.3 228 5600 male 2009"     
## [239] "Gentoo Biscoe 43.4 14.4 218 4600 female 2009"   
## [240] "Gentoo Biscoe 51.3 14.2 218 5300 male 2009"     
## [241] "Gentoo Biscoe 47.5 14 212 4875 female 2009"     
## [242] "Gentoo Biscoe 52.1 17 230 5550 male 2009"       
## [243] "Gentoo Biscoe 47.5 15 218 4950 female 2009"     
## [244] "Gentoo Biscoe 52.2 17.1 228 5400 male 2009"     
## [245] "Gentoo Biscoe 45.5 14.5 212 4750 female 2009"   
## [246] "Gentoo Biscoe 49.5 16.1 224 5650 male 2009"     
## [247] "Gentoo Biscoe 44.5 14.7 214 4850 female 2009"   
## [248] "Gentoo Biscoe 50.8 15.7 226 5200 male 2009"     
## [249] "Gentoo Biscoe 49.4 15.8 216 4925 male 2009"     
## [250] "Gentoo Biscoe 46.9 14.6 222 4875 female 2009"   
## [251] "Gentoo Biscoe 48.4 14.4 203 4625 female 2009"   
## [252] "Gentoo Biscoe 51.1 16.5 225 5250 male 2009"     
## [253] "Gentoo Biscoe 48.5 15 219 4850 female 2009"     
## [254] "Gentoo Biscoe 55.9 17 228 5600 male 2009"       
## [255] "Gentoo Biscoe 47.2 15.5 215 4975 female 2009"   
## [256] "Gentoo Biscoe 49.1 15 228 5500 male 2009"       
## [257] "Gentoo Biscoe 47.3 13.8 216 4725 NA 2009"       
## [258] "Gentoo Biscoe 46.8 16.1 215 5500 male 2009"     
## [259] "Gentoo Biscoe 41.7 14.7 210 4700 female 2009"   
## [260] "Gentoo Biscoe 53.4 15.8 219 5500 male 2009"     
## [261] "Gentoo Biscoe 43.3 14 208 4575 female 2009"     
## [262] "Gentoo Biscoe 48.1 15.1 209 5500 male 2009"     
## [263] "Gentoo Biscoe 50.5 15.2 216 5000 female 2009"   
## [264] "Gentoo Biscoe 49.8 15.9 229 5950 male 2009"     
## [265] "Gentoo Biscoe 43.5 15.2 213 4650 female 2009"   
## [266] "Gentoo Biscoe 51.5 16.3 230 5500 male 2009"     
## [267] "Gentoo Biscoe 46.2 14.1 217 4375 female 2009"   
## [268] "Gentoo Biscoe 55.1 16 230 5850 male 2009"       
## [269] "Gentoo Biscoe 44.5 15.7 217 4875 NA 2009"       
## [270] "Gentoo Biscoe 48.8 16.2 222 6000 male 2009"     
## [271] "Gentoo Biscoe 47.2 13.7 214 4925 female 2009"   
## [272] "Gentoo Biscoe NA NA NA NA NA 2009"              
## [273] "Gentoo Biscoe 46.8 14.3 215 4850 female 2009"   
## [274] "Gentoo Biscoe 50.4 15.7 222 5750 male 2009"     
## [275] "Gentoo Biscoe 45.2 14.8 212 5200 female 2009"   
## [276] "Gentoo Biscoe 49.9 16.1 213 5400 male 2009"     
## [277] "Chinstrap Dream 46.5 17.9 192 3500 female 2007" 
## [278] "Chinstrap Dream 50 19.5 196 3900 male 2007"     
## [279] "Chinstrap Dream 51.3 19.2 193 3650 male 2007"   
## [280] "Chinstrap Dream 45.4 18.7 188 3525 female 2007" 
## [281] "Chinstrap Dream 52.7 19.8 197 3725 male 2007"   
## [282] "Chinstrap Dream 45.2 17.8 198 3950 female 2007" 
## [283] "Chinstrap Dream 46.1 18.2 178 3250 female 2007" 
## [284] "Chinstrap Dream 51.3 18.2 197 3750 male 2007"   
## [285] "Chinstrap Dream 46 18.9 195 4150 female 2007"   
## [286] "Chinstrap Dream 51.3 19.9 198 3700 male 2007"   
## [287] "Chinstrap Dream 46.6 17.8 193 3800 female 2007" 
## [288] "Chinstrap Dream 51.7 20.3 194 3775 male 2007"   
## [289] "Chinstrap Dream 47 17.3 185 3700 female 2007"   
## [290] "Chinstrap Dream 52 18.1 201 4050 male 2007"     
## [291] "Chinstrap Dream 45.9 17.1 190 3575 female 2007" 
## [292] "Chinstrap Dream 50.5 19.6 201 4050 male 2007"   
## [293] "Chinstrap Dream 50.3 20 197 3300 male 2007"     
## [294] "Chinstrap Dream 58 17.8 181 3700 female 2007"   
## [295] "Chinstrap Dream 46.4 18.6 190 3450 female 2007" 
## [296] "Chinstrap Dream 49.2 18.2 195 4400 male 2007"   
## [297] "Chinstrap Dream 42.4 17.3 181 3600 female 2007" 
## [298] "Chinstrap Dream 48.5 17.5 191 3400 male 2007"   
## [299] "Chinstrap Dream 43.2 16.6 187 2900 female 2007" 
## [300] "Chinstrap Dream 50.6 19.4 193 3800 male 2007"   
## [301] "Chinstrap Dream 46.7 17.9 195 3300 female 2007" 
## [302] "Chinstrap Dream 52 19 197 4150 male 2007"       
## [303] "Chinstrap Dream 50.5 18.4 200 3400 female 2008" 
## [304] "Chinstrap Dream 49.5 19 200 3800 male 2008"     
## [305] "Chinstrap Dream 46.4 17.8 191 3700 female 2008" 
## [306] "Chinstrap Dream 52.8 20 205 4550 male 2008"     
## [307] "Chinstrap Dream 40.9 16.6 187 3200 female 2008" 
## [308] "Chinstrap Dream 54.2 20.8 201 4300 male 2008"   
## [309] "Chinstrap Dream 42.5 16.7 187 3350 female 2008" 
## [310] "Chinstrap Dream 51 18.8 203 4100 male 2008"     
## [311] "Chinstrap Dream 49.7 18.6 195 3600 male 2008"   
## [312] "Chinstrap Dream 47.5 16.8 199 3900 female 2008" 
## [313] "Chinstrap Dream 47.6 18.3 195 3850 female 2008" 
## [314] "Chinstrap Dream 52 20.7 210 4800 male 2008"     
## [315] "Chinstrap Dream 46.9 16.6 192 2700 female 2008" 
## [316] "Chinstrap Dream 53.5 19.9 205 4500 male 2008"   
## [317] "Chinstrap Dream 49 19.5 210 3950 male 2008"     
## [318] "Chinstrap Dream 46.2 17.5 187 3650 female 2008" 
## [319] "Chinstrap Dream 50.9 19.1 196 3550 male 2008"   
## [320] "Chinstrap Dream 45.5 17 196 3500 female 2008"   
## [321] "Chinstrap Dream 50.9 17.9 196 3675 female 2009" 
## [322] "Chinstrap Dream 50.8 18.5 201 4450 male 2009"   
## [323] "Chinstrap Dream 50.1 17.9 190 3400 female 2009" 
## [324] "Chinstrap Dream 49 19.6 212 4300 male 2009"     
## [325] "Chinstrap Dream 51.5 18.7 187 3250 male 2009"   
## [326] "Chinstrap Dream 49.8 17.3 198 3675 female 2009" 
## [327] "Chinstrap Dream 48.1 16.4 199 3325 female 2009" 
## [328] "Chinstrap Dream 51.4 19 201 3950 male 2009"     
## [329] "Chinstrap Dream 45.7 17.3 193 3600 female 2009" 
## [330] "Chinstrap Dream 50.7 19.7 203 4050 male 2009"   
## [331] "Chinstrap Dream 42.5 17.3 187 3350 female 2009" 
## [332] "Chinstrap Dream 52.2 18.8 197 3450 male 2009"   
## [333] "Chinstrap Dream 45.2 16.6 191 3250 female 2009" 
## [334] "Chinstrap Dream 49.3 19.9 203 4050 male 2009"   
## [335] "Chinstrap Dream 50.2 18.8 202 3800 male 2009"   
## [336] "Chinstrap Dream 45.6 19.4 194 3525 female 2009" 
## [337] "Chinstrap Dream 51.9 19.5 206 3950 male 2009"   
## [338] "Chinstrap Dream 46.8 16.5 189 3650 female 2009" 
## [339] "Chinstrap Dream 45.7 17 195 3650 female 2009"   
## [340] "Chinstrap Dream 55.8 19.8 207 4000 male 2009"   
## [341] "Chinstrap Dream 43.5 18.1 202 3400 female 2009" 
## [342] "Chinstrap Dream 49.6 18.2 193 3775 male 2009"   
## [343] "Chinstrap Dream 50.8 19 210 4100 male 2009"     
## [344] "Chinstrap Dream 50.2 18.7 198 3775 female 2009"
```

---
# Why use `reduce()`

This is one that I use a fair bit, but have a hard time coming up with good examples for.

The tidyverse makes it less needed, generally.

Still a good "tool" to have

---
# Wrap up
* Lots more to `{purrr}` but we've covered a lot
  
* Functional programming can *really* help your efficiency, and even if it slows you down initially, I'd recommend always striving toward it, because it will ultimately be a huge help.

.center[

### Questions?

]

--
If we have any time left - let's work on the homework

---
class: inverse-green middle
# Next time (fully remote)
## Functions
Beginning next class, the focus of the course will shift