Parallel Iterations

class: center, middle, inverse, title-slide

# Parallel Iterations
### Daniel Anderson
### Week 5, Class 1

---

layout: true

---

# Agenda
* Finish up slides from last week

* Discuss `map2_*` and `pmap_*`

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

---
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))
head(conditions)
```

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

```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] -8.89 11.59 -6.16 -7.1 3.81
##  $ : num [1:10] -5.967 -14.048 -0.242 -17.481 8.045 ...
##  $ : num [1:15] 5.103 -0.816 -5.772 -13.166 10.696 ...
##  $ : num [1:20] 3.3 -14.74 -4.39 7.39 -20.82 ...
##  $ : num [1:25] -5.64 17.8 -12.89 1.92 -1.03 ...
##  $ : num [1:30] -4.47 7.7 3.9 -3.88 10.41 ...
##  $ : num [1:35] 11.3211 5.2912 -0.0202 0.7237 2.5658 ...
##  $ : num [1:40] -5.36 14.02 1.73 -3.9 -6.73 ...
##  $ : num [1:45] 1.32 13.89 19.73 4.62 -16.47 ...
##  $ : num [1:50] -19.54 -1.02 -6.72 -8.03 12.07 ...
##  $ : num [1:55] -3.96 -2.31 -6.94 17.01 6.67 ...
##  $ : num [1:60] 0.169 6.871 -0.963 1.925 -6.045 ...
##  $ : num [1:65] 10.64 -16.511 -0.575 -6.33 -15.544 ...
##  $ : num [1:70] 4.84 -1.43 9.39 9.09 22.67 ...
##  $ : num [1:75] -7.264 0.494 -10.131 -15.417 -12.42 ...
##  $ : num [1:80] -4.49 5.16 -16.3 3.68 -5.99 ...
##  $ : num [1:85] -7 20.57 -3.59 -10.93 -6.08 ...
##  $ : num [1:90] 1.27 -4.96 7.39 10.66 -1.2 ...
##  $ : num [1:95] -0.562 -16.97 10.457 -0.644 -1.726 ...
##  $ : num [1:100] -7.589 0.757 -7.241 -19.506 -1.601 ...
##  $ : num [1:105] 8.71 -1.01 -18.79 -2.35 -7.49 ...
##  $ : num [1:110] 3.1764 -6.171 12.3225 -1.2728 -0.0135 ...
##  $ : num [1:115] 11.52 -13.41 -20.38 10.52 -5.49 ...
##  $ : num [1:120] 0.756 -3.554 3.401 10.524 4.408 ...
##  $ : num [1:125] -0.694 18.045 -8.461 -11.264 -0.76 ...
##  $ : num [1:130] -11.984 -0.623 6.513 8.497 -5.168 ...
##  $ : num [1:135] -21.403 -11.645 -9.269 -5.855 0.682 ...
##  $ : num [1:140] 1.07 24.46 -8.59 4.96 -7.9 ...
##  $ : num [1:145] -7.9 22.38 4.88 -17.46 -2.74 ...
##  $ : num [1:150] 10.382 1.299 5.264 9.303 0.339 ...
##  $ : num [1:5] -15.64 4 4.01 2.83 -10.27
##  $ : num [1:10] -8.106 2.349 0.694 -2.7 15.969 ...
##  $ : num [1:15] -11.2 4.66 -8.72 1.45 -11.5 ...
##  $ : num [1:20] 5.59 6.56 2.8 -11.09 -5.93 ...
##  $ : num [1:25] -1.21 -8.23 7.41 -12.76 -19.67 ...
##  $ : num [1:30] 8.88 7.94 -11.45 -14.62 -10.42 ...
##  $ : num [1:35] 1.784 -13.075 -0.478 -0.291 -11.102 ...
##  $ : num [1:40] -6.31 -10.05 16.66 14.96 -26.49 ...
##  $ : num [1:45] 8.83 -14.74 -8.99 -2.57 -1.22 ...
##  $ : num [1:50] -1.15 0.95 21.68 11.86 8.68 ...
##  $ : num [1:55] 5.36 5.78 -15.29 -25.96 35.88 ...
##  $ : num [1:60] -16.84 -5.44 23.98 -15.03 11.3 ...
##  $ : num [1:65] -0.488 8.281 -6.468 -2.943 -8.026 ...
##  $ : num [1:70] 2.93 19.19 -9.55 1.23 -14.18 ...
##  $ : num [1:75] -13.933 -8.524 -13.295 0.174 -1.812 ...
##  $ : num [1:80] -7.776 8.925 11.965 -9.403 0.907 ...
##  $ : num [1:85] 1 7.14 8.37 7.58 4.64 ...
##  $ : num [1:90] -8.472 -22.605 -0.167 7.205 3.682 ...
##  $ : num [1:95] -18.543 3.599 -4.158 0.354 16.679 ...
##  $ : num [1:100] -9.67 -1.67 -17.5 2.63 -8.83 ...
##  $ : num [1:105] -17.43 12.64 -26.39 -7.97 -7.1 ...
##  $ : num [1:110] -7.33 -5.85 2.23 11.65 2.53 ...
##  $ : num [1:115] -5.82 -2.02 -17.5 -15.41 1.88 ...
##  $ : num [1:120] 11.1 -16.58 -3.48 13.61 -14.24 ...
##  $ : num [1:125] 3.865 15.589 5.162 12.538 0.507 ...
##  $ : num [1:130] 16.49 17.13 -12.78 -2.82 9.04 ...
##  $ : num [1:135] 4.446 7.547 -0.351 15.577 14.497 ...
##  $ : num [1:140] -1.18 5.34 3.57 -1.66 -4.78 ...
##  $ : num [1:145] -11.33 10 -4.82 -17.41 -2.2 ...
##  $ : num [1:150] -10.78 -8.64 -13.54 10.11 2.79 ...
##  $ : num [1:5] -4.18 2.32 5.3 -21.26 -3.18
##  $ : num [1:10] -1.2 4.28 -11.33 6.81 2.87 ...
##  $ : num [1:15] 4.76 6.38 1.89 -1.25 -5.29 ...
##  $ : num [1:20] -1.42 11.05 -7.19 -7.92 3.51 ...
##  $ : num [1:25] 4.19 -6.71 11.5 -16.6 -6.23 ...
##  $ : num [1:30] -14.55 -2.81 27.39 -11.79 -0.21 ...
##  $ : num [1:35] -0.253 -0.412 2.388 -13.155 -13.82 ...
##  $ : num [1:40] -0.645 -6.149 5.5 4.469 2.458 ...
##  $ : num [1:45] -5.22 12.14 -16.84 -29.1 16.07 ...
##  $ : num [1:50] 4.395 -1.531 0.943 11.338 0.676 ...
##  $ : num [1:55] 11.37 0.406 16.455 1.067 10.846 ...
##  $ : num [1:60] -14.41 15.92 8.45 -3.95 -2.15 ...
##  $ : num [1:65] -20.1 -3.08 4.21 -14.95 -1.5 ...
##  $ : num [1:70] 17.62 -1.94 -27.02 12.75 -7.39 ...
##  $ : num [1:75] -6.24 -4.78 6.29 -17.02 1.21 ...
##  $ : num [1:80] 5.35 2.4 -11.44 -16.15 3.97 ...
##  $ : num [1:85] 13.676 11.162 20.833 0.483 7.093 ...
##  $ : num [1:90] 2.9 2.78 -19.45 -1.66 -4.31 ...
##  $ : num [1:95] -7.05 2.94 -16.43 -6.69 -7.67 ...
##  $ : num [1:100] 2.86 -11 -14.43 -12.48 -1.75 ...
##  $ : num [1:105] 8.283 -8.015 -0.538 1.435 -0.652 ...
##  $ : num [1:110] -19.57 -5.17 -0.63 -10.41 -9.36 ...
##  $ : num [1:115] -9.057 -4.536 -16.9 0.344 3.115 ...
##  $ : num [1:120] -6.55 -6.24 -8.16 -5.36 3.27 ...
##  $ : num [1:125] 7.04 -9.77 10.9 -12.86 -3.91 ...
##  $ : num [1:130] -5.82 -15.64 3.57 5.93 -1.12 ...
##  $ : num [1:135] 11.79 2.96 10.5 -10.76 20.74 ...
##  $ : num [1:140] 17.3 -13.55 -17.74 1.35 -1.3 ...
##  $ : num [1:145] -4.29 7.35 -1.4 6.87 -12.03 ...
##  $ : num [1:150] 22.11 3.6 -6.21 -3.42 -9.64 ...
##  $ : num [1:5] -7.7 -6.975 -0.467 -30.599 2.568
##  $ : num [1:10] 12.76 3.38 1.68 9.99 -7.66 ...
##  $ : num [1:15] -8.78 -6.38 10.45 -11.93 11.27 ...
##  $ : num [1:20] 12.53 9.32 -1.09 -1.62 -6.46 ...
##  $ : num [1:25] -4.93 3.37 -16.83 -5.97 11.93 ...
##  $ : num [1:30] 1.5 3.81 -12.67 11.05 17.08 ...
##  $ : num [1:35] -13.17 6 6.27 -15.47 -4.61 ...
##  $ : num [1:40] -23.094 -3.444 0.187 3.589 0.726 ...
##  $ : num [1:45] -5.35 -1.12 -4.32 -2.25 12.18 ...
##   [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 x 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 x 3
##        n    mu         sim
##    <dbl> <dbl>       <dbl>
##  1     5    -2   7.570682 
##  2     5    -2  -2.268110 
##  3     5    -2 -11.52502  
##  4     5    -2  -5.372831 
##  5     5    -2  -4.913946 
##  6    10    -2   0.5334139
##  7    10    -2   6.504093 
##  8    10    -2 -20.98906  
##  9    10    -2  13.81959  
## 10    10    -2 -16.19086  
## # … 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 x 3
##        n    mu        sim
##    <dbl> <dbl>      <dbl>
##  1     5    -2  13.95355 
##  2     5    -2  31.02931 
##  3     5    -2  -2.671605
##  4     5    -2  -9.867195
##  5     5    -2  -2.897235
##  6    10    -2 -19.70467 
##  7    10    -2  10.80861 
##  8    10    -2   7.158649
##  9    10    -2   2.255881
## 10    10    -2   5.924527
## # … 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 x 7
##    newspaper             circ2004 circ2013 pctchg_circ num_finals1990_2003 num_finals2004_2014
##    <chr>                    <dbl>    <dbl>       <int>               <int>               <int>
##  1 USA Today              2192098  1674306         -24                   1                   1
##  2 Wall Street Journal    2101017  2378827          13                  30                  20
##  3 New York Times         1119027  1865318          67                  55                  62
##  4 Los Angeles Times       983727   653868         -34                  44                  41
##  5 Washington Post         760034   474767         -38                  52                  48
##  6 New York Daily News     712671   516165         -28                   4                   2
##  7 New York Post           642844   500521         -22                   0                   0
##  8 Chicago Tribune         603315   414930         -31                  23                  15
##  9 San Jose Mercury News   558874   583998           4                   4                   2
## 10 Newsday                 553117   377744         -32                  12                   6
## # … with 40 more rows, and 1 more variable: 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 x 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
pulitzer%>%
	filter(newspaper == "Wall Street Journal") %>%
  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: Wall Street Journal",
       x = "Total number of winners",
       y = "") 
```

![](w5p1_files/figure-html/one-plot-pulitzer-1.png)

---
# Nest data

```r
pulitzer%>%
	group_by(newspaper) %>%
	nest()
```

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

---
# Look at a couple plots

.pull-left[

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

![](w5p1_files/figure-html/p1-1.png)

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

![](w5p1_files/figure-html/p1-2.png)
]

.pull-right[

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

![](w5p1_files/figure-html/p2-1.png)

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

![](w5p1_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 x 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))
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
```

```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] -1.536 -2.266 -1.205 -2.234 -0.944
##  $ : num [1:10] -0.841 -2.637 -2.427 -1.969 -2.696 ...
##  $ : num [1:15] -2.397 -1.581 -1.349 -1.856 0.587 ...
##  $ : num [1:20] -1.767 -3.026 -1.364 -2.747 -0.197 ...
##  $ : num [1:25] -2.56 -2.13 -1.33 -2.9 -3.86 ...
##  $ : num [1:30] -3.95 -1.88 -1.21 -1.5 -1.5 ...
##  $ : num [1:35] -1.56 -1.69 -4.14 -3.05 -1.93 ...
##  $ : num [1:40] -2.979 -1.432 -1.607 -1.905 -0.628 ...
##  $ : num [1:45] -1.829 -1.772 -1.432 -0.315 -2.345 ...
##  $ : num [1:50] -2.25 -1.93 -3.97 -1.59 -2.74 ...
##  $ : num [1:55] -3.562 -0.966 -1.718 -1.661 -1.809 ...
##  $ : num [1:60] -1.35 -1.15 -1.74 -2.3 -3.64 ...
##  $ : num [1:65] -2.47 -1.24 -2.43 -2.27 -2.34 ...
##  $ : num [1:70] -1.349 -2.941 -0.651 -2.054 -1.731 ...
##  $ : num [1:75] -2.262 -1.153 -2.463 -0.941 -2.026 ...
##  $ : num [1:80] -0.862 -1.633 -0.92 -1.897 -0.685 ...
##  $ : num [1:85] -2.82 -1.138 -0.755 -1.535 -2.639 ...
##  $ : num [1:90] -2.89 -0.924 -2.09 -1.869 -1.388 ...
##  $ : num [1:95] -3.61 -2.58 -2.46 -2.15 -2.22 ...
##  $ : num [1:100] -3.74 -1.3 -2.5 -2.19 -1.95 ...
##  $ : num [1:105] -2.52 -2.337 -0.567 -1.741 -3.519 ...
##  $ : num [1:110] -3.96 -2.1 -1.03 -2.82 -2.35 ...
##  $ : num [1:115] -0.819 -2.511 -2.104 0.287 -1.549 ...
##  $ : num [1:120] -1.39 -2.06 -1.83 -2.53 -2.2 ...
##  $ : num [1:125] -0.98651 -0.05067 -2.90015 0.00538 -2.95578 ...
##  $ : num [1:130] -1.129 -1.764 -0.516 -1.5 -3.097 ...
##  $ : num [1:135] -2.417 -2.952 -1.637 -2.34 -0.543 ...
##  $ : num [1:140] -2.4 -2.578 -1.755 -2.448 -0.388 ...
##  $ : num [1:145] -1.37 -2.65 -1.09 -1.27 -2.24 ...
##  $ : num [1:150] -0.807 -4.206 -3.005 -2.991 -1.578 ...
##  $ : num [1:5] -2.263 -1.338 -0.222 -1.286 -2.412
##  $ : num [1:10] -2.155 0.857 0.579 -0.713 -1.845 ...
##  $ : num [1:15] -0.896 -0.965 0.598 -0.374 -2.154 ...
##  $ : num [1:20] -2.078 -1.458 -1.439 -0.411 -2.641 ...
##  $ : num [1:25] -1.441 -1.34 0.311 -2.675 -1.602 ...
##  $ : num [1:30] -0.955 -1.28 -2.141 0.261 -2.227 ...
##  $ : num [1:35] -1.829 1.785 -0.205 -1.888 -2.248 ...
##  $ : num [1:40] -4.433 -0.982 -4.026 -1.432 -1.963 ...
##  $ : num [1:45] -2.429 -2.694 -1.752 -1.913 -0.509 ...
##  $ : num [1:50] -3.4296 -1.5497 -1.394 0.0127 -2.3191 ...
##  $ : num [1:55] -1.368 -0.958 -1.292 -2.09 0.502 ...
##  $ : num [1:60] -3.716 -2.458 -1.584 -1.055 -0.939 ...
##  $ : num [1:65] -1.709 -0.466 -2.235 -2.929 -0.658 ...
##  $ : num [1:70] -2.693 -0.761 -1.057 -0.342 0.492 ...
##  $ : num [1:75] -1.79 -2.4 -1.08 -1.79 -3.38 ...
##  $ : num [1:80] -2.2032 -1.8056 -2.5973 -1.5846 -0.0132 ...
##  $ : num [1:85] -2.4 -1.24 -1.78 -2.58 -2 ...
##  $ : num [1:90] -0.497 -1.768 -2.999 -1.153 -3.143 ...
##  $ : num [1:95] -1.902 -3.569 -0.919 0.559 -1.739 ...
##  $ : num [1:100] -2.62 -1.88 -3 -1.68 -1.14 ...
##  $ : num [1:105] -2.06 -1.75 -1.34 -2.44 -1.79 ...
##  $ : num [1:110] 0.189 -3.496 -2.917 -3.467 -2.244 ...
##  $ : num [1:115] -1.071 -1.245 -3.165 -0.424 -2.995 ...
##  $ : num [1:120] -0.82 -1.886 -2.771 -2.312 -0.653 ...
##  $ : num [1:125] -1.09 -1.82 -1.25 -2.06 -1.21 ...
##  $ : num [1:130] -2.161 -3.677 -0.812 -1.201 -0.453 ...
##  $ : num [1:135] -4.855 -0.877 -1.915 -3.513 -1.959 ...
##  $ : num [1:140] -1.353 -0.682 -1.759 -2.018 -0.129 ...
##  $ : num [1:145] -2.56 -1.59 -2.67 -3.09 -1.71 ...
##  $ : num [1:150] -1.65 -1.56 -1.9 -2.06 -1.36 ...
##  $ : num [1:5] -1.26 -1.26 -2.27 -1.97 -0.6
##  $ : num [1:10] -2.225 -1.227 -1.044 -0.568 -1.137 ...
##  $ : num [1:15] -1.81 -2.06 -2.22 -1.35 -2.94 ...
##  $ : num [1:20] -2.26 -1.72 -1.47 -1.74 -2.59 ...
##  $ : num [1:25] -1.36 -1.29 -3.56 -2.96 -3.11 ...
##  $ : num [1:30] 0.0545 -0.9368 -2.6742 -0.2815 -0.112 ...
##  $ : num [1:35] -1.404 -0.526 -3.585 -1.257 -2.43 ...
##  $ : num [1:40] -2.51 -1.53 -1.49 -1.68 -1.45 ...
##  $ : num [1:45] -0.806 -1.678 -1.745 -2.101 -0.455 ...
##  $ : num [1:50] -0.388 0.121 -2.335 -2.316 0.194 ...
##  $ : num [1:55] -1.65 -2.55 -1.92 -2.17 -4.24 ...
##  $ : num [1:60] -1.917 -0.577 -1.115 -2.226 -2.317 ...
##  $ : num [1:65] 0.482 -0.792 0.225 -2.623 -2.502 ...
##  $ : num [1:70] -0.768 -0.491 -0.996 -1.195 -1.094 ...
##  $ : num [1:75] -1.434 -2.759 -2.471 -1.138 0.346 ...
##  $ : num [1:80] 0.301 -1.918 -0.6 -2.349 -0.694 ...
##  $ : num [1:85] -2.56 -1.51 -1.73 -1.43 -1.74 ...
##  $ : num [1:90] -0.843 -1.781 -2.555 -1.108 -1.852 ...
##  $ : num [1:95] -1.898 -1.193 -2.47 -2.968 -0.565 ...
##  $ : num [1:100] -2.6637 -0.0529 -2.4872 -1.6588 -1.3545 ...
##  $ : num [1:105] -2.82 -2.25 -3 -1.91 -2.15 ...
##  $ : num [1:110] -1.478 -1.774 -0.072 -2.056 -2.089 ...
##  $ : num [1:115] -1.79 -0.582 -0.232 -2.57 -1.586 ...
##  $ : num [1:120] -2.04 -1.806 -0.234 -0.287 -1.114 ...
##  $ : num [1:125] -0.2575 -0.5272 -2.1321 -1.2277 0.0714 ...
##  $ : num [1:130] -2.1145 -0.9458 -1.3888 -2.4251 0.0279 ...
##  $ : num [1:135] -0.523 -1.32 -1.951 -1.915 0.564 ...
##  $ : num [1:140] -0.208 -1.821 -1.85 -2.347 -0.99 ...
##  $ : num [1:145] -1.654 -1.422 -1.579 -1.979 0.136 ...
##  $ : num [1:150] -0.534 -1.139 -2.15 -0.905 -1.074 ...
##  $ : num [1:5] -1.783 -0.155 -0.386 -1.179 0.557
##  $ : num [1:10] 1.26 -1.07 -2.07 -1.57 -2.47 ...
##  $ : num [1:15] -0.786 -0.522 0.468 -1.124 -0.967 ...
##  $ : num [1:20] 0.0891 -1.575 -1.7176 -1.871 -2.6302 ...
##  $ : num [1:25] -3.389 -0.322 -1.518 -0.846 -0.735 ...
##  $ : num [1:30] 0.79 -1.901 -2.854 -0.12 -0.397 ...
##  $ : num [1:35] 0.259 -0.37 -1.102 -1.955 -1.142 ...
##  $ : num [1:40] -0.806 -2.416 -1.666 -1.395 -2.467 ...
##  $ : num [1:45] -1.868 -0.112 -0.217 -1.265 -2.04 ...
##   [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] -2.15 -2.45 -2.93 -3.36 -1.7
##  $ : num [1:10] -1.777 -0.882 -2.075 -2.826 -2.757 ...
##  $ : num [1:15] -1.52044 -1.45154 -0.30679 -0.16667 -0.00603 ...
##  $ : num [1:20] -0.627 -1.191 -0.71 -2.197 -1.363 ...
##  $ : num [1:25] -1.7 -1.83 -1.86 -2.42 -2.94 ...
##  $ : num [1:30] -1.32 -2.53 -1.6 -1.02 -3.23 ...
##  $ : num [1:35] 0.0321 -0.798 -1.0541 -1.5625 -0.2858 ...
##  $ : num [1:40] -2.44 -1.53 -2.03 -2.34 -2.6 ...
##  $ : num [1:45] -2.22 -2.16 -3.81 -2.88 -0.73 ...
##  $ : num [1:50] -1.81 -3.08 -1.15 -2.61 -2.3 ...
##  $ : num [1:55] -1.87 -2.224 -3.966 -0.403 -2.465 ...
##  $ : num [1:60] -2.86 -2.27 -2.95 -2.23 -3.99 ...
##  $ : num [1:65] 0.54 -1.2 -0.74 -2.49 -2.55 ...
##  $ : num [1:70] -0.342 -3.39 -2.711 0.766 -0.39 ...
##  $ : num [1:75] -1.81 -1.652 -2.138 -0.816 -2.304 ...
##  $ : num [1:80] -1.747 -2.63 -1.113 -2.78 -0.929 ...
##  $ : num [1:85] -2.21 -1.85 -2.64 -0.85 -1.15 ...
##  $ : num [1:90] -2.881 -0.366 -0.616 -2.621 -2.1 ...
##  $ : num [1:95] -0.416 -2.188 -2.981 -1.865 -2.759 ...
##  $ : num [1:100] -1.1 -1.33 -2.38 -3.78 -2.04 ...
##  $ : num [1:105] -1.52 -3.03 -1.24 -2.55 -1.31 ...
##  $ : num [1:110] -1.84 -1.99 -2.39 -3.7 -2.22 ...
##  $ : num [1:115] -2.61 -1.77 -1.53 -1.49 -2.08 ...
##  $ : num [1:120] -0.144 -2.653 -2.151 -2.956 -2.816 ...
##  $ : num [1:125] -0.713 -1.665 -1.942 -0.271 -3.397 ...
##  $ : num [1:130] -4.212 -2.855 -0.891 -1.408 -2.459 ...
##  $ : num [1:135] -2.13 -3.3 -2.81 -1.45 -1.08 ...
##  $ : num [1:140] -0.885 -1.899 -1.883 -2.05 -2.224 ...
##  $ : num [1:145] -1.568 -2.834 -1.103 -0.573 -0.326 ...
##  $ : num [1:150] -2.769 -3.933 -2.555 -2.899 -0.652 ...
##  $ : num [1:5] -2.15 -1.455 -0.923 -2.791 -1.041
##  $ : num [1:10] -0.785 -3.017 -1.327 -2.766 -2.03 ...
##  $ : num [1:15] -0.594 -4.592 -2.286 -2.583 -2.246 ...
##  $ : num [1:20] -1.821 -0.527 -1.11 -2.759 -3.365 ...
##  $ : num [1:25] 1.185 -3.176 -1.88 -0.506 -1.392 ...
##  $ : num [1:30] -0.7988 -0.0641 -1.4638 -1.6671 -3.3243 ...
##  $ : num [1:35] -3.35 -4.02 -1.15 -1.66 -3.27 ...
##  $ : num [1:40] -1.625 -3.416 -2.233 -0.807 -1.334 ...
##  $ : num [1:45] -2.36 -3.14 -2.76 -2 -3.15 ...
##  $ : num [1:50] -2.06 -1.3 -1.75 -2.38 -1.2 ...
##  $ : num [1:55] -0.916 -2.838 -3.182 -2.224 1.434 ...
##  $ : num [1:60] -2.63 -1.46 -3.83 -2.16 -1.79 ...
##  $ : num [1:65] -1.15 -2.72 -2.21 -1.86 -2.05 ...
##  $ : num [1:70] -1.19 -2.71 -2.11 -1.3 -1.53 ...
##  $ : num [1:75] -1.489 -1.233 -0.523 -3.204 -1.966 ...
##  $ : num [1:80] -2.38 -2.283 -0.325 -1.147 -2.292 ...
##  $ : num [1:85] -1.71 -1.63 -1.29 -1.17 -2.08 ...
##  $ : num [1:90] -3.48 -1.1 -1.89 -1.68 -3.77 ...
##  $ : num [1:95] -1.849 0.0107 -2.6893 -2.5238 -0.0639 ...
##  $ : num [1:100] -1.694 -0.288 -1.509 -1.837 -2.627 ...
##  $ : num [1:105] -2.43 -1.82 -1.37 -2.04 -1.75 ...
##  $ : num [1:110] -1.6 -1.97 -2.35 -2.4 -3.82 ...
##  $ : num [1:115] -2.38 -1.11 -2.35 -3.95 -2.17 ...
##  $ : num [1:120] -2.354 -3.289 -1.679 -1.972 -0.384 ...
##  $ : num [1:125] -1.79 -3.03 -2.01 -1.63 -1.98 ...
##  $ : num [1:130] -1.04 -1.63 -1.75 -1.75 -1.76 ...
##  $ : num [1:135] -1.02 -2.44 -1.24 -1.82 -2.9 ...
##  $ : num [1:140] -1.947 -2.696 -1.286 -0.904 -0.126 ...
##  $ : num [1:145] -3.4 -1.82 -1.34 -2.3 -1.56 ...
##  $ : num [1:150] -2.07 -2.67 -2.49 -2.13 -2.04 ...
##  $ : num [1:5] -0.0561 -0.7266 -0.989 -1.0046 -3.0633
##  $ : num [1:10] -2.217 -1.59 -2.841 -0.508 -1.318 ...
##  $ : num [1:15] -2.38 -1.84 -2.28 -1.31 -1.88 ...
##  $ : num [1:20] -0.987 -3.491 -1.836 -3.222 -2.567 ...
##  $ : num [1:25] -0.276 -0.783 0.238 -0.226 -0.978 ...
##  $ : num [1:30] -1.59 -2.08 -2.84 -1.74 -1.51 ...
##  $ : num [1:35] -2.21 -3.17 -2.23 -1.94 -1.81 ...
##  $ : num [1:40] -0.4 -3.42 -0.924 -0.348 -1.034 ...
##  $ : num [1:45] -1.71 1.07 1.27 -1.54 -1.88 ...
##  $ : num [1:50] -2.66 -1.044 -2.125 -0.914 -2.567 ...
##  $ : num [1:55] -3.11 -1.768 -2.062 -1.028 -0.546 ...
##  $ : num [1:60] -0.883 -0.711 -4.1 -1.876 -2.073 ...
##  $ : num [1:65] -3.14513 -2.7894 -1.86605 0.00441 -0.17555 ...
##  $ : num [1:70] -1.466 -1.795 -0.643 -1.793 -2.022 ...
##  $ : num [1:75] -0.513 -3.371 -2.01 -0.451 -1.495 ...
##  $ : num [1:80] -1.08 -2.33 -1.28 -3.05 -1.09 ...
##  $ : num [1:85] -0.50768 -2.30933 -0.00181 -1.80426 -0.44882 ...
##  $ : num [1:90] -1.534 -0.365 -2.928 -1.573 -3.251 ...
##  $ : num [1:95] -0.768 -1.619 -1.675 -1.523 -3.053 ...
##  $ : num [1:100] -3.19 -1.14 -0.29 -1.06 -2.91 ...
##  $ : num [1:105] -0.8324 -2.8997 -2.1596 -2.0736 -0.0653 ...
##  $ : num [1:110] -2.41 -1.17 -1.18 -3.23 -1.41 ...
##  $ : num [1:115] -1.41 -1.59 -1.53 -2.86 -0.78 ...
##  $ : num [1:120] -0.995 -0.544 -1.598 -2.371 0.511 ...
##  $ : num [1:125] -2.35 -1.29 -2.55 -2.75 -1.29 ...
##  $ : num [1:130] -4.294 0.355 -1.556 0.801 -0.822 ...
##  $ : num [1:135] -2.843 -1.701 -0.396 -0.291 -1.538 ...
##  $ : num [1:140] -2.283 -0.665 -1.567 -3.486 -2.585 ...
##  $ : num [1:145] -0.0606 -0.2198 -1.5754 -2.0383 -2.4226 ...
##  $ : num [1:150] -1.752 -2.476 -1.458 -1.395 0.188 ...
##  $ : num [1:5] 0.948 1.077 -0.292 0.306 -1.144
##  $ : num [1:10] -0.481 -2.02 -1.348 -1.668 -0.986 ...
##  $ : num [1:15] -1.196 1.02 -0.548 -0.916 -0.943 ...
##  $ : num [1:20] -1.504 -1.351 -0.906 -2.073 -0.998 ...
##  $ : num [1:25] -1.94 -1.8 -2.18 -2.4 -3 ...
##  $ : num [1:30] -0.651 -0.9 -0.406 -2.767 -0.995 ...
##  $ : num [1:35] -0.212 -2.165 -0.775 -0.564 -0.582 ...
##  $ : num [1:40] -0.22 -0.982 -0.99 -0.411 -0.471 ...
##  $ : num [1:45] 0.247 -2.077 -0.266 -0.624 0.473 ...
##   [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] -2.28 -1.9 -2.7 -3.14 -1.78
##  $ : num [1:10] -1.21 -3.23 -0.97 -1.28 -2.06 ...
##  $ : num [1:15] -2.07 -1.24 -1.65 -0.27 -1.99 ...
##  $ : num [1:20] -1.772 -2.843 -0.871 -1.46 -0.763 ...
##  $ : num [1:25] 0.685 -0.119 -2.856 -0.688 -1.409 ...
##  $ : num [1:30] -1.743 -2.922 -1.887 -2.03 -0.667 ...
##  $ : num [1:35] -0.723 -1.828 -2.902 -0.284 -0.931 ...
##  $ : num [1:40] -2.258 -3.35 -0.359 -0.794 -1.552 ...
##  $ : num [1:45] -1.48 -1.92 -2.06 -1.99 -2.11 ...
##  $ : num [1:50] -1.77 -1.84 -1.22 -1.44 -1.53 ...
##  $ : num [1:55] -2.09 -2.6 -2.34 -3.12 -2.65 ...
##  $ : num [1:60] -0.874 -0.363 -1.584 -1.647 -0.697 ...
##  $ : num [1:65] -2.614 0.0316 -2.2096 -1.3851 -2.8877 ...
##  $ : num [1:70] -1.833 -3.959 -1.424 -0.366 -3.91 ...
##  $ : num [1:75] -2.525 -3.851 -2.547 -0.777 -1.011 ...
##  $ : num [1:80] -1.974 -0.704 -3.198 -2.399 -2.608 ...
##  $ : num [1:85] -3.173 -2.2 -2.965 -0.122 -2.95 ...
##  $ : num [1:90] -1.13 -3.17 -1.88 -1.2 -3.8 ...
##  $ : num [1:95] -1.862 -2.626 -0.544 -2.125 -2.76 ...
##  $ : num [1:100] -1.386 -1.704 1.781 -0.196 -0.76 ...
##  $ : num [1:105] -2.105 -2.706 -0.952 -1.631 -0.158 ...
##  $ : num [1:110] -1.68 -2.81 -3.05 -1.12 -2.03 ...
##  $ : num [1:115] -1.1 -1.73 -3.13 -2.96 -1.22 ...
##  $ : num [1:120] -1.4 -2.73 -2.03 -2.72 -1.74 ...
##  $ : num [1:125] -1.89 -1.52 -3.05 -1.38 -1.34 ...
##  $ : num [1:130] -2.283 -1.87 -0.643 -3.037 -2.963 ...
##  $ : num [1:135] -3.12 -1.28 -2.57 -2.12 -4.4 ...
##  $ : num [1:140] -1.536 0.517 -1.5 -1.554 -3.014 ...
##  $ : num [1:145] -2.711 -4.006 -2.072 -0.645 -3.781 ...
##  $ : num [1:150] -0.577 -1.987 -2.5 -1.292 -1.443 ...
##  $ : num [1:5] -2.178 -2.156 -0.833 -0.68 -3.071
##  $ : num [1:10] -2.572 -2.861 -2.25 -0.192 -0.631 ...
##  $ : num [1:15] -2.4465 -1.2752 0.0537 -3.473 -3.5255 ...
##  $ : num [1:20] -2.311 -1.859 -1.169 -0.695 -0.355 ...
##  $ : num [1:25] -2.249 -1.955 -3.404 -0.827 -1.223 ...
##  $ : num [1:30] -1.084 -0.819 -2.507 -0.789 -0.759 ...
##  $ : num [1:35] -2.541 -0.313 -3.554 -2.763 -3.561 ...
##  $ : num [1:40] -1.331 -1.798 -1.66 -1.539 -0.686 ...
##  $ : num [1:45] -2.027 -2.526 -0.529 -3.618 -1.236 ...
##  $ : num [1:50] -2.07 -2.05 -1.5 -3.21 -2.03 ...
##  $ : num [1:55] -1.22 -1.924 -1.446 -1.52 -0.958 ...
##  $ : num [1:60] -0.385 -2.353 -3.338 -2.524 -0.507 ...
##  $ : num [1:65] -1.84 -2.296 -1.053 -0.787 -1.104 ...
##  $ : num [1:70] -1.003 -2.191 -1.335 0.158 -2.166 ...
##  $ : num [1:75] -3.445 -0.219 -2.692 -0.65 -2.058 ...
##  $ : num [1:80] -1.504 -0.545 -2.231 -0.995 -1.946 ...
##  $ : num [1:85] -3.095 -2.434 -1.092 -1.478 -0.942 ...
##  $ : num [1:90] -0.406 0.405 -2.241 -3.095 -0.688 ...
##  $ : num [1:95] -2.082 -2.501 -2.125 -3.098 -0.545 ...
##  $ : num [1:100] -1.99 -2.18 -2.76 -1.23 -1.3 ...
##  $ : num [1:105] -0.623 -2.443 -2.883 -0.733 -1.265 ...
##  $ : num [1:110] -1.79 -2.23 -1.67 -1.55 -1.65 ...
##  $ : num [1:115] -0.494 -2.707 -1.158 0.198 -1.645 ...
##  $ : num [1:120] -1.702 -1.82 -0.179 -2.122 -1.1 ...
##  $ : num [1:125] -2.89 -1.72 -2.62 -1.56 -1.34 ...
##  $ : num [1:130] -0.707 -2.066 -2.257 -2.559 -1.533 ...
##  $ : num [1:135] -1.18 -1.2 -2.54 -1.17 -1.15 ...
##  $ : num [1:140] -1.019 -2.102 -0.813 -1.88 -1.331 ...
##  $ : num [1:145] -2.88 -2.42 -3.67 -2.83 -1.69 ...
##  $ : num [1:150] -0.95403 -2.71064 -0.00388 -2.28287 -1.99366 ...
##  $ : num [1:5] -2.299 -0.449 -1.123 -0.322 -0.712
##  $ : num [1:10] -2.422 -2.682 -0.663 -1.59 -0.312 ...
##  $ : num [1:15] -2.198 0.276 -0.194 0.243 -3.398 ...
##  $ : num [1:20] -2.75 -1.3 -2.59 -1.54 -2.53 ...
##  $ : num [1:25] -2.46 -1.37 -2.98 -2.17 -2.64 ...
##  $ : num [1:30] -1.06 0.795 -1.722 -2.103 -1.231 ...
##  $ : num [1:35] -2.395 -2.373 -0.539 -2.531 -1.835 ...
##  $ : num [1:40] -0.507 -1.933 -2.315 -1.173 -1.324 ...
##  $ : num [1:45] -3.574 -1.158 -1.389 -3.243 -0.358 ...
##  $ : num [1:50] 0.0716 -1.3999 -0.297 -2.9978 -1.5942 ...
##  $ : num [1:55] -1.662 -1.894 -0.745 -4.105 -0.771 ...
##  $ : num [1:60] -2.72 -3.065 -0.775 -1.59 -1.514 ...
##  $ : num [1:65] -2.33 -1.55 -2.45 -2.9 -3.22 ...
##  $ : num [1:70] -1.6414 -2.4184 -3.1205 -1.1381 -0.0615 ...
##  $ : num [1:75] -1.29 -2.19 -1.86 -2.06 -2.54 ...
##  $ : num [1:80] -0.973 -1.99 -1.385 -0.765 -2.101 ...
##  $ : num [1:85] -1.487 -1.185 -1.066 -0.859 -1.014 ...
##  $ : num [1:90] -3.153 -0.197 -1.399 0.484 -1.939 ...
##  $ : num [1:95] -0.694 -0.905 -0.984 0.813 -1.924 ...
##  $ : num [1:100] -1.617 -1.874 -2.269 -0.855 -2.222 ...
##  $ : num [1:105] -1.039 -1.863 -3.459 -1.276 0.671 ...
##  $ : num [1:110] -2.133 -3.227 0.253 -1.422 -0.769 ...
##  $ : num [1:115] -2.301 -2.512 -0.254 -1.807 -1.687 ...
##  $ : num [1:120] -3.052 -2.437 0.492 -1.163 -1.715 ...
##  $ : num [1:125] -2.382 -2.207 -0.729 -1.653 -0.544 ...
##  $ : num [1:130] -1.952 1.473 -2.208 -0.636 -0.921 ...
##  $ : num [1:135] -0.168 -2.362 -2.218 -2.683 -0.998 ...
##  $ : num [1:140] 0.579 -3.581 -2.371 -1.968 -1.829 ...
##  $ : num [1:145] -0.9203 -1.2718 -2.3872 -2.2341 0.0542 ...
##  $ : num [1:150] -0.802 -0.22 -1.189 -2.149 -2.411 ...
##  $ : num [1:5] -0.441 -2.207 -1.598 -1.914 -1.844
##  $ : num [1:10] -0.583 -0.916 -0.504 -0.158 1.169 ...
##  $ : num [1:15] -3.725 -2.738 -1.077 -0.886 -0.747 ...
##  $ : num [1:20] -1.288 -1.995 -2.593 -1.747 0.514 ...
##  $ : num [1:25] -0.691 -2.092 -1.742 -2.113 -0.812 ...
##  $ : num [1:30] -0.891 0.793 -0.14 -1.519 -0.837 ...
##  $ : num [1:35] -1.966 -1.311 -0.326 -1.336 -0.674 ...
##  $ : num [1:40] -1.98 -1.78 -1.82 -1.76 -3.3 ...
##  $ : num [1:45] 0.236 -1.342 -1.601 -1.899 -1.537 ...
##   [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 x 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 x 4
##        n    mu    sd        sim
##    <dbl> <dbl> <dbl>      <dbl>
##  1     5    -2     1 -0.9769357
##  2     5    -2     1 -1.026245 
##  3     5    -2     1 -1.983785 
##  4     5    -2     1 -1.262065 
##  5     5    -2     1 -0.3616705
##  6    10    -2     1 -1.136676 
##  7    10    -2     1 -1.665104 
##  8    10    -2     1 -3.062858 
##  9    10    -2     1 -4.412271 
## 10    10    -2     1 -0.9440350
## # … 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 x 4
##        n    mu    sd        sim
##    <dbl> <dbl> <dbl>      <dbl>
##  1     5    -2     1 -4.246447 
##  2     5    -2     1 -3.394033 
##  3     5    -2     1 -2.688046 
##  4     5    -2     1 -4.004150 
##  5     5    -2     1 -2.636363 
##  6    10    -2     1 -0.5402081
##  7    10    -2     1 -2.075118 
##  8    10    -2     1 -1.927610 
##  9    10    -2     1 -2.871188 
## 10    10    -2     1 -0.9080542
## # … 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 x 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 x 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
tmp <- pulitzer%>%
	filter(newspaper == "Wall Street Journal")

ggplot(tmp, 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(tmp$label)
  )
```

---
class: middle

![](w5p1_files/figure-html/plot-cap1-eval-1.png)

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

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

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

---
# Produce plots

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

![](w5p1_files/figure-html/p3-1.png)

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

![](w5p1_files/figure-html/p3-2.png)
]

.pull-right[

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

![](w5p1_files/figure-html/p4-1.png)

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

![](w5p1_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 x 4
## # Rowwise:  newspaper, label
##    newspaper                    label                                            data plots 
##    <chr>                        <glue>                             <list<tibble[,3]>> <list>
##  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 next week.

--
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/2020-21/c3-fp-2021/plots/pulitzers/usa-today.png"                   
##  [2] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/wall-street-journal.png"         
##  [3] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/new-york-times.png"              
##  [4] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/los-angeles-times.png"           
##  [5] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/washington-post.png"             
##  [6] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/new-york-daily-news.png"         
##  [7] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/new-york-post.png"               
##  [8] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/chicago-tribune.png"             
##  [9] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/san-jose-mercury-news.png"       
## [10] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/newsday.png"                     
## [11] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/houston-chronicle.png"           
## [12] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/dallas-morning-news.png"         
## [13] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/san-francisco-chronicle.png"     
## [14] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/arizona-republic.png"            
## [15] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/chicago-sun-times.png"           
## [16] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/boston-globe.png"                
## [17] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/atlanta-journal-constitution.png"
## [18] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/newark-star-ledger.png"          
## [19] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/detroit-free-press.png"          
## [20] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/minneapolis-star-tribune.png"    
## [21] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/philadelphia-inquirer.png"       
## [22] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/cleveland-plain-dealer.png"      
## [23] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/san-diego-union-tribune.png"     
## [24] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/tampa-bay-times.png"             
## [25] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/denver-post.png"                 
## [26] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/rocky-mountain-news.png"         
## [27] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/oregonian.png"                   
## [28] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/miami-herald.png"                
## [29] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/orange-county-register.png"      
## [30] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/sacramento-bee.png"              
## [31] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/st.-louis-post-dispatch.png"     
## [32] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/baltimore-sun.png"               
## [33] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/kansas-city-star.png"            
## [34] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/detroit-news.png"                
## [35] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/orlando-sentinel.png"            
## [36] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/south-florida-sun-sentinel.png"  
## [37] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/new-orleans-times-picayune.png"  
## [38] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/columbus-dispatch.png"           
## [39] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/indianapolis-star.png"           
## [40] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/san-antonio-express-news.png"    
## [41] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/pittsburgh-post-gazette.png"     
## [42] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/milwaukee-journal-sentinel.png"  
## [43] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/tampa-tribune.png"               
## [44] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/fort-woth-star-telegram.png"     
## [45] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/boston-herald.png"               
## [46] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/seattle-times.png"               
## [47] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/charlotte-observer.png"          
## [48] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/daily-oklahoman.png"             
## [49] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/plots/pulitzers/louisville-courier-journal.png"  
## [50] "/Users/daniel/Teaching/data_sci_specialization/2020-21/c3-fp-2021/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 x 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 x 0
```

---
# 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-green middle
# Next time
### Looping variants