concision

# concision
### Gina Reynolds

---

## In human language shorter is often better.

## "If I had more time I would have written a shorter letter"

## Tips:

- Avoid prepositional phrases:  the baker's daughter, not: the daughter of the baker

---

# DRY?

## Don't Repeat Yourself

---

## We've been intentionally verbose

## We have repeated ourselves...

## Training *your neural net* with lots of data/experience/practice

Lessons from AI: neural networks do better at new tasks when they have lots of data/experience/practice

---

# But ...

```r
gapminder %>% 
  filter(year == 2002) %>% 
  ggplot() +
  aes(x = gdpPercap) +
  aes(y = lifeExp) +
  geom_point() +
  aes(color = continent) +
  labs(x = "GDP per Capita (US$)") +
  labs(y = "Life Expectancy")
```

---

# Can be ...

```r
gapminder %>% 
  filter(year == 2002) %>% 
  ggplot() +
* aes(x = gdpPercap,
*     y = lifeExp,
*     color = continent) +
  geom_point() +
* labs(x = "GDP per Capita (US$)",
*      y = "Life Expectancy")
```

---

###  Another quick note on style (not related to concision)

### Non-mainstream, but fully supported (ggplot2: Elegant Graphics for Data Analysis 2009, uses it)

```r
gapminder %>% 
  filter(year == 2002) %>% 
  ggplot() +
* aes(x = gdpPercap,
*     y = lifeExp,
*     color = continent) +
  geom_point() 
```

---

### Mainstream, (ggplot2: Elegant Graphics for Data Analysis 2016 does **not** use + aes())

```r
gapminder %>% 
  filter(year == 2002) %>% 
* ggplot(aes(x = gdpPercap,
*            y = lifeExp,
*            color = continent)) +
  geom_point() 
```

### I personally prefer unnested, sequential declarations

### Some really bristle at it - it's likely to be unfamiliar - not the style of mainstream training resources.

---

# ggplot DRY strategies

---

# last_plot()

Add stuff to existing, full, plot to produce second version

---

# g1
# g2 <- g1 + facet_wrap(~ continent)

## Add stuff to existing plot to produce second version, save both as objects

---

# g1
# g1 %+% data2

## Changes out data with new pipe operator %+%

---

```r
*library(gapminder)
```
]
 
.right-output-last_plot-auto[

]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
*gapminder
```
]
 
.right-output-last_plot-auto[

```
  # A tibble: 1,704 x 6
     country     continent  year lifeExp      pop gdpPercap
     <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
   1 Afghanistan Asia       1952    28.8  8425333      779.
   2 Afghanistan Asia       1957    30.3  9240934      821.
   3 Afghanistan Asia       1962    32.0 10267083      853.
   4 Afghanistan Asia       1967    34.0 11537966      836.
   5 Afghanistan Asia       1972    36.1 13079460      740.
   6 Afghanistan Asia       1977    38.4 14880372      786.
   7 Afghanistan Asia       1982    39.9 12881816      978.
   8 Afghanistan Asia       1987    40.8 13867957      852.
   9 Afghanistan Asia       1992    41.7 16317921      649.
  10 Afghanistan Asia       1997    41.8 22227415      635.
  # … with 1,694 more rows
```
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
* filter(year == 2002)
```
]
 
.right-output-last_plot-auto[

```
  # A tibble: 142 x 6
     country     continent  year lifeExp       pop gdpPercap
     <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
   1 Afghanistan Asia       2002    42.1  25268405      727.
   2 Albania     Europe     2002    75.7   3508512     4604.
   3 Algeria     Africa     2002    71.0  31287142     5288.
   4 Angola      Africa     2002    41.0  10866106     2773.
   5 Argentina   Americas   2002    74.3  38331121     8798.
   6 Australia   Oceania    2002    80.4  19546792    30688.
   7 Austria     Europe     2002    79.0   8148312    32418.
   8 Bahrain     Asia       2002    74.8    656397    23404.
   9 Bangladesh  Asia       2002    62.0 135656790     1136.
  10 Belgium     Europe     2002    78.3  10311970    30486.
  # … with 132 more rows
```
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
* ggplot()
```
]
 
.right-output-last_plot-auto[
<img src="concision_files/figure-html/last_plot_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
* aes(x = gdpPercap)
```
]
 
.right-output-last_plot-auto[
<img src="concision_files/figure-html/last_plot_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
* aes(y = lifeExp)
```
]
 
.right-output-last_plot-auto[
<img src="concision_files/figure-html/last_plot_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
* geom_point()
```
]
 
.right-output-last_plot-auto[
<img src="concision_files/figure-html/last_plot_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
* aes(color = continent)
```
]
 
.right-output-last_plot-auto[
<img src="concision_files/figure-html/last_plot_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent)

# Plot2
*last_plot()
```
]
 
.right-output-last_plot-auto[
<img src="concision_files/figure-html/last_plot_auto_9_output-1.png" width="100%" /><img src="concision_files/figure-html/last_plot_auto_9_output-2.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent)

# Plot2
last_plot() +  
* facet_wrap(~continent)
```
]
 
.right-output-last_plot-auto[
<img src="concision_files/figure-html/last_plot_auto_10_output-1.png" width="100%" /><img src="concision_files/figure-html/last_plot_auto_10_output-2.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent)

# Plot2
last_plot() +  
  facet_wrap(~continent) +  
* theme(legend.position = "none")
```
]
 
.right-output-last_plot-auto[
<img src="concision_files/figure-html/last_plot_auto_11_output-1.png" width="100%" /><img src="concision_files/figure-html/last_plot_auto_11_output-2.png" width="100%" />
]

---

```r
*library(gapminder)
```
]
 
.right-output-object-auto[

]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
*gapminder
```
]
 
.right-output-object-auto[

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
* filter(year == 2002)
```
]
 
.right-output-object-auto[

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
* ggplot()
```
]
 
.right-output-object-auto[
<img src="concision_files/figure-html/object_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
* aes(x = gdpPercap)
```
]
 
.right-output-object-auto[
<img src="concision_files/figure-html/object_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
* aes(y = lifeExp)
```
]
 
.right-output-object-auto[
<img src="concision_files/figure-html/object_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
* geom_point()
```
]
 
.right-output-object-auto[
<img src="concision_files/figure-html/object_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
* aes(color = continent)
```
]
 
.right-output-object-auto[
<img src="concision_files/figure-html/object_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) ->  
*g1
```
]
 
.right-output-object-auto[

]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) ->  
g1

# Plot2
*g1
```
]
 
.right-output-object-auto[
<img src="concision_files/figure-html/object_auto_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) ->  
g1

# Plot2
g1 +  
* facet_wrap(~continent)
```
]
 
.right-output-object-auto[
<img src="concision_files/figure-html/object_auto_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) ->  
g1

# Plot2
g1 +  
  facet_wrap(~continent) +  
* theme()
```
]
 
.right-output-object-auto[
<img src="concision_files/figure-html/object_auto_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
library(gapminder)

# Plot1
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) ->  
g1

# Plot2
g1 +  
  facet_wrap(~continent) +  
  theme() ->  
*g2
```
]
 
.right-output-object-auto[

]

---

```r
*gapminder
```
]
 
.right-output-new_data-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(year == 2002)
```
]
 
.right-output-new_data-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
* filter(continent == "Americas")
```
]
 
.right-output-new_data-auto[

```
  # A tibble: 25 x 6
     country            continent  year lifeExp       pop gdpPercap
     <fct>              <fct>     <int>   <dbl>     <int>     <dbl>
   1 Argentina          Americas   2002    74.3  38331121     8798.
   2 Bolivia            Americas   2002    63.9   8445134     3413.
   3 Brazil             Americas   2002    71.0 179914212     8131.
   4 Canada             Americas   2002    79.8  31902268    33329.
   5 Chile              Americas   2002    77.9  15497046    10779.
   6 Colombia           Americas   2002    71.7  41008227     5755.
   7 Costa Rica         Americas   2002    78.1   3834934     7723.
   8 Cuba               Americas   2002    77.2  11226999     6341.
   9 Dominican Republic Americas   2002    70.8   8650322     4564.
  10 Ecuador            Americas   2002    74.2  12921234     5773.
  # … with 15 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Americas") %>%  
* ggplot()
```
]
 
.right-output-new_data-auto[
<img src="concision_files/figure-html/new_data_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Americas") %>%  
  ggplot() +  
* aes(x = gdpPercap)
```
]
 
.right-output-new_data-auto[
<img src="concision_files/figure-html/new_data_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Americas") %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
* aes(y = lifeExp)
```
]
 
.right-output-new_data-auto[
<img src="concision_files/figure-html/new_data_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Americas") %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
* geom_point()
```
]
 
.right-output-new_data-auto[
<img src="concision_files/figure-html/new_data_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Americas") %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
* labs(title = "Americas")
```
]
 
.right-output-new_data-auto[
<img src="concision_files/figure-html/new_data_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Americas") %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  labs(title = "Americas") ->  
*g1
```
]
 
.right-output-new_data-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Americas") %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  labs(title = "Americas") ->  
g1

# Change out data with special pipe
*g1
```
]
 
.right-output-new_data-auto[
<img src="concision_files/figure-html/new_data_auto_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

# Change out data with special pipe
g1 %+%  
* (gapminder %>%
*   filter(year == 2002) %>%
*   filter(continent == "Europe"))
```
]
 
.right-output-new_data-auto[
<img src="concision_files/figure-html/new_data_auto_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

# Change out data with special pipe
g1 %+%  
  (gapminder %>%  
    filter(year == 2002) %>%  
    filter(continent == "Europe")) +  
* labs(title = "Europe")
```
]
 
.right-output-new_data-auto[
<img src="concision_files/figure-html/new_data_auto_12_output-1.png" width="100%" />
]

---

# DRY part II

## writing functions

## writing packages (especially for themes)

### See end of [covariance walk-through](https://evamaerey.github.io/statistics/covariance_correlation.html)

---

Generative work:

- synthetic data

- generative art

- minimal examples

- hypothetical outcome plots

- conceptual work (flatten the curve)

---

# Generating synthetic data

- rnorm() - random normal
- runif() - sample from random uniform distribution
- sample() - sample from a vector
- sample_frac() - sample rows from a data frame (bootstrapping + hops)

---

```r
# where does randomization seed start
*set.seed(128347)
```
]
 
.right-output-gen_art-auto[

]

---
class: split-40
count: false

```r
# where does randomization seed start
set.seed(128347)  
# create a dataframe with input vectors
*tibble(x = runif(200),
*      y = runif(200),
*      z = x + y)
```
]
 
.right-output-gen_art-auto[

```
  # A tibble: 200 x 3
         x     y     z
     <dbl> <dbl> <dbl>
   1 0.309 0.407 0.716
   2 0.483 0.531 1.01 
   3 0.304 0.278 0.582
   4 0.570 0.367 0.937
   5 0.165 0.903 1.07 
   6 0.536 0.457 0.993
   7 0.661 0.519 1.18 
   8 0.188 0.120 0.309
   9 0.280 0.739 1.02 
  10 0.929 0.987 1.92 
  # … with 190 more rows
```
]

---
class: split-40
count: false

```r
# where does randomization seed start
set.seed(128347)  
# create a dataframe with input vectors
tibble(x = runif(200),  
       y = runif(200),  
       z = x + y) %>%  
* ggplot()
```
]
 
.right-output-gen_art-auto[
<img src="concision_files/figure-html/gen_art_auto_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# where does randomization seed start
set.seed(128347)  
# create a dataframe with input vectors
tibble(x = runif(200),  
       y = runif(200),  
       z = x + y) %>%  
  ggplot() +  
* aes(x = x, y = y, color = z)
```
]
 
.right-output-gen_art-auto[
<img src="concision_files/figure-html/gen_art_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# where does randomization seed start
set.seed(128347)  
# create a dataframe with input vectors
tibble(x = runif(200),  
       y = runif(200),  
       z = x + y) %>%  
  ggplot() +  
  aes(x = x, y = y, color = z) +  
* geom_point()
```
]
 
.right-output-gen_art-auto[
<img src="concision_files/figure-html/gen_art_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# where does randomization seed start
set.seed(128347)  
# create a dataframe with input vectors
tibble(x = runif(200),  
       y = runif(200),  
       z = x + y) %>%  
  ggplot() +  
  aes(x = x, y = y, color = z) +  
  geom_point() +  
* ggforce::geom_voronoi_tile(alpha = .8)
```
]
 
.right-output-gen_art-auto[
<img src="concision_files/figure-html/gen_art_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# where does randomization seed start
set.seed(128347)  
# create a dataframe with input vectors
tibble(x = runif(200),  
       y = runif(200),  
       z = x + y) %>%  
  ggplot() +  
  aes(x = x, y = y, color = z) +  
  geom_point() +  
  ggforce::geom_voronoi_tile(alpha = .8) +  
* aes(fill = z)
```
]
 
.right-output-gen_art-auto[
<img src="concision_files/figure-html/gen_art_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# where does randomization seed start
set.seed(128347)  
# create a dataframe with input vectors
tibble(x = runif(200),  
       y = runif(200),  
       z = x + y) %>%  
  ggplot() +  
  aes(x = x, y = y, color = z) +  
  geom_point() +  
  ggforce::geom_voronoi_tile(alpha = .8) +  
  aes(fill = z) +  
* scale_fill_viridis_c()
```
]
 
.right-output-gen_art-auto[
<img src="concision_files/figure-html/gen_art_auto_8_output-1.png" width="100%" />
]

---

For creating a manageable example...

Creating a toy dataset

[d'Hondt/Jefferson seat allocation toy example](https://evamaerey.github.io/flipbooks/dhondt_jefferson_allocation/dhondt_jefferson_allocation#70)

---

Also, being able to generate some data in your software can be helpful for creating [minimal working examples](https://community.rstudio.com/t/faq-whats-a-reproducible-example-reprex-and-how-do-i-do-one/5219) for asking for help as on forums like stack overflow.

---

# Hypothetical outcome plots - hops

---

## Visualizing a confidence interval

---

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  geom_point(size = 3, alpha = .3) +  
  geom_smooth(method = lm)  
```
]
 
.right-output-conf_interval-1[
<img src="concision_files/figure-html/conf_interval_1_1_output-1.png" width="100%" />
]

---

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  sample_frac(1, replace = T) %>%  
  ggplot() +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  geom_point(size = 3, alpha = .3) +  
  geom_smooth(method = lm, se = F) +  
  coord_cartesian(xlim = c(0,45), ylim = c(70,84))  
```
]
 
.right-output-conf_interval_boot-20[
<img src="concision_files/figure-html/conf_interval_boot_20_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

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class: split-40
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class: split-40
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count: false

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class: split-40
count: false

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class: split-40
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class: split-40
count: false

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class: split-40
count: false

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class: split-40
count: false

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class: split-40
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class: split-40
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class: split-40
count: false

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class: split-40
count: false

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class: split-40
count: false

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class: split-40
count: false

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class: split-40
count: false

---
class: split-40
count: false

---

# making a HOP with gganimate

---

```r
*gapminder
```
]
 
.right-output-hops-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(year == 2002)
```
]
 
.right-output-hops-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
* filter(continent == "Europe")
```
]
 
.right-output-hops-auto[

```
  # A tibble: 30 x 6
     country                continent  year lifeExp      pop gdpPercap
     <fct>                  <fct>     <int>   <dbl>    <int>     <dbl>
   1 Albania                Europe     2002    75.7  3508512     4604.
   2 Austria                Europe     2002    79.0  8148312    32418.
   3 Belgium                Europe     2002    78.3 10311970    30486.
   4 Bosnia and Herzegovina Europe     2002    74.1  4165416     6019.
   5 Bulgaria               Europe     2002    72.1  7661799     7697.
   6 Croatia                Europe     2002    74.9  4481020    11628.
   7 Czech Republic         Europe     2002    75.5 10256295    17596.
   8 Denmark                Europe     2002    77.2  5374693    32167.
   9 Finland                Europe     2002    78.4  5193039    28205.
  10 France                 Europe     2002    79.6 59925035    28926.
  # … with 20 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
* crossing(trial = 1:20)
```
]
 
.right-output-hops-auto[

```
  # A tibble: 600 x 7
     country continent  year lifeExp     pop gdpPercap trial
     <fct>   <fct>     <int>   <dbl>   <int>     <dbl> <int>
   1 Albania Europe     2002    75.7 3508512     4604.     1
   2 Albania Europe     2002    75.7 3508512     4604.     2
   3 Albania Europe     2002    75.7 3508512     4604.     3
   4 Albania Europe     2002    75.7 3508512     4604.     4
   5 Albania Europe     2002    75.7 3508512     4604.     5
   6 Albania Europe     2002    75.7 3508512     4604.     6
   7 Albania Europe     2002    75.7 3508512     4604.     7
   8 Albania Europe     2002    75.7 3508512     4604.     8
   9 Albania Europe     2002    75.7 3508512     4604.     9
  10 Albania Europe     2002    75.7 3508512     4604.    10
  # … with 590 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
* group_by(trial)
```
]
 
.right-output-hops-auto[

```
  # A tibble: 600 x 7
  # Groups:   trial [20]
     country continent  year lifeExp     pop gdpPercap trial
     <fct>   <fct>     <int>   <dbl>   <int>     <dbl> <int>
   1 Albania Europe     2002    75.7 3508512     4604.     1
   2 Albania Europe     2002    75.7 3508512     4604.     2
   3 Albania Europe     2002    75.7 3508512     4604.     3
   4 Albania Europe     2002    75.7 3508512     4604.     4
   5 Albania Europe     2002    75.7 3508512     4604.     5
   6 Albania Europe     2002    75.7 3508512     4604.     6
   7 Albania Europe     2002    75.7 3508512     4604.     7
   8 Albania Europe     2002    75.7 3508512     4604.     8
   9 Albania Europe     2002    75.7 3508512     4604.     9
  10 Albania Europe     2002    75.7 3508512     4604.    10
  # … with 590 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
  group_by(trial) %>%  
* sample_frac(frac = 1, replace = T)
```
]
 
.right-output-hops-auto[

```
  # A tibble: 600 x 7
  # Groups:   trial [20]
     country         continent  year lifeExp      pop gdpPercap trial
     <fct>           <fct>     <int>   <dbl>    <int>     <dbl> <int>
   1 Albania         Europe     2002    75.7  3508512     4604.     1
   2 Slovak Republic Europe     2002    73.8  5410052    13639.     1
   3 Slovak Republic Europe     2002    73.8  5410052    13639.     1
   4 Sweden          Europe     2002    80.0  8954175    29342.     1
   5 Poland          Europe     2002    74.7 38625976    12002.     1
   6 Montenegro      Europe     2002    74.0   720230     6557.     1
   7 Czech Republic  Europe     2002    75.5 10256295    17596.     1
   8 Turkey          Europe     2002    70.8 67308928     6508.     1
   9 Greece          Europe     2002    78.3 10603863    22514.     1
  10 Greece          Europe     2002    78.3 10603863    22514.     1
  # … with 590 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
  group_by(trial) %>%  
  sample_frac(frac = 1, replace = T) %>%  
* ggplot()
```
]
 
.right-output-hops-auto[
<img src="concision_files/figure-html/hops_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
  group_by(trial) %>%  
  sample_frac(frac = 1, replace = T) %>%  
  ggplot() +  
* aes(x = gdpPercap/1000)
```
]
 
.right-output-hops-auto[
<img src="concision_files/figure-html/hops_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
  group_by(trial) %>%  
  sample_frac(frac = 1, replace = T) %>%  
  ggplot() +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
* facet_wrap(~trial)
```
]
 
.right-output-hops-auto[
<img src="concision_files/figure-html/hops_auto_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
  group_by(trial) %>%  
  sample_frac(frac = 1, replace = T) %>%  
  ggplot() +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  facet_wrap(~trial) +  
* geom_point(size = 3, alpha = .3)
```
]
 
.right-output-hops-auto[
<img src="concision_files/figure-html/hops_auto_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
  group_by(trial) %>%  
  sample_frac(frac = 1, replace = T) %>%  
  ggplot() +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  facet_wrap(~trial) +  
  geom_point(size = 3, alpha = .3) +  
* geom_smooth(method = lm, se = F)
```
]
 
.right-output-hops-auto[
<img src="concision_files/figure-html/hops_auto_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
  group_by(trial) %>%  
  sample_frac(frac = 1, replace = T) %>%  
  ggplot() +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  facet_wrap(~trial) +  
  geom_point(size = 3, alpha = .3) +  
  geom_smooth(method = lm, se = F) +  
* facet_null()
```
]
 
.right-output-hops-auto[
<img src="concision_files/figure-html/hops_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  crossing(trial = 1:20) %>%  
# sample with replacement for each trial
  group_by(trial) %>%  
  sample_frac(frac = 1, replace = T) %>%  
  ggplot() +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  facet_wrap(~trial) +  
  geom_point(size = 3, alpha = .3) +  
  geom_smooth(method = lm, se = F) +  
  facet_null() +  
* gganimate::transition_manual(frames = trial)
```
]
 
.right-output-hops-auto[
<img src="concision_files/figure-html/hops_auto_14_output-1.gif" width="100%" />
]

---

# caching work w/ knitr

```r
knitr::opts_chunk$set(
  echo = F, eval = T,
  message = F, warning = F, 
* cache = T
  )
```

---