{ggxmean}

class: inverse
background-image: url(https://images.unsplash.com/photo-1572291720677-d8d28ac52a5b?ixid=MXwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHw%3D&ixlib=rb-1.2.1&auto=format&fit=crop&w=1556&q=80)
background-size: cover

# [{ggxmean}](https://github.com/EvaMaeRey/ggxmean)
<br><br><br><br><br>
<br><br><br><br><br>
<br><br><br><br><br>
<br><br><br><br><br>
#### Photo credit: Hans Veth

---
class: inverse, center, middle

# viz the mean and conditional means

---

```r
*library(tidyverse)
*library(ggxmean)
*palmerpenguins::penguins %>%
* drop_na() %>%
* ggplot() +
* aes(x = bill_length_mm) +
* geom_rug(alpha = .3) +
* geom_histogram(alpha = .4)
```
]
 
.panel2-vizthemean1d-user[
![](flipbook_files/figure-html/vizthemean1d_user_01_output-1.png)
]

---
count: false

---
count: false

```r
library(tidyverse)
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  geom_rug(alpha = .3) +
  geom_histogram(alpha = .4) +
  geom_x_mean() +
* aes(color = species)
```
]
 
.panel2-vizthemean1d-user[
![](flipbook_files/figure-html/vizthemean1d_user_03_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  geom_rug(alpha = .3) +
  geom_histogram(alpha = .4) +
  geom_x_mean() +
  aes(color = species) +
* aes(fill = species)
```
]
 
.panel2-vizthemean1d-user[
![](flipbook_files/figure-html/vizthemean1d_user_04_output-1.png)
]

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

```r
library(tidyverse)
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  geom_rug(alpha = .3) +
  geom_histogram(alpha = .4) +
  geom_x_mean() +
  aes(color = species) +
  aes(fill = species) +
  facet_grid(rows = vars(species)) +
  facet_grid(rows = vars(species, sex)) +
  geom_rug(alpha = .6) +
  geom_x_quantile(quantile = .5,
                  linetype = "dashed") +
* geom_x_percentile(percentile = 75,
*                   color = "goldenrod")
```
]
 
.panel2-vizthemean1d-user[
![](flipbook_files/figure-html/vizthemean1d_user_09_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  geom_rug(alpha = .3) +
  geom_histogram(alpha = .4) +
  geom_x_mean() +
  aes(color = species) +
  aes(fill = species) +
  facet_grid(rows = vars(species)) +
  facet_grid(rows = vars(species, sex)) +
  geom_rug(alpha = .6) +
  geom_x_quantile(quantile = .5,
                  linetype = "dashed") +
  geom_x_percentile(percentile = 75,
                    color = "goldenrod") +
* geom_x_median(color = "black")
```
]
 
.panel2-vizthemean1d-user[
![](flipbook_files/figure-html/vizthemean1d_user_10_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  geom_rug(alpha = .3) +
  geom_histogram(alpha = .4) +
  geom_x_mean() +
  aes(color = species) +
  aes(fill = species) +
  facet_grid(rows = vars(species)) +
  facet_grid(rows = vars(species, sex)) +
  geom_rug(alpha = .6) +
  geom_x_quantile(quantile = .5,
                  linetype = "dashed") +
  geom_x_percentile(percentile = 75,
                    color = "goldenrod") +
  geom_x_median(color = "black") +
* geom_x_quantile(quantile = .25, linetype = "dashed")
```
]
 
.panel2-vizthemean1d-user[
![](flipbook_files/figure-html/vizthemean1d_user_11_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  geom_rug(alpha = .3) +
  geom_histogram(alpha = .4) +
  geom_x_mean() +
  aes(color = species) +
  aes(fill = species) +
  facet_grid(rows = vars(species)) +
  facet_grid(rows = vars(species, sex)) +
  geom_rug(alpha = .6) +
  geom_x_quantile(quantile = .5,
                  linetype = "dashed") +
  geom_x_percentile(percentile = 75,
                    color = "goldenrod") +
  geom_x_median(color = "black") +
  geom_x_quantile(quantile = .25, linetype = "dashed") +
* geom_boxplot(y = 0,
*              width = 3,
*              fill = "white",
*              color = "black")
```
]
 
.panel2-vizthemean1d-user[
![](flipbook_files/figure-html/vizthemean1d_user_12_output-1.png)
]

---
class: inverse, center, middle

# and the mean of y

---

```r
*library(ggxmean)
*palmerpenguins::penguins %>%
* drop_na() %>%
* ggplot() +
* aes(x = bill_length_mm) +
* aes(y = flipper_length_mm) +
* geom_point()
```
]
 
.panel2-vizthemean2d-user[
![](flipbook_files/figure-html/vizthemean2d_user_01_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
* geom_x_mean()
```
]
 
.panel2-vizthemean2d-user[
![](flipbook_files/figure-html/vizthemean2d_user_02_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
* geom_y_mean()
```
]
 
.panel2-vizthemean2d-user[
![](flipbook_files/figure-html/vizthemean2d_user_03_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
* aes(color = species)
```
]
 
.panel2-vizthemean2d-user[
![](flipbook_files/figure-html/vizthemean2d_user_04_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
  aes(color = species) +
* facet_wrap(facets = vars(species))
```
]
 
.panel2-vizthemean2d-user[
![](flipbook_files/figure-html/vizthemean2d_user_05_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  drop_na() %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
  aes(color = species) +
  facet_wrap(facets = vars(species)) +
* facet_grid(cols = vars(species),
*            rows = vars(sex))
```
]
 
.panel2-vizthemean2d-user[
![](flipbook_files/figure-html/vizthemean2d_user_06_output-1.png)
]

---

```r
*set.seed(1456)
```
]
 
.panel2-ttest-auto[

]

---
count: false

```r
set.seed(1456)
*ggplot(starwars %>% sample_n(30))
```
]
 
.panel2-ttest-auto[
![](flipbook_files/figure-html/ttest_auto_02_output-1.png)
]

---
count: false

```r
set.seed(1456)
ggplot(starwars %>% sample_n(30)) +
* aes(x = height)
```
]
 
.panel2-ttest-auto[
![](flipbook_files/figure-html/ttest_auto_03_output-1.png)
]

---
count: false

```r
set.seed(1456)
ggplot(starwars %>% sample_n(30)) +
  aes(x = height) +
* geom_rug()
```
]
 
.panel2-ttest-auto[
![](flipbook_files/figure-html/ttest_auto_04_output-1.png)
]

---
count: false

```r
set.seed(1456)
ggplot(starwars %>% sample_n(30)) +
  aes(x = height) +
  geom_rug() +
* geom_dotplot()
```
]
 
.panel2-ttest-auto[
![](flipbook_files/figure-html/ttest_auto_05_output-1.png)
]

---
count: false

```r
set.seed(1456)
ggplot(starwars %>% sample_n(30)) +
  aes(x = height) +
  geom_rug() +
  geom_dotplot() +
* geom_x_mean(linetype = "dashed")
```
]
 
.panel2-ttest-auto[
![](flipbook_files/figure-html/ttest_auto_06_output-1.png)
]

---
count: false

```r
set.seed(1456)
ggplot(starwars %>% sample_n(30)) +
  aes(x = height) +
  geom_rug() +
  geom_dotplot() +
  geom_x_mean(linetype = "dashed") +
* ggxmean:::geom_tdist(
*   height = 50,
*   alpha = .5
*   )
```
]
 
.panel2-ttest-auto[
![](flipbook_files/figure-html/ttest_auto_07_output-1.png)
]

---
count: false

```r
set.seed(1456)
ggplot(starwars %>% sample_n(30)) +
  aes(x = height) +
  geom_rug() +
  geom_dotplot() +
  geom_x_mean(linetype = "dashed") +
  ggxmean:::geom_tdist(
    height = 50,
    alpha = .5
    ) +
* ggxmean:::geom_ttestconf(
*   size = 2,
*   conf.level = .9,
*   y = -.05
*   )
```
]
 
.panel2-ttest-auto[
![](flipbook_files/figure-html/ttest_auto_08_output-1.png)
]

---

```r
ggplot(tibble(height = runif(20))) +
  aes(x = height) +
  geom_rug() +
  geom_x_mean(
    linetype = "dashed",
    color = "mediumvioletred",
    size = 1.5
    ) +
  ggxmean:::geom_tdist(
    height = 1,
    alpha = .5,
    fill = "mediumvioletred"
    ) +
    ggxmean:::geom_ttestconf(
    size = 2,
    color = "mediumvioletred",
    alpha = .75,
    y = -.01,
    conf.level = .90) +
  scale_x_continuous(limits = c(0, 1)) +
  scale_y_continuous(limits = c(0, 1.7))
```
]
 
.panel2-multittest-20[
![](flipbook_files/figure-html/multittest_20_01_output-1.png)
]

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

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count: false

---
count: false

---
count: false

---
class: inverse, center, middle

# OLS

---

```r
*palmerpenguins::penguins %>%
* filter(species == "Chinstrap" & sex == "male") %>%
* ggplot() +
* aes(x = flipper_length_mm) +
* aes(y = body_mass_g) +
* geom_point()
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_01_output-1.png)
]

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
* geom_lm()
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_02_output-1.png)
]

---
count: false

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
* geom_x_line(alpha = .2,
*             linetype = "dotted")
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_04_output-1.png)
]

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
  geom_x_line(alpha = .2,
              linetype = "dotted") +
  geom_lm_fitted(color = "blue", size = 2.5) +
  geom_lm_residuals(color = "tomato3") +
  geom_y_mean() +
  geom_x_mean() +
  geom_xy_means(size = 3, color = "lightpink4") +
  geom_lm_formula() +
# plug and chug to calculate an
# expected value of y for a given x
  annotate(geom = "point",
           x = 208,
           y = 40.3 * 208 - 4110,
           size = 5,
           shape = "diamond") +
* ggxmean:::geom_lm_run(color = "violetred4")
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_12_output-1.png)
]

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
  geom_x_line(alpha = .2,
              linetype = "dotted") +
  geom_lm_fitted(color = "blue", size = 2.5) +
  geom_lm_residuals(color = "tomato3") +
  geom_y_mean() +
  geom_x_mean() +
  geom_xy_means(size = 3, color = "lightpink4") +
  geom_lm_formula() +
# plug and chug to calculate an
# expected value of y for a given x
  annotate(geom = "point",
           x = 208,
           y = 40.3 * 208 - 4110,
           size = 5,
           shape = "diamond") +
  ggxmean:::geom_lm_run(color = "violetred4") +
* ggxmean:::geom_lm_rise(color = "violetred4")
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_13_output-1.png)
]

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
  geom_x_line(alpha = .2,
              linetype = "dotted") +
  geom_lm_fitted(color = "blue", size = 2.5) +
  geom_lm_residuals(color = "tomato3") +
  geom_y_mean() +
  geom_x_mean() +
  geom_xy_means(size = 3, color = "lightpink4") +
  geom_lm_formula() +
# plug and chug to calculate an
# expected value of y for a given x
  annotate(geom = "point",
           x = 208,
           y = 40.3 * 208 - 4110,
           size = 5,
           shape = "diamond") +
  ggxmean:::geom_lm_run(color = "violetred4") +
  ggxmean:::geom_lm_rise(color = "violetred4") +
* ggxmean:::geom_lm_run10(color = "chartreuse3")
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_14_output-1.png)
]

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
  geom_x_line(alpha = .2,
              linetype = "dotted") +
  geom_lm_fitted(color = "blue", size = 2.5) +
  geom_lm_residuals(color = "tomato3") +
  geom_y_mean() +
  geom_x_mean() +
  geom_xy_means(size = 3, color = "lightpink4") +
  geom_lm_formula() +
# plug and chug to calculate an
# expected value of y for a given x
  annotate(geom = "point",
           x = 208,
           y = 40.3 * 208 - 4110,
           size = 5,
           shape = "diamond") +
  ggxmean:::geom_lm_run(color = "violetred4") +
  ggxmean:::geom_lm_rise(color = "violetred4") +
  ggxmean:::geom_lm_run10(color = "chartreuse3") +
* ggxmean:::geom_lm_rise10(color = "chartreuse3")
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_15_output-1.png)
]

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
  geom_x_line(alpha = .2,
              linetype = "dotted") +
  geom_lm_fitted(color = "blue", size = 2.5) +
  geom_lm_residuals(color = "tomato3") +
  geom_y_mean() +
  geom_x_mean() +
  geom_xy_means(size = 3, color = "lightpink4") +
  geom_lm_formula() +
# plug and chug to calculate an
# expected value of y for a given x
  annotate(geom = "point",
           x = 208,
           y = 40.3 * 208 - 4110,
           size = 5,
           shape = "diamond") +
  ggxmean:::geom_lm_run(color = "violetred4") +
  ggxmean:::geom_lm_rise(color = "violetred4") +
  ggxmean:::geom_lm_run10(color = "chartreuse3") +
  ggxmean:::geom_lm_rise10(color = "chartreuse3") +
* ggxmean:::geom_lm_predictx(alpha = .2)
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_16_output-1.png)
]

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
  geom_x_line(alpha = .2,
              linetype = "dotted") +
  geom_lm_fitted(color = "blue", size = 2.5) +
  geom_lm_residuals(color = "tomato3") +
  geom_y_mean() +
  geom_x_mean() +
  geom_xy_means(size = 3, color = "lightpink4") +
  geom_lm_formula() +
# plug and chug to calculate an
# expected value of y for a given x
  annotate(geom = "point",
           x = 208,
           y = 40.3 * 208 - 4110,
           size = 5,
           shape = "diamond") +
  ggxmean:::geom_lm_run(color = "violetred4") +
  ggxmean:::geom_lm_rise(color = "violetred4") +
  ggxmean:::geom_lm_run10(color = "chartreuse3") +
  ggxmean:::geom_lm_rise10(color = "chartreuse3") +
  ggxmean:::geom_lm_predictx(alpha = .2) +
* ggxmean:::geom_lm_predicty(alpha = .2)
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_17_output-1.png)
]

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
  geom_x_line(alpha = .2,
              linetype = "dotted") +
  geom_lm_fitted(color = "blue", size = 2.5) +
  geom_lm_residuals(color = "tomato3") +
  geom_y_mean() +
  geom_x_mean() +
  geom_xy_means(size = 3, color = "lightpink4") +
  geom_lm_formula() +
# plug and chug to calculate an
# expected value of y for a given x
  annotate(geom = "point",
           x = 208,
           y = 40.3 * 208 - 4110,
           size = 5,
           shape = "diamond") +
  ggxmean:::geom_lm_run(color = "violetred4") +
  ggxmean:::geom_lm_rise(color = "violetred4") +
  ggxmean:::geom_lm_run10(color = "chartreuse3") +
  ggxmean:::geom_lm_rise10(color = "chartreuse3") +
  ggxmean:::geom_lm_predictx(alpha = .2) +
  ggxmean:::geom_lm_predicty(alpha = .2) +
* ggxmean:::geom_lm_intercept()
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_18_output-1.png)
]

---
count: false

```r
palmerpenguins::penguins %>%
  filter(species == "Chinstrap" & sex == "male") %>%
  ggplot() +
  aes(x = flipper_length_mm) +
  aes(y = body_mass_g) +
  geom_point() +
  geom_lm() +
  geom_rug(aes(y = NULL)) +
  geom_x_line(alpha = .2,
              linetype = "dotted") +
  geom_lm_fitted(color = "blue", size = 2.5) +
  geom_lm_residuals(color = "tomato3") +
  geom_y_mean() +
  geom_x_mean() +
  geom_xy_means(size = 3, color = "lightpink4") +
  geom_lm_formula() +
# plug and chug to calculate an
# expected value of y for a given x
  annotate(geom = "point",
           x = 208,
           y = 40.3 * 208 - 4110,
           size = 5,
           shape = "diamond") +
  ggxmean:::geom_lm_run(color = "violetred4") +
  ggxmean:::geom_lm_rise(color = "violetred4") +
  ggxmean:::geom_lm_run10(color = "chartreuse3") +
  ggxmean:::geom_lm_rise10(color = "chartreuse3") +
  ggxmean:::geom_lm_predictx(alpha = .2) +
  ggxmean:::geom_lm_predicty(alpha = .2) +
  ggxmean:::geom_lm_intercept() +
* ggxmean:::geom_lm_interceptcoords(hjust = 0)
```
]
 
.panel2-ols-user[
![](flipbook_files/figure-html/ols_user_19_output-1.png)
]

---
class: inverse

# Correlation

# Don't worry about the overall importance of the problem; 
--
work on it if it looks interesting.  
--
I think there's a sufficient correlation between interest and importance.  
--
- David Blackwell

---

```r
*library(ggxmean)
*palmerpenguins::penguins %>%
* mutate(id = row_number()) %>%
* ggplot() +
* aes(x = bill_length_mm) +
* aes(y = flipper_length_mm) +
* geom_point() +
* geom_x_mean()
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_01_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  mutate(id = row_number()) %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
* geom_y_mean()
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_02_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  mutate(id = row_number()) %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
* geom_y_line(alpha = .02)
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_03_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  mutate(id = row_number()) %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
  geom_y_line(alpha = .02) +
* geom_x_line(alpha = .02)
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_04_output-1.png)
]

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  mutate(id = row_number()) %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
  geom_y_line(alpha = .02) +
  geom_x_line(alpha = .02) +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_diffsmultiplied() +
  ggxmean:::geom_x1sd() +
  ggxmean:::geom_y1sd() +
* ggxmean:::geom_rsq1(fill = "blue")
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_10_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  mutate(id = row_number()) %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
  geom_y_line(alpha = .02) +
  geom_x_line(alpha = .02) +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_diffsmultiplied() +
  ggxmean:::geom_x1sd() +
  ggxmean:::geom_y1sd() +
  ggxmean:::geom_rsq1(fill = "blue") +
* ggxmean:::geom_xy1sd(fill = "green")
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_11_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  mutate(id = row_number()) %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
  geom_y_line(alpha = .02) +
  geom_x_line(alpha = .02) +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_diffsmultiplied() +
  ggxmean:::geom_x1sd() +
  ggxmean:::geom_y1sd() +
  ggxmean:::geom_rsq1(fill = "blue") +
  ggxmean:::geom_xy1sd(fill = "green") +
* ggxmean:::geom_xydiffsmean(alpha = 1,
*                  fill = "plum3") +
* ggxmean:::geom_corrlabel()
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_12_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  mutate(id = row_number()) %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
  geom_y_line(alpha = .02) +
  geom_x_line(alpha = .02) +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_diffsmultiplied() +
  ggxmean:::geom_x1sd() +
  ggxmean:::geom_y1sd() +
  ggxmean:::geom_rsq1(fill = "blue") +
  ggxmean:::geom_xy1sd(fill = "green") +
  ggxmean:::geom_xydiffsmean(alpha = 1,
                   fill = "plum3") +
  ggxmean:::geom_corrlabel() +
* geom_lm()
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_13_output-1.png)
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  mutate(id = row_number()) %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  geom_x_mean() +
  geom_y_mean() +
  geom_y_line(alpha = .02) +
  geom_x_line(alpha = .02) +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_diffsmultiplied() +
  ggxmean:::geom_x1sd() +
  ggxmean:::geom_y1sd() +
  ggxmean:::geom_rsq1(fill = "blue") +
  ggxmean:::geom_xy1sd(fill = "green") +
  ggxmean:::geom_xydiffsmean(alpha = 1,
                   fill = "plum3") +
  ggxmean:::geom_corrlabel() +
  geom_lm() +
* facet_wrap(facet = vars(species))
```
]
 
.panel2-vizthecorrelation-user[
![](flipbook_files/figure-html/vizthecorrelation_user_14_output-1.png)
]

---

```r
*cars
```
]
 
.panel2-suprising-auto[

```
   speed dist
1      4    2
2      4   10
3      7    4
4      7   22
5      8   16
6      9   10
7     10   18
8     10   26
9     10   34
10    11   17
11    11   28
12    12   14
13    12   20
14    12   24
15    12   28
16    13   26
17    13   34
18    13   34
19    13   46
20    14   26
21    14   36
22    14   60
23    14   80
24    15   20
25    15   26
26    15   54
27    16   32
28    16   40
29    17   32
30    17   40
31    17   50
32    18   42
33    18   56
34    18   76
35    18   84
36    19   36
37    19   46
38    19   68
39    20   32
40    20   48
41    20   52
42    20   56
43    20   64
44    22   66
45    23   54
46    24   70
47    24   92
48    24   93
49    24  120
50    25   85
```
]

---
count: false

```r
cars %>%
* ggplot()
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_02_output-1.png)
]

---
count: false

```r
cars %>%
  ggplot() +
* aes(x = speed)
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_03_output-1.png)
]

---
count: false

```r
cars %>%
  ggplot() +
  aes(x = speed) +
* geom_rug()
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_04_output-1.png)
]

---
count: false

```r
cars %>%
  ggplot() +
  aes(x = speed) +
  geom_rug() +
* geom_x_mean()
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_05_output-1.png)
]

---
count: false

```r
cars %>%
  ggplot() +
  aes(x = speed) +
  geom_rug() +
  geom_x_mean() +
* geom_x_mean_label()
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_06_output-1.png)
]

---
count: false

```r
cars %>%
  ggplot() +
  aes(x = speed) +
  geom_rug() +
  geom_x_mean() +
  geom_x_mean_label() +
* aes(y = dist)
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_07_output-1.png)
]

---
count: false

```r
cars %>%
  ggplot() +
  aes(x = speed) +
  geom_rug() +
  geom_x_mean() +
  geom_x_mean_label() +
  aes(y = dist) +
* geom_y_mean()
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_08_output-1.png)
]

---
count: false

```r
cars %>%
  ggplot() +
  aes(x = speed) +
  geom_rug() +
  geom_x_mean() +
  geom_x_mean_label() +
  aes(y = dist) +
  geom_y_mean() +
* geom_y_mean_label()
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_09_output-1.png)
]

---
count: false

```r
cars %>%
  ggplot() +
  aes(x = speed) +
  geom_rug() +
  geom_x_mean() +
  geom_x_mean_label() +
  aes(y = dist) +
  geom_y_mean() +
  geom_y_mean_label() +
* geom_point()
```
]
 
.panel2-suprising-auto[
![](flipbook_files/figure-html/suprising_auto_10_output-1.png)
]

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