Data Visualization

class: center, middle, inverse, title-slide

# Data Visualization
## Visualizing 2D categorical and continuous by categorical, plus facets
### Ron Yurko
### 06/04/2020

---

## Crimes against bar charts

.center[![](https://pbs.twimg.com/media/DiaDinUX0AQ2Hel?format=jpg&name=large)]

_Please do NOT never make something like this_...

---

## Revisiting MVP Mike Trout's batted balls in 2019

Created dataset of batted balls by the American League MVP Mike Trout in 2019 season using [`baseballr`](http://billpetti.github.io/baseballr/)

```r
library(tidyverse)
trout_batted_balls <- 
  read_csv("http://www.stat.cmu.edu/cmsac/sure/materials/data/xy_examples/trout_2019_batted_balls.csv")
head(trout_batted_balls)
```

```
## # A tibble: 6 x 7
##   pitch_type batted_ball_type hit_x hit_y exit_velocity launch_angle
##   <chr>      <chr>            <dbl> <dbl>         <dbl>        <dbl>
## 1 FF         fly_ball          10.2 150.           91           30.2
## 2 FF         fly_ball         -70.6  82.7          82.2         49.7
## 3 FF         ground_ball      -21.6  18.5          49.1         13.8
## 4 SI         fly_ball         -23.5 191.          111.          25.5
## 5 FF         ground_ball      -14.5  41.8          82.1         -6  
## 6 FF         fly_ball          27.1 114.           96.8         52  
## # … with 1 more variable: outcome <chr>
```

- each row / observation is a batted ball from Trout's 2019 season
- __Categorical__ / qualitative variables: `pitch_type`, `batted_ball_type`, `outcome`
- __Continuous__ / quantitative variables: `hit_x`, `hit_y`, `exit_velocity`, `launch_angle`

---

## First - more fun with [`forcats`](https://forcats.tidyverse.org/)

Variables of interest: [`pitch_type`](https://library.fangraphs.com/pitch-type-abbreviations-classifications/) and `batted_ball_type` - but how many levels does `pitch_type` have?

```r
table(trout_batted_balls$pitch_type)
```

```
## 
##   CH   CU   FC   FF   FS   FT   KC null   SI   SL 
##   38   23   21  139    1   33    1    6   29   63
```

We can manually [`fct_recode`](https://forcats.tidyverse.org/reference/fct_recode.html) `pitch_type` (see [Chapter 15 of `R` for Data Science](https://r4ds.had.co.nz/factors.html) for more on factors)

```r
trout_batted_balls <- trout_batted_balls %>%
  filter(pitch_type != "null") %>% 
* mutate(pitch_type = fct_recode(pitch_type, "Changeup" = "CH", "Breaking ball" = "CU",
*                     "Fastball" = "FC", "Fastball" = "FF", "Fastball" = "FS",  "Fastball" = "FT",
*                     "Breaking ball" = "KC",  "Fastball" = "SI",  "Breaking ball" = "SL"))
table(trout_batted_balls$pitch_type)
```

```
## 
##      Changeup Breaking ball      Fastball 
##            38            87           223
```

---

## 2D Categorical visualization (== more bar charts!)

.pull-left[

__Stacked__: a bar chart of _spine_ charts

```r
trout_batted_balls %>%
  ggplot(aes(x = batted_ball_type,
*            fill = pitch_type)) +
  geom_bar() + theme_bw()
```

]
.pull-right[

__Side-by-Side__: a bar chart _of bar charts_

```r
trout_batted_balls %>%
  ggplot(aes(x = batted_ball_type,
             fill = pitch_type)) + 
* geom_bar(position = "dodge") + theme_bw()
```

<img src="04-2dcatcolorfacet_files/figure-html/side-by-side-bars-1.png" width="504" />
]

---

## Which do you prefer?

.pull-left[

]
.pull-right[

<img src="04-2dcatcolorfacet_files/figure-html/unnamed-chunk-2-1.png" width="504" />
]

- Stacked bar charts emphasize __marginal__ distribution of `x` variable, 
  - e.g. `$P$` (`batted_ball_type` = fly_ball)

- Side-by-side bar charts are useful to show the __conditional__ distribution of `fill` variable given `x`,
  - e.g. `$P$` (`pitch_type` = Fastball | `batted_ball_type` = fly_ball)

---

## Brief review of joint, marginal, and conditional probabilities

__Joint distribution__: frequency of intersection, `$P(X = x, Y = y)$`

```r
library(gt)
trout_batted_balls %>%
  group_by(batted_ball_type, pitch_type) %>%
  summarize(joint_prob = n() / nrow(trout_batted_balls)) %>%
  pivot_wider(names_from = batted_ball_type, values_from = joint_prob) %>%
  gt()
```

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</style>
<div id="mzsrfzbahv" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;"><table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1">pitch_type</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">fly_ball</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">ground_ball</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">line_drive</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">popup</th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr>
      <td class="gt_row gt_center">Changeup</td>
      <td class="gt_row gt_right">0.04022989</td>
      <td class="gt_row gt_right">0.03448276</td>
      <td class="gt_row gt_right">0.02586207</td>
      <td class="gt_row gt_right">0.00862069</td>
    </tr>
    <tr>
      <td class="gt_row gt_center">Breaking ball</td>
      <td class="gt_row gt_right">0.10344828</td>
      <td class="gt_row gt_right">0.04597701</td>
      <td class="gt_row gt_right">0.06896552</td>
      <td class="gt_row gt_right">0.03160920</td>
    </tr>
    <tr>
      <td class="gt_row gt_center">Fastball</td>
      <td class="gt_row gt_right">0.22126437</td>
      <td class="gt_row gt_right">0.17241379</td>
      <td class="gt_row gt_right">0.19827586</td>
      <td class="gt_row gt_right">0.04885057</td>
    </tr>
  </tbody>
  
  
</table></div>

--
__Marginal distribution__: row / column sums, e.g. `$P(X = \text{popup}) = \sum_{y \in \text{pitch types}} P(X = \text{popup}, Y = y)$`

--
__Conditional distribution__: probability event `$X$` __given__ second event `$Y$`, 
- e.g. `$P(X = \text{popup} | Y = \text{Fastball}) = \frac{P(X = \text{popup}, Y = \text{Fastball})}{P(Y = \text{Fastball})}$`

---

## What about independence? Can we visualize it?

--
Two variables are __independent__ if knowing the level of one tells us nothing about the other
- i.e.  `$P(X = x | Y = y) = P(X = x)$`, and that `$P(X = x, Y = y) = P(X = x) \times P(Y = y)$`

.pull-left[

Create a __mosaic__ plot using [`vcd`](https://cran.r-project.org/web/packages/vcdExtra/vignettes/vcd-tutorial.pdf) package

```r
library(vcd)
mosaic(~ pitch_type + batted_ball_type, 
       data = trout_batted_balls)
```

- spine chart _of spine charts_

- height = marginal distribution of `pitch_type`

- width = conditional distribution of `batted_ball_type` | `pitch_type`

- area = joint distribution

__[`ggmosaic`](https://github.com/haleyjeppson/ggmosaic) has issues...__
]
.pull-right[
<img src="04-2dcatcolorfacet_files/figure-html/unnamed-chunk-3-1.png" width="504" />
]

---

## Continuous by categorical: side-by-side and color

.pull-left[

```r
trout_batted_balls %>%
* ggplot(aes(x = pitch_type,
             y = exit_velocity)) +
  geom_violin() +
  geom_boxplot(width = .2) +
  theme_bw()
```

<img src="04-2dcatcolorfacet_files/figure-html/unnamed-chunk-4-1.png" width="504" />
  
]
.pull-right[

```r
trout_batted_balls %>%
  ggplot(aes(x = exit_velocity,
*            color = pitch_type)) +
  stat_ecdf() + 
  theme_bw() +
  theme(legend.position = "bottom")
```

<img src="04-2dcatcolorfacet_files/figure-html/unnamed-chunk-5-1.png" width="504" />
]

---

## What about for histograms?

.pull-left[

```r
trout_batted_balls %>%
  ggplot(aes(x = exit_velocity,
*            fill = pitch_type)) +
  geom_histogram() +
  theme_bw() +
  theme(legend.position = "bottom")
```

<img src="04-2dcatcolorfacet_files/figure-html/unnamed-chunk-6-1.png" width="504" />
  
]
.pull-right[

```r
trout_batted_balls %>%
  ggplot(aes(x = exit_velocity,
*            color = pitch_type)) +
  geom_freqpoly() +
  theme_bw() +
  theme(legend.position = "bottom")
```

<img src="04-2dcatcolorfacet_files/figure-html/unnamed-chunk-7-1.png" width="504" />
]

---

## We can always facet instead...

.pull-left[

```r
trout_batted_balls %>%
  ggplot(aes(x = exit_velocity)) + 
  geom_histogram() +
  theme_bw() +
* facet_wrap(~ pitch_type, ncol = 2)
```

<img src="04-2dcatcolorfacet_files/figure-html/unnamed-chunk-8-1.png" width="504" />
  
]
.pull-right[

```r
trout_batted_balls %>%
  ggplot(aes(x = exit_velocity)) + 
  geom_histogram() +
  theme_bw() +
* facet_grid(pitch_type ~., margins = TRUE)
```

<img src="04-2dcatcolorfacet_files/figure-html/unnamed-chunk-9-1.png" width="504" />
]

---

## Facets make it easy to move beyond 2D

```r
trout_batted_balls %>%
  ggplot(aes(x = pitch_type,
             fill = batted_ball_type)) + 
  geom_bar() + theme_bw() +
  facet_wrap(~ outcome, ncol = 5) +
  theme(legend.position = "bottom")
```