Welcome_to_R_5.Rnw

---
title: "ggplot2 intro 2"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
                      warning = FALSE,
                      message = FALSE)
```

Load in the package and read Statcast data.

```{r}
library(data.table)
library(ggplot2)
sc <- fread("https://raw.githubusercontent.com/bayesball/ABWRdata/master/data/statcast2017.txt")
```

Compare two Philly pitchers -- Aaron Nola and Nick Pivetta for 2017 season.  Compare season performances.

Create a data frame with variables

- Pitcher - name of pitcher
- pitch_type
- plate_x, plate_z
- type
- woba_value
- release_speed
- pfx_x, pfx_z

```{r}
two_pitchers <- sc[pitcher %in% c(601713, 605400)]
two_pitchers <- two_pitchers[, 
            Pitcher := ifelse(pitcher == 601713,
                     "Nick Pivetta", "Aaron Nola") ]
two_pitchers <- two_pitchers[, 
            .(Pitcher, pitch_type, plate_x, 
              plate_z, type, woba_value, 
              release_speed, pfx_x, pfx_z)]
```

#### What pitches do they throw?

```{r}
(S <- two_pitchers[, .N, 
                   by = .(Pitcher, pitch_type)])
```
Construct barplots of the frequencies of the different pitch types for both pitchers.

```{r}
ggplot(S, aes(x = Pitcher, y = N, 
              fill = pitch_type)) +
   geom_bar(stat = "identity", position = "dodge")
```

Alternative display using a point geom instead of a bar geom.

```{r}
ggplot(S, aes(x = Pitcher, y = N, 
              color = pitch_type)) +
   geom_point()
```

#### Compare release speeds of the pitchers

```{r}
ggplot(two_pitchers[ pitch_type != "SL"],
       aes(Pitcher, release_speed)) +
   geom_jitter() +
   facet_wrap(~ pitch_type)
```

Instead of jittered points, can use boxplots to summarize the speeds.

```{r}
ggplot(two_pitchers[ pitch_type != "SL"],
       aes(Pitcher, release_speed)) +
   geom_boxplot() +
   facet_wrap(~ pitch_type)
```

Or we could try parallel violin plots.

```{r}
ggplot(two_pitchers[ pitch_type != "SL"],
       aes(Pitcher, release_speed)) +
   geom_violin() +
   facet_wrap(~ pitch_type)
```

#### Compare horizontal and vertical breaks

```{r}
ggplot(two_pitchers[ pitch_type != "SL"],
       aes(pfx_x, pfx_z, color = Pitcher)) +
   geom_point() +
   facet_wrap(~ pitch_type)
```


#### Compare pitch locations 

Below I am adding a graph of the zone.  Here is a special function add_zone() that consists of a ggplot2 layer.

```{r}
add_zone <- function(Color = "red") {
    topKzone <- 3.5
    botKzone <- 1.6
    inKzone <- -0.85
    outKzone <- 0.85
    kZone <- data.frame(x = c(inKzone, inKzone, outKzone, outKzone, 
        inKzone), y = c(botKzone, topKzone, topKzone, botKzone, 
        botKzone))
    geom_path(aes(.data$x, .data$y), data = kZone, lwd = 1, col = Color)
}
```


```{r}
ggplot(two_pitchers[ pitch_type != "SL"],
       aes(plate_x, plate_z)) +
   geom_point(aes(color = Pitcher)) +
   facet_wrap(~ pitch_type) + 
   add_zone('black')
```

Focus on the locations for curve balls and four-seamers.

```{r}
ggplot(two_pitchers[ pitch_type %in% c("FF", "CU")],
       aes(plate_x, plate_z)) +
   geom_point() +
   facet_grid(pitch_type ~ Pitcher) + 
   add_zone('red')
```

#### What happens to balls in play?

```{r}
ggplot(two_pitchers[ pitch_type %in% 
            c("FF", "CU") & type == "X"],
       aes(plate_x, plate_z)) +
   geom_point(aes(color = 
                     as.character(woba_value))) +
   facet_grid(pitch_type ~ Pitcher) + 
   add_zone('black')
```