chapter03.R

# 3章
library(tidyverse)
hof <- read_csv("data/hofbatting.csv")
hof <- hof %>%
  mutate(MidCareer = (From + To) / 2,
         Era = cut(MidCareer,
                   breaks = c(1800, 1900, 1919, 1941,
                              1960, 1976, 1993, 2050),
                   labels = c("19th Century", "Dead Ball",
                              "Lively Ball", "Integration",
                              "Expansion", "Free Agency",
                              "Long Ball")))
hof_eras <- summarize(group_by(hof, Era), N = n())
hof_eras

ggplot(hof, aes(x = Era)) + geom_bar()
ggplot(hof, aes(Era)) +
  geom_bar() +
  xlab("Baseball Era") +
  ylab("Frequency") +
  ggtitle("Era of the Nonpitching Hall of Famers")
ggplot(hof_eras, aes(Era, N)) +
  geom_point() +
  xlab("Baseball Era") +
  ylab("Frequency") +
  ggtitle("Era of the Nonpitching Hall of Famers") +
  coord_flip()
ggplot(hof, aes(Era)) +
  geom_bar() +
  xlab("Baseball Era") +
  ylab("Frequency") +
  ggtitle("Era of the Nonpitching Hall of Famers")
ggsave("bargraph.png")

pdf("graphs.pdf")
ggplot(hof, aes(Era)) + geom_bar()
ggplot(hof_eras, aes(Era, N)) + geom_point()
dev.off()

ggplot(hof, aes(x = OPS, y = 1)) +
  geom_jitter(height = 0.6) + ylim(-1, 3) +
  theme(axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank()) +
  coord_fixed(ratio = 0.03)
ggplot(hof, aes(x = OPS)) +
  geom_histogram()

crcblue <- "#2905a1"
ggplot(hof, aes(x = OPS)) +
  geom_histogram(breaks = seq(0.4, 1.2, by = 0.1),
                 color = crcblue, fill = "white")
ggplot(hof, aes(MidCareer, OPS)) +
  geom_point() +
  geom_smooth()

library(ggrepel)
ggplot(hof, aes(MidCareer, OPS)) +
  geom_point() +
  geom_smooth() +
  geom_text_repel(data = filter(hof, OPS > 1.05 | OPS < .5),
                  aes(MidCareer, OPS, label = Player))
(p <- ggplot(hof, aes(OBP, SLG)) + geom_point())
(p <- p +
    xlim(0.25, 0.50) + ylim(0.28, 0.75) +
    xlab("On Base Percentage") +
    ylab("Slugging Percentage"))
p <- p +
  scale_x_continuous("On Base Percentage",
                     limits = c(0.25, 0.50)) +
  scale_y_continuous("Slugging Percentage",
                     limits = c(0.28, 0.75))
(p <- p + geom_abline(slope = -1,
                      intercept = seq(0.7, 1, by = 0.1)))
p + annotate("text",
             x = rep(.27, 4) , y = c(.42, .52, .62, .72),
             label = paste("OPS = ",
                           c(0.7, 0.8, 0.9, 1.0)))
ops_labels <- tibble(
  OBP = rep(0.3, 4),
  SLG = seq(0.4, 0.7, by = 0.1),
  label = paste("OPS =", OBP + SLG)
)
p + geom_text(data = ops_labels, hjust = "right",
              aes(label = label))

hof <- mutate(hof, hr_rate = HR / AB)
ggplot(hof, aes(hr_rate, Era)) +
  geom_jitter(height = 0.1)
ggplot(hof, aes(Era, hr_rate)) +
  geom_boxplot() + coord_flip()

library(Lahman)
get_birthyear <- function(Name){
  Names <- unlist(strsplit(Name, " "))
  People %>%
    filter(nameFirst == Names[1],
           nameLast == Names[2]) %>%
    mutate(birthyear = ifelse(birthMonth >= 7,
                              birthYear + 1, birthYear),
           Player = paste(nameFirst, nameLast)) %>%
    select(playerID, Player, birthyear)
}
PlayerInfo <- bind_rows(get_birthyear("Babe Ruth"),
                        get_birthyear("Hank Aaron"),
                        get_birthyear("Barry Bonds"),
                        get_birthyear("Alex Rodriguez")
)
Batting %>%
  inner_join(PlayerInfo, by = "playerID") %>%
  mutate(Age = yearID - birthyear) %>%
  select(Player, Age, HR) %>%
  group_by(Player) %>%
  mutate(CHR = cumsum(HR)) -> HRdata
ggplot(HRdata, aes(x = Age, y = CHR, linetype = Player)) +
  geom_line()

fields <- read_csv("data/fields.csv")
data1998 <- read_csv("data/all1998.csv",
                     col_names = pull(fields, Header))
sosa_id <- People %>%
  filter(nameFirst == "Sammy", nameLast == "Sosa") %>%
  pull(retroID)
mac_id <- People %>%
  filter(nameFirst == "Mark", nameLast == "McGwire") %>%
  pull(retroID)
hr_race <- data1998 %>%
  filter(BAT_ID %in% c(sosa_id, mac_id))

library(lubridate)
cum_hr <- function(d){
  d %>%
  mutate(Date = ymd(str_sub(GAME_ID, 4, 11))) %>%
  arrange(Date) %>%
  mutate(HR = ifelse(EVENT_CD == 23, 1, 0),
         cumHR = cumsum(HR)) %>%
  select(Date, cumHR)
}
hr_ytd <- hr_race %>%
  split(pull(., BAT_ID)) %>%
  map_df(cum_hr, .id = "BAT_ID") %>%
  inner_join(People, by = c("BAT_ID" = "retroID"))
ggplot(hr_ytd, aes(Date, cumHR, linetype = nameLast)) +
  geom_line() +
  geom_hline(yintercept = 62, color = crcblue) +
  annotate("text", ymd("1998-04-15"), 65,
           label = "62", color = crcblue) +
  ylab("Home Runs in the Season")