data(Salaries)

# what years are included?
summary(Salaries$yearID)

# how many players included each year?
table(Salaries$yearID)

# Team salary data

require("dplyr")
require("ggplot2")

# Total team salaries by league, team and year
teamSalaries <- Salaries %>%
                  group_by(lgID, teamID, yearID) %>%
                  summarise(Salary = sum(as.numeric(salary))) %>%
                  group_by(yearID, lgID) %>%
                  arrange(desc(Salary))

#######################################
# Highest paid players each year:
maxSal <- Salaries %>%
            group_by(yearID) %>%
            filter(salary == max(salary)) 
maxPlayers <- bind_rows(lapply(maxSal$playerID, playerInfo)) %>%
                  select(-playerID)
maxSal <- bind_cols(maxPlayers, maxSal)

# Plot maximum MLB salary by year (1985-present)           
ggplot(maxSal, aes(x = yearID, y = salary/1e6)) +
   geom_point() +
   geom_smooth(se = FALSE) +
   labs(x = "Year", y = "Salary (millions)")

# Plot salary distributions by year for all players
ggplot(Salaries, aes(x = factor(yearID), y = salary/1e5)) +
   geom_boxplot(fill = "lightblue", outlier.size = 1) +
   labs(x = "Year", y = "Salary ($100,000)") +
   coord_flip()

# Plot median MLB salary per year
Salaries %>%
   group_by(yearID) %>%
   summarise(medsal = median(salary)) %>%
   ggplot(., aes(x = yearID, y = medsal/1e6)) +
   geom_point() +
   geom_smooth() +
   labs(x = "Year", y = "Median MLB salary (millions)")

# add salary to Batting data
batting <- Batting %>%
              filter(yearID >= 1985) %>%
              left_join(select(Salaries, playerID, yearID, teamID, salary), 
                         by=c("playerID", "yearID", "teamID"))
str(batting)

#######################################
# Average salaries by teams, over years
#######################################

# Some franchises are multiply named, so add a new variable
# 'franchise' to the Salaries data as a lookup table

franchise <- c(`ANA` = "LAA", `ARI` = "ARI", `ATL` = "ATL", 
               `BAL` = "BAL", `BOS` = "BOS", `CAL` = "LAA",
               `CHA` = "CHA", `CHN` = "CHN", `CIN` = "CIN", 
               `CLE` = "CLE", `COL` = "COL", `DET` = "DET", 
               `FLO` = "MIA", `HOU` = "HOU", `KCA` = "KCA", 
               `LAA` = "LAA", `LAN` = "LAN", `MIA` = "MIA", 
               `MIL` = "MIL", `MIN` = "MIN", `ML4` = "MIL", 
               `MON` = "WAS", `NYA` = "NYA", `NYM` = "NYN", 
               `NYN` = "NYN", `OAK` = "OAK", `PHI` = "PHI", 
               `PIT` = "PIT", `SDN` = "SDN", `SEA` = "SEA",
               `SFG` = "SFN", `SFN` = "SFN", `SLN` = "SLN", 
               `TBA` = "TBA", `TEX` = "TEX", `TOR` = "TOR",
               `WAS` = "WAS")

Salaries$franchise <- unname(franchise[Salaries$teamID])

# Average salaries annual salaries by team, in millions USD
avg_team_salaries <- Salaries %>%
    group_by(yearID, franchise, lgID) %>%
    summarise(salary= mean(salary)/1e6) %>%
    filter(!(franchise == "CLE" & lgID == "NL"))

# Spaghetti plot of team salary over time by team
# Yankees have largest average team salary since 2003
ggplot(avg_team_salaries, 
       aes(x = yearID, y = salary, group = factor(franchise))) +
       geom_path() +
       labs(x = "Year", y = "Average team salary (millions USD)")