# NOT RUN {
data(Teams)
library("dplyr")
library("tidyr")
# Add some selected measures to the Teams data frame
# Restrict to AL and NL in modern era
teams <- Teams %>%
filter(yearID >= 1901 & lgID %in% c("AL", "NL")) %>%
group_by(yearID, teamID) %>%
mutate(TB = H + X2B + 2 * X3B + 3 * HR,
WinPct = W/G,
rpg = R/G,
hrpg = HR/G,
tbpg = TB/G,
kpg = SO/G,
k2bb = SO/BB,
whip = 3 * (H + BB)/IPouts)
# Function to create a ggplot by year for selected team stats
# Both arguments are character strings
yrPlot <- function(yvar, label)
{
require("ggplot2")
ggplot(teams, aes_string(x = "yearID", y = yvar)) +
geom_point(size = 0.5) +
geom_smooth(method="loess") +
labs(x = "Year", y = paste(label, "per game"))
}
## Run scoring in the modern era by year
yrPlot("rpg", "Runs")
## Home runs per game by year
yrPlot("hrpg", "Home runs")
## Total bases per game by year
yrPlot("tbpg", "Total bases")
## Strikeouts per game by year
yrPlot("kpg", "Strikeouts")
## Plot win percentage vs. run differential (R - RA)
ggplot(teams, aes(x = R - RA, y = WinPct)) +
geom_point(size = 0.5) +
geom_smooth(method="loess") +
geom_hline(yintercept = 0.5, color = "orange") +
geom_vline(xintercept = 0, color = "orange") +
labs(x = "Run differential", y = "Win percentage")
## Plot attendance vs. win percentage by league, post-1980
teams %>% filter(yearID >= 1980) %>%
ggplot(., aes(x = WinPct, y = attendance/1000)) +
geom_point(size = 0.5) +
geom_smooth(method="loess", se = FALSE) +
facet_wrap(~ lgID) +
labs(x = "Win percentage", y = "Attendance (1000s)")
## Teams with over 4 million attendance in a season
teams %>%
filter(attendance >= 4e6) %>%
select(yearID, lgID, teamID, Rank, attendance) %>%
arrange(desc(attendance))
## Average season HRs by park, post-1980
teams %>%
filter(yearID >= 1980) %>%
group_by(park) %>%
summarise(meanHRpg = mean((HR + HRA)/Ghome), nyears = n()) %>%
filter(nyears >= 10) %>%
arrange(desc(meanHRpg)) %>%
head(., 10)
## Home runs per game at Fenway Park and Wrigley Field,
## the two oldest MLB parks, by year. Fenway opened in 1912.
teams %>%
filter(yearID >= 1912 & teamID %in% c("BOS", "CHN")) %>%
mutate(hrpg = (HR + HRA)/Ghome) %>%
ggplot(., aes(x = yearID, y = hrpg, color = teamID)) +
geom_line(size = 1) +
geom_point() +
labs(x = "Year", y = "Home runs per game", color = "Team") +
scale_color_manual(values = c("red", "blue"))
## Ditto for total strikeouts per game
teams %>%
filter(yearID >= 1912 & teamID %in% c("BOS", "CHN")) %>%
mutate(kpg = (SO + SOA)/Ghome) %>%
ggplot(., aes(x = yearID, y = kpg, color = teamID)) +
geom_line(size = 1) +
geom_point() +
labs(x = "Year", y = "Strikeouts per game", color = "Team") +
scale_color_manual(values = c("red", "blue"))
# }
# NOT RUN {
if(require(googleVis)) {
motion1 <- gvisMotionChart(as.data.frame(teams),
idvar="teamID", timevar="yearID", chartid="gvisTeams",
options=list(width=700, height=600))
plot(motion1)
#print(motion1, file="gvisTeams.html")
# Merge with avg salary for years where salary is available
teamsal <- Salaries %>%
group_by(yearID, teamID) %>%
summarise(Salary = sum(salary, na.rm = TRUE)) %>%
select(yearID, teamID, Salary)
teamsSal <- teams %>%
filter(yearID >= 1985) %>%
left_join(teamsal, by = c("yearID", "teamID")) %>%
select(yearID, teamID, attendance, Salary, WinPct) %>%
as.data.frame(.)
motion2 <- gvisMotionChart(teamsSal, idvar="teamID", timevar="yearID",
xvar="attendance", yvar="salary", sizevar="WinPct",
chartid="gvisTeamsSal", options=list(width=700, height=600))
plot(motion2)
#print(motion2, file="gvisTeamsSal.html")
}
# }
Run the code above in your browser using DataCamp Workspace