knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-", warning = FALSE, message=FALSE ) library(baseballDBR)
install.packages("baseballDBR")
devtools::install_github("keberwein/baseballDBR")
The baseballDBR
package requires data that is formatted similar to the Baseball Databank or Sean Lahman's Baseball Database. The package also contains the get_bbdb()
function, which allows us to download the most up-to-date tables directly from the Chadwick Bureau's GitHub repository. For example, we can easily load the "Batting" table into our R environment.
library(baseballDBR) get_bbdb(table = "Batting") head(Batting)
library(Lahman) library(baseballDBR) Batting <- Lahman::Batting head(Batting)
Simple batting metrics can be easily added to any batting data frame. For example, we can add slugging percentage, on-base percentage and on-base plus slugging. Note that OPS and OBP appears as "NA" for the years before IBB was tracked.
library(baseballDBR) Batting$SLG <- SLG(Batting) Batting$OBP <- OBP(Batting) head(Batting, 3)
The package includes a suite of advanced metrics such as wOBA, RAA, and FIP, among others. Many of the advanced metrics require multiple tables. For example, the wOBA metric requires the Batting, Pitching, and Fielding tables in order to establish a player's regular defensive position.
library(baseballDBR) get_bbdb(table = c("Batting", "Pitching", "Fielding")) Batting$wOBA <- wOBA(Batting, Pitching, Fielding, Fangraphs = T) head(Batting, 3)
The code above uses Fangraphs wOBA values. The default behavior is to uses Tom Tango's adapted SQL formula. Other options include Sep.Leagues
, which may act as a buffer to any bias created by the designated hitter.
library(baseballDBR) get_bbdb(table = c("Batting", "Pitching", "Fielding")) Batting$wOBA <- wOBA(Batting, Pitching, Fielding, Fangraphs = F, Sep.Leagues = T) head(Batting, 3)
We can also produce a data frame that only shows the wOBA multipliers. Notice the Fangraphs wOBA multipliers slightly differ from the Tango multipliers.
library(baseballDBR) get_bbdb(table = c("Batting", "Pitching", "Fielding")) fangraphs_woba <- wOBA_values(Batting, Pitching, Fielding, Fangraphs=T) head(fangraphs_woba, 3) tango_woba <- wOBA_values(Batting, Pitching, Fielding, Fangraphs=F) head(tango_woba, 3)
A relational database is not needed to work with these data. However, we may want to store the data to be called more quickly at a later time. We can download all of the tables at once with the get_bbdb()
function and then write them to an empty schema in our favorite database. The example uses a newly created PostgreSQL instance, but other database tools can be used assuming an appropriate R package exists.
library(baseballDBR) library(RPostgreSQL) # Load all tables into the Global Environment. get_bbdb(AllTables = TRUE) # Make a list of all data frames. dbTables <- names(Filter(isTRUE, eapply(.GlobalEnv, is.data.frame))) # Load data base drivers and load all data frames in a loop. drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, host= "localhost", dbname= "lahman", user= "YOUR_USERNAME", password = "YOUR_PASSWORD") for (i in 1:length(dbTables)) { dbWriteTable(con, name = dbTables[i], value = get0(dbTables[i]), overwrite = TRUE) } # Disconnect from database. dbDisconnect(con) rm(con, drv)
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