Salaries table

Description

Player salary data.

Usage

data(Salaries)

Format

A data frame with 23141 observations on the following 5 variables.

yearID

Year

teamID

Team; a factor

lgID

League; a factor

playerID

Player ID code

salary

Salary

Details

There is no real coverage of player's salaries until 1985.

Source

Lahman, S. (2010) Lahman's Baseball Database, 1871-2012, 2012 version, http://baseball1.com/statistics/

Examples

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

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1980    1990    2000    2000    2010    2010

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

## 
## 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 
##  550  738  627  663  711  867  685  769  923  884  986  931  925  998 1006 
## 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 
##  836  860  846  827  831  831  819  842  856  813  830  839  848

# add salary to Batting data
batting <- merge(Batting, 
                 Salaries[,c("playerID", "yearID", "teamID", "salary")], 
                 by=c("playerID", "yearID", "teamID"), all.x=TRUE)
str(batting)

## 'data.frame':    96600 obs. of  25 variables:
##  $ playerID : chr  "aardsda01" "aardsda01" "aardsda01" "aardsda01" ...
##  $ yearID   : int  2004 2006 2007 2008 2009 2010 2012 1954 1955 1956 ...
##  $ teamID   : Factor w/ 149 levels "ALT","ANA","ARI",..: 117 35 33 16 116 116 93 80 80 80 ...
##  $ stint    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ lgID     : Factor w/ 7 levels "AA","AL","FL",..: 5 5 2 2 2 2 2 5 5 5 ...
##  $ G        : int  11 45 25 47 73 53 1 122 153 153 ...
##  $ G_batting: int  11 43 2 5 3 4 NA 122 153 153 ...
##  $ AB       : int  0 2 0 1 0 0 NA 468 602 609 ...
##  $ R        : int  0 0 0 0 0 0 NA 58 105 106 ...
##  $ H        : int  0 0 0 0 0 0 NA 131 189 200 ...
##  $ X2B      : int  0 0 0 0 0 0 NA 27 37 34 ...
##  $ X3B      : int  0 0 0 0 0 0 NA 6 9 14 ...
##  $ HR       : int  0 0 0 0 0 0 NA 13 27 26 ...
##  $ RBI      : int  0 0 0 0 0 0 NA 69 106 92 ...
##  $ SB       : int  0 0 0 0 0 0 NA 2 3 2 ...
##  $ CS       : int  0 0 0 0 0 0 NA 2 1 4 ...
##  $ BB       : int  0 0 0 0 0 0 NA 28 49 37 ...
##  $ SO       : int  0 0 0 1 0 0 NA 39 61 54 ...
##  $ IBB      : int  0 0 0 0 0 0 NA NA 5 6 ...
##  $ HBP      : int  0 0 0 0 0 0 NA 3 3 2 ...
##  $ SH       : int  0 1 0 0 0 0 NA 6 7 5 ...
##  $ SF       : int  0 0 0 0 0 0 NA 4 4 7 ...
##  $ GIDP     : int  0 0 0 0 0 0 NA 13 20 21 ...
##  $ G_old    : int  11 45 2 5 NA NA NA 122 153 153 ...
##  $ salary   : int  300000 NA 387500 403250 419000 2750000 500000 NA NA NA ...