Supported by Dr. Osamu Ogasawara and  providing  . |
|
Last data update: 2014.03.03 |
Managers tableDescriptionManagers table: information about individual team managers, teams they managed and some basic statistics for those teams in each year. Usagedata(Managers) FormatA data frame with 3370 observations on the following 10 variables.
SourceLahman, S. (2015) Lahman's Baseball Database, 1871-2014, 2015 version, http://baseball1.com/statistics/ Examples
####################################
# Basic career summaries by manager
####################################
library('plyr')
mgrsumm <- function(d) {
df <- data.frame(with(d,
nyear = length(unique(yearID)),
yearBegin = min(yearID),
yearEnd = max(yearID),
nTeams = length(unique(teamID)),
nfirst = sum(rank == 1L),
W = sum(W),
L = sum(L),
WinPct = round(W/(W + L), 3)))
df
}
mgrTotals <- ddply(Managers, .(playerID), summarise,
nyear = length(unique(yearID)),
yearBegin = min(yearID),
yearEnd = max(yearID),
nTeams = length(unique(teamID)),
nfirst = sum(rank == 1L),
games = sum(W + L),
W = sum(W),
L = sum(L),
WinPct = round(sum(W)/sum(W + L), 3))
mgrTotals <- merge(mgrTotals,
subset(Master, !is.na(playerID),
select = c('playerID', 'nameLast', 'nameFirst')),
by = 'playerID')
##########################
# Some basic queries
##########################
# Top 20 managers in terms of years of service:
head(arrange(mgrTotals, -nyear), 20)
# Top 20 winningest managers (500 games minimum)
head(arrange(subset(mgrTotals, games >= 500), -WinPct), 20)
# Hmm. Most of these are 19th century managers.
# How about the modern era?
head(arrange(subset(mgrTotals, yearBegin >= 1900 & games >= 500), -WinPct), 20)
# Top 10 managers in terms of percentage of titles (league or divisional) -
# should bias toward managers post-1970 since more first place finishes
# are available
head(arrange(subset(mgrTotals, yearBegin >= 1900 & games >= 500),
-round(nfirst/nyear, 3)), 10)
# How about pre-1969?
head(arrange(subset(mgrTotals,
yearBegin >= 1900 & yearEnd <= 1969 & games >= 500),
-round(nfirst/nyear, 3)), 10)
##############################################
# Density plot of the number of games managed:
##############################################
library('ggplot2')
ggplot(mgrTotals, aes(x = games)) + geom_density(fill = 'red', alpha = 0.3) +
labs(x = 'Number of games managed')
# Who managed more than 4000 games?
subset(mgrTotals, games >= 4000)
# Connie Mack had an advantage: he owned the Philadelphia A's :)
# Table of Tony LaRussa's team finishes:
with(subset(Managers, playerID == 'larusto01'), table(rank))
# To include zero frequencies, one alternative is the tabulate() function:
with(subset(Managers, playerID == 'larusto01'), tabulate(rank, 7))
##############################################
# Scatterplot of winning percentage vs. number of games managed (min 100)
##############################################
ggplot(subset(mgrTotals, yearBegin >= 1900 & games >= 100),
aes(x = games, y = WinPct)) + geom_point() + geom_smooth() +
labs(x = 'Number of games managed')
############################################
# Division titles
############################################
# Plot of number of first place finishes by managers with at least 8 years
# of experience in the divisional era (>= 1969):
divMgr <- subset(mgrTotals, yearBegin >= 1969 & nyear >= 8)
# Response is the number of titles
ggplot(divMgr, aes(x = nyear, y = nfirst)) +
geom_point(position = position_jitter(w = 0.2)) +
labs(x = 'Number of years', y = 'Number of divisional titles') +
geom_smooth()
# Response is the proportion of titles
ggplot(divMgr, aes(x = nyear, y = round(nfirst/nyear, 3))) +
geom_point(position = position_jitter(w = 0.2)) +
labs(x = 'Number of years', y = 'Proportion of divisional titles') +
geom_smooth()
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(Lahman)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Lahman/Managers.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Managers
> ### Title: Managers table
> ### Aliases: Managers
> ### Keywords: datasets
>
> ### ** Examples
>
> ####################################
> # Basic career summaries by manager
> ####################################
>
> library('plyr')
> mgrsumm <- function(d) {
+ df <- data.frame(with(d,
+ nyear = length(unique(yearID)),
+ yearBegin = min(yearID),
+ yearEnd = max(yearID),
+ nTeams = length(unique(teamID)),
+ nfirst = sum(rank == 1L),
+ W = sum(W),
+ L = sum(L),
+ WinPct = round(W/(W + L), 3)))
+ df
+ }
>
> mgrTotals <- ddply(Managers, .(playerID), summarise,
+ nyear = length(unique(yearID)),
+ yearBegin = min(yearID),
+ yearEnd = max(yearID),
+ nTeams = length(unique(teamID)),
+ nfirst = sum(rank == 1L),
+ games = sum(W + L),
+ W = sum(W),
+ L = sum(L),
+ WinPct = round(sum(W)/sum(W + L), 3))
> mgrTotals <- merge(mgrTotals,
+ subset(Master, !is.na(playerID),
+ select = c('playerID', 'nameLast', 'nameFirst')),
+ by = 'playerID')
>
> ##########################
> # Some basic queries
> ##########################
>
> # Top 20 managers in terms of years of service:
> head(arrange(mgrTotals, -nyear), 20)
playerID nyear yearBegin yearEnd nTeams nfirst games W L WinPct
1 mackco01 53 1894 1950 2 9 7679 3731 3948 0.486
2 larusto01 33 1979 2011 3 12 5093 2728 2365 0.536
3 mcgrajo01 33 1899 1932 3 11 4711 2763 1948 0.586
4 coxbo01 29 1978 2010 2 15 4505 2504 2001 0.556
5 harribu01 29 1924 1956 5 3 4377 2158 2219 0.493
6 torrejo01 29 1977 2010 5 13 4323 2326 1997 0.538
7 andersp01 26 1970 1995 2 7 4028 2194 1834 0.545
8 mauchge01 26 1960 1987 4 2 3939 1902 2037 0.483
9 mckecbi01 25 1915 1946 5 4 3619 1896 1723 0.524
10 stengca01 25 1934 1965 4 10 3747 1905 1842 0.508
11 durocle01 24 1939 1973 4 3 3717 2008 1709 0.540
12 mccarjo99 24 1926 1950 3 9 3458 2125 1333 0.615
13 alstowa01 23 1954 1976 2 7 3653 2040 1613 0.558
14 pinielo01 23 1986 2010 5 6 3548 1835 1713 0.517
15 wrighha01 23 1871 1893 4 6 2110 1225 885 0.581
16 leylaji99 22 1986 2013 4 6 3497 1769 1728 0.506
17 ansonca01 21 1875 1898 3 5 2243 1296 947 0.578
18 dykesji01 21 1934 1961 6 0 2947 1406 1541 0.477
19 lasorto01 21 1976 1996 1 8 3038 1599 1439 0.526
20 willidi02 21 1967 1988 6 6 3022 1571 1451 0.520
nameLast nameFirst
1 Mack Connie
2 LaRussa Tony
3 McGraw John
4 Cox Bobby
5 Harris Bucky
6 Torre Joe
7 Anderson Sparky
8 Mauch Gene
9 McKechnie Bill
10 Stengel Casey
11 Durocher Leo
12 McCarthy Joe
13 Alston Walter
14 Piniella Lou
15 Wright Harry
16 Leyland Jim
17 Anson Cap
18 Dykes Jimmy
19 Lasorda Tom
20 Williams Dick
>
> # Top 20 winningest managers (500 games minimum)
> head(arrange(subset(mgrTotals, games >= 500), -WinPct), 20)
playerID nyear yearBegin yearEnd nTeams nfirst games W L WinPct
1 mccarjo99 24 1926 1950 3 9 3458 2125 1333 0.615
2 mutriji99 9 1883 1891 2 3 1077 658 419 0.611
3 comisch01 12 1883 1894 3 4 1381 840 541 0.608
4 seleefr99 16 1890 1905 2 5 2146 1284 862 0.598
5 southbi01 13 1929 1951 2 4 1748 1044 704 0.597
6 chancfr01 11 1905 1923 3 4 1594 946 648 0.593
7 mcgrajo01 33 1899 1932 3 11 4711 2763 1948 0.586
8 lopezal01 17 1951 1969 2 2 2414 1410 1004 0.584
9 weaveea99 17 1968 1986 1 6 2540 1480 1060 0.583
10 cochrmi01 5 1934 1938 1 2 598 348 250 0.582
11 wrighha01 23 1871 1893 4 6 2110 1225 885 0.581
12 ansonca01 21 1875 1898 3 5 2243 1296 947 0.578
13 dyered01 5 1946 1950 1 1 771 446 325 0.578
14 rowlapa99 4 1915 1918 1 1 586 339 247 0.578
15 clarkfr01 19 1897 1915 2 4 2783 1602 1181 0.576
16 mcgunbi01 5 1888 1896 4 2 575 327 248 0.569
17 wardjo01 7 1880 1894 4 0 732 412 320 0.563
18 johnsda02 17 1984 2013 5 6 2443 1372 1071 0.562
19 moranpa01 9 1915 1923 2 2 1334 748 586 0.561
20 girarjo01 8 2006 2014 2 3 1296 726 570 0.560
nameLast nameFirst
1 McCarthy Joe
2 Mutrie Jim
3 Comiskey Charlie
4 Selee Frank
5 Southworth Billy
6 Chance Frank
7 McGraw John
8 Lopez Al
9 Weaver Earl
10 Cochrane Mickey
11 Wright Harry
12 Anson Cap
13 Dyer Eddie
14 Rowland Pants
15 Clarke Fred
16 McGunnigle Bill
17 Ward Monte
18 Johnson Davey
19 Moran Pat
20 Girardi Joe
>
> # Hmm. Most of these are 19th century managers.
> # How about the modern era?
> head(arrange(subset(mgrTotals, yearBegin >= 1900 & games >= 500), -WinPct), 20)
playerID nyear yearBegin yearEnd nTeams nfirst games W L WinPct
1 mccarjo99 24 1926 1950 3 9 3458 2125 1333 0.615
2 southbi01 13 1929 1951 2 4 1748 1044 704 0.597
3 chancfr01 11 1905 1923 3 4 1594 946 648 0.593
4 lopezal01 17 1951 1969 2 2 2414 1410 1004 0.584
5 weaveea99 17 1968 1986 1 6 2540 1480 1060 0.583
6 cochrmi01 5 1934 1938 1 2 598 348 250 0.582
7 dyered01 5 1946 1950 1 1 771 446 325 0.578
8 rowlapa99 4 1915 1918 1 1 586 339 247 0.578
9 johnsda02 17 1984 2013 5 6 2443 1372 1071 0.562
10 moranpa01 9 1915 1923 2 2 1334 748 586 0.561
11 girarjo01 8 2006 2014 2 3 1296 726 570 0.560
12 oneilst01 14 1935 1954 4 1 1861 1040 821 0.559
13 alstowa01 23 1954 1976 2 7 3653 2040 1613 0.558
14 coxbo01 29 1978 2010 2 15 4505 2504 2001 0.556
15 dierkla01 5 1997 2001 1 5 783 435 348 0.556
16 huggimi01 17 1913 1929 2 6 2547 1413 1134 0.555
17 terrybi01 10 1932 1941 1 3 1484 823 661 0.555
18 martibi02 16 1969 1988 5 6 2266 1253 1013 0.553
19 littlgr99 4 2002 2007 2 0 648 358 290 0.552
20 johnswa01 7 1929 1935 2 0 961 529 432 0.550
nameLast nameFirst
1 McCarthy Joe
2 Southworth Billy
3 Chance Frank
4 Lopez Al
5 Weaver Earl
6 Cochrane Mickey
7 Dyer Eddie
8 Rowland Pants
9 Johnson Davey
10 Moran Pat
11 Girardi Joe
12 O'Neill Steve
13 Alston Walter
14 Cox Bobby
15 Dierker Larry
16 Huggins Miller
17 Terry Bill
18 Martin Billy
19 Little Grady
20 Johnson Walter
>
> # Top 10 managers in terms of percentage of titles (league or divisional) -
> # should bias toward managers post-1970 since more first place finishes
> # are available
> head(arrange(subset(mgrTotals, yearBegin >= 1900 & games >= 500),
+ -round(nfirst/nyear, 3)), 10)
playerID nyear yearBegin yearEnd nTeams nfirst games W L WinPct
1 dierkla01 5 1997 2001 1 5 783 435 348 0.556
2 howsedi01 8 1978 1986 2 5 932 507 425 0.544
3 freyji99 5 1980 1986 2 3 610 323 287 0.530
4 coxbo01 29 1978 2010 2 15 4505 2504 2001 0.556
5 brenlbo01 4 2001 2004 1 2 565 303 262 0.536
6 kenneke99 4 1993 1996 2 2 582 309 273 0.531
7 manuech01 12 2000 2013 2 6 1826 1000 826 0.548
8 mattido01 4 2011 2014 1 2 647 354 293 0.547
9 morgajo01 4 1988 1991 1 2 563 301 262 0.535
10 gardero01 13 2002 2014 1 6 2107 1068 1039 0.507
nameLast nameFirst
1 Dierker Larry
2 Howser Dick
3 Frey Jim
4 Cox Bobby
5 Brenly Bob
6 Kennedy Kevin
7 Manuel Charlie
8 Mattingly Don
9 Morgan Joe
10 Gardenhire Ron
>
> # How about pre-1969?
> head(arrange(subset(mgrTotals,
+ yearBegin >= 1900 & yearEnd <= 1969 & games >= 500),
+ -round(nfirst/nyear, 3)), 10)
playerID nyear yearBegin yearEnd nTeams nfirst games W L WinPct
1 cochrmi01 5 1934 1938 1 2 598 348 250 0.582
2 stengca01 25 1934 1965 4 10 3747 1905 1842 0.508
3 mccarjo99 24 1926 1950 3 9 3458 2125 1333 0.615
4 chancfr01 11 1905 1923 3 4 1594 946 648 0.593
5 huggimi01 17 1913 1929 2 6 2547 1413 1134 0.555
6 colliji01 6 1901 1906 1 2 831 455 376 0.548
7 streega01 6 1929 1938 2 2 697 365 332 0.524
8 southbi01 13 1929 1951 2 4 1748 1044 704 0.597
9 terrybi01 10 1932 1941 1 3 1484 823 661 0.555
10 carribi02 7 1913 1929 1 2 989 489 500 0.494
nameLast nameFirst
1 Cochrane Mickey
2 Stengel Casey
3 McCarthy Joe
4 Chance Frank
5 Huggins Miller
6 Collins Jimmy
7 Street Gabby
8 Southworth Billy
9 Terry Bill
10 Carrigan Bill
>
> ##############################################
> # Density plot of the number of games managed:
> ##############################################
>
> library('ggplot2')
> ggplot(mgrTotals, aes(x = games)) + geom_density(fill = 'red', alpha = 0.3) +
+ labs(x = 'Number of games managed')
>
> # Who managed more than 4000 games?
> subset(mgrTotals, games >= 4000)
playerID nyear yearBegin yearEnd nTeams nfirst games W L WinPct
12 andersp01 26 1970 1995 2 7 4028 2194 1834 0.545
113 coxbo01 29 1978 2010 2 15 4505 2504 2001 0.556
243 harribu01 29 1924 1956 5 3 4377 2158 2219 0.493
335 larusto01 33 1979 2011 3 12 5093 2728 2365 0.536
364 mackco01 53 1894 1950 2 9 7679 3731 3948 0.486
400 mcgrajo01 33 1899 1932 3 11 4711 2763 1948 0.586
618 torrejo01 29 1977 2010 5 13 4323 2326 1997 0.538
nameLast nameFirst
12 Anderson Sparky
113 Cox Bobby
243 Harris Bucky
335 LaRussa Tony
364 Mack Connie
400 McGraw John
618 Torre Joe
> # Connie Mack had an advantage: he owned the Philadelphia A's :)
>
> # Table of Tony LaRussa's team finishes:
> with(subset(Managers, playerID == 'larusto01'), table(rank))
rank
1 2 3 4 5 7
12 4 8 5 4 1
>
> # To include zero frequencies, one alternative is the tabulate() function:
> with(subset(Managers, playerID == 'larusto01'), tabulate(rank, 7))
[1] 12 4 8 5 4 0 1
>
>
> ##############################################
> # Scatterplot of winning percentage vs. number of games managed (min 100)
> ##############################################
>
> ggplot(subset(mgrTotals, yearBegin >= 1900 & games >= 100),
+ aes(x = games, y = WinPct)) + geom_point() + geom_smooth() +
+ labs(x = 'Number of games managed')
>
> ############################################
> # Division titles
> ############################################
>
> # Plot of number of first place finishes by managers with at least 8 years
> # of experience in the divisional era (>= 1969):
>
> divMgr <- subset(mgrTotals, yearBegin >= 1969 & nyear >= 8)
>
> # Response is the number of titles
> ggplot(divMgr, aes(x = nyear, y = nfirst)) +
+ geom_point(position = position_jitter(w = 0.2)) +
+ labs(x = 'Number of years', y = 'Number of divisional titles') +
+ geom_smooth()
Warning messages:
1: Removed 1 rows containing non-finite values (stat_smooth).
2: Removed 1 rows containing missing values (geom_point).
>
> # Response is the proportion of titles
> ggplot(divMgr, aes(x = nyear, y = round(nfirst/nyear, 3))) +
+ geom_point(position = position_jitter(w = 0.2)) +
+ labs(x = 'Number of years', y = 'Proportion of divisional titles') +
+ geom_smooth()
Warning messages:
1: Removed 1 rows containing non-finite values (stat_smooth).
2: Removed 1 rows containing missing values (geom_point).
>
>
>
>
>
>
> dev.off()
null device
1
>
|
Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and