Using dplyr
Brad Thiessen
June 18, 2014
Load required packages
Let’s load the Mosaic package:
require(dplyr)
require(mosaic)
require(ggplot2)
require(Lahman)Data manipulation
In the last document, I showed how to use R to explore datasets using summary statistics and visualizations. We often need to manipulate our data to get it into a format where it’s easy to create these visualizations.
In this document, we’ll use the dplyr package to manipulate a large dataset.
To learn more about dplyr, you can run the command: vignette("introduction", package = "dplyr")
First, let’s load some baseball data:
### Lists datasets available in the Lahman package
data(package="Lahman")
# Loads Batting data
data(Batting)
# Gets dimensions of Batting data
dim(Batting)## [1] 96600 24This Batting dataset has 96,600 rows and 24 columns. Let’s get an idea of what those rows and columns represent:
head(Batting, 8)## playerID yearID stint teamID lgID G G_batting AB R H X2B X3B HR
## 1 aardsda01 2004 1 SFN NL 11 11 0 0 0 0 0 0
## 2 aardsda01 2006 1 CHN NL 45 43 2 0 0 0 0 0
## 3 aardsda01 2007 1 CHA AL 25 2 0 0 0 0 0 0
## 4 aardsda01 2008 1 BOS AL 47 5 1 0 0 0 0 0
## 5 aardsda01 2009 1 SEA AL 73 3 0 0 0 0 0 0
## 6 aardsda01 2010 1 SEA AL 53 4 0 0 0 0 0 0
## 7 aardsda01 2012 1 NYA AL 1 NA NA NA NA NA NA NA
## 8 aaronha01 1954 1 ML1 NL 122 122 468 58 131 27 6 13
## RBI SB CS BB SO IBB HBP SH SF GIDP G_old
## 1 0 0 0 0 0 0 0 0 0 0 11
## 2 0 0 0 0 0 0 0 1 0 0 45
## 3 0 0 0 0 0 0 0 0 0 0 2
## 4 0 0 0 0 1 0 0 0 0 0 5
## 5 0 0 0 0 0 0 0 0 0 0 NA
## 6 0 0 0 0 0 0 0 0 0 0 NA
## 7 NA NA NA NA NA NA NA NA NA NA NA
## 8 69 2 2 28 39 NA 3 6 4 13 122Each row represents the batting statistics for a baseball player in a single year playing for a single team. So, for example, if a player played for 2 teams in a single season, that player would have two rows (one for each “stint”).
From the header displayed above, we can see the player “aardsda01” has data from the 2004-2012 seasons (where he played for San Francisco, both Chicago teams, Boston, Seattle, and the Yankees).
Suppose I want to know which 5 players in MLB history have batted in the most games. To do this, I need to add up the games played (G) for all the rows for each player.
The dplyr package allows us to do these kind of data manipulations easily. We want to:
Group the rows of the dataset by playerID
Summarize those groups by adding up the games played
Arrange the data by listing the 5 players with the most games played
To do this step-by-step, we would use:
## Group rows of data by playerID
players <- group_by(Batting, playerID)
## Summarize the groups by taking the sum of games played
games <- summarize(players, total = sum(G))
## Arrange the data by games played (in descending order) and list the top 5
head(arrange(games, desc(total)), 5)## Source: local data frame [5 x 2]
##
## playerID total
## 1 rosepe01 3562
## 2 yastrca01 3308
## 3 aaronha01 3298
## 4 henderi01 3081
## 5 cobbty01 3035Those commands took a small fraction of a second to complete. From the output, we can see the top five players with most games played are: Pete Rose, Carl Yastrzemski, Hank Aaron, Ricky Henderson, and Ty Cobb.
We could have also done this manipulation by chaining operations together with the %.% operator:
Batting %.%
group_by(playerID) %.%
summarize(total = sum(G)) %.%
arrange(desc(total)) %.%
head(5)## Source: local data frame [5 x 2]
##
## playerID total
## 1 rosepe01 3562
## 2 yastrca01 3308
## 3 aaronha01 3298
## 4 henderi01 3081
## 5 cobbty01 3035You can think of %.% as an arrow pointing to the right –>. It tells the computer to move on to the next command in the pipeline.
Let’s see some other manipulation commands we may want to use:
Select columns (variables) of interest
This Batting dataset has 24 variables. Suppose we’re only interested in 14 of those columns: playerID, yearID, teamID, at-bats, hits, doubles, triples, homeruns, stolen bases, number of times caught stealing, strikeouts, walks, hit-by-pitch numbers, and sacrifice flies.
We can select a subset of columns from our dataset with the select command:
Batting %.%
select(playerID, yearID, teamID, AB, H, X2B, X3B, HR, SB, CS, SO, BB, HBP, SF) %.%
head(10)## playerID yearID teamID AB H X2B X3B HR SB CS SO BB HBP SF
## 1 aardsda01 2004 SFN 0 0 0 0 0 0 0 0 0 0 0
## 2 aardsda01 2006 CHN 2 0 0 0 0 0 0 0 0 0 0
## 3 aardsda01 2007 CHA 0 0 0 0 0 0 0 0 0 0 0
## 4 aardsda01 2008 BOS 1 0 0 0 0 0 0 1 0 0 0
## 5 aardsda01 2009 SEA 0 0 0 0 0 0 0 0 0 0 0
## 6 aardsda01 2010 SEA 0 0 0 0 0 0 0 0 0 0 0
## 7 aardsda01 2012 NYA NA NA NA NA NA NA NA NA NA NA NA
## 8 aaronha01 1954 ML1 468 131 27 6 13 2 2 39 28 3 4
## 9 aaronha01 1955 ML1 602 189 37 9 27 3 1 61 49 3 4
## 10 aaronha01 1956 ML1 609 200 34 14 26 2 4 54 37 2 7As you can see, our dataset now has these 14 columns. Suppose we wanted to add some new columns:
Batting average = hits / at-bats
On-base percentage = (hits + walks + hit-by-pitch) / (at-bats + walks + hbp + sacrifice flies)
Slugging percentage = (singles + 2doubles + 3triples + 4HRs) / (at-bats)
It’s easy to add columns using the mutate command in dplyr:
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, AB, H, X2B, X3B, HR, SB, CS, SO, BB, HBP, SF) %.%
mutate(Avg = H/AB,
OBP = (H + BB + HBP)/(AB+BB+HBP+SF),
SLG = (((H-X2B-X3B-HR)+(2*X2B)+(3*X3B)+(4*HR))/AB)) %.%
head(12)## playerID yearID teamID AB H X2B X3B HR SB CS SO BB HBP SF Avg
## 1 aardsda01 2004 SFN 0 0 0 0 0 0 0 0 0 0 0 NaN
## 2 aardsda01 2006 CHN 2 0 0 0 0 0 0 0 0 0 0 0.0000
## 3 aardsda01 2007 CHA 0 0 0 0 0 0 0 0 0 0 0 NaN
## 4 aardsda01 2008 BOS 1 0 0 0 0 0 0 1 0 0 0 0.0000
## 5 aardsda01 2009 SEA 0 0 0 0 0 0 0 0 0 0 0 NaN
## 6 aardsda01 2010 SEA 0 0 0 0 0 0 0 0 0 0 0 NaN
## 7 aardsda01 2012 NYA NA NA NA NA NA NA NA NA NA NA NA NA
## 8 aaronha01 1954 ML1 468 131 27 6 13 2 2 39 28 3 4 0.2799
## 9 aaronha01 1955 ML1 602 189 37 9 27 3 1 61 49 3 4 0.3140
## 10 aaronha01 1956 ML1 609 200 34 14 26 2 4 54 37 2 7 0.3284
## 11 aaronha01 1957 ML1 615 198 27 6 44 1 1 58 57 0 3 0.3220
## 12 aaronha01 1958 ML1 601 196 34 4 30 4 1 49 59 1 3 0.3261
## OBP SLG
## 1 NaN NaN
## 2 0.0000 0.0000
## 3 NaN NaN
## 4 0.0000 0.0000
## 5 NaN NaN
## 6 NaN NaN
## 7 NA NA
## 8 0.3221 0.4466
## 9 0.3663 0.5399
## 10 0.3649 0.5583
## 11 0.3778 0.6000
## 12 0.3855 0.5458As you can see, the command worked for rows 8-12. We can see, for example, that Hank Aaron (aaronha01) batted .279 in 1954. We can also see the command gave us “NaN” and “NA” in several of those rows.
The NA represents missing data, while NaN means “not a number.” We got NaN results when we tried to divide by zero in our calculations. For example, the first row lists a player with no at-bats. When we tried to calculate a batting average for that row, the calculation was 0/0 = NaN. To get around this problem, we can filter the dataset to include only players who have at least one at-bat in a season:
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, AB, H, X2B, X3B, HR, SB, CS, SO, BB, HBP, SF) %.%
filter(AB>0) %.%
mutate(Avg = H/AB,
OBP = (H + BB + HBP)/(AB+BB+HBP+SF),
SLG = (((H-X2B-X3B-HR)+(2*X2B)+(3*X3B)+(4*HR))/AB)) %.%
head(5)## playerID yearID teamID AB H X2B X3B HR SB CS SO BB HBP SF Avg
## 1 aardsda01 2006 CHN 2 0 0 0 0 0 0 0 0 0 0 0.0000
## 2 aardsda01 2008 BOS 1 0 0 0 0 0 0 1 0 0 0 0.0000
## 3 aaronha01 1954 ML1 468 131 27 6 13 2 2 39 28 3 4 0.2799
## 4 aaronha01 1955 ML1 602 189 37 9 27 3 1 61 49 3 4 0.3140
## 5 aaronha01 1956 ML1 609 200 34 14 26 2 4 54 37 2 7 0.3284
## OBP SLG
## 1 0.0000 0.0000
## 2 0.0000 0.0000
## 3 0.3221 0.4466
## 4 0.3663 0.5399
## 5 0.3649 0.5583Let’s find the top 10 season slugging percentages in MLB history:
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, AB, H, X2B, X3B, HR, SB, CS, SO, BB, HBP, SF) %.%
filter(AB>0) %.%
mutate(Avg = H/AB,
OBP = (H + BB + HBP)/(AB+BB+HBP+SF),
SLG = (((H-X2B-X3B-HR)+(2*X2B)+(3*X3B)+(4*HR))/AB)) %.%
arrange(desc(SLG)) %.%
head(11)## playerID yearID teamID AB H X2B X3B HR SB CS SO BB HBP SF Avg OBP SLG
## 1 chacigu01 2010 HOU 1 1 0 0 1 0 0 0 0 0 0 1 1 4
## 2 hernafe02 2008 SEA 1 1 0 0 1 0 0 0 0 0 0 1 1 4
## 3 lefebbi01 1938 BOS 1 1 0 0 1 0 0 0 0 0 NA 1 NA 4
## 4 motagu01 1999 MON 1 1 0 0 1 0 0 0 0 0 0 1 1 4
## 5 narumbu01 1963 BAL 1 1 0 0 1 0 0 0 0 0 0 1 1 4
## 6 perrypa02 1988 CHN 1 1 0 0 1 0 0 0 0 0 0 1 1 4
## 7 quirkja01 1984 CLE 1 1 0 0 1 0 0 0 0 0 0 1 1 4
## 8 rogered01 2005 BAL 1 1 0 0 1 0 2 0 0 0 0 1 1 4
## 9 sleatlo01 1958 DET 1 1 0 0 1 0 0 0 0 0 0 1 1 4
## 10 yanes01 2000 TBA 1 1 0 0 1 0 0 0 0 0 0 1 1 4
## 11 altroni01 1924 WS1 1 1 0 1 0 0 0 0 0 0 NA 1 NA 3As you can see, there were 10 players who had a homerun in their single at-bat during the season (which yields a slugging percentage of 4.00). Let’s filter these results to only include players with at least 100 at-bats in a season:
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, AB, H, X2B, X3B, HR, SB, CS, SO, BB, HBP, SF) %.%
filter(AB>100) %.%
mutate(Avg = H/AB,
OBP = (H + BB + HBP)/(AB+BB+HBP+SF),
SLG = (((H-X2B-X3B-HR)+(2*X2B)+(3*X3B)+(4*HR))/AB)) %.%
arrange(desc(SLG)) %.%
head(10)## playerID yearID teamID AB H X2B X3B HR SB CS SO BB HBP SF Avg
## 1 bondsba01 2001 SFN 476 156 32 2 73 13 3 93 177 9 2 0.3277
## 2 ruthba01 1920 NYA 457 172 36 9 54 14 14 80 150 3 NA 0.3764
## 3 ruthba01 1921 NYA 540 204 44 16 59 17 13 81 145 4 NA 0.3778
## 4 bondsba01 2004 SFN 373 135 27 3 45 6 1 41 232 9 3 0.3619
## 5 bondsba01 2002 SFN 403 149 31 2 46 9 2 47 198 9 2 0.3697
## 6 ruthba01 1927 NYA 540 192 29 8 60 7 6 89 137 0 NA 0.3556
## 7 gehrilo01 1927 NYA 584 218 52 18 47 10 8 84 109 3 NA 0.3733
## 8 ruthba01 1923 NYA 522 205 45 13 41 17 21 93 170 4 NA 0.3927
## 9 hornsro01 1925 SLN 504 203 41 10 39 5 3 39 83 2 NA 0.4028
## 10 mcgwima01 1998 SLN 509 152 21 0 70 1 0 155 162 6 4 0.2986
## OBP SLG
## 1 0.5151 0.8634
## 2 NA 0.8490
## 3 NA 0.8463
## 4 0.6094 0.8123
## 5 0.5817 0.7990
## 6 NA 0.7722
## 7 NA 0.7654
## 8 NA 0.7644
## 9 NA 0.7560
## 10 0.4699 0.7525From this, we see Barry Bonds had the highest slugging percentage in 2001 (when he hit 73 home runs). Let’s use a similar set of commands to see which players had the highest number of strikeouts:
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, SO) %.%
arrange(desc(SO)) %.%
head(5)## playerID yearID teamID SO
## 1 reynoma01 2009 ARI 223
## 2 dunnad01 2012 CHA 222
## 3 reynoma01 2010 ARI 211
## 4 stubbdr01 2011 CIN 205
## 5 reynoma01 2008 ARI 204Mark Reynolds, in 2009, struck-out 223 times. Adam Dunn was a close second, with 222 strikeouts in 2012 for the White Sox.
Who had the most career strikeouts? Let’s see:
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, SO) %.%
group_by(playerID) %.%
summarize(totalSO = sum(SO)) %.%
arrange(desc(totalSO)) %.%
head(5)## Source: local data frame [5 x 2]
##
## playerID totalSO
## 1 jacksre01 2597
## 2 thomeji01 2548
## 3 sosasa01 2306
## 4 rodrial01 2032
## 5 dunnad01 2031Reggie Jackson has struck-out more times (2597 times) than any other player in MLB history. Does he have the highest strike-out rate (strikeouts per at-bat)? Let’s see (for players with at least 2000 at-bats in their careers):
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, AB, SO) %.%
group_by(playerID) %.%
summarize(totalSO = sum(SO),
totalAB = sum(AB)) %.%
filter(totalAB>2000) %.%
mutate(Krate = totalSO/totalAB) %.%
arrange(desc(Krate)) %.%
head(5)## Source: local data frame [5 x 4]
##
## playerID totalSO totalAB Krate
## 1 custja01 819 2107 0.3887
## 2 branyru01 1118 2934 0.3810
## 3 reynoma01 1122 2973 0.3774
## 4 deerro01 1409 3881 0.3631
## 5 jacksbo01 841 2393 0.3514A Google search tells me the leader, striking out nearly 39% of the time, is Jack Cust (who amassed most of his strikeouts in Oakland from 2007-2009).
I wonder who had the highest strikeout rate while playing for the Detroit Tigers…
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, AB, SO) %.%
group_by(playerID) %.%
filter(teamID=="DET") %.%
summarize(totalSO = sum(SO),
totalAB = sum(AB)) %.%
filter(totalAB>2000) %.%
mutate(Krate = totalSO/totalAB) %.%
arrange(desc(Krate)) %.%
head(5)## Source: local data frame [5 x 4]
##
## playerID totalSO totalAB Krate
## 1 ingebr01 1189 4626 0.2570
## 2 clarkto02 721 2831 0.2547
## 3 fieldce01 926 3674 0.2520
## 4 grandcu01 618 2579 0.2396
## 5 gibsoki01 930 4170 0.2230Brandon Inge. What about the lowest strike-out rate? To do this, I just sort the data in ascending order (deleting the desc command):
## Add new columns (BA, OBP, SLG)
Batting %.%
select(playerID, yearID, teamID, AB, SO) %.%
group_by(playerID) %.%
filter(teamID=="DET") %.%
summarize(totalSO = sum(SO),
totalAB = sum(AB)) %.%
filter(totalAB>2000) %.%
mutate(Krate = totalSO/totalAB) %.%
arrange(Krate) %.%
head(5)## Source: local data frame [5 x 4]
##
## playerID totalSO totalAB Krate
## 1 cramedo01 86 2720 0.03162
## 2 kellge01 107 3303 0.03239
## 3 bassljo01 73 2240 0.03259
## 4 gehrich01 372 8860 0.04199
## 5 kuennha01 205 4372 0.04689Google tells me it’s Doc Cramer, who played for Detroit from 1942-1948.
My favorite baseball player is Alan Trammell, a shortstop for the Detroit Tigers. To access his data, I need to know his playerID. I’ll look it up by typing in the first letters of his last name:
playerInfo("tramm")## playerID nameFirst nameLast
## 14308 trammal01 Alan Trammell
## 14309 trammbu01 Bubba TrammellFrom this, we see there were two Trammells in MLB: Alan and Bubba. I want playerID trammal01. Let’s take a look at his batting statistics each season. Since I already know his playerID and teamID, I’ll eliminate those columns (using a minus - in the select command):
Batting %.%
filter(playerID=="trammal01") %.%
select(-playerID, -teamID, -stint, -lgID, -G_batting, -G_old)## yearID G AB R H X2B X3B HR RBI SB CS BB SO IBB HBP SH SF GIDP
## 1 1977 19 43 6 8 0 0 0 0 0 0 4 12 0 0 1 0 1
## 2 1978 139 448 49 120 14 6 2 34 3 1 45 56 0 2 6 3 12
## 3 1979 142 460 68 127 11 4 6 50 17 14 43 55 0 0 12 5 6
## 4 1980 146 560 107 168 21 5 9 65 12 12 69 63 2 3 13 7 10
## 5 1981 105 392 52 101 15 3 2 31 10 3 49 31 2 3 16 3 10
## 6 1982 157 489 66 126 34 3 9 57 19 8 52 47 0 0 9 6 5
## 7 1983 142 505 83 161 31 2 14 66 30 10 57 64 2 0 15 4 7
## 8 1984 139 555 85 174 34 5 14 69 19 13 60 63 2 3 6 2 8
## 9 1985 149 605 79 156 21 7 13 57 14 5 50 71 4 2 11 9 6
## 10 1986 151 574 107 159 33 7 21 75 25 12 59 57 4 5 11 4 7
## 11 1987 151 597 109 205 34 3 28 105 21 2 60 47 8 3 2 6 11
## 12 1988 128 466 73 145 24 1 15 69 7 4 46 46 8 4 0 7 14
## 13 1989 121 449 54 109 20 3 5 43 10 2 45 45 1 4 3 5 9
## 14 1990 146 559 71 170 37 1 14 89 12 10 68 55 7 1 3 6 11
## 15 1991 101 375 57 93 20 0 9 55 11 2 37 39 1 3 5 1 7
## 16 1992 29 102 11 28 7 1 1 11 2 2 15 4 0 1 1 1 6
## 17 1993 112 401 72 132 25 3 12 60 12 8 38 38 2 2 4 2 7
## 18 1994 76 292 38 78 17 1 8 28 3 0 16 35 1 1 2 0 8
## 19 1995 74 223 28 60 12 0 2 23 3 1 27 19 4 0 3 2 8
## 20 1996 66 193 16 45 2 0 1 16 6 0 10 27 0 0 1 3 3From this, we can see he played 20 seasons from 1977-1996. We can list his 5 best seasons in terms of on-base percentage:
## Add new columns (BA, OBP, SLG)
Batting %.%
filter(playerID=="trammal01") %.%
select(-playerID, -teamID, -stint, -lgID, -G_batting, -G_old) %.%
mutate(Avg = H/AB,
OBP = (H + BB + HBP)/(AB+BB+HBP+SF)) %.%
arrange(desc(OBP)) %.%
head(5)## yearID G AB R H X2B X3B HR RBI SB CS BB SO IBB HBP SH SF GIDP
## 1 1987 151 597 109 205 34 3 28 105 21 2 60 47 8 3 2 6 11
## 2 1993 112 401 72 132 25 3 12 60 12 8 38 38 2 2 4 2 7
## 3 1983 142 505 83 161 31 2 14 66 30 10 57 64 2 0 15 4 7
## 4 1984 139 555 85 174 34 5 14 69 19 13 60 63 2 3 6 2 8
## 5 1990 146 559 71 170 37 1 14 89 12 10 68 55 7 1 3 6 11
## Avg OBP
## 1 0.3434 0.4024
## 2 0.3292 0.3883
## 3 0.3188 0.3852
## 4 0.3135 0.3823
## 5 0.3041 0.3770We can also get his career totals:
Batting %.%
filter(playerID=="trammal01") %.%
group_by(playerID) %.%
summarize(seasons = n(),
games = sum(G),
AB = sum(AB),
hits = sum(H),
dbl = sum(X2B),
trpl = sum(X3B),
HR = sum(HR),
Avg = (sum(H) / sum(AB)),
OBP = ((sum(H)+sum(BB)+sum(HBP))/(sum(AB)+sum(BB)+sum(HBP)+sum(SF))),
SB = sum(SB),
SBpct = (sum(SB)/(sum(CS)+sum(SB))))## Source: local data frame [1 x 12]
##
## playerID seasons games AB hits dbl trpl HR Avg OBP SB SBpct
## 1 trammal01 20 2293 8288 2365 412 55 185 0.2854 0.3515 236 0.6841Over his 20 seasons, Alan Trammell batted .285, hit 185 HR, and successfully stole bases 68.4% of the time.
Visualizations
Let’s see the relationship between career homeruns and strikeouts for every player in MLB history
### Minimum 400 games over 5 seasons with at least one SO and HR
### na.rm=TRUE is a command to remove NA values from each variable
hrSO <- Batting %.%
group_by(playerID) %.%
summarize(seasons = n(),
games = sum(G, na.rm=TRUE),
atbats = sum(AB, na.rm=TRUE),
HR = sum(HR, na.rm=TRUE),
Avg = (sum(H, na.rm=TRUE) / sum(AB, na.rm=TRUE)),
SO = sum(SO, na.rm=TRUE)) %.%
filter(seasons>5 & games>400 & atbats>1000 & SO>0 & HR>0)
### Create plot
ggplot(hrSO, aes(HR, SO)) +
geom_point(alpha = 1/2) +
geom_smooth() +
scale_size_area()## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
This plot shows the general positive relationship between strikeouts and homeruns. This could be simply due to some players having more at-bats than others, so let’s plot the relationship between “home runs per at-bat” and “strikeouts per at-bat”
### Minimum 400 games over 5 seasons with at least one SO and HR
### na.rm=TRUE is a command to remove NA values from each variable
hrSO2 <- Batting %.%
group_by(playerID) %.%
summarize(seasons = n(),
games = sum(G, na.rm=TRUE),
atbats = sum(AB, na.rm=TRUE),
HR = sum(HR, na.rm=TRUE),
Avg = (sum(H, na.rm=TRUE) / sum(AB, na.rm=TRUE)),
SO = sum(SO, na.rm=TRUE),
HRperAB = (HR/atbats),
SOperAB = (SO/atbats)) %.%
filter(seasons>5 & games>400 & atbats>1000 & SO>0 & HR>0)
### Create plot
ggplot(hrSO2, aes(HRperAB, SOperAB)) +
geom_point(alpha = 1/2) +
geom_smooth() +
scale_size_area()## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
I wonder if this same relationship holds for pitchers. Do pitchers with more career strikeouts give up more career homeruns? Let’s see (controlling for innings pitched)…
### Minimum 3000 innings pitched
### na.rm=TRUE is a command to remove NA values from each variable
PitcherHRso <- Pitching %.%
group_by(playerID) %.%
summarize(seasons = n(),
IPouts = sum(IPouts, na.rm=TRUE),
HR = sum(HR, na.rm=TRUE),
SO = sum(SO, na.rm=TRUE),
HRperIP = (HR/IPouts),
SOperIP = (SO/IPouts)) %.%
filter(IPouts > 3000)
### Create plot
ggplot(PitcherHRso, aes(HRperIP, SOperIP)) +
geom_point(aes(size=IPouts), alpha = .4) +
geom_smooth() +
scale_size_area()## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
The size of the dots indicate the number of innings pitched for each pitcher. This relationship is a bit more complicated than it was for batters.
Final example - team statistics
See if you can figure out what this plot shows:
TeamSummary <- Batting %.%
group_by(teamID) %.%
summarize(games = sum(G, na.rm=TRUE),
atbats = sum(AB, na.rm=TRUE),
HR = sum(HR, na.rm=TRUE),
Avg = (sum(H, na.rm=TRUE) / sum(AB, na.rm=TRUE)),
SO = sum(SO, na.rm=TRUE),
HRperGame = (HR/games),
SOperGame = (SO/games)) %.%
filter(HRperGame>0 & SOperGame>0)
ggplot(TeamSummary, aes(HRperGame, SOperGame)) +
geom_point(aes(size=Avg), alpha = 1/2) +
geom_smooth() +
scale_size_area()## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.