CMSC 205 Homework 3
Kate Kilgus
Administrative
Please indicate
- Roughly how much time you spent on this HW so far: 1 hour
- The URL of the RPubs published URL here.
- What gave you the most trouble: the graphic
- Any comments you have: thanks for the extension!
Problem 1.
Define two new variables in the Teams data frame: batting average (BA) and slugging percentage (SLG). Batting average is the ratio of hits (H) to at-bats (AB), and slugging percentage is the total bases divided by at-bats. To compute the total bases, you get 1 for a single, 2 for a double, 3 for a triple, and 4 for a home run.
library(Lahman)
library(tidyverse)## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr## Conflicts with tidy packages ----------------------------------------------## filter(): dplyr, stats
## lag(): dplyr, statsTeams_new <- mutate(Teams, BA = H/AB, SLG = ((H + (2*X2B) + (3*X3B) + (4*HR))/AB))
head(Teams_new)## yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin LgWin
## 1 1871 NA BS1 BNA <NA> 3 31 NA 20 10 <NA> <NA> N
## 2 1871 NA CH1 CNA <NA> 2 28 NA 19 9 <NA> <NA> N
## 3 1871 NA CL1 CFC <NA> 8 29 NA 10 19 <NA> <NA> N
## 4 1871 NA FW1 KEK <NA> 7 19 NA 7 12 <NA> <NA> N
## 5 1871 NA NY2 NNA <NA> 5 33 NA 16 17 <NA> <NA> N
## 6 1871 NA PH1 PNA <NA> 1 28 NA 21 7 <NA> <NA> Y
## WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER ERA CG SHO SV
## 1 <NA> 401 1372 426 70 37 3 60 19 73 NA NA NA 303 109 3.55 22 1 3
## 2 <NA> 302 1196 323 52 21 10 60 22 69 NA NA NA 241 77 2.76 25 0 1
## 3 <NA> 249 1186 328 35 40 7 26 25 18 NA NA NA 341 116 4.11 23 0 0
## 4 <NA> 137 746 178 19 8 2 33 9 16 NA NA NA 243 97 5.17 19 1 0
## 5 <NA> 302 1404 403 43 21 1 33 15 46 NA NA NA 313 121 3.72 32 1 0
## 6 <NA> 376 1281 410 66 27 9 46 23 56 NA NA NA 266 137 4.95 27 0 0
## IPouts HA HRA BBA SOA E DP FP name
## 1 828 367 2 42 23 225 NA 0.83 Boston Red Stockings
## 2 753 308 6 28 22 218 NA 0.82 Chicago White Stockings
## 3 762 346 13 53 34 223 NA 0.81 Cleveland Forest Citys
## 4 507 261 5 21 17 163 NA 0.80 Fort Wayne Kekiongas
## 5 879 373 7 42 22 227 NA 0.83 New York Mutuals
## 6 747 329 3 53 16 194 NA 0.84 Philadelphia Athletics
## park attendance BPF PPF teamIDBR teamIDlahman45
## 1 South End Grounds I NA 103 98 BOS BS1
## 2 Union Base-Ball Grounds NA 104 102 CHI CH1
## 3 National Association Grounds NA 96 100 CLE CL1
## 4 Hamilton Field NA 101 107 KEK FW1
## 5 Union Grounds (Brooklyn) NA 90 88 NYU NY2
## 6 Jefferson Street Grounds NA 102 98 ATH PH1
## teamIDretro BA SLG
## 1 BS1 0.3104956 0.5021866
## 2 CH1 0.2700669 0.4431438
## 3 CL1 0.2765599 0.4603710
## 4 FW1 0.2386059 0.3324397
## 5 NY2 0.2870370 0.3960114
## 6 PH1 0.3200625 0.5144418#this is assuming that H gives singles Teams_newProblem 2.
Plot a time series of SLG since 1954 by league (lgID). Is slugging percentage typically higher in the American League (AL) or the National League?
Teams_new %>%
ggplot(aes(x = yearID, y = SLG, color = lgID)) +
geom_smooth(se = FALSE) +
xlim(1954, 2020)## `geom_smooth()` using method = 'gam'## Warning: Removed 1247 rows containing non-finite values (stat_smooth).
#SLG is typically higher in the American League Problem 3.
Display the top 15 teams ranked in terms of slugging percentage in MLB history. Repeat this using teams since 1969.
Teams_new %>%
arrange(desc(SLG)) %>%
head(n = 15)## yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin
## 1 2003 AL BOS BOS E 2 162 81 95 67 N Y
## 2 1927 AL NYA NYY <NA> 1 155 77 110 44 <NA> <NA>
## 3 1997 AL SEA SEA W 1 162 81 90 72 Y N
## 4 1996 AL SEA SEA W 2 161 81 85 76 N N
## 5 1930 AL NYA NYY <NA> 3 154 76 86 68 <NA> <NA>
## 6 1994 AL CLE CLE C 2 113 51 66 47 <NA> <NA>
## 7 2001 NL COL COL W 5 162 81 73 89 N N
## 8 1936 AL NYA NYY <NA> 1 155 77 102 51 <NA> <NA>
## 9 2009 AL NYA NYY E 1 162 81 103 59 Y N
## 10 2004 AL BOS BOS E 2 162 81 98 64 N Y
## 11 1995 AL CLE CLE C 1 144 72 100 44 Y N
## 12 2000 NL HOU HOU C 4 162 81 72 90 N N
## 13 1930 NL CHN CHC <NA> 2 156 79 90 64 <NA> <NA>
## 14 2003 NL ATL ATL E 1 162 81 101 61 Y N
## 15 1999 AL TEX TEX W 1 162 81 95 67 Y N
## LgWin WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER
## 1 N N 961 5769 1667 371 40 238 620 943 88 35 53 64 809 729
## 2 Y Y 975 5347 1644 291 103 158 635 605 90 64 NA NA 599 494
## 3 N N 925 5614 1574 312 21 264 626 1110 89 40 NA NA 833 770
## 4 N N 993 5668 1625 343 19 245 670 1052 90 39 NA NA 895 828
## 5 N N 1062 5448 1683 298 110 152 644 569 91 60 NA NA 898 741
## 6 <NA> <NA> 679 4022 1165 240 20 167 382 629 131 48 NA NA 562 493
## 7 N N 923 5690 1663 324 61 213 511 1027 132 54 61 50 906 841
## 8 Y Y 1065 5591 1676 315 83 182 700 594 77 40 NA NA 731 649
## 9 Y Y 915 5660 1604 325 21 244 663 1014 111 28 54 39 753 687
## 10 Y Y 949 5720 1613 373 25 222 659 1189 68 30 69 55 768 674
## 11 Y N 840 5028 1461 279 23 207 542 766 132 53 NA NA 607 554
## 12 N N 938 5570 1547 289 36 249 673 1129 114 52 83 61 944 865
## 13 N N 998 5581 1722 305 72 171 588 635 70 NA NA NA 870 748
## 14 N N 907 5670 1608 321 31 235 545 933 68 22 49 49 740 663
## 15 N N 945 5651 1653 304 29 230 611 937 111 54 NA NA 859 809
## ERA CG SHO SV IPouts HA HRA BBA SOA E DP FP name
## 1 4.48 5 6 36 4394 1503 153 488 1141 113 130 0.982 Boston Red Sox
## 2 3.20 82 11 20 4167 1403 42 409 431 196 123 0.960 New York Yankees
## 3 4.79 9 8 38 4341 1500 192 598 1207 123 143 0.980 Seattle Mariners
## 4 5.21 4 4 34 4293 1562 216 605 1000 110 155 0.980 Seattle Mariners
## 5 4.88 65 7 15 4101 1566 93 524 572 208 132 0.960 New York Yankees
## 6 4.36 17 5 21 3054 1097 94 404 666 90 119 0.980 Cleveland Indians
## 7 5.29 8 8 26 4290 1522 239 598 1058 96 167 0.984 Colorado Rockies
## 8 4.17 77 6 21 4200 1474 84 663 624 163 148 0.970 New York Yankees
## 9 4.26 3 8 51 4350 1386 181 574 1260 86 131 0.985 New York Yankees
## 10 4.18 4 12 36 4354 1430 159 447 1132 118 129 0.981 Boston Red Sox
## 11 3.83 10 10 50 3903 1261 135 445 926 101 142 0.980 Cleveland Indians
## 12 5.42 8 2 30 4313 1596 234 598 1064 133 149 0.978 Houston Astros
## 13 4.80 67 6 12 4209 1642 111 528 601 170 167 0.970 Chicago Cubs
## 14 4.10 4 7 51 4369 1425 147 555 992 121 166 0.981 Atlanta Braves
## 15 5.07 6 9 47 4306 1626 186 509 979 119 167 0.980 Texas Rangers
## park attendance BPF PPF teamIDBR teamIDlahman45
## 1 Fenway Park II 2724165 105 103 BOS BOS
## 2 Yankee Stadium I 1164015 98 94 NYY NYA
## 3 Kingdome 3192237 98 98 SEA SEA
## 4 Kingdome 2723850 100 99 SEA SEA
## 5 Yankee Stadium I 1169230 96 93 NYY NYA
## 6 Jacobs Field 1995174 99 97 CLE CLE
## 7 Coors Field 3166821 122 122 COL COL
## 8 Yankee Stadium I 976913 97 93 NYY NYA
## 9 Yankee Stadium III 3719358 105 103 NYY NYA
## 10 Fenway Park II 2837294 106 105 BOS BOS
## 11 Jacobs Field 2842745 101 99 CLE CLE
## 12 Enron Field 3056139 107 107 HOU HOU
## 13 Wrigley Field 1463624 101 98 CHC CHN
## 14 Turner Field 2401084 101 100 ATL ATL
## 15 The Ballpark at Arlington 2771469 105 105 TEX TEX
## teamIDretro BA SLG
## 1 BOS 0.2889582 0.6033975
## 2 NYA 0.3074621 0.5922947
## 3 SEA 0.2803705 0.5908443
## 4 SEA 0.2866972 0.5906845
## 5 NYA 0.3089207 0.5904919
## 6 CLE 0.2896569 0.5900050
## 7 COL 0.2922671 0.5880492
## 8 NYA 0.2997675 0.5871937
## 9 NYA 0.2833922 0.5818021
## 10 BOS 0.2819930 0.5807692
## 11 CLE 0.2905728 0.5799523
## 12 HOU 0.2777379 0.5797127
## 13 CHN 0.3085469 0.5791077
## 14 ATL 0.2835979 0.5790123
## 15 TEX 0.2925146 0.5783047Teams_new %>%
filter(yearID >= 1969) %>%
arrange(desc(SLG)) %>%
head(n = 15)## yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin
## 1 2003 AL BOS BOS E 2 162 81 95 67 N Y
## 2 1997 AL SEA SEA W 1 162 81 90 72 Y N
## 3 1996 AL SEA SEA W 2 161 81 85 76 N N
## 4 1994 AL CLE CLE C 2 113 51 66 47 <NA> <NA>
## 5 2001 NL COL COL W 5 162 81 73 89 N N
## 6 2009 AL NYA NYY E 1 162 81 103 59 Y N
## 7 2004 AL BOS BOS E 2 162 81 98 64 N Y
## 8 1995 AL CLE CLE C 1 144 72 100 44 Y N
## 9 2000 NL HOU HOU C 4 162 81 72 90 N N
## 10 2003 NL ATL ATL E 1 162 81 101 61 Y N
## 11 1999 AL TEX TEX W 1 162 81 95 67 Y N
## 12 1996 AL CLE CLE C 1 161 80 99 62 Y N
## 13 2000 NL SFN SFG W 1 162 81 97 65 Y N
## 14 1997 NL COL COL W 3 162 81 83 79 N N
## 15 2001 AL TEX TEX W 4 162 82 73 89 N N
## LgWin WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER
## 1 N N 961 5769 1667 371 40 238 620 943 88 35 53 64 809 729
## 2 N N 925 5614 1574 312 21 264 626 1110 89 40 NA NA 833 770
## 3 N N 993 5668 1625 343 19 245 670 1052 90 39 NA NA 895 828
## 4 <NA> <NA> 679 4022 1165 240 20 167 382 629 131 48 NA NA 562 493
## 5 N N 923 5690 1663 324 61 213 511 1027 132 54 61 50 906 841
## 6 Y Y 915 5660 1604 325 21 244 663 1014 111 28 54 39 753 687
## 7 Y Y 949 5720 1613 373 25 222 659 1189 68 30 69 55 768 674
## 8 Y N 840 5028 1461 279 23 207 542 766 132 53 NA NA 607 554
## 9 N N 938 5570 1547 289 36 249 673 1129 114 52 83 61 944 865
## 10 N N 907 5670 1608 321 31 235 545 933 68 22 49 49 740 663
## 11 N N 945 5651 1653 304 29 230 611 937 111 54 NA NA 859 809
## 12 N N 952 5681 1665 335 23 218 671 844 160 50 NA NA 769 702
## 13 N N 925 5519 1535 304 44 226 709 1032 79 39 51 66 747 675
## 14 N N 923 5603 1611 269 40 239 562 1060 137 65 NA NA 908 835
## 15 N N 890 5685 1566 326 23 246 548 1093 97 32 75 55 968 913
## ERA CG SHO SV IPouts HA HRA BBA SOA E DP FP
## 1 4.48 5 6 36 4394 1503 153 488 1141 113 130 0.982
## 2 4.79 9 8 38 4341 1500 192 598 1207 123 143 0.980
## 3 5.21 4 4 34 4293 1562 216 605 1000 110 155 0.980
## 4 4.36 17 5 21 3054 1097 94 404 666 90 119 0.980
## 5 5.29 8 8 26 4290 1522 239 598 1058 96 167 0.984
## 6 4.26 3 8 51 4350 1386 181 574 1260 86 131 0.985
## 7 4.18 4 12 36 4354 1430 159 447 1132 118 129 0.981
## 8 3.83 10 10 50 3903 1261 135 445 926 101 142 0.980
## 9 5.42 8 2 30 4313 1596 234 598 1064 133 149 0.978
## 10 4.10 4 7 51 4369 1425 147 555 992 121 166 0.981
## 11 5.07 6 9 47 4306 1626 186 509 979 119 167 0.980
## 12 4.35 13 9 46 4356 1530 173 484 1033 124 156 0.980
## 13 4.21 9 15 47 4333 1452 151 623 1076 93 173 0.985
## 14 5.25 9 5 38 4296 1697 196 566 870 111 202 0.980
## 15 5.71 4 3 37 4315 1670 222 596 951 114 167 0.981
## name park attendance BPF PPF
## 1 Boston Red Sox Fenway Park II 2724165 105 103
## 2 Seattle Mariners Kingdome 3192237 98 98
## 3 Seattle Mariners Kingdome 2723850 100 99
## 4 Cleveland Indians Jacobs Field 1995174 99 97
## 5 Colorado Rockies Coors Field 3166821 122 122
## 6 New York Yankees Yankee Stadium III 3719358 105 103
## 7 Boston Red Sox Fenway Park II 2837294 106 105
## 8 Cleveland Indians Jacobs Field 2842745 101 99
## 9 Houston Astros Enron Field 3056139 107 107
## 10 Atlanta Braves Turner Field 2401084 101 100
## 11 Texas Rangers The Ballpark at Arlington 2771469 105 105
## 12 Cleveland Indians Jacobs Field 3318174 99 98
## 13 San Francisco Giants PacBell Park 3318800 93 92
## 14 Colorado Rockies Coors Field 3888453 122 123
## 15 Texas Rangers The Ballpark at Arlington 2831021 104 105
## teamIDBR teamIDlahman45 teamIDretro BA SLG
## 1 BOS BOS BOS 0.2889582 0.6033975
## 2 SEA SEA SEA 0.2803705 0.5908443
## 3 SEA SEA SEA 0.2866972 0.5906845
## 4 CLE CLE CLE 0.2896569 0.5900050
## 5 COL COL COL 0.2922671 0.5880492
## 6 NYY NYA NYA 0.2833922 0.5818021
## 7 BOS BOS BOS 0.2819930 0.5807692
## 8 CLE CLE CLE 0.2905728 0.5799523
## 9 HOU HOU HOU 0.2777379 0.5797127
## 10 ATL ATL ATL 0.2835979 0.5790123
## 11 TEX TEX TEX 0.2925146 0.5783047
## 12 CLE CLE CLE 0.2930822 0.5766590
## 13 SFG SFN SFN 0.2781301 0.5760101
## 14 COL COL COL 0.2875245 0.5755845
## 15 TEX TEX TEX 0.2754617 0.5753738Problem 4.
The Angles have at times been called the California Angles (CAL), the Anaheim Angels (ANA), and the Los Angeles Angels (LAA). Find the 10 most successful seasons in Angels history. Have they ever won the world series?
Teams_new %>%
filter(teamID == c("CAL", "ANA", "LAA")) %>%
arrange(desc(W)) %>%
head(n = 10)## yearID lgID teamID franchID divID Rank G Ghome W L DivWin WCWin
## 1 1982 AL CAL ANA W 1 162 81 93 69 Y <NA>
## 2 1985 AL CAL ANA W 2 162 79 90 72 N <NA>
## 3 1979 AL CAL ANA W 1 162 81 88 74 Y <NA>
## 4 1991 AL CAL ANA W 7 162 81 81 81 N <NA>
## 5 1995 AL CAL ANA W 2 145 72 78 67 N N
## 6 1988 AL CAL ANA W 4 162 81 75 87 N <NA>
## 7 1961 AL LAA ANA <NA> 8 162 82 70 91 <NA> <NA>
## 8 1963 AL LAA ANA <NA> 9 161 81 70 91 <NA> <NA>
## 9 1968 AL CAL ANA <NA> 8 162 81 67 95 <NA> <NA>
## LgWin WSWin R AB H X2B X3B HR BB SO SB CS HBP SF RA ER
## 1 N N 814 5532 1518 268 26 186 613 760 55 53 NA NA 670 621
## 2 N N 732 5442 1364 215 31 153 648 902 106 51 NA NA 703 633
## 3 N N 866 5550 1563 242 43 164 589 843 100 53 NA NA 768 692
## 4 N N 653 5470 1396 245 29 115 448 928 94 56 NA NA 649 591
## 5 N N 801 5019 1390 252 25 186 564 889 58 39 NA NA 697 645
## 6 N N 714 5582 1458 258 31 124 469 819 86 52 NA NA 771 698
## 7 N N 744 5424 1331 218 22 189 681 1068 37 28 NA NA 784 689
## 8 N N 597 5506 1378 208 38 95 448 916 43 30 NA NA 660 569
## 9 N N 498 5331 1209 170 33 83 447 1080 62 50 NA NA 615 548
## ERA CG SHO SV IPouts HA HRA BBA SOA E DP FP name
## 1 3.82 40 10 27 4392 1436 124 482 728 106 171 0.98 California Angels
## 2 3.91 22 8 41 4371 1453 171 514 767 112 202 0.98 California Angels
## 3 4.34 46 9 33 4308 1463 131 573 820 135 172 0.97 California Angels
## 4 3.69 18 10 50 4323 1351 141 543 990 102 156 0.98 California Angels
## 5 4.52 8 9 42 3852 1310 163 486 901 95 120 0.98 California Angels
## 6 4.32 26 9 33 4365 1503 135 568 817 135 175 0.97 California Angels
## 7 4.31 25 5 34 4314 1391 180 713 973 192 154 0.96 Los Angeles Angels
## 8 3.52 30 13 31 4365 1317 120 578 889 163 155 0.97 Los Angeles Angels
## 9 3.43 29 11 31 4311 1234 131 519 869 140 156 0.97 California Angels
## park attendance BPF PPF teamIDBR teamIDlahman45
## 1 Anaheim Stadium 2807360 100 99 CAL CAL
## 2 Anaheim Stadium 2567427 100 100 CAL CAL
## 3 Anaheim Stadium 2523575 96 96 CAL CAL
## 4 Anaheim Stadium 2416236 99 100 CAL CAL
## 5 Anaheim Stadium 1748680 99 99 CAL CAL
## 6 Anaheim Stadium 2340925 97 97 CAL CAL
## 7 Wrigley Field (LA) 603510 111 112 LAA LAA
## 8 Dodger Stadium 821015 94 94 LAA LAA
## 9 Anaheim Stadium 1025956 95 97 CAL CAL
## teamIDretro BA SLG
## 1 CAL 0.2744035 0.5198843
## 2 CAL 0.2506431 0.4592062
## 3 CAL 0.2816216 0.5102703
## 4 CAL 0.2552102 0.4447898
## 5 CAL 0.2769476 0.5405459
## 6 CAL 0.2611967 0.4591544
## 7 LAA 0.2453909 0.4773230
## 8 LAA 0.2502724 0.4155467
## 9 CAL 0.2267867 0.3714125Teams_new %>%
filter(teamID == c("CAL", "ANA", "LAA"), WSWin == "Y")## [1] yearID lgID teamID franchID
## [5] divID Rank G Ghome
## [9] W L DivWin WCWin
## [13] LgWin WSWin R AB
## [17] H X2B X3B HR
## [21] BB SO SB CS
## [25] HBP SF RA ER
## [29] ERA CG SHO SV
## [33] IPouts HA HRA BBA
## [37] SOA E DP FP
## [41] name park attendance BPF
## [45] PPF teamIDBR teamIDlahman45 teamIDretro
## [49] BA SLG
## <0 rows> (or 0-length row.names)#The Angels never won the world series