Schools table
Schools.RdInformation on schools players attended, by school
Usage
data(Schools)Format
A data frame with 1241 observations on the following 5 variables.
schoolIDschool ID code
name_fullschool name
citycity where school is located
statestate where school's city is located
countrycountry where school is located
Source
Lahman, S. (2024) Lahman's Baseball Database, 1871-2023, 2024 version, http://www.seanlahman.com/
Examples
require("dplyr")
# How many different schools are listed in each state?
table(Schools$state)
#>
#> AL AR AZ Baltimore Berkeley
#> 33 17 14 1 1
#> Bradenton Brooklyn Brookville Buffalo CA
#> 1 2 1 1 127
#> CO CT Catonsville DC DE
#> 11 15 1 7 4
#> Davis Erie Essex FL GA
#> 1 1 1 54 25
#> HI Houston IA ID IL
#> 3 1 25 8 61
#> IN Irvine KS KY Keokuk
#> 24 1 27 17 1
#> Kirkwood LA La Jolla Los Angeles MA
#> 1 17 1 1 28
#> MD ME MI MN MO
#> 21 8 31 12 30
#> MS Mankato Miami Monroeville Moorhead
#> 22 1 3 1 1
#> Morris NC ND NE NH
#> 1 39 2 12 5
#> NJ NM NV NY New Brunswick
#> 22 7 3 50 1
#> New York OH OK OR PA
#> 1 42 23 17 69
#> Pomona RI Riverside Rockville SC
#> 1 7 1 1 19
#> SD San Luis Obispo Santa Barbara St. Louis TN
#> 1 1 1 1 39
#> TX Teaneck UT VA VT
#> 79 1 10 27 4
#> WA WI WV WY West Mifflin
#> 25 16 13 1 1
# How many different schools are listed in each country?
table(Schools$country)
#>
#> CA FL IA MD MN MO NJ NY PA TX USA
#> 9 4 1 4 3 2 2 5 3 1 1173
# Top 20 schools
schoolInfo <- Schools %>% select(-country)
schoolCount <- CollegePlaying %>%
group_by(schoolID) %>%
summarise(players = length(schoolID)) %>%
left_join(schoolInfo, by = "schoolID") %>%
arrange(desc(players))
head(schoolCount, 20)
#> # A tibble: 20 × 5
#> schoolID players name_full city state
#> <chr> <int> <chr> <chr> <chr>
#> 1 texas 265 University of Texas at Austin "Austin" TX
#> 2 usc 250 University of Southern California "Los An… CA
#> 3 stanford 248 Stanford University "Palo A… CA
#> 4 arizonast 236 Arizona State University "Tempe" AZ
#> 5 michigan 191 University of Michigan "Ann Ar… MI
#> 6 ucla 180 University of California " Los A… Los …
#> 7 holycross 167 College of the Holy Cross "Worces… MA
#> 8 california 162 University of California " Berke… Berk…
#> 9 arizona 161 University of Arizona "Tucson" AZ
#> 10 alabama 155 University of Alabama "Tuscal… AL
#> 11 unc 154 University of North Carolina at Chapel Hill "Chapel… NC
#> 12 floridast 152 Florida State University "Tallah… FL
#> 13 lsu 149 Louisiana State University "Baton … LA
#> 14 illinois 141 University of Illinois at Urbana-Champaign "Champa… IL
#> 15 clemson 138 Clemson University "Clemso… SC
#> 16 florida 138 University of Florida "Gaines… FL
#> 17 gatech 137 Georgia Institute of Technology "Atlant… GA
#> 18 oklahoma 135 University of Oklahoma "Norman" OK
#> 19 notredame 134 University of Notre Dame "South … IN
#> 20 okstate 132 Oklahoma State University "Stillw… OK
# sum counts by state
schoolStates <- schoolCount %>%
group_by(state) %>%
summarise(players = sum(players),
schools = length(state))
str(schoolStates)
#> tibble [76 × 3] (S3: tbl_df/tbl/data.frame)
#> $ state : chr [1:76] "AL" "AR" "AZ" "Baltimore" ...
#> $ players: int [1:76] 459 165 524 3 162 39 13 3 2412 80 ...
#> $ schools: int [1:76] 27 15 13 1 1 1 2 1 114 7 ...
summary(schoolStates)
#> state players schools
#> Length:76 Min. : 1.0 Min. : 1.00
#> Class :character 1st Qu.: 16.0 1st Qu.: 1.00
#> Mode :character Median : 98.0 Median : 6.00
#> Mean : 228.3 Mean : 13.66
#> 3rd Qu.: 358.0 3rd Qu.: 19.00
#> Max. :2412.0 Max. :114.00