Description Usage Format Source Examples
Information on schools players attended, by school
1 |
A data frame with 749 observations on the following 5 variables.
schoolID
school ID code
schoolName
school name
schoolCity
city where school is located
schoolState
state where school's city is located
schoolNick
nickname for school's baseball team
Lahman, S. (2014) Lahman's Baseball Database, 1871-2013, 2014 version, http://baseball1.com/statistics/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | require(plyr)
# how many different schools are listed in each state?
table(Schools$schoolState)
# top 20 schools
schoolInfo <- Schools[, c("schoolID", "schoolName", "schoolCity", "schoolState")]
schoolCount <- ddply(SchoolsPlayers, .(schoolID), summarise,
players = length(schoolID))
schoolCount <- merge(schoolCount, schoolInfo, by="schoolID", all.x=TRUE)
# Arrange in decreasing order:
schoolCount <- arrange(schoolCount, desc(players))
head(schoolCount, 20)
# sum counts by state
schoolStates <- ddply(schoolCount, .(schoolState), summarise,
players = sum(players),
schools = length(schoolState))
str(schoolStates)
summary(schoolStates)
## Not run:
if(require(zipcode)) {
# in lieu of more precise geocoding via schoolName,
# find lat/long of Schools from zipcode file
zips <- ddply(zipcode, .(city, state), summarize,
latitude=mean(latitude), longitude=mean(longitude))
colnames(zips)[1:2] <- c("schoolCity", "schoolState")
str(zips)
# merge lat/long from zips
schoolsXY <- merge(Schools, zips, by=c("schoolCity", "schoolState"), all.x=TRUE)
str(schoolsXY)
# plot school locations
with(subset(schoolsXY, schoolState != 'HI'),
plot(jitter(longitude), jitter(latitude))
)
}
## End(Not run)
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