Schools table

Description

Information on schools players attended, by school

Usage

data(Schools)

Format

A data frame with 1207 observations on the following 5 variables.

schoolID

school ID code

name_full

school name

city

city where school is located

state

state where school's city is located

country

country where school is located

Source

Lahman, S. (2016) Lahman's Baseball Database, 1871-2015, 2015 version, http://www.seanlahman.com/baseball-archive/statistics/

Examples

require("dplyr")

# How many different schools are listed in each state?
table(Schools$state)
 
# How many different schools are listed in each country?
table(Schools$country)

# 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)

# sum counts by state
schoolStates <- schoolCount %>%
                  group_by(state) %>%
                  summarise(players = sum(players),
                            schools = length(state))
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 <- zipcode %>%
            group_by(city, state) %>%
            summarise(latitude=mean(latitude), 
                      longitude=mean(longitude))
  names(zips)[1:2] <- c("city", "state")  
  str(zips)

  # merge lat/long from zips
  schoolsXY <- merge(Schools, zips, by=c("city", "state"), all.x=TRUE)
  str(schoolsXY)

  # plot school locations
  with(subset(schoolsXY, schoolState != 'HI'),
    plot(jitter(longitude), jitter(latitude))
  )
}

## End(Not run)