Published
Edited
Sep 23, 2019
md`
1.data <-Batting%>%
filter(playerID =="ruthba01"| playerID == "mayswi01" | playerID == "bondsba01")%>%
select(playerID,yearID,HR)
write_json(data,"data.json",pretty=TRUE)
install.packages("tidyverse")
> install.packages("Lahman")
> library(jsonlite)
> library(Lahman)
> library(tidyverse)
> data
`
md`2.Master%>%
filter(nameLast == "Sosa")%>%
select(playerID,nameFirst,nameLast)
#mcgwima01 sosasa01 bondsba01
Type in box bellow Master at first to get Master list
`
md`
3.Then delet what you have on the top and put this
data<- Batting%>%
filter(playerID == "mcgwima01"| playerID == "sosasa01" | playerID == "bondsba01")%>%
select(playerID,yearID,HR,H,SO)
write_json(data,"data.json",pretty=TRUE)
`
md`
Practice problem 1 R code. filtered so we got years after 1913
data<- Teams%>%
filter(name == "New York Yankees" | name == "Boston Red Sox")%>%
filter(yearID >= 1913)%>%
select(name,yearID,W)
write_json(data,"data.json",pretty=TRUE)
`
md` Midterm for class.
x<- seq(-4,4,.1)
y<- dnorm(x)
area <- 1-pnorm(x)
data<- tibble (x=x,y=y,area=area)
write_json(data,"data.json",pretty=TRUE)
`
md`second example problem
data<- Salaries%>%
filter(yearID == 2016)%>%
filter(salary>= 15000000)%>%
select (salary)
write_json(data,"data.json",pretty=TRUE)
`
md`
Other problems
x<- seq(-10,10,.1)
data<- tibble (x=x)
write_json(data,"data.json",pretty=TRUE)
>
> write_json(data,"data.json",pretty=TRUE)
>
> x<- seq(-10,10,.1)
> data<- tibble (x=x)
> write_json(data,"data.json",pretty=TRUE)
>
`
md`
Post Midterm, grouping
Batting%>%
filter(playerID== "ruthba01")%>%
group_by(playerID)%>%
summarise(careerHR= sum(HR))
`
md`
Group, mumeraize, generate top 10 list
Batting%>%
group_by(playerID)%>%
summarise(careerHR= sum(HR))%>%
top_n(10)
data<-Batting%>%
group_by(playerID)%>%
summarise(careerHR= sum(HR))%>%
top_n(10)
write_json(data,"data.json",pretty=TRUE)
`
md`
baseball example 2
data<-Batting%>%
filter(playerID=="luzingr01" | playerID== "schmimi01")%>%
select(playerID,HR)
write_json(data,"data.json",pretty=TRUE)
`
md`
hitters<-Batting%>%
filter(yearID<= 1980)%>%
filter(yearID>= 1976)%>%
filter(teamID == "PHI")%>%
group_by(playerID)%>%
summarize(totalHR=sum(HR))%>%
top_n(10)%>%
select(playerID)
then do
allhitters<-Batting%>%
filter(yearID<= 1980)%>%
filter(yearID>= 1976)%>%
filter(teamID == "PHI")%>%
select(playerID,yearID,HR)
allhitters<-Batting%>%
filter(yearID<= 1980)%>%
filter(yearID>= 1976)%>%
filter(teamID == "PHI")%>%
select(playerID,yearID,HR)
inner_join(hitters,allhitters,by=c("playerID"))
`
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