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"))
`
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.