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data-week3-videos.R
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# Subsetting
set.seed(13435)
X<-data.frame("var1"=sample(1:5),"var2"=sample(6:10),"var3"=sample(11:15))
X<-X[sample(1:5),]; X$var2[c(1,3)] = NA
# return column 1
X[,1]
# return column 1 by name
X[,"var1"]
# return the first two rows and the second column
X[1:2,"var2"]
# all the rows of X where var1 <=3 AND var3>11
X[(X$var1<=3 & X$var3>11),]
# or
X[(X$var1<=3 | X$var3>15),]
# which returns the indices (to deal with the NAs)
X[which(X$var2>8),]
sort(X$var1)
sort(X$var1,decreasing=TRUE)
sort(X$var2,na.last=TRUE)
X[order(X$var1),]
X[order(X$var1,X$var3),]
library(plyr)
arrange(X,var1)
arrange(X,desc(var1))
# assign a new row in the data set
X$var4<-rnorm(5)
Y<-cbind(X,rnorm(5))
########################################################
# Summarizzing data
if(!file.exists(".data")){dir.create("./data")}
fileUrl<-"https://data.baltimorecity.gov/api/views/k5ry-ef3g/rows.csv?accessType=DOWNLOAD"
download.file(fileUrl,destfile="./data/restaurants.csv",method="curl")
restData<-read.csv("./data/restaurants.csv")
head(restData,n=3)
tail(restData,n=3)
summary(restData)
str(restData)
quantile(restData$councilDistrict,na.rm=TRUE)
quantile(restData$councilDistrict,probs=c(0.5,0.75,0.9))
table(restData$zipCode,useNA="ifany")
table(restData$councilDistrict,restData$zipCode)
sum(is.na(restData$councilDistrict))
any(is.na(restData$councilDistrict))
all(restData$zipCode>0)
colSums(is.na(restData))
all(colSums(is.na(restData))==0)
table(restData$zipCode %in% c("21212"))
table(restData$zipCode %in% c("21212","21213"))
restData[restData$zipCode %in% c("21212","21213"),]
data(UCBAdmissions)
DF=as.data.frame(UCBAdmissions)
summary(DF)
xt<-xtabs(Freq ~ Gender + Admit, data=DF)
xt
warpbreaks$replicate<-rep(1:9,len=54)
xt<-xtabs(breaks ~., data=warpbreaks)
xt
ftable(xt)
fakeData=rnorm(1e5)
object.size(fakeData)
print(object.size(fakeData),units="Mb")
s1<-seq(1,10,by=2) ; s1
s2<-seq(1,10,length=3) ; s2
x<-c(1,3,8,25,100); seq(along=x)
restData$nearMe = restData$neighborhood %in% c("Roland Park","Homeland")
table(restData$nearMe)
restData$zipWrong=ifelse(restData$zipCode<0,TRUE,FALSE)
table(restData$zipWrong,restData$zipCode<0)
restData$zipGroups=cut(restData$zipCode,breaks=quantile(restData$zipCode))
table(restData$zipGroups)
table(restData$zipGroups,restData$zipCode)
library(Hmisc)
restData$zipGroups=cut2(restData$zipCode,g=4)
table(restData$zipGroups)
restData$zcf<-factor(restData$zipCode)
restData$zcf[1:10]
class(restData$zcf)
yesno<-sample(c("yes","no"),size=10,replace=TRUE)
yesnofac=factor(yesno,levels=c("yes","no"))
relevel(yesnofac,ref="yes")
as.numeric(yesnofac)
library(Hmisc); library(plyr)
restData2=mutate(restData,zipGroups=cut2(zipCode,g=4))
table(restData$zipGroups)
library(reshape2)
head(mtcars)
mtcars$carname<-rownames(mtcars)
carMelt<-melt(mtcars,id=c("carname","gear","cyl"),measure.vars=c("mpg","hp"))
head(carMelt,n=3)
tail(carMelt,n=3)
cylData<-dcast(carMelt,cyl ~ variable)
cylData
cylData<-dcast(carMelt,cyl ~ variable,mean)
cylData
head(InsectSprays)
tapply(InsectSprays$count,InsectSprays$spray,sum)
spIns=split(InsectSprays$count,InsectSprays$spray)
spIns
sprCount=lapply(spIns,sum)
sprCount
unlist(sprCount)
sapply(spIns,sum)
ddply(InsectSprays,.(spray),summarize,sum=sum(count))
spraySums<-ddply(InsectSprays,.(spray),summarize,sum=ave(count,FUN=sum))
dim(spraySums)
head(spraySums)
# merge
if(!file.exists("./data")){dir.create("./data")}
fileUrl1="https://dl.dropboxusercontent.com/u/7710864/data/reviews-apr29.csv"
fileUrl2="https://dl.dropboxusercontent.com/u/7710864/data/solutions-apr29.csv"
download.file(fileUrl1,destfile="./data/reviews.csv",method="curl")
download.file(fileUrl2,destfile="./data/solutions.csv",method="curl")
reviews=read.csv("./data/reviews.csv"); solutions<-read.csv("./data/solutions.csv")
head(reviews,2)
head(solutions,2)
names(reviews)
names(solutions)
mergedData=merge(reviews,solutions,by.x="solution_id",by.y="id",all=TRUE)
head(mergedData)
intersect(names(solutions),names(reviews))
mergeData2=merge(reviews,solutions,all=TRUE)
head(mergeData2)
# merge using plyr
df1=data.frame(id=sample(1:10),x=rnorm(10))
df2=data.frame(id=sample(1:10),y=rnorm(10))
arrange(join(df1,df2),id)
df1=data.frame(id=sample(1:10),x=rnorm(10))
df2=data.frame(id=sample(1:10),y=rnorm(10))
df3=data.frame(id=sample(1:10),=rnorm(10))
join_all(list(df1,df2,df3))