FIltering DataFrame Manual

library(datasets)
data(iris)

iris
#atau 
iris[,]

typeof(iris)
a=iris[1:5,1]
iris[1:5,1:2]
iris[1:5,1:3]


dim(iris)
str(iris)
col=length(iris) #5#jika data banyak kolom dia baca banyak kolom
bar=length(iris$Sepal.Length) #jika data hanya 1 kolom dia baca banyak baris
#iris[1:2] #prioritas kolom

DFtranspose = df[FALSE,]
#DFtranspose <- as.data.frame(matrix(nrow = 1, ncol = 5))
for(i in 1:bar){
    if(iris$Sepal.Length[i]>7.5){
      baca=list((iris[i,]))
      df= as.data.frame((as.data.frame(baca)))
      DFtranspose <- rbind(DFtranspose, df)
    }
}

baca=list((iris[1,]))
df1= as.data.frame((as.data.frame(baca)))

baca=list((iris[2,]))
df2= as.data.frame((as.data.frame(baca)))

baca=list((iris[3,]))
df3= as.data.frame((as.data.frame(baca)))

DFtranspose <- rbind(df1, df2)


for(i in 1:jd){
 print(MyList[[i]])
}

df= as.data.frame((as.data.frame(MyList)))
rownames(df)<-NULL
df


for (aName in c("name1", "name2")){
  MyList[[aName]] <- list(aName)
}


names(iris)
attributes(iris)
iris$Sepal.Length[1:10]
summary(iris)
 

hist(iris$Sepal.Length)
table(iris$Species)
pie(table(iris$Species))

x = iris[,-5]
y = iris$Species


library(dbscan)
iris2 <- as.matrix(iris[,1:4])
res <- dbscan(iris2, eps = .4, minPts = 5)
res$cluster
pie(table(res$cluster))



A=c(7,0,35,27,21,17,38,55,82,60,81,64,65,107,104,103,153,109,130,129,114,149,113,196,106,181,218,247,218,337,219,330,399,316,282,297,380,407,325,327,185,375,283,357,436,396,275,214,415,260,347,433,292,349,395,484,367,338,336,533,387,233,484,689,568,490,529,489,496,486,693,973,634,949,526,479,415,686,687,678,557,700,467,609,684,585,703,993,672,847,1043,1240,979,1111,1014,857,1017,1106,1031,1331,1041,1226,862,954,1051,1113,1178,1240,1385,1198,1082,1293,1385,1624,1301,1447,1607,1209,2000,1190)
D=c(1,0,3,1,0,0,2,12,6,7,6,10,1,6,3,20,9,15,12,8,14,21,13,11,10,7,11,12,19,40,26,21,46,26,60,10,27,24,15,47,8,26,19,12,42,31,23,22,8,11,8,8,31,14,19,8,23,35,13,16,14,18,16,21,15,33,13,59,43,30,21,36,48,25,21,19,27,55,23,24,53,40,28,22,35,23,49,31,50,32,40,36,41,48,43,43,64,33,45,63,34,56,36,35,35,38,47,63,37,34,51,71,58,53,49,53,82,70,90,5)

alldata= cbind(A,D)

res <- dbscan(alldata, eps = 50, minPts = 5)
res

res$cluster
pie(table(res$cluster))





length(A)
length(D)
jd=length(A)

newdata <- x[1:5,] + rnorm(10, 0, .2)
predict(res, newdata, data = x)




x <- list(a=1,b=2,c=3)
list.append(x,d=4,e=5)
list.append(x,d=4,f=c(2,3))

listoflists=list(c(1,'a',3,4),c(5,'b',6,7))
#Convert to a dataframe, transpose, and convert the resulting matrix back to a dataframe
df= as.data.frame(t(as.data.frame(listoflists)))
#Strip out the rownames if desired
rownames(df)<-NULL
#Show result
df