Thank you so much! Sorry Michael, I will insert in the next.
Best regards. [image: Mailtrack] <https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=signaturevirality5&;> Remetente notificado por Mailtrack <https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=signaturevirality5&;> 14/12/20 19:11:49 Em sáb., 12 de dez. de 2020 às 14:06, Michael Dewey <li...@dewey.myzen.co.uk> escreveu: > Dear Jovani > > If you cross-post on CrossValidated as well as here it is polite to give > a link so people do not answer here when someone has already answered > there, or vice versa. > > Michael > > On 12/12/2020 15:27, Jovani T. de Souza wrote: > > So, I and some other colleagues developed a hierarchical clustering > > algorithm to basically find the main clusters involving agricultural > > industries according to a particular city (e.g. London city).. We > > structured this algorithm in R. It is working perfectly. So, according to > > our filters that we inserted in the algorithm, we were able to generate 6 > > clustering scenarios to London city. For example, the first scenario > > generated 2 clusters, the second scenario 5 clusters, and so on. I would > > therefore like some help on how I can choose the most appropriate one. I > > saw that there are some packages that help in this process, like > `pvclust`, > > but I couldn't use it for my case. I am inserting a brief executable code > > below to show the essence of what I want. > > > > Any help is welcome! If you know how to use using another package, feel > > free to describe. > > > > Best Regards. > > > > > > library(rdist) > > library(geosphere) > > library(fpc) > > > > > > df<-structure(list(Industries = c(1,2,3,4,5,6), > > + Latitude = c(-23.8, -23.8, -23.9, -23.7, > > -23.7,-23.7), > > + Longitude = c(-49.5, -49.6, -49.7, -49.8, > > -49.6,-49.9), > > + Waste = c(526, 350, 526, 469, 534, 346)), > class = > > "data.frame", row.names = c(NA, -6L)) > > > > df1<-df > > > > #clusters > > coordinates<-df[c("Latitude","Longitude")] > > d<-as.dist(distm(coordinates[,2:1])) > > fit.average<-hclust(d,method="average") > > > > clusters<-cutree(fit.average, k=2) > > df$cluster <- clusters > > > df > > Industries Latitude Longitude Waste cluster > > 1 1 -23.8 -49.5 526 1 > > 2 2 -23.8 -49.6 350 1 > > 3 3 -23.9 -49.7 526 1 > > 4 4 -23.7 -49.8 469 2 > > 5 5 -23.7 -49.6 534 1 > > 6 6 -23.7 -49.9 346 2 > > > > > clusters1<-cutree(fit.average, k=5) > > df1$cluster <- clusters1 > > > df1 > > Industries Latitude Longitude Waste cluster > > 1 1 -23.8 -49.5 526 1 > > 2 2 -23.8 -49.6 350 1 > > 3 3 -23.9 -49.7 526 2 > > 4 4 -23.7 -49.8 469 3 > > 5 5 -23.7 -49.6 534 4 > > 6 6 -23.7 -49.9 346 5 > > > > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > -- > Michael > http://www.dewey.myzen.co.uk/home.html > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
