[R] R Help

[Uwe Ligges]

Better ask for local help if you can't reduce your code to some minimal 
examples so that we can understand easily what you are looking for.


On 02.03.2015 10:38, Rami Alzebdieh wrote:
> Dear Sir,
>
> I start using (R) 3 months ago,  and I am still learning,

Same for me .... after more than 16 years.

Best,
Uwe Ligges




 > I have a project and I am using R in this project, my friend helped 
me to build a code for this project and it's working perfect, but I need 
to make a small change in, it looks very simple but for me it's very 
complicated. I insert the code and I hope if you can help me this 
problem. I highlighted what exactly I need to change. This project is 
calculating the market and industry weighted returns for each based on 
the date levels.
>
> sync = read.csv("country-14.csv",header=T)
> id.country = 14
>
> sync = sync[sync$country!="country" & sync$country==id.country,-c(2,5)]
> sync$price=as.numeric(as.character(sync$price))
> sync$mv=as.numeric(as.character(sync$mv))
> attach(sync)
>
> #### Calculate returns and add to the dataset
> n.comp = nlevels(as.factor(as.character(sync$company_name)))
> comp.names = levels(as.factor(as.character(sync$company_name)))
> data = vector("list",n.comp)
> for(i in 1:n.comp){
>    temp = sync[sync$company_name==comp.names[i],]
>    data[[i]] = cbind(temp,c(NA,diff(temp$price)/temp$price[1:(length(temp$price)-1)]))
> }
> sync = do.call(rbind,data)
> names(sync)[7] = "returns"
> detach(sync)
> attach(sync)
>
> #### Fill industry_code column
> industry_code=rep(NA,dim(sync)[1])
> for(i in 1:dim(sync)[1]){
>    if(nchar(as.character(company_code[i])) == 3){
>      industry_code[i] = as.numeric(substr(as.character(company_code[i]),1,1))
>    } else {
>      industry_code[i] = as.numeric(substr(as.character(company_code[i]),1,2))
>    }
>    print((i/dim(sync)[1])*100)
> }
> sync = cbind(sync,as.factor(industry_code))
> names(sync)[8] = "industry_code"
> detach(sync)
> attach(sync)
>
> #### Calculate market weighted returns and add to the dataset
> market_returns = rep(NA,dim(sync)[1])
> industry_returns = rep(NA,dim(sync)[1])
> for(i in 1:nlevels(date)){
>      data = sync[date==levels(date)[i],]
>      data$company_name = as.factor(as.character(data$company_name))
>      for(m in 1:nlevels(data$company_name)){
>        index1 = data$company_name == levels(data$company_name)[m]
>        index2 = date==levels(date)[i] & company_name==levels(data$company_name)[m]
>        market_returns[index2] = (sum(data$returns*(data$mv/sum(data$mv,na.rm=TRUE)),na.rm=TRUE) -
>          (data$returns[index1]*(data$mv[index1]/sum(data$mv,na.rm=TRUE))))/(nlevels(data$company_name)-1) ## this what I need to change, instead of using the number of levels companies in the dataset (nlevels(data$company_name) , I need to put the number of returns values(data$returns) without NA (by the way this code is calculating returns at the date level as you can see from above)
>      }
>    print(i/nlevels(date))
> }
>
> sync = cbind(sync,market_returns)
> names(sync)[9] = "market_returns"
> detach(sync)
> attach(sync)
>
> #### Calculate industry weighted returns and add to the dataset
> for(i in 1:nlevels(date)){
>      for(k in 1:nlevels(as.factor(as.character(industry_code)))){
>        data1 = sync[date==levels(date)[i] & industry_code==levels(as.factor(as.character(industry_code)))[k],]
>        data1$company_name = as.factor(as.character(data1$company_name))
>        for(l in 1:nlevels(data1$company_name)){
>          index3 = data1$company_name == levels(data1$company_name)[l]
>          index4 = date==levels(date)[i] & company_name==levels(data1$company_name)[l]
>          industry_returns[index4] = (sum(data1$returns*(data1$mv/sum(data1$mv,na.rm=TRUE)),na.rm=TRUE) -
>            (data1$returns[index3]*(data1$mv[index3]/sum(data1$mv,na.rm=TRUE))))/(nlevels(data1$company_name)-1) ## also here I need to change, instead of using the number of levels companies in the dataset (nlevels(data1$company_name) , I need to put the number of returns values(data1$returns) without NA (by the way this code is calculating returns at the date level and industry level as you can see from above)
>
>        }
>      }
>    print(i/nlevels(date))
> }
>
> sync = cbind(sync,industry_returns)
> names(sync)[10] = "industry_returns"
> detach(sync)
> attach(sync)
>
> year = apply(as.matrix(sync$date),1,function(x) as.factor(substr(as.character(x),7,10)))
> sync = cbind(sync,as.factor(year))
> names(sync)[11] = "year"
> sync = sync[sync$year!="1999",]
> sync$year = as.factor(as.character(sync$year))
> detach(sync)
> attach(sync)
>
> year = as.factor(as.character(year))
> industry_code = as.factor(as.character(industry_code))
> comp.per.ind = rep(NA, dim(sync)[1])
> for(i in 1:nlevels(year)){
>    for(j in 1:nlevels(industry_code)){
>      index = year==levels(year)[i] & industry_code==levels(industry_code)[j]
>      data = sync[index,]
>      comp.per.ind[index] = nlevels(as.factor(as.character(data$company_name)))
>    }
> }
>
> sync = cbind(sync,as.factor(comp.per.ind))
> names(sync)[12] = "comp.per.ind"
> detach(sync)
> attach(sync)
>
> write.csv(sync,paste("Returns_data",id.country,".csv",sep=""))
>
>
>
>
> Thank you for your help
>
> Rami Alzebdieh
>
>
>
> 	[[alternative HTML version deleted]]
>
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