neuralnet:ANN results not reproducible even after setting seed - r

Question

In nutshell will explain the code;
Am trying to forecast by creating 24 hourly models in a single day and collating the results in the data frame.Basic issue is not able to reproduce #the output even after setting seed.Please anyone help me.some custom functions #and objects i have made and there is no randomization in them.(Just FYI).
f <- as.formula("actual~ lag.1 + last3.avg+monsoon+mon.thurs+wdaySaturday+wdaySunday+holiday
") #Defining the formula for neural network
require(dplyr);require(neuralnet)
set.seed(123456)
nnet.hour=data.frame()#Initializing a dataframe
#k=0
#x=list()
for(i in 1:24){#Running it for 24 hours in a day
sub<-new.day.ahead[new.day.ahead$hour==i,]
sub$lag.1<-lag(sub$actual,1)
for(i in 1:nrow(sub)){
sub$last3.avg[i]=sum(lag(sub$actual,1)[i],lag(sub$actual,2)[i],lag(sub$actual,3)[i],na.rm=TRUE)/3
}
ind=which(sub$mod.date==ymd(t[1]));ind#t[1] is basically a date #initialisation,getting the index
monsoon=as.factor(sub$Monsoon.Dummy)
wday=as.factor(sub$wday.dummy)
holiday=as.factor(sub$holiday)
sub=as.data.frame(cbind(sub[,c(4,16,17)],cbind(
monsoon=model.matrix(~monsoon)[,-1],
wday=model.matrix(~wday)[,-1],
holiday=model.matrix(~holiday)[,-1]
)))
names(sub)[5]<-"mon.thurs"
##Normalising the data for training in a neural net
sub[,2][1]=0
maxs <- apply(sub, 2, max)
mins <- apply(sub, 2, min)
scaled <- as.data.frame(scale(sub, center = mins, scale = maxs - mins))
train<- scaled[1:I(ind-1),]
test<- scaled[ind,]
set.seed(123456)
nn <- neuralnet(f,data=train,hidden =7,linear.output = TRUE)
pr.nn<-neuralnet::compute(nn,test[,-1])
#Normalising back
pr.nn.<- pr.nn$net.result*(max(sub$actual)-min(sub$actual))+min(sub$actual)
test.r <- (test$actual)*(max(sub$actual)-min(sub$actual))+min(sub$actual)
u=mape(as.numeric(test.r),as.numeric(pr.nn.));u#Calculating Mean Absolute Percentage Error
if(i==1){
nnet.hour=data.frame(actual=as.numeric(test.r),forecast1=as.numeric(pr.nn.),mape=u)
}else{
nnet.hour=rbind(nnet.hour,data.frame(data.frame(actual=as.numeric(test.r),forecast1=as.numeric(pr.nn.),mape=u)))
nnet.hour=data.frame(nnet.hour)
}
}

Answer 1

Yes.This is solved.Actually for some iterations I failed to invoke 'dplyr' package ,so the lag variables i was creating using lag(function 'lag' is both in base as well as dplyr package) function were returning just the same series as the variable I was trying to forecast courtesy which errors were ~negligible.
Once I invoke dplyr package Results are reproducible.
Thanks.