I don't think to parallelize on a single PC (2-4 cores) is the answer. There are plenty of lower hanging fruits to pick.
1) RF models increase in complexity with number of training samples. The average tree depth would be something like log(480,000/5)/log(2) = 16.5 intermediary nodes. In the vast majority of examples 2000-10000 samples per tree is fine. If you competing to win on kaggle, a small extra performance really matters, as winner takes all. In practice, you probably don't need that.
2) Don't clone you data set in your R code and try to only keep one copy of your data set (pass by reference is of course fine). It's not a big problem for this data set, as the dataset is not that big (~38Mb) even for R.
3) Don't use formula interface with randomForest algorithm for large datasets. It will make an extra copy of the data set. But again memory is not that much of a problem.
4) Use a faster RF algorithm: extraTrees, ranger or Rborist are available for R. extraTrees is not exactly a RF algorithm but pretty close.
5) avoid categorical features with more than 10 categories. RF can handle up to 32, but becomes super slow as any 2^32 possible split has to be evaluated. extraTrees and Rborist handle more categories by only testing some random selected splits (which works fine). Another solution as in the python-sklearn every category are assigned a unique integer, and the feature is handled as numeric. You can convert your categorical features with as.numeric and before runing randomForest to do the same trick.
6) For much bigger data. Split the data set in random blocks and train a few(~10) trees on each. Combine forests or save forests separate. This will slightly increase the tree correlation. There are some nice cluster implementation to train like these. But won't be necessary for datasets below 1-100Gb, depending on tree complexity etc.
#below I use solution 1-3) and get a run time of some minutes
library(randomForest)
#simulate data
dataset <- data.frame(replicate(12,rnorm(400000)))
dataset$Clip_pm25 = dataset[,1]+dataset[,2]^2+dataset[,4]*dataset[,3]
#dati <- data.frame(dataset) #no need to keep the data set, an extra time in memory
#attach(dati) #if you attach dati you don't need to write data$Clip_pm25, just Clip_pm25
#but avoid formula interface for randomForest for large data sets because it cost extra memory and time
#split data in X and y manually
y = dataset$Clip_pm25
X = dataset[,names(dataset) != "Clip_pm25"]
rm(dataset);gc()
object.size(X) #38Mb, no problemo
#if you were using formula interface
#output.forest <- randomForest(dati$Clip_pm25 ~ dati$e_1 + dati$Clipped_so + dati$Clip_no2 + dati$t2m_1 + dati$tp_1 + dati$Clipped_nh + dati$Clipped_co + dati$Clipped_o3 + dati$ssrd_1 + dati$Clipped_no + dati$Clip_pm10 + dati$sp_1, data=trainSet, ntree=250)
#output.forest <- randomForest(dati$Clip_pm25 ~ ., ntree=250) # use dot to indicate all variables
#start small, and scale up slowly
rf = randomForest(X,y,sampsize=1000,ntree=5) #runtime ~15 seconds
print(rf) #~67% explained var
#you probably really don't need to exeed 5000-10000 samples per tree, you could grow 2000 trees to sample most of training set
rf = randomForest(X,y,sampsize=5000,ntree=500) # runtime ~5 minutes
print(rf) #~87% explained var
#regarding parallel
#here you could implement some parallel looping
#.... but is it really worth for a 2-4 x speedup?
#coding parallel on single PC is fun but rarely worth the effort
#If you work at some company or university with a descent computer cluster,
#then you can spawn the process across 20-80-200 nodes and get a ~10-60-150 x speedup
#I can recommend the BatchJobs package
