Uploading Large csv file from Local File

I have a 6GB csv file which I created on my local machine which is named TrainDF.csv. I can't upload it directly as it exceeds the 1.95GB size limit uncompressed. However, I tried saving it as an RData file (as well as a zip file of the RData
file) and uploading that, however my R code throws an "unable to open connection" error when I try to load this data with an R Script with this code.
load("src/TrainDF.RData"); maml.mapOutputPort("TrainDF");
I have even tried:
load("/src/TrainDF.RData");
load("./src/TrainDF.RData");
load("~/src/TrainDF.RData");
TrainDF <- load("/src/TrainDF.RData");
TrainDF <- load("./src/TrainDF.RData");
TrainDF <- load("~/src/TrainDF.RData");
Same error for everything. What would be the simplest most straight-forward way to get a 6GB csv file usable as an input for an experiment from my local machine? I may need step by step instructions on this one depending on the answer.
Also, if a complete step by step answer to this question can be found somewhere please post a link to it.
Thanks in advance,
Bob
P.S. I know the data is actually there, I am able to download it.

Yes, all appears to work now. I have a bad habit of interchanging a csv file with a dataframe in R when I talk because they are so trivial to interchange. I meant a saved (RData) object containing a dataframe that was originally loaded from
a csv file. My problems were not knowing specifically how to write the path to the src folder and I didn't even think about using a different input. Hopefully documentation should be coming out in the future which clarifies these things.
In my opinion information on how to get info in and out of Azure ML is somewhat lacking. Although, I must say this is truly impressive work you guys are doing and not only that you are getting it done at a mind-boggling pace. So good job on that.
Thanks for your great help and fast response!

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