Obtaining the Data and Calculating the actual Fama-French Factors for top NDXT companies

I am very new to the world of finance (a statistician) and I would like to run time series regressions for 3 and 5-factor Fama-French model in R but before I do that, I am very puzzled about obtaining the actual data:

1. If I use quantmod, from my understanding I get the following variables on let's say some top performers in NDXT, let's say GOOG, CSCO, KLAC, ADSK, AKAM: so for each day, I'd get: High ,Low, Close, Volume, Adjusted (and I'm still having a hard time grasping what those concepts mean in terms of a bigger picture of finance)
2. From these variables, or whatever variables I can possibly get through quantmod (given they only provide the publicly available data from Google Finance, Yahoo, Fred, and Oanda), how do I calculate SMB and HML for NDXT? From Fama and French website,

SMB = 1/3 (Small Value + Small Neutral + Small Growth) - 1/3 (Big Value + Big Neutral + Big Growth).

and:
HML = 1/2 (Small Value + Big Value) - 1/2 (Small Growth + Big Growth).

Then, using the data I can obtain via quantmod, how do I calculate:

• Small Value, Small Neutral, Small Growth
• Big Value, Big Neutral, Big Growth
• the average return on the two growth portfolios
• the average return on the two growth portfolios

and to begin with, how do I find the two growth portfolios? Or it is already calculated somewhere on Fama and French website for NDXT and I'm just not looking at the right data files?

I think my confusion got even further after I read this response How to get real-time data for Fama-French model? on Stack Exchange:

Basically, what they do is divide the world up a grid of 5x5 portfolios: size on one axis, value on the other. If you have access to market cap and B/E you could follow their procedure that they outline. Hence, these factors are themselves average returns, so their calculation is dependent on the stocks you choose to input.

I can see that you are mentioning quantmod so you are probably using R, but in python it's quite straightforward to download the FF values through pandas.