When talking about R to an experienced tech guy, he will probably come out with two main objections to the language:

• Its steep learning curve
• Its difficulty in handling large datasets

You will soon discover that those are actually the two main weaknesses of the language. Nevertheless, not even pretending that R is a perfect language, we are going to tackle those weaknesses here, showing effective ways to overcome them. We can actually consider the first of the mentioned objections temporary, at least on an individual basis, since once the user gets through the valley of despair, he will never come back to it and the weakness will be forgotten. You do not know about the valley of despair? Let me show you a plot, and then we can discuss it:

It is common wisdom that every man who starts to learn something new and complex enough will go through three different phases:

• The honeymoon, where he falls in love with the new stuff and feels confident to be able to easily master it
• The valley of despair, where everything starts looking impossible and disappointing
• During the rest of the story, where he starts having a more realistic view of the new topic, his mastery of it starts increasing, and so does his level of confidence

Moving on to the second weakness, we have to say that R's difficulty in handling large datasets is a rather more structural aspect of the language, and therefore requires some structural changes to the language, and strategical cooperation between it and other tools. In two new paragraphs, we will go through both of the aforementioned weaknesses.