Why has R, despite quirks, been so successful?

Re:Why has R, despite quirks, been so successful? by c3534l on Thursday June 11, 2015 @02:11AM (#9697925)

I think the real reason is that no matter what crazy machine learning idea you want to implement, you basically just import the data and pass it to a function. Bam. You just trained a convolutional neural net. Calculate the hamming distance? Sure. No idea what that is, but I'll throw it into a random forest with a couple other idea when I'm done, then plot the most important variables. You never really have to learn a new API, which I'm always doing in Python. I don't want to learn a whole new framework: I heard of a thing and I want to see what it does with my data. Python never lets things be as simple as:

    from machinelearning import svm
    with open('/home/me/programming/data.csv') as f:
        data = f.read()

    print(svm(data))

That's why R, which is an awful, buggy, and weird language, is so pleasant to use for stats and ML.

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Re:Why has R, despite quirks, been so successful? by bowyakka on Thursday June 11, 2015 @03:26AM (#9698176)

Not to poke holes in this but is this actually that hard ?

    import pandas as pd
    from sklearn.linear import SVC

    df = pd.read_csv('/home/me/programming/data.csv')
    y = df['label']
    X = df.drop('label', axis=1)

    clf = SVC()
    clf.fit(X, y)

... Most of the things have a similar API, except for when I veer off into say deep-learning land.

This is not to say that R is a bad language, or that R does not have equally nice API's for this stuff; but I feel that in your case its a familiarity of language thing.

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Re:Why has R, despite quirks, been so successful? by Lofkin on Tuesday June 16, 2015 @06:15PM (#9729039)

You clearly haven't used pythons major data science packages.

Scikitlearn and statsmodels are known as being specifically easy to use with a uniform API that disparate R packages lack(though this is somewhat fixed with caret).

Further, for bayesian inference, Pymc 3 is more powerful and concise than training a model in stan.

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