4 ms·

> "Dr. Matloff is grossly underestimating how painful it is for beginners to, eg, figure out when they are dealing with a column of strings vs a column of facto

• ACow_Adonis 7y ago
  Pay no attention to the myriad of other apply functions hiding behind the curtain and the difficulty in remembering what each of them does and why you can't just use map, eh? :) So to answer your question, no, its not just simply that.
  • ImaCake 7y ago
    Looking at the documentation; "sapply" is itself a wrapper for "lapply", and will do a number of different things depending on what arguments are passed into the function or not.
  • nonbel 7y ago
    There is nothing to remember to solve that problem besides the wrote memorization of sapply(df, class).
  • Zelazny7 7y ago
    I use a simply device to keep these straight: - (l)apply: List apply always returns a list - (s)apply: simplify apply tries to return simplified result - (v)apply: verify apply checks the return type conforms to user supplied example - (m)apply: multiple apply applies FUN to multiple vectors - (r)apply: recursive apply is essentially a flatmap - apply : no device here, only use on matrices, never data.frames
    • BoiledCabbage 7y ago
      Love it or hate it, it's pretty clear these fns had very little api design thought go into them. As much as I think people can lean too much on the concept, single responsibility principle would've gone a long way here.
    • roenxi 7y ago
      After reading your explanation I still, as has been the case for years, don't understand what sapply or rapply does, and vapply sounds weird. That isn't going to change, because I'm just not going to use them or 'invest' the time in finding out what some statistician-of-yore's interpretation of a map is. Instead I'll stick to tidyverse map - returns a list. Or tidyverse map_[int/chr/dbl/etc, etc] if I want a vector of [int/chr/dbl/etc, etc]. That and the data frame manipulation verbs covers the most useful 80% of cases where *apply would otherwise be needed. If the base R team were implementing functions that way in the base, stats professors wouldn't need to complain about mass exoduses from base R.
      • Zelazny7 7y ago
        Yeah, that's a valid point. And it took me a couple years to internalize these differences -- mostly by being burned on numerous occasions. Base R has a lot of idiosyncrasies and they have to be memorized, unfortunately. I develop R packages for my colleagues and so I stick to Base R whenever possible. I don't want my packages depending on the tidyverse at all. But for EDA, I am agnostic about what my colleagues do. They should use the tools that stay out of the way and let them get their hands around the dataset intuitively. For me that's Base R, for others it's data.table or tidyverse.
      • nonbel 7y ago
        > After reading your explanation I still, as has been the case for years, don't understand what sapply or rapply does, There is nothing complicated about what sapply does... It simply means loop over the elements of the first argument (which must be a list; btw a dataframe, df, is internally the same as a list) and apply some function. lapply does this and returns a list, sapply does this and can optionally "simplify" the results into a vector, etc. So: lapply(df, class) = loop over the elements of df and tell me the class, return this in the form of a list sapply(df, class) = loop over the elements of df and tell me the class, return this as a character vector This is basically lapply: res = NULL for(i in 1:length(df)){ res = append(res, class(df[i])) } return(res)
• kgwgk 7y ago
  Or simply str(df) or summary(df)