3 ms·

R data frames have excellent support for missing and categorical data. Everything else is just as much of a problem.

• wesm 9y ago
  Disagree. Factors (categorical data) in R only support strings
  • sin7 9y ago
    But they can be ordered and are represented as integers. So you can turn your numerics to factors and keep them in order and do whatever goofy math you want to do on your categorical data.
    • wesm 9y ago
      Right, but I don't think it's fair to say that R's support for categorical data is "excellent" if only strings can be category labels/levels. Categories (aka dictionary-encoded data in other systems) semantically may be any type in practice (strings, timestamps, numbers, etc.). The function as.factor in R is lossy because the input type is coerced to string.
      • zzleeper 9y ago
        > the input type is coerced to string. Wow. Didn't knew that.. strings is the one thing I always minimize in my datasets, due to speed and memory considerations.. BTW, I wanted to thank you for your 2012 slides on how you used hashes to group and join data. It led me to learn more about categoricals and I ended up implementing a Factor() object [1] in the other tool I use (Stata) that ended up being a life saver. In fact, once you have a powerful and fast categorical type, with a set of key functions, you can do anything from group the data, to count distinct categories, to run fixed effect regressions in no time. [1] http://fmwww.bc.edu/repec/scon2017/Baltimore17_Correia.pdf http://fmwww.bc.edu/repec/scon2017/Baltimore17_Correia.pdf
        • rcthompson 9y ago
          Memory isn't the issue. Both factors and string vectors only store each unique string once in memory. The issue is the limitation that factor levels can only be strings and not any other data type. Also, I think testing strings for equality is O(1) since it should simplify to a pointer comparison.
          • stewbrew 9y ago
            That's not right. Factor _levels_ can only be integers, factor _labels_ can only be strings since labels are the printable representation of a level. Maybe you could store the "real" values of a level as attribute (I.e. metadata) of the factor. Anyway, I think that's as solvable problem.
            • rcthompson 9y ago
              Well, I don't know if there's a standard terminology used elsewhere, but what you call labels, R calls levels.
              • stewbrew 9y ago
                Erm ... Well, I admit it's somewhat confusing. factor(x = character(), levels, labels = levels, exclude = NA, ordered = is.ordered(x), nmax = NA) [...] levels : an optional vector of the values (as character strings) that x might have taken. The default is the unique set of values taken by as.character(x), sorted into increasing order of x. Note that this set can be specified as smaller than sort(unique(x)). labels : either an optional character vector of (unique) labels for the levels (in the same order as levels after removing those in exclude), or a character string of length 1. This way, you can do something like that: > x <- 1:3 > factor(x, levels = 1:2, labels = c("foo", "bar")) [1] foo bar <NA> Levels: foo bar But this actually is: > factor(as.character(x), levels = c("1", "2"), labels = c("foo", "bar")) [1] foo bar <NA> Levels: foo bar
      • rcthompson 9y ago
        I'm trying to think of an example where you would want to use a factor with levels that are timestamps rather than just a vector of timestamps. And likewise for other data types, even strings. The only case I can think of would be if you wanted to limit the possible values to specific set. My impression is that factors in R are borderline-deprecated, especially in the tidyverse, in favor of just using the equivalent non-factor vector.
      • stewbrew 9y ago
        I think you're mixing up scales. Timestamps and numbers can be recoded to categories but when doing this you're throwing away information -- the same way you lose information when converting an integer to a string. When you work with factors you're no longer interested in qualities the data would have on its original scale like distance etc. Could you please provide a real world example where this actually is a problem.