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  • r - Why use as. factor() instead of just factor() - Stack Overflow
    ‘factor(x, exclude = NULL)’ applied to a factor without ‘NA’s is a no-operation unless there are unused levels: in that case, a factor with the reduced level set is returned ‘as factor’ coerces its argument to a factor It is an abbreviated (sometimes faster) form of ‘factor’ Performance: as factor > factor when input is a factor
  • when to use factor () when plotting with ggplot in R?
    Is the general rule to use factor when the variable being used to determine the shape size colour is discrete, and not continuous? Or is there another use of factor in this context? It seems like the first command can be made like the second with the right legend, even without factor thanks edit: I get this when I use the colour=gear:
  • r - summarizing counts of a factor with dplyr - Stack Overflow
    I want to group a data frame by a column (owner) and output a new data frame that has counts of each type of a factor at each observation The real data frame is fairly large, and there are 10 different factors Here is some example input:
  • How to Find the Branching Factor of a Tree - Stack Overflow
    The branching factor is one characteristic of a node next to depth and gives a clue how complex a tree gets For example, for the GO Game on a 19x19 board, the branching factor on the first level is 361, after 4 more moves at depth 4 you end up having 10 billion nodes (possible moves)
  • How to change the number of replicas of a Kafka topic?
    Option "[replication-factor]" can't be used with option"[alter]" It is funny that you can change number of partitions on the fly (which is often hugely destructive action when done in runtime), but cannot increase replication factor, which should be transparent But remember, it is 0 10, not 10 0
  • Pandas - make a column dtype object or Factor - Stack Overflow
    Factor and Categorical are the same, as far as I know I think it was initially called Factor, and then changed to Categorical To convert to Categorical maybe you can use pandas Categorical from_array, something like this:




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