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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 The word "no-operation" is a bit ambiguous
r - list all factor levels of a data. frame - Stack Overflow with dplyr::glimpse(data) I get more values, but no infos about number values of factor-levels Is there an automatic way to get all level informations of all factor vars in a data frame?
r - How to convert a factor to integer\numeric without loss of . . . See the Warning section of ?factor: In particular, as numeric applied to a factor is meaningless, and may happen by implicit coercion To transform a factor f to approximately its original numeric values, as numeric(levels(f))[f] is recommended and slightly more efficient than as numeric(as character(f)) The FAQ on R has similar advice
r - Changing factor levels with dplyr mutate - Stack Overflow 19 From my understanding, the currently accepted answer only changes the order of the factor levels, not the actual labels (i e , how the levels of the factor are called) To illustrate the difference between levels and labels, consider the following example:
Convert existing dataframe variable to factor in Tidyverse When you have an existing character variable in a dataframe, is there an easy method for converting that variable to a factor using the tidyverse format? For example, the 2nd line of code below won't reorder the factor levels, but the last line will
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 diff
Convert data. frame column format from character to factor The complete conversion of every character variable to factor usually happens when reading in data, e g , with stringsAsFactors = TRUE, but this is useful when say, you've read data in with read_excel() from the readxl package and want to train a random forest model that doesn't accept character variables
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: