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Bookmark or Share- r - Changing factor levels with dplyr mutate - Stack OverflowFrom 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:
- How to force R to use a specified factor level as reference in a ...When creating the factor from b you can specify the ordering of the levels using factor(b, levels = c(3,1,2,4,5)). Do this in a data processing step outside the lm() call though. My answer below uses the relevel() function so you can create a factor and then shift the reference level around to suit as you need to. –
- Confusion between factor levels and factor labels - Stack Overflowwhich you want to transform into a factor, in which the different levels are coded as a, b, and c; and for which you want the levels to be labeled as Treatment A etc. The factor function will look for the values a, b and c, convert them to numerical factor classes, and add the label values to the level attribute of the factor. This attribute is ...
- r - How to convert a factor to integer\numeric without loss of ...But annoyingly, there is nothing to handle the factor -> numeric conversion. As an extension of Joshua Ulrich's answer, I would suggest to overcome this omission with the definition of your own idiomatic function: as.double.factor
- 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
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