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What does the manual say As.factor > factor when input is a factor performance Watch for unused or na levels The levels of a factor are stored as character data type anyway (attributes(f)), so i don't think there is anything wrong with as.numeric(paste(f))
Perhaps it would be better to think why (in the specific context) you are getting a factor in the first place, and try to stop that E.g., is the dec argument in read.table set correctly? 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.
The dplyr command to modify a data column is mutate All factors have an order for their levels The difference between an ordered = true factor and a regular factor is how the. You should do the data processing step outside of the model formula/fitting
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. 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.
Asked 11 years ago modified 3 years, 7 months ago viewed 229k times 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)). Is there an automatic way to get all level informations of all factor vars in a data.frame?
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