Handling categorical variables in R

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In summary, in R, nominal categorical variables must be converted into factors and then to dummy variables before using them in a statistical model. The lm() function in R automatically does this conversion, but it may not apply to other models. Python does not have factors, so the intermediate "factor" step does not apply. It is possible to convert categorical variables directly to dummy variables in R without the factor step, but this may limit the ability to choose different contrasts.
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fog37
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Handling categorical variables in R
Hello R users,

My general understanding is that, in R, nominal categorical variables (with 2 or more levels) must be first converted into factors and THEN to dummy variables (k-1 dummy variables for k levels). Is that correct?

Once we accomplish categorical variable -> factor -> dummy variables, we can then use the dummy variable as an independent or dependent variable in a statistical model (P.S. : when using the function ##lm()## in R, the function ##lm()## automatically does the dummy variable conversion but I am not sure that being true for other models).

What if we converted the categorical variable to dummy variables without the intermediate factor step? Would that still work in R?

Python does not have factors so that intermediate "factor" step does not apply...

Thanks!
 
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Can you give a code example? I'm not sure what the factor step is but seeing what's actually called might help.
 
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fog37 said:
TL;DR Summary: Handling categorical variables in R

What if we converted the categorical variable to dummy variables without the intermediate factor step? Would that still work in R?
I have never tried this, but from my experience I would think that yes you could do that. You would lose the ability to choose different contrasts, since that would be your dummy variables. But I don’t see why it wouldn’t work
 

FAQ: Handling categorical variables in R

How do I convert a categorical variable to a factor in R?

To convert a categorical variable to a factor in R, you can use the `factor()` function. For example, if you have a character vector `categories`, you can convert it to a factor by using `factor(categories)`. This will treat the variable as a categorical variable with distinct levels.

How can I check the levels of a factor in R?

You can check the levels of a factor in R using the `levels()` function. For example, if you have a factor variable `f`, you can see its levels by calling `levels(f)`. This will return a vector of the unique levels in the factor.

How do I handle missing values in categorical variables in R?

To handle missing values in categorical variables in R, you can use the `na.omit()` function to remove rows with missing values or use the `forcats` package to handle missing values in a more nuanced way. For example, `forcats::fct_explicit_na()` can be used to treat NA values as a separate category.

How can I create dummy variables from a factor in R?

To create dummy variables from a factor in R, you can use the `model.matrix()` function or the `dummy_cols()` function from the `fastDummies` package. For example, `model.matrix(~ factor_variable - 1)` will create a matrix of dummy variables for the factor `factor_variable`.

How do I reorder the levels of a factor in R?

You can reorder the levels of a factor in R using the `factor()` function with the `levels` argument or using the `forcats` package's `fct_relevel()` function. For example, `factor(f, levels = c("level3", "level1", "level2"))` will reorder the levels of factor `f` to the specified order.

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