How does lm() know which predictors are categorical?

Question

Normally, me and you(assuming you're not a bot) are easily able to identify whether a predictor is categorical or quantitative. Like, for example, gender is obviously categorical. Your last vote can be classified categorically.
Basically, we can identify categorical predictors easily. But what happens when we input some data in R, and it's lm function makes dummy variables for a predictor? How does it do that?

Somewhat related Question on StackOverflow.

Answer 1

Search R factor function. Here is a small demo, first model uses number of cylinder as a numerical valuable. Second model uses it as a categorical variable.

> summary(lm(mpg~cyl,mtcars))

Call:
lm(formula = mpg ~ cyl, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9814 -2.1185  0.2217  1.0717  7.5186 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  37.8846     2.0738   18.27  < 2e-16 ***
cyl          -2.8758     0.3224   -8.92 6.11e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.206 on 30 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.7171 
F-statistic: 79.56 on 1 and 30 DF,  p-value: 6.113e-10

> summary(lm(mpg~factor(cyl),mtcars))

Call:
lm(formula = mpg ~ factor(cyl), data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.2636 -1.8357  0.0286  1.3893  7.2364 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   26.6636     0.9718  27.437  < 2e-16 ***
factor(cyl)6  -6.9208     1.5583  -4.441 0.000119 ***
factor(cyl)8 -11.5636     1.2986  -8.905 8.57e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.223 on 29 degrees of freedom
Multiple R-squared:  0.7325,    Adjusted R-squared:  0.714 
F-statistic:  39.7 on 2 and 29 DF,  p-value: 4.979e-09

Answer 2

Hxd1011 adressed the more difficult case, when a categorical variable is stored as a number and therefore R understands by default that it is a numerical value - and if this is not the desired behaviour we must use factor function.

Your example with predictor ShelveLoc in dataset Carseats is easier because it's a text (character) variable, and therefore it can only be a categorical variable.

> head(Carseats$ShelveLoc)
[1] Bad    Good   Medium Medium Bad    Bad   
Levels: Bad Good Medium