What are the practical differences between 'factor' and 'string' data types in R? - r

Question

From other programming languages I am familiar with the string data type. In addition to this data type, R also has the factor data type. I am new to the R language, so I am trying to wrap my head around the intent behind this new data type.
Question: What are the practical differences between 'factor' and 'string' data types in R?
I get that (on a conceptual/philosophical level) the factor data type is supposed to encode the values of a categorical random variable, but I do not understand (on a practical level) why the string data type would be insufficient for this purpose.
Seemingly having duplicate data types which serve the same practical purpose would be bad design. However, if R were truly poorly designed on such a fundamental level, it would be much less likely to have achieved the level of popularity it has. So either a very improbable event has happened, or I am misunderstanding the practical significance/purpose of the factor data type.
Attempt: The one thing I could think of is the concept of "factor levels", whereby one can assign an ordering to factors (which one can't do for strings), which is helpful when describing "ordinal categorical variables", i.e. categorical variables with an order (e.g. "Low", "Medium", "High").
(Although even this wouldn't seem to make factors strictly necessary. Since the ordering is always linear, i.e. no true partial orders, on countable sets, we could always just accomplish the same with a map from some subset of the integers to the strings in question -- however in practice that would probably be a pain to implement over and over again, and a naive implementation would probably not be as efficient as the implementation of factors and factor levels built into R.)
However, not all categorical variables are ordinal, some are "nominal" (i.e. have no order). And yet "factors" and "factor levels" still seem to be used with these "nominal categorical variables". Why is this? I.e. what is the practical benefit to using factors instead of strings for such variables?
The only other information I could find on this subject is the following quote here:
Furthermore, storing string variables as factor variables is a more efficient use of memory.
What is the reason for this? Is this only true for "ordinal categorical variables", or is it also true for "nominal categorical variables"?
Related but different questions: These questions seem relevant, but don't specifically address the heart of my question -- namely, the difference between factors and strings, and why having such a difference is useful (from a programming perspective, not a statistical one).
Difference between ordered and unordered factor variables in R
Factors ordered vs. levels
Is there an advantage to ordering a categorical variable?
factor() command in R is for categorical variables with hierarchy level only?

Answer 1

Practical differences:
If x is a string it can take any value. If x is a factor it can only take a values from a list of all levels. That makes these variables more memory effecient as well.
example:
> x <- factor(c("cat1","cat1","cat2"),levels = c("cat1","cat2") )
> x
[1] cat1 cat1 cat2
Levels: cat1 cat2
> x[3] <- "cat3"
Warning message:
In `[<-.factor`(`*tmp*`, 3, value = "cat3") :
invalid factor level, NA generated
> x
[1] cat1 cat1 <NA>
Levels: cat1 cat2
As you said, you can have ordinal factors. Meaning that you can add extra information aout your variable that for instance level1 < level2 < level3. Characters don't have that. However, the order doesn't necessarily have to be linear, not sure where you found that.