lobstr | Understanding complex R objects with tools similar to str

I want to take a user's code and convert reactive() calls into functions like this.

I am building a react login page that uses Django Rest Token authentication. I am at the process of creating error messages in the login page. But one of the errors I created is not displaying correctly. The error message is supposed to display after a user has clicked the login button (handleSubmit). But the error displays after the user additionally inputs a change (handleChange )in the input. I can't seem to trace where I did an error in my code. Some help would be appreciated. Thanks.

The error that does not display correctly is, it is in my code below, I enclosed it in ** so you could easily identify it. :-

I am trying to evaluate a maximum likelihood expression using data masks. The idea is to allow parameters and variables to be called by name within the function while avoiding multiple calls to attach() and detach(). This is a small very simplified example and the real functions are quite large and complex.

Configuration:

I'm learning R and currently I'm reading this book. To make sure I understand the concept, I ran the following test which turned out to be quite confusing for me and I'd appreciate if you could clarify it. Here is the test, which I ran directly in the R shell from the terminal (not using RStudio or Emacs ESS).

I get cumulating memory using the following code. Every time I switch between the action button 1 and 2 the used memory increases.

Post edit: Important Remark: The behavior here reported seems to be happening in RStudio only, and not from the R terminal.

My RStudio version is: 1.2.1511.

I am trying to understand when an object in R is subject to changes in place or when is it following copy-on-modify semantics.

Take this example from Hadley's Advanced R book.

In that example, Hadley is illustrating how an object in R can be modified in place. He talks of two cases: objects with single name binding and environments.

I tried his example with vector v but I do not get the address of vector v being preserved after changing one of its values.

After changing value 3 at position 3 of vector v the memory address of v changes from 0x5583a1461fb8 to 0x5583a2c5f608.

So my question is why? This seems to contradict Hadley's book example.

Curious: can someone give the explanation as to why df1 and df3 are finally assigned the same attribute even though df3 is the one explicitly modified? I would have assumed that because df1 and df3 have different memory addresses they would not be both modified. Piping issue?--How are objects being bound together?