Post edit: Preserving the below post, which I think highlights some of the issues in the parent I'm responding to's example, but it turns out he is correct on a

Post edit: Preserving the below post, which I think highlights some of the issues in the parent I'm responding to's example, but it turns out he is correct on another issue -- narrowly, in his example, the `[` subset command dispatches differently on tibbles than data.frames, so you can narrowly produce weird behaviour. So to anyone reading, please consider upvoting parent and reading rest of thread Original post follows: Right off the bat, the problem is not "using" tibbles, it's that you've incorrectly constructed one by passing the data through the tibble() constructor rather than using as_tibble(). The tibble constructor -- for pretty good reasons in other circumstances that seem crazy to you here because of your intent -- infers that you want the entire data frame to be a single column inside the tibble, called "iris". It does this because it evaluates the variable name passed to the tibble constructor as both the intended column name and the data to be placed inside the column. This demonstrates nesting, which is one of the great features of tibbles and otherwise used for a bunch of stuff. If you had done `tb_iris <- as_tibble(iris)`, it would have worked fine. `as_tibble()` is the function to convert an existing data structure to a tibble. R is obviously not "type safe" in any way, but you can engage in defensive programming, and one way you can do that is being hyper-aware of the steps you take during type conversions. If you check the documentation for `tibble()`, it tells you explicitly to "Use as_tibble() to turn an existing object into a tibble." Is there a reason you didn't? Imagine this related example: my_string <- "10" numeric(my_string) as.numeric(my_string) Would we conclude that "using the numeric type can be dangerous" because the constructor interpreted the argument different than the conversion helper? Second, I suspect you must be using extremely old versions of things, because on more recent versions, your nunique function would fail, not produce 1. I correctly get "Error: Can't find column `Species` in `.data`." This error message is maybe a little confusing if you don't check the structure `str(tb_iris)` of tb_iris to see what I mentioned above, but is the correct error to output in light of it. You'd also be able to flag this by just checking `colnames(tb_iris)` or `View(tb_iris)` if you're working in RStudio or using the embedded environment pane or really any other way of looking at the data. But your broader point is also false. Once a tibble has been formed, it should work EXACTLY the way a data.frame works because R objects can have multiple classes. The only thing that makes a tibble different than a data.frame is that it has an additional class label. All dispatches that work on data.frame objects work on tibbles because of how multiple classing works in R. This has been a goal since the beginning of tibble. The one exception I'm aware of is external functions that incorrectly check `if(class(obj) == "data.frame")` instead of using `is.data.frame()` or `if("data.frame" %in% class(obj))`. The former is and always has been incorrect because of how multiple dispatch is designed to work in R and should generate an error with multi-classed objects because the if statement evaluates to a vector of logicals instead of a logical. Once way you can tell that tibbles and data frames are identical save the above caveat is to run the following code: df_iris <- iris tb_iris <- as_tibble(iris) identical(df_iris, tb_iris) class(tb_iris) <- "data.frame" identical(df_iris, tb_iris) Note that you are not "downconverting" a tibble into a data.frame in this code (but that would work too) -- you are taking the tibble exactly as is and hacking its class label to look like a data frame. It's identical because a tibble was always a data frame.