data(zips) has duplicate column 'area'

```r
data(zips)
sum(names(zips) == "area")
# [1] 2
```

This makes using {dplyr} with it more onerous. They are not identical:

```r
head(zips[abs(zips[[3]] - zips[[6]]) > 1e-5, ])
# lon lat area plotOrder lineOrder area perimeter zip lonCent latCent
# 686 -95.48580 30.38495 0.01263513 2483 1 0.01252809 0.6709048 77304 -95.52267 30.31832
# 687 -95.48561 30.37375 0.01263513 2483 2 0.01252809 0.6709048 77304 -95.52267 30.31832
# 688 -95.48392 30.35110 0.01263513 2483 3 0.01252809 0.6709048 77304 -95.52267 30.31832
# 689 -95.48286 30.34627 0.01263513 2483 4 0.01252809 0.6709048 77304 -95.52267 30.31832
# 690 -95.47842 30.33369 0.01263513 2483 5 0.01252809 0.6709048 77304 -95.52267 30.31832
# 691 -95.47407 30.32233 0.01263513 2483 6 0.01252809 0.6709048 77304 -95.52267 30.31832
```

Start from the data(zips) entry point and locate the package source or bundled data that defines zips. Inspect the duplicated area columns and their provenance, then check any data-related validation or package tests; done means the dataset has one clearly named area column without changing the other zip geometry fields.

Written by the indexing model from the issue text.