…iaData#1042)

Add compatibility with pre-contrasts ModelFrame constructor

…ise for speed improvement (JuliaData#1070)

Completely remove support for DataArrays.

This depends on PRs moving these into NullableArrays.jl.
Also use isequal() instead of ==, as the latter is in Base and
unlikely to change its semantics.

groupby() did not follow the order of levels, and wasn't robust to reordering
levels. Add tests for corner cases.

Use the fallbacks for now, should be added back after
JuliaData/CategoricalArrays.jl#12 is fixed.

Not sure what I meant by this. If it was really serious, we'll discover
it sooner or later.

This is a much more general issue (JuliaStats/NullableArrays.jl#85) which
can be tackled later.

For now, preserve the current semantics: conversion to NullableArray
does not happen via insert!().

Again a broader issue which doesn't particularly affect DataFrames. Cf.
JuliaStats/NullableArrays.jl#143

Better handle that separately.

Shorter written that way for now. Filed as JuliaStats/NullableArrays.jl#144.

This depends on a CategoricalArrays change by which levels are sorted when
creating the array.

There's no inconsistency here: when the input is a Matrix, there's no
point in returning a NullableArray. Anyway, these are test methods.

We don't have to handle this right now.

Keep this in DataFrames for now, renaming it to the more explicit
sharepools(). Also relax signatures to accept non-Nullable categorical arrays.

These were not exercized by the tests, and the use case for them isn't obvious.
(They were formerly methods of DataArrays.PooledDataArray().)

For NullableArrays, even current git master is not enough at this time.

Tests pass, but the Nullable{Any} results could be annoying for users.

New type merging NominalArray and OrdinalArray in 0.0.5.

These shouldn't live in DataFrames.

…w references

…pveyor

The full code isn't that longer, and we can go back to the original code
when RDataset will have been ported.

Since we give them the same semantics for now, there's no point in keeping
separate functions.

More logical name for the constructor provided by CategoricalArray.
categorical!() is the in-place equivalent provided only by DataFrames. Also
move the compacting logic into CategoricalArrays, as it's both a cleaner
design and a more efficient one.

We can't rely on the exact contents of a NULL.

Port to NullableArrays and CategoricalArrays

* Only sort duplicated columns once

* Added comments

* moved check for identical arrays inside of for loop

* Don't mention PR under /src, only mention them under /test

Resolve "WARNING: [a] concatenation is deprecated; use collect(a) instead"

Use vcat() instead of collect() in colwise(), and identity() instead of abs(),
since the latter do not work with Nullable.

Also switch from mkdocs output to Documenter's HTML output.

make GroupApplied immutable by adding subframe type parameter

* avoid [:], use reshape()
* avoid unnecessary Symbol<->String conversions

…uliaData#1078)

Misc minor enhancements

…Data#845)

* handle -1 and add tests

* replace `import Base.==` with `Base.:(==)`

* typo and error test

Also return a NullableCategoricalArray from sharepools() since
the code currently doesn't check that no null values are present.
anyway this function is internal and the change imposes no overhead.

* Better display of Nullables

* Don't write trailing space in Latex output

Also fix missing newline in show test

)

* limit attribute of IOContext is used for html generation

* fixup

Closes JuliaData#1103

I apparently missed these occurrences when removing these functions.