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* Patch test to be compatible with dev tibble
* Changed deprecation message of `extract_numeric()` to point to
`readr::parse_number()` rather than `readr::parse_numeric()`
# tidyr 0.6.0
## API changes
* `drop_na()` removes observations which have `NA` in the given variables. If no
variables are given, all variables are considered (#194, @janschulz).
* `extract_numeric()` has been deprecated (#213).
* Renamed `table4` and `table5` to `table4a` and `table4b` to make their
connection more clear. The `key` and `value` variables in `table2` have
been renamed to `type` and `count`.
## Bug fixes and minor improvements
* `expand()`, `crossing()`, and `nesting()` now silently drop zero-length
inputs.
* `crossing_()` and `nesting_()` are versions of `crossing()` and `nesting()`
that take a list as input.
* `full_seq()` works correctly for dates and date/times.
# tidyr 0.5.1
* Restored compatibility with R < 3.3.0 by avoiding `getS3method(envir = )` (#205, @krlmlr).
# tidyr 0.5.0
## New functions
* `separate_rows()` separates observations with multiple delimited values into
separate rows (#69, @aaronwolen).
## Bug fixes and minor improvements
* `complete()` preserves grouping created by dplyr (#168).
* `expand()` (and hence `complete()`) preserves the ordered attribute of
factors (#165).
* `full_seq()` preserve attributes for dates and date/times (#156),
and sequences no longer need to start at 0.
* `gather()` can now gather together list columns (#175), and
`gather_.data.frame(na.rm = TRUE)` now only removes missing values
if they're actually present (#173).
* `nest()` returns correct output if every variable is nested (#186).
* `separate()` fills from right-to-left (not left-to-right!) when fill = "left"
(#170, @dgrtwo).
* `separate()` and `unite()` now automatically drop removed variables from
grouping (#159, #177).
* `spread()` gains a `sep` argument. If not-null, this will name columns
as "key<sep>value". Additionally, if sep is `NULL` missing values will be
converted to `<NA>` (#68).
* `spread()` works in the presence of list-columns (#199)
* `unnest()` works with non-syntactic names (#190).
* `unnest()` gains a `sep` argument. If non-null, this will rename the
columns of nested data frames to include both the original column name,
and the nested column name, separated by `.sep` (#184).
* `unnest()` gains `.id` argument that works the same way as `bind_rows()`.
This is useful if you have a named list of data frames or vectors (#125).
* Moved in useful sample datasets from the DSR package.
* Made compatible with both dplyr 0.4 and 0.5.
* tidyr functions that create new columns are more aggresive about re-encoding
the column names as UTF-8.
# tidyr 0.4.1
* Fixed bug in `nest()` where nested data was ending up in the wrong row (#158).
# tidyr 0.4.0
## Nested data frames
`nest()` and `unnest()` have been overhauled to support a useful way of structuring data frames: the __nested__ data frame. In a grouped data frame, you have one row per observation, and additional metadata define the groups. In a nested data frame, you have one __row__ per group, and the individual observations are stored in a column that is a list of data frames. This is a useful structure when you have lists of other objects (like models) with one element per group.
* `nest()` now produces a single list of data frames called "data" rather
than a list column for each variable. Nesting variables are not included
in nested data frames. It also works with grouped data frames made
by `dplyr::group_by()`. You can override the default column name with `.key`.
* `unnest()` gains a `.drop` argument which controls what happens to
other list columns. By default, they're kept if the output doesn't require
row duplication; otherwise they're dropped.
* `unnest()` now has `mutate()` semantics for `...` - this allows you to
unnest transformed columns more easily. (Previously it used select semantics).
## Expanding
* `expand()` once again allows you to evaluate arbitrary expressions like
`full_seq(year)`. If you were previously using `c()` to created nested
combinations, you'll now need to use `nesting()` (#85, #121).
* `nesting()` and `crossing()` allow you to create nested and crossed data
frames from individual vectors. `crossing()` is similar to
`base::expand.grid()`
* `full_seq(x, period)` creates the full sequence of values from `min(x)` to
`max(x)` every `period` values.
## Minor bug fixes and improvements
* `fill()` fills in `NULL`s in list-columns.
* `fill()` gains a direction argument so that it can fill either upwards or
downwards (#114).
* `gather()` now stores the key column as character, by default. To revert to
the previous behaviour of using a factor (which allows you to preserve the
ordering of the columns), use `key_factor = TRUE` (#96).
* All tidyr verbs do the right thing for grouped data frames created by
`group_by()` (#122, #129, #81).
* `seq_range()` has been removed. It was never used or announced.
* `spread()` once again creates columns of mixed type when `convert = TRUE`
(#118, @jennybc). `spread()` with `drop = FALSE` handles zero-length
factors (#56). `spread()`ing a data frame with only key and value columns
creates a one row output (#41).
* `unite()` now removes old columns before adding new (#89, @krlmlr).
* `separate()` now warns if defunct ... argument is used (#151, @krlmlr).
# tidyr 0.3.1
* Fixed bug where attributes of non-gather columns were lost (#104)
# tidyr 0.3.0
## New features
* New `complete()` provides a wrapper around `expand()`, `left_join()` and
`replace_na()` for a common task: completing a data frame with missing
combinations of variables.
* `fill()` fills in missing values in a column with the last non-missing
value (#4).
* New `replace_na()` makes it easy to replace missing values with something
meaningful for your data.
* `nest()` is the complement of `unnest()` (#3).
* `unnest()` can now work with multiple list-columns at the same time.
If you don't supply any columns names, it will unlist all
list-columns (#44). `unnest()` can also handle columns that are
lists of data frames (#58).
## Bug fixes and minor improvements
* tidyr no longer depends on reshape2. This should fix issues if you also
try to load reshape (#88).
* `%>%` is re-exported from magrittr.
* `expand()` now supports nesting and crossing (see examples for details).
This comes at the expense of creating new variables inline (#46).
* `expand_` does SE evaluation correctly so you can pass it a character vector
of columns names (or list of formulas etc) (#70).
* `extract()` is 10x faster because it now uses stringi instead of
base R regular expressions. It also returns NA instead of throwing
an error if the regular expression doesn't match (#72).
* `extract()` and `separate()` preserve character vectors when
`convert` is TRUE (#99).
* The internals of `spread()` have been rewritten, and now preserve all
attributes of the input `value` column. This means that you can now
spread date (#62) and factor (#35) inputs.
* `spread()` gives a more informative error message if `key` or `value` don't
exist in the input data (#36).
* `separate()` only displays the first 20 failures (#50). It has
finer control over what happens if there are two few matches:
you can fill with missing values on either the "left" or the "right" (#49).
`separate()` no longer throws an error if the number of pieces aren't
as expected - instead it uses drops extra values and fills on the right
and gives a warning.
* If the input is NA `separate()` and `extract()` both return silently
return NA outputs, rather than throwing an error. (#77)
* Experimental `unnest()` method for lists has been removed.
# tidyr 0.2.0
## New functions
* Experimental `expand()` function (#21).
* Experiment `unnest()` function for converting named lists into
data frames. (#3, #22)
## Bug fixes and minor improvements
* `extract_numeric()` preserves negative signs (#20).
* `gather()` has better defaults if `key` and `value` are not supplied.
If `...` is ommitted, `gather()` selects all columns (#28). Performance
is now comparable to `reshape2::melt()` (#18).
* `separate()` gains `extra` argument which lets you control what happens
to extra pieces. The default is to throw an "error", but you can also
"merge" or "drop".
* `spread()` gains `drop` argument, which allows you to preserve missing
factor levels (#25). It converts factor value variables to character vectors,
instead of embedding a matrix inside the data frame (#35).
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