Better reporting for error calls from
vec_as_location() (#1237).
Mention median() in Recovery section of
vignette("numbers") (#1197).
trunc_mat() now returns a value with a different
structure. This is considered an implementation detail that can change
in the future, do not rely on it. The only guarantee is that calling
print() will display the input like a tibble (#1059).dim_desc() in reexports.class over .subclass in
rlang::error_cnd() (#1015, #1060).set_num_opts() and set_char_opts() are
reexported from pillar (#959).view() uses
rlang::expr_deparse(width = Inf) to avoid errors with long
|> pipes (#957).new_tibble() checks that the nrow argument
is nonnegative and less than 2^31 (#916).tbl_sum.tbl_df() has an ellipsis in its formals for
extensibility."tibble.view_max" option for
lazy tables (#954).as.data.frame.tbl_df() strips inner column names
(#837).new_tibble() allows omitting the nrow
argument again (#781).vignette("digits"),
vignette("numbers"), ?num and
?char from the pillar package here (#913).iris by trees (#943).?tibble_options help page (#912).x[i, j] <- one_row_value avoids explicit recycling
of the right-hand side, the recycling happens implicitly in
vctrs::vec_assign() for performance (#922).new_tibble() uses vctrs::new_data_frame()
internally (#726, @DavisVaughan).tbl[row, col] <- rhs treats an all-NA
logical vector as a missing value both for existing data (#773) and for
the right-hand side value (#868). This means that a column initialized
with NA (of type logical) will change its type
when a row is updated to a value of a different type.[[<-() supports symbols (#893).as_tibble_row() supports arbitrary vectors (#797).enframe() and deframe() support arbitrary
vectors (#730).tibble() and tibble_row() ignore all
columns that evaluate to NULL, not only those where a
verbatim NULL is passed (#895, #900).new_tibble() is now faster (#901, @mgirlich).pillar::dim_desc() (#859).num() and char() are reexported from
pillar (#880).tribble() and frame_matrix() give an error
if values are named (#871, @lorenzwalthert).cli.num_colors option (#410).new_tibble() examples for compatibility with pillar
1.6.0.has_rownames() now works correctly for data frames
with a "row.names" attribute malformed due to a problem in
structure() (#852).
tbl[FALSE, "column"] <- x adds new column again
(#846).
Importing pillar 1.5.0, cli and crayon are now suggested packages (#475).
size_sum() is now reexported from pillar (#850,
@topepo).
as_tibble() hints more often to use the
.name_repair argument if column names are invalid
(#855).
as_tibble.table() mentions .name_repair
argument in the error message (#839).
Remove compatibility code for pillar < 1.5.0 (#861).
Moved most functions to the “stable” lifecycle (#860).
vec_ptype_abbr.tbl_df() and
type_sum.tbl_df() now uses the name of the topmost class
for subclasses of "tbl_df" (#843).formats.Rmd vignette.Establish compatibility with upcoming pillar 1.5.0 (#818).
tbl_sum() shows “data frame” instead of “tibble” for
objects inheriting from "tbl" but not "tbl_df"
(#818).
Register format.tbl() and print.tbl()
methods only if pillar doesn’t (#816).
Use vctrs::num_as_location() internally for subset
assignment of rows and columns for better error messages
(#746).
Adapt tests to the development version of testthat.
Fix documentation link to base::Extract.
add_row(df) adds an empty row again (#809, @DavisVaughan).
Fix test compatibility with rlang 0.4.7.
Fix warning about needs_dots arguments with pillar
>= 1.4.5 (#798).
[[ works with classed indexes again, e.g. created
with glue::glue() (#778).
add_column() works without warning for 0-column data
frames (#786).
tribble() now better handles named inputs (#775) and
objects of non-vtrs classes like lubridate::Period (#784)
and formattable::formattable (#785).
Subsetting and subassignment are faster (#780, #790, #794).
is.null() is preferred over is_null()
for speed.
Implement continuous benchmarking (#793).
is_vector_s3() is no longer reexported from pillar
(#789).[<-.tbl_df() coerces matrices to data frames
(#762).
Use delayed import for cli to work around unload problems in downstream packages (#754).
More soft-deprecation warnings are actually visible.
If .name_repair is a function, no repair messages
are shown (#763).
Remove superseded signal for as_tibble.list(),
because as_tibble_row() only works for size 1.
as_tibble(validate = ) now always triggers a
deprecation warning.
Subsetting and subassignment of rows with one-column matrices work again, with a deprecation warning (#760).
Attempts to update a tibble row with an atomic vector give a clearer error message. Recycling message for subassignment appears only if target size is != 1.
Tweak title of “Invariants” vignette.
Subset assignment (“subassignment”) and also subsetting has become stricter. Symptoms:
Error: No common type for …
Error: Assigned data ... must be compatible with
…
i must have one dimension, not 2
Error: Lossy cast from … to …
The “invariants” article at https://tibble.tidyverse.org/dev/articles/invariants.html describes the invariants that the operations follow in tibble, and the most important differences to data frames. We tried to make subsetting and subassignment as safe as possible, so that errors are caught early on, while introducing as little friction as possible.
List classes are no longer automatically treated as vectors. Symptoms:
Error: All columns in a tibble must be vectors
Error: Expected a vector, not a ... object
If you implement a class that wraps a list as S3 vector, you need to
include "list" in the class:
structure(x, class = c("your_s3_class", "list"))Alternatively, implement a vec_proxy() method as
described in https://vctrs.r-lib.org/reference/vec_data.html, or
construct your class with list_of().
Added experimental support for inner names for all columns, of
the form tibble(a = c(b = 1)). Inner names are no longer
stripped when creating a tibble. They are maintained for slicing
operations but not yet updated when assigning with a row subscript. This
is a change that may break existing comparison tests that don’t expect
names in columns (#630). Symptoms:
tibble() now splices anonymous data frames,
tibble(tibble(a = 1), b = a) is equivalent to
tibble(a = 1, b = a). This means that
tibble(trees) now has three columns, use
tibble(trees = trees) if the intention is to create a
packed data frame (#581).
The name-repair help topic is gone, refer to
?vctrs::vec_as_names instead.
expression() columns are converted to lists as a
workaround for lacking support in vctrs (#657).
tribble() is now stricter when combining values. All
values in a column must be compatible, otherwise an error occurs (#204).
The criteria for wrapping in a list column are now based on vctrs
principles: non-vectors or vectors with vctrs::vec_size()
unequal 1 are wrapped in lists.
$ warns unconditionally if column not found,
[[ doesn’t warn.
add_row() now uses vctrs::vec_rbind()
under the hood, this means that all columns are combined with
vctrs::vec_c(). In particular, factor columns will be
converted to character if one of the columns is a character
column.
Soft-deprecate subclass argument to
new_tibble().
Soft-deprecate as_tibble() without arguments
(#683).
Preparing to move glimpse() and
tbl_sum() to the pillar package. If your package implements
these methods, please import the generics from pillar as soon as they
become available there.
Internals now make heavy use of the vctrs package, following most of the invariants defined there. Name repair is the responsibility of vctrs now (#464).
All errors emitted directly by the package inherit from the
"tibble_error" and "rlang_error" classes. In
some cases, "vctrs_error" errors may be passed through. The
exact subclass is subject to change.
Example: tibble(a = quote(b)) raises an error that
inherits from "tibble_error_column_must_be_vector",
"tibble_error" and "rlang_error", and from
"error" and "condition" like all errors. Do
not rely on the wording of
"tibble_error_column_must_be_vector", this is likely to
change.
Use the following pattern to catch errors emitted by tibble:
tryCatch(
your_code(),
tibble_error = function(cnd) {
}
)New tibble_row() constructs tibbles that have
exactly one row, or fails. Non-vector objects are automatically wrapped
in a list, vectors (including lists) must have length one
(#205).
New as_tibble_row() and as_tibble_col()
convert a bare vector to a one-row or one-column tibble, respectively.
as_tibble_col() also works for non-bare vectors. Using
as_tibble() for bare vectors is superseded (#447).
as_tibble.data.frame() uses implicit row names if
asked to create a column from row names. This allows lossless direct
conversion of matrices with row names to tibbles (#567, @stufield).
Implement str.tbl_df() (#480).
tribble() now returns columns with
"unspecified" type for 0-row tibbles.
add_row() and add_column() now restore
attributes to avoid errors when appending to sf objects or other tibble
subclasses (#662).
add_column() gains .name_repair
argument. If not given, .data must have unique columns,
with a deprecation message.
Allow POSIXlt columns, they are now better supported
by dplyr and other tools thanks to vctrs (#626).
tibble() ignores NULL arguments, named or unnamed
(#580).
view() works for remote data sources by applying the
same strategy as print() and glimpse(). The
maximum number of rows in this case can be specified using the new
n argument, by default it is taken from the new
"tibble.view_max" option (#695).
Formatting dimensions never uses scientific notation.
glimpse() uses “Rows” and “Columns” instead of
“Variables” and “Observations”, because we’re not sure if the data is
tidy here (#614).
view() now uses the created (or passed) title
argument (#610, @xvrdm).
Import lifecycle package (#669).
new_tibble() removes redundant subclasses from the
"class" attribute.
Using classed conditions. All classes start with
"tibble_error_" and also contain
"tibble_error" (#659).
The magrittr pipe %>% is reexported.
Relax version requirements.
Fix test failing after pillar upgrade.
Three dots are used even for "unique" name repair
(#566).
add_row(), add_case() and
add_column() now signal a warning once per session if the
input is not a data frame (#575).
Fix view() for the case when an object named
x exists in the global environment (#579).
tibble names can again be set to NULL within
RStudio, as some R routines within RStudio relied on this behaviour
(#563, @kevinushey).
as_tibble.matrix(validate = TRUE) works again, with
a lifecycle warning (#558).
Replace new_list_along() by rep_along()
to support rlang 0.3.1 (#557, @lionel-).
The tibble() and as_tibble() functions, and
the low-level new_tibble() constructor, have undergone a
major overhaul to improve consistency. We suspect that package code will
be affected more than analysis code.
To improve compatibility with existing code, breaking changes were
reduced to a minimum and in some cases replaced with a warning that
appears once per session. Call
tibble:::scoped_lifecycle_errors() when updating your
packages or scripts to the new semantics API to turn these warnings into
errors. The compatibility code will be removed in tibble 3.0.0.
All optional arguments have moved past the ellipsis, and must be
specified as named arguments. This affects mostly the n
argument to as_tibble.table(), passing n
unnamed still works (with a warning).
new_tibble() has been optimized for performance, the
function no longer strips dimensions from 1d arrays and no longer checks
correctness of names or column lengths. (It still checks if the object
is named, except for zero-length input.) Use the new
validate_tibble() if you need these checks (#471).
The nrow argument to new_tibble() is
now mandatory. The class argument replaces the now
deprecated subclass argument, the latter will be supported
quietly for some time (#518).
Setting names on a tibble via names(df) <- ...
now also requires minimal names, otherwise a warning is issued once per
session (#466).
In as_tibble(), checking names is also enabled by
default, even for tibbles, matrices and other matrix-like objects: names
must exist, NA names are not allowed. Coercing a matrix
without column names will trigger a warning once per session. (This
corresponds to the "minimal" checks described
below.).
The validate argument to as_tibble()
has been deprecated, see below for alternatives. (The
as_tibble.tbl_df() method has been removed, the
as_tibble.data.frame() method will be used for
tibbles.)
as_tibble() always checks that all columns are 1D or
2D vectors and not of type POSIXlt, even with
validate = FALSE (which is now deprecated).
Calling as_tibble() on a vector now warns once per
session. Use enframe(name = NULL) for converting a vector
to a one-column tibble, or enframe() for converting a named
vector to a two-column tibble.
data_frame() and frame_data() are
soft-deprecated, please use tibble() or
tribble() (#111).
tibble_(), data_frame_(), and
lst_() are soft-deprecated. Please use
tibble() or lst() (#111, #509).
as.tibble() and as_data_frame() are
officially deprecated and not generic anymore, please use/implement
as_tibble() (#111).
as_tibble.data.frame() (and also
as_tibble.matrix()) strip row names by default. Code that
relies on tibbles keeping row names now will see:
rownames() or
row.names(),NA values when subsetting rows with with a
character vector,
e.g. as_tibble(mtcars)["Mazda RX4", ].Call pkgconfig::set_config("tibble::rownames", NA) to
revert to the old behavior of keeping row names. Packages that import
tibble can call set_config() in their
.onLoad() function (#114).
as_tibble() drops extra classes, in particular
as_tibble.grouped_df() now removes grouping
(#535).
column_to_rownames() now always coerces to a data
frame, because row names are no longer supported in tibbles
(#114).
In all *_rownames() functions, the first argument
has been renamed to .data for consistency (#412).
Subsetting one row with [..., , drop = TRUE] returns
a tibble (#442).
The print.tbl_df() method has been removed, the
print.tbl() method handles printing (#519).
tibble() supports columns that are matrices or data
frames (#416).
The new .rows argument to tibble() and
as_tibble() allows specifying the expected number of rows
explicitly, even if it’s evident from the data. This allows writing more
defensive code.
Column name repair has more direct support, via the new
.name_repair argument to tibble() and
as_tibble(). It takes the following values:
"minimal": No name repair or checks, beyond basic
existence."unique": Make sure names are unique and not
empty."check_unique": (default value), no name repair, but
check they are unique."universal": Make the names unique and
syntactic..name_repair = make.names or
.name_repair = ~make.names(., unique = TRUE) for names in
the style of base R).The validate argument of as_tibble() is
deprecated but supported (emits a message once per session). Use
.name_repair = "minimal" instead of
validate = FALSE, and
.name_repair = "check_unique" instead of
validate = TRUE. If you need to support older versions of
tibble, pass both .name_repair and validate
arguments in a consistent way, no message will be emitted in this case
(#469, @jennybc).
Row name handling is stricter. Row names are never (and never
were) supported in tibble() and new_tibble(),
and are now stripped by default in as_tibble(). The
rownames argument to as_tibble() supports:
NULL: remove row names (default),NA: keep row names,The old default can be restored by calling
pkgconfig::set_config("tibble::rownames", NA), this also
works for packages that import tibble.
new_tibble() and as_tibble() now also
strip the "dim" attribute from columns that are
one-dimensional arrays. (tibble() already did this
before.)
Internally, all as_tibble() implementation forward
all extra arguments and ... to
as_tibble.list() where they are handled. This means that
the common .rows and .name_repair can be used
for all inputs. We suggest that your implementations of this method do
the same.
enframe() (with name = NULL) and
deframe() now support one-column tibbles (#449).
Improved S4 support by adding exportClass(tbl_df) to
NAMESPACE (#436, @jeffreyhanson and @javierfajnolla).
New validate_tibble() checks a tibble for internal
consistency (#471).
Bring error message for invalid column type in line with allowed matrix/df cols (#465, @maxheld83).
view() function that always returns
its input invisibly and calls utils::View() only in
interactive mode (#373).The set_tidy_names() and tidy_names()
helpers the list of new names using a bullet list with at most six items
(#406).
A one-character ellipse (cli::symbol$ellipsis) is
printed instead of "..." where available, this affects
glimpse() output and truncated lists (#403).
Column names and types are now formatted identically with
glimpse() and print.tbl_df().
tidy_names() quotes variable names when reporting on
repair (#407).
All error messages now follow the tidyverse style guide (#223).
as_tibble() prints an informative error message when
using the rownames argument and the input data frame or
matrix does not have row names (#388, @anhqle).
column_to_rownames() uses the real variable name in
its error message (#399, @alexwhan).
Lazy tibbles with exactly 10 rows no longer show “…with more rows” (#371).
glimpse() shows information obtained from
tbl_sum(), e.g. grouping information for
grouped_df from dplyr (#550).
glimpse() takes coloring into account when computing
column width, the output is no longer truncated prematurely when
coloring is enabled.
glimpse() disambiguates outputs for factors if the
levels contain commas (#384, @anhqle).
print.tbl_df() with a negative value for
n behaves as if n was omitted (#371).
Fixed output for extra column names that contain spaces.
Skip dplyr in tests if unavailable (#420, @QuLogic).
Skip mockr in tests if unavailable (#454, @Enchufa2).
Use fansi::strwrap_ctl() instead of own string
wrapping routine.
tibble() uses recycled values during construction
but unrecycled values for validation.
tibble() is now faster for very wide
tibbles.
Subsetting with the [ operator is faster
(#544).
Avoid use of stop() in examples if packages are not
installed (#453, @Enchufa2).
Fix as_tibble() examples by using correct argument
names in requireNamespace() call (#424, @michaelweylandt).
as_tibble() checks column length only once (#365,
@anhqle).
Using rlang::list2() (#391, @lionel-).
tibble.width option is honored again (#369).tbl[1, , drop = TRUE] now behaves identically to data
frames (#367).glimpse() returns its input for zero-column data
frames.enframe(NULL) now returns the same as
enframe(logical()) (#352).tribble() now ignores trailing commas (#342, @anhqle).nrow() and head() in
glimpse(), not ncol().The new pillar package is now responsible for formatting tibbles. Pillar will try to display as many columns as possible, if necessary truncating or shortening the output. Colored output highlights important information and guides the eye. The vignette in the tibble package describes how to adapt custom data types for optimal display in a tibble.
add_case() an alias for add_row()
(#324, @LaDilettante).as_tibble() gains rownames argument (#288,
#289).as_tibble.matrix() repairs column names.`[.tbl_df`() supports drop = TRUE and
omits the warning if j is passed. The calls
df[i, j, drop = TRUE] and df[j, drop = TRUE]
are now compatible with data frames again (#307, #311).glimpse() (#328).add_column() from dropping classes and
attributes by removing the use of cbind(). Additionally
this ensures that add_column() can be used with grouped
data frames (#303, @DavisVaughan).add_column() to an empty zero-row tibble with a
variable of nonzero length now produces a correct error message
(#319).has_name() from rlang, instead of
forwarding, to avoid warning when importing both rlang and tibble.tibble() call are recycled
prior to evaluating subsequent arguments, improving consistency with
mutate() (#213).tibble() call
maintains their class (#284).add_row() now always preserves the column data types of
the input data frame the same way as rbind() does
(#296).lst() now again handles duplicate names, the value
defined last is used in case of a clash.validate argument is now also supported in
as_tibble.tbl_df(), with default to FALSE
(#278). It must be passed as named argument, as in
as_tibble(validate = TRUE).format_v() now always surrounds lists with
[] brackets, even if their length is one. This affects
glimpse() output for list columns (#106).glimpse()
(#280).tibble() gives a consistent error message in the case
of duplicate column names (#291).format() and print() methods for
both tbl and tbl_df classes, to protect
against malformed tibbles that inherit from "tbl_df" but
not "tbl", as created e.g. by ungroup() in
dplyr 0.5.0 and earlier (#256, #263).tidy_names(syntactic = TRUE, quiet = FALSE) if not all
names are fixed (#260, @imanuelcostigan).set_tidy_names() and tidy_names(), a
simpler version of repair_names() which works unchanged for
now (#217).rowid_to_column() that adds a rowid
column as first column and removes row names (#243, @barnettjacob).all.equal.tbl_df() method has been removed, calling
all.equal() now forwards to
base::all.equal.data.frame(). To compare tibbles ignoring
row and column order, please use dplyr::all_equal()
(#247).x again instead of the Unicode
multiplication sign, to avoid encoding issues (#216).print(), format(), and
tbl_sum() methods are now implemented for class
"tbl" and not for "tbl_df". This allows
subclasses to use tibble’s formatting facilities. The formatting of the
header can be tweaked by implementing tbl_sum() for the
subclass, which is expected to return a named character vector. The
print.tbl_df() method is still implemented for
compatibility with downstream packages, but only calls
NextMethod().print.data.frame()
anymore. Now providing format.tbl_df() and full support for
Unicode characters in names and data, also for glimpse()
(#235).rlang instead of lazyeval (#225,
@lionel-), and
rlang functions (#244).tribble() now handles values that have a class (#237,
@NikNakk).any(is.na()) with
anyNA() (#229, @csgillespie).microbenchmark package is now used conditionally
(#245).pkgdown website.mts and
ts) are now supported in as_tibble()
(#184).all_equal() function (called by
all.equal.tbl_df()) now forwards to dplyr and
fails with a helpful message if not installed. Data frames with list
columns cannot be compared anymore, and differences in the declared
class (data.frame vs. tbl_df) are ignored. The
all.equal.tbl_df() method gives a warning and forwards to
NextMethod() if dplyr is not installed; call
all.equal(as.data.frame(...), ...) to avoid the warning.
This ensures consistent behavior of this function, regardless if
dplyr is loaded or not (#198).as.tibble() as an alias to as_tibble()
(#160, @LaDilettante).frame_matrix(), similar to
frame_data() but for matrices (#140, #168, @LaDilettante).deframe() as reverse operation to
enframe() (#146, #214).assertthat.add_column() can add columns of length 1 (#162, #164,
@LaDilettante).add_row() for a grouped data frame
results in a helpful error message (#179).x if it cannot be
represented in the current locale (#192, @ncarchedi).NA names in printing (#206, #207, @jennybc).glimpse() now uses type_sum() also for S3
objects (#185, #186, @holstius).max.print option is ignored when printing a tibble
(#194, #195, @t-kalinowski).obj_sum documentation (#193, @etiennebr).tribble() (#191, @kwstat).tibble.width option is used for
glimpse() only if it is finite (#153, @kwstat).as_tibble.poly() to support conversion of a
poly object to a tibble (#110).add_row() now correctly handles existing columns of
type list that are not updated (#148).all.equal() doesn’t throw an error anymore if one of
the columns is named na.last, decreasing or
method (#107, @BillDunlap).add_column(), analogously to add_row()
(#99).print.tbl_df() gains n_extra method and
will have the same interface as trunc_mat() from now
on.add_row() and add_column() gain
.before and .after arguments which indicate
the row (by number) or column (by number or name) before or after which
the new data are inserted. Updated or added columns cannot be named
.before or .after (#99).frame_data() to tribble(), stands
for “transposed tibble”. The former is still available as alias (#132,
#143).add_row() now can add multiple rows, with recycling
(#142, @jennybc).× instead of x when
printing dimensions (#126). Output tests had to be disabled for this on
Windows.dttm instead of time for
POSIXt values (#133), which is now used for columns of the
difftime class.print.tbl_df(), now using data
from nycflights13 instead of Lahman (#121),
with guidance to install nycflights13 package if necessary
(#152).Follow-up release.
tibble() is no longer an alias for
frame_data() (#82).tbl_df() (#57).$ returns NULL if column not found,
without partial matching. A warning is given (#109).[[ returns NULL if column not found
(#109).# and contains more text (#95)), removed empty line,
showing number of hidden rows and columns (#51). The trailing metadata
also begins with hash # (#101). Presence of row names is
indicated by a star in printed output (#72).NA values in character columns as
<NA>, like print.data.frame() does
(#69).glimpse() shows nesting structure for lists and uses
angle brackets for type (#98).POSIXlt columns can be printed now, the
text <POSIXlt> is shown as placeholder to encourage
usage of POSIXct (#86).type_sum() shows only topmost class for S3
objects.ncol are
supported. Passing a matrix or an array now raises an error in any case
(#83).NULL row names (#75).stop() and warning() are now always called
with call. = FALSE..Dim attribute is silently stripped from columns
that are 1d matrices (#84).as_tibble.data.frame() preserves attributes, and uses
as_tibble.list() to calling overriden methods which may
lead to endless recursion.has_name() (#102).tibble() and as_tibble() over
data_frame() and as_data_frame() in code and
documentation (#82).is.tibble() and is_tibble()
(#79).enframe() that converts vectors to two-column
tibbles (#31, #74).obj_sum() and type_sum() show
"tibble" instead of "tbl_df" for tibbles
(#82).as_tibble.data.frame() gains validate
argument (as in as_tibble.list()), if TRUE the
input is validated.as_tibble.default() (#71,
hadley/dplyr#1752).has_rownames() supports arguments that are not data
frames.[[ works (#58, #63).x[]) also removes
row names.as_tibble.tbl_df() for subclasses
(#60).knitr internals for testing (#78).knitr 1.13 (#76).knit_print() tests.tbl_sum.tbl_sql()
and tbl_sum.tbl_grouped_df() to allow dplyr
release before a tibble release.format_v() (#98).NULL value of
tbl_sum().expect_output_file() from
testthat.Initial CRAN release
Extracted from dplyr 0.4.3
Exported functions:
tbl_df()as_data_frame()data_frame(), data_frame_()frame_data(), tibble()glimpse()trunc_mat(), knit_print.trunc_mat()type_sum()lst() and lst_() create lists in the
same way that data_frame() and data_frame_()
create data frames (hadley/dplyr#1290). lst(NULL) doesn’t
raise an error (#17, @jennybc), but always uses deparsed
expression as name (even for NULL).add_row() makes it easy to add a new row to data
frame (hadley/dplyr#1021).rownames_to_column() and
column_to_rownames() (#11, @zhilongjia).has_rownames() and remove_rownames()
(#44).repair_names() fixes missing and duplicate names
(#10, #15, @r2evans).is_vector_s3().Features
as_data_frame.table() with argument n
to control name of count column (#22, #23).tibble prefix for options (#13, #36).glimpse() now (invisibly) returns its argument
(hadley/dplyr#1570). It is now a generic, the default method dispatches
to str() (hadley/dplyr#1325). The default width is obtained
from the tibble.width option (#35, #56).as_data_frame() is now an S3 generic with methods for
lists (the old as_data_frame()), data frames (trivial),
matrices (with efficient C++ implementation) (hadley/dplyr#876), and
NULL (returns a 0-row 0-column data frame) (#17, @jennybc).frame_data() and
tibble() (including lists) creates list-valued columns
(#7). These functions return 0-row but n-col data frame if no data.Bug fixes
frame_data() properly constructs rectangular tables
(hadley/dplyr#1377, @kevinushey).Minor modifications
setOldClass(c("tbl_df", "tbl", "data.frame")) to
help with S4 (hadley/dplyr#969).tbl_df() automatically generates column names
(hadley/dplyr#1606).tbl_dfs gain $ and [[ methods
that are ~5x faster than the defaults, never do partial matching
(hadley/dplyr#1504), and throw an error if the variable does not exist.
[[.tbl_df() falls back to regular subsetting when used with
anything other than a single string (#29).
base::getElement() now works with tibbles (#9).all_equal() allows to compare data frames ignoring row
and column order, and optionally ignoring minor differences in type
(e.g. int vs. double) (hadley/dplyr#821). Used by
all.equal() for tibbles. (This package contains a pure R
implementation of all_equal(), the dplyr code
has identical behavior but is written in C++ and thus faster.)data_frame() and
as_data_frame() have been aligned, so
as_data_frame() will now automatically recycle length-1
vectors. Both functions give more informative error messages if you are
attempting to create an invalid data frame. You can no longer create a
data frame with duplicated names (hadley/dplyr#820). Both functions now
check that you don’t have any POSIXlt columns, and tell you
to use POSIXct if you do (hadley/dplyr#813).
data_frame(NULL) raises error “must be a 1d atomic vector
or list”.trunc_mat() and print.tbl_df() are
considerably faster if you have very wide data frames. They will now
also only list the first 100 additional variables not already on screen
- control this with the new n_extra parameter to
print() (hadley/dplyr#1161). The type of list columns is
printed correctly (hadley/dplyr#1379). The width argument
is used also for 0-row or 0-column data frames (#18).[.tbl_df() does not change class (#41,
@jennybc). Improve
[.tbl_df() error message.Documentation
vignette("tibble") describes the difference between
tbl_dfs and regular data frames (hadley/dplyr#1468).Code quality
obj_type() to obj_sum(),
improvements, better integration with type_sum().