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Changelog

Source: NEWS.md

tibble 3.2.1

Internal

  • Use symbol instead of string in .Call().

tibble 3.2.0

CRAN release: 2023-03-08

Features

Breaking changes

Bug fixes

  • Allow glue() and other classed characters for subassignment (#1150, #1503).

Performance

  • Reduce overhead of single-column subset assignment (#1363).

Documentation

Internal

  • Require vctrs >= 0.4.1 and pillar >= 1.8.1

  • Use cli for formatting conditions (#1387).

  • Use vec_as_location(missing = "error") for better error messages (#741, #1511).

  • Remove compatibility code for RSDA package which is broken anyway due to other changes (#923, #1509).

  • Skip tests if suggested packages not available (#1246, @MichaelChirico).

  • Remove obsolete tests (#1513).

tibble 3.1.8

CRAN release: 2022-07-22

Documentation

tibble 3.1.7

CRAN release: 2022-05-03

Breaking change

  • 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).

Documentation

  • Avoid listing dim_desc() in reexports.
  • Add more examples for data frame and matrix columns (#978, #1012).

Internal

tibble 3.1.6

CRAN release: 2021-11-07

  • 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 3.1.5

CRAN release: 2021-09-30

  • Avoid necessity to set "tibble.view_max" option for lazy tables (#954).
  • Avoid blanket import for lifecycle package for compatibility with upcoming rlang (#955, @romainfrancois).

tibble 3.1.4

CRAN release: 2021-08-25

Features

  • as.data.frame.tbl_df() strips inner column names (#837).
  • new_tibble() allows omitting the nrow argument again (#781).

Documentation

Performance

  • 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).

Internal

tibble 3.1.3

CRAN release: 2021-07-23

Bug fixes

  • 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).

Features

Internal

  • Establish compatibility with rlang > 0.4.11 (#908).
  • Use pillar::dim_desc() (#859).
  • Establish compatibility with testthat > 3.0.3 (#896, @lionel-).
  • Bump required versions of ellipsis and vctrs to avoid warning during package load.

tibble 3.1.2

CRAN release: 2021-05-16

  • Bump required versions of ellipsis and vctrs to avoid warning during package load.

tibble 3.1.1

CRAN release: 2021-04-18

tibble 3.1.0

CRAN release: 2021-02-25

Bug fixes

  • 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).

Features

  • 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).

Internal

  • Remove compatibility code for pillar < 1.5.0 (#861).

  • Moved most functions to the “stable” lifecycle (#860).

tibble 3.0.6

CRAN release: 2021-01-29

  • vec_ptype_abbr.tbl_df() and type_sum.tbl_df() now uses the name of the topmost class for subclasses of "tbl_df" (#843).
  • Ignore errors in formats.Rmd vignette.
  • Avoid tidy evaluation in pillar compatibility code.

tibble 3.0.5

CRAN release: 2021-01-15

  • Use testthat edition 3, compatible with testthat 3.0.1 (#827, #832).

tibble 3.0.4

CRAN release: 2020-10-12

Compatibility

  • 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.

Bug fixes

tibble 3.0.3

CRAN release: 2020-07-10

  • Fix test compatibility with rlang 0.4.7.

  • Fix warning about needs_dots arguments with pillar >= 1.4.5 (#798).

tibble 3.0.2

CRAN release: 2020-07-07

Bug fixes

Performance

  • Subsetting and subassignment are faster (#780, #790, #794).

  • is.null() is preferred over is_null() for speed.

  • Implement continuous benchmarking (#793).

Compatibility

  • is_vector_s3() is no longer reexported from pillar (#789).

tibble 3.0.1

CRAN release: 2020-04-20

Compatibility fixes

  • [<-.tbl_df() coerces matrices to data frames (#762).

  • Use delayed import for cli to work around unload problems in downstream packages (#754).

Bug fixes

  • 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.

Enhancements

  • 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.

tibble 3.0.0

CRAN release: 2020-03-30

Major breaking changes

  • 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:

    • “names for target but not for current” when comparing

Breaking changes

  • 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 deprecations

  • 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.

Features

  • 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).

Output

  • 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).

Performance

Internal

  • 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.

tibble 2.1.3

CRAN release: 2019-06-06

  • Fix compatibility with R 3.5 and earlier, regression introduced in tibble 2.1.2.

tibble 2.1.2

CRAN release: 2019-05-29

  • Relax version requirements.

  • Fix test failing after pillar upgrade.

tibble 2.1.1

CRAN release: 2019-03-16

  • 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 2.0.1

CRAN release: 2019-01-12

  • 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-).

tibble 2.0.0

CRAN release: 2019-01-04

Breaking changes

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:

    • a different result when calling rownames() or row.names(),
    • rows full of 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).

New features

  • 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.
    • a function: apply custom name repair (e.g., .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,
    • A string: the name of the new column that will contain the existing row names, which are no longer present in the result.

    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).

New functions

  • Added experimental view() function that always returns its input invisibly and calls utils::View() only in interactive mode (#373).

Output

  • 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).

Bug fixes

  • 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.

Internal

tibble 1.4.2

CRAN release: 2018-01-22

Bug fixes

  • Fix OS X builds.
  • The tibble.width option is honored again (#369).
  • tbl[1, , drop = TRUE] now behaves identically to data frames (#367).
  • Fix error message when accessing columns using a logical index vector (#337, @mundl).
  • glimpse() returns its input for zero-column data frames.

Features

  • enframe(NULL) now returns the same as enframe(logical()) (#352).
  • tribble() now ignores trailing commas (#342, @anhqle).
  • Updated vignettes and website documentation.

Performance

tibble 1.4.1

CRAN release: 2017-12-25

New formatting

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.

New features

Bug fixes

  • Improved compatibility with remote data sources for glimpse() (#328).
  • Logical indexes are supported, a warning is raised if the length does not match the number of rows or 1 (#318).
  • Fixed width for word wrapping of the extra information (#301).
  • Prevent 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).

Internal changes

  • Reexporting has_name() from rlang, instead of forwarding, to avoid warning when importing both rlang and tibble.
  • Compatible with R 3.1 (#323).
  • Remove Rcpp dependency (#313, @patperry).

tibble 1.3.4

CRAN release: 2017-08-22

Bug fixes

  • Values of length 1 in a tibble() call are recycled prior to evaluating subsequent arguments, improving consistency with mutate() (#213).
  • Recycling of values of length 1 in a 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.
  • Adding columns to zero-row data frames now also works when mixing lengths 1 and 0 in the new columns (#167).
  • The 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).

Formatting

  • format_v() now always surrounds lists with [] brackets, even if their length is one. This affects glimpse() output for list columns (#106).
  • Factor levels are escaped when printing (#277).
  • Non-syntactic names are now also escaped in glimpse() (#280).
  • tibble() gives a consistent error message in the case of duplicate column names (#291).

tibble 1.3.3

CRAN release: 2017-05-28

Bug fixes

  • Added 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).
  • The column width for non-syntactic columns is computed correctly again (#258).
  • Printing a tibble doesn’t apply quote escaping to list columns.
  • Fix error in tidy_names(syntactic = TRUE, quiet = FALSE) if not all names are fixed (#260, @imanuelcostigan).
  • Remove unused import declaration for assertthat.

tibble 1.3.1

CRAN release: 2017-05-17

Bug fixes

  • Subsetting zero columns no longer returns wrong number of rows (#241, @echasnovski).

Interface changes

Formatting

  • Printing now uses x again instead of the Unicode multiplication sign, to avoid encoding issues (#216).
  • String values are now quoted when printing if they contain non-printable characters or quotes (#253).
  • The 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().
  • Own printing routine, not relying on print.data.frame() anymore. Now providing format.tbl_df() and full support for Unicode characters in names and data, also for glimpse() (#235).

Misc

  • Improve formatting of error messages (#223).
  • Using rlang instead of lazyeval (#225, @lionel-), and rlang functions (#244).
  • tribble() now handles values that have a class (#237, @NikNakk).
  • Minor efficiency gains by replacing any(is.na()) with anyNA() (#229, @csgillespie).
  • The microbenchmark package is now used conditionally (#245).
  • pkgdown website.

tibble 1.3.0

CRAN release: 2017-04-01

Bug fixes

  • Time series matrices (objects of class mts and ts) are now supported in as_tibble() (#184).
  • The 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).

Interface changes

Features

General

Input validation

  • An attempt to read or update a missing column now throws a clearer warning (#199).
  • An attempt to call add_row() for a grouped data frame results in a helpful error message (#179).

Printing

Documentation

Internal

  • Using registration of native routines.

tibble 1.2

CRAN release: 2016-08-26

Bug fixes

  • The tibble.width option is used for glimpse() only if it is finite (#153, @kwstat).
  • New 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).

Interface changes

  • New 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).
  • Rename frame_data() to tribble(), stands for “transposed tibble”. The former is still available as alias (#132, #143).

Features

  • add_row() now can add multiple rows, with recycling (#142, @jennybc).
  • Use multiply character × instead of x when printing dimensions (#126). Output tests had to be disabled for this on Windows.
  • Back-tick non-semantic column names on output (#131).
  • Use dttm instead of time for POSIXt values (#133), which is now used for columns of the difftime class.
  • Better output for 0-row results when total number of rows is unknown (e.g., for SQL data sources).

Documentation

  • New object summary vignette that shows which methods to define for custom vector classes to be used as tibble columns (#151).
  • Added more examples for print.tbl_df(), now using data from nycflights13 instead of Lahman (#121), with guidance to install nycflights13 package if necessary (#152).
  • Minor changes in vignette (#115, @helix123).

tibble 1.1

CRAN release: 2016-07-04

Follow-up release.

Breaking changes

  • tibble() is no longer an alias for frame_data() (#82).
  • Remove tbl_df() (#57).
  • $ returns NULL if column not found, without partial matching. A warning is given (#109).
  • [[ returns NULL if column not found (#109).

Output

  • Reworked output: More concise summary (begins with hash # 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).
  • Format NA values in character columns as <NA>, like print.data.frame() does (#69).
  • The number of printed extra cols is now an option (#68, @lionel-).
  • Computation of column width properly handles wide (e.g., Chinese) characters, tests still fail on Windows (#100).
  • glimpse() shows nesting structure for lists and uses angle brackets for type (#98).
  • Tibbles with 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.

Error reporting

  • Strict checking of integer and logical column indexes. For integers, passing a non-integer index or an out-of-bounds index raises an error. For logicals, only vectors of length 1 or ncol are supported. Passing a matrix or an array now raises an error in any case (#83).
  • Warn if setting non-NULL row names (#75).
  • Consistently surround variable names with single quotes in error messages.
  • Use “Unknown column ‘x’” as error message if column not found, like base R (#94).
  • stop() and warning() are now always called with call. = FALSE.

Coercion

  • The .Dim attribute is silently stripped from columns that are 1d matrices (#84).
  • Converting a tibble without row names to a regular data frame does not add explicit row names.
  • as_tibble.data.frame() preserves attributes, and uses as_tibble.list() to calling overriden methods which may lead to endless recursion.

New features

Bug fixes

  • Two-dimensional indexing with [[ works (#58, #63).
  • Subsetting with empty index (e.g., x[]) also removes row names.

Documentation

  • Document behavior of as_tibble.tbl_df() for subclasses (#60).
  • Document and test that subsetting removes row names.

Internal

  • Don’t rely on knitr internals for testing (#78).
  • Fix compatibility with knitr 1.13 (#76).
  • Enhance knit_print() tests.
  • Provide default implementation for tbl_sum.tbl_sql() and tbl_sum.tbl_grouped_df() to allow dplyr release before a tibble release.
  • Explicit tests for format_v() (#98).
  • Test output for NULL value of tbl_sum().
  • Test subsetting in all variants (#62).
  • Add missing test from dplyr.
  • Use new expect_output_file() from testthat.

Version 1.0

CRAN release: 2016-03-23

  • Initial CRAN release

  • Extracted from dplyr 0.4.3

  • Exported functions:

  • Features

    • New as_data_frame.table() with argument n to control name of count column (#22, #23).
    • Use 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).
    • Non-scalar input to 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

  • Minor modifications

    • Uses 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.)
    • The internals of 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).
    • When used in list-columns, S4 objects only print the class name rather than the full class hierarchy (#33).
    • Add test that [.tbl_df() does not change class (#41, @jennybc). Improve [.tbl_df() error message.

  • Documentation

  • Code quality

    • Test using new-style Travis-CI and AppVeyor. Full test coverage (#24, #53). Regression tests load known output from file (#49).
    • Renamed obj_type() to obj_sum(), improvements, better integration with type_sum().
    • Internal cleanup.