Regression: Awkward output for zero-row tibbles #18
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krlmlr
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Jan 7, 2016
- Non-scalar input to `frame_data()` and `tibble()` creates list-valued columns (#7). - `frame_data()` and `tibble()` create empty `data_frame` if no rows are given (#20). - `as_data_frame(NULL)` is 0-row 0-column data frame (#17, @jennybc). - `lst(NULL)` doesn't raise an error anymore (#17, @jennybc), but always uses deparsed expression as name (even for `NULL`). - `trunc_mat()` and `print()` use `width` argument also for zero-row and zero-column data frames (#18).
krlmlr
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Jan 7, 2016
- Non-scalar input to `frame_data()` and `tibble()` creates list-valued columns (#7). - `frame_data()` and `tibble()` create empty `data_frame` if no rows are given (#20). - `as_data_frame(NULL)` is 0-row 0-column data frame (#17, @jennybc). - `lst(NULL)` doesn't raise an error anymore (#17, @jennybc), but always uses deparsed expression as name (even for `NULL`). - `trunc_mat()` and `print()` use `width` argument also for zero-row and zero-column data frames (#18).
krlmlr
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Mar 2, 2016
- Functions related to `tbl` and `src` stay in `dplyr` (#26). Remove unused `make_tbl()`. - Non-scalar input to `frame_data()` and `tibble()` (including lists) creates list-valued columns (#7). - Use C++ implementation for `as_data_frame.matrix()` (#14). Also add former `matrixToDataFrame()` tests, and fix unwanted conversion to factor. - `as_data_frame(NULL)` is 0-row 0-column data frame (#17, @jennybc). `frame_data()` and `tibble()` create empty `data_frame` if no rows are given (#20). - `data_frame(NULL)` raises error "must be a 1d atomic vector or list". - `lst(NULL)` doesn't raise an error anymore (#17, @jennybc), but always uses deparsed expression as name (even for `NULL`). - `trunc_mat()` and `print()` use `width` argument also for zero-row and zero-column data frames (#18). - `glimpse()` now (invisibly) returns `x`, so it can be used within a chain of `dplyr` verbs (@edwindj). - `base::getElement()` now works with tibbles (#9). - Remove spurious usage of "dplyr" in documentation (#3). - Almost full test coverage.
krlmlr
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Mar 22, 2016
- 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()` - New `lst()` and `lst_()` create lists in the same way that `data_frame()` and `data_frame_()` create data frames (tidyverse/dplyr#1290). `lst(NULL)` doesn't raise an error (#17, @jennybc), but always uses deparsed expression as name (even for `NULL`). - New `add_row()` makes it easy to add a new row to data frame (tidyverse/dplyr#1021). - New `rownames_to_column()` and `column_to_rownames()` (#11, @zhilongjia). - New `has_rownames()` and `remove_rownames()` (#44). - New `repair_names()` fixes missing and duplicate names (#10, #15, @r2evans). - New `is_vector_s3()`. - 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 (tidyverse/dplyr#1570). It is now a generic, the default method dispatches to `str()` (tidyverse/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) (tidyverse/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 - `frame_data()` properly constructs rectangular tables (tidyverse/dplyr#1377, @kevinushey). - Minor modifications - Uses `setOldClass(c("tbl_df", "tbl", "data.frame"))` to help with S4 (tidyverse/dplyr#969). - `tbl_df()` automatically generates column names (tidyverse/dplyr#1606). - `tbl_df`s gain `$` and `[[` methods that are ~5x faster than the defaults, never do partial matching (tidyverse/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) (tidyverse/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 (tidyverse/dplyr#820). Both functions now check that you don't have any `POSIXlt` columns, and tell you to use `POSIXct` if you do (tidyverse/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()` (tidyverse/dplyr#1161). The type of list columns is printed correctly (tidyverse/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 - Update README, with edits (#52, @bhive01) and enhancements (#54, @jennybc). - `vignette("tibble")` describes the difference between tbl_dfs and regular data frames (tidyverse/dplyr#1468). - 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.
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