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as_cells.matrix() (#52 @billdenney)NULL POSIXct (dates)
(#39 @romainfrancois)"NNW" to "up-left" etc. Compass
directions still work, but the built-in dataset purpose has
different names and documentation will gradually change to the new
terms..name_repair compatibility (#2144
@krlmlr)tidyselect from dplyr
functions.behead_if() is for tiered headers within the same row
or column. It takes filter functions similarly to
dplyr::filter() to decide which cells to treat as headers,
and can be applied more than once to the same row or column of headers
until every tier has been dealt with.merge_rows() and merge_cols() combines
header text when it is split over multiple cells.behead(), enhead(),
partition() and rectify() give a more
informative error message for non-distinct cells, for example when
trying to pass cells from more than one sheet to these functions (@gregrs-uk, #15).This version makes some big breaking changes for the sake of a more intuitive grammar. It comes with much more documentation in the online book Spreadsheet Munging Strategies.
The main new workhorses:
behead() takes one level of headers from a pivot table
and make it part of the data. Chain this function to gradually strip
every level of header away until you have tidy data.spatter() is a data-type aware version of
tidyr::spread() and is a common final step.partition() breaks up small-multiples on a single
sheet, so you can handle them individually.rectify() visualises the cells in the console as they
would look in a spreadsheet.The previous version can be installed as follows.
devtools::install_version("unpivotr", version = "0.3.1", repos = "http://cran.us.r-project.org")NNW() etc. has been removed in
favour of the verbose join_header(), which has itself been
renamed to enhead() to suggest its similarity to
behead() (though they are not complements).enhead() (formerly join_header() now
follows the tidyverse convention of fct for ‘factor’ and
ord for ‘ordered factor’.enhead() (formerly join_header()) now uses
col_names and row_names as arguments instead
of colnames and rownames, for consistency with
tidyr.behead() is takes one level of headers from a pivot
table and make it part of the data. Think of it like
tidyr::gather(), except that it works when there is more
than one row of headers (or more than one column of row-headers), and it
only works on tables that have first come through enhead()
(formerly join_header() or
tidyxl::xlsx_cells().rectify() displays cells as though in a spreadsheet,
rather than in the ‘melted’ form of enhead() (formerly
join_header()) and tidyxl::xlsx_cells(). This
is useful for understanding the structure of a pivot table as a human,
when planning how to unpivot it. A print method is available to render
large datasets in the browser or the RStudio viewer pane.partition() divides a grid of cells into partitions
containing individual tables. Give it the corner cells of each table on
a spreadsheet.pack() packs cells values from separate columns per
data type into one list-column. unpack() is the
complement.isolate_sentinels() move sentinel values into a
separate column, leaving NA behind (or NULL
for list-columns).spatter() is like tidyr::spread(), but
preserves mixed data types.enhead() (formerly join_header()) now
returns a data_type column that names the column that
contains the value of a cell, similar to
tidyxl::xlsx_cells().enhead() (formerly join_header() now
follows the tidyverse convention of fct for ‘factor’ and
ord for ‘ordered factor’.enhead() (formerly join_header()) gains a
drop = TRUE argument to control whether to discard cells
that don’t have a matching header (e.g. ones that are left of the
leftmost header in enhead(x, y, "NNW")).justify() moves one set of cells to the same positions
as another set. This is useful when header cells aren’t at the corner of
the cells they describle. Put the header cells into
justify(), along with cells that are at the
corner.purpose (built-in dataset) gains a new list-member
small-multiples.tidy_table().dplyr::distinct(), which doesn’t handle list columns).dplyr/rlang
combination instead of the old dplyr/lazyeval
one.This release overhauls the tidy_table() function of
unpivotr to preserve the original data types of table cells and to
support HTML tables.
tidytable() has been renamed tidy_table().
tidytable() is an error, rather than a deprecation warning,
because tidy_table() is so different from before.tidy_table() method and vignette for
HTML.tidy_table() method for matrices. Convert
matrices to data.frames first, choosing what to do with row and column
names.tidy_table() returns only relevant columns, according
to the data types of the columns in the given data frame. It uses
tibble::type_sum() to determine the column type and to name
the columns, so whereas characters used to be returned in a column
called character, they are now returned in a column called
chr. The full list of column names is in
?tidy_table and is chr, cplx,
cplx, dbl, fctr,
int, lgl, list. The columns
fctr and list are list-columns, where each
element is itself a list. This means that factors with different levels
are kept separate. For HTML tables, an html column is
returned containing the standalone HTML of each cell.rowname and colname arguments to
tidy_table() now default to FALSE.tibble.inst/extdata to
vignettes.NEWS.md file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.