unpivotr-package: Un-pivot complex and irregular data layouts.
In unpivotr: Unpivot Complex and Irregular Data Layouts
unpivotr-package R Documentation
Un-pivot complex and irregular data layouts.
Description
'Unpivotr' provides tools for converting data from complex or irregular
layouts to a columnar structure. For example, tables with multi-level column
or row headers, or spreadsheets of several tables, nested HTML tables, and
data that uses several different sentinel values.
Details
The best way to learn unpivotr is the free online book Spreadsheet Munging Strategies.
Header and data cells can selected by their contents, position, data type and
formatting, and can be associated with one other by their relative positions.
The input data must be a data frame with the columns 'row' and 'col' to
describe the position of a 'cell' of data. For cells that are to be
interpreted as data, further columns containing the 'value' of the cell are,
of course, necessary for there to be any point in using this package, though
they are not actually required for any of the given functions.
Data frames and HTML tables can be converted into a format meeting these
requirements by using the as_cells() function. Excel (.xlsx)
files can be imported directly into the required format with the 'tidyxl'
package, available at https://github.com/nacnudus/tidyxl, which has the
advantage that it retains cell formatting and comments.
Author(s)
Maintainer: Duncan Garmonsway nacnudus@gmail.com
See Also
Useful links:
unpivotr documentation built on Jan. 23, 2023, 5:40 p.m.
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| unpivotr-package | R Documentation |
Un-pivot complex and irregular data layouts.
Description
'Unpivotr' provides tools for converting data from complex or irregular layouts to a columnar structure. For example, tables with multi-level column or row headers, or spreadsheets of several tables, nested HTML tables, and data that uses several different sentinel values.
Details
The best way to learn unpivotr is the free online book Spreadsheet Munging Strategies.
Header and data cells can selected by their contents, position, data type and formatting, and can be associated with one other by their relative positions.
The input data must be a data frame with the columns 'row' and 'col' to describe the position of a 'cell' of data. For cells that are to be interpreted as data, further columns containing the 'value' of the cell are, of course, necessary for there to be any point in using this package, though they are not actually required for any of the given functions.
Data frames and HTML tables can be converted into a format meeting these
requirements by using the as_cells() function. Excel (.xlsx)
files can be imported directly into the required format with the 'tidyxl'
package, available at https://github.com/nacnudus/tidyxl, which has the
advantage that it retains cell formatting and comments.
Author(s)
Maintainer: Duncan Garmonsway nacnudus@gmail.com
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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