New memisc release 0.99.20.1 improves compatibility with RStudio and “tidyverse”¶
22 November 2019
Release 0.99.20.1. has been published on CRAN. It improves the way the
package interoperates with RStudio and “tidyverse”. In particular:
A function view() provides a generic interface to the GUI function
View() in base R and RStudio. It makes it possible to extend it to
data objects of the classes “data.set”,
“descriptions”, and “importer”.
A method for “data.set” objects allows to transfer these
objects more easiliy into the “tidyverse”, i.e. facilitates the use of
functions from these package ecosystem on data sets imported or created with
memisc. An as_haven() function translates “data.set” objects into
“tibbles” with that extra information that the “haven” package adds to
“tibbles” imported with the help of that package. This should allow to view
and post-process data imported with memisc more or less the same way as if
the data were imported with “haven”.
A List() function adds names to its elements by deparsing arguments in the
same way as data.frame() does.
A new function Groups() allows to split a data frame or a “data.set” into
group based on factors in a more convenient way. There are methods of
and within() to deal with resulting objects of class “grouped.data”. For
example, the within() method allows to substract group means from the
observations within groups. withinGroups() allows to split a data frame or
“data.set” objects into groups, make within-group computations and recombine
the groups into the order of the original data frame or “data.set” object.
Stata.file() now handles files in format rev. 117 and
later as they are created by Stata version later than 13.
User definded missing values are now reported in separate tables in entries
created by codebook() even if these entries refer to items
measurement level “interval” or “ratio”.
If the annotation or the labels of a non-item is set to NULL this no longer
causes an error.
The function and now work with
objects in the same sensible way as they do with data frames.
The function recode() behaves more coherently: If a labelled vector is the
result of recode() it gets the measurement level
“nominal”. Factor levels
explictly created first come first in the order of factor levels.