New memisc release 0.99.20.1 improves compatibility with RStudio and “tidyverse”¶
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 “
descriptions”, and “
- 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”.
- When a “
data.set” object is translated into a data frame using
viewPrep()(the internal function used by
view()to prepare data for being viewed) the “description”
annotationof the variables (or “
item” objects) are retained as “label” attributes, so that labels are visible when viewed in RStudio.
List()function adds names to its elements by deparsing arguments in the same way as
- 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
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 “
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
codebook()even if these entries refer to items with measurement level “interval” or “ratio”.
- If the annotation or the labels of a non-item is set to
NULLthis no longer causes an error.
- Changing varible names to lowercase while importing data sets with
spss.system.file()is now optional.
- Importer methods
spss.system.file()now have optional arguments that allow to deal with variable labels or value labels in non-native encoding (e.g.
- A function
spss.file()acts as a common interface to
- The function and now work with
data.set” and “
importer” 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.