These three functions, along with names<-
and 1d numeric [
(i.e. x[loc]
) methods, provide a minimal interface for extending dplyr
to work with new data frame subclasses. This means that for simple cases
you should only need to provide a couple of methods, rather than a method
for every dplyr verb.
These functions are a stop-gap measure until we figure out how to solve the problem more generally, but it's likely that any code you write to implement them will find a home in what comes next.
Arguments
- data
A tibble. We use tibbles because they avoid some inconsistent subset-assignment use cases
- i
A numeric or logical vector that indexes the rows of
.data
.- cols
A named list used modify columns. A
NULL
value should remove an existing column.- template
Template to use for restoring attributes
Basic advice
This section gives you basic advice if you want to extend dplyr to work with your custom data frame subclass, and you want the dplyr methods to behave in basically the same way.
If you have data frame attributes that don't depend on the rows or columns (and should unconditionally be preserved), you don't need to do anything.
If you have scalar attributes that depend on rows, implement a
dplyr_reconstruct()
method. Your method should recompute the attribute depending on rows now present.If you have scalar attributes that depend on columns, implement a
dplyr_reconstruct()
method and a 1d[
method. For example, if your class requires that certain columns be present, your method should return a data.frame or tibble when those columns are removed.If your attributes are vectorised over rows, implement a
dplyr_row_slice()
method. This gives you access toi
so you can modify the row attribute accordingly. You'll also need to think carefully about how to recompute the attribute indplyr_reconstruct()
, and you will need to carefully verify the behaviour of each verb, and provide additional methods as needed.If your attributes that are vectorised over columns, implement
dplyr_col_modify()
, 1d[
, andnames<-
methods. All of these methods know which columns are being modified, so you can update the column attribute according. You'll also need to think carefully about how to recompute the attribute indplyr_reconstruct()
, and you will need to carefully verify the behaviour of each verb, and provide additional methods as needed.
Current usage
arrange()
,filter()
,slice()
,semi_join()
, andanti_join()
work by generating a vector of row indices, and then subsetting withdplyr_row_slice()
.mutate()
generates a list of new column value (usingNULL
to indicate when columns should be deleted), then passes that todplyr_col_modify()
.summarise()
works similarly tomutate()
but the data modified bydplyr_col_modify()
comes fromgroup_data()
.select()
uses 1d[
to select columns, thennames<-
to rename them.rename()
just usesnames<-
.relocate()
just uses 1d[
.inner_join()
,left_join()
,right_join()
, andfull_join()
coercesx
to a tibble, modify the rows, then usesdplyr_reconstruct()
to convert back to the same type asx
.nest_join()
usesdplyr_col_modify()
to cast the key variables to common type and add the nested-df thaty
becomes.distinct()
does amutate()
if any expressions are present, then uses 1d[
to select variables to keep, thendplyr_row_slice()
to select distinct rows.
Note that group_by()
and ungroup()
don't use any these generics and
you'll need to provide methods directly.