Functions :
eat
function as an improved join to grow data framesDifferent types of checks to be use in check
argument through the following
codes :
"c"
to check conflicts of columns"b"
like "by" checks if by
parameter was given explicitly"u"
like unique to check that the join columns form an unique key on x
"v"
to check that the join columns form an unique key on y
"m"
like match to check that all rows of x
have a match"n"
to check that all rows of y
have a match"e"
like expand to check that all combinations of joining columns are
present in x
"f"
to check that all combinations of joining columns are present in y
"l"
like levels to check that join columns are consistent in term of
factor levels"t"
like type to check that joining columns have same class and typeSolve conflict between columns by applying a function on pairs of conflicted
columns through the conflict
argument :
coalesce
or tibble
(to nest)"patch"
to keep values from rhs only when it matchesFeatures of eat
...
argument to select columns from .y
and leverages the select helpers from dplyr, allowing also things like renaming, negative selection, quasi-quotation....agg
before joining.fill
NA
values from the rhs with a given valueFuzzy joins special features:
match_fun
supports formula notationmulti_by
and multi_match_fun
from the fuzzyjoin package, the by
argument should use a formula such as
~ X("var1") > Y("var2") & X("var3") < Y("var4")
, it can either return a
logical vector or a data.frame containing a logical first column and other
columns that will be added to the resultAdd the following code to your website.
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