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Sign upall.equal() problems #2751
all.equal() problems #2751
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I think that's ok, although I feel like there's probably quite a few places where I rely on An alternative fix would be to write our own |
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@hadley: Are we including this in dplyr 0.6.0? |
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No, it'll have to wait until the next version. |
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Is it clear when this fix will be included in dplyr? |
Most are related to tidyverse/dplyr#2751
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Are there any thoughts on when/if attribute checking might be implemented? I'm creating a For now I'm using a (rather janky) workaround with a custom |
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In case this is useful, dplyr's library(tibble)
d1 <- tibble(id = 1:3, x = tibble(a = 1:3, y = 1:3))
d2 <- tibble(id = 1:3, x = tibble(a = 1:3, y = NA))
# works fine
all.equal(d1, d1)
#> [1] TRUE
all.equal(d1, d2)
#> [1] "Component \"x\": Component \"y\": Modes: numeric, logical"
#> [2] "Component \"x\": Component \"y\": target is numeric, current is logical"
library(dplyr, warn.conflicts = FALSE)
# broken
all.equal(d1, d1)
#> Error: Can't join on 'x' x 'x' because of incompatible types (tbl_df/tbl/data.frame / tbl_df/tbl/data.frame)
all.equal(d1, d2)
#> Error: Can't join on 'x' x 'x' because of incompatible types (tbl_df/tbl/data.frame / tbl_df/tbl/data.frame)Created on 2019-02-04 by the reprex package (v0.2.1.9000) |
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I have been beaten by this (and went crazy expect_equal(as.data.frame(object), as.data.frame(expected))HTH |
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Quick question: the tolerance issue mentioned as the first bullet point of the first message hasn't been resolved or am I missing something? It would really be nice to be able to account for small differences like |
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I think we should plan to rip out the existing |
And update newly failing tests. Fixes #2751
And update newly failing tests. Fixes #2751
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This old issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with reprex) and link to this issue. https://reprex.tidyverse.org/ |
dplyrWould it be too much of a change to get rid of this override altogether? I propose to emit a warning that
all_equal()should be used on tibbles in v0.6, and then remove the specialization in v0.7.CC @hadley @jennybc.