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dplyr::filter
and stats::filter
are two functions from two popular packages that have the same name. And
this leads to erros, when dplyr::filter is confused for
stats::filter. We use the conflicted::conflict_prefer
to resolve this issue.
This is an issue related to namespaces.
Below return the environment on the search path via rlang::search_envs():
print(rlang::search_envs())## [[1]] $ <env: global>
## [[2]] $ <env: .conflicts>
## [[3]] $ <env: tools:rstudio>
## [[4]] $ <env: package:stats>
## [[5]] $ <env: package:graphics>
## [[6]] $ <env: package:grDevices>
## [[7]] $ <env: package:utils>
## [[8]] $ <env: package:datasets>
## [[9]] $ <env: package:reticulate>
## [[10]] $ <env: package:conflicted>
## [[11]] $ <env: package:formatR>
## [[12]] $ <env: package:REconTools>
## [[13]] $ <env: package:kableExtra>
## [[14]] $ <env: package:knitr>
## [[15]] $ <env: package:lubridate>
## [[16]] $ <env: package:forcats>
## [[17]] $ <env: package:stringr>
## [[18]] $ <env: package:dplyr>
## [[19]] $ <env: package:purrr>
## [[20]] $ <env: package:readr>
## ... and 7 more environmentsWe can use tidyverse_conflicts()
to “lists all the conflicts between packages in the tidyverse and other
packages that you have loaded”. We can see that we have problems due to
filter, lag, and group_rows.
tidyverse_conflicts()## ── Conflicts ────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ stats::filter() masks dplyr::filter()
## ✖ kableExtra::group_rows() masks dplyr::group_rows()
## ✖ stats::lag() masks dplyr::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsFor example, the code below fails:
library(stats)
library(dplyr)
as_tibble(mtcars, rownames = "car") %>% filter(car == "Valiant")
# Error message
# > as_tibble(mtcars, rownames = "car") %>% filter(car == "Valiant")
# Error: object 'car' not foundThe code below works, because we explicitly write
dplyr::filter:
library(stats)
library(dplyr)
print(as_tibble(mtcars, rownames = "car") %>% dplyr::filter(car == "Valiant"))To deal with this, we use the conflicted::conflict_prefer
to resolve this issue. Now we can run the filter function
safely, knowing that it is the dplyr::filter function will
be used.
library(conflicted)
conflict_prefer("filter", "dplyr", "stats")
library(stats)
library(dplyr)
print(as_tibble(mtcars, rownames = "car") %>% filter(car == "Valiant"))
# > conflict_prefer("filter", "dplyr", "stats")
# [conflicted] Will prefer dplyr::filter over stats::filter.
# > library(stats)
# > library(dplyr)
# > print(as_tibble(mtcars, rownames = "car") %>% filter(car == "Valiant"))
# # A tibble: 1 × 12
# car mpg cyl disp hp drat wt qsec vs am gear carb
# <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1