Closed robjhyndman closed 2 years ago
This is using stats::filter()
. You want dplyr::filter()
.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tsibble)
#>
#> Attaching package: 'tsibble'
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, union
tourism %>%
filter(Region == "Adelaide")
#> # A tsibble: 320 x 5 [1Q]
#> # Key: Region, State, Purpose [4]
#> Quarter Region State Purpose Trips
#> <qtr> <chr> <chr> <chr> <dbl>
#> 1 1998 Q1 Adelaide South Australia Business 135.
#> 2 1998 Q2 Adelaide South Australia Business 110.
#> 3 1998 Q3 Adelaide South Australia Business 166.
#> 4 1998 Q4 Adelaide South Australia Business 127.
#> 5 1999 Q1 Adelaide South Australia Business 137.
#> 6 1999 Q2 Adelaide South Australia Business 200.
#> 7 1999 Q3 Adelaide South Australia Business 169.
#> 8 1999 Q4 Adelaide South Australia Business 134.
#> 9 2000 Q1 Adelaide South Australia Business 154.
#> 10 2000 Q2 Adelaide South Australia Business 169.
#> # … with 310 more rows
Created on 2021-09-19 by the reprex package (v2.0.0)
Of course. The filter clash bites me again. Thanks
What's going on here?
Created on 2021-09-19 by the reprex package (v2.0.1)