I'm using dplyr
's automatic SQL backend to query subtable from a database table. E.g.
my_tbl <- tbl(my_db, "my_table")
where my_table
in the database looks like
batch_name value
batch_A_1 1
batch_A_2 2
batch_A_2 3
batch_B_1 8
batch_B_2 9
...
I just want the data from batch_A_#
, regardless of the number.
If I were writing this in SQL, I could use
select * where batch_name like 'batch_A_%'
If I were writing this in R, I could use a few ways to get this: grepl()
, %in%
, or str_detect()
# option 1
subtable <- my_tbl %>% select(batch_name, value) %>%
filter(grepl('batch_A_', batch_name, fixed = T))
# option 2
subtable <- my_tbl %>% select(batch_name, value) %>%
filter(str_detect(batch_name, 'batch_A_'))
All of these give the following Postgres error: HINT: No function matches the given name and argument types. You might need to add explicit type casts
So, how do I pass in SQL string functions or matching functions to help make the generated dplyr SQL query able to use a more flexible range of functions in filter
?
(FYI the %in%
function does work, but requires listing out all possible values. This would be okay combined with paste
to make a list, but does not work in a more general regex case)
A "dplyr
-only" solution would be this
tbl(my_con, "my_table") %>%
filter(batch_name %like% "batch_A_%") %>%
collect()
Full reprex:
suppressPackageStartupMessages({
library(dplyr)
library(dbplyr)
library(RPostgreSQL)
})
my_con <-
dbConnect(
PostgreSQL(),
user = "my_user",
password = "my_password",
host = "my_host",
dbname = "my_db"
)
my_table <- tribble(
~batch_name, ~value,
"batch_A_1", 1,
"batch_A_2", 2,
"batch_A_2", 3,
"batch_B_1", 8,
"batch_B_2", 9
)
copy_to(my_con, my_table)
tbl(my_con, "my_table") %>%
filter(batch_name %like% "batch_A_%") %>%
collect()
#> # A tibble: 3 x 2
#> batch_name value
#> * <chr> <dbl>
#> 1 batch_A_1 1
#> 2 batch_A_2 2
#> 3 batch_A_2 3
dbDisconnect(my_con)
#> [1] TRUE
This works because any functions that dplyr doesn't know how to
translate will be passed along as is, see
?dbplyr::translate\_sql
.
Hat-tip to @PaulRougieux for his recent comment here
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