Closed rfherrerac closed 8 months ago
Thanks for reporting this @rfherrerac, can you share the settings you used with the function and I will investigate this?
Thanks @edward-burn
cdm <- generateDenominatorCohortSet(
cdm = cdm,
name = "denominator",
cohortDateRange = as.Date(c("2018-01-01", "2020-12-31")),#c(lubridate::ymd("2021-01-01"), lubridate::ymd("2023-01-31")),
ageGroup = list(c(18,44), c(45,64),
c(65,74), c(75,100)),
sex = c("Male", "Female", "Both"),
daysPriorObservation = 1,
requirementInteractions=FALSE
)
Thanks @rfherrerac, let me take a look and get back to you. I'm actually preparing a new release so hopefully we can get this fixed in that. I only have a got access to a small redshift test database, so it would be great if you could test this new release on your data if that would be ok?
For sure! happy to do so.
@rfherrerac I'm not seeing anything obvious that I've changed that would of caused this (but I'll keep looking). Can I just check what versions of dbplyr and RPostgres you have installed? I'm just wondering if it might relate to https://github.com/r-dbi/RPostgres/issues/457
Hi @edward-burn I have RPostgres 1.4.6. and dbplyr 2.4.0
Hi @rfherrerac, could you please try with the 0.7 version of IncidencePrevalence that is now out on cran? I realised that a dependency I was using was collecting data into R, and so with this fixed I´m hoping your issue will be solved but would be great if you could confirm
Hi @edward-burn, it worked perfectly. Thanks a lot!
Describe the bug When running generateDenominatorCohortSet in a US large dataset in redshift, the memory ram is consumed vastly. And takes forever
R version 4.2.3 (2023-03-15) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Red Hat Enterprise Linux 8.7 (Ootpa)
Matrix products: default BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.15.so
Version 0.4.1 did not have that issue ran pretty fast.