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I have been successful in running gnr_resolve on a data set with a few 100 rows to get matched names. However i'm getting the following error (Error: Request Entity Too Large (HTTP 413)) when running my bigger data set (79,298 rows), even with 'http="post"'. Is there any way I can easily overcome this? My data set will get larger and larger in the future so not being able to run large data sets will cause me a real headache. I know the wormsbynames() function of the worms package which I'm also using processes the data into chunks to avoid this I think. Does taxsize gnr_resolve() have an equivalent? What is the max data set size gnr_resolve() can handle?
what I run below
Taxize_test<-gnr_resolve(Formatted_Benthic_Biomass_Data_WW_TW_FINAL$Nomen,resolve_once = FALSE,best_match_only = TRUE,canonical = TRUE, http="post",fields="all",preferred_data_sources=9)
Error: Request Entity Too Large (HTTP 413)
Any help greatly appreciated.
session_info()
─ Session info ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.0 (2022-04-22 ucrt)
os Windows 10 x64 (build 19044)
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United Kingdom.utf8
ctype English_United Kingdom.utf8
tz Europe/London
date 2022-09-30
rstudio 2022.02.2+485 Prairie Trillium (desktop)
pandoc NA
need to split the data table for taxize due to size.
chunk <- 1000
n <- nrow(Formatted_Benthic_Biomass_Data_WW_TW_FINAL)
r <- rep(1:ceiling(n/chunk),each=chunk)[1:n]
d <- split(Formatted_Benthic_Biomass_Data_WW_TW_FINAL,r)
output <- list()
for (i in seq_along(d)){
Taxize_test<-gnr_resolve(d[[i]]$Nomen,resolve_once = FALSE,best_match_only = TRUE,canonical = TRUE, http="post",fields="all",preferred_data_sources=9)
output[[i]] <- Taxize_test
}
#rejoin tibbles of gnr_resolve
Taxize_test<-bind_rows(output)
Hello
I have been successful in running gnr_resolve on a data set with a few 100 rows to get matched names. However i'm getting the following error (Error: Request Entity Too Large (HTTP 413)) when running my bigger data set (79,298 rows), even with 'http="post"'. Is there any way I can easily overcome this? My data set will get larger and larger in the future so not being able to run large data sets will cause me a real headache. I know the wormsbynames() function of the worms package which I'm also using processes the data into chunks to avoid this I think. Does taxsize gnr_resolve() have an equivalent? What is the max data set size gnr_resolve() can handle?
what I run below
Any help greatly appreciated.
session_info()
─ Session info ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.0 (2022-04-22 ucrt)
os Windows 10 x64 (build 19044)
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United Kingdom.utf8
ctype English_United Kingdom.utf8
tz Europe/London
date 2022-09-30
rstudio 2022.02.2+485 Prairie Trillium (desktop)
pandoc NA
─ Packages ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
ape 5.6-2 2022-03-02 [1] CRAN (R 4.2.1)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.2.0)
backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
bit 4.0.4 2020-08-04 [1] CRAN (R 4.2.0)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
bold 1.2.0 2021-05-11 [1] CRAN (R 4.2.1)
broom 0.8.0 2022-04-13 [1] CRAN (R 4.2.0)
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callr 3.7.0 2021-04-20 [1] CRAN (R 4.2.0)
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cli 3.3.0 2022-04-25 [1] CRAN (R 4.2.0)
codetools 0.2-18 2020-11-04 [2] CRAN (R 4.2.0)
colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.2.0)
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crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0)
crul 1.3 2022-09-03 [1] CRAN (R 4.2.1)
curl 4.3.2 2021-06-23 [1] CRAN (R 4.2.0)
data.table 1.14.2 2021-09-27 [1] CRAN (R 4.2.0)
DBI 1.1.2 2021-12-20 [1] CRAN (R 4.2.0)
dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.2.0)
devtools * 2.4.4 2022-07-20 [1] CRAN (R 4.2.1)
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haven 2.5.0 2022-04-15 [1] CRAN (R 4.2.0)
hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0)
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stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0)
stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.2.0)
taxize * 0.9.100 2022-04-22 [1] CRAN (R 4.2.1)
terra 1.5-21 2022-02-17 [1] CRAN (R 4.2.0)
tibble * 3.1.7 2022-05-03 [1] CRAN (R 4.2.0)
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tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.2.0)
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