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tests/testthat:

GITHUB
warlicks/NOAATides: Collect Ocean Data from NOAA

", "Ford Prefect"),
book = c(NA, NA),
towel = c(NA, NA

tests/testthat:

CRAN
noaaoceans: Collect Ocean Data from NOAA

", "Ford Prefect"),
book = c(NA, NA),
towel = c(NA, NA

tests/testthat:

GITHUB
warlicks/noaaoceans: Collect Ocean Data from NOAA

", "Ford Prefect"),
book = c(NA, NA),
towel = c(NA, NA

tests/testthat/test_license_creation.R:

GITHUB
warlicks/cookiecutteR: What the Package Does (One Line, Title Case)

<- fs::path_expand(file.path("~", "license_dir"))
author <- "Ford Prefect"
# Create directory

R/romanddbb.09_list_emp.succesion.R:

GITHUB
miquelvazquez/romanddbb: romanddbb

as Praetorian Prefect'),
diadumedian = c('[diadumedian] associated as co-emperor with Macrinus'),
elagabalus = c

R/03_romanddbb.l_emp.succesion.R:

GITHUB
mvazquezs/romanddbb: romanddbb

as Praetorian Prefect'),
diadumedian = c('[diadumedian] associated as co-emperor with Macrinus'),
elagabalus = c

README.Rmd:

GITHUB
dirkschumacher/llr: A lisp like language on top of R

-of-conduct and also be aware that this a fun project,
so things will break and progress is valued prefect code (at the moment

README.md:

GITHUB
dirkschumacher/llr: A lisp like language on top of R

project, so things will break and progress is valued prefect code
(at the moment).
- However everyone

vignettes/siteonly/data-matching.Rmd:

GITHUB
2DegreesInvesting/pacta: Paris Agreement Capital Transition Assessment

match score. After that all resutls are split into 2 main groups: prefect matches and matches that require verification

R/old_code_staging.R:

GITHUB
amcrisan/GEViTRec: GEViTRec

if(nrow(link_strength)>0){
#when there is a prefect match free up the objects

R/old_code_staging.R:

GITHUB
amcrisan/epivis: GEViTRec

if(nrow(link_strength)>0){
#when there is a prefect match free up the objects

R/AppendixS4.R:

GITHUB
mjevans26/eaglesFWS:

to the budget
################################################
#prefect info

tests/testthat/test_02_dbi.R:

GITHUB
hannesmuehleisen/MonetDBLite-R: In-Process Version of 'MonetDB'

(a=as.integer(c(84, 42, 12)),
b=c(84.5, 42.5, NA),
c=c("Ford Prefect", "Zaphod Beeblebrox", NA),

tests/testthat/test_02_dbi.R:

CRAN
MonetDBLite: In-Process Version of 'MonetDB'

(a=as.integer(c(84, 42, 12)),
b=c(84.5, 42.5, NA),
c=c("Ford Prefect", "Zaphod Beeblebrox", NA),

vignettes/introduction.Rmd:

CRAN
tfdatasets: Interface to 'TensorFlow' Datasets

also parallelize the reading of data from storage by requesting that a buffer of records be prefected. You do