Prep for CRAN lazysf 0.1.0

[mdsumner]

• doc of DBI functions, expand or bundle? d259835 thanks to RSQLite
• see and review https://github.com/tidyverse/dbplyr/blob/master/vignettes/backend-2.Rmd
• do we document dplyr verbs? collect() is special because of st_as_sf() subtlety?
• vignette on OGRSQL vagaries (that we know)
• try other DBs (Manifold, MSSQLServer)
• discuss copy_to() - i.e. not possible atm

Prepare for release:

• ensure pkgdown.yml matches the topics
• Check that description is informative
• Check licensing of included files
• devtools::build_readme()
• usethis::use_cran_comments()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• Update cran-comments.md
• Draft blog post

Submit to CRAN:

• usethis::use_version('minor')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_news_md()
• usethis::use_github_release()
• usethis::use_dev_version()
• Update install instructions in README
• Finish blog post
• Tweet
• Add link to blog post in pkgdown news menu

passing arguments to st_read

haven't checked everything but seems ok

checked explicitly:

• query
• wkt_filter
• quiet
• fid_column_name
• drivers

[mdsumner]

the verbs work fine, haven't checked set (union, setdiff, intersect, etc) not sure how

library(lazysf)
library(dplyr)

# tbl, collapse, collect, do, head, pull
# arrange,  distinct,  filter,  group_by, mutate, select, rename, rename_with, relocate
# slice_min, slice_max, slice_sample

# inner_join, left_join, right_join, full_join, semi_join, anti_join,
#
# intersection, union, union_all, setdiff
# -- can't work
# copy_to

db <- dbConnect(SFSQL(), "inst/extdata/multi.gpkg")
  tbl(db, "centre") %>% dplyr::filter(state  %in% c("Tasmania", "Victoria")) %>%
    dplyr::select(state) %>%
  dplyr::inner_join(tbl(db, "state"), c("state" = "NAME")) %>%
   # rename(here = state)
  #head(2) %>% collect()
  #rename_with(~paste(.x, "abc", sep = "_"))
  #  mutate(a = 1) %>%  relocate(a)  ## doesn't work with geom
  group_by(state) %>% slice_sample(state, n= 2)
  
  
tbl(db, "centre") %>%  union( tbl(db, "state"),c("state" = "NAME") )