Can I use this answer to ask exactly for what it's mentioned. R and Postgis mostly for Easter files. Can you point books, online courses, tutorials, GitHub pages, anything, to better understand this? I had been struggling to find info.
Thanks! El vie., 25 may. 2018 1:35, Tom Philippi <tephili...@gmail.com> escribió: > What Roger said (as always). > > Note that if you use tidyverse and magrittr, dplyr and tidyverse tools work > well with databases via DBI. sqldf also works with multiple SQL database > backends if you're an ol dog like me and don't use tidyverse much. > > Also, since this is r-sig-*GEO*, note that postgreSQL has postGIS for > spatial data, which does far more than the automatic tiling of large > rasters in package raster. I'm seeing wonderful performance working with a > 340M observation >100GB dataset of bird observation data in R via postGIS, > even with "only" 32GB RAM and constrained to running win7, not linux/unix. > > One alternative is that if your database is running on massive hardware > (tons of memory, many cores, etc.), it is possible to run R within both > postgreSQL and now MS SQL Server, the first free, the second an additional > cost add-on, and both usually at the cost of painful negotiations with DA > administrators for permissions to run your ad hoc R code on their SQL > server. If you have the hardware, you can even run R with hadoop, although > I've never done that with spatial data. > > Tom 0 > > > On Thu, May 24, 2018 at 5:04 AM, Roger Bivand <roger.biv...@nhh.no> wrote: > > > On Thu, 24 May 2018, Yaya Bamba wrote: > > > > Thanks to all of you. I will try with the package RMySQL and see. > >> > > > > Maybe look more generally through the packages depending on and importing > > from DBI (https://cran.r-project.org/package=DBI) to see what is > > available - there are many more than RMySQL. > > > > and use the Official Statistics and HPC Task Views: > > > > https://cran.r-project.org/view=OfficialStatistics > > > > https://cran.r-project.org/view=HighPerformanceComputing > > > > to see how typical workflows (not necessarily DB-based) can be handled. > > The HPC TV has a section on large memory and out-of-memory approaches. If > > your data are spatial in raster format, the raster package provides some > > out-of-memory functionality. In sf, spatial vector data may be read from > > databases too. > > > > Roger > > > > > > > >> 2018-05-24 11:33 GMT+00:00 Andres Diaz Loaiza <madi...@gmail.com>: > >> > >> Hello Yaya, > >>> > >>> Many years ago I work with a database in MySQL connected to R through > the > >>> package RMySQL. The data was stored in the MySQL and I was connecting > >>> and > >>> using the data from R > >>> > >>> you should have a look in: > >>> > >>> https://cran.r-project.org/web/packages/RMySQL/index.html > >>> > >>> Cheers, > >>> > >>> Andres > >>> > >>> > >> > >> > >> > >> > > -- > > Roger Bivand > > Department of Economics, Norwegian School of Economics, > > Helleveien 30, N-5045 Bergen, Norway. > > voice: +47 55 95 93 55; e-mail: roger.biv...@nhh.no > > http://orcid.org/0000-0003-2392-6140 > > https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en > > _______________________________________________ > > R-sig-Geo mailing list > > R-sig-Geo@r-project.org > > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo