[MonetDBLite](https:\/\/www.monetdb.org\/blog\/monetdblite-r) (for R) was announced\/released today and, while the examples they provide are compelling there’s a “gotcha” for potential new folks using SQL in general and SQL + MonetDB + R together. The toy example on the site shows dumping `mtcars` with `dbWriteTable` and then doing things. Real-world CSV files have headers and commas (MonetDB by default expects no headers and `|` as a separator). Also, you need to make a MonetDB table (with a schema) before copying your _giant_ CSV file full of data into it. That’s a pain to do by hand.<\/p>\n
Here’s another toy example that shows how to:<\/p>\n
– use a specific directory for the embedded MonetDB files
\n– *auto-generate* the `CREATE TABLE` syntax from a sample of the real-world CSV file
\n– load the data from the real-world CSV file (i.e. skipping the header and using a `,` as a delimiter
\n– wire it up to R & dplyr<\/p>\n
It’s very similar to the MonetDBLite toy example but may help folks get up and running in the real world with less frustration.<\/p>\n
library(MonetDBLite)\r\nlibrary(MonetDB.R)\r\nlibrary(dplyr)\r\n\r\n# use built-in mtcars to make a CS File\r\n# we're more likely to find a file in this format vs what dbWriteTable produces\r\n# i.e. it has a header and commas for separator\r\nwrite.csv(add_rownames(mtcars, \"auto\"), \"mtcars.csv\", row.names=FALSE)\r\n\r\n# make a connection and get rid of the old table if it exists since\r\n# we are just playing around. in real life you prbly want to keep\r\n# the giant table there vs recreate it every time\r\nmdb <- dbConnect(MonetDBLite(), \"\/full\/path\/to\/your\/preferred\/monetdb\/data\/dir\")\r\ntry(invisible(dbSendQuery(mdb, \"DROP TABLE mtcars\")), silent=TRUE)\r\n\r\n# now we guess the column types by reading in a small fraction of the rows\r\nguess <- read.csv(\"mtcars.csv\", stringsAsFactors=FALSE, nrows=1000)\r\ncreate <- sprintf(\"CREATE TABLE mtcars ( %s )\", \r\n paste0(sprintf('\"%s\" %s', colnames(guess), \r\n sapply(guess, dbDataType, dbObj=mdb)), collapse=\",\"))\r\n\r\n# we build the table creation dynamically from what we've learned from guessing\r\ninvisible(dbSendQuery(mdb, create))\r\n\r\n# and then we load the data into the database, skipping the header and specifying a comma\r\ninvisible(dbSendQuery(mdb, \"COPY OFFSET 2 \r\n INTO mtcars \r\n FROM '\/full\/path\/to\/where\/you\/wrote\/the\/csv\/to\/mtcars.csv' USING DELIMITERS ','\"))\r\n\r\n# now wire it up to dplyr\r\nmdb_src <- src_monetdb(embedded=\"\/full\/path\/to\/your\/preferred\/monetdb\/data\/dir\")\r\nmdb_mtcars <- tbl(mdb_src, \"mtcars\")\r\n\r\n# and have some fun\r\ncount(mdb_mtcars, cyl)\r\n\r\n## Source: MonetDB ()\r\n## From: [?? x 2]\r\n## \r\n## cyl n\r\n## (int) (dbl)\r\n## 1 6 7\r\n## 2 4 11\r\n## 3 8 14\r\n## .. ... ...<\/pre>\n","protected":false},"excerpt":{"rendered":"[MonetDBLite](https:\/\/www.monetdb.org\/blog\/monetdblite-r) (for R) was announced\/released today and, while the examples they provide are compelling there’s a “gotcha” for potential new folks using SQL in general and SQL + MonetDB + R together. The toy example on the site shows dumping `mtcars` with `dbWriteTable` and then doing things. Real-world CSV files have headers and commas (MonetDB […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"activitypub_content_warning":"","activitypub_content_visibility":"","activitypub_max_image_attachments":3,"activitypub_interaction_policy_quote":"anyone","activitypub_status":"","footnotes":""},"categories":[763,91],"tags":[810],"class_list":["post-3784","post","type-post","status-publish","format-standard","hentry","category-monetdb","category-r","tag-post"],"yoast_head":"\n
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