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library(nycflights13)
library(dplyr)
dim(flights) # 336,776 x 19
as_uri <- function(x, base_uri = "x:") paste0(base_uri, x)
uri_flights <- flights %>%
mutate(tailnum = as_uri(tailnum),
carrier = as_uri(carrier))
df1 <- rdflib:::normalize_table(airlines, key = "carrier", prefix = "x:")
df2 <- rdflib:::normalize_table(planes, key = "tailnum", prefix = "x:")
df3 <- rdflib:::normalize_table(uri_flights, key = NULL, prefix = "x:")
df <- bind_rows(df1,df2,df3)
dim(df3) # 6,398,744 x 4
df %>%
filter(predicate %in% c("carrier", "name", "manufacturer", "model", "dep_delay")) %>%
count(predicate)
## We can recover an individual table
df %>%
filter(predicate %in% c("manufacturer", "model")) %>%
select(subject, predicate, object) %>%
tidyr::spread(predicate, object) %>%
rename(tailnum = subject) ## Also need to apply datatype...
## ... and join manually....
## What about recovering the "joined" table?
df %>%
filter(predicate %in% c("carrier", "name", "manufacturer", "model", "dep_delay")) %>%
select(subject, predicate, object) %>% tidyr::spread(predicate, object) %>%
filter(!is.na(carrier))
## Compare to pure approach on original tables
flights %>%
left_join(airlines, by = "carrier") %>%
left_join(planes, by = "tailnum") %>%
select(carrier, name, manufacturer, model, dep_delay) %>%
distinct()
#### All fits in memory anyway
#library(MonetDBLite)
#library(DBI)
#triplestore <- rappdirs::user_data_dir("rdflib")
#con <- dbConnect(MonetDBLite::MonetDBLite(), triplestore)
# Could use append=TRUE instead to extend triplestore later
#DBI::dbWriteTable(con, "flights", df, overwrite = TRUE)
## size as flat files on disk:
readr::write_tsv(flights, "flights.tsv") # 29.6 MB
readr::write_tsv(df, "triplestore.tsv") # 319 MB
readr::write_tsv(df, "triplestore.tsv.bz2") # 17 MB
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