View source: R/csv_to_sqlite.R
csv_to_sqlite | R Documentation |
sqlite
databaseThe table can be a comma separated (csv
) or a tab separated (tsv
) or any
other delimited text file. The file is read in chunks. Each chunk is copied
in the same sqlite
table database before the next chunk is loaded into
memory.
See the INBO tutorial Handling large files in R
to learn more.
csv_to_sqlite(
csv_file,
sqlite_file,
table_name,
delim = ",",
pre_process_size = 1000,
chunk_size = 50000,
show_progress_bar = TRUE,
...
)
csv_file |
Name of the text file to convert. |
sqlite_file |
Name of the newly created |
table_name |
Name of the table to store the data table in the |
delim |
Text file delimiter (default ","). |
pre_process_size |
Number of lines to check the data types of the individual columns (default 1000). |
chunk_size |
Number of lines to read for each chunk (default 50000). |
show_progress_bar |
Show progress bar (default TRUE). |
... |
Further arguments to be passed to |
a SQLite
database
The callback
argument in the read_delim_chunked
function call
refers to the custom written callback function append_to_sqlite
applied
to each chunk.
Other Data_handling_utilities:
convertdf_enc()
,
df_factors_to_char()
## Not run:
library(R.utils)
library(dplyr)
csv.name <- "2016-04-20-processed-logs-big-file-example.csv"
db.name <- "2016-04-20-processed-logs-big-file-example.db"
# download the CSV file example
csv.url <- paste("https://s3-eu-west-1.amazonaws.com/lw-birdtracking-data/",
csv.name, ".gz",
sep = ""
)
download.file(csv.url, destfile = paste0(csv.name, ".gz"))
gunzip(paste0(csv.name, ".gz"))
# Make a SQLite database
sqlite_file <- "example2.sqlite"
table_name <- "birdtracks"
csv_to_sqlite(
csv_file = csv.name,
sqlite_file = sqlite_file,
table_name = table_name
)
# Get access to SQLite database
my_db <- src_sqlite(sqlite_file, create = FALSE)
bird_tracking <- tbl(my_db, "birdtracks")
# Example query via dplyr
results <- bird_tracking %>%
filter(device_info_serial == 860) %>%
select(date_time, latitude, longitude, altitude) %>%
filter(date_time < "2014-07-01") %>%
filter(date_time > "2014-03-01") %>%
as_tibble()
head(results)
## End(Not run)
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