Wrapper around JsonTableReader to read a newline-delimited JSON (ndjson) file into a data frame or Arrow Table.
Arguments
- file
A character file name or URI, literal data (either a single string or a raw vector), an Arrow input stream, or a
FileSystem
with path (SubTreeFileSystem
).If a file name, a memory-mapped Arrow InputStream will be opened and closed when finished; compression will be detected from the file extension and handled automatically. If an input stream is provided, it will be left open.
To be recognised as literal data, the input must be wrapped with
I()
.- col_select
A character vector of column names to keep, as in the "select" argument to
data.table::fread()
, or a tidy selection specification of columns, as used indplyr::select()
.- as_data_frame
Should the function return a
tibble
(default) or an Arrow Table?- schema
Schema that describes the table.
- ...
Additional options passed to
JsonTableReader$create()
Details
If passed a path, will detect and handle compression from the file extension
(e.g. .json.gz
).
If schema
is not provided, Arrow data types are inferred from the data:
JSON null values convert to the
null()
type, but can fall back to any other type.JSON booleans convert to
boolean()
.JSON numbers convert to
int64()
, falling back tofloat64()
if a non-integer is encountered.JSON strings of the kind "YYYY-MM-DD" and "YYYY-MM-DD hh:mm:ss" convert to
timestamp(unit = "s")
, falling back toutf8()
if a conversion error occurs.JSON arrays convert to a
list_of()
type, and inference proceeds recursively on the JSON arrays' values.Nested JSON objects convert to a
struct()
type, and inference proceeds recursively on the JSON objects' values.
When as_data_frame = TRUE
, Arrow types are further converted to R types.
Examples
tf <- tempfile()
on.exit(unlink(tf))
writeLines('
{ "hello": 3.5, "world": false, "yo": "thing" }
{ "hello": 3.25, "world": null }
{ "hello": 0.0, "world": true, "yo": null }
', tf, useBytes = TRUE)
read_json_arrow(tf)
#> # A tibble: 3 x 3
#> hello world yo
#> <dbl> <lgl> <chr>
#> 1 3.5 FALSE thing
#> 2 3.25 NA NA
#> 3 0 TRUE NA
# Read directly from strings with `I()`
read_json_arrow(I(c('{"x": 1, "y": 2}', '{"x": 3, "y": 4}')))
#> # A tibble: 2 x 2
#> x y
#> <int> <int>
#> 1 1 2
#> 2 3 4