Export data to JSON file(s)

Usage

write_json_polars(.data, file, ..., pretty = FALSE, row_oriented = FALSE)

Arguments

.data: A Polars DataFrame.
file: File path to which the result should be written.
...: Ignored.
pretty: Pretty serialize JSON.
row_oriented: Write to row-oriented JSON. This is slower, but more common.

Value

The input DataFrame.

Examples

dest <- tempfile(fileext = ".json")
mtcars |>
  as_polars_df() |>
  write_json_polars(dest)

jsonlite::fromJSON(dest)
#> $columns
#>    name datatype bit_settings
#> 1   mpg  Float64             
#> 2   cyl  Float64             
#> 3  disp  Float64             
#> 4    hp  Float64             
#> 5  drat  Float64             
#> 6    wt  Float64             
#> 7  qsec  Float64             
#> 8    vs  Float64             
#> 9    am  Float64             
#> 10 gear  Float64             
#> 11 carb  Float64             
#>                                                                                                                                                                                                                            values
#> 1                                  21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26.0, 30.4, 15.8, 19.7, 15.0, 21.4
#> 2                                                                                                                                  6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
#> 3       160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0, 304.0, 350.0, 400.0, 79.0, 120.3, 95.1, 351.0, 145.0, 301.0, 121.0
#> 4                                                                           110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
#> 5                                  3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11
#> 6  2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.520, 3.435, 3.840, 3.845, 1.935, 2.140, 1.513, 3.170, 2.770, 3.570, 2.780
#> 7  16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.87, 17.30, 15.41, 17.05, 18.90, 16.70, 16.90, 14.50, 15.50, 14.60, 18.60
#> 8                                                                                                                                  0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
#> 9                                                                                                                                  1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
#> 10                                                                                                                                 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
#> 11                                                                                                                                 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
#>