data-write.md
In pysparklyr: Provides a 'PySpark' Back-End for the 'sparklyr' Package

CSV works

Code
  spark_read_csv(sc = sc, name = "csv_1", path = file_name, overwrite = TRUE,
    repartition = 2)
Output
  # Source:   table<`csv_1`> [?? x 11]
  # Database: spark_connection
       mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
   1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
   2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
   3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
   4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
   5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
   6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
   7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
   8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
   9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
  10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
  # i more rows

Code
  spark_read_csv(sc = sc, name = "csv_2", path = file_name, overwrite = TRUE,
    columns = paste0(names(mtcars), "t"))
Output
  # Source:   table<`csv_2`> [?? x 11]
  # Database: spark_connection
     mpgt  cylt  dispt hpt   dratt wtt   qsect vst   amt   geart carbt
     <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
   1 21.0  6.0   160.0 110.0 3.9   2.62  16.46 0.0   1.0   4.0   4.0  
   2 21.0  6.0   160.0 110.0 3.9   2.875 17.02 0.0   1.0   4.0   4.0  
   3 22.8  4.0   108.0 93.0  3.85  2.32  18.61 1.0   1.0   4.0   1.0  
   4 21.4  6.0   258.0 110.0 3.08  3.215 19.44 1.0   0.0   3.0   1.0  
   5 18.7  8.0   360.0 175.0 3.15  3.44  17.02 0.0   0.0   3.0   2.0  
   6 18.1  6.0   225.0 105.0 2.76  3.46  20.22 1.0   0.0   3.0   1.0  
   7 14.3  8.0   360.0 245.0 3.21  3.57  15.84 0.0   0.0   3.0   4.0  
   8 24.4  4.0   146.7 62.0  3.69  3.19  20.0  1.0   0.0   4.0   2.0  
   9 22.8  4.0   140.8 95.0  3.92  3.15  22.9  1.0   0.0   4.0   2.0  
  10 19.2  6.0   167.6 123.0 3.92  3.44  18.3  1.0   0.0   4.0   4.0  
  # i more rows

Code
  spark_read_csv(sc = sc, name = "csv_3", path = file_name, overwrite = TRUE,
    memory = TRUE)
Output
  # Source:   table<`csv_3`> [?? x 11]
  # Database: spark_connection
       mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
   1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
   2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
   3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
   4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
   5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
   6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
   7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
   8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
   9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
  10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
  # i more rows

Parquet works

Code
  spark_read_parquet(sc, "csv_1", file_name, overwrite = TRUE)
Output
  # Source:   table<`csv_1`> [?? x 11]
  # Database: spark_connection
       mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
   1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
   2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
   3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
   4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
   5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
   6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
   7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
   8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
   9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
  10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
  # i more rows

ORC works

Code
  spark_read_orc(sc, "csv_1", file_name, overwrite = TRUE)
Output
  # Source:   table<`csv_1`> [?? x 11]
  # Database: spark_connection
       mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
   1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
   2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
   3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
   4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
   5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
   6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
   7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
   8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
   9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
  10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
  # i more rows

JSON works

Code
  spark_read_json(sc, "csv_1", file_name, overwrite = TRUE)
Output
  # Source:   table<`csv_1`> [?? x 11]
  # Database: spark_connection
        am  carb   cyl  disp  drat  gear    hp   mpg  qsec    vs    wt
     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
   1     1     4     6  160   3.9      4   110  21    16.5     0  2.62
   2     1     4     6  160   3.9      4   110  21    17.0     0  2.88
   3     1     1     4  108   3.85     4    93  22.8  18.6     1  2.32
   4     0     1     6  258   3.08     3   110  21.4  19.4     1  3.22
   5     0     2     8  360   3.15     3   175  18.7  17.0     0  3.44
   6     0     1     6  225   2.76     3   105  18.1  20.2     1  3.46
   7     0     4     8  360   3.21     3   245  14.3  15.8     0  3.57
   8     0     2     4  147.  3.69     4    62  24.4  20       1  3.19
   9     0     2     4  141.  3.92     4    95  22.8  22.9     1  3.15
  10     0     4     6  168.  3.92     4   123  19.2  18.3     1  3.44
  # i more rows