2 Reading and Writing Data
This chapter contains recipes related to reading and writing data using Apache Arrow. When reading files into R using Apache Arrow, you can choose to read in your file as either a data frame or as an Arrow Table object.
There are a number of circumstances in which you may want to read in the data as an Arrow Table:
* your dataset is large and if you load it into memory, it may lead to performance issues
* you want faster performance from your dplyr queries
* you want to be able to take advantage of Arrow’s compute functions
2.1 Converting from a data frame to an Arrow Table
You want to convert an existing data.frame or tibble object into an Arrow Table.
2.2 Converting data from an Arrow Table to a data frame
You want to convert an Arrow Table to a data frame to view the data or work with it
in your usual analytics pipeline. You can use either as.data.frame() or
dplyr::collect() to do this.
2.2.1 Solution
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## 11 7 NA 6.9 74 5 11
## 12 16 256 9.7 69 5 12
## 13 11 290 9.2 66 5 13
## 14 14 274 10.9 68 5 14
## 15 18 65 13.2 58 5 15
## 16 14 334 11.5 64 5 16
## 17 34 307 12.0 66 5 17
## 18 6 78 18.4 57 5 18
## 19 30 322 11.5 68 5 19
## 20 11 44 9.7 62 5 20
## 21 1 8 9.7 59 5 21
## 22 11 320 16.6 73 5 22
## 23 4 25 9.7 61 5 23
## 24 32 92 12.0 61 5 24
## 25 NA 66 16.6 57 5 25
## 26 NA 266 14.9 58 5 26
## 27 NA NA 8.0 57 5 27
## 28 23 13 12.0 67 5 28
## 29 45 252 14.9 81 5 29
## 30 115 223 5.7 79 5 30
## 31 37 279 7.4 76 5 31
## 32 NA 286 8.6 78 6 1
## 33 NA 287 9.7 74 6 2
## 34 NA 242 16.1 67 6 3
## 35 NA 186 9.2 84 6 4
## 36 NA 220 8.6 85 6 5
## 37 NA 264 14.3 79 6 6
## 38 29 127 9.7 82 6 7
## 39 NA 273 6.9 87 6 8
## 40 71 291 13.8 90 6 9
## 41 39 323 11.5 87 6 10
## 42 NA 259 10.9 93 6 11
## 43 NA 250 9.2 92 6 12
## 44 23 148 8.0 82 6 13
## 45 NA 332 13.8 80 6 14
## 46 NA 322 11.5 79 6 15
## 47 21 191 14.9 77 6 16
## 48 37 284 20.7 72 6 17
## 49 20 37 9.2 65 6 18
## 50 12 120 11.5 73 6 19
## 51 13 137 10.3 76 6 20
## 52 NA 150 6.3 77 6 21
## 53 NA 59 1.7 76 6 22
## 54 NA 91 4.6 76 6 23
## 55 NA 250 6.3 76 6 24
## 56 NA 135 8.0 75 6 25
## 57 NA 127 8.0 78 6 26
## 58 NA 47 10.3 73 6 27
## 59 NA 98 11.5 80 6 28
## 60 NA 31 14.9 77 6 29
## 61 NA 138 8.0 83 6 30
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 65 NA 101 10.9 84 7 4
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 72 NA 139 8.6 82 7 11
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 75 NA 291 14.9 91 7 14
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 83 NA 258 9.7 81 7 22
## 84 NA 295 11.5 82 7 23
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 39 83 6.9 81 8 1
## 94 9 24 13.8 81 8 2
## 95 16 77 7.4 82 8 3
## 96 78 NA 6.9 86 8 4
## 97 35 NA 7.4 85 8 5
## 98 66 NA 4.6 87 8 6
## 99 122 255 4.0 89 8 7
## 100 89 229 10.3 90 8 8
## 101 110 207 8.0 90 8 9
## 102 NA 222 8.6 92 8 10
## 103 NA 137 11.5 86 8 11
## 104 44 192 11.5 86 8 12
## 105 28 273 11.5 82 8 13
## 106 65 157 9.7 80 8 14
## 107 NA 64 11.5 79 8 15
## 108 22 71 10.3 77 8 16
## 109 59 51 6.3 79 8 17
## 110 23 115 7.4 76 8 18
## 111 31 244 10.9 78 8 19
## 112 44 190 10.3 78 8 20
## 113 21 259 15.5 77 8 21
## 114 9 36 14.3 72 8 22
## 115 NA 255 12.6 75 8 23
## 116 45 212 9.7 79 8 24
## 117 168 238 3.4 81 8 25
## 118 73 215 8.0 86 8 26
## 119 NA 153 5.7 88 8 27
## 120 76 203 9.7 97 8 28
## 121 118 225 2.3 94 8 29
## 122 84 237 6.3 96 8 30
## 123 85 188 6.3 94 8 31
## 124 96 167 6.9 91 9 1
## 125 78 197 5.1 92 9 2
## 126 73 183 2.8 93 9 3
## 127 91 189 4.6 93 9 4
## 128 47 95 7.4 87 9 5
## 129 32 92 15.5 84 9 6
## 130 20 252 10.9 80 9 7
## 131 23 220 10.3 78 9 8
## 132 21 230 10.9 75 9 9
## 133 24 259 9.7 73 9 10
## 134 44 236 14.9 81 9 11
## 135 21 259 15.5 76 9 12
## 136 28 238 6.3 77 9 13
## 137 9 24 10.9 71 9 14
## 138 13 112 11.5 71 9 15
## 139 46 237 6.9 78 9 16
## 140 18 224 13.8 67 9 17
## 141 13 27 10.3 76 9 18
## 142 24 238 10.3 68 9 19
## 143 16 201 8.0 82 9 20
## 144 13 238 12.6 64 9 21
## 145 23 14 9.2 71 9 22
## 146 36 139 10.3 81 9 23
## 147 7 49 10.3 69 9 24
## 148 14 20 16.6 63 9 25
## 149 30 193 6.9 70 9 26
## 150 NA 145 13.2 77 9 27
## 151 14 191 14.3 75 9 28
## 152 18 131 8.0 76 9 29
## 153 20 223 11.5 68 9 302.3 Writing a Parquet file
You want to write Parquet files to disk.
2.4 Reading a Parquet file
You want to read a Parquet file.
2.4.1 Solution
## group score
## 1 A 99
## 2 B 97
## 3 C 99As the argument as_data_frame was left set to its default value of TRUE, the file was read in as a data.frame object.
## [1] "data.frame"2.4.2 Discussion
If you set as_data_frame to FALSE, the file will be read in as an Arrow Table.
my_table_arrow_table <- read_parquet("my_table.parquet", as_data_frame = FALSE)
my_table_arrow_table## Table
## 3 rows x 2 columns
## $group <string>
## $score <double>
##
## See $metadata for additional Schema metadata## [1] "Table" "ArrowTabular" "ArrowObject" "R6"2.5 Read a Parquet file from S3
You want to read a Parquet file from S3.
2.5.2 See also
For more in-depth instructions, including how to work with S3 buckets which require authentication, you can find a guide to reading and writing to/from S3 buckets here: https://arrow.apache.org/docs/r/articles/fs.html.
2.6 Filter columns while reading a Parquet file
You want to specify which columns to include when reading in a Parquet file.
2.6.1 Solution
# Create table to read back in
dist_time <- Table$create(data.frame(distance = c(12.2, 15.7, 14.2), time = c(43, 44, 40)))
# Write to Parquet
write_parquet(dist_time, "dist_time.parquet")
# Read in only the "time" column
time_only <- read_parquet("dist_time.parquet", col_select = "time")
time_only## time
## 1 43
## 2 44
## 3 402.7 Write an IPC/Feather V2 file
You want to read in a Feather file.
2.7.1 Solution
2.7.2 Discussion
For legacy support, you can write data in the original Feather format by setting the version parameter to 1.
2.8 Read a Feather file
You want to read a Feather file.
2.9 Write Streaming IPC Files
You want to write to the IPC stream format.
2.10 Read Streaming IPC Files
You want to read from the IPC stream format.
2.11 Reading and Writing CSV files
You can use write_csv_arrow() to save an Arrow Table to disk as a CSV.
You can use read_csv_arrow() to read in a CSV file as an Arrow Table.
2.12 Read JSON files
You want to read a JSON file.
2.12.1 Solution
# Create a file to read back in
tf <- tempfile()
writeLines('
{"country": "United Kingdom", "code": "GB", "long": -3.44, "lat": 55.38}
{"country": "France", "code": "FR", "long": 2.21, "lat": 46.23}
{"country": "Germany", "code": "DE", "long": 10.45, "lat": 51.17}
', tf, useBytes = TRUE)
# Read in the data
countries <- read_json_arrow(tf, col_select = c("country", "long", "lat"))
countries## # A tibble: 3 × 3
## country long lat
## <chr> <dbl> <dbl>
## 1 United Kingdom -3.44 55.4
## 2 France 2.21 46.2
## 3 Germany 10.4 51.22.13 Write Partitioned Data
You want to save data to disk in partitions based on columns in the data.
2.13.1 Solution
write_dataset(airquality, "airquality_partitioned", partitioning = c("Month", "Day"))
list.files("airquality_partitioned")## [1] "Month=5" "Month=6" "Month=7" "Month=8" "Month=9"As you can see, this has created folders based on the first partition variable supplied, Month.
If you take a look in one of these folders, you will see that the data is then partitioned by the second partition variable, Day.
## [1] "Day=1" "Day=10" "Day=11" "Day=12" "Day=13" "Day=14" "Day=15" "Day=16"
## [9] "Day=17" "Day=18" "Day=19" "Day=2" "Day=20" "Day=21" "Day=22" "Day=23"
## [17] "Day=24" "Day=25" "Day=26" "Day=27" "Day=28" "Day=29" "Day=3" "Day=30"
## [25] "Day=31" "Day=4" "Day=5" "Day=6" "Day=7" "Day=8" "Day=9"Each of these folders contains 1 or more Parquet files containing the relevant partition of the data.
## [1] "part-9.parquet"2.14 Reading Partitioned Data
You want to read partitioned data.
2.14.1 Solution
## FileSystemDataset with 153 Parquet files
## Ozone: int32
## Solar.R: int32
## Wind: double
## Temp: int32
## Month: int32
## Day: int32
##
## See $metadata for additional Schema metadata