stephhazlitt commented on code in PR #159: URL: https://github.com/apache/arrow-cookbook/pull/159#discussion_r1006295671
########## r/content/datasets.Rmd: ########## @@ -0,0 +1,291 @@ +<!--- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--> + +# Manipulating Data - Datasets + +## Introduction + +While Arrow allows you to work with single files imported as Arrow Tables, you can +also work with multi-files imported as an Arrow Dataset. Partitioning allows you to split data across +multiple files/folders, avoiding problems associated with storing all your data in a single file. This can provide +further advantages when using Arrow, as Arrow will only +read in the necessary partitioned files needed for any given analysis. + +It's possible to read in partitioned data in Parquet, Feather (aka Arrow), and CSV (or +other text-delimited) formats. If you are choosing a partitioned or multifile format, we +recommend Parquet or Feather, both of which can have improved performance +when compared to CSVs due to their capabilities around metadata and compression. + +## Write a data to disk (Parquet) + +You want to write data to disk in a single Parquet file. + +### Solution + +```{r, write_dataset_basic} +write_dataset(dataset = starwars, path = "starwars_data") +``` + +```{r, test_write_dataset_basic, opts.label = "test"} + +test_that("write_dataset_basic works as expected", { + + expect_true(file.exists("starwars_data")) + expect_length(list.files("starwars_data"), 1) + +}) + +``` + +### Discussion + +The default format for `open_dataset()` is Parquet. + +## Write a multifile dataset to disk partitioned based on a variable + +You want to write data to disk partitioned across multiple files. + +### Solution + +```{r, write_dataset_partitioned} +write_dataset(dataset = starwars, + path = "starwars_data_partitioned", + partitioning = "homeworld") +``` + + +```{r, test_write_dataset_partitioned, opts.label = "test"} + +test_that("write_dataset_partitioned works as expected", { + + expect_true(file.exists("starwars_data_partitioned")) + expect_length(list.files("starwars_data_partitioned", recursive = TRUE), 49) + +}) + +``` + +### Discussion + +The data is written to separate folders based on the values in the `homeworld` +column. The default behaviour is to use Hive-style (i.e. "col_name=value" folder names) +partitions. + +```{r} +# Take a look at the files in this directory +list.files("starwars_data_partitioned", recursive = TRUE) +``` + +Note that in the example above, when there was an `NA` value in the `homeworld` +column, these values are written to the `homeworld=__HIVE_DEFAULT_PARTITION__` +directory. + +You can specify multiple partitioning variables to add extra levels of partitioning. + +```{r, write_dataset_partitioned_deeper} +write_dataset( + dataset = starwars, + path = "starwars_partitioned_twice", + partitioning = c("homeworld", "species") +) +``` + +```{r, test_write_dataset_partitioned_deeper, opts.label = "test"} + +test_that("write_dataset_partitioned_deeper works as expected", { + + expect_true(file.exists("starwars_partitioned_twice")) + expect_length(list.files("starwars_partitioned_twice", recursive = TRUE), 58) + +}) + +``` + +```{r} +# Take a look at the files in this directory +list.files("starwars_partitioned_twice", recursive = TRUE) +``` + +There are two different ways to specify variables to use for partitioning - +either via the `partitioning` variable as above, or by using `dplyr::group_by()` on your data - the group variables will form the partitions. + +```{r, write_dataset_partitioned_groupby} +library(dplyr) +write_dataset(dataset = group_by(starwars, homeworld, species), + path = "starwars_groupby") +``` + +```{r, test_write_dataset_partitioned_groupby, opts.label = "test"} + +test_that("write_dataset_partitioned_groupby works as expected", { + + expect_true(file.exists("starwars_groupby")) + expect_length(list.files("starwars_groupby", recursive = TRUE), 58) + +}) + +``` + +```{r} +# Take a look at the files in this directory +list.files("starwars_groupby", recursive = TRUE) +``` +## Write data to disk - Feather format + +You want to write data to disk in a single Feather file. + +### Solution + +```{r, write_dataset_feather} +write_dataset(dataset = starwars, + path = "starwars_data_feather", + format = "feather") +``` + +```{r, test_write_dataset_feather, opts.label = "test"} + +test_that("write_dataset_feather works as expected", { + + expect_true(file.exists("starwars_data_feather")) + expect_length(list.files("starwars_data_feather"), 1) + +}) + +``` + +## Write data to disk - CSV format + +You want to write data to disk in a single CSV file. + +### Solution + +```{r, write_dataset_csv} +# Need to update this example as we can't write list columns to CSV :( Review Comment: I refactored to stick with the `airquality` dataset, to be more consistent with the rest fo the read/write material in the R cookbook. -- This is an automated message from the Apache Git Service. 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