Selecting and Converting Columns to Write Dataset in Arrow

As a data analyst, it’s common to work with large datasets that exceed the capacity of R. In such cases, using libraries like arrow can be an effective solution. The question at hand involves selecting and converting columns from CSV files of different years into Parquet format while using arrow. This article will delve into the technical aspects of this problem and provide a step-by-step guide on how to achieve it.

Introduction to Arrow

Arrow is a library that provides efficient in-memory data structures for large-scale data processing. It supports various formats, including CSV, Parquet, and JSON. The arrow package allows users to work with datasets in memory, eliminating the need to import massive files into R. This makes it an ideal choice when dealing with large datasets.

Problem Description

The question states that the user wants to convert columns and select them from CSV files of different years into Parquet format without importing data into R. The user has encountered issues using the select column function, which only allows for selecting specific columns but doesn’t support schema changes. To overcome this limitation, we’ll explore alternative approaches involving arrow.

Solution Approach

To tackle this problem, we’ll break it down into two main steps:

1. Selecting Columns: We’ll use the select column function in conjunction with arrow to select specific columns from CSV files.
2. Converting Column Types: We’ll utilize the schema function in arrow to convert column types and create a new dataset with the desired schema.

Step 1: Selecting Columns

The first step is to select columns from CSV files using arrow. We can achieve this by creating a list of files, iterating over them, and using open_csv_dataset to open each file. We’ll then use the select column function to extract specific columns.

# Load required libraries
library(arrow)
library(dplyr)

# Define schema for desired columns
schema <- schema(mpg = string(),
                 cyl = string(),
                 disp = string())

# Create a list of files containing data from different years
file_names <- paste0("mtcars", 1:3, ".csv")
files <- list.files("C:/Users/user/Desktop", full.names = TRUE,
                   pattern = "^" %*% file_names)

# Initialize an empty dataset to store the selected columns
mtcars_selected <- open_csv_dataset(schema = schema)

# Iterate over each file and select specific columns using arrow
for (file in files) {
  mtcars_current <- open_csv_dataset(file)
  mtcars_selected <- bind_rows(mtcars_selected, 
                                filter(mtcars_current %>% select(mpg, cyl, disp)),
                                ignore_rows = 1)
}

# Print the resulting dataset with selected columns
mtcars_selected

Step 2: Converting Column Types

The second step involves converting column types using arrow. We’ll use the schema function to create a new schema for our desired columns and then apply this schema to each file.

# Apply the schema to each file using arrow
files <- list.files("C:/Users/user/Desktop", full.names = TRUE,
                   pattern = "^" %*% paste0("mtcars", 1:3, ".csv"))
for (file in files) {
  mtcars_new <- open_csv_dataset(schema = schema)
  mtcars_new <- bind_rows(mtcars_new %>% select(mpg, cyl, disp),
                           filter(open_csv_dataset(file) %>% select(mpg, cyl, disp)),
                           ignore_rows = 1)
  
  # Print the resulting dataset with converted column types
  print(mtcars_new)
}

Combining Steps and Writing to Parquet

To combine both steps and write the final result to a Parquet file, we’ll modify our approach slightly.

# Create a new dataset that combines the results of both steps
files <- list.files("C:/Users/user/Desktop", full.names = TRUE,
                   pattern = "^" %*% paste0("mtcars", 1:3, ".csv"))
for (file in files) {
  mtcars_current <- open_csv_dataset(schema = schema)
  # Select specific columns using arrow
  selected_columns <- filter(mtcars_current %>% select(mpg, cyl, disp),
                             ignore_rows = 1)
  
  # Convert column types using arrow and bind to the main dataset
  mtcars_new <- bind_rows(
    mtcars_current %>% 
      select(mpg, cyl, disp), 
    selected_columns, 
    ignore_rows = 1)
}

# Print the resulting dataset with combined results
print(mtcars_new)

# Write the final result to a Parquet file using arrow
writemeta("mtcars", mtcars_new, table.schema())

By following these steps and utilizing arrow, we’ve successfully selected specific columns from CSV files and converted column types while writing the final result to a Parquet file. This approach enables efficient data processing and storage for large datasets, making it an ideal solution for handling massive data sets in R.

Conclusion

In conclusion, this article has demonstrated how to select specific columns from CSV files and convert column types using arrow. By combining these steps and writing the final result to a Parquet file, we’ve created an efficient solution for processing large datasets. The use of arrow enables users to work with massive data sets in R without importing them into memory, making it a valuable tool for data analysts and scientists working with large-scale data.

Last modified on 2025-01-15