Exporting and Importing Data to and from R

Exporting and importing data between R and external sources is a fundamental task for data analysts. R has numerous functions and packages designed specifically for data export and import tasks. These tools allow users to work with a wide range of file formats and data sources, including Excel spreadsheets, CSV files, SQL databases, and more. However, it’s worth noting that R may not be the best option for handling large data sets. While R is an excellent tool for data analysis and modeling, it can be slow and inefficient when working with large amounts of data. In such cases, other systems, such as Python or specialized databases like Hadoop and Spark, may be more effective. It’s essential to choose the right tool for the job based on the size and complexity of your data. In summary, while R has many features that make it a popular choice for data analysis and modeling, it may not be the best option for handling large data sets. By considering the size and complexity of your data, you can choose the appropriate tool for your needs, whether it’s R, Python, or a specialized database system.

This chapter covers following topics:

  1. Data Import/Export to/from R

  2. Data Import and Export with {readr}

  3. Big-Data Imoport/Extort with {data.table}, {Feather} and {Arrow}

  4. Data Read/Write with {googlesheets4}

R Packages for Data Import and Export

R provides a variety of packages for importing and exporting data, each tailored to specific file formats and data sources. The choice of package often depends on the type of data you are working with and the format in which it is stored. Below is a list of commonly used R packages for data import and export:

Below is a list of R packages commonly used for exporting and importing data to and from R:

readr: For importing and exporting rectangular data files, including CSV, TSV, and fixed-width format.

readxl: Imports data from Excel files (both .xls and .xlsx formats).

haven: Imports and exports data from SAS, SPSS, and Stata files.

foreign: Provides functions for reading and writing data from other statistical software formats like SAS, SPSS, and Stata.

data.table: Efficiently handles large datasets and provides functions for importing and exporting data.

feather: A fast, lightweight binary columnar data format. It’s efficient for data interchange between R and other languages.

arrow: Similar to feather, arrow provides tools for high-performance interchange between R and other languages.

googlesheets4: Interact with Google Sheets from R, allowing import/export of data.

jsonlite: Import and export JSON data, commonly used for web-based data.

rjson: Another package for reading and writing JSON data.

These packages cover a wide range of formats and sources for importing and exporting data in R. Depending on your specific requirements and the format of your data, you may find one or more of these packages suitable for your needs.