Unlock the potential of R for Excel file management with our curated list of the top R packages tailored for reading and writing XLS and XLSX file formats. Whether handling extensive datasets or complex spreadsheets, these packages are designed to streamline data operations, allowing seamless extraction, manipulation, and exportation of Excel files. Join us as we explore the tools that revolutionize Excel file management in R.
Here’s our roundup of the 34 best R packages for reading and writing Excel files:
1. The basictabler Package
basictabler is an R package that provides tools for creating tables. It includes functions for adding rows and columns, formatting cells, and exporting tables to various formats.
If you’re seeking detailed insights into the basictabler package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the basictabler package: Christopher Bailiss <[email protected]>.
2. The bulkreadr Package
bulkreadr is an R package that provides a set of tools for reading large amounts of text data into R. This can be useful in natural language processing and text mining.
If you’re seeking detailed insights into the bulkreadr package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the bulkreadr package: Ezekiel Ogundepo <[email protected]>.
3. The condformat Package
condformat is an R package that provides tools for conditional formatting in data frames. It includes functions for defining rules, applying formatting, and displaying results in various formats.
If you’re seeking detailed insights into the condformat package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the condformat package: Sergio Oller Moreno <[email protected]>.
4. The DataClean Package
DataClean is a package that provides functions for cleaning and preprocessing data in R. It supports various types of cleaning operations, including handling missing values, removing outliers, and correcting inconsistent data.
If you’re seeking detailed insights into the DataClean package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the DataClean package: Xiaorui(Jeremy) Zhu <[email protected]>.
5. The DataLoader Package
DataLoader is a package that provides functions for loading and pre-processing data in R. It supports various types of data sources, including databases, text files, and web APIs, and includes features for handling missing values, recoding variables, and transforming data.
If you’re seeking detailed insights into the DataLoader package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the DataLoader package: Srivenkatesh Gandhi <[email protected]>.
6. The excel.link Package
excel.link is an R package that provides an interface between R and Excel. It includes functions for reading and writing data, executing R code from Excel, and calling Excel functions from R.
If you’re seeking detailed insights into the excel.link package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the excel.link package: Gregory Demin <[email protected]>.
7. The exceldata Package
exceldata is an R package that provides tools for importing and exporting data between R and Excel. It supports various data types, and it includes functions for reading and writing worksheets, handling cell styles, and managing workbook properties.
If you’re seeking detailed insights into the exceldata package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the exceldata package: Lisa Avery <[email protected]>.
8. The ExcelFunctionsR Package
ExcelFunctionsR is an R package that provides a set of functions in R that mimic Excel functions, making it easier for Excel users to transition to R. It includes functions for basic calculations, text manipulation, date and time handling, and financial calculations.
If you’re seeking detailed insights into the ExcelFunctionsR package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the ExcelFunctionsR package: Nika Salia <[email protected]>.
9. The excelR Package
excelR is an R package that provides an interface to the ‘xlsx’ JavaScript library, allowing for interactive manipulation of Excel files in R. The package includes functions for reading and writing data, formatting cells, and creating formulas.
If you’re seeking detailed insights into the excelR package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the excelR package: Swechhya Bista <[email protected]>.
10. The janitor Package
The janitor package provides functions for cleaning and formatting data in R. It offers tools for handling missing values, converting data types, renaming variables, and performing other data cleaning tasks.
If you’re seeking detailed insights into the janitor package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the janitor package: Sam Firke <[email protected]>.
11. The joinXL Package
The joinXL package in R is designed to facilitate the joining and merging of Excel files in R, providing a toolset that makes it possible to perform complex data manipulation tasks with Excel files directly in R. It offers capabilities to join Excel sheets based on common columns, similar to how SQL queries work, making it a useful tool for data cleaning and preparation.
If you’re seeking detailed insights into the joinXL package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the joinXL package: Yvonne Glanville <[email protected]>.
12. The knitxl Package
The knitxl package is designed to allow the knitr package to work with Excel spreadsheets, by providing a mechanism to read and write Excel files from R. It provides a bridge between the knitr and the xlsx packages.
If you’re seeking detailed insights into the knitxl package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the knitxl package: Denis Dreano <[email protected]>.
13. The modgetxl Package
The modgetxl package in R is a tool for getting data from Excel files. It simplifies the process of importing data into R from Excel, offering functionality like reading specific ranges and handling missing data.
If you’re seeking detailed insights into the modgetxl package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the modgetxl package: Yadu Balehosur <[email protected]>.
14. The openxlsx Package
The openxlsx package offers tools for reading, writing, and editing Excel files in R. It provides functionalities that allow for a high level of customization without the need for Java.
If you’re seeking detailed insights into the openxlsx package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the openxlsx package: Philipp Schauberger <[email protected]>.
15. The openxlsx2 Package
The openxlsx2 package in R is a comprehensive solution for dealing with Excel files in R. It allows users to read, write, and format Excel worksheets, giving users full control over cell content and styling.
If you’re seeking detailed insights into the openxlsx2 package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the openxlsx2 package: Jan Marvin Garbuszus <[email protected]>.
16. The pdftables Package
The pdftables package is an R interface to the PDFTables API. This package allows users to convert PDF tables into Excel, CSV or XML format, easing the process of extracting tabular data from PDF files.
If you’re seeking detailed insights into the pdftables package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the pdftables package: Eric Persson <[email protected]>.
17. The pivottabler Package
The pivottabler package is a powerful tool in R for creating pivot tables. It allows users to easily summarize and explore data, providing a high level of customization to suit various analysis needs.
If you’re seeking detailed insights into the pivottabler package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the pivottabler package: Christopher Bailiss <[email protected]>.
18. The readOffice Package
The readOffice package is a handy R tool designed for reading text out of modern (xml-based) MS Office files. It enables the easy extraction of text from Word, PowerPoint, and Excel files.
If you’re seeking detailed insights into the readOffice package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the readOffice package: Mark Ewing <[email protected]>.
19. The readtext Package
The readtext package simplifies the process of importing and handling textual data in R. It offers flexible ways to import text from multiple file formats under several directory structures, which proves useful in text mining and text analysis.
If you’re seeking detailed insights into the readtext package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the readtext package: Kenneth Benoit <[email protected]>.
20. The readxl Package
The readxl package provides tools to read Excel files (.xls and .xlsx) into R. It simplifies the process of importing Excel data, catering to a wide range of users including data analysts, statisticians, and scientists.
If you’re seeking detailed insights into the readxl package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the readxl package: Jennifer Bryan <[email protected]>.
21. The readxlsb Package
The readxlsb package in R is designed to read Excel Binary (.xlsb) files. It offers a straightforward method to import and handle Excel Binary data, which is particularly useful when dealing with larger and more complex datasets that can’t be efficiently stored in standard Excel files.
If you’re seeking detailed insights into the readxlsb package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the readxlsb package: Michael Allen <[email protected]>.
22. The SheetReader Package
The SheetReader package allows users to read Google Sheets directly into R, providing a useful tool for data analysts who frequently use Google’s software suite.
If you’re seeking detailed insights into the SheetReader package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the SheetReader package: Felix Henze <[email protected]>.
23. The shinypivottabler Package
The shinypivottabler package allows developers to create pivot tables within shiny applications. It offers a highly customizable and interactive interface that enables users to summarize and analyze data effectively.
If you’re seeking detailed insights into the shinypivottabler package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the shinypivottabler package: Benoit Thieurmel <[email protected]>.
24. The tablaxlsx Package
The tablaxlsx package provides functionality to read and write Excel files while preserving tabular data semantics. This makes it easy to handle Excel data in the R environment, effectively linking spreadsheet software and data analysis.
If you’re seeking detailed insights into the tablaxlsx package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the tablaxlsx package: Jesus Maria Rodriguez Rodriguez <[email protected]>.
25. The tablexlsx Package
The tablexlsx package provides a lightweight, fast, and flexible way to write data frames or tibbles to Excel worksheets, without the need for Java or other dependencies.
If you’re seeking detailed insights into the tablexlsx package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the tablexlsx package: Damien Dotta <[email protected]>.
26. The tidyxl Package
The tidyxl package is designed to import Excel files into R. Unlike other Excel-reading packages, it preserves the structure and metadata of the original spreadsheet, which can be useful for dealing with complex or poorly structured spreadsheets.
If you’re seeking detailed insights into the tidyxl package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the tidyxl package: Duncan Garmonsway <[email protected]>.
27. The writexl Package
The writexl package is a lightweight, no-dependency package that can write Excel files in R. It supports writing of both .xlsx and .xls files and does not require any external dependencies, making it faster and more efficient.
If you’re seeking detailed insights into the writexl package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the writexl package: Jeroen Ooms <[email protected]>.
28. The WriteXLS Package
The WriteXLS package in R allows users to quickly and easily write data from R to Excel, without requiring any installation of Excel. It supports writing of multiple named sheets to an Excel file.
If you’re seeking detailed insights into the WriteXLS package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the WriteXLS package: Marc Schwartz <[email protected]>.
29. The XLConnect Package
The XLConnect package is a comprehensive and platform-independent R package for manipulating Microsoft Excel files from within R. It allows the reading, writing and manipulation of Excel files, supporting both .xls and .xlsx file formats.
If you’re seeking detailed insights into the XLConnect package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the XLConnect package: Martin Studer <[email protected]>.
30. The xlcutter Package
The xlcutter package provides functions to split Excel files based on their content. It is particularly useful when dealing with large Excel files that need to be divided into smaller, manageable chunks.
If you’re seeking detailed insights into the xlcutter package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the xlcutter package: Hugo Gruson <[email protected]>.
31. The xlsx Package
The xlsx package is another R library that facilitates interaction with Excel files. It provides comprehensive functionality for reading and writing .xlsx files, ensuring seamless integration of Excel data in R-based analyses.
If you’re seeking detailed insights into the xlsx package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the xlsx package: Cole Arendt <[email protected]>.
32. The xlsx2dfs Package
The xlsx2dfs package provides functions for converting Excel files to data frames in R. This enables easier manipulation and analysis of Excel data within the familiar and powerful data frame structure of R.
If you’re seeking detailed insights into the xlsx2dfs package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the xlsx2dfs package: Gwang-Jin Kim <[email protected]>.
33. The xlsxjars Package
The xlsxjars package is a part of the larger rJava package in R and is used as a dependency for the xlsx package. It provides the necessary Java binaries for the xlsx package to read, write, and manipulate Excel files.
If you’re seeking detailed insights into the xlsxjars package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the xlsxjars package: Adrian A. Dragulescu <[email protected]>.
34. The xmlconvert Package
The xmlconvert package provides functions for converting data between XML and other data formats within R. It supports conversion to and from data frames, lists, and matrices, providing a versatile tool for XML data interoperability.
If you’re seeking detailed insights into the xmlconvert package’s functionalities, you can review our comprehensive reference guide. For getting started with the package, check out our beginner’s guide.
You can also contact the maintainer of the xmlconvert package: Joachim Zuckarelli <[email protected]>.