11  Useful Packages in R

R offers a diverse ecosystem of packages that extend its capabilities in various domains. Below is a curated list of packages categorized by their primary functions, along with brief descriptions and links for further exploration. Tidyverse packages are excluded from this list.

11.0.1 Data Import and Management

  • haven: Simplifies the import and export of data between R and statistical software packages like SPSS, Stata, and SAS, enabling easier data integration in mixed software environments. Learn more.

  • openxlsx Reading and writing excel documents.

  • here: Helps manage file paths in an R project, making it easier to work with files without worrying about the working directory. Learn more. ### Data summaries and missing values

  • skimr: Provides compact and flexible summaries of data which makes it easier to start the data analysis with a clear overview. Learn more.

  • mice: Provides methods for dealing with missing data, specifically through multiple imputation, enhancing the robustness of statistical inferences from incomplete data sets. Learn more.

  • naniar provides principled, tidy ways to summarise, visualise, and manipulate missing data with minimal deviations from the workflows in ggplot2 and tidy data Learn more.

11.0.2 Tables

  • flextable: Provides a flexible way to create beautiful tables for reporting and publication, with advanced functions for formatting and displaying tables in multiple document formats such as HTML, Word, and PDF. Learn more.
  • tinytable: Small but powerful R package to draw beautiful tables in a variety of formats: HTML, LaTeX, Word, PDF, PNG, Markdown, and Typst. The user interface is minimalist and easy to learn, while giving users access to powerful frameworks to create endlessly customizable tables. Learn more.
  • janitor: Offers simple functions for examining and cleaning dirty data, making it easier to reformat data and get quick summaries. Learn more.
  • DT HTML-tables, provides filtering, pagination, sorting, and many other features Learn more.
  • GT HTML, LaTeX, and RTF output formats Learn more.

11.0.3 Model object summaries

  • Broom: Simplifies the process of turning statistical analysis objects from R into tidy format, making it easier to work with model outputs. Learn more.
  • modelsummary: Allows for easy creation of beautiful, customizable tables summarizing statistical models using a wide array of model types. Learn more.
  • parameters: Provides utilities for processing the parameters of various statistical models, aiding in model diagnostics and summaries. Learn more.
  • gtsummary The {gtsummary} package provides an elegant and flexible way to create publication-ready analytical and summary tables using the R programming language. Learn more.
  • sjPlot Collection of plotting and table output functions for data visualization. Learn more
  • marginaleffectsLearn more

11.0.4 Data Manipulation

  • dtracker: A package designed for tracking data changes and manipulations within an R workflow, enhancing data management and reproducibility, creates flow charts of your data manipulations. Learn more.
  • Flowchart a package for drawing participant flow diagrams directly from a dataframe using tidyverse. Learn more
  • data.table: Provides an alternative to R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. Learn more.
  • janitor: Facilitates data cleaning tasks, such as cleaning column names and examining data distributions, making it easier to prepare data for analysis.

11.0.5 Data Visualization

  • patchwork: A package designed to simplify the process of arranging ggplot2 plots into a composite plot, enhancing the presentation of complex data visualizations. Learn more.
  • scales: Provides tools for scaling data for visualization, such as creating custom scales and formatting numbers for human readability. Learn more.
  • ggforce:
  • ggtext:

11.0.6 Colors

Learn more Learn more

11.0.7 Statistical Modeling and Analysis

  • tidymodels: A collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse. Learn more.
  • easystats: A suite of packages that aims to make statistical analysis in R easy, providing a consistent and easy-to-use framework. Learn more.

11.0.8 Miscellaneous

11.0.9 Learning

  • datasciencebox: Videos of tidyverse functions Learn more https://r4ds.hadley.nz/ https://psyteachr.github.io/AITutoR/ https://github.com/Cghlewis/data-wrangling-functions/wiki/File-System?s=09 https://github.com/pgmj/RstudioQuartoIntro?tab=readme- https://tidyr.tidyverse.org/index.html https://bookdown.org/daniel_dauber_io/r4np_book/ https://jessesadler.github.io/notes-tidyverse/ https://www.tidyverse.org/blog/ https://support.posit.co/hc/en-us/articles/200549016-Customizing-the-RStudio-IDE https://docs.posit.co/ide/user/ https://utdata.github.io/rwdir/ https://allisonhorst.com/r-packages-functions

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