ggplot2 VS dplyr

Compare ggplot2 vs dplyr and see what are their differences.

ggplot2

An implementation of the Grammar of Graphics in R (by tidyverse)
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ggplot2 dplyr
62 40
6,316 4,652
1.2% 0.7%
9.4 7.4
5 days ago 20 days ago
R R
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ggplot2

Posts with mentions or reviews of ggplot2. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-02.

dplyr

Posts with mentions or reviews of dplyr. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-15.

What are some alternatives?

When comparing ggplot2 and dplyr you can also consider the following projects:

Altair - Declarative statistical visualization library for Python

worldfootballR - A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat and fotmob

tmap - R package for thematic maps

Rustler - Safe Rust bridge for creating Erlang NIF functions

vega - A visualization grammar.

nx - Multi-dimensional arrays (tensors) and numerical definitions for Elixir

explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir

glue - Glue strings to data in R. Small, fast, dependency free interpreted string literals.

rmarkdown - Dynamic Documents for R

deneb - Deneb is a custom visual for Microsoft Power BI, which allows developers to use the declarative JSON syntax of the Vega or Vega-Lite languages to create their own data visualizations.

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more