RMySQL vs jsonlite

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RMySQL		jsonlite
	Project
1	Mentions	1
207	Stars	369
0.5%	Growth	-
4.7	Activity	5.8
5 months ago	Latest Commit	23 days ago
C	Language	C
-	License	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.

RMySQL

Posts with mentions or reviews of RMySQL. We have used some of these posts to build our list of alternatives and similar projects.

How to convert Blob from SQL to string?
1 project | /r/rstats | 20 Feb 2023

Google came up with this, though I can't say it really looks like a likely winner.

jsonlite

Posts with mentions or reviews of jsonlite. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-13.

For fans of Spotify Wrapped, you can download your chronological streaming history for more detailed analysis. No genres or moods, but they provide every song played along with timestamp. [OC]
3 projects | /r/dataisbeautiful | 13 Dec 2021

I read these JSON files with the jsonlite R package and wrangled them using dplyr before plotting with ggplot2.

What are some alternatives?

When comparing RMySQL and jsonlite you can also consider the following projects:

mydumper - Official MyDumper project [Moved to: https://github.com/mydumper/mydumper]

dplyr - dplyr: A grammar of data manipulation

mydumper - Official MyDumper Project

writexl - Portable, light-weight data frame to xlsx exporter for R

Collapse Launcher - An Advanced Launcher for miHoYo Games

ssh - Native SSH client in R based on libssh

catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

qs - Quick serialization of R objects

kunlun - KunlunBase is a distributed relational database management system(RDBMS) with complete NewSQL capabilities and robust transaction ACID guarantees and is compatible with standard SQL. Applications which used PostgreSQL or MySQL can work with KunlunBase as-is without any code change or rebuild because KunlunBase supports both PostgreSQL and MySQL connection protocols and DML SQL grammars. MySQL DBAs can quickly work on a KunlunBase cluster because we use MySQL as storage nodes of KunlunBase. KunlunBase can elastically scale out as needed, and guarantees transaction ACID under error conditions, and KunlunBase fully passes TPC-C, TPC-H and TPC-DS test suites, so it not only support OLTP workloads but also OLAP workloads. Application developers can use KunlunBase to build IT systems that handles terabytes of data, without any effort on their part to implement data sharding, distributed transaction processing, distributed query processing, crash safety, high availability, strong consistency

OHMySQL - Swift + MySQL = ❤️