tidyverse
tidyquery
| tidyverse | tidyquery | |
|---|---|---|
| 2 | 2 | |
| 1,602 | 167 | |
| 0.9% | - | |
| 4.9 | 0.0 | |
| 5 months ago | over 1 year ago | |
| R | R | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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.
tidyverse
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Discrimination of R in companies
What’s the original? Tidyverse, as it exists now, had it’s initial release in 2016. Pandas initial release was in 2009. AFAIK, ggplot2 and reshape are the only individual Tidyverse packages older than that.
- R packages installation error
tidyquery
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Can "dplyr" code automatically be converted to SQL code?
tidyquery
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ClickHouse as an alternative to Elasticsearch for log storage and analysis
> SQL is a perfect language for analytics.
Slightly off topic, but I strongly agree with this statement and wonder why the languages used for a lot of data science work (R, Python) don't have such a strong focus on SQL.
It might just be my brain, but SQL makes so much logical sense as a query language and, with small variances, is used to directly query so many databases.
In R, why learn the data.tables (OK, speed) or dplyr paradigms, when SQL can be easily applied directly to dataframes? There are libraries to support this like sqldf[1], tidyquery[2] and duckdf[3] (author). And I'm sure the situation is similar in Python.
This is not a post against great libraries like data.table and dplyr, which I do use from time to time. It's more of a question about why SQL is not more popular as the query language de jour for data science.
[1] https://cran.r-project.org/web/packages/sqldf/index.html
[2] https://github.com/ianmcook/tidyquery
[3] https://github.com/phillc73/duckdf
What are some alternatives?
targets - Function-oriented Make-like declarative workflows for R
duckdf - 🦆 SQL for R dataframes, with ducks
tidyqpcr - quantitative PCR analysis with the tidyverse
clickhousedb_fdw - PostgreSQL's Foreign Data Wrapper For ClickHouse
janitor - simple tools for data cleaning in R
meilisearch-js-plugins - The search client to use Meilisearch with InstantSearch.
drake - An R-focused pipeline toolkit for reproducibility and high-performance computing
tidyquant - Bringing financial analysis to the tidyverse
tidytext - Text mining using tidy tools :sparkles::page_facing_up::sparkles:
tidylog - Tidylog provides feedback about dplyr and tidyr operations. It provides wrapper functions for the most common functions, such as filter, mutate, select, and group_by, and provides detailed output for joins.
desctable - An R package to produce descriptive and comparative tables
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow