congressapi
targets
| congressapi | targets | |
|---|---|---|
| 1 | 10 | |
| 5 | 871 | |
| - | 1.8% | |
| 4.3 | 9.6 | |
| 9 months ago | 8 days ago | |
| R | R | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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.
congressapi
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I'm developing the congressapi package, would love feedback!
Recently, Congress released their official API for data on bills, amendments, congressional sessions, congress people, and more. I've been developing an R client package called congressapi that facilitates easy retrieval and analysis of all that yummy data!
targets
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Advice on Best Practices
Is this it https://github.com/ropensci/targets?
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Does anyone else feel in a tricky spot about their use of R?
I'll chime in with others to say that using targets can help with the memory load as well. If you partition your data adequately (e.g. grouping by subjects), you can take advantage of the way targets maps data so it only loads what it needs to. Moreover, if you use the memory = "transient" option, it will unload objects between steps -- adding a little bit of time overhead but saving you on memory. targets and tidytable together have enabled me to work on pretty sizeable datasets while rarely running into memory issues. In fact, the only time I ran into a data memory hog was because I didn't adequately partition the data across worker nodes.
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What are your favorite R Libraries?
targets
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Is there a better way to update an entire series of scripts?
I highly recommend the holy grail of workflow orchestrators / executors in the R ecosystem: targets.
- The new Drake ropensci targets: Function-oriented Make-like declarative workflows for R {R}
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How do you manage, distribute and schedule jobs written in R?
That said, you might want to check out the ‘targets’ package, which provides a DSL for specifying complex workflow descriptions in R. When repeatedly running the same jobs on changing data, this package helps ensure that only necessary work is performed (suitable intermediate results are reused), and scripts are run reproducibly. This might help with sceduling.
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How do I do something like this as a parallel programming in R?
It may be worth it to put these individual steps into a targets pipeline. targets is designed to support parallelization with future and make it easier to visualize downstream dependencies.
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Tips re: workflow, organization, file hygiene and similar?
Given your requirements, I recommend you check out ‘targets’, which specifically addresses the needs of reusable workflows in R, and it seems like it fits your requirements to a T.
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Your impression of {targets}? (r package)
The targets package is the official successor to Drake, and has the same primary author (Will Landau). He has explained why he created targets, which includes stronger guardrails for users and better UX.
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Data engineering with R?
I use it for ETL. I use targets as the workflow management software, and, like others, have a cron job set up to run nightly builds.
What are some alternatives?
drake - An R-focused pipeline toolkit for reproducibility and high-performance computing
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
shinyjs - 💡 Easily improve the user experience of your Shiny apps in seconds
sf - Simple Features for R
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
crul - R6 based http client for R (for developers)
tidyverse - Easily install and load packages from the tidyverse
fastverse - An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R
targets-tutorial - Short course on the targets R package
dbt - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. [Moved to: https://github.com/dbt-labs/dbt-core]
vim-textobj-sentence - Improving on Vim's native sentence text object and motion