# Automatic resumes of your R-developer portfolio from your R-Universe

**Valerio Gherardi**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

# Hi R-bloggers π

Starting from today, all posts from this blog in the `R`

category will also appear on R-bloggers. I would like to thank Tal for aggregating my blog, and say βhi!β to all R-bloggers readers. Iβm a particle physicist with a passion for R, Statistics and Machine Learning. If you want to find out something more about me, you can take a look at my website, and links therein.

# Introduction

R-universe is a cool initiative from rOpenSci, which allows you to create your own CRAN-like repository. The latter is synced with the GitHub repositories (main or specific branches, or releases) associated to your R packages, so that using an R-universe is a very effortless way to organize and share your personal package ecosystem.

If you want to setup your own R-universe, follow the instructions in this blog post. In this post, I assume that you have created your own R-universe, and show you how to retrieve metadata on your packages using the R-universe API.

# Retrieving packages descriptions from your R-universe API

Once you will have it set up, your R-universe will be available at the URL `your-user-name.r-universe.dev`

. For instance, mine is vgherard.r-universe.dev. From your R-universe home page, you can access the documentation of the API. We will use the command:

GET /stats/descriptions NDJSON stream with data from package DESCRIPTION files.

The JSON stream can be read with `jsonlite`

, as follows:

con <- url("https://vgherard.r-universe.dev/stats/descriptions") pkgs <- jsonlite::stream_in(con) Found 6 records... Imported 6 records. Simplifying...

The result is a dataframe with alll the entries of your packagesβ DESCRIPTION file, e.g.:

pkgs[, c("Package", "Title", "Version")] Package Title 1 r2r R-Object to R-Object Hash Maps 2 kgrams Classical k-gram Language Models 3 scribblr A Notepad Inside RStudio 4 gsample Efficient Weighted Sampling Without Replacement 5 sbo Text Prediction via Stupid Back-Off N-Gram Models 6 fcci Feldman-Cousins Confidence Intervals Version 1 0.1.1.9000 2 0.1.0 3 0.2.0.9000 4 0.1.0 5 0.5.0 6 1.0.0

I use this query on my personal website to automatically generate a resume of the packages available on my R-universe (this is combined with a GitHub Action scheduled workflow which periodically updates the `Code`

section of my website). More precisely, I define an R string `txt`

containing the Markdown code for my resume, and I inline it in R Markdown using the synthax ``r ``

. This is the code I use on my website:

txt <- "" for (i in seq_len(nrow(pkgs))) { txt <- paste0( txt, "### [`", pkgs[i, "Package"], "`](", pkgs[i, "RemoteUrl"], ")", "\n", "[![CRAN status](https://www.r-pkg.org/badges/version/", pkgs[i,"Package"], ")](https://CRAN.R-project.org/package=",pkgs[i, "Package"], ")", "\n\n", "*", pkgs[i, "Title"], ".* ", pkgs[i, "Description"], "\n\n" ) }

and this is the output:

`r2r`

*R-Object to R-Object Hash Maps.* Implementation of hash tables (hash sets and hash maps) in R, featuring arbitrary R objects as keys, arbitrary hash and key-comparison functions, and customizable behaviour upon queries of missing keys.

`kgrams`

*Classical k-gram Language Models.* Tools for training and evaluating k-gram language models in R, supporting several probability smoothing techniques, perplexity computations, random text generation and more.

`scribblr`

*A Notepad Inside RStudio.* A project aware notepad inside RStudio, for taking quick project-related notes without distractions. RStudio addin.

`gsample`

*Efficient Weighted Sampling Without Replacement.* Sample without replacement using the Gumbel-Max trick (c.f. ).

`sbo`

*Text Prediction via Stupid Back-Off N-Gram Models.* Utilities for training and evaluating text predictors based on Stupid Back-Off N-gram models (Brants et al., 2007, https://www.aclweb.org/anthology/D07-1090/).

`fcci`

*Feldman-Cousins Confidence Intervals.* Provides support for building Feldman-Cousins confidence intervals [G. J. Feldman and R. D. Cousins (1998) doi:10.1103/PhysRevD.57.3873].

**leave a comment**for the author, please follow the link and comment on their blog:

**Valerio Gherardi**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.