---
title: "readr (R package): papers citing this CRAN package"
description: "5 papers in the tracked journals link to readr (R package) in their Crossref records, the first in 2018; most of them (2) in 2020."
url: https://scholar4ai.com/code/cran-readr
markdown_url: https://scholar4ai.com/code/cran-readr.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "5 papers in the tracked journals link to readr (R package) in their Crossref records, the first in 2018; most of them (2) in 2020. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# readr (R package): papers linking to it

Research papers in the 480 tracked journals that link to readr (R package) (https://CRAN.R-project.org/package=readr), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- CRAN
- https://CRAN.R-project.org/package=readr

All repositories: https://scholar4ai.com/code.md

## Key findings

- 5 papers in the tracked journals link to readr (R package) in their Crossref records, the first in 2018; most of them (2) in 2020.
- By category: AI (3), Marketing (1) and Management (1); the journals with the most are Journal of Marketing Analytics (1), Neural Computing and Applications (1) and Data Mining and Knowledge Discovery (1).

## By year

*Papers linking to readr (R package) by publication year*

| Year | Papers |
|---|---:|
| 2018 | 1 |
| 2020 | 2 |
| 2023 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to readr (R package) most*

| Journal | Papers |
|---|---:|
| [Journal of Marketing Analytics](https://scholar4ai.com/journals/journal-of-marketing-analytics.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 1 |
| [The Leadership Quarterly](https://scholar4ai.com/journals/the-leadership-quarterly.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |

## Papers

*Papers linking to readr (R package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-07-06 | [Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms](https://doi.org/10.1007/s00521-020-05172-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis | 177 | cites it |
| 2018-12-27 | [Inducing non-orthogonal and non-linear decision boundaries in decision trees via interactive basis functions](https://doi.org/10.1016/j.eswa.2018.12.041) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Antonio Páez, Fernando A. López Hernández, Manuel Ruiz Marín et al. | 20 | cites it |
| 2025-04-24 | [Does expressing uncertainty help or harm leaders?](https://doi.org/10.1016/j.leaqua.2025.101880) | [The Leadership Quarterly](https://scholar4ai.com/journals/the-leadership-quarterly.md) | Shilaan Alzahawi, Francis J. Flynn | 9 | cites it |
| 2023-11-03 | [Marketing analytics with RStudio: a software review](https://doi.org/10.1057/s41270-023-00264-0) | [Journal of Marketing Analytics](https://scholar4ai.com/journals/journal-of-marketing-analytics.md) | David Dege, Philipp Brüggemann | 8 | cites it |
| 2020-03-16 | [ptype: probabilistic type inference](https://doi.org/10.1007/s10618-020-00680-1) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Taha Ceritli, Christopher K. I. Williams, James Geddes | 5 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). readr (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-readr

```bibtex
@misc{scholar4ai-code-cran-readr,
  author = {{Scholar4AI}},
  title = {readr (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-readr}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).