PapersFlow Research Brief
Data Analysis with R
Research Guide
What is Data Analysis with R?
Data Analysis with R is the practice of using the R programming language and its package ecosystem to import, transform, visualize, and statistically model data to answer scientific or applied research questions.
The research cluster labeled Data Analysis with R contains 224,908 works focused on statistical computing and data analysis using the R language. Widely cited foundations include mixed-effects modeling with "Fitting Linear Mixed-Effects Models Usinglme4" (2015), data visualization with "ggplot2: Elegant Graphics for Data Analysis" (2009) and "ggplot2" (2016), and integrated workflows described in "Welcome to the Tidyverse" (2019). High-impact application areas represented in the most-cited papers include ecology and biological research methods (e.g., "Biometry: The Principles and Practice of Statistics in Biological Research" (1969) and "Mixed effects models and extensions in ecology with R" (2009)) and microbiome analysis tooling ("phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data" (2013)).
Topic Hierarchy
Research Sub-Topics
Linear Mixed-Effects Models in R
This sub-topic develops and applies lme4 and related packages for fitting hierarchical and longitudinal data models in R. Researchers extend methods for hypothesis testing and complex random effects structures.
Data Visualization with ggplot2
Focuses on the grammar of graphics paradigm in ggplot2 for creating layered, publication-quality plots and exploratory visualizations. Studies compare ggplot2 workflows with base R and lattice graphics.
Tidyverse Ecosystem
This area explores data wrangling, transformation, and analysis pipelines using dplyr, tidyr, and purrr within the tidyverse framework. Research evaluates performance and scalability for big data workflows.
Time Series Analysis in R
Covers forecasting models like ARIMA, state-space methods, and packages such as forecast and tsibble for univariate and multivariate time series. Applications span econometrics, climate, and finance.
Meta-Analysis in R
This sub-topic implements metafor and meta packages for fixed/random-effects models, heterogeneity assessment, and publication bias detection. Researchers advance multivariate and network meta-analysis techniques.
Why It Matters
Data Analysis with R matters because it provides concrete, widely adopted methods and software for statistical inference and reproducible analysis in domains where model structure and uncertainty must be handled explicitly. In mixed-effects modeling, "Fitting Linear Mixed-Effects Models Usinglme4" (2015) describes obtaining maximum likelihood or restricted maximum likelihood (REML) estimates for linear mixed-effects models via the lmer function, enabling analyses with hierarchical or grouped data common in experiments and longitudinal studies; its uptake is reflected in 79,986 citations. For inference on fitted mixed models, Kuznetsova et al. (2017) in "lmerTest Package: Tests in Linear Mixed Effects Models" (2017) directly addressed the practical need for p values for F and t tests for lmer outputs, supporting hypothesis testing workflows used in applied research; the paper has 21,510 citations. In microbiome research, McMurdie and Holmes (2013) introduced "phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data" (2013) as an open-source package available via GitHub and Bioconductor, linking data handling and graphics for microbiome census data; the work has 20,741 citations. For synthesis of evidence across studies, Viechtbauer (2010) in "Conducting Meta-Analyses inRwith themetaforPackage" (2010) described fixed- and random-effects meta-analytic models and meta-regression with moderators, supporting quantitative review in fields that rely on aggregating heterogeneous study results; it has 16,896 citations.
Reading Guide
Where to Start
Start with "Welcome to the Tidyverse" (2019) because it provides an explicit entry point to an integrated set of R packages that share common data representations and API design, which helps new readers form a coherent workflow before specializing in modeling or domain packages.
Key Papers Explained
A common progression is workflow → visualization → modeling → inference → synthesis. Wickham (2009) in "ggplot2: Elegant Graphics for Data Analysis" and Wickham (2016) in "ggplot2" anchor visualization practice, while Wickham et al. (2019) in "Welcome to the Tidyverse" frames interoperable packages for data handling and analysis workflows. Bates et al. (2015) in "Fitting Linear Mixed-Effects Models Usinglme4" provides the estimation and model-specification core for linear mixed-effects models in R, and Kuznetsova et al. (2017) in "lmerTest Package: Tests in Linear Mixed Effects Models" layers on practical hypothesis testing for lmer outputs. Viechtbauer (2010) in "Conducting Meta-Analyses inRwith themetaforPackage" extends beyond single-study modeling to cross-study evidence synthesis using fixed/random effects and meta-regression with moderators.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
For advanced work, the provided list points to specialization by domain and model class rather than a single universal pipeline. "phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data" (2013) exemplifies domain-specific package design for complex biological data, while "Mixed effects models and extensions in ecology with R" (2009) represents field-specific modeling practice layered on mixed-effects foundations. A practical frontier for many researchers is integrating mixed-effects estimation (Bates et al., 2015) with formal testing (Kuznetsova et al., 2017) and then carrying results into quantitative synthesis workflows described by Viechtbauer (2010).
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Fitting Linear Mixed-Effects Models Using<b>lme4</b> | 2015 | Journal of Statistical... | 80.0K | ✓ |
| 2 | Biostatistical Analysis | 1996 | Ecology | 35.4K | ✕ |
| 3 | ggplot2 | 2016 | Use R! | 29.6K | ✕ |
| 4 | <b>lmerTest</b> Package: Tests in Linear Mixed Effects Models | 2017 | Journal of Statistical... | 21.5K | ✓ |
| 5 | Biometry: The Principles and Practice of Statistics in Biologi... | 1969 | — | 21.1K | ✕ |
| 6 | phyloseq: An R Package for Reproducible Interactive Analysis a... | 2013 | PLoS ONE | 20.7K | ✓ |
| 7 | ggplot2: Elegant Graphics for Data Analysis | 2009 | — | 20.5K | ✕ |
| 8 | Welcome to the Tidyverse | 2019 | The Journal of Open So... | 19.2K | ✓ |
| 9 | Mixed effects models and extensions in ecology with R | 2009 | Statistics for biology... | 17.2K | ✓ |
| 10 | Conducting Meta-Analyses in<i>R</i>with the<b>metafor</b>Package | 2010 | Journal of Statistical... | 16.9K | ✓ |
In the News
R for Bioinformatics: Analyzing Genetic Data for Breakthrough Discoveries - FasterCapital
5\. Analyzing Sequence Data with R 6\. Machine Learning in Bioinformatics with R 7\. Integrating R with Other Bioinformatics Tools 8\. Breakthrough Discoveries Enabled by R
Research Grants (R series) - NIDCD - NIH
for a subsequent R01 application.
NLM Research Grants in Biomedical Informatics and Data Science (R01 Clinical Trial Optional)
Office of Data Science Strategy ( ODSS ) Funding Opportunity Title NLM Research Grants in Biomedical Informatics and Data Science (R01 Clinical Trial Optional) Activity Code R01 Research Project G...
Theories, Models and Methods for Analysis of Complex Data from the Brain (R01 Clinical Trial Not Allowed)
This notice of funding opportunity (NOFO) seeks applications to developtheories, models and methods (TMM) as tools that will advance a quantitative and predictive understanding of brain function ac...
Autism Data Science Initiative Funding Opportunities
Task II: Data Generation|Must be accompanied by activities under Task III –cannot do Task II alone | Task III: Data Analysis|Can be proposed as a stand-alone activity; or in conjunction with Task I...
Code & Tools
> > A curated list of awesome R frameworks, libraries, tools, and resources for data analysis, statistics, and visualization. > ## Contents * Lib...
## Repository files navigation # Data Analysis in R ### UBC Library Research Commons Link to workshop: https://ubc-library-rc.github.io/data-ana...
This repository contains the source of R for Data Science book. The book is built using Quarto . ## Images ### Omnigraffle drawings * Font: 12p...
The tidyverse is a set of packages that work in harmony because they share common data representations and API design. The**tidyverse**package is d...
caret (Classification And Regression Training) R package that contains misc functions for training and plotting classification and regression model...
Recent Preprints
Data Analysis in Excel and R: A Comparative Evaluation
casting doubt on their suitability in utilizing Excel in higher levels of analysis. More rigorous data analysis R is an ideal programming environment with more complex statistical modeling and enha...
Exploratory Data Analysis (EDA) - R and RStudio in Digital ...
EDA in R is often done using tidyverse packages, especially dplyr for data manipulation and ggplot2 for visualization. You begin by inspecting the structure of the dataset using functions like glim...
Introduction to R for Data Analysis Workshop - LibGuides
**Workshop Description:** R is a free and open-source programming language used for statistical data analysis and data visualization. In this workshop, we'll cover the basics of how to use R to ana...
R - Software for Data Analysis
Orange County has an R User Group . The group explores and discusses R and how it''s being used in data analysis, visualization, data mining, and predictive analytics.
Text Analysis Using R - Guides - University of Pennsylvania
##### A companion to our R/RStudio Libguide , this guide will take you through how to use several text analysis tools using R. R is a statistical programming language that can be used in text analy...
Latest Developments
Recent developments in Data Analysis with R research as of February 2026 include ongoing project ideas and practical applications, such as visualizations of domestic terrorism trends and AI-powered analysis tools like Positron’s Databot, as well as the continued prominence of R in statistical computing, machine learning, and data visualization, supported by frameworks like tidymodels and mlr3 (DataCamp, rfortherestofus.com, carmatec.com, mlr-org.com).
Sources
Frequently Asked Questions
What is Data Analysis with R in practice, and what kinds of tasks does it cover?
Data Analysis with R refers to using R to carry out end-to-end analytical work, including statistical analysis, visualization, and statistical modeling. The provided topic description explicitly includes time series analysis, spatial and geospatial data analysis, machine learning, parallel computing, and big data within this cluster of R-based work.
How do researchers fit linear mixed-effects models in R, and which paper is the standard reference?
"Fitting Linear Mixed-Effects Models Usinglme4" (2015) explains that maximum likelihood or restricted maximum likelihood (REML) estimates for linear mixed-effects model parameters can be obtained using the lmer function in the lme4 package. The paper also emphasizes formula-based model specification in the lmer call, aligning mixed-model setup with common R model-fitting conventions.
How can I obtain p values for tests in linear mixed-effects models fit with lme4?
Kuznetsova et al. (2017) in "lmerTest Package: Tests in Linear Mixed Effects Models" (2017) state that a frequent user question is how to get p values for F and t tests for objects returned by lmer. They describe that lmerTest extends the lme4 'lmerMod' class by overloading anova and summary to provide these tests for mixed-model outputs.
Which R references are most commonly cited for data visualization workflows?
Wickham’s "ggplot2: Elegant Graphics for Data Analysis" (2009) and "ggplot2" (2016) are the most-cited visualization-focused references in the provided list, with 20,536 and 29,569 citations respectively. These works are routinely cited when researchers justify or document grammar-of-graphics plotting in R.
How is meta-analysis conducted in R, and what does the canonical paper claim the package supports?
Viechtbauer (2010) in "Conducting Meta-Analyses inRwith themetaforPackage" (2010) states that the metafor package provides functions for conducting meta-analyses in R. The abstract specifies support for fixed- and random-effects models and the inclusion of moderators (study-level covariates) via meta-regression.
Which paper is a key reference for microbiome data analysis and graphics in R?
McMurdie and Holmes (2013) introduced "phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data" (2013) as an open-source R package available via GitHub and Bioconductor. The work is a central citation for workflows that combine microbiome census data management with interactive analysis and graphics in R.
Open Research Questions
- ? How can inferential procedures for linear mixed-effects models be standardized across R implementations so that p values and test statistics are comparable when using lmer-based workflows, as motivated by "lmerTest Package: Tests in Linear Mixed Effects Models" (2017) and "Fitting Linear Mixed-Effects Models Usinglme4" (2015)?
- ? What methodological guidance best links ecological study design and hierarchical modeling choices to practical mixed-effects implementations in R, building from the applied framing in "Mixed effects models and extensions in ecology with R" (2009) and the estimation approach in "Fitting Linear Mixed-Effects Models Usinglme4" (2015)?
- ? How should microbiome data structures and graphics be organized to maximize reproducibility and interactive exploration in R package form, extending the software and workflow goals stated in "phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data" (2013)?
- ? Which meta-analytic model choices and moderator specifications are most robust in typical applied settings when implemented through the functions described in "Conducting Meta-Analyses inRwith themetaforPackage" (2010)?
Recent Trends
The provided corpus metadata indicates a large, mature area (224,908 works) organized around interoperable R workflows, with highly cited anchors in mixed-effects modeling and visualization.
The most-cited methodological software paper in the list is "Fitting Linear Mixed-Effects Models Usinglme4" with 79,986 citations, and closely related inference support is represented by "lmerTest Package: Tests in Linear Mixed Effects Models" (2017) with 21,510 citations, highlighting sustained emphasis on mixed-model estimation plus hypothesis testing.
2015Visualization and workflow standardization remain prominent via Wickham’s "ggplot2" references ("ggplot2: Elegant Graphics for Data Analysis" , 20,536 citations; "ggplot2" (2016), 29,569 citations) and the integrated package framing in "Welcome to the Tidyverse" (2019) with 19,209 citations, indicating that reusable, consistent analysis pipelines are a central organizing theme in highly cited R-based data analysis literature.
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