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Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

The “Final_matrices” excel file contains research output related to the paper “From exports to value added to income: Accounting for bilateral income transfers”. Details on how the data are assembled can be found in the paper and in its online appendix. Replication files (R-files and Matlab-codes) as well as the raw data needed for replication of all empirical results in the paper are available upon request from the author.

A routine was developed in R ('bathy_plots.R') to plot bathymetry data over time during individual CEAMARC events. This is so we can analyse benthic data in relation to habitat, ie. did we trawl over a slope or was the sea floor relatively flat. Note that the depth range in the plots is autoscaled to the data, so a small range in depths appears as a scatetring of points. As long as you look at the depth scale though interpretation will be ok. The R files need a file of bathymetry data in '200708V3_one_minute.csv' which is a file containing a data export from the underway PostgreSQL ship database and 'events.csv' which is a stripped down version of the events export from the ship board events database export. If you wish to run the code again you may need to change the pathnames in the R script to relevant locations. If you have opened the csv files in excel at any stage and the R script gets an error you may need to format the date/time columns as yyyy-mm-dd hh;mm:ss, save and close the file as csv without opening it again and then run the R script. However, all output files are here for every CEAMARC event. Filenames contain a reference to CEAMARC event id. Files are in eps format and can be viewed using Ghostview which is available as a free download on the internet.

This R script file contains the different scripts used to obtain metadata, join it, export it and produce the paper's graphs (except for the taxonomic graph, which was done using data exported from R into the Krona Excel macro template, which can be found on Github). The CSV files needed for this script are in a separate ZIP file.

Background: Malaria remains one of the leading causes of death in Sub-Saharan Africa (SSA). The scoping review mapped evidence in research on existing studies on malaria genome-wide association studies (GWAS) in SSA.Methods: A scoping review was conducted to map existing studies in genome-wide association on malaria in SSA, with a review period between 1st January 2000 and 31st December 2024. The searches were made with the last search done in January 2025. The extracted data were analyzed using R software and SRplot. Relevant studies were identified through electronic searching of Google Scholar, Pubmed, Scopus, and Web of Science databases. Two independent reviewers followed the inclusion-exclusion criteria to extract relevant studies. Data from the studies were collected and synthesized using Excel and Zotero software.Results: We identified 89 studies for inclusion. Most of these studies (n = 42, ) used a case-control study design, while the rest used cross-sectional, cohort, longitudinal, family-based, and experimental study designs. These studies were conducted between 2000 and 2024, with a noticeable increase in publications from 2012. Most studies were carried out in Kenya (n = 23), Gambia (n = 18), Cameroon (n = 15), and Tanzania (n = 9), primarily exploring genetic variants associated with malaria susceptibility, resistance, and severity.Conclusion: Many case-control studies in Kenya and Gambia reported genetic variants in malaria susceptibility, resistance, and severity. GWAS on malaria is scarce in SSA, and even fewer studies are model-based. Consequently, there is a pressing need for more genome-wide research on malaria in SSA.Keywords: Genome-wide association studies, malaria, Sub-Saharan Africa, scoping review.

Using Web of Science and Unpaywall data, we here provide an update of Open Access (OA) levels of Dutch universities, for 2016 and 2017.

Our previous analysis (10.5281/zenodo.1133759 and 10.7287/peerj.preprints.3520v1) looked at OA classification as included in Web of Science (gold and green OA, based on Unpaywall data), and supplemented that with a breakdown of gold OA into pure gold, hybrid and bronze, taken from Unpaywall data (formerly OADOI) directly. Here, we improve on this by running all DOIs retrieved from WoS through Unpaywall data (using their web interface that allows batch checking of up to 10,000 DOIs at a time). Unlike WoS, Unpaywall data itself includes author-submitted versions in their green OA classification, resulting in more complete green OA levels.

In addition, since our initial analysis of December 2017, Unpaywall data has considerably expanded its coverage of institutional repositories (IRs) (see https://unpaywall.org/sources). This now includes coverage of the IRs from all Dutch universities.

Taken together, the current data show higher levels of green open access, including author-submitted versions, compared to our previous analysis.

In this update, we include output (articles and reviews) from 2016 and 2017 for all 14 universities in the Netherlands.

The following categories are distinguished (description taken from Piwowar at al., 2018, doi: 10.7717/peerj.4375)

Pure gold: Published in an open-access journal (as defined by the DOAJ)

Hybrid: Free under an open license in a toll-access journal

Bronze: Free to read on the publisher page, but without a license

Green: Available from an institutional or disciplinary repository (including PubMedCentral)

Data for Dutch universities were collected from Web of Science using the organization-enhanced field. Only articles and reviews were included. DOIs were extracted from the Web of Science export, run through the Unpaywall data Simple Query Tool. From the resulting data from Unpaywall, OA classification was done using a simple formula in Excel (to be replaced by an R script in a future update). The Excel template used is included in this dataset, as is the OADOI API output for each Dutch university's article subset, and the lists of DOIs derived from Web of Science. The dataset also includes summarized data and three charts generated from these data, showing levels of different types of OA for 2016, 2017 and the two years compared.