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/

titanic5 Dataset Created by David Beltran del Rio March 2016.

Notes This is the final (for now) version of my update to the Titanic data. I think it’s finally ready for publishing if you’d like. What I did was to strip all the passenger and crew data from the Encyclopedia Titanica (ET) web pages (excluding channel crossing passengers), create a unique ID for each passenger and crew member (Name_ID), then (painstakingly and hopefully 100% correctly) match to your earlier titanic3 dataset, in order to compare the two and to get your sibsp and parch variables. Since the ET is updated occasionally the work put into the ID and matching can be reused and refined later. I did eventually hear back from the ET people, they are willing to make the underlying database available in the future, I have not yet taken them up on it.

The two datasets line up nicely, most of the differences in the newer titanic5 dataset are in the age variable, as I had mentioned before - the new set has less missing ages - 51 missing (vs 263) out of 1309.

I am in the process of refining my analysis of the data as well, based on your comments below and your Regression Modeling Strategies example.

titanic3_wID data can be matched to titanic5 using the Name_ID variable. Tab titanic5 Metadata has the variable descriptions and allowable values for Class and Class/Dept.

A note about the ages - instead of using the add 0.5 trick to indicate estimated birth day / date I have a flag that indicates how the “final” age (Age_F) was arrived at. It’s the Age_F_Code variable - the allowable values are in the Titanic5_metadata tab in the attached excel. The reason for this is that I already had some fractional ages for infants where I had age in months instead of years and I wanted to avoid confusion for 6 month old infants, although I don’t think there are any in the data! Also, I was thinking to make fractional ages or age in days for all passengers for whom I have DoB, but I have not yet done so.

Here’s what the tabs are:

Titanic5_all - all (mostly cleaned) Titanic passenger and crew records Titanic5_work - working dataset, crew removed, unnecessary variables removed - this is the one I import into SAS / R to work on Titanic5_metadata - Variable descriptions and allowable values titanic3_wID - Original Titanic3 dataset with Name_ID added for merging to Titanic5 I have a csv, R dataset, and SAS dataset, but the variable names are an older version, so I won’t send those along for now to avoid confusion.

If it helps send my contact info along to your student in case any questions arise. Gmail address probably best, on weekends for sure: davebdr@gmail.com

The tabs in titanic5.xls are

Titanic5_all Titanic5_passenger (the one to be used for analysis) Titanic5_metadata (used during analysis file creation) Titanic3_wID

Firm-level data from 2009 to 2018 of 34 large gold mines in Developing countries. The data is used to compute the deterministic, dynamic environmental and technical efficiencies of large gold mines in developing countries. Steps to reproduce1. Run the R command to generate dynamic technical and dynamic inefficiencies per every two subsequent period (i.e period t and t+1)2. combine the results files of inefficiencies per period generated in R into a panel (see the Excel files in the results folder)3. Import the excel folder into Stata and generate the final results indicated in the paper.

Data files for the paper: Huston, D.C. et al. 2021. Stable isotope signatures of an acanthocephalan and trematode from the herbivorous marine fish Kyphosus bigibbus (Perciformes: Kyphosidae). Journal of Parasitology. 107(5) 726–730

Includes raw data, .csv files for import of data into R, R script file, and excel spreadsheet file used to create Figure 1.

Dataset Overview: This dataset pertains to the examination results of students who participated in a series of academic assessments at a fictitious educational institution named "University of Exampleville." The assessments were administered across various courses and academic levels, with a focus on evaluating students' performance in general management and domain-specific topics.

Columns: The dataset comprises 12 columns, each representing specific attributes and performance indicators of the students. These columns encompass information such as the students' names (which have been anonymized), their respective universities, academic program names (including BBA and MBA), specializations, the semester of the assessment, the type of examination domain (general management or domain-specific), general management scores (out of 50), domain-specific scores (out of 50), total scores (out of 100), student ranks, and percentiles.

Data Collection: The examination data was collected during a standardized assessment process conducted by the University of Exampleville. The exams were designed to assess students' knowledge and skills in general management and their chosen domain-specific subjects. It involved students from both BBA and MBA programs who were in their final year of study.

Data Format: The dataset is available in a structured format, typically as a CSV file. Each row represents a unique student's performance in the examination, while columns contain specific information about their results and academic details.

Data Usage: This dataset is valuable for analyzing and gaining insights into the academic performance of students pursuing BBA and MBA degrees. It can be used for various purposes, including statistical analysis, performance trend identification, program assessment, and comparison of scores across domains and specializations. Furthermore, it can be employed in predictive modeling or decision-making related to curriculum development and student support.

Data Quality: The dataset has undergone preprocessing and anonymization to protect the privacy of individual students. Nevertheless, it is essential to use the data responsibly and in compliance with relevant data protection regulations when conducting any analysis or research.

Data Format: The exam data is typically provided in a structured format, commonly as a CSV (Comma-Separated Values) file. Each row in the dataset represents a unique student's examination performance, and each column contains specific attributes and scores related to the examination. The CSV format allows for easy import and analysis using various data analysis tools and programming languages like Python, R, or spreadsheet software like Microsoft Excel.

Here's a column-wise description of the dataset:

Name OF THE STUDENT: The full name of the student who took the exam. (Anonymized)

UNIVERSITY: The university where the student is enrolled.

PROGRAM NAME: The name of the academic program in which the student is enrolled (BBA or MBA).

Specialization: If applicable, the specific area of specialization or major that the student has chosen within their program.

Semester: The semester or academic term in which the student took the exam.

Domain: Indicates whether the exam was divided into two parts: general management and domain-specific.

GENERAL MANAGEMENT SCORE (OUT of 50): The score obtained by the student in the general management part of the exam, out of a maximum possible score of 50.

Domain-Specific Score (Out of 50): The score obtained by the student in the domain-specific part of the exam, also out of a maximum possible score of 50.

TOTAL SCORE (OUT of 100): The total score obtained by adding the scores from the general management and domain-specific parts, out of a maximum possible score of 100.

We imported the excel sheet FGD patients’ characteristics into the Stata software for conducting simple descriptive analysis. Therefore, a saved dataset and its do file has been shared with editors and reviewers for their reference. (ZIP)