Dataset

This dataset was created by Mustafa Almitamy

Released under Apache 2.0

Contents

Boxplots have become an extremely popular display of distribution summaries for collections of data, especially when we need to visualize summaries for several collections simultaneously. The whiskers in the boxplot show only the extent of the tails for most of the data (with outside values denoted separately); more detailed information about the shape of the tails, such as skewness and “weight” relative to a standard reference distribution, is much better displayed via quantile–quantile (q-q) plots. We incorporate the q-q plot’s tail information into the traditional boxplot by replacing the boxplot’s whiskers with the tails from a q-q plot, and display these tails with confidence bands for the tails that would be expected from the tails of the reference distribution. We describe the construction of the “q-q boxplot” and demonstrate its advantages over earlier proposed boxplot modifications on data from economics and neuroscience, which illustrate the q-q boxplots’ effectiveness in showing important tail behavior especially for large datasets. The package qqboxplot (an extension to the ggplot2 package) is available for the R programming language. Supplementary files for this article are available online.

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

This module utilizes a user-friendly database exploring data selection, box-and-whisker plot, and correlation analysis. It also guides students on how to make a poster of their data and conclusions.

Data table for publication Illus. 6.13. Box plots of diversity scores for building interior and exterior.

This dataset contains 15 minute average mobile unit box plot imagery of CO, NO2, O3, PM10, and SO2 collected during the MILAGRO field project.

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/

RSV box-and-whisker diagram data for the search terms "malnutrition," "frailty," "sarcopenia," and "cachexia" from January 1, 2018 to January 1, 2022. The data is divided before and after the declaration of the COVID-19 pandemic.

Context

Collectively collected some of the Data Sets to practice and understand the business improvements.

Content

You can find number of various attributes and cleaning is the part from where we have to start and then moving onto Visualizations and moving over to Modelling.

Acknowledgements

I would like to thank GOD for Giving me the opportunity to Study ad implement Data Science technology along with colleagues like you who are too contributing in making this world a better place.

Inspiration

Visualizations implemented such as BOX PLOTS/VIOLINS PLOT, best metric to impute data or the appropriate way to approach a data Set and lot more with the help of each other!

Let's Begin!

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Context

Data Visualization

Content

a. Scatter plot

  i. The webapp should allow the user to select genes from datasets and plot 2D scatter plots between 2 variables(expression/copy_number/chronos) for 
    any pair of genes.

  ii. The user should be able to filter and color data points using metadata information available in the file “metadata.csv”.

  iii. The visualization could be interactive - It would be great if the user can hover over the data-points on the plot and get the relevant information (hint - 
    visit https://plotly.com/r/, https://plotly.com/python)

  iv. Here is a quick reference for you. The scatter plot is between chronos score for TTBK2 gene and expression for MORC2 gene with coloring defined by
    Gender/Sex column from the metadata file.

b. Boxplot/violin plot

  i. User should be able to select a gene and a variable (expression / chronos / copy_number) and generate a boxplot to display its distribution across 
   multiple categories as defined by user selected variable (a column from the metadata file)

 ii. Here is an example for your reference where violin plot for CHRONOS score for gene CCL22 is plotted and grouped by ‘Lineage’

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

The box and whisker plots were used to check for the variability between self reports activities and accelerometer blocks of activities

Blockchain data dashboard: Sort box-chart

S4 Table. Box plot and the statistical analysis for the diameters measured for the NCLPs obtained by AFM.

BOX reported $1.61B in Assets for its fiscal quarter ending in September of 2025. Data for BOX - Assets including historical, tables and charts were last updated by Trading Economics this last December in 2025.

% ==================================================================== %
% Data directory for 3D spherical, thermal convection models presented %
% in Fuchs and Becker (2022) with a strain-dependent weakening and %
% hardening rheology following the formulation of %
% Fuchs and Becker (2019). %
% %
% The directory contains an input_files, MATLAB_Scripts, and each %
% model directory with a certain name. %
% input_file Contains all the input files for CitcomS %
% MATLAB_Scripts Contains all the MATLAB scripts required to %
% reproduce the figures in the manuscript %
% $ModelName Contains a MATLAB directory for individual %
% data from each model, %
% a TPR directory for toroidal-poloidal data for %
% each degree, and, %
% a txt_data for data picked from CitcomS models %
% which is then visualized in MATLAB. %
% For the models discussed in the paper, surface %
% maps plots and surface grd-files are available %
% within those directories as well. %
% ==================================================================== %

MATLAB_Scripts directory:
-------------------------
To visualize certain data for each model you can run the script
Analyze_Citcom_Models in the MALTAB_Scripts directory, e.g.,

Analyze_Citcom_Models(Name,PlotParam,PlotParam2,S)

where,
Name is the model name as a string variable,
PlotParam a switch to save (1) or not save (0) the figures,
PlotParam2 a switch to activate (1) or deactivate (0) plotting,
S a switch to define the scaling of the model parameter,
no scaling (0), scaling with the diffusion time scale (1),
or scaling with the overturn time OT (2).

With this script one can visualize all the time-dependent data picked
from the CitcomS models. The CitcomS models can be reproduce with the
input-files given in the input_files directory.

To reproduce the box whisker plots in figures 2, 3, S7, and S8 one
needs to run the script CompStat. This scripts reads in the data
from all models (from the txt_files directory in the $ModelName
directory) and creates a box whisker plot for each model period and
plots them again the average lithospheric damage (gamma_L).

For more details to each MATLAB script see the help comments within the
script.

In case of any questions, do not hesitate to contact me via email:

lukas.fuchs84 at gmail dot com

% ==================================================================== %
% =============================== END ================================ %
% ==================================================================== %

The data used to produce the box plots of the figure 5 are presented in this table

This database studies the performance inconsistency on the biomass HHV ultimate analysis. The research null hypothesis is the consistency in the rank of a biomass HHV model. Fifteen biomass models are trained and tested in four datasets. In each dataset, the rank invariability of these 15 models indicates the performance consistency.

The database includes the datasets and source codes to analyze the performance consistency of the biomass HHV. These datasets are stored in tabular on an excel workbook. The source codes are the biomass HHV machine learning model through the MATLAB Objected Orient Program (OOP). These machine learning models consist of eight regressions, four supervised learnings, and three neural networks.

An excel workbook, "BiomassDataSetUltimate.xlsx," collects the research datasets in six worksheets. The first worksheet, "Ultimate," contains 908 HHV data from 20 pieces of literature. The names of the worksheet column indicate the elements of the ultimate analysis on a % dry basis. The HHV column refers to the higher heating value in MJ/kg. The following worksheet, "Full Residuals," backups the model testing's residuals based on the 20-fold cross-validations. The article (Kijkarncharoensin & Innet, 2021) verifies the performance consistency through these residuals. The other worksheets present the literature datasets implemented to train and test the model performance in many pieces of literature.

A file named "SourceCodeUltimate.rar" collects the MATLAB machine learning models implemented in the article. The list of the folders in this file is the class structure of the machine learning models. These classes extend the features of the original MATLAB's Statistics and Machine Learning Toolbox to support, e.g., the k-fold cross-validation. The MATLAB script, name "runStudyUltimate.m," is the article's main program to analyze the performance consistency of the biomass HHV model through the ultimate analysis. The script instantly loads the datasets from the excel workbook and automatically fits the biomass model through the OOP classes.

The first section of the MATLAB script generates the most accurate model by optimizing the model's higher parameters. It takes a few hours for the first run to train the machine learning model via the trial and error process. The trained models can be saved in MATLAB .mat file and loaded back to the MATLAB workspace. The remaining script, separated by the script section break, performs the residual analysis to inspect the performance consistency. Furthermore, the figure of the biomass data in the 3D scatter plot, and the box plots of the prediction residuals are exhibited. Finally, the interpretations of these results are examined in the author's article.

Reference : Kijkarncharoensin, A., & Innet, S. (2022). Performance inconsistency of the Biomass Higher Heating Value (HHV) Models derived from Ultimate Analysis [Manuscript in preparation]. University of the Thai Chamber of Commerce.

Script graphs box plots of DBI scores for all metro areas, grouping by year and metropolitan area population size (larger or smaller than 250,000 people). Additional scripts create different graphs. Data are provided in both "long" and "tall" formats.