Data visualization, bar naked: A free tool for creating interactive graphics

Although bar graphs are designed for categorical data, they are routinely used to present continuous data in studies that have small sample sizes. This presentation is problematic, as many data distributions can lead to the same bar graph, and the actual data may suggest different conclusions from t...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of biological chemistry 2017-12, Vol.292 (50), p.20592-20598
Main Authors: Weissgerber, Tracey L., Savic, Marko, Winham, Stacey J., Stanisavljevic, Dejana, Garovic, Vesna D., Milic, Natasa M.
Format: Article
Language:English
Subjects:
Animals
bar graphs
Biomedical Research - ethics
Biomedical Research - methods
Biomedical Research - standards
Cluster Analysis
Computer Graphics - standards
data visualization
Electronic Data Processing
Humans
interactive graphics
Internet
Methods and Resources
Models, Statistical
open science
Periodicals as Topic
Reproducibility of Results
Sample Size
Scientific Misconduct - ethics
Statistical Distributions
transparency
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Although bar graphs are designed for categorical data, they are routinely used to present continuous data in studies that have small sample sizes. This presentation is problematic, as many data distributions can lead to the same bar graph, and the actual data may suggest different conclusions from the summary statistics. To address this problem, many journals have implemented new policies that require authors to show the data distribution. This paper introduces a free, web-based tool for creating an interactive alternative to the bar graph (http://statistika.mfub.bg.ac.rs/interactive-dotplot/). This tool allows authors with no programming expertise to create customized interactive graphics, including univariate scatterplots, box plots, and violin plots, for comparing values of a continuous variable across different study groups. Individual data points may be overlaid on the graphs. Additional features facilitate visualization of subgroups or clusters of non-independent data. A second tool enables authors to create interactive graphics from data obtained with repeated independent experiments (http://statistika.mfub.bg.ac.rs/interactive-repeated-experiments-dotplot/). These tools are designed to encourage exploration and critical evaluation of the data behind the summary statistics and may be valuable for promoting transparency, reproducibility, and open science in basic biomedical research.
ISSN:0021-9258
1083-351X
DOI:10.1074/jbc.RA117.000147