Welcome to the Correlation Plot App

Summary:

This web application allows you to graph correlation plots to visualize the correlation between variables. In clinical trial data, correlation refers to the statistical relationship between two variables. The correlation coefficient, r, is a measure of linear association. Correlation provides information on direction and strength of linear association.

Please note that data should be explored graphically before estimating the correlation. It is possible to have a high correlation coefficient but have a non-linear relationship. On the other hand, it is possible to have a low correlation coefficient and still have a linear relationship.

Detail and Uses:

The plots and summary statistics provided in this app can be downloaded and used for publications, presentations, or simply for internal reference. This app will take your formatted data and automatically create high quality graphs. The user has the ability to select stylistic choices including axis and plot titles, and download quality.

Disclaimer:

We do not take responsibility for any misuse or misinterpretation of this application or the results produced.

BERD web apps are hosted on Amazon Web Services’ (AWS) HIPAA compliant infrastructure. Data are not permanently stored.

References:

Jonathan McPherson, Alan Dipert and Barbara Borges (2021). shiny: Web Application Framework for R. R package version 1.7.1. https://CRAN.R-project.org/package=shiny

Winston Chang (2021). shinythemes: Themes for Shiny. R package version 1.2.0. https://CRAN.R-project.org/package=shinythemes

Carson Sievert and Joe Cheng (2021). bslib: Custom "Bootstrap" "Sass" Themes for "shiny" and "rmarkdown". R package version 0.3.1.https://CRAN.R-project.org/package=bslib

Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. https://CRAN.R-project.org/package=dplyr

Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. URL http://www.jstatsoft.org/v40/i01/.

Wickham H, Bryan J (2022). _readxl: Read Excel Files_. R package version 1.4.1, <https://CRAN.R-project.org/package=readxl>.

R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Kassambara A (2022). _ggcorrplot: Visualization of a Correlation Matrix using "ggplot2"_. R package version 0.1.4, <https://CRAN.R-project.org/package=ggcorrplot>.

Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, Elberg A, Crowley J (2021). _GGally: Extension to "ggplot2"_. R package version 2.1.2, <https://CRAN.R-project.org/package=GGally>.

Funding:

This app was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Columbia University Irving Medical Center CTSA Grant Number UL1TR001873. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.


Version 1.0
Developed by
Gonghao Liu

Accepted format for upload data include csv, xlsx, and xls.

Description of Variables
Variables on X-axis:

These variables will be used to calculate with the variables on Y-axis for the correlation coefficient or will be used to graph for correlation with the variables on Y-axis. These variables must be numeric variables.

Variables on Y-axis:

These variables will be used to calculate with the variables on X-axis for the correlation coefficient or will be used to graph for correlation with the variables on X-axis. These variables must be numeric variables.

Below is a sample data set that satisfies the above criteria. This data can be downloaded and used as a sample for visualization.

A scatterplot is a graphic representation of the relationship between two continuous variables. Generally, the outcome (dependent) variable is plotted on the y-axis and the predictor (independent) variable is plotted on the x-axis. A scatterplot provides an idea of the overall pattern of data.

Please cite this web application wherever used in published work:

BERD Apps Group. (2023) Visualization Apps [Web application]. Irving Institute for Clinical and Translational Research. Retrieved from https://www.irvinginstitute.columbia.edu/services/visualization-apps [Accessed 2025-04-16 ]

The scatter plot matrix is a graphical display of a combination set of correlation scatter plots. Each block represents a scatter plot of the variable on the x-axis and the variable on the y-axis. In the first block of each row, a density plot has been generated for each variable.

Please cite this web application wherever used in published work:

BERD Apps Group. (2023) Visualization Apps [Web application]. Irving Institute for Clinical and Translational Research. Retrieved from https://www.irvinginstitute.columbia.edu/services/visualization-apps [Accessed 2025-04-16 ]

The correlation matrix is a table that displays the correlation coefficients between a set of variables in a data set. In each cell, the value of the correlation coefficient "r" was calculated for the corresponding variable on the x-axis and the corresponding variable on the y-axis. The correlation coefficient is positive if as one variable’s values increases, the other variable’s values also increase. On the other hand, if as one variable’s values increases the other variable’s values decreases, the coefficient is negative. In terms of the strength of linear association, here is a general interpretation of the correlation coefficient: if r = 0, indicating no linear association; if 0 < |r| <= 0.3, indicating weak linear association; if 0.3 < |r| <= 0.5, indicating medium linear association; if 0.5 < |r| <= 0.7, indicating strong linear association; if 0.7 < |r| < 1, very strong linear association; if |r| = 1, indicating perfect linear association.

Please cite this web application wherever used in published work:

BERD Apps Group. (2023) Visualization Apps [Web application]. Irving Institute for Clinical and Translational Research. Retrieved from https://www.irvinginstitute.columbia.edu/services/visualization-apps [Accessed 2025-04-16 ]