Data Visualization Packages For Latex: Pgfplots, Gnuplot, R, And More

The Problem of Data Visualization in LaTeX

LaTeX offers unparalleled typesetting capabilities, but creating high-quality data visualizations poses unique challenges. Unlike graphical programs like Excel or dedicated visualization software, LaTeX lacks built-in tools for plotting charts or graphs. This forces users to utilize special packages and write custom code to visualize quantitative data.

Common pain points for LaTeX users looking to render visualizations include the following:

  • Steep learning curve for visualization packages
  • Compilation errors when using multiple packages
  • Limited built-in chart types and customization options
  • Difficulty ensuring consistent style and appearance across graphs
  • Rendering quality issues with bitmap graphics in vector output

In the sections below, we survey some of the most popular and full-featured data visualization packages for LaTeX. With the right tools and coding techniques, LaTeX can produce publication-quality data graphics alongside its professional text and mathematical typesetting.

Powerful Data Visualization Packages

Pgfplots - Flexible and Full-Featured Plotting Package

The Pgfplots package is likely the most versatile, customizable, and highest-quality data visualization solution for LaTeX. Built on top of the PGF/TikZ graphics system, Pgfplots offers advanced capabilities like:

  • Line plots, scatter plots, bar charts, pie charts, and more
  • Extensive options for axes, legends, labels, ticks, and gridlines
  • Multiplot split frames and superimposed plots
  • 3D and contour visualizations
  • PicTeX and LuaLaTeX support
  • Export animations and interactive charts

Below demonstrates creating a basic 2D line chart with Pgfplots by plotting a sine wave:

    \addplot {sin(deg(x))};

Gnuplot - Popular Package for Scientific Visualizations

Gnuplot is another common choice for rendering visualizations from LaTeX code. It focuses on 2D and 3D scientific and mathematical charts like:

  • Functions
  • Data sets
  • Parametric, polar, and contour plots
  • Histograms
  • Heatmaps and image plots

To utilize Gnuplot, LaTeX documents must interface with an external installation of the Gnuplot plotting program. Below shows a basic example:

  plot [x=-10:10] sin(x)  

Integrating R and LaTeX

R is the world's most popular programming language for statistical analysis and data science. Luckily, multiple packages exist to render R data visualizations directly within LaTeX:

  • Sweave - Insert and run R code chunks
  • knitr - More advanced R integration
  • rmarkdown - Output PDF/LaTeX reports from R notebooks

Below uses knitr and R to plot a sample chart:


The knitr package replaces the R plot with the resulting image in the final LaTeX output. This enables incorporating dynamic data analysis and visualizations done in R directly alongside LaTeX text and equations.

Advanced Visualization Techniques

In additional to basic 2D charts, some LaTeX visualization packages support more advanced plot types like:

3D Plots with Pgfplots

Pgfplots can generate 3D surface and contour plots from plotted mathematical functions or data points. For example:

    \addplot3 [surf] {exp(-x^2-y^2)};

Interactive Charts and Graphs

Using LaTeX with HTML output or specialized packages like arara enables creating interactive JavaScript or D3 visualizations.

Animations and Video Exports

Pgfplots and others allow programmatically animating plots across slideshow frames or directly exporting the output as embedded videos.

Customizing Axes, Legends, and Styles

Consistent, publication-quality visualizations require fine-grained control over styling and customization options. Packages like Pfgplots provide settings for attributes like:

  • Font choices for text labels
  • Line and marker styles
  • Legend appearance and location
  • Tick mark intervals
  • Grid line styles
  • Plot colors, fills, and transparencies

For example, to format the axes and legend in a Pgfplots line chart:

  xlabel= {Time},
  legend style={at={(0.5,-0.2)}, anchor=north},
  grid style={dashed, gray!50}
\addplot {sin(x)} 
node[above right] {sine wave};  

This degree of customizability ensures visualizations share stylistic conventions across figures in publications typeset with LaTeX.

Troubleshooting Common Issues

Some common pitfalls when rendering graphics from LaTeX include:

Debugging Compilation Errors

Bugs in visualization code can lead to cryptic LaTeX compiler errors. Online resources like StackExchange provide solutions to many common issues.

Improving Graphic Image Quality

Poor quality raster graphics or font rendering issues arise at times. Troubleshooting output settings, graphic file types, and LaTeX compiler choice can help.

FAQs and Solutions for Charting Problems

Consult documentation and forums to find fixes for problems like missing plot data, distorted axes, label overlaps, and more.

Next Steps and Additional Resources

To continue enhancing LaTeX data visualization skills, refer to the following helpful links:

  • CTAN - Central LaTeX package repository
  • Pgfplots - Download and documentation
  • Gnuplot - Official website and docs
  • The R Project - Open-source statistical computing

Some ideas for expanding knowledge include learning interactive JavaScript charting, linking LaTeX with databases to visualize query results, or exporting LaTeX charts for use in presentations.

Leave a Reply

Your email address will not be published. Required fields are marked *