Charts

Basic Building Blocks

Points: points, jittered points

Lines: line, path, diagonal line, horizontal line, vertical line, segment, step-function

Areas: area, ribbon

Polygons: polygon, map

Tiles: tiles, rectangles, raster plot

Text: text, label

Examples:

• Population mobility and COVID-19
• Bayesian inference
• Line vs. path
• Inset map of Kotlin island
• Formatting labels on plots
• Labels on charts
• geom_text() features
• Image on map, the 'use_crs' parameter

Discrete

bar, pie, lollipop, boxplot

Examples:

• Bar geometry
• Identity stat
• Pie
• Pie chart on map
• Annotations for pie
• Lollipop plot
• Count stat
• Comparisons
• General purpose stats

• Using scales: continuous, discrete, identity, manual, brewer colors, viridis colors, grey-scale colors

• Dodge position

Ordering Categories, as_discrete()

as_discrete()

Learn more: Function as_discrete().

Examples:

• Guide to ordering
• Discover trends with smoothing

Contours

contours, filled contours

Examples:

• Contours
• How to draw curve fast
• 3d distributions

Visualization of Distribution

histogram, density, dotplot, ydotplot, violin, ridgeline, frequency polygon

Examples:

• Histogram
• Distributions
• 1d distributions
• Dot plots
• Violin plot
• Ridgeline plot
• Netflix movies
• Orientation

• Using scales: continuous, diverging, qualitative

Marginal Plots

ggmarginal

Examples:

• Marginal plots

Visualization of Errors

crossbar, errorbar, linerange, pointrange

Examples:

• Error bars
• Horizontal error bars and vertical 'dodge'
• Comparisons

Smoothing

smoothing line

Examples:

• Simple linear smoothing
• Draw a scatter plot
• Discover trends with smoothing

Bivariate Distribution

2d bins, 2d density, filled 2d density

Examples:

• 2d density
• General purpose stats
• 2d distributions

Time Series

scale_x_datetime(), scale_y_datetime(), scale_x_time(), scale_y_time()

Examples:

• Scale time
• Time series visualizations

Images

geom_imshow(), matrix of images

Examples:

• geom_imshow()
• The 'extent' parameter
• Parameters 'norm', 'vmain', 'vmax' and 'cmap'
• The alpha parameter in geom_imshow()
• NaN values in geom_imshow()
• "Fisher boat": 'geom_imshow()' and 'geom_raster()'
• DEM image on map
• Image on map, the 'use_crs' parameter
• Simple image matrix
• Image matrix

Facets

facet_grid(), facet_wrap()

Examples:

• Facets
• Free scales on a faceted plot
• Population mobility and COVID-19

Coordinate Systems

coord_cartesian(), coord_fixed(), coord_flip(), coord_map()

Examples:

• Coordinate systems
• Flipping coordinates
• Map coordinates

‘bistro’ Plots

Exploratory Data Analysis (EDA) is an open-ended, highly interactive, iterative process, whose actual steps are segments of a stubbily branching, tree-like pattern of possible actions.

Learn more about instruments for EDA in Lets-Plot: ‘bistro’ Plots.

GeoPandas Shapes

GeoPandas GeoDataFrame is supported by the following geometry layers: polygon, map, point, pie, text, path, rect.

Learn more: GeoPandas Support.

Examples:

• Inset map of Kotlin island
• Using Lets-Plot with GeoPandas to Create Maps
• Plotting Airbnb prices Boston
• Spatial prediction of soil pollutants with multi-output Gaussian processes
• DEM image on map
• Using exotic map projections

Grouping Plots

GGBunch and gggrid shows a collection of plots on one figure.

Examples:

• The GGBunch class
• The gggrid() function

Presentation Options

theme(), ggtitle(), ggsize(), xlab(), ylab(), labs(), guide_legend(), guide_colorbar()

Predefined themes:

minimal2, bw, grey, classic, light, minimal, none

Color schemes (flavors):

darcula, solarized light, solarized dark, high contrast light, high contrast dark

Examples:

• Default theme
• Themes overview
• Theme flavors
• Legend and axis
• Multiple lines for legend text
• Tooltip customization
• Comparisons
• Legend guide
• Legend place
• Title, subtitle, caption
• Tooltips theme
• Set font faces
• Panel borders
• Axis position
• Rotation of axis labels

Cookbooks

• Lets-Plot API overview
• Multiple color scales
• Showing quantiles on density plots
• Scale functions with parameter aesthetic
• Stackable position adjustments

Articles

• Picking the Perfect Data Visualization: Line Plots

• Picking the Perfect Data Visualization: Barplots

Examples

Show more

Key Features

ggplot2-like API

A bridge between R (ggplot2) and Python Data visualization.

Customizable Tooltips

You can customize the content, values formatting and appearance of tooltip for any geometry layer in your plot. Learn more.

Suitable for Scientist and Developer

Works in computational notebooks (Jupyter, Datalore, Kaggle, Colab, Deepnote) and in JetBrains professional IDEs - PyCharm.

Formatting

Lets-Plot supports formatting of numeric and date-time values in tooltips, legends, on the axes and text geometry layer. Learn more.

Kotlin API

R, Python, what’s next? Right. Lets-Plot Kotlin API enables data visualization in JVM and Kotlin/JS applications as well as in scientific notebooks like Jupyter and Datalore.

Sampling

Sampling is a special technique of data transformation, which helps to deal with large datasets and overplotting. Learn more.

Geospatial Visualization

Find spatial objects with the help of our powerful and easy to use Geocoding module. In case you already have GeoDataFrame on hand - plot it straight away.

Export to SVG, HTML and PNG

The ggsave() function is an easy way to export plot to a file in SVG, HTML or PNG formats. Learn more.

Interactive Maps

Interactive maps allow zooming and panning around your geospatial data with customizable vector or raster basemaps as a backdrop. Learn more.

‘No Javascript’ and Offline Mode

In the ‘no javascript’ mode Lets-Plot generates plots as bare-bones SVG images. Plots in the notebook with option offline=True will be working without an Internet connection. Learn more.