Most basic

Most basic stacked area chart you can build with R and ggplot2, using the geom_area function.

Small multiple

Small multiple is probably the best alternative, making obvious the evolution of each gropup.

Most basic

Most basic area chart you can build in base R using the polygon function.

Most basic barplot

The most basic barplot you can do with geom_bar(), using default settings.

Custom color

A few examples showing how to custom barplot color.

Horizontal barchart

It makes sense to make your barchart horizontal: group labels are now much easier to read

Bar width

You can control bar width using the width argument of geom_bar()

Control group order

Reordering categories in the barchart is a crucial step for an insightful figure: learn how to do it.

Variable width

A barplot with variable width to represent categories sample size.

Error bars

How to add error bars on a barplot, and why should be careful about it.

Most basic

Most basic usage of the barplot() function.

Control color

How to control barplot color, how to pick a nice color palette.

General customization

Title, axis labels, axis limits, and more.

Horizontal barplot

A horizontal version of the barplot, thanks to the horiz argument.

Bar width

Play with bar width and space between bars.

Bar texture

Change bar texture with the density and angle parameters of the barplot function.

Axis features

Change font, color and size of axis labels and titles

Increase margin

Increase margin size with the mar argument of the par function

Add error bars

Add error bars on barplot to show confidence interval or standard deviation

Number of obs

Add number of observation on top of barplot, and other customization

Parameters reminder

A cheatsheet to quickly reminder what option to use with what value to customize your chart.

Base R margins

A post dedicated to margin management in base R.

Increase margin

Increase margin size with the mar argument of the par function

Basic barplot

The basic barplot hides information: how does the underlying distribution look like? What are the category sample sizes?

Add individual observation

See how low group C sample size actually is?

Violin plot

See group B? It would be a shame to miss out this bimodal distribution.

Most basic boxplot

The most basic boxplot you do using ggplot2.

geom_boxplot() options

An overview of the boxplot options offered by ggplot2 to custom chart appearance.

Control group order

Changing group order in a boxplot is a crucial step. Learn why and discover 3 methods to do so.

Control color

Several examples showing most usual color customization: uniform, discrete, using colorBrewer, Viridis and more.

Most basic histogram 2d

Most basic histogram 2d using the geom_bin2d() function of ggplot2

Color and bin size

Learn how to customize the color and the bin size of your 2d histogram

Most basic

Most basic, default parameters

change theme

Use the theme_bw() for better appearance

Color and bin size

Learn how to customize the color and the bin size of your hexbin chart

Hexbin package

Build a hexbin chart with the hexbin package and color it with RColorBrewer

Scatter on top of 2d distribution

Add a scatterplot on top of the ggplot2 2d density chart

3D knot

A 3D knot to illustrate what is possible to do with rgl.

Nifty graph

A contribution by Matt Asher.

Scatter / bubble

Check how to animate a bubble or scatterplot to visualize evolution over time.

Small multiple

Any customization offered by ggplot2 can be used in gganimate. Here is an illustration using small multiple.

Barplot transition

A transition between 2 states in a barplot, smoothly.

Line apparition

A line chart progressively displayed.

Most basic

A Count-down animation made with R and Image Magick.

Most basic

Most basic area chart with R and ggplot2

Improve appearance

Use a theme, highlight top line, add points if needed, and more options.

Log transform

How to use a log transformation for the Y axis with scale_x_log10().

Highlight a group

Learn how to highlight a group on your chart to convey your message more efficiently.

Grouped boxplot

A grouped boxplot displays the distribution of several categories organized in groups and subgroups

Faceting in boxplot

An alternative to grouped boxplot where each group or each subgroup is displayed in a distinct panel.

Variable width

It is possible to make the box widths proportionnal to category sample size.

Boxplot from continuous variable.

how to build a boxplot with ggplot2 where categories are actually bins of a numeric variable.

Add mean value.

Explaines how to add mean value on top of boxplot. (remember boxplot displays the median, not the mean).

Add individual observation

Boxplot downside is to hide information. You can reveal box underlying distribution showing individual observations with jitter.

Marginal distribution

Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function.

Boxplot on top of histogram

How to add a boxplot on top of a histogram.

Boxplot with custom colors

Color specific groups in this base R boxplot using ifelse statement.

X axis labels on several lines

How to display the X axis labels on several lines: an application to boxplot to show sample size of each group.

Boxplot with jitter

Show individual observations on top of boxes, with jittering to avoid dot overlap.

Order categories by median

Learn how to reorder categories using increasing median.

Boxplot with specific order

Learn how to reorder categories with a specific order.

Grouped and ordered boxplot

How to build a grouped boxplot (groups & subgroups) and order it by increasing median.

Boxplot with variable width

Make box size proportional to category sample size.

Boxplot with labels on top

Add labels on top of each category to display custom information like category sample size.

Tukey test

Tukey test compares the mean of all pairs of category. Here is how to perform it and represent its result on a boxplot.

Box type around plot

Learn how the bty argument of the par() function allows to custom the box around base R plot.

Split plot window with layout()

Layout() divides the device up into as many rows and columns as need, with custom proportions.

Basic bubble chart

The most basic bubble chart you can build with R and gglot2 with geom_point()

Control bubble size

Control bubble size with scale_size(): allows to set minimum and maximum size.

Improve appearance

Make your chart pretty with nice color scale, general theme, stroke around cirle and more..

Add Markers

Learn how to add a marker on a map with leaflet. Circle, rectangle, pointer and more. Link it to tooltip.

Bubble map

Lear to map the marker size to a numeric value, resulting in a bubble map.

Interactive legend

You can add tags to marker and build an interactive legend that allows to toggle their visibility.

Cartography pkg

Check the cartography package to build great maps in minutes with R.

Most basic map

Before building any cartogram, you need to understand how to build a basic map background. Here is how.

Most basic cartogram

Learn how to distort region shape using the cartogram package.

Switch to choropleth

Next step is to color each region according to their value, on top changing their size.

Basic Hexbin Map

Start with an usual hexbin map.

Apply Cartogram method

Apply a cartogram algorithm to distort hexagon size according to a numeric value.

Customization

Customize the previous chart: legend, color palette, title, state name and more..

Data from the package

The cartography comes with a set of geospatial data included. Learn how to use it to build a choropleth map.

Data from the package

The cartography comes with a set of geospatial data included. Learn how to use it to build a choropleth map.

Data from the package

The cartography comes with a set of geospatial data included. Learn how to use it to build a choropleth map.

Most basic

The most basic circular packing you can build..

Color customization

Change colors, map the palette to a variable, customize background and more.

Space between bubbles

Add space between bubbles by reducing bubble size according to a constant.

Most basic

The most basic circular packing you can build with R and ggraph

Add color

Learn how to map the bubble color to the hierarchy level

Add labels

Add labels to nodes to give more insight to the chart

Hide levels

Hide some levels of the hierarchy for a more stylish chart

Specific labels

Add labels to specific levels of the hierarchy

Most basic

The most basic circular barchart you can build, to illustrate how coord_polar() works

Add labels

Add labels with geom_text(). Tricky, since you have to compute label angles first.

Add a gap

Lear how to add a gap in the circle. The same technique will then be used to add gaps between groups

Gap between groups

The space between groups allows to highlight them.

Order

As usual, it makes sense to order bars of each group to get the ranking more easily

Customization

Add scale, group names and more

Basic usage

Basic usage to build a 5 color long palette.

Longer palette

Get a longer color palette from RColorBrewer.

Available palette

See the complete list of available palettes.

Categorical palette

How to map a color to a categorical variable.

Continuous palette

Same thing for a continuous variable.

Two histograms on same Axis

Compare the distribution of 2 variables with this double histogram built with base R function.

Histogram with colored tails

Coloring tails sometimes allow to highlight specific areas of the distribution.

Boxplot with custom colors

Color specific groups in this base R boxplot using ifelse statement.

Number of obs

Add number of observation on top of barplot, and other customization

Color and legend

Customize color and legend of the dendrogram.

Most basic

Most basic scatterplot with R and ggplot2

Custom appearance

Classic appearance customization with theme_ipsum and more

Connected Scatter for evolution

Use connected scatterplot with 3 numeric variable to show an evolution.

How to add a legend to base R plot

The legend() function allows to add a legend. See how to use it with a list of available customization.

Image on the chart background

The rasterImage function allows to add an image on the background of the chart.

Manage date data

Learn why it is important that your date is recognized at the date format, and how to do so.

Parameters reminder

A cheatsheet to quickly reminder what option to use with what value to customize your chart.

Background map and circle

Start by remembering how to plot a absic background map with circles to show 3 cities

Add a few connections

Add a few connections using great circles and the lines() function.

Loop the process

Build a loop to display many connections. Add city names. Gert fancy.

Scatterplot matrix

Basic scatteprlot matrix with the ggpairs() function.

Pearson correlation

Visualize correlation with ggcorr().

Show groups

You can use the ggplot2 syntax to add aesthetics and ths map color to categories.

Square and Pie

How to use the corrgram() function to represent correlation with square and pie charts.

Ellipse + Scatterplot

Visualize correlation with corrgram(). Use ellipses and scatterplots

Half matrix

You can display correlation in half the matrix only.

Ellipse package

The ellipse package allows to visualize a correlation matrix with ellipses.

plot() function

The plot function is straightforward way to build a basic scatterplot matrix.

car package

The car package probides a scatterplotMatrix function to build correlogram.

Most basic dendrogram

Most basic usage of ggraph, applied on 2 types of input data format.

Dendrogram customization

Go further with ggraph: edge style, general layout, node features, adding labels, and more.

Customized circular dendrogram

Learn how to build a circular dendrogram with proper labels.

Basic clustering process

Learn how to format your data, compute distance between samples, run the clusterisation and visualize the result.

Zoom / Select

Zoom on a specific part of the tree to study a particular group more in detail.

Color and legend

Customize color and legend of the dendrogram.

The set() function

An introduction to the set function that allows to customize node and label features.

Horizontal

Flip the tree to get it horizontal, and use coloring to highlight clusters.

Highlight a group

Highlight a specific group with a rectangle on top.

Bottom bar

Add a colored bar on the bottom of the tree to check an expected distribution.

Face to face

Put 2 dendrogram face to face to compare their clustering result.

Most basic

The most basic density plot you can do with ggplot2.

Custom appearance

Use the theme_ipsum to improve the general appearance of the previous basic density chart.

Mirror density chart

Put 2 density charts face to face to compare the distribution of 2 numeric variables.

Multi density chart

Explains how to display several density charts on the same axis, and the potential associated caveats.

Multi density with annotation

It is a good practice to write group names next to shapes instead of adding a legend beside the chart.

Small multiple

If you have several groups, plotting them on the same axis often results in a cluttered and unreadable figure. Use small multiple to avoid that.

Stacked density chart

Stack groups on of top of each other. Allows to study the whole, but makes it hard to study each group.

Marginal distribution

Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function.

Most basic

How to build the most basic donut chart with R and ggplot2

Customization

Make it prettier with labels, nice color palette and better general appearance

Ring width

Control ring thinckness with xlim

Most basic

Basic donut chart with base R, no library involved.

Annotate with geom_text

geom_text() allows to add annotation to one, several or all markers of your chart.

Annotate with geom_label

Very close to geom_text, geom_label produces a label wrapped in a rectangle. This example also explains how to apply labels to a selection of markers.

Rectangle

Learn how to use the annotate function to add a rectangle on a specific part of the chart.

Segment

How to add one or several segments to the chart.

Arrow

Basically the same as for a segment, with just one additional argument.

Abline

Add horizontal or vertical ablines with geom_hline and geom_vline.

Scatterplot with rug

Add rug on X and Y axis to describe the numeric variable distribution. Show how geom_rug() works.

Marginal distribution

Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function.

Marginal boxplot

Using boxplots is another way to show the marginal distribution. Find more in this post.

Axis

Customize ggplot2 axis: title, line, labels and ticks.

Background

Customize ggplot2 background: color, major and minor grid lines.

Legend

Customize ggplot2 legend: position, title, text, key symbol and more.

Basic

Learn how to add a title with ggtitle, default options.

Appearance

Learn how to change title font, size, color, text and more.

Position

Check how to change title position.

Facet_wrap

Facet wrap allows to build small multiples using one categorical variable.

Facet_grid

Same idea, but using 2 categorical variables for the faceting.

Customized

An advanced examples to make sure you know how to make your small multiple pretty.

Strip features

Customize the general layout with the strip option.

Mixing charts

How to combine several charts together with ggplot2.

Violin + boxplot + sample size

This examples provides 2 tricks: one to add a boxplot into the violin, the other to add sample size of each group on the X axis

Add individual observation

See how low group C sample size actually is?

Map marker feature to variable

Ggplot2 makes it a breeze to map a variable to a marker feature. Here is an example where marker color depends on its category.

Improve appearance

Make your chart pretty with nice color scale, general theme, stroke around cirle and more..

Custom appearance

Classic appearance customization with theme_ipsum and more

Improve general appearance

Add title, use a theme, change color palette, control variable orders and more

Conditional color

Change shape parameters depending on where the data points is located compared to a threshold.

Cleveland dot plot

A variation of the lollipop chart to study several categories on the same chart.

Customization

Add scale, group names and more

Small multiple

Small multiple is probably the best alternative, making obvious the evolution of each gropup.

Customization

Usual customizations like title, theme, color palette and more.

Droid

A BB8 droid made using R and ggplot2.

Random Shapes

A piece of generative art built with R and ggplot2.

R Snail

R Snail, a generative figure made by Christophe Cariou, built with R.

Most basic heatmap

The most basic heatmap you can build with R, using the heatmap() function.

Control color

Control the color palette used in the heatmap. Several methods shown.

Color scheme on the side

Add color beside the heatmap to compare actual structure with the expected one.

Most basic

Most basic usage of geom_tile to build heatmap

Control color

Control the color palette used in the heatmap. Several methods shown.

Interactive version

It's very easy to turn your heatmap interactive with ggplotly, check it out!

Most basic

Most basic use of the levelplot() function of the lattice package.

Reorder and swap X and Y axis

How to control row and column order on your heatmap.

Color palettes

How to use common color palette: R natives, R Color Brewer and Viridis.

Heatmap with smoothing

The latticeExtra allows to add a layer of heatmap with smoothing.

Most basic

The most basic hexbin map you can do, just plotting the boundaries.

Add state names

This examples explains how to compute the hexagone baricenters to add labels.

Basic Choropleth

Color each hexagone with a color that represents a numeric value. It's a hexbin choropleth map.

Start with a hierarchical network

Hierarchical Edge Bundling is based on a hierarchy. An original node gives underlying nodes and so on. Final nodes are called leaves, displayed around the circle.

Draw a connection

The second required level is connection, stored in another dataset. Leaves are connected with curves instead of straight lines.

Most Basic Edge Bundling

This is the most basic hierarchical edge bundling you can build. It displays many connection between leaves.

Uniform color

You can use one unique color for all connections

Color depending on value or index

Color can also depends on value to represent the strength of the connection, or on the the node index.

Custom nodes

Change node features to display one more level of information on the chart

Most basic

The most basic histogram you can do with R and ggplot2.

Control bin size

Playing with histogram bin size is an important step. Histogram appearance can greatly change, and so does the message you're trying to convey.

Mirror histogram

The mirror histogram allows to compare the distribution of 2 numeric variables.

Multi histogram

How to display several histograms on the same X axis

Small multiple

Using small multiple and histogram allows to compare the distribution of many groups with cluttering the figure.

Marginal distribution

Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function.

Two histograms on same Axis

Compare the distribution of 2 variables with this double histogram built with base R function.

Two histograms on split windows

Compare the distribution of 2 variables plotting 2 histograms one beside the other.

Boxplot on top of histogram

How to add a boxplot on top of a histogram.

Histogram with colored tails

Coloring tails sometimes allow to highlight specific areas of the distribution.

Mirrored histogram in base R

A mirrored histogram allows to compare the distribution of 2 variables. Here is how to build one in base R.

Get rid of borders

Just a small tip to get rid of histogram borders and improve the general appearance.

Most basic

Most basic line chart with R and ggplot2

Basic customization

Basic customization to improve the line chart: size, color, type, theme, title and more

Log transform

How to use a log transformation for the Y axis with scale_x_log10().

Deal with date

How to avoid struggling with dates on the X axis

Basic Grouped line chart

How to build a line chart representing several groups

Customized Grouped

Pick up a nice color palette, use a theme, add titles, and more.

Linear trend

geom_smooth allows to add the result of a model to your scatterplot, with confidence interval as well.

Annotation

Annotation allows to highlight main features of a chart. Learn how to add text, circles, lines and more.

Geom_smooth()

geom_smooth allows to add the result of a model to your scatterplot, with confidence interval as well.

Geom_ribbon()

If you already know the upper and lower limits of the error envelop, geom_ribbon will plot it for you.

Spaghetti chart

Too many lines with 10+ legend entries? You are building a spaghetti chart and readers will struggle to get info from it.

Annotation

Try to understand what really matters, highlight it and annotate it to make sure people get your point.

Small multiple

Split the layout: each group having its own space. It makes it easier to understand what's happening for each them.

Line color and Y value

Change the line color according to the Y axis value

How to add a legend to base R plot

The legend() function allows to add a legend. See how to use it with a list of available customization.

Reversed Y axis

Learn how to flip the Y axis upside down using the ylim argument.

Polynomial curve

Line charts are often used to represent the result of a statistical model.

Parameters reminder

A cheatsheet to quickly reminder what option to use with what value to customize your chart.

Line chart with 2 series

A classic line chart with 2 series, only 1 Y axis.

Dual Y axis line chart

Add a second Y axis for the second sery. See how chart conclusion gets different and wrong.

Add legend

Add a legend to specify what color is linked to what value.

Most basic

Understand the basics of lollipop chart with this most simple version.

Customize markers

See the different options allowing to customize the marker on top of the stem.

Customize stems

See the different options allowing to customize the stems.

General appearance

How to use the theme() function to improve your chart genereal appearance.

Horizontal version

Make your lollipop chart horizontal → your labels will be easier to read

Change baseline

Change the baseline to highlight an interesting threshold.

Control group order

Changing group order in a lollipop chart is important to add insight to the chart. Learn why and discover 3 methods to do so.

Cleveland dot plot

A variation of the lollipop chart to study several categories on the same chart.

Annotation

Highlight one or several groups to convey your message more efficiently. Play with group appearance and add text annotation.

Conditional color

Change shape parameters depending on where the data points is located compared to a threshold.

Most basic

The most basic background map you can do with R and the leaflet package. Default options only.

Zoom and Location

Learn how to zoom on a specific part of the map with the setView function.

Change tile

Several background tiles are offered by leaflet. Learn how to load them, and check the possibilities.

Read and plot Shapefiles

learn how to read and plot shapefiles thanks to the rgdal package.

Read and plot geoJSON files

learn how to read and plot geoJSON files thanks to the geojsonio package.

Select

How to select one or several regions in a geospatial object, and plot it.

Simplify

Simplifying a geospatial object allows to get a lighter object that will be plotted faster.

Centroid

Computing region centroids is a common task: it allows to add region labels afterwards.

Intro

An introduction to network diagram with R and the igraph package.

Customization

Explore all the parameters offered by the igraph package to customize chart appearance.

Layout algorithm

Several layout algorithm are offered by the igraph package. Learn how to use them and what are the possibilities.

Map variable to feature

Learn how to change node and link features according to external variables.

Clustering viz

Network diagrams can be used to visualize the result of correlation matrix.

Node size and edge #

Learn how to map node size to its number of links.

Most basic parallel chart

Most basic option using the ggparcoord() function of the ggally package

Improve general appearance

Add title, use a theme, change color palette, control variable orders and more

Highlight a group

Highlight a group of interest to help people understand your story

Most basic parallel chart

Most basic option using the parcoord() function of the MASS package

Reorder variables

Reordering variables allows to avoid line crossings, and thus makes the chart more understandable

Highlight a group

You are interested in a group compared to all the others? Highlight it.

Most basic

Most basic piechart using the pie() function of base R.

General customization

Learn how to change pie shape, zoom in, add stripes, change labels and more.

Colors

How to pick a nice color palette and apply it to the piechart

Most basic

Explains how to use coord_polar() on a barchart to get a pie chart

Make it clean

Clean the basic piechart: remove background, grid, axis labels

Add Labels

Compute and add labels on each slice, the trickiest part of the job.

Most basic ridgeline

The most basic ridgeline plot you can build with the ridgelines R package.

Distribution shape

Custom the distribution shape, here applying a histogram shape using binning.

Color gradient

Apply a color gradient to the chart. Makes the color scaled to the numeric variable.

Basic scatterplot

The most basic scatterplot you can build with R and ggplot2.

Simply explains how to call the geom_point() function.

Custom marker features

The geom_point() function has option to custom color, stroke, shape, size and more. Learn how to call them.

Map marker feature to variable

Ggplot2 makes it a breeze to map a variable to a marker feature. Here is an example where marker color depends on its category.

Map to several features

Extension of the previous concept: several features can be mapped to variables in the same time

Annotate with geom_text

geom_text() allows to add annotation to one, several or all markers of your chart.

Annotate with geom_label

Very close to geom_text, geom_label produces a label wrapped in a rectangle. This example also explains how to apply labels to a selection of markers.

Scatterplot with rug

Add rug on X and Y axis to describe the numeric variable distribution. Show how geom_rug() works.

Marginal distribution

Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function.

Linear trend

geom_smooth allows to add the result of a model to your scatterplot, with confidence interval as well.

Most basic scatterplot

The most basic scatterplot you can build with R, using the plot() function.

plot() parameters

Custom your scatterplot with the arguments of the plot() function.

Polynomial curve

Set a linear model with lm(), and plot it on top of your scatterplot with line().

Confidence interval

Add a confidence interval around the polynomial model with polygon().

Lattice XYplot()

The lattice XYplot() allows to build one scatterplot for each level of a factor automatically.

Correlation of discrete variables

Make the circle size proportional to number of data points when working with discrete variables.

the mtext() function

mtext() allows to add text in margin. Useful to add an unique title for several charts.

split_screen() function

Split screen allows to split the chart window in several sections.

Split screen with par(mfrow())

The easiest way to split the graphic window is to use par(mfrow()).

Parameters reminder

A cheatsheet to quickly reminder what option to use with what value to customize your chart.

Most basic scatterplot

The most basic scatterplot you can build with R, using the plot() function.

plot() parameters

Custom your scatterplot with the arguments of the plot() function.

Polynomial curve

Set a linear model with lm(), and plot it on top of your scatterplot with line().

Confidence interval

Add a confidence interval around the polynomial model with polygon().

Most basic radar

Start with a basic version, learn how to format your input dataset

Radar chart customizations

How to customize the chart appearance, polygon, net, labels and more.

Basic multi-group radar chart

Start with a basic version, learn how to format your input dataset

Customization

Customization option offered by the fmsb package

About axis limits

Learn how to control chart axis limits

Most basic

Most basic stacked area chart you can build with R and ggplot2, using the geom_area function.

Reorder

Learn different methods to reorder the groups from top to bottom

Percent stacked

Switch to a stacked percent area graph where the sum of each time point is 100

Customization

Usual customizations like title, theme, color palette and more.

The issue with stacking.

Try to understand how the green group evolved on the period. Pretty hard isn't it?

Line chart.

Switch to a line chart to understand better how each group behaved during the period.

Small multiple

Small multiple is probably the best alternative, making obvious the evolution of each gropup.

Most basic

Most basic area chart you can build in base R using the polygon function.

Basic grouped barplot

The most basic grouped barplot you can build with R and ggplot2.

Basic Stacked barplot

Instead of having subgroups one beside another, they are on top of each other.

Percent stacked

A parcent stacked barchart with R and ggplot2: each bar goes to 1, and show the proportion of each subgroup.

Customization

Apply some classic customization like title, color palette, theme and more.

Small multiple

Small multiple can be an alternartive to grouped barplot.

Most basic streamchart

The most basic streamchart you can build with R and the streamgraph package.

Line chart.

Switch to a line chart to understand better how each group behaved during the period.

Small multiple

Small multiple is probably the best alternative, making obvious the evolution of each gropup.

Grouped barplot

How to build a grouped barchart with base R

Stacked barplot

Put subgroups on top of each other in a stacked barplot

Percent stacked barplot

Compare proportion in the whole with a percent stacked barchart

Likert package

The Likert package allows to build visualization for questionnaire answers

Most basic streamchart

The most basic streamchart you can build with R and the streamgraph package.

Change offset

By default a streamchart is displayed all around a horizontal line. You can change that thanks to the offset option

Change shape

Leave the smooth and rounded option to get something similar to a stacked area chart or to a stacked barplot.

Change color

Several ways to change the color palette used on the chart.

Most basic

Most basic line chart with R and ggplot2 for time series data visualization

X labels

Customize the X axis labels with any date format

Time frame

Select the time frame of interest in your input data

Annotation

Annotation allows to highlight main features of a chart. Learn how to add text, circles, lines and more.

Most basic

The most basic time sery visual you can build with the dygraphs library. Explanation about possible input formats.

Several series

How to display several series on the same chart.

Area chart

Build an interactive area chart for time series with dygraphs.

Candlestick

Build an interactive Candlestick chart for time series with dygraphs.

Confidence interval

Add confidence interval around interactive line chart for time series with dygraphs.

Step plot

Build an interactive step plot for time series with dygraphs.

Line chart with 2 series

A classic line chart with 2 series, only 1 Y axis.

Dual Y axis line chart

Add a second Y axis for the second sery. See how chart conclusion gets different and wrong.

Add legend

Add a legend to specify what color is linked to what value.

Most basic treemap

Start with the most basic treemap you can build. No customization, no subgroups, easy code.

Multi-level treemap

How to build a treemap with group and subgroups.

Customization

Customize treemap labels, borders, color palette and more

Most basic Venn diagram

This is the most basic venn diagram you can build with R and the VennDiagram package.

Most basic Venn diagram

Description of the long list of options available to customize the venn diagram.

Most basic Venn diagram

Application to a real dataset: the lyrics of 3 famous French singers.

Most basic violin chart

Most basic violin using default parameters.
Focus on the 2 input formats you can have: long and wide

Control group order

Changing group order in your violin chart is important. It adds insight to the chart. Learn why and discover 3 methods to do so.

Horizontal version

Flipping X and Y axis allows to get a horizontal version. Group labels become much more readable

Violin + boxplot + sample size

This examples provides 2 tricks: one to add a boxplot into the violin, the other to add sample size of each group on the X axis

Grouped violin chart

A grouped violin displays the distribution of a variable for groups and subgroups. Here is an implementation with R and ggplot2

Vioplot package

The vioplot package allows to build violin charts. Learn how it works.

Most basic wordcloud

See what input file is needed to run the basic wordcloud2 function with default parameters.

Text color: random

Change word color to random dark

Text color: 2 colors

Provide a vector of color.

Background color

How to change the background color with the backgroundColor argument

Available shapes

A few shapes are available to be used as Masks.

Your image as a mask

You can use any image as a mask.

Text rotation

Chinese version

Use a letter as a Mask

Use a word as a Mask

Most basic

See what input file is needed to build this basic wordcloud.

Text analysis

A text analysis by Benjamin Tovarcis for document classification.

Text analysis

A text analysis by Benjamin Tovarcis for document classification.