Time Series

---

ggplot2 offers great features when it comes to visualize time series. The date format will be recognized automatically, resulting in neat X axis labels. The scale_x_data() makes it a breeze to customize those labels. Last but not least, plotly can turn the resulting chart interactive in one more line of code.

---

The ggplotly() function of the plotly library makes it a breeze to build an interactive version. Try to hover circles to get a tooltip, or select an area of interest for zooming. Double click to reinitialize.

Get code

---

The dygraphs package is a html widget. It allows to make interactive time series chart: you can zoom and hover data points to get additional information. Start by reading the chart #316 for quick introduction and input description. Then, the graph #317 gives an overview of the different types of charts that are offered. To go further, check the graph #318 (interactive version below).

The dygraph package offers zooming, hovering, minimaps and much more. Try it on the example below!

Get code

---

Heatmaps can be a very good alternative to visualize time series, especially when the time frame you study is repeating, like weeks. Here is a customized example, but visit the heatmap section for more.

Code Heatmap section

---

A Bump plot is an effective alternative for visualizing time series with a limited number of data points, such as data collected every 5 or 10 years.

The following example demonstrates how to highlight specific lines, avoiding a spaghetti chart.

Code

---

Warning: a dual Y axis line chart represents the evolution of 2 series, each plotted according to its own Y scale. This kind of chart must be avoided, since playing with Y axis limits can lead to completely different conclusions. Visit data-to-viz for more info.

Why you should avoid it

---

A bump chart is a variation of the parallel coordinate plot. It visualizes the rank order and changes in rank of categorical data over time. It is thus suitable for timeseries.

In R, the ggbump package makes it a breeze to build one as shown in the following examples:

---

The web is full of astonishing R charts made by awesome bloggers. The R graph gallery tries to display some of the best creations and explain how their source code works. If you want to display your work here, please drop me a word or even better, submit a Pull Request!