Time Series

Time series aim to study the evolution of one or several variables through time. This section gives examples using R. A focus is made on the tidyverse: the lubridate package is indeed your best friend to deal with the date format, and ggplot2 allows to plot it efficiently. The dygraphs package is also considered to build stunning interactive charts.

Struggling with the Date format? You will the lubridate package.

Time series with ggplot2

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.

Interactive version: plotly

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.

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Time series with dygraph

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!

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Heatmap for time series

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
heatmap for time series in R

Dual Y axis with latticeExtra or ggplot2

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

Related chart types

Line plot
Stacked area
Time Series