Time Series

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• Building time series requires the time variable to be at the date format. The first step of your analysis must be to double check that R read your data correctly, i.e. at the date format. This is possible thanks to the str() function:





• Getting this date format can be a pain, and the lubridate package is such a life saver. It offers several function which name are composed by 3 letters: year (y), month (m) and day (d). Example:

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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.

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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

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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 Time series section

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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

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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