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.
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Building time series requires the time variable to be at the
dateformat. The first step of your analysis must be to double check that R read your data correctly, i.e. at thedateformat. This is possible thanks to thestr()function: -
Getting this
dateformat can be a pain, and thelubridatepackage is such a life saver. It offers several function which name are composed by 3 letters: year (y), month (m) and day (d). Example:
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
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.
Bump chart with ggbump
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:
🌐 From the web
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!
Related chart types
Line plot
Area
Stacked area
Streamchart
Time Series
