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R Programming By Example

You're reading from   R Programming By Example Practical, hands-on projects to help you get started with R

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788292542
Length 470 pages
Edition 1st Edition
Languages
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Authors (2):
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Omar Trejo Navarro Omar Trejo Navarro
Author Profile Icon Omar Trejo Navarro
Omar Trejo Navarro
Omar Trejo Navarro Omar Trejo Navarro
Author Profile Icon Omar Trejo Navarro
Omar Trejo Navarro
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to R 2. Understanding Votes with Descriptive Statistics FREE CHAPTER 3. Predicting Votes with Linear Models 4. Simulating Sales Data and Working with Databases 5. Communicating Sales with Visualizations 6. Understanding Reviews with Text Analysis 7. Developing Automatic Presentations 8. Object-Oriented System to Track Cryptocurrencies 9. Implementing an Efficient Simple Moving Average 10. Adding Interactivity with Dashboards 11. Required Packages

Looking at dynamic data with time-series

Now we are going to focus on another very common type of graph: time-series. Our objective is to understand how our data is behaving for the last n days, and, as we have done before, we want to further disaggregate using colors, like the graph below shows:

If you have read all the chapter up until this point, you should be able to understand most of what the function is doing. The only new function is scale_x_date(). It allows us to specify date formats for the axis ticks other than the default. In this case, we want to use breaks by day (as we had done in some examples before), but we want the format of the labels to be similar to July 30, 2017, for example. To do so we make use of the date formats mentioned in a previous section in this chapter and send the desired string structure to the date_labels parameter:

graph_last_n_days &lt...
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