Summary
1. Summary
This is it! You made it. In this course, you went from very basic looking, standard graphics to highly customized and aesthetic visualizations.2. <<<New Slide>>>
In the end you put everything into a final RMarkdown report. This report contains everything to fully reproduce how you visualized the relationship between weekly working hours and hourly compensation in European countries. In the last chapter, you've also learnt how to customize RMarkdown reports.3. The last step in the Tidyverse process
This knowledge, together with what you've learnt about ggplot2 themes, now enables you to create outstanding reports in the Tidyverse. Remember: Communication is key. Always invest enough time in it, and your data science efforts won't go unnoticed.4. <<<New Slide>>>
Here are more examples to get you hooked on visualizing data in the Tidyverse. The first one is a thematic map of Switzerland I created some time ago. It shows the average age in Swiss municipalities – the darker the older, the brighter the younger. Besides some geospatial packages for data preprocessing, this map was created using only ggplot2, including the legend at the bottom.5. <<<New Slide>>>
Here's another map example by Henrik Lindberg. It shows European population density, using the geom_line geometry of ggplot2. Such a display is rather uncommon, but it is actually made with Tidyverse packages only, totalling less than 30 lines of code. So you see, with Tidyverse packages and some investment in customization, you can come up with pretty unique and cool visualizations some people won't even recognize as made in R.6. Next steps
In this course you got a glimpse of different techniques for effectively communicating your results. But that was only the tip of the iceberg. How to go further from here? Datacamp has a whole track on Data Visualization. There, you get an in-depth look at ggplot2, but also at other visualization libraries in R – the base and lattice graphics, which have their advantages, too. If you want to dive even deeper into RMarkdown and get to know even more customizations, there are also courses on RMarkdown. Reports can also be made interactive with a framework called Shiny – Datacamp has got you covered here, too.7. Congratulations!
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