Whats next?

Last updated on 2025-04-15 | Edit this page

Overview

Questions

  • “What is the next step in learning to plot?”

Objectives

  • “Provide tips on where to locate data for plotting”
  • “Provide tips for finding inspiration for plotting”

What should I do next?

First of all: If you do not have data you want to visualise already. Find some!

Kaggle host competitions in machine learning. For the use of those competitions, they give access to a lot of interesting datasets to work with. With more than 200.000 datasets at time of writing, it can be a bit overwhelming, so consider looking at the “Data Visualization” category. The link provided only shows datasets saved as CSV-files, and has only about 2.500 datasets.

Play around!

ggplot2 comes with a lot of functionality. This is the list of build-in geoms in ggplot2:

OUTPUT 

 [1] "geom_abline"            "geom_area"              "geom_bar"
 [4] "geom_bin_2d"            "geom_bin2d"             "geom_blank"
 [7] "geom_boxplot"           "geom_col"               "geom_contour"
[10] "geom_contour_filled"    "geom_count"             "geom_crossbar"
[13] "geom_curve"             "geom_density"           "geom_density_2d"
[16] "geom_density_2d_filled" "geom_density2d"         "geom_density2d_filled"
[19] "geom_dotplot"           "geom_errorbar"          "geom_errorbarh"
[22] "geom_freqpoly"          "geom_function"          "geom_hex"
[25] "geom_histogram"         "geom_hline"             "geom_jitter"
[28] "geom_label"             "geom_line"              "geom_linerange"
[31] "geom_map"               "geom_path"              "geom_point"
[34] "geom_pointrange"        "geom_polygon"           "geom_qq"
[37] "geom_qq_line"           "geom_quantile"          "geom_raster"
[40] "geom_rect"              "geom_ribbon"            "geom_rug"
[43] "geom_segment"           "geom_sf"                "geom_sf_label"
[46] "geom_sf_text"           "geom_smooth"            "geom_spoke"
[49] "geom_step"              "geom_text"              "geom_tile"
[52] "geom_violin"            "geom_vline"

ggplot2 is also build as an extensible package, making it relatively easy to build extensions, that does things that ggplot2 is not able to on its own. This page contains a collection of these.

Get some inspiration!

The website/book Fundamentals of Data Visualization is a great resource for tips, tricks and thinking about visualizations, especially the directory of visualizations. Note, however, that the author does not provide examples of the code you need to write to make the plots.

There exist an online challenge, #30DayChartChallenge, that challenges you to create a data visualization on a certain topic each day of april. That can be a bit of a mouthful and we are not going to participate ourselves. But! The collection of visualizations from 2022 is humongous, and a great place to find ideas.

Sometimes you have a pretty good idea about what you want to visualise. But you are uncertain about what graphs might be used for that. Or you know what graph you want to make, but can’t quite figure out how to write the code. The R Graph Gallery have a wide selection of chart types with code!

Other online courses

EdX offers a multitude of interesting courses - here we link to their ggplot-related courses.

Codecademy also offers free courses (and a certificate of completion if you pay) This course offers a more indepth introduction to ggplot2.

Extensions

ggplot2 is build for extensions. And there are many.

Key Points

  • “ggplot2 is extensible - a LOT of extensions are available”