This course has given you a brief introduction to the tools that can help you read in, analyse and plot your data.
This has been only a brief introduction. You can learn more from these excellent free online resources:
R for Data Science, by Garrett Grolemund and Hadley Wickham - this is a must-read book that really shows you how to do data science with R.
ggplot2: Elegant Graphics for Data Analysis, by Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen - this is an excellent book that show how to use ggplot for drawing graphs.
Advanced R, by Hadley Wickham - this book goes deeper into the R programming language.
Text mining with R: A Tidy approach, by Julia Silge and David Robinson - this book shows how to use tidy principles to perform text mining and sentiment analysis.
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, by Chester Ismay and Albert Y. Kim - this book teaches statistical techniques, using R and the Tidyverse.
The R Graph Gallery - a large showcase of different ggplot graphs covering pretty every type of data visualisation you would want to perform. Examples with code! Find an example that matches the type of graph that you want to draw and then adapt that to your own data.
Mastering Shiny, by Hadley Wickham - This book teaches you how to use Shiny to turn your Tidyverse R data analysis and visualisations into interactive web pages and dashboards that you can share with your collaborators.
BONUS EXERCISE
Looking at ggplot2: Elegant Graphics for Data Analysis, can you find how to make the graph of average decadal temperature plotted in the last section prettier? Change the axis titles to
Temperature / °CandYear, and change the points colour to red.[Optional] Change the colour of the points so that low temperatures are blue and higher temperatures are red.
Once you’re happy with the concepts in this course, you may also want to look at the Text and Sentiment Analysis in R course which starts to show how to perform text and sentiment analysis using R.
This workshop is based on Introduction to Data Analysis in Python, by Matt Williams. All text is published under a Creative Commons Attribution 4.0 International License with all code snippets licensed as MIT.
The source for the material can be found on GitHub where fixes are welcome.