ggplot2 is one of the most powerful packages in r for data visualizations and it is essential to master the underlying grammar of graphics to fully utilize its power. While the theming system of ggplot2 allows you to customize the appearance of the plot according to our needs in practice, it is always a frustration to identify the elements on the plot you want to change as you may find it difficult to remember the element names and their corresponding functions to modify, at least this is the case for me.

The reticulate package provides a comprehensive set of tools for interoperability between Python and R. With reticulate, you can call Python from R in a variety of ways including importing Python modules into R scripts, writing R Markdown Python chunks, sourcing Python. If you like this post, you may like another one about how to learn ggplot2 on paper. This entry was posted in R Programming and tagged data visualization, ggplot, ggplot2, ggplot2 theme elements, ggplot2 themes, R, R Cheat Sheet on April 25, 2020 by Henry.

• This article is an attempt to make a nice guide or a cheat sheet for some common types of plots from basic to advanced level. For doing all the exercises here, I used RStudio. Hopefully, that’s good for you as well. Let’s start with the basics. This is the command to install the ggplot2 if you do not have it already: install.packages('ggplot2').
• 27 likert data cheat sheet. 27.1 link is below; 28 Categorical Data Visualization Cheat Sheet; 29 Video Introduction to the Replication Crisis in Psychology; 30 A Video Tutorial for Creating a Data Story in Tableau. 30.1 Introduction; 30.2 Data Source; VI Live Tutorials; 31 Tableau intro tutorial explained with proset2; 32 Solutioning a Data.

Continue reading

5.1 Plotting with ggplot2

ggplot2 is a plotting package that makes it simple to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties. Therefore, we only need minimal changes if the underlying data change or if we decide to change from a bar plot to a scatter plot. This helps in creating publication quality plots with minimal amounts of adjustments and tweaking.

ggplot2 functions like data in the ‘long’ format, i.e., a column for every dimension, and a row for every observation. Well-structured data will save you lots of time when making figures with ggplot2

ggplot graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.

To build a ggplot, we will use the following basic template that can be used for different types of plots:

• use the ggplot() function and bind the plot to a specific data frame using the data argument

Ggplot2 Example

• define a mapping (using the aesthetic (aes) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.

• add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars). ggplot2 offers many different geoms; we will use some common ones today, including:

• geom_point() for scatter plots, dot plots, etc.
• geom_boxplot() for, well, boxplots!

• geom_line() for trend lines, time series, etc.

To add a geom to the plot use the + operator. Because we have two continuous variables, let’s use geom_point() first:

The + in the ggplot2 package is particularly useful because it allows you to modify existing ggplot objects. This means you can easily set up plot templates and conveniently explore different types of plots, so the above plot can also be generated with code like this:

Notes

Ggplot2 Cheat Sheet 2020 Free

• Anything you put in the ggplot() function can be seen by any geom layers that you add (i.e., these are universal plot settings). This includes the x- and y-axis mapping you set up in aes().
• You can also specify mappings for a given geom independently of the mappings defined globally in the ggplot() function.
• The + sign used to add new layers must be placed at the end of the line containing the previous layer. If, instead, the + sign is added at the beginning of the line containing the new layer, ggplot2 will not add the new layer and will return an error message.