Gain insights into using ggplot2 in R to create compelling visualizations. Learn about scatter plots, bar charts, customizing themes, and building interactive maps to enhance data storytelling.

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Data visualization in R using ggplot2 is an essential skill for data professionals and enthusiasts to efficiently discover and convey insights from datasets. Mastering ggplot2 allows you to uncover hidden patterns and trends in data, boosting decision-making and storytelling for a wide range of audiences. 

In this comprehensive course, you’ll begin by learning data visualization principles and the grammar of graphics. Progressing to ggplot2’s core functionalities, you’ll create scatter plots, bar charts, line graphs, histograms, and more explore advanced visualization through customizations and themes to create visually engaging plots. You’ll also be building interactive visualizations and captivating maps. 

By the end of this course, you’ll confidently generate engaging data visualizations in R, transforming yourself into an effective data communicator. You’ll also be able to elevate your data visualization skills with ggplot2 and excel as a data storyteller while enhancing your data science career.

Data Visualizations with ggplot2 in R

# Customizing fonts and colors

After building a data visualization, the next step can be customization. We can customize the font and colors of various plot elements in `ggplot2` to effectively communicate our data and enhance the aesthetic appeal of the plot using the `theme()` function.

Using the `element_text()` function inside the `theme()` function, we can customize the font, font family, and color of plot elements such as the title, subtitle, caption, and tag. 

Let’s first create a simple pie chart using the `housing` dataset from the `MASS` package. We’ll also add a title, subtitle, caption, and tag to the plot and save it in an object named `chart`. Then, we’ll customize this plot.

chart<-ggplot(housing) +
  geom_col(aes(x = "", y = Freq, fill =Type)) +
  coord_polar(theta = "y")+
  theme_bw()+
  scale_fill_manual(values = c("#0b7a75","#1DD3B0","#5DA9E9","#03045E"))+
  labs(title = "Distribution of Housing Types",
   	  subtitle = "Pie chart showing different types of housing",
   	  caption = "Source: housing (MASS package)",
   	  tag = "Figure 1")
chart

* **Line 1:** We create a new object named `chart` and use the `<-` assignment operator for storing the graph in the `chart` object.  We initialize a new `ggplot` object with the `ggplot()` function and pass the `housing` dataset. Using the `+` operator, we add a layer to the `ggplot` object.
* **Line 2:**  We use the `geom_col()` function to create a bar chart where the height of each bar is determined by the `Freq` variable and the fill color is determined by the `Type` variable.
* **Line 3:** Next, we use the `coord_polar()` function to add a polar coordinate layer to the plot. With this function, the height of the bars is converted into angles around the center of the plot. We also set the `theta` argument to `"y"`, because of which the angles will be proportional to the values on the y-axis.
* **Lines 4–5:** We apply the `ggplot` theme to the plot using the `theme_bw()` function and set specific colors to the plot using the `scale_fill_manual()` function.
* **Lines 6–9:** We give the plot a title, subtitle, caption, and tag using the `labs()` function.


Learn how to customize the fonts and colors of ggplot2 charts.

Welcome to the Course

Introduction to ggplot2

Drawing Basic Plots Using ggplot2

Advanced Plots

Plot Customization

Creating Panel of Plots

Building Interactive Plots

Uber Data Analysis Using the R Language

Summing Up

Appendix

Final Assessment

Stock Data Analysis with R

Customizing Fonts and Colors with ggplot2

Customizing fonts and colors