Data visualization in R

class: center, middle, inverse, title-slide

.title[
# Data visualization in R
]
.author[
### Mikhail Dozmorov
]
.institute[
### Virginia Commonwealth University
]
.date[
### 2025-08-25
]

---

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.small { font-size: 70%; }
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## ggplot2 package

- **ggplot2** is a data visualization package, part of the tidyverse ecosystem created by Hadley Wickham. It’s built on the concept of the Grammar of Graphics (Leland Wilkinson), which provides a consistent, layered framework for creating plots. .small[ https://ggplot2.tidyverse.org/ ]

Key Features
- **Layered approach** – Start with data and progressively add plot elements.
- **Aesthetic mapping** – Easily link variables to colors, sizes, shapes, and other visual attributes.
- **Wide variety of geoms** – From scatter plots and bar charts to boxplots and heatmaps.
- **Highly customizable** – Control scales, coordinate systems, annotations, and themes for publication-quality visuals.
- **Works well with tidy data** – Integrates seamlessly with dplyr and other tidyverse packages.

---
## The basics of ggplot2 graphics

- Data mapped to graphical elements
- Add graphical layers and transformations
- Commands are chained with "+" sign

| Object     |       | Description                                                        |
|------------|-------|--------------------------------------------------------------------|
| Data       |       | The raw data that you want to plot                                 |
| Aethetics  | aes() | How to map your data on x, y axis, color, size, shape (aesthetics) |
| Geometries | geom_ | The geometric shapes that will represent the data                  |
data +

aesthetic mappings of data to plot coordinates +

geometry to represent the data

---
## Basic ggplot2 syntax

**Specify data, aesthetics and geometric shapes**

`ggplot(data, aes(x=, y=, color=, shape=, size=, fill=)) +`   
`geom_point()`, or `geom_histogram()`, or `geom_boxplot()`, etc.

- This combination is very effective for exploratory graphs.

- The data must be a data frame in a **long** (not wide) format

- The `aes()` function maps **columns** of the data frame to aesthetic properties of geometric shapes to be plotted.

- `ggplot()` defines the plot; the `geoms` show the data; layers are added with `+`

<!---
## Examples of ggplot2 graphics

``` r
diamonds %>% filter(cut == "Good", color == "E") %>% 
  ggplot(aes(x = price, y = carat)) +
  geom_point()  # aes(size = price) +
```

Try other geoms

```
  geom_smooth() # method = lm
  geom_line()
  geom_boxplot()
  geom_bar(stat="identity")
  geom_histogram()
```
-->

---
## Moving beyond `ggplot` + `geoms`

Customizing scales

* Scales control the mapping from data to aesthetics and provide tools to read the plot (ie, axes and legends).

* Every aesthetic has a default scale. To add or modify a scale, use a `scale` function.

* All scale functions have a common naming scheme:
`scale` `_` name of aesthetic `_` name of scale

* Examples: `scale_y_continuous`, `scale_color_discrete`, `scale_fill_manual`

---
## ggplot2 example - update scale for y-axis

``` r
ggplot(iris, aes(x = Petal.Width, y = Sepal.Width, 
                 color=Species)) + geom_point() +
  scale_y_continuous(limits=c(0,5), breaks=seq(0,5,0.5)) +
  scale_color_manual(name="Iris Species", 
                     values=c("red","blue","black"))
```

---
## Moving beyond `ggplot` + `geoms`

Split plots

* A natural next step in exploratory graphing is to create plots of subsets of data. These are called facets in ggplot2.

* Use `facet_wrap()` if you want to facet by one variable and have `ggplot2` control the layout. Example:   
  + `facet_wrap( ~ var)`

- Use `facet_grid()` if you want to facet by one and/or two variables and control layout yourself. Examples:    
  + `facet_grid(. ~ var1)` - facets in columns   
  + `facet_grid(var1 ~ .)` - facets in rows   
  + `facet_grid(var1 ~ var2)` - facets in rows and columns

---
## ggplot2 example - `facet_wrap`

Note free x scales

``` r
ggplot(iris, aes(x = Petal.Width, y = Sepal.Width)) + 
  geom_point() + geom_smooth(method="lm") +
  facet_wrap(~ Species, scales = "free_x")
```

---
## stat functions

- All `geoms` perform a default statistical transformation.

- For example, `geom_histogram()` bins the data before plotting. `geom_smooth()` fits a line through the data according to a specified method.

- In some cases the transformation is the "identity", which just means plot the raw data. For example, `geom_point()`

- These transformations are done by `stat` functions. The naming scheme is `stat_` followed by the name of the transformation. For example, `stat_bin`, `stat_smooth`, `stat_boxplot`

- **Every geom has a default stat, every stat has a default geom.**

---
## stat functions

``` r
# Case 1: Directly on a categorical variable (Species)
head(iris)
```

```
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
```

``` r
# geom_bar() uses stat_count() by default
ggplot(iris, aes(x = Species, fill = Species)) +
  geom_bar() +
  ggtitle("geom_bar() with default stat = 'count'") 
```

---
## stat functions

``` r
# Case 2: Using pre-computed counts with dplyr + stat = 'identity'
(species_counts <- iris %>%
  group_by(Species) %>%
  summarise(Count = n(), .groups = "drop"))
```

```
## # A tibble: 3 × 2
##   Species    Count
##   <fct>      <int>
## 1 setosa        50
## 2 versicolor    50
## 3 virginica     50
```

``` r
ggplot(species_counts, aes(x = Species, y = Count, fill = Species)) +
  geom_bar(stat = "identity") +
  ggtitle("geom_bar(stat = 'identity') with summarised counts")
```

---
## Update themes and labels

* The default ggplot2 theme is excellent. It follows the advice of several landmark papers regarding statistics and visual perception. (Wickham 2009, p. 141)

* However you can change the theme using ggplot2's themeing system. To date, there are seven built-in themes: `theme_gray` (_default_), `theme_bw`, `theme_linedraw`, `theme_light`, `theme_dark`, `theme_minimal`, `theme_classic`

* You can also update axis labels and titles using the `labs` function.

---
## ggplot2 enhancements
.pull-left[

``` r
ggplot(iris, aes(x = Petal.Width, y = Sepal.Width, color = Species)) +
  geom_point(size = 2, alpha = 0.8) +
  labs( title    = "Sepal vs. Petal",
        subtitle = "Iris dataset",
        x        = "Petal Width (cm)",
        y        = "Sepal Width (cm)",
        color    = "Species") +
  theme_minimal(base_size = 14) +
  theme(axis.text.x      = element_text(angle = 45, hjust = 1),
        plot.title       = element_text(face = "bold", hjust = 0.5),
        plot.subtitle    = element_text(hjust = 0.5),
        legend.position  = "top",
        panel.grid.minor = element_blank())
```

<img src="01c_ggplot2_files/figure-html/unnamed-chunk-6-1.png" style="display: block; margin: auto;" />
]
.pull-right[
- `theme_minimal()` for a clean look

- Larger points with slight transparency (size, alpha)

- Subtitle added in `labs()`

- Rotated x-axis labels with `element_text(angle = 45)`

- Centered bold title & subtitle

- Legend moved to the top

- Minor gridlines removed
]

---
## Summary: Fine tuning ggplot2 graphics

| Parameter                   |        | Description                                                                                                                     | 
|-----------------------------|--------|---------------------------------------------------------------------------------------------------------------------------------| 
| Facets                      | facet_ | Split one plot into multiple plots based on a grouping variable                                                                 | 
| Scales                      | scale_ | Maps between the data ranges and the dimensions of the plot                                                                     | 
| Visual Themes               | theme  | The overall visual defaults of a plot: background, grids, axe, default typeface, sizes, colors, etc.                            | 
| Statistical transformations | stat_  | Statistical summaries of the data that can be plotted, such as quantiles, fitted curves (loess, linear models, etc.), sums etc. | 
| Coordinate systems          | coord_ | Expressing coordinates in a system other than Cartesian                                                                         | 
---
## R Graphics Resources

* **Plotly** – An interactive graphing library that works with R and integrates with **ggplot2**. It allows zooming, tooltips, and interactive exploration of data in the browser or RStudio Viewer.

* **GoogleVis** – An R package that provides an interface to the **Google Charts API**. It creates interactive charts (motion charts, maps, gauges, etc.) that can be viewed in a web browser.

* **ggbio** – An extension of **ggplot2** that implements the grammar of graphics for **biological data**. It provides specialized visualizations for genomic features, tracks, and annotation data.

.small[https://plotly.com/r/  
https://cran.r-project.org/web/packages/googleVis/vignettes/googleVis_intro.html  
https://bioconductor.org/packages/ggbio/
]