Introduction to ggplot2

class: center, middle, inverse, title-slide

.title[
# Introduction to ggplot2
]
.subtitle[
## From Base R to Grammar of Graphics
]
.author[
### Bioinfomatics Core Facility
]
.institute[
### CECAD
]
.date[
### 2025-09-18
]

---

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# Slides & Code

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[Intermediate_R_course](https://github.com/CECADBioinformaticsCoreFacility/Intermediate_R_Course_2025)

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### Github webpage

[https://cecadbioinformaticscorefacility.github.io/Intermediate_R_Course_2025/](https://cecadbioinformaticscorefacility.github.io/Intermediate_R_Course_2025/)

]

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]

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---
class: inverse, center, middle

# From Base R to ggplot2
## Why Make the Switch?

---

# Learning Objectives

.learning-objective[
**By the end of this session, you will be able to:**

1. 🎯 **Understand** the difference between base R and ggplot2 plotting approaches
2. 🔧 **Create** basic plots using ggplot2 syntax
3. 🎨 **Customize** plots with themes, colors, and labels
4. 📊 **Choose** appropriate geoms for different data types
5. 🚀 **Apply** the Grammar of Graphics to build complex visualizations
]

---

# Base R vs ggplot2: Comparison

.panelset.sideways[

.panel[.panel-name[Base R Approach]

.pull-left[
**Procedural Thinking**
- "Draw a canvas"
- "Add points"
- "Add lines"
- "Modify colors"

**Characteristics:**
- ✅ Quick for simple plots
- ✅ Part of base R (no dependencies)
- ✅ Fast execution
- ❌ Hard to modify complex plots
- ❌ Inconsistent parameter names
- ❌ Limited layering capabilities

*_Evidence provided in following slides_
]

.pull-right[

``` r
# Base R approach
plot(iris$Sepal.Length, iris$Sepal.Width,
     col = as.numeric(iris$Species),
     pch = 16, cex = 1.2,
     main = "Sepal Length vs Width",
     xlab = "Sepal Length (cm)",
     ylab = "Sepal Width (cm)")
legend("topright", legend = levels(iris$Species),
       col = 1:3, pch = 16)
```

<img src="Day1_ggplot2-tmp-only_files/figure-html/base-r-example-1.png" width="100%" />
]
]

.panel[.panel-name[ggplot2 Approach]

.pull-left[
**Declarative Thinking**
- "Map data to aesthetics"
- "Choose geometric objects"
- "Apply transformations"
- "Layer components"

**Characteristics:**
- ✅ Consistent grammar
- ✅ Easy to modify and extend
- ✅ Excellent for complex plots
- ✅ Beautiful defaults
- ❌ Steeper learning curve
- ❌ Verbose for simple plots
- ❌ Additional dependency
- ❌ Can be slower for large datasets
]

.pull-right[

``` r
# ggplot2 approach
library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width, 
                 color = Species)) +
  geom_point(size = 2) +
  labs(title = "Sepal Length vs Width",
       x = "Sepal Length (cm)",
       y = "Sepal Width (cm)") +
  theme_minimal()
```

<img src="Day1_ggplot2-tmp-only_files/figure-html/ggplot2-example-1.png" width="100%" />
]
]

.panel[.panel-name[Fair Assessment]

.comparison-box[
**🤔 Both approaches have trade-offs:**

**Base R Strengths:** Speed, simplicity, no dependencies
**Base R Weaknesses:** Consistency, modification difficulty

**ggplot2 Strengths:** Grammar, extensibility, beautiful output  
**ggplot2 Weaknesses:** Learning curve, verbosity, performance
]

**Key Question:** What matters most for your use case?
- Quick exploration → Base R might win
- Publication plots → ggplot2 often wins
- Team collaboration → ggplot2 usually wins

]

---

# Evidence: Base R Inconsistency Issues

.pull-left[
**Problem 1: Parameter Name Inconsistency**

``` r
# Different functions, different parameter names
plot(x, y, col = "red")      # col
barplot(height, color = "red") # color  
hist(x, col = "red")         # col again
boxplot(x, col = "red")      # col
pie(x, col = "red")          # col
```

**Problem 2: Color Management**

``` r
# Base R: Good practice uses variables
my_colors <- c("red", "blue", "green")
plot(..., col = my_colors)
legend(..., col = my_colors) # Consistent!

# But still requires manual legend management
# ggplot2: Automatic color/legend coordination
ggplot(..., aes(color = group)) + geom_point()
```
]

.pull-right[
**Problem 3: Limited Layering**

``` r
# Base R: Hard to add multiple layers elegantly
plot(iris$Sepal.Length, iris$Sepal.Width, 
     col = "lightgray", pch = 16)
# Adding trend line requires separate function
abline(lm(Sepal.Width ~ Sepal.Length, data = iris), col = "blue")
```

``` r
# Adding species-specific trends? Gets complicated...
```

**These aren't "dealbreakers", but they require workarounds**
]

---

# Evidence: ggplot2 Disadvantages

.pull-left[
**Problem 1: Learning Curve**

``` r
# Beginners often struggle with:
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = "red")) +
  geom_point()  # Why is there a legend for "red"?

# vs the simpler Base R:
plot(iris$Sepal.Length, iris$Sepal.Width, col = "red")
```

**Problem 2: Verbosity for Simple Tasks**

``` r
# Base R: 1 line
hist(iris$Sepal.Length)

# ggplot2: 2-3 lines minimum  
ggplot(iris, aes(x = Sepal.Length)) + 
  geom_histogram()
```
]

.pull-right[
**Problem 3: Performance with Large Data**

``` r
# For millions of points, base R can be faster
system.time(plot(big_data$x, big_data$y))
# vs
system.time(ggplot(big_data, aes(x, y)) + geom_point())
```

**Problem 4: Dependency Management**

``` r
# Base R plots work everywhere
plot(x, y)

# ggplot2 requires installation and loading
library(ggplot2)  # What if it's not available?
ggplot(data, aes(x, y)) + geom_point()
```

]

---

# The Grammar of Graphics: Building Blocks

.pull-left[
**Think in Components:**

1. 📊 **Data** - Your dataset
2. 🎨 **Aesthetics** - Visual properties (x, y, color)
3. 🔷 **Geometries** - Plot type (points, lines, bars)
4. 📐 **Scales** - How data maps to visuals
5. 🎯 **Facets** - Subplots
6. 🎨 **Themes** - Overall appearance

**Like LEGO blocks:** Combine simple pieces → complex structures
]

.pull-right[

``` r
# Step-by-step building
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +  # Data + Aesthetics
  geom_point(aes(color = Species), size = 3) +          # Geometry
  scale_color_manual(values = c("#E74C3C", "#3498DB", "#2ECC71")) + # Scale
  labs(title = "Grammar of Graphics in Action") +       # Labels
  theme_minimal()                                       # Theme
```

<img src="Day1_ggplot2-tmp-only_files/figure-html/grammar-demo-1.png" width="100%" />
]

**Key Insight:** Each `+` adds a layer. Order matters!

---

# Building Plots Step by Step

.panelset[

.panel[.panel-name[Step 1: Data + Aesthetics]

``` r
# Start with data and aesthetic mappings
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species))
```

**Result:** Empty plot with scales, but no visual elements yet
]

.panel[.panel-name[Step 2: Add Geometry]

``` r
# Add geometric objects to represent data
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 2)
```

**Result:** Now we can see the data points!
]

.panel[.panel-name[Step 3: Enhance]

``` r
# Add labels and theme
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 2) +
  labs(title = "Sepal Dimensions", x = "Length (cm)", y = "Width (cm)") +
  theme_minimal()
```

**Result:** Professional-looking plot ready for presentation
]

]

---

# Essential ggplot2 Components

.panelset.sideways[

.panel[.panel-name[Aesthetics (aes)]

.pull-left[
**Aesthetics map data to visual properties:**

- `x`, `y` - Position
- `color` - Color of points/lines
- `fill` - Fill color of shapes
- `size` - Size of points
- `alpha` - Transparency
- `shape` - Point shapes
- `linetype` - Line styles

**Global vs Local aesthetics:**

``` r
# Global - applies to all layers
ggplot(data, aes(x = var1, y = var2))

# Local - applies to specific geom
geom_point(aes(color = var3))
```
]

.pull-right[

``` r
# Multiple aesthetics example
ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width)) +
  geom_point(aes(color = Species,
                 size = Petal.Length,
                 shape = Species),
             alpha = 0.7) +
  labs(title = "Multiple Aesthetics in Action") +
  theme_minimal()
```

.pro-tip[
💡 **Remember:** Aesthetics go inside `aes()`, fixed values go outside!
]
]
]

.panel[.panel-name[Common Geometries]

.pull-left[
**Choose the right geom for your data:**

- **Continuous vs Continuous:** `geom_point()`, `geom_smooth()`
- **Categorical vs Continuous:** `geom_boxplot()`, `geom_violin()`
- **Distributions:** `geom_histogram()`, `geom_density()`
- **Categorical data:** `geom_bar()`, `geom_col()`
- **Time series:** `geom_line()`, `geom_area()`

**Quick decision guide:**
- 1 variable → histogram, density
- 2 variables → scatterplot, boxplot
- Time series → line plot
- Categories → bar chart
]

.pull-right[
<div class="image-with-caption">
  <img src="images/gallery.png" alt="ggplot2 geoms gallery" width="100%", height="350">
  <p class="caption"><font size="3"><b>source:</b> https://r-graph-gallery.com/</font></p>
</div>

.comparison-box[
**🎯 Rule of Thumb:** Start with the data relationship, then choose the geom that best shows that relationship.
]
]
]

.panel[.panel-name[Base R vs ggplot2 Geoms]

.pull-left[
**.base-r[Base R Functions]**

``` r
# Scatterplot
plot(x, y)

# Histogram  
hist(x)

# Boxplot
boxplot(y ~ group)

# Barplot
barplot(table(x))

# Line plot
plot(x, y, type = "l")
```
]

.pull-right[
**.ggplot2[ggplot2 Equivalents]**

``` r
# Scatterplot
geom_point()

# Histogram
geom_histogram()

# Boxplot  
geom_boxplot()

# Barplot
geom_bar() # or geom_col()

# Line plot
geom_line()
```
]

.comparison-box[
**Key Difference:** ggplot2 geoms are more flexible and can be easily combined!
]

]

---

# Hands-On: Your First ggplot2 Plot

.panelset.sideways[

.panel[.panel-name[The Challenge]

.learning-objective[
**🎯 Your Turn:** Create a scatterplot comparing Base R and ggplot2

**Goal:** Plot Sepal.Length vs Sepal.Width, colored by Species

**Steps:**
1. First, try it in Base R
2. Then, recreate it in ggplot2
3. Compare the code complexity
4. Add one enhancement to the ggplot2 version
]

.pro-tip[
💡 **Tip:** Don't just copy-paste! Type it out to build muscle memory.
]

]

.panel[.panel-name[Base R Solution]

.pull-left[

``` r
# Base R approach
plot(iris$Sepal.Length, 
     iris$Sepal.Width,
     col = c("red", "green", "blue")[iris$Species],
     pch = 16,
     cex = 1.2,
     main = "Sepal Dimensions by Species",
     xlab = "Sepal Length (cm)",
     ylab = "Sepal Width (cm)")

# Add legend (gets tricky!)
legend("topright", 
       legend = levels(iris$Species),
       col = c("red", "green", "blue"), 
       pch = 16,
       title = "Species")
```

]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/base-r-solution-show-1.png" width="100%" />

**Observations:**
- 🔴 Manual color assignment
- 🔴 Separate legend command
- 🔴 Hard to modify later
- ✅ Quick for simple plots
]
]

.panel[.panel-name[ggplot2 Solution]

.pull-left[

``` r
# ggplot2 approach
library(ggplot2)

ggplot(iris, 
       aes(x = Sepal.Length, 
           y = Sepal.Width, 
           color = Species)) +
  geom_point(size = 2.5) +
  labs(title = "Sepal Dimensions by Species",
       x = "Sepal Length (cm)",
       y = "Sepal Width (cm)") +
  theme_minimal()
```

**Observations:**
- ✅ Automatic color mapping
- ✅ Automatic legend
- ✅ Easy to modify
- ✅ Consistent syntax
- ✅ Beautiful defaults
]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/ggplot2-solution-show-1.png" width="100%" />
]
]

.panel[.panel-name[Enhancement Challenge]

.pull-left[
**Now let's enhance the ggplot2 version:**

``` r
ggplot(iris, 
       aes(x = Sepal.Length, 
           y = Sepal.Width, 
           color = Species)) +
  geom_point(size = 3, alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Sepal Dimensions by Species",
       subtitle = "With trend lines",
       x = "Sepal Length (cm)",
       y = "Sepal Width (cm)",
       caption = "Data: iris dataset") +
  theme_minimal() +
  theme(legend.position = "bottom")
```

**💭 Try this in Base R... Good luck!**
]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/enhanced-plot-show-1.png" width="100%" />

.pro-tip[
💡 **See the power?** Adding trend lines in ggplot2 = one line of code!
]
]
]

]

---

# Base R Solution: The Challenge Accepted!

.pull-left[
**Challenge:** Recreate the same enhanced plot in Base R

**Requirements:**
- Scatter plot with colored points by species
- Different colors for each species
- Trend lines for each species
- Professional labels (title, subtitle, axis labels)
- Legend with proper positioning
- Alpha transparency for points

**Let's see the complexity...**
]

.pull-right[

``` r
# Base R equivalent - much more complex!
# Set up colors and transparency
colors <- c("setosa" = "#F8766D", "versicolor" = "#00BA38", "virginica" = "#619CFF")
alpha_hex <- "B3"  # 70% transparency in hex

# Create the plot
plot(iris$Sepal.Length, iris$Sepal.Width,
     col = paste0(colors[iris$Species], alpha_hex),
     pch = 16, cex = 1.5,
     main = "",
     xlab = "", ylab = "",
     xlim = range(iris$Sepal.Length),
     ylim = range(iris$Sepal.Width))

# Add trend lines for each species (complex!)
for(species in levels(iris$Species)) {
  subset_data <- iris[iris$Species == species, ]
  lm_model <- lm(Sepal.Width ~ Sepal.Length, data = subset_data)
  x_seq <- seq(min(subset_data$Sepal.Length), max(subset_data$Sepal.Length), 
               length.out = 100)
  y_pred <- predict(lm_model, newdata = data.frame(Sepal.Length = x_seq))
  lines(x_seq, y_pred, col = colors[species], lwd = 2)
}

# Add all the labels manually
title(main = "Sepal Dimensions by Species", 
      sub = "With trend lines",
      xlab = "Sepal Length (cm)", 
      ylab = "Sepal Width (cm)")

# Add legend
legend("bottomright", legend = levels(iris$Species),
       col = colors, pch = 16, pt.cex = 1.5,
       title = "Species", bty = "n")

# Add caption (requires manual positioning)
mtext("Data: iris dataset", side = 1, line = 3, adj = 1, cex = 0.8, col = "gray50")
```

<img src="Day1_ggplot2-tmp-only_files/figure-html/base-r-enhanced-1.png" width="100%" />
]

---

# Complexity Comparison: ggplot2 vs Base R

.comparison-box[

.pull-left[
### ggplot2 Version

``` r
ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width, 
                 color = Species)) +
  geom_point(size = 3, alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Sepal Dimensions by Species",
       subtitle = "With trend lines",
       x = "Sepal Length (cm)",
       y = "Sepal Width (cm)",
       caption = "Data: iris dataset") +
  theme_minimal() +
  theme(legend.position = "bottom")
```
]

.pull-right[
### Base R Version

``` r
# Set up colors and transparency
colors <- c("setosa" = "#F8766D", "versicolor" = "#00BA38", 
            "virginica" = "#619CFF")
alpha_hex <- "B3"

# Create base plot
plot(iris$Sepal.Length, iris$Sepal.Width,
     col = paste0(colors[iris$Species], alpha_hex),
     pch = 16, cex = 1.5, main = "", xlab = "", ylab = "")

# Manual trend lines (complex loop!)
for(species in levels(iris$Species)) {
  subset_data <- iris[iris$Species == species, ]
  lm_model <- lm(Sepal.Width ~ Sepal.Length, data = subset_data)
  x_seq <- seq(min(subset_data$Sepal.Length), 
               max(subset_data$Sepal.Length), length.out = 100)
  y_pred <- predict(lm_model, newdata = data.frame(Sepal.Length = x_seq))
  lines(x_seq, y_pred, col = colors[species], lwd = 2)
}

# Manual labels and legend
title(main = "Sepal Dimensions by Species", 
      sub = "With trend lines", xlab = "Sepal Length (cm)", 
      ylab = "Sepal Width (cm)")
legend("bottomright", legend = levels(iris$Species),
       col = colors, pch = 16, pt.cex = 1.5, title = "Species")
mtext("Data: iris dataset", side = 1, line = 3, adj = 1, cex = 0.8)
```
]
]

.pro-tip[
🎯 **Key Insight:** ggplot2's power becomes clear when creating complex, multi-layered visualizations. The grammar of graphics makes complex plots simple and intuitive!
]

---

# Plot Types: Base R vs ggplot2 Comparison

.panelset.sideways[

.panel[.panel-name[Histograms]

.pull-left[
**.base-r[Base R]**

``` r
# Base R histogram
hist(iris$Sepal.Length,
     breaks = 20,
     col = "lightblue",
     main = "Distribution of Sepal Length",
     xlab = "Sepal Length (cm)",
     border = "white")
```

**Issues:**
- Fixed color for all data
- Hard to show groups
- Limited customization
]

.pull-right[
**.ggplot2[ggplot2]**

``` r
# ggplot2 histogram
ggplot(iris, aes(x = Sepal.Length, 
                 fill = Species)) +
  geom_histogram(bins = 20, 
                 alpha = 0.7,
                 position = "identity") +
  labs(title = "Distribution of Sepal Length",
       x = "Sepal Length (cm)",
       y = "Count") +
  theme_minimal()
```

**Advantages:**
- Easy grouping by Species
- Transparency control
- Professional appearance
]

.pull-left[
<img src="Day1_ggplot2-tmp-only_files/figure-html/base-hist-show-1.png" width="100%" />
]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/gg-hist-show-1.png" width="100%" />
]

]

.panel[.panel-name[Boxplots]

.pull-left[
**.base-r[Base R]**

``` r
# Base R boxplot
boxplot(Sepal.Length ~ Species, 
        data = iris,
        col = c("red", "green", "blue"),
        main = "Sepal Length by Species",
        xlab = "Species",
        ylab = "Sepal Length (cm)")
```

**Manual work needed:**
- Specify colors manually
- Limited styling options
- Hard to add additional layers
]

.pull-right[
**.ggplot2[ggplot2]**

``` r
# ggplot2 boxplot
ggplot(iris, aes(x = Species, 
                 y = Sepal.Length,
                 fill = Species)) +
  geom_boxplot(alpha = 0.7) +
  geom_jitter(width = 0.2, alpha = 0.5) +
  labs(title = "Sepal Length by Species",
       x = "Species",
       y = "Sepal Length (cm)") +
  theme_minimal() +
  theme(legend.position = "none")
```

**Easy enhancements:**
- Automatic colors
- Easy to add data points
- Beautiful themes
]

.comparison-box[
**Compare the complexity:**
]

.pull-left[
<img src="Day1_ggplot2-tmp-only_files/figure-html/base-box-show-1.png" width="100%" />
]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/gg-box-show-1.png" width="100%" />
]

]

.panel[.panel-name[Complex Plots]

**.ggplot2[What's nearly impossible in Base R but easy in ggplot2:]**

``` r
# Faceted plot with multiple geoms
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point(aes(color = Species, size = Petal.Length), alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE, color = "black", linetype = "dashed") +
  facet_wrap(~ Species, scales = "free") +
  labs(title = "Sepal Relationships Across Species",
       subtitle = "Point size represents petal length",
       x = "Sepal Length (cm)", y = "Sepal Width (cm)") +
  theme_minimal() +
  theme(legend.position = "bottom")
```

.pro-tip[
💡 **Challenge:** Try recreating this plot in Base R... You'll appreciate ggplot2's elegance!
]

]

---

# Base R Solution: "Nearly Impossible" Challenge

.pull-left[
**ggplot2 Version (8 lines):**

``` r
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point(aes(color = Species, size = Petal.Length), 
             alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE, 
              color = "black", linetype = "dashed") +
  facet_wrap(~ Species, scales = "free") +
  labs(title = "Sepal Relationships Across Species",
       subtitle = "Point size represents petal length") +
  theme_minimal()
```

**✅ What ggplot2 handles automatically:**
- Faceting across species
- Color/size mapping
- Consistent scales
- Professional styling
- Easy modifications
]

.pull-right[
**Base R Attempt:**

<button onclick="showFullCode()" style="background-color: #FF6B6B; color: white; border: none; padding: 8px 16px; border-radius: 4px; cursor: pointer; font-weight: bold; margin: 10px 0;">
🔍 Click to see the FULL Base R solution (MUCH more lines!)
</button>

<div id="fullCodeModal" style="display: none; position: fixed; z-index: 1000; left: 0; top: 0; width: 100%; height: 100%; background-color: rgba(0,0,0,0.5);">
  <div style="background-color: white; margin: 5% auto; padding: 20px; border-radius: 8px; width: 80%; height: 80%; overflow-y: auto; position: relative;">
    <span onclick="closeFullCode()" style="color: #aaa; float: right; font-size: 28px; font-weight: bold; cursor: pointer;">&times;</span>
    <h3>Complete Base R Solution</h3>
    <div class="tiny-code" style="font-size: 10px;">
      <pre><code># Base R faceting with legends

# Layout: 3 plots + 1 legend panel
layout(matrix(1:4, 1), widths = c(1, 1, 1, 0.6))
par(mar = c(4, 4, 3, 1), oma = c(0, 0, 4, 0))

cols <- c(setosa="#F8766D", versicolor="#00BA38", virginica="#619CFF")
sz   <- function(x) (x - min(iris$Petal.Length)) / diff(range(iris$Petal.Length)) * 2 + 0.5
xr   <- range(iris$Sepal.Length); yr <- range(iris$Sepal.Width)

for (sp in levels(iris$Species)) {
  d <- iris[iris$Species == sp, ]
  plot(Sepal.Width ~ Sepal.Length, d, col=paste0(cols[sp], "B3"), pch=16,
       cex=sz(d$Petal.Length), xlim=xr, ylim=yr, main=sp,
       xlab=ifelse(sp=="versicolor", "Sepal Length (cm)", ""),
       ylab=ifelse(sp=="setosa", "Sepal Width (cm)", ""))
  abline(lm(Sepal.Width ~ Sepal.Length, d), lty=2, lwd=1.5)
}

mtext("Sepal Relationships Across Species", side=3, outer=TRUE, line=2.5, cex=1.2, font=2)
mtext("Point size represents petal length", side=3, outer=TRUE, line=1, col="gray40")

# Legend panel
par(mar=c(2,2,2,2)); plot.new()
legend("topleft", names(cols), col=cols, pch=16, title="Species", bty="n", cex=1.1)
vals <- with(iris, c(min(Petal.Length), median(Petal.Length), max(Petal.Length)))
legend("topright", paste0(round(vals,1), " cm"), pt.cex=sz(vals), pch=16, col="gray50",
       title="Petal Length", bty="n", cex=1.1)
</code></pre>
    </div>
  </div>
</div>

function closeFullCode() {
    document.getElementById("fullCodeModal").style.display = "none";
}

// Close modal when clicking outside of it
window.onclick = function(event) {
    var modal = document.getElementById("fullCodeModal");
    if (event.target == modal) {
        modal.style.display = "none";
    }
}
</script>

**❌ Base R Problems:**
- 20+ lines vs 6 lines!
- Manual everything
- Fragile and hard to modify
- Legends require separate plots
]

---

# Why Base R Makes This "Nearly Impossible"
.comparison-box[

.pull-left[

### ggplot2 Version (Concise & Powerful)

**Lines of code:** \~8
**Complexity:** Low
**Maintainability:** High

✅ **Automatic faceting** with `facet_wrap()`
✅ **Consistent scales** handled automatically
✅ **Color/size mapping** with `aes()`
✅ **Professional themes** built-in
✅ **Easy to modify** any component

``` r
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point(aes(color = Species, size = Petal.Length), 
             alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE, 
              color = "black", linetype = "dashed") +
  facet_wrap(~ Species) +
  labs(title = "Sepal Relationships Across Species",
       subtitle = "Point size represents petal length",
       x = "Sepal Length (cm)", y = "Sepal Width (cm)") +
  theme_minimal()
```

]

.pull-right[

### Base R Version (Verbose & Manual)

**Lines of code:** \~25–30
**Complexity:** High
**Maintainability:** Low

❌ **Manual layout management** with `layout()` / `par()`
❌ **Scale coordination** requires `xlim` / `ylim`
❌ **Custom size scaling** with helper functions
❌ **Legends need extra steps** for unified placement
❌ **Styling (themes, transparency)** requires hacks

**Key Challenges:**

1. No built-in faceting → must loop and set ranges manually
2. Legends → often need a dedicated panel
3. Point size mapping → requires writing a scaling function
4. Layout and titles → managed with `par()` / `mtext()`
5. Transparency → only via hex color codes

**Result:** Works, but verbose, fragile, and much harder to maintain than `ggplot2`.
]
]

.pro-tip[
🎯 **The Point:** This demonstrates why ggplot2 exists - it makes complex visualizations simple and maintainable!
]

---

# Customization: Making Beautiful Plots

.panelset.sideways[

.panel[.panel-name[Themes & Styling]

.pull-left[
**Built-in themes:**

``` r
# Clean and minimal
theme_minimal()

# Classic look  
theme_classic()

# Black and white
theme_bw()

# Void (no axes)
theme_void()

# Dark theme
theme_dark()
```

**Custom theme elements:**

``` r
theme(
  plot.title = element_text(size = 16),
  axis.text = element_text(size = 12),
  legend.position = "bottom",
  panel.grid = element_blank()
)
```
]

.pull-right[

``` r
library(gridExtra)

p1 <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() + labs(title = "theme_minimal()") + theme_minimal()

p2 <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() + labs(title = "theme_classic()") + theme_classic()

p3 <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() + labs(title = "theme_bw()") + theme_bw()

p4 <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() + labs(title = "theme_dark()") + theme_dark()

grid.arrange(p1, p2, p3, p4, ncol = 2)
```

<img src="Day1_ggplot2-tmp-only_files/figure-html/theme-comparison-1.png" width="100%" />
]
]

.panel[.panel-name[Colors & Scales]

.pull-left[
**Color customization:**

``` r
# Manual colors
ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width, 
                 color = Species)) +
  geom_point(size = 3) +
  scale_color_manual(
    values = c("setosa" = "#FF6B6B",
               "versicolor" = "#4ECDC4", 
               "virginica" = "#45B7D1")) +
  labs(title = "Custom Color Palette") +
  theme_minimal()
```

**Built-in palettes:**

``` r
# Viridis (colorblind-friendly)
scale_color_viridis_d()

# Brewer palettes
scale_color_brewer(palette = "Set1")

# Manual specification
scale_color_manual(values = c(...))
```
]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/color-demo-show-1.png" width="100%" />

.pro-tip[
💡 **Tip:** You can think about using colorblind-friendly palettes 
like viridis or ColorBrewer!
]
]
]

.panel[.panel-name[Labels & Annotations]

.pull-left[
**Comprehensive labeling:**

``` r
ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width, 
                 color = Species)) +
  geom_point(size = 3, alpha = 0.7) +
  labs(
    title = "Iris Sepal Measurements",
    subtitle = "Relationship between length and width",
    x = "Sepal Length (cm)",
    y = "Sepal Width (cm)",
    color = "Iris Species",
    caption = "Data source: iris dataset"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16),
    plot.subtitle = element_text(hjust = 0.5, size = 12)
  )
```
]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/labels-demo-show-1.png" width="100%" />

.comparison-box[
**Remember:** Good plots tell a story. Use titles, subtitles, and captions to guide your reader!
]
]
]

]

---

# Advanced ggplot2 Features

.panelset.sideways[

.panel[.panel-name[Faceting]

.pull-left[
**Split your data into subplots:**

**facet_wrap()** - One variable

``` r
facet_wrap(~ Species)
facet_wrap(~ Species, ncol = 2)
```

**facet_grid()** - Two variables

``` r
facet_grid(Species ~ .)
facet_grid(. ~ Species)
facet_grid(var1 ~ var2)
```

**Free scales:**

``` r
facet_wrap(~ Species, scales = "free")
facet_wrap(~ Species, scales = "free_x")
```
]

.pull-right[

``` r
# Create size categories for demonstration
iris$Size <- ifelse(iris$Sepal.Length > median(iris$Sepal.Length), 
                    "Large", "Small")

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Species)) +
  geom_point(size = 2) +
  facet_wrap(~ Size, labeller = label_both) +
  labs(title = "Petal Dimensions by Size Category") +
  theme_minimal()
```

.pro-tip[
💡 **Use Case:** Perfect for comparing patterns across groups or conditions!
]
]
]

.panel[.panel-name[Multiple Geoms]

.pull-left[
**Layer different plot types:**

``` r
ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  # Box plot base
  geom_boxplot(aes(fill = Species), 
               alpha = 0.7, width = 0.5) +
  # Add individual points
  geom_jitter(width = 0.2, alpha = 0.6, size = 2) +
  # Add mean points
  stat_summary(fun = mean, geom = "point", 
               size = 4, color = "red", shape = 17) +
  labs(title = "Multiple Geoms in One Plot",
       subtitle = "Boxplot + Points + Means",
       y = "Sepal Length (cm)") +
  theme_minimal() +
  theme(legend.position = "none")
```

**Each layer can have different aesthetics!**
]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/multi-geom-show-1.png" width="100%" />

.comparison-box[
**Layering Power:** Each `+` adds a new layer. Order matters - layers are drawn bottom to top!
]
]
]

.panel[.panel-name[Statistical Transformations]

.pull-left[
**Built-in statistical functions:**

``` r
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point(aes(color = Species), size = 2) +
  # Linear regression
  geom_smooth(method = "lm", se = TRUE, color = "black") +
  # Separate regression by species
  geom_smooth(aes(color = Species), method = "lm", se = FALSE) +
  labs(title = "Linear Relationships",
       subtitle = "Overall trend (black) vs by species") +
  theme_minimal()
```

**Available stat functions:**
- `stat_smooth()` - Regression lines
- `stat_summary()` - Summary statistics  
- `stat_density()` - Density estimates
- `stat_count()` - Counts
]

.pull-right[
<img src="Day1_ggplot2-tmp-only_files/figure-html/stats-demo-show-1.png" width="100%" />

.pro-tip[
💡 **Power Feature:** ggplot2 can calculate statistics on the fly - no need to pre-compute!
]
]
]

]

---

# Common Mistakes & Troubleshooting

.panelset.sideways[

.panel[.panel-name[Syntax Errors]

.pull-left[
**❌ Common Mistakes:**

1. **Forgetting the `+`**

``` r
# Wrong
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))
geom_point()
```

2. **Using `%>%` instead of `+`**

``` r
# Wrong (dplyr syntax)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) %>%
  geom_point()
```
]

.pull-right[
**❌ More Common Issues:**

3. **Aesthetics outside `aes()`**

``` r
# Wrong - color should be inside aes()
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, 
                 color = "blue")) + geom_point()
```

4. **Missing data argument**

``` r
# Wrong
ggplot(aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point()
```
]

.pro-tip[
💡 **Debug Tip:** Build your plot step by step. Add one layer at a time!
]
]

.panel[.panel-name[Aesthetic Confusion]

.comparison-box[
**🤔 When to use `aes()` vs when not to?**

**Inside `aes()`:** When mapping data variables to visual properties

**Outside `aes()`:** When setting fixed visual properties
]

.pull-left[
**❌ Wrong:**

``` r
# This creates a legend for "red"!
ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width,
                 color = "red")) +
  geom_point() +
  labs(title = "Oops! Unwanted legend")
```

<img src="Day1_ggplot2-tmp-only_files/figure-html/aes-wrong-show-1.png" width="100%" />
]

.pull-right[
**✅ Right:**

``` r
# Fixed color for all points
ggplot(iris, aes(x = Sepal.Length, 
                 y = Sepal.Width)) +
  geom_point(color = "red") +
  labs(title = "Clean! No unwanted legend")
```

<img src="Day1_ggplot2-tmp-only_files/figure-html/aes-right-show-1.png" width="100%" />
]

]

.panel[.panel-name[Quick Fixes]

.pull-left[
**Problem: Plot doesn't show**

``` r
# Store plot in variable
p <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point()
# Don't forget to display it!
p  # or print(p)
```
**Problem: Overlapping text**

``` r
# Rotate axis labels
theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
**Problem: Legend takes too much space**

``` r
# Move legend
theme(legend.position = "bottom")
# Or remove it
theme(legend.position = "none")
```
]

.pull-right[
**Problem: Need larger text**

``` r
# Increase base size
theme_minimal(base_size = 14)
# Or specific elements
theme(text = element_text(size = 14))
```
**Problem: Too many decimal places**

``` r
# Format scales
scale_x_continuous(labels = scales::number_format(accuracy = 0.1))
```
**Problem: Want to save plot**

``` r
ggsave("my_plot.png", width = 8, height = 6, dpi = 300)
```
]

]

---

# Key Takeaways & Next Steps

.panelset.sideways[

.panel[.panel-name[What We Learned]

.learning-objective[
**🎯 Key Concepts Mastered:**

✅ **Grammar of Graphics** - Building plots layer by layer

✅ **Base R vs ggplot2** - When and why to choose ggplot2

✅ **Essential Components** - Data, aesthetics, geometries, themes

✅ **Customization** - Colors, themes, labels, and scales

✅ **Advanced Features** - Faceting, multiple geoms, statistical layers

✅ **Troubleshooting** - Common mistakes and how to fix them
]

.comparison-box[
**🧠 Mental Model Change:**

**Before:** "How do I draw this specific plot?"

**After:** "How do I map my data to visual elements and layer components?"
]

]

.panel[.panel-name[ggplot2 vs Base R Summary]

.pull-left[
**.base-r[Base R - Use When:]**
- ✅ Quick exploratory plots
- ✅ Simple, one-off visualizations  
- ✅ Don't want extra dependencies
- ✅ Working with base R workflow

**.ggplot2[ggplot2 - Use When:]**
- 🎨 Need professional-quality plots
- 🔧 Want easy customization
- 📊 Creating complex visualizations
- 🚀 Building reproducible reports
- 👥 Sharing/presenting to others
]

.pull-right[
**The Verdict:**

.pro-tip[
💡 **Best Practice:** Learn both! Use Base R for quick checks, ggplot2 for final presentations.

**Career Tip:** ggplot2 skills are highly valued in data science roles!
]

**Remember:**
- Start with simple plots
- Build complexity gradually  
- Focus on telling a story with your data
- Practice makes perfect!
]

]

.panel[.panel-name[Next Steps]

.learning-objective[

**Immediate Practice:**
1. Recreate your favorite Base R plots in ggplot2
2. Explore different themes and color palettes
3. Try the exercises with your own data

**Advanced Topics to Explore:**
- Animations with `gganimate`  
- Extensions: `ggridges`, `ggbeeswarm`, `patchwork`
- Custom themes for your lab
]

- 💡 [TidyTuesday](https://github.com/rfordatascience/tidytuesday) for practice

.pull-right[
**Resources:**
- 📚 [R for Data Science](https://r4ds.had.co.nz/) (Chapter 3)
- 🎨 [ggplot2 Gallery](https://r-graph-gallery.com/)
- 📖 [ggplot2 Book](https://ggplot2-book.org/)

]

.pro-tip[
💡 **Final Tip:** The best way to learn ggplot2 is to use it regularly. Start incorporating it into your daily data analysis workflow!
]

]

---
class: inverse, center, middle

# Thank You!

**Remember:** Every expert was once a beginner! 🌱→🌳