Data Visualization and Design

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# Data Visualization and Design
## Using ggplot2 
### Omni Analytics Group

---

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# POLISHING PLOTS

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# Visual Appearance

---

## Built-In Themes

![](polishing-plots_files/figure-html/unnamed-chunk-2-1.png)

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## Other Themes: ggthemes a ggplot2 extension

![](polishing-plots_files/figure-html/unnamed-chunk-3-1.png)

---

## Setting Themes

You can globally set a theme with the `theme_set()` function:

```r
theme_set(theme_bw())

ggplot(df, aes(x = Wallet, y = Volatility, colour = Type)) + geom_point()
```

![](polishing-plots_files/figure-html/unnamed-chunk-4-1.png)

---

## Elements in a theme

The function `theme()` is used to control non-data parts of the graph including:

- **Line elements**: axis lines, minor and major grid lines, plot panel border, axis ticks background color, etc.  
- **Text elements**: plot title, axis titles, legend title and text, axis tick mark labels, etc.  
- **Rectangle elements**: plot background, panel background, legend background, etc.

There is a specific function to modify each of these three elements :

- `element_line()` to modify the line elements of the theme
- `element_text()` to modify the text elements
- `element_rect()` to change the appearance of the rectangle elements
- `element_blank()` to draw nothing and assign no space

**Note**: `rel()` is used to specify sizes relative to the parent, `margins()` is used to specify the margins of elements.

---

## Modifying a plot

```r
p1 <- ggplot(punks) + 
 geom_bar(aes(x = Type, colour = TypeSkin, fill = TypeSkin))

p2 <- p1 + theme_classic() + theme(
 ## modify plot background
 plot.background = element_rect(fill = "lightskyblue1",colour = "pink",size = 0.5, linetype = "longdash")
 )
```

![](polishing-plots_files/figure-html/unnamed-chunk-6-1.png)

---

## Plot Legends

```r
p3 <- p2 + theme(
 ### move and modify legend 
 legend.title = element_blank(),
 legend.position = "top", 
 legend.key = element_rect(fill = "lightskyblue1", color = "lightskyblue1"),
 legend.background = element_rect( fill = "lightskyblue1",color = "pink", size = 0.5,linetype = "longdash")
 )
```

![](polishing-plots_files/figure-html/unnamed-chunk-8-1.png)

---

## Modifying Axes

```r
p4 <- p3 + theme(
 ### remove axis ticks
 axis.ticks=element_blank(),
 ### modify axis lines
 axis.line.y = element_line(colour = "pink", size = 1, linetype = "dashed"),
 axis.line.x = element_line(colour = "pink", size = 1.2, linetype = "dashed"))
```

![](polishing-plots_files/figure-html/unnamed-chunk-10-1.png)

---

## Plot Labels

Can be modified in several ways:
- `labs()`, `xlab()`, `ylab()`, `ggtitle()`
- You can also set axis and legend labels in the individual scales (using the first argument, the name)
.

```r
p5 <- p4 + labs(x = "Class of car", 
 y = "",
 title = "Cars by class and manufacturer",
 subtitle = "With a custom theme!!")
```

![](polishing-plots_files/figure-html/unnamed-chunk-12-1.png)

---

## Zooming

```r
p <- ggplot(df, aes(x = Wallet, y = Volatility, colour = Type)) + geom_point()
p_zoom_in <- p + xlim(2, 4) + ylim(10, 25)
p_zoom_out <- p + xlim(0,7) + ylim(0, 45)
```

![](polishing-plots_files/figure-html/unnamed-chunk-14-1.png)

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# Interactive graphics

---
## Plotly

```r
p <- ggplot(df, aes(x = Wallet, y = Volatility, colour = Type)) + geom_point() + scale_color_locuszoom()

library(plotly)
ggplotly(p)
```

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---
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# Saving graphics

---

## Saving your Work

We can save the results of a plot to a file (as an image) using the `ggsave()` function:

```r
p1 <- ggplot(df, aes(x = Wallet, y = Volatility, colour = Type)) + geom_point()

ggsave("Volatilify_by_wallet.pdf", plot = p1)
```

---

## Your Turn

1. Create a scatterplot of End.Price versus Start.Price from the `cryptoart` data, colored by Platform
2. Use the black and white theme
3. Include an informative title
4. Move the legend to the bottom
5. Save your plot to a pdf file and open it in a pdf viewer.

```r
ggplot(data = cryptoart, aes(x = Start.Price, y = End.Price))
```

![](polishing-plots_files/figure-html/unnamed-chunk-17-1.png)

---

## Answers

```r
p1 <- ggplot(data = cryptoart, aes(x = Start.Price, y = End.Price, colour = Platform)) +
 geom_point() +
 ggtitle("End Price vs Start Price by Platform") +
 theme(
 legend.position = "bottom"
 )
p1

ggsave("yourturn.pdf", plot = p1)
```