第2讲 ggplot2数据可视化

class: center, middle, inverse, title-slide

.title[
# 量化金融与金融编程
]
.subtitle[
## L02 ggplot2 数据可视化
]
.author[
### 曾永艺
]
.institute[
### 厦门大学管理学院
]
.date[
### 2022-09-23
]

---

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- ## .font120[<svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>] 的绘图系统

- ## `ggplot2` 入门

- ### The Layered Grammar of Graphics
  
  - ### 将数据映射为几何对象的图形属性
  
  - ### 统计变换、位置调整、坐标、分面
  
  - ### 标签、标注、标度、主题等

- ## `ggplot2` extensions

---

### 一图胜千言

---
class: inverse, center, middle

# 1. .font120[<svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>] 的绘图系统

---

### .font120[<svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>] 的绘图系统

---

.pull-left[

- .font120[高阶绘图函数]
    - **`plot()`**
    - `barplot()`、`pie()`、`dotchart()`、`boxplot()`、`hist()`、
    `stripchart()`、`stem()`、`smoothScatter()`、`spineplot()`、
    `mosaicplot()`、`pairs()`、`matplot()`、`image()`、
    `contour()`、`persp()` ……

- .font120[低阶绘图函数]
    - `points()`、`lines()`、`segments()`、`arrows()`、`xspline()`、`rect()`、`polygon()`、`polypath()`、`rasterImage()`、`title()`、`text()`、`legend()` 等

]

.center[示例]

.pull-right[
.code70[

```r
EUSM <- window(EuStockMarkets, 1992, 1994)

plot(EUSM[, "FTSE"], ylim = range(EUSM), 
     xlab = "Time", ylab = "Price", adj = 1, 
     cex.main = 1.5)
lines(EUSM[, "DAX"], lty = "dashed")
title(main = "Index's Price")
legend(1992, 3450, c("FTSE", "DAX"), 
       lty = c("solid", "dashed"), bty = "n")
```

<img src="L02_Visualization_files/figure-html/EUSM-1.png" style="display: block; margin: auto;" />
]
]

---
class: inverse, center, middle
background-image: url(imgs/logo-ggplot2.svg)
background-size: 12%
background-position: 18% 50%

# 2. `ggplot2`.font60[v3.3.6] 入门

---
layout: true

### .bold[2.1 The Layered Grammar of Graphics]

---

.font130[_Data Visualization_：将.bold[数据]映射为.bold[几何对象的图形属性]]

.font110[

- 数据（.red[data]）：我们想要可视化的对象，包含变量
- 几何对象（.red[geom]etries)：用来呈现数据的几何图形对象，如点、条形、线条等
- 图形属性（.red[aes]thetic）：几何对象的视觉属性，如`x`坐标和`y`坐标位置、颜色、形状等

]

---

.font130[`ggplot2` 的语法模板]

.code150[

```r
*ggplot(data = <DATA>) +
* <GEOM_FUNCTION>(
* mapping = aes(<MAPPINGS>),
 stat = <STAT>, 
 position = <POSITION>
 ) +
 <COORDINATE_FUNCTION> +
 <FACET_FUNCTION>
```
]

---
layout: true

### .bold[2.2 将.red[数据]映射为几何对象的图形属性]

---

```r
library(tidyverse)
```

```r
mpg  # print(mpg)
```

```
#> # A tibble: 234 × 11
#> manufacturer model displ year cyl trans drv cty hwy fl class 
#> <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr> 
#> 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
#> 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
#> 3 audi a4 2 2008 4 manual(m6) f 20 31 p compact
#> # … with 231 more rows
```

```r
# 查看ggplot2包内置数据集mpg的帮助文档
?mpg  # help(mpg)
```

1. `displ`: a car's engine size, in litres.
2. `hwy`: a car's fuel efficiency on the highway, in miles per gallon (mpg). 
3. `class`: "type" of car  
...

---
layout: true

### .bold[2.2 将数据.red[映射为几何对象的图形属性]]

---

```r
ggplot(data = mpg) +                   # 数据集
  geom_point(                          # 几何对象函数
    mapping = aes(x = displ, y = hwy)  # 映射：2个变量 -> 2种图形属性x坐标和y坐标
  )
```

---

```r
ggplot(data = mpg) + 
  geom_point(
*   mapping = aes(x = displ, y = hwy, colour = class)  # 3个映射
  )
```

---

```r
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))
```

```
#> Warning: The shape palette can deal with a maximum of 6 discrete values because more than
#> 6 becomes difficult to discriminate; you have 7. Consider specifying shapes
#> manually if you must have them.
```

```
#> Warning: Removed 62 rows containing missing values (geom_point).
```

---

.font120[
- R内置有25个形状（shape），在`ggplot2`中可用名字或数字进行设定
]
.pull-left[
<img src="L02_Visualization_files/figure-html/shapes-name-1.png" width="95%" style="display: block; margin: auto;" />

]

.pull-right[
<img src="L02_Visualization_files/figure-html/shapes-id-1.png" width="95%" style="display: block; margin: auto;" />

]

.font120[
- 设定图形属性相关的说明文档 .content-box-yellow.font80[`vignette("ggplot2-specs")`]
]

---

```r
ggplot(data = mpg) + 
  geom_point(
    mapping = aes(x = displ, y = hwy), 
*   shape = "triangle", size = 3, colour = "red", alpha = 0.3
    # 变量无关（“大家都一样”）的几何对象图形属性应放在aes()外
  )
```

---

```r
# layered!
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
* geom_smooth(mapping = aes(x = displ, y = hwy))  # layered!
```

---

```r
ggplot(
  data = mpg,
* mapping = aes(x = displ, y = hwy)  # 共用映射，提前至ggplot()中
) +
  geom_point() +
  geom_smooth()
```

---

```r
*ggplot(mpg, aes(displ, hwy)) +       # 省略常用参数名
* geom_point(aes(colour = class)) +  # 图层自用映射
* geom_smooth(data = filter(mpg, class == "pickup"))  # 不同的数据集
```

---
layout: false
class: hide_logo

## 🙋‍♂️ Your Turn!

.pull-left[

.font120[在以下空白处填入恰当的内容，从而得到右边所示的图形：]

<code class ='r hljs remark-code'>library(ggplot2) economics %>% &nbsp;&nbsp;ggplot() + &nbsp;&nbsp;geom &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;( &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; = 3.5, &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; = 'white', &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; = 2 &nbsp;&nbsp;) + &nbsp;&nbsp;geom &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;( &nbsp;&nbsp;&nbsp;&nbsp;aes(x = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;y = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;) &nbsp;&nbsp;)</code>
]

.pull-right[

]

---
layout: true

### .bold[2.3 .red[统计变换]、位置调整、坐标、分面]

---

```r
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))
```

.red.font110[Where comes the "count" in y-axis? 😅]

---

.font110[下面的代码会得到和使用 `geom_bar()` 相同的结果]

```r
ggplot(data = diamonds) + 
* stat_count(mapping = aes(x = cut))
```

---

```r
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, 
                         y = after_stat(prop),   # ?aes_eval
                         group = 1))             # ?aes_group_order
```

---

```r
ggplot(data = diamonds %>% group_by(cut) %>% count()) +  # what's this?!
  geom_bar(
    mapping = aes(x = cut, y = n),
*   stat = "identity"
  )
```

---
layout: true

### .bold[2.3 统计变换、.red[位置调整]、坐标、分面]

---

```r
ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity))
# geom_bar()默认的position = "stack"
```

---

```r
ggplot(data = diamonds) + 
  geom_bar(
    mapping = aes(x = cut, fill = clarity),
*   position = "dodge"  # position = position_dodge(width = 0.9)
  )
```

---

```r
ggplot(data = mpg) + 
  geom_point(
    mapping = aes(x = displ, y = hwy),
*   position = "jitter"   # same as `ggplot() + geom_jitter()`
  )
```

---
layout: true

### .bold[2.3 统计变换、位置调整、.red[坐标]、分面]

---

```r
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot() +
* coord_flip()  # ggplot2 3.3.0后直接调换x，y参数即可
```

---

```r
bar <- ggplot(data = diamonds) + # 将图形对象存为变量bar，可在Environment标签页中检查其内容
 geom_bar(
 mapping = aes(x = cut, fill = cut), show.legend = FALSE, width = 1
 ) + 
 theme(aspect.ratio = 1) + labs(x = NULL, y = NULL)
*bar + coord_polar()
```

---
layout: true

### .bold[2.3 统计变换、位置调整、坐标、.red[分面]]

---

```r
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(data = transform(mpg, class = NULL), colour = "grey") +
  geom_point() + 
* facet_wrap(vars(class), nrow = 2) # facet_wrap(~ class, nrow = 2)
```

---

```r
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(data = transform(mpg, cyl = NULL), colour = "grey") +
  geom_point() + 
* facet_grid(rows = vars(drv), cols = vars(cyl))  # facet_grid(drv ~ cyl)
```

---
layout: true

### .bold[2.4 .red[标签]、标注、标度、主题等]

---

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
* labs(  # 标题、小标题等
*   title = "Fuel efficiency",
*   subtitle = "... generally decreases with engine size",
*   caption = "Data from fueleconomy.gov"
* )
```

---

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
* labs( # 图形属性 = 标签
*   x = "Engine displacement (L)",
*   y = "Highway fuel economy (mpg)",
*   colour = "Car type"
* )
```

---
layout: true

### .bold[2.4 标签、.red[标注]、标度、主题等]

---

```r
# 生成辅助数据集
best <- mpg %>%
 group_by(class) %>%
 filter(row_number(desc(hwy)) == 1)
```

.pull-left[

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
* geom_text(
    aes(label = model), 
    data = best
  )
```

]

.pull-right[

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
* geom_label(
    aes(label = model), data = best, 
    nudge_y = 2, alpha = 0.5
  )
```

]

---

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
* geom_point(size = 3, shape = 1, data = best) +
* ggrepel::geom_label_repel(aes(label = model), data = best)  # install ggrepel
```

---

.pull-left.code75[

```r
library(ggtext)  # install it!

# 辅助数据
beetle <- mpg %>% 
 filter(model == "new beetle") %>% 
 filter(hwy == max(hwy)) %>% 
 mutate(model = 
 "<img src='imgs/beetle.png' 
 width='25'/>")

ggplot(mpg, aes(displ, hwy)) + 
 geom_point(aes(colour = class)) + 
* geom_richtext(
 data = beetle,
 aes(label = model),
 fill = NA, colour = NA, 
 hjust = 0, vjust = 0.5
 ) + 
 labs(
 title = "Fuel efficiency 
 
 ... generally **_decreases_** with
 engine size"
 ) -> p
```
]

.pull-right.code75[

```r
p + theme(
  plot.title.position = "plot",
* plot.title = element_textbox_simple(
    size = 15, lineheight = 1.2,
    padding = margin(5.5, 5.5, 5.5, 5.5),
    margin = margin(0, 0, 5.5, 0),
    linetype = 1,
    r = grid::unit(8, "pt"),
    fill = "#F0F7FF"
  ))
```

<img src="L02_Visualization_files/figure-html/label4-2-1.png" width="100%" style="display: block; margin: auto;" />
]

---
layout: true

### .bold[2.4 标签、标注、.red[标度]、主题等]

---

```r
ggplot(diamonds, aes(carat, price)) +
  geom_bin2d() + 
* scale_x_log10() +
* scale_y_log10()
```

---

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
* scale_y_continuous(
    name = "Highway fuel economy (mpg)",
    breaks = seq(0, 50, by = 10),
    limits = c(0, 50)
  )
```

---

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = drv, shape = drv)) +
* scale_colour_brewer(palette = "Set1")   # RColorBrewer::display.brewer.all()
```

---

```r
ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
* theme(legend.position = "bottom") +
  guides(
*   colour = guide_legend(nrow = 1, override.aes = list(size = 3))
  )
```

---
layout: true

### .bold[2.4 标签、标注、标度、.red[主题]等]

---

.pull-left[

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
* theme_minimal()
```

<img src="L02_Visualization_files/figure-html/theme1-1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  geom_smooth(se = FALSE) +
* ggthemes::theme_stata()   # install it!
```

<img src="L02_Visualization_files/figure-html/theme2-1.png" width="100%" style="display: block; margin: auto;" />
]

---

.code80[

```r
theme(line, rect, text, title, aspect.ratio, axis.title, axis.title.x,
      axis.title.x.top, axis.title.x.bottom, axis.title.y, axis.title.y.left,
      axis.title.y.right, axis.text, axis.text.x, axis.text.x.top,
      axis.text.x.bottom, axis.text.y, axis.text.y.left, axis.text.y.right,
      axis.ticks, axis.ticks.x, axis.ticks.x.top, axis.ticks.x.bottom, axis.ticks.y,
      axis.ticks.y.left, axis.ticks.y.right, axis.ticks.length, axis.ticks.length.x,
      axis.ticks.length.x.top, axis.ticks.length.x.bottom, axis.ticks.length.y,
      axis.ticks.length.y.left, axis.ticks.length.y.right, axis.line, axis.line.x,
      axis.line.x.top, axis.line.x.bottom, axis.line.y, axis.line.y.left,
      axis.line.y.right, legend.background, legend.margin, legend.spacing,
      legend.spacing.x, legend.spacing.y, legend.key, legend.key.size,
      legend.key.height, legend.key.width, legend.text, legend.text.align,
      legend.title, legend.title.align, legend.position, legend.direction,
      legend.justification, legend.box, legend.box.just, legend.box.margin,
      legend.box.background, legend.box.spacing, panel.background, panel.border,
      panel.spacing, panel.spacing.x, panel.spacing.y, panel.grid, panel.grid.major,
      panel.grid.minor, panel.grid.major.x, panel.grid.major.y, panel.grid.minor.x,
      panel.grid.minor.y, panel.ontop, plot.background, plot.title,
      plot.title.position, plot.subtitle, plot.caption, plot.caption.position,
      plot.tag, plot.tag.position, plot.margin, strip.background, strip.background.x,
      strip.background.y, strip.placement, strip.text, strip.text.x, strip.text.y,
      strip.switch.pad.grid, strip.switch.pad.wrap, ..., complete = FALSE,
      validate = TRUE)
```
]

---
layout: false
class: inverse, center, middle

# 3. `ggplot2` extensions and `plotly`

---
class: hide_logo
background-image: url(imgs/logo-esquisse.png)
background-size: 5%
background-position: 5% 3%

### &emsp;&emsp;`esquisse`.font60[v1.1.2]

.pull-left.font110[

> This `esquisse` addin allows you to interactively 
> explore your data by visualizing it with the `ggplot2` 
> package. It allows you to draw bar plots, curves, 
> scatter plots, histograms, boxplot and sf objects, 
> then export the graph or retrieve the code to reproduce 
> the graph.

```r
# install.packages("esquisse")

# RStudio Addins -> 'ggplot2' builder
esquisse::esquisser()
```

]

.pull-right[

]

---
class: hide_logo
background-image: url(imgs/logo-patchwork.png)
background-size: 5%
background-position: 5% 3%

### &emsp;&emsp;`patchwork`.font60[v1.1.2]

.pull-left.font110[

> `patchwork` expands the API to allow 
> for arbitrarily complex composition of 
> plots by, among others, providing 
> mathematical operators for combining 
> multiple plots.

```r
# install.packages("patchwork")
library(patchwork)

p <- ggplot(mtcars)

p1 <- p + geom_point(aes(mpg, disp))

p2 <- p + geom_boxplot(
 aes(gear, disp, group = gear))

p3 <- p + geom_smooth(aes(disp, qsec))

p4 <- p + geom_bar(aes(carb))
```

]

--
  
.pull-right[

```r
p1 + plot_spacer() + {
  p2 + p3 + plot_layout(ncol = 1)
} + plot_layout(nrow = 1, 
                widths = c(1, 0.25, 1)) -
  p4 + plot_layout(ncol = 1) +
  plot_annotation(title = "Fancy plot!", 
                  tag_levels = "A")
```

<img src="L02_Visualization_files/figure-html/patchwork-1.png" width="95%" style="display: block; margin: auto;" />
]

---
class: hide_logo
background-image: url(imgs/logo-gganimate.png)
background-size: 5%
background-position: 5% 3%

### &emsp;&emsp;`gganimate`.font60[v1.0.8]

.pull-left[

```r
# install.packages("gganimate")
# install.packages("gifski")
# install.packages("gapminder")

library(gganimate)
library(gapminder)

p <- ggplot(gapminder,
 aes(gdpPercap, lifeExp, 
 size = pop, 
 colour = country)) +
 geom_point(alpha = 0.7, 
 show.legend = FALSE) +
 scale_colour_manual(
 values = country_colors
 ) +
 scale_size(range = c(2, 12)) +
 scale_x_log10() +
 facet_wrap(vars(continent))
```
]

--
  
.pull-right[

```r
# Here comes the gganimate specific bits
*p + labs(title = 'Year: {frame_time}',
         x = 'GDP per capita', 
         y = 'life expectancy') +  
* transition_time(year) +
* ease_aes('linear')
```

]

---
class: hide_logo
background-image: url(imgs/logo-plotly.png)
background-size: 5%
background-position: 5% 3%

### &emsp;&emsp;`plotly`.font60[v4.10.0]

.pull-left.font110[

> Create interactive web graphics from `ggplot2` graphs 
> and/or a custom interface to the 'plotly.js' 
> inspired by the _grammar of graphics_.

```r
# install.packages("plotly")
*library(plotly)

p <- gapminder %>%
 filter(year == 2007) %>%
 ggplot(aes(gdpPercap, lifeExp, 
 size = pop, 
 color = continent)) + 
 geom_point() + 
 scale_x_log10() + 
 theme_bw()
 
*ggplotly(p)
```
]

.pull-right[

<div id="htmlwidget-8df4120ed34ce736658c" style="width:100%;height:65%;" class="plotly html-widget"></div>
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]

---
class: inverse, center, middle

# 课后作业

---
class: middle

.font120[
🕐 根据 L2 课程讲义的打印稿，在 📑 _R Script_ 中键入并完成全部代码的运行

🕑 请学习 📖 .bold[[{{_R for Data Science_}}](https://r4ds.had.co.nz/)] * 一书关于数据可视化的第3章和第28章（以及第4、6、8章关于工作流程）的内容

🕒 从 📖 .bold[[{{_R Graphics Cookbook_}}](https://r-graphics.org/)] * 一书中挑选.red[__两章__]，复制性代码练习

🕓 下载（打印）📰 .bold[[{{ggplot2-cheatsheet}}](docs/ggplot2-cheatsheet.pdf)] 并浏览之

🕔 登录浏览 🖼 .bold[[{{The R Graph Gallery}}](https://www.r-graph-gallery.com/index.html)]

🕕 登录浏览 📦 .bold[[{{ggplot2 extentions}}](https://exts.ggplot2.tidyverse.org/gallery/)]，进一步了解 `ggplot2` 包的“生态圈”

🕖 _**进阶内容**_：R 对中文字体的支持不够理想，可参考 📝 .bold[[{{_RJournal_的文章}}](https://journal.r-project.org/archive/2015-1/qiu.pdf)]以及帖子 .bold[[{{showtext}}](https://cosx.org/2014/01/showtext-interesting-fonts-and-graphs)] 中提到的解决方案和有趣示例

]

.footnote.red[
\* 若英文看起来实在吃力 😵， 这两本书的中文翻译版都有电子版发行，请同学们自行学习对应章节。我个人建议同学们（可等网上商城打折时）购买实体书，放在案头作学习参考。]

---
class: middle

.font120[
🕐 根据 L2 课程讲义的打印稿，在 📑 _R Script_ 中键入并完成全部代码的运行
]

- .bold[打开新的 R script 文件]
  - `Ctrl+Shift+N` 打开 -> `Ctrl+S` 存盘为“_L02_coding_practice.R_”
  - 注：文件保存路径和文件名最好为英文，中间不要有空格

--
  
- .bold[输入与运行代码]
  - 长代码手动断行，最好不要超过80个字符
  - 灵活使用快捷键，如 `Tab`（代码提示与自动补全）、`Ctrl+A`（选中全部代码） -> `Ctrl+Shift+A`（代码重新格式化）等
  - `Ctrl+Enter` 运行光标所在行或选中代码（`Ctrl+Alt+R` 运行全部代码，`Ctrl+Shift+P` 重新运行代码，`Ctrl+Alt+P` 运行光标前的代码）

- .bold[注释]
  - 以 # 作为注释开始标记，如说明性文字、区分不同代码块（Ctrl+Shift+R）
  - 最后加入一段注释说明，简单说说使用ggplot2包的体会
  
--

- .bold[提交代码]

- 可结队编写代码，共同署名，分别提交，但不能完全雷同
  - 2022年9月26日22:00前通过 [{{坚果云链接}}](https://workspace.jianguoyun.com/inbox/collect/a06f3a70ea64480691024f239e216976/submitv2) 直接提交代码文档

---
class: center middle
background-image: url(imgs/xaringan.png)
background-size: 12%
background-position: 50% 40%

<hr color='#f00' size='2px' width='80%'>
 
.Large.red[_**本网页版讲义的制作由 R 包 [{{`xaringan`}}](https://github.com/yihui/xaringan) 赋能！**_]