tidyverse_ggplot2_gganimate.Rmd

# ggplot2之让你的数据动起来 {#tidyverse-ggplot2-gganimate}


这节课，我们讲如何让我们的图动起来。（因为渲染需要花费很长时间，所以文档中的动图代码都没有执行。）


## 为什么要使用动图

- 改进了图形在时间上和空间上的重新定位
- 传递更多信息
- 引人注意


## `gganimate`宏包

动图可以将其理解为多张静态图堆在一起，当然不是随意的堆放，而是按照一定的规则，比如按照时间的顺序，或者类别的顺序。一般而言，动图制作包括两个步骤: 静态图制作及图形组装。静态图制作，前面几章我们讲过主要用ggplot2宏包实现；对于图形组装，需要用到今天我们要讲Thomas Lin Pedersen的`gganimate`宏包，来自同一工厂的产品，用起来自然是无缝衔接啦。

```{r ggplot2-gganimate-1, eval=FALSE}
install.packages("gganimate")
```


### 先来一张静态图
```{r ggplot2-gganimate-2, eval=FALSE}
library(tidyverse)
library(covdata) # remotes::install_github("kjhealy/covdata")
library(gganimate)
```



```{r ggplot2-gganimate-3, eval=FALSE}
covdata::covnat %>%
  dplyr::filter(iso3 == "USA") %>%
  dplyr::filter(cu_cases > 0) %>%
  ggplot(aes(x = date, y = cases)) +
  geom_path() +
  labs(
    title = "美国新冠肺炎累积确诊病例",
    subtitle = "数据来源https://kjhealy.github.io/covdata/"
  )
```


让它动起来，我们只需要增加一行代码！

``` {r, eval=FALSE}
covdata::covnat %>%
  dplyr::filter(iso3 == "USA") %>%
  dplyr::filter(cu_cases > 0) %>%
  ggplot(aes(x = date, y = cases)) +
  geom_path() +
  labs(
    title = "美国新冠肺炎累积确诊病例 {frame_along}",
    subtitle = "数据来源https://kjhealy.github.io/covdata/"
  ) +
  transition_reveal(along = date)
```


### 相对复杂点的例子

```{r ggplot2-gganimate-4, eval=FALSE}
library(datasauRus)
ggplot(datasaurus_dozen) +
  aes(x, y, color = dataset) +
  geom_point()
```

用分面展示
```{r ggplot2-gganimate-5, eval=FALSE}
ggplot(datasaurus_dozen) +
  aes(x, y, color = dataset) +
  geom_point() +
  facet_wrap(~dataset)
```

可以用动图展示
```{r ggplot2-gganimate-6, eval=FALSE}
ggplot(datasaurus_dozen) +
  aes(x, y, color = dataset) +
  geom_point() +
  transition_states(dataset, 3, 1) + # <<
  labs(title = "Dataset: {closest_state}")
```

是不是很炫酷，下面我们就一个个讲解其中的函数。



## The grammar of animation

使用`gganimate`做动画，只需要掌握以下五类函数：

- `transition_*()`: 定义动画是根据哪个变量进行”动”，以及如何”动”
- `view_*()`: 定义坐标轴随数据变化.
- `shadow_*()`: 影子（旧数据的历史记忆）?定义点相继出现的方式.
- `enter_*()/exit_*()`: 定义新数据出现和旧数据退去的方式.
- `ease_aes()`: 美观定义，控制变化的节奏(如何让整个动画看起来更舒适).


下面通过案例依次讲解这些函数功能。


## 希望动画随哪个变量动起来

变量如何选择，这需要从变量类型和变量代表的信息来确定。


### transition_states

- `transition_states(states = )`, 这里的参数states往往带有分组信息，可以等价于静态图中的分面。
```{r ggplot2-gganimate-7, eval=FALSE}
diamonds %>%
  ggplot(aes(carat, price)) +
  geom_point()
```

```{r ggplot2-gganimate-8, eval=FALSE}
diamonds %>%
  ggplot(aes(carat, price)) +
  geom_point() +
  facet_wrap(vars(color))
```

```{r ggplot2-gganimate-9, eval=FALSE}
diamonds %>%
  ggplot(aes(carat, price)) +
  geom_point() +
  transition_states(states = color, transition_length = 3, state_length = 1)
```

### transition_time

- `transition_time(time = )`, 这里的time一般认为是**连续**的值，相比于`transition_states`，没有了`transtion_length`这个选项，是因为`transtion_length`默认为time. 事实上，`transition_time`是`transition_states`的一种特例，但其实也有分组的要求

```{r, out.width = "95%"}
library(ggplot2)
library(gganimate)
library(gapminder)

plot <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = gapminder::country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(vars(continent)) +
  labs(title = "Year: {frame_time}", x = "GDP Per capita", y = "life expectancy") +
  transition_time(year) +
  ease_aes("linear")
plot
# save as a GIF
# animate(plot, fps = 30, width = 750, height = 750)
# anim_save("gapminder.gif")
```





```{r ggplot2-gganimate-10, eval=FALSE}
p <- gapminder::gapminder %>%
  ggplot(aes(x = gdpPercap, y = lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  labs(
    x = "GDP per capita",
    y = "life expectancy"
  )
p
```

```{r ggplot2-gganimate-11, eval=FALSE}
anim <- p +
  transition_time(time = year) +
  labs(title = "year: {frame_time}")
anim
```



### transition_reveal
- `transition_reveal(along =  )`,  along 这个词可以看出，它是按照某个变量**依次**显示的意思，比如顺着x轴显示

```{r ggplot2-gganimate-12, eval=FALSE}
ggplot(data = economics) +
  aes(x = date, y = unemploy) +
  geom_line()
```



```{r ggplot2-gganimate-13, eval=FALSE}
ggplot(economics) +
  aes(x = date, y = unemploy) +
  geom_line() +
  transition_reveal(along = date) +
  labs(title = "now is {frame_along}")
```



### transition_filter

- `transition_filter( 至少2个筛选条件，transition_length = ,   filter_length =)`, 动图将会在这些筛选条件对应的子图之间转换
```{r ggplot2-gganimate-14, eval=FALSE}
diamonds %>%
  ggplot(aes(carat, price)) +
  geom_point() +
  transition_filter(
    transition_length = 3,
    filter_length = 1,

    cut == "Ideal",
    Deep = depth >= 60
  )
```


### transition_layers

- `transition_layers()`: 依次显示每个图层

```{r ggplot2-gganimate-15, eval=FALSE}
mtcars %>%
  ggplot(aes(mpg, disp)) +
  geom_point() +
  geom_smooth(colour = "grey", se = FALSE) +
  geom_smooth(aes(colour = factor(gear))) +
  transition_layers(
    layer_length = 1, transition_length = 2,
    from_blank = FALSE, keep_layers = c(Inf, 0, 0)
  ) +
  enter_fade() +
  exit_fade()
```


### 其他

- `transition_manual()`
- `transition_components()`
- `transition_events()`





## 希望坐标轴随数据动起来

动画过程中，绘图窗口怎么变化呢？

```{r views-tbl, echo=FALSE, eval=FALSE}
tribble(
  ~Function, ~Description,
  "view_follow", "完全跟随当前数据的范围",
  "view_step", "类似 view_follow, 除了过渡的时候要静止下下",
  "view_step_manual", "类似 view_step, 除了需要手动定义坐标轴的范围",
  "view_zoom", "与view_step一样, 但更变化过程更平滑",
  "view_zoom_manual", "同上，除了需要手动定义坐标轴的范围"
)
```

### view_follow

```{r ggplot2-gganimate-16, eval=FALSE}
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() +
  labs(title = "{closest_state}") +
  transition_states(Species, transition_length = 4, state_length = 1) +
  view_follow()
```

### 其它

- `view_step()`
- `view_step_manual()`
- `view_zoom()`
- `view_zoom_manual()`




## 希望动画有个记忆

- `shadow_wake(wake_length =,  ) `         旧数据消退时，制造点小小的尾迹的效果（wake除了叫醒，还有尾迹的意思，合起来就是记忆_尾迹）
- `shadow_trail(distance = 0.05)`         旧数据消退时，制造面包屑一样的残留痕迹（记忆_零星残留）
- `shadow_mark(past = TRUE, future = FALSE)` 将旧数据和新数据当作背景（记忆_标记）


### shadow_wake()

```{r ggplot2-gganimate-17, eval=FALSE}
p +
  transition_time(time = year) +
  labs(title = "year: {frame_time}") +
  shadow_wake(wake_length = 0.1, alpha = FALSE)
```


```{r ggplot2-gganimate-18, eval=FALSE}
ggplot(iris, aes(Petal.Length, Sepal.Length)) +
  geom_point(size = 2) +
  labs(title = "{closest_state}") +
  transition_states(Species, transition_length = 4, state_length = 1) +
  shadow_wake(wake_length = 0.1)
```

### shadow_trail()
```{r ggplot2-gganimate-19, eval=FALSE}
p +
  transition_time(time = year) +
  labs(title = "year: {frame_time}") +
  shadow_trail(distance = 0.1)
```

```{r ggplot2-gganimate-20, eval=FALSE}
ggplot(iris, aes(Petal.Length, Sepal.Length)) +
  geom_point(size = 2) +
  labs(title = "{closest_state}") +
  transition_states(Species, transition_length = 4, state_length = 1) +
  shadow_trail(distance = 0.1)
```


### shadow_mark()

```{r ggplot2-gganimate-21, eval=FALSE}
p +
  transition_time(time = year) +
  labs(title = "year: {frame_time}") +
  shadow_mark(alpha = 0.3, size = 0.5)
```


```{r ggplot2-gganimate-22, eval=FALSE}
ggplot(airquality, aes(Day, Temp)) +
  geom_line(color = "red", size = 1) +
  transition_time(Month) +
  shadow_mark(colour = "black", size = 0.75)
```



## 定义新数据出现和旧数据退去的方式

出现和退去的函数是成对的

```{r enter-exit-tbl, echo=FALSE, eval=FALSE}
tribble(
  ~Function, ~Description,
  "enter_appear/exit_disappear", "突然出现，或者闪退",
  "enter_fade/exit_fade", "使用透明度上的变化",
  "enter_grow/exit_shrink", "使用大小上的变化",
  "enter_recolour/exit_recolour", "使用颜色上的变化",
  "enter_fly/exit_fly", "使用位置上飞来飞去的效果",
  "enter_drift/exit_drift", "使用位置上漂移的效果",
  "enter_reset/exit_reset", "使用清空之前的效果"
)
```

### enter/exit_fade() 

透明度上的变化，我这里用柱状图展示，效果要明显一点。

```{r ggplot2-gganimate-23, eval=FALSE}
tibble(
  x = month.name,
  y = sample.int(12)
) %>%
  ggplot(aes(x = x, y = y)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  transition_states(states = month.name)
```


```{r ggplot2-gganimate-24, eval=FALSE}
tibble(
  x = month.name,
  y = sample.int(12)
) %>%
  ggplot(aes(x = x, y = y)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  transition_states(states = month.name) +
  shadow_mark(past = TRUE) +
  enter_fade()
```


```{r ggplot2-gganimate-25, eval=FALSE}
p +
  transition_time(time = year) +
  labs(title = "year: {frame_time}") +
  enter_fade()
```

### enter_grow()/exit_shrink()

大小上的变化

```{r ggplot2-gganimate-26, eval=FALSE}
tibble(
  x = month.name,
  y = sample.int(12)
) %>%
  ggplot(aes(x = x, y = y)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  transition_states(states = month.name) +
  shadow_mark(past = TRUE) +
  enter_grow()
```


```{r ggplot2-gganimate-27, eval=FALSE}
p +
  transition_time(time = year) +
  labs(title = "year: {frame_time}") +
  enter_grow() +
  enter_fade()
```


## 控制变化的节奏

控制数据点变化的快慢


```r
p + ease_aes({aesthetic} = {ease})
p + ease_aes(x = "cubic")
```


```{r ggplot2-gganimate-28, out.width='100%', fig.align='left'}
knitr::include_graphics("images/ease.png")
```

Source: https://easings.net/



看下面的案例：
```{r ggplot2-gganimate-29, eval=FALSE}
diamonds %>%
  ggplot(aes(carat, price)) +
  geom_point() +
  transition_states(color, transition_length = 3, state_length = 1) +
  ease_aes("cubic-in") # Change easing of all aesthetics
```

```{r ggplot2-gganimate-30, eval=FALSE}
diamonds %>%
  ggplot(aes(carat, price)) +
  geom_point() +
  transition_states(color, transition_length = 3, state_length = 1) +
  ease_aes(x = "elastic-in") # Only change `x` (others remain “linear”)
```



## 标签

我们可能需要在标题中加入每张动画的信息，常用罗列如下

```{r ggplot2-gganimate-31, eval=FALSE}
transition_states(states = ) +
  labs(title = "previous is {previous_state}, 
                current is {closest_state}, 
                next is {next_state}")

transition_layers() +
  labs(title = "previous is {previous_layers}, 
                current is {closest_layers}, 
                next is {next_layers}")

transition_time(time = ) +
  labs(title = "now is {frame_time}")


transition_reveal(along = ) +
  labs(title = "now is {frame_along}")
```



## 保存

### Renderer options

```{r renderer-tbl, echo=FALSE}
tibble::tribble(
  ~Function, ~Description,
  "gifski_renderer", "Default, super fast gif renderer.",
  "magick_renderer", "Somewhat slower gif renderer.",
  "ffmpeg_renderer", "Uses ffmpeg to create a video from the animation.",
  "av_renderer", "Uses the av package to create a video (using ffmpeg).",
  "file_renderer", "Dumps a list of image frames from the animation.",
  "sprite_renderer", "Creates a spritesheet from frames of the animation."
)
```

### 常用方法

一般用`anim_save()`保存为 gif 格式，方法类似`ggsave()`

```{r ggplot2-gganimate-32, eval=F}
animation_to_save <- diamonds %>%
  ggplot(aes(carat, price)) +
  geom_point() +
  transition_states(color, transition_length = 3, state_length = 1) +
  ease_aes("cubic-in")

anim_save("first_saved_animation.gif", animation = animation_to_save)
```


## 案例演示一


<!-- 这是网上有段时间比较火的racing_bar图 -->
<!-- ```{r} -->
<!-- ranked_by_year <- -->
<!--   gapminder %>% -->
<!--   select(country, pop, year, continent) %>% -->
<!--   group_by(year) %>% -->
<!--   arrange(year, desc(pop)) %>% -->
<!--   mutate(rank = 1:n()) %>% -->
<!--   filter(rank <= 10) -->
<!-- ``` -->


<!-- ```{r} -->
<!-- ranked_by_year %>% -->
<!--   ungroup() %>% -->
<!--   mutate(pop = pop / 1000000) %>% -->
<!--   ggplot(aes( -->
<!--     x = rank, -->
<!--     y = country, -->
<!--     group = country, -->
<!--     label = country, -->
<!--     fill = continent -->
<!--   )) + -->
<!--   geom_tile( -->
<!--     aes( -->
<!--       y = pop / 2, -->
<!--       height = pop, -->
<!--       width = 0.9, -->
<!--       fill = continent -->
<!--     ), -->
<!--     alpha = 0.8, -->
<!--     show.legend = F -->
<!--   ) + -->
<!--   geom_text(aes( -->
<!--     y = pop, -->
<!--     label = country, -->
<!--     color = ifelse(pop > 1100, "#ffffff", "#000000"), -->
<!--     hjust = ifelse(pop > 1100, 1, 0) -->
<!--   )) + -->
<!--   scale_x_reverse( -->
<!--     breaks = c(1:10), -->
<!--     label = c(1:10) -->
<!--   ) + -->
<!--   theme_minimal() + -->
<!--   coord_flip(clip = "off", expand = FALSE) + -->
<!--   labs( -->
<!--     title = "year {closest_state}", -->
<!--     x = "", -->
<!--     y = "Population (millions)", -->
<!--     caption = "Source: github/corydonbaylor" -->
<!--   ) + -->
<!--   transition_states(year, -->
<!--     transition_length = 4, -->
<!--     state_length = 1, -->
<!--     wrap = TRUE -->
<!--   ) + -->
<!--   ease_aes("cubic-in-out") -->
<!-- ``` -->



这是网上有段时间比较火的racing_bar图

```{r ggplot2-gganimate-33, eval=FALSE}
ranked_by_date <- covdata::covnat %>%
  group_by(date) %>%
  arrange(date, desc(cu_cases)) %>%
  mutate(rank = 1:n()) %>%
  filter(rank <= 10) %>%
  ungroup()
```


```{r ggplot2-gganimate-34, eval=FALSE}
ranked_by_date %>%
  filter(date >= "2020-05-01") %>%
  ggplot(
    aes(x = rank, y = cname, group = cname, fill = cname)
  ) +
  geom_tile(
    aes(
      y = cu_cases / 2,
      height = cu_cases,
      width = 0.9
    ),
    alpha = 0.8,
    show.legend = F
  ) +
  geom_text(aes(
    y = cu_cases,
    label = cname
  ),
  show.legend = FALSE
  ) +
  scale_x_reverse(
    breaks = c(1:10),
    label = c(1:10)
  ) +
  theme_minimal() +
  coord_flip(clip = "off", expand = FALSE) +
  labs(
    title = "日期: {closest_state}",
    x = "",
    caption = "Source: github/kjhealy/covdata"
  ) +
  transition_states(date,
    transition_length = 4,
    state_length = 1,
    wrap = TRUE
  ) +
  ease_aes("cubic-in-out")
```


## 案例演示二

```{r ggplot2-gganimate-35, eval=FALSE}
bats <- readr::read_csv("./demo_data/bats-subset.csv") %>%
  dplyr::mutate(id = factor(id))
```



```{r ggplot2-gganimate-36, eval=FALSE}
bats %>%
  ggplot(aes(
    x = longitude,
    y = latitude,
    group = id,
    color = id
  )) +
  geom_point()
```




### 常规的方法

```{r ggplot2-gganimate-37, eval=FALSE}
bats %>%
  ggplot(aes(
    x = longitude,
    y = latitude,
    group = id,
    color = id
  )) +
  geom_point() +
  transition_time(time) +
  shadow_mark(past = TRUE)
```



- geom_path()是按照数据点出现的先后顺序
- geom_line()是按照数据点在x轴的顺序

```{r ggplot2-gganimate-38, eval=FALSE}
bats %>%
  ggplot(aes(
    x = longitude,
    y = latitude,
    group = id,
    color = id
  )) +
  geom_path() +
  transition_time(time) +
  shadow_mark(past = TRUE)
```


### 炫酷点的

<!-- 此时geom_path() + transition_reveal(time) = transition_time(time) -->


```{r ggplot2-gganimate-39, eval=FALSE}
bats %>%
  dplyr::mutate(
    image = "images/bat-cartoon.png"
  ) %>%
  ggplot(aes(
    x = longitude,
    y = latitude,
    group = id,
    color = id
  )) +
  geom_path() +
  ggimage::geom_image(aes(image = image), size = 0.1) +
  transition_reveal(time)
```


## 案例演示三

全球R-Ladies组织，会议活动的情况，我们在地图上用动图展示
```{r ggplot2-gganimate-40, eval=FALSE}
rladies <- read_csv("./demo_data/rladies.csv")
rladies
```

这里需要一个地图，可以这样
```{r ggplot2-gganimate-41, eval=FALSE}
ggplot() +
  ggplot2::borders("world", colour = "gray85", fill = "gray80") +
  ggthemes::theme_map()
```


当然，最好是这样
```{r ggplot2-gganimate-42, eval=FALSE}
library(maps)
world <- map_data("world") 

world_map <- ggplot() +
  geom_polygon(data = world, 
               aes(x = long, y = lat, group = group),
               color = "white", fill = "gray80"
               ) +
  ggthemes::theme_map()

world_map 
```


然后把点打上去
```{r ggplot2-gganimate-43, eval=FALSE}
world_map +
  geom_point(
    data = rladies,
    aes(x = lon, y = lat, size = followers),
    colour = "purple", alpha = .5
  ) +
  scale_size_continuous(
    range = c(1, 8),
    breaks = c(250, 500, 750, 1000)
  ) +
  labs(size = "Followers")
```



用动图展示（这种方法常用在流行病传播的展示上）

```{r ggplot2-gganimate-44, eval=FALSE}
world_map +
  geom_point(aes(x = lon, y = lat, size = followers),
    data = rladies,
    colour = "purple", alpha = .5
  ) +
  scale_size_continuous(
    range = c(1, 8),
    breaks = c(250, 500, 750, 1000)
  ) +
  transition_states(created_at) +
  shadow_mark(past = TRUE) +
  labs(title = "Day: {closest_state}")
```





## 课后作业

### 作业1

把下图弄成你喜欢的样子
```{r ggplot2-gganimate-45, eval=FALSE}
library(gapminder)
theme_set(theme_bw())

ggplot(gapminder) +
  aes(
    x = gdpPercap, y = lifeExp,
    size = pop, colour = country
  ) +
  geom_point(show.legend = FALSE) +
  scale_x_log10() +
  scale_color_viridis_d() +
  scale_size(range = c(2, 12)) +
  labs(x = "GDP per capita", y = "Life expectancy") +
  transition_time(year) +
  labs(title = "Year: {frame_time}")
```





### 作业2

那请说说这以下三个的区别？
```{r ggplot2-gganimate-46, eval=FALSE}
bats %>%
  dplyr::filter(id == 1) %>%
  ggplot(
    aes(
      x = longitude,
      y = latitude
    )
  ) +
  geom_point() +
  transition_reveal(time) # <<



bats %>%
  dplyr::filter(id == 1) %>%
  ggplot(
    aes(
      x = longitude,
      y = latitude
    )
  ) +
  geom_point() +
  transition_states(time) # <<



bats %>%
  dplyr::filter(id == 1) %>%
  ggplot(
    aes(
      x = longitude,
      y = latitude
    )
  ) +
  geom_point() +
  transition_time(time) # <<
```





```{r ggplot2-gganimate-47, echo = F}
# remove the objects
# rm(list=ls())
#rm(anim, bats, p, ranked_by_date, rladies, world, world_map)
#rm(anim, bats, p, ranked_by_date, rladies)
```



```{r ggplot2-gganimate-48, echo = F, message = F, warning = F, results = "hide"}
pacman::p_unload(pacman::p_loaded(), character.only = TRUE)
```