gganimate: ggplot2拓展之让你的数据high起来

gganimate：让ggplot2图 随时而动。

常用参数

transition_*() defines how the data should be spread out and how it relates to itself across time.
view_*() defines how the positional scales should change along the animation.
shadow_*() defines how data from other points in time should be presented in the given point in time.
enter_*()/exit_*() defines how new data should appear and how old data should disappear during the course of the animation.
ease_aes() defines how different aesthetics should be eased during transitions.

实践的真知

R包安装

install.package("gganimate")
install.packages("gifski")

Preliminaries: Load gganimate & datas

rm(list = ls())  # Clear the environment
options(warn=-1) # Turn off warning message globally
library(ggplot2)
library(gganimate)

先赏为敬

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

庖丁解牛

library(gapminder)
theme_set(theme_bw())
head(gapminder)

country	continent	year	lifeExp	pop	gdpPercap
<fct>	<fct>	<int>	<dbl>	<int>	<dbl>
Afghanistan	Asia	1952	28.8	8425333	779.
Afghanistan	Asia	1957	30.3	9240934	821.
Afghanistan	Asia	1962	32.0	10267083	853.
Afghanistan	Asia	1967	34.0	11537966	836.
Afghanistan	Asia	1972	36.1	13079460	740.
Afghanistan	Asia	1977	38.4	14880372	786.

ggplot(gapminder, aes(gdpPercap, lifeExp, size=pop, color=continent,frame=year)) +
  geom_point() +
  scale_x_log10() +
  transition_time(year)+
  shadow_wake(wake_length = 0.05)

图按照年份变化

ggplot(gapminder, aes(gdpPercap, lifeExp, size=pop))+
  geom_point()+
  geom_point(aes(colour = factor(year)))+
  scale_x_log10() +
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year)

一般来说我们都是将时间映射给frame，这也符合我们的直觉，但是这并不意味着我们只能将时间映射给frame，我们可以将任何想要的变量映射给frame。

ggplot(gapminder, aes(gdpPercap, lifeExp, size=pop, frame=continent))+
  geom_point(color="blue")+
  scale_x_log10() +
  transition_states(continent)

有一篇介绍本R包的比较好的一篇文章，大家可以自行比对学习脚本： 使用gganimate制作数据可视化gif动图

生物的坑

以下内容copy自如何让你的基因放烟花。
注：此部分内容仅觉得好玩，我还没有想到这些处理怎么展示会比较好，主要是学习以下代码。

load("fireworks.Rdata")
class(test)

>class(test)
[1] "data.frame"

ggplot(test,aes(x=subgroup,y=expression,fill=subgroup))+
  geom_boxplot()+
  geom_jitter()+
  theme_bw()

分面，表示不同基因的表达量

p <- ggplot(test,aes(x=subgroup,y=expression,fill=subgroup))+
  geom_boxplot()+
  geom_jitter()+
  theme_bw()
p +facet_grid(.~gene)

transition_*()选择分面并动态显示，transition_time()专指时间。

p <- ggplot(test,aes(x=subgroup,y=expression,fill=subgroup))+
  geom_boxplot()+
  geom_jitter()+
  theme_bw()
p +transition_states(gene,
                     state_length = 0)+
  labs(title = "{closest_state} expression(TPM)")

基因在不同分组中的动态数据展示

p <- ggplot(test,aes(x=gene,y=expression))+
  geom_boxplot(aes(color=gene),size=2)+
  geom_jitter()+
  theme_bw()
p +transition_states(subgroup,
                     transition_length = 0.1,
                     state_length = 0)+
  labs(title = "{closest_state}")

去除边界，放飞变量

p <- ggplot(test,aes(x=gene,y=expression,color=gene))+
  #geom_boxplot()+
  geom_jitter()+
  theme_bw()
p +transition_states(subgroup,
                     transition_length = 0.1,
                     state_length = 0)+
  labs(title = "{closest_state}")

巨人的肩膀

gganimate: A Grammar of Animated Graphics
Reference manual
使用gganimate制作数据可视化gif动图
如何让你的基因放烟花

生物的坑数据 提取码: a55g