单位GDP与寿命的关系统计绘图 
library(ggplot2) library(gganimate) theme_set(theme_bw()) library(gapminder) head(gapminder) p <- ggplot( gapminder, aes(x = gdpPercap, y=lifeExp, size = pop, colour = country) ) geom_point(show.legend = FALSE, alpha = 0.7) scale_color_viridis_d() scale_size(range = c(2, 12)) scale_x_log10() labs(x = "GDP per capital", y = "Life expectancy") p 转换不同州的时间数据
library(ggplot2) library(gganimate) theme_set(theme_bw()) library(gapminder) head(gapminder) p <- ggplot( gapminder, aes(x = gdpPercap, y=lifeExp, size = pop, colour = country) ) geom_point(show.legend = FALSE, alpha = 0.7) scale_color_viridis_d() scale_size(range = c(2, 12)) scale_x_log10() labs(x = "GDP per capital", y = "Life expectancy") p transition_time(year) labs(title = "Year: {frame_time}") 根据不同的大洲创造不同的面：
library(ggplot2) library(gganimate) theme_set(theme_bw()) library(gapminder) head(gapminder) p <- ggplot( gapminder, aes(x = gdpPercap, y=lifeExp, size = pop, colour = country) ) geom_point(show.legend = FALSE, alpha = 0.7) scale_color_viridis_d() scale_size(range = c(2, 12)) scale_x_log10() labs(x = "GDP per capital", y = "Life expectancy") p facet_wrap(~continent) transition_time(year) labs(title = "Year: {frame_time}") 让视图跟随每帧数据变化
p transition_time(year) labs(title = "Year: {frame_time}") view_follow(fixed_y = TRUE) Show preceding frames with gradual falloff This shadow is meant to draw a small wake after data by showing the latest frames up to the current. You can choose to gradually diminish the size and/or opacity of the shadow. The length of the wake is not given in absolute frames as that would make the animation susceptible to changes in the framerate. Instead it is given as a proportion of the total length of the animation.
Show the original data as background marks This shadow lets you show the raw data behind the current frame. Both past and/or future raw data can be shown and styled as you want.
p transition_time(year) labs(title = "Year: {frame_time}") shadow_wake(wake_length = 0.1, alpha = FALSE) Reveal data along a given dimension This transition allows you to let data gradually appear, based on a given time dimension.
Static plot
p transition_time(year) labs(title = "Year: {frame_time}") shadow_mark(alpha = 0.3, size = 0.5) Let data gradually appear Reveal by day (x-axis)
library(ggplot2) library(gganimate) theme_set(theme_bw()) library(gapminder) p <- ggplot( airquality, aes(Day, Temp, group = Month, color = factor(Month)) ) geom_line() scale_color_viridis_d() labs(x = "Day of Month", y = "Temperature") theme(legend.position = "top") p Show points:
p transition_reveal(Day) Points can be kept by giving them a unique group:
p geom_point() transition_reveal(Day) Transition between several distinct stages of the data Create a bar plot of mean temperature:
p geom_point(aes(group = seq_along(Day))) transition_reveal(Day) transition_states():
library(dplyr) mean.temp <- airquality %>% group_by(Month) %>% summarise(Temp = mean(Temp)) mean.temp p <- ggplot(mean.temp, aes(Month, Temp, fill = Temp)) geom_col() scale_fill_distiller(palette = "Reds", direction = 1) theme_minimal() theme( panel.grid = element_blank(), panel.grid.major.y = element_line(color = "white"), panel.ontop = TRUE ) p library(dplyr) mean.temp <- airquality %>% group_by(Month) %>% summarise(Temp = mean(Temp)) mean.temp p <- ggplot(mean.temp, aes(Month, Temp, fill = Temp)) geom_col() scale_fill_distiller(palette = "Reds", direction = 1) theme_minimal() theme( panel.grid = element_blank(), panel.grid.major.y = element_line(color = "white"), panel.ontop = TRUE ) p transition_states(Month, wrap = FALSE) shadow_mark() enter_grow() enter_fade()
p transition_sttes(Month, wrap = FALSE) +
shadow_mark() +
enter_grow() +
enter_fade()
参考资料：
https://www.datanovia.com/en/blog/gganimate-how-to-create-plots-with-beautiful-animation-in-r/ https://cloud.tencent.com/developer/article/1675209 TED：https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seenlanguage=zh-TW