R语言gganimate疫情数据动态可视化

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R语言gganimate学习
所需加载包
+ u0 S- [6 n) O" G% i3 w! elibrary(av)
library(ggplot2)
, J. k: T) j5 _1 [+ F( V6 p- Ylibrary(gganimate)
library(tidyverse)
library(lubridate)
library(scales)
& s; t4 f0 j7 {; N/ E: Slibrary(ggrepel)
library(cowplot)
5 d- g- K; {( t* Q2 }8 w  C数据

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ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
2 [" e/ {3 d: B9 b. k" O. x    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
5 R! x4 A+ H/ p    #theme_bw()+
8 A; q6 i. }* x2 c   #theme(legend.position="none") +
) s8 r5 E8 l3 ]: C% [    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
    theme(panel.background=element_rect(fill='transparent'))+
+ r% A: B3 m' M4 h* o+ \8 O- \  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
1 T8 p* j* k: F: @  g  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
- }" }* q% o/ j2 Z/ F1 e3 `   theme(panel.grid =element_blank()) +   ## 删去网格线
& I! G' h# T4 f: P7 @- ]" e! B  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
4 T; v- _; G7 }& T) F6 m# Here comes the gganimate specific bits
# s6 ~  G7 t; q" `- D#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
#annotate("text",x=0,y=40,label=C,parse=T)+
( G  Q. X9 f( h1 Scoord_flip()+
! N. p6 _9 Q. T' w8 m2 P, h/ [transition_manual(frames=as.Date(Date)) +
  K' c6 ?/ L1 ^#ggdark::dark_theme_bw() + #设置黑色主题
, Q1 Y$ A; J" e+ i# e  mlabs(title = paste('日期:', '{current_frame}'),x = '', y ='各省累计确诊病例增长(除湖北省外)')+
, t0 K2 }+ N9 S/ _  p% C3 b6 ~ease_aes('linear')
ps
结果展示
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+ d# q1 X4 I, ~! Q8 b/ v% M
8 _7 ^# ?4 S7 ?( n视频格式转化，加载BGM
5 l* d$ B/ C5 Y* `- Q, ?% y4 ]#df <- animate(ps, renderer = av_renderer('animation.mp4'),
6 t( [) c, ]) J& M, j! v, J         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
9 G: w& ]3 S! X0 N全国新冠状肺炎26天增长状况

  e) @9 h) C' Npc<-ggplot(data=CNdata_s,aes(x=variable,y=value,fill=variable,frame=Date))+
   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
4 m7 \3 V) l5 q    theme(legend.position="none") +
, W  i1 l" G- I0 o/ U. [  theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
; \- P9 c( |6 M1 f  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
8 t. v% f% s, [: G. u  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
( _/ c. K% U( d! x3 F5 ~! F  #scale_x_continuous(limits = c(0,6))+
# Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
#annotate("text",x=0,y=40,label=C,parse=T)+
    # coord_flip()+
+ \0 s4 X( V! G3 [3 ?+ `     transition_manual(frames=as.Date(Date)) +
    # ggdark::dark_theme_bw() + #设置黑色主题
     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
, W3 Y* I8 l- \1 Z     ease_aes('linear')
4 A# o/ U3 E3 }0 s# Q7 ~3 ]pc
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动态图合并
library(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
5 ^" K/ {! G2 h$ E  A+ P* Nps_gifs <- image_read(ps_gif)
pc_gifs <- image_read(pc_gif)
7 H9 Y8 E7 f  e  l) g8 w$ N5 p8 D5 knew_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
9 E: U+ T0 l) W6 O2 o+ a: ^) Q9 ifor(i in 2:length(pc_gifs)){
0 ?/ C% c7 J4 u  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
}
* [- |, B* |4 k3 ?new_gif
结果展示


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