R语言gganimate疫情数据动态可视化

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R语言gganimate学习
) k. N8 v+ N7 |% T1 ^/ f* h. D所需加载包
library(av)
library(ggplot2)
8 x1 A  {( ^7 R$ l& I" M5 Olibrary(gganimate)
8 Z4 B/ B6 ~& |library(tidyverse)
library(lubridate)
3 r4 o( A- b' e( t7 u% zlibrary(scales)
library(ggrepel)
. @. U/ ]3 Z- M* P) c2 rlibrary(cowplot)
0 B" u0 O/ o: J  H6 t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)) +
5 y# P. U; [9 U  @1 J     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
! G5 P8 z, C; O4 I) T    #theme_bw()+
   #theme(legend.position="none") +
    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'))+
% ]+ J6 @  R6 ~1 {( {2 P0 a- r6 n  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
. L3 S$ t. r* {2 _  M  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
, }6 B: O# z$ Z2 _0 R) f  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
5 E' @# L! D8 q; v, C# 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()+
, n( e3 [* h2 Ntransition_manual(frames=as.Date(Date)) +
. h5 d  _( F9 i' u5 n1 n. x- S% X#ggdark::dark_theme_bw() + #设置黑色主题
labs(title = paste('日期:', '{current_frame}'),x = '', y ='各省累计确诊病例增长(除湖北省外)')+
& `9 ~6 `" c3 Q6 m8 a. w/ Rease_aes('linear')
ps
* N' e$ N3 w$ m) l, _3 U结果展示


' C1 a* r/ P$ c- D5 `7 u2 E6 \. @9 t5 B视频格式转化，加载BGM
/ h% K0 {0 H4 M& X' Y#df <- animate(ps, renderer = av_renderer('animation.mp4'),
         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
+ F- ^) b/ ]4 B/ H, _" v& s: j; T# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
全国新冠状肺炎26天增长状况

pc<-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)+
    theme(legend.position="none") +
4 Q" n* `' J* `5 a+ c7 J) s  _  theme(panel.background=element_rect(fill='transparent'))+
; {: U0 h; J+ [  h2 i+ K/ |! `+ g  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
* A: N5 @6 B" t" ^  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
$ K9 K( F* t& E, Q# y) ~  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
! S7 L, B, ]0 d* i0 A  #scale_x_continuous(limits = c(0,6))+
# Here comes the gganimate specific bits
8 l: v2 q, g* `/ g#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()+
     transition_manual(frames=as.Date(Date)) +
    # ggdark::dark_theme_bw() + #设置黑色主题
5 N' F0 v4 }2 a( i3 X     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
" h# k# S3 @4 G" b- }5 c     ease_aes('linear')
9 l5 P, r# F& t! h& G9 gpc
. H' \3 y0 @$ [1 ?2 j& i9 C

动态图合并
( g. T+ d' N3 p2 J$ D8 X2 |6 q0 Slibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
pc_gifs <- image_read(pc_gif)
new_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
for(i in 2:length(pc_gifs)){
  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
}
new_gif
- o8 e  n: M8 Q; D结果展示
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