R语言gganimate疫情数据动态可视化

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R语言gganimate学习
5 d: }: h# B6 \! J5 Q) h. T$ o所需加载包
  y9 v* f$ G1 G8 N  l. glibrary(av)
1 L- \# U3 f/ P* ]+ F% H7 F7 Wlibrary(ggplot2)
3 a/ ~# t, a) L5 M$ ~% n/ ^5 R$ ]library(gganimate)
/ d  {! D$ i5 N0 x1 u# h8 Z4 w3 Ulibrary(tidyverse)
1 J) O% U2 p9 dlibrary(lubridate)
+ E4 y9 t0 ?3 `$ a: Z, Slibrary(scales)
+ Y( {6 L9 ]; P. N3 Klibrary(ggrepel)
0 `: ~% G: Q* W. flibrary(cowplot)
数据

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$ q9 y& I) ^7 R8 Y# }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) +
    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
0 j7 L9 W' x# q2 }8 z0 d    #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'))+
" a; l$ g; s$ ]" p' _# [2 Q5 I  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))+
1 ?& _& x8 i) r5 `   theme(panel.grid =element_blank()) +   ## 删去网格线
: B2 W2 O- \( c& q$ {2 V  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
  D" B2 f" f  p( c5 o  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
# Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
( w0 b' I3 k% ^" _; c9 E#annotate("text",x=0,y=40,label=C,parse=T)+
5 R# z- V! S/ X! ~coord_flip()+
transition_manual(frames=as.Date(Date)) +
#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)')+
ease_aes('linear')
ps
结果展示

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视频格式转化，加载BGM
#df <- animate(ps, renderer = av_renderer('animation.mp4'),
         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
3 i( |; r9 r6 m! |4 C# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
全国新冠状肺炎26天增长状况
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pc<-ggplot(data=CNdata_s,aes(x=variable,y=value,fill=variable,frame=Date))+
5 w" @7 t1 z$ F. o) D. ~; D   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
) g/ l5 k1 T% \( \0 \) ]9 |    theme(legend.position="none") +
, P! C) g5 o9 G. R( B  theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
, n0 H% o  P9 F4 |6 T) w  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
- E4 v/ i9 y0 U- i. N) y  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
9 G* g& A: h2 i0 P  S! y  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
# P( M& F; u# d3 Q) z/ p4 g  #scale_x_continuous(limits = c(0,6))+
# Here comes the gganimate specific bits
. c: ~9 P7 M1 r#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
' ?4 ?0 i$ z0 C1 L( @7 y#annotate("text",x=0,y=40,label=C,parse=T)+
/ F! ~' l+ n7 R3 z& u9 j- \    # coord_flip()+
5 o  g% G. w: o- c& L/ i' O     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 ='')+
     ease_aes('linear')
6 C9 e9 ^5 h; u" p2 cpc

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& k; Y9 F+ ]& \7 B8 l& ]* q动态图合并
, }5 e4 y& _5 V- \4 vlibrary(magick)
% D: l# e2 m1 f# r1 U. V* ~ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
" v, ~: @1 A% C2 X$ @pc_gifs <- image_read(pc_gif)
4 u7 z+ H( o( e# L2 bnew_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
for(i in 2:length(pc_gifs)){
: }* n7 Z( V. v  Z, x  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
6 r4 Z4 E  {9 {6 n5 i9 @}
  T* |( Q( B/ e: L3 Wnew_gif
结果展示
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