R语言gganimate疫情数据动态可视化

1047521767

R语言gganimate学习
所需加载包
( e$ A! e1 u" ?library(av)
library(ggplot2)
library(gganimate)
5 L. e3 S0 Y' n) l2 Hlibrary(tidyverse)
9 \3 P! [1 R7 U; ]library(lubridate)
library(scales)
7 u/ a  h2 d' G6 flibrary(ggrepel)
+ b  ?' p4 J2 D" ^7 y: ^- v$ t2 Alibrary(cowplot)
% b% k  F6 P7 n% j数据
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: r( U3 d1 h+ D3 k) t4 u! i0 Y3 Vps = 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) +
$ s9 m) S+ {3 q# B( V* I    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
) r9 J( z% _! }    #theme_bw()+
% d& S+ l8 |5 y1 P   #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'))+
3 D2 _2 P' n: ^1 D& z) @0 t  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
  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
7 M  J1 V  W- F/ a, [! e  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
' a5 E" T. B) Q6 Z3 @) o# 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()+
transition_manual(frames=as.Date(Date)) +
$ w0 t( b* c; T) T9 [#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)')+
# F6 ~0 Q* W, i9 Qease_aes('linear')
ps
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+ \0 {8 z% f# Y# X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
# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
) N. `, u) W. O! S全国新冠状肺炎26天增长状况
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/ F& H4 U- b& [1 b+ _+ w! apc<-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)+
6 s7 Q2 E8 @. K0 T3 o    theme(legend.position="none") +
) Z+ _. _  ]* d" b: R! v, W5 R  theme(panel.background=element_rect(fill='transparent'))+
7 b& A% C: |6 u  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
; g! c3 b9 s$ N* q5 e( \, k  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
; O% C4 a" s5 R  ~! y  H1 U" q6 |   theme(panel.grid =element_blank()) +   ## 删去网格线
" J# P$ C5 O5 N" [! |  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
$ B, A, s* \- z4 m: v" ^0 ~0 p- }  #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') +
0 v0 K: O( r! t& h: }" k#annotate("text",x=0,y=40,label=C,parse=T)+
* X5 l. v* Y  }/ Z  J. y0 l. a    # coord_flip()+
3 n. f/ Y( O* \) R# w     transition_manual(frames=as.Date(Date)) +
7 q: ?7 W7 s5 M  ]& S    # 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 ='')+
/ j' f1 S/ B4 N# y     ease_aes('linear')
pc

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动态图合并
library(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
* l' e) R8 V8 L! dpc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
pc_gifs <- image_read(pc_gif)
! u% G/ P, S. pnew_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
/ ^' j- u% |1 Tfor(i in 2:length(pc_gifs)){
, M# y1 y6 [3 k# P2 e; l* O  x  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
}
# R  i& c0 n3 {& m5 nnew_gif
) p: T. H; x, {: S+ L& D0 R结果展示
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