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
library(av)
6 Z3 I+ m( `% K6 H, ^% llibrary(ggplot2)
: E9 S: |6 H) K0 D' f/ D$ |library(gganimate)
( a* U& x# k# d% a* blibrary(tidyverse)
library(lubridate)
library(scales)
library(ggrepel)
2 O# Z, l# M1 y6 A. I) slibrary(cowplot)
数据


ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
5 M( H& v, @- d( \" v7 `# n     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
, z9 N# p  G, X6 B+ b    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
    #theme_bw()+
   #theme(legend.position="none") +
) g; u( y& `0 ~' k; Q2 P! q- q7 f    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'))+
  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
  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
9 @" w' }% ~) u! L" n+ t* i  Q# Here comes the gganimate specific bits
+ M0 }. B& B: m. z  b1 T8 U# i& I7 t#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)) +
1 \. G/ U1 W, d#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)')+
5 l+ ^  Q5 D4 V" n* A$ Dease_aes('linear')
ps
+ v4 j2 [+ n9 g结果展示


/ \& }6 @# z/ v" L9 I视频格式转化，加载BGM
#df <- animate(ps, renderer = av_renderer('animation.mp4'),
$ _* f8 M# M9 n# Y. L" j         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
6 j# s7 F! A( O# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
2 I" |9 v/ ?& t3 U全国新冠状肺炎26天增长状况
3 K0 O' K  s' b( y9 \+ q
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)+
5 b' p- D9 S( o& S    theme(legend.position="none") +
+ G1 W% z, V* e5 U" D  theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
) L% Z; u; S% i) a6 q  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
  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
, h- G( `4 C9 w6 z  #scale_x_continuous(limits = c(0,6))+
% d6 K) g" ]# A# s. S, \ # Here comes the gganimate specific bits
# b' p: ]- I# N1 }; N2 w#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
1 y3 i( [# j9 x. k, a#annotate("text",x=0,y=40,label=C,parse=T)+
- H/ {6 ]+ g% D3 v    # coord_flip()+
4 k0 }& x0 E' M" I0 D     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 ='')+
$ h; H5 f0 m) B) G. {* _6 e     ease_aes('linear')
* K% L7 M: `* Y0 S2 t" \pc


& L# k1 [' o$ K) _: G动态图合并
' Z' T/ R2 y( x/ plibrary(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)){
" H/ j) P. g& p) r) }1 o  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
& H; `4 Z0 z' p9 b- s# f  G# q}
+ H2 C. a1 n6 r. d  }- J/ `1 K0 k7 hnew_gif
8 h' F$ S, w# B( y3 P6 o结果展示
2 {) X( C/ d! H% Y/ m, ]

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