R语言gganimate疫情数据动态可视化

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R语言gganimate学习
/ V. u9 ^8 ?2 I: |2 @6 t所需加载包
8 a4 N  z8 J" v. v  o( U1 d; N2 l7 P: ~  Ilibrary(av)
library(ggplot2)
library(gganimate)
' Y) a* X' }! n; }library(tidyverse)
) n) F3 N" U4 N- I3 k* ulibrary(lubridate)
library(scales)
library(ggrepel)
( x6 ]$ m! D5 p' U" Hlibrary(cowplot)
$ d, S* I% F0 t数据
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! A) s: \& i( d" O4 Hps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
" Q- J) }2 o2 U% L1 a     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
  ?; P/ f' @1 B- M    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
    #theme_bw()+
   #theme(legend.position="none") +
1 h1 W( G! A8 @2 p$ U    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
* S3 _4 E9 N! A6 i5 h/ r1 z5 x! O- l    theme(panel.background=element_rect(fill='transparent'))+
9 Q- ]: z! R3 h( Q  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
0 p: d- G# A! r! ]' U  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
; w- P6 [% b& s# S   theme(panel.grid =element_blank()) +   ## 删去网格线
* H9 r' |9 y; Y2 p2 x5 h$ v9 m/ }  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
) X8 B  O! P( D" M2 a9 N" \  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') +
$ e: ?# a, H7 p4 y/ K#annotate("text",x=0,y=40,label=C,parse=T)+
coord_flip()+
transition_manual(frames=as.Date(Date)) +
' k6 B' `% ^6 H! F$ N, `#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


& \7 U* p0 W8 a$ o$ O3 s视频格式转化，加载BGM
% A* ]5 }. |$ {#df <- animate(ps, renderer = av_renderer('animation.mp4'),
# b# p; _) ]7 a: m         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
全国新冠状肺炎26天增长状况

1 M; a3 a" _0 `, v( s7 r9 s+ ?, Mpc<-ggplot(data=CNdata_s,aes(x=variable,y=value,fill=variable,frame=Date))+
2 N/ W- s+ j# k! N   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
" K: j7 a" z2 N$ V( N   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
    theme(legend.position="none") +
  theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
  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
( M7 x* s0 K2 v7 u/ T1 s  #scale_x_continuous(limits = c(0,6))+
2 m: T: ^- x, L- k  o+ k # 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()+
9 ?  D$ G' Q- `) h, \     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 ='')+
+ ^+ J7 r/ V4 i4 n     ease_aes('linear')
pc
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动态图合并
, V  w3 h' j# a7 [* g7 nlibrary(magick)
$ ^  l0 v) W1 x1 B6 s- rps_gif <- animate(ps,,width = 720, height = 480)
# W; @2 e$ D* h( B8 wpc_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]))
& j. L, E0 H9 t) X( w4 r$ b$ kfor(i in 2:length(pc_gifs)){
  combined <- image_append(c(pc_gifs, ps_gifs))
1 r! i0 I7 ^! a3 k. q  new_gif <- c(new_gif, combined)
}
new_gif
! m! ~; V% i- |0 o1 U结果展示


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