R语言gganimate疫情数据动态可视化

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R语言gganimate学习
! y7 P- ^9 c# _% [所需加载包
5 s2 c% F$ ^. e* W1 Jlibrary(av)
library(ggplot2)
library(gganimate)
library(tidyverse)
( m' e) ]2 [+ E7 Q+ b6 zlibrary(lubridate)
# u& z( u1 n6 ?! L; @- O2 H3 Jlibrary(scales)
library(ggrepel)
library(cowplot)
/ h& O0 u" b& N- t( j+ x数据

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  p7 w4 V  C) P" p7 mps = 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)+
" F7 c. _( r: [9 q9 V1 a8 }+ ]    #theme_bw()+
4 R# H" U% f# V$ }) }" O/ {, [   #theme(legend.position="none") +
% ^' r( d) Q2 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))+
/ D( P8 n- J5 }( f0 A1 [' f  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
3 |& R7 S) }" @# N2 O   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
: I1 V8 |. q  ?6 y$ Y# Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
& V& R5 Z/ T: M& K) p! c, s: e#annotate("text",x=0,y=40,label=C,parse=T)+
, e; O* e- C! R* u. p1 q0 E, a: Ccoord_flip()+
transition_manual(frames=as.Date(Date)) +
: j5 l( v0 o- z#ggdark::dark_theme_bw() + #设置黑色主题
0 q! v" v% T# u5 T8 T" t: Ilabs(title = paste('日期:', '{current_frame}'),x = '', y ='各省累计确诊病例增长(除湖北省外)')+
# C0 {; G9 c6 O1 ]. H: _" J# Zease_aes('linear')
ps
: Z5 q: b3 U+ ?2 N& o& b结果展示
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视频格式转化，加载BGM
7 G9 p$ `& e6 h- A#df <- animate(ps, renderer = av_renderer('animation.mp4'),
         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
# i+ Y8 V4 _9 d$ ^$ v9 {# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
6 }1 E5 @( O) L$ L9 p8 ]$ o9 p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))+
: C+ M& S# k, S/ D( c, R   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
9 _8 g  t; e) H' s   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
' x& w5 d) |) ]1 [    theme(legend.position="none") +
' Z  G! Z) C9 W1 J( h+ |2 H  theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
2 l, Q' p- A9 e1 R( f- y+ E7 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
  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
8 c4 D' h9 N5 W/ P' A4 _  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
  #scale_x_continuous(limits = c(0,6))+
# Here comes the gganimate specific bits
  J7 `/ L5 Y7 v4 u. D# U6 ]& J#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
5 t% j- J1 {* I& `# v  F; L9 J) l#annotate("text",x=0,y=40,label=C,parse=T)+
. }7 d' {5 V( f+ c    # coord_flip()+
$ _8 _% B3 L, ]* B* {     transition_manual(frames=as.Date(Date)) +
    # ggdark::dark_theme_bw() + #设置黑色主题
0 x: T5 @% |1 x% C; U6 n     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
) q$ C+ t' p% m, s/ h' h+ ^5 K     ease_aes('linear')
0 ~+ w- R- A9 p/ r/ n( ~pc

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动态图合并
( w7 r3 R8 ]9 P$ g" g% |library(magick)
, C: j$ C1 S3 M& Z. Lps_gif <- animate(ps,,width = 720, height = 480)
3 E2 F& K5 q+ r) ^pc_gif <- animate(pc, width = 360, height = 480)
3 h) Y2 o. T  P6 b# u& wps_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)){
: @0 F6 ~8 p% G  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
/ e8 E0 ~# N) x6 [}
# v7 G1 z6 I/ n! J6 H5 Lnew_gif
9 s0 [; X0 j" m* b, W结果展示
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