R语言gganimate疫情数据动态可视化

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R语言gganimate学习
: b1 N# t) l, a& e& a所需加载包
library(av)
library(ggplot2)
library(gganimate)
: }4 G0 @- W7 c. Alibrary(tidyverse)
library(lubridate)
library(scales)
library(ggrepel)
( `$ R9 r0 N, r: K! wlibrary(cowplot)
1 C: [* q; i, ^" [5 r/ Y$ ~* Q+ Q( X数据
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: z- V) r% e5 u3 O- _, yps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
, ~7 g/ p0 S- V) {0 O+ K7 G     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
* Q6 ^4 t  i3 R1 F7 z2 `  R( [6 S    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
% E$ L3 d9 h6 j5 K5 H    #theme_bw()+
   #theme(legend.position="none") +
' v+ T3 }: t4 O7 Y$ B. V: \    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))+
- u( P( ]7 `' v2 H; l  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
4 v# }: d  _! W: ^' W/ {   theme(panel.grid =element_blank()) +   ## 删去网格线
  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
3 V: e  @6 X0 ~  O/ K2 L6 ~  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
3 p: c. u$ G, {4 @  K( k# Here comes the gganimate specific bits
# h! o) ^4 y, A9 R9 j( v1 E$ |, u; k#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
( b2 n- U4 ]; ^: N3 C#annotate("text",x=0,y=40,label=C,parse=T)+
coord_flip()+
  L& Y" _/ ?4 r, C! etransition_manual(frames=as.Date(Date)) +
- W3 t& z* a) V, v; [& o1 Q2 \#ggdark::dark_theme_bw() + #设置黑色主题
  n: m9 p, T/ P$ o0 J4 a* glabs(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')
  {6 M$ R, k8 J/ T; z1 \. d" hps
结果展示


' \5 T8 R& F% \$ c3 B4 I视频格式转化，加载BGM
' J" u/ u! C- M#df <- animate(ps, renderer = av_renderer('animation.mp4'),
! i7 Z6 C7 d: V" H- ^         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
) G! D  t. s4 K8 W, O全国新冠状肺炎26天增长状况

. y( b1 H! |! J& M2 fpc<-ggplot(data=CNdata_s,aes(x=variable,y=value,fill=variable,frame=Date))+
' v9 g  ?+ @- E  @8 W1 f3 u% P   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
$ l5 v- l* N8 ]' ]   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
    theme(legend.position="none") +
2 P4 b) \( J% G. ~, J" \  theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
0 G1 u9 h8 e. h; h0 L- ]  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
2 y! H% e7 k7 h7 U! k  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
& r: c: b$ W' x, ^9 V9 j  #scale_x_continuous(limits = c(0,6))+
& C( S, P7 L; _6 S; V* _6 _( s # Here comes the gganimate specific bits
; i6 n( z# h7 w( |#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
#annotate("text",x=0,y=40,label=C,parse=T)+
0 ^2 m' G, I, o1 o    # coord_flip()+
     transition_manual(frames=as.Date(Date)) +
5 B" ]+ Z/ R1 A7 X# z( j    # 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')
pc

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5 D# N2 o9 j! i& f  J6 M0 S& ^动态图合并
- ]! a) `$ u3 O, E" clibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
; n- Y) `; N" z: Cps_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)){
6 p* {" f5 B9 ?- c  combined <- image_append(c(pc_gifs, ps_gifs))
) b, n& U  r/ [5 d8 Z; T9 [6 e% w3 ]  new_gif <- c(new_gif, combined)
}
new_gif
结果展示

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