R语言gganimate疫情数据动态可视化

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R语言gganimate学习
4 E( V4 C% m( n1 ?所需加载包
# @' z! G. K  W9 S: r; R2 Llibrary(av)
5 `! ^8 R2 N+ D% ~: Clibrary(ggplot2)
library(gganimate)
library(tidyverse)
library(lubridate)
library(scales)
library(ggrepel)
& z+ h# ?4 u; a& k. Nlibrary(cowplot)
2 o9 A& S/ ~  A! i9 T数据


ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
4 x. k# b" W, `     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
) l  v+ i' K9 N' n  n% ]2 R( }+ e% U    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
: m6 N* E, w9 B7 Z    #theme_bw()+
   #theme(legend.position="none") +
2 `9 F0 C/ ~3 `0 a! R, Z1 R    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
4 S$ a' L8 ?3 V4 [# q( r    theme(panel.background=element_rect(fill='transparent'))+
4 W/ H: x" ?# A3 r9 C# @# s  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
  R  H6 _/ \" u% a, f# y  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
: M# s) ]/ K5 V6 K  B  c   theme(panel.grid =element_blank()) +   ## 删去网格线
  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
. h, s/ G2 K5 c& r  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') +
#annotate("text",x=0,y=40,label=C,parse=T)+
coord_flip()+
+ |2 D" o+ Y. L* b6 @/ {# ktransition_manual(frames=as.Date(Date)) +
* J7 V" d& ~) Y$ C7 Y7 `#ggdark::dark_theme_bw() + #设置黑色主题
$ p; E) Y* Y1 ~2 |4 U7 g, {labs(title = paste('日期:', '{current_frame}'),x = '', y ='各省累计确诊病例增长(除湖北省外)')+
* t+ P, Y2 E" g+ Q% I6 R0 V7 ~; u. qease_aes('linear')
ps
结果展示
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视频格式转化，加载BGM
#df <- animate(ps, renderer = av_renderer('animation.mp4'),
* g8 r& f1 Q. f( Z4 X- R4 f) [; W         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
" D4 O1 q- ^6 d6 f- {& {全国新冠状肺炎26天增长状况

3 F9 H; |; z( Q" U  @0 G/ Hpc<-ggplot(data=CNdata_s,aes(x=variable,y=value,fill=variable,frame=Date))+
( ]" H% M# d  f' d6 ~   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
8 G4 p, d7 ^: N, _1 [: f   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
' ~4 K7 R1 ]' d& B! f    theme(legend.position="none") +
3 k, V8 \; l6 v  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
; L$ N# n( [+ {  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
) ~9 M; \) l# S/ B$ p  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
( [1 \2 g9 A$ p( r  #scale_x_continuous(limits = c(0,6))+
4 q" |7 U6 R) Q! }' f8 l # Here comes the gganimate specific bits
% z& @0 f6 H2 G. C; G9 k3 t* J#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()+
# r1 Y3 \+ M0 F/ p- B+ @) V" T$ c     transition_manual(frames=as.Date(Date)) +
; |$ e) H. b, i7 c    # 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 ='')+
& \7 P0 t2 `$ \9 E. S2 s: y0 ^     ease_aes('linear')
pc
9 w! b! i* h( ~: N0 u9 m

; D5 t8 J& j" G! n/ w% d动态图合并
library(magick)
6 h9 J1 s; P) Q' A& [ps_gif <- animate(ps,,width = 720, height = 480)
, i! ~& _' O3 L, I1 H( ?$ \. N8 npc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
9 m; r( \( p3 a; q, [1 \* Opc_gifs <- image_read(pc_gif)
new_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
for(i in 2:length(pc_gifs)){
  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
}
new_gif
4 ?# x1 y3 j. f3 d  k结果展示


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