R语言gganimate疫情数据动态可视化

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R语言gganimate学习
; N# ?2 o4 x, U, c) L- y7 N所需加载包
& q5 a" s6 J0 ^0 D- R6 ]6 tlibrary(av)
+ H; p% E2 E$ Clibrary(ggplot2)
" `7 e. e# A: `+ Olibrary(gganimate)
library(tidyverse)
library(lubridate)
library(scales)
  v7 [! p; e1 j1 T& R# N) f, Ylibrary(ggrepel)
library(cowplot)
8 }1 N( K3 `6 E9 _1 ^( b数据


ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
) W+ G) J& w4 [     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
+ x2 J+ |9 h2 T- ?* c1 F/ z) E" b7 W/ ]    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
! h: U: F4 l; K1 X5 x# \    #theme_bw()+
   #theme(legend.position="none") +
0 }. b# h) F3 N/ Q4 J! D0 u    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))+
- Q" y0 f8 N( O3 n) k  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
2 L( ?% ?5 S5 p4 q( K   theme(panel.grid =element_blank()) +   ## 删去网格线
: u' x3 L# l7 j  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
2 O2 r; L; F5 c$ K, }5 _" u  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
  P% g7 n3 k$ _5 @3 p" Q& G4 U# Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
2 y4 i* P. i( `, F#annotate("text",x=0,y=40,label=C,parse=T)+
6 E! l5 w  L0 ?coord_flip()+
transition_manual(frames=as.Date(Date)) +
#ggdark::dark_theme_bw() + #设置黑色主题
: x0 \/ G) l# A8 a% tlabs(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
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视频格式转化，加载BGM
#df <- animate(ps, renderer = av_renderer('animation.mp4'),
         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
1 o; k0 A2 V5 |" F( z: _+ B" |" d全国新冠状肺炎26天增长状况

  N8 e- a! H. Q% K9 F: t- ]# n# Kpc<-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)+
    theme(legend.position="none") +
+ v' p7 p$ T4 n6 G  theme(panel.background=element_rect(fill='transparent'))+
$ x  U# [$ V! k2 N( ~7 D) z; W  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
# Q2 H# {6 o- k7 \4 H) S0 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
/ }% L/ G8 P' h  k6 i  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
  #scale_x_continuous(limits = c(0,6))+
4 u$ Q: o& E' f4 R* F0 k # Here comes the gganimate specific bits
5 t& L/ J' R9 ~. L; O% |#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
#annotate("text",x=0,y=40,label=C,parse=T)+
: x; g' Q7 O! d) `    # coord_flip()+
% l& @7 x" I. @. m- e- P     transition_manual(frames=as.Date(Date)) +
    # ggdark::dark_theme_bw() + #设置黑色主题
: L( D7 w. Q* m1 Q     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
$ m  ]$ F( Q5 u5 t( I$ a     ease_aes('linear')
pc
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0 u5 w* Q; Q4 n动态图合并
library(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
; i' z  u4 C0 x7 Pps_gifs <- image_read(ps_gif)
pc_gifs <- image_read(pc_gif)
/ ~' A6 E" N/ Q0 U) H8 \0 Cnew_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
6 M0 g! ^) X) n' O3 Y& mfor(i in 2:length(pc_gifs)){
7 e  `" m9 Y2 c6 k. D9 d6 b* j  combined <- image_append(c(pc_gifs, ps_gifs))
$ J, o+ R9 ^  t0 m  new_gif <- c(new_gif, combined)
}
new_gif
# j2 G& W4 Y0 j% M8 d2 B结果展示
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