R语言gganimate疫情数据动态可视化

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R语言gganimate学习
7 V# b: T0 H: R2 H) U+ l# c所需加载包
' s# p' h  N! F' _3 Plibrary(av)
library(ggplot2)
5 Y5 o: E8 i1 V4 k8 T- N% t7 Tlibrary(gganimate)
! H3 g9 P( E# }- G* Z( klibrary(tidyverse)
& c! \/ w- d0 ^2 K$ wlibrary(lubridate)
library(scales)
library(ggrepel)
' A1 b6 E: M3 g3 L! C, A9 qlibrary(cowplot)
8 R7 L4 L$ u( [( ?' ~' r数据
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  A" W- W. g) Y  Z$ J5 ~ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
  }( C9 \) `0 p, t  \     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
$ n! e- p4 G: y7 v2 n+ m+ b/ v% ^+ @    #theme_bw()+
/ ?6 T+ G/ @# `6 J2 b! A) c   #theme(legend.position="none") +
    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))+
  theme(axis.text.x=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
  z& j/ u' [4 N1 N5 ~- _8 `  z# Here comes the gganimate specific bits
3 x; c. I& H( g  Y#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()+
transition_manual(frames=as.Date(Date)) +
  Q2 O+ j  ]+ s* ~. E' v. t) w#ggdark::dark_theme_bw() + #设置黑色主题
* E+ q; c; k  e, Rlabs(title = paste('日期:', '{current_frame}'),x = '', y ='各省累计确诊病例增长(除湖北省外)')+
8 n3 I  q" L, n: ]. tease_aes('linear')
. S6 K$ O5 A" |ps
; A" p, k0 o% @! ]% r, \结果展示

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8 ~  t& H: m" B1 ?& z1 K5 v视频格式转化，加载BGM
3 W0 [8 i: B6 b: J/ ~#df <- animate(ps, renderer = av_renderer('animation.mp4'),
         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
' M6 {$ }) w1 B5 u# ]& X# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
8 w  `0 ^4 ^! Z1 b; h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))+
# z% J, S5 I) u" n+ A" Q: A& k   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
& ?! ]% S$ p' r    theme(legend.position="none") +
" y. S+ K) f* u( r( a  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
% Z7 K; y8 B8 a* H+ f' G$ ^  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
  #scale_x_continuous(limits = c(0,6))+
8 y0 g, y$ C0 P" ? # Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
  E! X! _+ |* m#annotate("text",x=0,y=40,label=C,parse=T)+
/ g2 _- o+ t7 _    # coord_flip()+
$ |; d* E  H2 c0 B7 G     transition_manual(frames=as.Date(Date)) +
) T7 K3 d. [: ^2 y  b+ G/ ]    # 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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动态图合并
library(magick)
- T% E" p# H: H6 a$ @0 e- z2 w0 Dps_gif <- animate(ps,,width = 720, height = 480)
# j5 A9 S+ ?( [! J4 b/ E6 F  Tpc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
0 B' V- |  S4 J6 J( ^, _8 h- }pc_gifs <- image_read(pc_gif)
6 Y( a. a& H! K; Nnew_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))
5 z8 R! D* e, v9 J4 J$ f  new_gif <- c(new_gif, combined)
7 l: x4 \; b5 E" A& e}
new_gif
结果展示
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