R语言gganimate疫情数据动态可视化

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R语言gganimate学习
1 S4 U% W7 r3 f# X所需加载包
library(av)
library(ggplot2)
library(gganimate)
2 \0 i% @7 n' z$ ?* Klibrary(tidyverse)
library(lubridate)
! O$ b8 ~' G( B9 B. K9 Wlibrary(scales)
; Z/ M+ N  e) K  u% Rlibrary(ggrepel)
library(cowplot)
数据
% Q2 F4 s2 ^; Y+ f
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- w+ j& u0 f% F" I" y& ?ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
" U" a9 s' ~6 w$ X  ~( r- F- }     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
$ G+ p$ p) l& M1 U4 r    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
    #theme_bw()+
   #theme(legend.position="none") +
    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
. Z0 Y6 s  X4 q7 L- R  F6 s! D+ W    theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
! c: N; J3 W1 R: v  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
) s, |9 R4 u* h1 w' ?( I5 X* f- R   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
. ], S% U" o0 M! o$ d# Here comes the gganimate specific bits
1 ]0 n% ]# a  Q3 Q# y#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
5 Y/ [- S& C% s* V#annotate("text",x=0,y=40,label=C,parse=T)+
coord_flip()+
transition_manual(frames=as.Date(Date)) +
) ?* n; s. W/ I#ggdark::dark_theme_bw() + #设置黑色主题
9 A4 \! T5 s3 ^6 Z1 Plabs(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')
* s$ A1 ?2 _& O9 tps
0 V) N$ h/ `- K+ e结果展示


# k1 N; ~: ]' M( y& u1 {8 a, F视频格式转化，加载BGM
: ~; y9 E" [& ?/ n: H$ {: ~; x#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")
全国新冠状肺炎26天增长状况
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pc<-ggplot(data=CNdata_s,aes(x=variable,y=value,fill=variable,frame=Date))+
% D, p" e* L( H( e   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
0 ^( k4 O" G& @# T0 \3 f    theme(legend.position="none") +
  theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
: |% k) Z  v! d7 c- L" }2 D  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
0 u6 j" n7 o0 t' ^2 k3 T8 x  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
  #scale_x_continuous(limits = c(0,6))+
1 M- w% |: [% o; X) j # Here comes the gganimate specific bits
6 \3 G& o, Z7 e9 c2 ^: B, ^#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
: }. t9 e% m2 e+ X2 `, l9 m# n9 l#annotate("text",x=0,y=40,label=C,parse=T)+
3 |2 x( ~" F  A) ^8 q- y    # coord_flip()+
     transition_manual(frames=as.Date(Date)) +
" f5 l' u- l& C; {( p    # ggdark::dark_theme_bw() + #设置黑色主题
& y, k! V  d) l3 w6 C$ B     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
1 w" X* c4 M0 Glibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
pc_gifs <- image_read(pc_gif)
new_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
  X8 d( ]) v: I5 _$ J  P8 w5 Zfor(i in 2:length(pc_gifs)){
) Y# g2 t+ ^0 a2 ]+ ?+ ^" A  combined <- image_append(c(pc_gifs, ps_gifs))
2 `& ~4 t  r. ?% h: |: O  new_gif <- c(new_gif, combined)
# y3 f: ]9 E5 n$ r5 q0 G& n}
new_gif
$ V7 G. H, [0 y% K7 z( C结果展示
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