R语言gganimate疫情数据动态可视化

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R语言gganimate学习
所需加载包
library(av)
library(ggplot2)
library(gganimate)
( q" V! A6 F& olibrary(tidyverse)
library(lubridate)
8 S5 W: u, B* N5 V* hlibrary(scales)
; F$ M/ Y/ d2 \& klibrary(ggrepel)
library(cowplot)
# ?, S: A8 j1 i# F数据
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4 l! K# v6 x+ l1 x
ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
3 `( Q2 p4 x4 j1 H6 H& V     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
" M% I1 B: z9 }/ S0 j# L4 n5 C    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
' }0 x! @0 x) Q; Z9 T9 I    #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") +
    theme(panel.background=element_rect(fill='transparent'))+
& E# i) t( N. P, L& L  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
  C$ i. ^& w' D8 t  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)+
8 P1 N. s$ c, R, S7 d/ `coord_flip()+
% D2 P+ M1 Y4 k9 P4 x! Mtransition_manual(frames=as.Date(Date)) +
9 q+ F3 p- n8 b#ggdark::dark_theme_bw() + #设置黑色主题
* b" Z, I$ D0 ]  y* n$ h) 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')
/ s8 o' o# s9 x$ N$ Dps
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
4 s8 W' M0 O, K" ?2 J8 H' M# 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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, \' u* D- F& m4 @' q, Cpc<-ggplot(data=CNdata_s,aes(x=variable,y=value,fill=variable,frame=Date))+
/ }* `; u% U* H1 e$ M: y$ @   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
8 L* j9 v* }8 b5 p' }: p5 [   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
    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))+
  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
: E. u  P7 H- ]) H3 x/ l  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
2 l: i3 k0 W  w) f  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
" x7 ]0 q- A. E" ]5 x2 o# y7 A6 r/ b1 i  #scale_x_continuous(limits = c(0,6))+
# Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
# y8 H; j  Z! t, ~' }5 X8 U( I#annotate("text",x=0,y=40,label=C,parse=T)+
& U5 g+ r( {. E5 G    # coord_flip()+
     transition_manual(frames=as.Date(Date)) +
    # ggdark::dark_theme_bw() + #设置黑色主题
. s, d4 X4 ]0 L' g4 l     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
& y3 `! ?: a, @; v+ o     ease_aes('linear')
pc

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动态图合并
& k+ ~/ \" K  M4 Ulibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
: s) R/ l. G$ s! G: M2 ]9 ypc_gifs <- image_read(pc_gif)
( c- R2 L9 B2 Qnew_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)
; S, F# Q( D7 a1 [% E" @}
new_gif
结果展示
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