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)
4 L5 s" V8 R1 O# J$ C: f1 n6 e9 ^library(gganimate)
4 t" s9 I' e& K/ E$ v  |library(tidyverse)
library(lubridate)
library(scales)
6 g6 ~" M& P9 O! Q6 G9 y4 w5 y( ]library(ggrepel)
* P+ F, Y: p9 _) M0 i" Hlibrary(cowplot)
7 @* G1 H  ]9 z) `" u; w数据

, q( l9 F( f* ?, Q( j) a' {6 k
ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
$ b; a+ ^) v8 v& m* z" U     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
4 C& m; h; N+ p$ H5 `7 e    #theme_bw()+
2 {. k$ U1 `2 L7 @5 ]( u   #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))+
, o; w9 A! r0 d/ V2 x# w   theme(panel.grid =element_blank()) +   ## 删去网格线
, E, f3 r7 p3 b) N9 P  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
8 _, d: @# f7 I5 j# Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
; Z% f$ [5 C! G, {  ?; ~# I#annotate("text",x=0,y=40,label=C,parse=T)+
coord_flip()+
; {5 N0 e- D- j( i+ h  mtransition_manual(frames=as.Date(Date)) +
#ggdark::dark_theme_bw() + #设置黑色主题
labs(title = paste('日期:', '{current_frame}'),x = '', y ='各省累计确诊病例增长(除湖北省外)')+
$ v1 @" W( N/ z  J0 o4 ]9 {ease_aes('linear')
ps
+ X4 u; @2 A3 n, @2 ^结果展示
+ u1 i, d. V. t. |
' p7 {9 Q/ h  s$ q# m  Z
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
/ G8 b8 i  a. C% }. |2 a  u) y+ b# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
: c% u  @7 N; q, o8 M; ?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))+
   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") +
  theme(panel.background=element_rect(fill='transparent'))+
+ e" P. u# {/ B. Z  u* m+ l  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
/ _( b+ r+ b4 e: F0 @! ^  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
7 C; J' y) n7 ]- f7 g  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))+
# Here comes the gganimate specific bits
2 e! t: |3 q. M8 L+ s6 S#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
#annotate("text",x=0,y=40,label=C,parse=T)+
* v  Y. h* @) d3 T$ f. B    # coord_flip()+
     transition_manual(frames=as.Date(Date)) +
' q4 R- h' C0 g% n% k    # 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 ='')+
( e! z% I2 d5 z1 r     ease_aes('linear')
pc


动态图合并
library(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
7 i. O$ e& F' Mps_gifs <- image_read(ps_gif)
3 q7 f( M2 {9 `+ w" g# P2 a. ^- `pc_gifs <- image_read(pc_gif)
/ X% \9 i2 E* Y1 S- {' [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))
: t% H, p! v# @  new_gif <- c(new_gif, combined)
}
, d( T/ s; {& t# j- W: B5 s8 \2 \new_gif
; B. q9 z9 M1 k/ c结果展示

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