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)
/ h0 d2 b" r0 o/ Plibrary(gganimate)
. v/ M- M5 a% T2 B& Dlibrary(tidyverse)
; i' _* [; r1 Ilibrary(lubridate)
5 Q5 A" G  R6 z/ R+ olibrary(scales)
library(ggrepel)
( U; a2 N, o; d# l* A' I. U1 rlibrary(cowplot)
数据


ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
# g* V) U4 q+ b6 L( I     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
    #theme_bw()+
6 I1 e3 h1 S! d3 B0 V* O   #theme(legend.position="none") +
0 T' ~9 M3 b2 |5 y% a" C7 z# |! I3 L    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'))+
- L9 y9 j, ^# N. C- [  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
7 @1 ]0 {- w* o$ o/ j  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
& I5 j# f& w( B0 A  s+ \# Y   theme(panel.grid =element_blank()) +   ## 删去网格线
) [. q0 B: K& v  u, h$ O9 l  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
6 N- S0 F- `' w# {& m1 O  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
- r0 r* w5 t6 ]: t# Here comes the gganimate specific bits
' J) {. ^9 F" z( ]#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
9 O6 R9 N) f7 S#annotate("text",x=0,y=40,label=C,parse=T)+
coord_flip()+
3 k2 v4 i# l6 y4 I, J% [transition_manual(frames=as.Date(Date)) +
: V0 D( D) N. Z& a#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)')+
ease_aes('linear')
ps
) q4 m* F2 Z. J4 R结果展示
0 @* |% n6 \  y

, w, [/ `5 P( k8 P! p5 O' B4 J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")
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))+
- {8 d$ ~( F4 \- r+ f3 D( k   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'))+
  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
' c) @# D0 `5 x1 g9 x6 K! G  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
0 d: I( z$ n: f# k* }5 E4 Y  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
8 w& `8 s' |2 L' l" L: h) a#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
! X* z  d! T6 r% E3 W: D5 l#annotate("text",x=0,y=40,label=C,parse=T)+
    # coord_flip()+
6 c6 R5 e4 E4 M2 f) t+ D5 z) y     transition_manual(frames=as.Date(Date)) +
    # 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 ='')+
8 R* Q) v& Q& l* L8 G     ease_aes('linear')
pc
- A) M% o4 h% }* ^. C

* J8 A5 K* |, t# X! @; C) P# }动态图合并
) U" V" @* @% p& H, xlibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
; E9 B$ |% Q( q( Q) xpc_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]))
6 S3 O) U4 X3 ~/ N4 f) x6 lfor(i in 2:length(pc_gifs)){
  combined <- image_append(c(pc_gifs, ps_gifs))
9 ?- b: r8 z6 l/ B  new_gif <- c(new_gif, combined)
3 P- ^  Y, d3 s( |2 l/ b0 f}
new_gif
9 C0 }# ~1 ^" X  D结果展示
6 L! U" c+ m& l
  d* k- |9 _9 O: `6 ~( d7 j
6 q9 B: _7 W6 \. U# M3 c& s

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