R语言gganimate疫情数据动态可视化

1047521767

R语言gganimate学习
! F' u& _( i0 z# d7 b所需加载包
library(av)
3 \2 i  |0 u- B! alibrary(ggplot2)
; W! P! q( q/ I% llibrary(gganimate)
$ {( x0 J& `- K( elibrary(tidyverse)
library(lubridate)
6 W" z" G* A9 a9 b- Nlibrary(scales)
library(ggrepel)
3 j, ^( G! h& j/ h7 llibrary(cowplot)
数据
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8 P, z2 D. o& D- ~% Zps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
     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()+
   #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'))+
( ~! `3 R1 y8 Y+ N  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
8 g# V  r- ?! T# t- B2 }  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
* G7 U" L- r* s( r2 m/ e   theme(panel.grid =element_blank()) +   ## 删去网格线
  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
7 W$ o- o# T, m7 L9 {' Y  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
# Here comes the gganimate specific bits
" O7 u8 o6 O, ]. _: \5 `#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
#annotate("text",x=0,y=40,label=C,parse=T)+
7 R6 ]4 J6 a3 E* B* e1 Ncoord_flip()+
- p6 l7 X. }) L+ b/ H6 ltransition_manual(frames=as.Date(Date)) +
+ _% L4 ]; ?# d) A4 s  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)')+
( d* g8 ~" \, V3 t& s0 H3 s4 rease_aes('linear')
ps
. ~$ b: k$ {( H1 ]! D结果展示
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视频格式转化，加载BGM
  q7 f& K  p/ |1 `! A: d, ?#df <- animate(ps, renderer = av_renderer('animation.mp4'),
         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
: s% @9 G; a  s* L+ T: T# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
) E% \7 {8 Q, w: _8 P全国新冠状肺炎26天增长状况
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3 k, T, q: V  D6 X3 f# hpc<-ggplot(data=CNdata_s,aes(x=variable,y=value,fill=variable,frame=Date))+
0 y- q( s$ h' z* t" j5 {1 C* P   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'))+
" ~# S1 ^/ a0 j/ C# C  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
# J* U/ w: d% R  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
5 z  m) r: ]8 K1 m5 I$ Q   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
. A5 n  T. y9 \  #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') +
$ y1 n9 j/ {) x' h8 `) I. S#annotate("text",x=0,y=40,label=C,parse=T)+
5 X) |% T: T0 Q    # coord_flip()+
7 G; O$ H8 }9 e8 n+ c5 d9 Q' {. J6 J$ a" u     transition_manual(frames=as.Date(Date)) +
    # ggdark::dark_theme_bw() + #设置黑色主题
' k4 h5 f- }) I! ~' H+ n     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
, \! X1 o0 B' n$ ~     ease_aes('linear')
  h- J0 @  a4 L1 d( f! @3 k* wpc


动态图合并
( w: A7 C6 {4 r0 E; i, ^6 ]# @9 Hlibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
& X2 O' P+ C& zpc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
3 K) `# r0 |( D# n; ^pc_gifs <- image_read(pc_gif)
0 x# [* h1 {; I; I, N0 Q8 C2 [new_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
, u( r/ \) X) b7 x/ Z& Dfor(i in 2:length(pc_gifs)){
  combined <- image_append(c(pc_gifs, ps_gifs))
) U& l9 d0 S7 ]# [0 r  new_gif <- c(new_gif, combined)
}
& W8 A( h8 g% E( c4 [new_gif
结果展示
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