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
2 s; O& R) O$ ~1 V5 E) O2 g' ]library(av)
library(ggplot2)
4 G) F" Z4 T0 Z6 Llibrary(gganimate)
library(tidyverse)
library(lubridate)
library(scales)
library(ggrepel)
library(cowplot)
数据
% R" _' j: Z7 @2 z* x/ p! k

ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
& X- W% Y7 G& |1 x  l     geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
: O( G8 H0 Z' ]& V; W    #theme_bw()+
6 _& ^' V, p9 ~3 I; b   #theme(legend.position="none") +
) |8 t2 Y; a0 J6 C/ @- {& w: G    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
/ I6 d, q$ n" m+ Q9 l8 s3 R    theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
" Z3 ^4 }! k% d2 X3 _2 X. D# t  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
' P9 ]. l# n$ Z# \2 a! ]   theme(panel.grid =element_blank()) +   ## 删去网格线
; e! g1 b7 b, Z" `$ t3 u+ M3 d$ a( ~  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
$ m6 u& u# \* {" P  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
" i; K+ W8 {- X/ g& y# Here comes the gganimate specific bits
1 C& z& D+ d% \8 @0 N9 Q#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
$ |, @! X+ f3 ~& A#annotate("text",x=0,y=40,label=C,parse=T)+
$ e) d% A/ u& ^coord_flip()+
transition_manual(frames=as.Date(Date)) +
#ggdark::dark_theme_bw() + #设置黑色主题
% o  }; s5 a8 D( r5 Xlabs(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
1 o5 O0 X9 m" E结果展示

2 w/ G. a: U. G0 @) |
' n6 `; o+ b9 [$ O7 b, @( I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
0 O% z$ n6 B- A7 ]/ R" n) Q# 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))+
   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
' u( H$ R3 H# o0 k* i% R   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
    theme(legend.position="none") +
; o. u4 X  M6 S9 a9 n' m. Y  theme(panel.background=element_rect(fill='transparent'))+
0 \! c" L# z" c  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
2 R' g4 Y0 k& m$ x" V. l) E  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
$ D1 s2 S) o+ p4 w7 _  f1 x: S2 O  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
8 x* q4 a) C* r3 Y  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
: ^4 \! T% b$ D) N3 g  #scale_x_continuous(limits = c(0,6))+
/ b& ]. ^8 P) U6 n: Q" B/ J # Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
0 Y- ]( g' a) P$ r, `#annotate("text",x=0,y=40,label=C,parse=T)+
    # coord_flip()+
     transition_manual(frames=as.Date(Date)) +
    # ggdark::dark_theme_bw() + #设置黑色主题
& ?5 D; d7 {" a  ^/ g9 G     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
9 `) I; M! @1 p! D3 a) ^     ease_aes('linear')
7 Z' a/ D: q6 P8 f4 t4 d; v$ f+ zpc

$ h/ N+ g  Q; B, g, j
7 W* ?0 p: y2 t# n动态图合并
$ ?  X  @/ O. u, rlibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
7 v9 J  K7 _0 y! E  X& mpc_gif <- animate(pc, width = 360, height = 480)
0 o$ Z( P$ f& x4 k# I' ?4 V/ n9 qps_gifs <- image_read(ps_gif)
pc_gifs <- image_read(pc_gif)
new_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
6 }! Z( V7 m) W0 Z' Bfor(i in 2:length(pc_gifs)){
  combined <- image_append(c(pc_gifs, ps_gifs))
5 R. f/ L- b0 \1 U- K: |& {" Y* a  new_gif <- c(new_gif, combined)
# z# J# M) O" ^. q}
new_gif
结果展示
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