R语言gganimate疫情数据动态可视化

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R语言gganimate学习
' p$ W0 m2 Y! t, F, Q所需加载包
. S7 O1 \! X, H3 Q0 l1 r" ~0 m- }library(av)
' E+ n1 v  K# z/ ]& wlibrary(ggplot2)
# p% ~7 n8 t/ Q7 }' Xlibrary(gganimate)
library(tidyverse)
2 H$ E+ f: Y: Q. {- i2 ?library(lubridate)
5 T" v: n/ @) `+ ~+ mlibrary(scales)
library(ggrepel)
library(cowplot)
数据
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ps = 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) +
9 F  v$ `- t1 ?, j5 y, m" U* B4 Z    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
( }+ ~$ \' G4 r/ E/ x    #theme_bw()+
   #theme(legend.position="none") +
  N) ~/ Y+ K2 @; F" e. b/ C5 ~    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
$ G  B6 `4 u# ~4 j/ r    theme(panel.background=element_rect(fill='transparent'))+
: v8 n& F# B4 s4 g4 ^" n" W& `0 d* p4 w  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
. Y) N0 r+ k9 D& r( h! a; F  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
3 f8 l7 [6 y. r% n4 G" x" g8 z   theme(panel.grid =element_blank()) +   ## 删去网格线
5 m. o+ k. F) i  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
% u& T/ N) E7 _; I% O. W6 c  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
# Here comes the gganimate specific bits
  x- _$ p+ R7 V6 }1 M% L#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
#annotate("text",x=0,y=40,label=C,parse=T)+
coord_flip()+
transition_manual(frames=as.Date(Date)) +
* }4 H* z: I- |7 g6 \1 [. ~( C2 o#ggdark::dark_theme_bw() + #设置黑色主题
5 Q: m3 q8 i' V! T' Q; H6 llabs(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')
! `) w1 }2 v9 n) b: J7 Cps
: y0 C. e' O# T9 D, [6 Z结果展示

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, }4 F8 O& I* ]2 T( S6 Z; P  a视频格式转化，加载BGM
#df <- animate(ps, renderer = av_renderer('animation.mp4'),
! A; ]& `2 w* B) a& c         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
: v& W0 S* I( s: P- G# 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) +
   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
9 B- T. z4 o, W5 E/ v    theme(legend.position="none") +
3 Q! G2 B& z. E+ F6 a, B2 [  theme(panel.background=element_rect(fill='transparent'))+
! K4 t$ q) l8 A$ u- a  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
, y7 V& Z; _: _; O( }* l  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
# G# q7 e$ T6 o& Q* u: q: c   theme(panel.grid =element_blank()) +   ## 删去网格线
' H. C- ?. L. c5 d" j  [, H8 b2 a" n  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))+
6 W5 y8 h& C& w/ @/ {) i, Y # Here comes the gganimate specific bits
: }0 K% Y: }3 D& k#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
% ?8 k0 D. V; J1 \7 q7 c#annotate("text",x=0,y=40,label=C,parse=T)+
) w# t2 d4 u- V4 `- K: J    # coord_flip()+
     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 ='')+
     ease_aes('linear')
pc
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动态图合并
. ^* D- Q* k. u3 W) R* i  W7 b5 a9 Klibrary(magick)
% Q! |2 P/ k4 r: ?ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
, r+ T! g/ C0 c2 G) }5 ipc_gifs <- image_read(pc_gif)
3 C, g! Z) J7 Q1 K' Unew_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
for(i in 2:length(pc_gifs)){
: M, ?& [9 N- T% H& j& P8 D  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
}
new_gif
9 J2 D8 s. b; L  e6 s) M9 }+ a结果展示
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