R语言gganimate疫情数据动态可视化

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R语言gganimate学习
! T9 N- ?1 o$ Z! \3 G% B6 W9 n" o所需加载包
! C# L! W+ L' I# tlibrary(av)
/ I! I. m4 b( F' L+ A+ ]# }+ p) r# Glibrary(ggplot2)
. d! G+ [6 T) wlibrary(gganimate)
library(tidyverse)
library(lubridate)
library(scales)
library(ggrepel)
library(cowplot)
: @$ I5 I) q1 v8 X- }5 ?6 N% F数据
: p7 C; d; Q3 Q
" W; R# l, J7 y6 @
ps = ggplot(mydatan, aes(x=reorder(省份, 累计确诊),y=累计确诊, fill=省份,frame=Date)) +
, p5 ~$ J- v1 ~# z/ L) h6 D  z     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()+
" X2 S1 o( T4 M6 K% S   #theme(legend.position="none") +
9 O& |* s% t1 O; T. A3 ~/ b  o" J' f& q    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
' a1 J# x- z' T    theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
4 R  Q! I5 G' P8 t  Z6 r/ D. D. x  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
' U( H) a! W" S; W8 k" Z   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
# Here comes the gganimate specific bits
8 u' T) C, d+ l7 ^  c8 \#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
0 t- |; o& J: B" U8 q#annotate("text",x=0,y=40,label=C,parse=T)+
5 a. P) }9 R' Fcoord_flip()+
2 A- {: F: k8 v; Wtransition_manual(frames=as.Date(Date)) +
" @0 S; s$ L  [8 n  F#ggdark::dark_theme_bw() + #设置黑色主题
$ E' \2 P, ?* t( J' [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')
, E2 S! C- H. b* b3 yps
4 _9 H5 B/ g* H. `结果展示

) B6 J( X7 R* ?
视频格式转化，加载BGM
$ T  u* _0 v0 @: ~#df <- animate(ps, renderer = av_renderer('animation.mp4'),
         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
6 L6 K4 b8 p' n2 W8 Q7 ?# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
全国新冠状肺炎26天增长状况

2 A2 F, L  S/ L1 ppc<-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) +
; A, W$ L: y" }' t8 x   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
    theme(legend.position="none") +
  K1 Z3 Q4 P, A2 p  theme(panel.background=element_rect(fill='transparent'))+
  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
1 m7 r9 t/ U5 b  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
$ |* `; H- N5 x7 B  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
+ t) Y! [& d$ ~" M  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
  #scale_x_continuous(limits = c(0,6))+
$ O8 o* @- g! s! |" m3 e; P* S& Q # Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
+ m8 l; j/ X9 b" V#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
3 L/ M: |) R, Y1 `! }2 u, r4 ]     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
9 _9 n7 Z: v' I5 r1 G     ease_aes('linear')
& Q" k9 c% k4 [6 U% W$ V9 zpc

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' b/ w! i) g$ J7 n动态图合并
; J- Y8 |4 A- z/ clibrary(magick)
+ |1 a/ M8 }0 Q3 T8 X; ips_gif <- animate(ps,,width = 720, height = 480)
& r3 s& \4 C) \pc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
, R* _  x  P" Q9 Spc_gifs <- image_read(pc_gif)
2 F, r$ o7 [) M" ^0 C5 ~* g# bnew_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
( j# [" _: Z8 {, V! i' n; |for(i in 2:length(pc_gifs)){
  combined <- image_append(c(pc_gifs, ps_gifs))
1 `+ z$ L# E+ O2 a/ m' a8 D  new_gif <- c(new_gif, combined)
+ \5 d) _9 {/ p9 F1 X# [* a2 x}
7 U, _  D5 t& @new_gif
& S8 @' `, E) B# `结果展示

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