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
% b& S0 ]3 v. y# D4 }8 c' alibrary(av)
% y. G, L6 |% ]; E2 ?8 zlibrary(ggplot2)
( U9 b8 T" U5 ~2 j; slibrary(gganimate)
7 p8 z( a# v/ g3 Llibrary(tidyverse)
library(lubridate)
4 J( E! `! ^0 s; mlibrary(scales)
library(ggrepel)
library(cowplot)
数据
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. U+ i" N4 d) L; U3 v* K2 _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) +
    geom_text(aes(label=paste0(累计确诊)),col="black",hjust=-0.2)+
2 j7 K  \! `- v" l    #theme_bw()+
   #theme(legend.position="none") +
' n% E# h( B, S; T& x/ g* o( W$ V    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
* V4 T/ [/ u. \8 T# j; L    theme(panel.background=element_rect(fill='transparent'))+
4 n% W. P' u5 e6 ?# e2 Y6 [* K  theme(axis.text.y=element_text(angle=0,colour="black",size=12,hjust=1))+
  theme(axis.text.x=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
) c$ n& m; F* m  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
8 |, e  l+ m- O8 k# A7 }* q# I7 V" _- p# Here comes the gganimate specific bits
+ a! Z% i5 k' T/ Y& d% s+ W#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)) +
#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)')+
' |5 U% x+ r" a6 X/ U' V8 m# X9 \; Z5 \ease_aes('linear')
3 r) h1 ?! d' ^+ X+ Lps
9 N- K. ~! h/ @$ o- k; U( ~结果展示

" }$ i) }% e  J; W% T
视频格式转化，加载BGM
5 k. Y) t* q; F" j#df <- animate(ps, renderer = av_renderer('animation.mp4'),
+ v7 T) v9 d" ]         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
& _8 ^' f$ R) q* u) c# [) F# 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 K! K6 f" z# O! C* ~# Y4 s7 ^   geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE,width=0.7) +
- A4 j6 V3 T$ @   geom_text(aes(label=paste0(value)),col="black",hjust=-0.2)+
    theme(legend.position="none") +
, }: U/ N1 @- I4 N2 ]8 N" o! X  theme(panel.background=element_rect(fill='transparent'))+
! W) |, @: K3 w: _7 A  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
' l2 M( \6 B# p1 c" L4 m0 M  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
9 d  @2 U9 H6 S( u  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
. {" M  |4 x2 s; q/ y" k! ?  #scale_x_continuous(limits = c(0,6))+
6 t4 g2 p1 E- W/ f5 \  M- d # Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
6 k) w9 M3 K1 L( a1 S5 y0 G#annotate("text",x=0,y=40,label=C,parse=T)+
    # coord_flip()+
# Z# |$ J2 N; \1 x2 L+ e; G  C     transition_manual(frames=as.Date(Date)) +
+ ~2 o* q5 a+ v1 b3 f+ }4 w2 a    # ggdark::dark_theme_bw() + #设置黑色主题
9 s" B3 k0 I; E8 x& ^# @) r     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
& }" o+ d5 k" B" W8 m/ D2 n! d" T     ease_aes('linear')
pc


动态图合并
; b! f. |% m( N( x1 l) f# ulibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
3 _& G6 f; f# b3 \* }pc_gif <- animate(pc, width = 360, height = 480)
  I4 y! c8 X, I, s. zps_gifs <- image_read(ps_gif)
pc_gifs <- image_read(pc_gif)
" H9 w- K" ^1 d5 B* Z, {) Y# dnew_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
  [8 I& z! R+ pfor(i in 2:length(pc_gifs)){
  combined <- image_append(c(pc_gifs, ps_gifs))
2 \% Q2 C; w8 w  new_gif <- c(new_gif, combined)
( ?9 `4 t. d+ {( F$ i' S- u}
new_gif
结果展示

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