R语言gganimate疫情数据动态可视化

1047521767

R语言gganimate学习
所需加载包
3 v: f  l7 k3 i  Y5 b/ e- l* zlibrary(av)
; ?* ~) W: f& }+ p5 alibrary(ggplot2)
library(gganimate)
2 L1 y# g5 H' b5 \* Plibrary(tidyverse)
library(lubridate)
library(scales)
library(ggrepel)
) O" x9 L* [7 k/ x( u- xlibrary(cowplot)
- {! D7 y. Z7 N% i  x数据

3 W; J6 {8 u7 b* W, s
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)+
. H$ _) x: q! D+ c' h) v3 ~( M    #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") +
) B" i: I0 b) j3 F! c4 Z; V    theme(panel.background=element_rect(fill='transparent'))+
* o- a: _( w# U  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))+
9 G- D9 }: Q! H7 M" h* \# ]0 l& {   theme(panel.grid =element_blank()) +   ## 删去网格线
! `( O9 o# j+ P+ P' L* b  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
/ @2 X" [8 {  @. a) z  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
; Q0 i5 B' q) x+ l' j, [1 B; M% Q3 Y( f# Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
; o7 C, L% A2 o# Y+ u# r) p/ P1 _) 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
labs(title = paste('日期:', '{current_frame}'),x = '', y ='各省累计确诊病例增长(除湖北省外)')+
; x4 j' T& P% Dease_aes('linear')
( S% l" q- S) e1 E( pps
结果展示


视频格式转化，加载BGM
9 J4 P2 p+ a8 n# h3 N+ C; S#df <- animate(ps, renderer = av_renderer('animation.mp4'),
         #     width = 1280, height = 720, res = 100, fps = 10)#视频制作
# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
8 v/ {0 [7 p5 ~/ A0 J" ]1 Q全国新冠状肺炎26天增长状况
& W) \& }/ n2 n7 X3 }
' I# v/ m9 W' F. i3 h. \  Upc<-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)+
$ d4 S4 d# f& D# \    theme(legend.position="none") +
1 T4 `2 z- ]( E* o0 J6 ?+ H+ _  theme(panel.background=element_rect(fill='transparent'))+
3 q3 l; `+ u+ u5 K7 l( J  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
0 j8 R$ n$ p$ t8 C8 g  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
   theme(panel.grid =element_blank()) +   ## 删去网格线
" ?- T) t% A2 m  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
9 U5 X# q2 n! n' l  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
: F& a$ @2 C% r0 Y- Q0 o" Z  #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') +
- P* U8 Y  y3 B) o5 i; H6 Y#annotate("text",x=0,y=40,label=C,parse=T)+
$ E2 {! L4 ^9 U. U    # coord_flip()+
; [4 N4 f% J) D; F     transition_manual(frames=as.Date(Date)) +
    # ggdark::dark_theme_bw() + #设置黑色主题
: Q4 j' ~2 y( }8 O2 i     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
6 m4 q1 _6 b- P6 p1 T- k" |     ease_aes('linear')
7 _% l3 |. k8 H. I0 L' ~- |pc
$ o8 S* J* s- S8 Y$ V: D
5 `, S' z9 ?2 B5 ]9 l* @8 `, E
动态图合并
! m  j$ o8 ~& z6 d! ^( Tlibrary(magick)
ps_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
; }' Q- W! @$ U7 p6 J% }6 l. i* {ps_gifs <- image_read(ps_gif)
pc_gifs <- image_read(pc_gif)
new_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
) S9 o; p4 e- R, b+ Cfor(i in 2:length(pc_gifs)){
! Q2 j6 K# E! h& d9 u4 E  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
9 z# h, R! Y( `3 F0 `  w}
4 K* P, {2 y, g" znew_gif
结果展示

/ x1 F2 {9 n8 M; ]

sjlxdn

111111111111