R语言gganimate疫情数据动态可视化

1047521767

R语言gganimate学习
8 p6 u, ^" y5 F( k# \所需加载包
9 `% k. u2 U3 u! o0 \' X$ Plibrary(av)
library(ggplot2)
library(gganimate)
library(tidyverse)
8 u) K  m0 Y0 [- J% Ylibrary(lubridate)
. v" l' |2 d8 i/ F4 @7 m7 X9 Xlibrary(scales)
9 F4 k9 f1 L4 glibrary(ggrepel)
2 U- t# G  j& M( h6 ?; Ilibrary(cowplot)
数据

7 m) {' _2 i" f$ L: U5 S- u
9 ~8 Z* D  S, P7 T3 {  O5 @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)+
5 n8 a1 v! @7 H6 I$ I8 E9 A    #theme_bw()+
- A4 Q2 Y$ I4 g. F2 p   #theme(legend.position="none") +
    theme(axis.text.x = element_text(size = 12,angle = 90, hjust = 0.2, vjust = 0.2),legend.position="none") +
    theme(panel.background=element_rect(fill='transparent'))+
- E' _4 T/ @4 Q) S  L4 R  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))+
6 L8 Y, D6 z9 L6 L   theme(panel.grid =element_blank()) +   ## 删去网格线
  theme(axis.text = element_blank()) +   ## 删去所有刻度标签
" j- k( I" p8 p4 W  theme(axis.ticks = element_blank()) +  ## 删去所有刻度线
# Here comes the gganimate specific bits
#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
9 q9 ~3 H4 z3 y2 M#annotate("text",x=0,y=40,label=C,parse=T)+
coord_flip()+
) u5 @- f# g2 y* G" @4 P) k3 ptransition_manual(frames=as.Date(Date)) +
% s- m8 d3 y, d$ q1 _1 l#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)')+
  D4 M; O" x! k5 x- aease_aes('linear')
ps
结果展示
8 o5 O# B; W! I# L6 M, _
6 i. U/ ?- h/ Z  g
9 j/ J4 Y: G& m: X% j( n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
/ N6 n; W/ N, w* A: d. E8 x# av_encode_video(df, 'output.mp4', framerate = 2,audio ="N.mp3")
全国新冠状肺炎26天增长状况

# Y0 B) r$ z$ }2 P. s& F. L  Ypc<-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)+
, h4 g" ?2 G% W$ D: E$ Q    theme(legend.position="none") +
  l( X/ S1 ]. @& H  theme(panel.background=element_rect(fill='transparent'))+
9 t7 b3 t6 g: ?3 J) X0 ~  theme(axis.text.x=element_text(angle=0,colour="black",size=15,hjust=1))+
  theme(axis.text.y=element_text(angle=0,colour="white",size=2,hjust=1))+
6 h, L, b* F3 _( J7 d7 w   theme(panel.grid =element_blank()) +   ## 删去网格线
. q- q. |# a6 u! X0 c& V" V  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))+
# Here comes the gganimate specific bits
% V0 t& {7 ?3 T/ w1 M% Z% M' X/ r# Y, g#labs(title = '日期:', x = '省份', y = '累计确诊病例') +
#annotate("text",x=0,y=40,label=C,parse=T)+
' `( c. h7 R# ^& v& Q    # coord_flip()+
     transition_manual(frames=as.Date(Date)) +
% i9 N2 o% w6 `" |1 C9 |    # ggdark::dark_theme_bw() + #设置黑色主题
& D/ U, s9 K2 E! L     labs(title = paste('日期:', '{current_frame}'),x = '全国新冠状肺炎增长', y ='')+
     ease_aes('linear')
pc

/ @" h" w8 _) e; T; e  X$ O
; p1 G2 l  L8 ]: P动态图合并
( R0 |( n# |# y' P5 zlibrary(magick)
8 S' b+ K! U0 ~$ w" I6 X$ ~1 T+ ips_gif <- animate(ps,,width = 720, height = 480)
pc_gif <- animate(pc, width = 360, height = 480)
ps_gifs <- image_read(ps_gif)
; J* S  ]. p$ b% T, _2 ?, q7 fpc_gifs <- image_read(pc_gif)
/ f3 u# ~2 ?$ T7 N& z% [: ?new_gif <- image_append(c(pc_gifs[1], ps_gifs[1]))
! m, }- q, l3 qfor(i in 2:length(pc_gifs)){
+ U0 v6 F7 {) f1 t  combined <- image_append(c(pc_gifs, ps_gifs))
  new_gif <- c(new_gif, combined)
}
new_gif
+ }3 b, Q! a- D: ~& M/ w" \- B结果展示

1 u! o" V8 B* E

sjlxdn

111111111111
6 j# A9 N$ [( O' J3 D# ~6 W- n