gganimate包-GIF绘制导出
Xwyturbo
/ 2022-12-17
1.1 加载数据集
原始数据：使用gapminder这个数据集合。
该数据一共有6列，依次为
country（国家）continent（洲）
year（年份）lifeExp（生活指数）
pop（人口） gdpPercap（国内生产总值）
## Warning: package 'gapminder' was built under R version 4.2.2
## Warning: package 'ggdark' was built under R version 4.2.2
library(ggplot2)
library(gganimate)
## Warning: package 'gganimate' was built under R version 4.2.2
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ✔ purrr 0.3.5
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
mydata <- gapminder %>%
filter(country == 'France'|
country == 'Italy'|
country == 'China'|
country == 'Japan'|
country == 'Austria'|
country == 'Brazil'|
country == 'Colombia'|
country == 'Cuba'|
country == 'Germany'|
country == 'India')
mydata
## # A tibble: 120 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Austria Europe 1952 66.8 6927772 6137.
## 2 Austria Europe 1957 67.5 6965860 8843.
## 3 Austria Europe 1962 69.5 7129864 10751.
## 4 Austria Europe 1967 70.1 7376998 12835.
## 5 Austria Europe 1972 70.6 7544201 16662.
## 6 Austria Europe 1977 72.2 7568430 19749.
## 7 Austria Europe 1982 73.2 7574613 21597.
## 8 Austria Europe 1987 74.9 7578903 23688.
## 9 Austria Europe 1992 76.0 7914969 27042.
## 10 Austria Europe 1997 77.5 8069876 29096.
## # … with 110 more rows
1.2 绘制动态条形图
使用gganimate这个包绘制动态图：
使用函数transition_time()添加动态，
并指定动态依据哪个变量变化，这里动态变量是year。
ps1 = ggplot(mydata, aes(x=reorder(country, lifeExp),
y=lifeExp, fill=country,frame=year)) +
geom_bar(stat= 'identity', position = 'dodge',show.legend = FALSE) +
geom_text(aes(label=paste0(lifeExp)),
col="black",hjust=-0.2)+
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'))+
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
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
coord_flip()+ #横纵坐标位置转换
transition_time(year) + #设置动态
labs(title = paste('Year:', '{frame_time}'),x = '',
y ='各国生活指数')+
ease_aes('linear')
ps1
1.3 绘制动态点图
使用gganimate这个包绘制动态图：
使用函数transition_time()添加动态，
并指定动态依据哪个变量变化，这里动态变量是year。
ps2 = ggplot(mydata, aes(x=year,y=lifeExp)) +
geom_point(aes(color = country)) +
theme_bw()+
transition_time(year) + #设置动态
labs(title = paste('Year:', '{frame_time}'),x = '',
y ='各国生活指数')+
ease_aes('linear')
ps2
1.4 绘制动态折线图
使用gganimate这个包绘制动态图：
使用函数transition_reveal()添加动态，
并指定动态依据哪个变量变化，这里动态变量是year。
ps3 = ggplot(mydata, aes(x=year,y=lifeExp)) +
geom_line(aes(color = country)) +
geom_point(aes(color = country)) +
theme_bw()+
transition_reveal(year) +
labs(x = 'Year',
y ='各国生活指数')+
ease_aes('linear')
ps3
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
1.5读取excel表数据，绘制动态折线图
library(tidyverse)
library(readxl)
library(ggplot2)
library(ggpubr)
library(patchwork)
library(gganimate)
x3 <- read_xlsx("D:/wenyu/BruceR/不同批次下训练的损失值.xlsx",sheet = 3)
x3
## # A tibble: 60 × 5
## epoch train_loss train_acc val_loss val_acc
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.686 0.577 0.662 0.649
## 2 2 0.678 0.579 0.66 0.622
## 3 3 0.666 0.582 0.618 0.648
## 4 4 0.559 0.613 0.411 0.89
## 5 5 0.343 0.911 0.264 0.922
## 6 6 0.274 0.912 0.26 0.931
## 7 7 0.265 0.904 0.19 0.944
## 8 8 0.184 0.934 0.156 0.944
## 9 9 0.238 0.909 0.204 0.934
## 10 10 0.187 0.929 0.142 0.959
## # … with 50 more rows
x31 <- x3[,c(1,2,4)] %>%
pivot_longer(-epoch,names_to = "type",
values_to = "loss")
x31
## # A tibble: 120 × 3
## epoch type loss
## <dbl> <chr> <dbl>
## 1 1 train_loss 0.686
## 2 1 val_loss 0.662
## 3 2 train_loss 0.678
## 4 2 val_loss 0.66
## 5 3 train_loss 0.666
## 6 3 val_loss 0.618
## 7 4 train_loss 0.559
## 8 4 val_loss 0.411
## 9 5 train_loss 0.343
## 10 5 val_loss 0.264
## # … with 110 more rows
ps31 = ggplot(x31, aes(x=epoch,y=loss)) +
geom_line(aes(color = type)) +
geom_point(aes(color = type)) +
theme_bw()+
transition_reveal(epoch) +
labs(x = 'epoch',
y ='loss')+
ease_aes('linear')
ps31
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
# ps31 <- animate(ps,fps = 24,duration = 15)
# anim_save("ps031.gif")