image.png热图展示不同国家历届足球世界杯的成绩，非常有意思，时间跨度是1982年到2018年，入选国家的标准是最少参加过四次世界杯，我们今天来重复一下这个图，自己这个伪球迷也来了解一下足球世界杯的相关知识。
推特上这个图还没有分享示例数据和代码，我们手动把数据整理下来，代码自己来写
部分示例数据截图
image.png最开始整理数据是直接按照图中的图例文字来标注的，想了一下用数字替代可能会更快一点，数字在读入R语言后可以用代码再次替换成图例的文本
三个图的作图代码是一样的，只是需要换一下数据就可以了
第一个图
library(readxl)
library(ggplot2)
library(tidyverse)
dat01<-read_excel('data/20221122/fifaworldcup.xlsx',
sheet = 'Sheet2')
dat01 %>%
pivot_longer(!country,names_to = 'year') %>%
mutate(`Best Achievement`=case_when(
value == 1 ~ 'Not Present',
value == 2 ~ 'Group Stage',
value == 3 ~ 'Round of 16',
value == 4 ~ 'Quarter Finals',
value == 5 ~ 'Semi Finals',
value == 6 ~ 'Winner',
TRUE ~ value
)) -> new.dat01
new.dat01 <- new.dat01 %>%
mutate(country=factor(country,
levels = c('Germany','Spain','Italy',
'England','France',
'Belgium','Netherlands',
'Portugal','Croatia',
'Denmark','Poland','Sweden',
'Switzerland','Russia','Scotland')))
ggplot()+
geom_tile(data=new.dat01,
aes(y=year,x=country,fill=`Best Achievement`),
color='white')+
theme_classic()+
theme(axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(angle = 60,hjust=0,vjust=0.5),
legend.position = 'bottom')+
guides(fill=guide_legend(title.position = 'top',byrow = TRUE))+
labs(x=NULL,y=NULL)+
scale_x_discrete(position = 'top')+
scale_fill_manual(values = c('Not Present'='#e5e5e5',
'Group Stage'='#440053',
'Round of 16'='#3c528b',
'Quarter Finals'='#218f8c',
'Semi Finals'='#5dc763',
'Winner'='#fde624'))+
ggtitle('Europe')+
coord_equal() -> p1
p1
image.png第二个图
dat02<-read_excel('data/20221122/fifaworldcup.xlsx',
sheet = 'Sheet3')
dat02 %>%
pivot_longer(!country,names_to = 'year') %>%
mutate(`Best Achievement`=case_when(
value == 1 ~ 'Not Present',
value == 2 ~ 'Group Stage',
value == 3 ~ 'Round of 16',
value == 4 ~ 'Quarter Finals',
value == 5 ~ 'Semi Finals',
value == 6 ~ 'Winner'
)) -> new.dat02
new.dat02 <- new.dat02 %>%
mutate(country=factor(country,
levels = c('Brazi','Argentina','Mexico',
'United States','Uruguay',
'Colombia','Costa Rica',
'Paraguay','Chile')
))
ggplot()+
geom_tile(data=new.dat02,
aes(y=year,x=country,fill=`Best Achievement`),
color='white')+
theme_classic()+
theme(axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(angle = 60,hjust=0,vjust=0.5),
legend.position = 'bottom')+
guides(fill=guide_legend(title.position = 'top',byrow = TRUE))+
labs(x=NULL,y=NULL)+
scale_x_discrete(position = 'top')+
scale_fill_manual(values = c('Not Present'='#e5e5e5',
'Group Stage'='#440053',
'Round of 16'='#3c528b',
'Quarter Finals'='#218f8c',
'Semi Finals'='#5dc763',
'Winner'='#fde624'))+
ggtitle('Americas')+
coord_equal() -> p2
p2
image.png第三个图
dat03<-read_excel('data/20221122/fifaworldcup.xlsx',
sheet = 'Sheet4')
dat03 %>%
pivot_longer(!country,names_to = 'year') %>%
mutate(`Best Achievement`=case_when(
value == 1 ~ 'Not Present',
value == 2 ~ 'Group Stage',
value == 3 ~ 'Round of 16',
value == 4 ~ 'Quarter Finals',
value == 5 ~ 'Semi Finals',
value == 6 ~ 'Winner'
)) -> new.dat03
new.dat03 <- new.dat03 %>%
mutate(country=factor(country,
levels = c('South Korea','Cameroon',
'Japan','Nigeria','Saudi Arabia',
'Algeria','Iran',
'Morocco','Australia','Tunisia')
))
ggplot()+
geom_tile(data=new.dat03,
aes(y=year,x=country,fill=`Best Achievement`),
color='white')+
theme_classic()+
theme(axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_text(angle = 60,hjust=0,vjust=0.5),
legend.position = 'bottom')+
guides(fill=guide_legend(title.position = 'top',byrow = TRUE))+
labs(x=NULL,y=NULL)+
scale_x_discrete(position = 'top')+
scale_fill_manual(values = c('Not Present'='#e5e5e5',
'Group Stage'='#440053',
'Round of 16'='#3c528b',
'Quarter Finals'='#218f8c',
'Semi Finals'='#5dc763',
'Winner'='#fde624'))+
ggtitle('Other')+
coord_equal() -> p3
p3
image.png最后是拼图
library(patchwork)
pdf(file = 'worldcup1982-2018.pdf',
width = 9.4,height = 4,family = 'serif')
p1+p2+theme(axis.text.y = element_blank())+
p3+theme(axis.text.y = element_blank())+
plot_layout(guides='collect')+
plot_annotation(theme = theme(legend.position = 'bottom'))
dev.off()
image.png推特上的图还用点标注了每届世界杯的东道主国家，这个如何实现在单独出一期推文进行介绍
示例数据和代码可以给推文点赞，点击在看，最后留言获取
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