看世界杯也能学画图：R语言ggplot2画热图展示不同国家历届足球世界杯的成绩

在twitter上看见有人分享了一个图

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

最开始整理数据是直接按照图中的图例来标注的，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

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

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

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()

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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