在一个篮球相关的公众号的文章里发现了一张图片
image.png
第一感觉应该是是R语言的ggplot2包做出来的，这么好的学习素材不重复一下岂不是可惜了，遂以关键词“Joel Embiids Points Per 100 Possessions When Guarded By”搜索找到了原文https://www.reddit.com/r/nba/comments/bjuiy4/oc_joel_embiids_points_per_100_possessions/
有的评论提到原始数据来自这个链接 https://stats.nba.com/player/203954/matchups/?sort=POSS&dir=1&Season=2018-19&SeasonType=Regular%20Season&DateFrom=04%2F29%2F2019&DateTo=04%2F30%2F2019
但是自己还没有研究出来如何在这个网站上找到特定的球员面对不同的防守者的得分的相关数据。
找到了原始代码
df %>%
ggplot(aes(x = DEF_PLAYER_NAME, y = pts.per.100, size = total.poss, fill = pts.per.100)) +
geom_point(alpha = .75, shape = 21, color = 'black') +
coord_flip() +
theme_custom() +
labs(size = "Total Possessions",
title = "Joel Embiid's Points Per 100 Possessions When Guarded By ___",
subtitle = "Among players that guarded Embiid at least 50 possesions in 2018-19 (Regular Season + Playoffs)",
y = "Points per 100 possessions",
x = "") +
scale_y_continuous(limits = c(15, 55), breaks = seq(15, 55, 5)) +
scale_size_continuous(range = c(2, 7)) +
theme(legend.position=c(0.15, 0.835), legend.background = element_rect(fill="floralwhite")) +
scale_fill_gradient2(guide=FALSE, low = ("#0571b0"), mid = "white",
high = ("#ca0020"), midpoint = 38.6) +
geom_hline(yintercept = 38.6, linetype = 2, color = 'gray55') +
theme(plot.title = element_text(face = 'bold', size = 11, hjust = 0.5)) +
theme(plot.subtitle = element_text(face = 'italic', size = 9, hjust = 0.5)) +
annotate(geom = 'label', x= 3, y = 45, label = "Season average\n38.6 pts per 100", family = 'Gill Sans MT', fontface = 'bold', fill = 'floralwhite') +
theme(plot.margin=unit(c(.75,.25,.5,0),"cm"))
那就不用原始数据了，根据图片和代码自己构造一份数据。
根据以上代码可以看到作图的数据总共有三列
- x是防守者的姓名
- y是恩比德面对不同的对手百回合得分
- 还有一列是恩比德面对不同的对手总共的回合数，用来控制点的大小
- 恩比德面对不同的对手百回合得分 用来映射点的颜色
根据图片可以看到总共有29个防守者，得分范围在每百回合15到55之间，总共的回合数是50到200之间。
首先是构造数据
def_player_name<-paste0("Player","_",1:29)
pts.per.100<-seq(15,55,by=0.5)
total.poss<-seq(50,200,by=1)
df<-data.frame(def_player_name=sample(def_player_name,29,replace = F),
pts.per.100=sample(pts.per.100,29,replace = F),
total.poss=sample(total.poss,29,replace = F))
dim(df)
head(df)
基本的散点图
library(ggplot2)
ggplot(df,aes(x=def_player_name,y=pts.per.100,
size=total.poss,fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')
image.png
接下来就是对这幅图进行美化
美化的内容包括
- 旋转90度
用到的代码是
ggplot(df,aes(x=def_player_name,y=pts.per.100,
size=total.poss,fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')+
coord_flip()
image.png
- 按照得分大小排序
ggplot(df,aes(x=reorder(def_player_name,pts.per.100),
y=pts.per.100,
size=total.poss,
fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')+
coord_flip()
image.png
注意这里是如何实现的，升序和降序是经常用到的
- 改变主题
原始代码用到的是 theme_custom（）这个函数。我暂时还不知道是哪个包里的，或者是他自己写的。这里我们就不管了，换成theme_bw（）这个函数
ggplot(df,aes(x=reorder(def_player_name,pts.per.100),
y=pts.per.100,
size=total.poss,
fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')+
coord_flip()+
theme_bw()
image.png
- 接下来是改变点的填充颜色和大小
ggplot(df,aes(x=reorder(def_player_name,pts.per.100),
y=pts.per.100,
size=total.poss,
fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')+
coord_flip()+
theme_bw()+
scale_fill_gradient2(guide=FALSE,
low = ("#0571b0"),
mid = "white",
high = ("#ca0020"),
midpoint = 38.6)+
scale_size_continuous(range = c(2, 7))
image.png
- 更改y轴的显示刻度，添加辅助线
ggplot(df,aes(x=reorder(def_player_name,pts.per.100),
y=pts.per.100,
size=total.poss,
fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')+
coord_flip()+
theme_bw()+
scale_fill_gradient2(guide=FALSE,
low = ("#0571b0"),
mid = "white",
high = ("#ca0020"),
midpoint = 38.6)+
scale_size_continuous(range = c(2, 20))+
scale_y_continuous(limits = c(15, 55), breaks = seq(15, 55, 5)) +
geom_hline(yintercept = 38.6, linetype = 2, color = 'gray55')
image.png
- 更改坐标轴的标签，添加标题
ggplot(df,aes(x=reorder(def_player_name,pts.per.100),
y=pts.per.100,
size=total.poss,
fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')+
coord_flip()+
theme_bw()+
scale_fill_gradient2(guide=FALSE,
low = ("#0571b0"),
mid = "white",
high = ("#ca0020"),
midpoint = 38.6)+
scale_size_continuous(range = c(2, 20))+
scale_y_continuous(limits = c(15, 55), breaks = seq(15, 55, 5)) +
geom_hline(yintercept = 38.6, linetype = 2, color = 'gray55')+
labs(size = "Total Possessions",
title = "Joel Embiid's Points Per 100 Possessions When Guarded By ___",
subtitle = "Among players that guarded Embiid at least 50 possesions in 2018-19 (Regular Season + Playoffs)",
y = "Points per 100 possessions",
x = "")
image.png
- 添加一个注释的标签
ggplot(df,aes(x=reorder(def_player_name,pts.per.100),
y=pts.per.100,
size=total.poss,
fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')+
coord_flip()+
theme_bw()+
scale_fill_gradient2(guide=FALSE,
low = ("#0571b0"),
mid = "white",
high = ("#ca0020"),
midpoint = 38.6)+
scale_size_continuous(range = c(2, 20))+
scale_y_continuous(limits = c(15, 55), breaks = seq(15, 55, 5)) +
geom_hline(yintercept = 38.6, linetype = 2, color = 'gray55')+
labs(size = "Total Possessions",
title = "Joel Embiid's Points Per 100 Possessions When Guarded By ___",
subtitle = "Among players that guarded Embiid at least 50 possesions in 2018-19 (Regular Season + Playoffs)",
y = "Points per 100 possessions",
x = "") +
annotate(geom = 'label', x= 3, y = 45,
label = "Season average\n38.6 pts per 100",
family = 'Times New Roman',
fontface = 'bold',
fill = 'floralwhite')
image.png
- 最后就是对主题的一些细节设置了
ggplot(df,aes(x=reorder(def_player_name,pts.per.100),
y=pts.per.100,
size=total.poss,
fill=pts.per.100))+
geom_point(alpha=0.75,shape=21,color='black')+
coord_flip()+
theme_bw()+
scale_fill_gradient2(guide=FALSE,
low = ("#0571b0"),
mid = "white",
high = ("#ca0020"),
midpoint = 38.6)+
scale_size_continuous(range = c(2, 20))+
scale_y_continuous(limits = c(15, 55), breaks = seq(15, 55, 5)) +
geom_hline(yintercept = 38.6, linetype = 2, color = 'gray55')+
labs(size = "Total Possessions",
title = "Joel Embiid's Points Per 100 Possessions When Guarded By ___",
subtitle = "Among players that guarded Embiid at least 50 possesions in 2018-19 (Regular Season + Playoffs)",
y = "Points per 100 possessions",
x = "") +
annotate(geom = 'label', x= 3, y = 45,
label = "Season average\n38.6 pts per 100",
family = 'Times New Roman',
fontface = 'bold',
fill = 'floralwhite') +
theme(legend.position=c(0.15, 0.835),
legend.background = element_rect(fill="floralwhite"),
plot.title = element_text(face = 'bold', size = 11, hjust = 0.5),
plot.subtitle = element_text(face = 'italic', size = 9, hjust = 0.5),
plot.margin=unit(c(.75,.25,.5,0),"cm"))
image.png
好了，今天的内容就到这里了。
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