---
title: "Describtion"
toc: true
toc-location: left
code-fold: true
warning: false
code-tools: true
---
# 总体描述性统计
## 所用球员特征表：
```{r}
# 安装并加载所需包（如未安装请取消注释下面行）
# install.packages("readxl")
# install.packages("dplyr")
library(readxl)
library(dplyr)
# 读取 Excel 文件
df <- read_excel("C:/Users/86133/Desktop/object/MVP/常规赛.xlsx")
# 查看列名和前几行
names(df)
head(df)
# 去掉非数值列（如名字、赛季），只保留数值型列
numeric_df <- df %>%
select(where(is.numeric))
# 基本描述性统计
summary(numeric_df)
# 计算均值、标准差、最小值、最大值等更全面的统计量
```
```{r}
# 安装必要包（首次运行时需要）
#install.packages("gt")
library(dplyr)
library(tidyr)
library(gt)
# 计算全面统计量（宽数据格式）
stat_wide <- numeric_df %>%
summarise_all(
list(
mean = ~mean(.x, na.rm = TRUE),
sd = ~sd(.x, na.rm = TRUE),
min = ~min(.x, na.rm = TRUE),
q1 = ~quantile(.x, 0.25, na.rm = TRUE),
median = ~median(.x, na.rm = TRUE),
q3 = ~quantile(.x, 0.75, na.rm = TRUE),
max = ~max(.x, na.rm = TRUE)
)
)
# 转换为长数据（变量与统计量分离）
stat_long <- stat_wide %>%
pivot_longer(
cols = everything(),
names_to = c("variable", "statistic"), # 拆分为变量名和统计量名
names_sep = "_" # 列名通过下划线分割（如"mpg_mean" → "mpg"和"mean"）
)
# 转换为宽数据（统计量作为列，变量作为行），并保留variable列
stat_table <- stat_long %>%
pivot_wider(
names_from = statistic, # 统计量名称作为列名
values_from = value # 统计量值作为列内容
) %>%
select(
variable, # 显式保留variable列（关键修正！）
starts_with("mean"),
starts_with("sd"),
starts_with("min"),
starts_with("q1"),
starts_with("median"),
starts_with("q3"),
starts_with("max")
) # 按顺序排列列（可选）
# 用gt绘制表格（此时stat_table包含variable列）
stat_table %>%
gt() %>%
tab_header(
title = "数值变量的描述性统计量",
subtitle = "包含均值、标准差、分位数等"
) %>%
cols_label(
variable = "变量", # 现在variable列存在，不会报错
mean = "均值",
sd = "标准差",
min = "最小值",
q1 = "第一四分位数 (25%)",
median = "中位数",
q3 = "第三四分位数 (75%)",
max = "最大值"
) %>%
fmt_number(
columns = everything(),
decimals = 2
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_title(groups = "title")
) %>%
tab_options(
column_labels.font.weight = "bold",
table.font.size = 12,
data_row.padding = px(8)
)
```
```{r}
library(readxl)
library(dplyr)
# 总样本量（原始数据行数）
total_rows <- nrow(df)
# 每列的缺失值个数
missing_per_column <- colSums(is.na(df))
# 缺失值总数
total_missing <- sum(is.na(df))
# 删除所有含有缺失值的行
df_no_na <- df %>% drop_na()
# 删除后剩余的行数
remaining_rows <- nrow(df_no_na)
# 被删除的行数
removed_rows <- total_rows - remaining_rows
# 汇总信息为表格
summary_table <- data.frame(
指标 = c("总样本量", "含缺失值的样本量", "删除后剩余样本量", "缺失值总个数","指标总数"),
数值 = c(total_rows, removed_rows, remaining_rows, total_missing,43)
)
print(summary_table)
```
## 指标随赛季变化折线图
```{r}
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
df <- read_excel("C:/Users/86133/Desktop/object/MVP/常规赛.xlsx")
# ---------------------------
# 步骤1：处理常数列并归一化（避免NaN）
# ---------------------------
# 选择数值列并识别常数列（max == min）
numeric_cols <- df %>% select(where(is.numeric)) %>% colnames()
constant_cols <- sapply(df[numeric_cols], function(col) {
isTRUE(all.equal(min(col, na.rm = TRUE), max(col, na.rm = TRUE)))
})
# 仅对非常数列进行归一化，常数列设为0（或根据需求处理）
df_scaled <- df %>%
mutate(
across(
all_of(numeric_cols[!constant_cols]), # 仅处理非常数列
~ (. - min(., na.rm = TRUE)) / (max(., na.rm = TRUE) - min(., na.rm = TRUE))
),
across(
all_of(numeric_cols[constant_cols]), # 常数列设为0（或其他合理值）
~ 0
),
Season = df$Season # 添加赛季列
) %>%
select(Season, all_of(numeric_cols)) # 整理列顺序（可选）
# ---------------------------
# 步骤2：计算各赛季均值（处理全NA情况）
# ---------------------------
mean_by_season <- df_scaled %>%
group_by(Season) %>%
summarise(
across(where(is.numeric),
~ ifelse(sum(is.na(.)) == length(.), # 检查是否全为NA
NA_real_, # 全NA则返回NA
mean(., na.rm = TRUE))), # 否则计算均值
.groups = "drop"
)
# ---------------------------
# 步骤3：转换为长格式并整理
# ---------------------------
mean_long <- mean_by_season %>%
pivot_longer(
cols = -Season,
names_to = "Variable",
values_to = "Mean"
) %>%
mutate(
Season = factor(Season, levels = sort(unique(Season))), # 控制赛季顺序
Variable = factor(Variable) # 确保变量为因子，正确分组
) %>%
filter(!is.na(Mean)) # 移除均值中的NA（可选，根据需求保留）
```
```{r}
library(ggplot2)
library(dplyr)
library(patchwork) # 用于图拼接
# 步骤1：变量分组
variables <- unique(mean_long$Variable)
n_vars <- length(variables)
group_size <- 5
group_ids <- ceiling(seq_len(n_vars) / group_size)
Group <- paste0("Group_", group_ids)
variable_group_map <- data.frame(Variable = variables, Group = Group)
mean_long_grouped <- mean_long %>%
left_join(variable_group_map, by = "Variable")
# 步骤2：为每个 group 生成一个图（含图例）
plots <- list()
groups <- unique(mean_long_grouped$Group)
for (g in groups) {
df_sub <- mean_long_grouped %>% filter(Group == g)
p <- ggplot(df_sub, aes(x = Season, y = Mean, color = Variable, group = Variable)) +
geom_line(size = 0.7, alpha = 0.5) +
theme_minimal(base_size = 8) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1, size = 4),
axis.text.y = element_text(size = 4),
strip.text = element_text(size = 5, face = "bold"),
legend.position = "top",
legend.text = element_text(size = 5),
legend.title = element_blank(),
plot.title = element_text(size = 5, face = "bold"),
) +
labs(
title = paste("归一化变量均值变化 -", g),
x = "赛季",
y = "归一化均值"
)
plots[[g]] <- p
}
# 步骤3：拼接所有图（2列排列）
final_plot <- wrap_plots(plots, ncol = 3)
final_plot
#ggsave("grouped_plots.png", final_plot, width = 15, height = 10, dpi = 500)
```
1.上世纪50年代，总投篮出手次数逐渐减少而后趋于稳定，而命中率上升，可能表示技术精进、精细，团队合作
2.上世纪80-90年代，命中率(主要是两分球)达到峰值后90-2000又下降（可能是防守强度上升），后又逐渐上升(技术精进)
3.三分球占总投篮比例(3PAr)在1966年达到峰值后下降到73年逐步上升至今（幅度很大）
4.三分球出手数稳步提升于2017年超过两分球，同时三分球，两分球等投篮命中率在2000年后稳步提升
5.ORB（进攻篮板）逐年下降趋势（下降明显），AST助攻近年逐步上升
6.PF犯规逐年递减
# 总体和MVP对比柱状图
赛季均值数据：
```{r}
# 加载所需的包
library(readxl)
library(dplyr)
library(writexl)
#install.packages("writexl")
# 读取 Excel 文件
data <- read_excel("C:/Users/86133/Desktop/object/MVP/常规赛.xlsx")
# 假设年份列为 "Season"，对其分组并求每列的均值
mean_by_year <- data %>%
group_by(Season) %>%
summarise(across(where(is.numeric), mean, na.rm = TRUE))
# 保存为新的 Excel 文件
write_xlsx(mean_by_year, path = "C:/Users/86133/Desktop/object/MVP/每年均值.xlsx")
```
## 2010-2023赛季 MVP 和 总体数据 对比柱状图：
```{r}
#| warning: false
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
# 读取数据
df <- read_excel("C:/Users/86133/Desktop/object/MVP/MVP_Mean/MVP_mean_2010_23.xlsx", sheet = "Sheet1")
# 提取年份和类型
df <- df %>%
mutate(
Season = str_extract(name, "\\d{4}"),
Type = ifelse(grepl("Mean", name), "League Mean", "MVP")
)
# 分别计算平均值（跨所有赛季）
mvp_mean <- df %>%
filter(Type == "MVP") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "MVP")
league_mean <- df %>%
filter(Type == "League Mean") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "League Mean")
# 合并
compare_df <- bind_rows(mvp_mean, league_mean)
# 绘图
ggplot(compare_df, aes(x = Metric, y = Value, fill = Type)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_text(
aes(label = round(Value, 1)),
position = position_dodge(width = 0.8),
vjust = -0.5,
size = 3
) +
scale_fill_manual(values = c("MVP" = "#1DA1F2", "League Mean" = "#7F8C8D")) +
labs(
title = "MVP vs. League Mean: Average Performance Across All Seasons",
x = "Metric",
y = "Average Value",
fill = "Type"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1,size=5),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "bottom"
)
```
2010-2023赛季 MVP 和 总体数据 对比柱状图：
```{r}
#| warning: false
# 将 MVP 和 League Mean 数据展开为宽格式
df_wide <- compare_df %>%
pivot_wider(names_from = Type, values_from = Value)
# 计算提升百分比（%），并构建新数据框
improvement_df <- df_wide %>%
mutate(`Improvement %` = (`MVP` - `League Mean`) / `League Mean` * 100) %>%
select(Metric, `Improvement %`) %>%
mutate(Type = "Improvement") %>%
rename(Value = `Improvement %`)
compare_all <- bind_rows(compare_df, improvement_df)
library(ggplot2)
ggplot(compare_all, aes(x = Metric, y = Value, fill = Type)) +
geom_col(
data = subset(compare_all, Type != "Improvement"),
position = position_dodge(width = 0.9),
width = 0.7
) +
geom_col(
data = subset(compare_all, Type == "Improvement"),
aes(fill = "Improvement"),
position = position_dodge(width = 0.9),
width = 0.7,
color = "black",
fill = "orange",
alpha = 0.1
) +
geom_text(
data = subset(compare_all, Type == "Improvement"),
aes(label = paste0(round(Value, 1), "%")),
vjust = -0.5,
size = 3,
position = position_dodge(width = 0.9)
) +
scale_fill_manual(
values = c("Improvement" = "orange","MVP" = "#1DA1F2", "League Mean" = "#7F8C8D")
) +
labs(
title = "MVP vs. League Mean + Improvement %",
subtitle = "Average performance and MVP boost by metric (2010–2023)",
x = "Metric",
y = "Value",
fill = "Type"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1,size=7),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "bottom"
)
```
1. **3PAr**(三分出手率（3PA / FGA），三分出手占总投篮的比例) 数据低于均值
2. 提升幅度最高的几项指标：**DBPM** 防守 Box Plus/Minus衡量球员对球队进攻影响, **BPM** 综合 Box Plus/Minus（越大越好）, **VORP** 替代值胜利（Value Over Replacement Player），表示比替补平均水平好多少, **OBPM** 进攻 Box Plus/Minus（衡量球员对球队进攻影响）, **OWS** 进攻 Box Plus/Minus（衡量球员对球队进攻影响）, **DWS** 防守胜利贡献值（Defensive Win Shares）, **WS** 总胜利贡献值（Win Shares）
## 2000-2009赛季：
```{r}
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
# 读取数据
df <- read_excel("C:/Users/86133/Desktop/object/MVP/MVP_Mean/MVP_mean_2000_09.xlsx", sheet = "Sheet1")
# 提取年份和类型
df <- df %>%
mutate(
Season = str_extract(name, "\\d{4}"),
Type = ifelse(grepl("Mean", name), "League Mean", "MVP")
)
# 分别计算平均值（跨所有赛季）
mvp_mean <- df %>%
filter(Type == "MVP") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "MVP")
league_mean <- df %>%
filter(Type == "League Mean") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "League Mean")
# 合并
compare_df <- bind_rows(mvp_mean, league_mean)
# 绘图
ggplot(compare_df, aes(x = Metric, y = Value, fill = Type)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_text(
aes(label = round(Value, 1)),
position = position_dodge(width = 0.8),
vjust = -0.5,
size = 3
) +
scale_fill_manual(values = c("MVP" = "#1DA1F2", "League Mean" = "#7F8C8D")) +
labs(
title = "MVP vs. League Mean: Average Performance Across All Seasons",
x = "Metric",
y = "Average Value",
fill = "Type"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1,size=5),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "bottom"
)
```
```{r}
#| warning: false
#| # 读取数据
df <- read_excel("C:/Users/86133/Desktop/object/MVP/MVP_Mean/MVP_mean_2000_09.xlsx", sheet = "Sheet1")
# 提取年份和类型
df <- df %>%
mutate(
Season = str_extract(name, "\\d{4}"),
Type = ifelse(grepl("Mean", name), "League Mean", "MVP")
)
# 分别计算平均值（跨所有赛季）
mvp_mean <- df %>%
filter(Type == "MVP") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "MVP")
league_mean <- df %>%
filter(Type == "League Mean") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "League Mean")
# 合并
compare_df <- bind_rows(mvp_mean, league_mean)
# 将 MVP 和 League Mean 数据展开为宽格式
df_wide <- compare_df %>%
pivot_wider(names_from = Type, values_from = Value)
# 计算提升百分比（%），并构建新数据框
improvement_df <- df_wide %>%
mutate(`Improvement %` = (`MVP` - `League Mean`) / `League Mean` * 100) %>%
select(Metric, `Improvement %`) %>%
mutate(Type = "Improvement") %>%
rename(Value = `Improvement %`)
compare_all <- bind_rows(compare_df, improvement_df)
library(ggplot2)
ggplot(compare_all, aes(x = Metric, y = Value, fill = Type)) +
geom_col(
data = subset(compare_all, Type != "Improvement"),
position = position_dodge(width = 0.9),
width = 0.7
) +
geom_col(
data = subset(compare_all, Type == "Improvement"),
aes(fill = "Improvement"),
position = position_dodge(width = 0.9),
width = 0.7,
color = "black",
fill = "orange",
alpha = 0.1
) +
geom_text(
data = subset(compare_all, Type == "Improvement"),
aes(label = paste0(round(Value, 1), "%")),
vjust = -0.5,
size = 3,
position = position_dodge(width = 0.9)
) +
scale_fill_manual(
values = c("Improvement" = "orange","MVP" = "#1DA1F2", "League Mean" = "#7F8C8D")
) +
labs(
title = "MVP vs. League Mean + Improvement %",
subtitle = "Average performance and MVP boost by metric (2000–2009)",
x = "Metric",
y = "Value",
fill = "Type"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1,size=7),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "bottom"
)
```
## 1990-1999赛季：
```{r}
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
# 读取数据
df <- read_excel("C:/Users/86133/Desktop/object/MVP/MVP_Mean/MVP_mean_1990_99.xlsx", sheet = "Sheet1")
# 提取年份和类型
df <- df %>%
mutate(
Season = str_extract(name, "\\d{4}"),
Type = ifelse(grepl("Mean", name), "League Mean", "MVP")
)
# 分别计算平均值（跨所有赛季）
mvp_mean <- df %>%
filter(Type == "MVP") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "MVP")
league_mean <- df %>%
filter(Type == "League Mean") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "League Mean")
# 合并
compare_df <- bind_rows(mvp_mean, league_mean)
# 绘图
ggplot(compare_df, aes(x = Metric, y = Value, fill = Type)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_text(
aes(label = round(Value, 1)),
position = position_dodge(width = 0.8),
vjust = -0.5,
size = 3
) +
scale_fill_manual(values = c("MVP" = "#1DA1F2", "League Mean" = "#7F8C8D")) +
labs(
title = "MVP vs. League Mean: Average Performance Across All Seasons",
x = "Metric",
y = "Average Value",
fill = "Type"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1,size=5),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "bottom"
)
```
```{r}
#| warning: false
#| # 读取数据
df <- read_excel("C:/Users/86133/Desktop/object/MVP/MVP_Mean/MVP_mean_1990_99.xlsx", sheet = "Sheet1")
# 提取年份和类型
df <- df %>%
mutate(
Season = str_extract(name, "\\d{4}"),
Type = ifelse(grepl("Mean", name), "League Mean", "MVP")
)
# 分别计算平均值（跨所有赛季）
mvp_mean <- df %>%
filter(Type == "MVP") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "MVP")
league_mean <- df %>%
filter(Type == "League Mean") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "League Mean")
# 合并
compare_df <- bind_rows(mvp_mean, league_mean)
# 将 MVP 和 League Mean 数据展开为宽格式
df_wide <- compare_df %>%
pivot_wider(names_from = Type, values_from = Value)
# 计算提升百分比（%），并构建新数据框
improvement_df <- df_wide %>%
mutate(`Improvement %` = (`MVP` - `League Mean`) / `League Mean` * 100) %>%
select(Metric, `Improvement %`) %>%
mutate(Type = "Improvement") %>%
rename(Value = `Improvement %`)
compare_all <- bind_rows(compare_df, improvement_df)
library(ggplot2)
ggplot(compare_all, aes(x = Metric, y = Value, fill = Type)) +
geom_col(
data = subset(compare_all, Type != "Improvement"),
position = position_dodge(width = 0.9),
width = 0.7
) +
geom_col(
data = subset(compare_all, Type == "Improvement"),
aes(fill = "Improvement"),
position = position_dodge(width = 0.9),
width = 0.7,
color = "black",
fill = "orange",
alpha = 0.1
) +
geom_text(
data = subset(compare_all, Type == "Improvement"),
aes(label = paste0(round(Value, 1), "%")),
vjust = -0.5,
size = 3,
position = position_dodge(width = 0.9)
) +
scale_fill_manual(
values = c("Improvement" = "orange","MVP" = "#1DA1F2", "League Mean" = "#7F8C8D")
) +
labs(
title = "MVP vs. League Mean + Improvement %",
subtitle = "Average performance and MVP boost by metric (1990–1999)",
x = "Metric",
y = "Value",
fill = "Type"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1,size=7),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "bottom"
)
```
## 1980-1989赛季：
```{r}
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
# 读取数据
df <- read_excel("C:/Users/86133/Desktop/object/MVP/MVP_Mean/MVP_mean_1980_89.xlsx", sheet = "Sheet1")
# 提取年份和类型
df <- df %>%
mutate(
Season = str_extract(name, "\\d{4}"),
Type = ifelse(grepl("Mean", name), "League Mean", "MVP")
)
# 分别计算平均值（跨所有赛季）
mvp_mean <- df %>%
filter(Type == "MVP") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "MVP")
league_mean <- df %>%
filter(Type == "League Mean") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "League Mean")
# 合并
compare_df <- bind_rows(mvp_mean, league_mean)
# 绘图
ggplot(compare_df, aes(x = Metric, y = Value, fill = Type)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_text(
aes(label = round(Value, 1)),
position = position_dodge(width = 0.8),
vjust = -0.5,
size = 3
) +
scale_fill_manual(values = c("MVP" = "#1DA1F2", "League Mean" = "#7F8C8D")) +
labs(
title = "MVP vs. League Mean: Average Performance Across All Seasons",
x = "Metric",
y = "Average Value",
fill = "Type"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1,size=5),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "bottom"
)
```
```{r}
#| warning: false
#| # 读取数据
df <- read_excel("C:/Users/86133/Desktop/object/MVP/MVP_Mean/MVP_mean_1980_89.xlsx", sheet = "Sheet1")
# 提取年份和类型
df <- df %>%
mutate(
Season = str_extract(name, "\\d{4}"),
Type = ifelse(grepl("Mean", name), "League Mean", "MVP")
)
# 分别计算平均值（跨所有赛季）
mvp_mean <- df %>%
filter(Type == "MVP") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "MVP")
league_mean <- df %>%
filter(Type == "League Mean") %>%
select(-name, -Season) %>%
summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Metric", values_to = "Value") %>%
mutate(Type = "League Mean")
# 合并
compare_df <- bind_rows(mvp_mean, league_mean)
# 将 MVP 和 League Mean 数据展开为宽格式
df_wide <- compare_df %>%
pivot_wider(names_from = Type, values_from = Value)
# 计算提升百分比（%），并构建新数据框
improvement_df <- df_wide %>%
mutate(`Improvement %` = (`MVP` - `League Mean`) / `League Mean` * 100) %>%
select(Metric, `Improvement %`) %>%
mutate(Type = "Improvement") %>%
rename(Value = `Improvement %`)
compare_all <- bind_rows(compare_df, improvement_df)
library(ggplot2)
ggplot(compare_all, aes(x = Metric, y = Value, fill = Type)) +
geom_col(
data = subset(compare_all, Type != "Improvement"),
position = position_dodge(width = 0.9),
width = 0.7
) +
geom_col(
data = subset(compare_all, Type == "Improvement"),
aes(fill = "Improvement"),
position = position_dodge(width = 0.9),
width = 0.7,
color = "black",
fill = "orange",
alpha = 0.1
) +
geom_text(
data = subset(compare_all, Type == "Improvement"),
aes(label = paste0(round(Value, 1), "%")),
vjust = -0.5,
size = 3,
position = position_dodge(width = 0.9)
) +
scale_fill_manual(
values = c("Improvement" = "orange","MVP" = "#1DA1F2", "League Mean" = "#7F8C8D")
) +
labs(
title = "MVP vs. League Mean + Improvement %",
subtitle = "Average performance and MVP boost by metric (1980–1989)",
x = "Metric",
y = "Value",
fill = "Type"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1,size=7),
plot.title = element_text(size = 16, face = "bold"),
legend.position = "bottom"
)
```
