# R Markdown是什麼？
# - 將R代碼、結果和文本結合在一個文檔中
# - 支持多種輸出格式（HTML、PDF、Word等）
# - 支持動態內容生成

# 安裝必要的包
install.packages(c("rmarkdown", "knitr", "tinytex"))

# 驗證安裝
library(rmarkdown)
library(knitr)

# 安裝TinyTeX用於PDF輸出
if (!requireNamespace("tinytex", quietly = TRUE)) {
  install.packages("tinytex")
  tinytex::install_tinytex()
}

# 在RStudio中創建R Markdown文檔：
# 1. File → New File → R Markdown
# 2. 選擇文檔類型（HTML/PDF/Word）
# 3. 填寫標題和作者
# 4. 點擊"OK"

# 或者使用代碼創建模板：
rmarkdown::draft(
  file = "my_report.Rmd",
  template = "html_document",
  package = "rmarkdown",
  create_dir = TRUE,
  edit = FALSE
)

# 查看模板文件結構
file.edit("my_report/my_report.Rmd")

# 這是一個示例R Markdown文檔的基本結構

---
title: "我的數據分析報告"
author: "你的名字"
date: "`r Sys.Date()`"
output: html_document
---

## 一級標題
### 二級標題
#### 三級標題

**粗體文本** 和 *斜體文本*

`行內代碼`

---

1. 有序列表項1
2. 有序列表項2
3. 有序列表項3

- 無序列表項1
- 無序列表項2
- 無序列表項3

[鏈接文本](https://www.example.com)

![圖片描述](圖片路徑)

> 引用文本

---

表格示例：

| 列1標題 | 列2標題 | 列3標題 |
|---------|---------|---------|
| 單元格1 | 單元格2 | 單元格3 |
| 單元格4 | 單元格5 | 單元格6 |

---

數學公式：

行內公式：$E = mc^2$

塊級公式：
$$
f(x) = \frac{1}{\sqrt{2\pi\sigma^2}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}
$$

# 基本代碼塊語法
```{r}
# 這是一個R代碼塊
x <- 1:10
y <- x^2
plot(x, y, main = "簡單散點圖")
```

# 帶參數的代碼塊
```{r, echo=TRUE, eval=TRUE, warning=FALSE, message=FALSE}
# echo: 是否顯示代碼
# eval: 是否執行代碼
# warning: 是否顯示警告
# message: 是否顯示消息
# results: 輸出方式 ('hide', 'asis', 'hold')
# fig.width, fig.height: 圖形尺寸
```

# 命名代碼塊（便於交叉引用）
```{r data_preparation}
# 數據準備代碼
data <- mtcars
summary(data)
```

# 在R中查看所有可用的代碼塊選項
names(knitr::opts_chunk$get())

# 常用代碼塊選項示例：
# 
# 基本控制：
# - echo: TRUE/FALSE - 顯示/隱藏代碼
# - eval: TRUE/FALSE - 執行/不執行代碼
# - include: TRUE/FALSE - 包含/排除整個代碼塊
# 
# 結果輸出：
# - results: 'markup', 'asis', 'hold', 'hide'
# - collapse: TRUE/FALSE - 合併輸出
# 
# 圖形設置：
# - fig.width: 圖形寬度（英寸）
# - fig.height: 圖形高度（英寸）
# - fig.align: 'left', 'center', 'right'
# - out.width: '70%' (相對大小)
# - out.height: '400px' (絕對大小)
# 
# 緩存：
# - cache: TRUE/FALSE - 啓用/禁用緩存
# - cache.path: 緩存路徑
# - dependson: 依賴的代碼塊
# 
# 表格輸出：
# - comment: 註釋字符（默認'##'）
# - highlight: TRUE/FALSE - 語法高亮

# 在文本中嵌入R代碼
數據集包含 `r nrow(mtcars)` 行和 `r ncol(mtcars)` 列。

平均油耗為 `r mean(mtcars$mpg) %>% round(2)` 英里/加侖。

當前日期是 `r Sys.Date()`。

# 條件文本
`r if (mean(mtcars$mpg) > 20) "平均油耗良好" else "需要改進"`

# 循環生成文本
`r paste0("車型包括：", paste(unique(mtcars$cyl), collapse = ", "))`

# 格式化數字
`r sprintf("平均值為 %.2f，標準差為 %.3f", 
          mean(mtcars$mpg), 
          sd(mtcars$mpg))`

# 使用knitr::kable創建表格
```{r, results='asis'}
library(knitr)
library(dplyr)

# 簡單表格
kable(head(mtcars), 
      caption = "汽車數據示例")

# 更美觀的表格
kable(mtcars %>% 
        group_by(cyl) %>% 
        summarize(
          count = n(),
          avg_mpg = mean(mpg) %>% round(2),
          avg_hp = mean(hp) %>% round(1)
        ),
      caption = "按氣缸數分組彙總",
      align = c('l', 'c', 'c', 'c'),
      col.names = c("氣缸數", "數量", "平均MPG", "平均馬力"))
```

```{r}
# 安裝並加載kableExtra
# install.packages("kableExtra")
library(kableExtra)
library(dplyr)

# 創建高度可定製的表格
mtcars %>%
  head() %>%
  kable(format = "html", 
        caption = "美化表格示例") %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    full_width = FALSE,
    position = "center"
  ) %>%
  row_spec(0, bold = TRUE, color = "white", background = "#4ECDC4") %>%
  column_spec(1, bold = TRUE) %>%
  add_header_above(c(" " = 1, "性能指標" = 2, "外觀尺寸" = 2)) %>%
  footnote(
    general = "數據來源: 1974年Motor Trend雜誌",
    number = "MPG: 英里/加侖"
  )
```

```{r}
# 創建交互式表格
# install.packages("DT")
library(DT)

datatable(
  mtcars,
  caption = "交互式汽車數據表",
  filter = "top",           # 頂部過濾器
  extensions = c('Buttons', 'ColReorder'),
  options = list(
    pageLength = 10,
    dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
    colReorder = TRUE,
    scrollX = TRUE
  )
)
```

```{r cars-plot, fig.width=10, fig.height=6, fig.cap="汽車數據可視化"}
library(ggplot2)

# 基本圖形
p <- ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3, alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title = "重量與油耗關係",
    subtitle = "按氣缸數着色",
    x = "重量（千磅）",
    y = "油耗（英里/加侖）",
    color = "氣缸數"
  ) +
  theme_minimal()

print(p)

# 多個圖形佈局
library(patchwork)

p1 <- ggplot(mtcars, aes(x = mpg)) + 
  geom_histogram(bins = 15, fill = "steelblue") +
  ggtitle("MPG分佈")

p2 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) + 
  geom_boxplot(fill = "lightgreen") +
  ggtitle("按氣缸數分組")

(p1 | p2) + plot_annotation(title = "汽車數據分析")
```

# 在文本中引用圖形

如\@ref(fig:cars-plot)所示，汽車的重量與油耗呈負相關關係。

# 也可以這樣寫：
如圖\@ref(fig:cars-plot)所示...

# 對於表格引用：
如表\@ref(tab:summary-table)所示...

# 注意：需要在代碼塊中設置標籤
# ```{r cars-plot, fig.cap="圖形標題"}
# # 圖形代碼
# ```

# YAML頭部需要添加：
# output:
#   bookdown::html_document2:
#     toc: true

---
title: "專業數據分析報告"
author: "數據分析師"
date: "`r format(Sys.Date(), '%Y年%m月%d日')`"
output:
  html_document:
    theme: united
    highlight: tango
    toc: true
    toc_float: true
    toc_depth: 3
    number_sections: true
    code_folding: show
    fig_width: 8
    fig_height: 6
    fig_caption: true
    df_print: paged
    css: styles.css
    includes:
      in_header: header.html
      before_body: before_body.html
      after_body: after_body.html
  pdf_document:
    toc: true
    number_sections: true
    latex_engine: xelatex
    includes:
      in_header: preamble.tex
  word_document:
    toc: true
    toc_depth: 3
abstract: |
  本報告分析了汽車數據集，探討了油耗與各種因素的關係。
  主要內容包括描述性統計分析、可視化展示和迴歸模型構建。
keywords:
  - 數據分析
  - 汽車油耗
  - 統計建模
  - R語言
subtitle: "汽車油耗影響因素分析"
lang: zh-CN
---

/* styles.css - 自定義CSS樣式 */

/* 整體樣式 */
body {
  font-family: 'Microsoft YaHei', 'Segoe UI', sans-serif;
  line-height: 1.6;
  color: #333;
  max-width: 1200px;
  margin: 0 auto;
  padding: 20px;
}

/* 標題樣式 */
h1 {
  color: #2c3e50;
  border-bottom: 3px solid #3498db;
  padding-bottom: 10px;
}

h2 {
  color: #34495e;
  border-left: 5px solid #3498db;
  padding-left: 15px;
}

h3 {
  color: #7f8c8d;
}

/* 代碼塊樣式 */
pre {
  background-color: #f8f9fa;
  border: 1px solid #dee2e6;
  border-radius: 5px;
  padding: 15px;
}

code {
  background-color: #f1f3f4;
  color: #d63384;
  padding: 2px 4px;
  border-radius: 3px;
}

/* 表格樣式 */
table {
  border-collapse: collapse;
  width: 100%;
  margin: 20px 0;
}

th {
  background-color: #3498db;
  color: white;
  padding: 12px;
  text-align: left;
}

td {
  padding: 10px;
  border-bottom: 1px solid #ddd;
}

tr:hover {
  background-color: #f5f5f5;
}

/* 引用樣式 */
blockquote {
  border-left: 4px solid #3498db;
  background-color: #f8f9fa;
  padding: 15px;
  margin: 20px 0;
  font-style: italic;
}

/* 鏈接樣式 */
a {
  color: #3498db;
  text-decoration: none;
}

a:hover {
  text-decoration: underline;
  color: #2980b9;
}

/* 特殊類 */
.note {
  background-color: #e7f3ff;
  border-left: 4px solid #2196f3;
  padding: 15px;
  margin: 20px 0;
}

.warning {
  background-color: #fff3cd;
  border-left: 4px solid #ffc107;
  padding: 15px;
  margin: 20px 0;
}

% preamble.tex - LaTeX前言
\usepackage{ctex}  % 中文支持
\usepackage{geometry}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage{caption}
\usepackage{float}
\usepackage{hyperref}

% 頁面設置
\geometry{a4paper, margin=2.5cm}

% 鏈接設置
\hypersetup{
    colorlinks=true,
    linkcolor=blue,
    filecolor=magenta,      
    urlcolor=cyan,
}

% 自定義命令
\newcommand{\R}{\textsf{R}}
\newcommand{\code}[1]{\texttt{#1}}

% 圖表標題格式
\captionsetup[figure]{labelfont=bf}
\captionsetup[table]{labelfont=bf}

---
title: "參數化分析報告"
author: "數據分析團隊"
date: "`r Sys.Date()`"
output: html_document
params:
  dataset: "mtcars"
  variable: "mpg"
  group_by: "cyl"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(dplyr)

# 使用參數
data <- get(params$dataset)
```

## 分析報告

正在分析數據集：`r params$dataset`

目標變量：`r params$variable`

分組變量：`r params$group_by`

```{r analysis}
# 動態分析
summary_stats <- data %>%
  group_by(across(params$group_by)) %>%
  summarize(
    count = n(),
    mean = mean(.data[[params$variable]]),
    sd = sd(.data[[params$variable]]),
    min = min(.data[[params$variable]]),
    max = max(.data[[params$variable]])
  )

kable(summary_stats, caption = "描述性統計")
```

```{r visualization, fig.width=10, fig.height=6}
# 動態可視化
ggplot(data, aes(x = .data[[params$group_by]], 
                 y = .data[[params$variable]])) +
  geom_boxplot(fill = "lightblue") +
  labs(
    title = paste(params$variable, "by", params$group_by),
    x = params$group_by,
    y = params$variable
  ) +
  theme_minimal()
```

# 使用htmlwidgets添加交互式元素

```{r}
# 交互式圖形 - plotly
library(plotly)

p <- ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3) +
  theme_minimal()

ggplotly(p) %>%
  layout(
    title = "交互式汽車數據圖",
    hovermode = "closest"
  )
```

```{r}
# 交互式表格 - DT
library(DT)

datatable(
  mtcars,
  options = list(
    pageLength = 5,
    dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel')
  ),
  filter = 'top',
  caption = '交互式數據表'
)
```

```{r}
# 交互式地圖 - leaflet
library(leaflet)

leaflet() %>%
  addTiles() %>%
  setView(lng = 116.4074, lat = 39.9042, zoom = 10) %>%
  addMarkers(lng = 116.4074, lat = 39.9042, 
             popup = "北京")
```

# 引用參考文獻

## 文獻引用

根據Smith的研究[@smith2020ml]，機器學習在數據分析中...

近期研究顯示[@jones2021dl]，深度學習...

## 參考文獻列表

---

# references.bib 文件內容示例
# @book{smith2020ml,
#   title={機器學習導論},
#   author={Smith, John},
#   year={2020},
#   publisher={科學出版社}
# }
# 
# @article{jones2021dl,
#   title={深度學習在金融中的應用},
#   author={Jones, Mary},
#   journal={計算機研究},
#   volume={48},
#   number={3},
#   pages={123--135},
#   year={2021}
# }

# YAML配置需要添加：
# bibliography: references.bib
# csl: chinese-gb7714-2005-numeric.csl  # 中文參考文獻格式

---
title: "數據分析演示文稿"
author: "演講者"
date: "`r Sys.Date()`"
output: 
  ioslides_presentation:
    widescreen: true
    smaller: true
    transition: faster
---

## 幻燈片1：標題頁

# 數據分析報告
### 汽車油耗影響因素分析
<br>
<br>
`r Sys.Date()`

---

## 幻燈片2：內容大綱

### 報告結構

1. 研究背景
2. 數據描述
3. 分析方法
4. 主要發現
5. 結論建議

---

## 幻燈片3：交互式內容

```{r slide3-plot, echo=FALSE, fig.height=4}
library(plotly)
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(aes(color = factor(cyl)), size = 3) +
  theme_minimal()
ggplotly(p)
```

汽車重量與油耗的關係如上圖所示。

---

## 幻燈片4：分欄佈局

:::::::::::::: {.columns}
::: {.column width="50%"}

### 左側內容

```{r left-col, echo=FALSE}
library(DT)
datatable(head(mtcars, 5))
```

:::
::: {.column width="50%"}

### 右側內容

- 要點1：油耗與重量負相關
- 要點2：氣缸數影響顯著
- 要點3：自動擋油耗更高

```{r right-col, echo=FALSE, fig.height=3}
boxplot(mpg ~ cyl, data = mtcars, col = "lightblue")
```

:::
::::::::::::::

---
title: "汽車油耗影響因素分析報告"
author: "數據分析團隊"
date: "`r format(Sys.Date(), '%Y年%m月%d日')`"
output:
  html_document:
    theme: flatly
    toc: true
    toc_float: true
    number_sections: true
    code_folding: hide
    fig_width: 10
    fig_height: 6
abstract: |
  本報告使用mtcars數據集，分析影響汽車油耗的主要因素。
  通過描述性統計、可視化分析和迴歸建模，探討了氣缸數、
  重量、馬力等變量對油耗的影響。
keywords:
  - 汽車油耗
  - 數據分析
  - 迴歸模型
  - R語言
---

```{r setup, include=FALSE}
# 初始化設置
knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE,
  fig.align = "center"
)

# 加載包
library(tidyverse)
library(knitr)
library(kableExtra)
library(ggplot2)
library(plotly)
library(DT)
```

# 執行摘要

本次分析的主要發現：

1. **油耗與汽車重量顯著負相關**（相關係數 = -0.87）
2. **氣缸數對油耗有顯著影響**（4缸車平均油耗最高）
3. **自動擋車輛油耗普遍低於手動擋**
4. 最佳的預測模型是多元線性迴歸模型（R² = 0.85）

```{r summary-stats, echo=FALSE}
# 計算關鍵統計指標
key_stats <- data.frame(
  指標 = c("平均油耗", "油耗標準差", "最低油耗", "最高油耗", "樣本數量"),
  數值 = c(
    round(mean(mtcars$mpg), 2),
    round(sd(mtcars$mpg), 2),
    min(mtcars$mpg),
    max(mtcars$mpg),
    nrow(mtcars)
  )
)

kable(key_stats, align = c('l', 'c')) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
```

# 1. 數據概覽

## 1.1 數據來源

本次分析使用R內置的`mtcars`數據集，包含1974年《Motor Trend》雜誌測試的32款汽車的11個變量。

## 1.2 變量説明

```{r variable-description, echo=FALSE}
var_desc <- data.frame(
  變量名 = names(mtcars),
  描述 = c(
    "油耗（英里/加侖）",
    "氣缸數",
    "排量",
    "馬力",
    "後軸比",
    "重量（千磅）",
    "1/4英里加速時間",
    "發動機類型（V型/直列）",
    "變速箱類型（0=自動，1=手動）",
    "前進檔數",
    "化油器數量"
  ),
  類型 = c(rep("數值型", 11))
)

kable(var_desc, caption = "變量説明") %>%
  kable_styling(bootstrap_options = "striped")
```

## 1.3 數據預覽

```{r data-preview, echo=FALSE}
DT::datatable(
  mtcars,
  caption = "數據集預覽（可交互）",
  options = list(
    pageLength = 10,
    dom = 'Bfrtip',
    buttons = c('copy', 'csv')
  ),
  filter = 'top'
)
```

# 2. 描述性分析

## 2.1 油耗分佈

```{r mpg-distribution, echo=FALSE, fig.cap="油耗分佈直方圖"}
ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(bins = 15, fill = "steelblue", alpha = 0.7) +
  geom_density(aes(y = after_stat(count)), color = "red", size = 1) +
  geom_vline(xintercept = mean(mtcars$mpg), 
             linetype = "dashed", 
             color = "darkgreen", 
             size = 1) +
  annotate("text", 
           x = mean(mtcars$mpg) + 3, 
           y = 5, 
           label = paste("均值:", round(mean(mtcars$mpg), 2)),
           color = "darkgreen") +
  labs(title = "油耗分佈", x = "油耗（mpg）", y = "頻數") +
  theme_minimal()
```

## 2.2 分組比較

```{r group-comparison, echo=FALSE, fig.cap="按氣缸數分組的油耗箱線圖"}
p1 <- ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
  geom_boxplot(alpha = 0.7) +
  labs(title = "按氣缸數分組", x = "氣缸數", y = "油耗", fill = "氣缸數") +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal()

p2 <- ggplot(mtcars, aes(x = factor(am), y = mpg, fill = factor(am))) +
  geom_boxplot(alpha = 0.7) +
  labs(title = "按變速箱類型分組", 
       x = "變速箱（0=自動，1=手動）", 
       y = "油耗",
       fill = "變速箱") +
  scale_fill_brewer(palette = "Set3") +
  theme_minimal()

library(patchwork)
p1 + p2 + plot_layout(guides = "collect")
```

## 2.3 相關性分析

```{r correlation, echo=FALSE, fig.cap="變量間相關矩陣熱力圖"}
library(corrplot)

cor_matrix <- cor(mtcars[, c("mpg", "cyl", "disp", "hp", "wt")])

corrplot(cor_matrix, 
         method = "color",
         type = "upper",
         order = "hclust",
         addCoef.col = "black",
         tl.col = "black",
         tl.srt = 45,
         diag = FALSE,
         title = "主要變量相關矩陣",
         mar = c(0, 0, 2, 0))
```

# 3. 迴歸分析

## 3.1 簡單線性迴歸

```{r simple-regression, echo=FALSE, fig.cap="油耗與重量關係的迴歸分析"}
lm_simple <- lm(mpg ~ wt, data = mtcars)

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = 3, alpha = 0.7, color = "steelblue") +
  geom_smooth(method = "lm", color = "red", se = TRUE) +
  labs(
    title = paste("油耗與重量關係 (R² =", 
                  round(summary(lm_simple)$r.squared, 3), ")"),
    x = "重量（千磅）",
    y = "油耗（mpg）"
  ) +
  theme_minimal() +
  annotate("text", 
           x = 4.5, 
           y = 30, 
           label = paste("公式: mpg =", 
                         round(coef(lm_simple)[1], 2), 
                         round(coef(lm_simple)[2], 2), "* wt"),
           color = "red",
           size = 5)
```

## 3.2 多元迴歸模型

```{r multiple-regression, echo=FALSE}
lm_multi <- lm(mpg ~ wt + hp + factor(cyl) + factor(am), data = mtcars)

# 創建迴歸結果表格
coef_table <- data.frame(
  變量 = names(coef(lm_multi)),
  係數估計值 = round(coef(lm_multi), 3),
  標準誤 = round(summary(lm_multi)$coefficients[, 2], 3),
  t值 = round(summary(lm_multi)$coefficients[, 3], 2),
  p值 = format.pval(summary(lm_multi)$coefficients[, 4], digits = 3)
)

kable(coef_table, caption = "多元迴歸模型係數") %>%
  kable_styling(bootstrap_options = "striped") %>%
  row_spec(which(coef_table$p值 < 0.05), bold = TRUE, color = "red")
```

```{r model-summary, echo=FALSE}
# 模型摘要
model_summary <- data.frame(
  指標 = c("R²", "調整R²", "F統計量", "F檢驗p值", "殘差標準誤"),
  值 = c(
    round(summary(lm_multi)$r.squared, 3),
    round(summary(lm_multi)$adj.r.squared, 3),
    round(summary(lm_multi)$fstatistic[1], 1),
    format.pval(pf(summary(lm_multi)$fstatistic[1],
                   summary(lm_multi)$fstatistic[2],
                   summary(lm_multi)$fstatistic[3],
                   lower.tail = FALSE), digits = 3),
    round(summary(lm_multi)$sigma, 3)
  )
)

kable(model_summary, align = c('l', 'c')) %>%
  kable_styling(bootstrap_options = "striped")
```

# 4. 結論與建議

## 4.1 主要發現

1. **重量是影響油耗的最主要因素**，重量每增加1000磅，油耗平均減少5.34 mpg
2. **氣缸數對油耗有顯著影響**，6缸和8缸車油耗明顯高於4缸車
3. **手動擋車輛油耗略高於自動擋**，但差異不顯著
4. **馬力對油耗的影響較小**，在控制其他變量後不顯著

## 4.2 建議

基於以上分析，我們建議：

1. **輕量化設計**：汽車製造商應注重輕量化設計以降低油耗
2. **發動機優化**：在保證動力的前提下，優化發動機效率
3. **消費者選擇**：消費者可選擇重量較輕、氣缸數較少的車型以降低油耗

# 附錄

## 附錄A：完整R代碼

```{r full-code, eval=FALSE, echo=TRUE}
# 完整分析代碼
library(tidyverse)
library(ggplot2)

# 數據加載
data(mtcars)

# 描述性分析
summary(mtcars$mpg)
hist(mtcars$mpg)

# 迴歸分析
model <- lm(mpg ~ wt + hp + factor(cyl), data = mtcars)
summary(model)

# 可視化
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_smooth(method = "lm")
```

## 附錄B：參考文獻

1. Henderson, H. V., & Velleman, P. F. (1981). Building multiple regression models interactively. *Biometrics*, 37(2), 391-411.

2. R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

---
*報告生成時間：`r Sys.time()`*
*報告版本：1.0*
*分析工具：R `r R.version.string`*

---
title: "銷售週報 - 第`r params$week`周"
author: "銷售分析部"
date: "`r Sys.Date()`"
output: html_document
params:
  week: 1
  region: "全國"
  start_date: !r as.Date("2023-01-01")
  end_date: !r as.Date("2023-12-31")
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
library(tidyverse)
library(ggplot2)
library(plotly)
library(lubridate)
```

# `r params$region`銷售週報（第`r params$week`周）

**報告期間：** `r format(params$start_date, "%Y年%m月%d日")` - `r format(params$end_date, "%Y年%m月%d日")`

**生成時間：** `r Sys.time()`

## 執行摘要

```{r summary-data, echo=FALSE}
# 生成模擬銷售數據
set.seed(params$week * 123)
n_days <- as.numeric(params$end_date - params$start_date) + 1

sales_data <- data.frame(
  date = seq(params$start_date, params$end_date, by = "day"),
  sales = 10000 + cumsum(rnorm(n_days, mean = 100, sd = 500)),
  orders = round(runif(n_days, 50, 200)),
  customers = round(runif(n_days, 30, 150))
)

# 計算周度彙總
weekly_summary <- sales_data %>%
  mutate(week = isoweek(date)) %>%
  filter(week == params$week) %>%
  summarize(
    total_sales = sum(sales),
    avg_daily_sales = mean(sales),
    total_orders = sum(orders),
    new_customers = sum(customers),
    avg_order_value = sum(sales) / sum(orders)
  )

# 顯示關鍵指標
metrics <- data.frame(
  指標 = c("總銷售額", "日均銷售額", "總訂單數", "新客户數", "平均訂單價值"),
  數值 = c(
    paste("¥", format(weekly_summary$total_sales, big.mark = ",")),
    paste("¥", format(round(weekly_summary$avg_daily_sales), big.mark = ",")),
    weekly_summary$total_orders,
    weekly_summary$new_customers,
    paste("¥", format(round(weekly_summary$avg_order_value), big.mark = ","))
  )
)

kable(metrics, align = c('l', 'c')) %>%
  kable_styling(bootstrap_options = "striped")
```

## 詳細分析

### 銷售額趨勢

```{r sales-trend, echo=FALSE, fig.width=12, fig.height=6}
weekly_sales <- sales_data %>%
  mutate(week = isoweek(date)) %>%
  group_by(week) %>%
  summarize(
    week_start = min(date),
    total_sales = sum(sales)
  )

ggplot(weekly_sales, aes(x = week_start, y = total_sales)) +
  geom_line(color = "steelblue", size = 1.5) +
  geom_point(color = "darkblue", size = 3) +
  geom_point(data = weekly_sales %>% filter(week == params$week), 
             color = "red", size = 5) +
  labs(
    title = paste("周銷售額趨勢（當前周：第", params$week, "周）"),
    x = "周開始日期",
    y = "周銷售額（¥）"
  ) +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma)
```

### 產品類別分析

```{r product-analysis, echo=FALSE, fig.width=10, fig.height=6}
# 模擬產品類別數據
product_data <- data.frame(
  category = c("電子產品", "家居用品", "服裝配飾", "食品飲料", "圖書音像"),
  sales = c(35000, 28000, 22000, 18000, 12000) * runif(5, 0.8, 1.2),
  growth = c(15, 12, 8, -5, 3) + rnorm(5, 0, 2)
)

ggplot(product_data, aes(x = reorder(category, sales), y = sales, fill = growth)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = paste("¥", format(round(sales), big.mark = ","))), 
            hjust = -0.2, size = 3.5) +
  geom_text(aes(label = paste0(ifelse(growth > 0, "+", ""), 
                               round(growth, 1), "%")), 
            hjust = 1.2, size = 3.5, color = "white") +
  scale_fill_gradient2(low = "red", mid = "white", high = "green", midpoint = 0) +
  coord_flip()