---
title: "APM Dahsboard"
output: 
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    theme: yeti
    source_code: embed
---

  
```{r setup, include=FALSE}
packages <- c(
  "flexdashboard",
  "tidyverse",
  "highcharter",
  "viridis",
  "DT",
  "gapminder",
  "jsonlite"
)

installed <- packages %in% rownames(installed.packages())
if (any(!installed)) {
  install.packages(packages[!installed])
}

# Load library
library(flexdashboard)
library(tidyverse)
library(highcharter)
library(viridis)
library(DT)
library(gapminder)
library(jsonlite)
```

Members {data-orientation=rows}
=======================================================================

* Foto Tim (kelompok)
* WAJIB DITAMPILKAN:
* Nama Lengkap
* NIM
* Program Studi

Objectives {data-orientation=rows}
=======================================================================

### A. Dataset Understanding & Exploratory Data Analysis (EDA)  
**(Weight: ±25%)**

Students are required to:

- Describe the dataset context and analytical objectives.
- Explain the data structure and variable types.
- Present key descriptive statistics.
- Identify and discuss:
  - missing values,
  - outliers,
  - data distributions.
- Provide at least **five (5) relevant data visualizations**.

---

### B. Relationship and Pattern Analysis  
**(Weight: ±20%)**

Students are required to:

- Analyze relationships among key variables.
- Apply appropriate analytical techniques (e.g., correlation, regression, cross-tabulation).
- Identify potential data issues (e.g., multicollinearity, heterogeneity).
- Interpret analytical results clearly and logically.

---

### C. Advanced Analysis (Context-Dependent)  
**(Weight: ±20%)**

Students are required to apply an advanced analytical approach that is appropriate to the dataset, such as:

- Time series analysis (if time-related variables exist),
- Clustering or segmentation,
- Risk or anomaly detection,
- Classification or forecasting.

---

### D. Analytical / Predictive Modeling  
**(Weight: ±25%)**

Students are required to:

- Develop at least **one analytical or predictive model**.
- Explain model selection and underlying assumptions.
- Evaluate model performance using appropriate metrics.
- Discuss model limitations and potential improvements.

---

### E. Insights, Conclusions, and Recommendations  
**(Weight: ±10%)**

Students are required to:

- Summarize key findings from the analysis.
- Present data-driven insights.
- Provide logical and actionable recommendations aligned with the dataset context.

---

Dataset {data-orientation=rows}
=======================================================================
  
### Table {data-height=520}

```{r}
df <- readr::read_csv(
  "https://raw.githubusercontent.com/dsciencelabs/dataset/refs/heads/master/bestsellers_with_categories.csv",
  show_col_types = FALSE
) %>% 
  dplyr::distinct(Name, .keep_all = TRUE) %>% 
  dplyr::rename(User_Rating = `User Rating`)
```
  
```{r}
# This is going to be a datatable
df1 <- df %>% 
  filter(User_Rating >= 4.5) %>% 
  arrange(desc(Reviews)) %>% 
  select(Name, Author,User_Rating,Reviews,Price,Year)

datatable(df1, 
          options=list(scrollX=TRUE),
          caption = htmltools::tags$caption(
            style = 'caption-side: bottom; text-align: center;',
            'Table: ', htmltools::em('Best Books from 2009 to 2019 By Users Rating Greateher Than 4.5.')
          ))
```

EDA {data-orientation=rows}
=======================================================================

## Column {.tabset .tabset-fade data-height=520}
-----------------------------------------------------------------------
  
### Chart 1 {data-width=600 data-height=510}
  
```{r}

```

### Chart 2 {data-width=600 data-height=510}
  
```{r}

```

### Chart 3 {data-width=600 data-height=510}
  
```{r}

```

### Chart 4 {data-width=600 data-height=510}
  
```{r}

```

### Chart 5 {data-width=600 data-height=510}
  
```{r}

```

Regresi {data-orientation=rows}
=======================================================================

Students are required to:

- Define the objective of the regression analysis.
- Identify dependent and independent variables.
- Justify the choice of regression model (e.g., linear, multiple, or other variants).
- Check and discuss key assumptions (e.g., linearity, normality, homoscedasticity, independence).
- Interpret model coefficients and statistical significance.
- Evaluate model performance using appropriate metrics (e.g., R², RMSE, MAE).
- Discuss limitations and possible improvements of the model.

---

Klasifikasi {data-orientation=rows}
=======================================================================

Students are required to:

- Clearly define the classification problem and target variable.
- Explain class distribution and potential imbalance issues.
- Select and justify the classification method used (e.g., logistic regression, decision tree, k-NN).
- Perform model training and testing using an appropriate validation strategy.
- Evaluate model performance using relevant metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC).
- Interpret results and discuss model strengths and weaknesses.

---

Klastering {data-orientation=rows}
=======================================================================

Students are required to:

- Explain the objective of the clustering analysis.
- Select relevant variables and justify preprocessing steps (e.g., scaling, normalization).
- Choose and justify the clustering method (e.g., k-means, hierarchical clustering).
- Determine the optimal number of clusters using appropriate criteria.
- Interpret the characteristics of each cluster.
- Discuss practical implications of the clustering results.

---

Time Series {data-orientation=rows}
=======================================================================

Students are required to:

- Describe the time-related structure of the dataset.
- Identify components of the time series (trend, seasonality, noise).
- Apply an appropriate time series method (e.g., decomposition, ARIMA, forecasting models).
- Justify model selection and parameter choices.
- Evaluate forecasting performance using suitable metrics.
- Interpret results and discuss potential sources of uncertainty.

---

Insights {data-orientation=rows}
=======================================================================

Students are required to:

- Integrate findings from all analyses conducted.
- Highlight the most important patterns, trends, or relationships discovered.
- Translate analytical results into meaningful insights.
- Provide data-driven conclusions supported by evidence.
- Offer actionable and realistic recommendations based on the analysis.