---
title: "Student Pressure Perception Analysis"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    theme: flatly
    social: menu
    source_code: embed
---


```{r setup, include=FALSE}
# Load required libraries
library(flexdashboard)
library(ggplot2)
library(tidyverse)
library(patchwork)
library(RColorBrewer)
library(igraph)
library(plotly)
library(shiny)

# Load and prepare data
df_imputed <- read.csv("df_imputed.csv")
df_imputed$Schoolwork_Pressure <- factor(df_imputed$Schoolwork_Pressure, 
                                        levels = c("None", "Very little", "Some", "A lot"))

df_imputed <- df_imputed %>%
  mutate(
    Pressure_Group = case_when(
      Schoolwork_Pressure %in% c("None", "Very little") ~ "Low Pressure",
      Schoolwork_Pressure %in% c("Some", "A lot") ~ "High Pressure"
    ),
    Schoolwork_Pressure_Binary = factor(Schoolwork_Pressure_Binary, 
                                       levels = c("Low Pressure", "High Pressure"))
  )

# Set color palette
pressure_colors <- c("Low Pressure" = "#6baed6", "High Pressure" = "#756bb1")
activity_colors <- c("#238b45", "#74c476")
```


Column {.sidebar}
-----------------------------------------------------------------------
- This dashboard explores how students perceive academic pressure and its relationship with their daily habits.

<br> 

- Built from student survey data, it reveals key behavioral trends that correlate with academic stress perception.

<br>

- Educators and parents can use these insights to identify at-risk patterns and promote balanced student lifestyles.

<br><br>  

___Why it matters is that early detection of stress factors can help improve student well-being and academic performance___




Row {data-height=375}
-----------------------------------------------------------------------

```{r}

# Create nodes data frame
nodes <- data.frame(
  id = c("center", "screen_time", "activity", "home_occupants", "sleep"),
  label = c("Perception on\nSchool Work Pressure", 
            "Mean Daily\nScreen Time", 
            "Mean Daily\nPhysical &\nSocial Activity",
            "Home \nOccupants", 
            "Average \nNight Sleep"),
  x = c(0, -2, 2, -2, 2),
  y = c(0, 2, 2, -2, -2),
  size = c(120, 120, 120, 120, 120)
)

# Create edges data frame (connections between nodes)
edges <- data.frame(
  from = c("screen_time", "activity", "home_occupants", "sleep"),
  to = rep("center", 4),
  arrow.size = 4
)

# Create the graph
g <- graph_from_data_frame(edges, vertices = nodes, directed = TRUE)

# Plot the graph
plot(g, 
     layout = as.matrix(nodes[, c("x", "y")]),
     vertex.label = nodes$label,
     vertex.size = nodes$size,
     vertex.color = c("darkseagreen3", rep("gold2", 5)),
     vertex.frame.color = "gray",
     vertex.label.color = "black",
     vertex.label.cex = 1,
     edge.arrow.size = 0.5,
     edge.arrow.width = 1,
     edge.color = "red4")


```



```{r}

valueBox(tags$div(style = "font-size: 28px; font-weight: bold;", 
           "Which of these factors are statistically significant for Pressure Perception of Students?"),
  caption = tags$div(style = "font-size: 18px;", "Analysis of Key Predictors"), 
  icon = icon("question-circle", "fa-3x"),
  color = "warning"
)
```










Row
-------------------------------------


### Pressure Perception Distribution

```{r,echo=FALSE, message=FALSE, warning=FALSE}

ggplot(df_imputed, aes(x = Schoolwork_Pressure, fill = Pressure_Group)) +
  geom_bar(color = "black", alpha = 0.8) +
  scale_fill_manual(values = pressure_colors) +
  theme_minimal(base_size = 14) +
  labs(
    subtitle = "Breakdown of students",
    x = "Pressure Level", 
    y = "Number of Students",
    fill = "Pressure Group"
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
    plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
    plot.subtitle = element_text(size = 12, hjust = 0.5, color = "gray40"),
    legend.position = "bottom"
  ) +
  guides(fill = guide_legend(title.position = "top"))

```


### Pressure Group Classification

```{r,echo=FALSE, message=FALSE, warning=FALSE}

ggplot(df_imputed, aes(x = Schoolwork_Pressure_Binary, fill = Schoolwork_Pressure_Binary)) +
  geom_bar(color = "black", alpha = 0.8, show.legend = FALSE) +
  scale_fill_manual(values = pressure_colors) +
  theme_minimal(base_size = 14) +
   labs(
    subtitle = "Students grouped into high/low pressure categories",
    x = "Pressure Category", 
    y = "Number of Students"
  ) +
  theme(
    plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
    plot.subtitle = element_text(size = 12, hjust = 0.5, color = "gray40"),
    axis.text.x = element_text(size = 12)
  ) +
  geom_text(stat = 'count', aes(label = ..count..), vjust = 1.5, size = 5)




```



Row
-----------------------------------------------------------------------

### Daily Average Screen Time

```{r,echo=FALSE, message=FALSE, warning=FALSE}
ggplot(df_imputed, aes(x = Mean_daily_screen_time)) +
  geom_density(fill = activity_colors[1], alpha = 0.6, color = NA) +
  theme_minimal(base_size = 14) +
  labs(
    x = "Daily Average Screen Time (hours)", 
    y = "Density"
  ) +
  theme(
    plot.title = element_text(size = 16, face = "bold")
  )
```


### Daily Average Screen Time Between Groups



```{r,echo=FALSE, message=FALSE, warning=FALSE}

ggplot(df_imputed, aes(x = Schoolwork_Pressure_Binary, y = Mean_daily_screen_time, 
                      fill = Schoolwork_Pressure_Binary)) +
  geom_boxplot(alpha = 0.8, show.legend = FALSE) +
  scale_fill_manual(values = pressure_colors) +
  theme_minimal(base_size = 14) +
  labs(
    x = "Pressure Group", 
    y = "Daily Average Screen Time (hours)"
  ) +
  theme(
    plot.title = element_text(size = 16, face = "bold")
  )



```




Row
-----------------------------------------------------------------------

### Daily Average Physical & Social Activity Time

```{r,echo=FALSE, message=FALSE, warning=FALSE}
ggplot(df_imputed, aes(x = Mean_daily_Physical_and_Social_Activity)) +
  geom_density(fill = activity_colors[2], alpha = 0.6, color = NA) +
  theme_minimal(base_size = 14) +
  labs(
    x = "Daily Average Physical & Social Activity Time", 
    y = "Density"
  ) +
  theme(
    plot.title = element_text(size = 16, face = "bold")
  )

```


### Daily Average Physical & Social Activity Time Between Groups


```{r,echo=FALSE, message=FALSE, warning=FALSE}


ggplot(df_imputed, aes(x = Schoolwork_Pressure_Binary, 
                     y = Mean_daily_Physical_and_Social_Activity,
                     fill = Schoolwork_Pressure_Binary)) +
  geom_boxplot(alpha = 0.8, show.legend = FALSE) +
  scale_fill_manual(values = pressure_colors) +
  theme_minimal(base_size = 14) +
  labs(
    x = "Pressure Group", 
    y = "Daily Average Physical & Social Activity Time"
  ) +
  theme(
    plot.title = element_text(size = 16, face = "bold")
  )

```



Row
-----------------------------------------------------------------------



### Predictors of Schoolwork Pressure: A Logistic Regression Analysis




```{r,echo=FALSE, message=FALSE, warning=FALSE}

df_selected <- df_imputed %>%
  select(Schoolwork_Pressure_Binary, Mean_daily_screen_time, Mean_daily_Physical_and_Social_Activity, 
         Home_Occupants, Avg_Night_Sleep)

full_model <- glm(Schoolwork_Pressure_Binary ~ ., data = df_selected , family = binomial)
summary.m <- summary(full_model)

knitr::kable(summary.m$coefficients)


```