---
title: "CBSE Results Analysis"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    theme: yeti
    social: menu
    source_code: embed
    
---

```{r setup, include=FALSE}

library(tidyverse)
library(ggplot2)
library(flexdashboard)
library(DT)
cbse_data <- read.csv("class12-2023.csv")
cbse_appd_data <- cbse_data %>% filter(Status == "Appd")

```
## Dataset Desciption {.tabset}

### Dataset View
```{r}
datatable(cbse_data,extensions = 'Buttons',options = list(dom='Bfrtip',Buttons=c('copy','csv','print')))

```
### Appeared Stud Data

```{r}

datatable(cbse_appd_data,extensions = 'Buttons',options = list(dom='Bfrtip',Buttons=c('copy','csv','print')))

```

### Summary
```{r}
summary(cbse_data)
```
### Assumptions

 **Data Fields**:  
      -The data set contains records for Class XII CBSE exam statistics from various regions in 2023.  
      -Key columns include the number of registered (Regd) and appeared (Appd) students across different school types         (CTSA, Govt, Govt Aided, Independent, JNV, KV)  
      -**"Class, Year, Region"** These fields likely represent the grade level, academic year, and geographical location         of the schools.  
      -**"Status"** This could indicate whether the school's registration is regular (Regd) or approved (Appd).
 
 
 
## Dataset Analysis {.tabset}

### Histogram Analysis
```{r}
hist_indep <- ggplot(cbse_appd_data, aes(x = INDEPENDENT)) +
  geom_histogram(binwidth = 5000, fill = "blue", color = "black") +
  labs(title = "Distribution of Independent School Students Appeared", x = "Students", y = "Frequency")+
  xlim(0,100000)+
  ylim(0,3)
print(hist_indep)

```
**Insights & Inference**: 
          -The majority of independent school registrations fall in the range of 60,000–90,000.
          
          


### Boxplot Analysis
```{r}
box_plot <- ggplot(cbse_appd_data, aes(x = Region, y = GOVT)) +
  geom_boxplot() +
  labs(title = "Government School Students Appeared by Region", x = "Region", y = "Government School Students") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
print(box_plot)

```
**Insights & Inference**: 
          -Region Delhi East has the highest variation in independent school registrations.  
          -Where the other regions has low registration.  
          -Chandigarh,Guwahati and Delhi West has the registration in range of 15,000 - 75,000.
          
          
          
### Scatterplot Analysis

```{r}
ggplot(cbse_data, aes(x = GOVT, y = INDEPENDENT)) +
  geom_point(color = "red") +
  labs(title = "Govt vs Independent School Registrations", x = "Government School Registrations", y = "Independent School Registrations")+
  xlim(0, 110000) +
  ylim(0, 110000) 


```
**Insights & Inference**:
          -There is a clear difference in the number of registrations between government and independent schools.  
          -As it goes on it has more students.