---
title: "EDA_23CSEA50"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
theme: flatly
social: menu
source_code: embed
navbar:
- { title: "Dataset Description", href: "#dataset-description" }
- { title: "Univariate Analysis", href: "#univariate-analysis" }
- { title: "Bivariate Analysis", href: "#bivariate-analysis" }
- { title: "Multivariate Analysis", href: "#multivariate-analysis" }
---
```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
library(readr)
library(dplyr)
library(DT)
# Load the dataset
data <- read_csv("JalanidhiDistrict.csv")
# Handling missing values by removing NA
data <- na.omit(data)
```
## Dataset Description {.tabset .active} {#dataset-description}
### View of dataset
```{r}
datatable(data, extensions = 'Buttons', options = list(dom='Bfrtip', buttons=c('copy','csv','print','pdf')))
```
### About the Dataset
```{r}
str(data)
```
### Summary of the Dataset
```{r}
summary(data)
```
### Head of the Dataset
```{r}
head(data)
```
## Univariate Analysis {.tabset} {#univariate-analysis}
### Histogram of Tap connections - No of Applications Received
```{r}
# Histogram of 'Tap Connections - No of applications received'
ggplot(data, aes(x = `Tap Connections - No of applications received`)) +
geom_histogram(binwidth = 50, fill = "green", color = "black") +
labs(title = "Histogram of Applications Received for Tap Connections",
x = "No. of Applications Received",
y = "Frequency") +
theme_minimal()
```
### Histogram of 'Tap Connections - No of applications approved'
```{r}
# Histogram of 'Tap Connections - No of applications approved'
ggplot(data, aes(x = `Tap Connections - No of applications approved`)) +
geom_histogram(binwidth = 50, fill = "orange", color = "black") +
labs(title = "Histogram of Applications Approved for Tap Connections",
x = "No. of Applications Approved",
y = "Frequency") +
theme_minimal()
```
## Bivariate Analysis {.tabset} {#bivariate-analysis}
### Box Plot for Tap Connections - No of applications approved
```{r}
# Boxplot of 'Tap Connections - No of applications approved'
ggplot(data, aes(y = `Tap Connections - No of applications approved`)) +
geom_boxplot(fill = "purple", color = "black") +
labs(title = "Boxplot of Applications Approved for Tap Connections",
y = "No. of Applications Approved") +
theme_minimal()
```
### Boxplot of Tap Connections - No of applications pending
```{r}
# Boxplot of 'Tap Connections - No of applications pending'
ggplot(data, aes(y = `Tap Connections - No of applications pending`)) +
geom_boxplot(fill = "red", color = "black") +
labs(title = "Boxplot of Applications Pending for Tap Connections",
y = "No. of Applications Pending") +
theme_minimal()
```
## Multivariate Analysis {.tabset} {#multivariate-analysis}
### Scatter Plot for Tap Connections - No of applications approved vs No of Tap Connections
```{r}
# Scatterplot of 'Tap Connections - No of applications approved' vs 'No of Tap Connections'
ggplot(data, aes(x = `Tap Connections - No of applications approved`,
y = `No of Tap Connections`)) +
geom_point(color = "purple") +
labs(title = "Scatterplot of Applications Approved vs No. of Tap Connections",
x = "No. of Applications Approved",
y = "No. of Tap Connections") +
theme_minimal()
```
### Scatterplot of Tap Connections - No of applications pending vs No of Tap Connections
```{r}
# Scatterplot of 'Tap Connections - No of applications pending' vs 'No of Tap Connections'
ggplot(data, aes(x = `Tap Connections - No of applications pending`,
y = `No of Tap Connections`)) +
geom_point(color = "red") +
labs(title = "Scatterplot of Applications Pending vs No. of Tap Connections",
x = "No. of Applications Pending",
y = "No. of Tap Connections") +
theme_minimal()
```
