View at Dataset

Dataset Description

Structure of the Dataset

'data.frame':   423 obs. of  9 variables:
 $ Property.ID                        : num  1e+12 1e+12 1e+12 1e+12 1e+12 ...
 $ Education.Institution.Category     : chr  "School" "School" "School" "School" ...
 $ Education.Institution.Maintained.by: chr  "Private" "Private" "Corporation" "Corporation" ...
 $ Education.Institution.Name         : chr  "AMRITA FUN SCHOOL" "SINDHI VIDYALAYA SCHOOL" "BABY SCHOOL" "CORPORATION SCHOOL" ...
 $ Ward.Number                        : int  14 14 14 15 15 15 15 15 22 22 ...
 $ Address                            : chr  "DR AMBEDKAR ROAD" "DR AMBEDKAR ROAD" "BHARATHIYAR STREET" "MARUTHAMALAI ROAD" ...
 $ Length..mtr.                       : num  92 352.3 134.1 321.9 77.1 ...
 $ Area..mtr.                         : num  522 5276 1008 4766 368 ...
 $ Education.Institution.ID           : num  1e+12 1e+12 1e+12 1e+12 1e+12 ...

Summary of the Dataset

  Property.ID        Education.Institution.Category
 Min.   :1.000e+12   Length:423                    
 1st Qu.:1.000e+12   Class :character              
 Median :1.001e+12   Mode  :character              
 Mean   :1.001e+12                                 
 3rd Qu.:1.001e+12                                 
 Max.   :1.001e+12                                 
 Education.Institution.Maintained.by Education.Institution.Name  Ward.Number   
 Length:423                          Length:423                 Min.   :10.00  
 Class :character                    Class :character           1st Qu.:40.00  
 Mode  :character                    Mode  :character           Median :58.00  
                                                                Mean   :54.91  
                                                                3rd Qu.:73.00  
                                                                Max.   :86.00  
   Address           Length..mtr.       Area..mtr.      
 Length:423         Min.   :  22.86   Min.   :    32.7  
 Class :character   1st Qu.:  93.17   1st Qu.:   459.3  
 Mode  :character   Median : 179.65   Median :  1672.4  
                    Mean   : 313.77   Mean   : 11018.1  
                    3rd Qu.: 367.97   3rd Qu.:  6918.1  
                    Max.   :4039.80   Max.   :530419.9  
 Education.Institution.ID
 Min.   :1.000e+12       
 1st Qu.:1.000e+12       
 Median :1.001e+12       
 Mean   :1.001e+12       
 3rd Qu.:1.001e+12       
 Max.   :1.001e+12

Univariate Analysis

Histogram for numerical columns

Bivariate Analysis

Scatter Plot

Box Plot

Multivariate Analysis

PCA

Importance of components:
                          PC1     PC2
Standard deviation     1.3765 0.32461
Proportion of Variance 0.9473 0.05269
Cumulative Proportion  0.9473 1.00000

Cluster Analysis

             Length Class  Mode   
cluster      423    -none- numeric
centers        6    -none- numeric
totss          1    -none- numeric
withinss       3    -none- numeric
tot.withinss   1    -none- numeric
betweenss      1    -none- numeric
size           3    -none- numeric
iter           1    -none- numeric
ifault         1    -none- numeric

Linear Regression


Call:
lm(formula = Area..mtr. ~ Length..mtr. + Education.Institution.Category, 
    data = Education)

Coefficients:
                             (Intercept)  
                               -12504.40  
                            Length..mtr.  
                                   85.67  
    Education.Institution.CategoryOthers  
                                 2018.95  
    Education.Institution.CategorySchool  
                                -3499.73  
Education.Institution.CategoryUniversity  
                               -14979.92  

---
title: "Assignment_Flexdashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    theme: paper
    social: ["facebook","whatsapp"]
    source_code: embed
---


```{r setup, include=TRUE}
library(flexdashboard)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(crosstalk)
library(DT)

Education <- read.csv("Educational_Institution_n1.csv")
```
# View at Dataset

```{r}
datatable(Education,extensions = 'Buttons',options = list(dom = 'Bfrtip',Buttons = c('print','pdf')))
```

## Dataset Description {.tabset}

### Structure of the Dataset

```{r}
str(Education)
```
### Summary of the Dataset

```{r}
summary(Education)
```


## Univariate Analysis

### Histogram for numerical columns

```{r}
Education %>% 
  select_if(is.numeric) %>% 
  gather() %>% 
  ggplot(aes(value)) + 
  geom_histogram(bins = 30) + 
  facet_wrap(~ key, scales = 'free_x')
```

## Bivariate Analysis {.tabset}

### Scatter Plot

```{r}
ggplot(Education, aes(x = Length..mtr., y = Area..mtr., color = Education.Institution.Category)) + 
  geom_point() + 
  labs(title = "Scatter Plot of Length vs Area", x = "Length (mtr)", y = "Area (mtr)")
```

### Box Plot
```{r}
ggplot(Education, aes(x = Education.Institution.Category, y = Area..mtr.)) + 
  geom_boxplot() + 
  labs(title = "Box Plot of Area by Education Institution Category", x = "Institution Category", y = "Area (m²)")
```

## Multivariate Analysis {.tabset}

### PCA
```{r}
pca <- prcomp(Education[, c("Length..mtr.", "Area..mtr.")], scale. = TRUE)
summary(pca)
```
### Cluster Analysis
```{r}
kmeans <- kmeans(Education[, c("Length..mtr.", "Area..mtr.")], centers = 3)
summary(kmeans)
```
### Linear Regression
```{r}
lm(Area..mtr. ~ Length..mtr. + Education.Institution.Category, data = Education)
```