Dataset Description {.tabset .active}

View of dataset

spc_tbl_ [74 × 16] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ state_name                 : chr [1:74] "Andhra Pradesh" "Andhra Pradesh" "Andhra Pradesh" "Andhra Pradesh" ...
 $ district_name              : chr [1:74] "Krishna" "Krishna" "Krishna" "Krishna" ...
 $ block_name                 : chr [1:74] "Kalidindi" "Kalidindi" "Kalidindi" "Kalidindi" ...
 $ panchayat_name             : chr [1:74] "Konduru" "Chinatadinada" "Chinatadinada" "Venkatapuram" ...
 $ village_name               : chr [1:74] "Konduru" "Tadinada" "Tadinada" "Venkatapuram" ...
 $ habitation_name            : chr [1:74] "Konduru Scl" "Pallepalem" "Harijanawada" "Harijanawada" ...
 $ habitation_id              : chr [1:74] "0000015089" "0000015094" "0000015095" "0000015105" ...
 $ households                 : num [1:74] 111 275 317 64 3 327 599 106 115 365 ...
 $ house_connection_by_pws    : num [1:74] 50 13 113 64 3 239 396 50 99 82 ...
 $ ispws                      : chr [1:74] "Yes" "Yes" "Yes" "Yes" ...
 $ gen_current_pop            : num [1:74] 0 994 651 232 4 ...
 $ sc_current_pop             : num [1:74] 397 13 477 0 9 654 201 5 194 0 ...
 $ st_current_pop             : num [1:74] 0 0 0 0 0 2 33 0 0 0 ...
 $ total_current_pop          : num [1:74] 397 1007 1128 232 13 ...
 $ water_quality_contamination: logi [1:74] NA NA NA NA NA NA ...
 $ update_date                : Date[1:74], format: "2024-06-07" "2024-06-07" ...
 - attr(*, "spec")=
  .. cols(
  ..   state_name = col_character(),
  ..   district_name = col_character(),
  ..   block_name = col_character(),
  ..   panchayat_name = col_character(),
  ..   village_name = col_character(),
  ..   habitation_name = col_character(),
  ..   habitation_id = col_character(),
  ..   households = col_double(),
  ..   house_connection_by_pws = col_double(),
  ..   ispws = col_character(),
  ..   gen_current_pop = col_double(),
  ..   sc_current_pop = col_double(),
  ..   st_current_pop = col_double(),
  ..   total_current_pop = col_double(),
  ..   water_quality_contamination = col_logical(),
  ..   update_date = col_date(format = "")
  .. )
 - attr(*, "problems")=<externalptr>

summary of the dataset

  state_name        district_name       block_name        panchayat_name    
 Length:74          Length:74          Length:74          Length:74         
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
 village_name       habitation_name    habitation_id        households   
 Length:74          Length:74          Length:74          Min.   :  3.0  
 Class :character   Class :character   Class :character   1st Qu.:111.5  
 Mode  :character   Mode  :character   Mode  :character   Median :252.0  
                                                          Mean   :269.5  
                                                          3rd Qu.:373.5  
                                                          Max.   :847.0  
 house_connection_by_pws    ispws           gen_current_pop  sc_current_pop  
 Min.   :  1.0           Length:74          Min.   :   0.0   Min.   :  0.00  
 1st Qu.: 26.0           Class :character   1st Qu.: 314.5   1st Qu.:  0.00  
 Median : 88.5           Mode  :character   Median : 835.0   Median :  5.00  
 Mean   :139.1                              Mean   : 968.3   Mean   : 83.45  
 3rd Qu.:188.5                              3rd Qu.:1353.2   3rd Qu.:116.00  
 Max.   :675.0                              Max.   :6785.0   Max.   :654.00  
 st_current_pop    total_current_pop water_quality_contamination
 Min.   :  0.000   Min.   :  13.0    Mode:logical               
 1st Qu.:  0.000   1st Qu.: 402.0    NA's:74                    
 Median :  0.000   Median : 923.5                               
 Mean   :  7.149   Mean   :1058.9                               
 3rd Qu.:  0.000   3rd Qu.:1377.0                               
 Max.   :144.000   Max.   :6929.0                               
  update_date        
 Min.   :2024-06-07  
 1st Qu.:2024-06-07  
 Median :2024-06-07  
 Mean   :2024-06-07  
 3rd Qu.:2024-06-07  
 Max.   :2024-06-07

Head of DataSet

# A tibble: 6 × 16
  state_name     district_name block_name panchayat_name village_name
  <chr>          <chr>         <chr>      <chr>          <chr>       
1 Andhra Pradesh Krishna       Kalidindi  Konduru        Konduru     
2 Andhra Pradesh Krishna       Kalidindi  Chinatadinada  Tadinada    
3 Andhra Pradesh Krishna       Kalidindi  Chinatadinada  Tadinada    
4 Andhra Pradesh Krishna       Kalidindi  Venkatapuram   Venkatapuram
5 Andhra Pradesh Krishna       Kalidindi  Korukollu      Korukollu   
6 Andhra Pradesh Krishna       Kalidindi  Lodida Lanka   Kalidindi   
# ℹ 11 more variables: habitation_name <chr>, habitation_id <chr>,
#   households <dbl>, house_connection_by_pws <dbl>, ispws <chr>,
#   gen_current_pop <dbl>, sc_current_pop <dbl>, st_current_pop <dbl>,
#   total_current_pop <dbl>, water_quality_contamination <lgl>,
#   update_date <date>

Univariate Analysis {.tabset}

Histogram for Households

Histogram for current population generation

Barplot

Bivariate Analysis {.tabset}

coorelation

                        households house_connection_by_pws gen_current_pop
households               1.0000000              0.83856282       0.6786421
house_connection_by_pws  0.8385628              1.00000000       0.6142656
gen_current_pop          0.6786421              0.61426562       1.0000000
sc_current_pop           0.1042510              0.07417009      -0.1165441
st_current_pop           0.3451441              0.33761499       0.7850502
total_current_pop        0.6937320              0.62561272       0.9899870
                        sc_current_pop st_current_pop total_current_pop
households                  0.10425101     0.34514412        0.69373195
house_connection_by_pws     0.07417009     0.33761499        0.62561272
gen_current_pop            -0.11654414     0.78505023        0.98998697
sc_current_pop              1.00000000    -0.09786525        0.02418007
st_current_pop             -0.09786525     1.00000000        0.78463029
total_current_pop           0.02418007     0.78463029        1.00000000

PCA

Muiltivariate Analysis {.tabset}

hierarchical clustering

Plot MDS results

---
title: "EDA for Households"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    theme: simplex
    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(DT)
library(dplyr)
library(readr)
data <- read_csv("home.csv")
```

## Dataset Description {.tabset .active} {#dataset-description}

### View of dataset

```{r}
str(data)
datatable(data,extensions = 'Buttons',options = list(dom='Bfrtip',Buttons=c('copy','csv','print','pdf')))
```

### summary of the dataset

```{r}
summary(data)
```

### Head of DataSet

```{r}
head(data)
```

## Univariate Analysis {.tabset} {#univariate-analysis}

### Histogram for Households

```{r}
hist(data$households, main="Distribution of Households", xlab="Households", col="lightblue", border="black")
```

### Histogram for current population generation

```{r}
hist(data$gen_current_pop, main="Distribution of current population generation ", xlab="Households", col="red", border="black")
```

### Barplot

```{r}
boxplot(data$households, main="Boxplot of Households", ylab="Households")
```

## Bivariate Analysis {.tabset} {#bivariate-analysis}

### coorelation

```{r}
correlation_matrix <- cor(data[, sapply(data, is.numeric)], use="complete.obs")
print(correlation_matrix)
```

### PCA

```{r}
pca <- prcomp(data[, sapply(data, is.numeric)], center = TRUE, scale. = TRUE)

biplot(pca, main = "PCA Biplot")
dist_matrix <- dist(data[, sapply(data, is.numeric)])
```

## Muiltivariate Analysis {.tabset} {#multivariate-analysis}

### hierarchical clustering

```{r}
hc <- hclust(dist_matrix)

# Plot dendrogram
plot(hc, main = "Hierarchical Clustering Dendrogram")

mds <- cmdscale(dist_matrix, k = 2)  # k is the number of dimensions
```

### Plot MDS results

```{r}
# Perform MDS
mds <- cmdscale(dist_matrix, k = 2) # k is the number of dimensions

# Plot MDS results
plot(mds, type = "n")
text(mds[, 1], mds[, 2], labels = 1:nrow(data), cex = 0.7)  
title("MDS Plot")

```