code.Rmd

---
title: "Bihar Assembly Election Analysis"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    vertical_layout: fill
---

```{r setup, include=FALSE}
packages <- c("plotly","readxl","flexdashboard","dplyr","ggplot2","sf","tmap","readr","DT","ggridges","ggthemes","knitr","rAmCharts")
if(!require(packages)){
    install.packages(packages)
}

library(flexdashboard)
library(ggplot2)
library(plotly)
library(dplyr)
#library(magrittr)
#library(cartography)
library(sf)
#library(haven)

#loading the Bihar Assambly Election Data


#read_csv("bihar-elec-data.csv")
library(readxl)
dataset<- read_excel("data/src/Bihar Assembly Election 2020 cleaned.xls")
#str(dataset)
#head(dataset)
#View(dataset)

#Dataset of all the candidates
dataset2 <-dataset %>% filter(dataset$`STATE/UT NAME`==dataset[[1]][1])
#str(dataset2)
#head(dataset2)
#View(dataset2)

#Dataset without NOTA
dataset3 <- dataset2 %>% filter(dataset2$PARTY !="NOTA")
#str(dataset3)
#head(dataset3)
#View(dataset3)

#Dataset without Independent candidate and NOTA
dataset4 <- dataset3 %>% filter(dataset3$PARTY !="IND")
#str(dataset4)
#head(dataset4)
#View(dataset4)

#Winning Candidates data
win_data_temp <- dataset3 %>% group_by(`AC NO.`) %>% filter(TOTAL==max(TOTAL))
colnames(win_data_temp)[colnames(win_data_temp)=="AC NO."]<-"AC_NO"
colnames(win_data_temp)[colnames(win_data_temp)=="CANDIDATE NAME"]<-"CANDIDATE_NAME"

```
Winning Candidates
==============================================================================

Column {data-width="1000"}
------------------------------------------------------------------------------

### **Winning Candidates data**

```{r,echo=F,out.width="100%",out.height="100%"}
#Winning Candidates data
win_data_temp <- dataset3 %>% group_by(`AC NO.`) %>% filter(TOTAL==max(TOTAL))
colnames(win_data_temp)[colnames(win_data_temp)=="AC NO."]<-"AC_NO"
colnames(win_data_temp)[colnames(win_data_temp)=="CANDIDATE NAME"]<-"CANDIDATE_NAME"
win_data_temp = as.data.frame(win_data_temp)
DT::datatable(win_data_temp,options = list(pageLength=20, scrollY=F),colnames=c("GENDER"="SEX", "STATE"="STATE/UT NAME"))
```


Interactive Maps
===============================================================================
Column {data-width="1000"}
------------------------------------------------------------------------------
```{r, echo=FALSE}

shape_data<-read_sf("Shape file/INDIA_AC.shp")
Bihar_shape_data<- shape_data %>% filter(shape_data$ST_CODE==10)
#View(Bihar_shape_data)

mapdata<-merge(Bihar_shape_data, win_data_temp, by="AC_NO")
#View(mapdata)
#names(mapdata)
#mapdata=View(mapdata)
#write.csv2(mapdata,"d.csv")
```

```{r, echo=FALSE}
#library(tmaptools)
pal=c("#05980D","#EE7B06","#CA4741","#EA3606","#E50505","#E72415","#AA6D6D",
      "#E5E5E5","#BC06C5","#8456C5","#737373","#0B461E","#1A1A1A")

#Map code----------------------------------------------------------------
#library
library(tmap)

#Intering Interective mode------------------------------------------------
tmap_mode("view")

#District Layer
Bihar_assembly_map<-tm_shape(mapdata)+
  #tm_fill(col =  "PARTY", alpha=1, palette = "Reds", legend.show = F)+
  tm_borders(col="black",lwd=1,alpha = 1)+
  tm_fill(id="AC NAME", col =  "PARTY", alpha=1, palette = pal, legend.show = T,
          popup.vars = c("AC NAME", "CANDIDATE_NAME", "SEX","AGE", "CATEGORY",
                         "PARTY", "% VOTES POLLED" ))+tm_text("AC NAME", size=0.6)+

  #tm_layout(style="cat", aes.palette = "Reds", aes.color = "Reds" )+
  #other customization
  #tm_borders(col="white",lwd=0.2)+
  #tm_layout(frame = T, outer.margins = 0, inner.margins = 0)+
  #tm_style("cobalt")+
  tm_view(set.view = 8, alpha = 0.9, set.zoom.limits=c(7,11))+
  tm_legend(legend.outside=T,legend.outside.position=c("right","top"),title="Bihar Assembly Election (Wining Candidates)" )

```

```{r interective-map, echo=FALSE, message=FALSE, warning=FALSE,out.width="100%", out.height="100%"}
Bihar_assembly_map
```



Data Summary
==============================================================================

Columns {data-width="300"}
------------------------------------------------------------------------------

### **Number of Candidates Fielded by Canstituency**

```{r}
#Number of Candidates Fielded by Canstituency
AC_cand = table(dataset3$`AC NAME`)
AC_cand = as.data.frame(AC_cand)
DT::datatable(AC_cand,options = list(pageLength=20, scrollY=F), colnames = c("ASSEMBLY CONSTITUENCY" = "Var1","CANDIDATES"="Freq"))
```

Columns {data-width="300"}
------------------------------------------------------------------------------

### **Number Of Candidates Fielded Party wise**

```{r}
#Number Of Candidates Fielded Party wise
DT::datatable(as.data.frame(table(dataset3$PARTY)),options = list( scrollY=F),colnames = c("PARTY" = "Var1","CANDIDATES"="Freq"))
```

### **Winning Candidates party wise**

```{r}
#Winning Candidates party wise
DT::datatable(as.data.frame(table(win_data_temp$PARTY)),options = list(scrollY=F),colnames = c("PARTY" = "Var1","CANDIDATES"="Freq"))
```

Columns {data-width="300"}
------------------------------------------------------------------------------

### **Female winning candidates**

```{r}
#Gender wie and partywise winning candidates
#FEMALES
win_data_temp_w <- win_data_temp %>% group_by(PARTY) %>% filter(SEX=="FEMALE")
DT::datatable(as.data.frame(table(win_data_temp_w$PARTY)),options = list(scrollY=F),colnames = c("PARTY" = "Var1","CANDIDATES"="Freq"))
```

### **Male winning candidates**

```{r}
#MALES
win_data_temp_m <- win_data_temp %>% group_by(PARTY) %>% filter(SEX=="MALE")
DT::datatable(as.data.frame(table(win_data_temp_m$PARTY)),options = list( scrollY=F),colnames = c("PARTY" = "Var1","CANDIDATES"="Freq"))

```

Columns {data-width="100"}
------------------------------------------------------------------------------

### **Winning candidates Gender Wise**

```{r}
#Winning candidates Gender Wise
knitr::kable(as.data.frame(table(win_data_temp$SEX)),col.names = c("Gender","Candidates"))
```

### **Winning Candidates Catagory wise**

```{r}
#Winning Candidates Catagory wise
knitr::kable(as.data.frame(table(win_data_temp$CATEGORY)),col.names = c("Catogary","Candidates"))
```

Plots
=============================================================================

Column {data-width="333"}
---------------------------------------------------------------------------
### **Catagory wise plot**

```{r}
#Catagory wise plot
p1 <- ggplot(dataset3, aes(x = CATEGORY, y = ..count../sum(..count..) )) +
  geom_bar()+
  labs(x="Catagory",title="Bihar Assembly Election Candidates By Catagory.",y="Percentage Of Candidates")+
  scale_y_continuous(labels=scales::percent)

ggplotly(p1)

```

### **Gender Wise plot**

```{r}
#Gender Wise plot
p2 <- ggplot(dataset3, aes(x = SEX,y = ..count../sum(..count..), fill=CATEGORY))+
  geom_bar(color="brown")+
  labs(x="Gender",title="Bihar Assembly Election Candidates By Gender.",y="Percentage Of Candidates")

ggplotly(p2)
```

Column {data-width="333"}
-----------------------------------------------------------------------------
### **Age distribution Gender wise**

```{r}
#Age distribution Gender wise
p3 <- ggplot(dataset3, aes(x = SEX, y = AGE) )+
  geom_boxplot()

ggplotly(p3)

```

### **Age distribution category wise**

```{r}
#Age distribution category wise
p4 <- ggplot(dataset3, aes(x=CATEGORY, y=AGE))+
  geom_boxplot()

ggplotly(p4)

```

Column {data-width="333"}
------------------------------------------------------------------------------

### **Age distribution of big parties (Boxplot)**

```{r}
#Age distribution of big parties (Boxplot)
Big_party_data <- filter(dataset3, dataset3$PARTY %in% c("BJP","JD(U)","RJD","INC","LJP","IND"))
p5 <- ggplot(Big_party_data,aes(x=PARTY,y=AGE))+ 
  geom_boxplot()
ggplotly(p5)
```

### **Age Distribution Of Big Parties (ridgesdensity plot)**

```{r, echo=FALSE,include=T,message=FALSE,error=F,warning=F}
#Age Distribution Of Big Parties (ridgesdensity plot)
library(ggridges)
p6 <- ggplot(Big_party_data,aes(x=AGE,y=PARTY,fill=PARTY))+ 
  geom_density_ridges()+
  theme_ridges()
p6
```

Static maps
==============================================================================

Columns {data-width="500"}
----------------------------------------------------------------------------
### **Male and Female Candidates**

```{r}
map_a <-ggplot(mapdata) +
 aes(fill = SEX) +
 geom_sf(shape = "circle", size = 0.5) +
 scale_fill_brewer(palette = "RdGy", 
 direction = 1) +
 labs(title = "Bihar Assembly Election Result", subtitle = "Assembly Election 2020", 
 caption = "data source:https://eci.gov.in/files/file/12787-bihar-legislative-election-2020", fill = "Male and Female Candidates") +
 ggthemes::theme_economist() +
 theme(plot.caption = element_text(face = "italic"))

map_a
```

Columns {data-width="500"}
----------------------------------------------------------------------------

### **Winning Candidates Party Wise**

```{r}
map_b <- ggplot(mapdata) +
 aes(fill = PARTY) +
 geom_sf(shape = "circle", size = 0.5) +
 scale_fill_manual(values = c(AIMIM = "#05980D", 
BJP = "#EE7B06", BSP = "#CA4741", CPI = "#EA3606", `CPI(ML)(L)` = "#E50505", CPIM = "#E72415", HAMS = "#AA6D6D", 
INC = "#E5E5E5", IND = "#BC06C5", `JD(U)` = "#8456C5", LJP = "#737373", RJD = "#0B461E", VSIP = "#1A1A1A"
)) +
 labs(title = "Bihar Assembly Election Result", subtitle = "Winning Candidates Party Wise", caption = "data source:https://eci.gov.in/files/file/12787-bihar-legislative-election-2020", 
 fill = "Party") +
 theme_minimal() +
 theme(plot.caption = element_text(face = "italic"))

map_b
```

Success Comparision Plot
============================================================================

Columns {data-width="500"}
--------------------------------------------------------------------------

```{r}
#setup chunk
#candidates won partywise without independent candidates
win_data_temp_tbl <-as.data.frame(table(win_data_temp$PARTY)) %>% filter(Var1 !="IND")
colnames(win_data_temp_tbl)<-c("PARTY","CANDIDATES WON")
win_part <- win_data_temp_tbl$PARTY


```

### **Winning Candidate Partywise**

```{r}

bar_a <- ggplot(win_data_temp_tbl) +
  aes(x = PARTY, weight = `CANDIDATES WON`) +
  geom_bar(fill = "#370E21") +
  labs(
    x = "Party",
    y = "No. Of Winning Candidate",
    title = "Winning Candidates",
    subtitle = "Winning Candidates Partywise inclusing Independent Candidates",
    caption = "Source: https://eci.gov.in/files/file/12787-bihar-legislative-election-2020/"
  ) +
  ggthemes::theme_wsj()
ggplotly(bar_a)
```



### **Candidate Fielded Partywise**

```{r}
#candidates fielded partywise without independent candidates
dataset3 %>% filter(PARTY %in% win_part )%>% filter(PARTY != "IND")  -> Can_field_partywise
Can_field_partywise=as.data.frame(table(Can_field_partywise$PARTY) )
colnames(Can_field_partywise) <- c("PARTY","CANDIDATES CONTESTED")

bar_b <- ggplot(Can_field_partywise) +
  aes(x = PARTY, weight = `CANDIDATES CONTESTED`) +
  geom_bar(fill = "#370E21") +
  labs(
    x = "Party",
    y = "Number Of Candidate Fielded",
    title = "Number Of Candidate Fielded Partywise",
    subtitle = "Fielded Candidates Partywise inclusing Independent Candidates",
    caption = "Source: https://eci.gov.in/files/file/12787-bihar-legislative-election-2020/"
  ) +
  ggthemes::theme_wsj()
ggplotly(bar_b)
```

Columns {data-width="500"}
-----------------------------------------------------------------------------

### ***Candidates Won Partywise (Pie chart)***
```{r,out.width="100%", out.height="100%"}
library(rAmCharts)
amPieChart(valueField = 'CANDIDATES WON', titleField = 'PARTY',
           theme = "light", dataProvider = win_data_temp_tbl) %>%
  setLegend(position = "bottom", useMarkerColorForLabels = TRUE)%>%
   addListener(name = "clickSlice", expression = paste("function (event) {",
                                                      "var obj = event.dataItem;",
                                                      "alert('Candidates Won: ' + obj.value);",
                                                      "}"))%>%
  plot()
```


### ***Candidates Fielded Partywise (Pie chart)***
```{r }
amPieChart(valueField = 'CANDIDATES CONTESTED', titleField = 'PARTY',
           theme = "light", dataProvider = Can_field_partywise) %>%
  setLegend(position = "bottom", useMarkerColorForLabels = TRUE)%>%
  addListener(name = "pullOutSlice", expression = paste("function (event) {",
                                                      "var obj = event.dataItem;",
                                                      "alert('Candidates Contested: ' + obj.value);",
                                                      "}"))%>%
  plot()
```

VOTE SHARE PLOT
============================================================================

Columns {data-width="1000"}
--------------------------------------------------------------------------

### ***Party Performance***
```{r}
Perform_data = cbind(Can_field_partywise,win_data_temp_tbl,by="PARTY")
amSerialChart(dataProvider = Perform_data, categoryField = 'PARTY',
              creditsPosition =  "bottom-right",theme = "light")%>%
  addGraph(valueField = "CANDIDATES WON", title = "Winning candidates")%>%
  addGraph(valueField = "CANDIDATES CONTESTED", title = "Candidates Contested")%>%
  setLegend(position = "bottom", useMarkerColorForLabels = TRUE)
  # we use the setter 'add' since the property titles is an array of 'Title' objects"

```

Other Plot
============================================================================

Columns {data-width="1000"}
--------------------------------------------------------------------------

### ***Layered barplot***
```{r}
amBarplot(x = "PARTY", y = c("CANDIDATES CONTESTED","CANDIDATES WON"), data = Perform_data, horiz = F,show_values = T,layered = T)

```