---
title: "Legislative Effectiveness for Lawmaking"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(maps)
load("~/BUAN514.RData")
df_law <- week8_LES
colnames(df_law)[5]<-"Year"
colnames(df_law)[c(6,53)]<-c("State","LES")
colnames(df_law)[25]<-"Number_terms"
```

House of Representatives Demographics {data-icon="fa-archive"}
=======================================================================

Row
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### Breakdown of Party Majority

```{r}
df_part<-df_law[c(5,8)]
colnames(df_part)<-c("Year","Party")

party<-rename(count(df_part,Year,Party),Freq=n)
Rep<-party[seq(1,nrow(party),3),]
Dem<-party[seq(2,nrow(party),3),]
thi<-party[seq(3,nrow(party),3),]

df_part1<-as.data.frame(cbind(Rep[,1],Rep[,3],Dem[,3],thi[,3]))
colnames(df_part1)<-c("Year","Republican","Democrat","Third Party")

df_new <- df_part1 %>% pivot_longer(cols=Republican:`Third Party`,
                                    names_to="Party",
                                    values_to="Freq")

col1<-c(Republican="#d92534",Democrat="#637aa6",`Third Party`="#d9ae30")

p1<-ggplot(df_new,aes(x=Year,y=Freq,group=Party,color=Party))+
  geom_line()+
  scale_color_manual(values=col1)+
  labs(color="Political Party",
       y="Number of Seats Held")

ggplotly(p1)
```


Row {.tabset}
-----------------------------------------------------------------------

### Percentage of Votes to Win Congressional Seat

```{r}
p2<-ggplot(df_law,aes(x=Percent.vote.received.to.enter.this.Congress))+
  geom_bar(fill="#637AA6")+
  labs(x="Percent of Votes Won",y="Count")

ggplotly(p2)
```

### Distribution of Terms Served

```{r}
p3<-ggplot(df_law,aes(x=Number_terms))+
  geom_bar(fill="#566D8C")+
  labs(x="Number of Terms",y="Count")

ggplotly(p3)
```


Legislative Effectiveness Score {data-icon="fa-archive"}
=======================================================================

Row
-----------------------------------------------------------------------


### Average bills passed per member

```{r}
max_bills <- round(mean(df_law$Total.bills.became.law),4)
valueBox(max_bills, icon ="fa-pencil")
```

### Average Terms Served

```{r}
df_na<-na.omit(df_law$Number_terms)
mean_term <- round(mean(df_na),4)
valueBox(mean_term,icon = "fa-eye")
```

### Average LES per year

```{r}
LES_mean <- round(mean(df_law$LES),4)
valueBox(LES_mean, icon = "fa-bell")
```

Row
-----------------------------------------------------------------------

### Bills the Became Laws by State

```{r}
df_state<-df_law[c(6,52)]
colnames(df_state)<-c("State","Laws")
df_state$Laws<- as.integer(df_state$Laws)

df_stfact<-data.frame(tapply(df_state$Laws,df_state$State,FUN=sum))
states <- rownames(df_stfact)
rownames(df_stfact) <- NULL
df_stfact<-cbind(states,df_stfact)
colnames(df_stfact)<-c("State","Laws")

df_stfact1<-df_stfact[-c(4,9,11,14,43,51),]
row.names(df_stfact1) <- NULL

state_abbs <- tibble(state = str_to_lower(state.name), abb = state.abb)

tbl_m <- left_join(df_stfact1, state_abbs, by = c("State" = "abb"))
tbl_m<-tbl_m[,-1]
tbl_m$region <- tolower(tbl_m$state)

state_map<-map_data("state")
map.df <- merge(state_map,tbl_m, by="region", all.x=T)
map.df <- map.df[order(map.df$order),]

p4<-ggplot(map.df,aes(x=long,y=lat,group=group,
                      text = paste("State:", region,"Laws:", Laws)))+
  geom_polygon(aes(fill=Laws))+
  geom_path()+
  scale_fill_gradientn(colours=rev(heat.colors(10)),na.value="grey90")+
  coord_map()+
  labs(x=" ",y=" ")

ggplotly(p4,tooltip = "text")
```

### LES for the States with the Most and Least Laws Passed

```{r}
df_ord<-df_stfact1%>%
  arrange(desc(Laws))

df_sub_up<-subset(df_law,State=="CA" | State=="NY" | State=="TX" |
               State=="IL" | State=="FL")
df_sub_up['val']="Most"

df_sub_dn<-subset(df_law,State=="DE" | State=="SD" | State=="ME" |
               State=="ND" | State=="VT")
df_sub_dn['val']="Least"

df_sub<-rbind(df_sub_up,df_sub_dn)

p5<-ggplot(df_sub, aes(factor(State), LES,col=val)) +
  geom_boxplot()+
  labs(x="State",y="LES",color="Laws Passed")+
  scale_color_manual(values=c("#f29f80","#566d8c"))+
  coord_flip()

 ggplotly(p5)
```


Row
-----------------------------------------------------------------------

### Bills Passed from 1973 to 2007

```{r}
yr_les<- df_law%>%
  group_by(Year)%>%
  summarise(Total.bills.became.law=sum(Total.bills.became.law))%>%
  `colnames<-`(c("Year","Bills Passed"))

p6<-ggplot(yr_les,aes(x=Year,y=`Bills Passed`))+
  geom_line(color="#566d8c")+
  labs(x="Year",y="Bills Passed")

ggplotly(p6)
```

### Histogram of LES

```{r}
p7<-ggplot(df_law,aes(LES))+
  geom_histogram(binwidth=0.25,fill="#637aa6")+
  labs(x="LES",y="Count")

ggplotly(p7)
```