---
title: "Final Projetc ANLY512"
author: Nikhil Chate
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    storyboard: true
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(lubridate)
library(tidyverse)
library(plotly)

CC<- read.csv("Chase5036_Activity_20180818.CSV")
CC$Trans_Date<- mdy(CC$Trans.Date)
CC$Post_Date<- mdy(CC$Post.Date)

```


###Which are most frequent type of charges on the credit card?
```{r}
ggplot(data=CC, mapping=aes(x=Trans_Date, y=Amount,color=Type))+
  geom_point(size=rel(2))+
  xlab("Transaction Date")+
  ylab("Amount in Dollers")+
  theme(plot.background = element_rect(fill='lightgreen'),panel.background = element_rect(fill = "white",colour = "blue",size = 2, linetype = "solid"),
        panel.grid = element_blank(), plot.title = element_text(size = rel(2)),
        axis.text = element_text(colour = "blue"), axis.title.y = element_text(size = rel(1.5)),axis.title.x = element_text(size = rel(1.5)),
        legend.position = "bottom",legend.key = element_rect(fill = "white", colour = "black"))+
  labs(title="Summary of type of transaction", caption="negative values are sales")



```




###Which is the most frequent charge on Credit Card? 

```{r}
sale<- filter(CC,Type=="Sale")

sale1<- transform(sale, freq.loc = ave(seq(nrow(sale)), Description, FUN=length))


ggplot(filter(sale1,freq.loc >=5),mapping=aes(x=Description,fill=Description))+
  geom_bar(show.legend = FALSE,width = 1)+
  labs(x = NULL, y = NULL)+
  theme(plot.background = element_rect(fill='lightgreen'),panel.background = element_rect(fill = "white",colour = "blue",size = 2, linetype = "solid"),
        panel.grid = element_blank(), plot.title = element_text(size = rel(2)),
        axis.text = element_text(colour = "blue"),aspect.ratio = 1)+
       labs(title="Top 5 Categories of Sale by count")+
       coord_polar()

```


###Which is the highest charge on Credit Card?

```{r}

doller<- sale1 %>% group_by(Description,freq.loc) %>%
summarise(Amount=sum(Amount)) %>%
  arrange((Amount))
doller = doller[1:5,]


p <- doller %>%
  group_by(Description,Amount) %>%
  summarise(Doller=sum((-1)*Amount)) %>%
  plot_ly(labels = ~Description, values = ~Doller) %>%
  add_pie(hole = 0.6) %>%
  layout(title = "Top Charges by doller Amount",  showlegend = F,
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

p


```




###Comparision of highest charge and most frequent charge(Doller)?

```{r}
doller = doller[1:5,]
Count =filter(sale1,freq.loc >=5)
Count =Count[1:5,]


ggplot(data = doller) + 
  geom_point(mapping = aes(x = Amount, y =freq.loc), color="red", size=4) + 
  xlab("Amount in Dollers")+
  ylab("Charge Frequency")+
  theme(panel.background = element_rect(fill = "lightblue"), axis.text = element_text(colour = "blue"),
        axis.title.y = element_text(size = rel(1.5)),axis.title.x = element_text(size = rel(1.5)))+
  labs(title="Doller vs frequency of Top 5 Doller",caption="negative values are sales")+
  facet_wrap(~Description , nrow = 2)
```

###Comparision of highest charge and most frequent charge(Count)?
```{r}
ggplot(data = Count) + 
  xlab("Amount in Dollers")+
  ylab("Charge Frequency")+
  geom_point(mapping = aes(x = Amount, y =freq.loc), color="red", size=4) + 
  theme(panel.background = element_rect(fill = "lightblue"), axis.text = element_text(colour = "blue"),
        axis.title.y = element_text(size = rel(1.5)),axis.title.x = element_text(size = rel(1.5)))+
  labs(title="Doller vs frequency of Top 5 Count",caption="negative values are sales")+
  facet_wrap(~Description , nrow = 2)


```