---
title: "ANLY512-Final Project"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
    storyboard: true
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(ggplot2)
library(tidyverse)
library(readxl)
library(dplyr)
library(xts)
library(zoo)
```

Column {data-width=650}
-----------------------------------------------------------------------
### Executive Summary
I collected my consume historical data from my credit card in 2018, to analyze my consume behavior across location and timeline in the past year. 

### What is the monthly expending in 2018 

```{r}
data1<-read_xlsx("/Users/jwu/Documents/ANLY512-Final Project-1.xlsx")
Month<- ggplot(data1, aes(x = data1$Month, y=data1$Amount)) + geom_bar(stat = "identity", fill = "Orange")+scale_x_discrete(limits=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")) +labs(title = "Monthly Expending", x = "Month", y = "Amount") +theme_minimal()
Month

```

### Which category that I spend most in 2018? 

```{r}
data2<-read_xlsx( "/Users/jwu/Documents/ANLY512-Final Project-2.xlsx")
category <- ggplot(data2, aes(x=data2$Category, y=data2$Amounts, fill=Category)) + geom_bar(stat = "identity", fill = "LightBlue")+labs(title = "Spending across Category", x = "Category", y = "Amount") + coord_flip()
category

```

### Which Location that my card was charged mostly in 2018?

```{r}
data3<-read_xlsx("/Users/jwu/Documents/ANLY512-Final Project-3.xlsx")
dotchart(data3$Amount,labels = data3$Location, cex=1, color = "red", main = "Expenditures by locations")
```

-----------------------------------------------------------------------

### What Category consume distribution during the first half year in 2018

```{r}
data4<-read_xlsx("/Users/jwu/Documents/ANLY512-Final Project-4.xlsx")
month1 <- ggplot(data4, aes(x=data4$Month, y=data4$Amount)) +   
  geom_bar(aes(fill = data4$Category),  stat="identity") +
  labs(title = "Monthly Expenditure by Category in Jan-June, 2018", x = "Month", y = "Amount") +
  scale_x_continuous(breaks=c(1,2,3,4,5,6)) +
  theme(legend.position = "Right") +
  scale_fill_discrete(name="Category") +
  theme(legend.position = "right")

month1
```
 
### What Category consume distribution during the second half year in 2018

```{r}
data5<-read_xlsx("/Users/jwu/Documents/ANLY512-Final Project-5.xlsx")
month2 <- ggplot(data5, aes(x=data5$Month, y=data5$Amount)) +   
  geom_bar(aes(fill = data5$Category),  stat="identity") +
  labs(title = "Monthly Expenditure by Category in Jul-Dec, 2018", x = "Month", y = "Amount") +
  scale_x_continuous(breaks=c(7,8,9,10,11,12)) +
  theme(legend.position = "Right") +
  scale_fill_discrete(name="Category") +
  theme(legend.position = "right")

month2
```