---
title: "Anly512_Quantified_Self"
author: "JIANWEI LI"
date: "4/21/2020"

output:
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source_code: embed
    vertical_layout: fill
---

```{r setup,include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(flexdashboard)
library(knitr)
library(readxl)
library(dplyr)
library(tidyverse)
library(ggplot2)

```


### Project Description

The Quantified Self movement grew from the popularity and growth of the internet of things, the mass collection of personal information, and mobile technologies (primarily wearable computing). This final class project uses a collection of 2 years of data on spending and payments captured by discover credit card.

The scope of thie project is visualize my personal creditcard expense using the flexdashboard,ggplots and other visualization package in RStudio. 

To begin with, I would like to list five questions that I will use the data visualization to depict and unfold my analysis 

1) What is the total spending by month in for the past year?
2) What is the top 10 merchant that I vist the most for the past year?
3) What is the top 
10 merchat that I spent the most for the past year?
4) In which dates I spend the most in past year ?
5) What is the top 10 Merchant Location I spend the most by month for the past year?
6) What is the total expense per week of day for the past year?

### Data Preprocessing

Data Description:

- Date: Transaction Posted Date
- Reference_Number: Unique Transaction reference Number
- Payee: Merchant's Name
- Address: Merchant's Location
- Amount: Payment 

```{r input,echo=FALSE}
setwd('C:/Users/JIANWEI LI/Documents/Rstudio/HU/512/FinalProject/')

data <-
    list.files(path = "./data/",
               pattern = "*.csv", 
               full.names = T) %>% 
    map_df(~read_csv(., col_types = cols(.default = "c"))) 


data$`Posted Date`<-as.Date(data$`Posted Date`,"%m/%d/%Y")
data$Amount<- as.numeric(data$Amount)
data$Amount = -data$Amount
data<-data[data$Amount>0,]

# sort by Posted Date
data<-data[order(data$`Posted Date`),]
colnames(data)[1]<-'Date'
colnames(data)[2]<-'Reference_Number'

kable(data[80:100,],caption = 'Sample data')

```


### Q1:What is the total spending by month in for the past year?
```{r, echo=FALSE}

library(lubridate)
library(zoo)
data$month<-month(as.POSIXlt(data$Date,format = '%d/%m/%Y'))
data$year<- year(as.POSIXlt(data$Date,format = '%d/%m/%Y'))



monthlyAmount<-aggregate(Amount~year+month,data,sum)

monthlyAmount<- monthlyAmount[
  with(monthlyAmount,order(year,month)),
]



monthlyAmount<-within(monthlyAmount, Date <- sprintf("%d-%02d", year, month))

monthlyAmount<- monthlyAmount[2:13,]


p<- ggplot(monthlyAmount, aes(x = month, y=Amount)) + 
  geom_bar(stat = "identity", fill = "Blue")+
  scale_x_discrete(limits=monthlyAmount$Date) +
  labs(title = "Spending per Month", x = "Month", y = "Total Amount") +
  theme_minimal()
p+theme(axis.text.x=element_text(angle=90))


```

***

From this graph above, I find that I will pay less during the spring. I might spend a lot during the pre holiday such as November, Februry. My expense spike on August,since I just move my apartment during that month.

### Q2:What is the top 10 merchant that I vist the most for the past year?
```{r, echo=FALSE}


q2<-aggregate(Amount~Payee,data,FUN = length)
q2<-q2[order(-q2$Amount),]
q2<-q2[c(1:10),]

ggplot(q2, mapping = aes(x = reorder(Payee, Amount), Amount)) + 
  geom_bar(stat = "identity")+
  labs(title = "Top 10 Visit Merchant", x = "Merchant", y = "Total Amount") +
  coord_flip()+
  theme_minimal()


```

***

The top visit is Costco, where I shop for most of my grocery.


### Q3:What is the top 10 merchat that I spent the most for the past year?
```{r, echo=FALSE}

q3<-aggregate(Amount~Payee,data,FUN = sum)
q3<-q3[order(-q3$Amount),]
q3<-q3[c(1:10),]

ggplot(q3, mapping = aes(x = reorder(Payee, Amount), Amount)) + 
  geom_bar(stat = "identity")+
  labs(title = "Top 10 Merchant by Expense", x = "Merchant", y = "Total Amount") +
  coord_flip()+
  theme_minimal()

```

***

The top expense is also Costco. But, we can also see the car insurance and other one time purchase on my list.

### Q4:In which dates I spend the most in past year ?
```{r, echo=FALSE}

q4<-aggregate(Amount~Date,data,FUN = sum)
q4<-q4[order(-q4$Amount),]
q4<-q4[c(1:20),]

ggplot(q4, mapping = aes(x = reorder(Date, Amount), Amount)) + 
  geom_bar(stat = "identity")+
  labs(title = "Top 20 Date by Expense", x = "Date", y = "Total Amount") +
  coord_flip()+
  theme_minimal()

```

***

In this graph, I can zoom in to see the specific date, I make high amount expense. For example, on 2020-02-16 I purchase a new car insurance on Sunday, but the cost is posted on 2020-02-17. 

### Q5:What is the top 10 Merchant Location I spend the most by month for the past year?
```{r, echo=FALSE}

q5<-aggregate(Amount~Address,data,FUN = length)
q5<-q5[order(-q5$Amount),]
q5<-q5[c(1:10),]

ggplot(q5, mapping = aes(x = reorder(Address, Amount), Amount)) + 
  geom_bar(stat = "identity")+
  labs(title = "Top 10 Visit Merchant", x = "Merchant", y = "Total Amount") +
  coord_flip()+
  theme_minimal()

```

***

From this graph, I can see where I mostly visit and make purchase. As a commuter between New York and New Jersey, it really make sense to me.

### Q6:What is the total expense per week of day for the past year?
```{r, echo=FALSE}
library(dbplyr)

data$weekday<- weekdays(as.Date(data$Date))

q6<-aggregate(Amount~weekday,data,FUN = sum)
q6<-q6[order(-q6$Amount),]



ggplot(q6, mapping = aes(x = reorder(weekday, Amount), Amount)) + 
  geom_bar(stat = "identity")+
  labs(title = "Top 10 Visit Merchant", x = "Merchant", y = "Total Amount") +
  coord_flip()+
  theme_minimal()

```

***

From this graph, I can see the Monday category spike, However, I notices that all Sunday charge is post on Mondays. However, I do notice that I didn't spend a lot on Saturday and Friday.