---
title: "**ANLY 512 - Final Project**"
author: "**Mithil Kashyap Vyas**"
date: "04/24/2023"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    social: menu
    source: embed
    html_document:
    df_print: paged
    pdf_document: default
---


```{r setup, include=FALSE}
library(flexdashboard)
library(DT)
library(ggplot2)
library(dplyr)
library(dygraphs)
library(tidyverse)
library(lubridate)
library(plotly)
library(webr)
library(reshape2)

data = read.csv("data.csv")
data$Date = as.Date(data$Date, format="%m/%d/%Y")


#Extracting Month & Year
data$month <- format(as.Date(data$Date, format="%m/%d/%Y"),"%m")
data$year <- format(as.Date(data$Date, format="%m/%d/%Y"),"%Y")


#Summarize Spending by each category and also finding total no of swipes per category
df = data %>% group_by(Category) %>% summarise(Freq = sum(Amount), total_count=n())


#Summarize Spending by each Month and also finding total no of swipes per Month
df2 = data %>% group_by(month) %>% summarise(Freq = sum(Amount) , total_count=n())


#Summarize Spending by each Month & category and also finding total no of swipes by Month & category
df3 = data %>% group_by(Category, month) %>% summarise(Freq = sum(Amount), total_count=n())
```



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# **Project Introduction**

## Table of Contents {.sidebar}

**Table of Contents:**

* **Project Introduction**

* **Data Overview**

* **How many swipes do I make every month?**

* **Which category did I spend the most?**

* **Can we predict the monthly credit card spending?**

* **Compare Insurance, shopping, groceries and travel expenses, is there any interesting pattern?**

* **How was my spending pattern based on each category?**

* **Summary**

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### **Quantified Self (QS)** 
The **Quantified Self (QS)** is a movement motivated to leverage the synergy of wearables, analytics, and
“Big Data”. This movement exploits the ease and convenience of data acquisition through the internet
of things (IoT) to feed the growing obsession of personal informatics and quotidian data. The website
http://quantifiedself.com/ is a great place to start to understand more about the QS movement.
The value of the QS for our class is that its core mandate is to visualize and generate questions and insights
about a topic that is of immense importance to most people – themselves. It also produces a wealth of data
in a variety of forms. Therefore, designing this project around the QS movement makes perfect sense because
it offers you the opportunity to be both the data and question provider, the data analyst, the vis designer,
and the end user. This means you will be in the unique position of being capable of providing feedback and
direction at all points along the data visualization/analysis life cycle.

### **Project Deliverables**

To complete this assignment you must deliver two items:

1. A ≤ 3 page write up of the questions, data acquisition, storage, manipulation, visualization methods and
a written summary of each visualization. This summary should be part of your final dashboard
not a separate document.

2. The final visualization dashboard.



Row
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### **Project Objective** 

I have developed a visualization dashboard based on a series of credit card payment data about my spending habits from Oct 2022 to April 2023. In future, this dashboard will fulfill the following key objectives:

**1. To provide a comprehensive view of spending habits**: The visualization dashboard will provide a clear overview of spending habits over the six-month period. This should include information on the total amount spent, spending patterns, and spending categories.

**2. To enable tracking of spending categories**: The dashboard will enable tracking of spending categories over time, allowing users to identify areas where they are spending more than they would like, and to make adjustments to their spending habits.

**3. To provide insights into monthly spending**: The dashboard will allow users to see how their spending habits change from month to month. This can help users to identify patterns in their spending and make changes to their behavior as needed.

**4. To provide alerts for unusual spending patterns**: The dashboard will include alerts for unusual spending patterns, such as sudden spikes in spending or unexpected purchases. This can help users to quickly identify potential fraud or other issues with their credit card.

**5. To provide data for financial planning**: The dashboard will provide data that can be used for financial planning, such as projections for future spending, estimated savings, and potential areas for cost-cutting. This can help users to make more informed financial decisions and achieve their financial goals.

**6. To enable customization and personalization**: The dashboard will be customizable and allow users to personalize their views based on their preferences and needs. This can help users to focus on the information that is most relevant to them and make the dashboard more user-friendly.



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**Data Overview**
===================================== 

Column 
------------------------

I manage my finances through Intuit's app **Mint**. Mint is a free budgeting app that lets you connect all of your financial accounts in one digital space so you get a high-level overview of your financial health. The app also allows users to track spending and savings and set and monitor budget goals.With Mint, users can sync bank accounts, money management accounts, retirement and investment accounts, credit cards and other financial accounts. You also can track all of your monthly bills through Mint and receive reminders so you can easily pay your bills on time.

After connecting your financial accounts, Mint tracks your transactions and categorizes them to simplify tracking. Users can stick with the default categories provided by Mint or create personalized categories to fit their needs. For added customization, you can add tags and reorganize transactions as necessary to better track your spending. If you’re like most users, many transactions don’t necessarily fall into one specific category. With Mint, you have the ability to separate a single transaction into multiple categories, including any fees charged.

**How To Download Transactions from Mint**:

1. Sign in to Mint.com and select “Transactions” in the left panel

2. If you only want to download a range of transactions, select “Filters” in the left dropdown menu.
  * Because you can only download 10,000 transactions at time, you may need to set date filters to download your entire transaction history. 
  * You can filter by category, tag, or date
  * You can also use search keywords to find specific transactions 

3. Select “Export Transactions”.

4. Your CSV file of transaction data will download to your computer.

**For each transaction, the Mint export CSV file includes**:

* Date
* Description
* Original Description
* Amount
* Transaction Type
* Category
* Account Name
* Labels
* Notes you’ve added




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**Question 1**
===================================== 

Column {data-width=250}
-------------------------------------

### **Question 1**

**How many swipes do I make every month?**

It is essential to note that my credit card usage varied every month between **October 2022** and **April 2023**, making it challenging to provide an exact figure for my spending. However, on average, I made approximately **`32`** transactions per month during this period. This translates to me making at least one credit card transaction every day, indicating that I frequently use my credit card for purchases.

Additionally, it is interesting to note that most of my credit card swipes were for the "Shopping" category, which is reflected in the data. In the month of **December**, I made **`47`** credit card swipes, which was the highest number of swipes during the period. Surprisingly, **March** had the second-highest number of swipes, with **`43`** credit card transactions, and **November** came in at third place with **`41`**  credit card swipes.

The high number of credit card swipes in November and December can be attributed to the holiday season and the associated expenses that come with it. However, the fact that March had a relatively high number of swipes is interesting and warrants further investigation. Perhaps there were significant expenses related to travel or other unforeseen circumstances that led to the high number of credit card transactions in March. Overall, the data highlights the importance of analyzing spending patterns and their potential underlying causes to gain a more comprehensive understanding of one's financial habits.


Regenerate response 


Column {.tabset .tabset-fade}
-------------------------------------

### **Spending Data**
```{r}
data %>% 
  datatable()
```    

### **No of Credit Card Swipes By Month**

```{r}
bar1 <- ggplot (data, aes(x = month, fill = Category)) +
  geom_bar() +
  theme_bw() +
  labs(title = "Credit Card Swipes By Month",
       x = "Month-Year",
       y = "No of Swipes")
bar1
```

### **No of Credit Card Swipes By Month by Category**

```{r}
bar1 <- ggplot (df3, aes(x = month, y = total_count, fill = Category)) +
  geom_bar(stat="identity", position=position_dodge()) +  
  geom_text(aes(label=total_count), position = position_dodge(0.9), vjust=-0.3, color="black", size=3.5) +
  theme_bw() +
  labs(title = "Credit Card Swipes By Month",
       x = "Month-Year",
       y = "No of Swipes")
bar1
```


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**Question 2**
===================================== 

Column {data-width=250}
-------------------------------------
### **Question 2**

**Which category did I spend the most?**

I spent the most money on my Rent. Rent accounts for almost **`38%`** or **`$5315`** total credit card spending. I live in Massachusetts and MA has very high rent prices. Some of the cities in MA are amongst the top 10 most expensive cities in the world. 

Shopping follows rent as the next highest category at **`23%`** or **`$3224`** of all credit card spending. I have been working from home since 2020. So I had to setup and upgrade my home office. I live and I work from so Rent and Shopping being the top 2 categories makes a lot of sense.

I am actually surprised that I have spent very little on groceries. I moved with my family for a few months during the time these transactions are recorded so I didn't have to spend a lot on groceries. Groceries only account for **`4%`** or approximately **`$580`** for the 6 month period. 


 
Column {.tabset .tabset-fade}
-------------------------------------
    
### **Credit Card Spending by Category %**

```{r}
plotly::plot_ly(df)%>%
  add_pie(df,labels=~Category,values= ~Freq,
          textinfo="label+percent",type='pie',hole=0.6)%>%
  layout(title="Credit Card Spending by Category %")
```

### **Total Credit Card Spending in $ By Category**

```{r}
p<-ggplot(df, aes(x=Category, y=Freq, fill=Category)) +  geom_bar(stat="identity") + theme_minimal() + theme_bw() +
  
  geom_text(aes(label=Freq), position = position_dodge(0.9), vjust=1.6, color="black", size=3.5) +
  
  theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
  
  labs(title = "Credit Card Spending By Category", x = "Category", y = "Total Spending")

p
```


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**Question 3**
===================================== 

Column {data-width=250}
-------------------------------------
### **Question 4**

**Can we predict the monthly credit card spending?**

While analyzing the boxplot, it appears that predicting my monthly expenditure accurately could be challenging. Nevertheless, based on the available data, we can determine the median **`$16.97`** and mean **`$63.25`** values for each transaction.

As per the findings of question 1, we can deduce that I use my credit card at least **`32`** times in a month on average. If we calculate the median value of each transaction and multiply it by 32, we arrive at an approximate amount of **`$543.04`**.

When we calculate the average value of the transactions and multiply it by 32, the estimated monthly spending is approximately **`$2,024`**. However, it is worth noting that the spending range may vary from **`$540`** to **`$2,024`** per month. This aligns with my current recurring credit card bills, which typically amount to around **`$1,800`** per month.
  
Column
-------------------------------------
    
### **Date**


```{r}
boxplot(Freq~month, data=df3, main="Spending by Month", xlab="Month", ylab="Amount", col="orange", border="brown")
```


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**Question 4**
===================================== 

Column {data-width=250}
-------------------------------------
### **Question 4**

**Compare Insurance, shopping, groceries and travel expenses, is there any interesting pattern?**

Each category of spending has unique patterns, and it is interesting to observe the differences in spending behavior across them. While credit card spending on groceries and shopping is inconsistent and may vary from time to time, insurance spending is relatively stable and consistent. The visual representation of the data makes it clear that I tend to purchase groceries at least once every other week, which is quite accurate. During the period when I spent more time with my family, I spent less money on groceries than I usually do, indicating a direct correlation between my spending habits and personal circumstances.

In contrast, my spending on travel shows a noticeable increase, especially after the new year. There are significant spikes in travel spending right after January, as highlighted in the visual representation. This could be attributed to my travel plans and holiday activities during that time.

The shopping category also reveals interesting trends. The months of November, December, and January show significant spikes in shopping, which is a direct result of the holiday shopping season for both myself and my family. The visual representation of the data reflects that the holiday season influences my shopping habits and that I tend to spend more during that time. Overall, each category of spending shows unique patterns, and it is important to consider the context and personal circumstances when interpreting the data.


Column {.tabset .tabset-fade}
-------------------------------------
    
### **Grocery**
```{r}
groceries <- data[data$Category == "Groceries", ]
ggplot(groceries, aes(x=Date, y=Amount)) +
  geom_line(color="#69b3a2", size=1, alpha=0.9, linetype=1) +
  ggtitle("Groceries Spending")
```

### **Shopping**
```{r}
shopping <- data[data$Category == "Shopping", ]
ggplot(shopping, aes(x=Date, y=Amount)) +
  geom_line(color="#FF5733", size=1, alpha=0.9, linetype=1) +
  ggtitle("Shopping Spending")
```

### **Insurance**

```{r}
ins <- data[data$Category == "Insurance", ]
ggplot(ins, aes(x=Date, y=Amount)) +
  geom_line(color="#69b3a2", size=1, alpha=0.9, linetype=1) +
  ggtitle("Insurance Spending")
```


### **Travel**

```{r}
travel <- data[data$Category == "Travel", ]
ggplot(travel, aes(x=Date, y=Amount)) +
  geom_line(color="#FF5733", size=1, alpha=0.9, linetype=1) +
  ggtitle("Travel Spending")
```



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**Question 5**
===================================== 

Column {data-width=250}
-------------------------------------
### **Question 5**

**How was my spending pattern based on each category?**

When analyzing my spending habits, it is clear that fixed costs such as rent, insurance, and utilities remain fairly consistent from month to month. However, in comparison, my spending on travel and shopping tends to vary quite a bit. Surprisingly, despite these fluctuations, shopping came in as the second-highest expense after rent, which makes sense given the circumstances. As I have been living with my family and working from home, my spending was mostly focused on setting up my office and purchasing gifts for my loved ones.

When it comes to travel expenses, the cost adds up quickly, but the value of spending quality time with family cannot be measured by a dollar amount. Although I may not have saved as much money as I had hoped, I cannot put a price on the time spent with my loved ones. Ultimately, these few months spent with family were worth every penny spent.

Column
-------------------------------------
    
### **Spending Habit**

```{r}
labels = c("Dinning","Groceries","Insurance","Rent","Shopping", "Travel","Utilities")

boxplot(Freq~Category, data=df3, names = labels, main="Spending Habits by Category", xlab="Category", ylab="Amount", 
        
        col="orange", border="brown")
```



**Summary**
===================================== 

Column
-------------------------------------
### **Summary**

From this Dashboard, we have answered the following questions:

* **Question 1: How many swipes do I make every month?**
* **Question 2:Which category did I spend the most?**
* **Question 3:Can we predict the monthly credit card spending?**
* **Question 4:Compare Insurance, shopping, groceries and travel expenses, is there any interesting pattern?**
* **Question 5:How was my spending pattern based on each category?**



The author uses Mint, a free budgeting app, to manage their finances and track their spending and savings. They made an average of 32 credit card transactions per month between October 2022 and April 2023, with most of them falling under the "Shopping" category. The highest number of credit card swipes occurred in December, followed by March, which the author finds interesting and potentially warrants further investigation. The data emphasizes the importance of analyzing spending patterns to gain a better understanding of one's financial habits.

The author's credit card spending is dominated by rent, which makes up almost 38% of their spending, followed by shopping at 23%. They live and work from home, which explains why these two categories are the highest. Because the author was living with his family for some time, the writer only spent around 4% of their total credit card spending on groceries. The available data shows that predicting monthly expenditure accurately is challenging. However, the median and mean transaction values were calculated. The median value per transaction multiplied by 32 equals an approximate of 543.04. The average value per transaction multiplied by 32 is about 2,024. The spending range per month may vary from 540 to 2,024, and it aligns with the current recurring credit card bills, which is approximately $1,800 per month.

The author's credit card spending patterns vary across different categories, with grocery and shopping spending being inconsistent while insurance spending is stable. Travel spending spikes after the new year, and shopping spending spikes during the holiday season. Personal circumstances influence the author's spending habits.The author's spending habits reveal that fixed costs like rent and insurance are consistent, while spending on shopping and travel fluctuates. Shopping is the second-highest expense due to setting up an office and buying gifts for family. Although travel expenses add up quickly, the value of spending time with loved ones cannot be measured by money. These few months with family were worth every penny spent.

<br />
<br />


References: 


* [Reference 1](https://www.forbes.com/advisor/banking/mint-budgeting-app-review/#:~:text=Mint%20is%20a%20personal%20budgeting,financial%20accounts%20in%20one%20place.)
* [Reference 2](https://www.tillerhq.com/exporting-mint-transaction-data-into-a-google-sheet-spreadsheet/)



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