---
title: "ANLY 512 PROJECT"
author: "Sowmya Puranam "
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
    orientation: columns
    vertical_layout: fill
---
```{r setup, include=FALSE}



knitr::opts_chunk$set(echo = FALSE)
library(flexdashboard)
library(knitr)
library(ggplot2)
library(rnoaa)
library(tidyverse)
library(maps)
library(scatterpie)
library(ggiraph)
library(plotly)
library(gdtools)
library(readxl)
library(dplyr)
library(xts)
library(zoo)

setwd("C:\\Users\\sowmy\\Desktop\\HU\\ANLY512\\Project")



```




###Overview
![Source: google](quantifiedselfimage.png)






****
The Quantified Self is a thrilling prospect that allows Self improvement by collecting and monitoring the data. I chose to collect and monitor my finances by utilizing the Discover credit card spend analyzer data which is available on the website that includes the spending summary for the past 6 months( March to Present). 

This has allowed me to get an understanding of what areas I have been spending the most & helps to keep track and manage the expenses. 
Based on this data, I consider the following questions: 
1) Which category has maximum spending ? 
2) During past 6 months,which month have I spent the most ? 
3)For Merchandise , what is the spending by month in 2017 ?
4)For Services , what is the spending by month ? 
5)For SuperMarkets , what is the spending by month ?



```{r,echo = FALSE, message = FALSE}

discoverdata = read.csv("C:\\Users\\sowmy\\Desktop\\HU\\ANLY512\\Project\\spendanalyzer1.csv")

```



  
###  Which category has maximum spending ? 


```{r}

q <- discoverdata%>%
 plot_ly(labels = ~Category, values = ~Amount) %>% add_pie(hole = 0.6)%>%
  layout(title = "Spending categories ",  showlegend = T,
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
q



```


***

It is important to know where you use your credit card the most i.e. the category of purchase.  I used a donut chart to understand the maximum spending category, because of its ease of understanding. Maximum expenses were seen to be coming from the Merchandise which is around 29.5%, and other major expenses are from Services (25.4%) and Supermarkets (15.6%). 

### Q.2 Which month has maximum spending ? 
```{r}
library(ggplot2)


new_date<- as.Date(discoverdata$Trans..Date, "%m/%d/%Y")


Month<- strftime(new_date, "%m")


plot2<- ggplot(discoverdata, aes(x = Month, y=Amount)) + 
  geom_bar(stat = "identity", fill = "Green")+

  labs(title = "Spending per Month", x = "Month", y = "Amount") +
  theme_minimal()
plot2



```

***
It is always interesting to note which month of the year you spent the most. I used a simple Bar chart to get an understanding this. Maximum payments were done in the Month of May this year which is more than $750.

###  Merchandise category spending by month 

```{r}
Merchandisedata<- subset(discoverdata, Category=='Merchandise')
new_date<- as.Date(Merchandisedata$Trans..Date, "%m/%d/%Y")

Month<- strftime(new_date, "%m")
p<- ggplot(Merchandisedata, aes(x = Month, y=Amount)) + 
  geom_bar(stat = "identity", fill = "Blue") +
  labs(title = "Spending on Merchandise per Month", x = "Month", y = "Amount") +coord_flip()+
  theme_minimal()
p
```


***
The bar plot explains the expense by month for the Merchandise Category.  The maximum spending for Merchandise is in the Months May, March , and July. 



 

### Services the spending by month ?



```{r}
Servicesdata<- subset(discoverdata, Category=='Services')
new_date<- as.Date(Servicesdata$Trans..Date, "%m/%d/%Y")

Month<- strftime(new_date, "%m")
p<- ggplot(Servicesdata, aes(x = Month, y=Amount)) + 
  geom_bar(stat = "identity", fill = "Orange") +
  labs(title = "Spending on Services per Month", x = "Month", y = "Amount") +
  theme_minimal()
p
```

***

The second most spending categories is Services. From the plot it is seen that maximum money was spent on Services during the month of April. I used my credit card for Services like Internet payment, Entry fees, Parking fees etc.There is both an increase and decrease in the payments, maximum being in the month of April.
  

### Q.5 SuperMarkets category spending by month ?



```{r}
Supermarketsdata<- subset(discoverdata, Category=='Supermarkets')
new_date<- as.Date(Supermarketsdata$Trans..Date, "%m/%d/%Y")

Month<- strftime(new_date, "%m")
p<- ggplot(Supermarketsdata, aes(x = Month, y=Amount)) + 
  geom_bar(stat = "identity", fill = "Purple") +
  labs(title = "Spending on Grocery per Month", x = "Month", y = "Amount") +
  theme_minimal()
p
```



***

Besides Merchandise and Services, one of the other major spending category is Supermarkets. I tried to dig deeper and identify the trend of the months in which I spent the most for Groceries.  I believe I used my Discover credit card for most of the expenses in the month of May.