---
title: "ANLY 512: Dashboard Laboratory"
author: "Venkata Sarath Pulipati"
Date: "12/18/2019"
output: 
 flexdashboard::flex_dashboard:
    theme: sandstone
    social: menu
    source_code: embed
    vertical_layout: fill
    orientation: rows
---

```{r, echo = TRUE, include = FALSE, message = FALSE}
library(tidyquant)
library(xts)
library(ggplot2)
library(flexdashboard)
library(dygraphs)
library(dplyr)
library(knitr)
library(tidyr)
library(plyr)
library(PerformanceAnalytics)
library(stocks)
```

Background
===========

### **Overview**  

Due to the inherent growth in the electronic production and storage of information, there is often a feeling of “information overload” or inundation when facing the process of quantitative decision making. As an analyst your job will often be to conduct analyses or create tools to support quantitative decision making.

A principle tool used in industry, goverment, non-profits, and academic fields to compensate for the information overload is the information dashboard. Functionally, a dashboard is meant to provide a user with a central resource to present in a clear and concise manner all the information neccessary to support day-to-day decision making and support operations.

Row {data-height=400}
----------------------
### **Objective** 

The objective of this laboratory is to plan, design, and create an information dashboard to support quantitative decision making. To accomplish this task you will have to complete a number of steps:

1. Delineate the necessary decision (I will do that)
2. Identify what information will be relevant to decision making.
3. Find and collect the data necessary to create your visualization plan.
4. Organize and summarize the collected data.
5. Design and create the best visualizations to present that information.
6. Finally organize the layout of those visualizations in a way that conforms to the theory of dashboarding.
7. Write a summary about what decisions you made based on the visualizations that you developed.

### **The Decision & Rules**

You make investments for an organization, your objective is to purchase securities/commodities for the key objective of maximizing profits. You want to make an investment in securities/commodities to make some short term gains. You are considering investing in one of any four companies, for example: Twitter (TWTR), Microsoft (MSFT), or Apple (AAPL) (don’t use these). Choose 4 companies or commodities and determine which one of the four will produce the most short term gains. Use your imagination.

### **Dates & Deliverables**

You are responsible for submitting a link to your dashboard hosted on the Rpubs site. The dashboard must include the ```source_code = TRUE``` parameter.

The due date for this project is XX at the start of class. This assignment is worth 75 points, 3x a normal homework, the additional time should allow you to spend the neccessary effort on this assignment.

You are welcome to work in groups of ≤2
 people. However, each person in a group must submit their own link to the assignment on moodle for grading! Each team member can submit the same link to a single rpubs account, however it may be a good idea for each of you to post your own copy to rpubs in case you want to share it to prospective employers ect.

There is one caveat to this project. While you can use any package to pull or obtain data, DO NOT use package like quantmod() to make your graphics. I want to see that you designed and built all the graphics yourself and did not use a precanned stock visualizing function like chartSeries(). There a number of great packages that allow you to use financial graphic types for which you build them see [candlestick dygraphs for examples](https://rstudio.github.io/dygraphs/gallery-candlestick.html).

Here is another great resource for visualizing financial time series data using ggplot [here](https://www.business-science.io/code-tools/2017/01/22/tidyquant-update-0-3-0.html)

### **Methods Help**
##### *Getting data*
There are lots of places we can get financial data to support these decision. The simplest would be to go to for instance to the Yahoo Finance (https://finance.yahoo.com/) for data on the Hershey Company (HSY) the URL would be: (https://finance.yahoo.com/quote/HSY/history?p=HSY) and collect historical price data, and other financial and company information.


Motivation & KPIs
=================

### **Motivation**  

In the coming years, the popular industries which are good to consider for serious investment will be Artificial Intelligence, IT Software & Services, Renewable energy. There are specific companies at this moment which are booming and have a great probability of becomming leaders in their respective industries. In this report, our focus is on the companies that are in the AI & robotics field and are one among the market leaders. The four companies, **IBM**, **NVIDIA**, **AMAZON** and **INTEL** are concentrated and will make an attempt to analyze these companies' market performance. This analysis is intended to help the investing company's direction to optimize and make investment plans eventually.

### **KPIs**

##### **Indicators**

+ **Open** - Price at openeing.
+ **High** - Price at highest rate.
+ **Low** - Price at the lowest rate.
+ **Close** - Closing rate.

Row {data-height=680}
-----------------------
### **KPI Table** 

```{r}
library(tidyquant)
library(dplyr)
AMZN  <- tq_get("AMZN", get = "stock.prices", from = "2018-01-01")
INTC  <- tq_get("INTC", get = "stock.prices", from = "2018-01-01")
IBM  <- tq_get("IBM", get = "stock.prices", from = "2018-01-01")
NVDA  <- tq_get("NVDA", get = "stock.prices", from = "2018-01-01")
bind_rows("AMZN" = AMZN, "INTC" = INTC, "IBM" = IBM, "NVDA" = NVDA, .id = "id")
```

Analysis
========

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------
### **Stock Prices from last three years**
```{r}
from = today() - years(3)
Stocks <- tq_get(c("NVDA","AMZN","INTC","IBM"), get = "stock.prices", from = from) %>%
group_by(symbol)
Stocks %>% 
  ggplot(aes(x = date, y = adjusted, color=symbol)) + 
  geom_line() +
   facet_wrap(~symbol)
```

### **Closing Prices by Company**
```{r}
tq_get(c("INTC","IBM","NVDA","AMZN"), get="stock.prices") %>%
  ggplot(aes(date, close, color=symbol)) +
  geom_line()
```

Conclusion
========

Among the four stocks, the stock prices of Amazon has been increasing consistently and it's other competitors are stable but the increase rate do not match Amazon. So the recommendation to invest on, will be Amazon.