---
title: "Dashboarding Presentation"
author: "Xiaoyu Wang"
date: "`r Sys.Date()`"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    orientation: columns
---

```{r setup, include=FALSE}
library(ggplot2)
library(flexdashboard)
```

# Introduction

With the advent of Chinese companies in America's Stock Market, our team is interested to look into the data from Yahoo Finance to find the best performers, the fastest growing runners, and understand the general trend of these companies' performance within a five-year time range. Furthermore, we would also like to summarize the observations with clear, straight-forward dashbaords. 

The tickers are:
JMEI,JD, BABA, XNET, WUBA, YY, BIDU, MOMO, NOAH, EHIC, CMGE, VIPS, WB, YOKU, DANG, WBAI, ATHM, BITA, SINA, 

The key questions are:
*Q1: Which E-Commerce brands keep the best momentum in price within recent three years?
*Q2: Which brands beat the market by overperforming ASHR Excess Return?
*Q3: Which brands are relatively cheaper (lower P/E Ratio) in 2017? 
*Q4: Which brands have the highest drawdown (high risk) in price in 2017?
*Q5: Which brands own the most market capitalization (biggest asset volumn) in 2017?



# ASHR 
## Column 1
### Q1.Which Chinese E-Commerce Brands keep the best momentum in price within recent three years?

```{r module_1, echo=FALSE}
data = read.csv("/Users/Jade/Desktop/Harrisburg University/Second Semester/Data Visualization ANLY 512 2017LateFall/Dashboard Theory/Stock Price.csv", stringsAsFactors=F)

#normalize stock price for multi-line chart
temp = data

for( i in 1:nrow(data) )
  {
for( j in 2:ncol(data))
  {
temp[i,j] = data[i,j]/data[nrow(data),j]
  }
}

#convert date into correct format
temp$Date = as.Date(temp$Date,format = "%m/%d/%y")
temp = temp[order(temp$Date, decreasing = TRUE),]

data_2 = data.frame()
tickers = c('BABA','JMEI','VIPS','ASHR')

for( i in 1:length(tickers))
{
  data_2_temp = temp[,c("Date",tickers[i])]
  colnames(data_2_temp) <- c("Date","Index")
  data_2_temp["Ticker"] = tickers[i]
  data_2 = rbind(data_2, data_2_temp)
}


yy_1 <- ggplot() +
  geom_line(data = data_2, aes(x = Date, y = Index, color = Ticker), size = 0.5)+
  labs(x = "Date", y = "Index")+
  theme_grey()
yy_1

print("Chinese stocks are known as China A-Shares. ASHR stands for the investment & fund info of Chinese A-Shares.Alibaba is overperforming ASHR index and it is so far the brand with highest momentum startning 2016.")

```

## Column 2
### Q2.Which brands beat the market by overperforming ASHR Excess Return?

```{r module_2, echo=FALSE}
data_er = read.csv("/Users/Jade/Desktop/Harrisburg University/Second Semester/Data Visualization ANLY 512 2017LateFall/Dashboard Theory/Excess Return.csv", stringsAsFactors=F)

ER<- ggplot(data=data_er, aes(x= reorder (Ticker,-Excess.Return) , y=Excess.Return,fill=Ticker)) + 
  geom_bar(stat = "identity", width=0.75) +
  geom_text(aes(label=Excess.Return), vjust=-0.3, size=3)+
  labs(x = "Ticker", y = "Excess Return")+
  theme(text = element_text(size=12),
        axis.text.x = element_text(angle=90, hjust=1))

ER

print("Excess Return is equal to the different between stock price and ASHR price divided by ASHR price, which represents the stock price momentum comparing to the benchmark. WB is overperforming other brands with highest Excess Returns.")
```

# P/E Ratio
## Column 1
### Q3. Which brands are relatively cheaper (lower P/E Ratio) in 2017? 
##### The P/E ratio is calculated by dividing a company's stock price by its earnings from the most recent?fiscal year. This places a relative price tag on earnings. Momo has the lowest P/E ratio, YY has the second lowest P/E ratio. In contrast, WUBA has the highest P/E ratio and WB has the second highest P/E ratio.

```{r module_3, echo=FALSE, fig.align='center'}
data_pe = read.csv("/Users/Jade/Desktop/Harrisburg University/Second Semester/Data Visualization ANLY 512 2017LateFall/Dashboard Theory/trend and pe ratio data visual.csv", stringsAsFactors=F)

PE <- ggplot(data= data_pe, aes(x= reorder (Brand,PE.Ratio) , y=PE.Ratio,fill=Brand)) + 
  geom_bar(stat = "identity", width=0.75) +
  geom_text(aes(label=PE.Ratio), vjust=-0.3, size=3)+
  labs(x = "Selected Brands", y = "P/E Ratio %")+
  theme_minimal()

PE

```

# Drawdown
## Column 1
### Which brands have the highest drawdown (high risk) in price in 2017?
##### A Drawdown is the peak-to-through decline during a specific recorded period of an investment, fund or commodity, which presents a significant risk to investors when considering the uptick in share price needed to overcome a drawdown. Among these Chinese Concepts Stocks, BIDU (Baidu) has the biggest drawdown, and BABA has the second largest drawdown and YY has the third.

```{r module_4, echo=FALSE, fig.align='center'}
MKT_CAP_DD <- read.csv("/Users/Jade/Desktop/Harrisburg University/Second Semester/Data Visualization ANLY 512 2017LateFall/Dashboard Theory/DATA MKT CAP _ DD.csv",stringsAsFactors=F)

dd<- ggplot(data=MKT_CAP_DD, aes(x= reorder (Ticker,-Drawdown) , y=Drawdown,fill=Ticker)) + 
  geom_bar(stat = "identity", width=0.75) +
  geom_text(aes(label=Drawdown), vjust=-0.3, size=2.5)+
  labs(x = "Ticker", y = "Drawdown")+
   theme(text = element_text(size=12),
        axis.text.x = element_text(angle=90, hjust=1))

dd
```

# Market Capitalization
## Column 1
### Which brands own the most market capitalization (biggest asset volumn) in 2017?
##### Among these stocks, BABA has the highest market capitalization. WBAI, JMEI, EHIC has the lowest. The lowest one (WBAI) owns less than 0.1% market cap compared to BABA.

```{r module_5, echo=FALSE, fig.align='center'}
mktc<- ggplot(data=MKT_CAP_DD, aes(x= reorder (Ticker,-Cap) , y=Cap,fill=Ticker)) + 
  geom_bar(stat = "identity", width=0.75) +
  geom_text(aes(label=Cap), vjust=-0.5, size=2.5)+
  labs(x = "Ticker", y = "Market Capitalization")+
   theme(text = element_text(size=12),
        axis.text.x = element_text(angle=90, hjust=1))
  

mktc

```