This is an analysis of high growth stocks traded over the first quarter of 2019. The top chart is a reactive candle stick chart, bottom chart shows the volume traded and the adjusted prices of each stock in a time series.

---
title: "Highest Growth Stock Analysis"
author: "Laxman Panthi"
output: 
  flexdashboard::flex_dashboard:
    theme: bootstrap
    orientation: rows
    social: menu
    source_code: embed
html_document:
    df_print: paged
---

```{r global, include=FALSE}

#load required libraries
library(quantmod)
library(tidyverse)
library(shiny)
library(dygraphs)



getSymbols(c("CVNA","CGC","SQ","VYGR"), from="2019-01-01", to ="2019-03-31")

ind_colnames <- c("Open","High","Low","Close","Volume","Adjusted")

cgc_mtx <- as.matrix(CGC)
colnames(cgc_mtx) <- ind_colnames

cvna_mtx <- as.matrix(CVNA)
colnames(cvna_mtx) <- ind_colnames

sq_mtx <- as.matrix(SQ)
colnames(sq_mtx) <- ind_colnames

vygr_mtx <- as.matrix(VYGR)
colnames(vygr_mtx) <- ind_colnames

all_data <- data.frame(CGC)%>%rownames_to_column("Day")%>%
  merge(data.frame(CVNA)%>%rownames_to_column("Day"))%>%
  merge(data.frame(SQ)%>%rownames_to_column("Day"))%>%
  merge(data.frame(VYGR)%>%rownames_to_column("Day"))%>%
  mutate(Date=as.Date(Day))

```



This is an analysis of high growth stocks traded over the first quarter of 2019.
The top chart is a reactive candle stick chart, bottom chart shows the volume traded and the adjusted prices of each stock in a time series.


Row {row.height=1500}
-----------------------------------------------------------------------

### Candlestick Chart - Canopy
    
```{r}
dygraph(cgc_mtx[,1:4]) %>%
  dyCandlestick()
```


### Candlestick Chart - Carvana
    
```{r}
dygraph(cvna_mtx[,1:4]) %>%
  dyCandlestick()
```


Row
-----------------------------------------------------------------------

### Candlestick Chart - Square
    
```{r}
dygraph(sq_mtx[,1:4]) %>%
  dyCandlestick()
```


### Candlestick Chart - Voyager
    
```{r}
dygraph(vygr_mtx[,1:4]) %>%
  dyCandlestick()
```


Row {row.height=1500}
-------------------------------------
    
### Volume Time-Series
    
```{r, fig.width=15, fig.height=5}
volume_data <- all_data %>%
  select(Date,CVNA.Volume,CGC.Volume,SQ.Volume,VYGR.Volume) %>%
  gather(key = "variable", value = "value", -Date)

ggplot(volume_data, aes(x = Date, y = value)) + 
  geom_line(aes(color = variable)) +
  scale_color_manual(labels = c("Canopy", "Carvana", "Square", "Voyager"),values = c("#00AFBB", "#E7B800", "#E88000","#120FAA")) +
  theme_minimal() + 
  theme(legend.position="top")

```

    
### Adjusted Time-Series

```{r, fig.width=15, fig.height=5}
adjusted_data <- all_data %>%
  select(Date,CVNA.Adjusted,CGC.Adjusted,SQ.Adjusted,VYGR.Adjusted) %>%
  gather(key = "variable", value = "value", -Date)

ggplot(volume_data, aes(x = Date, y = value)) + 
  geom_line(aes(color = variable)) +
  scale_color_manual(labels = c("Canopy", "Carvana", "Square", "Voyager"),values = c("#00AFBB", "#E7B800", "#E88000","#120FAA")) +
  theme_minimal() + 
  theme(legend.position="top")
```