output: flexdashboard::flex_dashboard: orientation: columns vertical_layout: fill social: menu source_code: embed

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(quantmod)

## Warning: package 'quantmod' was built under R version 4.3.3

## Loading required package: xts

## Warning: package 'xts' was built under R version 4.3.3

## Loading required package: zoo

## Warning: package 'zoo' was built under R version 4.3.3

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## Loading required package: TTR

## Warning: package 'TTR' was built under R version 4.3.3

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(flexdashboard)

## Warning: package 'flexdashboard' was built under R version 4.3.3

library(dygraphs)

## Warning: package 'dygraphs' was built under R version 4.3.3

library(plyr)

## Warning: package 'plyr' was built under R version 4.3.3

# Names of the stocks we are interested in
tickers <- c("NFLX", "AMZN", "META", "NVDA", "GOOG")
tickers

## [1] "NFLX" "AMZN" "META" "NVDA" "GOOG"

Sidebar

Finding the Right Stock for Investment

This dashboard is a collection of data visualization of various stock prices. The goal is to enable us make a decision on which stock is best to invest in.

For the purpose of this exercise, we looked at the following stocks “NETFLIX”, “AMAZON”, “META”, “NVIDIA” and “GOOGLE”, - some of the best performing stock.

Column

###Netflix

timeWindow<-c("2022-07-01", "2023-07-01")
invisible(getSymbols("NFLX", src = "yahoo", from='2022-07-01'))
NFLX_x <- NFLX
dygraph(NFLX_x[, -5], main = "NETFLIX") %>%
  dyCandlestick() %>%
  dyAxis("y", label="SHARE PRICE in USD") %>%
  dyOptions(colors= RColorBrewer::brewer.pal(5, "Set1")) %>%
  dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
  dyRangeSelector(dateWindow = timeWindow, height = 15, strokeColor = "")

Amazon

timeWindow<-c("2022-07-01", "2023-07-01")
invisible(getSymbols("AMZN", src = "yahoo", from='2022-07-01'))
AMZN_x <- AMZN
dygraph(AMZN_x[, -5], main = "AMAZON") %>%
  dyCandlestick() %>%
  dyAxis("y", label="SHARE PRICE in USD") %>%
  dyOptions(colors= RColorBrewer::brewer.pal(5, "Set1")) %>%
  dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
  dyRangeSelector(dateWindow = timeWindow, height = 15, strokeColor = "")

Meta

timeWindow<-c("2022-07-01", "2023-07-01")
invisible(getSymbols("META", src = "yahoo", from='2022-07-01'))
FB_x <- META
dygraph(FB_x[, -5], main = "META") %>%
  dyCandlestick() %>%
  dyAxis("y", label="SHARE PRICE in USD") %>%
  dyOptions(colors= RColorBrewer::brewer.pal(5, "Set1")) %>%
  dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
  dyRangeSelector(dateWindow = timeWindow, height = 15, strokeColor = "")

Nvidia

timeWindow<-c("2022-07-01", "2023-07-01")
invisible(getSymbols("NVDA", src = "yahoo", from='2022-07-01'))
NVDA_x <- NVDA
dygraph(NVDA_x[, -5], main = "NVIDIA") %>%
  dyCandlestick() %>%
  dyAxis("y", label="SHARE PRICE in USD") %>%
  dyOptions(colors= RColorBrewer::brewer.pal(5, "Set1")) %>%
  dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
  dyRangeSelector(dateWindow = timeWindow, height = 15, strokeColor = "")

###Google

timeWindow<-c("2022-07-01", "2023-07-01")
invisible(getSymbols("GOOG", src = "yahoo", from='2022-07-01'))
GOOG_x <- GOOG
dygraph(GOOG_x[, -5], main = "GOOGLE") %>%
  dyCandlestick() %>%
  dyAxis("y", label="SHARE PRICE in USD") %>%
  dyOptions(colors= RColorBrewer::brewer.pal(5, "Set1")) %>%
  dyHighlight(highlightSeriesOpts = list(strokeWidth = 3)) %>%
  dyRangeSelector(dateWindow = timeWindow, height = 15, strokeColor = "")

column

KEY FINANCIAL METRICS

what_metrics <- yahooQF(c("Price/Sales", 
                          "P/E Ratio",
                          "Price/EPS Estimate Next Year",
                          "PEG Ratio",
                          "Dividend Yield", 
                          "Market Capitalization"))

tickers <- c("NFLX", "AMZN", "META",  "NVDA", "GOOG")
metrics <- getQuote(paste(tickers, sep="", collapse=";"), what=what_metrics)
metrics <- data.frame(Symbol=tickers, metrics[,2:length(metrics)]) 

#Rename the metrics columns
colnames(metrics) <- c("SYMBOL", "EARNINGS MULTIPLE","EARNINGS MULTIPLE (Forward)", "DIVIDEND", "MARKET CAP")

DT::datatable(metrics)

column

SUMMARY

Based on the visuals of the various stocks, we believe NVIDIA is the best stock to invest in because it shows growth potential. It shows the highest earnnings and dividend compared to the other stock.