APPLE Stock Price

---
title: "dashboard Lab1"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

APPLE Stock Price

```{r setup, include=FALSE}

```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A

Yearly trend shows increase in stock price.

```{r,echo = FALSE, message=FALSE}
library(flexdashboard)
library(lubridate)      # easily work with dates and times
library(fpp2)          # working with time series data
library(zoo)           # working with time series data
library(data.table)
library(tidyverse)
AAPL<- read.csv("AAPL.csv", header=TRUE)
AAPL$Date1<- as.Date(AAPL$Date,format="%m/%d/%Y")
APPLE <- AAPL %>%
  select(Date1,  Close) %>%
  mutate(sma5 = rollmean(Close, k = 5, fill = NA,align = "right" ),
         sma9= rollmean(Close, k = 9, fill = NA,align = "right"),
         sma21 = rollmean(Close, k = 21, fill = NA,align = "right"))

 APPLE1<- APPLE %>%
  gather(metric, value, sma5:sma21) %>%
  ggplot(mapping=aes(Date1, value, color = metric)) +
  geom_line()+
  geom_point(mapping=aes(y=Close),color="Black")

APPLE1 + labs(x="Date", y="Price",title="APPLE Stock Price", 
              subtitle="Indicators=simple moving averages", caption="(based on yahoo finance data)")

```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart B
Small simple moving average croses a large simple moving average signals strong buying sentiment.In following chart on 10th July 2018, 5 day moving average has croosed 21 day moving signaling strong indiction to buy the stock for short term profit
```{r}
library(flexdashboard)
library(lubridate)      # easily work with dates and times
library(fpp2)          # working with time series data
library(zoo)           # working with time series data
library(data.table)
library(tidyverse)
AAPL<- read.csv("AAPL.csv", header=TRUE)
AAPL$Date1<- as.Date(AAPL$Date,format="%m/%d/%Y")
APPLE <- AAPL %>%
  select(Date1,  Close) %>%
  mutate(sma5 = rollmean(Close, k = 5, fill = NA,align = "right" ),
         sma9= rollmean(Close, k = 9, fill = NA,align = "right"),
         sma21 = rollmean(Close, k = 21, fill = NA,align = "right"))

APPLE30<- tail(APPLE,30)

APPLE2<- APPLE30 %>%
  gather(metric, value, sma5:sma21) %>%
  ggplot(mapping=aes(Date1, value, color = metric)) +
  geom_line()+
  geom_point(mapping=aes(y=Close),color="Black") +
  geom_vline(aes(xintercept =as.numeric(APPLE30$Date1[26])),linetype=8, na.rm=FALSE,color="Purple")

APPLE2 + labs(x="Date", y="Price",title="APPLE Stock Price Analysis last 30 days", 
              subtitle="sma5 crosses sma21 signals buying", caption="(based on yahoo finance data)")


  

```