Peer-graded Assignment: R Markdown Presentation & Plotly

By Sandy Sng
5 June 2018

Instructions

Create a web page presentation using R Markdown that features a plot created with Plotly.
Host your webpage on either GitHub Pages, RPubs, or NeoCities.
Your webpage must contain the date that you created the document, and it must contain a plot created with Plotly. We would love to see you show off your creativity!

GOOG Historical NASDAQ Stock Prices

Below is an interactive multi-line graph of GOOG Historical Stock Prices (retrieved on NASDAQ)
Results for: 10 Years, From 04-JUN-2008 TO 04-JUN-2018
Source: https://www.nasdaq.com/symbol/goog/historical

Load packages and dataset

library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(tidyr) 
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

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

GOOG <- as.data.frame(read.csv("~/Desktop/R Files/HistoricalQuotes.csv"))

Data exploration

head(GOOG)

##         date     close       volume      open      high       low
## 1      16:00  1,139.29    1,889,579  1,122.33  1,141.89 1,122.005
## 2 2018/06/04 1139.2900 1881046.0000 1122.3300 1141.8900 1122.0050
## 3 2018/06/01 1119.5000 2416755.0000 1099.3500 1120.0000 1098.5000
## 4 2018/05/31 1084.9900 3085325.0000 1067.5600 1097.1900 1067.5600
## 5 2018/05/30 1067.8000 1129958.0000 1063.0300 1069.2100 1056.8300
## 6 2018/05/29 1060.3200 1858676.0000 1064.8900 1073.3700 1055.2200

str(GOOG)

## 'data.frame':    1056 obs. of  6 variables:
##  $ date  : Factor w/ 1056 levels "16:00","2014/03/27",..: 1 1056 1055 1054 1053 1052 1051 1050 1049 1048 ...
##  $ close : Factor w/ 1039 levels "1,139.29","1001.5200",..: 1 134 124 99 79 72 87 92 94 82 ...
##  $ volume: Factor w/ 1055 levels "1,889,579","1002313.0000",..: 1 626 805 886 81 613 996 980 35 54 ...
##  $ open  : Factor w/ 1022 levels "1,122.33","1001.5000",..: 1 124 111 81 75 77 91 90 79 97 ...
##  $ high  : Factor w/ 1034 levels "1,141.89","1016.1000",..: 1 132 119 102 72 76 87 82 83 92 ...
##  $ low   : Factor w/ 1039 levels "1,122.005","1000.6600",..: 1 125 109 88 73 71 95 84 78 86 ...

Data Cleaning

Remove 1st row: Incomplete data in row for 05-Jun-2018
Remove 3rd column: Volume of GOOG (irrelevant here)
Temporarily remove date column
New (cleaned) variable is GOOG2 Change all prices to numeric format

GOOG2 <- GOOG[-1,-3] 
GOOG2 <- GOOG2[nrow(GOOG2):1,] # Arrange 2014 first
GOOG2 = GOOG2[,-1] # Remove date from analysis for now

names(GOOG2)

## [1] "close" "open"  "high"  "low"

GOOG2$close <- as.numeric(gsub(",","",GOOG2$close))
GOOG2$open <- as.numeric(gsub(",","",GOOG2$open))
GOOG2$high <- as.numeric(gsub(",","",GOOG2$high))
GOOG2$low <- as.numeric(gsub(",","",GOOG2$low))

# Check cleaned data
summary(GOOG2)

##      close             open             high             low        
##  Min.   : 492.6   Min.   : 494.6   Min.   : 496.0   Min.   : 487.6  
##  1st Qu.: 571.2   1st Qu.: 571.3   1st Qu.: 576.6   1st Qu.: 565.5  
##  Median : 737.8   Median : 736.5   Median : 742.0   Median : 731.0  
##  Mean   : 751.6   Mean   : 751.6   Mean   : 757.6   Mean   : 745.2  
##  3rd Qu.: 915.4   3rd Qu.: 915.4   3rd Qu.: 922.2   3rd Qu.: 909.6  
##  Max.   :1175.8   Max.   :1177.3   Max.   :1186.9   Max.   :1172.0

Plotting the Data

GOOGstock <- as.data.frame(GOOG2) %>% 
        gather(result, price)

plot_ly(GOOGstock, x = GOOG2$date, y = ~price, color = ~result)

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter

## No scatter mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode