Quiz on Ch1: Prices
Vic Richardson
9/18/2018
- Q1 What is the closing price of the stock on December 13, 2002?
- Q2 How many variables (columns) does your data have after conversion?
- 6 columns
- Q3 During what year (of the study period) did the stock reach the highest price?
- 2018 WAS THE HIGHEST STOCK PRICE.
- Q4 What is the highest price the stock ever reached? Consider only the study period.
Go to Yahoo Finance and import Netflix stock prices from December 13, 2002 to July 31, 2018.
Q1 What is the closing price of the stock on December 13, 2002?
$0.89
# Load csv file
data.NFLX <- read.csv("data/NFLX.csv", header = TRUE)
head(data.NFLX)
## Date Open High Low Close Adj.Close Volume
## 1 2002-12-13 0.900000 0.914286 0.860714 0.892857 0.892857 1395800
## 2 2002-12-16 0.898571 0.927857 0.867857 0.913571 0.913571 2098600
## 3 2002-12-17 0.914286 0.927143 0.892857 0.906429 0.906429 1680000
## 4 2002-12-18 0.896429 0.898571 0.763571 0.785000 0.785000 13378400
## 5 2002-12-19 0.770714 0.799286 0.758571 0.771429 0.771429 3781400
## 6 2002-12-20 0.764286 0.771429 0.728571 0.764286 0.764286 4384800
tail(data.NFLX)
## Date Open High Low Close Adj.Close Volume
## 3929 2018-07-24 366.94 367.40 354.56 357.32 357.32 12851500
## 3930 2018-07-25 357.57 363.28 355.65 362.87 362.87 8467800
## 3931 2018-07-26 358.19 365.54 356.63 363.09 363.09 6993700
## 3932 2018-07-27 366.85 367.00 351.65 355.21 355.21 8949500
## 3933 2018-07-30 351.93 352.03 334.02 334.96 334.96 18260700
## 3934 2018-07-31 331.51 342.50 328.00 337.45 337.45 14085400Convert the date variable from a Factor to a Date.
class(data.NFLX$Date)
## [1] "factor"
date <- as.Date(data.NFLX$Date, format = "%Y-%m-%d")
class(date)
## [1] "Date"Combine date and data.NFLX.
data.NFLX <- cbind(date, data.NFLX[, -1])
head(data.NFLX)
## date Open High Low Close Adj.Close Volume
## 1 2002-12-13 0.900000 0.914286 0.860714 0.892857 0.892857 1395800
## 2 2002-12-16 0.898571 0.927857 0.867857 0.913571 0.913571 2098600
## 3 2002-12-17 0.914286 0.927143 0.892857 0.906429 0.906429 1680000
## 4 2002-12-18 0.896429 0.898571 0.763571 0.785000 0.785000 13378400
## 5 2002-12-19 0.770714 0.799286 0.758571 0.771429 0.771429 3781400
## 6 2002-12-20 0.764286 0.771429 0.728571 0.764286 0.764286 4384800
tail(data.NFLX)
## date Open High Low Close Adj.Close Volume
## 3929 2018-07-24 366.94 367.40 354.56 357.32 357.32 12851500
## 3930 2018-07-25 357.57 363.28 355.65 362.87 362.87 8467800
## 3931 2018-07-26 358.19 365.54 356.63 363.09 363.09 6993700
## 3932 2018-07-27 366.85 367.00 351.65 355.21 355.21 8949500
## 3933 2018-07-30 351.93 352.03 334.02 334.96 334.96 18260700
## 3934 2018-07-31 331.51 342.50 328.00 337.45 337.45 14085400Sort the data in chronological order.
data.NFLX <- data.NFLX[order(data.NFLX$date),]
head(data.NFLX)
## date Open High Low Close Adj.Close Volume
## 1 2002-12-13 0.900000 0.914286 0.860714 0.892857 0.892857 1395800
## 2 2002-12-16 0.898571 0.927857 0.867857 0.913571 0.913571 2098600
## 3 2002-12-17 0.914286 0.927143 0.892857 0.906429 0.906429 1680000
## 4 2002-12-18 0.896429 0.898571 0.763571 0.785000 0.785000 13378400
## 5 2002-12-19 0.770714 0.799286 0.758571 0.771429 0.771429 3781400
## 6 2002-12-20 0.764286 0.771429 0.728571 0.764286 0.764286 4384800
tail(data.NFLX)
## date Open High Low Close Adj.Close Volume
## 3929 2018-07-24 366.94 367.40 354.56 357.32 357.32 12851500
## 3930 2018-07-25 357.57 363.28 355.65 362.87 362.87 8467800
## 3931 2018-07-26 358.19 365.54 356.63 363.09 363.09 6993700
## 3932 2018-07-27 366.85 367.00 351.65 355.21 355.21 8949500
## 3933 2018-07-30 351.93 352.03 334.02 334.96 334.96 18260700
## 3934 2018-07-31 331.51 342.50 328.00 337.45 337.45 14085400Convert data.frame object to xts object.
Q2 How many variables (columns) does your data have after conversion?
Hint: Now that the data is converted to xts from data frame, the date column has become row names.
6 columns
class(data.NFLX)
## [1] "data.frame"
library(xts)
data.NFLX <- xts(data.NFLX[, 2:7], order.by = data.NFLX[, 1])
class(data.NFLX)
## [1] "xts" "zoo"Rename variables
names(data.NFLX)
## [1] "Open" "High" "Low" "Close" "Adj.Close" "Volume"
names(data.NFLX) <- paste(c("NFLX.Open", "NFLX.High", "NFLX.Low",
"NFLX.Close", "NFLX.Adjusted", "NFLX.Volume"))
head(data.NFLX)
## NFLX.Open NFLX.High NFLX.Low NFLX.Close NFLX.Adjusted
## 2002-12-13 0.900000 0.914286 0.860714 0.892857 0.892857
## 2002-12-16 0.898571 0.927857 0.867857 0.913571 0.913571
## 2002-12-17 0.914286 0.927143 0.892857 0.906429 0.906429
## 2002-12-18 0.896429 0.898571 0.763571 0.785000 0.785000
## 2002-12-19 0.770714 0.799286 0.758571 0.771429 0.771429
## 2002-12-20 0.764286 0.771429 0.728571 0.764286 0.764286
## NFLX.Volume
## 2002-12-13 1395800
## 2002-12-16 2098600
## 2002-12-17 1680000
## 2002-12-18 13378400
## 2002-12-19 3781400
## 2002-12-20 4384800
tail(data.NFLX)
## NFLX.Open NFLX.High NFLX.Low NFLX.Close NFLX.Adjusted
## 2018-07-24 366.94 367.40 354.56 357.32 357.32
## 2018-07-25 357.57 363.28 355.65 362.87 362.87
## 2018-07-26 358.19 365.54 356.63 363.09 363.09
## 2018-07-27 366.85 367.00 351.65 355.21 355.21
## 2018-07-30 351.93 352.03 334.02 334.96 334.96
## 2018-07-31 331.51 342.50 328.00 337.45 337.45
## NFLX.Volume
## 2018-07-24 12851500
## 2018-07-25 8467800
## 2018-07-26 6993700
## 2018-07-27 8949500
## 2018-07-30 18260700
## 2018-07-31 14085400Plot the data
Q3 During what year (of the study period) did the stock reach the highest price?
2018 WAS THE HIGHEST STOCK PRICE.
plot(data.NFLX$NFLX.Close)
Q4 What is the highest price the stock ever reached? Consider only the study period.
Hint: Use the closing price in the summary statistics below.
$418.97
summary(data.NFLX)
## Index NFLX.Open NFLX.High
## Min. :2002-12-13 Min. : 0.6929 Min. : 0.7086
## 1st Qu.:2006-11-08 1st Qu.: 3.7343 1st Qu.: 3.8243
## Median :2010-10-06 Median : 10.6107 Median : 10.8643
## Mean :2010-10-07 Mean : 45.9126 Mean : 46.6075
## 3rd Qu.:2014-09-03 3rd Qu.: 60.8068 3rd Qu.: 61.5161
## Max. :2018-07-31 Max. :421.3800 Max. :423.2100
## NFLX.Low NFLX.Close NFLX.Adjusted
## Min. : 0.6843 Min. : 0.6907 Min. : 0.6907
## 1st Qu.: 3.6429 1st Qu.: 3.7286 1st Qu.: 3.7286
## Median : 10.3607 Median : 10.6179 Median : 10.6179
## Mean : 45.1841 Mean : 45.9355 Mean : 45.9355
## 3rd Qu.: 60.1032 3rd Qu.: 60.7368 3rd Qu.: 60.7368
## Max. :413.0800 Max. :418.9700 Max. :418.9700
## NFLX.Volume
## Min. : 866300
## 1st Qu.: 7126450
## Median : 12444600
## Mean : 18683695
## 3rd Qu.: 22590575
## Max. :323414000