Disclaimer: The content of this RMarkdown note came from a course called Intermediate R for Finance in datacamp.

Intermediate R for Finance

Chapter 1: Dates

1.1 What day is it?

How to identify dates.

# What is the current date?
Sys.Date()
## [1] "2017-06-21"

# What is the current date and time?
Sys.time()
## [1] "2017-06-21 14:09:40 UTC"

# Create the variable today
today <- Sys.Date()


# Confirm the class of today
class(today)
## [1] "Date"

1.2 From char to date

# Create crash
crash <- ("2008-09-29")

# Print crash
crash
## [1] "2008-09-29"

# crash as a numeric
as.numeric(crash)
## [1] NA

# Current time as a numeric
as.numeric(Sys.time())
## [1] 1498054180

# Incorrect date format
#as.Date("09/29/2008") # Gives "Error: character string is not in a standard unambiguous format"

1.3 Many dates

How to declare singular dates and name them.

# Create dates from "2017-02-05" to "2017-02-08" inclusive.
dates <- c("2017-02-05", "2017-02-06", "2017-02-07", "2017-02-08")

# Add names to dates
names(dates) = c("Sunday", "Monday", "Tuesday", "Wednesday")

# Subset dates to only return the date for Monday
dates["Monday"]
##       Monday 
## "2017-02-06"

1.4 Date formats (1)

Proper format for corresponding dates.

# "08,30,30"
as.Date("08,30,1930", format = "%m, %d, %Y")
## [1] "1930-08-30"

# "Aug 30,1930"
as.Date("Aug 30,1930", format = "%b %d, %Y")
## [1] "1930-08-30"

# "30aug1930"
as.Date("30aug1930", format = "%d%b%Y")
## [1] "1930-08-30"

1.5 Date formats (2)

Not only can you convert characters to dates, but you can convert objects that are already dates to differently formatted dates using format().

# char_dates
char_dates <- c("1jan17", "2jan17", "3jan17", "4jan17", "5jan17")

# Create dates using as.Date() and the correct format 
dates <- as.Date(char_dates, format = "%d%b%y")

# Use format() to go from "2017-01-04" -> "Jan 04, 17"
format(dates, format = "%b %d, %y")
## [1] "Jan 01, 17" "Jan 02, 17" "Jan 03, 17" "Jan 04, 17" "Jan 05, 17"

# Use format() to go from "2017-01-04" -> "01,04,2017"
format(dates, format = "%m,%d,%Y")
## [1] "01,01,2017" "01,02,2017" "01,03,2017" "01,04,2017" "01,05,2017"

1.6 Subtraction of dates

Just like with numerics, arithmetic can be done on dates. In particular, you can find the difference between two dates, in days, by using subtraction.

# Dates
dates <- as.Date(c("2017-01-01", "2017-01-02", "2017-01-03"))

# Create the origin
origin <- as.Date("1970-01-01")

# Use as.numeric() on dates
as.numeric(dates)
## [1] 17167 17168 17169

# Find the difference between dates and origin
dates - origin
## Time differences in days
## [1] 17167 17168 17169

1.7 months() and weekdays() and quarters(), oh my!

As a final lesson on dates, there are a few functions that are useful for extracting date components. One of those is months().

# dates
dates <- as.Date(c("2017-01-02", "2017-05-03", "2017-08-04", "2017-10-17"))

# Extract the months
months(dates)
## [1] "January" "May"     "August"  "October"

# Extract the quarters
quarters(dates)
## [1] "Q1" "Q2" "Q3" "Q4"

# dates2
dates2 <- as.Date(c("2017-01-02", "2017-01-03", "2017-01-04", "2017-01-05"))

# Assign the weekdays() of dates2 as the names()
names(dates2) <- weekdays(dates2)

# Print dates2
dates2
##       Monday      Tuesday    Wednesday     Thursday 
## "2017-01-02" "2017-01-03" "2017-01-04" "2017-01-05"

Chapter 2: If Statements and Operators

2.1 Relational practice

This allows us to compare data and determine if they are true or false.

# Stock prices
apple <- 48.99
micr <- 77.93

# Apple vs Microsoft
apple > micr
## [1] FALSE

# Not equals
apple != micr
## [1] TRUE

# Dates - today and tomorrow
today <- as.Date(Sys.Date())
tomorrow <- as.Date(Sys.Date() + 1)

# Today vs Tomorrow
tomorrow < today
## [1] FALSE

2.2 Vectorized operations

You can extend the concept of relational operators to vectors.

date <- as.Date(c("2017-01-20", "2017-01-23", "2017-01-24", "2017-01-25"))
ibm <- c(170.55, 171.03, 175.90, 178.29)
panera <- c(216.65, 216.06, 213.55, 212.22)
stocks <- data.frame(date = date, ibm = ibm, panera = panera)
stocks
##         date    ibm panera
## 1 2017-01-20 170.55 216.65
## 2 2017-01-23 171.03 216.06
## 3 2017-01-24 175.90 213.55
## 4 2017-01-25 178.29 212.22
# Print stocks
stocks
##         date    ibm panera
## 1 2017-01-20 170.55 216.65
## 2 2017-01-23 171.03 216.06
## 3 2017-01-24 175.90 213.55
## 4 2017-01-25 178.29 212.22

# IBM range
stocks$ibm_buy <- stocks$ibm < 175

# Panera range
stocks$panera_sell <- stocks$panera > 213

# IBM vs Panera
stocks$ibm_vs_panera <- stocks$ibm > stocks$panera

# Print stocks
stocks
##         date    ibm panera ibm_buy panera_sell ibm_vs_panera
## 1 2017-01-20 170.55 216.65    TRUE        TRUE         FALSE
## 2 2017-01-23 171.03 216.06    TRUE        TRUE         FALSE
## 3 2017-01-24 175.90 213.55   FALSE        TRUE         FALSE
## 4 2017-01-25 178.29 212.22   FALSE       FALSE         FALSE

2.3 And / Or

How to check multiple relations at once.

# Restore stocks
stocks[, 4:6] <- NULL

# IBM buy range
stocks$ibm_buy_range <- stocks$ibm > 171 & stocks$ibm < 176

# Panera spikes
stocks$panera_spike <- stocks$panera < 213.20 | stocks$panera > 216.50

# Date range
stocks$good_dates <- stocks$date > as.Date("2017-01-21") & stocks$date < as.Date("2017-01-25")

# Print stocks
stocks
##         date    ibm panera ibm_buy_range panera_spike good_dates
## 1 2017-01-20 170.55 216.65         FALSE         TRUE      FALSE
## 2 2017-01-23 171.03 216.06          TRUE        FALSE       TRUE
## 3 2017-01-24 175.90 213.55          TRUE        FALSE       TRUE
## 4 2017-01-25 178.29 212.22         FALSE         TRUE      FALSE

2.4 Not!

# IBM range
! (stocks$ibm > 176)
## [1]  TRUE  TRUE  TRUE FALSE

# Missing data
missing <- c(24.5, 25.7, NA, 28, 28.6, NA)

# Is missing?
is.na(missing)
## [1] FALSE FALSE  TRUE FALSE FALSE  TRUE

# Not missing?
! is.na(missing)
## [1]  TRUE  TRUE FALSE  TRUE  TRUE FALSE

2.5 Logicals and subset()

stocks[, 4:6] <- NULL
# Panera range
subset(stocks, panera > 216)
##         date    ibm panera
## 1 2017-01-20 170.55 216.65
## 2 2017-01-23 171.03 216.06

# Specific date
subset(stocks, date == as.Date("2017-01-23"))
##         date    ibm panera
## 2 2017-01-23 171.03 216.06

# IBM and Panera joint range
subset(stocks, ibm < 175 & panera < 216.50)
##         date    ibm panera
## 2 2017-01-23 171.03 216.06

2.6 All together now!

date <- seq(from = as.Date("2016-12-01"), to = as.Date("2016-12-30"), by = "days")
date <- date[-26]
apple <- c(109.49, 109.90, NA, NA, 109.11, 109.95, 111.03, 112.12, 113.95, NA, NA, 113.30,
           115.19, 115.19, 115.82, 115.97, NA, NA, 116.64, 116.95, 117.06, 116.29, 116.52,
           NA, NA, 117.26, 116.76, 116.73, 115.82)
micr <- c(59.20, 59.25, NA, NA, 60.22, 59.95, 61.37, 61.01, 61.97, NA,  NA, 62.17, 62.98,
          62.68, 62.58, 62.30, NA, NA, 63.62, 63.54, 63.54, 63.55, 63.24, NA, NA, 63.28,
          62.99, 62.90, 62.14)
stocks <- data.frame(date = date, apple = apple, micr = micr) 

# View stocks
stocks
##          date  apple  micr
## 1  2016-12-01 109.49 59.20
## 2  2016-12-02 109.90 59.25
## 3  2016-12-03     NA    NA
## 4  2016-12-04     NA    NA
## 5  2016-12-05 109.11 60.22
## 6  2016-12-06 109.95 59.95
## 7  2016-12-07 111.03 61.37
## 8  2016-12-08 112.12 61.01
## 9  2016-12-09 113.95 61.97
## 10 2016-12-10     NA    NA
## 11 2016-12-11     NA    NA
## 12 2016-12-12 113.30 62.17
## 13 2016-12-13 115.19 62.98
## 14 2016-12-14 115.19 62.68
## 15 2016-12-15 115.82 62.58
## 16 2016-12-16 115.97 62.30
## 17 2016-12-17     NA    NA
## 18 2016-12-18     NA    NA
## 19 2016-12-19 116.64 63.62
## 20 2016-12-20 116.95 63.54
## 21 2016-12-21 117.06 63.54
## 22 2016-12-22 116.29 63.55
## 23 2016-12-23 116.52 63.24
## 24 2016-12-24     NA    NA
## 25 2016-12-25     NA    NA
## 26 2016-12-27 117.26 63.28
## 27 2016-12-28 116.76 62.99
## 28 2016-12-29 116.73 62.90
## 29 2016-12-30 115.82 62.14

# Weekday investigation
stocks$weekday <- weekdays(stocks$date)

# View stocks again
stocks
##          date  apple  micr   weekday
## 1  2016-12-01 109.49 59.20  Thursday
## 2  2016-12-02 109.90 59.25    Friday
## 3  2016-12-03     NA    NA  Saturday
## 4  2016-12-04     NA    NA    Sunday
## 5  2016-12-05 109.11 60.22    Monday
## 6  2016-12-06 109.95 59.95   Tuesday
## 7  2016-12-07 111.03 61.37 Wednesday
## 8  2016-12-08 112.12 61.01  Thursday
## 9  2016-12-09 113.95 61.97    Friday
## 10 2016-12-10     NA    NA  Saturday
## 11 2016-12-11     NA    NA    Sunday
## 12 2016-12-12 113.30 62.17    Monday
## 13 2016-12-13 115.19 62.98   Tuesday
## 14 2016-12-14 115.19 62.68 Wednesday
## 15 2016-12-15 115.82 62.58  Thursday
## 16 2016-12-16 115.97 62.30    Friday
## 17 2016-12-17     NA    NA  Saturday
## 18 2016-12-18     NA    NA    Sunday
## 19 2016-12-19 116.64 63.62    Monday
## 20 2016-12-20 116.95 63.54   Tuesday
## 21 2016-12-21 117.06 63.54 Wednesday
## 22 2016-12-22 116.29 63.55  Thursday
## 23 2016-12-23 116.52 63.24    Friday
## 24 2016-12-24     NA    NA  Saturday
## 25 2016-12-25     NA    NA    Sunday
## 26 2016-12-27 117.26 63.28   Tuesday
## 27 2016-12-28 116.76 62.99 Wednesday
## 28 2016-12-29 116.73 62.90  Thursday
## 29 2016-12-30 115.82 62.14    Friday

# Remove missing data
stocks_no_NA <- subset(stocks, !is.na(apple))

# Apple and Microsoft joint range
subset(stocks_no_NA, apple > 117 | micr > 63)
##          date  apple  micr   weekday
## 19 2016-12-19 116.64 63.62    Monday
## 20 2016-12-20 116.95 63.54   Tuesday
## 21 2016-12-21 117.06 63.54 Wednesday
## 22 2016-12-22 116.29 63.55  Thursday
## 23 2016-12-23 116.52 63.24    Friday
## 26 2016-12-27 117.26 63.28   Tuesday

2.7 If this

# micr
micr <- 48.55

# Fill in the blanks
if( micr < 55 ) {
    print("Buy!")
}
## [1] "Buy!"

2.8 If this, Else that

# micr
micr <- 57.44

# Fill in the blanks
if( micr < 55 ) {
    print("Buy!")
} else {
    print("Do nothing!")
}
## [1] "Do nothing!"

2.9 If this, Else If that, Else that other thing

# micr
micr <- 105.67

# Fill in the blanks
if( micr < 55 ) {
    print("Buy!")
} else if( micr >= 55 & micr < 75 ){
    print("Do nothing!")
} else { 
    print("Sell!")
}
## [1] "Sell!"

2.10 Can you If inside an If?

# micr
micr <- 105.67
shares <- 1

# Fill in the blanks
if( micr < 55 ) {
    print("Buy!")
} else if( micr >= 55 & micr < 75 ) {
    print("Do nothing!")
} else { 
    if( shares >= 1 ) {
        print("Sell!")
    } else {
        print("Not enough shares to sell!")
    }
}
## [1] "Sell!"

2.11 ifelse()

# Define stocks
date <- seq(from = as.Date("2016-12-01"), to = as.Date("2016-12-30"), by = "days")
date <- date[-c(3,4,10,11,17,18,24,25,26)]
apple <- c(109.49, 109.90, 109.11, 109.95, 111.03, 112.12, 113.95, 113.30,
           115.19, 115.19, 115.82, 115.97, 116.64, 116.95, 117.06, 116.29, 116.52,
           117.26, 116.76, 116.73, 115.82)
micr <- c(59.20, 59.25, 60.22, 59.95, 61.37, 61.01, 61.97, 62.17, 62.98,
          62.68, 62.58, 62.30, 63.62, 63.54, 63.54, 63.55, 63.24, 63.28,
          62.99, 62.90, 62.14)
stocks <- data.frame(date = date, apple = apple, micr = micr) 
# Microsoft test
stocks$micr_buy <- ifelse(test = stocks$micr > 60 & stocks$micr < 62, yes = 1, no = 0)

# Apple test
stocks$apple_date <- ifelse(test = stocks$apple > 117, yes = stocks$date, no = NA)

# Print stocks
stocks
##          date  apple  micr micr_buy apple_date
## 1  2016-12-01 109.49 59.20        0         NA
## 2  2016-12-02 109.90 59.25        0         NA
## 3  2016-12-05 109.11 60.22        1         NA
## 4  2016-12-06 109.95 59.95        0         NA
## 5  2016-12-07 111.03 61.37        1         NA
## 6  2016-12-08 112.12 61.01        1         NA
## 7  2016-12-09 113.95 61.97        1         NA
## 8  2016-12-12 113.30 62.17        0         NA
## 9  2016-12-13 115.19 62.98        0         NA
## 10 2016-12-14 115.19 62.68        0         NA
## 11 2016-12-15 115.82 62.58        0         NA
## 12 2016-12-16 115.97 62.30        0         NA
## 13 2016-12-19 116.64 63.62        0         NA
## 14 2016-12-20 116.95 63.54        0         NA
## 15 2016-12-21 117.06 63.54        0      17156
## 16 2016-12-22 116.29 63.55        0         NA
## 17 2016-12-23 116.52 63.24        0         NA
## 18 2016-12-27 117.26 63.28        0      17162
## 19 2016-12-28 116.76 62.99        0         NA
## 20 2016-12-29 116.73 62.90        0         NA
## 21 2016-12-30 115.82 62.14        0         NA

# Change the class() of apple_date.
class(stocks$apple_date) <- "Date"

# Print stocks again
stocks
##          date  apple  micr micr_buy apple_date
## 1  2016-12-01 109.49 59.20        0       <NA>
## 2  2016-12-02 109.90 59.25        0       <NA>
## 3  2016-12-05 109.11 60.22        1       <NA>
## 4  2016-12-06 109.95 59.95        0       <NA>
## 5  2016-12-07 111.03 61.37        1       <NA>
## 6  2016-12-08 112.12 61.01        1       <NA>
## 7  2016-12-09 113.95 61.97        1       <NA>
## 8  2016-12-12 113.30 62.17        0       <NA>
## 9  2016-12-13 115.19 62.98        0       <NA>
## 10 2016-12-14 115.19 62.68        0       <NA>
## 11 2016-12-15 115.82 62.58        0       <NA>
## 12 2016-12-16 115.97 62.30        0       <NA>
## 13 2016-12-19 116.64 63.62        0       <NA>
## 14 2016-12-20 116.95 63.54        0       <NA>
## 15 2016-12-21 117.06 63.54        0 2016-12-21
## 16 2016-12-22 116.29 63.55        0       <NA>
## 17 2016-12-23 116.52 63.24        0       <NA>
## 18 2016-12-27 117.26 63.28        0 2016-12-27
## 19 2016-12-28 116.76 62.99        0       <NA>
## 20 2016-12-29 116.73 62.90        0       <NA>
## 21 2016-12-30 115.82 62.14        0       <NA>

Chapter 3: Loops

3.1 Repeat, repeat, repeat

# Stock price
stock_price <- 126.34

repeat {
  # New stock price
  stock_price <- stock_price * runif(1, .985, 1.01)
  print(stock_price)
  
  # Check
  if(stock_price < 125) {
    print("Stock price is below 124.5! Buy it while it's cheap!")
    break
  }
}
## [1] 125.4911
## [1] 125.7271
## [1] 126.151
## [1] 126.1773
## [1] 124.4303
## [1] "Stock price is below 124.5! Buy it while it's cheap!"

3.2 When to break?

# Stock price
stock_price <- 67.55

repeat {
  # New stock price
  stock_price <- stock_price * .995
  print(stock_price)
  
  # Check
  if(stock_price < 66) {
    print("Stock price is below 66! Buy it while it's cheap!")
    break
  }
  
}
## [1] 67.21225
## [1] 66.87619
## [1] 66.54181
## [1] 66.2091
## [1] 65.87805
## [1] "Stock price is below 66! Buy it while it's cheap!"

3.3 While with a print

# Initial debt
debt <- 5000

# While loop to pay off your debt
while (debt > 0) {
  debt <- debt - 500
  print(paste("Debt remaining", debt))
}
## [1] "Debt remaining 4500"
## [1] "Debt remaining 4000"
## [1] "Debt remaining 3500"
## [1] "Debt remaining 3000"
## [1] "Debt remaining 2500"
## [1] "Debt remaining 2000"
## [1] "Debt remaining 1500"
## [1] "Debt remaining 1000"
## [1] "Debt remaining 500"
## [1] "Debt remaining 0"

3.4 While with a plot

debt <- 5000    # initial debt
i <- 0          # x axis counter
x_axis <- i     # x axis
y_axis <- debt  # y axis

# Initial plot
plot(x_axis, y_axis, xlim = c(0,10), ylim = c(0,5000))


# Graph your debt
while (debt > 0) {

  # Updating variables
  debt <- debt - 500
  i <- i + 1
  x_axis <- c(x_axis, i)
  y_axis <- c(y_axis, debt)
  
  # Next plot
  plot(x_axis, y_axis, xlim = c(0,10), ylim = c(0,5000))
}

3.5 Break it

# debt and cash
debt <- 5000
cash <- 4000

# Pay off your debt...if you can!
while (debt > 0) {
  debt <- debt - 500
  cash <- cash - 500
  print(paste("Debt remaining:", debt, "and Cash remaining:", cash))

  if (cash == 0) {
   print("You ran out of cash!")
   break
  }
}
## [1] "Debt remaining: 4500 and Cash remaining: 3500"
## [1] "Debt remaining: 4000 and Cash remaining: 3000"
## [1] "Debt remaining: 3500 and Cash remaining: 2500"
## [1] "Debt remaining: 3000 and Cash remaining: 2000"
## [1] "Debt remaining: 2500 and Cash remaining: 1500"
## [1] "Debt remaining: 2000 and Cash remaining: 1000"
## [1] "Debt remaining: 1500 and Cash remaining: 500"
## [1] "Debt remaining: 1000 and Cash remaining: 0"
## [1] "You ran out of cash!"

3.6 Loop over a vector

# Sequence
seq <- c(1:10)

# Print loop
for (value in seq) {
    print(value)
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10

# A sum variable
sum <- 0

# Sum loop
for (value in seq) {
    sum <- sum + value
    print(sum)
}
## [1] 1
## [1] 3
## [1] 6
## [1] 10
## [1] 15
## [1] 21
## [1] 28
## [1] 36
## [1] 45
## [1] 55

3.7 Loop over data frame rows

# Define stock
date <- seq(from = as.Date("2016-12-01"), to = as.Date("2016-12-30"), by = "days")
date <- date[-c(3,4,10,11,17,18,24,25,26)]
apple <- c(109.49, 109.90, 109.11, 109.95, 111.03, 112.12, 113.95, 113.30,
           115.19, 115.19, 115.82, 115.97, 116.64, 116.95, 117.06, 116.29, 116.52,
           117.26, 116.76, 116.73, 115.82)
stock <- data.frame(date = date, apple = apple)

# Loop over stock rows
for (row in 1:nrow(stock)) {
    price <- stock[row, "apple"]
    date  <- stock[row, "date"]

    if(price > 116) {
        print(paste("On", date, 
                    "the stock price was", price))
    } else {
        print(paste("The date:", date, 
                    "is not an important day!"))
    }
}
## [1] "The date: 2016-12-01 is not an important day!"
## [1] "The date: 2016-12-02 is not an important day!"
## [1] "The date: 2016-12-05 is not an important day!"
## [1] "The date: 2016-12-06 is not an important day!"
## [1] "The date: 2016-12-07 is not an important day!"
## [1] "The date: 2016-12-08 is not an important day!"
## [1] "The date: 2016-12-09 is not an important day!"
## [1] "The date: 2016-12-12 is not an important day!"
## [1] "The date: 2016-12-13 is not an important day!"
## [1] "The date: 2016-12-14 is not an important day!"
## [1] "The date: 2016-12-15 is not an important day!"
## [1] "The date: 2016-12-16 is not an important day!"
## [1] "On 2016-12-19 the stock price was 116.64"
## [1] "On 2016-12-20 the stock price was 116.95"
## [1] "On 2016-12-21 the stock price was 117.06"
## [1] "On 2016-12-22 the stock price was 116.29"
## [1] "On 2016-12-23 the stock price was 116.52"
## [1] "On 2016-12-27 the stock price was 117.26"
## [1] "On 2016-12-28 the stock price was 116.76"
## [1] "On 2016-12-29 the stock price was 116.73"
## [1] "The date: 2016-12-30 is not an important day!"

3.8 Loop over matrix elements

# Define corr
corr <- matrix(c(1.00, 0.96, 0.88, 0.96, 1.00, 0.74, 0.88, 0.74, 1.00), 3, 3)
row.names(corr) <- c("apple", "ibm", "micr")
colnames(corr) <- c("apple", "ibm", "micr")

# Print out corr
corr
##       apple  ibm micr
## apple  1.00 0.96 0.88
## ibm    0.96 1.00 0.74
## micr   0.88 0.74 1.00
##       apple  ibm micr
## apple  1.00 0.96 0.88
## ibm    0.96 1.00 0.74
## micr   0.88 0.74 1.00

# Create a nested loop
for(row in 1:nrow(corr)) {
    for(col in 1:ncol(corr)) {
        print(paste(colnames(corr)[col], "and", rownames(corr)[row], 
                    "have a correlation of", corr[row,col]))
    }
}
## [1] "apple and apple have a correlation of 1"
## [1] "ibm and apple have a correlation of 0.96"
## [1] "micr and apple have a correlation of 0.88"
## [1] "apple and ibm have a correlation of 0.96"
## [1] "ibm and ibm have a correlation of 1"
## [1] "micr and ibm have a correlation of 0.74"
## [1] "apple and micr have a correlation of 0.88"
## [1] "ibm and micr have a correlation of 0.74"
## [1] "micr and micr have a correlation of 1"

3.9 Break and next

# Define apple
apple <- c(109.49, 109.90, NA, NA, 109.11, 109.95, 111.03, 112.12, 113.95, NA, NA,
           113.30, 115.19, 115.19, 115.82, 115.97, NA, NA, 116.64, 116.95, 117.06,
           116.29, 116.52, NA, NA, 117.26, 116.76, 116.73, 115.82)
# Print apple
apple
##  [1] 109.49 109.90     NA     NA 109.11 109.95 111.03 112.12 113.95     NA
## [11]     NA 113.30 115.19 115.19 115.82 115.97     NA     NA 116.64 116.95
## [21] 117.06 116.29 116.52     NA     NA 117.26 116.76 116.73 115.82

# Loop through apple. Next if NA. Break if above 117.
for (value in apple) {
    if(is.na(value)) {
        print("Skipping NA")
        next
    }
    
    if(value > 117) {
        print("Time to sell!")
        break
    } else {
        print("Nothing to do here!")
    }
}
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Skipping NA"
## [1] "Skipping NA"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Skipping NA"
## [1] "Skipping NA"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Skipping NA"
## [1] "Skipping NA"
## [1] "Nothing to do here!"
## [1] "Nothing to do here!"
## [1] "Time to sell!"

Chapter 4: Functions

4.1 Function help and documentation

# subset help
?subset(help)

# Sys.time help
?Sys.time(help)

4.2 Optional arguments

# Round 5.4
round(5.4)
## [1] 5

# Round 5.4 with 1 decimal place
round(5.4, digits = 1)
## [1] 5.4
# numbers
numbers <- c(.002623, pi, 812.33345)

# Round numbers to 3 decimal places
round(numbers, digits = 3)
## [1]   0.003   3.142 812.333

4.3 Functions in functions

# cbind() the stocks
stocks <- cbind(apple, ibm, micr)

# cor() to create the correlation matrix
cor(stocks)
##       apple        ibm       micr
## apple     1         NA         NA
## ibm      NA 1.00000000 0.03846558
## micr     NA 0.03846558 1.00000000

# All at once! Nest cbind() inside of cor()
cor(cbind(apple, ibm, micr))
##       apple        ibm       micr
## apple     1         NA         NA
## ibm      NA 1.00000000 0.03846558
## micr     NA 0.03846558 1.00000000

4.4 Your first function

# Percent to decimal function
percent_to_decimal <- function(percent) {
    percent / 100
}

# Use percent_to_decimal() on 6
percent_to_decimal(6)
## [1] 0.06

# Example percentage
pct <- 8

# Use percent_to_decimal() on pct
percent_to_decimal(pct)
## [1] 0.08

4.5 Multiple arguments (1)

# Percent to decimal function
percent_to_decimal <- function(percent, digits = 2) {
    decimal <- percent / 100
    
    round(decimal, digits)
}

# percents
percents <- c(25.88, 9.045, 6.23)

# percent_to_decimal() with default digits
percent_to_decimal(percents)
## [1] 0.26 0.09 0.06

# percent_to_decimal() with digits = 4
percent_to_decimal(percents, digits = 4)
## [1] 0.2588 0.0904 0.0623

4.6 Multiple arguments (2)

# Present value function
pv <- function(cash_flow, i, year) {
    
    # Discount multiplier
    mult <- 1 + percent_to_decimal(i)
    
    # Present value calculation
    cash_flow * mult ^ -year
}

# Calculate a present value
pv(1200, 7, 3)
## [1] 979.5575

4.7 tidyquant package

# Library tidquant
library(tidyquant)

# Pull Apple stock data
apple <- tq_get("AAPL", get = "stock.prices", 
                from = "2007-01-03", to = "2017-06-05")

# Take a look at what it returned
head(apple)
## # A tibble: 6 × 7
##         date  open  high   low    close    volume adjusted
##       <date> <dbl> <dbl> <dbl>    <dbl>     <dbl>    <dbl>
## 1 2007-01-03 86.29 86.58 81.90 83.80000 309579900 10.81246
## 2 2007-01-04 84.05 85.95 83.82 85.66000 211815100 11.05245
## 3 2007-01-05 85.77 86.20 84.40 85.04999 208685400 10.97374
## 4 2007-01-08 85.96 86.53 85.28 85.47000 199276700 11.02793
## 5 2007-01-09 86.45 92.98 85.15 92.57000 837324600 11.94403
## 6 2007-01-10 94.75 97.80 93.45 97.00000 738220000 12.51562

# Plot the stock price over time
plot(apple$date, apple$adjusted, type = "l")


# Calculate daily stock returns for the adjusted price
apple <- tq_mutate(data = apple,
                   ohlc_fun = Ad,
                   mutate_fun = dailyReturn)

# Sort the returns from least to greatest
sorted_returns <- sort(apple$daily.returns)

# Plot them
plot(sorted_returns)

Chapter 5: Apply

5.1 lapply() on a list

# Define Stock_Return
apple <- c(0.37446342, -0.71883530,  0.76986527, 0.98226467,  0.98171665,  1.63217981, -0.57042563, 1.66813769, 0.00000000,  0.54692248, 0.12951131,  0.57773562, 0.26577503, 0.09405729, -0.65778233,  0.19778141, 0.63508411, -0.42640287, -0.02569373, -0.77957680)
ibm <- c(0.1251408, -0.1124859, 0.3190691, 2.7689429, 0.3458948, 0.7014998,  -0.6125390,  1.6858006, 0.1307267, -0.2907839, -0.7677657, -0.0299886, 0.5519558, -0.1610979, -0.1613578, -0.2095056, 0.2579329, -0.5683858,  0.2467056, -0.3661465)
micr <- c(0.08445946, 1.63713080, -0.44835603, 2.36864053, -0.58660583, 1.57351254,  0.32273681, 1.30287920, -0.47634170, -0.15954052, -0.44742729, 2.11878010, -0.12574662, 0.00000000,  0.01573812, -0.48780488,  0.06325111, -0.45828066, -0.14287982, -1.20826709)
stock_return <- list(apple = apple, ibm = ibm, micr = micr)

# Define percent_to_decimal
percent_to_decimal <- function(percent, digits = 2) {
    decimal <- percent / 100
    
    round(decimal, digits)
}
# Print stock_return
stock_return
## $apple
##  [1]  0.37446342 -0.71883530  0.76986527  0.98226467  0.98171665
##  [6]  1.63217981 -0.57042563  1.66813769  0.00000000  0.54692248
## [11]  0.12951131  0.57773562  0.26577503  0.09405729 -0.65778233
## [16]  0.19778141  0.63508411 -0.42640287 -0.02569373 -0.77957680
## 
## $ibm
##  [1]  0.1251408 -0.1124859  0.3190691  2.7689429  0.3458948  0.7014998
##  [7] -0.6125390  1.6858006  0.1307267 -0.2907839 -0.7677657 -0.0299886
## [13]  0.5519558 -0.1610979 -0.1613578 -0.2095056  0.2579329 -0.5683858
## [19]  0.2467056 -0.3661465
## 
## $micr
##  [1]  0.08445946  1.63713080 -0.44835603  2.36864053 -0.58660583
##  [6]  1.57351254  0.32273681  1.30287920 -0.47634170 -0.15954052
## [11] -0.44742729  2.11878010 -0.12574662  0.00000000  0.01573812
## [16] -0.48780488  0.06325111 -0.45828066 -0.14287982 -1.20826709

# lapply to change percents to decimal
lapply(stock_return, FUN = percent_to_decimal)
## $apple
##  [1]  0.00 -0.01  0.01  0.01  0.01  0.02 -0.01  0.02  0.00  0.01  0.00
## [12]  0.01  0.00  0.00 -0.01  0.00  0.01  0.00  0.00 -0.01
## 
## $ibm
##  [1]  0.00  0.00  0.00  0.03  0.00  0.01 -0.01  0.02  0.00  0.00 -0.01
## [12]  0.00  0.01  0.00  0.00  0.00  0.00 -0.01  0.00  0.00
## 
## $micr
##  [1]  0.00  0.02  0.00  0.02 -0.01  0.02  0.00  0.01  0.00  0.00  0.00
## [12]  0.02  0.00  0.00  0.00  0.00  0.00  0.00  0.00 -0.01

5.2 lapply() on a data frame

# Define Stock_Return
stock_return <- lapply(stock_return, function(x) {x/100})
stock_return <- data.frame(stock_return)

# Print stock_return
stock_return
##            apple          ibm          micr
## 1   0.0037446342  0.001251408  0.0008445946
## 2  -0.0071883530 -0.001124859  0.0163713080
## 3   0.0076986527  0.003190691 -0.0044835603
## 4   0.0098226467  0.027689429  0.0236864053
## 5   0.0098171665  0.003458948 -0.0058660583
## 6   0.0163217981  0.007014998  0.0157351254
## 7  -0.0057042563 -0.006125390  0.0032273681
## 8   0.0166813769  0.016858006  0.0130287920
## 9   0.0000000000  0.001307267 -0.0047634170
## 10  0.0054692248 -0.002907839 -0.0015954052
## 11  0.0012951131 -0.007677657 -0.0044742729
## 12  0.0057773562 -0.000299886  0.0211878010
## 13  0.0026577503  0.005519558 -0.0012574662
## 14  0.0009405729 -0.001610979  0.0000000000
## 15 -0.0065778233 -0.001613578  0.0001573812
## 16  0.0019778141 -0.002095056 -0.0048780488
## 17  0.0063508411  0.002579329  0.0006325111
## 18 -0.0042640287 -0.005683858 -0.0045828066
## 19 -0.0002569373  0.002467056 -0.0014287982
## 20 -0.0077957680 -0.003661465 -0.0120826709

# lapply to get the average returns
lapply(stock_return, FUN = mean)
## $apple
## [1] 0.002838389
## 
## $ibm
## [1] 0.001926806
## 
## $micr
## [1] 0.002472939

# Sharpe ratio
sharpe <- function(returns) {
    (mean(returns) - .0003) / sd(returns)
}

# lapply to get the sharpe ratio
lapply(stock_return, FUN = sharpe)
## $apple
## [1] 0.3546496
## 
## $ibm
## [1] 0.2000819
## 
## $micr
## [1] 0.218519

5.3 FUN arguments

# sharpe
sharpe <- function(returns, rf = .0003) {
    (mean(returns) - rf) / sd(returns)
}

# First lapply()
lapply(stock_return, FUN = sharpe, rf = .0004)
## $apple
## [1] 0.3406781
## 
## $ibm
## [1] 0.1877828
## 
## $micr
## [1] 0.2084626

# Second lapply()
lapply(stock_return, FUN = sharpe, rf = .0009)
## $apple
## [1] 0.2708209
## 
## $ibm
## [1] 0.1262875
## 
## $micr
## [1] 0.1581807

5.4 sapply() VS lapply()

# lapply() on stock_return
lapply(stock_return, FUN = sharpe)
## $apple
## [1] 0.3546496
## 
## $ibm
## [1] 0.2000819
## 
## $micr
## [1] 0.218519

# sapply() on stock_return
sapply(stock_return, FUN = sharpe)
##     apple       ibm      micr 
## 0.3546496 0.2000819 0.2185190

# sapply() on stock_return with optional arguments
sapply(stock_return, FUN = sharpe, simplify = FALSE, USE.NAMES = FALSE)
## $apple
## [1] 0.3546496
## 
## $ibm
## [1] 0.2000819
## 
## $micr
## [1] 0.218519

5.5 Failing to simplify

# Market crash with as.Date()
market_crash <- list(dow_jones_drop = 777.68, 
                     date = as.Date("2008-09-28"))
                     
# Find the classes with sapply()
sapply(market_crash, FUN = class)
## dow_jones_drop           date 
##      "numeric"         "Date"

# Market crash with as.POSIXct()
market_crash2 <- list(dow_jones_drop = 777.68, 
                      date = as.POSIXct("2008-09-28"))

# Find the classes with lapply()
lapply(market_crash2, FUN = class)
## $dow_jones_drop
## [1] "numeric"
## 
## $date
## [1] "POSIXct" "POSIXt"

# Find the classes with sapply()
sapply(market_crash2, FUN = class)
## $dow_jones_drop
## [1] "numeric"
## 
## $date
## [1] "POSIXct" "POSIXt"

5.6 vapply() VS sapply()

# Market crash with as.POSIXct()
market_crash2 <- list(dow_jones_drop = 777.68, 
                      date = as.POSIXct("2008-09-28"))
# Find the classes with sapply()
sapply(market_crash2, class)
## $dow_jones_drop
## [1] "numeric"
## 
## $date
## [1] "POSIXct" "POSIXt"

5.7 More vapply()

# Sharpe ratio for all stocks
vapply(stock_return, sharpe, FUN.VALUE = numeric(1))
##     apple       ibm      micr 
## 0.3546496 0.2000819 0.2185190

# Summarize Apple
summary(stock_return$apple)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.007796 -0.001259  0.002318  0.002838  0.006688  0.016680

# Summarize all stocks
vapply(stock_return, summary, FUN.VALUE = numeric(6))
##             apple        ibm       micr
## Min.    -0.007796 -0.0076780 -0.0120800
## 1st Qu. -0.001259 -0.0022980 -0.0045080
## Median   0.002318  0.0004758 -0.0006287
## Mean     0.002838  0.0019270  0.0024730
## 3rd Qu.  0.006688  0.0032580  0.0056780
## Max.     0.016680  0.0276900  0.0236900

5.8 Anonymous functions

# Max and min
vapply(stock_return, 
       FUN = function(x) { c(max(x), min(x)) }, 
       FUN.VALUE = numeric(2))
##             apple          ibm        micr
## [1,]  0.016681377  0.027689429  0.02368641
## [2,] -0.007795768 -0.007677657 -0.01208267

Quiz 2

If statement

Samsung <- 68.49

if( Samsung < 59 ) {
    print("Buy!")
} else {
    print("Do nothing!")
}
## [1] "Do nothing!"

loop

iced_coffee <- 100 
while (iced_coffee > 0) {
  iced_coffee <- iced_coffee - 5
  print(paste("iced_coffee remainng", iced_coffee))
}
## [1] "iced_coffee remainng 95"
## [1] "iced_coffee remainng 90"
## [1] "iced_coffee remainng 85"
## [1] "iced_coffee remainng 80"
## [1] "iced_coffee remainng 75"
## [1] "iced_coffee remainng 70"
## [1] "iced_coffee remainng 65"
## [1] "iced_coffee remainng 60"
## [1] "iced_coffee remainng 55"
## [1] "iced_coffee remainng 50"
## [1] "iced_coffee remainng 45"
## [1] "iced_coffee remainng 40"
## [1] "iced_coffee remainng 35"
## [1] "iced_coffee remainng 30"
## [1] "iced_coffee remainng 25"
## [1] "iced_coffee remainng 20"
## [1] "iced_coffee remainng 15"
## [1] "iced_coffee remainng 10"
## [1] "iced_coffee remainng 5"
## [1] "iced_coffee remainng 0"

Create a function to takes a vector as an input and print its maximum and minimum.

ibm_stock <- c(159.82, 160.02, 159.84)

range(ibm_stock) 
## [1] 159.82 160.02

Given a data frame of stock returns of three stocks, can you write a code in a single line that prints both maximum and minimum return of each stock.

    apple <- c(109.49, 109.90, 109.11, 109.95, 111.03, 112.12, 113.95, 113.30, 115.19, 115.19, 115.82, 115.97, 116.64, 116.95, 117.06, 116.29, 116.52, 117.26, 116.76, 116.73, 115.82)
ibm <- c(159.82, 160.02, 159.84, 160.35, 164.79, 165.36, 166.52, 165.50, 168.29, 168.51, 168.02, 166.73, 166.68, 167.60, 167.33, 167.06, 166.71, 167.14, 166.19, 166.60, 165.99)
micr <- c(59.20, 59.25, 60.22, 59.95, 61.37, 61.01, 61.97, 62.17, 62.98, 62.68, 62.58, 62.30, 63.62, 63.54, 63.54, 63.55, 63.24, 63.28, 62.99, 62.90, 62.14)

range(apple, ibm, micr)
## [1]  59.20 168.51

Intermediate R for Finance

Eric Mazzola

6/12/2017

Intermediate R for Finance

Chapter 1: Dates

1.1 What day is it?

1.2 From char to date

1.3 Many dates

1.4 Date formats (1)

1.5 Date formats (2)

1.6 Subtraction of dates

1.7 months() and weekdays() and quarters(), oh my!

Chapter 2: If Statements and Operators

2.1 Relational practice

2.2 Vectorized operations

2.3 And / Or

2.4 Not!

2.5 Logicals and subset()

2.6 All together now!

2.7 If this

2.8 If this, Else that

2.9 If this, Else If that, Else that other thing

2.10 Can you If inside an If?

2.11 ifelse()

Chapter 3: Loops

3.1 Repeat, repeat, repeat

3.2 When to break?

3.3 While with a print

3.4 While with a plot

3.5 Break it

3.6 Loop over a vector

3.7 Loop over data frame rows

3.8 Loop over matrix elements

3.9 Break and next

Chapter 4: Functions

4.1 Function help and documentation

4.2 Optional arguments

4.3 Functions in functions

4.4 Your first function

4.5 Multiple arguments (1)

4.6 Multiple arguments (2)

4.7 tidyquant package

Chapter 5: Apply

5.1 lapply() on a list

5.2 lapply() on a data frame

5.3 FUN arguments

5.4 sapply() VS lapply()

5.5 Failing to simplify

5.6 vapply() VS sapply()

5.7 More vapply()

5.8 Anonymous functions

Quiz 2