Course Notes: Introduction to R for Finance

Course Notes

Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! The datasets used in this course are available in the datasets folder.

The order in which you perform your mathematical operations is critical to get the correct answer. The correct sequence of "order of operation" is:

Parenthesis, Exponentiation, Multiplication and Division, Addition and Subtraction

Or PEMDAS for short!

105 = 100 * 1.05

Or in terms of variables:

post_jan_cash <- starting_cash * jan_mult

A quick way to get the multiplier is:

multiplier = 1 + (return / 100)

**To get started, here are some of R's most basic data types:

Numerics are decimal numbers like 4.5. A special type of numeric is an integer, which is a numeric without a decimal piece. Integers must be specified like 4L. Logicals are the boolean values TRUE and FALSE. Capital letters are important here; true and false are not valid. Characters are text values like "hello world".**

Up until now, you have been determining what data type a variable is just by looks. There is actually a better way to check this.

class(my_var)

In R, ls() is a function that lists the objects (e.g. variables, functions, datasets) in the current working environment. When you call the ls() function with no arguments, it will return a character vector of the names of all objects in the current workspace.

vector is a collection of objects of the same data type. Remember, you create a vector using the combine function, c(), and each element you add is separated by a comma.

For example, this is a vector of Apple's stock prices from December, 2016:

apple_stock <- c(109.49, 109.90, 109.11, 109.95, 111.03, 112.12)

And this is a character vector of bond credit ratings:

credit_rating <- c("AAA", "AA", "BBB", "BB", "B")

A logical vector

logic <- c( TRUE, FALSE, TRUE)

The hierarchy for coercion is:

logical < integer < numeric < character

Inside of plot(), you can change the type of your graph using type =. The default is "p" for points, but you can also change it to "l" for line.

Here is the 12 month return vector:

ret <- c(5, 2, 3, 7, 8, 3, 5, 9, 1, 4, 6, 3)

Select the first month: ret[1].

Select the first month by name: ret["Jan"].

Select the first three months: ret[1:3] or ret[c(1, 2, 3)]

Correlation is a measure of association between two things and is represented by a number from -1 to 1

A 1 represents perfect positive correlation, a -1 represents perfect negative correlation, and 0 correlation means that the stocks move independently of each other

Data frames are great because of their ability to hold a different type of data in each column.

To select the entire first row, leave the col empty: stocks[1, ]

To select the first two rows: stocks[1:2, ] or stocks[c(1,2), ]

To select an entire column, leave the row empty: stocks[, 1]

You can also select an entire column by name: stocks[, "apple"]

data.frame(company = c("A", "A", "B"), cash_flow = c(100, 200, 300), year = c(1, 3, 2)) company <- c("A", "A", "B") cash_flow <- c(100, 200, 300) year <- c(1, 3, 2)

data.frame(company, cash_flow, year)

head() - Returns the first few rows of a data frame. By default, 6. To change this, use head(cash, n = ___) tail() - Returns the last few rows of a data frame. By default, 6. To change this, use tail(cash, n = ___) str() - Check the structure of an object. This fantastic function will show you the data type of the object you pass in (here, data.frame), and will list each column variable along with its data type.

cash$cash_flow is the same as cash[,"cash_flow"]

You can delete a column by assigning it NULL subset(cash, company == "A")

Creating new columns in your data frame is as simple as assigning the new information to data_frame$new_column

present_value <- cash_flow * (1 + interest / 100) ^ -year

Note that in the example below, you are creating the plot from a factor and not a character vector. R will throw an error if you try and plot a character vector!

if list was not created...

my_list <- list(my_words = words, my_numbers = numbers)

or use this if list was already created...

my_list <- list(words, numbers) names(my_list) <- c("my_words", "my_numbers")

To access the elements in the list, use [ ]. This will always return another list.

my_list[1]

$my_words [1] "I <3 R"

my_list[c(1,2)]

$my_words [1] "I <3 R"

$my_numbers [1] 42 24 To pull out the data inside each element of your list, use [[ ]].

my_list[[1]]

[1] "I <3 R" If your list is named, you can use the my_words. This is the same as using [[ ]] to return the inner data.

Add weight: 20% Apple, 80% IBM

portfolio$weight <- c(apple = 0.2, ibm = 0.8)

to remove from list

my_list$dans_movie <- NULL

If your list is not named, you can also remove elements by position using my_list[1] <- NULL or my_list[[1]] <- NULL

Create a grouping to split on, and use split() to create a list of two data frames.

grouping <- cash$company split_cash <- split(cash, grouping)

split_cash

$A company cash_flow year 1 A 1000 1 2 A 4000 3 3 A 550 4

$B company cash_flow year 4 B 1500 1 5 B 1100 2 6 B 750 4 7 B 6000 5 To get your original data frame back, use unsplit(split_cash, grouping).

Take Notes

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