Learning R, Part 2

Now that we know some basics, we'll cover

• importing data from a spreadsheet
• data types
• functions

• Probably the most common format for data storage is Excel
• Unfortunately, you can't import Excel files with base R
• There are packages that will do it
• But the best way to import spreadsheet data is to use the CSV file format

• First, open your Excel file
• Go to “Save As”
• For the “Save as type” option, select “CSV (Comma delimited)”
• You'll only be able to save one sheet (CSV won't support multiple sheets)

• Let's say you named the file “mydata.csv” in a folder called “Documents” in your C: drive
• In R, use the following code

data <- read.csv(file = "C:/Documents/mydata.csv")

• R will assume that the cells in the first row of your spreadsheet contain column headers
• The variable data will be a data frame

• We'll cover the following data types in R (some of which we touched on in part 1)
  • vectors
  • matrices
  • data frames
  • lists

• Vectors are variables with ordered, or indexed, values
• All of the values in a vector must be the same class
• Some basic data classes in R are character, integer, numeric, and logical

• To retrieve a value using the index of a vector we use brackets
• Here we create a vector of character values and retrieve the 2nd value

x <- c("red", "white", "blue")
x[2]

[1] "white"

• A matrix is a rectangular array of values
• In R, all values in a matrix must be of the same class
• So, you can have a matrix of numbers or a matrix of characters, but not both
• If you mix them, R will convert all values in the matrix to the most general class, which would be character

• Below we create a matrix using the matrix() function
• We use the colon to create an ordered sequence (e.g., 1:3 creates the vector c(1, 2, 3))

m <- matrix(data = 1:12, nrow = 3)
m

     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12

• We use brackets to retrieve a value, or a subset of values, [rows, columns]
• Here, we retrieve the first column of the matrix from the previous slide

m[, 1]

[1] 1 2 3

• Here we retrieve the second row of the matrix

m[2, ]

[1]  2  5  8 11

• And here we select the value in the 3rd row, 4th column

m[3, 4]

[1] 12

• To select more than one row or column, use vectors for rows and columns
• Here we select the 4 values in the lower right hand corner of the matrix

m[c(2, 3), c(3, 4)]

     [,1] [,2]
[1,]    8   11
[2,]    9   12

• Data frames are like matrices, but the difference is that all of the values don't have to have the same class
• You can think of a column of a data frame as a vector with a certain class
• The main restriction is that each column has to be the same length as all the others

Here's the example from part 1:

price <- c(1000, 4000, 2000, 5000, 500)
carat <- c(0.4, 0.55, 0.45, 0.65, .2)
color <- c("G", "H", "D", "E", "G")
diamonds <- data.frame(price, carat, color)
diamonds

  price carat color
1  1000  0.40     G
2  4000  0.55     H
3  2000  0.45     D
4  5000  0.65     E
5   500  0.20     G

• Like with matrices, we use brackets to retrieve values or subsets
• Here we get the last two prices from the diamonds data frame

diamonds[c(4, 5), 1]

[1] 5000  500

• When columns (or rows) have names, we can use those instead of numbers

diamonds[c(4, 5), "price"]

[1] 5000  500

• And to get a column, we can also use the $ symbol

diamonds$price

[1] 1000 4000 2000 5000  500

• Our last data type is the list
• It's like a vector, but each “value” in a list is not restricted to one number or character string
• A member of a list can be a vector, a matrix, a data frame, or even another list

• Lists are handy because you can create a data object that isn't restricted to the table format
• For example, we could create an object that contains the diamonds data frame along with some other information

sales <- list(product = diamonds, store = "downtown", month = "january")
sales

$product
  price carat color
1  1000  0.40     G
2  4000  0.55     H
3  2000  0.45     D
4  5000  0.65     E
5   500  0.20     G

$store
[1] "downtown"

$month
[1] "january"

• Functions have the form function() and take arguments in the parenthesis
• If you want to know what arguments are required for a function to work, type a question mark in front of it and run it
• In RStudio, the helpfile will appear in the bottom right panel

• You can specify each argument when you run the function, or you can supply the argument in the order they appear in the help file

matrix(data = 1:12, nrow = 3)

is equivalent to

matrix(1:12, 3)

• Part 3 of this series explains how to write your own functions, as well as a few more advanced topics
• These presentations were created using RStudio's “R Presentations”
• The code for this presentation can be found here: https://github.com/NateByers/IntroRpresentation2