Quick Intro to R

R Style and Nomenclature

• R variable names frequently contain periods and underscores. Example: male.cholesterol or male_cholesterol

• R users tend to use the word objects to refer to R variables, functions, datasets, etc. This refers to something more than object in an object-oriented programming style.

• John Chambers: Everything that exists in R is an object. Everything that happens is a function call.

So, in R, all action occurs via functions. Even something as simple as

1 + 2

## [1] 3

is computed via a function call.

• You can use = or <- for assignment, but <- is better

Vectors and Indexing

• R uses 1-based indexing!

• R has no concept of a scalar. A scalar is simply a vector of length

• R can handle both numeric and non-numeric (non-numeric) data. Beware R shows its statistical origins in that some non-numeric data may be automatically converted to factors.

• Combining numeric and non-numeric into vectors causes silent coercion into the type that can accommodate the result!

c(1, 2, "foo", "bar")

## [1] "1"   "2"   "foo" "bar"

• Logicals in R are TRUE and FALSE.

• Indexing can be done with integers, names, logical values etc.

x <- 1:10
x[x %% 2 == 0]

## [1]  2  4  6  8 10

x[-(1:5)]

## [1]  6  7  8  9 10

• Lists are versatile data structures that can grow or shrink and contain heterogeneous data. They are constructed using the list function:

(aList <- list(a = 1, b = 2, c = list(1, 2, "abc")))

## $a
## [1] 1
## 
## $b
## [1] 2
## 
## $c
## $c[[1]]
## [1] 1
## 
## $c[[2]]
## [1] 2
## 
## $c[[3]]
## [1] "abc"

Note how a list prints differently.

• With lists, the individual elements can also be accessed using the dollar ($) notation.

aList$c

## [[1]]
## [1] 1
## 
## [[2]]
## [1] 2
## 
## [[3]]
## [1] "abc"

• R has a notion of a missing value, NA. This is not the same as NULL, which indicates nothing is present.

miss1 <- c(1.0, NA, 2.0)
2 * miss1

## [1]  2 NA  4

Some numerical computations will have to provide hints on how to handle the missing values. For example, the mean function computes the average of a set of numbers.

## No hint to process missing values
mean(miss1)

## [1] NA

## Remove missing values before processing
mean(miss1, na.rm = TRUE)

## [1] 1.5

One can check for missing-ness or nullity using the is family of functions.

is.null(c())

## [1] TRUE

is.null(NA)

## [1] FALSE

## This should produce a warning
is.na(c())

## Warning in is.na(c()): is.na() applied to non-(list or vector) of type
## 'NULL'

## logical(0)

is.na(NA)

## [1] TRUE

There are many others: is.numeric, is.list, is.vector, etc.

Numerics

• Standard operations. When you perform arithmetic on vectors, the operations happen on all elements.

## Add two vectors
1:3 + 2:4

## [1] 3 5 7

## Multiply a vector by 2
2 * 1:3

## [1] 2 4 6

## Better to have parenthesis
2 * (1:3)

## [1] 2 4 6

## Divide
c(2, 4, 6) / c(2, 4, 6)

## [1] 1 1 1

## Halve
c(2, 4, 6) / 2

## [1] 1 2 3

## R recycles shorter vector to match length
c(2, 4, 6, 8) / c(1, 2)

## [1] 2 2 6 4

The last operation shows how R tries to make two vectors conform in length and provides no warning. It is easy to avoid such constructs.

• The usual comparison operators are available: == for equality, != for not equal to, >= for greater than or equal to, etc.

xx <- 1:3
xx == xx

## [1] TRUE TRUE TRUE

## 1 is expanded to match length of xx
xx > 1

## [1] FALSE  TRUE  TRUE

Matrices

The function matrix can be used for creating matrices which are two-dimensional arrays.

## Create a 3 by 2 matrix.
m <- matrix(1:6, nrow = 3)
m

##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6

Another way is to use existing vectors to bind into a matrix.

xx <- 1:3
yy <- 4:6
## Bind by columns
(m2 <- cbind(xx, yy))

##      xx yy
## [1,]  1  4
## [2,]  2  5
## [3,]  3  6

## Bind by rows
(m3 <- rbind(xx, yy))

##    [,1] [,2] [,3]
## xx    1    2    3
## yy    4    5    6

The matrix m2 has the same content as m above, but the columns have names xx and yy which can be used in subsetting/indexing.

## Access element in row 1, column 2
m[1, 2]

## [1] 4

## Access second column
m[ , 2]

## [1] 4 5 6

## Do the same with matrix m2
m2[, "yy"]

## [1] 4 5 6

## Access the third row of m
m[3, ]

## [1] 3 6

• Sparse Matrices are provided by specialized packages such as Matrix or slam.

• R provides a large suite of functions for numerical analysis, optimization etc.