• Course overview and mechanics
• Built-in data types
• Built-in functions and operators
• First data structures: Vectors and arrays

• Independence: Otherwise, you rely on someone else having given you exactly the right tool

• Honesty: Otherwise, you end up distorting your problem to match the tools you have

• Clarity: Making your method something a machine can do disciplines your thinking and makes it public; that's science

• No programming knowledge presumed
• Some stats. knowledge presumed
• General programming mixed with data-manipulation and statistical inference
• Class will be very cumulative

• Lectures: concepts, methods, examples
• In-class Assignments to try stuff out and get fast feedback
• HW weekly to do longer and more complex things
• Projects:
  • Project 1: Descriptive statistics
  • Project 2: Inferencial Statistics
• Midterm and Final: in class, hands-on

Download and review at https://www.r-project.org/

2 sorts of things (objects): data and functions

• Data: things like 7, “seven”, \( 7.000 \), the matrix \( \left[ \begin{array}{ccc} 7 & 7 & 7 \\ 7 & 7 & 7\end{array}\right] \)

• Functions: things like \( \log{} \), \( + \) (two arguments), \( < \) (two), \( \mod{} \) (two), mean (one)

A function is a machine which turns input objects (arguments) into an output object (return value), possibly with side effects, according to a definite rule

Different kinds of data object

All data is represented in binary format, by bits (TRUE/FALSE, YES/NO, 1/0)

• Booleans Direct binary values: TRUE or FALSE in R
• Integers: whole numbers (positive, negative or zero), represented by a fixed-length block of bits
• Characters fixed-length blocks of bits, with special coding; strings = sequences of characters
• Floating point numbers: a fraction (with a finite number of bits) times an exponent, like \( 1.87 \times {10}^{6} \), but in binary form
• Missing or ill-defined values: NA, NaN, etc.

7+5

[1] 12

7-5

[1] 2

7*5

[1] 35

7^5

[1] 16807

Comparisons are also binary operators; they take two objects, like numbers, and give a Boolean

7 > 5

[1] TRUE

7 < 5

[1] FALSE

7 >= 7

[1] TRUE

7 <= 5

[1] FALSE

Basically “and” and “or”:

(5 > 7) & (6*7 == 42)

[1] FALSE

(5 > 7) | (6*7 == 42)

[1] TRUE

typeof() function returns the type

is.foo() functions return Booleans for whether the argument is of type foo

as.foo() (tries to) “cast” its argument to type foo — to translate it sensibly into a foo-type value

We can give names to data objects; these give us variables

A few variables are built in:

pi

[1] 3.141593

Variables can be arguments to functions or operators, just like constants:

pi*10

[1] 31.41593

cos(pi)

[1] -1

What names have you defined values for?

ls()

[1] "approx.pi"               "circumference.in.cubits"
[3] "diameter.in.cubits"     

objects()

[1] "approx.pi"               "circumference.in.cubits"
[3] "diameter.in.cubits"     

Getting rid of variables:

rm("circumference.in.cubits")
ls()

[1] "approx.pi"          "diameter.in.cubits"

Group related data values into one object, a data structure

A vector is a sequence of values, all of the same type

x <- c(7, 8, 10, 45)
x

[1]  7  8 10 45

is.vector(x)

[1] TRUE

c() function returns a vector containing all its arguments in order

x[1] is the first element, x[4] is the 4th element
x[-4] is a vector containing all but the fourth element

Operators apply to vectors “pairwise” or “elementwise”:

y <- c(-7, -8, -10, -45)
x+y

[1] 0 0 0 0

x*y

[1]   -49   -64  -100 -2025

Recycling repeats elements in shorter vector when combined with longer

x + c(-7,-8)

[1]  0  0  3 37

x^c(1,0,-1,0.5)

[1] 7.000000 1.000000 0.100000 6.708204

Single numbers are vectors of length 1 for purposes of recycling:

2*x

[1] 14 16 20 90

Lots of functions take vectors as arguments:

• mean(), median(), sd(), var(), max(), min(), length(), sum(): return single numbers
• sort() returns a new vector
• hist() takes a vector of numbers and produces a histogram, a highly structured object, with the side-effect of making a plot
• Similarly ecdf() produces a cumulative-density-function object
• summary() gives a five-number summary of numerical vectors
• any() and all() are useful on Boolean vectors

Vector of indices:

x[2];x[4]

[1] 8

[1] 45

x[c(2,4)]

[1]  8 45

Vector of negative indices

x[c(-1,-3)]

[1]  8 45

(why that, and not 8 10?)

You can give names to elements or components of vectors

names(x) <- c("v1","v2","v3","fred")
names(x)

[1] "v1"   "v2"   "v3"   "fred"

x[c("fred","v1")]

fred   v1 
  45    7 

note the labels in what R prints; not actually part of the value

• We write programs by composing functions to manipulate data
• The basic data types let us represent Booleans, numbers, and characters
• Data structure let us group related values together
• Vectors let us group values of the same type
• Use variables rather a profusion of magic constants
• Name components of structures to make data more meaningful