this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
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An Intoduction to R
1. An Introduction to R
Programming Language Implementation and Design
PhD. Saied Farzi
Lecture by Mahmoud Shiri Varamini
Amin Khaleghi
K.N.Toosi university of tech.
autumn 2016
shirivaramini@gmail.com
2. 2
What is R
R is a language and environment for statistical computing
and graphics. It is a GNU project which is similar to the S
language.
●Created in 1993, license: GNU GPL
●Interpreted
●C-like syntax
●Functional programming language semantics (Lisp, APL)
●Object oriented (3 different OO systems)
●Garbage collector
●Mostly call-by-value
●Lexical scope
●Function closure
3. 3
Popularity
● Github: 12
● Stackoverflow: 117341 questions (Java: 978006, Python:
507653)
● Most popular tool for statistical data analysis
4. 4
History
R is an implementation of the S programming language
combined with lexical scoping semantics inspired by Scheme.
S was created by John Chambers while at Bell Labs. There
are some important differences, but much of the code written
for S runs unaltered.
5. 5
History
R was created by Ross Ihaka and Robert Gentleman at the
University of Auckland, New Zealand, and is currently
developed by the R Development Core Team, of which
Chambers is a member. The project was conceived in 1992,
with an initial version released in 1995 and a stable beta
version in 2000.
6. 6
Usage
●Statistics (frequentist and bayesian)
●Machine learning and data mining
●Science (mathematics, chemistry, physics, medical, ecology,
genetics,economy, history, …)
●Finance
●Natural Language Processing
●Data visualization
●Analyzing spatial, spatio-temporal data and time series
●…
11. 11
Data Types (Modes)
• Numeric
• Character
• Logical (TRUE / FALSE)
• Complex
• Raw (bytes)
12. 12
Data structure
R is an object-oriented language: an object in R is anything
(constants, data structures, functions, graphs) that can be
assigned to a variable:
● Data Objects: used to store real or complex numerical
values, logical values or characters. These objects are always
vectors: there are no scalars in R.
● Language Objects: functions, expressions
13. 13
Data structure types
Vectors: one-dimensional arrays used to store collection
data of the same mode
●Numeric Vectors (mode: numeric)
●Complex Vectors (mode: complex)
●Logical Vectors (model: logical)
●Character Vector or text strings (mode: character)
Matrices: two-dimensional arrays to store collections of data
of the same mode. They are accessed by two integer indices.
14. 14
Data structure types
Arrays: similar to matrices but they can be multi-dimensional
(more than two dimensions)
Factors: vectors of categorical variables designed to group
the components of another vector with the same size
Lists: ordered collection of objects, where the elements can
be of different types
Data Frames: generalization of matrices where different
columns can store different mode data.
Functions: objects created by the user and reused to make
specific operations.
16. 16
Numeric Vectors
There are several ways to assign values to a variable:
> a <- 1.7
> 1.7 -> a
> a = 1.7
> assign("a", 1.7)
To show the values:
> a
[1] 1.7
> print(a)
[1] 1.7
17. 17
Numeric Vectors
To generate a vector with several numeric values:
> a <- c(10, 11, 15, 19)
The operations are always done over all the elements of the
numeric array:
> 1/a
[1] 0.10000000 0.09090909 0.06666667 0.05263158
> b <- a-1
> b
[1] 9 10 14 18
To generate a sequence:
> 2:10
[1] 2 3 4 5 6 7 8 9 10
19. 19
Character Vectors
a <- "This is an example"
> a
[1] "This is an example"
We can concatenate vectors after converting them into
character vectors:
> x <- 1.5
> y <- -2.7
> paste("Point is (",x,",",y,")", sep="")
[1] "Point is (1.5,-2.7)"
20. 20
Matrices
A matrix is a bi-dimensional collection of data:
> a <- matrix(1:12, nrow=3, ncol=4)
> a
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
> dim(a)
[1] 3 4
21. 21
Arrays
They are similar to the matrices although they can have 2 o
more dimensions.
> z <- array(1:24, dim=c(2,3,4))
> z
, , 1
[,1] [,2] [,3]
[1,] 1 3 5
[2,] 2 4 6
...
, , 4
[,1] [,2] [,3]
[1,] 19 21 23
[2,] 20 22 24
22. 22
Factors
Factors are vectors that contain categorical information useful
to group the values of other vectors of the same size. Let’s
see an example:
> bv <- c(0.92,0.97,0.87,0.91,0.92,1.04,0.91,0.94,0.96,
+ 0.90,0.96,0.86,0.85) # (B-V) colours
from 13 galaxies
23. 23
Lists
Lists are ordered collections of objects, where the elements
can be of a different type (a list can be a combination of
matrices, vectors, other lists, etc.) They are created using the
list() function:
> gal <- list(name="NGC3379", morf="E", T.RC3=-5,
colours=c(0.53,0.96))
> gal
$name
[1] "NGC3379"
$morf
[1] "E"
$T.RC3
[1] -5
$colours
[1] 0.53 0.96
24. 24
Data Frames (Tables)
A Data Frame is an special type of list very useful for the
statistical work. There are some restrictions to guarantee that
they can be used for this statistical purpose.
Among other restrictions, a Data Frame must verify that:
●List components must be vectors (numeric, character or
logical vectors), factors, numeric matrices or other data
frames.
●Vectors, which are the variables in the data frame, must be
of the same length.
26. 26
if statements
The syntax of if statement is:
if (test_expression) {
statement
}
The syntax of if...else statement is:
if (test_expression) {
statement1
} else {
statement2
}
27. 27
for loops
for ( name in expr_1 )
expr_2
expr: expression
Ex:
for (i in 1:10) {
if (!i %% 2){
next
}
print(i)
}
[1] 1 3 5 7 9
30. 30
Operators
We have the following types of operators in R programming:
● Arithmetic Operators ( + , - , * , / , %% [give the
remainder] , %/% [result of division or quotient] , ^
[exponent] )
● Relational Operators ( > , < , == , <= , >= , != )
● Logical Operators ( & , | , ! , && , || )
● Assignment Operators ( ← or = or < [Called Left
Assignment ] , → or →> [Called Right Assignment] )
●Miscellaneous Operators ( : [creates the series of numbers
in sequence for a vector] , %in% [This operator is used to
identify if an element belongs to a vector] , %*% [multiply a
matrix with its transpose] )
31. 31
read and write
In R, we can read data from files stored outside the R
environment. We can also write data into files which will be
stored and accessed by the operating system. R can read
and write into various file formats like csv, excel, xml etc.
read.table()
Ex:
Mydata ← read.table(“c:/test/data.txt”)
if you want to use back slash you should do this:
Mydata ← read.table(“c:testdata.txt”)
write.table(x, file=” ”)
write.csv2(x, file=”*.csv”)
32. 32
User Interfaces for R
Rstudio Integrated development environment (IDE) for R
Rattle Gnome cross platform GUI for Data Mining using
RRed-R Open source visual programming interface for
Rdeducer Intuitive, cross-platform graphical data analysis
system
RKWard Easy to use, transparent frontend
JGR Universal and unified graphical user interface for
R
R Commander Basic-Statistics GUI for R
terminal Linux terminal
33. 33
Real world scenario :
Mandelbrot set
Short R code calculating Mandelbrot set through the first 20
iterations of equation z = z^2 + c plotted for different complex
constants c. This example demonstrates:
● use of community-developed external libraries (called
packages), in this case caTools package
● handling of complex numbers
● multidimensional arrays of numbers used as basic data
type, see variables C, Z and X.
34. 34
Real world scenario :
Mandelbrot set
install.packages("caTools") # install external package
library(caTools) # external package providing write.gif function
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan",
"#7FFF7F",
"yellow", "#FF7F00", "red", "#7F0000"))
dx <- 400 # define width
dy <- 400 # define height
C <- complex( real=rep(seq(-2.2, 1.0, length.out=dx), each=dy ),
imag=rep(seq(-1.2, 1.2, length.out=dy), dx ) )
C <- matrix(C,dy,dx) # reshape as square matrix of complex numbers
Z <- 0 # initialize Z to zero
X <- array(0, c(dy,dx,20)) # initialize output 3D array
for (k in 1:20) { # loop with 20 iterations
Z <- Z^2+C # the central difference equation
X[,,k] <- exp(-abs(Z)) # capture results
}
write.gif(X, "Mandelbrot.gif", col=jet.colors, delay=900)
