1. R programming language
Dardan Xhymshiti
15-JUN-2016

2. What is R? Statistical programming language:
Statistical computing
Graphics
R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.
R is an open source project licensed under GNU General Public License and currently being developed by R Development Core Team.

3. R statistical features
There are a lot of libraries out there for R.
These libraries implement a wide variety of statistical and graphical techniques:
Linear and nonlinear modeling,
Classical statistical tests,
Time-series analysis,
Classification,
Clustering etc.
Able to create publication-quality graphs, including math symbols.
Dynamic and interactive graphs.
R has Rd, its own LaTeX-like document format.

4. R programming features:
Interpreted programming language
Dynamically typed language
Syntax similar to MATLAB
Data structures:
Vectors, matrices, arrays, data frames (similar to tables in relational databases), lists.
A scalar is represented as a vector with length one.
R has more than 7,801 additional libraries.

5. Downloading and installing
1. Open in your browser
2. Click on CRAN. You will see a list of mirror sites, organized by country.
3. Select a site near you.
4. Click “Download R for windows”
5. Click on “base”.
6. Click on the link for downloading the latest version of R.
7. When the download completes, double-click on the .exe file and answer the usual questions.

6. 1. Entering commands To get started, treat R as a calculator. Write:
1+1
max(1, 5, 10)
min(1, 5,
+ 10)
5*4-3+1

7. 2. Getting help on a Function
Display the documentation for the function
help(function_name)
Use args for a quick remainder of the function arguments
args(function_name)
Use example to see examples of using the function
example(function_name)

8. 3. Printing something
If you enter the variable name or expression at the command prompt, R will print the value: pi
sqrt(2)
Print function knows how to format any R value for printing, including structured values such as matrices and lists:
print(matrix(c(1,2,3,4),2,2))
print(list("a", "b", "c"))
Use cat instead of print, to combine multiple items into a continuous output:
cat("The zero occurs at", 2*pi, "radians.", "\n")

9. 3. Setting variables There is no need to declare variables.
To initialize a variable use the assignment operator (<−):
Assigning different values to the same variable:
x <- 3
x <- 4
z <- sqrt(x^2 + y^2)
print(z)
x <- 3
print(x)
[1] 3
x <- c(“one”, “two”, “three”)
[1] <- “one” “two” “three”

10. 3. Setting variables Setting a global variable: x≪−3
Other assigning operators: =
−>
Write mode(variable_name) to get the runtime type of the variable
> foo = 3
> print(foo)
[1] 3
> 5 -> fum
> print(fum)
[1] 5

11. 4. Creating a vector To create a vector we use the 𝑐(…)
A vector can contain either numbers, strings, or logical values but not a mixture.
C(1,1,2,3,5,8,13,21)
[1]
C(1*pi, 2*pi, 3*pi, 4*pi)
[1] , , ,
C(“one”, “two”, “three”)
[1] “one” “two” “three”
C(TRUE, TRUE, FALSE, TRUE)
[1] TRUE TRUE FALSE TRUE

12. 5. Computing basic statistics
Mean
Mean(x)
Median
Median(x)
Standard deviation
Sd(x)
Variance
Var(x)
Correlation
Cor(x,y)
Covariance
Cov(x,y)

13. 6. Creating sequences
Use n:m expression to create the simple sequence 𝑛, 𝑛+1, 𝑛+2, …,𝑚
1:5
[1]
Seq(from=1, to=5, by=2)
[1] 1 3 5
Rep(1, times=5)
[1]

14. 7. Comparing vectors
Comparing a vector with another one, or comparing a vector with a scalar.
Comparison operators: ==, !=, <, >, <=, >=
Result: a vector of TRUE and FALSE logical values:
v <- c(3, pi, 4)
w <- c(pi, pi, pi)
v == w
[1] FALSE TRUE FALSE
v < w
[1] TRUE FALSE FALSE
v != w
[1] TRUE FALSE TRUE

15. 8. Defining a function
Create a function by using the function keyword followed by a list of parameters and the function body.
Syntax: function(param_1,..., param_n) { expressions }
A function can be passed as parameter to another function.
fn <- function(x) sd(x)/mean(x)
fn(1:10)
[1]
gcd <- function(a,b){
+ if(b==0) return (a)
+ else return(gcd(b,a%%b)) +}

16. 9. Accessing Built-in Datasets
Dataset package comes already with R installation.
Dataset package contains 104 datasets.
You can list the available datasets by writing: data()
To access datasets in other packages, use the data function like this:
data(dataset_name, package=“package_name”)
If you want to know more about a dataset write: help(dataset_name)
Get some statistics about a dataset by writing: summary(dataset_name)

17. 10. Packages library() View the list of installed packages
install.packages(“package_name”)
Installing packages from CRAN

18. 11. Getting and Setting the Working Directory
getwd()
Get the workspace directory
setwd(“path”)
Set the workspace directory

19. 12. Reading tabular data files
Insert a dataset.txt in your directory.
phones <- read.table(“phones.txt”)
phones <- read.table(“phones.txt”, sep=“:“)
phones <- read.table(“phones.txt”, stringsAsFactor=FALSE)
By default, every attribute is considered as factor.
Defining stringsAsFactor we can tell R to show the real attribute types.
Use class(ph$Vi)where i=1,…,d to get the type of the attribute
phones <- read.table(“phones_header.txt”, header=TRUE, stringsAsFactor=FALSE)
It considers the names of attributes.