The document provides an introduction to R programming fundamentals including installation, basic data types, operations, and data visualization techniques. It covers control structures, functions, and examples of statistical analysis tools within R. The content emphasizes R's user-friendliness and graphical capabilities compared to other programming languages.

1. R Programming
Fundamentals
Ragia A. Ibrahim

2. Objectives
• How to program in R ?
• Installing and sitting working directory?
• Introduction , what is R?
Data Types ,Operations on them
• Formulating First Script .
• Visualize data

3. WHAT &WHY
Ross Ihaka
Robert
Gentleman
•R dialect From S
•S is a statistical programming language
•R developed in 1991, to public in 1993,1995 R developers uses
GNU and R become Free
•GNU
• Lots of help available online
• similar to Matlab( not free) , but more user friendly.
•Excellent graphics capabilities
•built-in basic & advanced statistical and numerical analysis tools
Why R ?
WHAT IS R?

4. Installing R
•Installing R on Windows ,
•Installing R on Rstudio , more enjoyable.
working directory
getwd()

5. Data Types & Operators
## comments
Operators
x<-5
︡x
[1] 5
<- assignment operator
: sequence
x <- 1:5
x
[1] 1 2 3 4 5
#Please Make
#it readable
Logical
Y==6
X>Y

6. Data Types & Operators
Create Vector
Data Types
X<-c(1,2,2)
X<-c(1,2,NA,3)
is.na(X)Missing Values
?
x<-c(1:4) nummeric
X<-(TRUE,FALSE) logical
bad<-is.na(X)
X[!bad]
mean(X)
mean(X[!bad])
max(X)
max(X[!bad]) #var..

7. Data Types & Operators
Data Types
Create Matrix (all obj the same)
m <- matrix(nrow=2, ncol=3) #dim(m), attributes(m)
m[1][3] <- 6 #.....
M <-matrix( 1:6, nrow=2,ncol=3)
M
M<-matrix(c(4,5,6,7,8,9), nrow=2,ncol=3)
x<-matrix(c(2:16),nrow=3,ncol=5)
M%*%x ; M+M;M*M
subsiting a matrix , M[1,2]; M[1,];M[1,]

8. Data Types & Operators
#[[]] , vectors with singel [] but lists not.
L <- list(“a”,1,TRUE)
Subseting a list
[[1]], [1]
single [] returns a list, [[]] different data type.
Data Types
Create List ((different elements)

9. Data Types & Operators
> list1<-c(4,5,6)
> list2<-c(5,6,1,2,3)
> my_l<-list(list1,list2)
 my_l
 my_l[[3]]<-c(4,1)
 my_l[[length(my_l)+1]]<-c(4,1)
Data Types
Create List OF Lists

10. Data Types & Operators
Data Frame (tabulated data)
each column is a list (each col. same type)
> Df<-
data.frame(name=c("Amal","Basem","Cren"))
> Df<-cbind(Df,degree=c(6.4,3.5,4))
> Df
Df$name , use $ to retrieve object that has name.
Mean, degree ?
Data Types
Data Frame

11. Control Structure [Control the flow]
If else –-----------------
for – execute loop for fixed number of
times
while – execute while the condition of
while is true
…
d

12. Control Structure [Cont….]
If (x>3)
{y<-2 }
Else
{y<-0}
…
X<-
c(“a”,”b”,”c”,”d”)
For ( i in 1:4)
print(x[i])
If (x>3)
{ print (“Pass”)}
For(i in 1:10)
{print(i) }
Count<-0
# careful ..infinite
loops
While(count<10)
{count<-count+1
Print(count)
}}
IF FOR While

13. Writing a function
Ready! Your first function…
# in your script.
My_add<-function(x,y)
{ x+y}
# propmpet
My_add(6,4)
[take 2 number and add them together]
# what about * / ?

14. Example
?faithful
names(faithful)
hist(faithful$eruptions)
hist(faithful$eruptions,xlab="duration, min")
hist(faithful$eruptions,xlab="duration, min",main="Old faithful eruption")
hist(faithful$eruptions,xlab="duration, min",main="Old faithful
eruption",col=c("red","green","blue","yellow","pink","cyan","violet", "orange"))

15. Thanks…