R is a statistical computing and graphics language used for statistical development and data analysis. It provides effective data handling, storage, and reporting with a wide range of statistical and graphical techniques. Some key features of R include its open source nature, large community support through CRAN, and ability to handle complex mathematical formulas and statistical tests easily. However, R also has a steep learning curve and some available packages can be buggy. Common R objects include vectors, lists, matrices, arrays, factors, and data frames to store and manipulate tabular data.

1. R
IsRa El Isa

2. What is R?
● A statistical computing and graphics language
● Used in statistical development and data analysis
● Effective data handling, storage, and reporting
● It provides a wide range of statistical and graphical techniques
○ Linear and nonlinear modeling
○ Classical statistical tests
○ Time-series analysis

3. What is R?
● Functional and procedural language
● Supports OOP with generic functions
● R software environment is written in C, Fortran, and R
● Available IDEs: RStudio, Red-R, Rattle
● R is open source
● R is case sensitive

4. Focus of R
● Better easier data analysis
● User-friendly
● Statistics
● Graphical models
● Makes it easier to use complex mathematical formulas
● Statistical tests and models are ready to use

5. History of R
● Released in 1995
● Stable beta version in 2000
● Created by Ross Ihaka and Robert Gentleman
● ‘R’ from Ross and Robert

6. Advantages of R
● Open source
● Makes complex formulas easy
● A huge community
● Data visualization
● CRAN (Comprehensive R Archive Network)
● It runs on UNIX platforms, Windows, and Mac OS

7. Disadvantages of R
● A steep learning curve
● Some of the available packages are buggy
● Many R commands don’t consider the memory management
● Documentation is sometimes not clear

8. R Objects
● Vectors
● Lists
● Matrices
● Arrays
● Factors
● Data Frames

9. Vectors
● Logical
is_car <- TRUE
● Numeric
width <- 12.3
● Integers
height <- 30

10. Vectors
● Complex
area <- width * height
● Characters
name <- “isra”

11. Matrices
● A collection of elements of the same datatype
● Arranged into a fixed number of rows and columns
matrix(1:9, byrow=TRUE, nrow = 3)
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9

12. Factors
● Created using a vector
● Returns the distinct vector elements
v <- c(1,1,1,2,3,3,4,4,7)
f <- factor(v)
f
[1] 1,1,1,2,3,3,4,4,7
Levels: 1,2,3,4,7

13. Data Frames
● Tabular data objects
● Unlike the matrix, each column may contain a different type of data
● A list of vectors of equal lengths
name age gender
1 Isra 25 f
2 Sara 23 f
3 John 36 m

14. Most Important Built-in Functions
● sum()
● mean()
● prod()
● abs()
● range()
● plot()

15. Example
● Read from csv file
● Save the csv file content into a variable of type data frame
● Plot the data
● Conclude some results

16. Resources
● https://www.r-project.org/about.html
● https://www.datacamp.com/home
● R for Data Science by Dan Toomey

17. Questions?