15  Матриці [EN]

15.1 What is matrices?

Matrix - a collection of elements of the same type (numeric,character, logical) with a fixed set of rows and columns. In the case where the matrix has only rows and columns, it is a two-dimensional data array.

The matrix is created using the matrix() function:

matrix(1:10, byrow = TRUE, nrow = 2)

A matrix: 2 × 5 of type int

1	2	3	4	5
6	7	8	9	10

where 1:10 - a set of elements of the matrix, it can also be a pre-formed vector (entered, by calculation, from a file, etc.),byrow = TRUE - means that the elements in the matrix will be written in rows, so in the pedestrian line contains the value 1:5, and the second6:10 (if we need to write information on the lines then we should use byrow = FASLE),nrow - the number of rows of the matrix.

sales1 <- c(12, 14, 15)
sales2 <- c(22, 15, 21)
sales <- c(sales1, sales2)
m <- matrix(sales, byrow= T, nrow = 2)
m

A matrix: 2 × 3 of type dbl

12	14	15
22	15	21

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15.2 Naming matrices

To specify the names of rows and columns of the matrix, use the functions rownames() and colnames():

m <- matrix(1:9, nrow = 3)
rownames(m) <- c("row1", "row2", "row3")
colnames(m) <- c("c1", "c2", "c3")
m

A matrix: 3 × 3 of type int

	c1	c2	c3
row1	1	4	7
row2	2	5	8
row3	3	6	9

---

15.3 Add rows and columns

Special methods cbind/rbind are used to change the number of elements in rows and columns of matrices, as well as to quickly combine them.

** The cbind ** function allows you to add one or more matrices and/or vectors behind one of the columns. That is, there is not a simple connection, but a comparison by key field. Consider an example:

m1 <- matrix(c(1:3, 101:103), nrow = 3)
colnames(m1) <- c("A", "B")

m2 <- matrix(c(201:203, 1001:1003), nrow = 3)
colnames(m2) <- c("C", "D")

m_bind <- cbind(m1, m2)

m1
m2
m_bind

A matrix: 3 × 2 of type int

A	B
1	101
2	102
3	103

A matrix: 3 × 2 of type int

C	D
201	1001
202	1002
203	1003

A matrix: 3 × 4 of type int

A	B	C	D
1	101	201	1001
2	102	202	1002
3	103	203	1003

---

15.4 Access to matrix elements

The elements of the matrix are accessed by the index of rows and columns. You can select ranges in a similar way to vectors.

Let’s look at an example:

m <- matrix(11:25, nrow = 3)
m

A matrix: 3 × 5 of type int

11	14	17	20	23
12	15	18	21	24
13	16	19	22	25

To display the 10th element of the matrix, you can use the entries (note that the account is from the right left corner of the columns):

m[10]    
m[[10]]

20

20

To display the same element using row and column indexes, write as follows:

# Row #1
# Column #4
m[1,4]

20

Question: What record should you use ti get 18?

Answer: m[2,3]

m[2,3]

18

If you want to output / use an entire row or a whole column, then the block with the index of unnecessary dimensionality can be left blank:

m[1, ] # first row only
m[c(1,3), ] # first and third row only

1. 11
2. 14
3. 17
4. 20
5. 23

A matrix: 2 × 5 of type int

11	14	17	20	23
13	16	19	22	25

m[, 1] # first column only
m[, c(1,3)] # first and third column only

1. 11
2. 12
3. 13

A matrix: 3 × 2 of type int

11	17
12	18
13	19

You can also specify a list of rows and columns to be output / received simultaneously:

m[c(1,3), 2:4]

A matrix: 2 × 3 of type int

14	17	20
16	19	22

You can exclude individual columns or rows by using indexes with minus signs (-):

m[-1, c(-2:-3)]

A matrix: 2 × 3 of type int

12	21	24
13	22	25

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15.5 Useful functions

15.5.1 Matrix dimmentions

To obtain information about the dimensions of the table, there are special functions: nrow(), ncol(), dim():

# Decalre matrix
m <- matrix(1:15, ncol = 3)
m

print(paste("Rows:", nrow(m)))
print(paste("Cols:", ncol(m)))

print(paste("Dim:", paste0(dim(m), collapse = " x ")))

A matrix: 5 × 3 of type int

1	6	11
2	7	12
3	8	13
4	9	14
5	10	15

[1] "Rows: 5"
[1] "Cols: 3"
[1] "Dim: 5 x 3"

Using nrow() andncol() allows you to access the last row and column of the matrix, respectively:

m[nrow(m), ] # last row
m[, ncol(m)] # last colum

1. 5
2. 10
3. 15

1. 11
2. 12
3. 13
4. 14
5. 15

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15.6 References

1. The Comprehensive R Archive NetworkRcran: Url: https://cran.r-project.org/
2. RStudio official website. Url: https://rstudio.com/
3. Anaconda official website. Url: https://www.anaconda.com/
4. Introduction to R. Datacamp interactive course. Url: https://www.datacamp.com/courses/free-introduction-to-r
5. Quanargo. Introduction to R. Url: https://www.quantargo.com/courses/course-r-introduction
6. R Coder Project. Begin your data science career with R language! Url: https://r-coder.com/
7. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-project.org/.
8. A.B. Shipunov, EM Baldin, P.A. Volkova, VG Sufiyanov. Visual statistics. We use R! - M .: DMK Press, 2012. - 298 p .: ill.
9. An Introduction to R. URL: https://cran.r-project.org/doc/manuals/r-release/R-intro.html
10. R programming. https://www.datamentor.io/r-programming
11. Learn R. R Functions. https://www.w3schools.com/r/r_functions.asp
12. UC Business Analytics R Programming Guide. Managing Data Frames. http://uc-r.github.io/dataframes
13. Learn R programming. R - Lists. https://www.tutorialspoint.com/r/r_lists.htm
14. Tutorial on the R Apply Family by Carlo Fanara. https://www.datacamp.com/community/tutorials/r-tutorial-apply-family