By this time, we referred to matrix elements by indices. But in the case of large matrices, it will be quite hard to remember and find where precisely necessary elements are.

This issue can be solved by using names on rows/columns. To set names (stored in `names` vector) of rows for matrix `m` use `rownames(m) <- names`. To set names of columns use the same syntax: `colnames(m) <- names`.

> Note
>
> Note that the length of vector names must equal the number of rows or columns respectively. For example, you can not assign 3 column names to a matrix with 4 columns.

For example, let's assign some names to the example matrix.
```
      [,1] [,2] [,3]
[1,]    1    2    3
[2,]    4    5    6
[3,]    7    8    9
```


# Vector of integers
num <- 1:9
# Matrix
m <- matrix(num, nrow = 3, ncol = 3, byrow = T)

# Assign names of rows
rownames(m) <- c('r1', 'r2', 'r3')
# Assign names of columns
colnames(m) <- c('c1', 'c2', 'c3')
m # Output the matrix

As you can see, there are names on both rows and columns. If you have names on rows and (or) columns, you can refer to a specific element(s) by using names. You can do it the same way as indexing: specify the name/names of row(s)/column(s) to extract. For example, from the matrix above, we can extract the element `4` (`r2` and `c1`) and the first row (`r1`).

num <- 1:9
m <- matrix(num, nrow = 3, ncol = 3, byrow = T)
rownames(m) <- c('r1', 'r2', 'r3')
colnames(m) <- c('c1', 'c2', 'c3')
# Extact element `4` using namings
m["r2", "c1"]
# Extract the first row
m["r1",]

In the second part of R Introduction, you will learn about multi-dimensional built-in structures, such as matrices, data frames, and lists.

In the first section, you will learn the two-dimensional structure that is widely used in math. In R, this data type is used not only for math purposes but also has many real-life purposes.

In the second section, you will learn data frames - an 'upgraded' version of the matrix. Unlike matrix, the data frame may contain data of different types. Most real-life tables can be represented as data frames. You will learn how to create/manipulate data frames and extract necessary data.

In the final section, you will learn the lists in R. This data type works as vectors but may contain data of different types.

Namings

Solução

Awesome!

Namings

Solução

Awesome!