Good! Accessing columns by their names is convenient. Can we filter the rows we want to output?

Indeed, we can. First, we can use indices (like it was for vectors or matrices). But usually, we do not know the positions of the rows but know some conditions we want to satisfy. For example, we may want to extract data for only Males or only people older than 30. You can do it by specifying necessary conditions within square brackets. You need to use the double sign `==` for equality.

Assume we have data frame `data` and want to filter to rows having the value `30` in column `age`. This can be done using the following syntax: `data[data$age == 30,]`. Note that you put condition as the first index within the square bracket. For example, for the same training data as before, let's extract the data of people older than 30 and males only.

# Data
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
# Creating a data frame
test <- data.frame(name, age, gender)

# People older than 30
test[test$age > 30, ]
# Males only
test[test$gender == 'M', ]

The output will be the following.
```
  name age gender
2 Julia  43      F
3  Finn  32      M

  name age gender
1 Alex  24      M
3 Finn  32      M
```
As you can see, that's correct.

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.