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Imagine you got many movie recomendations from your friends and compiled all of the recomendations in a table, with specific info about each movie.

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movie <- list("Toy Story", 1995, c("Animation", "Adventure", "Comedy"))

In the code above, the variable movie contains a list of 3 objects, which are a string, a numeric value, and a vector of strings. Easy, eh? Now let's print the content of the list. We just need to call its name.

movie

A list has a sequence and each element of a list has a position in that sequence, which starts from 1. If you look at our previous example, you can see that each element has its position represented by double square brackets "**[[ ]]**".

### Accessing items in a list | Доступ до елементів списку

The index number 2 returns the second element of a list, if that element exists:

movie[2]

Or you can select a part or interval of elements of a list. In our next example we are retrieving the 1st, 2nd, and 3rd elements:

movie[2:3]

It looks a little confusing, but lists can also store names for its elements.

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movie <- list(name = "Toy Story",

Let me explain that: the list **movie** has some named objects within it. **name**, for example, is an object of type **character**, **year** is an object of type **number**, and **genre** is a vector with objects of type **character**.

movie

You can also get separated information from the list. You can use **listName\$selectorName**. The _dollar-sign operator_ **$** will give you the block of data that is related to selectorName.

movie$genre

Another way of selecting the genre column:

movie["genre"]

You can also use numerical selectors like an array. Here we are selecting elements from 2 to 3.

movie[2:3]

The function __class()__ returns the type of a object. You can use that function to retrieve the type of specific elements of a list:

class(movie$name)

### Adding, modifying, and removing items | Додавання, модифікація і видалення елементів

Adding a new element is also very easy. The code below adds a new field named **age** and puts the numerical value 5 into it. In this case we use the double square brackets operator, because we are directly referencing a list member (and we want to change its content).

movie[["age"]] <- 5

In order to modify, you just need to reference a list member that already exists, then change its content.

movie[["age"]] <- 6

And removing is also easy! You just put **_NULL_**, which means missing value/data, into it.

movie[["age"]] <- NULL

### Concatenating lists | Зчеплення списків

# We split our previous list in two sublists

Lists are really handy for organizing different types of elements in R, and also easy to use. Additionally, lists are also important since this type of data structure is essential to create data frames, our next covered topic.

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Data frame is a structure that is used for storing data tables. Underneath it all, a data frame is a list of vectors of same length, exactly like a table (each vector is a column). We call a function called  __data.frame()__ to create a data frame and pass vector, which are our columns, as arguments. It is required to name the columns that will compose the data frame.

movies <- data.frame(name = c("Toy Story", "Akira", "The Breakfast Club", "The Artist",

Let's print its content of our recently created data frame:

movies

It's very easy! You can note how it looks like a table.

movies$name

You retrieve data using numeric indexing, like in lists:

# This returns the first (1st) column

The function called __str()__ is one of most useful functions in R. With this function you can obtain textual information about an object. In this case,  it delivers information about the objects whitin a data frame. Let's see what it returns:

str(movies)

It shouws this data frame has 8 observations, for 2 columns, so called __name__ and __year__. The "name" column is a factor with 8 levels and "year" is a numerical column.

The class() function works for data frames as well. You can use it to determine the type of a column of a data frame.

class(movies$year)

You can use numerical selectors to reach information inside the table.

movies[1,2] #1-Toy Story, 2-1995

The **_head()_** function is very useful when you have a large table and you need to take a peek at the first elements. This function returns the first 6 values of a data frame (or event a list).

head(movies)

Similar to the previous function, **_tail()_** returns the last 6 values of a data frame or list.

tail(movies)

Now let's try to add a new column to our data frame with the length of each movie in minutes.

movies['length'] <- c(81, 125, 97, 100, 87, 139, 130, 119)

A new column was included into our data frame with just one line of code. We just needed to add a vector to data frame, then it will be our new column.

Now let's try to add a new movie to our data set.

movies <- rbind(movies, c(name="Dr. Strangelove", year=1964, length=94))

REMEMBER!!!! You can't add a list with more variables than the data frame, and vice-versa.

We don't need this movie anymore, let's delete it. Here we are deleting row 12

movies <- movies[-12,]

To delete a column you can just set it as **_NULL_**.

movies[["length"]] <- NULL

That is it! You learned a lot about data frames and how easy it is to work with them.

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#### Scaling R with big data | Масштабування R з використанням великих даних

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### About the Author: | Про автора:

LISTS and DATAFRAMES in R | СПИСКИ і ФРЕЙМИ ДАНИХ¶

Table of Contents | Зміст¶

About the Dataset | Про базу даних¶

Lists | Списки¶

Accessing items in a list | Доступ до елементів списку¶

Named lists | Іменовані списки¶

Adding, modifying, and removing items | Додавання, модифікація і видалення елементів¶

Concatenating lists | Зчеплення списків¶

Data Frames | Фрейми даних (Датафрейми)¶

Scaling R with big data | Масштабування R з використанням великих даних¶

About the Author: | Про автора:¶

About the Translator: | Про перекладача:¶