Understanding R Objects: Naming, Data Structures and Classes in R

This is why R is so powerful: all kind of objects and possibilites. Naming objects, understand structures is crucial to learn

Rstudio
Objects
Author

Jorge Roa

Published

April 16, 2023

Objects in R

One of the things that define R as an amazing tool to do multiple things are objects. R work with objects that essentialy are data structures that can hold values, but also othr types of data and can be assigned names for easy reference. Objects in R are useful because we can organize and store data in meaningful ways. One of the most importants thigns when you are learning R is understand how to work with these objects because you will have the confidence and knowledge to use it at your better convenience. There are several types of objects in R, including vectors, matrices, data frames, lists, and factors, each with their own characteristics and uses.

Assign objects
  • To create an object, give it a name followed by the assignment operator, followed by the value/data type.
  • Assignment operator <-
  • Shortcut: “Alt + -” on PC, “Option + -” on Mac
Execute R code in the website
  • You can execute R code within our website!!
  • You can install plots with this code webr::install("ggplot2"). However, it may take 2 minutes to run.
  • You can check in this link which packages are available and datasets

 

 

In this image, you can see the different types of objects that we have and this is the best way to visualize. However, to really understand what is going on, you need to start writing code and start trying using this different type of objects to know them better. In the next lines, I’ll write explanations, examples and exercises.

 

Naming Objects

It can be headache naming objects in R since you need to have the creativity to write a short but precise name for your object. Here are some reasons why naming objects in R is so essential.

  • Use descriptive names: Choose names that describe the contents or purpose of the object. Avoid using single-letter variable names or abbreviations that are unclear or confusing. For example, use “number_of_widgets” instead of “n” or “num_wgts”.

  • Use lowercase letters: R is case-sensitive, so it’s a good practice to use lowercase letters for object names to avoid confusion.

  • Use underscores to separate words: Use underscores to separate words in object. For example, use “customer_id” instead of “customerId”.

  • Avoid using reserved words: Avoid using reserved words such as “if”, “else”, “while”, “repeat”, “function”, “for”, etc. as object names since they can be take as functions that have exactly the same name.

  • Keep names short but meaningful: Object names should be short and easy to type, but also meaningful and descriptive. Avoid overly long names that are cumbersome to type and read.

Personally, I like to name my objects according to the next table:

Alarid et. al. (2019)

Alarid et. al. (2019)

I like it since it gives you structure and clarity about how to name your objects. For example:

This logics applies the same with matrices, arrays, data frames, etc. It’s a good practice to follow this criteria since is going to be much easier to identify your objects in your environment.

 

Single Values

Numeric values

A numeric object can be stored as a number.

x <- 6

class(x)
[1] "numeric"
x
[1] 6

Character values

Stores a sequence of characters, such as letters, numbers, and symbols. A character value is created by enclosing the sequence of characters within quotation marks, either single or double.

character <- "Welcome to the Hertie Coding Club"   # creating a character value

Character values can be combined using the paste() or paste0() functions, which concatenate two or more character values into a single character value.

x <- "Welcome to"
y <- "the Hertie Coding Club"
z <- paste(x, y, sep = ": ")


class(z)
[1] "character"
z
[1] "Welcome to: the Hertie Coding Club"

 

Vectors

Vectors are one-dimensional arrays of data that contain elements of the same data type, such as numbers or characters. They are a fundamental building block in R and are used extensively in data analysis and statistical computing.

Tip

Vectors can be created using the c() function, or by using functions such as seq() and rep(). Vectors can be manipulated using R’s built-in vectorized functions, such as sum(), mean(), length(), and many more.

 

In R, you can assign a name to an object using the assignment operator “<-” or the equal sign “=”. Here are multiple way to assign a name to an object.

x <- 2

x = 2

assign("x", 2)

 

Create a numeric vector

x <- c(1, 2, 3, 4, 5)

x
[1] 1 2 3 4 5

 

Access elements of a vector

Returns the second element of x, which is 2

x[2] 
[1] 2

 

Perform arithmetic operations

Adds 2 to each element of x, resulting in the vector c(3, 4, 5, 6, 7)

x + 2 
[1] 3 4 5 6 7

 

Find the length of a vector:

length(x) 
[1] 5

 

Modify a vector

Changes the third element of x to 10

x[3] <- 10

x
[1]  1  2 10  4  5

 

Apply functions to a vector

Returns the sum of the elements in x, which is 22

sum(x) 
[1] 22

Returns the mean (average) of the elements in x, which is 4.4

mean(x)
[1] 4.4

Returns the maximum value in x, which is 10

max(x)
[1] 10

Returns the minimum value in x, which is 1

min(x)
[1] 1

 

In addition to numeric values, you can put a wide variety of other data types in a vector in R. Here are some examples:

Character values

names <- c("Hertie School", "Berlin", "Public Policy")

names
[1] "Hertie School" "Berlin"        "Public Policy"

 

Logical values

flags <- c(TRUE, FALSE, TRUE)

flags
[1]  TRUE FALSE  TRUE

 

Times:

times <- as.POSIXct(c("2022-01-01 10:00:00", "2022-01-01 11:00:00", "2022-01-01 12:00:00"))

times
[1] "2022-01-01 10:00:00 EET" "2022-01-01 11:00:00 EET"
[3] "2022-01-01 12:00:00 EET"

 

Exercises

1.-Create a numeric vector x with values 1 through 10. Then, calculate the sum of the first 5 elements of x.






Answer





Code

# Create a numeric vector containing the numbers 1 through 10
x <- 1:10

# Find the sum of the first five elements of the vector x using indexing
sum(x[1:5])








2.-Create a character vector names with the names of your three favorite foods. Then, use indexing to extract the second element of names.






  





Answer





Code

# Create a character vector containing the names "pizza", "tacos", and "soup"
names <- c("pizza", "tacos", "soup")

# Extract the second element of the vector using indexing
names[2]








3.-Create a logical vector flags with values TRUE, FALSE, TRUE, TRUE. Then, use the which() function to find the positions of the TRUE values in flags.






  





Answer





Code

# create a logical vector containing the values TRUE, FALSE, TRUE, and TRUE
flags <- c(TRUE, FALSE, TRUE, TRUE)

# use the which() function to find the indices of the TRUE values in the vector
which(flags)








4.-Create a date vector dates with the dates “2022-01-01”, “2022-02-01”, “2022-03-01”, “2022-04-01”, “2022-05-01”. Then, use the month() function to extract the month of the second date in dates.


Answer





Code

# Create a Date vector containing five dates using the as.Date() function
dates <- as.Date(c("2022-01-01", "2022-02-01", "2022-03-01", "2022-04-01", "2022-05-01"))

# Extract the second element of the vector using indexing and find its month using the month() function
month(dates[2])








5.-Create a vector x with values 1, 2, 3, 4, 5, and a vector y with values 6, 7, 8, 9, 10. Then, concatenate x and y into a single vector z.






  





Answer





Code

# Create two numeric vectors containing 
# the numbers 1 through 5 and 6 through 10, respectively
x <- c(1:5)
y <- c(6:10)

# Concatenate the two vectors to create a 
# new vector called z using the c() function
z <- c(x, y)






 






Lists














A list allow you to store multiple objects of different types. Lists are similar to vectors in that they can store multiple elements, but unlike vectors, the elements of a list can be of different types, such as numeric, character, or even other lists. Each element of a list can be named, allowing you to access and manipulate specific elements of the list by name.


Lists are handy since they can allow you to store different outputs simultaneously. For example, when you are running models, the results are stored in lists and you can access to them by specifying [[]] and the name of the list inside of the squared brackets.




A list with different type of vectors




my_list <- list(numbers = c(1, 2, 3), names = c("Alejandro", "Eduardo", "Fernanda"), statement = c(TRUE, FALSE, TRUE))

my_list




$numbers
[1] 1 2 3

$names
[1] "Alejandro" "Eduardo"   "Fernanda" 

$statement
[1]  TRUE FALSE  TRUE










Access an element of the list using indexing


We can access an element of the list using indexing, which is really useful when you are doing functions and storing results or calling them.


Here this returns the second element of my_list, which is the character vector “names”




my_list[[2]]




[1] "Alejandro" "Eduardo"   "Fernanda" 






or also we can access an element of the list using named indexing:




my_list[["names"]]




[1] "Alejandro" "Eduardo"   "Fernanda" 










Add a new element to the list:


Adds a new numeric vector “ages” to my_list




my_list[["ages"]] <- c(25, 30, 35)




We can do the same even with a dataframe inside a list




df <- data.frame(name = c("Alejandro", "Eduardo", "Marifer"), 
                 age = c(30, 27, 17)) # Create the dataframe

my_list[["my_df"]] <- df

my_list




$numbers
[1] 1 2 3

$names
[1] "Alejandro" "Eduardo"   "Fernanda" 

$statement
[1]  TRUE FALSE  TRUE

$ages
[1] 25 30 35

$my_df
       name age
1 Alejandro  30
2   Eduardo  27
3   Marifer  17










Create a nested list with two elements:




nested_list <- list(numbers = c(1, 2, 3), list_2 = list(letter = "A", number = 42))

nested_list




$numbers
[1] 1 2 3

$list_2
$list_2$letter
[1] "A"

$list_2$number
[1] 42






For access a nested element of the list:




nested_list[["list_2"]][["number"]]  




[1] 42






As you can see, we can access the outputs of the list we want. This is useful since you can apply functions to retrieve your data from a nested list or access to a specific output of your model, for example.








Exercises




1.-Create a list with three elements: a numeric vector, a character vector, and a logical vector. Then, add a new element to the list containing a data frame with three columns: “name”, “age”, and “gender”.






  





Answer





Code

# Create a list with three elements: 
# a numeric vector, a character vector, and a logical vector
l_exercise1 <- list(numbers = c(1, 2, 3), 
                    names = c("Alice", "Bob", "Charlie"), 
                    logical = c(TRUE, FALSE, TRUE))

# Create a data frame with three columns
df <- data.frame(name = c("Alice", "Bob", "Charlie"), 
                 age = c(25, 30, 35), 
                 gender = c("F", "M", "M"))

# Add the data frame as a fourth element 
# to the list using the [[]] operator
l_exercise1[["df"]] <- df








2.-Create a nested list with two elements: a character vector containing the names of three countries, and a list containing the populations and areas of those countries.






  





Answer





Code

# The first element is a character vector containing the names of three countries
# The second element is a list containing two named vectors: population and area

l_exercise2 <- list(countries = c("USA", "China", "India"), 
                    data = list(population = c(328, 1394, 1380), 
                                area = c(9.8, 9.6, 3.3)))








3.- Create a list with four elements: two numeric vectors, a character vector, and a data frame. Then, extract the second element of the character vector and the third column of the data frame.






  





Answer





Code

# Create a list with four elements: two numeric vectors, a character vector, and a data frame
l_exercise3 <- list(x = c(1, 2, 3), y = c(4, 5, 6), 
                    names = c("Guillermo", "Alonso", "Chivo"), 
                    df = data.frame(name = c("Guillermo", "Alonso", "Chivo"), 
                                    age = c(25, 21, 45), 
                                    gender = c("M", "M", "M")))

# Extract the second element of the names vector using the [[]] operator
l_exercise3[["names"]][2]

# Extract the third column of the df data frame using the [[]] operator
l_exercise3[["df"]][3]








4.-Create a list with two elements: a numeric vector and a data frame. Then, use the str() function to display the structure of the list.






  





Answer





Code

# Create a list with two elements: a numeric vector and a data frame
my_list <- list(x = c(1, 2, 3), 
                df = data.frame(name = c("Trejo", "Adame", "Rose"), 
                                age = c(25, 30, 35), 
                                gender = c("M", "M", "F")))

# Display the structure of the list using the str() function
str(my_list)








5.-Create a list with three elements: a numeric vector, a character vector, and a logical vector. Then, use the names() function to assign names to the elements of the list.






  





Answer





Code

# Create a list with three elements: a numeric vector, a character vector, and a logical vector
l_exercise5 <- list(numbers = c(1, 2, 3), 
                    names = c("Mariana", "Bobby", "Niurka", "Ricardo"), 
                    logical = c(TRUE, FALSE, TRUE))

# Use the names() function to assign names to the elements of the list
names(l_exercise5) <- c("num", "nam", "log")








6.-Create a list with three elements: a numeric vector, a character vector, and a logical vector. Then, use the length() function to find the length of the character vector.






  





Answer





Code

# Create a list with three elements: a numeric vector, a character vector, and a logical vector
l_exercise6 <- list(numbers = c(1, 2, 3), 
                names = c("Mariana", "Bobby", "Niurka"), 
                logical = c(TRUE, FALSE, TRUE))

# Use the length() function to find the length of the "names" vector
length(l_exercise6[["names"]])






 






Matrices and arrays














A matrix is a two-dimensional data structure that contains a fixed number of rows and columns. As it showed in the image, matrix can have the same functions as a dataframe, but the difference is how you approach to this object. For example, the elements of a matrix must all be of the same data type, such as numeric or character. Matrices are similar to data frames, but they do not have column names or row names. Usually, this ones are used to mathematic operations or modeling. Arrays, on the other hand, can have any number of dimensions, even including two dimensions for matrices. Arrays can contain elements of different data types, and they are useful for storing and manipulating large amounts of data with complex structures.




Createing a matrix


Create a matrix with three rows and four columns:




m_example <- matrix(1:12, nrow = 3, ncol = 4)

m_example




     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12










Access an element of the matrix using indexing


Returns the element in the second row and third column of my_matrix, which is 7




m_example[2, 3] 




[1] 8










Modify an element of the matrix


Changes the element in the first row and fourth column of my_matrix to 20




m_example[1, 4] <- 20  

m_example




     [,1] [,2] [,3] [,4]
[1,]    1    4    7   20
[2,]    2    5    8   11
[3,]    3    6    9   12










Create an array with three dimensions




arr_example <- array(1:24, dim = c(2, 3, 4))

arr_example




, , 1

     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6

, , 2

     [,1] [,2] [,3]
[1,]    7    9   11
[2,]    8   10   12

, , 3

     [,1] [,2] [,3]
[1,]   13   15   17
[2,]   14   16   18

, , 4

     [,1] [,2] [,3]
[1,]   19   21   23
[2,]   20   22   24










Access an element of the array using indexing


Returns the element in the second row, third column, and fourth layer of my_array, which is 23




arr_example[2, 3, 4]




[1] 24










Modify an element of the array


Changes the element in the second row, second column, and first layer of arr_example to 10




arr_example[2, 2, 1] <- 10 

arr_example




, , 1

     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2   10    6

, , 2

     [,1] [,2] [,3]
[1,]    7    9   11
[2,]    8   10   12

, , 3

     [,1] [,2] [,3]
[1,]   13   15   17
[2,]   14   16   18

, , 4

     [,1] [,2] [,3]
[1,]   19   21   23
[2,]   20   22   24












Exercises




1.-Create a matrix with four rows and three columns, where each element is the square of its row number.






  





Answer





Code

# Create a matrix with 4 rows and 3 columns 
# containing the squares of the numbers 1 through 4
m_exercise1 <- matrix((1:4)^2, nrow = 4, ncol = 3)

# Display the matrix
m_exercise1

# Output

#      [,1] [,2] [,3]
# [1,]    1    1    1
# [2,]    4    4    4
# [3,]    9    9    9
# [4,]   16   16   16








2.-Create a three-dimensional array with dimensions 2 x 3 x 4, where each element is the sum of its three indices.






  





Answer





Code

l_exercise2 <- list(countries = c("USA", "China", "India"), 
                    data = list(population = c(328, 1394, 1380), 
                                area = c(9.8, 9.6, 3.3)))

# $countries
# [1] "USA"   "China" "India"
# 
# $data
# $data$population
# [1]  328 1394 1380
# 
# $data$area
# [1] 9.8 9.6 3.3








3.-Create a matrix with five rows and two columns, where each element is a random number between 1 and 10.






  





Answer





Code

# Create a 5 x 2 matrix with random values between 1 and 10
m_exercise3 <- matrix(sample(1:10, 10), nrow = 5, ncol = 2)

# Display the matrix
m_exercise3


# Output

#      [,1] [,2]
# [1,]    3    8
# [2,]    4    9
# [3,]    2    7
# [4,]    6    1
# [5,]    5   10








4.-Use matrix multiplication to multiply a matrix of dimensions 3 x 2 by a matrix of dimensions 2 x 3.


Hint: Use %*% for matrix multiplication.






  





Answer





Code

# cCeate a 3 x 2 matrix containing the numbers 1 through 6
m_exercise4_1 <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, ncol = 2)

# Create a 2 x 3 matrix containing the numbers 1 through 6 in a different order
m_exercise4_2 <- matrix(c(2, 1, 4, 3, 6, 5), nrow = 2, ncol = 3)

# Perform matrix multiplication on the two matrices
m_multiplication <- m_exercise4_1 %*% m_exercise4_2

# Display the result of the matrix multiplication
m_multiplication

# Output
#      [,1] [,2] [,3]
# [1,]    6   16   26
# [2,]    9   23   37
# [3,]   12   30   48








5.-Use indexing to extract a 2 x 3 sub-array from a 3 x 4 array.






  





Answer





Code

# Create a 3 x 4 array containing the numbers 1 through 12
array_exercise5 <- array(1:12, dim = c(3, 4))

# Extract a 2 x 3 sub-array from rows 2 and 3 and columns 2 through 4
sub_array_ex5 <- array_exercise5[2:3, 2:4]

# Display the sub-array
sub_array_ex5


# Output

#      [,1] [,2] [,3]
# [1,]    5    8   11
# [2,]    6    9   12






 






Data Frame














A data frame is the a common object, specially for people that does data wrangling and analyze data. Thos popular object is a tabular data structure where the rows represent observations and columns represent variables.












Warning







Data Frames must have columns representing a vector of the same length. If you try to merge a vector of 5 elements and a vector of 4 elements, it will give you an error.








Create a Data Frame: multiple ways


1.- Using data.frame() function: This is the most common way to create a data frame in R. You can put as many vectors (variables) as you want.




df <- data.frame(x = c(1, 2, 3), 
                 y = c("a", "b", "c"), 
                 z = c(TRUE, FALSE, TRUE))




2.- Using read_csv2() function: You can also create a data frame by importing data from an external file using the read.table() or read.csv() function.




df <- read.table("data.txt", header = TRUE)




3.- Using read_excel() function from the readxl package




library(readxl)
df <- read_excel("data.xlsx")




4.- Using tibble() function from the tibble package




library(tibble)
df <- tibble(x = c(1, 2, 3), y = c("a", "b", "c"), z = c(TRUE, FALSE, TRUE))




You can check our other tutorials to handle data frames and start practicing. We also recommend these books from the website for you to go into more detail.















References














References







Alarid-Escudero, F., Krijkamp, E. M., Pechlivanoglou, P., Jalal, H., Kao, S. Z., Yang, A., & Enns, E. A. (2019). A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling. PharmacoEconomics, 37(11), 1329–1339. URL
















Note







Cite this page: Roa, J. (2023, April 16). Understanding R Objects: Data Structures and Classes in R. Hertie Coding Club. URL