This is my first post about R and I will be covering an important topic here. R is a object oriented language. What I mean is every variable in R is an object. So, even a simple integer has attributes. The attribute can be None or, be a list of attributes including names, dimnames, dim and class.
In R, we have some data types. Everyone is an object from a different class. Let's look at the code bellow:
# The class character
x <- 'hello R users'
x
The <- is an assignment signal. You can use = as well.
So, we created an object named x and assigned it the value hello R. In R, you do not have to explicitly define the type of the object. R takes care of it. In this example, x is an object from the character class.
So, we can use the class() function to get the class of a specific object:
class(x)
Now, let's create an object from each R class:
numeric
y <- 10
y
class(y)
integer
z <- 10L # note the L after the integer
z
class(z)
logical
p <- FALSE
q <- T # you can use the first letter only for logical variable
p
q
class(c(p,q))
complex
# R can deal with complex numbers as well
r <- 21+2i
r
class(r)
In R, we have some data structures as well. They are very powerful objects in terms of data analysis and manipulation. They are atomic vectors, list, matrix, and data frame. Let's see how we can create everyone bellow:
atomic vector or JUST vector
# we can use th c() function to create a vector
v1 <- c(1,2,3,4,5) # a numeric vector
v2 <- c("a", "b", "c", "d") # a character vector
v1
v2
class(c(v1, v2))
list
l1 <- list(vector = c(1,2,3,4,5), number = 10, logical = TRUE, character= 'hello R users')
l1
# Note I created a list with different class objects inside. You can name every object inside the list, or you can omit the names
l2 <- list(c(1,2,3,4,5), 10, TRUE, 'hello R users')
l2
class(c(l1,l2))
matrix
vals <- rnorm(100, 10, 2) # the rnorm() function returns random values with a normal distribution.
# The rnorm() function arguments are the number of values (100), the mean (10) and the standard deviation (2)
m <- matrix(vals, 10, 10) # we pass to the matrix() function the values and the matrix dimension
m
class(m)
data frame
cities <- c("Ribeirão Preto", "Presidente Prudente", "Piracicaba", "São Paulo")
population <- c(694534, 207610, 297767, 12176866)
lived <- c("yes", "yes", "no", "yes")
df <- data.frame(City = cities,
Population_2018 = population,
Has_lived = lived)
df
class(df)
Now I'm showing the attributes these objects have. I'm using the same matrix m I've already created. Let's look the object attributes:
attributes(m)
So, we have only de dim() attribute. This returns the dimension of the object:
dim(m)
It's a 10 x 10 matrix (rows x columns). We can create more attributes to the object:
# Creating the rows and columns names
dimnames(m) <- list(c(paste("L", 1:10, sep = "")), c(paste("C", 1:10, sep = "")))
attributes(m)
And we can assign a name for every value in this matrix. I will be using a repetition structure for this. If you are not familiar with this, no worries. We will be covering this in details further:
for (i in 1:length(m)) {
names(m)[i] <- paste("val_", i, sep = "")
}
names(m)
Basically, I used a for loop to assign a name for every value in the matrix m. Now, is possible to access a specific value in the matrix:
m["val_57"]
And the m attributes now is this:
attributes(m)
So, this is the end of the first tutorial. We covered the basic data types in R. I hope the object oriented concept was clear and helped you to understand how variables are treated in R.