Learning any programming language requires learning the basic data types and data structures it supports. R is no different. For the most part, you will find most languages use the same data types and similar data structures.

Data Types

R supports 6 basic data types.

  • character
  • numeric
  • integer
  • logical (boolean)
  • complex
  • It isn’t typically necessary to force a numeric value to be integer. R does a pretty good job at handling this for you.

R has a few functions to examine the objects (R calls everything an object).

  • class() - provides the type of object
  • typeof() - describe what kind of object a data structure contains
  • length() - how long the object is
  • attributes() - additional metadata associated with the object

> char <- "some random characters"
> num <- 1.2
> int <- 2L
> bool <- TRUE
> comp <- 1 + 3i

# Try out some of the 
> class(char)
[1] "character"
> typeof(char)
[1] "character"
> length(char)
[1] 1
> class(complex)
[1] "complex"
> typeof(comp)
[1] "comp"
> length(complex)
[1] 1

There are some other special values in R.

  • NA - missing value
  • Inf - infinity
  • NaN - not a number

> NA
[1] NA
> 1/0
[1] Inf
> 0/0
[1] NaN

These basic data types are also called scalars, which just means they are single values. Next we’ll learn different ways to combine them.

Data Structures

Data structures are ways of combining scalars and even other data structures together to make them easier to work with. There are a few basic ones we’ll cover here. You are likely to encounter more, since many packages have created their own data structure as well. The data structures most commonly encountered include (but aren’t limited to)

  • vectors
  • lists
  • matrixes
  • data frames

We will be just covering the basics here, which may seem like a lot!

Vectors

Vectors are one-dimensional arrays composed of a single data type. There are a number of ways to create a vector.


> vector()  # default type is logical
logical(0)
> vector("character", 4)  # empty character vector
[1] "" "" "" ""
> numeric(10)
 [1] 0 0 0 0 0 0 0 0 0 0
> x <- c(1, 2, 5)   # creating a vector directly.
> x
[1] 1 2 5
> v <- c(1:10)
> v
 [1]  1  2  3  4  5  6  7  8  9 10
> s <- seq(1, 10, 0.1)
> s
 [1]  1.0  1.1  1.2  1.3  1.4  1.5  1.6  1.7  1.8  1.9  2.0  2.1  2.2  2.3  2.4  2.5  2.6
[18]  2.7  2.8  2.9  3.0  3.1  3.2  3.3  3.4  3.5  3.6  3.7  3.8  3.9  4.0  4.1  4.2  4.3
[35]  4.4  4.5  4.6  4.7  4.8  4.9  5.0  5.1  5.2  5.3  5.4  5.5  5.6  5.7  5.8  5.9  6.0
[52]  6.1  6.2  6.3  6.4  6.5  6.6  6.7  6.8  6.9  7.0  7.1  7.2  7.3  7.4  7.5  7.6  7.7
[69]  7.8  7.9  8.0  8.1  8.2  8.3  8.4  8.5  8.6  8.7  8.8  8.9  9.0  9.1  9.2  9.3  9.4
[86]  9.5  9.6  9.7  9.8  9.9 10.0

You can use the functions shown above to explore vectors.


> class(x)
[1] "numeric"
> typeof(x)
[1] "double"
> length(x)
[1] 3

You will often need to add items to a vector.


> c(x, 20)
[1]  1  2  5 20
> x         # The change to x isn't saved
[1] 1 2 5
> x <- c(x, 20)  # This puts the new vector into x 
> x
[1]  1  2  5 20
> x <- c(x, c(NA, 31))  # Combining two vectors and using missing value.
> x
[1]  1  2  5 20 NA 31
> x <- c(x, "42")   # If you mix types, R can do some odd things.
> x
[1] "1"  "2"  "5"  "20" NA   "31" "42"

And you can get parts of a vector.


> x[1]   # One item
[1] "1"
> x[1:3]  # Slice 
[1] "1" "2" "5"
> x[c(1,3,5)]  # Specific items
[1] "1" "5" NA

NOTE: In R the indexes start at 1, not 0 as in many other languages.

Lists

Lists are like vectors except the items can be of different types.


> l <- list(1, TRUE, "sheep", 3 + 4i)
> l
[[1]]
[1] 1

[[2]]
[1] TRUE

[[3]]
[1] "sheep"

[[4]]
[1] 3+4i

You can access elements of a list using double bracket notation.

 
> l[[3]]
[1] "sheep"

You can name list elements too. This can make your R code far more readable and understandable.


> named_list <- list(name="Random Ciizen", age=32, vect=c(1, 3, 4))
> named_list
$name
[1] "Random Ciizen"

$age
[1] 32

$vect
[1] 1 3 4

> named_list[["name"]]  # one way to access by name
[1] "Random Ciizen"

> named_list$vect   # more readable way to access by name
[1] 1 3 4

> names(named_list)
[1] "name" "age"  "vect"

Matrices

Matrices are essentially two dimensional vectors. Like vectors, they must be composed of the same data type. You can me them as follows.


> m1 <- matrix(1:20, nrow=5, ncol=4)
     [,1] [,2] [,3] [,4]
[1,]    1    6   11   16
[2,]    2    7   12   17
[3,]    3    8   13   18
[4,]    4    9   14   19
[5,]    5   10   15   20

> m2 <- matrix(1:20, nrow=5, ncol=4, byrow = TRUE)
     [,1] [,2] [,3] [,4]
[1,]    1    2    3    4
[2,]    5    6    7    8
[3,]    9   10   11   12
[4,]   13   14   15   16
[5,]   17   18   19   20

# You can do useful things like this too. 
# The vector must be a mulitple of the total number of cells.
> m3 <- matrix(1:5, nrow=5, ncol=4)
     [,1] [,2] [,3] [,4]
[1,]    1    1    1    1
[2,]    2    2    2    2
[3,]    3    3    3    3
[4,]    4    4    4    4
[5,]    5    5    5    5


# You can add row and columns names as well.
> cells <- c(1,2,3,5)
> rnames <- c("Row 1", "Row 2")
> cnames <- c("Col 1", "Col 2")
> m4 <- matrix(cells, nrow=2, ncol=2, byrow=TRUE, dimnames=list(rnames, cnames))
      Col 1 Col 2
Row 1     1     2
Row 2     3     5

You can access cells, rows are columns in a matrix using subscripts.


# Getting a single value
> m1[1,2]
[1] 6

# Getting a row
> m1[1,]
[1]  1  6 11 16

# Getting a column
> m1[,2]
[1]  6  7  8  9 10

# Getting mulitple rows
> m1[2:3,]
     [,1] [,2] [,3] [,4]
[1,]    2    7   12   17
[2,]    3    8   13   18

# Getting multiple columns
> m1[,3:4]
     [,1] [,2]
[1,]   11   16
[2,]   12   17
[3,]   13   18
[4,]   14   19
[5,]   15   20

Data Frames

The most frequently used data structure in R is the Data Frame. It is essentially a list of vectors, so that each column must be composed of the same data type, but each row can be composed of a mix of data types. The primary constraint on data frames is that each column be the same length. It may look like a matrix but it is much more general because each column can be of a different data type.

Data frames are typicaly how data from Excel or CSV files are imported which we will learn later. There are several other ways to make a data frame as well, but the method shown below is very common.


> names <- c("Jill", "Joe", "Fido")
> age <- c(4, 7, 8)
> adopted <- c(TRUE, FALSE, TRUE)
> dogs_df <- data.frame(names, age, adopted)
> dogs_df

  names age adopted
1  Jill   4    TRUE
2   Joe   7   FALSE
3  Fido   8    TRUE

You can access parts of a data frame much like matrix using subscripts.


> dogs_df[1,2]
[1] 4

> dogs_df[1,]
  names age adopted
1  Jill   4    TRUE

> dogs_df[,2:3]
  age adopted
1   4    TRUE
2   7   FALSE
3   8    TRUE

You can also access columns using the $ notation.


> dogs_df$age
[1] 4 7 8

You can also select rows based on a certain criterion.

 
> dogs_df[which(df$age > 5),]
  names age adopted
2   Joe   7   FALSE
3  Fido   8    TRUE

You will often need to recode a variable or add a new column (variable) to your data frame. This can usually done very easily as follows.


# This creates a new column named dog_age
> dogs_df$dog_age <- dogs_df$age * 7
> dogs_df
  names age adopted dog_age
1  Jill   4    TRUE      28
2   Joe   7   FALSE      49
3  Fido   8   FALSE      56

# This recodes age to months, overwriting
# the original age in years.
> dogs_df$age <- dogs_df$age * 12
> dogs_df
  names age adopted dog_age
1  Jill  48    TRUE      28
2   Joe  84   FALSE      49
3  Fido  96   FALSE      56

In most cases it is preferable to create a new column rather than overwrite one that exists.

Try it out

  1. Create a vector of logical values (TRUE, FALSE, TRUE, FALSE)

  2. Create a column that displays the length of the dog’s name.