R Variables

DRS Training

Doing Maths in R

R can just be used directly as a calculator but mathematical manipulations come up all the time as parts of R scripts. You can use the R console as a place to test commands and as a calculator.

Examples in this document are largely drawn from

https://swcarpentry.github.io/r-novice-gapminder/ https://swcarpentry.github.io/r-novice-inflammation/ Which may be useful resources to expand your Knowledge of R

Task 1

Try in the Console

The number in square brackets [1] is the index number of the output element at the start of the output line

Task 2:

This output may be clearer with a more complex example

This is an example of R vectorization which we will revisit later.

When using R as a calculator, the order of operations is standard. From highest to lowest precedence: * Parentheses: (, ) * Exponents: ^ or ** * Multiply: Divide: / * Add: + * Subtract: -

Task 3:

Try:

and

Really small or large numbers get a scientific notation:

( shorthand for 2 * 10^(-4) )

Mathmatical functions

R has many built in mathematical functions. To call a function, we can type its name, followed by open and closing parentheses. Functions take arguments as inputs, anything we type inside the parentheses of a function is considered an argument. Trigonometry and Logarithm functions are good examples: (Ignore the # comments)

Don’t worry about trying to remember every function in R. You can look them up on Google, or if you can remember the start of the function’s name, use the tab completion in RStudio.

Incomplete commands and Ctrl-C

If you enter a command on the command line and get the plus symbol + in response, R considers the entered command incomplete and is stuck until you provide more information.

Task 4

Try an incompete command

Try competing the calculation or alternatively use Ctrl C

NOTE If you enter a command that is incorrect or incomplete and R gets ‘stuck’ in a way that you can’t resolve use Ctrl C or Esc to ‘escape’ the situation. (Ctrl C may be more generally applicable if you are using R outside of Rstudio)

Comparing Things

Comparing numbers/variables to get a TRUE or FALSE response is a key element of programming languages

NOTE/WARNING

You should never use == to compare two numbers unless they are integers (a data type which can specifically represent only whole numbers.) Force numbers to integer status with as.integer() ). Computers may only represent decimal numbers with a certain degree of precision, so two numbers which look the same when printed out by R, may actually have different underlying representations and therefore be different by a small margin of error (and therefore not exactly equal). The all.equal() function can be used.

Variables in R

Before you can do anything to process data with R the data needs to be assigned to different variables. We can think of a variable as a container with a name, such as x, current_temp, or subject_id that contains one or more values. We can create a new variable and assign a value to it using <- e.g:

Technically you could use an = to assign values to a variable but in R there is a strong convention that the = sign is used to assign values to function parameters.

Once a variable is created, we can use the variable name to refer to the value it was assigned. The variable name now acts as a tag. Whenever R reads that tag (weight_kg), it substitutes the value (55).

Entering a variable at the command prompt or running it in Rstudio returns the variable content

Once a variable is in memory R can act on it e.g:

A variable can be updated by changing its value

check the Environment tab in Rstudio

New variables can be created using the values of old variables

Main R data types

  • numeric – (3, 6.7, 121) Decimals or Integers (defaults to double)
  • double - (123.321. 6.6) Decimals
  • integer – (2L, 42L) where ‘L’ declares this as an integer/real number
  • logical – (TRUE,FALSE)
  • character – (‘a’, “B”, ‘c is third’, ‘Hello’, ‘666’)

More info on R data types

https://www.r-bloggers.com/2023/09/understanding-data-types-in-r/

Factors

Factors are used to work with categorical variables, variables that have a fixed and known set of possible values. Generally vectors of character variables are converted or treated as factors for certain types of statistical manipulations. Factors are not used much in the R Tidyverse but are necessary or created by default for some older R libraries / functions and they can cause some confusion if your data has been transformed to a factor unintentionally.

Vectors

A vector in R describes a set of values in a certain order of the same data type (numeric, character etc). Vectors are generally created using the c() construct

try the commands typeof() and str() on one of the variables

Subsetting vectors

If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:

Try:

Subsetting logical vectors

Alternatively another common way of sub-setting is by using a logical vector. TRUE will select the element with the same index, while FALSE will not

Generally the conditional vector id generated by other functions or tests

Lists

A list is a vector but with heterogeneous data elements. A list in R is created with the use of the list() function. Lists can be very important / complex nested data objects in R

check the Environment tab in Rstudio for the list structure

Lists are a special type of vector and can be subsetted in very similar ways

Matrix

Matrices are two-dimensional, homogeneous data structures

i.e. they are tables of data of the same type most frequently numeric.

They are created using the matrix() function

Matrix parameters:

  • data – values you want to enter
  • nrow – no. of rows
  • ncol – no. of columns
  • byrow – logical clue, if ‘true’ value will be assigned by rows
  • dimnames – names of rows and columns

Matrix example

N.b. Rows and columns can be named

Other matrix examples

Subsetting a Matrix

Elements of a matrix can be referenced by specifying the index along each dimension (e.g. ‘row’ and “column”) in single square brackets.

For Example:

Omitting the column coordinate gives you the whole row referenced Omitting the row coordinate gives you the whole column referenced

Matrices are often imported in several ways from stored datafiles.

Dataframe

Dataframes are a bit like matrices but can contain different datatypes. One column can be character, then another can be numeric and a third logical. However, each column has to contain the same kind of data. They can be thought of as the data equivalent of a type of spreadsheet.

Dataframes come in several variants, the main one used in the tidyverse is called the tibble https://tibble.tidyverse.org/ We will be exploring dataframes in detail in a later section

Variable Naming Conventions

Variable names can contain letters, numbers, underscores and periods but no spaces. They must start with a letter or a period followed by a letter (they cannot start with a number nor an underscore). Variables beginning with a period are hidden variables.

Naming Conventions

Over time different conventions for long variable names have arisen.

These include: * periods.between.words (old style R) * underscores_between_words (recommended) * camelCaseToSeparateWords (ugly but used in several languages) There are several R naming convention/style guides on the internet including https://style.tidyverse.org/syntax.html