Entering Input and Evaluation

1 Entering Input

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At the R prompt we type expressions. The <- symbol is the assignment operator.

x <- 1
print(x)

[1] 1

x

[1] 1

msg <- "hello"
msg

[1] "hello"

The grammar of the language determines whether an expression is complete or not.

x <-  ## Incomplete expression

The # character indicates a comment. Anything to the right of the # (including the # itself) is ignored. This is the only comment character in R. Unlike some other languages, R does not support multi-line comments or comment blocks.

2 Evaluation

When a complete expression is entered at the prompt, it is evaluated and the result of the evaluated expression is returned. The result may be auto-printed.

x <- 5   ## nothing is printed
x        ## auto-printing occurs

[1] 5

print(x)  ## explicit printing

[1] 5

The [1] shown in the output indicates that x is a vector and 5 is its first element.

Typically with interactive work, we do not explicitly print objects with the print function; it is much easier to just auto-print them by typing the name of the object and hitting return/enter. However, when writing scripts, functions, or longer programs, there is sometimes a need to explicitly print objects because auto-printing does not work in those settings.

When an R vector is printed you will notice that an index for the vector is printed in square brackets [] on the side. For example, see this integer sequence of length 20.

x <- 11:30
x

 [1] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

The numbers in the square brackets are not part of the vector itself, they are merely part of the printed output.

With R, it’s important that one understand that there is a difference between the actual R object and the manner in which that R object is printed to the console. Often, the printed output may have additional bells and whistles to make the output more friendly to the users. However, these bells and whistles are not inherently part of the object.

Note that the : operator is used to create integer sequences.

3 Reference:

R Programming for Data Science, by Roger D. Peng