Datacamp course - intermediate R
Achmelvich Beach
Nick Hood

Datacamp course - intermediate R

2020, Jul 10    

Continuing my journey into R, the next course in the R programming track at DataCamp is Intermediate R. This course is presented by Filip Schouwenaars. It teaches language syntax and programming conventions, building on the last course.

Conditionals and Control Flow

Relational operators

This section begins with a talk-through the main relational operators in R, with simple examples, followed by exercises in the virtual lab.

> TRUE == TRUE			# Equality
[1] TRUE
> 'oranges' != 'apples'	# Inequality
[1] TRUE
> 'oranges' > 'apples'	# Strings compare alphabetically
[1] TRUE
> 'oranges' < 'apples'
> vec <- c('apples', 'bananas', 'dragon fruit', 'tomato')
> vec > 'oranges'		# Works on vectors (and matrices)

TRUE coerces to the value 1, FALSE, 0. So truth is greater!

Logical operators

Syntax for these familiar operators is &, | and !, for logical AND, OR and NOT, respectively. They have high precedence and therefore do not need brackets around expressions:

> 4 > 3 & 8 <= 9
[1] TRUE

Logical operators may be used on matrices and vectors:

> !c(TRUE, FALSE, 1 > 0)

Note that double-signed operators like && work only on the first element of a vector.

Conditional statements

Again, familiar syntax here, with the conditional test in brackets; code blocks in curly braces; and two statement words, if and else:

x <- 0
if (x < 0) { 
    print ('x is negative')
} else if (x == 0) { 
  print ('x is zero') 
} else { 
    print ('x is positive') 

Notice that the else and else if statements come on the same line as the closign curly brace of the associated if statement. Once a conditional test evaluates TRUE, the corresponding code block is executed and the remaining code within the if control structure is ignored. Conditional statements may be nested.

Evaluation and next steps

There is a greater teacher presence in this course than the previous, through the use of video presentations to support the hands-on interactive labs.

Thus far into the R Programming Track with Datacamp, I have stopped because I have hit an unexpected paywall. Continuing requires a commitment of at least $25 per month, which is good value if I were continuing with courses several hours per day but not appropriate for my current ad-hoc engagement. The day job takes priority, which means all of the available time mostly. I’ll be switching to other resources from now, probably starting with R for Data Science1, or at least the online version.


  1. Grolemund, G. and Wickham, H. (2016) R for Data Science, O’Reilly Media.