🗓️ Session 2: First steps in R

In this session, you will learn about how to use the integrated development environment R-Studio to edit and execute R script and about the basic commands in R. Moreover, you will be introduced to two fundamental concepts: assignments and functions.

👨‍🏫 Lecture Slides

Either click on the slide area below or click here to download the slides.

The R script of this session

	# 1. Basic commands-----------
	# This is an addition:
	2 + 4
	4 - 9 # This is substraction
	4/9
	3*9
	2**3
	# This is
	# a comment that spans
	# over several lines.
	# 2. Intermediate task I -----------
	5+12
	(2*3)**2
	2*5.8 # Beware: decimal sign in R is '.', not ','
	(8**2 + 5**4) / 3
	## 3. Assignments -----------
	assign("int_result", 2 + 4)
	int_result <- 2 + 4 # Does the same as previous line
	a1 <- 2
	a1 <- 4
	a1 # Second assignment got overridden
	# 4. Intermediate task II -----------
	a_ <- 2+3
	b_ <- (5*a_)/2
	c_ <- (b_+1)**2
	d_ <- sqrt(c_) # c_**0.5
	# Hint: if you get the error "Object not found" such as here:
	obj_x
	# ...then you should always check the following:
	#. 1. Did you misspell the name?
	#. 2. Did you forget the do the assignment in the script? -> search for the name
	# Also, if you get a weird error, always check to which objects the involved
	# names actually point
	# 5. Functions -----------
	t_vec <- c(1, 2, 3, 4)
	# The following four lines do the same thing:
	mean(t_vec)
	mean(c(1,2,3,4))
	mean(x = t_vec) # The name of the mandatory argument 'x' is used explicitly
	mean(x = c(1,2,3,4))
	help(mean) # Get information about arguments
	# Using optional arguments:
	t_vec <- c(1,2,3,4, NA)
	mean(t_vec)
	mean(t_vec, na.rm = FALSE)
	mean(t_vec, na.rm = TRUE)
	# From the fact that `mean(t_vec, na.rm = FALSE)` gives the same result as
	# `mean(t_vec)` we can conclude that by default `na.rm = FALSE`.
	# 6. Intermediate task III -----------
	task_vector <- c(-2, 2, 4, 6, 9, NA)
	median(task_vector)
	median(task_vector, na.rm = TRUE)
	is.na(task_vector)
	anyNA(task_vector)
	sum(task_vector)
	sum(task_vector, na.rm = TRUE)
	# If in doubt use `?` or `help()`.
	# This also explains that var() computes the sample variance:
	var(task_vector, na.rm = TRUE)
	# To compute the population variance you can write your own function:
	var_pop <- function(input_vector) {
	mean((input_vector - mean(input_vector))**2)
	}
	# Or correct the result of var():
	n_obs <- length(task_vector) # get the number of observations
	var(task_vector)*(n_obs-1)/n_obs
	# Although this is slightly less accurate because we count the NA
	# 7. Defining your own functions --------------
	add_two_numbers <- function(nb1, nb2){
	result <- nb1 + nb2
	return(result)
	}
	add_two_numbers(1, 4)
	add_two_numbers(nb1 = 1, nb2 = 4) # equivalent, but mandatory arguments set explicitly
	nb1 # Not defined outside the function body!
	# 8. Defining function - exercise I --------------
	compute_eq <- function(x_1, x_2){
	result <- x_1**2 + 2*x_1*x_2 + x_2**2
	return(result)
	}
	compute_eq(2, 4)
	# 8. Defining function - exercise II --------------
	normalize_vec <- function(input_vector){
	(input_vector - min(input_vector)) / (max(input_vector) - min(input_vector))
	}
	normalize_vec(c(1,2,3,4))

view raw Session-02-Script.R hosted with ❤ by GitHub

🎥 Accompanying lecture videos

All the videos are available via this playlist.

Expand to access the videos directly

📚 Mandatory Reading

• Tutorial First steps in R
• Chapter 2 in Wickham et al. (2023).

🏆 Further readings

• R Studio Cheat Sheet
• Chapter 4 in Wickham et al. (2023).
• Chapter 8 in Wickham et al. (2023).

✍️ Coursework

• Do the Basics exercises of the package DataScienceExercises

Quick code for starting the exercises

learnr::run_tutorial(
  name = "Basics", 
  package = "DataScienceExercises", 
  shiny_args=list("launch.browser"=TRUE))

• Do the Functions exercises of the package DataScienceExercises

Quick code for starting the exercises

learnr::run_tutorial(
  name = "Functions", 
  package = "DataScienceExercises", 
  shiny_args=list("launch.browser"=TRUE))

• If you have questions or problems, please post them in the Moodle forum

References

Wickham, H., Çetinkaya-Rundel, M. and Grolemund, G. (2023) R for data science: Import, tidy, transform, visualize, and model data, 2nd edition., Beijing et al.: O’Reilly, available at https://r4ds.hadley.nz/.