RStudio Guide

To get started, click on the 'workspace' folder in the bottom right pane of RStudio. Click on the file entitled "Step 1...". You may want to drag the console pane to be smaller so that you have more room to work. You'll complete each task for Step 1 in that R Markdown file. Remember, you must run the cells with the play button at the top right of each cell for a task before moving onto the next task in the R Markdown file. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Getting Started with R: Variables and Data Types

To review the concepts covered in this step, please refer to the Getting Started with R module of the Programming with R course.

Understanding variables and data types is important because they are the basic building blocks of any programming language. In R, we have several data types including numeric, character, logical, and raw. This step will help you practice creating variables and assigning different data types to them.

Let's dive into the world of R programming by practicing the creation of variables and assigning different data types to them. The goal here is to familiarize yourself with the syntax of R and understand how different data types work. You will use the assignment operator (<-) to create variables and assign values to them. You will also practice checking the data type of a variable using the class() function.

Task 1.1: Creating a Numeric Variable

Create a numeric variable named num_var and assign the value 10 to it.

num_var <- 10

Task 1.2: Creating a Character Variable

Create a character variable named char_var and assign the string 'Hello, R!' to it.

char_var <- 'Hello, R!'

Task 1.3: Creating a Logical Variable

Create a logical variable named log_var and assign the boolean value TRUE to it.

log_var <- TRUE

Task 1.4: Checking the Data Type of a Variable

Check the data type of the variable num_var using the class() function.

class(num_var)

Exploring Vectors and Factors

To review the concepts covered in this step, please refer to the Exploring Vectors and Factors module of the Programming with R course.

Working with vectors and factors is important because they are fundamental data structures in R used to store and manipulate data. This step will help you practice creating and manipulating vectors and factors.

Let's explore the power of vectors and factors in R. The goal here is to understand how to create vectors, assign values to them, and perform operations on them. You will use the c() function to create vectors and the factor() function to create factors. You will also practice ordering and sorting vectors, and performing set operations on them.

Task 2.1: Creating a Vector

Create a vector named my_vector with the numbers 1 through 10 using the c() function.

my_vector <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

# optionally, use `:` to select consecutive numbers
my_vector <- 1:10

Task 2.2: Assigning Values to a Vector

Assign the value 100 to the 5th element of my_vector.

my_vector[5] <- 100

Task 2.3: Performing Operations on a Vector

Multiply each element of my_vector by 2 and assign the result back to my_vector.

my_vector <- my_vector * 2

Task 2.4: Creating a Factor

Create a factor named my_factor with the values 'high', 'low', 'medium', 'low', 'high' using the factor() function. Make sure the factor levels are in the right order.

factor(c('value1', 'value2', ...), levels = c('level1', 'level2', ...))

my_factor <- factor(c('high', 'low', 'medium', 'low', 'high'), levels = c('low', 'medium', 'high'))

Task 2.5: Ordering a Vector

Order my_vector in ascending order and assign the result to a new vector named ordered_vector.

ordered_vector <- sort(my_vector)

Task 2.6: Performing Set Operations on Vectors

Create a new vector named another_vector with the numbers 5 through 15. Then, find the intersection of my_vector and another_vector and assign the result to a new vector named intersection_vector.

another_vector <- 5:15
intersection_vector <- intersect(my_vector, another_vector)

Using Matrices, Arrays, and Lists

To review the concepts covered in this step, please refer to the Using Matrices, Arrays, and Lists module of the Programming with R course.

Understanding matrices, arrays, and lists is important because they are more complex data structures in R that allow you to store and manipulate multi-dimensional data. This step will help you practice creating and manipulating matrices, arrays, and lists.

Let's take a step further into the world of R programming by practicing the creation and manipulation of matrices, arrays, and lists. The goal here is to understand how these data structures work and how they can be used to store and manipulate multi-dimensional data. You will use the matrix(), array(), and list() functions to create these data structures, and practice selecting elements from them and performing operations on them.

Task 3.1: Creating a Matrix

Create a matrix with 3 rows and 3 columns using the matrix() function. Fill the matrix with numbers from 1 to 9. Assign the matrix to a variable named m.

m <- matrix(data = 1:9, nrow = 3, ncol = 3)

Task 3.2: Selecting Elements from a Matrix

Using the matrix created in the previous step, select the element in the 2nd row and 3rd column.

m[2,3]

Task 3.3: Creating an Array

Create a 3-dimensional array with dimensions 2x2x2 and fill it with numbers from 1 to 8 using the array() function. Assign the array to a variable called a.

a <- array(data = 1:8, dim = c(2,2,2))

Task 3.4: Selecting Elements from an Array

Using the array created in the previous task, select the element in the 1st row, 2nd column, and 2nd layer.

a[1,2,2]

Task 3.5: Creating a List

Create a list containing a numeric vector, a character vector, and a logical vector using the list() function. Assign the list to a variable named l

l <- list(c(1,2,3), c('a','b','c'), c(TRUE,FALSE,TRUE))

Task 3.6: Selecting Elements from a List

Using the list created in the previous task, select the 2nd element (the character vector).

l[[2]]

Working with Data Frames

To review the concepts covered in this step, please refer to the Working with Data Frames module of the Programming with R course.

Working with data frames is important because they are the most commonly used data structure in R for data analysis. This step will help you practice creating and manipulating data frames.

Let's delve into the world of data frames in R. The goal here is to understand how to create data frames, insert data into them, and manipulate them. You will use the data.frame() function to create data frames, and practice selecting elements from them, adding rows and columns to them, and using the tidyverse package to manipulate them.

Task 4.1: Create a Data Frame

Create a data frame named df with three columns: name, age, and gender. The name column should contain the names 'John', 'Jane', and 'Joe'. The age column should contain the ages 23, 25, and 22. The gender column should contain the genders 'Male', 'Female', and 'Male'.

df <- data.frame(
  name = c('John', 'Jane', 'Joe'),
  age = c(23, 25, 22),
  gender = c('Male', 'Female', 'Male')
)

Task 4.2: Select Elements from a Data Frame

Select the age column from the df data frame and assign it to a variable named ages.

ages <- df$age

Task 4.3: Add a Row to a Data Frame

Add a new row to the df data frame with the name 'Jill', age 24, and gender 'Female'.

new_row <- data.frame(name = 'Jill', age = 24, gender = 'Female')
df <- rbind(df, new_row)

Task 4.4: Add a Column to a Data Frame

Add a new column named income to the df data frame with the values 50000, 60000, 55000, and 58000.

df$income <- c(50000, 60000, 55000, 58000)

Task 4.5: Manipulate a Data Frame with tidyverse

Load the tidyverse package (it is already installed in this environment). Then use the filter() function to select the rows from the df data frame where age is greater than 23. Assign the result to a new data frame named df_filtered.

library('tidyverse')

df_filtered <- df %>% 
  filter(age > 23)

Managing Control Statements

To review the concepts covered in this step, please refer to the Managing Control Statements module of the Programming with R course.

Understanding control statements is important because they allow you to control the flow of execution in your program. This step will help you practice using different types of control statements in R, including if, else, and switch statements, and for, while, and repeat loops.

Let's practice controlling the flow of execution in your R programs. The goal here is to understand how to use different types of control statements in R, including if, else, and switch statements, and for, while, and repeat loops. You will practice writing conditional statements, using the switch command, and creating different types of loops.

Task 5.1: Creating an If-Else Statement

Create a numeric variable named num and assign the value 10. Write an if-else statement that prints 'Greater' if num is greater than 100 and 'Equal or Less' if not.

if (condition) {
  #a
} else {
  #b
}

num <- 10

if (num > 100) {
  print('Greater')
} else {
  print('Equal or Less')
}

Task 5.2: Using the Switch Statement

Create a character variable named month_name and assign the value "January". Create a switch statement to print the abbreviation of any given month_name based on its full name (Jan for January, Feb for February, etc.).

month_name <- "January"

switch(month_name,
"January" = "Jan",
 "February" = "Feb",
 "March" = "Mar",
 "April" = "Apr",
 "May" = "May",
 "June" = "Jun",
 "July" = "Jul",
 "August" = "Aug",
 "September" = "Sep",
 "October" = "Oct",
 "November" = "Nov",
 "December" = "Dec")

Task 5.3: Creating a For Loop

Write a for loop that prints the numbers from 1 to 10.

for (variable in sequence) {
  # code to execute during each iteration
}

for (i in 1:10) {
  print(i)
}

Task 5.4: Creating a While Loop

Write a while loop that prints the numbers from 1 to 10.

while (condition) {
  # code to execute as long as the condition is true
}

i <- 1

while (i <= 10) {
  print(i)
  i <- i + 1
}

Task 5.5: Creating a Repeat Loop

Write a repeat loop that prints the numbers from 1 to 10 and then breaks.

repeat {
  # code to execute
  if (condition) {
    break
  }
}

i <- 1

repeat {
  print(i)
  i <- i + 1
  if (i > 10) {
    break
  }
}

Building Your First Function

To review the concepts covered in this step, please refer to the Building Your First Function module of the Programming with R course.

Understanding how to create functions is important because functions allow you to write reusable code and make your programs more modular and efficient. This step will help you practice creating your own functions in R.

Let's wrap up our practice session by building your first function in R. The goal here is to understand the components of a function and how to create one. You will practice writing a function, passing data as input to the function, and using control statements within the function.

Task 6.1: Creating a Basic Function

Create a function named my_function that takes one argument and returns the square of that argument.

function_name <- function(arg1, arg2, ...) {
  # function body
}

my_function <- function(x) {
  return(x^2)
}

Task 6.2: Testing Your Function

Test your function by calling it with the argument 5. Store the result in a variable named result, then print result.

result <- my_function(5)
print(result)

Task 6.3: Adding Control Statements to Your Function

Modify your function to check if the input is numeric. If it is not, the function should return the message 'Input must be numeric.'

my_function <- function(x) {
  if (!is.numeric(x)) {
    return("Input must be numeric.")
  }
  return(x^2)
}

Task 6.4: Testing Your Modified Function

Test your modified function by calling it with the argument 'five'. Store the result in a variable named result2. Then print result2.

result2 <- my_function('five')
print(result2)