Muhammad Imdad Ullah, Author at R Programming FAQs

ggplot Visualizations Quiz 30

April 15, 2025April 15, 2025 by Muhammad Imdad Ullah

Test your ggplot2 skills with this 20-question multiple-choice quiz! The ggplot Visualizations Quiz covers essential topics in data visualizations in R’s ggplot2 package, including:

Creating basic plots (scatter plots, line plots)
Customizing visuals with geoms (geom_smooth, geom_text_repel)
Using scales (scale_color_gradient, scale_color_brewer)
Advanced techniques like scatterplot matrices and geographic maps

Whether you are a beginner or looking to refine your ggplot2 expertise, the quiz will challenge your understanding of building, customizing, and interpreting data visualizations in R. Let us start with the ggplot Visualizations Quiz now.

Online ggplot Visualizations Quiz with Answers

Say you have data that looks like this, saved to the object my_dat:
time   unit   value
1        a        5
1    b       10
2        a        6
2        b    9
3        a        7
3        b        8
Which is the correct series of functions for creating a line plot with time on the x-axis, value on the y-axis, and two different lines with different styles, one with a line for unit a and another with a line for unit b?
What is the basic ggplot function for adding text to a plot without drawing a rectangle around the text?
Say you had a dataset named my_dat that summarizes the height and weight of a group of people. The first two rows look like this:
name   height   weight
Steve   6            170
Amy     5.5         140
You want a scatter plot with each person’s name at the correct x-y coordinate for height and weight. Which command is correct?
If you wanted to plot the points in a scatter plot but move the text label down three units, what is the correct modification?
What is the value of geom_text_repel()?
What does a scale do?
Review the code below, where variable1, variable2, and variable3 are continuous numeric variables: ggplot(data,aes(x=variable1,y=variable2,color=variable3)+ geom_point()+ scale_color_gradient(low=”blue”,high=”yellow”)
What is scale_color_gradient telling R to do?
Why would you want to use scale_color_brewer?
What is the default method for fitting a best-fit line with geom_smooth?
What function is required to make a scatterplot matrix?
What geom do you need to use to draw a Cleveland dot plot?
In the ggcorrplot() function, what is the role of the “type=” argument?
What is the correct geom for filling in the area underneath a line in a line plot?
What structure do you need your data to be in to make a dumbbell plot?
Using the ggalt package, what is the geom used to draw a dumbbell chart?
What is the aes() that you need to set in order to create a stacked area chart?
Which of these geoms is required to create a complete alluvial diagram?
In conjunction with ggplot and packcircles, what geoms are used to make a labelled packed circle plot?
What geom is used to draw geographic borders using ggplot?
What geom is used to place points on a map using latitude and longitude data?

Exploratory Data Analysis Quiz

ggplot2 Data Visualization Quiz 29

April 10, 2025April 10, 2025 by Muhammad Imdad Ullah

Test your ggplot() function in R skills with this interactive ggplot2 data visualization quiz! Perfect for R users. The ggplot2 Data Visualization Quiz covers key ggplot2 concepts, syntax, and best practices. See how well you know ggplot2—take the challenge now!” Let us start with the ggplot2 Data Visualization Quiz now.

Please go to ggplot2 Data Visualization Quiz 29 to view the test

Online ggplot2 Data Visualization Quiz with Answers

Which of these is NOT a component of a ggplot figure?
What is facetting?
What is the first argument in the ggplot() function in R?
What kind of data would be the best candidate for scatter plotting?
Say you had data saved to an object in R named “my_data” that looked like this:
Height   Weight   Gender
5           120          Male
5.5        130          Female
and so on.
What is the correct ggplot() command for creating a scatter plot with weight on the x-axis, height on the y-axis, and the changing the color of the point based on gender?
What does it mean to set the aesthetic mappings in ggplot?
Say you have data saved to the my_data object that looks like this:
City State Population
(categorical) (categorical) (numeric values)
Which of these will draw a histogram for cities in the state of California?
Say you have data saved to the my_data object that looks like this:
City State Population
(categorical) (categorical) (numeric values)
Which of these will draw a boxplot of the population for cities in California?
Say you have data saved to the my_data object that looks like this:
City State Population
(categorical) (categorical) (numeric values)
Which of these will draw a density plot of the population for cities in California?
What can you do if you have a problem with overplotting in a scatter plot?
What does it mean to modify the binning of a histogram?
How do you modify a ggplot() command to tell R to make a bar plot?
What is the difference between using geom_bar() and geom_bar(stat=”identity”)?
Say you had a plot that you started with this ggplot() function in R. Assume that variable1 and variable2 are categorical variables. ggplot(my_data,aes(x=variable1,fill=variable2)) What do you add to create a stacked barplot, so counts of different values of variable2 “stack” up to equal the sum of counts for the different values of variable1?
Say you had a plot that you started with this ggplot() function in R. Assume that variable1 and variable2 are categorical variables. ggplot(my_data,aes(x=variable1,fill=variable2)) What do you add to create a grouped barplot, so counts of different values of variable2 are grouped by values of variable1?
Let’s say you drew a bar plot where the bars were filled with colors based on some value in the data. R will automatically generate a legend. Which of these is the correct way to remove the legend?
Say you are starting with a ggplot() command that looks like this, ggplot(my_data,aes(y=variable1,x=time)) where variable1 is a continuous numeric variable and time is a set of years from 1999 to 2000. You want a line plot that tracks the value of variable1 across these years. What do you add to draw the line that will connect variable1 values across years?
Say you have data that looks like this, saved to the object my_dat:
time    unit     value
1         a          5
1         b         10
2         a          6
2         b          9
3         a          7
3         b          8
Which is a correct series of functions for creating a line plot with time on the x-axis, value on the y-axis, and two different lines on the same plot for unit a and unit b?
Say you have data that looks like this, saved to the object my_dat:
time   unit    value
1        a         5
1        b        10
2        a         6
2        b         9
3        a         7
3        b         8
Which is the correct series of functions for creating a line plot with time on the x-axis, value on the y-axis, and two different lines with different colors on the same plot for unit a and unit b?
Say you have data that looks like this, saved to the object my_dat:
time   unit    value
1        a         5
1        b        10
2        a         6
2        b         9
3        a         7
3        b         8
Which is the correct series of functions for creating a line plot with time on the x-axis, value on the y-axis, and two different plots, one with a line for unit a and another with a line for unit b?

Try Neural Networks Quiz

Functions in R

April 4, 2025 by Muhammad Imdad Ullah

Functions in R programming are reusable blocks of code that perform specific tasks, improving efficiency and readability. This guide covers how to write functions in R, their key features (lexical scoping, closures, generics), and practical examples for data science & automation. It is perfect for beginners and advanced users!

What are Functions in R Language?

A function is a chunk of code written to carry out a specified task. It can or cannot accept arguments (also called parameters), and it can or cannot return one or more values. In R, functions are objects in their own right. Hence, we can work with them the same way we work with any other type of object.

Objects in the function are local to the function. One can return the object as any data type.

What is Function Definition?

An R function is created using the keyword function. The basic syntax of an R function definition is as follows –

Function_name <- function(arg_1, arg_2, …) {
    Function body 
}

What are the Components of R functions?

The different components of a function are:

Function Name: Function Name is the actual name of the function because it is stored in the R environment as an object with this name.
Arguments: An argument is a placeholder. When a function is invoked, we pass a value to the Argument. Arguments are optional; that is, a function may contain no arguments. Arguments can also have default values.
Functions Body: In a function body, statements can be collected. It defines what the function does.
Return Value: The return value of a function is the last expression in the function body to check.

What are the Key Features of R Functions?

The following are key features of R functions:

Generic Functions: Work differently based on input class (e.g., print(), plot()).
First-class Objects: First-class Objects can be assigned, passed as arguments, and returned.
Lexical Scoping: Variables are looked up where the function is defined.
Flexible Arguments: Default values, optional args, and ... (variable-length args).
Closures: Can remember their environment (useful in functional programming).

What are Generic Functions in R?

Generic Functions in R behave differently based on the class of their input arguments. They use method dispatch to call the appropriate version (method) of the function for a specific object type. The generic function allows one function name to work for different object types (e.g., print(), plot(), and summary()).

What is the Attribute Function in R?

To get or set a single attribute, you can use the attr() function. This function takes two important arguments. The first argument is the object we want to examine, and the second argument is the name of the attribute we want to see or change. If the attribute we ask for does not exist, R simply returns NULL.

What is an arbitrary function in R?

Arbitrary function means any function. Generally, an arbitrary function refers to a function that belongs to the same class of functions we are discussing (its freedom is limited). For example, when talking about continuous real-valued functions defined on the bounded closed interval of the real line, an arbitrary function may refer to a function of the same type.

What are the Types of Functions in R?

In R, the following are types of functions:

Built-in Functions: R has many built-in functions such as sum(), mean(), and plot().

numbers <- c(2, 4, 6, 8)
mean(numbers)  

## Output: 5

User-defined Functions: Custom functions created by users, for example,

# Define a function to add two numbers
add_numbers <- function(a, b) {
  return(a + b)
}

# Call the function
add_numbers(5, 3)  

## Output: 8

Generic Functions (Polymorphic Behavior): Generic functions behave differently based on input class. For example, print() behaves differently for numbers and lm models.

Recursive Functions: Recursive functions call themselves (useful for iterative algorithms).

# Recursive factorial function
factorial <- function(n) {
  if (n == 0) return(1)
  else return(n * factorial(n - 1))
}

factorial(5)  

## Output: 120

What are the Best Practices for Writing Functions in R?

The following are considered best practices when writing functions in R Programming Language.

✅ Use Descriptive Names (e.g., calculate_mean() instead of f1()).
✅ Keep Functions Short & Focused (Single Responsibility Principle).
✅ Add Comments for clarity.
✅ Use Default Arguments for flexibility.
✅ Test Functions with different inputs.

Functions in R make your code modular, reusable, and efficient. Whether you’re performing data analysis, building models, or creating visualizations, mastering functions will significantly improve your R programming skills.

Machine Learning Quiz