Muhammad Imdad Ullah

Student and Instructor of Statistics and business mathematics. Completed my Ph.D. in Statistics from the Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan. l like Applied Statistics, Mathematics and Statistical Computing. Statistical and Mathematical software used is SAS, STATA, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel. Like to use type-setting LaTeX for composing Articles, thesis, etc.

ggplot2 Data Visualization Quiz 29

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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.

online ggplot2 Data Visualization Quiz with Answer

Online ggplot2 Data Visualization Quiz

1. What does it mean to modify the binning of a histogram?

 
 
 

2. What is facetting?

 
 
 

3. How do you modify a ggplot() command to tell R to make a bar plot?

 
 
 

4. 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?

 
 
 

5. Say you had a plot that you started with this ggplot() function. 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?

 
 
 

6. What kind of data would be the best candidate for scatter plotting?

 
 
 
 
 

7. Which of these is NOT a component of a ggplot figure?

 
 
 
 
 
 
 

8. 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?

 
 
 

9. What is the difference between using geom_bar() and geom_bar(stat=”identity”)?

 
 
 

10. What is the first argument in the ggplot() function?

 
 
 

11. 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?

 
 
 

12. 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?

 
 
 

13. 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?

 
 
 

14. Say you had a plot that you started with this ggplot() function. 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?

 
 
 

15. 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?

 
 
 

16. 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?

 
 
 

17. 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?

 
 
 

18. What does it mean to set the aesthetic mappings in ggplot?

 
 
 

19. 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?

 
 
 

20. What can you do if you have a problem with overplotting in a scatter plot?

 
 
 

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

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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






Functions in R Language




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.





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Special Values in R Language
		

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R is a powerful language for statistical computing and data analysis. While working with data, one may encounter special values in R Language. There are several special values in R Language (such as NA, NULL, Inf, and NaN) representing missing data, undefined results, or mathematical operations. Understanding their differences and how to handle them correctly is crucial for effective R programming. Misunderstanding these special values can lead to bugs in your R programming code or incorrect analysis. 









This guide about special values in R Language covers:





    

  • NA: Missing or undefined data.
    • 




  • NULL: Absence of a value or object.
    • 




  • Inf / -Inf: Infinity from calculations like division by zero.
    • 




  • NaN: “Not a Number” for undefined math operations.
    • 







Special values in R Programming Language




Let us explore each special value with examples.





NA – Not Available (Missing Data)












NA represents missing or unavailable data in vectors, matrices, and data frames. The key properties of special value NA are





    

  • Used in all data types (logical, numeric, character).
    • 




  • Functions like is.na() detect NA values.
    • 






x <- c(1, 2, NA, 4)
is.na(x)    # Returns FALSE FALSE TRUE FALSE





Note that Operations involving NA usually result in NA unless explicitly handled with na.rm = TRUE. NA is not the same as "NA" (a character string). Also note that type-specific NAs are NA_integer_, NA_real_, NA_complex_, NA_character_.





NULL – Absence of a Value





NULL signifies an empty or undefined object, often returned by functions expecting no result. It is different from NA because NULL means the object does not exist, while NA means a value is missing. The key properties are:





    

  • NULL is a zero-length object, while NA has a placeholder.
    • 




  • Cannot be part of a vector.
    • 




  • Functions return NULL if they operate on a NULL object.
    • 




  • Use is.null() to check for NULL.
    • 






y <- NULL
is.null(y) # Returns TRUE





Note that NaN is a subtype of NA (is.na(NaN) returns TRUE). Also note that it is used for invalid numerical operations.






Special Values in R Programming Language




Inf and -Inf – Infinity





Inf and -Inf represent positive and negative infinity in R. These values occur when numbers exceed the largest finite representable value. Inf arises from operations like division by zero or overflow. The key properties are:





    

  • Often results from division by zero.
    • 




  • Can be used in comparisons (Inf > 1000 returns TRUE).
    • 






1 / 0            # Returns Inf
log(0)           # Returns -Inf
is.infinite(1/0) # TRUE





Note that Infinite values can be checked with is.infinite(x). Inf and -Inf results in NaN.





NaN – Not a Number





NaN results from undefined mathematical operations, like 0/0. One can check NaN values by using is.nan() function. Let us see how to check for NaN using R example:





0 / 0 # Returns NaN
is.nan(0 / 0) # TRUE
is.na(NaN) # TRUE (NaN is a type of NA)





Note that NULL is different from NA and NaN; it means no value exists. It is commonly used for empty lists, missing function arguments, or when an object is undefined.





FALSE and TRUE (Boolean Values)





Results in logical values used in conditions and expressions.





b <- TRUE
c <- FALSE
as.numeric(b)  # 1
as.numeric(c)  # 0





Note that Logical values are stored as integers (TRUE = 1, FALSE = 0). These are useful for indexing and conditional statements.





Comparison between NA, NULL, Inf, NaN





ValueMeaningCheck Function
NAMissing datais.na()
NULLEmpty objectis.null()
InfInfinityis.infinite()
NaNNot a Numberis.nan()











Common Pitfalls and Best Practices





    

  1. NA vs. NULL: Use NA for missing data in datasets; NULL for empty function returns.
    2. 




  2. Math with Inf/NaN: Use is.finite() to filter valid numbers.
    3. 




  3. Debugging Tip: Check for NA before calculations to avoid unexpected NaNs.
    4. 






Handling Special Values in R





To manage special values in R efficiently, use the following functions:





    

  • is.na(x): Check for NA values.
    • 




  • is.null(x): Check for NULL values.
    • 




  • is.infinite(x): Check for Inf or -Inf.
    • 




  • is.nan(x): Check for NaN.
    • 






Practical Tips When Using Special Values in R Language





The following are some important practical tips when making use of special values in R Language:





    

  1. Handling Missing Data (NA)

      

    • Use na.omit(x) or complete.cases(x) to remove NA values.
      • 




    • Use replace(x, is.na(x), value) to fill in missing values.
      • 

    

    2. 




  2. Avoiding NaN Issues

      

    • Check for potential division by zero.
      • 




    • Use ifelse(is.nan(x), replacement, x) to handle NaN.
      • 

    

    3. 




  3. Checking for Special Values in R

      

    • is.na(x), is.nan(x), is.infinite(x), and is.null(x) help identify special values.
      • 

    

    4. 




  4. Using Default Values with NULL

      

    • Set default function arguments as NULL and use if (is.null(x)) to assign a fallback value.
      • 

    

    5. 











Summary of Special Values in R Language





Understanding special values in R is essential for data analysis and statistical computing. Properly handling NA, NULL, Inf, and NaN ensures accurate calculations and prevents errors in your R scripts. By using built-in functions, one can effectively manage these special values in R and improve the workflow.





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