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.

How to Do a T-Test in R

by

Master essential R functions for statistical testing. Learn how to perform correlation, covariance, and t-test in R (One-Sample, Independent, Paired) in R. Perfect for data analysts, students, and job test preparation with practical code examples.

How can one compute correlation and covariances in R?

Computing correlations and covariances is a fundamental task in R, and the language provides several straightforward and powerful ways to do it. One can compute the correlation by using cor() function and cov() function to compute the covariance.

What are the different methods for computing correlation in R?

The cor() function allows you to choose the calculation method. The most common are:

  • "pearson": Standard correlation coefficient for linear relationships. Assumes data is normally distributed. It is the default method for computing Pearson’s Correlation Coefficient.
  • "spearman": Spearman’s rank correlation. A non-parametric method based on ranks, good for monotonic (consistently increasing or decreasing, but not necessarily linear) relationships.
  • "kendall": Kendall’s rank correlation. Another non-parametric method, often used for small data sets or when many tied ranks exist.

Explain how t-test is performed in R?

In R, the t.test() function produces a variety of t-tests. The t-test is the most common test in statistics and is used to determine whether the means of two groups are equal to each other.

The primary function for all t-tests in R is t.test(). Its usage changes slightly depending on the type of test you want to perform (one-sample, independent two-sample, or paired).

One-Sample T-Test

To determine if the mean of a single sample is significantly different from a known or hypothesized population mean. The general syntax of the t-test in R is

t.test(x, mu = hypothesized_mean, alternative = "two.sided")

The description of the argument are:

  • x: A numeric vector of data.
  • mu: The hypothesized true population mean.
  • alternative: The alternative hypothesis. Can be "two.sided", "less", or "greater".

Independent Two-Sample T-Test

To compare the means of two independent groups to see if they are significantly different from each other. The general syntax of the two-sample t-test is

t.test(x, y, alternative = "two.sided", var.equal = FALSE)

The description of the important argument is:

  • x: A numeric vector of data for group 1.
  • y: A numeric vector of data for group 2.
  • var.equal: A crucial argument.
    • var.equal = FALSE: Uses the Welch’s t-test, which does not assume the two groups have equal variances. This is the recommended and safer choice in most real-world situations. It is the default argument value.
    • var.equal = TRUE: Uses the Student’s classic t-test, which does assume equal variances.

The two-sample t-test can be computed by using t.test() function in formula format

t.test(numeric_variable ~ group_variable, data = my_data, ...)

Paired T-Test

To compare the means of the same group at two different times (e.g., before and after a treatment). The data is “paired” because each subject is measured twice. The general syntax for the paired sample t-test is

t.test(x, y, paired = TRUE, alternative = "two.sided")

What are the output objects of t.test() the function, and how can these be extracted?

The t.test() function returns a list object containing all the results. You can store it and extract specific values for reporting.

my_test <- t.test(mtcars$mpg, mu = 15)
How to Do a T-Test in R Language

One can extract specific values from t.test() function.

  • my_test$statistic # t-value
  • my_test$parameter # degrees of freedom (df)
  • my_test$p.value # p-value
  • my_test$estimate # estimated mean (or means)
  • my_test$conf.int # confidence interval

One can print a clean summary of objects

cat("t(", my_test$parameter, ") = ", round(my_test$statistic, 2), ", p = ", format.pval(my_test$p.value, digits=2), sep = "")

Perform Correlation Analysis

Perform Testing of Hypothesis

Object Oriented Programming in R

by

Answering the top questions on Object Oriented Programming in R: What is S4? What is a Reference Class? When should I use them? This post provides definitive answers on S4 class features, RC key characteristics, and how generics enable multiple dispatch. Level up your R programming skills today.

Object Oriented Programming in R

What is OOP in R?

OOP stands for Object Oriented Programming in R, and it is a popular programming language. OOP allows us to construct modular pieces of code that are used as building blocks for large systems. R is a functional language. It also supports exists for programming in an object-oriented style. OOP is a superb tool to manage complexity in larger programs. It is particularly suited to GUI development.

Object Oriented Programming in R is a paradigm for structuring your code around objects, which are data structures that have attributes (data) and methods (functions). However, unlike most other languages, R has three distinct object-oriented systems:

  1. S3: The simplest and most common system. Informal and flexible.
  2. S4: A more formal and rigorous version of S3.
  3. R6 (and others): A modern system that supports more familiar OOP features like reference semantics (objects that can be modified in place).

What is S4 Class in R?

S4 Class in R is a formal object-oriented programming (OOP) system in R. It is a more structured and rigorous evolution of the simpler S3 system. While S3 is informal and flexible, S4 introduces formal class definitions, validity checks, and a powerful feature called multiple dispatch.

One can think of it as providing a blueprint for your objects, ensuring they are constructed correctly and used properly.

When to use S4 Class in R?

Use S4 when you are building large, complex systems or packages where the integrity of your objects is critical. It’s heavily used in the Bioconductor project, which manages complex biological data, because its rigor helps prevent bugs and ensures interoperability between packages. For simpler, more interactive tasks, S3 or R6 is often preferable.

What is the Reference Class?

The Reference Class (often abbreviated RC) is another object-oriented system in R, introduced in the methods package around 2010. It was the precursor to the more modern and robust R6 system.

What are the key features of Reference Class?

  1. Encapsulation: Methods (functions) and fields (data) are defined together within the class. You use the $ operator to access both.
  2. Mutable State: Because of reference semantics, the object’s internal state can be changed by its methods.
  3. Inheritance: RC supports single inheritance, allowing a class to inherit fields and methods from a parent class.
  4. Built-in: They are part of the base methods package, so no additional installations are needed (unlike R6, which is a separate package, though also very popular).

When to use Reference Class?

  • When maintaining legacy code that already uses them.
  • When you need mutable state and reference semantics and cannot rely on an external package (though R6 is a lightweight, recommended package).
  • For modeling real-world entities that have a changing identity over time (e.g., a game character, a bank account, a connected device).

What is S4 Generic Function?

An S4 generic function is a fundamental concept in R’s S4 object-oriented system. It’s the mechanism that enables polymorphism, allowing the same function name to perform different actions depending on the class of its arguments.

What are the key features of S4 Class in R?

  1. Multiple Dispatch: This is the superpower of S4. While S3 generics only dispatch on the first argument, S4 generics can look at the class of multiple arguments to choose the right method.
  2. Formal Definition: S4 generics are formally defined, which makes the system more robust and less prone to error than the informal S3 system.
  3. Existing Generics: You can define new methods for existing generics (like show, plot) without creating a new generic function. This is very common.

Learn Statistics Software

R Language MCQs Test 33

by

Test your R programming expertise with this 20-question MCQ quiz! R Language MCQs Test designed for both learners and professionals, this quiz covers essential topics like data wrangling with dplyr (group_by, summarize, pipes), string manipulation, lubridate, tidymodels, and predictive modeling. Perfect for preparing for data scientist job interviews, brushing up on core R concepts, and mastering the tidyverse ecosystem. Let us start with the R Language MCQs Test now.

Online R Language MCQs Test

Online R Language Programming Quiz with Answers

1. What’s the point of using group_by()?

 
 
 
 

2. Which function can you use to read a text file that uses the “%” character as a delimiter?

 
 
 
 

3. When grouping data and calculating the mean of each group as part of your exploratory data analysis, you typically use the group_by() function with which other function?

 
 
 
 

4. How can the factor() function be used to map R onto a relational database management system (RDBMS)?

 
 
 
 

5. When using the predict() function in R, what is the default confidence level?

 
 
 
 

6. Assume you have a dataset called “new_dataset”, a predictor variable called X, and a target called Y, and you want to fit a simple linear regression model. Which command should you use?

 
 
 
 

7. You’ve got some messy data that looks like this:
my_strings<-c(
"xyztiger",
" i33tiger",
"898natiger "
)
You want to use a function to do a logical test for whether the character string “tiger” is present in any of the items in this vector. What is the correct function?

 
 
 
 

8. You’ve still got this same messy data:
my_strings<-c(
"xyztiger",
" i33tiger",
"898natiger "
)
You want to use a function to take this data and create a column of data that looks like this:

“tiger”
“tiger”
“tiger”
What is the correct function?

 
 
 
 

9. Assume you have a dataset called “new_dataset”, two predictor variables called X and Y, and a target variable called Z, and you want to fit a multiple linear regression model. Which command should you use?

 
 
 
 

10. What is the main similarity between the summarize() and group_by() functions?

 
 
 
 

11. Let’s say you want to calculate how many days passed from 14 July, 1789 until 1 December 1941. How can you calculate that?

 
 
 
 

12. What is the result of the following statement?

sub_airline %>% map(~sum(is.na(.)))

 
 
 
 

13. Which of the following can you accomplish using the spread() function?

 
 
 
 

14. You have a character vector that looks like this:
my_dates<-c(
“05-28-1984”,
“07-15-1981”,
“9-12-1986”,
“1-15-1982”)
You want to extract the year values from this vector, using the tools in lubridate. Which is correct?

 
 
 
 

15. Which functions do you use together to correct data types in all columns of your dataset?

 
 
 
 

16. Which tidymodels function do you use to create the grid for a grid search?

 
 
 
 

17. You have a variable called “Status” that contains a status code in the format “error_type-severity_level”, for example “10-07”, and you want to reformat the column so that the “error_type” and “severity_level” are in different columns. What is the correct function to do this?

 
 
 
 

18. What is the purpose of the pipe (%>%) operator?

 
 
 
 

19. You are checking your data using the glimpse() function before beginning your analysis, and determine that the data type of a variable called TimeStamp is in a character format. What should you do next?

 
 
 
 

20. Say you want to split a character vector and split the strings, so you have a matrix with two columns, splitting the string as indicated. Your character vector looks like this:
my_strings<-c(
"paper_store1",
"pens_store1",
"pencils_store1")
You want to split the strings at the underscore. What function do you use?

 
 
 
 

Question 1 of 20

Online R Language MCQs Test with Answers

  • What’s the point of using group_by()?
  • Which tidymodels function do you use to create the grid for a grid search?
  • What is the purpose of the pipe (%>%) operator?
  • You are checking your data using the glimpse() function before beginning your analysis, and determine that the data type of a variable called TimeStamp is in a character format. What should you do next?
  • How can the factor() function be used to map R onto a relational database management system (RDBMS)?
  • Which function can you use to read a text file that uses the “%” character as a delimiter?
  • What is the main similarity between the summarize() and group_by() functions?
  • What is the result of the following statement?
    sub_airline %>% map(~sum(is.na(.)))
  • Which functions do you use together to correct data types in all columns of your dataset?
  • You have a variable called “Status” that contains a status code in the format “error_type-severity_level”, for example “10-07”, and you want to reformat the column so that the “error_type” and “severity_level” are in different columns. What is the correct function to do this?
  • Which of the following can you accomplish using the spread() function?
  • When grouping data and calculating the mean of each group as part of your exploratory data analysis, you typically use the group_by() function with which other function?
  • Assume you have a dataset called “new_dataset”, a predictor variable called X, and a target called Y, and you want to fit a simple linear regression model. Which command should you use?
  • When using the predict() function in R, what is the default confidence level?
  • Assume you have a dataset called “new_dataset”, two predictor variables called X and Y, and a target variable called Z, and you want to fit a multiple linear regression model. Which command should you use?
  • You’ve got some messy data that looks like this:
    my_strings<-c( “xyztiger”, ” i33tiger”, “898natiger ” )
    You want to use a function to do a logical test for whether the character string “tiger” is present in any of the items in this vector. What is the correct function?
  • You’ve still got this same messy data:
    my_strings<-c( “xyztiger”, ” i33tiger”, “898natiger ” )
    You want to use a function to take this data and create a column of data that looks like this:
    “tiger”
    “tiger”
    “tiger”
    What is the correct function?
  • Say you want to split a character vector and split the strings, so you have a matrix with two columns, splitting the string as indicated. Your character vector looks like
    this: my_strings<-c( “paper_store1”, “pens_store1”, “pencils_store1”)
    You want to split the strings at the underscore. What function do you use?
  • You have a character vector that looks like this:
    my_dates<-c( “05-28-1984”, “07-15-1981”, “9-12-1986”, “1-15-1982”)
    You want to extract the year values from this vector, using the tools in lubridate. Which is correct?
  • Let’s say you want to calculate how many days passed from 14 July, 1789 until 1 December 1941. How can you calculate that?

Try Online Correlation Regression Quiz