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Calculating Descriptive Statistics in R

Arimoro Olayinka Imisioluwa

Welcome to this 2-hour long project-based course Calculating Descriptive Statistics in R. In this project, you will learn how to perform extensive descriptive statistics on both quantitative and qualitative variables in R. You will also learn how to calculate the frequency and percentage of categorical variables and check the distribution of quantitative variables. By extension, you will learn how to perform univariate and bivariate statistics for univariate and bivariate variables in R.

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Welcome to this 2-hour long project-based course Calculating Descriptive Statistics in R. In this project, you will learn how to perform extensive descriptive statistics on both quantitative and qualitative variables in R. You will also learn how to calculate the frequency and percentage of categorical variables and check the distribution of quantitative variables. By extension, you will learn how to perform univariate and bivariate statistics for univariate and bivariate variables in R.

Note: You do not need to be a Data Scientist to be successful in this guided project, just a familiarity with basic statistics and using R suffice for this project. If you are not familiar with R and want to learn the basics, start with my previous guided project titled “Getting Started with R”.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Instructors are recognized for work in x
Suitable for beginners with basic statistics and R familiarity
Develops skills useful for personal growth and development
Covers quantitative and qualitative variables
Teaches univariate and bivariate statistics
Builds a strong foundation for beginners

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Reviews summary

Concise, practical r for descriptive statistics

According to learners, this course is a highly effective and practical introduction to descriptive statistics in R. Many praise the clear explanations and concise, project-based approach, which helps solidify understanding and enables immediate application. Students find the hands-on coding activities particularly useful for building confidence. While largely seen as a strong foundation for beginners and those needing a quick refresh, some note that it may be too basic for more experienced R users or those seeking a deeper theoretical understanding of statistics. Overall, it's considered an excellent 2-hour guided project for quickly acquiring R skills.
Perfectly suited for newcomers to R or statistics.
"This was a fantastic introduction to descriptive statistics in R... for someone relatively new to R."
"It's perfect for a quick refresher or if I'm a complete beginner in R."
"This is a good course for beginners like me."
"This course was exactly what I needed! It's ideal for learning how to calculate descriptive stats."
Instructor explanations are highly lucid.
"The instructor's explanations were incredibly clear and easy to follow, even for someone relatively new to R."
"I found the course concise, practical, and to the point. The R demonstrations were clear and the instructor's pace was just right."
"The instructor clearly knows his stuff and delivers it efficiently."
"The instructions were precise and the practical exercises were very effective for me."
Effective for applying R skills immediately.
"The project-based approach really helped solidify my understanding and I felt confident applying the concepts immediately."
"This is a very useful project. It covers the basics well, and the hands-on nature is great for me."
"I loved the hands-on coding. It made learning R for descriptive stats a breeze. I highly recommend it for practical application."
"The practical exercises were very effective, and this course delivered exactly what it promised."
Some found the pace too fast without prior R familiarity.
"I found some parts a bit rushed in this course."
"I found the explanations sometimes confusing, especially if I'm not already familiar with R. I had to pause frequently to re-read."
"It felt a bit too fast for me to follow comfortably without prior R knowledge."
Might be too basic for experienced users or theoretical understanding.
"My only minor critique is that it's quite basic, so if I already know a fair bit of R or statistics, I might find it moves a bit slowly."
"As someone with some stats background, I was hoping for a bit more nuance and depth."
"I felt it lacked depth in explaining *why* certain statistical measures are used, focusing more on *how* to calculate them in R."
"For more advanced users like myself, it might feel a bit slow or too basic."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Calculating Descriptive Statistics in R with these activities:
Organize and review your course materials
Stay organized and up-to-date with the course material by creating a comprehensive compilation.
Show steps
  • Create a dedicated folder or notebook for the course.
  • Organize your notes, assignments, quizzes, and exams into the folder or notebook.
  • Review your materials regularly to reinforce your understanding and identify areas for improvement.
Follow a guided tutorial on calculating descriptive statistics in R
Get hands-on experience with R by following a guided tutorial on how to calculate descriptive statistics.
Show steps
  • Find a reputable online tutorial or video on calculating descriptive statistics in R.
  • Follow the steps in the tutorial to calculate descriptive statistics on a dataset.
  • Experiment with different functions and options to customize your analysis.
Complete practice exercises on calculating descriptive statistics in R
Solidify your understanding of descriptive statistics in R by completing practice exercises.
Show steps
  • Find practice exercises online or in a textbook.
  • Solve the exercises to test your skills.
  • Review your answers and identify areas for improvement.
Three other activities
Expand to see all activities and additional details
Show all six activities
Join a study group and discuss descriptive statistics concepts
Enhance your understanding of descriptive statistics by discussing the concepts with peers.
Show steps
  • Form or join a study group with other students taking this course.
  • Meet regularly to discuss course material, share insights, and work on assignments together.
  • Prepare for each session by reviewing the relevant material and identifying specific questions or topics to discuss.
Read a book on descriptive statistics in R
Expand your knowledge of descriptive statistics in R by reading a comprehensive book on the subject.
View R for Data Science: Import, Tidy, Transform,... on Amazon
Show steps
  • Purchase or borrow a copy of the book.
  • Read the chapters on descriptive statistics.
  • Work through the examples and exercises to reinforce your understanding.
Start a project that involves analyzing and interpreting descriptive statistics
Apply your knowledge of descriptive statistics to a real-world project.
Show steps
  • Identify a topic or question that you are interested in exploring.
  • Gather data from reliable sources.
  • Calculate descriptive statistics on the data and analyze the results.
  • Interpret the statistics and draw conclusions based on your analysis.
  • Write a report or presentation to share your findings.

Career center

Learners who complete Calculating Descriptive Statistics in R will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts play a crucial role in analyzing and interpreting data to gain insights and inform decision-making. The Calculating Descriptive Statistics in R course provides a solid foundation for Data Analysts by equipping them with the skills to perform extensive descriptive statistics on both quantitative and qualitative variables. This course teaches the calculation of frequency and percentage of categorical variables, checking the distribution of quantitative variables, and performing univariate and bivariate statistics. These skills are essential for Data Analysts to effectively summarize and analyze data, identify patterns and trends, and draw meaningful conclusions.
See salaries and explore the career path for Data Analyst
Data Scientist
Data Scientists leverage advanced statistical techniques to extract knowledge from data. The Calculating Descriptive Statistics in R course provides a valuable foundation for Data Scientists by introducing them to the basics of descriptive statistics in R. By mastering these concepts, Data Scientists can effectively explore, analyze, and summarize data, which is crucial for building predictive models, identifying patterns and insights, and making data-driven decisions.
See salaries and explore the career path for Data Scientist
Market Researcher
Market Researchers gather and analyze data to understand market trends, consumer behavior, and industry dynamics. The Calculating Descriptive Statistics in R course can enhance the skills of Market Researchers by providing them with the ability to perform comprehensive descriptive statistics on both quantitative and qualitative data. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills are essential for Market Researchers to effectively analyze survey data, customer feedback, and market trends, enabling them to provide valuable insights and inform marketing strategies.
See salaries and explore the career path for Market Researcher
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to assess financial risks and make investment decisions. The Calculating Descriptive Statistics in R course provides a strong foundation for Quantitative Analysts by introducing them to the basics of descriptive statistics in R. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills are essential for Quantitative Analysts to effectively analyze financial data, identify patterns and trends, and develop quantitative models for risk assessment and investment决策.
See salaries and explore the career path for Quantitative Analyst
Statistician
Statisticians collect, analyze, interpret, and present statistical data. The Calculating Descriptive Statistics in R course provides a strong foundation for Statisticians by introducing them to the basics of descriptive statistics in R. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills are essential for Statisticians to effectively analyze data, draw meaningful conclusions, and communicate statistical findings in various fields.
See salaries and explore the career path for Statistician
Business Analyst
Business Analysts use data analysis techniques to solve business problems and improve decision-making. The Calculating Descriptive Statistics in R course can enhance the skills of Business Analysts by providing them with the ability to perform descriptive statistics on both quantitative and qualitative data. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills are essential for Business Analysts to effectively analyze business data, identify trends and patterns, and develop data-driven solutions to improve business outcomes.
See salaries and explore the career path for Business Analyst
Data Engineer
Data Engineers design, build, and maintain data architectures and systems. The Calculating Descriptive Statistics in R course may be helpful for Data Engineers who want to enhance their understanding of descriptive statistics and its applications in data management. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Data Engineers in designing data pipelines, optimizing data storage and retrieval, and ensuring data quality.
See salaries and explore the career path for Data Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. The Calculating Descriptive Statistics in R course may be helpful for Machine Learning Engineers who want to strengthen their understanding of descriptive statistics and its role in machine learning. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Machine Learning Engineers in data preprocessing, feature engineering, and model evaluation.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The Calculating Descriptive Statistics in R course may be helpful for Software Engineers who want to enhance their understanding of descriptive statistics and its applications in software development. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Software Engineers in data analysis, debugging, and performance optimization.
See salaries and explore the career path for Software Engineer
Financial Analyst
Financial Analysts evaluate and make recommendations on investments and financial products. The Calculating Descriptive Statistics in R course may be helpful for Financial Analysts who want to strengthen their understanding of descriptive statistics and its applications in financial analysis. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Financial Analysts in analyzing financial data, assessing risk and return, and making investment decisions.
See salaries and explore the career path for Financial Analyst
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risk in insurance and finance. The Calculating Descriptive Statistics in R course may be helpful for Actuaries who want to enhance their understanding of descriptive statistics and its applications in actuarial science. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Actuaries in risk assessment, premium pricing, and financial modeling.
See salaries and explore the career path for Actuary
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to improve the efficiency and effectiveness of operations and processes. The Calculating Descriptive Statistics in R course may be helpful for Operations Research Analysts who want to strengthen their understanding of descriptive statistics and its applications in operations research. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Operations Research Analysts in data analysis, optimization modeling, and decision-making.
See salaries and explore the career path for Operations Research Analyst
Epidemiologist
Epidemiologists investigate the distribution and causes of diseases and health conditions in populations. The Calculating Descriptive Statistics in R course may be helpful for Epidemiologists who want to enhance their understanding of descriptive statistics and its applications in epidemiology. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Epidemiologists in data analysis, outbreak investigation, and disease surveillance.
See salaries and explore the career path for Epidemiologist
Biostatistician
Biostatisticians apply statistical principles and methods to the design, analysis, and interpretation of biomedical research studies. The Calculating Descriptive Statistics in R course may be helpful for Biostatisticians who want to strengthen their understanding of descriptive statistics and its applications in biostatistics. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Biostatisticians in clinical trial design, data analysis, and medical research.
See salaries and explore the career path for Biostatistician
Survey Researcher
Survey Researchers design, conduct, and analyze surveys to collect data on a wide range of topics. The Calculating Descriptive Statistics in R course may be helpful for Survey Researchers who want to enhance their understanding of descriptive statistics and its applications in survey research. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Survey Researchers in data analysis, report writing, and survey design.
See salaries and explore the career path for Survey Researcher

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Calculating Descriptive Statistics in R.
Cover image
R Programming for Data Science
Save
Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers topics such as data manipulation, visualization, and statistical modeling, making it an excellent reference for those who want to learn more about R and its capabilities.
R Programming for Data Science
Paperback
$$
Cover image
R for Data Science
Save
Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers topics such as data manipulation, visualization, and statistical modeling, making it an excellent reference for those who want to learn more about R and its capabilities.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Statistical Inference Via Data Science
Save
Introduces statistical inference through the lens of data science, using R and the tidyverse. It covers topics such as probability, hypothesis testing, regression, and Bayesian analysis, making it a great resource for those who want to learn more about statistical inference in a modern context.
Statistical Inference via Data Science: A...
Paperback
$$$
Statistical Inference via Data Science: A...
Kindle Edition
$$$
Cover image
The Art of Machine Learning
Save
Provides a comprehensive overview of the R programming language, covering topics such as data manipulation, visualization, and statistical modeling. It valuable resource for those who want to learn more about the advanced features of R and how to use them effectively.
The Art of Machine Learning: A Hands-On Guide to...
Paperback
$$
The Art of Machine Learning: A Hands-On Guide to...
Kindle Edition
$$
Cover image
The R Book
Save
Comprehensive reference for the R programming language, covering topics such as data manipulation, visualization, and statistical modeling. It valuable resource for those who want to learn more about the advanced features of R and how to use them effectively.
The R Book
Hardcover
$$$
The R Book
Kindle Edition
$$$
Modern Statistics with R
Save
Provides a modern introduction to statistics, using R for data analysis. It covers topics such as probability, hypothesis testing, regression, and Bayesian analysis, making it a great resource for those who want to learn more about statistics in a modern context.
Modern Statistics with R: From Wrangling and...
Kindle Edition
$$$
Cover image
Statistical Modeling with R
Save
Provides a modern introduction to statistics, using R for data analysis. It covers topics such as probability, hypothesis testing, regression, and Bayesian analysis, making it a great resource for those who want to learn more about statistics in a modern context.
Statistical Modeling With R: a dual frequentist and...
Paperback
$$
Statistical Modeling With R: a dual frequentist and...
Kindle Edition
$$
Cover image
Advanced R, Second Edition
Save
Provides a comprehensive introduction to the ggplot2 package for creating graphics in R. It covers topics such as data visualization, aesthetics, and themes. It valuable resource for those who want to learn more about how to create beautiful and informative graphics in R.
Advanced R, Second Edition (Chapman & Hall/CRC The...
Paperback
$$$
Advanced R, Second Edition (Chapman & Hall/CRC The...
Kindle Edition
$$$
Cover image
Data Manipulation with R
Save
Provides a comprehensive overview of data manipulation in R, covering topics such as data import, cleaning, and transformation. It valuable resource for those who want to learn more about how to work with data in R.
Data Manipulation with R (Use R!)
Paperback
$$$
Data Manipulation with R (Use R!)
Kindle Edition
$$$
Cover image
R Markdown
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Provides a comprehensive introduction to the R Markdown format for creating dynamic reports and documents. It covers topics such as syntax, output formats, and extensions. It valuable resource for those who want to learn more about how to create beautiful and informative reports in R.
R Markdown: The Definitive Guide (Chapman &...
Paperback
$$
R Markdown: The Definitive Guide (Chapman &...
Kindle Edition
$$
Cover image
R Graphics Cookbook
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Provides a collection of recipes for creating various types of graphs in R, covering topics such as bar charts, line charts, and scatterplots. It valuable resource for those who want to learn more about how to create beautiful and informative graphics in R.
R Graphics Cookbook: Practical Recipes for...
Paperback
$$$
R Graphics Cookbook: Practical Recipes for...
Paperback
$$
(中文) R Graphics Cookbook: Practical Recipes for...
Paperback
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R Graphics Cookbook: Practical Recipes for...
Kindle Edition
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R Graphics Cookbook: Practical Recipes for...
Kindle Edition
$$
Cover image
R for Everyone
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Provides a gentle introduction to the R programming language, with a focus on data analysis and visualization. It great resource for those who are new to R and want to learn the basics.
R for Everyone: Advanced Analytics and Graphics...
Paperback
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R for Everyone: Advanced Analytics and Graphics...
Kindle Edition
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R for Everyone: Advanced Analytics and Graphics...
Unknown Binding
$$$

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Course image
Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Arimoro Olayinka Imisioluwa
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Instructors are recognized for work in x
Suitable for beginners with basic statistics and R familiarity
Develops skills useful for personal growth and development
Covers quantitative and qualitative variables
Teaches univariate and bivariate statistics
Builds a strong foundation for beginners
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