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Introduction to Statistics in R - A Practical Approach

Name: Introduction to Statistics in R - A Practical Approach
Rating: 4.5 (41 reviews)

Learn descriptive statistics in R applying your knowledge with mini projects, quizzes, and a final exam

Created byEstefania Cassingena Navone

Last updated 11/2020

English

What you'll learn

How to apply basic statistical knowledge to solve real-world scenarios using R
How to create, read, and work with CSV files in RStudio
Fundamental concepts such as population, sample, sampling, bias, data, statistic, and parameter
How to find and interpret frequency, relative frequency, and cumulative relative frequency
How to find and interpret the Mean, Median, and Mode
How to find and interpret Variance and Standard Deviation
How to find quartiles (Q1, Q2, Q3), the interquartile range (IQR), and outliers
How to create, read and interpret bar plots, histograms, and box plots

Course content

13 sections • 142 lectures • 4h 45m total length

Welcome | Course Overview2:19
Content:
A brief overview of the topics covered during the course and the mini projects that you will work on.
Important Course Information and Resources1:31
Content:
A summary of the main course guidelines and tips such as how to format code, adjust image quality, and use the online classroom.
Introduction to Statistics as a Science1:17
Content:
Concept of Statistics.
Its two main areas and goals.
(PDF Resource) Section Handout0:05
Content:
A handout with the essential terminology that you will need during the course (PDF File).
Population4:00
Content:
Concept of Population.
Examples.
Importance of Samples.
Sample and Sampling6:59
Content:
Concept of Sample.
Sample size.
Examples.
What makes a good sample and how to select it.
Simple random sampling.
Biased Samples1:10
Content:
Concept of Biased Sample.
Sources of Bias.
Examples.
Bias in Research0:21
Content:
A link to an interesting scientific reading on bias in research.
An optional assignment to discuss the content of the article.
Basic Statistical Concepts
Variables and Data5:17
Content:
Concept of Variable.
Concept of Data.
Types of data: quantitative and qualitative.
Types of quantitative data: discrete and continuous.
Examples and practice.
Dataset0:10
Content:
Concept of dataset.
Levels of Measurement1:24
Content:
The characteristics of the four levels of measurement used to classify data: nominal, ordinal, interval, and ratio.
Parameter vs. Statistic5:13
Content:
Concept of Parameter and examples.
Concepts of Statistic and examples.
How they are related in statistics and their main differences.
Identify the Key Elements - Scenario #13:24
Content:
A practical scenario where we will identify the main elements such as population, sample, sample size, statistic, and parameter.
Identify the Key Elements - Scenario #23:21
Content:
A practical scenario where we will identify the main elements such as population, sample, sample size, statistic, and parameter.
Larger Samples, More Reliable Results0:49
Content:
A fundamental principle of statistics: that larger samples generate more accurate results.
Examples.
Section Review
Mini Project | Identify the Elements
(PDF Version) Mini Project | Identify the Elements0:11
Content:
The PDF version of the instructions for the mini project for students who prefer to work with the PDF file and students who take the course on the mobile app.
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Section Introduction0:20
Content:
A very brief summary of the topics that will be covered in this section.
Downloading and Installing RStudio (Open-Source Version)1:30
Content:
Instructions to download R studio from the official website.
(PDF Resource) Basic R Commands and R Studio0:06
Content:
A handout with the R commands used in this course and their purpose (PDF File).
Programming: Basic Terminology0:43
Content:
Basic programming terminology that you will find during the course.
Tour of RStudio: Overview of the User Interface and Toolbars8:03
Content:
A tour of the user interface of R studio including the main panels.
Basic Functionality4:55
Content:
An introduction to the basic functionality of R studio.
How to create and save a file.
How to run code.
How to use the interactive console.
How to delete environment variables.
What Is That Number Before the Output?1:13
Content:
The meaning of the number displayed within square brackets at the beginning of each line of the output.
Factors in R0:58
Content:
How to create and work with factors in R.
Introduction to CSV Files3:23
Content:
A detailed description of the structure of CSV files and how you can create your own CSV file using Google Sheets.
Key terminology such as variables and observations.
How to Create CSV Files Using Google Sheets3:16
Content:
How to create CSV files using Google Sheets.
How to Read CSV Files4:49
Content:
How to read a CSV file in R and assign it to a variable.
Introduction to Data Frames5:11
Content:
Introduction to Data Frames.
How to access a column of a data frame.
How to visualize data frames.
How to find the number of observations in a data frame.
How to find the number of variables in a data frame.
How to Read the R Documentation and Comment R Code3:37
Content:
How to access the documentation of a function in R studio and the main components of this type of articles.
How to Install Packages in R0:47
Content:
How to install packages in R studio in the package tab.
Section Review
Mini Project | International Flights
(PDF Version) Mini Project | International Flights0:12
Content:
The PDF version of the instructions for the mini project for students who prefer to work with the PDF file and students who take the course on the mobile app.
CSV file(s) with data for the mini project.
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Section Introduction0:18
Content:
A very brief summary of the topics that will be covered in this section.
(PDF Resource) Section Handout0:06
Content:
A PDF Handout with a summary of the main aspects of this section.
Introduction to Frequency2:44
Content:
Concept of frequency.
Examples and Practice.
Frequency Tables4:05
Content:
Importance of frequency tables.
Practical Examples.
How to read frequency tables.
How to build frequency tables from raw data.
Example of a Frequency Table0:31
Content:
Practical Example of a Frequency Table.
Generate a Frequency Table in R3:31
Content:
How to generate a frequency table in R using the table() command.
Visualize Frequency with a Bar Plot7:33
Content:
Concept and importance of bar plots.
How to read and interpret bar plots.
Practical examples.
Create a Bar Plot in R3:56
Content:
How to generate a bar plot in R using the plot() function.
How to save your bar plot as an image file.
Create a Bar Plot in R using barplot()0:30
Content:
How to create a bar plot in R using the barplot() function.
Grouped Data in a Frequency Table0:48
Content:
How to read and interpret a frequency table for grouped data.
Generate a Frequency Table with Intervals in R1:23
Content:
How to generate a frequency table in R using intervals.
A detailed description of the purpose of each line in the program.
A .zip file with the R file and CSV file.
Section Review
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Section Introduction0:24
Content:
A very brief summary of the topics that will be covered in this section.
Introduction to Relative Frequency7:08
Content:
Concept of Relative Frequency.
How to calculate relative frequency.
Practical Examples and Guidelines.
Relative Frequency Table | Example0:29
Content:
An example of a relative frequency table with diagrams and illustrations.
Finding Relative Frequency in R3:14
Content:
How to use R to find the relative frequency of all the values in a sequence.
Generate a Relative Frequency Table in R with Intervals1:53
Content:
How to generate a relative frequency table in R with Intervals.
How each line of the program works to display a table with data already grouped into intervals.
A .zip file with the program and CSV file.
Section Review
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Section Introduction0:24
Content:
A very brief summary of the topics that will be covered in this section.
Introduction to Cumulative Relative Frequency5:10
Content:
Concept of Cumulative Relative Frequency.
How to calculate it.
How to find the cumulative relative frequency using a table.
Why the last value has to have a cumulative relative frequency of 1.
How interpret the cumulative relative frequency.
Find Cumulative Relative Frequency in R3:03
Content:
How to find the cumulative relative frequency in R using the cumsum() function.
How to Find Relative Frequency from Cumulative Relative Frequency1:15
Content:
General formula to find the relative frequency of a value from its cumulative relative frequency and the cumulative relative frequency of the previous value.
Example.
Cumulative Relative Frequency in R with Intervals1:50
Content:
How to find the cumulative relative frequency in R with the data already grouped into specific intervals.
How each line of the program works behind the scenes.
A .zip file with the R file and a CSV file.
Frequency vs. Relative Frequency vs. Cumulative Relative Frequency0:46
Content:
A summary of the main characteristics and differences between the frequency, relative frequency, and cumulative relative frequency.
Section Review
Mini Project | Online Learning
(PDF Version) Mini Project | Online Learning0:11
Content:
The PDF version of the instructions for the mini project for students who prefer to work with the PDF file and students who take the course on the mobile app.
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Section Introduction0:24
Content:
A very brief summary of the topics that will be covered in this section.
(PDF Resource) Section Handout0:06
Content:
A PDF Handout with a summary of the main aspects of this section.
Why Do We Need to Know The Center of The Data?1:25
Content:
A brief explanation of the relevance of the center of the data in statistics.
Math Notation: Sigma1:17
Content:
How to read and interpret sigma notation used to represent summation.
Introduction to the Mean6:14
Content:
Concept of Mean.
Formal Notation.
Importance.
How to calculate it.
Applications.
Sample Mean vs. Population Mean0:41
Content:
Differences between the sample mean and population mean including concept, formulas, and formal notation.
Find the Mean in R (Approach #1)4:20
Content:
A practical session in R using the first approach to find the mean.
Find the Mean in R (Approach #2)4:15
Content:
A practical session in R using the second approach to find the mean with the values and their relative frequencies.
Find the Mean of Grouped Data0:42
Content:
How to find the mean from a frequency table with grouped data.
Other Types of Mean0:59
Content:
Weighted Mean.
Geometric Mean.
Harmonic Mean.
Their formulas, meaning, and interesting applications.
Section Review
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Section Introduction0:24
Content:
A very brief summary of the topics that will be covered in this section.
Introduction to the Median7:01
Content:
Concept and relevance as a measure of the center of the data.
How to find the median.
Practice finding the median when there is an even or odd number of elements in the data.
Find the Median in R1:29
Content:
A practical session showing how you can find the median in R.
Median of Grouped Data0:56
Content:
How to find the median of grouped data.
Introduction to the Mode4:08
Content:
Concept and relevance of the mode as a measure of the center.
How to identify it using bar plots.
How datasets are classified according to the number of modes.
Concept of modal class.
When the Mode Doesn't Exist0:10
Content:
Conditions to determine if a dataset doesn't have a mode.
Find the Mode in R1:41
Content:
A custom R function used to find the mode(s) of a dataset.
A line-by-line explanation of the code.
Mean vs. Median vs. Mode0:35
Content:
A brief summary of the main differences between the mean, median, and mode and their characteristics.
Section Review
Mini Project | Black Friday Sales
(PDF Version) Mini Project | Black Friday Sales0:12
Content:
The PDF version of the instructions for the mini project for students who prefer to work with the PDF file and students who take the course on the mobile app.
CSV file(s) with data for the mini project.
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Quick Tip: How to Leave or Update Your Review0:21
Section Introduction0:19
Content:
A very brief summary of the topics that will be covered in this section.
(PDF Resource) Section Handout0:06
Content:
A PDF Handout with a summary of the main aspects of this section.
Why do We Need The Spread of The Data?3:20
Content:
A very brief description of the relevance of the spread of the data in statistics.
Introduction to Variance5:33
Content:
Concept of Variance.
Formal notation.
Concept of deviation from the mean.
How to find the variance (formula).
How to compare variance graphically using bar plots.
Find the Variance (First Approach)4:31
Content:
A step-by-step practical example of how to find the variance of the sales of a donut shop.
Find the Variance in R (First Approach)3:31
Content:
A practical session showing how to find variance in R.
Find the Variance (Second Approach with Frequency Tables)7:04
Content:
A step-by-step practical example of an alternative approach used to find the variance from a frequency table.
Find the Variance in R (Second Approach with Frequency Tables)6:45
Content:
A practical session in R showing how to find the variance in R using the alternative approach from a frequency table.
Find the Variance of Grouped Data0:49
Content:
How to find the variance of Grouped Data.
Population Variance vs. Sample Variance0:51
Content:
Main characteristics of the population variance and the sample variance.
Their main differences.
Differences in their formulas.
Section Review
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Section Introduction0:27
Content:
A very brief summary of the topics that will be covered in this section.
Introduction to the Standard Deviation2:25
Content:
Concept of Standard Deviation.
Formal Terminology.
Examples.
How the standard deviation is used as a reference of distance from the mean.
Standard Deviation as a Measure of Distance from the Mean3:37
Content:
How the standard deviation is used as a reference of distance from the mean.
Practice | Standard Deviation as Reference3:18
Content:
Practical exercises and examples of how you can use the standard deviation as a reference of distance of a value from the mean.
Find the Standard Deviation in R1:54
Content:
A practical session of how to find the standard deviation in R.
Find the Standard Deviation of Grouped Frequency Tables0:09
Content:
How to find an estimation of the standard deviation when you have a frequency table with grouped data.
Skewness2:24
Content:
Concept of skewness as an indication of assymetry.
What it means for a dataset to have no skew, positive skew, and negative skew.
Skewness and Relative Location of the Mean, Median, and Mode1:36
Content:
How you can determine the relative location of the mean, median, and mode when we know that the distribution of the data has no skew, positive skew, or negative skew.
Chebyshev's Theorem0:25
Content:
Chebyshev's Theorem, which applies to all datasets.
Section Review
Mini Project | Waiting Times
(PDF Version) Mini Project | Waiting Times0:12
Content:
The PDF version of the instructions for the mini project for students who prefer to work with the PDF file and students who take the course on the mobile app.
CSV file(s) with data for the mini project.
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Section Introduction0:23
Content:
A very brief summary of the topics that will be covered in this section.
(PDF Resource) Section Handout0:05
Content:
A PDF Handout with a summary of the main aspects of this section.
Importance of Graphs and Plots in Statistics2:38
Content:
Why visual representations of the data in the form of graphs and plots are extremely important for descriptive statistics.
Graph vs. Chart vs. Plot0:09
Content:
Subtle difference between the term "graph" and "plot".
Frequency Histograms0:03
Content:
Type of histogram that you will learn during this section.
Introduction to Histograms4:34
Content:
Concept of histogram.
Use cases.
Elements.
How to read and interpret them.
Class, Class Limits, and Class Boundaries0:29
Content:
Concept of class, class limit, and class boundary.
Interpret Histograms | Examples and Practice6:27
Content:
Practical examples showing how to read and interpret histograms.
How to Make a Histogram Manually0:45
Content:
Guidelines to draw histograms manually based on the characteristics of the data.
How to Create and Customize Histograms in R5:40
Content:
Practical session to generate a histogram in R.
How to save your histogram as an image file.
Section Review
Mini Project | Wood for Sawmills
(PDF Version) Mini Project | Wood for Sawmills0:12
Content:
The PDF version of the instructions for the mini project for students who prefer to work with the PDF file and students who take the course on the mobile app.
CSV files with data for the mini project.
Collect and Share Your Badge0:07
Download and share a special badge to celebrate this milestone.

Requirements

No previous programming experience required.
Download and install RStudio (Free Open-Source Edition).
You can work with the R console during the course as well.
Google Sheets to create CSV files.

Description

Learn statistics using R with mini projects, hands-on practice, and carefully designed visual explanations. Understand how fundamental statistical concepts work behind the scenes and apply your knowledge to new scenarios.

Descriptive Statistics in R is Your First Step Into the In-demand and Powerful World of Statistics and Data Science

Analyze real-world scenarios by identifying key elements such as population, sample, statistic, and parameter.
Measure the center of the data with the mean, median, and mode. Describe their key differences and use cases.
Measure the spread of the data with variance and standard deviation.
Learn how to create and interpret bar plots, histograms and box plots.
Find quartiles and the interquartile range (IQR). Use them to identify potential outliers.
Apply your knowledge in practical mini projects.
Check your knowledge with a final exam that covers all the topics of the course.

Add New Statistical Skills To Your Resume

Statistics is one of the most in-demand skills of our current time. If you want a career in data science, computer science, or mathematics, learning statistics is the first step that you need to take. When you combine theoretical statistical skills with practical R programming skills, you have the perfect skill set that employers around the world are looking for.

This course provides a detailed and engaging introduction to descriptive statistics using the R programming language and RStudio, the main tool used in industry to work with programming for statistical purposes.

No programming experience is required to take this course. Lectures combine the theoretical aspects of statistics with the practical and applied aspects that R programming brings to this amazing field. You will be analyzing small datasets and working on practical mini projects that simulate simplified real-world scenarios.

Learning the fundamentals of statistics is your first step towards mastering a career in data science, computer science, and mathematics.

Content & Overview

With high-quality video lectures that include customized graphics and presentations, you will learn and work with these concepts:

Population
Sample
Sampling
Data
Variable
Statistic
Parameter
Frequency
Relative Frequency
Cumulative Relative Frequency
Bar plots
Mean
Median
Mode
Variance
Standard Deviation
Histograms
Quartiles
Interquartile Range (IQR)
Outliers
Box Plots
.and more.

You will apply your knowledge in practical mini projects throughout the course and you will check your understanding with a final exam that will test your knowledge of all the topics covered in the course.

Learning Material & Resources

Throughout the course, you will find these resources:

Video lectures: carefully designed graphics and explanations.
Mini Projects: apply your knowledge with practical mini projects that represent simplified real-world scenarios.
Solutions: each mini project has its corresponding solution, so you can check your answers immediately.
Coding Sessions: practical lectures cover how to apply your new statistical knowledge in R and RStudio.
PDF Handouts: you will find unique study guides with key aspects of each section.
Quizzes: check your knowledge interactively after each section with short quizzes (unlimited attempts!).
Articles: read complementary articles specifically written for this course to expand your knowledge on various topics.
Discussion Forums: ask questions on the discussion forums and discuss interesting topics with your peers.

Why is this course unique?

This course is unique because of its emphasis on providing visual and detailed explanations of how statistics works behind the scenes, so you will not only learn how to find statistical results using R, you will actually understand what they mean and what each line of code does behind the scenes.

During the course, you will apply your knowledge by completing mini projects that simulate simplified real-world scenarios such as analyzing Black Friday sales, online learning patterns, waiting times of a taxi company, delivery times of a wood transportation company, light bulb life, and house prices across three different neighborhoods.

By the end of this course, you will be able to combine your new theoretical knowledge of statistics with practical R skills to interpret results.

Unique study materials complement the course experience. You will find PDF handouts specifically written for the course with key aspects of each section.

You will check your knowledge with short quizzes that provide instant feedback, so you can check the correct answer immediately. These questions were designed to make you think more deeply about the topics presented.

You will receive a certificate of completion that you can add to your social media profiles to showcase your new skills.

You will also have lifetime access to the course.

You are very welcome to watch the preview lectures and check out the full course curriculum.

If you are looking for an engaging, visual, and practical course, you've found it.

Add Descriptive Statistics in R to your resume and showcase your new skills!

Who this course is for:

Students who are new to descriptive statistics and to R programming.
Learners who want to combine existing statistical knowledge with R programming.
Professionals who wish to expand their skills with practical statistical knowledge to solve real-world problems.

Introduction to Statistics in R - A Practical Approach

What you'll learn

Explore related topics

Course content

Introduction to Statistics and Basic Concepts17 lectures • 38min

Introduction to R and RStudio16 lectures • 39min

Introduction to Frequency12 lectures • 26min

Relative Frequency6 lectures • 13min

Cumulative Relative Frequency8 lectures • 13min

Measures of the Center of the Data: Mean11 lectures • 21min

Measures of the Center of the Data: Median and Mode10 lectures • 17min

Measures of the Spread of the Data: Variance12 lectures • 33min

Measures of the Spread of the Data: Standard Deviation11 lectures • 17min

Introduction to Histograms12 lectures • 22min

Requirements

Description

Who this course is for: