Melina A.
Before 365:
Information Design | UAM
After 365:
Data Analyst | TELUS Digital
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Online Course
Introduction to R Programming
with
Simona Dobreva
and
Iliya Valchanov
Master R programming for data science: manipulate, analyze, and visualize data with the best programming language for statistical analysis
18344 students already have enrolled
Skill level:
Basic
Duration:
6 hours
-
Lessons (6 hours)
-
Projects (7 hours)
- Lessons (6 hours)
- Projects (7 hours)
CPE credits:
8.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Get a comprehensive introduction to the world of R programming.
- Write well-structured and professional R code in RStudio.
- Create, import, and manipulate data frames, vectors, and matrices.
- Learn how to use R for statistical analysis and data visualization.
- Solve hypothesis testing and linear regression tasks in R.
Topics & tools
programmingdata analysisdata visualizationdata processingr
Your instructor
Head of Product at Team-GPT | Psychology Graduate | Data-Driven Marketing & Product Leader
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Psychology
Media and recognition
- Daniel Garrad Prize, University of Edinburgh
- Edinburgh Award for student leadership and volunteering
CEO of Team-GPT | AI Educator | Co-founder of 365 Data Science & 3veta
Track record
Bringing real-world expertise from leading global companies
Academic background
Bachelor's degree, International Economics, Management, and Finance
Course OVERVIEW
Description
CPE Credits: 8.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
R is one of the best programming languages specifically designed for statistics and graphics. Programming in R is a fast and effective way to perform advanced data analyses and manipulations. In this course, you will learn how to use R and utilize the many data analysis techniques, methods, and functions it has to offer to the professional data scientist.
Prerequisites
- R (latest version from cran.r-project.org) and RStudio (free version available at posit.co)
Advanced preparation
- None
Curriculum
87 lessons
60 exercises
1 project
1 exam
- 2. The building blocks of R33 MinIn this section, you will learn about objects and coercion rules in R, functions in R, and how to use R’s console. Not only that, but by the end of the section, you will have built your first function; it will be able to draw cards from a deck, so you can play your favorite board game even if you don’t have the physical cards in front of you.Creating an object in R FreeCoding exercise FreeData types in R (Integers and doubles) FreeExercise FreeCoding exercise FreeData types in R (Characters and logicals) FreeCoding exercise FreeCoercion rules in R FreeExercise FreeCoding exercise FreeFunctions in R FreeCoding exercise FreeFunctions and arguments FreeCoding exercise FreeBuilding a function in R FreeCoding exercise FreeUsing the script vs. using the console Free
- 3. Vectors and vector operations29 MinNow that we have covered the basics, in this section, we are about to drill deeper into R’s most widely used object type – the vector. You will learn how to create vectors and how to perform vector arithmetic operations. You will also see how to index and access elements from a vector, and how vectors recycle. Then, you will see how to change the dimensions of a vector and create a two-dimensional object from it. That will be our nice little segue into matrices.
- 4. Matrices48 MinIn this section, you will learn how to create and rename matrices, and how to index and slice matrices. All of this will lay a super solid foundation for the big star of data analysis: the data frame. Not only that, but we will also talk about factors, which are related to the statistics part of the course. Finally, we will cover lists: R’s way of storing hierarchical data.Creating a matrixCoding exerciseFaster code - creating a matrix in a single line of codeCoding exerciseDo matrices recycleIndexing an element from a matrixCoding exerciseSlicing a matrixCoding exerciseMatrix arithmeticCoding exerciseMatrix operationsCoding exerciseCategorical dataCreating a factor in RCoding exerciseLists in RCoding exercise
- 5. Fundamentals Of Programming With R43 MinIn this section of the course, we will go through some of the fundamental tools you need to learn when programming with R (and many other programming languages). We will cover relational operators, logical operators, vectors, IF, ELSE, and different types of loops (for, while, and repeat) in R. Some of these topics will have already been introduced to you in our Python training, but here you will have the chance to reinforce what you have learned and see things with R in mind.Relational operators in RCoding exerciseLogical operators in RCoding exerciseLogical operators and vectorsCoding exerciseIf else else-if statementsCoding exerciseIf else else-if keep-in-minds'sFor loops in RCoding exerciseWhile loops in RCoding exerciseRepeat loops in RCoding exerciseBuilding a function in R 2.0Coding exerciseBuilding a function in R 2.0 ScopingCoding exercise
- 6. Data frames in R36 MinIn this section, we will focus our attention on how to create and import data frames into R. How to quickly get a sense of your data frame by using the str() function, summary(), col-and row-names, and so on. We’ll learn about accessing individual elements of your data frame for further use. And about extending a data frame with either new observations or variables (or row and columns). Furthermore, we will talk about dealing with missing data because in real life that happens more often than we’d like. And we’ll discuss exporting data frames once we’re happy with their general state and are ready to share them with the world.What does section 6 coverCreating a data frameCoding exerciseThe Tidyverse packageData import into RCoding exerciseImporting a CSV into RData export in RGetting a sense of your data frameCoding exerciseIndexing and slicing a data frame in RCoding exerciseExtending a data frame in RCoding exerciseDealing with missing dataCoding exercise
- 7. Manipulating data with R25 MinHere, we will be talking about data transformation with the dplyr package. More specifically, how to filter(), arrange(), mutate(), and transmute() your data, as well as how to sample() fractions and a fixed number of elements from it. You will also learn what tidy data is, why it is extremely important for the efficiency of your work to tidy your data sets in the most meaningful way, and how to achieve this by using the tidyr package. You will be tidying several messy real-life data sets, and you will learn how to combine multiple operations in an intuitive way by using the pipe operator.
- 8. Visualizing data with R42 MinPlotting and graphing data is the most elegant way to understand your data and present your findings to others. In this section, we are going to learn about the grammar of graphics and the seven layers that comprise a visualization. Then, we will jump straight into creating graphs and plots, with the ggplot2 package. Starting with the histogram, we will continue on to the bar chart, then onto the box and whiskers plot, and finally, the scatterplot. You will notice that with each new type of plot, you will also be learning about a new layer or two while getting familiarized with ggplot2 and its inner workings in an incremental way.
- 9. Exploratory data analysis with R25 MinIn this part of the course, we start applying R for statistical analysis. We are ready to discuss several exploratory data analysis topics: population vs. sample; mean, median, and mode; skewness; variance, standard deviation, and the coefficient of variability; and covariance and correlation.
- 10. Hypothesis Testing56 MinAt this point, you are already familiar with hypothesis testing. We covered it in one of our earlier modules – Statistics. What we will do here is a natural continuation – you will learn how to carry out hypothesis testing in R.DistributionsStandard Error and Confidence IntervalsHypothesis TestingType I and Type II errorsTest for the mean. Population variance knownCoding exerciseThe P-valueTest for the mean. Population variance unknownCoding exerciseDependent samplesCoding exerciseComparing two means. Independent samplesCoding exercise
- 11. Linear Regression Analysis in R25 MinRegression analysis is another topic we covered earlier in our program. As with hypothesis testing, this is a great opportunity to apply the theory you have learned previously in R.
- 12. Course project and exam435 Min
Free preview
1.1 What does this course cover
5 min
1.2 What does section 1 cover
1 min
1.3 Downloading and installing R and RStudio
3 min
1.4 Quick guide to the RStudio user interface
8 min
1.7 Changing the appearance of RStudio
2 min
1.8 Installing packages and using the library
5 min
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ACCREDITED certificates
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