Introduction to R Programming

Curriculum

• Introduction & Getting Started Free

  6 Lessons 24 Min

  In this introductory part of the course, we will go for a walk in the R environment. First, we are going to install R and RStudio together. Then, we’ll dive straight into RStudio and learn about its interface, and how to make use of the main windows and tabs there. We will also talk about setting your working directory and getting additional help.

  What does this course cover Free What does section 1 cover Free Downloading and installing R and RStudio Free Quick guide to the RStudio user interface Free Changing the appearance of RStudio Free Installing packages and using the library Free
• The building blocks of R Free

  8 Lessons 33 Min

  In 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 Free Data types in R (Integers and doubles) Free Data types in R (Characters and logicals) Free Coercion rules in R Free Functions in R Free Functions and arguments Free Building a function in R Free Using the script vs. using the console Free
• Vectors and vector operations

  7 Lessons 29 Min

  Now 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.

  What does section 3 cover Introduction to vectors Vector recycling Naming a vector Getting help with R Slicing and indexing a vector Changing the dimensions of an object in R
• Matrices

  10 Lessons 48 Min

  In 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 matrix Faster code - creating a matrix in a single line of code Do matrices recycle Indexing an element from a matrix Slicing a matrix Matrix arithmetic Matrix operations Categorical data Creating a factor in R Lists in R
• Fundamentals Of Programming With R

  10 Lessons 43 Min

  In 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 R Logical operators in R Logical operators and vectors If else else-if statements If else else-if keep-in-minds's For loops in R While loops in R Repeat loops in R Building a function in R 2.0 Building a function in R 2.0 Scoping
• Data frames in R

  10 Lessons 36 Min

  In 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 cover Creating a data frame The Tidyverse package Data import into R Importing a CSV into R Data export in R Getting a sense of your data frame Indexing and slicing a data frame in R Extending a data frame in R Dealing with missing data
• Manipulating data with R

  7 Lessons 25 Min

  Here, 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.

  What does section 7 cover Data transformation with R - the Dplyr package - Part I Data transformation with R - the Dplyr package - Part II Sampling data with the Dplyr package Using the pipe operator Tidying your data - gather() and separate() Tidying your data - unite() and spread()
• Visualizing data with R

  8 Lessons 42 Min

  Plotting 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.

  What does section 8 cover Intro to data visualisation Intro to ggplot2 Variables revisited Building a histogram with ggplot2 Building a bar chart with ggplot2 Building a box and whiskers plot with ggplot2 Building a scatterplot with ggplot2
• Exploratory data analysis with R

  5 Lessons 25 Min

  In 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.

  Population vs Sample Mean, median, mode Skewness Variance, standard deviation, and coefficient of variability Covariance and correlation
• Hypothesis Testing

  9 Lessons 56 Min

  At 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.

  Distributions Standard Error and Confidence Intervals Hypothesis Testing Type I and Type II errors Test for the mean. Population variance known The P-value Test for the mean. Population variance unknown Dependent samples Comparing two means. Independent samples
• Linear Regression Analysis in R

  7 Lessons 25 Min

  Regression 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.

  The Linear Regression Model Correlation vs Regression Geometrical Representation Doing the Regression in R How to interpret the regression table Decomposition of variability R-Squared