IIRF Online > Development > Data Science > R (programming language) > R Programming for Statistics and Data Science
R Programming for Statistics and Data Science by Udemy
R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R
Course Highlights
- Learn the fundamentals of programming in R
- Work with R’s conditional statements, functions, and loops
- Build your own functions in R
- Get your data in and out of R
- Learn the core tools for data science with R
- Manipulate data with the Tidyverse ecosystem of packages
- Systematically explore data in R
- The grammar of graphics and the ggplot2 package
- Visualise data: plot different types of data & draw insights
- Transform data: best practices of when and how
- Index, slice, and subset data
- Learn the fundamentals of statistics and apply them in practice
- Hypothesis testing in R
- Understand and carry out regression analysis in R
- Work with dummy variables
- Learn to make decisions that are supported by the data!
- Have fun by taking apart Star Wars and Pokemon data, as well some more serious data sets
Skills you will learn!
Curriculum
1 Topic
Ten Things You Will Learn in This Course
6 Topics
Intro
Downloading and installing R & RStudio
Quick guide to the RStudio user interface
RStudio's GUI
Changing the appearance in RStudio
Installing packages in R and using the library
16 Topics
Creating an object in R
Exercise 1 Creating an object in R
Data types in R - Integers and doubles
Data types in R - Characters and logicals
Objects and Data Types
Exercise 2 Data types in R
Coercion rules in R
Exercise 3 Coercion rules in R
Functions in R
Exercise 4 Using functions in R
Functions and arguments
Exercise 5 The arguments of a function
Building a function in R (basics)
Objects and Functions
Exercise 6 Building a function in R
Using the script vs. using the console
14 Topics
Intro
Introduction to vectors
Vector recycling
Exercise 7 Vector recycling
Naming a vector in R
Exercise 8 Vector attributes - names
Introduction to vectors
Getting help with R
Getting Help with R
Slicing and indexing a vector in R
Extracting elements from a vector
Exercise 9 Indexing and slicing a vector
Changing the dimensions of an object in R
Exercise 10 Vector attributes - dimensions
20 Topics
Creating a matrix in R
Faster code: creating a matrix in a single line of code
Creating a matrix
Exercise 11 Creating a matrix in R
Do matrices recycle?
Indexing an element from a matrix
Slicing a matrix in R
Exercise 12 Indexing and slicing a matrix
Matrix arithmetic
Exercise 13 Matrix arithmetic
Matrix operations in R
Matrix operations
Exercise 14 Matrix operations
Categorical data
Creating a factor in R
Factors in R
Exercise 15 Creating a factor in R
Lists in R
Exercise: Lists in R
Completed 33% of the course
18 Topics
Relational operators in R
Logical operators in R
Vectors and logicals operators
Relational and Logical operators in R
Exercise Logical operators
If else else if statements in R
Exercise If else else if statements in R
If else else if statements - Keep-In-Mind's
For loops in R
Exercise: For Loops in R
While loops in R
Exercise: While loops in R
Repeat loops in R
Loops in R
Building a function in R 2.0
Building a function in R 2.0 - Scoping
Exercise Scoping
Completed 50% of the course
15 Topics
Intro
Creating a data frame in R
Exercise 16 Creating a data frame in R
The Tidyverse package
Data import in R
Importing a CSV in R
Data export in R
Exercise 17 Importing and exporting data in R
Creating data frames
Getting a sense of your data frame
Indexing and slicing a data frame in R
Data frame operations
Extending a data frame in R
Exercise 18 Data frame operations
Dealing with missing data in R
11 Topics
Intro
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 in R
Manipulating data
Exercise 19 Data transformation with Dplyr
Tidying data in R - gather() and separate()
Tidying data in R - unite() and spread()
Tidying data
Exercise 20 Data tidying with Tidyr
12 Topics
Intro
Intro to data visualization
Intro to ggplot2
Variables: revisited
Building a histogram with ggplot2
Exercise 21 Building a histogram with ggplot2
Building a bar chart with ggplot2
Exercise 22 Building a bar chart with ggplot2
Building a box and whiskers plot with ggplot2
Exercise 23 Building a box plot with ggplot2
Building a scatterplot with ggplot2
Exercise 24 Building a scatterplot with ggplot2
7 Topics
Population vs. sample
Mean median mode
Skewness
Exercise 25 Determining Skewness
Variance standard deviation and coefficient of variability
Covariance and correlation
Exercise 26 Practical example with real estate data
12 Topics
Distributions
Standard Error and Confidence Intervals
Hypothesis testing
Type I and Type II errors
Test for the mean - population variance known
Exercise: Test for the mean - population variance known
The P-value
Test for the mean - Population variance unknown
Exercise: Test for the mean - population variance unknown
Comparing two means - Dependent samples
Exercise: Comparing two means - Dependent samples
Comparing two means - Independent samples
9 Topics
The linear regression model
Correlation vs regression
Geometrical representation
First regression in R
How to interpret the regression table
Exercise: Doing a regression in R
Decomposition of variability: SST SSR SSE
R-squared
Completed 100% of the course
R Programming for Statistics and Data Science
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