Introduction to R Programming

Providing you with the skills to manipulate, analyze, and visualize data with the best programming language for statistical analysis for data science.

with Simona Dobreva and Iliya Valchanov

Course Overview

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.

87 High Quality Lessons

7 Practical Tasks

6 Hours of Video

Certificate of Achievement

Skills you will gain

data analysisdata processingdata visualizationprogrammingr

What You'll Learn

Here, you will learn how to work on statistics and graphics with R. Become a professional data scientist by applying specific R techniques, methods, and functions.

Use RStudio

Understand objects and coercion rules in R

Create functions in R and use the R's console

Create vectors and matrices

Create and import data frames into R

Manipulate and visualize data

Curriculum

Introduction & Getting Started Free

6 Lesson 24 Min

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 Lesson 33 Min

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 Lesson 29 Min

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 Lesson 48 Min

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 Lesson 43 Min

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 Lesson 36 Min

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 Lesson 25 Min

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 Lesson 42 Min

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 Lesson 25 Min

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

9 Lesson 56 Min

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 Lesson 25 Min

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

“Learning R programming is a valuable asset to get you where you want to be in your data science career. In this course, I have drawn from years of experience in data analysis and have summarized all in a fast and effective training. I will be super proud and happy to share my R journey with you! ”

Simona Dobreva

Worked at 365 Data Science

Courses You May Like

100 lessons 9 hours

The Complete Data Visualization Course with Python, R, Tableau, and Excel

with Elitsa Kaloyanova

68 lessons 7 hours

Data Preprocessing with NumPy

with Viktor Mehandzhiyski

27 lessons 2 hours

Data Cleaning and Preprocessing with pandas

with Martin Ganchev

242 lessons 8 hours

with Martin Ganchev, Vladimir Saev

128 lessons 11 hours

Python Programmer Bootcamp

with Giles McMullen-Klein

9 lessons 1 hours

Introduction to Jupyter

with Martin Ganchev

20 lessons 1 hours

with Martin Ganchev

12 lessons 1 hours

with Giles McMullen-Klein

40 lessons 2 hours

Introduction to Python

with Martin Ganchev

46 lessons 2 hours

Dates and Times in Python

with Ivan Manov, Martin Ganchev

Introduction to R Programming

with Simona Dobreva and Iliya Valchanov