Introduction to Programming R a Modern Approach
4.8 (48 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
186 students enrolled

Introduction to Programming R a Modern Approach

Introduction to fundamentals of R programming - Writing effective, simple, and modern R codes using tidyverse and more
Highest Rated
4.8 (48 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
186 students enrolled
Created by Robert Jeutong
Last updated 5/2020
English
Current price: $34.99 Original price: $49.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 8.5 hours on-demand video
  • 1 article
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Basics of programming in R
  • Data wrangling manipulations
  • Make use of the tidyverse packages which includes but not limited to purrr, dplyr, ggplot2, etc..
  • Create pipelines using the pipe operator to chain instruction and transform a data frame to another
  • Transform data frames then pipe it to ggplot for EDA or professional looking graphs
  • Showcase working directories and projects
  • Teach the fundamentals of how R works beyond this course
  • Understanding functions and how use existing ones or how to create your own
  • Teach modern techniques used in R by data scientists
  • Install and load packages such as lubridate, readxl, esquisse, etc...
  • Read and write different types of data
  • Group and summarize data using the dplyr verbs
  • Transpose data with dplyr pivoting functions or using the soon to be deprecated gather and spread functions
Course content
Expand all 42 lectures 08:17:31
+ Installing R and R Studio Demo
2 lectures 16:07

Here we show you how to install R and R studio

Preview 11:20

Set the working directory. The working directory is the folder for your project. It is where you save your scripts, data, and everything you need for your project. Setting it in R makes it easy to read and write files among other things

R Studio Setup and Working Directory
04:47
+ Base R Fundamentals - Base R Data Structure and Slicing
15 lectures 01:56:08

What are variables and what are they used for? How to create them?

Variables Lecture
02:16

A demo on how to create variable and how to use them

Preview 06:21

Definition of different types of vectors.

Vectors Lecture
04:55

Demo on different types of atomic vectors

Vectors Demo
19:42

Lecture on what are matrices and arrays

Matrices and Arrays Lecture
01:39

Demo session on matrices and arrays

Matrices and Arrays Demo
07:54

Quick into to lists

Lists Lecture
01:52

List demo

Lists Demo
08:56

Intro to data frames

Data Frames Lecture
02:13

Quick demo on data frames

Data Frames Demo
10:48

Slicing, filtering, or subsetting the different types of data.

Preview 03:56

Demo about slicing or subsetting atomic vectors

Vectors Slicing
11:27

Demo about slicing or subsetting atomic lists

List Slicing
07:19

Demo about slicing or subsetting atomic data frames

Data Frames Slicing 1
14:24

Importing data and render it to a data frame and filtering it

Preview 12:26
+ Packages - Modernizing Your R Script with Tidyverse Packages and More
10 lectures 01:57:52

Definition of packages and how to install them

What are Packages
03:46

About the packages in the tidyverse set of packages

Tidyverse Pakages Explained
03:06

Import and export data. readr import and export are faster and return tibbles rather than data frames

Import and Export data
15:39

Modern ways to slice (filter), select variables, creating new variables (mutate), and sort (arrange) data. More efficient than base R

Dplyr Verbs
16:51

Create a pipeline of a chain of instructions to be taken on a dataset.

Preview 11:06

Use the tidyverse packages to summarize the data neatly.

Summarize Data
13:03

The power of piping actions

More on the Pipe Operator
13:37

Transform data from wide to long format and from long to wide format

Pivoting
15:43

Lecture about merging data frames together. Using the dplyr joins to merge data

Preview 06:44

Demo of dplyr joins

Relational Data Demo
18:17
+ Base R Must - if(), loops, functions, and Base R plot
5 lectures 01:34:49

The syntax of running if else in R

If Else
16:40

What are loops? How to write for and while loops in R

Loops - for and while loops
21:41

Some Base R functions. And explanation of parameters

Intro to Functions
10:36

We show examples of how to write your own funcion

Create Your Own Function
22:51

An introduction to plotting with Base R

Intro to Base R Plots
23:01
+ Plots with ggplot2 Package
7 lectures 01:52:21

Some cool plots to begin. Data from the National Football League (NFL)

Intro to ggplot2
18:46

Add layers to combine multiple geoms. Also modify the global and local aesthetic mappings

Layers
14:10

A transformation of the game data set

Bonus - How did I Transpose the Game Data Set
19:48

More on different types of charts.

Other Types of Charts
27:46

Showcase the idea of faceting in ggplot

Faceting
10:21

Add a tittle or subtitle to your chart. Also change x and y-axis labels. Change the position of the legend and more

ggplot Options
10:27

Use the esquisse package as a helper to get you started with ggplot

The Esquisse Package
11:03
+ Comprehensive Project
2 lectures 38:37
The Comprehensive Project
00:20
Solution to the Comprehensive Project
38:17
Requirements
  • Simple understanding of data or a data set
  • Understanding of data in tabular form
  • Simple algebra
Description

Are you nervous or excited about learning how to code? Are you a beginner who wants to get better at learning R the right way? Do you want to learn how to make cool looking and insightful charts? If so, you are in the right place.

Learning how to code in R is a good way to start. R is one of the top languages used by data scientists, data analysts, statisticians, etc. The best thing about it is its simplicity.

R was introduced to me in the summer of 2008 as an intern at a marketing firm. Ever since I have been hooked. Along with SAS, I use it daily to conduct data analysis and reporting. R is one of my top go-to tools. I start with the basics showing you how I learned it and then I teach it at a pace comfortable for a beginner.

We are living in exciting times and the future looks bright for those skilled in programming. Industries are using data more and more to make important decisions. They need skilled analysts to help design data collection processes and to analyze it. Where do you fit in this picture now and tomorrow? Learning R sets you now and will sustain you for the future.

R was designed mainly for statisticians or those who did not have a computer science background, hence its intuitiveness. R is a free and open-source programming language. It will not cost you anything to have R installed and running on your computer. R is open-source meaning that contributors can improve its usability by creating packages. Packages contain functions to help users solve specific problems that R’s founders did not think about. It would be a pleasure to see you grow to become a contributor to R someday.

Although R itself is immensely powerful, it is not the best place to write R codes. We will write R codes (or scripts) in R studio. R studio is a powerful editor for R. You will learn all about it in this course.

Here are some of the things you will learn in this course:

1. Download and install R and R studio

2. The different data structures such as atomic vectors, lists, data frames, and tibbles. How to create and use them

3. How to import an excel or a CSV file into R

4. Create functions

5. How to execute chunks of code following an if-else logic

6. Lean R studio short cut keys to increase your efficiency and productivity

7. How to summarize data

8. How to transpose data from long format to wide format and backward

9. How to create powerful easy to read pipelines using purrr and dplyr packages

10. Introduction to base R plots

11. Ggplots

12. And more…

Thanks for taking the time to check out my course. I cannot wait to help you get started with R and R studio. If you have any questions about the course feel free to message me or check out the free preview lecture to learn more.

Who this course is for:
  • Aspiring data scientists, statisticians, or data analyts
  • Beginner R developers curious about data science
  • Non computer programmers who willing to learn a fun and useful coding language
  • Data scientists eager to learn R