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Course

R For SAS Users

BasicSkill Level

4.7+

Updated 08/2024

Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.

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RProgramming4 hr15 videos56 Exercises4,700 XP4,486Statement of Accomplishment

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Course Description

If you have experience with SAS and want to learn R, this is the course for you. R is FREE (cost) and OPEN (license) and is one of the fastest growing software languages for statistics and data science. This course is a gentle introduction to the R language with every chapter providing a detailed mapping of R functions to SAS procedures highlighting similarities and differences. You will orient yourself in the R environment and discover how to wrangle, visualize, and model data plus customize your output for final presentation. Throughout the course, you will follow a consistent workflow of data quality checking and cleaning, exploring relationships, modeling, and presenting results. You will leave this course with coded examples that provide a template to use immediately with a dataset of your own.

Prerequisites

There are no prerequisites for this course

1

Getting Started with R

This first chapter will get you oriented into the R programming environment. You'll learn how to get help, load a dataset, and increase functionality by adding packages. You'll begin working with the abalone dataset (through the dplyr package workflow) to get descriptive statistics and create helpful visualizations (using the ggplot2 package).

Get help and load data in R

Getting help

Load dataset and get details

Add functionality with packages

Dataset contents and descriptive statistics

Load external dataset

Dataset contents

Descriptive statistics

Graphical visualizations

Boxplots and violin plots

Scatterplots

2

Data Wrangling

Now that you are oriented in the R environment, this chapter will advance your understanding of R's versatility working with data objects. You'll learn how to create and modify variables in the abalone data set. Using your ggplot2 visualization skills, you will discover the data errors in the abalone data and then create a final cleaned data set ready for analysis and modeling.

Objects - the building blocks of R

Create data objects in R

Create composite object types

Selecting elements from objects

Determine variable types

Select elements in objects

Manipulating datasets and data objects

Create new variables

Recode variables

Object type conversion

Data quality and cleaning

Variables inspection

Illogical weights

Check dimension measurements

Check final dataset

3

Data Exploration

Once your data set has been cleaned, the next step is exploration. In chapter 3 you will learn how to compute descriptive statistics, explore associations (e.g., correlations) among the variables, and perform bi-variate statistical tests (e.g., t-tests and chi-square tests). You will also create graphical visualizations which illustrate the bi-variate associations and group comparison tests.

Exploratory data analysis

Descriptive statistics and function masking

Specific statistics for one or more variables

Summary statistics by group

Correlations and t-tests

Bivariate correlations

Scatterplots

Correlations by sex

Tests for two groups

Categorical data: analyze and visualize

Chi-square tests

Mosaic plots

Age categories by shellweight categories

4

Models and Presentation

In this final chapter, you will learn how to work with one of the most versatile data object types in R called a list. These skills will enable you to save and manipulate your output from descriptive statistics, associations, and group comparison computations. You will also learn how to perform ANOVA (analysis of variance) and linear regression in R. All your skills are put to use in the final exercises to create the best models for predicting abalone ages from their sex, size, and weight measurements.

Working with output objects

Descriptive statistics output

Summarise output

Group_by output

Working with lists

Hmisc describe output

Correlations output

t-tests output

Chi-square tests output

ANOVA and linear models

Linear regression

Final models evaluation

Abalone age predictors

Best model by sex

Course summary and recommendations

R For SAS Users

Course
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Don’t just take our word for it

*4.7

from 27 reviews

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Yaseen

2 months ago

Tung

2 months ago

.

Stanislau

2 months ago

Vitalii

3 months ago

Fabrice

3 months ago

Good overview of general R possibilities and its equivalent in SAS. As a daily SAS user, it would be nice to present what is the equivalent of "SAS macro" with R, how to loop, and how to create rtf, doc or pdf documents.

Damir

3 months ago

Yaseen

Stanislau

Vitalii

FAQs

Is this course suitable for beginners?

Yes, this course is suitable for beginners. It is a gentle introduction to R and does not require prior experience in the language.

Will I receive a certificate at the end of the course?

Yes, certificate of completion can be obtained at the end of the course.

What topics will be covered in this course?

This course covers a variety of topics related to R, including getting started with the R programming environment, data wrangling, data exploration, models, and presentation.

Does this course focus on data wrangling or modeling?

This course focuses on both data wrangling and modeling. It provides a comprehensive overview of how to wrangle, explore, and model data, as well as how to customize your output for presentation.

Who will benefit from this course?

Anyone looking to gain knowledge in data analysis and programming would benefit from this course. It may be particularly useful for individuals with a SAS background who are looking to transition to R programming.

What packages will be used in this course?

The primary packages used in this course are dplyr and ggplot2.

Will I learn how to create graphical visualizations?

Yes, you will learn how to create graphical visualizations using the ggplot2 package.

What kind of data will the course focus on?

The course will focus primarily on the abalone dataset and will showcase data wrangling, exploration, modeling, and presentation techniques with this data.

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