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Kurs

R For SAS Users

GrundläggandeKunskapsnivå

Uppdaterad 2024-08

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

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RProgramming

4 tim

15 videor

56 Övningar

4,700 XP

4,526

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Kursbeskrivning

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.

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Det finns inga förkunskapskrav för den här kursen

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

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