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Introduction to R

Moffitt Cancer Center

📆 Mondays June 7 - July 19, 2021

⏰ 3:00 pm

🏢 via Zoom (see links below)

💻 Moffitt

Setup

The course will be hosted through Zoom with video recordings posted on Ponopto after. Links to recorded lectures will be emailed out after class.

Slides

  • Introduction and R and Rmarkdown
  • Data Manipulation I and II
  • Visualization with ggplot
  • Statistical testing
  • Best practices

Code

Find our live coded notes in notes/ as we commit them.

Questions?

Feel free to ask questions in the GitHub Issues.

Overview

This is a six week course designed to introduce future users to R and Rstudio. We will cover data cleaning using the tidyverse, creating visuals with ggplot, basic statistical analysis and writing documents with Rmarkdown. In the end you should be able to:

  • import and manipulate data into different formats
  • create new variables and recode existing ones
  • plot data using the appropriate figure type
  • perform basic statistics
  • write Rmarkdown reports

Who is this course designed for?

Have you never written any code in R or any other programming language? Are you familiar with R, but hoping to bulk up your basic skills? Have you used R but are new to the tidyverse framework?

Learning objectives

Introduction to R/Rstudio and Rmarkdown

  • Get familiar with the R and Rstudio environment
  • Understand basic R code structure (packages, data structures)
  • Create and render Rmarkdown documents in multiple formats

Data import and manipulation - Part I

  • Import various data types
  • Explain the fundamentals of "tidy data" and the tidyverse
  • Clean data sets using dplyr verbs

Data manipulation and cleaning - Part II

  • Merge data sets
  • Transform data

Visualization

  • Understand the principles of ggplot2
  • Determine appropriate visualizations
  • Plot - geom_smooth to see a model over the data

Basic statistics

  • ttest, correlation and chisq
  • Model data
  • Report findings from R output

Final coding project

  • Organize projects and name objects in a uniform manner
  • Create a reproducible workflow
  • Setting up your own R installation

Materials/RStudio Cloud

Materials will be made available on GitHub. If you are using an organization-issued laptop, you may want to verify before you arrive that you can access GitHub.

In this course, we will be using a version of RStudio available online. The class workspace can be accessed here. Please create a user account before the first day of class (or connect with either a GitHub account or Google account).

Schedule and Links

Instructors

This course is taught by members of Moffitt staff including Jordan Creed, a data analyst in the Department of Health Informatics, Zachary Thompson and Ram Thapa, biostatisticians in the Bioinformatics and Biostatistics Core.

About

Materials and information on Moffitt Cancer Center's introductory course for R and Rstudio.

Resources

Readme
Activity
Custom properties

Stars

4 stars

Watchers

4 watching

Forks

6 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
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Introduction to R
Moffitt Cancer Center
📆 Mondays June 7 - July 19, 2021
⏰ 3:00 pm
🏢 via Zoom (see links below)
💻 Moffitt
Setup
The course will be hosted through Zoom with video recordings posted on
Ponopto after. Links to recorded lectures will be emailed out after class.
Slides
  • Introduction and R and Rmarkdown
  • Data Manipulation I and II
  • Visualization with ggplot
  • Statistical testing
  • Best practices
Code
Find our live coded notes in notes/ as we commit them.
Questions?
Feel free to ask questions in the GitHub Issues.
Overview
This is a six week course designed to introduce future users to R and Rstudio.
We will cover data cleaning using the tidyverse, creating visuals with ggplot,
basic statistical analysis and writing documents with Rmarkdown. In the end
you should be able to:
  • import and manipulate data into different formats
  • create new variables and recode existing ones
  • plot data using the appropriate figure type
  • perform basic statistics
  • write Rmarkdown reports
Who is this course designed for?
Have you never written any code in R or any other programming language?
Are you familiar with R, but hoping to bulk up your basic skills? Have
you used R but are new to the tidyverse framework?
Learning objectives
Introduction to R/Rstudio and Rmarkdown
  • Get familiar with the R and Rstudio environment
  • Understand basic R code structure (packages, data structures)
  • Create and render Rmarkdown documents in multiple formats
Data import and manipulation - Part I
  • Import various data types
  • Explain the fundamentals of "tidy data" and the tidyverse
  • Clean data sets using dplyr verbs
Data manipulation and cleaning - Part II
  • Merge data sets
  • Transform data
Visualization
  • Understand the principles of ggplot2
  • Determine appropriate visualizations
  • Plot - geom_smooth to see a model over the data
Basic statistics
  • ttest, correlation and chisq
  • Model data
  • Report findings from R output
Final coding project
  • Organize projects and name objects in a uniform manner
  • Create a reproducible workflow
  • Setting up your own R installation
Materials/RStudio Cloud
Materials will be made available on
GitHub. If you are
using an organization-issued laptop, you may want to verify before you
arrive that you can access GitHub.
In this course, we will be using a version of RStudio available online. The class
workspace can be accessed here. Please create a user account before the first day of class
(or connect with either a GitHub account or Google account).
Schedule and Links
Instructors
This course is taught by members of Moffitt staff including Jordan
Creed, a data analyst in the Department of Health Informatics, Zachary
Thompson and Ram Thapa, biostatisticians in the Bioinformatics and
Biostatistics Core.
About
Materials and information on Moffitt Cancer Center's introductory course for R and Rstudio. 
Resources
Readme
Activity
Custom properties
Stars
4 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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Introduction to R
Moffitt Cancer Center
📆 Mondays June 7 - July 19, 2021
⏰ 3:00 pm
🏢 via Zoom (see links below)
💻 Moffitt
Setup
The course will be hosted through Zoom with video recordings posted on
Ponopto after. Links to recorded lectures will be emailed out after class.
Slides
  • Introduction and R and Rmarkdown
  • Data Manipulation I and II
  • Visualization with ggplot
  • Statistical testing
  • Best practices
Code
Find our live coded notes in notes/ as we commit them.
Questions?
Feel free to ask questions in the GitHub Issues.
Overview
This is a six week course designed to introduce future users to R and Rstudio.
We will cover data cleaning using the tidyverse, creating visuals with ggplot,
basic statistical analysis and writing documents with Rmarkdown. In the end
you should be able to:
  • import and manipulate data into different formats
  • create new variables and recode existing ones
  • plot data using the appropriate figure type
  • perform basic statistics
  • write Rmarkdown reports
Who is this course designed for?
Have you never written any code in R or any other programming language?
Are you familiar with R, but hoping to bulk up your basic skills? Have
you used R but are new to the tidyverse framework?
Learning objectives
Introduction to R/Rstudio and Rmarkdown
  • Get familiar with the R and Rstudio environment
  • Understand basic R code structure (packages, data structures)
  • Create and render Rmarkdown documents in multiple formats
Data import and manipulation - Part I
  • Import various data types
  • Explain the fundamentals of "tidy data" and the tidyverse
  • Clean data sets using dplyr verbs
Data manipulation and cleaning - Part II
  • Merge data sets
  • Transform data
Visualization
  • Understand the principles of ggplot2
  • Determine appropriate visualizations
  • Plot - geom_smooth to see a model over the data
Basic statistics
  • ttest, correlation and chisq
  • Model data
  • Report findings from R output
Final coding project
  • Organize projects and name objects in a uniform manner
  • Create a reproducible workflow
  • Setting up your own R installation
Materials/RStudio Cloud
Materials will be made available on
GitHub. If you are
using an organization-issued laptop, you may want to verify before you
arrive that you can access GitHub.
In this course, we will be using a version of RStudio available online. The class
workspace can be accessed here. Please create a user account before the first day of class
(or connect with either a GitHub account or Google account).
Schedule and Links
Instructors
This course is taught by members of Moffitt staff including Jordan
Creed, a data analyst in the Department of Health Informatics, Zachary
Thompson and Ram Thapa, biostatisticians in the Bioinformatics and
Biostatistics Core.
About
Materials and information on Moffitt Cancer Center's introductory course for R and Rstudio. 
Resources
Readme
Activity
Custom properties
Stars
4 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Introduction to R
Moffitt Cancer Center
📆 Mondays June 7 - July 19, 2021
⏰ 3:00 pm
🏢 via Zoom (see links below)
💻 Moffitt
Setup
The course will be hosted through Zoom with video recordings posted on
Ponopto after. Links to recorded lectures will be emailed out after class.
Slides
  • Introduction and R and Rmarkdown
  • Data Manipulation I and II
  • Visualization with ggplot
  • Statistical testing
  • Best practices
Code
Find our live coded notes in notes/ as we commit them.
Questions?
Feel free to ask questions in the GitHub Issues.
Overview
This is a six week course designed to introduce future users to R and Rstudio.
We will cover data cleaning using the tidyverse, creating visuals with ggplot,
basic statistical analysis and writing documents with Rmarkdown. In the end
you should be able to:
  • import and manipulate data into different formats
  • create new variables and recode existing ones
  • plot data using the appropriate figure type
  • perform basic statistics
  • write Rmarkdown reports
Who is this course designed for?
Have you never written any code in R or any other programming language?
Are you familiar with R, but hoping to bulk up your basic skills? Have
you used R but are new to the tidyverse framework?
Learning objectives
Introduction to R/Rstudio and Rmarkdown
  • Get familiar with the R and Rstudio environment
  • Understand basic R code structure (packages, data structures)
  • Create and render Rmarkdown documents in multiple formats
Data import and manipulation - Part I
  • Import various data types
  • Explain the fundamentals of "tidy data" and the tidyverse
  • Clean data sets using dplyr verbs
Data manipulation and cleaning - Part II
  • Merge data sets
  • Transform data
Visualization
  • Understand the principles of ggplot2
  • Determine appropriate visualizations
  • Plot - geom_smooth to see a model over the data
Basic statistics
  • ttest, correlation and chisq
  • Model data
  • Report findings from R output
Final coding project
  • Organize projects and name objects in a uniform manner
  • Create a reproducible workflow
  • Setting up your own R installation
Materials/RStudio Cloud
Materials will be made available on
GitHub. If you are
using an organization-issued laptop, you may want to verify before you
arrive that you can access GitHub.
In this course, we will be using a version of RStudio available online. The class
workspace can be accessed here. Please create a user account before the first day of class
(or connect with either a GitHub account or Google account).
Schedule and Links
Instructors
This course is taught by members of Moffitt staff including Jordan
Creed, a data analyst in the Department of Health Informatics, Zachary
Thompson and Ram Thapa, biostatisticians in the Bioinformatics and
Biostatistics Core.
About
Materials and information on Moffitt Cancer Center's introductory course for R and Rstudio. 
Resources
Readme
Activity
Custom properties
Stars
4 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
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Introduction to R
Moffitt Cancer Center
📆 Mondays June 7 - July 19, 2021
⏰ 3:00 pm
🏢 via Zoom (see links below)
💻 Moffitt
Setup
The course will be hosted through Zoom with video recordings posted on
Ponopto after. Links to recorded lectures will be emailed out after class.
Slides
  • Introduction and R and Rmarkdown
  • Data Manipulation I and II
  • Visualization with ggplot
  • Statistical testing
  • Best practices
Code
Find our live coded notes in notes/ as we commit them.
Questions?
Feel free to ask questions in the GitHub Issues.
Overview
This is a six week course designed to introduce future users to R and Rstudio.
We will cover data cleaning using the tidyverse, creating visuals with ggplot,
basic statistical analysis and writing documents with Rmarkdown. In the end
you should be able to:
  • import and manipulate data into different formats
  • create new variables and recode existing ones
  • plot data using the appropriate figure type
  • perform basic statistics
  • write Rmarkdown reports
Who is this course designed for?
Have you never written any code in R or any other programming language?
Are you familiar with R, but hoping to bulk up your basic skills? Have
you used R but are new to the tidyverse framework?
Learning objectives
Introduction to R/Rstudio and Rmarkdown
  • Get familiar with the R and Rstudio environment
  • Understand basic R code structure (packages, data structures)
  • Create and render Rmarkdown documents in multiple formats
Data import and manipulation - Part I
  • Import various data types
  • Explain the fundamentals of "tidy data" and the tidyverse
  • Clean data sets using dplyr verbs
Data manipulation and cleaning - Part II
  • Merge data sets
  • Transform data
Visualization
  • Understand the principles of ggplot2
  • Determine appropriate visualizations
  • Plot - geom_smooth to see a model over the data
Basic statistics
  • ttest, correlation and chisq
  • Model data
  • Report findings from R output
Final coding project
  • Organize projects and name objects in a uniform manner
  • Create a reproducible workflow
  • Setting up your own R installation
Materials/RStudio Cloud
Materials will be made available on
GitHub. If you are
using an organization-issued laptop, you may want to verify before you
arrive that you can access GitHub.
In this course, we will be using a version of RStudio available online. The class
workspace can be accessed here. Please create a user account before the first day of class
(or connect with either a GitHub account or Google account).
Schedule and Links
Instructors
This course is taught by members of Moffitt staff including Jordan
Creed, a data analyst in the Department of Health Informatics, Zachary
Thompson and Ram Thapa, biostatisticians in the Bioinformatics and
Biostatistics Core.
About
Materials and information on Moffitt Cancer Center's introductory course for R and Rstudio. 
Resources
Readme
Activity
Custom properties
Stars
4 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction to R
Moffitt Cancer Center
📆 Mondays June 7 - July 19, 2021
⏰ 3:00 pm
🏢 via Zoom (see links below)
💻 Moffitt
Setup
The course will be hosted through Zoom with video recordings posted on
Ponopto after. Links to recorded lectures will be emailed out after class.
Slides
  • Introduction and R and Rmarkdown
  • Data Manipulation I and II
  • Visualization with ggplot
  • Statistical testing
  • Best practices
Code
Find our live coded notes in notes/ as we commit them.
Questions?
Feel free to ask questions in the GitHub Issues.
Overview
This is a six week course designed to introduce future users to R and Rstudio.
We will cover data cleaning using the tidyverse, creating visuals with ggplot,
basic statistical analysis and writing documents with Rmarkdown. In the end
you should be able to:
  • import and manipulate data into different formats
  • create new variables and recode existing ones
  • plot data using the appropriate figure type
  • perform basic statistics
  • write Rmarkdown reports
Who is this course designed for?
Have you never written any code in R or any other programming language?
Are you familiar with R, but hoping to bulk up your basic skills? Have
you used R but are new to the tidyverse framework?
Learning objectives
Introduction to R/Rstudio and Rmarkdown
  • Get familiar with the R and Rstudio environment
  • Understand basic R code structure (packages, data structures)
  • Create and render Rmarkdown documents in multiple formats
Data import and manipulation - Part I
  • Import various data types
  • Explain the fundamentals of "tidy data" and the tidyverse
  • Clean data sets using dplyr verbs
Data manipulation and cleaning - Part II
  • Merge data sets
  • Transform data
Visualization
  • Understand the principles of ggplot2
  • Determine appropriate visualizations
  • Plot - geom_smooth to see a model over the data
Basic statistics
  • ttest, correlation and chisq
  • Model data
  • Report findings from R output
Final coding project
  • Organize projects and name objects in a uniform manner
  • Create a reproducible workflow
  • Setting up your own R installation
Materials/RStudio Cloud
Materials will be made available on
GitHub. If you are
using an organization-issued laptop, you may want to verify before you
arrive that you can access GitHub.
In this course, we will be using a version of RStudio available online. The class
workspace can be accessed here. Please create a user account before the first day of class
(or connect with either a GitHub account or Google account).
Schedule and Links
Instructors
This course is taught by members of Moffitt staff including Jordan
Creed, a data analyst in the Department of Health Informatics, Zachary
Thompson and Ram Thapa, biostatisticians in the Bioinformatics and
Biostatistics Core.
About
Materials and information on Moffitt Cancer Center's introductory course for R and Rstudio. 
Resources
Readme
Activity
Custom properties
Stars
4 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction to R
Moffitt Cancer Center
📆 Mondays June 7 - July 19, 2021
⏰ 3:00 pm
🏢 via Zoom (see links below)
💻 Moffitt
Setup
The course will be hosted through Zoom with video recordings posted on
Ponopto after. Links to recorded lectures will be emailed out after class.
Slides
  • Introduction and R and Rmarkdown
  • Data Manipulation I and II
  • Visualization with ggplot
  • Statistical testing
  • Best practices
Code
Find our live coded notes in notes/ as we commit them.
Questions?
Feel free to ask questions in the GitHub Issues.
Overview
This is a six week course designed to introduce future users to R and Rstudio.
We will cover data cleaning using the tidyverse, creating visuals with ggplot,
basic statistical analysis and writing documents with Rmarkdown. In the end
you should be able to:
  • import and manipulate data into different formats
  • create new variables and recode existing ones
  • plot data using the appropriate figure type
  • perform basic statistics
  • write Rmarkdown reports
Who is this course designed for?
Have you never written any code in R or any other programming language?
Are you familiar with R, but hoping to bulk up your basic skills? Have
you used R but are new to the tidyverse framework?
Learning objectives
Introduction to R/Rstudio and Rmarkdown
  • Get familiar with the R and Rstudio environment
  • Understand basic R code structure (packages, data structures)
  • Create and render Rmarkdown documents in multiple formats
Data import and manipulation - Part I
  • Import various data types
  • Explain the fundamentals of "tidy data" and the tidyverse
  • Clean data sets using dplyr verbs
Data manipulation and cleaning - Part II
  • Merge data sets
  • Transform data
Visualization
  • Understand the principles of ggplot2
  • Determine appropriate visualizations
  • Plot - geom_smooth to see a model over the data
Basic statistics
  • ttest, correlation and chisq
  • Model data
  • Report findings from R output
Final coding project
  • Organize projects and name objects in a uniform manner
  • Create a reproducible workflow
  • Setting up your own R installation
Materials/RStudio Cloud
Materials will be made available on
GitHub. If you are
using an organization-issued laptop, you may want to verify before you
arrive that you can access GitHub.
In this course, we will be using a version of RStudio available online. The class
workspace can be accessed here. Please create a user account before the first day of class
(or connect with either a GitHub account or Google account).
Schedule and Links
Instructors
This course is taught by members of Moffitt staff including Jordan
Creed, a data analyst in the Department of Health Informatics, Zachary
Thompson and Ram Thapa, biostatisticians in the Bioinformatics and
Biostatistics Core.
About
Materials and information on Moffitt Cancer Center's introductory course for R and Rstudio. 
Resources
Readme
Activity
Custom properties
Stars
4 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages

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Introduction to R
Moffitt Cancer Center
📆 Mondays June 7 - July 19, 2021
⏰ 3:00 pm
🏢 via Zoom (see links below)
💻 Moffitt
Setup
The course will be hosted through Zoom with video recordings posted on
Ponopto after. Links to recorded lectures will be emailed out after class.
Slides
  • Introduction and R and Rmarkdown
  • Data Manipulation I and II
  • Visualization with ggplot
  • Statistical testing
  • Best practices
Code
Find our live coded notes in notes/ as we commit them.
Questions?
Feel free to ask questions in the GitHub Issues.
Overview
This is a six week course designed to introduce future users to R and Rstudio.
We will cover data cleaning using the tidyverse, creating visuals with ggplot,
basic statistical analysis and writing documents with Rmarkdown. In the end
you should be able to:
  • import and manipulate data into different formats
  • create new variables and recode existing ones
  • plot data using the appropriate figure type
  • perform basic statistics
  • write Rmarkdown reports
Who is this course designed for?
Have you never written any code in R or any other programming language?
Are you familiar with R, but hoping to bulk up your basic skills? Have
you used R but are new to the tidyverse framework?
Learning objectives
Introduction to R/Rstudio and Rmarkdown
  • Get familiar with the R and Rstudio environment
  • Understand basic R code structure (packages, data structures)
  • Create and render Rmarkdown documents in multiple formats
Data import and manipulation - Part I
  • Import various data types
  • Explain the fundamentals of "tidy data" and the tidyverse
  • Clean data sets using dplyr verbs
Data manipulation and cleaning - Part II
  • Merge data sets
  • Transform data
Visualization
  • Understand the principles of ggplot2
  • Determine appropriate visualizations
  • Plot - geom_smooth to see a model over the data
Basic statistics
  • ttest, correlation and chisq
  • Model data
  • Report findings from R output
Final coding project
  • Organize projects and name objects in a uniform manner
  • Create a reproducible workflow
  • Setting up your own R installation
Materials/RStudio Cloud
Materials will be made available on
GitHub. If you are
using an organization-issued laptop, you may want to verify before you
arrive that you can access GitHub.
In this course, we will be using a version of RStudio available online. The class
workspace can be accessed here. Please create a user account before the first day of class
(or connect with either a GitHub account or Google account).
Schedule and Links
Instructors
This course is taught by members of Moffitt staff including Jordan
Creed, a data analyst in the Department of Health Informatics, Zachary
Thompson and Ram Thapa, biostatisticians in the Bioinformatics and
Biostatistics Core.
About
Materials and information on Moffitt Cancer Center's introductory course for R and Rstudio. 
Resources
Readme
Activity
Custom properties
Stars
4 stars
Watchers
4 watching
Forks
6 forks
Report repository
Releases
Packages
Contributors
Languages