RStudio → Posit

andrewheiss · May 29, 2023 · 93696bc · 93696bc
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diff --git a/assignment/03-exercise.qmd b/assignment/03-exercise.qmd
@@ -9,9 +9,9 @@ knitr::opts_chunk$set(fig.align = "center")
 
 ## Getting started
 
-You'll be doing all your R work in R Markdown this time (and from now on). You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You'll be doing all your R work in R Markdown this time (and from now on). You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
-You'll need to download three CSV files and put them somewhere on your computer or upload them to RStudio.cloud—preferably in a folder named `data` in your project folder:
+You'll need to download three CSV files and put them somewhere on your computer or upload them to Posit.cloud—preferably in a folder named `data` in your project folder:
 
 - [{{< fa file-csv >}} `The_Fellowship_Of_The_Ring.csv`](/files/data/external_data/The_Fellowship_Of_The_Ring.csv)
 - [{{< fa file-csv >}} `The_Two_Towers.csv`](/files/data/external_data/The_Two_Towers.csv)

diff --git a/assignment/04-exercise.qmd b/assignment/04-exercise.qmd
@@ -13,9 +13,9 @@ The New York City Department of Buildings (DOB) maintains a list of construction
 
 For this exercise, you're going to use this data to visualize the amounts or proportions of different types of essential projects in the five boroughs of New York City (Brooklyn, Manhattan, the Bronx, Queens, and Staten Island). 
 
-You'll be doing all your R work in R Markdown this time (and from now on). You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You'll be doing all your R work in R Markdown this time (and from now on). You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
-You'll need to download one CSV file and put it somewhere on your computer or upload it to RStudio.cloud—preferably in a folder named `data` in your project folder. You can download the data from [the DOB's map](https://www1.nyc.gov/assets/buildings/html/essential-active-construction.html), or use this link to get it directly:
+You'll need to download one CSV file and put it somewhere on your computer or upload it to Posit.cloud—preferably in a folder named `data` in your project folder. You can download the data from [the DOB's map](https://www1.nyc.gov/assets/buildings/html/essential-active-construction.html), or use this link to get it directly:
 
 - [{{< fa file-csv >}} `EssentialConstruction.csv`](/files/data/external_data/EssentialConstruction.csv)
 

diff --git a/assignment/05-exercise.qmd b/assignment/05-exercise.qmd
@@ -11,7 +11,7 @@ knitr::opts_chunk$set(fig.align = "center")
 
 For this assignment, you're going to work with data compiled by [data journalist Duncan Greere](https://www.duncangeere.com/) related to 48 Soviet dogs who flew as test subjects in USSR's space program in the 1950s and 60s. [The original data can be found here](https://airtable.com/universe/expG3z2CFykG1dZsp/sovet-space-dogs).
 
-You'll need to download one CSV file and put them somewhere on your computer or upload them to RStudio.cloud—preferably in a folder named `data` in your project folder:
+You'll need to download one CSV file and put them somewhere on your computer or upload them to Posit.cloud—preferably in a folder named `data` in your project folder:
 
 - [{{< fa file-csv >}} `Dogs-Database.csv`](/files/data/external_data/Dogs-Database.csv)
 

diff --git a/assignment/06-exercise.qmd b/assignment/06-exercise.qmd
@@ -11,7 +11,7 @@ knitr::opts_chunk$set(fig.align = "center")
 
 For this exercise you'll revisit Hans Rosling's gapminder data on health and wealth.
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 You don't need to download any CSV files for this assignment. If you run `library(gapminder)` you'll have access to a data frame named `gapminder` that contains all the data. 
 

diff --git a/assignment/07-exercise.qmd b/assignment/07-exercise.qmd
@@ -11,7 +11,7 @@ knitr::opts_chunk$set(fig.align = "center")
 
 For this exercise you'll use precinct-level data from the 2016 presidential election to visualize relationships between variables. This data comes from the [MIT Election Data and Science Lab](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VOQCHQ).
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 - [{{< fa file-csv >}} `results_2016.csv`](/files/data/external_data/results_2016.csv)
 

diff --git a/assignment/08-exercise.qmd b/assignment/08-exercise.qmd
@@ -11,7 +11,7 @@ knitr::opts_chunk$set(fig.align = "center")
 
 For this exercise you'll use state-level unemployment data from 2006 to 2016 that comes from the US Bureau of Labor Statistics (if you're curious, [I describe how I built this dataset down below](#postscript-how-i-got-this-unemployment-data)). 
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 - [{{< fa file-csv >}} `unemployment.csv`](/files/data/external_data/unemployment.csv)
 

diff --git a/assignment/09-exercise.qmd b/assignment/09-exercise.qmd
@@ -11,7 +11,7 @@ knitr::opts_chunk$set(fig.align = "center")
 
 For this exercise, you'll use whatever data you want to make a plot and add annotations to it. Use a dataset from a past exercise, use one of the built-in datasets like `mpg` or `gapminder` from the {gapminder} package, download stuff from the World Bank using the {WDI} package, or use something from [this list of datasets](/resource/data/).
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 **To help you**, I've created a skeleton R Markdown file with a template for this exercise, along with some code to help you clean and summarize the data. Download that here and include it in your project:
 

diff --git a/assignment/10-exercise.qmd b/assignment/10-exercise.qmd
@@ -14,7 +14,7 @@ This exercise is a little different from past ones because you will not knit to
 
 For this exercise, you'll use whatever data you want to create an interactive HTML plot and a dashboard. Use a dataset from a past exercise, use one of the built-in datasets like `mpg` or `gapminder` from the {gapminder} package, download stuff from the World Bank using the {WDI} package, or use something from [this list of datasets](/resource/data/).
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 **To help you**, I've created a skeleton R Markdown file with a template for this exercise, along with some code to help you clean and summarize the data. Download that here and include it in your project:
 

diff --git a/assignment/11-exercise.qmd b/assignment/11-exercise.qmd
@@ -11,7 +11,7 @@ knitr::opts_chunk$set(fig.align = "center")
 
 For this exercise, you'll visualize something over time. You can use whatever data you want. Use a dataset from a past exercise, use one of the built-in datasets like `gapminder` from the {gapminder} package, download stuff from the World Bank with the {WDI} package, or download stuff from FRED using the {tidyquant} package. 
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 **To help you**, I've created a skeleton R Markdown file with a template for this exercise, along with some code to help you clean and summarize the data. Download that here and include it in your project:
 

diff --git a/assignment/12-exercise.qmd b/assignment/12-exercise.qmd
@@ -16,7 +16,7 @@ Download these two data files:
 - [{{< fa table >}} `share-of-individuals-using-the-internet-1990-2015.csv`](/data/share-of-individuals-using-the-internet-1990-2015.csv)
 - [{{< fa file-archive >}} `ne_110m_admin_0_countries.zip`](/data/ne_110m_admin_0_countries.zip). This is the ["110m Admin 0—Countries"](https://www.naturalearthdata.com/downloads/110m-cultural-vectors/) shapefile from Natural Earth. It will download as a .zip file. Unzip the file and move the entire `ne_110m_admin_0_countries` directory into your data folder.
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 **To help you**, I've created a skeleton R Markdown file with a template for this exercise, along with some code to help you clean and join the two datasets. Download that here and include it in your project:
 

diff --git a/assignment/13-exercise.qmd b/assignment/13-exercise.qmd
@@ -11,7 +11,7 @@ knitr::opts_chunk$set(fig.align = "center")
 
 For this exercise, you'll download some books from Project Gutenberg and visualize patterns in the words.
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 **To help you**, I've created a skeleton R Markdown file with a template for this exercise, along with some helpful starter code. Download that here and include it in your project:
 

diff --git a/assignment/14-exercise.qmd b/assignment/14-exercise.qmd
@@ -15,7 +15,7 @@ I have given you 100% of the R code you need to use. All you have to do is run i
 
 - [{{< fa table >}} `hot-dog-contest-winners.csv`](/data/hot-dog-contest-winners.csv)
 
-You should use an RStudio Project to keep your files well organized (either on your computer or on RStudio.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
+You should use an RStudio Project to keep your files well organized (either on your computer or on Posit.cloud). Either create a new project for this exercise only, or make a project for all your work in this class.
 
 **To help you**, I've created a skeleton R Markdown file with a template for this exercise, along with all the code you'll need. Download that here and include it in your project:
 

diff --git a/lesson/01-lesson.qmd b/lesson/01-lesson.qmd
@@ -16,12 +16,12 @@ You'll learn some of the basics of R, as well as some powerful methods for manip
 Complete these:
 
 - **The Basics**
-	- [Visualization Basics](https://rstudio.cloud/learn/primers/1.1)
-	- [Programming Basics](https://rstudio.cloud/learn/primers/1.2)
+	- [Visualization Basics](https://posit.cloud/learn/primers/1.1)
+	- [Programming Basics](https://posit.cloud/learn/primers/1.2)
 - **Work with Data**
-	- [Working with Tibbles](https://rstudio.cloud/learn/primers/2.1)
-	- [Isolating Data with dplyr](https://rstudio.cloud/learn/primers/2.2)
-	- [Deriving Information with dplyr](https://rstudio.cloud/learn/primers/2.3)
+	- [Working with Tibbles](https://posit.cloud/learn/primers/2.1)
+	- [Isolating Data with dplyr](https://posit.cloud/learn/primers/2.2)
+	- [Deriving Information with dplyr](https://posit.cloud/learn/primers/2.3)
 
 The content from these primers comes from the (free and online!) book [*R for Data Science* by Garrett Grolemund and Hadley Wickham](https://r4ds.had.co.nz/). I highly recommend the book as a reference and for continuing to learn and use R in the future (like running regression models and other types of statistical analysis)
 

diff --git a/lesson/03-lesson.qmd b/lesson/03-lesson.qmd
@@ -14,14 +14,14 @@ For the first part of today's lesson, you need to work through RStudio's introdu
 It seems like there are a lot, but they're short and go fairly quickly (especially as you get the hang of the `ggplot()` syntax). Complete these:
 
 - **Visualize Data**
-  - [Exploratory Data Analysis](https://rstudio.cloud/learn/primers/3.1)
-  - [Bar Charts](https://rstudio.cloud/learn/primers/3.2)
-  - [Histograms](https://rstudio.cloud/learn/primers/3.3)
-  - [Boxplots and Counts](https://rstudio.cloud/learn/primers/3.4)
-  - [Scatterplots](https://rstudio.cloud/learn/primers/3.5)
-  - [Line plots](https://rstudio.cloud/learn/primers/3.6)
-  - [Overplotting and Big Data](https://rstudio.cloud/learn/primers/3.7)
-  - [Customize Your Plots](https://rstudio.cloud/learn/primers/3.8)
+  - [Exploratory Data Analysis](https://posit.cloud/learn/primers/3.1)
+  - [Bar Charts](https://posit.cloud/learn/primers/3.2)
+  - [Histograms](https://posit.cloud/learn/primers/3.3)
+  - [Boxplots and Counts](https://posit.cloud/learn/primers/3.4)
+  - [Scatterplots](https://posit.cloud/learn/primers/3.5)
+  - [Line plots](https://posit.cloud/learn/primers/3.6)
+  - [Overplotting and Big Data](https://posit.cloud/learn/primers/3.7)
+  - [Customize Your Plots](https://posit.cloud/learn/primers/3.8)
 
 
 ## Part 2: Reshaping data with {tidyr}
@@ -31,7 +31,7 @@ For the last part of today's lesson, you'll work through just one RStudio primer
 Complete this:
 
 - **Tidy Your Data**
-  - [Reshape Data](https://rstudio.cloud/learn/primers/4.1)
+  - [Reshape Data](https://posit.cloud/learn/primers/4.1)
 
 ::: {.callout-note}
 ### Pivoting

diff --git a/syllabus.qmd b/syllabus.qmd
@@ -71,9 +71,9 @@ I also highly recommend subscribing to the [R Weekly newsletter](https://rweekly
 
 You will do all of your analysis with the open source (and free!) programming language [R](https://cran.r-project.org/). You will use [RStudio](https://www.rstudio.com/) as the main program to access R. **Think of R as an engine and RStudio as a car dashboard**—R handles all the calculations produces the actual statistics and graphical output, while RStudio provides a nice interface for running R code.
 
-R is free, but it can sometimes be a pain to install and configure. To make life easier, you can (and should!) use the free [RStudio.cloud](http://rstudio.cloud/) service, which lets you run a full instance of RStudio in your web browser. This means you won't have to install anything on your computer to get started with R! We will have a shared class workspace in RStudio.cloud that will let you quickly copy templates for examples, exercises, and mini projects.
+R is free, but it can sometimes be a pain to install and configure. To make life easier, you can (and should!) use the free [Posit.cloud](http://posit.cloud/) service, which lets you run a full instance of RStudio in your web browser. This means you won't have to install anything on your computer to get started with R! We will have a shared class workspace in Posit.cloud that will let you quickly copy templates for examples, exercises, and mini projects.
 
-RStudio.cloud is convenient, but it can be slow and it is not designed to be able to handle larger datasets or more complicated analysis and graphics. You also can't use your own custom fonts with RStudio.cloud. Over the course of the semester, you'll probably want to get around to installing R, RStudio, and other R packages on your computer and wean yourself off of RStudio.cloud. This isn't 100% necessary, but it's helpful.
+Posit.cloud is convenient, but it can be slow and it is not designed to be able to handle larger datasets or more complicated analysis and graphics. You also can't use your own custom fonts with Posit.cloud. Over the course of the semester, you'll probably want to get around to installing R, RStudio, and other R packages on your computer and wean yourself off of Posit.cloud. This isn't 100% necessary, but it's helpful.
 
 You can [find instructions for installing R, RStudio, and all the tidyverse packages here.](/resource/install/)