differences for PR #869

swcarpentry · Nov 18, 2023 · a2286df · a2286df
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diff --git a/01-rstudio-intro.md b/01-rstudio-intro.md
@@ -7,8 +7,8 @@ source: Rmd
 
 ::::::::::::::::::::::::::::::::::::::: objectives
 
-- Describe the purpose and use of each pane in RStudio 
-- Locate buttons and options in RStudio 
+- Describe the purpose and use of each pane in the RStudio IDE
+- Locate buttons and options in the RStudio IDE
 - Define a variable
 - Assign data to a variable
 - Manage a workspace in an interactive R session
@@ -29,39 +29,38 @@ source: Rmd
 
 
 
+## Motivation
+
+Science is a multi-step process: once you've designed an experiment and collected
+data, the real fun begins! This lesson will teach you how to start this process using
+R and RStudio. We will begin with raw data, perform exploratory analyses, and learn
+how to plot results graphically. This example starts with a dataset from
+[gapminder.org](https://www.gapminder.org) containing population information for many
+countries through time. Can you read the data into R? Can you plot the population for
+Senegal? Can you calculate the average income for countries on the continent of Asia?
+By the end of these lessons you will be able to do things like plot the populations
+for all of these countries in under a minute!
+
 ## Before Starting The Workshop
 
 Please ensure you have the latest version of R and RStudio installed on your machine. This is important, as some packages used in the workshop may not install correctly (or at all) if R is not up to date.
 
 - [Download and install the latest version of R here](https://www.r-project.org/)
 - [Download and install RStudio here](https://www.rstudio.com/products/rstudio/download/#download)
 
+## Introduction to RStudio
 
-## Why use R and R studio?
-
-Welcome to the R portion of the Software Carpentry workshop!
-
-Science is a multi-step process: once you've designed an experiment and collected
-data, the real fun begins with analysis! Throughout this lesson, we're going to teach you some of the fundamentals of the R language as well as some best practices for organizing code for scientific projects that will make your life easier.
-
-Although we could use a spreadsheet in Microsoft Excel or Google sheets to analyze our data, these tools are limited in their flexibility and accessibility. Critically, they also are difficult to share steps which explore and change the raw data, which is key to ["reproducible" research](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285). 
+Welcome to the R portion of the Software Carpentry workshop.
 
-Therefore, this lesson will teach you how to begin exploring your data using R and RStudio. The R program is available for Windows, Mac, and Linux operating systems, and is a freely-available where you downloaded it above. To run R, all you need is the R program. 
+Throughout this lesson, we're going to teach you some of the fundamentals of
+the R language as well as some best practices for organizing code for
+scientific projects that will make your life easier.
 
-However, to make using R easier, we will use the program RStudio, which we also downloaded above. RStudio is a free, open-source, Integrated Development
+We'll be using RStudio: a free, open-source R Integrated Development
 Environment (IDE). It provides a built-in editor, works on all platforms (including
 on servers) and provides many advantages such as integration with version
 control and project management.
 
-## Overview  
-
-We will begin with raw data, perform exploratory analyses, and learn how to plot results graphically. This example starts with a dataset from [gapminder.org](https://www.gapminder.org) containing population information for many
-countries through time. Can you read the data into R? Can you plot the population for
-Senegal? Can you calculate the average income for countries on the continent of Asia?
-By the end of these lessons you will be able to do things like plot the populations
-for all of these countries in under a minute!
-
-
 **Basic layout**
 
 When you first open RStudio, you will be greeted by three panels:
@@ -159,8 +158,7 @@ the first element of the line being printed in the console. For more information
 on indexing vectors, see [Episode 6: Subsetting Data](https://swcarpentry.github.io/r-novice-gapminder/06-data-subsetting/index.html).
 
 If you type in an incomplete command, R will wait for you to
-complete it. If you are familiar with Unix Shell's bash, you may recognize this  
-behavior from bash.
+complete it. If you are familiar with Unix Shell's bash, you may recognize this behavior from bash.
 
 ```r
 > 1 +
@@ -544,7 +542,7 @@ min-length
 One final thing to be aware of is that R is *vectorized*, meaning that
 variables and functions can have vectors as values. In contrast to physics and
 mathematics, a vector in R describes a set of values in a certain order of the
-same data type. For example
+same data type. For example:
 
 
 ```r
@@ -644,7 +642,7 @@ function (name, pos = -1L, envir = as.environment(pos), all.names = FALSE,
     }
     else all.names
 }
-<bytecode: 0x55bec8f02a70>
+<bytecode: 0x55a7ed239a00>
 <environment: namespace:base>
 ```
 

diff --git a/02-project-intro.md b/02-project-intro.md
@@ -190,7 +190,7 @@ ls -lh data/gapminder_data.csv
 ```
 
 ```{.output}
--rw-r--r-- 1 runner docker 80K Nov 16 22:05 data/gapminder_data.csv
+-rw-r--r-- 1 runner docker 80K Nov 18 14:04 data/gapminder_data.csv
 ```
 
 The file size is 80K.

diff --git a/03-seeking-help.md b/03-seeking-help.md
@@ -181,8 +181,8 @@ attached base packages:
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 loaded via a namespace (and not attached):
-[1] compiler_4.3.1 tools_4.3.1    yaml_2.3.7     knitr_1.45     xfun_0.41     
-[6] renv_1.0.3     evaluate_0.23 
+[1] compiler_4.3.1    tools_4.3.1       rstudioapi_0.15.0 yaml_2.3.7       
+[5] knitr_1.43        xfun_0.40         renv_1.0.3        evaluate_0.21    
 ```
 
 Will print out your current version of R, as well as any packages you

diff --git a/13-dplyr.md b/13-dplyr.md
@@ -28,23 +28,23 @@ do these operations using the normal base R operations:
 
 
 ```r
-mean(gapminder$gdpPercap[gapminder$continent == "Africa"])
+mean(gapminder[gapminder$continent == "Africa", "gdpPercap"])
 ```
 
 ```{.output}
 [1] 2193.755
 ```
 
 ```r
-mean(gapminder$gdpPercap[gapminder$continent == "Americas"])
+mean(gapminder[gapminder$continent == "Americas", "gdpPercap"])
 ```
 
 ```{.output}
 [1] 7136.11
 ```
 
 ```r
-mean(gapminder$gdpPercap[gapminder$continent == "Asia"])
+mean(gapminder[gapminder$continent == "Asia", "gdpPercap"])
 ```
 
 ```{.output}

diff --git a/fig/06-rmd-generate-figures.sh b/fig/06-rmd-generate-figures.sh
diff --git a/fig/12-plyr-generate-figures.sh b/fig/12-plyr-generate-figures.sh
diff --git a/md5sum.txt b/md5sum.txt
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