All in One View

Last updated on 2026-10-21 | Edit this page

Overview

Questions

How do you write a lesson using R Markdown and sandpaper?

Objectives

Explain how to use markdown with the new lesson template
Demonstrate how to include pieces of code, figures, and nested challenge blocks

Introduction

This is a lesson created via The Carpentries Workbench. It is written in Pandoc-flavored Markdown for static files and R Markdown for dynamic files that can render code into output. Please refer to the Introduction to The Carpentries Workbench for full documentation.

What you need to know is that there are three sections required for a valid Carpentries lesson template:

questions are displayed at the beginning of the episode to prime the learner for the content.
objectives are the learning objectives for an episode displayed with the questions.
keypoints are displayed at the end of the episode to reinforce the objectives.

Challenge

Challenge 1: Can you do it?

What is the output of this command?

R

paste("This", "new", "lesson", "looks", "good")

Output

OUTPUT

[1] "This new lesson looks good"

Challenge

Challenge 2: how do you nest solutions within challenge blocks?

Show me the solution

You can add a line with at least three colons and a solution tag.

Figures

You can also include figures generated from R Markdown:

R

pie(
  c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), 
  init.angle = 315, 
  col = c("deepskyblue", "yellow", "yellow3"), 
  border = FALSE
)

pie chart illusion of a pyramid — Sun arise each and every morning

Or you can use standard markdown for static figures with the following syntax:

![optional caption that appears below the figure](figure url){alt='alt text for accessibility purposes'}

You belong in The Carpentries!

Callout

Callout sections can highlight information.

They are sometimes used to emphasise particularly important points but are also used in some lessons to present “asides”: content that is not central to the narrative of the lesson, e.g. by providing the answer to a commonly-asked question.

Math

One of our episodes contains $\LaTeX$ equations when describing how to create dynamic reports with {knitr}, so we now use mathjax to describe this:

$\alpha = \dfrac{1}{(1 - \beta)^2}$ becomes: $\alpha = \dfrac{1}{(1 - \beta)^2}$

Cool, right?

Key Points

Use .md files for episodes when you want static content
Use .Rmd files for episodes when you need to generate output
Run sandpaper::check_lesson() to identify any issues with your lesson
Run sandpaper::build_lesson() to preview your lesson locally

Content from Environments

Last updated on 2026-10-21 | Edit this page

Overview

Questions

What is a project environment and why does it matter?
How can we create a project environment?

Objectives

Explain the purpose of an project environment.
Create a project environment using renv.
Use use renv::snapshot() to take implicit and explicit snapshots of the environment.
Restore an environment using a lockfile or using a DESCRIPTION file.

Running someone else’s script

A colleague emails you an R script called sleep_analysis.R and asks you to reproduce their results while they are away.

Before running anything, create an R project to organise your work.

Create an R project

Before working with the script, create an R project in RStudio:

Go to File > New Project > New Directory > New Project
Name the directory rworkshop
Create as a subdirectory of your home directory (for example: C:/Users/username/)
Click Create Project

If you want to come back to your work later, open the R Project file you created (rworkshop.Rproj). Double‑clicking this file launches RStudio, sets the working directory, and restores your project as you left it.

Save the script

Create a new R script called sleep_analysis.R inside the rworkshop folder and save the file after pasting in the code your colleague sent you:

R

library(obscurepackage)
library(ggplot2)

data <- sleep
none_transformed_data <- none_transform(data$extra)

p <- ggplot(data, aes(x = group, y = none_transformed_data)) +
  geom_boxplot() +
  labs(
    title = "Extra Sleep by Drug Group",
    x = "Drug Group",
    y = "Extra sleep (hours)"
  )

print(p)

Run the script

Run sleep_analysis.R to see what happens.

In RStudio, you can run the script using your editor’s Source button.

You will probably hit an error resulting in a message that looks like the one below.

R

Error in library(obscurepackage) : 
  there is no package called ‘obscurepackage’

The script tries to load:

R

library(obscurepackage)

On your machine, R looks for packages in its configured R libraries. You might already have ggplot2 installed, but you probably do not have obscurepackage installed, so the script fails.

Install the required packages

You may already have ggplot2 installed, but if not, you can install it with:

R

install.packages("ggplot2")

The obscurepackage is not on CRAN.

First, install the remotes package, which allows you to install packages from GitHub:

R

install.packages("remotes")

Then, install obscurepackage:

R

remotes::install_github("stellaprins/obscurepackage")

Challenge

Reproduce the results

Now that you have installed the required packages, are you able to reproduce the plot below?

If not, what happens, and why do you think the results differ?

Hint: visit the obscurepackage GitHub repository and explore its commit history. Have any changes been made that could explain the difference?

Show me the solution

You will not be able to reproduce your colleague’s original plot using the latest version of obscurepackage.

Instead, you should see something like this:

The difference is caused by a bug introduced in a newer version of obscurepackage.

The none_transform() function was originally designed to return its input unchanged:

R

none_transform <- function(x) {
  return(x)
}

However, a bug introduced in the newer version causes the function to always return NA, regardless of its input:

R

none_transform <- function(x) {
  return(NA)
}

As a result, the analysis produces missing values instead of the expected data, preventing the original boxplot from being reproduced.

To reproduce your colleague’s results, you would need to install an earlier version of obscurepackage that does not contain the bug.

Changes to software dependencies can affect reproducibility, even when the code and data remain unchanged. This highlights the importance of recording and managing package versions.

Project environments

Different versions of an R package can behave differently.

This can be due to new features, altered defaults, or bugs, and causes the same code to produce different results.

A version mismatch happens when your script relies on a package whose behaviour has changed between releases.

A project environment helps prevent this by isolating the packages your project uses and recording their versions.

This keeps your work separate from whatever happens to be installed globally on your system and avoids conflicts between R projects that require different or incompatible dependencies.

Recording package versions in a project environment also helps you rebuild the setup, for example when someone else needs to run your analysis or you return to the project in the future.

Callout

Two different meanings of environment in R

1) project environments
These are project‑specific libraries created by the renv package. They store packages and their versions, helping you reproduce your project on other machines.

2) R environments
These store variables and track where functions were created, and control how R finds values. They enable features like lexical scoping and namespaces.

In this workshop, we are going to use an R package called renv to manage our project environment.

To install renv:

R

install.packages("renv")

Challenge

Find out more about `renv`

Open the renv help page in the R console:

R

?renv

Explore the help page and useful links.

In your own words, explain:

What does renv do?
How can renv help when handing the project over to a colleague?

Show me the solution

renv is an R package that helps create reproducible environments.

renv gives every R project its own library of packages, separate from everything else on your computer, and records the package versions the project uses.

When you hand a project to someone else, renv can restore the package versions recorded in renv.lock.

This helps them recreate your project environment and run the analysis using the same package versions.

Differences in R versions, operating systems, or system libraries can still affect the results.

Initialise renv:

R

renv::init()

This will:

Set up project infrastructure inside your working directory that ensures renv will be used in future sessions
Discover packages used in your project and add them and their dependencies to the project library
Create a “lockfile” named renv.lock that records the versions of your project’s package dependencies
Restart R if you are using RStudio.

Install packages in the project environment

Before using renv, we installed packages using install.packages(). Packages are installed into an R library, which may be shared by several projects. When renv is active, install.packages() normally uses the project library.

This installs ggplot2 only into the project’s renv library, leaving the global library untouched:

R

renv::install("ggplot2")

Challenge

Install a GitHub package

Use the help page for renv::install to learn how to install packages inside the renv environment.

R

?renv::install

Use the help page to find out how to install a package from GitHub using renv::install()
Use what you learned to install v0.1.0 of the GitHub repository stellaprins/obscurepackage.

Hint: Explore the repository’s commit history to find the commit corresponding to v0.1.0, and check how to specify a commit hash when using renv::install().

Show me the solution

From the help page, GitHub packages can be installed by using the username/repository string, and specific versions can be installed by supplying a commit SHA.

The commit SHA for v0.1.0 of stellaprins/obscurepackage is 9e7cfcba9bfbfd1cbea4dbc1600f8d5283c71831.

R

renv::install("stellaprins/obscurepackage@9e7cfcba9bfbfd1cbea4dbc1600f8d5283c71831")

To install a specific version of a GitHub package inside a renv project, use the username/repo@version format:

R

renv::install("stellaprins/obscurepackage@v0.1.0")

Restart your R session to make sure R uses the newly installed version of obscurepackage.

In RStudio, select Session → Restart R, or run:

R

.rs.restartR()

Note: .rs.restartR() only works in RStudio, not in a standard R terminal or other R editors.

Then run sleep_analysis.R again.

You should be able to replicate the results of your colleague!

Recording your environment with a lockfile

You have now reproduced your colleague’s results by installing a specific version of obscurepackage.

But how can you make sure that you, or someone else, can reproduce these results again in the future?

This is where a lockfile comes in.

When you initialise renv, it creates a file called renv.lock.

This file records information about your project’s software dependencies, including:

The R version used by the project
The packages required by the project
The exact versions of those packages
Where the packages were installed from, such as CRAN or GitHub

Now that you have installed a different version of obscurepackage, your project environment has changed.

These changes are not automatically recorded in your renv.lock file.

To check whether your project environment is consistent with your lockfile, use:

R

renv::status()

renv::status() compares three things:

renv project library
the renv.lock file
What your project actually uses

If these differ, renv::status() will report inconsistencies such as:

packages installed but not recorded
packages recorded but not installed
packages where the installed version ≠ recorded version

Once you have reviewed these differences, you can use renv::snapshot() to update the lockfile and record the current state of your project environment.

Challenge

Update your lockfile using a snapshot

Run:

R

renv::status()

Look at the output.

Now take a snapshot using renv::snapshot():

R

renv::snapshot()

Then, run renv::status() again:

R

renv::status()

Compare the renv::status outputs before and after taking a snapshot:

Which inconsistencies disappeared?
Which (if any) remain?
Why did snapshotting fix these issues?

Show me the solution

After taking the snapshot, renv::status() may report:

R

No issues found -- the project is in a consistent state.

renv::snapshot() updates the lockfile to record the installed versions of your project’s dependencies and the R version.

This can resolve inconsistencies between the lockfile and the project library. However, it does not install missing packages. Some issues may still require renv::install() or renv::restore().

Use renv::snapshot() to record your project’s current package dependencies and their versions in the lockfile.

R

renv::snapshot()

Restoring an environment from a lockfile

While renv::snapshot() records your current environment, renv::restore() recreates the environment described by the lockfile.

Command	Purpose
`renv::status()`	Check whether the project environment and lockfile are consistent
`renv::snapshot()`	Record the current environment in the lockfile
`renv::restore()`	Recreate the environment described by the lockfile

Use renv::restore() when:

You open a project on a new computer
You clone someone else’s project from GitHub
You want to return to a known working state

`DESCRIPTION` files

You receive an email from your colleague. Apparently the earlier file (sleep_analysis.R) was the wrong one.

This time, your colleague sent you two files: sleep_analysis.R and DESCRIPTION.

In their message, your colleague explains that you should use the DESCRIPTION file to create the correct R environment for running the analysis.

Download the DESCRIPTION file and save it in your rworkshop folder.

Next, create a new R script called sleep_analysis_final.R in the same folder. Copy the following code into it and save the file.

Keep your original sleep_analysis.R file. We will compare the dependencies detected in both scripts later.

R

library(dplyr)
library(ggplot2)
library(broom)

data <- sleep

# Compute group means for bar plot
summary_tbl <- data |>
  group_by(group) |>
  summarise(
    mean_extra = mean(extra),
    sd_extra   = sd(extra),
    n          = n(),
    se_extra   = sd_extra / sqrt(n)
  )

# Compute prolongation (drug - placebo) per subject
sleep1 <- data |>
  group_by(ID) |>
  summarise(
    prolongation = extra[group == 2] - extra[group == 1]
  )

# Paired t-test
tt <- t.test(sleep1$prolongation, mu = 0)
tt_tidy <- tidy(tt)

p_value <- signif(tt_tidy$p.value, 3)
t_stat  <- signif(tt_tidy$statistic, 3)

# Bar plot with t-test annotation
ggplot(summary_tbl, aes(x = factor(group), y = mean_extra)) +
  geom_col(fill = "steelblue", alpha = 0.8, width = 0.6) +
  geom_errorbar(aes(ymin = mean_extra - se_extra,
                    ymax = mean_extra + se_extra),
                width = 0.2, linewidth = 0.8) +
  labs(
    title = "Extra Sleep by Drug",
    x = "Drug",
    y = "Extra Sleep (hours)"
  ) +
  annotate(
    "text",
    x = 1.5,
    y = max(summary_tbl$mean_extra) + 1,
    label = paste0("t = ", t_stat, "\n",
                   "p = ", p_value),
    size = 5,
    hjust = 0.5
  ) +
  theme_minimal(base_size = 14)

Your project folder should now contain these files:

rworkshop/
├── rworkshop.Rproj
├── sleep_analysis.R
├── sleep_analysis_final.R
└── DESCRIPTION

Challenge

Learn more about `DESCRIPTION` files

Open the DESCRIPTION file in RStudio. What information does the file contain?

Show me the solution

The DESCRIPTION file contains information about the project, including its name, version, a short description, and the packages it depends on.

R

Package: sleepanalysis
Title: Sleep Analysis
Version: 0.1.0
Description: Dependencies for the sleep analysis.
Imports:
    dplyr (>= 1.1.0),
    tidyr (>= 1.3.0),
    ggplot2 (>= 3.4.0),
    broom (>= 0.8.0)

The Imports field lists the packages required by the project and their minimum versions.

The >= operator means greater than or equal to, so the specified version or any newer version can be installed.

For example, dplyr (>= 1.1.0) means that version 1.1.0 or any newer version can be used.

A DESCRIPTION file is normally used in R packages to record information such as the package name, version, authors, and dependencies.

It can also be used in an R analysis project to document the packages required to run the project. In this case, a minimal DESCRIPTION file containing only an Imports field is sufficient to declare the project’s dependencies for use with renv.

Challenge

Why specify minimum package versions?

In DESCRIPTION files, it is common to use >= to specify the minimum required version of each package. This means that the specified version or any newer version can be installed.

Discuss the following questions: 1. What are the benefits of allowing newer package versions? 2. What potential pitfalls could this introduce?

Think back to your earlier experience with obscurepackage.

Show me the solution

Specifying minimum versions provides flexibility, allowing projects to benefit from bug fixes, security updates, and new features without constantly updating dependency requirements.

However, newer versions can also introduce breaking changes, alter default behaviour, or contain bugs, as you saw with obscurepackage.

Therefore, specifying minimum versions alone does not guarantee reproducibility.

While DESCRIPTION defines the project’s dependency requirements, renv.lock records the exact package versions used in a particular environment.

Reset the environment

You will now create a fresh project environment to explore how renv works with a DESCRIPTION file.

First, make sure your rworkshop project is open in RStudio. Restart R by selecting Session > Restart R to unload any packages from your previous session.

Before resetting the environment, remove the packages installed during the previous exercise:

R

renv::remove(c("ggplot2", "obscurepackage"))

Then deactivate renv and remove the existing project environment:

R

renv::deactivate(clean = TRUE)

This removes the project’s renv infrastructure, including its project library and lockfile, without affecting your R scripts or DESCRIPTION file.

After R restarts, initialise a new environment:

R

renv::init(bare = TRUE)

Setting bare = TRUE creates an empty project environment without automatically discovering and installing the packages used in your project.

Because your project contains a DESCRIPTION file, renv will ask how it should identify the project’s dependencies:

This project contains a DESCRIPTION file.
Which files should renv use for dependency discovery in this project?

1: Use only the DESCRIPTION file. (explicit mode)
2: Use all files in this project. (implicit mode)

These options represent two approaches to managing project dependencies:

Explicit mode: Uses the dependencies declared in DESCRIPTION when creating a lockfile.
Implicit mode: Discovers dependencies by scanning project files, such as R scripts, when creating a lockfile.

Select option 1 (explicit mode) because your colleague has provided a DESCRIPTION file declaring the project’s required packages.

This setting controls which dependencies renv records when taking a “snapshot”. It does not automatically install the packages.

Your new project environment is now active, but the packages required for the analysis have not yet been installed.

Try running sleep_analysis_final.R.

Does it work? What error message do you get?

The script should fail because the required packages are not yet available in the project library.

In the next steps, you will identify and install these dependencies.

Find dependencies in a project

Before installing anything, it is useful to check which dependencies renv detects.

renv::dependencies() scans your project files and reports every package it finds, along with where each one came from.

R

renv::dependencies()

Challenge

`renv::dependencies()`

Run renv::dependencies():

R

renv::dependencies()

Examine at the Package, Source, and Type columns.

Which packages does renv detect?
Which files are these packages found in?
Are all the detected packages listed in DESCRIPTION?

Show me the solution

Your output may look similar to this (file paths will vary):

Finding R package dependencies ... Done!


     Type                                             Source        Package Require Version   Dev
1 Imports           C:/Users/username/rworkshop/DESCRIPTION          dplyr      >=   1.1.0 FALSE
2 Imports           C:/Users/username/rworkshop/DESCRIPTION          tidyr      >=   1.3.0 FALSE
3 Imports           C:/Users/username/rworkshop/DESCRIPTION        ggplot2      >=   3.4.0 FALSE
4 Imports           C:/Users/username/rworkshop/DESCRIPTION          broom      >=   0.8.0 FALSE
5    <NA>      C:/Users/username/rworkshop/sleep_analysis.R        ggplot2                 FALSE
6    <NA>      C:/Users/username/rworkshop/sleep_analysis.R obscurepackage                 FALSE
7    <NA> C:/Users/username/rworkshop/sleep_analysis_final.R          broom                 FALSE
8    <NA> C:/Users/username/rworkshop/sleep_analysis_final.R          dplyr                 FALSE
9    <NA> C:/Users/username/rworkshop/sleep_analysis_final.R        ggplot2                 FALSE

Which packages does renv detect?

renv detects five different packages: dplyr, tidyr, ggplot2, broom, and obscurepackage.

Some packages appear more than once because they are found in multiple files.

Which files are these packages found in?

The Source column shows where each package was found:

DESCRIPTION lists dplyr, tidyr, ggplot2, and broom.
sleep_analysis.R uses ggplot2 and obscurepackage.
sleep_analysis_final.R uses dplyr, ggplot2, and broom.

The Type column shows Imports for packages declared in the Imports field of DESCRIPTION. For packages detected in the R scripts, it shows <NA>.

Are all the detected packages listed in DESCRIPTION?

No. There are two differences:

obscurepackage is used in sleep_analysis.R but is not listed in DESCRIPTION.
tidyr is listed in DESCRIPTION but is not used in either R script.

This shows that the packages declared in DESCRIPTION do not always match those used in the project’s scripts.

Install dependencies from `DESCRIPTION` file

Even though we selected explicit mode earlier, renv::dependencies() still scans both DESCRIPTION and the R scripts. Explicit mode tells renv to use the dependencies declared in DESCRIPTION when creating the lockfile with renv::snapshot().

To install the packages listed in DESCRIPTION, we can use the remotes package.

First, install remotes if you haven’t already:

R

install.packages("remotes")

Then run:

R

remotes::install_deps()

This installs the packages listed in the DESCRIPTION file and their dependencies.

Challenge

Run both analysis scripts

Now that you have installed the dependencies from DESCRIPTION, try running both sleep_analysis.R and sleep_analysis_final.R.

Do both scripts run successfully? Why (not)?

Show me the solution

sleep_analysis_final.R should run successfully.

This script uses dplyr, ggplot2, and broom, which are all listed in DESCRIPTION and have now been installed.

sleep_analysis.R should fail.

The original script requires obscurepackage, which is not listed in DESCRIPTION and therefore was not installed by remotes::install_deps().

This shows that the DESCRIPTION does not necessarily install every package used in the project’s R scripts and must be kept up to date with the packages required by the project.

The sleep_analysis_final.R script should now run without errors, because every required package is available inside the renv project library.

Callout

The DESCRIPTION file usually specifies minimum required versions, not exact pins.

This flexibility allows newer versions to be installed so your project can benefit from bug fixes, performance improvements, and security updates.

In contrast, the lockfile records the exact package versions used in a specific environment.

This means your colleague’s lockfile may differ from yours even if you share the same DESCRIPTION file.

Choosing which dependencies to record

Earlier, you used renv::snapshot() to record your project’s package versions in a lockfile.

But remember what you found when running renv::dependencies()? Your original sleep_analysis.R script uses obscurepackage, even though it isn’t listed in DESCRIPTION or needed for the final analysis.

When taking a snapshot, we can choose how renv identifies which packages to record.

Challenge

Snapshot types

Use the help system to find out what snapshot types are available and how they differ:

R

?renv::snapshot

What is the default snapshot type when calling renv::snapshot()?
How do implicit and explicit snapshots differ?
Why is an explicit snapshot the better choice in this situation?

Show me the solution

The default snapshot type is implicit.

An implicit snapshot records installed packages detected by renv::dependencies() in project files and their dependencies.
An explicit snapshot records installed packages declared in the Imports or Depends fields of DESCRIPTION and their dependencies.

Both approaches record the installed versions of the selected packages and their dependencies in the lockfile.

In our project, an implicit snapshot would identify obscurepackage because it is used in the original script.

An explicit snapshot instead uses the dependencies declared in DESCRIPTION, including tidyr, even though it is not used directly in either script.

Since our colleague has provided a DESCRIPTION file listing the packages needed for the final analysis, explicit snapshotting is the better choice here.

Earlier, you used renv::snapshot() without specifying a snapshot type.

This time, use:

R

renv::snapshot(type = "explicit")

After taking the explicit snapshot, confirm that the project is now consistent.

R

renv::status()

You should see a message indicating that there are no issues.

You now have a lockfile for your new analysis, based on the dependencies specified by your colleague in the DESCRIPTION file they sent you.

Key Points

A project environment tracks the R and package versions used in a project.
renv creates isolated, project‑specific libraries for reproducible workflows.
Reproducibility depends on stable, well‑defined environments.

All in One View

Overview

Questions

Objectives

Introduction

Challenge 1: Can you do it?

R

Output

OUTPUT

Challenge 2: how do you nest solutions within challenge blocks?

Show me the solution

Figures

R

Math

Overview

Questions

Objectives

Running someone else’s script

Create an R project

Save the script

R

Run the script

R

R

Install the required packages

R

R

R

Reproduce the results

Show me the solution

R

R

Project environments

Two different meanings of environment in R

R

Find out more about renv

R

Show me the solution

R

Install packages in the project environment

R

Install a GitHub package

R

Show me the solution

R

R

R

Recording your environment with a lockfile

R

Update your lockfile using a snapshot

R

R

R

Show me the solution

R

R

Restoring an environment from a lockfile

DESCRIPTION files

R

Learn more about DESCRIPTION files

Show me the solution

R

Why specify minimum package versions?

Show me the solution

Reset the environment

R

R

R

Find dependencies in a project

R

renv::dependencies()

R

Show me the solution

Install dependencies from DESCRIPTION file

R

R

Run both analysis scripts

Show me the solution

Choosing which dependencies to record

Snapshot types

Find out more about `renv`

`DESCRIPTION` files

Learn more about `DESCRIPTION` files

`renv::dependencies()`

Install dependencies from `DESCRIPTION` file