Content from Using Markdown
Last updated on 2023-07-24 | Edit this page
Overview
Questions
- How do you write a lesson using Markdown and sandpaper?
Objectives
- Explain how to use markdown with The Carpentries Workbench
- 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:
-
questionsare displayed at the beginning of the episode to prime the learner for the content. -
objectivesare the learning objectives for an episode displayed with the questions. -
keypointsare displayed at the end of the episode to reinforce the objectives.
Challenge 1: Can you do it?
What is the output of this command?
R
paste("This", "new", "lesson", "looks", "good")
OUTPUT
[1] "This new lesson looks good"Challenge 2: how do you nest solutions within challenge blocks?
You can add a line with at least three colons and a
solution tag.
Figures
You can use standard markdown for static figures with the following syntax:
{alt='alt text for accessibility purposes'}
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
.mdfiles for episodes when you want static content - Use
.Rmdfiles 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 What is Data Visualization
Last updated on 2024-05-16 | Edit this page
Overview
Questions
- TODO
Objectives
- Imagine that different visualizations of the same data can tell different stories
- Identify the components of a data visualization and explain their function
Terminology
The purpose of data visualization
Elements of a figure
Content from Understanding Your Data
Last updated on 2024-05-14 | Edit this page
Overview
Questions
- TODO
Objectives
- Recognize properties of a dataset (categorical vs. continuous,
grouping, dependency in time) suitable for visualization.
- Review your dataset.
- Identify and list any critical context for understanding the information/results.
- Make decisions for what to share in a visual format.
- Determine parameters for data you intend to visualize including outliers, etc.
- Build a narrative or “story” for your data that you intend to visualize.
Item
Item
Item
Content from Creating Data Visualizations
Last updated on 2024-05-16 | Edit this page
Overview
Questions
- How do I choose a data visualization?
- How do I help readers understand my data?
- How do I visualize uncertainty and weird data?
Objectives
- Compare different approaches to creating a data visualization.
- Choose visual aesthetics (position, shape, color, size) appropriate for aspects of a dataset (quantity, label, etc.).
- Demonstrate different approaches to showing uncertainty.
- Contrast different visual strategies for outliers
Different data visualizations for a dataset
Challenge text, code, and other information goes here
Hints
Solution text, code, and other information
Takeaways
- You can make your data visualization more accessible with appropriate design choices.
- Your choices matter to your reader.
Content from Evaluating Data Visualization
Last updated on 2024-05-15 | Edit this page
Overview
Questions
- TODO
Objectives
- interpret data visualizations to generate a narrative about
unfamiliar data
- Compare and contrast interpretations of different visualizations
from the same dataset
- Propose questions about choices made during the data visualization process to explore potential hidden features of the data
Develop Qs Evaluating a Whisker Plot
Come up with a set of questions to help you evaluate the clarity, usefulness, completeness, and objectivity of this whisker plot about Flu shots.
** EXAMPLE ** 
Some things to consider are: - n of participants
visual display
the statistical choices of the authors
any comparisons or relationships among the plotted elements
sample responses: - are the data in each of the groups mutually exclusive?
are there outliers excluded? how?
how are the data combined to form each point & whisker plot?
how are the CIs (confidence intervals) computed? [in this case, because the quantitative axis is % increase, CI is a little funky here as an estimate of error around the value, and not an estimate of variability within a group]
are the number of subjects in each group equal (or approximately equal)?
is there a grouping of interventions that suggest generalizations? (e.g. 1 text vs 2 texts, content of reminder, format of reminder)
is there an effect of time of day that a reminder is sent/received (vs. relative to an appointment time)
how robust are the estimates to the selection of the regression model?
soooo, what’s the takeaway from this figure? 😄
Content from Error Awareness
Last updated on 2024-05-15 | Edit this page
Overview
Questions
- TODO
Objectives
- Recognize common mistakes responsible for misleading plots or misinterpretations.
- Evaluate a plot for areas of potential sources of confusion.
- Suggest improvements to plots that clarify its points.
Mighty Challenge grik grak grok
Which questions would you say this visualization fails to answer?
- are the data in each of the groups mutually exclusive?
- are there outliers excluded? how?
- how are the data combined to form each point & whisker plot?
- how are the CIs (confidence intervals) computed? [in this case, because the quantitative axis is % increase, CI is a little funky here as an estimate of error around the value, and not an estimate of variability within a group]
- are the number of subjects in each group equal (or approximately equal)?
- is there a grouping of interventions that suggest generalizations? (e.g. 1 text vs 2 texts, content of reminder, format of reminder)
- is there an effect of time of day that a reminder is sent/received (vs. relative to an appointment time)
- how robust are the estimates to the selection of the regression model?
- soooo, what’s the takeaway from this figure?