Material for updated (third) edition of Resampling: The New Statistics, second edition by Julian L. Simon.
The new edition is by Julian L. Simon with Matthew Brett and Stéfan van der Walt.
The latest version will always be at the book website.
We release the material in this repository under
a CC-BY-ND license, unless
otherwise specified. See LICENSE.md in this directory for details.
The source text that we build to the book is in the source directory.
There are source (Markdown) versions of chapters from the second edition in the
unported directory. As we fill out the third edition, we move these files
into the source directory, and edit them there.
Typically, you will want to install Python build dependencies in a virtual
environment. You can place such an
environment anywhere you like. This is how to create it in ~/envs:
mkdir ~/envs
python -m venv ~/envs/resampling
export PIP_INSTALL_CMD="pip install"
make build-init
You will also need rsvg-convert, inkscape, and pandoc. On macOS,
those can be installed with:
brew install librsvg inkscape pandoc
On Fedora, with:
sudo dnf install R-rsvg inkscape pandoc
See the Pandoc installation guide for
suggestions to install rsvg-convert on other platforms.
Make sure that your rmarkdown package is sufficiently up to date to work
with your pandoc version. Versions of pandoc >= 2.11 use --citeproc and
not --filter pandoc-citeproc; if your rmarkdown version is older than 2.5
(library(rmarkdown); sessionInfo()), it won't know that, and therefore will
raise an error on book build - see RMarkdown release
notes. Upgrade with
install.packages('rmarkdown').
We use Quarto as the build machinery for the website and for PDF.
See the Quarto installation instructions. Afterwards, install the matching R package.
Rscript -e 'install.packages("quarto")'
If it complains about the CRAN mirror not set, add the following to ~/.Rprofile and try again:
local({r <- getOption("repos")
r["CRAN"] <- "http://cran.r-project.org"
options(repos=r)})
The process may fail if it cannot find curl and openssl development headers. The error message explains how to install those headers on the various systems. For example, on Fedora it'd be:
sudo dnf install libcurl-devel openssl-devel
Finally, check the installation:
quarto check install
quarto check knitr
Quarto uses various Pandoc markdown extensions, as do we (Div and Span elements for custom inline elements and blocks).
Follow the build instructions above.
Ensure that your virtual environment is activated:
source ~/envs/resampling-with/bin/activate
Make sure you can build the whole book in your current environment with:
make clean && make python-book
make clean && make r-book
from the top-level repository directory. If this doesn't work, make an Issue on Github.
Be careful - and note the make cleans above - it seems that caching can trip
up the build. In general, try make clean if you run into puzzling build
problems with data frames not defined, that are clearly defined, or missing
imports that are not missing.
After you've confirmed you can build both the Python and the R edition, you may want to work on only one of these editions — say the Python book, and clean up the R book later (or the other way round).
Matthew and Peter know R reasonably well — we can help with R cleanup.
See the ./source/_quarto.yml.template file for
a list of the chapters currently in the book build.
Let's say you want to start work on one of the chapters, and you've see this in the _quarto.yml.template file:
- reliability_average.Rmd
The procedure is:
Before you start:
- Make a new Git branch, and check out the branch.
cd source
Editing:
- Edit the matching file - here
source/reliability_average.Rmd. - You might find it useful to have the original PDF chapter open; see the
original book in PDF. The PDFs
are also in the
./unporteddirectory of the repository. The filenames in the./unporteddirectory begin with the original file stem followed by the new file stem. For example, the PDF corresponding tosource/reliability_average.Rmdis26-Chap-22_reliability_average.pdf. Also see the chapter mappings at the end of this page. - Rebuild the file you're working on from time to time.
From the
sourcedirectory:You can now open the generated HTML file in your browser.ninja ../python-book/reliability_average.html - If you get an obscure Python or R error from the build, try
ninja cleanbefore rerunning the build, as in:ninja clean && ninja ../python-book/reliability_average.html - When in some kind of shape that is ready for other people to look at, make your commits with the changes, and do a pull-request to the main repository. See the initial port checklist below.
- In due course you may want to remove the boilerplate warning text at the top of the page.
- Check sections.
- Fix any tables — but see below - are they auto-generated? Search for
ketableinintro.Rmdfor an example. git rmany now-unused.pngfiles.- Update any chapter or section or figure or example cross-references from e.g.
see Chapter 14tosee @sec-some-named-section. - Port any Resampling-stats code to Python and R.
- Put in notebook markers.
Make a notebook section with e.g.
::: {.notebook name="ambulances" title="Ambulances"}
Stuff
```{python}
# A Python cell - it only gets run in the Python edition.
k = 1
```
```{r}
# An R cell - it only gets run in the R edition
k <- 1
```
The value of `k` is `r get_var('k')`.
:::
The notebooks (above) get written out as separate documents. By default, they may lack some context, if the reader can't see the preceding text in the chapter. So, sometimes it's useful to put an introductory paragraph at the top of the notebook to give the context, but where that paragraph does not appear in the main text, like this:
::: {.notebook name="another_notebook" title="Another Notebook"}
::: nb-only
This appears only in the output notebook file, and not in the main text.
:::
```{python}
# A Python cell.
k = 1
```
```{r}
# An R cell.
k <- 1
```
:::
You have inline and block markup to put content into just the R edition, or just the Python edition:
::: r
This only appears in the R edition
:::
::: python
This only appears in the Python edition
:::
This content appears [only in the R edition]{.r}[just in the Python
book]{.python}.
If you find yourself doing that often, you can define a version-dependent variable in the text_variables.yml file.
Use it with e.g.
Here I have text that depends on the version of the book — {{< var my_var >}}
— as determined by the `text_variables.yml` file.
Citations are in Pandoc format, as implemented in Quarto's citations.
Check that the reference is not already in source/simon_refs.bib. Add it if
so, following reference name standard in that file (e.g.
@article{christensen2005fisher,). Cite with e.g. This is a terrible idea [@christensen2005fisher] or As Christensen notes [-@christensen2005fisher],
or There are many good ways to do this [see @knuth1984, pp. 33-35; also @wickham2015, chap. 1]. See Quarto link above for other examples.
See Quarto footnotes)
Examples (from that page):
Here is a footnote reference,[^1] and another.[^longnote]
[^1]: Here is the footnote.
[^longnote]: Here's one with multiple blocks.
Subsequent paragraphs are indented to show that they
belong to the previous footnote.
Here is an inline note.^[Inlines notes are easier to write,
since you don't have to pick an identifier and move down to
type the note.]
Notice that you'll need full 4-space indentation to keep the footnote paragraphs within the footnote.
See Quarto callouts
:::{.callout-note}
## A title for the note
Some text
:::
Also .callout-warning, -tip, -important, -caution.
HTML comments:
<!---
More here on something and something else
-->
Note the triple dash in the first line, as in <!---.
See Cross-references in
Quarto. Summary for
section reference: add {#sec-name-for-your-ref} to the target section title,
reference with Please see section @name-for-your-ref for details.
| Col1 | Col2 | Col3 |
|------|------|------|
| A | B | C |
| E | F | G |
| A | G | G |
: My Caption {#tbl-letters}
See @tbl-letters.
These support per-column alignment:
| Default | Left | Right | Center |
|---------|:-----|------:|:------:|
| 12 | 12 | 12 | 12 |
| 123 | 123 | 123 | 123 |
| 1 | 1 | 1 | 1 |
: A pipe table {#tbl-pipe}
See @tbl-pipe.
See Quarto tables
These are the most flexible, but are fiddly to edit because they care about spacing. Use these for headers, footer, fusing cells, etc.
See: Pandoc grid tables and
inference_ideas.Rmd for examples.
There is a good summary of Markdown tables in this page
Install Jupyter:
pip install jupyter
Start jupyterlab in the source directory, right-click on an .Rmd
file, and "Open as notebook". If this option does not appear, ensure that
Jupytext is installed.
SVG figures need to be converted to PNG for the HTML build and PDF for the PDF build. We automate this with the build system. To use the automation:
- Create the
.svgfile and put it in thediagramsdirectory. - Insert a block like the one below, to include the figure.
include_svg('diagrams/ships_gold_silver.svg')
Where ships_gold_silver.svg is the file in diagrams, fi-ships-gold-silver
is the reference label for the figure, opts.label='svg_fig' sets the correct
display options for the figure, and fig.cap give the caption.
You can then refer to the figure elsewhere (for this example) as
@fig-ships-gold-silver.
Search for include_svg in the source directory for other examples.
Sometimes we generate figures and tables on the fly using code that should not
appear in the final book. For examples, see more_sampling_tools.Rmd.
The code could be Python or R.
By default, the build system executes Python code chunks only in the Python
edition, and R code chunks in the R edition. For supporting code, to be run in
both editions, you need to tell Quarto (in fact, Knitr) to always execute the
code (regardless of whether we are currently building the R or Python edition);
use the option eval=TRUE.
Nearly always, you will want to hide the source code for those chunks. Use the chunk option echo=FALSE to do that.
Sometimes (sometimes not) you will want to suppress the output. You can add
results=FALSE to the options to suppress the output, or use include=FALSE
to suppress the code and output (equivalent to echo=FALSE, results=FALSE).
Here's an example, from intro.Rmd:
```{python, eval=TRUE, echo=FALSE}
import os.path as op
import numpy as np
import pandas as pd
lake = pd.read_csv(op.join('data', 'lough_erne.csv'))
yearly_srp = lake.loc[:, ['Year', 'SRP']].copy()
```
```{r, eval=TRUE, echo=FALSE}
ketable(py$yearly_srp,
caption = "Soluble Reactive Phosphorus in Lough Erne {#tbl-yearly-srp}")
```
Notice in the case above, that there is no output from the first chunk (so we
don't need to suppress it with results=FALSE or include=FALSE). We do want
the output results from the second chunk.
See the Knitr chunk options documentation for more detail.
For the Jupyter notebook, you might want to enable the R magics, to allow you to run both the R code and the Python code, in the same notebook, without any extra cells.
First, install rpy2 into your virtualenv:
pip install rpy2
Find your IPython default configuration directory. From Jupyter:
get_ipython().profile_dir.startup_dirThen make a file in that directory with name such as 03_rpy2.ipy, with
contents including:
try:
import rpy2
except ImportError:
pass
else:
%load_ext rpy2.ipythonThe .ipy extension is to allow the % magic commands. Now you should be
able to work with the %%R cell magics.
- The original book in PDF. MB has the print book.
See the notes repository for more discussions of various concepts in the book, and how we are thinking about them.
These are the mappings between the files in the ./source directory, and the
original chapters from the second edition
website. You can find basic Markdown
ports of the original second edition PDF chapters in the ./unported directory
of the repository.
See also ./source/_quarto.yml.template for files making up chapters in current
built book.
| Third edition file | Second edition file(s) | Third edition chapter title |
|---|---|---|
| preface_third.Rmd | N/A | Preface to the third edition |
| preface_second.Rmd | 01-Preface | Preface to the second edition |
| intro.Rmd | 02-Intro, 04-Afternote-2 | Introduction |
| monty_hall.Rmd | N/A | N/A |
| dramatizing_resampling.Rmd | 03-Afternote-1 | N/A |
| resampling_method.Rmd | 05-Chap-1 | The resampling method |
| about_technology.Rmd | N/A | Introducing Python ... |
| resampling_with_code.Rmd | N/A | Resampling with code |
| resampling_with_code2.Rmd | N/A | More resampling with code |
| what_is_probability.Rmd | 06-Chap-2, 07-Chap-3 | What is probability? |
| probability_theory_1a.Rmd | 08-Chap-4 | NA |
| probability_theory_1b.Rmd | 09-Chap-5 | NA |
| probability_theory_2_compound.Rmd | 10-Chap-6 | NA |
| probability_theory_3.Rmd | 11-Chap-7 | NA |
| probability_theory_4_finite.Rmd | 12-Chap-8 | NA |
| sampling_variability.Rmd | 13-Chap-9 | NA |
| monte_carlo.Rmd | 14-Chap-10 | NA |
| inference_ideas.Rmd | 15-Chap-11 | NA |
| inference_intro.Rmd | 16-Chap-12 | NA |
| point_estimation.Rmd | 17-Chap-13 | NA |
| framing_questions.Rmd | 18-Chap-14 | NA |
| testing_counts_1.Rmd | 19-Chap-15 | NA |
| significance.Rmd | 20-Chap-16 | NA |
| testing_counts_2.Rmd | 21-Chap-17 | NA |
| testing_measured.Rmd | 22-Chap-18 | NA |
| testing_procedures.Rmd | 23-Chap-19 | NA |
| confidence_1.Rmd | 24-Chap-20 | NA |
| confidence_2.Rmd | 25-Chap-21 | NA |
| reliability_average.Rmd | 26-Chap-22 | NA |
| correlation_causation.Rmd | 27-Chap-23 | NA |
| how_big_sample.Rmd | 28-Chap-24 | NA |
| bayes_simulation.Rmd | 29-Chap-25 | NA |
| exercise_solutions.Rmd | 30-Exercise-sol | NA |
| acknowlegements.Rmd | acknow | NA |
| technical_note.Rmd | Technical | NA |
Initial text for this table generated using:
grep ed2_fname *.Rmd | grep -v _main | sed 's/:.*ed2_fname//' | sort -t ':' -k 2 -