knitr::opts_chunk$set(comment = "") options(width = 120, max.print = 20) library(ArchaeoPhases)
ArchaeoPhases 1.5 adds new read, plot, and statistical functions
designed to encourage reproducibility. This vignette describes some of these
functions and illustrates how to use them to reproduce an analysis.
New read functions
intended to replace the general purpose function,
ImportCSV(). The new
functions are built on the function
read_csv(), which is fast and able to read
remote files, as well as local files. The new functions return S3 objects that
can identify the file that produced them. This facility might be useful in
situations where an analysis is based on a remote file that isn't under the
analyst's control, or when files are shared electronically and potentially
subject to corruption.
The following code block illustrates this capability. After the
package has been loaded, the
read_oxcal function is used to read a
OxCal file and assign an S3 object to the variable
original_file method of the
oxc object checks
if the original file used to create it has changed since the
object was created. If the original file still exists and is unchanged, then
the function returns
TRUE. If the original file cannot be found, or has
changed, then the function returns
## load ArchaeoPhases library(ArchaeoPhases) ## read remote file data(oxc) ## returns TRUE, if ox.csv has not changed on the server original_file(oxc)
The new plot functions,
occurrence_plot() are functional replacements for the originals with camelCase
tempo_plot(). They return S3 objects with
reproduce() methods that inherit from data frame and can be
passed to statistical functions.
The following code block illustrates the
reproduce() methods. The
call to the
marginal_plot function draws a plot of the first marginal
posterior in the
oxc object and returns an S3 object, which is assigned to the
oxc.mar. The call to the
plot method of the
oxc.mar object draws
the same plot and also returns an S3 object. Note that the S3 objects returned
plot() differ because the calls that created them
differ. Nevertheless, the data returned by the two calls are identical, as
expected. The call,
reproduce(oxc.mar) checks that the original file is
accessible and has not changed, then recreates the plot. If successful, the
object it returns is identical with the object it reproduces.
## create mariginal plot object oxc.mar <- marginal_plot(oxc) ## use plot method to reproduce marginal plot oxc.mar.plot <- plot(oxc.mar) ## check for identity returns FALSE because calls differ identical(oxc.mar, oxc.mar.plot) ## check for data identity returns TRUE identical(oxc.mar$x, oxc.mar.plot$x) ## reproduce the marginal plot object oxc.mar.rep <- reproduce(oxc.mar) ## check for object identity returns TRUE identical(oxc.mar.rep, oxc.mar)
The objects returned by the
read_*() functions behave like data frames. The
multi_marginal_statistics, expects a data frame as its first
argument. When passed the
oxc object, it returns an object with various
summary statistics of the marginals, as if it had been passed a standard data
oxc.stats <- multi_marginal_statistics(oxc) oxc.stats$statistics
The ability to reproduce an
ArchaeoPhases analysis might find several uses.
One use case is when work on a project is interrupted. In this use case, objects
produced during an earlier session and saved to disk can be read into the new
R session. The
reproduce methods of the objects will indicate whether or not
the Bayesian calibration output is still available, and if so, whether or not it
Another use case is a collaboration where one might wish to distribute an MCMC
file and the
R code required to make an informative plot. An email message
with the MCMC file and the saved
ArchaeoPhases object attached is a reasonable
way this might be accomplished. In this situation, the recipient can point the
reproduce() function to the local MCMC file and verify that the analysis
carried out by the sender can actually be reproduced.
A third use case is one you should know how to avoid. The S3 objects produced by
ArchaeoPhases can be manipulated to include malicious code and an unscrupulous
scoundrel might intend to harm you in this way. The best defense is, as usual,
to choose trustworthy collaborators and not try to reproduce objects from
unknown sources. In any event, a simple way to check for malicious code is to
call attribute of the object, as shown in the following source
code block. If the call looks good, then all should be well.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.