Note: some changes in this version were suggested by anonymous reviewers from the journal we submitted our manuscipt to. We are those reviewers very grateful for going through our code so thoroughly!
# we need special treatment for very prevalent full names, they are likely!
# e.g. as.mo("Staphylococcus aureus")
@ -1544,7 +1544,7 @@ exec_as.mo <- function(x,
# this will save the uncertain items as attribute, so they can be bound to `uncertainties` in the uncertain_fn() function
x<-structure(x,uncertainties=uncertainties)
}
if (old_mo_warning==TRUE&property!="mo"){
warning("The input contained old microorganism IDs from previous versions of this package.\nPlease use `as.mo()` on these old IDs to transform them to the new format.\nSUPPORT FOR THIS WILL BE DROPPED IN A FUTURE VERSION.",call.=FALSE)
#' This helper function is used by [as.mo()] to determine the most probable match of taxonomic records, based on user input.
#' @param x Any user input value(s)
#' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
#' @param uncertainty The level of uncertainty set in [as.mo()], see `allow_uncertain` in that function (here, it defaults to 1, but is automatically determined in [as.mo()] based on the number of transformations needed to get to a result)
#' @section Matching score for microorganisms:
#' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m} is calculated as:
#' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m}, ranging from 0 to 100%, is calculated as:
#' * \eqn{n} is a taxonomic name (genus, species and subspecies);
#' * \eqn{l_{n}}{l_n} is the length of the taxonomic name;
#' * \eqn{\operatorname{lev}}{lev} is the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) function;
#' * \eqn{p} is the human pathogenic prevalence, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see *Details* in `?as.mo`), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
#' * \eqn{k} is the kingdom index, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
#' * \eqn{n} is a taxonomic name (genus, species and subspecies) as found in [`microorganisms$fullname`][microorganisms];
#' * \eqn{l_{n}}{l_n} is the length of \eqn{n};
#' * \eqn{\operatorname{lev}}{lev} is the [Levenshtein distance function](https://en.wikipedia.org/wiki/Levenshtein_distance);
#' * \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see *Details* in `?as.mo`), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
#' * \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
#'
#' This means that the user input `x = "E. coli"` gets for *Escherichia coli* a matching score of `r percentage(mo_matching_score("E. coli", "Escherichia coli"), 1)` and for *Entamoeba coli* a matching score of `r percentage(mo_matching_score("E. coli", "Entamoeba coli"), 1)`.
#'
#' All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">1.3.0.9030</span>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">1.3.0.9031</span>
</span>
</div>
@ -388,17 +388,18 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
<p>With ambiguous user input in <code>as.mo()</code> and all the <code><ahref='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code><ahref='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\) is calculated as:</p>
<p>With ambiguous user input in <code>as.mo()</code> and all the <code><ahref='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code><ahref='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\), ranging from 0 to 100%, is calculated as:</p>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies);</p></li>
<li><p>\(l_{n}\) is the length of the taxonomic name;</p></li>
<li><p>\(\operatorname{lev}\) is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance</a> function;</p></li>
<li><p>\(p\) is the human pathogenic prevalence, categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code>?as.mo</code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k\) is the kingdom index, set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies) as found in <code><ahref='microorganisms.html'>microorganisms$fullname</a></code>;</p></li>
<li><p>\(l_{n}\) is the length of \(n\);</p></li>
<li><p>\(\operatorname{lev}\) is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance function</a>;</p></li>
<li><p>\(p_{n}\) is the human pathogenic prevalence of \(n\), categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code>?as.mo</code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k_{n}\) is the kingdom index of \(n\), set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
</ul>
<p>This means that the user input <code>x = "E. coli"</code> gets for <em>Escherichia coli</em> a matching score of 68.8% and for <em>Entamoeba coli</em> a matching score of 7.9%.</p>
<p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p>
<h2class="hasAnchor"id="catalogue-of-life"><aclass="anchor"href="#catalogue-of-life"></a>Catalogue of Life</h2>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">1.3.0.9030</span>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">1.3.0.9031</span>
</span>
</div>
@ -255,27 +255,24 @@
<th>n</th>
<td><p>A full taxonomic name, that exists in <code><ahref='microorganisms.html'>microorganisms$fullname</a></code></p></td>
</tr>
<tr>
<th>uncertainty</th>
<td><p>The level of uncertainty set in <code><ahref='as.mo.html'>as.mo()</a></code>, see <code>allow_uncertain</code> in that function (here, it defaults to 1, but is automatically determined in <code><ahref='as.mo.html'>as.mo()</a></code> based on the number of transformations needed to get to a result)</p></td>
</tr>
</table>
<h2class="hasAnchor"id="matching-score-for-microorganisms"><aclass="anchor"href="#matching-score-for-microorganisms"></a>Matching score for microorganisms</h2>
<p>With ambiguous user input in <code><ahref='as.mo.html'>as.mo()</a></code> and all the <code><ahref='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code>mo_matching_score()</code>. This matching score \(m\) is calculated as:</p>
<p>With ambiguous user input in <code><ahref='as.mo.html'>as.mo()</a></code> and all the <code><ahref='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code>mo_matching_score()</code>. This matching score \(m\), ranging from 0 to 100%, is calculated as:</p>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies);</p></li>
<li><p>\(l_{n}\) is the length of the taxonomic name;</p></li>
<li><p>\(\operatorname{lev}\) is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance</a> function;</p></li>
<li><p>\(p\) is the human pathogenic prevalence, categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code><ahref='as.mo.html'>?as.mo</a></code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k\) is the kingdom index, set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies) as found in <code><ahref='microorganisms.html'>microorganisms$fullname</a></code>;</p></li>
<li><p>\(l_{n}\) is the length of \(n\);</p></li>
<li><p>\(\operatorname{lev}\) is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance function</a>;</p></li>
<li><p>\(p_{n}\) is the human pathogenic prevalence of \(n\), categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code><ahref='as.mo.html'>?as.mo</a></code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k_{n}\) is the kingdom index of \(n\), set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
</ul>
<p>This means that the user input <code>x = "E. coli"</code> gets for <em>Escherichia coli</em> a matching score of 68.8% and for <em>Entamoeba coli</em> a matching score of 7.9%.</p>
<p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">1.3.0.9030</span>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">1.3.0.9031</span>
</span>
</div>
@ -350,17 +350,18 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
<p>With ambiguous user input in <code><ahref='as.mo.html'>as.mo()</a></code> and all the <code>mo_*</code> functions, the returned results are chosen based on their matching score using <code><ahref='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\) is calculated as:</p>
<p>With ambiguous user input in <code><ahref='as.mo.html'>as.mo()</a></code> and all the <code>mo_*</code> functions, the returned results are chosen based on their matching score using <code><ahref='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\), ranging from 0 to 100%, is calculated as:</p>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies);</p></li>
<li><p>\(l_{n}\) is the length of the taxonomic name;</p></li>
<li><p>\(\operatorname{lev}\) is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance</a> function;</p></li>
<li><p>\(p\) is the human pathogenic prevalence, categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code><ahref='as.mo.html'>?as.mo</a></code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k\) is the kingdom index, set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies) as found in <code><ahref='microorganisms.html'>microorganisms$fullname</a></code>;</p></li>
<li><p>\(l_{n}\) is the length of \(n\);</p></li>
<li><p>\(\operatorname{lev}\) is the <ahref='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance function</a>;</p></li>
<li><p>\(p_{n}\) is the human pathogenic prevalence of \(n\), categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code><ahref='as.mo.html'>?as.mo</a></code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k_{n}\) is the kingdom index of \(n\), set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
</ul>
<p>This means that the user input <code>x = "E. coli"</code> gets for <em>Escherichia coli</em> a matching score of 68.8% and for <em>Entamoeba coli</em> a matching score of 7.9%.</p>
<p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p>
<h2class="hasAnchor"id="catalogue-of-life"><aclass="anchor"href="#catalogue-of-life"></a>Catalogue of Life</h2>
@ -146,20 +146,22 @@ If the unlying code needs breaking changes, they will occur gradually. For examp
\section{Matching score for microorganisms}{
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m} is calculated as:
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, ranging from 0 to 100\%, is calculated as:
\item \eqn{n} is a taxonomic name (genus, species and subspecies);
\item \eqn{l_{n}}{l_n} is the length of the taxonomic name;
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} function;
\item \eqn{p} is the human pathogenic prevalence, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k} is the kingdom index, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
\item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
\item \eqn{l_{n}}{l_n} is the length of \eqn{n};
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
\item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
}
This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
\item{n}{A full taxonomic name, that exists in \code{\link[=microorganisms]{microorganisms$fullname}}}
\item{uncertainty}{The level of uncertainty set in \code{\link[=as.mo]{as.mo()}}, see \code{allow_uncertain} in that function (here, it defaults to 1, but is automatically determined in \code{\link[=as.mo]{as.mo()}} based on the number of transformations needed to get to a result)}
}
\description{
This helper function is used by \code{\link[=as.mo]{as.mo()}} to determine the most probable match of taxonomic records, based on user input.
}
\section{Matching score for microorganisms}{
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m} is calculated as:
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, ranging from 0 to 100\%, is calculated as:
\item \eqn{n} is a taxonomic name (genus, species and subspecies);
\item \eqn{l_{n}}{l_n} is the length of the taxonomic name;
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} function;
\item \eqn{p} is the human pathogenic prevalence, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k} is the kingdom index, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
\item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
\item \eqn{l_{n}}{l_n} is the length of \eqn{n};
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
\item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
}
This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
@ -126,20 +126,22 @@ If the unlying code needs breaking changes, they will occur gradually. For examp
\section{Matching score for microorganisms}{
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m} is calculated as:
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, ranging from 0 to 100\%, is calculated as:
\item \eqn{n} is a taxonomic name (genus, species and subspecies);
\item \eqn{l_{n}}{l_n} is the length of the taxonomic name;
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} function;
\item \eqn{p} is the human pathogenic prevalence, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k} is the kingdom index, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
\item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
\item \eqn{l_{n}}{l_n} is the length of \eqn{n};
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
\item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
}
This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
