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AMR
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(v1.1.0.9009) lose dependencies

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dr. M.S. (Matthijs) Berends 2020-05-18 10:30:53 +02:00
parent 071bdc5a9e
commit bf0402a653
29 changed files with 405 additions and 374 deletions
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6
.gitlab-ci.yml
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@ -93,7 +93,6 @@ R-release:
allow_failure: true
script:
- Rscript -e 'sessionInfo()'
- Rscript -e 'AMR::eucast_rules(AMR::example_isolates_unclean, info = TRUE)'
# install missing and outdated packages
- Rscript -e 'source(".gitlab-ci.R"); gl_update_pkg_all(repos = "https://cran.rstudio.com", quiet = TRUE, install_pkgdown = TRUE, install_lintr = TRUE)'
# remove vignettes folder and get VignetteBuilder field out of DESCRIPTION file
@ -102,6 +101,8 @@ R-release:
# build package
- R CMD build . --no-build-vignettes --no-manual
- PKG_FILE_NAME=$(ls -1t *.tar.gz | head -n 1)
- Rscript -e 'devtools::install_local("${PKG_FILE_NAME}")'
- Rscript -e 'AMR::eucast_rules(AMR::example_isolates_unclean, info = TRUE)'
- Rscript -e 'Sys.setenv(NOT_CRAN = "true")'
- R CMD check "${PKG_FILE_NAME}" --no-build-vignettes --no-manual --as-cran
artifacts:
@ -121,7 +122,6 @@ R-devel:
allow_failure: true
script:
- Rscriptdevel -e 'sessionInfo()'
- Rscriptdevel -e 'AMR::eucast_rules(AMR::example_isolates_unclean, info = TRUE)'
# install missing and outdated packages
- Rscriptdevel -e 'source(".gitlab-ci.R"); gl_update_pkg_all(repos = "https://cran.rstudio.com", quiet = TRUE)'
# remove vignettes folder and get VignetteBuilder field out of DESCRIPTION file
@ -130,6 +130,8 @@ R-devel:
# build package
- Rdevel CMD build . --no-build-vignettes --no-manual
- PKG_FILE_NAME=$(ls -1t *.tar.gz | head -n 1)
- Rscript -e 'devtools::install_local("${PKG_FILE_NAME}")'
- Rscript -e 'AMR::eucast_rules(AMR::example_isolates_unclean, info = TRUE)'
- Rscript -e 'Sys.setenv(NOT_CRAN = "true")'
- Rdevel CMD check "${PKG_FILE_NAME}" --no-build-vignettes --no-manual --as-cran
artifacts:

4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 1.1.0.9008
Date: 2020-05-17
Version: 1.1.0.9009
Date: 2020-05-18
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(role = c("aut", "cre"),

4
NEWS.md
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@ -1,5 +1,5 @@
# AMR 1.1.0.9008
## <small>Last updated: 17-May-2020</small>
# AMR 1.1.0.9009
## <small>Last updated: 18-May-2020</small>
### Breaking
* Removed previously deprecated function `p.symbol()` - it was replaced with `p_symbol()`

2
R/atc_online.R
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@ -132,7 +132,7 @@ atc_online_property <- function(atc_code,
for (i in seq_len(length(atc_code))) {
progress$tick()$print()
progress$tick()
atc_url <- sub("%s", atc_code[i], url, fixed = TRUE)

41
R/first_isolate.R
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@ -46,12 +46,9 @@
#'
#' All isolates with a microbial ID of `NA` will be excluded as first isolate.
#'
#' The functions [filter_first_isolate()] and [filter_first_weighted_isolate()] are helper functions to quickly filter on first isolates. The function [filter_first_isolate()] is essentially equal to:
#' The functions [filter_first_isolate()] and [filter_first_weighted_isolate()] are helper functions to quickly filter on first isolates. The function [filter_first_isolate()] is essentially equal to one of:
#' ```
#' x %>%
#' mutate(only_firsts = first_isolate(x, ...)) %>%
#' filter(only_firsts == TRUE) %>%
#' select(-only_firsts)
#' x %>% filter(first_isolate(., ...))
#' ```
#' The function [filter_first_weighted_isolate()] is essentially equal to:
#' ```
@ -60,7 +57,7 @@
#' mutate(only_weighted_firsts = first_isolate(x,
#' col_keyantibiotics = "keyab", ...)) %>%
#' filter(only_weighted_firsts == TRUE) %>%
#' select(-only_weighted_firsts)
#' select(-only_weighted_firsts, -keyab)
#' ```
#' @section Key antibiotics:
#' There are two ways to determine whether isolates can be included as first *weighted* isolates which will give generally the same results:
@ -451,7 +448,7 @@ filter_first_isolate <- function(x,
col_patient_id = NULL,
col_mo = NULL,
...) {
filter(x, first_isolate(x = x,
subset(x, first_isolate(x = x,
col_date = col_date,
col_patient_id = col_patient_id,
col_mo = col_mo,
@ -466,15 +463,23 @@ filter_first_weighted_isolate <- function(x,
col_mo = NULL,
col_keyantibiotics = NULL,
...) {
tbl_keyab <- x %>%
mutate(keyab = suppressMessages(key_antibiotics(.,
col_mo = col_mo,
...))) %>%
mutate(firsts = first_isolate(.,
col_date = col_date,
col_patient_id = col_patient_id,
col_mo = col_mo,
col_keyantibiotics = "keyab",
...))
x[which(tbl_keyab$firsts == TRUE), ]
y <- x
if (is.null(col_keyantibiotics)) {
# first try to look for it
col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics")
# still NULL? Then create it since we are calling filter_first_WEIGHTED_isolate()
if (is.null(col_keyantibiotics)) {
y$keyab <- suppressMessages(key_antibiotics(x,
col_mo = col_mo,
...))
col_keyantibiotics <- "keyab"
}
}
subset(x, first_isolate(x = y,
col_date = col_date,
col_patient_id = col_patient_id,
col_mo = col_mo,
col_keyantibiotics = col_keyantibiotics,
...))
}

4
R/key_antibiotics.R
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@ -214,11 +214,9 @@ key_antibiotics <- function(x,
warning("only using ", length(gram_negative), " different antibiotics as key antibiotics for Gram-negatives. See ?key_antibiotics.", call. = FALSE)
}
# join to microorganisms data set
x <- x %>% as.data.frame(stringsAsFactors = FALSE)
x <- as.data.frame(x, stringsAsFactors = FALSE)
x[, col_mo] <- as.mo(x[, col_mo, drop = TRUE])
x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL)
x$key_ab <- NA_character_
# Gram +

1
_pkgdown.yml
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@ -96,7 +96,6 @@ reference:
- "`eucast_rules`"
- "`guess_ab_col`"
- "`mo_source`"
- "`read.4D`"
- title: "Enhancing your data"
desc: >
Functions to add new data to your existing data, such as the determination

2
docs/404.html
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@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="https://msberends.gitlab.io/AMR/index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>

2
docs/LICENSE-text.html
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@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>

506
docs/articles/AMR.html
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@ -39,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>
@ -186,7 +186,7 @@
<h1 data-toc-skip>How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">15 April 2020</h4>
<h4 class="date">18 May 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/AMR.Rmd"><code>vignettes/AMR.Rmd</code></a></small>
<div class="hidden name"><code>AMR.Rmd</code></div>
@ -195,15 +195,15 @@
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 15 April 2020.</p>
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 18 May 2020.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
<p>Conducting antimicrobial resistance analysis unfortunately requires in-depth knowledge from different scientific fields, which makes it hard to do right. At least, it requires:</p>
<ul>
<li>Good questions (always start with these!)</li>
<li>Good questions (always start with those!)</li>
<li>A thorough understanding of (clinical) epidemiology, to understand the clinical and epidemiological relevance and possible bias of results</li>
<li>A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment</li>
<li>A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment, as well as understanding intrinsic and acquired microbial resistance</li>
<li>Experience with data analysis with microbiological tests and their results, to understand the determination and limitations of MIC values and their interpretations to RSI values</li>
<li>Availability of the biological taxonomy of microorganisms and probably normalisation factors for pharmaceuticals, such as defined daily doses (DDD)</li>
<li>Available (inter-)national guidelines, and profound methods to apply them</li>
@ -226,21 +226,21 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2020-04-15</td>
<td align="center">2020-05-18</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2020-04-15</td>
<td align="center">2020-05-18</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2020-04-15</td>
<td align="center">2020-05-18</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -258,7 +258,7 @@
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">AMR</span>)
<span class="co"># (if not yet installed, install with:)</span>
<span class="co"># install.packages(c("tidyverse", "AMR"))</span></pre></body></html></div>
<span class="co"># install.packages(c("dplyr", "ggplot2", "AMR"))</span></pre></body></html></div>
</div>
</div>
<div id="creation-of-data" class="section level1">
@ -301,24 +301,24 @@
<div id="put-everything-together" class="section level2">
<h2 class="hasAnchor">
<a href="#put-everything-together" class="anchor"></a>Put everything together</h2>
<p>Using the <code><a href="https://rdrr.io/r/base/sample.html">sample()</a></code> function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the <code>prob</code> parameter.</p>
<p>Using the <code><a href="https://dplyr.tidyverse.org/reference/sample.html">sample()</a></code> function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the <code>prob</code> parameter.</p>
<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">sample_size</span> <span class="kw">&lt;-</span> <span class="fl">20000</span>
<span class="no">data</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span>(<span class="kw">date</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="no">dates</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>),
<span class="kw">patient_id</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="no">patients</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>),
<span class="kw">hospital</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="no">hospitals</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="no">data</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span>(<span class="kw">date</span> <span class="kw">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="no">dates</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>),
<span class="kw">patient_id</span> <span class="kw">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="no">patients</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>),
<span class="kw">hospital</span> <span class="kw">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="no">hospitals</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">prob</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.30</span>, <span class="fl">0.35</span>, <span class="fl">0.15</span>, <span class="fl">0.20</span>)),
<span class="kw">bacteria</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="no">bacteria</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">bacteria</span> <span class="kw">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="no">bacteria</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">prob</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.50</span>, <span class="fl">0.25</span>, <span class="fl">0.15</span>, <span class="fl">0.10</span>)),
<span class="kw">AMX</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="no">ab_interpretations</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">AMX</span> <span class="kw">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="no">ab_interpretations</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">prob</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.60</span>, <span class="fl">0.05</span>, <span class="fl">0.35</span>)),
<span class="kw">AMC</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="no">ab_interpretations</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">AMC</span> <span class="kw">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="no">ab_interpretations</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">prob</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.75</span>, <span class="fl">0.10</span>, <span class="fl">0.15</span>)),
<span class="kw">CIP</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="no">ab_interpretations</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">CIP</span> <span class="kw">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="no">ab_interpretations</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">prob</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.80</span>, <span class="fl">0.00</span>, <span class="fl">0.20</span>)),
<span class="kw">GEN</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="no">ab_interpretations</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">GEN</span> <span class="kw">=</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="no">ab_interpretations</span>, <span class="kw">size</span> <span class="kw">=</span> <span class="no">sample_size</span>, <span class="kw">replace</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
<span class="kw">prob</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.92</span>, <span class="fl">0.00</span>, <span class="fl">0.08</span>)))</pre></body></html></div>
<p>Using the <code><a href="https://dplyr.tidyverse.org/reference/mutate-joins.html">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">data</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate-joins.html">left_join</a></span>(<span class="no">patients_table</span>)</pre></body></html></div>
<p>Using the <code><a href="https://dplyr.tidyverse.org/reference/join.html">left_join()</a></code> function from the <code>dplyr</code> package, we can ‘map’ the gender to the patient ID using the <code>patients_table</code> object we created earlier:</p>
<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">data</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/join.html">left_join</a></span>(<span class="no">patients_table</span>)</pre></body></html></div>
<p>The resulting data set contains 20,000 blood culture isolates. With the <code><a href="https://rdrr.io/r/utils/head.html">head()</a></code> function we can preview the first 6 rows of this data set:</p>
<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="no">data</span>)</pre></body></html></div>
<table class="table">
@ -335,64 +335,64 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2015-12-27</td>
<td align="center">Y9</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">2013-06-23</td>
<td align="center">V9</td>
<td align="center">Hospital C</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2014-01-17</td>
<td align="center">X10</td>
<td align="center">Hospital C</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">2016-12-25</td>
<td align="center">S10</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">F</td>
</tr>
<tr class="odd">
<td align="center">2015-10-31</td>
<td align="center">I3</td>
<td align="center">2011-01-27</td>
<td align="center">Z10</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2014-10-30</td>
<td align="center">D9</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2011-02-15</td>
<td align="center">P6</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2011-10-07</td>
<td align="center">G2</td>
<td align="center">Hospital C</td>
<td align="center">2010-09-14</td>
<td align="center">N1</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2017-10-31</td>
<td align="center">W1</td>
<td align="center">Hospital D</td>
<td align="center">Klebsiella pneumoniae</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2014-03-29</td>
<td align="center">L4</td>
<td align="center">Hospital B</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</td>
<td align="center">S</td>
@ -412,11 +412,12 @@
<p>For example, for the <code>gender</code> variable:</p>
<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="no">data</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(<span class="no">gender</span>) <span class="co"># this would be the same: freq(data$gender)</span></pre></body></html></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor (numeric)<br>
<p>Class: character<br>
Length: 20,000<br>
Levels: 2: F, M<br>
Available: 20,000 (100%, NA: 0 = 0%)<br>
Unique: 2</p>
<p>Shortest: 1<br>
Longest: 1</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
@ -430,16 +431,16 @@ Unique: 2</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">M</td>
<td align="right">10,277</td>
<td align="right">51.39%</td>
<td align="right">10,277</td>
<td align="right">51.39%</td>
<td align="right">10,293</td>
<td align="right">51.47%</td>
<td align="right">10,293</td>
<td align="right">51.47%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">F</td>
<td align="right">9,723</td>
<td align="right">48.62%</td>
<td align="right">9,707</td>
<td align="right">48.54%</td>
<td align="right">20,000</td>
<td align="right">100.00%</td>
</tr>
@ -476,10 +477,10 @@ Unique: 2</p>
<p>This <code>AMR</code> package includes this methodology with the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:</p>
<div class="sourceCode" id="cb15"><html><body><pre class="r"><span class="no">data</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="kw">first</span> <span class="kw">=</span> <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span>(<span class="no">.</span>))
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
<span class="co"># NOTE: Using column `patient_id` as input for `col_patient_id`.</span></pre></body></html></div>
<p>So only 28.3% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<span class="co"># [34mNOTE: Using column `[1mbacteria[22m` as input for `col_mo`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mdate[22m` as input for `col_date`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mpatient_id[22m` as input for `col_patient_id`.[39m</span></pre></body></html></div>
<p>So only 28.6% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb16"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="no">first</span> <span class="kw">==</span> <span class="fl">TRUE</span>)</pre></body></html></div>
<p>For future use, the above two syntaxes can be shortened with the <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
@ -489,7 +490,7 @@ Unique: 2</p>
<div id="first-weighted-isolates" class="section level2">
<h2 class="hasAnchor">
<a href="#first-weighted-isolates" class="anchor"></a>First <em>weighted</em> isolates</h2>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient R8, sorted on date:</p>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient N1, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -505,10 +506,10 @@ Unique: 2</p>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-03-01</td>
<td align="center">R8</td>
<td align="center">2010-03-16</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -516,8 +517,8 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-04-16</td>
<td align="center">R8</td>
<td align="center">2010-04-19</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -527,8 +528,8 @@ Unique: 2</p>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-04-18</td>
<td align="center">R8</td>
<td align="center">2010-06-26</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -538,10 +539,10 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-05-08</td>
<td align="center">R8</td>
<td align="center">2010-07-17</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -549,30 +550,30 @@ Unique: 2</p>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-06-06</td>
<td align="center">R8</td>
<td align="center">2010-09-14</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-06-14</td>
<td align="center">R8</td>
<td align="center">2010-09-23</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2010-07-28</td>
<td align="center">R8</td>
<td align="center">2010-12-14</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -582,19 +583,19 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2010-07-31</td>
<td align="center">R8</td>
<td align="center">2011-07-30</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2010-08-25</td>
<td align="center">R8</td>
<td align="center">2011-08-21</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -604,27 +605,27 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2010-09-30</td>
<td align="center">R8</td>
<td align="center">2011-09-09</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
</tbody>
</table>
<p>Only 1 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The <code><a href="../reference/key_antibiotics.html">key_antibiotics()</a></code> function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.</p>
<p>Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The <code><a href="../reference/key_antibiotics.html">key_antibiotics()</a></code> function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.</p>
<p>If a column exists with a name like ‘key(…)ab’ the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:</p>
<div class="sourceCode" id="cb18"><html><body><pre class="r"><span class="no">data</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="kw">keyab</span> <span class="kw">=</span> <span class="fu"><a href="../reference/key_antibiotics.html">key_antibiotics</a></span>(<span class="no">.</span>)) <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="kw">first_weighted</span> <span class="kw">=</span> <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span>(<span class="no">.</span>))
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
<span class="co"># NOTE: Using column `patient_id` as input for `col_patient_id`.</span>
<span class="co"># NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.</span></pre></body></html></div>
<span class="co"># [34mNOTE: Using column `[1mbacteria[22m` as input for `col_mo`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mbacteria[22m` as input for `col_mo`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mdate[22m` as input for `col_date`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mpatient_id[22m` as input for `col_patient_id`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mkeyab[22m` as input for `col_keyantibiotics`. Use [1mcol_keyantibiotics = FALSE[22m to prevent this.[39m</span></pre></body></html></div>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -641,10 +642,10 @@ Unique: 2</p>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-03-01</td>
<td align="center">R8</td>
<td align="center">2010-03-16</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -653,20 +654,20 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-04-16</td>
<td align="center">R8</td>
<td align="center">2010-04-19</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-04-18</td>
<td align="center">R8</td>
<td align="center">2010-06-26</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -677,35 +678,35 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-05-08</td>
<td align="center">R8</td>
<td align="center">2010-07-17</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-06-06</td>
<td align="center">R8</td>
<td align="center">2010-09-14</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-06-14</td>
<td align="center">R8</td>
<td align="center">2010-09-23</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
@ -713,32 +714,32 @@ Unique: 2</p>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2010-07-28</td>
<td align="center">R8</td>
<td align="center">2010-12-14</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2010-07-31</td>
<td align="center">R8</td>
<td align="center">2011-07-30</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2010-08-25</td>
<td align="center">R8</td>
<td align="center">2011-08-21</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -749,23 +750,23 @@ Unique: 2</p>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2010-09-30</td>
<td align="center">R8</td>
<td align="center">2011-09-09</td>
<td align="center">N1</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
</tbody>
</table>
<p>Instead of 1, now 7 isolates are flagged. In total, 75.2% of all isolates are marked ‘first weighted’ - 46.9% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>Instead of 2, now 4 isolates are flagged. In total, 75.4% of all isolates are marked ‘first weighted’ - 46.8% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>As with <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code>, there’s a shortcut for this new algorithm too:</p>
<div class="sourceCode" id="cb19"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="../reference/first_isolate.html">filter_first_weighted_isolate</a></span>()</pre></body></html></div>
<p>So we end up with 15,034 isolates for analysis.</p>
<p>So we end up with 15,085 isolates for analysis.</p>
<p>We can remove unneeded columns:</p>
<div class="sourceCode" id="cb20"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">&lt;-</span> <span class="no">data_1st</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(-<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">first</span>, <span class="no">keyab</span>))</pre></body></html></div>
@ -773,6 +774,7 @@ Unique: 2</p>
<div class="sourceCode" id="cb21"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="no">data_1st</span>)</pre></body></html></div>
<table class="table">
<thead><tr class="header">
<th></th>
<th align="center">date</th>
<th align="center">patient_id</th>
<th align="center">hospital</th>
@ -789,28 +791,14 @@ Unique: 2</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2015-12-27</td>
<td align="center">Y9</td>
<td align="center">Hospital A</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2014-01-17</td>
<td align="center">X10</td>
<td>1</td>
<td align="center">2013-06-23</td>
<td align="center">V9</td>
<td align="center">Hospital C</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-positive</td>
@ -818,43 +806,30 @@ Unique: 2</p>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>2</td>
<td align="center">2016-12-25</td>
<td align="center">S10</td>
<td align="center">Hospital D</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">2015-10-31</td>
<td align="center">I3</td>
<td>3</td>
<td align="center">2011-01-27</td>
<td align="center">Z10</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2014-10-30</td>
<td align="center">D9</td>
<td align="center">Hospital A</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">2011-02-15</td>
<td align="center">P6</td>
<td align="center">Hospital D</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
@ -864,18 +839,51 @@ Unique: 2</p>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2011-10-07</td>
<td align="center">G2</td>
<td align="center">Hospital C</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">S</td>
<td align="center">S</td>
<td>4</td>
<td align="center">2010-09-14</td>
<td align="center">N1</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>5</td>
<td align="center">2017-10-31</td>
<td align="center">W1</td>
<td align="center">Hospital D</td>
<td align="center">B_KLBSL_PNMN</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Klebsiella</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>7</td>
<td align="center">2010-03-02</td>
<td align="center">W2</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
</tbody>
@ -897,8 +905,8 @@ Unique: 2</p>
<div class="sourceCode" id="cb23"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(<span class="no">genus</span>, <span class="no">species</span>)</pre></body></html></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 15,034<br>
Available: 15,034 (100%, NA: 0 = 0%)<br>
Length: 15,085<br>
Available: 15,085 (100%, NA: 0 = 0%)<br>
Unique: 4</p>
<p>Shortest: 16<br>
Longest: 24</p>
@ -915,33 +923,33 @@ Longest: 24</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">7,486</td>
<td align="right">49.79%</td>
<td align="right">7,486</td>
<td align="right">49.79%</td>
<td align="right">7,418</td>
<td align="right">49.17%</td>
<td align="right">7,418</td>
<td align="right">49.17%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Staphylococcus aureus</td>
<td align="right">3,727</td>
<td align="right">24.79%</td>
<td align="right">11,213</td>
<td align="right">74.58%</td>
<td align="right">3,716</td>
<td align="right">24.63%</td>
<td align="right">11,134</td>
<td align="right">73.81%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">2,273</td>
<td align="right">15.12%</td>
<td align="right">13,486</td>
<td align="right">89.70%</td>
<td align="right">2,404</td>
<td align="right">15.94%</td>
<td align="right">13,538</td>
<td align="right">89.74%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">1,548</td>
<td align="right">10.30%</td>
<td align="right">15,034</td>
<td align="right">1,547</td>
<td align="right">10.26%</td>
<td align="right">15,085</td>
<td align="right">100.00%</td>
</tr>
</tbody>
@ -953,7 +961,7 @@ Longest: 24</p>
<p>The functions <code><a href="../reference/proportion.html">resistance()</a></code> and <code><a href="../reference/proportion.html">susceptibility()</a></code> can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions <code><a href="../reference/proportion.html">proportion_S()</a></code>, <code><a href="../reference/proportion.html">proportion_SI()</a></code>, <code><a href="../reference/proportion.html">proportion_I()</a></code>, <code><a href="../reference/proportion.html">proportion_IR()</a></code> and <code><a href="../reference/proportion.html">proportion_R()</a></code> can be used to determine the proportion of a specific antimicrobial outcome.</p>
<p>As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (<code><a href="../reference/proportion.html">proportion_R()</a></code>, equal to <code><a href="../reference/proportion.html">resistance()</a></code>) and susceptibility as the proportion of S and I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equal to <code><a href="../reference/proportion.html">susceptibility()</a></code>). These functions can be used on their own:</p>
<div class="sourceCode" id="cb24"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span>(<span class="no">AMX</span>)
<span class="co"># [1] 0.4703339</span></pre></body></html></div>
<span class="co"># [1] 0.4658933</span></pre></body></html></div>
<p>Or can be used in conjuction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb25"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(<span class="no">hospital</span>) <span class="kw">%&gt;%</span>
@ -966,19 +974,19 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4760956</td>
<td align="center">0.4701310</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4711409</td>
<td align="center">0.4702970</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4700967</td>
<td align="center">0.4575579</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4605220</td>
<td align="center">0.4574359</td>
</tr>
</tbody>
</table>
@ -996,23 +1004,23 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4760956</td>
<td align="center">4518</td>
<td align="center">0.4701310</td>
<td align="center">4503</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4711409</td>
<td align="center">5215</td>
<td align="center">0.4702970</td>
<td align="center">5454</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4700967</td>
<td align="center">2274</td>
<td align="center">0.4575579</td>
<td align="center">2203</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4605220</td>
<td align="center">3027</td>
<td align="center">0.4574359</td>
<td align="center">2925</td>
</tr>
</tbody>
</table>
@ -1032,27 +1040,27 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Escherichia</td>
<td align="center">0.9181138</td>
<td align="center">0.8947368</td>
<td align="center">0.9927865</td>
<td align="center">0.9207334</td>
<td align="center">0.8959288</td>
<td align="center">0.9936641</td>
</tr>
<tr class="even">
<td align="center">Klebsiella</td>
<td align="center">0.9153747</td>
<td align="center">0.8959948</td>
<td align="center">0.9948320</td>
<td align="center">0.9198449</td>
<td align="center">0.9004525</td>
<td align="center">0.9974144</td>
</tr>
<tr class="odd">
<td align="center">Staphylococcus</td>
<td align="center">0.9246042</td>
<td align="center">0.9203112</td>
<td align="center">0.9946338</td>
<td align="center">0.9203445</td>
<td align="center">0.9233046</td>
<td align="center">0.9938105</td>
</tr>
<tr class="even">
<td align="center">Streptococcus</td>
<td align="center">0.6159261</td>
<td align="center">0.6110649</td>
<td align="center">0.0000000</td>
<td align="center">0.6159261</td>
<td align="center">0.6110649</td>
</tr>
</tbody>
</table>
@ -1180,7 +1188,7 @@ Longest: 24</p>
</div>
<div class="pkgdown">
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.0.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
</div>
</footer>

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@ -39,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>
@ -186,7 +186,7 @@
<h1 data-toc-skip>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">15 April 2020</h4>
<h4 class="date">18 May 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/benchmarks.Rmd"><code>vignettes/benchmarks.Rmd</code></a></small>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -196,13 +196,14 @@
<p>One of the most important features of this package is the complete microbial taxonomic database, supplied by the <a href="http://catalogueoflife.org">Catalogue of Life</a>. We created a function <code><a href="../reference/as.mo.html">as.mo()</a></code> that transforms any user input value to a valid microbial ID by using intelligent rules combined with the taxonomic tree of Catalogue of Life.</p>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark()</a></code> runs different input expressions independently of each other and measures their time-to-result.</p>
<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">microbenchmark</span>)
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">AMR</span>)</pre></body></html></div>
<p>Using the <code>microbenchmark</code> package, we can review the calculation performance of this function. Its function <code>microbenchmark()</code> runs different input expressions independently of each other and measures their time-to-result.</p>
<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="no">microbenchmark</span> <span class="kw">&lt;-</span> <span class="kw pkg">microbenchmark</span><span class="kw ns">::</span><span class="no"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">AMR</span>)
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">dplyr</span>)</pre></body></html></div>
<p>In the next test, we try to ‘coerce’ different input values into the microbial code of <em>Staphylococcus aureus</em>. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.</p>
<p>The actual result is the same every time: it returns its microorganism code <code>B_STPHY_AURS</code> (<em>B</em> stands for <em>Bacteria</em>, the taxonomic kingdom).</p>
<p>But the calculation time differs a lot:</p>
<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">S.aureus</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(
<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">S.aureus</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"sau"</span>), <span class="co"># WHONET code</span>
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"stau"</span>),
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"STAU"</span>),
@ -220,25 +221,40 @@
<span class="kw">times</span> <span class="kw">=</span> <span class="fl">10</span>)
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">S.aureus</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">2</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 10.0 11.0 11 11.0 11.0 11 10</span>
<span class="co"># as.mo("stau") 39.0 43.0 54 44.0 70.0 72 10</span>
<span class="co"># as.mo("STAU") 43.0 44.0 52 47.0 68.0 71 10</span>
<span class="co"># as.mo("staaur") 9.6 9.8 11 11.0 12.0 13 10</span>
<span class="co"># as.mo("STAAUR") 9.6 11.0 17 11.0 11.0 45 10</span>
<span class="co"># as.mo("S. aureus") 15.0 17.0 26 18.0 43.0 50 10</span>
<span class="co"># as.mo("S aureus") 16.0 17.0 23 18.0 21.0 44 10</span>
<span class="co"># as.mo("Staphylococcus aureus") 6.0 6.0 12 6.9 7.2 40 10</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 660.0 740.0 780 780.0 810.0 870 10</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 370.0 430.0 470 450.0 460.0 760 10</span>
<span class="co"># as.mo("MRSA") 9.5 9.6 13 11.0 11.0 35 10</span>
<span class="co"># as.mo("VISA") 25.0 26.0 38 30.0 50.0 58 10</span>
<span class="co"># as.mo("VRSA") 25.0 29.0 55 55.0 56.0 150 10</span>
<span class="co"># as.mo(22242419) 130.0 150.0 160 160.0 170.0 170 10</span></pre></body></html></div>
<span class="co"># expr min lq mean median uq max</span>
<span class="co"># as.mo("sau") 7.5 9.0 21.0 11.0 37.0 43.0</span>
<span class="co"># as.mo("stau") 130.0 130.0 170.0 170.0 180.0 280.0</span>
<span class="co"># as.mo("STAU") 130.0 140.0 150.0 150.0 170.0 180.0</span>
<span class="co"># as.mo("staaur") 7.3 9.7 13.0 11.0 11.0 37.0</span>
<span class="co"># as.mo("STAAUR") 7.3 9.2 13.0 9.8 10.0 45.0</span>
<span class="co"># as.mo("S. aureus") 10.0 11.0 23.0 12.0 12.0 120.0</span>
<span class="co"># as.mo("S aureus") 9.9 12.0 23.0 13.0 40.0 43.0</span>
<span class="co"># as.mo("Staphylococcus aureus") 5.7 6.4 7.4 7.2 8.8 9.5</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 850.0 860.0 870.0 870.0 880.0 910.0</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 330.0 350.0 360.0 360.0 370.0 390.0</span>
<span class="co"># as.mo("MRSA") 7.7 9.2 12.0 9.7 11.0 39.0</span>
<span class="co"># as.mo("VISA") 19.0 21.0 25.0 22.0 24.0 55.0</span>
<span class="co"># as.mo("VRSA") 18.0 22.0 30.0 25.0 26.0 57.0</span>
<span class="co"># as.mo(22242419) 140.0 150.0 170.0 150.0 190.0 210.0</span>
<span class="co"># neval</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span></pre></body></html></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="562.5"></p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">M.semesiae</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"metsem"</span>),
<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">M.semesiae</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"metsem"</span>),
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"METSEM"</span>),
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"M. semesiae"</span>),
<span class="fu"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"M. semesiae"</span>),
@ -246,37 +262,37 @@
<span class="kw">times</span> <span class="kw">=</span> <span class="fl">10</span>)
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">M.semesiae</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">4</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq</span>
<span class="co"># as.mo("metsem") 1629.000 1648.000 1694.000 1681.000 1734.000</span>
<span class="co"># as.mo("METSEM") 1577.000 1604.000 1675.000 1685.000 1700.000</span>
<span class="co"># as.mo("M. semesiae") 17.390 17.470 26.150 18.020 43.560</span>
<span class="co"># as.mo("M. semesiae") 17.390 17.880 27.570 18.310 45.910</span>
<span class="co"># as.mo("Methanosarcina semesiae") 6.988 7.067 7.443 7.262 7.733</span>
<span class="co"># max neval</span>
<span class="co"># 1825.000 10</span>
<span class="co"># 1793.000 10</span>
<span class="co"># 48.390 10</span>
<span class="co"># 50.960 10</span>
<span class="co"># 8.381 10</span></pre></body></html></div>
<p>That takes 5.6 times as much time on average. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<span class="co"># expr min lq mean median uq</span>
<span class="co"># as.mo("metsem") 146.500 150.900 173.300 183.300 191.800</span>
<span class="co"># as.mo("METSEM") 145.700 153.400 169.300 172.300 185.300</span>
<span class="co"># as.mo("M. semesiae") 8.586 8.790 9.909 10.140 10.250</span>
<span class="co"># as.mo("M. semesiae") 8.613 8.719 12.350 9.756 10.210</span>
<span class="co"># as.mo("Methanosarcina semesiae") 6.153 6.357 9.325 6.729 7.826</span>
<span class="co"># max neval</span>
<span class="co"># 193.60 10</span>
<span class="co"># 187.40 10</span>
<span class="co"># 12.52 10</span>
<span class="co"># 38.46 10</span>
<span class="co"># 31.48 10</span></pre></body></html></div>
<p>Looking up arbitrary codes of less prevalent microorganisms costs the most time. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-6-1.png" width="900"></p>
<p>Uncommon microorganisms take a lot more time than common microorganisms. To relieve this pitfall and further improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
<p>Uncommon microorganisms take some more time than common microorganisms. To further improve performance, two important calculations take almost no time at all: <strong>repetitive results</strong> and <strong>already precalculated results</strong>.</p>
<div id="repetitive-results" class="section level3">
<h3 class="hasAnchor">
<a href="#repetitive-results" class="anchor"></a>Repetitive results</h3>
<p>Repetitive results are unique values that are present more than once. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. We will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">dplyr</span>)
<span class="co"># take all MO codes from the example_isolates data set</span>
<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">dplyr</span>)</pre></body></html></div>
<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="co"># take all MO codes from the example_isolates data set</span>
<span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">example_isolates</span>$<span class="no">mo</span> <span class="kw">%&gt;%</span>
<span class="co"># keep only the unique ones</span>
<span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span>() <span class="kw">%&gt;%</span>
<span class="co"># pick 50 of them at random</span>
<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>(<span class="fl">50</span>) <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>(<span class="fl">50</span>) <span class="kw">%&gt;%</span>
<span class="co"># paste that 10,000 times</span>
<span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span>(<span class="fl">10000</span>) <span class="kw">%&gt;%</span>
<span class="co"># scramble it</span>
<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span>()
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/sample.html">sample</a></span>()
<span class="co"># got indeed 50 times 10,000 = half a million?</span>
<span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">x</span>)
@ -287,30 +303,30 @@
<span class="co"># [1] 50</span>
<span class="co"># now let's see:</span>
<span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="no">x</span>),
<span class="kw">times</span> <span class="kw">=</span> <span class="fl">100</span>)
<span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="no">x</span>),
<span class="kw">times</span> <span class="kw">=</span> <span class="fl">10</span>)
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">run_it</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">3</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># mo_name(x) 1730 1790 1830 1810 1860 2030 100</span></pre></body></html></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 1.81 seconds (1814 ms). You only lose time on your unique input values.</p>
<span class="co"># mo_name(x) 1660 1700 1760 1750 1810 1910 10</span></pre></body></html></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 1.75 seconds. You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
<a href="#precalculated-results" class="anchor"></a>Precalculated results</h3>
<p>What about precalculated results? If the input is an already precalculated result of a helper function like <code><a href="../reference/mo_property.html">mo_name()</a></code>, it almost doesn’t take any time at all (see ‘C’ below):</p>
<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="kw">A</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"B_STPHY_AURS"</span>),
<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="kw">A</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"B_STPHY_AURS"</span>),
<span class="kw">B</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"S. aureus"</span>),
<span class="kw">C</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"Staphylococcus aureus"</span>),
<span class="kw">times</span> <span class="kw">=</span> <span class="fl">10</span>)
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">run_it</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">3</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 7.26 7.390 8.020 7.450 7.500 11.100 10</span>
<span class="co"># B 15.30 15.700 20.500 15.800 17.000 56.300 10</span>
<span class="co"># C 0.58 0.718 0.735 0.726 0.747 0.927 10</span></pre></body></html></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0007 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="kw">A</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 5.650 6.030 6.36 6.430 6.660 7.130 10</span>
<span class="co"># B 9.860 9.890 14.20 10.900 11.200 46.300 10</span>
<span class="co"># C 0.232 0.237 0.30 0.302 0.355 0.369 10</span></pre></body></html></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0003 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="kw">A</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),
<span class="kw">B</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),
<span class="kw">C</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"Staphylococcus aureus"</span>),
<span class="kw">D</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_family</a></span>(<span class="st">"Staphylococcaceae"</span>),
@ -322,21 +338,21 @@
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">run_it</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">3</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 0.380 0.386 0.397 0.394 0.397 0.434 10</span>
<span class="co"># B 0.413 0.417 0.444 0.424 0.430 0.627 10</span>
<span class="co"># C 0.574 0.680 0.707 0.730 0.735 0.776 10</span>
<span class="co"># D 0.407 0.419 0.430 0.430 0.435 0.470 10</span>
<span class="co"># E 0.374 0.381 0.401 0.397 0.424 0.438 10</span>
<span class="co"># F 0.366 0.380 0.389 0.387 0.395 0.422 10</span>
<span class="co"># G 0.367 0.377 0.388 0.383 0.392 0.420 10</span>
<span class="co"># H 0.368 0.374 0.386 0.382 0.390 0.430 10</span></pre></body></html></div>
<span class="co"># A 0.209 0.214 0.229 0.221 0.229 0.299 10</span>
<span class="co"># B 0.199 0.206 0.225 0.209 0.214 0.373 10</span>
<span class="co"># C 0.201 0.207 0.217 0.213 0.222 0.247 10</span>
<span class="co"># D 0.200 0.203 0.214 0.206 0.222 0.266 10</span>
<span class="co"># E 0.200 0.200 0.213 0.209 0.216 0.264 10</span>
<span class="co"># F 0.195 0.205 0.216 0.207 0.217 0.284 10</span>
<span class="co"># G 0.191 0.194 0.206 0.203 0.206 0.261 10</span>
<span class="co"># H 0.190 0.195 0.205 0.198 0.209 0.256 10</span></pre></body></html></div>
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
<h3 class="hasAnchor">
<a href="#results-in-other-languages" class="anchor"></a>Results in other languages</h3>
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost does’t take extra time:</p>
<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"en"</span>) <span class="co"># or just mo_name("CoNS") on an English system</span>
<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"en"</span>) <span class="co"># or just mo_name("CoNS") on an English system</span>
<span class="co"># [1] "Coagulase-negative Staphylococcus (CoNS)"</span>
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"es"</span>) <span class="co"># or just mo_name("CoNS") on a Spanish system</span>
@ -345,7 +361,7 @@
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"nl"</span>) <span class="co"># or just mo_name("CoNS") on a Dutch system</span>
<span class="co"># [1] "Coagulase-negatieve Staphylococcus (CNS)"</span>
<span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span>(<span class="kw">en</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"en"</span>),
<span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="kw">en</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"en"</span>),
<span class="kw">de</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"de"</span>),
<span class="kw">nl</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"nl"</span>),
<span class="kw">es</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"es"</span>),
@ -356,13 +372,13 @@
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">run_it</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">4</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 26.72 28.52 32.86 29.31 30.18 69.18 100</span>
<span class="co"># de 27.86 30.67 40.23 31.44 46.42 170.40 100</span>
<span class="co"># nl 34.45 36.94 44.89 37.95 40.54 76.41 100</span>
<span class="co"># es 27.89 30.51 38.46 31.33 32.89 170.10 100</span>
<span class="co"># it 29.15 30.37 35.85 31.04 32.39 65.33 100</span>
<span class="co"># fr 28.18 30.37 35.80 31.11 32.25 78.14 100</span>
<span class="co"># pt 28.23 30.55 36.89 31.32 32.77 67.79 100</span></pre></body></html></div>
<span class="co"># en 20.58 21.00 24.57 21.31 21.81 65.96 100</span>
<span class="co"># de 21.28 21.81 25.34 22.15 22.73 62.52 100</span>
<span class="co"># nl 25.15 25.68 32.14 26.03 27.25 167.40 100</span>
<span class="co"># es 21.35 21.78 27.39 22.14 23.29 67.97 100</span>
<span class="co"># it 21.42 21.83 26.19 22.31 22.84 71.71 100</span>
<span class="co"># fr 21.43 21.82 27.43 22.25 23.39 69.29 100</span>
<span class="co"># pt 21.42 21.92 28.71 22.23 22.89 187.40 100</span></pre></body></html></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>
@ -380,7 +396,7 @@
</div>
<div class="pkgdown">
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.0.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
</div>
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@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>

2
docs/authors.html
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@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>

2
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@ -43,7 +43,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>

12
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@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9006</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>
@ -229,13 +229,13 @@
<small>Source: <a href='https://gitlab.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
</div>
<div id="amr-1-1-0-9006" class="section level1">
<h1 class="page-header" data-toc-text="1.1.0.9006">
<a href="#amr-1-1-0-9006" class="anchor"></a>AMR 1.1.0.9006<small> Unreleased </small>
<div id="amr-1-1-0-9009" class="section level1">
<h1 class="page-header" data-toc-text="1.1.0.9009">
<a href="#amr-1-1-0-9009" class="anchor"></a>AMR 1.1.0.9009<small> Unreleased </small>
</h1>
<div id="last-updated-16-may-2020" class="section level2">
<div id="last-updated-18-may-2020" class="section level2">
<h2 class="hasAnchor">
<a href="#last-updated-16-may-2020" class="anchor"></a><small>Last updated: 16-May-2020</small>
<a href="#last-updated-18-may-2020" class="anchor"></a><small>Last updated: 18-May-2020</small>
</h2>
<div id="breaking" class="section level3">
<h3 class="hasAnchor">

2
docs/pkgdown.yml
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@ -10,7 +10,7 @@ articles:
WHONET: WHONET.html
benchmarks: benchmarks.html
resistance_predict: resistance_predict.html
last_built: 2020-05-17T09:54Z
last_built: 2020-05-18T08:29Z
urls:
reference: https://msberends.gitlab.io/AMR/reference
article: https://msberends.gitlab.io/AMR/articles

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@ -82,7 +82,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9004</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>
@ -362,17 +362,14 @@
<p><strong>WHY THIS IS SO IMPORTANT</strong> <br />
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode <a href='https://www.ncbi.nlm.nih.gov/pubmed/17304462'>(ref)</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <a href='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p>
<p>All isolates with a microbial ID of <code>NA</code> will be excluded as first isolate.</p>
<p>The functions <code>filter_first_isolate()</code> and <code>filter_first_weighted_isolate()</code> are helper functions to quickly filter on first isolates. The function <code>filter_first_isolate()</code> is essentially equal to:</p><pre> <span class='no'>x</span> <span class='kw'>%&amp;gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span>(<span class='kw'>only_firsts</span> <span class='kw'>=</span> <span class='fu'>first_isolate</span>(<span class='no'>x</span>, <span class='no'>...</span>)) <span class='kw'>%&amp;gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>only_firsts</span> <span class='kw'>==</span> <span class='fl'>TRUE</span>) <span class='kw'>%&amp;gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/select.html'>select</a></span>(-<span class='no'>only_firsts</span>)</pre>
<p>The functions <code>filter_first_isolate()</code> and <code>filter_first_weighted_isolate()</code> are helper functions to quickly filter on first isolates. The function <code>filter_first_isolate()</code> is essentially equal to one of:</p><pre> <span class='no'>x</span> <span class='kw'>%&amp;gt;%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='fu'>first_isolate</span>(<span class='no'>.</span>, <span class='no'>...</span>))</pre>
<p>The function <code>filter_first_weighted_isolate()</code> is essentially equal to:</p><pre> <span class='no'>x</span> <span class='kw'>%&amp;gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span>(<span class='kw'>keyab</span> <span class='kw'>=</span> <span class='fu'><a href='key_antibiotics.html'>key_antibiotics</a></span>(<span class='no'>.</span>)) <span class='kw'>%&amp;gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span>(<span class='kw'>only_weighted_firsts</span> <span class='kw'>=</span> <span class='fu'>first_isolate</span>(<span class='no'>x</span>,
<span class='kw'>col_keyantibiotics</span> <span class='kw'>=</span> <span class='st'>"keyab"</span>, <span class='no'>...</span>)) <span class='kw'>%&amp;gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>only_weighted_firsts</span> <span class='kw'>==</span> <span class='fl'>TRUE</span>) <span class='kw'>%&amp;gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/select.html'>select</a></span>(-<span class='no'>only_weighted_firsts</span>)</pre>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/select.html'>select</a></span>(-<span class='no'>only_weighted_firsts</span>, -<span class='no'>keyab</span>)</pre>
<h2 class="hasAnchor" id="key-antibiotics"><a class="anchor" href="#key-antibiotics"></a>Key antibiotics</h2>

2
docs/reference/index.html
View File

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9008</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9009</span>
</span>
</div>

7
man/first_isolate.Rd
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@ -98,10 +98,7 @@ To conduct an analysis of antimicrobial resistance, you should only include the
All isolates with a microbial ID of \code{NA} will be excluded as first isolate.
The functions \code{\link[=filter_first_isolate]{filter_first_isolate()}} and \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} are helper functions to quickly filter on first isolates. The function \code{\link[=filter_first_isolate]{filter_first_isolate()}} is essentially equal to:\preformatted{ x \%>\%
mutate(only_firsts = first_isolate(x, ...)) \%>\%
filter(only_firsts == TRUE) \%>\%
select(-only_firsts)
The functions \code{\link[=filter_first_isolate]{filter_first_isolate()}} and \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} are helper functions to quickly filter on first isolates. The function \code{\link[=filter_first_isolate]{filter_first_isolate()}} is essentially equal to one of:\preformatted{ x \%>\% filter(first_isolate(., ...))
}
The function \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} is essentially equal to:\preformatted{ x \%>\%
@ -109,7 +106,7 @@ The function \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_i
mutate(only_weighted_firsts = first_isolate(x,
col_keyantibiotics = "keyab", ...)) \%>\%
filter(only_weighted_firsts == TRUE) \%>\%
select(-only_weighted_firsts)
select(-only_weighted_firsts, -keyab)
}
}
\section{Key antibiotics}{

2
tests/appveyor/appveyor_tool.ps1
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@ -191,7 +191,7 @@ Function Bootstrap {
}
Progress "Downloading and installing travis-tool.sh"
cp "tests\appveyor\travis_tool.sh" "..\travis_tool.sh"
cp "tests\appveyor\travis_tool.sh" "..\travis-tool.sh"
# Invoke-WebRequest https://raw.githubusercontent.com/krlmlr/r-appveyor/master/r-travis/scripts/travis-tool.sh -OutFile "..\travis-tool.sh"
echo '@bash.exe ../travis-tool.sh %*' | Out-File -Encoding ASCII .\travis-tool.sh.cmd
cat .\travis-tool.sh.cmd

10
tests/testthat/test-first_isolate.R
View File

@ -188,5 +188,13 @@ test_that("first isolates work", {
test_unknown$mo <- ifelse(test_unknown$mo == "UNKNOWN", NA, test_unknown$mo)
expect_equal(sum(first_isolate(test_unknown)),
1062)
# shortcuts
expect_identical(filter_first_isolate(example_isolates),
subset(example_isolates, first_isolate(example_isolates)))
ex <- example_isolates
ex$keyab <- key_antibiotics(ex)
expect_identical(filter_first_weighted_isolate(example_isolates),
subset(example_isolates, first_isolate(ex)))
})

2
vignettes/AMR.Rmd
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@ -30,7 +30,7 @@ knitr::opts_chunk$set(
Conducting antimicrobial resistance analysis unfortunately requires in-depth knowledge from different scientific fields, which makes it hard to do right. At least, it requires:
* Good questions (always start with these!)
* Good questions (always start with those!)
* A thorough understanding of (clinical) epidemiology, to understand the clinical and epidemiological relevance and possible bias of results
* A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment, as well as understanding intrinsic and acquired microbial resistance
* Experience with data analysis with microbiological tests and their results, to understand the determination and limitations of MIC values and their interpretations to RSI values

1
vignettes/benchmarks.Rmd
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@ -54,6 +54,7 @@ ggplot.bm <- function(df, title = NULL) {
```{r, message = FALSE}
microbenchmark <- microbenchmark::microbenchmark
library(AMR)
library(dplyr)
```
In the next test, we try to 'coerce' different input values into the microbial code of *Staphylococcus aureus*. Coercion is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties.