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(v0.7.1.9030) eucast_rules() fix

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dr. M.S. (Matthijs) Berends 2019-08-08 15:52:07 +02:00
parent f67c739892
commit 22a206ffd8
70 changed files with 470 additions and 420 deletions
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26
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 0.7.1.9029
Date: 2019-08-07
Version: 0.7.1.9030
Date: 2019-08-08
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(
@ -15,11 +15,6 @@ Authors@R: c(
email = "c.f.luz@umcg.nl",
role = "aut",
comment = c(ORCID = "0000-0001-5809-5995")),
person(
given = c("Erwin", "E.", "A."),
family = "Hassing",
email = "e.hassing@certe.nl",
role = "ctb"),
person(
given = "Corinna",
family = "Glasner",
@ -37,7 +32,22 @@ Authors@R: c(
family = "Sinha",
email = "b.sinha@umcg.nl",
role = c("aut", "ths"),
comment = c(ORCID = "0000-0003-1634-0010")))
comment = c(ORCID = "0000-0003-1634-0010")),
person(
given = c("Erwin", "E.", "A."),
family = "Hassing",
email = "e.hassing@certe.nl",
role = "ctb"),
person(
given = c("Bart", "C."),
family = "Meijer",
email = "b.meijerg@certe.nl",
role = "ctb"),
person(
given = "Dennis",
family = "Souverein",
email = "d.souvereing@streeklabhaarlem.nl",
role = "ctb"))
Description: Functions to simplify the analysis and prediction of Antimicrobial
Resistance (AMR) and to work with microbial and antimicrobial properties by
using evidence-based methods.

6
NEWS.md
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@ -1,4 +1,4 @@
# AMR 0.7.1.9029
# AMR 0.7.1.9030
### Breaking
* Function `freq()` has moved to a new package, [`clean`](https://github.com/msberends/clean) ([CRAN link](https://cran.r-project.org/package=clean)). Creating frequency tables is actually not the scope of this package (never was) and this function has matured a lot over the last two years. Therefore, a new package was created for data cleaning and checking and it perfectly fits the `freq()` function. The [`clean`](https://github.com/msberends/clean) package is available on CRAN and will be installed automatically when updating the `AMR` package, that now imports it. In a later stage, the `skewness()` and `kurtosis()` functions will be moved to the `clean` package too.
@ -36,6 +36,7 @@
### Changed
* Added more informative errors and warnings to `eucast_rules()`
* Fixed a bug in `eucast_rules()` where antibiotic columns would be read as lists instead of characters
* Added tibble printing support for classes `rsi`, `mic`, `ab` and `mo`. When using tibbles containing antibiotic columns, values `S` will print in green, values `I` will print in yellow and values `R` will print in red:
```r
(run this on your own console, as this page does not support colour printing)
@ -63,6 +64,9 @@
* Speed improvement for `guess_ab_col()` which is now 30 times faster for antibiotic abbreviations
* Using factors as input for `eucast_rules()` now adds missing factors levels when the function changes antibiotic results
#### Other
* Added Dr Bart Meijer and Dr Dennis Souverein as contributors
# AMR 0.7.1
#### New

17
R/eucast_rules.R
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@ -416,10 +416,12 @@ eucast_rules <- function(x,
},
error = function(e) {
txt_error()
stop(paste0("Error in row(s) ", paste(rows[1:min(length(rows), 10)], collapse = ","),
'... while writing value "', to,
stop(paste0("In row(s) ", paste(rows[1:min(length(rows), 10)], collapse = ","),
ifelse(length(rows) > 10, "...", ""),
' while writing value "', to,
'" to column(s) `', paste(cols, collapse = "`, `"),
"` (data class:", paste(class(x_original), collapse = "/"), "):\n", e$message), call. = FALSE)
"` (data class: ", paste(class(x_original), collapse = "/"), "):\n", e$message),
call. = FALSE)
}
)
@ -520,6 +522,9 @@ eucast_rules <- function(x,
x <- trimws(unlist(strsplit(x, ",", fixed = TRUE)))
y <- character(0)
for (i in 1:length(x)) {
if (is.function(get(x[i]))) {
stop("Column ", x[i], " is also a function. Please create an issue on github.com/msberends/AMR/issues.")
}
y <- c(y, tryCatch(get(x[i]), error = function(e) ""))
}
y[y != "" & y %in% colnames(df)]
@ -736,7 +741,7 @@ eucast_rules <- function(x,
mutate(plural = ifelse(n > 1, "s", ""),
txt = paste0(formatnr(n), " test result", plural, " added as ", new)) %>%
pull(txt) %>%
paste(" *", ., collapse = "\n") %>%
paste(" -", ., collapse = "\n") %>%
cat()
}
@ -765,7 +770,7 @@ eucast_rules <- function(x,
mutate(plural = ifelse(n > 1, "s", ""),
txt = paste0(formatnr(n), " test result", plural, " changed from ", old, " to ", new)) %>%
pull(txt) %>%
paste(" *", ., collapse = "\n") %>%
paste(" -", ., collapse = "\n") %>%
cat()
cat("\n")
}
@ -774,7 +779,7 @@ eucast_rules <- function(x,
if (verbose == FALSE & nrow(verbose_info) > 0) {
cat(paste("\nUse", bold("eucast_rules(..., verbose = TRUE)"), "(on your original data) to get a data.frame with all specified edits instead.\n\n"))
} else if (verbose == TRUE) {
cat(paste(red("\nUsed 'Verbose mode' (verbose = TRUE)"), ", which returns a data.frame with all specified edits.\nUse", bold("verbose = FALSE"), "to apply the rules on your data.\n\n"))
cat(paste0("\nUsed 'Verbose mode' (", bold("verbose = TRUE"), "), which returns a data.frame with all specified edits.\nUse ", bold("verbose = FALSE"), " to apply the rules on your data.\n\n"))
}
}

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R/sysdata.rda
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2
data-raw/eucast_rules.tsv
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@ -126,7 +126,7 @@ genus_species is Providencia stuartii aminopenicillins, AMC, CZO, CXM, tetracy
genus is Raoultella aminopenicillins, TIC R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
genus_species is Serratia marcescens aminopenicillins, AMC, CZO, FOX, CXM, DOX, TCY, polymyxins, NIT R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
genus_species is Yersinia enterocolitica aminopenicillins, AMC, TIC, CZO, FOX R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
genus_species is Yersinia pseudotuberculosis poly, COL R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
genus_species is Yersinia pseudotuberculosis PLB, COL R Table 01: Intrinsic resistance in Enterobacteriaceae Expert Rules
genus one_of Achromobacter, Acinetobacter, Alcaligenes, Bordatella, Burkholderia, Elizabethkingia, Flavobacterium, Ochrobactrum, Pseudomonas, Stenotrophomonas PEN, FOX, CXM, glycopeptides, FUS, macrolides, LIN, streptogramins, RIF, DAP, LNZ R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
genus_species is Acinetobacter baumannii aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules
genus_species is Acinetobacter pittii aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY R Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria Expert Rules

Can't render this file because it contains an unexpected character in line 6 and column 94.

2
docs/LICENSE-text.html
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@ -78,7 +78,7 @@
</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">0.7.1.9029</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9030</span>
</span>
</div>

466
docs/articles/AMR.html
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@ -40,7 +40,7 @@
</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">0.7.1.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">07 August 2019</h4>
<h4 class="date">08 August 2019</h4>
<div class="hidden name"><code>AMR.Rmd</code></div>
@ -194,7 +194,7 @@
<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 07 August 2019.</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 08 August 2019.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
@ -210,21 +210,21 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2019-08-07</td>
<td align="center">2019-08-08</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">2019-08-07</td>
<td align="center">2019-08-08</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">2019-08-07</td>
<td align="center">2019-08-08</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -320,69 +320,69 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2017-01-02</td>
<td align="center">V8</td>
<td align="center">Hospital A</td>
<td align="center">2017-01-03</td>
<td align="center">T3</td>
<td align="center">Hospital D</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2012-11-05</td>
<td align="center">F9</td>
<td align="center">Hospital D</td>
<td align="center">Streptococcus pneumoniae</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">2011-12-06</td>
<td align="center">I5</td>
<td align="center">Hospital C</td>
<td align="center">Klebsiella pneumoniae</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2013-05-08</td>
<td align="center">W2</td>
<td align="center">Hospital B</td>
<td align="center">Staphylococcus aureus</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>
</tr>
<tr class="odd">
<td align="center">2015-08-15</td>
<td align="center">R1</td>
<td align="center">Hospital B</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2012-01-09</td>
<td align="center">X9</td>
<td align="center">Hospital D</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="odd">
<td align="center">2014-06-18</td>
<td align="center">Z7</td>
<td align="center">2011-03-10</td>
<td align="center">B9</td>
<td align="center">Hospital D</td>
<td align="center">Streptococcus 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">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2015-02-18</td>
<td align="center">F4</td>
<td align="center">Hospital A</td>
<tr class="odd">
<td align="center">2010-06-27</td>
<td align="center">A2</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">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2017-12-11</td>
<td align="center">F8</td>
<td align="center">Hospital A</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">R</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">M</td>
</tr>
</tbody>
@ -406,8 +406,8 @@
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------- -------- ----------- -------------
# 1 M 10,401 52.0% 10,401 52.0%
# 2 F 9,599 48.0% 20,000 100.0%</code></pre>
# 1 M 10,468 52.3% 10,468 52.3%
# 2 F 9,532 47.7% 20,000 100.0%</code></pre>
<p>So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values <code>M</code> and <code>F</code>. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.</p>
<p>The data is already quite clean, but we still need to transform some variables. The <code>bacteria</code> column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The <code><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate()</a></code> function of the <code>dplyr</code> package makes this really easy:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1">data &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span></a>
@ -437,14 +437,14 @@
<a class="sourceLine" id="cb15-18" data-line-number="18"><span class="co"># Pasteurella multocida (no values changed)</span></a>
<a class="sourceLine" id="cb15-19" data-line-number="19"><span class="co"># Staphylococcus (no values changed)</span></a>
<a class="sourceLine" id="cb15-20" data-line-number="20"><span class="co"># Streptococcus groups A, B, C, G (no values changed)</span></a>
<a class="sourceLine" id="cb15-21" data-line-number="21"><span class="co"># Streptococcus pneumoniae (1,456 values changed)</span></a>
<a class="sourceLine" id="cb15-21" data-line-number="21"><span class="co"># Streptococcus pneumoniae (1,480 values changed)</span></a>
<a class="sourceLine" id="cb15-22" data-line-number="22"><span class="co"># Viridans group streptococci (no values changed)</span></a>
<a class="sourceLine" id="cb15-23" data-line-number="23"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-24" data-line-number="24"><span class="co"># EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)</span></a>
<a class="sourceLine" id="cb15-25" data-line-number="25"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1,287 values changed)</span></a>
<a class="sourceLine" id="cb15-25" data-line-number="25"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1,293 values changed)</span></a>
<a class="sourceLine" id="cb15-26" data-line-number="26"><span class="co"># Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no values changed)</span></a>
<a class="sourceLine" id="cb15-27" data-line-number="27"><span class="co"># Table 03: Intrinsic resistance in other Gram-negative bacteria (no values changed)</span></a>
<a class="sourceLine" id="cb15-28" data-line-number="28"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2,759 values changed)</span></a>
<a class="sourceLine" id="cb15-28" data-line-number="28"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2,772 values changed)</span></a>
<a class="sourceLine" id="cb15-29" data-line-number="29"><span class="co"># Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no values changed)</span></a>
<a class="sourceLine" id="cb15-30" data-line-number="30"><span class="co"># Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no values changed)</span></a>
<a class="sourceLine" id="cb15-31" data-line-number="31"><span class="co"># Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no values changed)</span></a>
@ -452,24 +452,24 @@
<a class="sourceLine" id="cb15-33" data-line-number="33"><span class="co"># Table 13: Interpretive rules for quinolones (no values changed)</span></a>
<a class="sourceLine" id="cb15-34" data-line-number="34"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-35" data-line-number="35"><span class="co"># Other rules</span></a>
<a class="sourceLine" id="cb15-36" data-line-number="36"><span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,330 values changed)</span></a>
<a class="sourceLine" id="cb15-37" data-line-number="37"><span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (108 values changed)</span></a>
<a class="sourceLine" id="cb15-36" data-line-number="36"><span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2,251 values changed)</span></a>
<a class="sourceLine" id="cb15-37" data-line-number="37"><span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (117 values changed)</span></a>
<a class="sourceLine" id="cb15-38" data-line-number="38"><span class="co"># Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no values changed)</span></a>
<a class="sourceLine" id="cb15-39" data-line-number="39"><span class="co"># Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no values changed)</span></a>
<a class="sourceLine" id="cb15-40" data-line-number="40"><span class="co"># Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no values changed)</span></a>
<a class="sourceLine" id="cb15-41" data-line-number="41"><span class="co"># Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no values changed)</span></a>
<a class="sourceLine" id="cb15-42" data-line-number="42"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-43" data-line-number="43"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb15-44" data-line-number="44"><span class="co"># EUCAST rules affected 6,604 out of 20,000 rows, making a total of 7,940 edits</span></a>
<a class="sourceLine" id="cb15-44" data-line-number="44"><span class="co"># EUCAST rules affected 6,550 out of 20,000 rows, making a total of 7,913 edits</span></a>
<a class="sourceLine" id="cb15-45" data-line-number="45"><span class="co"># =&gt; added 0 test results</span></a>
<a class="sourceLine" id="cb15-46" data-line-number="46"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-47" data-line-number="47"><span class="co"># =&gt; changed 7,940 test results</span></a>
<a class="sourceLine" id="cb15-48" data-line-number="48"><span class="co"># * 110 test results changed from S to I</span></a>
<a class="sourceLine" id="cb15-49" data-line-number="49"><span class="co"># * 4,715 test results changed from S to R</span></a>
<a class="sourceLine" id="cb15-50" data-line-number="50"><span class="co"># * 1,119 test results changed from I to S</span></a>
<a class="sourceLine" id="cb15-51" data-line-number="51"><span class="co"># * 307 test results changed from I to R</span></a>
<a class="sourceLine" id="cb15-52" data-line-number="52"><span class="co"># * 1,664 test results changed from R to S</span></a>
<a class="sourceLine" id="cb15-53" data-line-number="53"><span class="co"># * 25 test results changed from R to I</span></a>
<a class="sourceLine" id="cb15-47" data-line-number="47"><span class="co"># =&gt; changed 7,913 test results</span></a>
<a class="sourceLine" id="cb15-48" data-line-number="48"><span class="co"># * 111 test results changed from S to I</span></a>
<a class="sourceLine" id="cb15-49" data-line-number="49"><span class="co"># * 4,763 test results changed from S to R</span></a>
<a class="sourceLine" id="cb15-50" data-line-number="50"><span class="co"># * 1,086 test results changed from I to S</span></a>
<a class="sourceLine" id="cb15-51" data-line-number="51"><span class="co"># * 349 test results changed from I to R</span></a>
<a class="sourceLine" id="cb15-52" data-line-number="52"><span class="co"># * 1,585 test results changed from R to S</span></a>
<a class="sourceLine" id="cb15-53" data-line-number="53"><span class="co"># * 19 test results changed from R to I</span></a>
<a class="sourceLine" id="cb15-54" data-line-number="54"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb15-55" data-line-number="55"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-56" data-line-number="56"><span class="co"># Use eucast_rules(..., verbose = TRUE) (on your original data) to get a data.frame with all specified edits instead.</span></a></code></pre></div>
@ -497,7 +497,7 @@
<a class="sourceLine" id="cb17-3" data-line-number="3"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `bacteria` as input for `col_mo`.</span></a>
<a class="sourceLine" id="cb17-4" data-line-number="4"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a>
<a class="sourceLine" id="cb17-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<a class="sourceLine" id="cb17-6" data-line-number="6"><span class="co"># =&gt; Found 5,676 first isolates (28.4% of total)</span></a></code></pre></div>
<a class="sourceLine" id="cb17-6" data-line-number="6"><span class="co"># =&gt; Found 5,610 first isolates (28.0% of total)</span></a></code></pre></div>
<p>So only 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="cb18"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb18-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb18-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(first <span class="op">==</span><span class="st"> </span><span class="ot">TRUE</span>)</a></code></pre></div>
@ -508,7 +508,7 @@
<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 M4, 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 W2, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -524,10 +524,10 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-04-02</td>
<td align="center">M4</td>
<td align="center">2010-02-15</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
@ -535,8 +535,8 @@
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-05-26</td>
<td align="center">M4</td>
<td align="center">2010-05-10</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -546,10 +546,10 @@
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-07-15</td>
<td align="center">M4</td>
<td align="center">2010-08-30</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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>
@ -557,8 +557,8 @@
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-09-11</td>
<td align="center">M4</td>
<td align="center">2010-09-27</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -568,32 +568,32 @@
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-11-13</td>
<td align="center">M4</td>
<td align="center">2010-10-28</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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">FALSE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-11-16</td>
<td align="center">M4</td>
<td align="center">2010-12-01</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-01-03</td>
<td align="center">M4</td>
<td align="center">2011-01-20</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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>
@ -601,30 +601,30 @@
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-03-16</td>
<td align="center">M4</td>
<td align="center">2011-02-09</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-04-21</td>
<td align="center">M4</td>
<td align="center">2011-03-08</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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">R</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-04-24</td>
<td align="center">M4</td>
<td align="center">2011-04-14</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -645,7 +645,7 @@
<a class="sourceLine" id="cb20-7" data-line-number="7"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `patient_id` as input for `col_patient_id`.</span></a>
<a class="sourceLine" id="cb20-8" data-line-number="8"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.</span></a>
<a class="sourceLine" id="cb20-9" data-line-number="9"><span class="co"># [Criterion] Inclusion based on key antibiotics, ignoring I.</span></a>
<a class="sourceLine" id="cb20-10" data-line-number="10"><span class="co"># =&gt; Found 14,973 first weighted isolates (74.9% of total)</span></a></code></pre></div>
<a class="sourceLine" id="cb20-10" data-line-number="10"><span class="co"># =&gt; Found 15,032 first weighted isolates (75.2% of total)</span></a></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -662,10 +662,10 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-04-02</td>
<td align="center">M4</td>
<td align="center">2010-02-15</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
@ -674,8 +674,8 @@
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-05-26</td>
<td align="center">M4</td>
<td align="center">2010-05-10</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -686,35 +686,35 @@
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-07-15</td>
<td align="center">M4</td>
<td align="center">2010-08-30</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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="even">
<td align="center">4</td>
<td align="center">2010-09-11</td>
<td align="center">M4</td>
<td align="center">2010-09-27</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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">2010-11-13</td>
<td align="center">M4</td>
<td align="center">2010-10-28</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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">FALSE</td>
@ -722,71 +722,71 @@
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-11-16</td>
<td align="center">M4</td>
<td align="center">2010-12-01</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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">S</td>
<td align="center">FALSE</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2011-01-03</td>
<td align="center">M4</td>
<td align="center">2011-01-20</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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>
<tr class="even">
<td align="center">8</td>
<td align="center">2011-03-16</td>
<td align="center">M4</td>
<td align="center">2011-02-09</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</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">2011-04-21</td>
<td align="center">M4</td>
<td align="center">2011-03-08</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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">R</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-04-24</td>
<td align="center">M4</td>
<td align="center">2011-04-14</td>
<td align="center">W2</td>
<td align="center">B_ESCHR_COL</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 2, now 7 isolates are flagged. In total, of all isolates are marked ‘first weighted’ - 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 8 isolates are flagged. In total, of all isolates are marked ‘first weighted’ - 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="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="../reference/first_isolate.html">filter_first_weighted_isolate</a></span>()</a></code></pre></div>
<p>So we end up with 14,973 isolates for analysis.</p>
<p>So we end up with 15,032 isolates for analysis.</p>
<p>We can remove unneeded columns:</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb22-1" data-line-number="1">data_1st &lt;-<span class="st"> </span>data_1st <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb22-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="op">-</span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(first, keyab))</a></code></pre></div>
@ -812,59 +812,43 @@
<tbody>
<tr class="odd">
<td>3</td>
<td align="center">2015-08-15</td>
<td align="center">R1</td>
<td align="center">Hospital B</td>
<td align="center">B_STRPT_PNE</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">2011-12-06</td>
<td align="center">I5</td>
<td align="center">Hospital C</td>
<td align="center">B_KLBSL_PNE</td>
<td align="center">R</td>
<td align="center">F</td>
<td align="center">Gram-positive</td>
<td align="center">Streptococcus</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">Klebsiella</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>4</td>
<td align="center">2012-01-09</td>
<td align="center">X9</td>
<td align="center">Hospital D</td>
<td align="center">B_STPHY_AUR</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>5</td>
<td align="center">2014-06-18</td>
<td align="center">Z7</td>
<td align="center">2011-03-10</td>
<td align="center">B9</td>
<td align="center">Hospital D</td>
<td align="center">B_STRPT_PNE</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">F</td>
<td align="center">R</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">Streptococcus</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>6</td>
<td align="center">2015-02-18</td>
<td align="center">F4</td>
<td align="center">Hospital A</td>
<tr class="odd">
<td>5</td>
<td align="center">2010-06-27</td>
<td align="center">A2</td>
<td align="center">Hospital D</td>
<td align="center">B_ESCHR_COL</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>
@ -874,31 +858,47 @@
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>6</td>
<td align="center">2017-12-11</td>
<td align="center">F8</td>
<td align="center">Hospital A</td>
<td align="center">B_STRPT_PNE</td>
<td align="center">R</td>
<td align="center">R</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">Streptococcus</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>7</td>
<td align="center">2010-11-14</td>
<td align="center">W5</td>
<td align="center">Hospital D</td>
<td align="center">B_STPHY_AUR</td>
<td align="center">S</td>
<td align="center">S</td>
<td>8</td>
<td align="center">2014-06-01</td>
<td align="center">W7</td>
<td align="center">Hospital C</td>
<td align="center">B_ESCHR_COL</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-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="even">
<td>8</td>
<td align="center">2010-12-25</td>
<td align="center">F4</td>
<td align="center">Hospital B</td>
<td>9</td>
<td align="center">2017-12-09</td>
<td align="center">A1</td>
<td align="center">Hospital C</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
@ -925,7 +925,7 @@
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/clean/topics/freq">freq</a></span>(genus, species)</a></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 14,973 (of which NA: 0 = 0.00%)<br>
Length: 15,032 (of which NA: 0 = 0.00%)<br>
Unique: 4</p>
<p>Shortest: 16<br>
Longest: 24</p>
@ -942,33 +942,33 @@ Longest: 24</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">7,385</td>
<td align="right">49.3%</td>
<td align="right">7,385</td>
<td align="right">49.3%</td>
<td align="right">7,421</td>
<td align="right">49.4%</td>
<td align="right">7,421</td>
<td align="right">49.4%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Staphylococcus aureus</td>
<td align="right">3,709</td>
<td align="right">24.8%</td>
<td align="right">11,094</td>
<td align="right">74.1%</td>
<td align="right">3,771</td>
<td align="right">25.1%</td>
<td align="right">11,192</td>
<td align="right">74.5%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">2,340</td>
<td align="right">15.6%</td>
<td align="right">13,434</td>
<td align="right">89.7%</td>
<td align="right">2,303</td>
<td align="right">15.3%</td>
<td align="right">13,495</td>
<td align="right">89.8%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">1,539</td>
<td align="right">10.3%</td>
<td align="right">14,973</td>
<td align="right">1,537</td>
<td align="right">10.2%</td>
<td align="right">15,032</td>
<td align="right">100.0%</td>
</tr>
</tbody>
@ -979,7 +979,7 @@ Longest: 24</p>
<a href="#resistance-percentages" class="anchor"></a>Resistance percentages</h2>
<p>The functions <code><a href="../reference/portion.html">portion_S()</a></code>, <code><a href="../reference/portion.html">portion_SI()</a></code>, <code><a href="../reference/portion.html">portion_I()</a></code>, <code><a href="../reference/portion.html">portion_IR()</a></code> and <code><a href="../reference/portion.html">portion_R()</a></code> can be used to determine the portion of a specific antimicrobial outcome. As per the EUCAST guideline of 2019, we calculate resistance as the portion of R (<code><a href="../reference/portion.html">portion_R()</a></code>) and susceptibility as the portion of S and I (<code><a href="../reference/portion.html">portion_SI()</a></code>). These functions can be used on their own:</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/portion.html">portion_R</a></span>(AMX)</a>
<a class="sourceLine" id="cb26-2" data-line-number="2"><span class="co"># [1] 0.4646363</span></a></code></pre></div>
<a class="sourceLine" id="cb26-2" data-line-number="2"><span class="co"># [1] 0.4640766</span></a></code></pre></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="cb27"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb27-1" data-line-number="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb27-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(hospital) <span class="op">%&gt;%</span><span class="st"> </span></a>
@ -992,19 +992,19 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4721851</td>
<td align="center">0.4620994</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4554570</td>
<td align="center">0.4646182</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4683715</td>
<td align="center">0.4689655</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4663838</td>
<td align="center">0.4625253</td>
</tr>
</tbody>
</table>
@ -1022,23 +1022,23 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4721851</td>
<td align="center">4494</td>
<td align="center">0.4620994</td>
<td align="center">4525</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4554570</td>
<td align="center">5186</td>
<td align="center">0.4646182</td>
<td align="center">5370</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4683715</td>
<td align="center">2229</td>
<td align="center">0.4689655</td>
<td align="center">2175</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4663838</td>
<td align="center">3064</td>
<td align="center">0.4625253</td>
<td align="center">2962</td>
</tr>
</tbody>
</table>
@ -1058,27 +1058,27 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Escherichia</td>
<td align="center">0.9243060</td>
<td align="center">0.8958700</td>
<td align="center">0.9932295</td>
<td align="center">0.9217087</td>
<td align="center">0.8948929</td>
<td align="center">0.9932624</td>
</tr>
<tr class="even">
<td align="center">Klebsiella</td>
<td align="center">0.8401559</td>
<td align="center">0.8992853</td>
<td align="center">0.9837557</td>
<td align="center">0.8217306</td>
<td align="center">0.9050098</td>
<td align="center">0.9915420</td>
</tr>
<tr class="odd">
<td align="center">Staphylococcus</td>
<td align="center">0.9280129</td>
<td align="center">0.9263953</td>
<td align="center">0.9967646</td>
<td align="center">0.9254840</td>
<td align="center">0.9252188</td>
<td align="center">0.9952267</td>
</tr>
<tr class="even">
<td align="center">Streptococcus</td>
<td align="center">0.6303419</td>
<td align="center">0.6139818</td>
<td align="center">0.0000000</td>
<td align="center">0.6303419</td>
<td align="center">0.6139818</td>
</tr>
</tbody>
</table>

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@ -40,7 +40,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>How to apply EUCAST rules</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">29 July 2019</h4>
<h4 class="date">08 August 2019</h4>
<div class="hidden name"><code>EUCAST.Rmd</code></div>

44
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@ -40,7 +40,7 @@
</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">0.7.1.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>How to determine multi-drug resistance (MDR)</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">07 August 2019</h4>
<h4 class="date">08 August 2019</h4>
<div class="hidden name"><code>MDR.Rmd</code></div>
@ -228,19 +228,19 @@
<p>The data set looks like this now:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/head">head</a></span>(my_TB_data)</a>
<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co"># 1 R R S S I S</span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co"># 2 S S S R R R</span></a>
<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co"># 3 S S S S R R</span></a>
<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co"># 4 S S S R S R</span></a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co"># 5 S R R R R R</span></a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># 6 S S R S S I</span></a>
<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co"># 1 S S S S R S</span></a>
<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co"># 2 S R S R R S</span></a>
<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co"># 3 R R S S S S</span></a>
<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co"># 4 I R R S S S</span></a>
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co"># 5 R I R S R R</span></a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># 6 R S S S S R</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># kanamycin</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># 1 S</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># 2 R</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># 3 S</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># 4 S</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># 5 S</span></a>
<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co"># 6 S</span></a></code></pre></div>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># 3 R</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># 4 I</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># 5 R</span></a>
<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co"># 6 R</span></a></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">my_TB_data<span class="op">$</span>mdr &lt;-<span class="st"> </span><span class="kw"><a href="../reference/mdro.html">mdr_tb</a></span>(my_TB_data)</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co"># </span><span class="al">NOTE</span><span class="co">: No column found as input for `col_mo`, assuming all records contain Mycobacterium tuberculosis.</span></a>
@ -272,32 +272,32 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistance</td>
<td align="right">3271</td>
<td align="right">3270</td>
<td align="right">65.4%</td>
<td align="right">3271</td>
<td align="right">3270</td>
<td align="right">65.4%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">643</td>
<td align="right">645</td>
<td align="right">12.9%</td>
<td align="right">3914</td>
<td align="right">3915</td>
<td align="right">78.3%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multidrug resistance</td>
<td align="right">582</td>
<td align="right">11.6%</td>
<td align="right">4496</td>
<td align="right">89.9%</td>
<td align="right">593</td>
<td align="right">11.9%</td>
<td align="right">4508</td>
<td align="right">90.2%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistance</td>
<td align="right">287</td>
<td align="right">5.7%</td>
<td align="right">275</td>
<td align="right">5.5%</td>
<td align="right">4783</td>
<td align="right">95.7%</td>
</tr>

24
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@ -40,7 +40,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">29 July 2019</h4>
<h4 class="date">08 August 2019</h4>
<div class="hidden name"><code>SPSS.Rmd</code></div>
@ -211,29 +211,33 @@
</li>
<li>
<p><strong>R is extremely flexible.</strong></p>
<p>Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, gathering, grouping, summarising and drawing plots is endless - with SPSS, SAS or Stata you are bound to their algorithms and styles. They may be a bit flexible, but you can probably never create that very specific publication-ready plot without using other (paid) software.</p>
<p>Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, gathering, grouping and summarising data, or drawing plots, is endless - with SPSS, SAS or Stata you are bound to their algorithms and format styles. They may be a bit flexible, but you can probably never create that very specific publication-ready plot without using other (paid) software. If you sometimes write syntaxes in SPSS to run a complete analysis or to ‘automate’ some of your work, you could do this a lot less time in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS. Still, as working with any statistical package, you will have to have knowledge about what you are doing (statistically) and what you are willing to accomplish.</p>
</li>
<li>
<p><strong>R can be easily automated.</strong></p>
<p>Over the last years, <a href="https://rmarkdown.rstudio.com/">R Markdown</a> has really made an interesting development. With R Markdown, you can very easily produce reports, whether format has to be Word, PowerPoint, a website, a PDF document or just the raw data to Excel. It even allows the use of a reference file containing the layout style (e.g. fonts and colours) of your organisation. I use this a lot to generate weekly and monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.</p>
<p>Over the last years, <a href="https://rmarkdown.rstudio.com/">R Markdown</a> has really made an interesting development. With R Markdown, you can very easily produce reports, whether the format has to be Word, PowerPoint, a website, a PDF document or just the raw data to Excel. It even allows the use of a reference file containing the layout style (e.g. fonts and colours) of your organisation. I use this a lot to generate weekly and monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.</p>
<p>For an even more professional environment, you could create <a href="https://shiny.rstudio.com/">Shiny apps</a>: live manipulation of data using a custom made website. The webdesign knowledge needed (JavaScript, CSS, HTML) is almost <em>zero</em>.</p>
</li>
<li>
<p><strong>R has a huge community.</strong></p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, almost <a href="https://stackoverflow.com/questions/tagged/r?sort=votes">300,000 R-related questions</a> have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
<p>Many R users just ask questions on websites like <a href="https://stackoverflow.com">StackOverflow.com</a>, the largest online community for programmers. At the time of writing, more than <a href="https://stackoverflow.com/questions/tagged/r?sort=votes">300,000 R-related questions</a> have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.</p>
</li>
<li>
<p><strong>R understands any data type, including SPSS/SAS/Stata.</strong></p>
<p>And that’s not vice versa I’m afraid. You can import data from any source into R. From SPSS, SAS and Stata (<a href="https://haven.tidyverse.org/">link</a>), from Minitab, Epi Info and EpiData (<a href="https://cran.r-project.org/package=foreign">link</a>), from Excel (<a href="https://readxl.tidyverse.org/">link</a>), from flat files like CSV, TXT or TSV (<a href="https://readr.tidyverse.org/">link</a>), or directly from databases and datawarehouses from anywhere on the world (<a href="https://dbplyr.tidyverse.org/">link</a>). You can even scrape websites to download tables that are live on the internet (<a href="https://github.com/hadley/rvest">link</a>) or get the results of an API call (<a href="https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread">link</a>).</p>
<p>And that’s not vice versa I’m afraid. You can import data from any source into R. For example from SPSS, SAS and Stata (<a href="https://haven.tidyverse.org/">link</a>), from Minitab, Epi Info and EpiData (<a href="https://cran.r-project.org/package=foreign">link</a>), from Excel (<a href="https://readxl.tidyverse.org/">link</a>), from flat files like CSV, TXT or TSV (<a href="https://readr.tidyverse.org/">link</a>), or directly from databases and datawarehouses from anywhere on the world (<a href="https://dbplyr.tidyverse.org/">link</a>). You can even scrape websites to download tables that are live on the internet (<a href="https://github.com/hadley/rvest">link</a>) or get the results of an API call and transform it into data in only one command (<a href="https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread">link</a>).</p>
<p>And the best part - you can export from R to most data formats as well. So you can import an SPSS file, do your analysis neatly in R and export the resulting tables to Excel files for sharing.</p>
</li>
<li>
<p><strong>R is completely free and open-source.</strong></p>
<p>No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying <a href="https://www.ibm.com/products/spss-statistics/pricing">between USD 1,300 and USD 8,500</a> per computer <em>per year</em>. SAS Analytics Pro costs <a href="https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html">around USD 10,000</a> per computer. Stata also has a business model with subscription fees, varying <a href="https://www.stata.com/order/new/bus/single-user-licenses/dl/">between USD 600 and USD 1,200</a> per computer per year, but lower prices come with a limitation of the number of variables you can work with.</p>
<p>No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying <a href="https://www.ibm.com/products/spss-statistics/pricing">between USD 1,300 and USD 8,500</a> per user <em>per year</em>. SAS Analytics Pro costs <a href="https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html">around USD 10,000</a> per computer. Stata also has a business model with subscription fees, varying <a href="https://www.stata.com/order/new/bus/single-user-licenses/dl/">between USD 600 and USD 2,800</a> per computer per year, but lower prices come with a limitation of the number of variables you can work with. And still they do not offer the above benefits of R.</p>
<p>If you are working at a midsized or small company, you can save it tens of thousands of dollars by using R instead of e.g. SPSS - gaining even more functions and flexibility. And all R enthousiasts can do as much PR as they want (like I do here), because nobody is officially associated with or affiliated by R. It is really free.</p>
</li>
<li>
<p><strong>R is (nowadays) the preferred analysis software in academic papers.</strong></p>
<p>At present, R is among the world most powerful statistical languages, and it is generally very popular in science (Bollmann <em>et al.</em>, 2017). For all the above reasons, the number of references to R as an analysis method in academic papers <a href="https://r4stats.com/2014/08/20/r-passes-spss-in-scholarly-use-stata-growing-rapidly/">is rising continuously</a> and has even surpassed SPSS for academic use (Muenchen, 2014).</p>
<p>I believe that the thing with SPSS is, that it has always had a great user interface which is very easy to learn and use. Back when they developed it, they had very little competition, let alone from R. R didn’t even had a professional user interface until the last decade (called RStudio, see below). How people used R between the nineties and 2010 is almost completely incomparable to how R is being used now. The language itself <a href="https://www.tidyverse.org/packages/">has been restyled completely</a> by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else that could compete. But now in 2019, I don’t see any reason why SPSS would be of any better use than R.</p>
</li>
</ul>
<p>If you sometimes write syntaxes in SPSS to run a complete analysis or to ‘automate’ some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS. Still, as working with any statistical package, you will have to have knowledge about what you are doing (statistically) and what you are willing to accomplish.</p>
<p>To demonstrate the first point:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="co"># not all values are valid MIC values:</span></a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="../reference/as.mic.html">as.mic</a></span>(<span class="fl">0.125</span>)</a>
@ -251,10 +255,10 @@
<a class="sourceLine" id="cb1-14" data-line-number="14">klebsiella_test &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/data.frame">data.frame</a></span>(<span class="dt">mo =</span> <span class="st">"klebsiella"</span>, </a>
<a class="sourceLine" id="cb1-15" data-line-number="15"> <span class="dt">amox =</span> <span class="st">"S"</span>,</a>
<a class="sourceLine" id="cb1-16" data-line-number="16"> <span class="dt">stringsAsFactors =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb1-17" data-line-number="17">klebsiella_test</a>
<a class="sourceLine" id="cb1-17" data-line-number="17">klebsiella_test <span class="co"># (our original data)</span></a>
<a class="sourceLine" id="cb1-18" data-line-number="18"><span class="co"># mo amox</span></a>
<a class="sourceLine" id="cb1-19" data-line-number="19"><span class="co"># 1 klebsiella S</span></a>
<a class="sourceLine" id="cb1-20" data-line-number="20"><span class="kw"><a href="../reference/eucast_rules.html">eucast_rules</a></span>(klebsiella_test, <span class="dt">info =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb1-20" data-line-number="20"><span class="kw"><a href="../reference/eucast_rules.html">eucast_rules</a></span>(klebsiella_test, <span class="dt">info =</span> <span class="ot">FALSE</span>) <span class="co"># (the edited data by EUCAST rules)</span></a>
<a class="sourceLine" id="cb1-21" data-line-number="21"><span class="co"># mo amox</span></a>
<a class="sourceLine" id="cb1-22" data-line-number="22"><span class="co"># 1 klebsiella R</span></a>
<a class="sourceLine" id="cb1-23" data-line-number="23"></a>

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@ -40,7 +40,7 @@
</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">0.7.1.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>How to work with WHONET data</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">07 August 2019</h4>
<h4 class="date">08 August 2019</h4>
<div class="hidden name"><code>WHONET.Rmd</code></div>

76
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@ -40,7 +40,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">29 July 2019</h4>
<h4 class="date">08 August 2019</h4>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -210,14 +210,14 @@
<a class="sourceLine" id="cb2-8" data-line-number="8"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb2-9" data-line-number="9"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(S.aureus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb2-10" data-line-number="10"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb2-11" data-line-number="11"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-12" data-line-number="12"><span class="co"># as.mo("sau") 8.3 8.4 15.0 8.9 9.5 57.0 10</span></a>
<a class="sourceLine" id="cb2-13" data-line-number="13"><span class="co"># as.mo("stau") 30.0 31.0 42.0 32.0 48.0 110.0 10</span></a>
<a class="sourceLine" id="cb2-14" data-line-number="14"><span class="co"># as.mo("staaur") 8.3 8.4 10.0 8.5 8.9 25.0 10</span></a>
<a class="sourceLine" id="cb2-15" data-line-number="15"><span class="co"># as.mo("STAAUR") 8.1 8.4 10.0 8.4 9.1 24.0 10</span></a>
<a class="sourceLine" id="cb2-16" data-line-number="16"><span class="co"># as.mo("S. aureus") 23.0 23.0 29.0 24.0 38.0 45.0 10</span></a>
<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="co"># as.mo("S. aureus") 22.0 22.0 24.0 23.0 24.0 38.0 10</span></a>
<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># as.mo("Staphylococcus aureus") 3.8 3.9 4.1 4.1 4.2 4.6 10</span></a></code></pre></div>
<a class="sourceLine" id="cb2-11" data-line-number="11"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-12" data-line-number="12"><span class="co"># as.mo("sau") 8.4 8.5 12 9.1 10.0 27.0 10</span></a>
<a class="sourceLine" id="cb2-13" data-line-number="13"><span class="co"># as.mo("stau") 30.0 31.0 33 32.0 32.0 48.0 10</span></a>
<a class="sourceLine" id="cb2-14" data-line-number="14"><span class="co"># as.mo("staaur") 8.4 8.6 11 9.0 9.9 26.0 10</span></a>
<a class="sourceLine" id="cb2-15" data-line-number="15"><span class="co"># as.mo("STAAUR") 8.5 8.5 16 8.7 9.7 56.0 10</span></a>
<a class="sourceLine" id="cb2-16" data-line-number="16"><span class="co"># as.mo("S. aureus") 22.0 22.0 24 22.0 23.0 40.0 10</span></a>
<a class="sourceLine" id="cb2-17" data-line-number="17"><span class="co"># as.mo("S. aureus") 22.0 22.0 36 24.0 41.0 98.0 10</span></a>
<a class="sourceLine" id="cb2-18" data-line-number="18"><span class="co"># as.mo("Staphylococcus aureus") 3.9 4.0 4 4.0 4.1 4.3 10</span></a></code></pre></div>
<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. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Thermus islandicus</em> (<code>B_THERMS_ISL</code>), a bug probably never found before in humans:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1">T.islandicus &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="kw"><a href="../reference/as.mo.html">as.mo</a></span>(<span class="st">"theisl"</span>),</a>
@ -229,12 +229,12 @@
<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(T.islandicus, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("theisl") 250 260 260 260 270 290 10</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("THEISL") 250 260 270 260 270 320 10</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("T. islandicus") 120 120 130 130 140 150 10</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("T. islandicus") 120 130 140 140 140 140 10</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Thermus islandicus") 45 46 55 56 62 67 10</span></a></code></pre></div>
<p>That takes 8.8 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. 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>Thermus islandicus</em>) are almost fast - these are the most probable input from most data sets.</p>
<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co"># as.mo("theisl") 260 270 280 280 290 310 10</span></a>
<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co"># as.mo("THEISL") 260 270 290 280 290 380 10</span></a>
<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co"># as.mo("T. islandicus") 130 140 150 150 150 160 10</span></a>
<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co"># as.mo("T. islandicus") 130 140 140 140 150 160 10</span></a>
<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co"># as.mo("Thermus islandicus") 47 50 58 62 65 68 10</span></a></code></pre></div>
<p>That takes 9.4 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. 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>Thermus islandicus</em>) are almost fast - these 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>Thermus islandicus</em> (which is very uncommon):</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/par">par</a></span>(<span class="dt">mar =</span> <span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/c">c</a></span>(<span class="dv">5</span>, <span class="dv">16</span>, <span class="dv">4</span>, <span class="dv">2</span>)) <span class="co"># set more space for left margin text (16)</span></a>
<a class="sourceLine" id="cb4-2" data-line-number="2"></a>
@ -280,8 +280,8 @@
<a class="sourceLine" id="cb5-24" data-line-number="24"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb5-25" data-line-number="25"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb5-26" data-line-number="26"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb5-27" data-line-number="27"><span class="co"># mo_fullname(x) 586 611 623 619 638 671 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.62 seconds (618 ms). You only lose time on your unique input values.</p>
<a class="sourceLine" id="cb5-27" data-line-number="27"><span class="co"># mo_fullname(x) 625 649 665 666 677 724 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.67 seconds (666 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -293,10 +293,10 @@
<a class="sourceLine" id="cb6-4" data-line-number="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb6-5" data-line-number="5"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb6-6" data-line-number="6"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb6-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-8" data-line-number="8"><span class="co"># A 6.350 6.600 7.050 6.870 7.35 8.37 10</span></a>
<a class="sourceLine" id="cb6-9" data-line-number="9"><span class="co"># B 21.300 21.500 25.300 22.200 22.70 48.20 10</span></a>
<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="co"># C 0.624 0.753 0.804 0.783 0.87 1.01 10</span></a></code></pre></div>
<a class="sourceLine" id="cb6-7" data-line-number="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-8" data-line-number="8"><span class="co"># A 6.440 6.65 6.880 6.840 7.15 7.48 10</span></a>
<a class="sourceLine" id="cb6-9" data-line-number="9"><span class="co"># B 22.400 22.90 26.200 23.600 25.20 44.00 10</span></a>
<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="co"># C 0.762 0.81 0.848 0.818 0.87 1.10 10</span></a></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_fullname("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0008 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"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1">run_it &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/microbenchmark/topics/microbenchmark">microbenchmark</a></span>(<span class="dt">A =</span> <span class="kw"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),</a>
<a class="sourceLine" id="cb7-2" data-line-number="2"> <span class="dt">B =</span> <span class="kw"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),</a>
@ -310,14 +310,14 @@
<a class="sourceLine" id="cb7-10" data-line-number="10"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">3</span>)</a>
<a class="sourceLine" id="cb7-11" data-line-number="11"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb7-12" data-line-number="12"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb7-13" data-line-number="13"><span class="co"># A 0.436 0.454 0.460 0.460 0.462 0.491 10</span></a>
<a class="sourceLine" id="cb7-14" data-line-number="14"><span class="co"># B 0.472 0.480 0.496 0.488 0.513 0.542 10</span></a>
<a class="sourceLine" id="cb7-15" data-line-number="15"><span class="co"># C 0.657 0.672 0.757 0.750 0.797 0.952 10</span></a>
<a class="sourceLine" id="cb7-16" data-line-number="16"><span class="co"># D 0.478 0.495 0.500 0.499 0.503 0.540 10</span></a>
<a class="sourceLine" id="cb7-17" data-line-number="17"><span class="co"># E 0.436 0.446 0.456 0.448 0.455 0.507 10</span></a>
<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="co"># F 0.437 0.447 0.455 0.454 0.460 0.478 10</span></a>
<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># G 0.428 0.441 0.449 0.447 0.455 0.477 10</span></a>
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># H 0.438 0.445 0.456 0.451 0.472 0.477 10</span></a></code></pre></div>
<a class="sourceLine" id="cb7-13" data-line-number="13"><span class="co"># A 0.437 0.456 0.499 0.482 0.560 0.607 10</span></a>
<a class="sourceLine" id="cb7-14" data-line-number="14"><span class="co"># B 0.474 0.484 0.534 0.509 0.588 0.627 10</span></a>
<a class="sourceLine" id="cb7-15" data-line-number="15"><span class="co"># C 0.621 0.712 0.799 0.812 0.829 1.020 10</span></a>
<a class="sourceLine" id="cb7-16" data-line-number="16"><span class="co"># D 0.469 0.482 0.534 0.513 0.595 0.654 10</span></a>
<a class="sourceLine" id="cb7-17" data-line-number="17"><span class="co"># E 0.415 0.434 0.493 0.459 0.557 0.678 10</span></a>
<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="co"># F 0.458 0.523 0.538 0.546 0.554 0.601 10</span></a>
<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co"># G 0.416 0.438 0.484 0.450 0.563 0.621 10</span></a>
<a class="sourceLine" id="cb7-20" data-line-number="20"><span class="co"># H 0.420 0.434 0.491 0.448 0.577 0.620 10</span></a></code></pre></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> too, 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">
@ -343,14 +343,14 @@
<a class="sourceLine" id="cb8-17" data-line-number="17"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb8-18" data-line-number="18"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/print">print</a></span>(run_it, <span class="dt">unit =</span> <span class="st">"ms"</span>, <span class="dt">signif =</span> <span class="dv">4</span>)</a>
<a class="sourceLine" id="cb8-19" data-line-number="19"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb8-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb8-21" data-line-number="21"><span class="co"># en 17.21 17.67 18.20 18.40 18.62 18.81 10</span></a>
<a class="sourceLine" id="cb8-22" data-line-number="22"><span class="co"># de 18.76 19.01 21.59 19.45 19.82 41.96 10</span></a>
<a class="sourceLine" id="cb8-23" data-line-number="23"><span class="co"># nl 24.15 24.57 29.11 25.67 26.49 45.25 10</span></a>
<a class="sourceLine" id="cb8-24" data-line-number="24"><span class="co"># es 18.31 19.02 19.33 19.37 19.93 20.09 10</span></a>
<a class="sourceLine" id="cb8-25" data-line-number="25"><span class="co"># it 18.96 19.27 23.48 19.58 20.91 41.27 10</span></a>
<a class="sourceLine" id="cb8-26" data-line-number="26"><span class="co"># fr 18.33 18.80 19.46 19.27 19.97 21.10 10</span></a>
<a class="sourceLine" id="cb8-27" data-line-number="27"><span class="co"># pt 18.89 19.50 20.54 19.70 20.36 27.83 10</span></a></code></pre></div>
<a class="sourceLine" id="cb8-20" data-line-number="20"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb8-21" data-line-number="21"><span class="co"># en 17.02 17.26 17.89 17.85 18.50 18.84 10</span></a>
<a class="sourceLine" id="cb8-22" data-line-number="22"><span class="co"># de 18.28 18.65 22.91 18.84 19.67 41.64 10</span></a>
<a class="sourceLine" id="cb8-23" data-line-number="23"><span class="co"># nl 24.07 24.31 32.74 24.60 25.02 105.60 10</span></a>
<a class="sourceLine" id="cb8-24" data-line-number="24"><span class="co"># es 18.59 19.00 19.99 19.32 19.81 26.42 10</span></a>
<a class="sourceLine" id="cb8-25" data-line-number="25"><span class="co"># it 18.28 18.40 22.59 19.07 20.38 39.47 10</span></a>
<a class="sourceLine" id="cb8-26" data-line-number="26"><span class="co"># fr 18.34 18.70 21.48 19.37 20.83 34.67 10</span></a>
<a class="sourceLine" id="cb8-27" data-line-number="27"><span class="co"># pt 18.60 18.92 19.25 19.19 19.59 20.14 10</span></a></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
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</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">0.7.1.9029</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9030</span>
</span>
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@ -40,7 +40,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>
@ -185,7 +185,7 @@
<h1>How to predict antimicrobial resistance</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">29 July 2019</h4>
<h4 class="date">08 August 2019</h4>
<div class="hidden name"><code>resistance_predict.Rmd</code></div>
@ -212,15 +212,17 @@
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./AMR.html">other AMR analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
<p>It is basically as easy as:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">tbl =</span> septic_patients, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>)</a>
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">tbl =</span> septic_patients, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</a>
<a class="sourceLine" id="cb2-3" data-line-number="3"></a>
<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="co"># or:</span></a>
<a class="sourceLine" id="cb2-5" data-line-number="5">septic_patients <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-6" data-line-number="6"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>)</a>
<a class="sourceLine" id="cb2-7" data-line-number="7"></a>
<a class="sourceLine" id="cb2-8" data-line-number="8"><span class="co"># to bind it to object 'predict_TZP' for example:</span></a>
<a class="sourceLine" id="cb2-9" data-line-number="9">predict_TZP &lt;-<span class="st"> </span>septic_patients <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-10" data-line-number="10"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>)</a></code></pre></div>
<a class="sourceLine" id="cb2-6" data-line-number="6"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</a>
<a class="sourceLine" id="cb2-7" data-line-number="7"> model <span class="st">"binomial"</span>)</a>
<a class="sourceLine" id="cb2-8" data-line-number="8"></a>
<a class="sourceLine" id="cb2-9" data-line-number="9"><span class="co"># to bind it to object 'predict_TZP' for example:</span></a>
<a class="sourceLine" id="cb2-10" data-line-number="10">predict_TZP &lt;-<span class="st"> </span>septic_patients <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-11" data-line-number="11"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</a>
<a class="sourceLine" id="cb2-12" data-line-number="12"> <span class="dt">model =</span> <span class="st">"binomial"</span>)</a></code></pre></div>
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model will be printed unless using <code><a href="../reference/resistance_predict.html">resistance_predict(..., info = FALSE)</a></code>.</p>
<pre><code># NOTE: Using column `date` as input for `col_date`.
@ -296,12 +298,12 @@
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo, <span class="dt">language =</span> <span class="ot">NULL</span>) <span class="op">==</span><span class="st"> "Gram-positive"</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"VAN"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"VAN"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb8-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()</a>
<a class="sourceLine" id="cb8-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
<p>You can define the model with the <code>model</code> parameter. The default model is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
<p>You can define the model with the <code>model</code> parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
<p>Valid values are:</p>
<table class="table">
<colgroup>

18
docs/authors.html
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@ -78,7 +78,7 @@
</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">0.7.1.9029</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9030</span>
</span>
</div>
@ -233,10 +233,6 @@
<p><strong><a href='https://www.rug.nl/staff/c.f.luz/'>Christian F. Luz</a></strong>. Author. <a href='https://orcid.org/0000-0001-5809-5995' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID' height='16'></a>
</p>
</li>
<li>
<p><strong>Erwin E. A. Hassing</strong>. Contributor.
</p>
</li>
<li>
<p><strong><a href='https://www.rug.nl/staff/c.glasner/'>Corinna Glasner</a></strong>. Author, thesis advisor. <a href='https://orcid.org/0000-0003-1241-1328' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID' height='16'></a>
</p>
@ -249,6 +245,18 @@
<p><strong><a href='https://www.rug.nl/staff/b.sinha/'>Bhanu N. M. Sinha</a></strong>. Author, thesis advisor. <a href='https://orcid.org/0000-0003-1634-0010' target='orcid.widget'><img src='https://members.orcid.org/sites/default/files/vector_iD_icon.svg' class='orcid' alt='ORCID' height='16'></a>
</p>
</li>
<li>
<p><strong>Erwin E. A. Hassing</strong>. Contributor.
</p>
</li>
<li>
<p><strong>Bart C. Meijer</strong>. Contributor.
</p>
</li>
<li>
<p><strong>Dennis Souverein</strong>. Contributor.
</p>
</li>
</ul>
</div>

2
docs/extra.js
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@ -80,6 +80,8 @@ $( document ).ready(function() {
x = x.replace("Corinna", "Dr Corinna");
x = x.replace("Alex", "Prof Dr Alex");
x = x.replace("Bhanu", "Prof Dr Bhanu");
x = x.replace("Bart", "Dr Bart");
x = x.replace("Dennis", "Dr Dennis");
x = x.replace(/Author, thesis advisor/g, "Doctoral advisor");
x = x.replace(/Authors/g, "aut_plural");
x = x.replace(/Author, maintainer[.]?/g, "");

2
docs/index.html
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@ -42,7 +42,7 @@
</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">0.7.1.9029</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9030</span>
</span>
</div>

44
docs/news/index.html
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@ -78,7 +78,7 @@
</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">0.7.1.9029</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9030</span>
</span>
</div>
@ -225,9 +225,9 @@
</div>
<div id="amr-0-7-1-9029" class="section level1">
<div id="amr-0-7-1-9030" class="section level1">
<h1 class="page-header">
<a href="#amr-0-7-1-9029" class="anchor"></a>AMR 0.7.1.9029<small> Unreleased </small>
<a href="#amr-0-7-1-9030" class="anchor"></a>AMR 0.7.1.9030<small> Unreleased </small>
</h1>
<div id="breaking" class="section level3">
<h3 class="hasAnchor">
@ -275,6 +275,7 @@
<ul>
<li>Added more informative errors and warnings to <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code>
</li>
<li>Fixed a bug in <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> where antibiotic columns would be read as lists instead of characters</li>
<li>
<p>Added tibble printing support for classes <code>rsi</code>, <code>mic</code>, <code>ab</code> and <code>mo</code>. When using tibbles containing antibiotic columns, values <code>S</code> will print in green, values <code>I</code> will print in yellow and values <code>R</code> will print in red:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">(run this on your own console, as this page does not support colour printing)</a>
@ -307,6 +308,13 @@
<li>Speed improvement for <code><a href="../reference/guess_ab_col.html">guess_ab_col()</a></code> which is now 30 times faster for antibiotic abbreviations</li>
<li><p>Using factors as input for <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> now adds missing factors levels when the function changes antibiotic results</p></li>
</ul>
<div id="other" class="section level4">
<h4 class="hasAnchor">
<a href="#other" class="anchor"></a>Other</h4>
<ul>
<li>Added Dr Bart Meijer and Dr Dennis Souverein as contributors</li>
</ul>
</div>
</div>
</div>
<div id="amr-0-7-1" class="section level1">
@ -382,9 +390,9 @@
</li>
</ul>
</div>
<div id="other" class="section level4">
<div id="other-1" class="section level4">
<h4 class="hasAnchor">
<a href="#other" class="anchor"></a>Other</h4>
<a href="#other-1" class="anchor"></a>Other</h4>
<ul>
<li>Fixed a note thrown by CRAN tests</li>
</ul>
@ -478,9 +486,9 @@ Please <a href="https://gitlab.com/msberends/AMR/issues/new?issue%5Btitle%5D=EUC
<li><p>Fix for <code><a href="../reference/mo_property.html">mo_shortname()</a></code> where species would not be determined correctly</p></li>
</ul>
</div>
<div id="other-1" class="section level4">
<div id="other-2" class="section level4">
<h4 class="hasAnchor">
<a href="#other-1" class="anchor"></a>Other</h4>
<a href="#other-2" class="anchor"></a>Other</h4>
<ul>
<li>Support for R 3.6.0 and later by providing support for <a href="https://developer.r-project.org/Blog/public/2019/02/14/staged-install/index.html">staged install</a>
</li>
@ -726,9 +734,9 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<li>if using different lengths of pattern and x in <code>%like%</code>, it will now return the call</li>
</ul>
</div>
<div id="other-2" class="section level4">
<div id="other-3" class="section level4">
<h4 class="hasAnchor">
<a href="#other-2" class="anchor"></a>Other</h4>
<a href="#other-3" class="anchor"></a>Other</h4>
<ul>
<li>Updated licence text to emphasise GPL 2.0 and that this is an R package.</li>
</ul>
@ -851,9 +859,9 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<li><p>Percentages will now will rounded more logically (e.g. in <code>freq</code> function)</p></li>
</ul>
</div>
<div id="other-3" class="section level4">
<div id="other-4" class="section level4">
<h4 class="hasAnchor">
<a href="#other-3" class="anchor"></a>Other</h4>
<a href="#other-4" class="anchor"></a>Other</h4>
<ul>
<li>New dependency on package <code>crayon</code>, to support formatted text in the console</li>
<li>Dependency <code>tidyr</code> is now mandatory (went to <code>Import</code> field) since <code>portion_df</code> and <code>count_df</code> rely on it</li>
@ -988,9 +996,9 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
</li>
</ul>
</div>
<div id="other-4" class="section level4">
<div id="other-5" class="section level4">
<h4 class="hasAnchor">
<a href="#other-4" class="anchor"></a>Other</h4>
<a href="#other-5" class="anchor"></a>Other</h4>
<ul>
<li>More unit tests to ensure better integrity of functions</li>
</ul>
@ -1117,9 +1125,9 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<li>Other small fixes</li>
</ul>
</div>
<div id="other-5" class="section level4">
<div id="other-6" class="section level4">
<h4 class="hasAnchor">
<a href="#other-5" class="anchor"></a>Other</h4>
<a href="#other-6" class="anchor"></a>Other</h4>
<ul>
<li>Added integration tests (check if everything works as expected) for all releases of R 3.1 and higher
<ul>
@ -1179,9 +1187,9 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<li>Functions <code>as.rsi</code> and <code>as.mic</code> now add the package name and version as attributes</li>
</ul>
</div>
<div id="other-6" class="section level4">
<div id="other-7" class="section level4">
<h4 class="hasAnchor">
<a href="#other-6" class="anchor"></a>Other</h4>
<a href="#other-7" class="anchor"></a>Other</h4>
<ul>
<li>Expanded <code>README.md</code> with more examples</li>
<li>Added <a href="https://orcid.org">ORCID</a> of authors to DESCRIPTION file</li>
@ -1218,7 +1226,7 @@ Using <code><a href="../reference/as.mo.html">as.mo(..., allow_uncertain = 3)</a
<div id="tocnav">
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#amr-0-7-1-9029">0.7.1.9029</a></li>
<li><a href="#amr-0-7-1-9030">0.7.1.9030</a></li>
<li><a href="#amr-0-7-1">0.7.1</a></li>
<li><a href="#amr-0-7-0">0.7.0</a></li>
<li><a href="#amr-0-6-1">0.6.1</a></li>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
docs/reference/AMR.html
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</button>
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<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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
docs/reference/age.html
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
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2
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@ -80,7 +80,7 @@
</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">0.7.1.9027</span>
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</span>
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2
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
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2
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@ -80,7 +80,7 @@
</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">0.7.1.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
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2
docs/reference/as.mo.html
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9027</span>
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</span>
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2
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@ -80,7 +80,7 @@
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<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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
</div>

2
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@ -80,7 +80,7 @@
</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">0.7.1.9027</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9029</span>
</span>
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@ -81,7 +81,7 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
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@ -80,7 +80,7 @@
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2
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@ -80,7 +80,7 @@
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@ -81,7 +81,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
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@ -81,7 +81,7 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
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@ -81,7 +81,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr
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@ -80,6 +80,8 @@ $( document ).ready(function() {
x = x.replace("Corinna", "Dr Corinna");
x = x.replace("Alex", "Prof Dr Alex");
x = x.replace("Bhanu", "Prof Dr Bhanu");
x = x.replace("Bart", "Dr Bart");
x = x.replace("Dennis", "Dr Dennis");
x = x.replace(/Author, thesis advisor/g, "Doctoral advisor");
x = x.replace(/Authors/g, "aut_plural");
x = x.replace(/Author, maintainer[.]?/g, "");

19
vignettes/SPSS.Rmd
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@ -39,32 +39,35 @@ As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come with major
* **R is extremely flexible.**
Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, gathering, grouping, summarising and drawing plots is endless - with SPSS, SAS or Stata you are bound to their algorithms and styles. They may be a bit flexible, but you can probably never create that very specific publication-ready plot without using other (paid) software.
Because you write the syntax yourself, you can do anything you want. The flexibility in transforming, gathering, grouping and summarising data, or drawing plots, is endless - with SPSS, SAS or Stata you are bound to their algorithms and format styles. They may be a bit flexible, but you can probably never create that very specific publication-ready plot without using other (paid) software. If you sometimes write syntaxes in SPSS to run a complete analysis or to 'automate' some of your work, you could do this a lot less time in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS. Still, as working with any statistical package, you will have to have knowledge about what you are doing (statistically) and what you are willing to accomplish.
* **R can be easily automated.**
Over the last years, [R Markdown](https://rmarkdown.rstudio.com/) has really made an interesting development. With R Markdown, you can very easily produce reports, whether format has to be Word, PowerPoint, a website, a PDF document or just the raw data to Excel. It even allows the use of a reference file containing the layout style (e.g. fonts and colours) of your organisation. I use this a lot to generate weekly and monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.
Over the last years, [R Markdown](https://rmarkdown.rstudio.com/) has really made an interesting development. With R Markdown, you can very easily produce reports, whether the format has to be Word, PowerPoint, a website, a PDF document or just the raw data to Excel. It even allows the use of a reference file containing the layout style (e.g. fonts and colours) of your organisation. I use this a lot to generate weekly and monthly reports automatically. Just write the code once and enjoy the automatically updated reports at any interval you like.
For an even more professional environment, you could create [Shiny apps](https://shiny.rstudio.com/): live manipulation of data using a custom made website. The webdesign knowledge needed (JavaScript, CSS, HTML) is almost *zero*.
* **R has a huge community.**
Many R users just ask questions on websites like [StackOverflow.com](https://stackoverflow.com), the largest online community for programmers. At the time of writing, almost [300,000 R-related questions](https://stackoverflow.com/questions/tagged/r?sort=votes) have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
Many R users just ask questions on websites like [StackOverflow.com](https://stackoverflow.com), the largest online community for programmers. At the time of writing, more than [300,000 R-related questions](https://stackoverflow.com/questions/tagged/r?sort=votes) have already been asked on this platform (which covers questions and answers for any programming language). In my own experience, most questions are answered within a couple of minutes.
* **R understands any data type, including SPSS/SAS/Stata.**
And that's not vice versa I'm afraid. You can import data from any source into R. From SPSS, SAS and Stata ([link](https://haven.tidyverse.org/)), from Minitab, Epi Info and EpiData ([link](https://cran.r-project.org/package=foreign)), from Excel ([link](https://readxl.tidyverse.org/)), from flat files like CSV, TXT or TSV ([link](https://readr.tidyverse.org/)), or directly from databases and datawarehouses from anywhere on the world ([link](https://dbplyr.tidyverse.org/)). You can even scrape websites to download tables that are live on the internet ([link](https://github.com/hadley/rvest)) or get the results of an API call ([link](https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread)).
And that's not vice versa I'm afraid. You can import data from any source into R. For example from SPSS, SAS and Stata ([link](https://haven.tidyverse.org/)), from Minitab, Epi Info and EpiData ([link](https://cran.r-project.org/package=foreign)), from Excel ([link](https://readxl.tidyverse.org/)), from flat files like CSV, TXT or TSV ([link](https://readr.tidyverse.org/)), or directly from databases and datawarehouses from anywhere on the world ([link](https://dbplyr.tidyverse.org/)). You can even scrape websites to download tables that are live on the internet ([link](https://github.com/hadley/rvest)) or get the results of an API call and transform it into data in only one command ([link](https://github.com/Rdatatable/data.table/wiki/Convenience-features-of-fread)).
And the best part - you can export from R to most data formats as well. So you can import an SPSS file, do your analysis neatly in R and export the resulting tables to Excel files for sharing.
* **R is completely free and open-source.**
No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying [between USD 1,300 and USD 8,500](https://www.ibm.com/products/spss-statistics/pricing) per computer *per year*. SAS Analytics Pro costs [around USD 10,000](https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html) per computer. Stata also has a business model with subscription fees, varying [between USD 600 and USD 1,200](https://www.stata.com/order/new/bus/single-user-licenses/dl/) per computer per year, but lower prices come with a limitation of the number of variables you can work with.
No strings attached. It was created and is being maintained by volunteers who believe that (data) science should be open and publicly available to everybody. SPSS, SAS and Stata are quite expensive. IBM SPSS Staticstics only comes with subscriptions nowadays, varying [between USD 1,300 and USD 8,500](https://www.ibm.com/products/spss-statistics/pricing) per user *per year*. SAS Analytics Pro costs [around USD 10,000](https://www.sas.com/store/products-solutions/sas-analytics-pro/prodPERSANL.html) per computer. Stata also has a business model with subscription fees, varying [between USD 600 and USD 2,800](https://www.stata.com/order/new/bus/single-user-licenses/dl/) per computer per year, but lower prices come with a limitation of the number of variables you can work with. And still they do not offer the above benefits of R.
If you are working at a midsized or small company, you can save it tens of thousands of dollars by using R instead of e.g. SPSS - gaining even more functions and flexibility. And all R enthousiasts can do as much PR as they want (like I do here), because nobody is officially associated with or affiliated by R. It is really free.
* **R is (nowadays) the preferred analysis software in academic papers.**
If you sometimes write syntaxes in SPSS to run a complete analysis or to 'automate' some of your work, you should perhaps do this in R. You will notice that writing syntaxes in R is a lot more nifty and clever than in SPSS. Still, as working with any statistical package, you will have to have knowledge about what you are doing (statistically) and what you are willing to accomplish.
At present, R is among the world most powerful statistical languages, and it is generally very popular in science (Bollmann *et al.*, 2017). For all the above reasons, the number of references to R as an analysis method in academic papers [is rising continuously](https://r4stats.com/2014/08/20/r-passes-spss-in-scholarly-use-stata-growing-rapidly/) and has even surpassed SPSS for academic use (Muenchen, 2014).
I believe that the thing with SPSS is, that it has always had a great user interface which is very easy to learn and use. Back when they developed it, they had very little competition, let alone from R. R didn't even had a professional user interface until the last decade (called RStudio, see below). How people used R between the nineties and 2010 is almost completely incomparable to how R is being used now. The language itself [has been restyled completely](https://www.tidyverse.org/packages/) by volunteers who are dedicated professionals in the field of data science. SPSS was great when there was nothing else that could compete. But now in `r max(2019, as.integer(format(Sys.Date(), "%Y")))`, I don't see any reason why SPSS would be of any better use than R.
To demonstrate the first point:
@ -80,8 +83,8 @@ mo_gramstain("E. coli")
klebsiella_test <- data.frame(mo = "klebsiella",
amox = "S",
stringsAsFactors = FALSE)
klebsiella_test
eucast_rules(klebsiella_test, info = FALSE)
klebsiella_test # (our original data)
eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules)
# hundreds of trade names can be translated to a name, trade name or an ATC code:
ab_name("floxapen")

14
vignettes/resistance_predict.Rmd
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@ -42,15 +42,17 @@ Our package contains a function `resistance_predict()`, which takes the same inp
It is basically as easy as:
```{r, eval = FALSE}
# resistance prediction of piperacillin/tazobactam (TZP):
resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "TZP")
resistance_predict(tbl = septic_patients, col_date = "date", col_ab = "TZP", model = "binomial")
# or:
septic_patients %>%
resistance_predict(col_ab = "TZP")
resistance_predict(col_ab = "TZP",
model "binomial")
# to bind it to object 'predict_TZP' for example:
predict_TZP <- septic_patients %>%
resistance_predict(col_ab = "TZP")
resistance_predict(col_ab = "TZP",
model = "binomial")
```
The function will look for a date column itself if `col_date` is not set.
@ -59,7 +61,7 @@ When running any of these commands, a summary of the regression model will be pr
```{r, echo = FALSE}
predict_TZP <- septic_patients %>%
resistance_predict(col_ab = "TZP")
resistance_predict(col_ab = "TZP", model = "binomial")
```
This text is only a printed summary - the actual result (output) of the function is a `data.frame` containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:
@ -92,13 +94,13 @@ Resistance is not easily predicted; if we look at vancomycin resistance in Gram
```{r}
septic_patients %>%
filter(mo_gramstain(mo, language = NULL) == "Gram-positive") %>%
resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE) %>%
resistance_predict(col_ab = "VAN", year_min = 2010, info = FALSE, model = "binomial") %>%
ggplot_rsi_predict()
```
Vancomycin resistance could be 100% in ten years, but might also stay around 0%.
You can define the model with the `model` parameter. The default model is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.
You can define the model with the `model` parameter. The model chosen above is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.
Valid values are: