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dr. M.S. (Matthijs) Berends 2019-05-29 00:36:48 +02:00
parent 27380fa021
commit 62e6f41961
63 changed files with 414 additions and 482 deletions
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18
R/age.R
View File

@ -50,8 +50,8 @@ age <- function(x, reference = Sys.Date()) {
as.integer(years_gap))
if (any(ages < 0, na.rm = TRUE)) {
warning("NAs introduced for ages below 0.")
ages[ages < 0] <- NA_integer_
warning("NAs introduced for ages below 0.")
}
if (any(ages > 120, na.rm = TRUE)) {
warning("Some ages are > 120.")
@ -71,7 +71,7 @@ age <- function(x, reference = Sys.Date()) {
#' The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).}
#' \item{A character:}
#' \itemize{
#' \item{\code{"children"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.}
#' \item{\code{"children"} or \code{"kids"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.}
#' \item{\code{"elderly"} or \code{"seniors"}, equivalent of: \code{c(65, 75, 85)}. This will split on 0-64, 65-74, 75-84, 85+.}
#' \item{\code{"fives"}, equivalent of: \code{1:20 * 5}. This will split on 0-4, 5-9, 10-14, ..., 90-94, 95-99, 100+.}
#' \item{\code{"tens"}, equivalent of: \code{1:10 * 10}. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.}
@ -107,17 +107,15 @@ age <- function(x, reference = Sys.Date()) {
#' # resistance of ciprofloxacine per age group
#' library(dplyr)
#' septic_patients %>%
#' mutate(first_isolate = first_isolate(.)) %>%
#' filter(first_isolate == TRUE,
#' mo == as.mo("E. coli")) %>%
#' filter_first_isolate) %>%
#' filter(mo == as.mo("E. coli")) %>%
#' group_by(age_group = age_groups(age)) %>%
#' select(age_group,
#' CIP) %>%
#' select(age_group, CIP) %>%
#' ggplot_rsi(x = "age_group")
age_groups <- function(x, split_at = c(12, 25, 55, 75)) {
if (is.character(split_at)) {
split_at <- split_at[1L]
if (split_at %like% "^(child|kid)") {
if (split_at %like% "^(child|kid|junior)") {
split_at <- c(0, 1, 2, 4, 6, 13, 18)
} else if (split_at %like% "^(elder|senior)") {
split_at <- c(65, 75, 85)
@ -133,10 +131,12 @@ age_groups <- function(x, split_at = c(12, 25, 55, 75)) {
}
split_at <- sort(unique(split_at))
if (!split_at[1] == 0) {
# add base number 0
split_at <- c(0, split_at)
}
split_at <- split_at[!is.na(split_at)]
if (length(split_at) == 1) {
# only 0 available
# only 0 is available
stop("invalid value for `split_at`.")
}

444
docs/articles/AMR.html
View File

@ -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="Released version">0.6.1.9037</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9040</span>
</span>
</div>
@ -334,69 +334,69 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2016-12-11</td>
<td align="center">T9</td>
<td align="center">2010-08-30</td>
<td align="center">X2</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">Staphylococcus aureus</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>
</tr>
<tr class="even">
<td align="center">2013-02-14</td>
<td align="center">A3</td>
<td align="center">Hospital B</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2011-05-14</td>
<td align="center">W4</td>
<td align="center">Hospital D</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">F</td>
</tr>
<tr class="even">
<td align="center">2010-02-09</td>
<td align="center">G3</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2016-04-24</td>
<td align="center">B7</td>
<td align="center">2015-08-09</td>
<td align="center">E8</td>
<td align="center">Hospital A</td>
<td align="center">Escherichia coli</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2014-05-04</td>
<td align="center">M8</td>
<td align="center">Hospital D</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2010-02-20</td>
<td align="center">L7</td>
<td align="center">2013-05-31</td>
<td align="center">J10</td>
<td align="center">Hospital B</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2014-07-13</td>
<td align="center">H9</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">Streptococcus pneumoniae</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>
</tr>
<tr class="even">
<td align="center">2015-03-14</td>
<td align="center">I6</td>
<td align="center">Hospital D</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">M</td>
</tr>
</tbody>
@ -418,8 +418,8 @@
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------- -------- ----------- -------------
# 1 M 10,367 51.8% 10,367 51.8%
# 2 F 9,633 48.2% 20,000 100.0%</code></pre>
# 1 M 10,432 52.2% 10,432 52.2%
# 2 F 9,568 47.8% 20,000 100.0%</code></pre>
<p>So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values <code>M</code> and <code>F</code>. From a researcher 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="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1">data &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span></a>
@ -449,14 +449,14 @@
<a class="sourceLine" id="cb14-18" title="18"><span class="co"># Pasteurella multocida (no new changes)</span></a>
<a class="sourceLine" id="cb14-19" title="19"><span class="co"># Staphylococcus (no new changes)</span></a>
<a class="sourceLine" id="cb14-20" title="20"><span class="co"># Streptococcus groups A, B, C, G (no new changes)</span></a>
<a class="sourceLine" id="cb14-21" title="21"><span class="co"># Streptococcus pneumoniae (1390 new changes)</span></a>
<a class="sourceLine" id="cb14-21" title="21"><span class="co"># Streptococcus pneumoniae (1464 new changes)</span></a>
<a class="sourceLine" id="cb14-22" title="22"><span class="co"># Viridans group streptococci (no new changes)</span></a>
<a class="sourceLine" id="cb14-23" title="23"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-24" title="24"><span class="co"># EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)</span></a>
<a class="sourceLine" id="cb14-25" title="25"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1264 new changes)</span></a>
<a class="sourceLine" id="cb14-25" title="25"><span class="co"># Table 01: Intrinsic resistance in Enterobacteriaceae (1265 new changes)</span></a>
<a class="sourceLine" id="cb14-26" title="26"><span class="co"># Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no new changes)</span></a>
<a class="sourceLine" id="cb14-27" title="27"><span class="co"># Table 03: Intrinsic resistance in other Gram-negative bacteria (no new changes)</span></a>
<a class="sourceLine" id="cb14-28" title="28"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2725 new changes)</span></a>
<a class="sourceLine" id="cb14-28" title="28"><span class="co"># Table 04: Intrinsic resistance in Gram-positive bacteria (2753 new changes)</span></a>
<a class="sourceLine" id="cb14-29" title="29"><span class="co"># Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no new changes)</span></a>
<a class="sourceLine" id="cb14-30" title="30"><span class="co"># Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no new changes)</span></a>
<a class="sourceLine" id="cb14-31" title="31"><span class="co"># Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no new changes)</span></a>
@ -464,24 +464,24 @@
<a class="sourceLine" id="cb14-33" title="33"><span class="co"># Table 13: Interpretive rules for quinolones (no new changes)</span></a>
<a class="sourceLine" id="cb14-34" title="34"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-35" title="35"><span class="co"># Other rules</span></a>
<a class="sourceLine" id="cb14-36" title="36"><span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2301 new changes)</span></a>
<a class="sourceLine" id="cb14-37" title="37"><span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (114 new changes)</span></a>
<a class="sourceLine" id="cb14-36" title="36"><span class="co"># Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2272 new changes)</span></a>
<a class="sourceLine" id="cb14-37" title="37"><span class="co"># Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (108 new changes)</span></a>
<a class="sourceLine" id="cb14-38" title="38"><span class="co"># Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no new changes)</span></a>
<a class="sourceLine" id="cb14-39" title="39"><span class="co"># Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no new changes)</span></a>
<a class="sourceLine" id="cb14-40" title="40"><span class="co"># Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no new changes)</span></a>
<a class="sourceLine" id="cb14-41" title="41"><span class="co"># Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no new changes)</span></a>
<a class="sourceLine" id="cb14-42" title="42"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-43" title="43"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb14-44" title="44"><span class="co"># EUCAST rules affected 6,521 out of 20,000 rows, making a total of 7,794 edits</span></a>
<a class="sourceLine" id="cb14-44" title="44"><span class="co"># EUCAST rules affected 6,521 out of 20,000 rows, making a total of 7,862 edits</span></a>
<a class="sourceLine" id="cb14-45" title="45"><span class="co"># =&gt; added 0 test results</span></a>
<a class="sourceLine" id="cb14-46" title="46"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-47" title="47"><span class="co"># =&gt; changed 7,794 test results</span></a>
<a class="sourceLine" id="cb14-48" title="48"><span class="co"># - 125 test results changed from S to I</span></a>
<a class="sourceLine" id="cb14-49" title="49"><span class="co"># - 4,678 test results changed from S to R</span></a>
<a class="sourceLine" id="cb14-50" title="50"><span class="co"># - 1,070 test results changed from I to S</span></a>
<a class="sourceLine" id="cb14-51" title="51"><span class="co"># - 286 test results changed from I to R</span></a>
<a class="sourceLine" id="cb14-52" title="52"><span class="co"># - 1,620 test results changed from R to S</span></a>
<a class="sourceLine" id="cb14-53" title="53"><span class="co"># - 15 test results changed from R to I</span></a>
<a class="sourceLine" id="cb14-47" title="47"><span class="co"># =&gt; changed 7,862 test results</span></a>
<a class="sourceLine" id="cb14-48" title="48"><span class="co"># - 112 test results changed from S to I</span></a>
<a class="sourceLine" id="cb14-49" title="49"><span class="co"># - 4,682 test results changed from S to R</span></a>
<a class="sourceLine" id="cb14-50" title="50"><span class="co"># - 1,085 test results changed from I to S</span></a>
<a class="sourceLine" id="cb14-51" title="51"><span class="co"># - 303 test results changed from I to R</span></a>
<a class="sourceLine" id="cb14-52" title="52"><span class="co"># - 1,657 test results changed from R to S</span></a>
<a class="sourceLine" id="cb14-53" title="53"><span class="co"># - 23 test results changed from R to I</span></a>
<a class="sourceLine" id="cb14-54" title="54"><span class="co"># --------------------------------------------------------------------------</span></a>
<a class="sourceLine" id="cb14-55" title="55"><span class="co"># </span></a>
<a class="sourceLine" id="cb14-56" title="56"><span class="co"># Use verbose = TRUE to get a data.frame with all specified edits instead.</span></a></code></pre></div>
@ -509,8 +509,8 @@
<a class="sourceLine" id="cb16-3" title="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="cb16-4" title="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="cb16-5" title="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="cb16-6" title="6"><span class="co"># =&gt; Found 5,627 first isolates (28.1% of total)</span></a></code></pre></div>
<p>So only 28.1% 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>
<a class="sourceLine" id="cb16-6" title="6"><span class="co"># =&gt; Found 5,661 first isolates (28.3% of total)</span></a></code></pre></div>
<p>So only 28.3% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" title="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb17-2" title="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>
<p>For future use, the above two syntaxes can be shortened with the <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
@ -536,8 +536,8 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-03-09</td>
<td align="center">J4</td>
<td align="center">2010-01-13</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -547,30 +547,30 @@
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-19</td>
<td align="center">J4</td>
<td align="center">2010-02-27</td>
<td align="center">L3</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">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">3</td>
<td align="center">2010-03-31</td>
<td align="center">J4</td>
<td align="center">2010-04-07</td>
<td align="center">L3</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">FALSE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-05-07</td>
<td align="center">J4</td>
<td align="center">2010-08-07</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
@ -580,10 +580,10 @@
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-06-21</td>
<td align="center">J4</td>
<td align="center">2010-08-09</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">I</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -591,21 +591,21 @@
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-07-10</td>
<td align="center">J4</td>
<td align="center">2010-09-28</td>
<td align="center">L3</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">FALSE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2010-11-26</td>
<td align="center">J4</td>
<td align="center">2010-10-07</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
@ -613,19 +613,19 @@
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2010-12-04</td>
<td align="center">J4</td>
<td align="center">2010-12-15</td>
<td align="center">L3</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">9</td>
<td align="center">2011-03-19</td>
<td align="center">J4</td>
<td align="center">2011-10-03</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -635,8 +635,8 @@
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-05-16</td>
<td align="center">J4</td>
<td align="center">2011-12-06</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -657,7 +657,7 @@
<a class="sourceLine" id="cb19-7" title="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="cb19-8" title="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="cb19-9" title="9"><span class="co"># [Criterion] Inclusion based on key antibiotics, ignoring I.</span></a>
<a class="sourceLine" id="cb19-10" title="10"><span class="co"># =&gt; Found 15,074 first weighted isolates (75.4% of total)</span></a></code></pre></div>
<a class="sourceLine" id="cb19-10" title="10"><span class="co"># =&gt; Found 15,116 first weighted isolates (75.6% of total)</span></a></code></pre></div>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -674,8 +674,8 @@
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-03-09</td>
<td align="center">J4</td>
<td align="center">2010-01-13</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -686,32 +686,32 @@
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-03-19</td>
<td align="center">J4</td>
<td align="center">2010-02-27</td>
<td align="center">L3</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">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">TRUE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-03-31</td>
<td align="center">J4</td>
<td align="center">2010-04-07</td>
<td align="center">L3</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">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-05-07</td>
<td align="center">J4</td>
<td align="center">2010-08-07</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">S</td>
@ -722,10 +722,10 @@
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-06-21</td>
<td align="center">J4</td>
<td align="center">2010-08-09</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">I</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
@ -734,44 +734,44 @@
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-07-10</td>
<td align="center">J4</td>
<td align="center">2010-09-28</td>
<td align="center">L3</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">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2010-11-26</td>
<td align="center">J4</td>
<td align="center">2010-10-07</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</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">2010-12-04</td>
<td align="center">J4</td>
<td align="center">2010-12-15</td>
<td align="center">L3</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">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2011-03-19</td>
<td align="center">J4</td>
<td align="center">2011-10-03</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -782,8 +782,8 @@
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2011-05-16</td>
<td align="center">J4</td>
<td align="center">2011-12-06</td>
<td align="center">L3</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">S</td>
<td align="center">S</td>
@ -794,11 +794,11 @@
</tr>
</tbody>
</table>
<p>Instead of 2, now 7 isolates are flagged. In total, 75.4% of all isolates are marked ‘first weighted’ - 47.2% 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, 75.6% of all isolates are marked ‘first weighted’ - 47.3% 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="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" title="1">data_1st &lt;-<span class="st"> </span>data <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb20-2" title="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 15,074 isolates for analysis.</p>
<p>So we end up with 15,116 isolates for analysis.</p>
<p>We can remove unneeded columns:</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" title="1">data_1st &lt;-<span class="st"> </span>data_1st <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb21-2" title="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>
@ -822,93 +822,93 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2016-12-11</td>
<td align="center">T9</td>
<td align="center">2010-08-30</td>
<td align="center">X2</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">R</td>
<td align="center">B_STPHY_AUR</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">Gram positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2013-02-14</td>
<td align="center">A3</td>
<td align="center">Hospital B</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2015-08-09</td>
<td align="center">E8</td>
<td align="center">Hospital A</td>
<td align="center">B_STPHY_AUR</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">2014-05-04</td>
<td align="center">M8</td>
<td align="center">Hospital D</td>
<td align="center">B_STPHY_AUR</td>
<td align="center">R</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>
<td align="center">Escherichia</td>
<td align="center">coli</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 align="center">2011-05-14</td>
<td align="center">W4</td>
<td align="center">Hospital D</td>
<td align="center">B_STRPT_PNE</td>
<tr class="even">
<td align="center">2013-05-31</td>
<td align="center">J10</td>
<td align="center">Hospital B</td>
<td align="center">B_STPHY_AUR</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">2014-07-13</td>
<td align="center">H9</td>
<td align="center">Hospital C</td>
<td align="center">B_STRPT_PNE</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">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 align="center">2010-02-09</td>
<td align="center">G3</td>
<td align="center">Hospital C</td>
<td align="center">B_ESCHR_COL</td>
<td align="center">2015-03-14</td>
<td align="center">I6</td>
<td align="center">Hospital D</td>
<td align="center">B_STPHY_AUR</td>
<td align="center">R</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>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">2016-04-24</td>
<td align="center">B7</td>
<td align="center">Hospital A</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">M</td>
<td align="center">Gram negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2010-02-20</td>
<td align="center">L7</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">M</td>
<td align="center">Gram negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">Gram positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
</tbody>
@ -928,9 +928,9 @@
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb23-1" title="1"><span class="kw"><a href="../reference/freq.html">freq</a></span>(<span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/paste">paste</a></span>(data_1st<span class="op">$</span>genus, data_1st<span class="op">$</span>species))</a></code></pre></div>
<p>Or can be used like the <code>dplyr</code> way, which is easier readable:</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb24-1" title="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(genus, species)</a></code></pre></div>
<p><strong>Frequency table of <code>genus</code> and <code>species</code> from a <code>data.frame</code> (15,074 x 13)</strong></p>
<p><strong>Frequency table of <code>genus</code> and <code>species</code> from a <code>data.frame</code> (15,116 x 13)</strong></p>
<p>Columns: 2<br>
Length: 15,074 (of which NA: 0 = 0.00%)<br>
Length: 15,116 (of which NA: 0 = 0.00%)<br>
Unique: 4</p>
<p>Shortest: 16<br>
Longest: 24</p>
@ -947,33 +947,33 @@ Longest: 24</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">7,504</td>
<td align="right">49.8%</td>
<td align="right">7,504</td>
<td align="right">49.8%</td>
<td align="right">7,472</td>
<td align="right">49.4%</td>
<td align="right">7,472</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">3,702</td>
<td align="right">24.6%</td>
<td align="right">11,206</td>
<td align="right">74.3%</td>
<td align="right">3,773</td>
<td align="right">25.0%</td>
<td align="right">11,245</td>
<td align="right">74.4%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">2,313</td>
<td align="right">15.3%</td>
<td align="right">13,519</td>
<td align="right">89.7%</td>
<td align="right">2,296</td>
<td align="right">15.2%</td>
<td align="right">13,541</td>
<td align="right">89.6%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">1,555</td>
<td align="right">10.3%</td>
<td align="right">15,074</td>
<td align="right">1,575</td>
<td align="right">10.4%</td>
<td align="right">15,116</td>
<td align="right">100.0%</td>
</tr>
</tbody>
@ -984,7 +984,7 @@ 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="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" title="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="cb25-2" title="2"><span class="co"># [1] 0.4647738</span></a></code></pre></div>
<a class="sourceLine" id="cb25-2" title="2"><span class="co"># [1] 0.4655994</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="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" title="1">data_1st <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb26-2" title="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>
@ -997,19 +997,19 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4603103</td>
<td align="center">0.4690460</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4641985</td>
<td align="center">0.4633959</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4670185</td>
<td align="center">0.4724376</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4706464</td>
<td align="center">0.4590056</td>
</tr>
</tbody>
</table>
@ -1027,23 +1027,23 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4603103</td>
<td align="center">4447</td>
<td align="center">0.4690460</td>
<td align="center">4539</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4641985</td>
<td align="center">5321</td>
<td align="center">0.4633959</td>
<td align="center">5218</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4670185</td>
<td align="center">2274</td>
<td align="center">0.4724376</td>
<td align="center">2322</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4706464</td>
<td align="center">3032</td>
<td align="center">0.4590056</td>
<td align="center">3037</td>
</tr>
</tbody>
</table>
@ -1063,27 +1063,27 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Escherichia</td>
<td align="center">0.9235075</td>
<td align="center">0.8956557</td>
<td align="center">0.9941365</td>
<td align="center">0.9243844</td>
<td align="center">0.8899893</td>
<td align="center">0.9945128</td>
</tr>
<tr class="even">
<td align="center">Klebsiella</td>
<td align="center">0.8135048</td>
<td align="center">0.8926045</td>
<td align="center">0.9858521</td>
<td align="center">0.8101587</td>
<td align="center">0.8996825</td>
<td align="center">0.9828571</td>
</tr>
<tr class="odd">
<td align="center">Staphylococcus</td>
<td align="center">0.9240951</td>
<td align="center">0.9289573</td>
<td align="center">0.9972988</td>
<td align="center">0.9207527</td>
<td align="center">0.9114763</td>
<td align="center">0.9912536</td>
</tr>
<tr class="even">
<td align="center">Streptococcus</td>
<td align="center">0.6182447</td>
<td align="center">0.6276132</td>
<td align="center">0.0000000</td>
<td align="center">0.6182447</td>
<td align="center">0.6276132</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="Released version">0.6.1.9037</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9040</span>
</span>
</div>

2
docs/articles/G_test.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="Released version">0.6.1.9037</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9040</span>
</span>
</div>

56
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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="Released version">0.6.1.9037</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9040</span>
</span>
</div>
@ -242,17 +242,17 @@
<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" title="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" title="2"><span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></a>
<a class="sourceLine" id="cb3-3" title="3"><span class="co"># 1 S S R S R S</span></a>
<a class="sourceLine" id="cb3-4" title="4"><span class="co"># 2 R R R S S R</span></a>
<a class="sourceLine" id="cb3-5" title="5"><span class="co"># 3 S S S S S S</span></a>
<a class="sourceLine" id="cb3-6" title="6"><span class="co"># 4 S S S S S R</span></a>
<a class="sourceLine" id="cb3-7" title="7"><span class="co"># 5 R R S S R R</span></a>
<a class="sourceLine" id="cb3-8" title="8"><span class="co"># 6 R S S R S S</span></a>
<a class="sourceLine" id="cb3-3" title="3"><span class="co"># 1 S S R R S R</span></a>
<a class="sourceLine" id="cb3-4" title="4"><span class="co"># 2 R S S S S R</span></a>
<a class="sourceLine" id="cb3-5" title="5"><span class="co"># 3 S S S S I I</span></a>
<a class="sourceLine" id="cb3-6" title="6"><span class="co"># 4 S I R I I S</span></a>
<a class="sourceLine" id="cb3-7" title="7"><span class="co"># 5 S R S R R R</span></a>
<a class="sourceLine" id="cb3-8" title="8"><span class="co"># 6 R R R R S S</span></a>
<a class="sourceLine" id="cb3-9" title="9"><span class="co"># kanamycin</span></a>
<a class="sourceLine" id="cb3-10" title="10"><span class="co"># 1 S</span></a>
<a class="sourceLine" id="cb3-11" title="11"><span class="co"># 2 S</span></a>
<a class="sourceLine" id="cb3-12" title="12"><span class="co"># 3 S</span></a>
<a class="sourceLine" id="cb3-13" title="13"><span class="co"># 4 R</span></a>
<a class="sourceLine" id="cb3-11" title="11"><span class="co"># 2 R</span></a>
<a class="sourceLine" id="cb3-12" title="12"><span class="co"># 3 R</span></a>
<a class="sourceLine" id="cb3-13" title="13"><span class="co"># 4 S</span></a>
<a class="sourceLine" id="cb3-14" title="14"><span class="co"># 5 R</span></a>
<a class="sourceLine" id="cb3-15" title="15"><span class="co"># 6 I</span></a></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set:</p>
@ -285,40 +285,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistance</td>
<td align="right">3,249</td>
<td align="right">65.0%</td>
<td align="right">3,249</td>
<td align="right">65.0%</td>
<td align="right">3,214</td>
<td align="right">64.3%</td>
<td align="right">3,214</td>
<td align="right">64.3%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">647</td>
<td align="right">12.9%</td>
<td align="right">3,896</td>
<td align="right">77.9%</td>
<td align="right">685</td>
<td align="right">13.7%</td>
<td align="right">3,899</td>
<td align="right">78.0%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multidrug resistance</td>
<td align="right">625</td>
<td align="right">12.5%</td>
<td align="right">4,521</td>
<td align="right">90.4%</td>
<td align="right">576</td>
<td align="right">11.5%</td>
<td align="right">4,475</td>
<td align="right">89.5%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistance</td>
<td align="right">281</td>
<td align="right">5.6%</td>
<td align="right">4,802</td>
<td align="right">96.0%</td>
<td align="right">294</td>
<td align="right">5.9%</td>
<td align="right">4,769</td>
<td align="right">95.4%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensive drug resistance</td>
<td align="right">198</td>
<td align="right">4.0%</td>
<td align="right">231</td>
<td align="right">4.6%</td>
<td align="right">5,000</td>
<td align="right">100.0%</td>
</tr>

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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="Released version">0.6.1.9037</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9040</span>
</span>
</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="Released version">0.6.1.9037</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9040</span>
</span>
</div>

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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="Released version">0.6.1.9037</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9040</span>
</span>
</div>

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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="Released version">0.6.1.9037</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9040</span>
</span>
</div>
@ -224,14 +224,14 @@
<a class="sourceLine" id="cb2-8" title="8"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb2-9" title="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" title="10"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb2-11" title="11"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-12" title="12"><span class="co"># as.mo("sau") 17.0 17 18.0 18 18.0 18.0 10</span></a>
<a class="sourceLine" id="cb2-13" title="13"><span class="co"># as.mo("stau") 48.0 48 66.0 48 92.0 92.0 10</span></a>
<a class="sourceLine" id="cb2-14" title="14"><span class="co"># as.mo("staaur") 17.0 17 23.0 18 19.0 62.0 10</span></a>
<a class="sourceLine" id="cb2-15" title="15"><span class="co"># as.mo("STAAUR") 17.0 18 18.0 18 18.0 19.0 10</span></a>
<a class="sourceLine" id="cb2-16" title="16"><span class="co"># as.mo("S. aureus") 28.0 28 37.0 28 29.0 73.0 10</span></a>
<a class="sourceLine" id="cb2-17" title="17"><span class="co"># as.mo("S. aureus") 28.0 28 44.0 28 28.0 140.0 10</span></a>
<a class="sourceLine" id="cb2-18" title="18"><span class="co"># as.mo("Staphylococcus aureus") 7.9 8 8.2 8 8.1 8.8 10</span></a></code></pre></div>
<a class="sourceLine" id="cb2-11" title="11"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb2-12" title="12"><span class="co"># as.mo("sau") 18.0 18 28 18 18.0 76 10</span></a>
<a class="sourceLine" id="cb2-13" title="13"><span class="co"># as.mo("stau") 48.0 48 70 48 92.0 130 10</span></a>
<a class="sourceLine" id="cb2-14" title="14"><span class="co"># as.mo("staaur") 18.0 18 18 18 18.0 19 10</span></a>
<a class="sourceLine" id="cb2-15" title="15"><span class="co"># as.mo("STAAUR") 17.0 17 27 18 18.0 63 10</span></a>
<a class="sourceLine" id="cb2-16" title="16"><span class="co"># as.mo("S. aureus") 28.0 28 29 28 29.0 29 10</span></a>
<a class="sourceLine" id="cb2-17" title="17"><span class="co"># as.mo("S. aureus") 28.0 28 29 28 29.0 30 10</span></a>
<a class="sourceLine" id="cb2-18" title="18"><span class="co"># as.mo("Staphylococcus aureus") 7.9 8 17 8 8.1 52 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" title="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>
@ -243,12 +243,12 @@
<a class="sourceLine" id="cb3-7" title="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" title="8"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb3-9" title="9"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb3-10" title="10"><span class="co"># as.mo("theisl") 470 470 500 500 520 530 10</span></a>
<a class="sourceLine" id="cb3-11" title="11"><span class="co"># as.mo("THEISL") 470 470 500 510 520 520 10</span></a>
<a class="sourceLine" id="cb3-12" title="12"><span class="co"># as.mo("T. islandicus") 75 75 82 76 76 130 10</span></a>
<a class="sourceLine" id="cb3-13" title="13"><span class="co"># as.mo("T. islandicus") 74 75 90 75 91 150 10</span></a>
<a class="sourceLine" id="cb3-14" title="14"><span class="co"># as.mo("Thermus islandicus") 73 74 90 74 120 130 10</span></a></code></pre></div>
<p>That takes 8.3 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" title="10"><span class="co"># as.mo("theisl") 470 470 500 520 520 520 10</span></a>
<a class="sourceLine" id="cb3-11" title="11"><span class="co"># as.mo("THEISL") 470 470 490 470 520 520 10</span></a>
<a class="sourceLine" id="cb3-12" title="12"><span class="co"># as.mo("T. islandicus") 74 74 79 74 75 120 10</span></a>
<a class="sourceLine" id="cb3-13" title="13"><span class="co"># as.mo("T. islandicus") 74 74 92 74 120 120 10</span></a>
<a class="sourceLine" id="cb3-14" title="14"><span class="co"># as.mo("Thermus islandicus") 73 73 80 73 73 140 10</span></a></code></pre></div>
<p>That takes 8.1 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" title="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" title="2"></a>
@ -294,8 +294,8 @@
<a class="sourceLine" id="cb5-24" title="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" title="25"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb5-26" title="26"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb5-27" title="27"><span class="co"># mo_fullname(x) 630 675 709 693 765 858 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.69 seconds (692 ms). You only lose time on your unique input values.</p>
<a class="sourceLine" id="cb5-27" title="27"><span class="co"># mo_fullname(x) 637 674 745 720 764 936 10</span></a></code></pre></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 0.72 seconds (720 ms). You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -307,11 +307,11 @@
<a class="sourceLine" id="cb6-4" title="4"> <span class="dt">times =</span> <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb6-5" title="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" title="6"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb6-7" title="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-8" title="8"><span class="co"># A 13.10 13.20 13.40 13.40 13.50 13.60 10</span></a>
<a class="sourceLine" id="cb6-9" title="9"><span class="co"># B 25.30 25.60 30.40 26.10 26.40 70.10 10</span></a>
<a class="sourceLine" id="cb6-10" title="10"><span class="co"># C 1.28 1.42 1.57 1.66 1.67 1.71 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 0.0017 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>
<a class="sourceLine" id="cb6-7" title="7"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb6-8" title="8"><span class="co"># A 13.10 13.2 13.50 13.40 13.60 14.30 10</span></a>
<a class="sourceLine" id="cb6-9" title="9"><span class="co"># B 25.50 25.8 30.60 25.80 26.70 70.70 10</span></a>
<a class="sourceLine" id="cb6-10" title="10"><span class="co"># C 1.62 1.8 1.85 1.81 1.92 2.04 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 0.0018 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" title="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" title="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>
<a class="sourceLine" id="cb7-3" title="3"> <span class="dt">C =</span> <span class="kw"><a href="../reference/mo_property.html">mo_fullname</a></span>(<span class="st">"Staphylococcus aureus"</span>),</a>
@ -324,14 +324,14 @@
<a class="sourceLine" id="cb7-10" title="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" title="11"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb7-12" title="12"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb7-13" title="13"><span class="co"># A 0.422 0.441 0.483 0.452 0.483 0.675 10</span></a>
<a class="sourceLine" id="cb7-14" title="14"><span class="co"># B 0.434 0.512 0.549 0.562 0.593 0.638 10</span></a>
<a class="sourceLine" id="cb7-15" title="15"><span class="co"># C 1.340 1.530 1.670 1.680 1.860 2.030 10</span></a>
<a class="sourceLine" id="cb7-16" title="16"><span class="co"># D 0.443 0.471 0.552 0.580 0.611 0.645 10</span></a>
<a class="sourceLine" id="cb7-17" title="17"><span class="co"># E 0.358 0.398 0.465 0.473 0.516 0.554 10</span></a>
<a class="sourceLine" id="cb7-18" title="18"><span class="co"># F 0.366 0.397 0.441 0.436 0.485 0.512 10</span></a>
<a class="sourceLine" id="cb7-19" title="19"><span class="co"># G 0.348 0.462 0.485 0.484 0.517 0.615 10</span></a>
<a class="sourceLine" id="cb7-20" title="20"><span class="co"># H 0.194 0.262 0.295 0.288 0.327 0.403 10</span></a></code></pre></div>
<a class="sourceLine" id="cb7-13" title="13"><span class="co"># A 0.517 0.559 0.605 0.582 0.648 0.771 10</span></a>
<a class="sourceLine" id="cb7-14" title="14"><span class="co"># B 0.549 0.602 0.681 0.684 0.720 0.856 10</span></a>
<a class="sourceLine" id="cb7-15" title="15"><span class="co"># C 1.640 1.710 1.830 1.790 2.000 2.090 10</span></a>
<a class="sourceLine" id="cb7-16" title="16"><span class="co"># D 0.620 0.672 0.768 0.759 0.846 0.985 10</span></a>
<a class="sourceLine" id="cb7-17" title="17"><span class="co"># E 0.487 0.576 0.659 0.673 0.749 0.834 10</span></a>
<a class="sourceLine" id="cb7-18" title="18"><span class="co"># F 0.489 0.590 0.639 0.637 0.692 0.751 10</span></a>
<a class="sourceLine" id="cb7-19" title="19"><span class="co"># G 0.471 0.478 0.646 0.689 0.753 0.856 10</span></a>
<a class="sourceLine" id="cb7-20" title="20"><span class="co"># H 0.200 0.251 0.348 0.341 0.454 0.531 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">
@ -358,13 +358,13 @@
<a class="sourceLine" id="cb8-18" title="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" title="19"><span class="co"># Unit: milliseconds</span></a>
<a class="sourceLine" id="cb8-20" title="20"><span class="co"># expr min lq mean median uq max neval</span></a>
<a class="sourceLine" id="cb8-21" title="21"><span class="co"># en 18.24 18.29 18.60 18.43 18.58 19.88 10</span></a>
<a class="sourceLine" id="cb8-22" title="22"><span class="co"># de 23.04 23.28 37.00 23.46 68.46 69.60 10</span></a>
<a class="sourceLine" id="cb8-23" title="23"><span class="co"># nl 36.67 36.75 37.54 37.09 37.17 42.45 10</span></a>
<a class="sourceLine" id="cb8-24" title="24"><span class="co"># es 23.08 23.19 23.38 23.39 23.58 23.63 10</span></a>
<a class="sourceLine" id="cb8-25" title="25"><span class="co"># it 22.97 23.30 23.40 23.35 23.54 23.92 10</span></a>
<a class="sourceLine" id="cb8-26" title="26"><span class="co"># fr 23.28 23.30 23.81 23.46 23.82 26.51 10</span></a>
<a class="sourceLine" id="cb8-27" title="27"><span class="co"># pt 23.10 23.15 27.87 23.35 23.76 68.33 10</span></a></code></pre></div>
<a class="sourceLine" id="cb8-21" title="21"><span class="co"># en 18.47 18.50 23.02 18.55 18.75 62.99 10</span></a>
<a class="sourceLine" id="cb8-22" title="22"><span class="co"># de 23.26 23.35 23.55 23.52 23.75 23.93 10</span></a>
<a class="sourceLine" id="cb8-23" title="23"><span class="co"># nl 36.79 36.90 50.46 37.27 81.55 81.90 10</span></a>
<a class="sourceLine" id="cb8-24" title="24"><span class="co"># es 23.28 23.40 27.94 23.50 23.64 67.84 10</span></a>
<a class="sourceLine" id="cb8-25" title="25"><span class="co"># it 23.12 23.23 23.38 23.33 23.60 23.67 10</span></a>
<a class="sourceLine" id="cb8-26" title="26"><span class="co"># fr 23.30 23.38 23.61 23.47 23.86 24.32 10</span></a>
<a class="sourceLine" id="cb8-27" title="27"><span class="co"># pt 23.33 23.38 23.63 23.64 23.87 23.95 10</span></a></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>

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</button>
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@ -199,7 +199,7 @@
<h1>How to create frequency tables</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">28 May 2019</h4>
<h4 class="date">29 May 2019</h4>
<div class="hidden name"><code>freq.Rmd</code></div>
@ -211,13 +211,23 @@
<div id="introduction" class="section level2">
<h2 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h2>
<p>Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the <code>freq</code> function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the <code>septic_patients</code> dataset (included in this AMR package) as example.</p>
<p>Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the <code><a href="../reference/freq.html">freq()</a></code> function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the <code>septic_patients</code> dataset (included in this AMR package) as example.</p>
</div>
<div id="frequencies-of-one-variable" class="section level2">
<h2 class="hasAnchor">
<a href="#frequencies-of-one-variable" class="anchor"></a>Frequencies of one variable</h2>
<p>To only show and quickly review the content of one variable, you can just select this variable in various ways. Let’s say we want to get the frequencies of the <code>gender</code> variable of the <code>septic_patients</code> dataset:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1">septic_patients <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(gender)</a></code></pre></div>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="co"># Any of these will work:</span></a>
<a class="sourceLine" id="cb1-2" title="2"><span class="co"># freq(septic_patients$gender)</span></a>
<a class="sourceLine" id="cb1-3" title="3"><span class="co"># freq(septic_patients[, "gender"])</span></a>
<a class="sourceLine" id="cb1-4" title="4"></a>
<a class="sourceLine" id="cb1-5" title="5"><span class="co"># Using tidyverse:</span></a>
<a class="sourceLine" id="cb1-6" title="6"><span class="co"># septic_patients$gender %&gt;% freq()</span></a>
<a class="sourceLine" id="cb1-7" title="7"><span class="co"># septic_patients[, "gender"] %&gt;% freq()</span></a>
<a class="sourceLine" id="cb1-8" title="8"><span class="co"># septic_patients %&gt;% freq("gender")</span></a>
<a class="sourceLine" id="cb1-9" title="9"></a>
<a class="sourceLine" id="cb1-10" title="10"><span class="co"># Probably the fastest and easiest:</span></a>
<a class="sourceLine" id="cb1-11" title="11">septic_patients <span class="op">%&gt;%</span><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(gender) </a></code></pre></div>
<p><strong>Frequency table of <code>gender</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
<p>Class: character<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
@ -489,8 +499,10 @@ Outliers: 15 (unique count: 12)</p>
<li><p><strong>Mean</strong></p></li>
<li><p><strong>Standard deviation</strong></p></li>
<li><p><strong>Coefficient of variation</strong> (CV), the standard deviation divided by the mean</p></li>
<li><p><strong>Five numbers of Tukey</strong> (min, Q1, median, Q3, max)</p></li>
<li><p><strong>Coefficient of quartile variation</strong> (CQV, sometimes called coefficient of dispersion), calculated as (Q3 - Q1) / (Q3 + Q1) using quantile with <code>type = 6</code> as quantile algorithm to comply with SPSS standards</p></li>
<li><p><strong>Mean absolute deviation</strong> (MAD), the median of the absolute deviations from the median - a more robust statistic than the standard deviation</p></li>
<li><p><strong>Five numbers of Tukey</strong>, namely: the minimum, Q1, median, Q3 and maximum</p></li>
<li><p><strong>Interquartile range</strong> (IQR), the distance between Q1 and Q3</p></li>
<li><p><strong>Coefficient of quartile variation</strong> (CQV, sometimes called <em>coefficient of dispersion</em>), calculated as (Q3 - Q1) / (Q3 + Q1) using <code><a href="https://www.rdocumentation.org/packages/stats/topics/quantile">quantile()</a></code> with <code>type = 6</code> as quantile algorithm to comply with SPSS standards</p></li>
<li><p><strong>Outliers</strong> (total count and unique count)</p></li>
</ul>
<p>So for example, the above frequency table quickly shows the median age of patients being 74.</p>
@ -498,7 +510,7 @@ Outliers: 15 (unique count: 12)</p>
<div id="frequencies-of-factors" class="section level2">
<h2 class="hasAnchor">
<a href="#frequencies-of-factors" class="anchor"></a>Frequencies of factors</h2>
<p>To sort frequencies of factors on factor level instead of item count, use the <code>sort.count</code> parameter.</p>
<p>To sort frequencies of factors on their levels instead of item count, use the <code>sort.count</code> parameter.</p>
<p><code>sort.count</code> is <code>TRUE</code> by default. Compare this default behaviour…</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb7-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id)</a></code></pre></div>
@ -551,7 +563,7 @@ Unique: 4</p>
</tr>
</tbody>
</table>
<p>… with this, where items are now sorted on count:</p>
<p>… to this, where items are now sorted on factor levels:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb8-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">sort.count =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>hospital_id</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
@ -603,7 +615,7 @@ Unique: 4</p>
</tr>
</tbody>
</table>
<p>All classes will be printed into the header (default is <code>FALSE</code> when using markdown like this document). Variables with the new <code>rsi</code> class of this AMR package are actually ordered factors and have three classes (look at <code>Class</code> in the header):</p>
<p>All classes will be printed into the header. Variables with the new <code>rsi</code> class of this AMR package are actually ordered factors and have three classes (look at <code>Class</code> in the header):</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb9-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(AMX, <span class="dt">header =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>AMX</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
@ -791,68 +803,14 @@ Group: Beta-lactams/penicillins<br>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb14-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(AMX, <span class="dt">na.rm =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>AMX</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
<p>Class: factor &gt; ordered &gt; rsi (numeric)<br>
Length: 2,000 (of which NA: 771 = 38.55%)<br>
Levels: 3: S &lt; I &lt; R<br>
Unique: 4</p>
<p>Drug: Amoxicillin (AMX, J01CA04)<br>
Group: Beta-lactams/penicillins<br>
%SI: 44.43%</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">(NA)</td>
<td align="right">771</td>
<td align="right">38.6%</td>
<td align="right">771</td>
<td align="right">38.6%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">R</td>
<td align="right">683</td>
<td align="right">34.2%</td>
<td align="right">1,454</td>
<td align="right">72.7%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">S</td>
<td align="right">543</td>
<td align="right">27.2%</td>
<td align="right">1,997</td>
<td align="right">99.8%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">I</td>
<td align="right">3</td>
<td align="right">0.2%</td>
<td align="right">2,000</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
</div>
<div id="parameter-row-names" class="section level3">
<h3 class="hasAnchor">
<a href="#parameter-row-names" class="anchor"></a>Parameter <code>row.names</code>
</h3>
<p>A frequency table shows row indices. To remove them, use <code>row.names = FALSE</code>:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb15-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">row.names =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb14-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">row.names =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>hospital_id</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
<p>Class: factor (numeric)<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
@ -902,58 +860,22 @@ Unique: 4</p>
<h3 class="hasAnchor">
<a href="#parameter-markdown" class="anchor"></a>Parameter <code>markdown</code>
</h3>
<p>The <code>markdown</code> parameter is <code>TRUE</code> at default in non-interactive sessions, like in reports created with R Markdown. This will always print all rows, unless <code>nmax</code> is set.</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb16-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">markdown =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<p><strong>Frequency table of <code>hospital_id</code> from a <code>data.frame</code> (2,000 x 49)</strong></p>
<p>Class: factor (numeric)<br>
Length: 2,000 (of which NA: 0 = 0.00%)<br>
Levels: 4: A, B, C, D<br>
Unique: 4</p>
<table class="table">
<thead><tr class="header">
<th align="left"></th>
<th align="left">Item</th>
<th align="right">Count</th>
<th align="right">Percent</th>
<th align="right">Cum. Count</th>
<th align="right">Cum. Percent</th>
</tr></thead>
<tbody>
<tr class="odd">
<td align="left">1</td>
<td align="left">D</td>
<td align="right">762</td>
<td align="right">38.1%</td>
<td align="right">762</td>
<td align="right">38.1%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">B</td>
<td align="right">663</td>
<td align="right">33.2%</td>
<td align="right">1,425</td>
<td align="right">71.2%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">A</td>
<td align="right">321</td>
<td align="right">16.0%</td>
<td align="right">1,746</td>
<td align="right">87.3%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">C</td>
<td align="right">254</td>
<td align="right">12.7%</td>
<td align="right">2,000</td>
<td align="right">100.0%</td>
</tr>
</tbody>
</table>
<p>The <code>markdown</code> parameter is <code>TRUE</code> at default in non-interactive sessions, like in reports created with R Markdown. This will always print all rows, unless <code>nmax</code> is set. Without markdown (like in regular R), a frequency table would print like:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb15-2" title="2"><span class="st"> </span><span class="kw"><a href="../reference/freq.html">freq</a></span>(hospital_id, <span class="dt">markdown =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb15-3" title="3"><span class="co"># Frequency table of `hospital_id` from a data.frame (2,000 x 49) </span></a>
<a class="sourceLine" id="cb15-4" title="4"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-5" title="5"><span class="co"># Class: factor (numeric)</span></a>
<a class="sourceLine" id="cb15-6" title="6"><span class="co"># Length: 2,000 (of which NA: 0 = 0.00%)</span></a>
<a class="sourceLine" id="cb15-7" title="7"><span class="co"># Levels: 4: A, B, C, D</span></a>
<a class="sourceLine" id="cb15-8" title="8"><span class="co"># Unique: 4</span></a>
<a class="sourceLine" id="cb15-9" title="9"><span class="co"># </span></a>
<a class="sourceLine" id="cb15-10" title="10"><span class="co"># Item Count Percent Cum. Count Cum. Percent</span></a>
<a class="sourceLine" id="cb15-11" title="11"><span class="co"># --- ----- ------ -------- ----------- -------------</span></a>
<a class="sourceLine" id="cb15-12" title="12"><span class="co"># 1 D 762 38.1% 762 38.1%</span></a>
<a class="sourceLine" id="cb15-13" title="13"><span class="co"># 2 B 663 33.2% 1,425 71.2%</span></a>
<a class="sourceLine" id="cb15-14" title="14"><span class="co"># 3 A 321 16.0% 1,746 87.3%</span></a>
<a class="sourceLine" id="cb15-15" title="15"><span class="co"># 4 C 254 12.7% 2,000 100.0%</span></a></code></pre></div>
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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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@ -81,7 +81,7 @@ top_freq can be used to get the top/bottom n items of a frequency table, with co
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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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@ -24,7 +24,7 @@ To split ages, the input can be:
The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).}
\item{A character:}
\itemize{
\item{\code{"children"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.}
\item{\code{"children"} or \code{"kids"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.}
\item{\code{"elderly"} or \code{"seniors"}, equivalent of: \code{c(65, 75, 85)}. This will split on 0-64, 65-74, 75-84, 85+.}
\item{\code{"fives"}, equivalent of: \code{1:20 * 5}. This will split on 0-4, 5-9, 10-14, ..., 90-94, 95-99, 100+.}
\item{\code{"tens"}, equivalent of: \code{1:10 * 10}. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.}
@ -61,12 +61,10 @@ age_groups(ages, c(1, 2, 4, 6, 13, 17))
# resistance of ciprofloxacine per age group
library(dplyr)
septic_patients \%>\%
mutate(first_isolate = first_isolate(.)) \%>\%
filter(first_isolate == TRUE,
mo == as.mo("E. coli")) \%>\%
filter_first_isolate) \%>\%
filter(mo == as.mo("E. coli")) \%>\%
group_by(age_group = age_groups(age)) \%>\%
select(age_group,
CIP) \%>\%
select(age_group, CIP) \%>\%
ggplot_rsi(x = "age_group")
}
\seealso{

36
vignettes/freq.Rmd
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@ -28,13 +28,23 @@ library(AMR)
## Introduction
Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the `septic_patients` dataset (included in this AMR package) as example.
Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq()` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. We take the `septic_patients` dataset (included in this AMR package) as example.
## Frequencies of one variable
To only show and quickly review the content of one variable, you can just select this variable in various ways. Let's say we want to get the frequencies of the `gender` variable of the `septic_patients` dataset:
```{r, echo = TRUE}
septic_patients %>% freq(gender)
# Any of these will work:
# freq(septic_patients$gender)
# freq(septic_patients[, "gender"])
# Using tidyverse:
# septic_patients$gender %>% freq()
# septic_patients[, "gender"] %>% freq()
# septic_patients %>% freq("gender")
# Probably the fastest and easiest:
septic_patients %>% freq(gender)
```
This immediately shows the class of the variable, its length and availability (i.e. the amount of `NA`), the amount of unique values and (most importantly) that among septic patients men are more prevalent than women.
@ -84,9 +94,13 @@ So the following properties are determined, where `NA` values are always ignored
* **Coefficient of variation** (CV), the standard deviation divided by the mean
* **Five numbers of Tukey** (min, Q1, median, Q3, max)
* **Mean absolute deviation** (MAD), the median of the absolute deviations from the median - a more robust statistic than the standard deviation
* **Coefficient of quartile variation** (CQV, sometimes called coefficient of dispersion), calculated as (Q3 - Q1) / (Q3 + Q1) using quantile with `type = 6` as quantile algorithm to comply with SPSS standards
* **Five numbers of Tukey**, namely: the minimum, Q1, median, Q3 and maximum
* **Interquartile range** (IQR), the distance between Q1 and Q3
* **Coefficient of quartile variation** (CQV, sometimes called *coefficient of dispersion*), calculated as (Q3 - Q1) / (Q3 + Q1) using `quantile()` with `type = 6` as quantile algorithm to comply with SPSS standards
* **Outliers** (total count and unique count)
@ -94,7 +108,7 @@ So for example, the above frequency table quickly shows the median age of patien
## Frequencies of factors
To sort frequencies of factors on factor level instead of item count, use the `sort.count` parameter.
To sort frequencies of factors on their levels instead of item count, use the `sort.count` parameter.
`sort.count` is `TRUE` by default. Compare this default behaviour...
@ -103,14 +117,14 @@ septic_patients %>%
freq(hospital_id)
```
... with this, where items are now sorted on count:
... to this, where items are now sorted on factor levels:
```{r, echo = TRUE}
septic_patients %>%
freq(hospital_id, sort.count = FALSE)
```
All classes will be printed into the header (default is `FALSE` when using markdown like this document). Variables with the new `rsi` class of this AMR package are actually ordered factors and have three classes (look at `Class` in the header):
All classes will be printed into the header. Variables with the new `rsi` class of this AMR package are actually ordered factors and have three classes (look at `Class` in the header):
```{r, echo = TRUE}
septic_patients %>%
@ -147,8 +161,6 @@ dim(my_df)
With the `na.rm` parameter you can remove `NA` values from the frequency table (defaults to `TRUE`, but the number of `NA` values will always be shown into the header):
```{r, echo = TRUE}
septic_patients %>%
freq(AMX, na.rm = FALSE)
septic_patients %>%
freq(AMX, na.rm = FALSE)
```
@ -162,9 +174,9 @@ septic_patients %>%
```
### Parameter `markdown`
The `markdown` parameter is `TRUE` at default in non-interactive sessions, like in reports created with R Markdown. This will always print all rows, unless `nmax` is set.
The `markdown` parameter is `TRUE` at default in non-interactive sessions, like in reports created with R Markdown. This will always print all rows, unless `nmax` is set. Without markdown (like in regular R), a frequency table would print like:
```{r, echo = TRUE}
```{r, echo = TRUE, results = 'markup'}
septic_patients %>%
freq(hospital_id, markdown = TRUE)
freq(hospital_id, markdown = FALSE)
```