(v1.1.0.9011) lose dependencies

v1.8.2
parent d659c9baef
commit 218fd08097
  1. 2
      .gitlab-ci.yml
  2. 5
      DESCRIPTION
  3. 2
      NAMESPACE
  4. 4
      NEWS.md
  5. 32
      R/aa_helper_functions.R
  6. 3
      R/atc_online.R
  7. 83
      R/bug_drug_combinations.R
  8. 2
      R/rsi_calc.R
  9. 10
      R/zzz.R
  10. 2
      docs/404.html
  11. 2
      docs/LICENSE-text.html
  12. 2
      docs/articles/index.html
  13. 2
      docs/authors.html
  14. 6
      docs/index.html
  15. 10
      docs/news/index.html
  16. 2
      docs/pkgdown.yml
  17. 2
      docs/reference/index.html
  18. 4
      index.md

@ -151,7 +151,7 @@ lintr:
- master
script:
# check all syntax with lintr
- Rscript -e 'lintr::lint_package()'
- Rscript -e 'lintr::lint_package(exclusions = list("R/aa_helper_functions_dplyr.R"))'
cache:
key: release
paths:

@ -1,5 +1,5 @@
Package: AMR
Version: 1.1.0.9010
Version: 1.1.0.9011
Date: 2020-05-18
Title: Antimicrobial Resistance Analysis
Authors@R: c(
@ -37,10 +37,8 @@ Description: Functions to simplify the analysis and prediction of Antimicrobial
Depends:
R (>= 3.1.0)
Imports:
backports,
cleaner,
pillar,
tidyr (>= 1.0.0),
vctrs
Suggests:
covr,
@ -51,6 +49,7 @@ Suggests:
rmarkdown,
rvest,
testthat,
tidyr,
utils
VignetteBuilder: knitr,rmarkdown
URL: https://msberends.gitlab.io/AMR, https://gitlab.com/msberends/AMR

@ -325,8 +325,6 @@ importFrom(stats,prcomp)
importFrom(stats,predict)
importFrom(stats,qchisq)
importFrom(stats,var)
importFrom(tidyr,pivot_longer)
importFrom(tidyr,pivot_wider)
importFrom(vctrs,vec_cast)
importFrom(vctrs,vec_cast.character)
importFrom(vctrs,vec_default_cast)

@ -1,4 +1,4 @@
# AMR 1.1.0.9010
# AMR 1.1.0.9011
## <small>Last updated: 18-May-2020</small>
### Breaking
@ -11,7 +11,7 @@
* Added official drug names to verbose output of `eucast_rules()`
### Other
* Removed dependency on **all** packages that were needed for the `AMR` package to work properly: `crayon`, `data.table`, `dplyr`, `ggplot2`, `R6`, `rlang` and `tidyr`. This is a major code change, but will probably not be noticeable by users. Making this package independent on especially the tidyverse (packages `dplyr`, `ggplot2` and `tidyr`) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. The only dependencies that remained are for extending methods of other packages, like `pillar` and `vctrs` for printing and working with tibbles using our classes `mo` and `ab`.
* Removed dependency on **all** packages that were needed for the `AMR` package to work properly: `backports`, `crayon`, `data.table`, `dplyr`, `ggplot2`, `R6`, `rlang` and `tidyr`. This is a major code change, but will probably not be noticeable by users. Making this package independent on especially the tidyverse (packages `dplyr`, `ggplot2` and `tidyr`) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. The only dependencies that remained are for extending methods of other packages, like `pillar` and `vctrs` for printing and working with tibbles using our classes `mo` and `ab`.
* Removed function `read.4d()`, that was only useful for reading from an old test database.
# AMR 1.1.0

@ -338,12 +338,6 @@ font_stripstyle <- function(x) {
}
progress_estimated <- function(n = 1, n_min = 0, ...) {
# initiate with:
# progress <- progressbar(n)
# on.exit(close(progress))
#
# update with:
# progress$tick()
if (n >= n_min) {
pb <- utils::txtProgressBar(max = n, style = 3)
pb$tick <- function() {
@ -431,3 +425,29 @@ percentage <- function(x, digits = NULL, ...) {
class = c("percentage", "numeric")),
digits = digits, ...)
}
# prevent dependency on package 'backports'
strrep = function(x, times) {
x = as.character(x)
if (length(x) == 0L)
return(x)
unlist(.mapply(function(x, times) {
if (is.na(x) || is.na(times))
return(NA_character_)
if (times <= 0L)
return("")
paste0(replicate(times, x), collapse = "")
}, list(x = x, times = times), MoreArgs = list()), use.names = FALSE)
}
trimws <- function (x, which = c("both", "left", "right")) {
which = match.arg(which)
mysub = function(re, x) sub(re, "", x, perl = TRUE)
if (which == "left")
return(mysub("^[ \t\r\n]+", x))
if (which == "right")
return(mysub("[ \t\r\n]+$", x))
mysub("[ \t\r\n]+$", mysub("^[ \t\r\n]+", x))
}
isFALSE <- function (x) {
is.logical(x) && length(x) == 1L && !is.na(x) && !x
}

@ -129,7 +129,8 @@ atc_online_property <- function(atc_code,
}
progress <- progress_estimated(n = length(atc_code))
on.exit(close(progress))
for (i in seq_len(length(atc_code))) {
progress$tick()

@ -32,7 +32,6 @@
#' @param ... arguments passed on to `FUN`
#' @inheritParams rsi_df
#' @inheritParams base::formatC
#' @importFrom tidyr pivot_longer
#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_IR = FALSE` (default) to test R vs. S+I and `combine_IR = TRUE` to test R+I vs. S.
#'
#' The language of the output can be overwritten with `options(AMR_locale)`, please see [translate].
@ -73,28 +72,41 @@ bug_drug_combinations <- function(x,
stop("`col_mo` must be set.", call. = FALSE)
}
select_rsi <- function(.data) {
.data[, c(col_mo, names(which(sapply(.data, is.rsi))))]
x <- as.data.frame(x, stringsAsFactors = FALSE)
x[, col_mo] <- FUN(x[, col_mo, drop = TRUE])
x <- x[, c(col_mo, names(which(sapply(x, is.rsi))))]
unique_mo <- sort(unique(x[, col_mo, drop = TRUE]))
out <- data.frame(
mo = character(0),
ab = character(0),
S = integer(0),
I = integer(0),
R = integer(0),
total = integer(0))
for (i in seq_len(length(unique_mo))) {
# filter on MO group and only select R/SI columns
x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(sapply(x, is.rsi)))]
# turn and merge everything
pivot <- lapply(x_mo_filter, function(x) {
m <- as.matrix(table(x))
data.frame(S = m["S", ], I = m["I", ], R = m["R", ], stringsAsFactors = FALSE)
})
merged <- do.call(rbind, pivot)
out_group <- data.frame(mo = unique_mo[i],
ab = rownames(merged),
S = merged$S,
I = merged$I,
R = merged$R,
total = merged$S + merged$I + merged$R)
out <- rbind(out, out_group)
}
x <- x %>% as.data.frame(stringsAsFactors = FALSE)
x$mo <- FUN(x[, col_mo, drop = TRUE])
x <- x %>%
select_rsi() %>%
pivot_longer(-mo, names_to = "ab") %>%
group_by(mo, ab) %>%
summarise(S = sum(value == "S", na.rm = TRUE),
I = sum(value == "I", na.rm = TRUE),
R = sum(value == "R", na.rm = TRUE)) %>%
ungroup() %>%
mutate(total = S + I + R) %>%
as.data.frame(stringsAsFactors = FALSE)
structure(.Data = x, class = c("bug_drug_combinations", class(x)))
structure(.Data = out, class = c("bug_drug_combinations", class(x)))
}
#' @importFrom tidyr pivot_wider
#' @exportMethod format.bug_drug_combinations
#' @export
#' @rdname bug_drug_combinations
@ -109,10 +121,10 @@ format.bug_drug_combinations <- function(x,
decimal.mark = getOption("OutDec"),
big.mark = ifelse(decimal.mark == ",", ".", ","),
...) {
x <- x %>% subset(total >= minimum)
x <- subset(x, total >= minimum)
if (remove_intrinsic_resistant == TRUE) {
x <- x %>% subset(R != total)
x <- subset(x, R != total)
}
if (combine_SI == TRUE | combine_IR == FALSE) {
x$isolates <- x$R
@ -137,7 +149,10 @@ format.bug_drug_combinations <- function(x,
}
remove_NAs <- function(.data) {
as.data.frame(sapply(.data, function(x) ifelse(is.na(x), "", x), simplify = FALSE))
cols <- colnames(.data)
.data <- as.data.frame(sapply(.data, function(x) ifelse(is.na(x), "", x), simplify = FALSE))
colnames(.data) <- cols
.data
}
create_var <- function(.data, ...) {
@ -161,14 +176,26 @@ format.bug_drug_combinations <- function(x,
" (", trimws(format(y$isolates, big.mark = big.mark)), "/",
trimws(format(y$total, big.mark = big.mark)), ")")) %>%
select(ab, ab_txt, mo, txt) %>%
arrange(mo) %>%
pivot_wider(names_from = mo, values_from = txt) %>%
arrange(mo)
# replace tidyr::pivot_wider() from here
for (i in unique(y$mo)) {
mo_group <- y[which(y$mo == i), c("ab", "txt")]
colnames(mo_group) <- c("ab", i)
rownames(mo_group) <- NULL
y <- y %>%
left_join(mo_group, by = "ab")
}
y <- y %>%
distinct(ab, .keep_all = TRUE) %>%
select(-mo, -txt) %>%
# replace tidyr::pivot_wider() until here
remove_NAs()
select_ab_vars <- function(.data) {
.data[, c("ab_group", "ab_txt", colnames(.data)[!colnames(.data) %in% c("ab_group", "ab_txt", "ab")])]
}
y <- y %>%
create_var(ab_group = ab_group(y$ab, language = language)) %>%
select_ab_vars() %>%
@ -177,13 +204,17 @@ format.bug_drug_combinations <- function(x,
create_var(ab_group = ifelse(y$ab_group != lag(y$ab_group) | is.na(lag(y$ab_group)), y$ab_group, ""))
if (add_ab_group == FALSE) {
y <- y %>% select(-ab_group) %>% rename("Drug" = ab_txt)
y <- y %>%
select(-ab_group) %>%
rename("Drug" = ab_txt)
colnames(y)[1] <- translate_AMR(colnames(y)[1], language = get_locale(), only_unknown = FALSE)
} else {
y <- y %>% rename("Group" = ab_group,
"Drug" = ab_txt)
colnames(y)[1:2] <- translate_AMR(colnames(y)[1:2], language = get_locale(), only_unknown = FALSE)
}
rownames(y) <- NULL
y
}

@ -45,7 +45,7 @@ rsi_calc <- function(...,
stop("`only_all_tested` must be logical", call. = FALSE)
}
dots_df <- ...elt(1) # it needs this evaluation
dots_df <- switch(1, ...) # it needs this evaluation
dots <- base::eval(base::substitute(base::alist(...)))
if ("also_single_tested" %in% names(dots)) {
stop("`also_single_tested` was replaced by `only_all_tested`. Please read Details in the help page (`?proportion`) as this may have a considerable impact on your analysis.", call. = FALSE)

@ -20,9 +20,6 @@
# ==================================================================== #
.onLoad <- function(libname, pkgname) {
# get new functions not available in older versions of R
backports::import(pkgname)
assign(x = "MO_lookup",
value = create_MO_lookup(),
envir = asNamespace("AMR"))
@ -34,7 +31,6 @@
assign(x = "mo_codes_v0.5.0",
value = make_trans_tbl(),
envir = asNamespace("AMR"))
}
# maybe add survey later: "https://www.surveymonkey.com/r/AMR_for_R"
@ -55,13 +51,13 @@ create_MO_lookup <- function() {
MO_lookup$subspecies)))
MO_lookup[MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname), "fullname_lower"] <- tolower(trimws(MO_lookup[MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname),
"fullname"]))
MO_lookup$fullname_lower <- gsub("[^.a-z0-9/ \\-]+", "",MO_lookup$fullname_lower)
MO_lookup$fullname_lower <- gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower)
# add a column with only "e coli" like combinations
MO_lookup$g_species <- gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO_lookup$fullname_lower)
# so arrange data on prevalence first, then kingdom, then full name
MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower),]
MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower), ]
}
create_MO.old_lookup <- function() {
@ -75,7 +71,7 @@ create_MO.old_lookup <- function() {
MO.old_lookup$g_species <- gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO.old_lookup$fullname_lower)
# so arrange data on prevalence first, then full name
MO.old_lookup[order(MO.old_lookup$prevalence, MO.old_lookup$fullname_lower),]
MO.old_lookup[order(MO.old_lookup$prevalence, MO.old_lookup$fullname_lower), ]
}
make_trans_tbl <- function() {

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="https://msberends.gitlab.io/AMR/index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9011</span>
</span>
</div>

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9011</span>
</span>
</div>

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9011</span>
</span>
</div>

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9011</span>
</span>
</div>

@ -43,7 +43,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9011</span>
</span>
</div>
@ -198,12 +198,12 @@ A methods paper about this package has been preprinted at bioRxiv (DOI: 10.1101/
<h3 class="hasAnchor">
<a href="#what-is-amr-for-r" class="anchor"></a>What is <code>AMR</code> (for R)?</h3>
<p><em>(<help title="Too Long, Didn't Read">TLDR</help> - to find out how to conduct AMR analysis, please <a href="./articles/AMR.html">continue reading here to get started</a>.</em></p>
<p><code>AMR</code> is a free and open-source <a href="https://www.r-project.org">R package</a> to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. <strong>Our aim is to provide a standard</strong> for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.</p>
<p><code>AMR</code> is a free, open-source and independent <a href="https://www.r-project.org">R package</a> to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. <strong>Our aim is to provide a standard</strong> for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.</p>
<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~70,000 distinct microbial species</strong></a> and all <a href="./reference/antibiotics.html"><strong>~550 antibiotic, antimycotic and antiviral drugs</strong></a> by name and code (including ATC, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
<p>We created this package for both routine data analysis and academic research (as part of our PhD theses) at the Faculty of Medical Sciences of the University of Groningen, the Netherlands, and the Medical Microbiology &amp; Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is <a href="./news">actively maintained</a> and is free software (see <a href="#copyright">Copyright</a>).</p>
<div class="main-content">
<p>
<a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a> <strong>Used in more than 100 countries</strong><br> Since its first public release in early 2018, this package has been downloaded from more than 100 countries <small>(as of March 2020, <a href="https://cran-logs.rstudio.com" target="_blank">CRAN logs</a>)</small>. Click the map to enlarge, to see the names of the countries.
<a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a> <strong>Used in more than 100 countries</strong><br> Since its first public release in early 2018, this package has been downloaded from more than 100 countries <small>(source: <a href="https://cran-logs.rstudio.com" target="_blank">CRAN logs</a>)</small>. Click the map to enlarge, to see the names of the countries.
</p>
<br><br>
</div>

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9011</span>
</span>
</div>
@ -229,9 +229,9 @@
<small>Source: <a href='https://gitlab.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
</div>
<div id="amr-1-1-0-9010" class="section level1">
<h1 class="page-header" data-toc-text="1.1.0.9010">
<a href="#amr-1-1-0-9010" class="anchor"></a>AMR 1.1.0.9010<small> Unreleased </small>
<div id="amr-1-1-0-9011" class="section level1">
<h1 class="page-header" data-toc-text="1.1.0.9011">
<a href="#amr-1-1-0-9011" class="anchor"></a>AMR 1.1.0.9011<small> Unreleased </small>
</h1>
<div id="last-updated-18-may-2020" class="section level2">
<h2 class="hasAnchor">
@ -260,7 +260,7 @@
<h3 class="hasAnchor">
<a href="#other" class="anchor"></a>Other</h3>
<ul>
<li>Removed dependency on <strong>all</strong> packages that were needed for the <code>AMR</code> package to work properly: <code>crayon</code>, <code>data.table</code>, <code>dplyr</code>, <code>ggplot2</code>, <code>R6</code>, <code>rlang</code> and <code>tidyr</code>. This is a major code change, but will probably not be noticeable by users. Making this package independent on especially the tidyverse (packages <code>dplyr</code>, <code>ggplot2</code> and <code>tidyr</code>) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. The only dependencies that remained are for extending methods of other packages, like <code>pillar</code> and <code>vctrs</code> for printing and working with tibbles using our classes <code>mo</code> and <code>ab</code>.</li>
<li>Removed dependency on <strong>all</strong> packages that were needed for the <code>AMR</code> package to work properly: <code>backports</code>, <code>crayon</code>, <code>data.table</code>, <code>dplyr</code>, <code>ggplot2</code>, <code>R6</code>, <code>rlang</code> and <code>tidyr</code>. This is a major code change, but will probably not be noticeable by users. Making this package independent on especially the tidyverse (packages <code>dplyr</code>, <code>ggplot2</code> and <code>tidyr</code>) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. The only dependencies that remained are for extending methods of other packages, like <code>pillar</code> and <code>vctrs</code> for printing and working with tibbles using our classes <code>mo</code> and <code>ab</code>.</li>
<li>Removed function <code>read.4d()</code>, that was only useful for reading from an old test database.</li>
</ul>
</div>

@ -10,7 +10,7 @@ articles:
WHONET: WHONET.html
benchmarks: benchmarks.html
resistance_predict: resistance_predict.html
last_built: 2020-05-18T09:07Z
last_built: 2020-05-18T11:59Z
urls:
reference: https://msberends.gitlab.io/AMR/reference
article: https://msberends.gitlab.io/AMR/articles

@ -81,7 +81,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9010</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9011</span>
</span>
</div>

@ -8,7 +8,7 @@
*(<help title="Too Long, Didn't Read">TLDR</help> - to find out how to conduct AMR analysis, please [continue reading here to get started](./articles/AMR.html).*
`AMR` is a free and open-source [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
`AMR` is a free, open-source and independent [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
After installing this package, R knows [**~70,000 distinct microbial species**](./reference/microorganisms.html) and all [**~550 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
@ -18,7 +18,7 @@ We created this package for both routine data analysis and academic research (as
<p>
<a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a>
<strong>Used in more than 100 countries</strong><br>
Since its first public release in early 2018, this package has been downloaded from more than 100 countries <small>(as of March 2020, <a href="https://cran-logs.rstudio.com" target="_blank">CRAN logs</a>)</small>. Click the map to enlarge, to see the names of the countries.</p><br><br>
Since its first public release in early 2018, this package has been downloaded from more than 100 countries <small>(source: <a href="https://cran-logs.rstudio.com" target="_blank">CRAN logs</a>)</small>. Click the map to enlarge, to see the names of the countries.</p><br><br>
</div>
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