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64 lines
3.5 KiB
R
64 lines
3.5 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2022 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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resistance_data <- structure(list(order = c("Bacillales", "Enterobacterales", "Enterobacterales"),
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genus = c("Staphylococcus", "Escherichia", "Klebsiella"),
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AMC = c(0.00425, 0.13062, 0.10344),
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CXM = c(0.00425, 0.05376, 0.10344),
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CTX = c(0.00000, 0.02396, 0.05172),
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TOB = c(0.02325, 0.02597, 0.10344),
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TMP = c(0.08387, 0.39141, 0.18367)),
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class = c("grouped_df", "tbl_df", "tbl", "data.frame"),
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row.names = c(NA, -3L),
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groups = structure(list(order = c("Bacillales", "Enterobacterales"),
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.rows = list(1L, 2:3)),
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row.names = c(NA, -2L),
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class = c("tbl_df", "tbl", "data.frame"),
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.drop = TRUE))
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pca_model <- pca(resistance_data)
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expect_inherits(pca_model, "pca")
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pdf(NULL) # prevent Rplots.pdf being created
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if (AMR:::pkg_is_available("ggplot2")) {
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ggplot_pca(pca_model, ellipse = TRUE)
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ggplot_pca(pca_model, arrows_textangled = FALSE)
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}
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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0")) {
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resistance_data <- example_isolates %>%
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group_by(order = mo_order(mo),
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genus = mo_genus(mo)) %>%
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summarise_if(is.rsi, resistance, minimum = 0)
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pca_result <- resistance_data %>%
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pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, "SXT")
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expect_inherits(pca_result, "prcomp")
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if (AMR:::pkg_is_available("ggplot2")) {
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ggplot_pca(pca_result, ellipse = TRUE)
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ggplot_pca(pca_result, ellipse = FALSE, arrows_textangled = FALSE, scale = FALSE)
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}
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}
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