2018-12-16 22:45:12 +01:00
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# ==================================================================== #
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# TITLE #
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2022-10-05 09:12:22 +02:00
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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2018-12-16 22:45:12 +01:00
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# #
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2019-01-02 23:24:07 +01:00
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# SOURCE #
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2020-07-08 14:48:06 +02:00
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# https://github.com/msberends/AMR #
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2018-12-16 22:45:12 +01:00
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# #
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2022-10-05 09:12:22 +02:00
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# CITE AS #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# doi:10.18637/jss.v104.i03 #
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# #
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2022-12-27 15:16:15 +01:00
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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2018-12-16 22:45:12 +01:00
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# #
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2019-01-02 23:24:07 +01:00
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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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2020-01-05 17:22:09 +01:00
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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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2020-10-08 11:16:03 +02:00
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# #
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# Visit our website for the full manual and a complete tutorial about #
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2021-02-02 23:57:35 +01:00
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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2018-12-16 22:45:12 +01:00
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# ==================================================================== #
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2023-01-24 10:20:27 +01:00
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sir <- random_sir(100)
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rsi <- sir
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class(rsi) <- gsub("sir", "rsi", class(rsi))
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mic <- random_mic(100)
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disk <- random_disk(100)
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expect_identical(summary(sir), summary(rsi))
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expect_identical(c(sir), c(rsi))
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expect_identical(suppressWarnings(suppressMessages(as.rsi(as.character(rsi)))),
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suppressWarnings(suppressMessages(as.sir(as.character(sir)))))
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expect_identical(suppressWarnings(suppressMessages(as.rsi(mic, mo = "Escherichia coli", ab = "CIP"))),
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suppressWarnings(suppressMessages(as.sir(mic, mo = "Escherichia coli", ab = "CIP"))))
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expect_identical(suppressWarnings(suppressMessages(as.rsi(disk, mo = "Escherichia coli", ab = "CIP"))),
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suppressWarnings(suppressMessages(as.sir(disk, mo = "Escherichia coli", ab = "CIP"))))
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expect_identical(suppressWarnings(suppressMessages(as.rsi(data.frame(CIP = mic, mo = "Escherichia coli")))),
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suppressWarnings(suppressMessages(as.sir(data.frame(CIP = mic, mo = "Escherichia coli")))))
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expect_identical(suppressWarnings(n_rsi(example_isolates$CIP)),
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suppressWarnings(n_sir(example_isolates$CIP)))
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expect_identical(suppressWarnings(rsi_df(example_isolates$CIP)),
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suppressWarnings(sir_df(example_isolates$CIP)))
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expect_identical(suppressWarnings(is.rsi.eligible(example_isolates)),
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suppressWarnings(is_sir_eligible(example_isolates)))
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if (pkg_is_available("ggplot2", also_load = FALSE)) {
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expect_identical(suppressWarnings(ggplot_rsi(example_isolates[, c("CIP", "GEN", "TOB")])),
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suppressWarnings(ggplot_sir(example_isolates[, c("CIP", "GEN", "TOB")])))
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p <- ggplot2::ggplot(example_isolates[, c("CIP", "GEN", "TOB")])
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expect_equal(suppressWarnings(p + geom_rsi() + scale_rsi_colours() + labels_rsi_count() + facet_rsi() + theme_rsi()),
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suppressWarnings(p + geom_sir() + scale_sir_colours() + labels_sir_count() + facet_sir() + theme_sir()))
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}
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