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big website update, licence txt update

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dr. M.S. (Matthijs) Berends
2019-01-02 23:24:07 +01:00
parent 4255707cb7
commit 6b2d464f8c
190 changed files with 8785 additions and 66176 deletions
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25
R/resistance_predict.R
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@ -2,18 +2,21 @@
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# AUTHORS #
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# This package is free software; you can redistribute it and/or modify #
# it under the terms of the GNU General Public License version 2.0, #
# as published by the Free Software Foundation. #
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# This R package is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License version 2.0 for more details. #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# #
# This R package was created for academic research and was publicly #
# released in the hope that it will be useful, but it comes WITHOUT #
# ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.gitab.io/AMR. #
# ==================================================================== #
#' Predict antimicrobial resistance
@ -45,7 +48,7 @@
#' @export
#' @importFrom stats predict glm lm
#' @importFrom dplyr %>% pull mutate mutate_at n group_by_at summarise filter filter_at all_vars n_distinct arrange case_when
# @importFrom tidyr spread
#' @inheritSection AMR Read more on our website!
#' @examples
#' \dontrun{
#' # use it with base R:
@ -137,7 +140,7 @@ resistance_predict <- function(tbl,
tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
}
tbl <- tbl %>%
tbl <- tbl %>%
mutate_at(col_ab, as.rsi) %>%
filter_at(col_ab, all_vars(!is.na(.)))
tbl[, col_ab] <- droplevels(tbl[, col_ab])