P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-07-09 04:02:19 +02:00
Code Releases Activity

new MOs, cleanup

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2018-09-01 21:19:46 +02:00
parent 5965d3c794
commit 75fe4d401f
20 changed files with 166 additions and 179 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
R/data.R
View File

@ -16,10 +16,10 @@
# GNU General Public License for more details. #
# ==================================================================== #
#' Dataset with 423 antibiotics
#' Data set with 423 antibiotics
#'
#' A dataset containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDD's. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
#' @format A data.frame with 423 observations and 18 variables:
#' A data set containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDDs. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
#' @format A \code{\link{tibble}} with 423 observations and 18 variables:
#' \describe{
#' \item{\code{atc}}{ATC code, like \code{J01CR02}}
#' \item{\code{certe}}{Certe code, like \code{amcl}}
@ -120,10 +120,10 @@
#
"antibiotics"
#' Dataset with ~2650 microorganisms
#' Data set with human pathogenic microorganisms
#'
#' A dataset containing 2,646 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
#' @format A data.frame with 2,646 observations and 12 variables:
#' A data set containing 2,664 (potential) human pathogenic microorganisms. MO codes can be looked up using \code{\link{guess_mo}}.
#' @format A \code{\link{tibble}} with 2,664 observations and 12 variables:
#' \describe{
#' \item{\code{mo}}{ID of microorganism}
#' \item{\code{bactsys}}{Bactsyscode of microorganism}
@ -151,10 +151,10 @@
#' @seealso \code{\link{guess_mo}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}
"microorganisms"
#' Translation table for UMCG with ~1100 microorganisms
#' Translation table for UMCG with ~1,100 microorganisms
#'
#' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$mo} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{mo}'s with \code{\link{guess_mo}}.
#' @format A data.frame with 1090 observations and 2 variables:
#' A data set containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$mo} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{MO}s with \code{\link{guess_mo}}.
#' @format A \code{\link{tibble}} with 1,090 observations and 2 variables:
#' \describe{
#' \item{\code{umcg}}{Code of microorganism according to UMCG MMB}
#' \item{\code{mo}}{Code of microorganism in \code{\link{microorganisms}}}
@ -163,10 +163,10 @@
#' @seealso \code{\link{guess_mo}} \code{\link{microorganisms}}
"microorganisms.umcg"
#' Dataset with 2000 blood culture isolates of septic patients
#' Data set with 2000 blood culture isolates of septic patients
#'
#' An anonymised dataset containing 2000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This \code{data.frame} can be used to practice AMR analysis. For examples, press F1.
#' @format A data.frame with 2000 observations and 49 variables:
#' An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This \code{data.frame} can be used to practice AMR analysis. For examples, press F1.
#' @format A \code{\link{tibble}} with 2,000 observations and 49 variables:
#' \describe{
#' \item{\code{date}}{date of receipt at the laboratory}
#' \item{\code{hospital_id}}{ID of the hospital, from A to D}
@ -185,13 +185,13 @@
#' # PREPARATION #
#' # ----------- #
#'
#' # Save this example dataset to an object, so we can edit it:
#' # Save this example data set to an object, so we can edit it:
#' my_data <- septic_patients
#'
#' # load the dplyr package to make data science A LOT easier
#' library(dplyr)
#'
#' # Add first isolates to our dataset:
#' # Add first isolates to our data set:
#' my_data <- my_data %>%
#' mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "mo"))
#'