added vanA to vanE positive Enterococci

NEWS.md

@@ -23,7 +23,7 @@
 * Introduction to AMR as a vignette
 
 #### Changed
-* Added 226 microorganisms to the `microorganisms` data set and removed the few viruses it contained, now *n* = 2,664 (2,225 bacteria, 285 fungi/yeasts, 153 parasites, 1 other)
+* Added 231 microorganisms to the `microorganisms` data set and removed the few viruses it contained, now *n* = 2,669 (2,230 bacteria, 285 fungi/yeasts, 153 parasites, 1 other)
 * Added three antimicrobial agents to the `antibiotics` data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)
 * Added 163 trade names to the `antibiotics` data set, it now contains 298 different trade names in total, e.g.:
   ```r

R/data.R

@@ -122,8 +122,8 @@
 
 #' Data set with human pathogenic microorganisms
 #'
-#' 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 16 variables:
+#' A data set containing 2,669 (potential) human pathogenic microorganisms. MO codes can be looked up using \code{\link{guess_mo}}.
+#' @format A \code{\link{tibble}} with 2,669 observations and 16 variables:
 #' \describe{
 #'   \item{\code{mo}}{ID of microorganism}
 #'   \item{\code{bactsys}}{Bactsyscode of microorganism}
@@ -158,7 +158,7 @@
 #' Translation table for UMCG with ~1,100 microorganisms
 #'
 #' 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:
+#' @format A \code{\link{tibble}} with 1,095 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}}}

README.md

@@ -44,7 +44,7 @@ This `AMR` package basically does four important things:
 
 1. It **cleanses existing data**, by transforming it to reproducible and profound *classes*, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:
 
-   * Use `as.mo` to get an ID of a microorganism. The IDs are quite obvious - the ID of *E. coli* is "ESCCOL" and the ID of *S. aureus* is "STAAUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even `as.mo("MRSA")` will return the ID of *S. aureus*. Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. To find bacteria based on your input, this package contains a freely available database of ~2,650 different (potential) human pathogenic microorganisms.
+   * Use `as.mo` to get an ID of a microorganism. The IDs are quite obvious - the ID of *E. coli* is "ESCCOL" and the ID of *S. aureus* is "STAAUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even `as.mo("MRSA")` will return the ID of *S. aureus*. Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. To find bacteria based on your input, this package contains a freely available database of almost 3,000 different (potential) human pathogenic microorganisms.
    * Use `as.rsi` to transform values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
    * Use `as.mic` to cleanse your MIC values. It produces a so-called factor (called *ordinal* in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
    * Use `as.atc` to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantin", "nitro" all return the ATC code of Nitrofurantoine.

man/microorganisms.Rd

@@ -4,7 +4,7 @@
 \name{microorganisms}
 \alias{microorganisms}
 \title{Data set with human pathogenic microorganisms}
-\format{A \code{\link{tibble}} with 2,664 observations and 16 variables:
+\format{A \code{\link{tibble}} with 2,669 observations and 16 variables:
 \describe{
   \item{\code{mo}}{ID of microorganism}
   \item{\code{bactsys}}{Bactsyscode of microorganism}
@@ -27,7 +27,7 @@
 microorganisms
 }
 \description{
-A data set containing 2,664 (potential) human pathogenic microorganisms. MO codes can be looked up using \code{\link{guess_mo}}.
+A data set containing 2,669 (potential) human pathogenic microorganisms. MO codes can be looked up using \code{\link{guess_mo}}.
 }
 \seealso{
 \code{\link{guess_mo}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}

man/microorganisms.umcg.Rd

@@ -4,7 +4,7 @@
 \name{microorganisms.umcg}
 \alias{microorganisms.umcg}
 \title{Translation table for UMCG with ~1,100 microorganisms}
-\format{A \code{\link{tibble}} with 1,090 observations and 2 variables:
+\format{A \code{\link{tibble}} with 1,095 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}}}

vignettes/AMR.Rmd

@@ -23,7 +23,7 @@ This `AMR` package basically does four important things:
 
 1. It **cleanses existing data**, by transforming it to reproducible and profound *classes*, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:
 
-   * Use `as.mo` to get an ID of a microorganism. The IDs are quite obvious - the ID of *E. coli* is "ESCCOL" and the ID of *S. aureus* is "STAAUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even `as.mo("MRSA")` will return the ID of *S. aureus*. Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. To find bacteria based on your input, this package contains a freely available database of ~2,650 different (potential) human pathogenic microorganisms.
+   * Use `as.mo` to get an ID of a microorganism. The IDs are quite obvious - the ID of *E. coli* is "ESCCOL" and the ID of *S. aureus* is "STAAUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even `as.mo("MRSA")` will return the ID of *S. aureus*. Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. To find bacteria based on your input, this package contains a freely available database of almost 3,000 different (potential) human pathogenic microorganisms.
    * Use `as.rsi` to transform values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
    * Use `as.mic` to cleanse your MIC values. It produces a so-called factor (called *ordinal* in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
    * Use `as.atc` to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantin", "nitro" all return the ATC code of Nitrofurantoine.