Use these selection helpers inside any function that allows Tidyverse selection helpers, such as select() and pivot_longer(). They help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations.

ab_class(ab_class)

aminoglycosides()

carbapenems()

cephalosporins()

cephalosporins_1st()

cephalosporins_2nd()

cephalosporins_3rd()

cephalosporins_4th()

cephalosporins_5th()

fluoroquinolones()

glycopeptides()

macrolides()

penicillins()

tetracyclines()

Arguments

ab_class

an antimicrobial class, like "carbapenems". The columns group, atc_group1 and atc_group2 of the antibiotics data set will be searched (case-insensitive) for this value.

Details

All columns will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.) in the antibiotics data set. This means that a selector like e.g. aminoglycosides() will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.

N.B. These functions require the tidyselect package to be installed, that comes with the dplyr package. An error will be thrown if the tidyselect package is not installed, or if the functions are used outside a function that allows Tidyverse selection helpers such as select() and pivot_longer()`.

Reference data publicly available

All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.

Read more on our website!

On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!

See also

filter_ab_class() for the filter() equivalent.

Examples

if (require("dplyr")) {

  # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
  example_isolates %>% 
    select(carbapenems())
    
  # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
  example_isolates %>% 
    select(mo, aminoglycosides())
    
  # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
  example_isolates %>% 
    select(mo, ab_class("mycobact"))
    
    
  # get bug/drug combinations for only macrolides in Gram-positives:
  example_isolates %>% 
    filter(mo_is_gram_positive()) %>% 
    select(mo, macrolides()) %>% 
    bug_drug_combinations() %>%
    format()
    
    
  data.frame(some_column = "some_value",
             J01CA01 = "S") %>%   # ATC code of ampicillin
    select(penicillins())         # only the 'J01CA01' column will be selected

}