# Data set with 2000 blood culture isolates of septic patients — septic_patients • AMR (for R)
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 data.frame can be used to practice AMR analysis. For examples, press F1.

# Data set with 2000 blood culture isolates of septic patients

`septic_patients.Rd`

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 `data.frame` can be used to practice AMR analysis. For examples, press F1.

    septic_patients

## Format

A `data.frame` with 2,000 observations and 49 variables:

`date`

  date of receipt at the laboratory

`hospital_id`

  ID of the hospital, from A to D

`ward_icu`

  logical to determine if ward is an intensive care unit

`ward_clinical`

  logical to determine if ward is a regular clinical ward

`ward_outpatient`

  logical to determine if ward is an outpatient clinic

`age`

  age of the patient

`gender`

  gender of the patient

`patient_id`

  ID of the patient, first 10 characters of an SHA hash containing irretrievable information

`mo`

  ID of microorganism created with `as.mo`, see also `microorganisms`

`peni:rifa`

  40 different antibiotics with class `rsi` (see `as.rsi`); these column names occur in `antibiotics` data set and can be translated with `abname`

## Examples

    # NOT RUN {
    # ----------- #
    # PREPARATION #
    # ----------- #

    # 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 data set:
    my_data <- my_data %>%
      mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "mo"))

    # -------- #
    # ANALYSIS #
    # -------- #

    # 1. Get the amoxicillin resistance percentages (p)
    #     and numbers (n) of E. coli, divided by hospital:

    my_data %>%
      filter(mo == guess_mo("E. coli"),
             first_isolates == TRUE) %>%
      group_by(hospital_id) %>%
      summarise(n = n_rsi(amox),
                p = portion_IR(amox))

    # 2. Get the amoxicillin/clavulanic acid resistance
    #    percentages of E. coli, trend over the years:

    my_data %>%
      filter(mo == guess_mo("E. coli"),
             first_isolates == TRUE) %>%
      group_by(year = format(date, "%Y")) %>%
      summarise(n = n_rsi(amcl),
                p = portion_IR(amcl, minimum = 20))
    # }