Title: Data Wrangling for Antimicrobial Resistance Studies
Version: 1.0.4
Description: Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC) dataset, a repository of freely accessible electronic health records. MIMER(MIMIC-enabled Research) package, offers a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes, particularly in antimicrobial resistance(AMR) studies. It simplifies complex data manipulation tasks, allowing researchers to focus on their primary inquiries without being bogged down by wrangling complexities.
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: AMR, dplyr, fuzzyjoin, rlang, stringr, tidyr, testthat, data.table, reshape2
Depends: R (≥ 4.1.0)
NeedsCompilation: no
Packaged: 2025-06-24 12:51:44 UTC; anoopvelluva
Author: Anoop Velluva ORCID iD [aut, cre], Alessandro Gerada ORCID iD [ctb], Alexander Howard ORCID iD [ctb]
Maintainer: Anoop Velluva <anoop.velluva@liverpool.ac.uk>
Repository: CRAN
Date/Publication: 2025-06-24 13:20:02 UTC

Add a column and check any previous events identified for a particular antibiotic

Description

This function helps to check any previous events identified or not (TRUE/FALSE)

Usage

check_previous_events(df,
      cols,
      sort_by_col,
      patient_id_col,
      event_indi_value="R",
      new_col_prefix="pr_event_",
      time_period_in_days,
      minimum_prev_events,
      default_na_date='9999-12-31 00:00:00')

Arguments

event_indi_value

(optional) Event value indicating Resistance (Default 'R' )

df

A data frame containing microbiology events

cols

Columns for each antibiotics which contains events

sort_by_col

A date column to order the input data frame

patient_id_col

Patient Id Column

new_col_prefix

(optional) Custom Prefix for new column(Default 'pr_event_' )

time_period_in_days

(optional) to check any previous events in last 'n' days or not

minimum_prev_events

(optional) to check any 'n' number of previous events happened or not

default_na_date

(optional) replacement date string for NA values in sort_by_col eg: '9999-12-31 00:00:00'

Value

Data Frame

Examples


#Example -1
test_data <- data.frame(subject_id = c(10000032,
                                       10000280,
                                       10000280,
                                       10000280,
                                       10000826,
                                       10000826),
                      chartdate = c('2150-10-12',
                                    '2150-10-12',
                                    '2151-03-17',
                                    '2146-12-08',
                                    '2187-09-26',
                                    '2188-07-01'),
                      AMIKACIN=c('R','R','S','S','S','R'))

check_previous_events(test_data,
                      cols="AMIKACIN",
                      sort_by_col='chartdate',
                      patient_id_col='subject_id',
                      event_indi_value='R')
#Example -2

test_data <- data.frame(subject_id=c('10016742', '10016742','10016742',
                                      '10016742','10016742','10038332',
                                      '10038332','10038332','10038332',
                                       '10038332','10038332'),
              chartdate= c('2178-07-03','2178-08-01','2178-07-22',
                          '2178-08-03','2178-09-25','2164-07-31',
                          '2164-12-22','2164-12-22','2165-01-07',
                          '2165-04-17','2165-05-05'),
               CEFEPIME=c('R','S','R','S','S','R','R','R','S','S','S'),
               CEFTAZIDIME=c('S','R','S','R','R','S','S','S','R','R','S'))

check_previous_events(test_data,
                      cols = c('CEFEPIME','CEFTAZIDIME'),
                      sort_by_col = 'chartdate',
                      patient_id_col = 'subject_id',
                      time_period_in_days = 62,
                      minimum_prev_events = 2)

Clean Antibiotics using Fuzzy Matching with AMR Packages's Antibiotics Data.

Description

This utility helps to identify and clean-up antibiotics names passed to it. Also helps to identify whether a medicine is antibiotic or not

Usage

clean_antibiotics(x , ...)

Arguments

x

character vector or a dataframe containing medicine details

...

column name with drug details (required only if first parameter is a dataframe)

Value

Character Vector or Data Frame

Examples


clean_antibiotics(c("Amoxicilin","Amoxicillin","Paracetamol"))


df <- data.frame(medicine = c("Amoxicilin","Amoxicillin","Paracetamol"))
clean_antibiotics(df,drug_col=medicine)

Cleanse organism names in Urine samples

Description

Function to preprocess organism names in urine samples. Removes specified strings, maps certain values to standard ones, and filters out unwanted values.

Usage

cleanse_urine_organism_names(
  data,
  column_name = "org_name",
  strings_to_remove = NULL,
  standard_mapping = NULL,
  filter_values = NULL
)

Arguments

data

The dataframe containing urine sample data.

column_name

The name of the column containing organism names.

strings_to_remove

A character vector of strings to be removed from the organism names.

standard_mapping

A named character vector specifying mappings of values to standard ones.

filter_values

A character vector of values to be filtered out from the organism names.

Value

The preprocessed dataframe.

Examples

data <- data.frame(org_name = c("PRESUMPTIVELY Streptococcus",
              "MODERATE Escherichia coli",
              "S. AUREUS POSITIVE",
              "CANCELLED Influenza A"))
data <- cleanse_urine_organism_names(data,
                                   column_name = "org_name",
                                   strings_to_remove = c("POSITIVE FOR",
                                                        "PRESUMPTIVELY","PRESUMPTIVE",
                                                        "PROBABLE", "IDENTIFICATION",
                                                        "RESEMBLING", "SEEN",
                                                        "MODERATE", "FEW", "BETA",
                                                         "METHICILLIN RESISTANT",
                                                         "NUTRITIONALLY VARIANT",
                                                         "NOT C. PERFRINGENS OR C. SEPTICUM",
                                                        "-LACTAMASE POSITIVE",
                                                        "-LACTAMASE NEGATIVE",
                                                         "VIRAL ANTIGEN",
                                                         "CANDIDA INCONSPICUA",
                                                        "/POSADASII",
                                                        "NOT FUMIGATUS, FLAVUS OR NIGER",
                                                        "MRSA POSITIVE", "MRSA NEGATIVE",
                                                        "HISTOLYTICA/DISPAR"),
                                   standard_mapping = c(
                                                        "NON-FERMENTER" = "STREPTOCOCCUS",
                                                        "ABIOTROPHIA/GRANULICATELLA" =
                                                                            "STREPTOCOCCUS",
                                                        "S. AUREUS POSITIVE" =
                                                                           "STAPHYLOCOCCUS AUREUS",
                                                        "ASPERGILLUS FUMIGATUS COMPLEX" =
                                                                           "ASPERGILLUS FUMIGATUS",
                                                        "(CRYPTOSPORIDIUM PARVUM OOCYSTS|
                                                        CUNNINGHAMELLA BERTHOLLETIAE|
                                                        EPIDERMOPHYTON FLOCCOSUM|
                                                        EXOPHIALA JEANSELMEI COMPLEX|
                                                        SCEDOSPORIUM|
                                                        NEOASCOCHYTA DESMAZIERI|
                                                        NEOSCYTALIDIUM DIMIDIATUM|
                                                        LOMENTOSPORA|NEUROSPORA|
                                                        PERONEUTYPA SCOPARIA|
                                                        SPOROTHRIX SCHENCKII COMPLEX|
                                                        ZYGOSACCHAROMYCES FERMENTATI)" =
                                                                            "UNKNOWN FUNGUS"
                                                        ),
                                   filter_values = c('CANCELLED|VIRUS|SIMPLEX|PARAINFLUENZA|
                                                     INFLUENZA A|INFLUENZA B|TICK|
                                                     AFB GROWN|GRAM VARIABLE RODS|HYMENOLEPIS'))

Convert All Directory Files to CSV - To be used in the Manual Build Process

Description

This function converts all text files in a directory to CSV format.

Usage

convert_all_directory_files_as_csv(extract_dir, delete_text_files = FALSE)

Arguments

extract_dir

The directory containing the text files to be converted.

delete_text_files

Logical indicating whether to delete the original text files after conversion.

Download and Convert NDC Files to CSV - To be used in the Manual Build Process

Description

This function downloads a zip file containing NDC (National Drug Code) text files from a specified URL, extracts the files to a directory, converts them to CSV format, and optionally deletes the original text files.

Usage

download_ndc_files_as_csv(
  zip_file_url = "https://www.accessdata.fda.gov/cder/ndctext.zip",
  zip_file_name = "ndctext.zip",
  extract_dir = "inst/extdata/ndctext"
)

Arguments

zip_file_url

The URL of the zip file containing NDC text files to download.

zip_file_name

The name of the downloaded zip file.

extract_dir

The directory where the NDC text files will be extracted.

Check 'route' is systemic or not

Description

Function to check 'route' is Systemic or not.

Usage

is_systemic_route(route, class_names)

Arguments

route

A vector containing route code.

class_names

A vector containing relevant_routes_administration class

  • Eg: PO/NG

Details

is_systemic_route

Value

Boolean

Check 'ndc' code is belongs to any Antimicrobial.

Description

Function to check input 'ndc' code is belongs to any Antimicrobial or not.

Usage

ndc_is_antimicrobial(ndc,
 class_names,
 include_missing_NDCs = TRUE)

Arguments

ndc

A vector containing ndc codes. Will be coerced to character vector.

class_names

A vector containing antibacterial classes

  • eg: c("antimicrobial", "antibacterial")

include_missing_NDCs

includes a hardcoded database of NDCs that are present in MIMIC-IV but not in NDC database.

Details

ndc_is_antimicrobial

#' Note: Certain medications, such as Rifaximin, may not be considered antibiotics for all purposes despite their classification. Specific cases should be handled based on context and requirements.

Value

Boolean vector for whether input ndc code corresponds to an antimicrobial

Convert 'ndc' code to corresponding Antibiotic code.

Description

Function to convert 'ndc' code to corresponding Antibiotic code.

Usage

ndc_to_antimicrobial(ndc,
 class_names,
 include_missing_NDCs = TRUE)

Arguments

ndc

A vector containing ndc codes. Will be coerced to character.

class_names

A vector containing antibacterial class names - eg: c("antimicrobial", "antibacterial").

include_missing_NDCs

includes a hardcoded database of NDCs that are present in MIMIC-IV but not in NDC database.

Details

ndc_to_antimicrobial

Value

Vector of antimicrobials in antibiotic class from AMR package.

Remove Files - To be used in the Manual Build Process

Description

This function removes files specified by their filenames from a given directory.

Usage

remove_files(extract_dir, filenames)

Arguments

extract_dir

The directory from which files will be removed.

filenames

A character vector containing the names of the files to be removed.

Transpose microbiology events dataset

Description

This function helps to transpose (rows to columns) microbiology events.

Usage

transpose_microbioevents(raw_df,
                                 key_columns,
                                 required_columns,
                                 transpose_key_column,
                                 transpose_value_column,
                                 fill="NA",
                                 non_empty_filter_column,
                                 remove_duplicates=TRUE)

Arguments

key_columns

(Optional) Primary Key/ Key columns for duplicate check : Default Value = c('subject_id','micro_specimen_id','isolate_num', 'org_name','ab_itemid')

raw_df

A data frame containing microbiology events

required_columns

(Optional) columns should contain in final dataset : Default Value c('subject_id','hadm_id','micro_specimen_id', 'order_provider_id', 'chartdate','charttime', 'spec_itemid','spec_type_desc','storedate', 'storetime','test_itemid','test_name', 'org_itemid','isolate_num','org_name')

transpose_key_column

(Optional) The column that should be transposed ( - distinct values of that column will become separate columns) :Default 'ab_name'

transpose_value_column

(optional) Values of 'transpose_key_column' column :Default 'interpretation'

fill

(optional) Fill character for empty columns- Default : "NA"

non_empty_filter_column

(optional) Filter input dataframe where 'non_empty_filter_column' is not empty or na. Default :'ab_itemid'

remove_duplicates

(optional) Default :TRUE

Value

Data Frame

Examples

test_data <- data.frame(subject_id=c('10016742','10016742','10016742',
                                     '10016742','10016742','10038332',
                                     '10038332','10038332','10038332',
                                     '10038332','10038332'),
                       chartdate= c('2178-07-03','2178-08-01','2178-08-01',
                                    '2178-08-01','2178-09-25','2164-07-31',
                                    '2164-12-22','2164-12-22','2165-01-07',
                                    '2165-04-17','2165-05-05'),
                       ab_name=c('CEFEPIME','CEFTAZIDIME','CEFEPIME',
                                'CEFEPIME','CEFTAZIDIME','CEFTAZIDIME',
                                'CEFEPIME','CEFEPIME','CEFTAZIDIME',
                                'CEFTAZIDIME','CEFEPIME'),
                       interpretation=c('S','R','S','R','R','S','S','S','R','R','S'))

transpose_microbioevents(test_data,
                         key_columns = c('subject_id','chartdate','ab_name'),
                         required_columns = c('subject_id','chartdate'),
                         transpose_key_column = 'ab_name',
                         transpose_value_column = 'interpretation',
                         fill = "N/A",
                         non_empty_filter_column = 'subject_id')