Description
Data Wrangling for Antimicrobial Resistance Studies.
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.
README.md
MIMER
MIMER is an R package designed for analyzing the MIMIC-IV dataset, a repository of pseudonymized electronic health records. It offers a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes, particularly in antimicrobial resistance (AMR) studies. MIMER simplifies complex data manipulation tasks, allowing researchers to focus on their primary inquiries without being bogged down by wrangling complexities. It integrates seamlessly with the AMR package and is ideal for R developers working in AMR research
Usages
MIMER::ndc_to_antimicrobial(ndc, class)
MIMER::ndc_is_antimicrobial(ndc, class)
MIMER::is_systemic_route(route, class)
MIMER::check_previous_events(df, cols, sort_by_col, patient_id_col,
event_indi_value="R", new_col_prefix="pr_event_",
time_period_in_days = 0, minimum_prev_events = 0)
MIMER::transpose_microbioevents(df, key_columns, required_columns, transpose_key_column,
transpose_value_column, fill = "N/A")
#not recommended to use
MIMER::clean_antibiotics(
x ,
...
)
Installation
You can install the development version of MIMER from GitHub with:
install.packages("devtools")
devtools::install_github("CAMO-NET-LIV/MIMER")
or install from CRAN using:
install.packages("MIMER")
Examples
This is a basic example which shows you how to solve a common problem:
library(MIMER)
## Warning: package 'MIMER' was built under R version 4.3.3
## basic example code
MIMER::ndc_to_antimicrobial(ndc='65649030303', class='antibacterial')
## Class 'ab'
## [1] RFX
library(MIMER)
## basic example code
MIMER::ndc_is_antimicrobial(ndc='65649030303')
## [1] TRUE
library(MIMER)
## basic example code
MIMER::is_systemic_route(route='PO/NG')
## [1] TRUE
library(MIMER)
## basic example code
df <- data.frame(subject_id=c('90916742','90916742','90916742','90916742',
'90916742','90938332','90938332','90938332',
'90938332','90938332','90938332'),
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'),
CEFEPIME=c('R','R','R','R','S','R','R','R','S','S','S'),
CEFTAZIDIME=c('S','R','S','R','R','S','S','S','R','R','S'))
MIMER::check_previous_events(df,
cols = c('CEFTAZIDIME'),
sort_by_col = 'chartdate',
patient_id_col = 'subject_id',
event_indi_value='R')
## Checking Previous Events for
## CEFTAZIDIME
## Total Antibiotics Column (Events) Added : 1
## # A tibble: 11 × 5
## subject_id chartdate CEFEPIME CEFTAZIDIME pr_event_CEFTAZIDIME
## <chr> <chr> <chr> <chr> <lgl>
## 1 90938332 2164-07-31 R S FALSE
## 2 90938332 2164-12-22 R S FALSE
## 3 90938332 2164-12-22 R S FALSE
## 4 90938332 2165-01-07 S R FALSE
## 5 90938332 2165-04-17 S R TRUE
## 6 90938332 2165-05-05 S S TRUE
## 7 90916742 2178-07-03 R S FALSE
## 8 90916742 2178-08-01 R R FALSE
## 9 90916742 2178-08-01 R S FALSE
## 10 90916742 2178-08-01 R R FALSE
## 11 90916742 2178-09-25 S R TRUE
## example with 'minimum_prev_events' parameter
df <- data.frame(subject_id=c('90916742','90916742','90916742','90916742',
'90916742','90938332','90938332','90938332',
'90938332','90938332','90938332'),
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'))
MIMER::check_previous_events(df,
cols = c('CEFEPIME'),
sort_by_col = 'chartdate',
patient_id_col = 'subject_id',
minimum_prev_events = 2)
## Checking Previous Events for
## CEFEPIME
## Total Antibiotics Column (Events) Added : 1
## # A tibble: 11 × 5
## subject_id chartdate CEFEPIME CEFTAZIDIME pr_event_CEFEPIME
## <chr> <chr> <chr> <chr> <lgl>
## 1 90938332 2164-07-31 R S FALSE
## 2 90938332 2164-12-22 R S FALSE
## 3 90938332 2164-12-22 R S FALSE
## 4 90938332 2165-01-07 S R TRUE
## 5 90938332 2165-04-17 S R TRUE
## 6 90938332 2165-05-05 S S TRUE
## 7 90916742 2178-07-03 R S FALSE
## 8 90916742 2178-07-22 R S FALSE
## 9 90916742 2178-08-01 S R TRUE
## 10 90916742 2178-08-03 S R TRUE
## 11 90916742 2178-09-25 S R TRUE
## example with 'time_period_in_days' parameter
df <- data.frame(subject_id=c('90916742','90916742','90916742','90916742',
'90916742','90938332','90938332','90938332',
'90938332','90938332','90938332'),
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'))
MIMER::check_previous_events(df,
cols = c('CEFTAZIDIME'),
sort_by_col = 'chartdate',
patient_id_col = 'subject_id',
time_period_in_days = 25)
## Checking Previous Events for
## CEFTAZIDIME
## Total Antibiotics Column (Events) Added : 1
## # A tibble: 11 × 5
## subject_id chartdate CEFEPIME CEFTAZIDIME pr_event_CEFTAZIDIME
## <chr> <chr> <chr> <chr> <lgl>
## 1 90938332 2164-07-31 R S FALSE
## 2 90938332 2164-12-22 R S FALSE
## 3 90938332 2164-12-22 R S FALSE
## 4 90938332 2165-01-07 S R FALSE
## 5 90938332 2165-04-17 S R FALSE
## 6 90938332 2165-05-05 S S TRUE
## 7 90916742 2178-07-03 R S FALSE
## 8 90916742 2178-07-22 R S FALSE
## 9 90916742 2178-08-01 S R FALSE
## 10 90916742 2178-08-03 S R TRUE
## 11 90916742 2178-09-25 S R FALSE
## example with 'time_period_in_days' & 'minimum_prev_events' parameters
df <- data.frame(subject_id=c('90916742','90916742','90916742','90916742',
'90916742','90938332','90938332',
'90938332','90938332','90938332','90938332'),
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'),
CEFEPIME=c('R','R','R','R','S','R','R','R','S','S','S'),
CEFTAZIDIME=c('S','R','S','R','R','S','S','S','R','R','S'))
MIMER::check_previous_events(df,
cols = c('CEFEPIME'),
sort_by_col = 'chartdate',
patient_id_col = 'subject_id',
time_period_in_days = 62,
minimum_prev_events = 2)
## Checking Previous Events for
## CEFEPIME
## Total Antibiotics Column (Events) Added : 1
## # A tibble: 11 × 5
## subject_id chartdate CEFEPIME CEFTAZIDIME pr_event_CEFEPIME
## <chr> <chr> <chr> <chr> <lgl>
## 1 90938332 2164-07-31 R S FALSE
## 2 90938332 2164-12-22 R S FALSE
## 3 90938332 2164-12-22 R S FALSE
## 4 90938332 2165-01-07 S R TRUE
## 5 90938332 2165-04-17 S R FALSE
## 6 90938332 2165-05-05 S S FALSE
## 7 90916742 2178-07-03 R S FALSE
## 8 90916742 2178-08-01 R R FALSE
## 9 90916742 2178-08-01 R S FALSE
## 10 90916742 2178-08-01 R R FALSE
## 11 90916742 2178-09-25 S R TRUE
##example for transpose_microbioevents
test_data <- data.frame(subject_id=c('90916742','90916742','90916742','90916742',
'90916742','90938332','90938332','90938332',
'90938332','90938332','90938332'),
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'))
MIMER::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')
## subject_id chartdate CEFEPIME CEFTAZIDIME
## 1 90916742 2178-07-03 S N/A
## 2 90916742 2178-08-01 N/A R
## 3 90916742 2178-09-25 N/A R
## 4 90938332 2164-07-31 N/A S
## 5 90938332 2165-01-07 N/A R
## 6 90938332 2165-04-17 N/A R
## 7 90938332 2165-05-05 S N/A
library(MIMER)
## basic example code
MIMER::clean_antibiotics(c("Amoxicilli"))
## [1] "Amoxicillin"
library(MIMER)
## basic example code
df <- data.frame(drug = c("Amoxicilln","moxicillin","Paracetamol") )
MIMER::clean_antibiotics(df, drug_col = drug)
## drug abx_name synonyms is_abx
## 1 Amoxicilln Amoxicillin Amoxicillin TRUE
## 2 moxicillin Amoxicillin Amoxicillin TRUE
## 3 Paracetamol <NA> <NA> FALSE