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new class bactid

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dr. M.S. (Matthijs) Berends
2018-07-23 14:14:03 +02:00
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@ -126,6 +126,86 @@ after
# 5 PSEAER R R - - R
```
Bacteria ID's can be retrieved with the `as.bactid` function. It uses any type of info about a microorganism as input. For example, all these will return value `STAAUR`, the ID of *S. aureus*:
```r
as.bactid("stau")
as.bactid("STAU")
as.bactid("staaur")
as.bactid("S. aureus")
as.bactid("S aureus")
as.bactid("Staphylococcus aureus")
as.bactid("MRSA") # Methicillin Resistant S. aureus
as.bactid("VISA") # Vancomycin Intermediate S. aureus
as.bactid("VRSA") # Vancomycin Resistant S. aureus
```
### New classes
This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`).
Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
```r
# Transform values to new classes
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
```
These functions also try to coerce valid values.
Quick overviews when just printing objects:
```r
mic_data
# Class 'mic': 7 isolates
#
# <NA> 0
#
# <=0.128 1 8 16 >=32
# 1 1 2 2 1
rsi_data
# Class 'rsi': 880 isolates
#
# <NA>: 0
# Sum of S: 474
# Sum of IR: 406
# - Sum of R: 370
# - Sum of I: 36
#
# %S %IR %I %R
# 53.9 46.1 4.1 42.0
```
A plot of `rsi_data`:
```r
plot(rsi_data)
```
![example1](man/figures/rsi_example.png)
A plot of `mic_data` (defaults to bar plot):
```r
plot(mic_data)
```
![example2](man/figures/mic_example.png)
Other epidemiological functions:
```r
# Determine key antibiotic based on bacteria ID
key_antibiotics(...)
# Selection of first isolates of any patient
first_isolate(...)
# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
rsi(...)
# Predict resistance levels of antibiotics
rsi_predict(...)
# Get name of antibiotic by ATC code
abname(...)
abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
```
### Frequency tables
Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`). It can be used in two ways:
```r
@ -235,79 +315,12 @@ Learn more about this function with:
?freq
```
### New classes
This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`).
Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
```r
# Transform values to new classes
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
```
These functions also try to coerce valid values.
Quick overviews when just printing objects:
```r
mic_data
# Class 'mic': 7 isolates
#
# <NA> 0
#
# <=0.128 1 8 16 >=32
# 1 1 2 2 1
rsi_data
# Class 'rsi': 880 isolates
#
# <NA>: 0
# Sum of S: 474
# Sum of IR: 406
# - Sum of R: 370
# - Sum of I: 36
#
# %S %IR %I %R
# 53.9 46.1 4.1 42.0
```
A plot of `rsi_data`:
```r
plot(rsi_data)
```
![example1](man/figures/rsi_example.png)
A plot of `mic_data` (defaults to bar plot):
```r
plot(mic_data)
```
![example2](man/figures/mic_example.png)
Other epidemiological functions:
```r
# Determine key antibiotic based on bacteria ID
key_antibiotics(...)
# Selection of first isolates of any patient
first_isolate(...)
# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
rsi(...)
# Predict resistance levels of antibiotics
rsi_predict(...)
# Get name of antibiotic by ATC code
abname(...)
abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
```
### Databases included in package
Datasets to work with antibiotics and bacteria properties.
```r
# Dataset with 2000 random blood culture isolates from anonymised
# septic patients between 2001 and 2017 in 5 Dutch hospitals
septic_patients # A tibble: 4,000 x 47
septic_patients # A tibble: 2,000 x 47
# Dataset with ATC antibiotics codes, official names, trade names
# and DDD's (oral and parenteral)