mo_source.RdThese functions can be used to predefine your own reference to be used in as.mo and consequently all mo_* functions like mo_genus and mo_gramstain.
This is the fastest way to have your organisation (or analysis) specific codes picked up and translated by this package.
set_mo_source(path) get_mo_source()
| path | location of your reference file, see Details |
|---|
The reference file can be a text file seperated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you need to have the readxl package installed.
set_mo_source will check the file for validity: it must be a data.frame, must have a column named "mo" which contains values from microorganisms$mo and must have a reference column with your own defined values. If all tests pass, set_mo_source will read the file into R and export it to "~/.mo_source.rds". This compressed data file will then be used at default for MO determination (function as.mo and consequently all mo_* functions like mo_genus and mo_gramstain). The location of the original file will be saved as option with options(mo_source = path). Its timestamp will be saved with options(mo_source_datetime = ...).
get_mo_source will return the data set by reading "~/.mo_source.rds" with readRDS. If the original file has changed (the file defined with path), it will call set_mo_source to update the data file automatically.
Reading an Excel file (.xlsx) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by get_mo_source in only a couple of microseconds (a millionth of a second).
Imagine this data on a sheet of an Excel file (mo codes were looked up in the `microorganisms` data set). The first column contains the organisation specific codes, the second column contains an MO code from this package:
| A | B | --|--------------------|-------------| 1 | Organisation XYZ | mo | 2 | lab_mo_ecoli | B_ESCHR_COL | 3 | lab_mo_kpneumoniae | B_KLBSL_PNE | 4 | | |
We save it as 'home/me/ourcodes.xlsx'. Now we have to set it as a source:
set_mo_source("home/me/ourcodes.xlsx")
# Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
It has now created a file "~/.mo_source.rds" with the contents of our Excel file. It it an R specific format with great compression.
And now we can use it in our functions:
as.mo("lab_mo_ecoli")
# B_ESCHR_COL
mo_genus("lab_mo_kpneumoniae")
# "Klebsiella"
If we edit the Excel file to, let's say, this:
| A | B | --|--------------------|-------------| 1 | Organisation XYZ | mo | 2 | lab_mo_ecoli | B_ESCHR_COL | 3 | lab_mo_kpneumoniae | B_KLBSL_PNE | 4 | lab_Staph_aureus | B_STPHY_AUR | 5 | | |
...any new usage of an MO function in this package will update your data:
as.mo("lab_mo_ecoli")
# Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
# B_ESCHR_COL
mo_genus("lab_Staph_aureus")
# "Staphylococcus"
To remove the reference completely, just use any of these:
set_mo_source("")
set_mo_source(NULL)
# Removed mo_source file '~/.mo_source.rds'.
On our website https://msberends.gitlab.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.