Now, let’s start the cleaning and the analysis!
So only 53.1% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
filter()
dplyr
So only 53.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
data_1st <- data %>% filter(first == TRUE)
data_1st <- data %>% filter_first_isolate()
So we end up with 10,615 isolates for analysis. Now our data looks like:
So we end up with 10,709 isolates for analysis. Now our data looks like:
head(data_1st)
data_1st %>% freq(genus, species)
Frequency table
Class: character -Length: 10,615 -Available: 10,615 (100.0%, NA: 0 = 0.0%) +Length: 10,709 +Available: 10,709 (100.0%, NA: 0 = 0.0%) Unique: 4
Shortest: 16 Longest: 24
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
proportion_R()
resistance()
proportion_SI()
susceptibility()
data_1st %>% resistance(AMX) -# [1] 0.5423457
Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:
group_by()
summarise()
data_1st %>% @@ -958,19 +958,19 @@ Longest: 24 Hospital A -0.5395931 +0.5425565 Hospital B -0.5418251 +0.5403611 Hospital C -0.5406250 +0.5634772 Hospital D -0.5486436 +0.5504023 @@ -989,23 +989,23 @@ Longest: 24 Hospital A -0.5395931 -3195 +0.5425565 +3231 Hospital B -0.5418251 -3682 +0.5403611 +3766 Hospital C -0.5406250 -1600 +0.5634772 +1599 Hospital D -0.5486436 -2138 +0.5504023 +2113 @@ -1026,27 +1026,27 @@ Longest: 24 Escherichia -0.7690646 -0.8751350 -0.9775329 +0.7672751 +0.8720122 +0.9778357 Klebsiella -0.8321859 -0.9001721 -0.9870912 +0.8333333 +0.8820132 +0.9768977 Staphylococcus -0.7908448 -0.8820246 -0.9795311 +0.8004261 +0.8895597 +0.9808239 Streptococcus -0.5395414 +0.5430495 0.0000000 -0.5395414 +0.5430495 @@ -1071,23 +1071,23 @@ Longest: 24 Hospital A -54.0% -26.6% +54.3% +26.3% Hospital B -54.2% -25.7% +54.0% +25.3% Hospital C -54.1% -28.1% +56.3% +27.4% Hospital D -54.9% -26.4% +55.0% +25.8% @@ -1171,16 +1171,16 @@ Longest: 24 mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 2 0.125 16 0.0625 2 0.001 0.0625 1 32 0.005 -# [11] 8 0.0625 0.001 0.002 128 32 4 2 0.002 32 -# [21] 128 16 1 4 256 16 128 64 0.5 8 -# [31] 0.5 0.25 1 4 16 0.025 0.125 1 1 4 -# [41] 0.002 16 4 16 128 0.002 8 0.001 0.001 0.005 -# [51] 0.25 1 0.125 64 0.001 1 8 1 0.005 8 -# [61] 2 0.025 0.001 0.125 0.025 0.001 256 0.01 2 0.002 -# [71] 128 0.25 32 8 32 0.001 256 2 4 32 -# [81] 64 0.002 0.25 4 8 32 0.25 0.5 0.0625 256 -# [91] 0.25 0.025 0.002 1 0.005 0.025 1 2 0.002 64
mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 2 0.125 16 0.0625 2 0.001 0.0625 1 32 0.005 -# [11] 8 0.0625 0.001 0.002 128 32 4 2 0.002 32 -# [21] 128 16 1 4 256 16 128 64 0.5 8 -# [31] 0.5 0.25 1 4 16 0.025 0.125 1 1 4 -# [41] 0.002 16 4 16 128 0.002 8 0.001 0.001 0.005 -# [51] 0.25 1 0.125 64 0.001 1 8 1 0.005 8 -# [61] 2 0.025 0.001 0.125 0.025 0.001 256 0.01 2 0.002 -# [71] 128 0.25 32 8 32 0.001 256 2 4 32 -# [81] 64 0.002 0.25 4 8 32 0.25 0.5 0.0625 256 -# [91] 0.25 0.025 0.002 1 0.005 0.025 1 2 0.002 64
# base R: plot(mic_values)
# base R: plot(disk_values, mo = "E. coli", ab = "cipro")
head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 I I I S S S -# 2 R I R I I R -# 3 S I I S I S -# 4 S S I R S S -# 5 R I R S I R -# 6 S R I R S S +# 1 R R S R R R +# 2 S I S R I I +# 3 S I I S R R +# 4 S R R S I S +# 5 R S R S I S +# 6 S I S S R R # kanamycin -# 1 R +# 1 S # 2 I -# 3 I -# 4 I +# 3 R +# 4 S # 5 S -# 6 R
We can now add the interpretation of MDR-TB to our data set. You can use:
mdro(my_TB_data, guideline = "TB")
All reference data (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply tab separated files that are machine-readable and suitable for input in any software program, such as laboratory information systems.
AMR
All reference data (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are reliable, up-to-date and freely available. We continually export our data sets to formats for use in R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated text files that are machine-readable and suitable for input in any software program, such as laboratory information systems.
On this page, we explain how to download them and how the structure of the data sets look like.
microorganisms
A data set with 70,764 rows and 16 columns, containing the following column names:mo, fullname, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, species_id, source, prevalence and snomed.
This data set is in R available as microorganisms, after you load the AMR package.
It was last updated on 26 August 2022 12:06:25 UTC. Find more info about the structure of this data set here.
It was last updated on 26 August 2022 21:26:31 UTC. Find more info about the structure of this data set here.
Direct download links:
NOTE: The exported files for Excel, SAS, SPSS and Stata contain only the first 50 SNOMED codes per record, as their file size would otherwise exceed 100 MB; the file size limit of GitHub. Advice? Use R instead.
microorganisms.old
A data set with 14,338 rows and 4 columns, containing the following column names:fullname, fullname_new, ref and prevalence.
Note: remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e. of column ‘fullname’. For the scientific reference of the new names, i.e. of column ‘fullname_new’, see the microorganisms data set.
This data set is in R available as microorganisms.old, after you load the AMR package.
antibiotics
A data set with 464 rows and 14 columns, containing the following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units and loinc.
This data set is in R available as antibiotics, after you load the AMR package.
antivirals
A data set with 102 rows and 9 columns, containing the following column names:atc, cid, name, atc_group, synonyms, oral_ddd, oral_units, iv_ddd and iv_units.
This data set is in R available as antivirals, after you load the AMR package.
rsi_translation
A data set with 20,369 rows and 11 columns, containing the following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R and uti.
This data set is in R available as rsi_translation, after you load the AMR package.
intrinsic_resistant
A data set with 134,956 rows and 2 columns, containing the following column names:mo and ab.
This data set is in R available as intrinsic_resistant, after you load the AMR package.
dosage
A data set with 169 rows and 9 columns, containing the following column names:ab, name, type, dose, dose_times, administration, notes, original_txt and eucast_version.
This data set is in R available as dosage, after you load the AMR package.
Note: to keep the package size as small as possible, we only included this vignette on CRAN. You can read more vignettes on our website about how to conduct AMR data analysis, determine MDRO’s, find explanation of EUCAST rules, and much more: https://msberends.github.io/AMR/articles/.
Note: to keep the package size as small as possible, we only included this vignette on CRAN. You can read more vignettes on our website about how to conduct AMR data analysis, determine MDROs, find explanation of EUCAST rules, and much more: https://msberends.github.io/AMR/articles/.
AMR is a free, open-source and independent R package (see Copyright) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
The AMR package is a free and open-source R package with zero dependencies to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
After installing this package, R knows ~71,000 distinct microbial species and all ~570 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
The AMR package is available in Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish and Swedish. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
The AMR package is available in English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish and Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). It was designed to work in any setting, including those with very limited resources. Since its first public release in early 2018, this package has been downloaded from more than 175 countries.
This package can be used for:
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.
This R package was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice Foundation and University Medical Center Groningen. This R package formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.192486375) but is actively and durably maintained (see changelog)) by two public healthcare organisations in the Netherlands.
This AMR package for R is free, open-source software and licensed under the GNU General Public License v2.0 (GPL-2). These requirements are consequently legally binding: modifications must be released under the same license when distributing the package, changes made to the code must be documented, source code must be made available when the package is distributed, and a copy of the license and copyright notice must be included with the package.
as.rsi()
NA
as.rsi(as.disk(NA), ...)
as.integer()
table()
factor
droplevels()
<mic>
droplevels(..., as.mic = TRUE)