A vector specifying the antimicrobials containing SIR values to include in the antibiogram (see Examples ). Will be evaluated using guess_ab_col()
. This can be:
@@ -464,7 +464,8 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
bacteria = rep ( "Escherichia coli" , 4 ) ,
antibiotic = c ( "amoxicillin" , "cipro" , "tobra" , "genta" ) ,
mics = as.mic ( c ( 0.01 , 1 , 4 , 8 ) ) ,
-
disks = as.disk ( c ( 6 , 10 , 14 , 18 ) )
+
disks = as.disk ( c ( 6 , 10 , 14 , 18 ) ) ,
+
guideline = c ( "EUCAST 2021" , "EUCAST 2022" , "EUCAST 2023" , "EUCAST 2024" )
)
# \donttest{
@@ -483,7 +484,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
mutate_if ( is.mic , as.sir ,
mo = "bacteria" ,
ab = "antibiotic" ,
-
guideline = "CLSI"
+
guideline = guideline
)
df_long %>%
mutate ( across (
@@ -648,166 +649,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
#> column ' microorganism ', EUCAST 2024 ...
#> NOTE
#> • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir .
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> Interpreting MIC values: ' amoxicillin ' (AMX), ' cipro ' (CIP, ciprofloxacin),
-
#> ' tobra ' (TOB, tobramycin), and ' genta ' (GEN, gentamicin) based on column
-
#> ' bacteria ', CLSI 2024 ...
-
#> OK
-
#> Warning: There was 1 warning in `mutate()`.
-
#> ℹ In argument: `mics = (function (x, ...) ...`.
-
#> Caused by warning:
-
#> ! Some MICs were converted to the nearest higher log2 level, following the
-
#> CLSI interpretation guideline.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> Interpreting MIC values: ' amoxicillin ' (AMX), ' cipro ' (CIP, ciprofloxacin),
-
#> ' tobra ' (TOB, tobramycin), and ' genta ' (GEN, gentamicin) based on column
-
#> ' bacteria ', CLSI 2024 ...
-
#> OK
-
#> Warning: There was 1 warning in `mutate()`.
-
#> ℹ In argument: `across(...)`.
-
#> Caused by warning:
-
#> ! Some MICs were converted to the nearest higher log2 level, following the
-
#> CLSI interpretation guideline.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> Interpreting MIC values: ' cipro ' (CIP, ciprofloxacin), CLSI 2024 ...
-
#> OK
-
#> Interpreting disk diffusion zones: ' tobra ' (TOB, tobramycin), CLSI 2024 ...
-
#> OK
-
#> Interpreting disk diffusion zones: ' genta ' (GEN, gentamicin), CLSI 2024 ...
-
#> OK
-
#> Warning: There was 1 warning in `mutate()`.
-
#> ℹ In argument: `cipro = (function (x, ...) ...`.
-
#> Caused by warning:
-
#> ! Some MICs were converted to the nearest higher log2 level, following the
-
#> CLSI interpretation guideline.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> Interpreting MIC values: ' cipro ' (CIP, ciprofloxacin), CLSI 2024 ...
-
#> OK
-
#> Interpreting disk diffusion zones: ' tobra ' (TOB, tobramycin), CLSI 2024 ...
-
#> OK
-
#> Interpreting disk diffusion zones: ' genta ' (GEN, gentamicin), CLSI 2024 ...
-
#> OK
-
#> Warning: There was 1 warning in `mutate()`.
-
#> ℹ In argument: `across(...)`.
-
#> Caused by warning:
-
#> ! Some MICs were converted to the nearest higher log2 level, following the
-
#> CLSI interpretation guideline.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> ℹ Assuming breakpoint_type = "animal" , since host is set.
-
#>
-
#> Interpreting MIC values: ' amoxicillin ' (AMX), ' cipro ' (CIP, ciprofloxacin),
-
#> ' tobra ' (TOB, tobramycin), and ' genta ' (GEN, gentamicin) based on column
-
#> ' bacteria ', CLSI 2024 ...
-
#> OK
-
#> Warning: There was 1 warning in `mutate()`.
-
#> ℹ In argument: `mics = (function (x, ...) ...`.
-
#> Caused by warning:
-
#> ! Some MICs were converted to the nearest higher log2 level, following the
-
#> CLSI interpretation guideline.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> ℹ Assuming breakpoint_type = "animal" , since host is set.
-
#>
-
#> Interpreting MIC values: ' amoxicillin ' (AMX), ' cipro ' (CIP, ciprofloxacin),
-
#> ' tobra ' (TOB, tobramycin), and ' genta ' (GEN, gentamicin) based on column
-
#> ' bacteria ', CLSI 2024 ...
-
#> OK
-
#> Warning: There was 1 warning in `mutate()`.
-
#> ℹ In argument: `across(...)`.
-
#> Caused by warning:
-
#> ! Some MICs were converted to the nearest higher log2 level, following the
-
#> CLSI interpretation guideline.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> ℹ Assuming breakpoint_type = "animal" , since host is set.
-
#>
-
#>
-
#> Interpreting MIC values: ' antibiotic ' (ASP, acetylspiramycin), CLSI 2024 ...
-
#> WARNING
-
#> • No CLSI 2024 MIC breakpoints available for acetylspiramycin (ASP).
-
#> Interpreting disk diffusion zones: ' antibiotic ' (ASP, acetylspiramycin),
-
#> CLSI 2024 ...
-
#> WARNING
-
#> • No CLSI 2024 DISK breakpoints available for acetylspiramycin (ASP).
-
#> Interpreting disk diffusion zones: ' antibiotic ' (ASP, acetylspiramycin),
-
#> CLSI 2024 ...
-
#> WARNING
-
#> • No CLSI 2024 DISK breakpoints available for acetylspiramycin (ASP).
-
#> Warning: There were 2 warnings in `mutate()`.
-
#> The first warning was:
-
#> ℹ In argument: `cipro = (function (x, ...) ...`.
-
#> Caused by warning:
-
#> ! The following animal host(s) could not be coerced: "animal_species"
-
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> ℹ Assuming breakpoint_type = "animal" , since host is set.
-
#>
-
#>
-
#> Interpreting MIC values: ' cipro ' (CIP, ciprofloxacin), CLSI 2024 ...
-
#> OK
-
#> Interpreting disk diffusion zones: ' tobra ' (TOB, tobramycin), CLSI 2024 ...
-
#> OK
-
#> Interpreting disk diffusion zones: ' genta ' (GEN, gentamicin), CLSI 2024 ...
-
#> OK
-
#> Warning: There were 2 warnings in `mutate()`.
-
#> The first warning was:
-
#> ℹ In argument: `across(...)`.
-
#> Caused by warning:
-
#> ! The following animal host(s) could not be coerced: "animal_species"
-
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> Interpreting disk diffusion zones: column ' nitrofuratoin ' (NIT,
-
#> nitrofurantoin), EUCAST 2024 ...
-
#> OK
-
#> ℹ Assuming value "urine" in column ' specimen ' reflects a urinary tract
-
#> infection.
-
#> Use as.sir(uti = FALSE) to prevent this.
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> Interpreting disk diffusion zones: column ' nitrofuratoin ' (NIT,
-
#> nitrofurantoin), EUCAST 2024 ...
-
#> OK
-
#>
-
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
-
#> all the details of the breakpoint interpretations.
-
#>
-
#> Interpreting MIC values: ' cipro ' (CIP, ciprofloxacin), EUCAST 2024 ...
-
#> OK
-
#> Interpreting disk diffusion zones: ' tobra ' (TOB, tobramycin), EUCAST
-
#> 2024 ...
-
#> OK
-
#> Interpreting disk diffusion zones: ' genta ' (GEN, gentamicin), EUCAST
-
#> 2024 ...
-
#> OK
-
#> microorganism amoxicillin cipro tobra genta ERY
-
#> 1 Escherichia coli 8 <NA> S S R
+
#> Error: object 'guideline' not found
## Using base R ------------------------------------------------
@@ -839,20 +681,20 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
# return a 'logbook' about the results:
sir_interpretation_history ( )
-
#> # A tibble: 65 × 17
+
#> # A tibble: 29 × 17
#> datetime index method ab_given mo_given host_given input_given
#> <dttm> <int> <chr> <chr> <chr> <chr> <chr>
-
#> 1 2025-04-14 07:56:09 1 DISK ampicillin Strep pneu human 18
-
#> 2 2025-04-14 07:56:09 1 DISK AMP Escherich… human 20
-
#> 3 2025-04-14 07:56:09 1 DISK AMP Escherich… human 20
+
#> 1 2025-04-18 14:09:31 1 DISK ampicillin Strep pneu human 18
+
#> 2 2025-04-18 14:09:31 1 DISK AMP Escherich… human 20
+
#> 3 2025-04-18 14:09:31 1 DISK AMP Escherich… human 20
#> 4 NA NA NA NA NA NA NA
-
#> 5 2025-04-14 07:56:10 1 DISK GEN Escherich… human 18
-
#> 6 2025-04-14 07:56:10 1 DISK TOB Escherich… human 16
-
#> 7 2025-04-14 07:56:11 1 MIC AMX B_STRPT_P… human 2
-
#> 8 2025-04-14 07:56:11 1 MIC AMX B_STRPT_P… human 0.01
-
#> 9 2025-04-14 07:56:11 2 MIC AMX B_STRPT_P… human 2
-
#> 10 2025-04-14 07:56:11 3 MIC AMX B_STRPT_P… human 4
-
#> # ℹ 55 more rows
+
#> 5 2025-04-18 14:09:31 1 DISK GEN Escherich… human 18
+
#> 6 2025-04-18 14:09:32 1 DISK TOB Escherich… human 16
+
#> 7 2025-04-18 14:09:32 1 MIC AMX B_STRPT_P… human 2
+
#> 8 2025-04-18 14:09:33 1 MIC AMX B_STRPT_P… human 0.01
+
#> 9 2025-04-18 14:09:33 2 MIC AMX B_STRPT_P… human 2
+
#> 10 2025-04-18 14:09:33 3 MIC AMX B_STRPT_P… human 4
+
#> # ℹ 19 more rows
#> # ℹ 10 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>,
#> # outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
#> # breakpoint_S_R <chr>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index d11ac283d..751ba01b7 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -1,5 +1,5 @@
-
Get ATC Properties from WHOCC Website — atc_online_property • AMR (for R)
+
Get ATC Properties from WHOCC Website — atc_online_property • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
-
2.1.1.9239
+
2.1.1.9242
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index 13acad976..e2d1766b2 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -1,5 +1,5 @@
-Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text • AMR (for R)
+Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/av_property.html b/reference/av_property.html
index 9a2f3610c..7371b8fba 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -1,5 +1,5 @@
-Get Properties of an Antiviral Drug — av_property • AMR (for R)
+Get Properties of an Antiviral Drug — av_property • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/availability.html b/reference/availability.html
index d7911964c..508ff891e 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -1,5 +1,5 @@
-Check Availability of Columns — availability • AMR (for R)
+Check Availability of Columns — availability • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index 197479572..0ab002333 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -1,5 +1,5 @@
-Determine Bug-Drug Combinations — bug_drug_combinations • AMR (for R)
+Determine Bug-Drug Combinations — bug_drug_combinations • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index 1a2d63b15..fd066b3d3 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -1,5 +1,5 @@
-Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints • AMR (for R) Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints • AMR (for R) AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/count.html b/reference/count.html
index 7ea9c92e0..ae02d797a 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -1,5 +1,5 @@
-Count Available Isolates — count • AMR (for R) Count Available Isolates — count • AMR (for R)
Skip to contents
@@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index b231aaafb..bb412ccb1 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -1,5 +1,5 @@
-Define Custom EUCAST Rules — custom_eucast_rules • AMR (for R)
+Define Custom EUCAST Rules — custom_eucast_rules • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/dosage.html b/reference/dosage.html
index f1c1e8028..c4cd3e30a 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -1,5 +1,5 @@
-Data Set with Treatment Dosages as Defined by EUCAST — dosage • AMR (for R)
+Data Set with Treatment Dosages as Defined by EUCAST — dosage • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index ab8356f4f..8bc5cb7d5 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -1,5 +1,5 @@
-Apply EUCAST Rules — eucast_rules • AMR (for R) Apply EUCAST Rules — eucast_rules • AMR (for R)
Skip to contents
@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index ad8ae8dc3..5e384f84b 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -1,5 +1,5 @@
-Data Set with 2 000 Example Isolates — example_isolates • AMR (for R)
+Data Set with 2 000 Example Isolates — example_isolates • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index c78664d80..f82ee61d5 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -1,5 +1,5 @@
-Data Set with Unclean Data — example_isolates_unclean • AMR (for R)
+Data Set with Unclean Data — example_isolates_unclean • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
index f3d6843db..3d841e6c5 100644
--- a/reference/export_ncbi_biosample.html
+++ b/reference/export_ncbi_biosample.html
@@ -1,5 +1,5 @@
-Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample • AMR (for R)
+Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 5cae20298..59beb3618 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -1,5 +1,5 @@
-Determine First Isolates — first_isolate • AMR (for R) Determine First Isolates — first_isolate • AMR (for R)
Skip to contents
@@ -9,7 +9,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/g.test.html b/reference/g.test.html
index 1ada34a23..f8ecda49e 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -1,5 +1,5 @@
-G-test for Count Data — g.test • AMR (for R)
+G-test for Count Data — g.test • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/get_episode.html b/reference/get_episode.html
index 35c85f43e..cdc86bb80 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -1,5 +1,5 @@
-Determine Clinical or Epidemic Episodes — get_episode • AMR (for R)
+Determine Clinical or Epidemic Episodes — get_episode • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 1b3186b7e..8b92cf458 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -1,5 +1,5 @@
-PCA Biplot with ggplot2 — ggplot_pca • AMR (for R)
+PCA Biplot with ggplot2 — ggplot_pca • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 8e3bd4e5e..6ca6ba4eb 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -1,5 +1,5 @@
-AMR Plots with ggplot2 — ggplot_sir • AMR (for R)
+AMR Plots with ggplot2 — ggplot_sir • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 8348b0762..985fee9bb 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -1,5 +1,5 @@
-Guess Antibiotic Column — guess_ab_col • AMR (for R)
+Guess Antibiotic Column — guess_ab_col • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/index.html b/reference/index.html
index 75563b80a..51d56535b 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -1,5 +1,5 @@
-Package index • AMR (for R)
+Package index • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index adcdd1656..65991658f 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -1,5 +1,5 @@
-Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant • AMR (for R)
+Data Set Denoting Bacterial Intrinsic Resistance — intrinsic_resistant • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 3cda8b9a6..12201349c 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -1,5 +1,5 @@
-Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy • AMR (for R)
+Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/join.html b/reference/join.html
index ac5992752..89cc0b2d1 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -1,5 +1,5 @@
-Join microorganisms to a Data Set — join • AMR (for R)
+Join microorganisms to a Data Set — join • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 416034e5d..c1ef811ab 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -1,5 +1,5 @@
-(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials • AMR (for R)
+(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials • AMR (for R)
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AMR (for R)
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diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 541d5a1a4..1482e59b9 100644
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-Kurtosis of the Sample — kurtosis • AMR (for R)
+Kurtosis of the Sample — kurtosis • AMR (for R)
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diff --git a/reference/like.html b/reference/like.html
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-Vectorised Pattern Matching with Keyboard Shortcut — like • AMR (for R)
+Vectorised Pattern Matching with Keyboard Shortcut — like • AMR (for R)
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AMR (for R)
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diff --git a/reference/mdro.html b/reference/mdro.html
index 8bbcec217..a4967cc82 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
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-Determine Multidrug-Resistant Organisms (MDRO) — mdro • AMR (for R)
+Determine Multidrug-Resistant Organisms (MDRO) — mdro • AMR (for R)
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diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
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-Calculate the Mean AMR Distance — mean_amr_distance • AMR (for R)
+Calculate the Mean AMR Distance — mean_amr_distance • AMR (for R)
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diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 9e54bbcd2..59b18ea21 100644
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-Data Set with 4 971 Common Microorganism Codes — microorganisms.codes • AMR (for R)
+Data Set with 4 971 Common Microorganism Codes — microorganisms.codes • AMR (for R)
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diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index 3be804a85..81127bf1a 100644
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-Data Set with 534 Microorganisms In Species Groups — microorganisms.groups • AMR (for R)
+Data Set with 534 Microorganisms In Species Groups — microorganisms.groups • AMR (for R)
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diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index a20794c72..e40e0a6a6 100644
--- a/reference/microorganisms.html
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-Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms • AMR (for R) Data Set with 78 679 Taxonomic Records of Microorganisms — microorganisms • AMR (for R)
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@@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
AMR (for R)
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diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index e338bfe47..81139ed86 100644
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-Calculate the Matching Score for Microorganisms — mo_matching_score • AMR (for R)
+Calculate the Matching Score for Microorganisms — mo_matching_score • AMR (for R)
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- 2.1.1.9239
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diff --git a/reference/mo_property.html b/reference/mo_property.html
index 10459c9fb..c172d2538 100644
--- a/reference/mo_property.html
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-Get Properties of a Microorganism — mo_property • AMR (for R)
+Get Properties of a Microorganism — mo_property • AMR (for R)
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- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 8fe9e4768..cdd06f804 100644
--- a/reference/mo_source.html
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-User-Defined Reference Data Set for Microorganisms — mo_source • AMR (for R) User-Defined Reference Data Set for Microorganisms — mo_source • AMR (for R)
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@@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
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diff --git a/reference/pca.html b/reference/pca.html
index abb7c3292..654459405 100644
--- a/reference/pca.html
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-Principal Component Analysis (for AMR) — pca • AMR (for R)
+Principal Component Analysis (for AMR) — pca • AMR (for R)
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- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/plot.html b/reference/plot.html
index 4758ada9e..b09923f83 100644
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-Plotting Helpers for AMR Data Analysis — plot • AMR (for R) Plotting Helpers for AMR Data Analysis — plot • AMR (for R)
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@@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
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diff --git a/reference/proportion.html b/reference/proportion.html
index 97cee4943..13c31beb0 100644
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@@ -1,5 +1,5 @@
-Calculate Antimicrobial Resistance — proportion • AMR (for R) Calculate Antimicrobial Resistance — proportion • AMR (for R)
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@@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
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diff --git a/reference/random.html b/reference/random.html
index e034497b4..bf65e153b 100644
--- a/reference/random.html
+++ b/reference/random.html
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-Random MIC Values/Disk Zones/SIR Generation — random • AMR (for R)
+Random MIC Values/Disk Zones/SIR Generation — random • AMR (for R)
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- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index f154ab275..59072c560 100644
--- a/reference/resistance_predict.html
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-Predict Antimicrobial Resistance — resistance_predict • AMR (for R) Predict Antimicrobial Resistance — resistance_predict • AMR (for R)
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@@ -9,7 +9,7 @@ NOTE: These functions are deprecated and will be removed in a future version. Us
AMR (for R)
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diff --git a/reference/skewness.html b/reference/skewness.html
index fa85cbd9f..dd8b9803f 100644
--- a/reference/skewness.html
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-Skewness of the Sample — skewness • AMR (for R) Skewness of the Sample — skewness • AMR (for R)
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@@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
AMR (for R)
- 2.1.1.9239
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diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html
index 14424fca5..03cc89b20 100644
--- a/reference/top_n_microorganisms.html
+++ b/reference/top_n_microorganisms.html
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-Filter Top n Microorganisms — top_n_microorganisms • AMR (for R)
+Filter Top n Microorganisms — top_n_microorganisms • AMR (for R)
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AMR (for R)
- 2.1.1.9239
+ 2.1.1.9242
diff --git a/reference/translate.html b/reference/translate.html
index 50be4aecf..bfe3a053d 100644
--- a/reference/translate.html
+++ b/reference/translate.html
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-Translate Strings from the AMR Package — translate • AMR (for R)
+Translate Strings from the AMR Package — translate • AMR (for R)
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- 2.1.1.9239
+ 2.1.1.9242
diff --git a/search.json b/search.json
index 40f14ccab..9f6b66cec 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\" mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\" mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Retrieved values from the microorganisms.codes data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> ℹ Microorganism translation was uncertain for four microorganisms. Run #> mo_uncertainties() to review these uncertainties, or use #> add_custom_microorganisms() to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #> dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella #> pneumoniae ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), #> Klebsiella pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii #> (0.500), Klebsiella planticola (0.500), Kingella potus (0.400), #> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis #> (0.363), and Kosakonia pseudosacchari (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #> Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #> (0.533), Staphylococcus pseudintermedius (0.532), Serratia #> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #> periodonticum (0.519) #> #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://amr-for-r.org/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>% mutate_at(vars(AMX:GEN), as.sir) # method 2, let the AMR package determine the eligible columns our_data <- our_data %>% mutate_if(is_sir_eligible, as.sir) # result: our_data #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 724 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a #> microbial ID was available) our_data_1st <- our_data %>% filter(first == TRUE) our_data_1st <- our_data %>% filter_first_isolate() our_data_1st #> # A tibble: 2,724 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE #> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,714 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #> patient_id hospital date #> Length:2724 Length:2724 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-07 #> Mode :character Mode :character Median :2015-06-03 #> Mean :2015-06-09 #> 3rd Qu.:2017-08-11 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :41.6% (n=1133) %S :52.6% (n=1432) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :16.4% (n=446) %I :12.2% (n=333) #> #2 :B_STPHY_AURS %R :42.0% (n=1145) %R :35.2% (n=959) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :52.5% (n=1431) %S :61.0% (n=1661) TRUE:2724 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.5% (n=176) %I : 3.0% (n=82) #> %R :41.0% (n=1117) %R :36.0% (n=981) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,724 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,… # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id hospital date bacteria AMX AMC CIP #> 260 3 1854 4 3 3 3 #> GEN first #> 3 1"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1518 #> 2 Staphylococcus aureus 730 #> 3 Streptococcus pneumoniae 426 #> 4 Klebsiella pneumoniae 326 our_data_1st %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1321 #> 2 Staphylococcus aureus 682 #> 3 Streptococcus pneumoniae 402 #> 4 Klebsiella pneumoniae 319"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>% select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,724 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2014-09-19 S #> 4 2015-12-10 S #> 5 2015-03-02 S #> 6 2018-03-31 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,724 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI R S #> 4 B_ESCHR_COLI S I #> 5 B_ESCHR_COLI S S #> 6 B_STPHY_AURS R S #> 7 B_ESCHR_COLI R S #> 8 B_ESCHR_COLI S S #> 9 B_STPHY_AURS S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,724 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI R S S S #> 4 B_ESCHR_COLI S I S S #> 5 B_ESCHR_COLI S S S S #> 6 B_STPHY_AURS R S R S #> 7 B_ESCHR_COLI R S S S #> 8 B_ESCHR_COLI S S S S #> 9 B_STPHY_AURS S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,714 more rows # filtering using AB selectors is also possible: our_data_1st %>% filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 981 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 971 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 more rows # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , …"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL) combined_ab"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10) # Recommended threshold: ≥30 example_isolates %>% top_n_microorganisms(n = 10) %>% group_by(age_group = age_groups(age, c(25, 50, 75)), gender) %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4203377 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.340 #> 2 B 0.551 #> 3 C 0.370"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"How to conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Klebsiella pneumoniae - assuming body site 'Non-meningitis'. my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 16.000 R #> 2 0.005 S #> 3 1.000 R #> 4 >=256.000 R #> 5 2.000 R #> 6 0.025 S #> 7 16.000 R #> 8 0.025 S #> 9 0.500 I #> 10 0.005 S #> # ℹ 90 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"How to conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25) ggplot(my_data, aes(x = group, y = MIC, colour = SIR)) + geom_jitter(width = 0.2, size = 2) + geom_boxplot(fill = NA, colour = \"grey40\") + scale_y_mic() + scale_colour_sir() + labs(title = \"MIC Distribution and SIR Interpretation\", x = \"Sample Groups\", y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-1-using-antimicrobial-selectors","dir":"Articles","previous_headings":"","what":"Example 1: Using Antimicrobial Selectors","title":"AMR with tidymodels","text":"leveraging power tidymodels AMR package, ’ll build reproducible machine learning workflow predict Gramstain microorganism two important antibiotic classes: aminoglycosides beta-lactams.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Prepare data: Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) # Your data could look like this: example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , … # Select relevant columns for prediction data <- example_isolates %>% # select AB results dynamically select(mo, aminoglycosides(), betalactams()) %>% # replace NAs with NI (not-interpretable) mutate(across(where(is.sir), ~replace_na(.x, \"NI\")), # make factors of SIR columns across(where(is.sir), as.integer), # get Gramstain of microorganisms mo = as.factor(mo_gramstain(mo))) %>% # drop NAs - the ones without a Gramstain (fungi, etc.) drop_na() #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antimicrobial selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>% step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #> #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>% set_engine(\"glm\") # Use the Generalised Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organises entire modelling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>% add_recipe(resistance_recipe) %>% # Add the preprocessing recipe add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #> #> • step_corr() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antimicrobial selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram stain 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split) # Testing set # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>% fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>% predict(testing_data) # Generate predictions probabilities <- fitted_workflow %>% predict(testing_data, type = \"prob\") # Generate probabilities predictions <- predictions %>% bind_cols(probabilities) %>% bind_cols(testing_data) # Combine with true labels predictions #> # A tibble: 394 × 24 #> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB #> #> 1 Gram-positive 1.07e- 1 8.93e- 1 Gram-p… 5 5 #> 2 Gram-positive 3.17e- 8 1.00e+ 0 Gram-p… 5 1 #> 3 Gram-negative 9.99e- 1 1.42e- 3 Gram-n… 5 5 #> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5 #> 5 Gram-negative 9.46e- 1 5.42e- 2 Gram-n… 5 5 #> 6 Gram-positive 1.07e- 1 8.93e- 1 Gram-p… 5 5 #> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5 #> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4 #> 9 Gram-negative 1 e+ 0 2.22e-16 Gram-n… 1 1 #> 10 Gram-positive 6.05e-11 1.00e+ 0 Gram-p… 4 4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK , KAN , PEN , OXA , FLC , #> # AMX , AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , IPM , MEM # Evaluate model performance metrics <- predictions %>% metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics metrics #> # A tibble: 2 × 3 #> .metric .estimator .estimate #>