Arguments
@@ -577,10 +578,10 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
# example data sets, with combined MIC values and disk zones
df_wide <- data.frame(
microorganism = "Escherichia coli",
-
AMP = as.mic(8),
-
CIP = as.mic(0.256),
-
GEN = as.disk(18),
-
TOB = as.disk(16),
+
amoxicillin = as.mic(8),
+
cipro = as.mic(0.256),
+
tobra = as.disk(16),
+
genta = as.disk(18),
ERY = "R"
)
df_long <- data.frame(
@@ -597,8 +598,8 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
df_wide %>% mutate_if(is.mic, as.sir)
df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
df_wide %>% mutate(across(where(is.mic), as.sir))
-
df_wide %>% mutate_at(vars(AMP:TOB), as.sir)
-
df_wide %>% mutate(across(AMP:TOB, as.sir))
+
df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)
+
df_wide %>% mutate(across(amoxicillin:tobra, as.sir))
# approaches that all work with additional arguments:
df_long %>%
@@ -613,17 +614,15 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
mo = "bacteria",
ab = "antibiotic",
guideline = "CLSI")))
-
df_long %>%
+
df_wide %>%
# given certain columns, e.g. from 'cipro' to 'genta'
mutate_at(vars(cipro:genta), as.sir,
mo = "bacteria",
-
ab = "antibiotic",
guideline = "CLSI")
-
df_long %>%
+
df_wide %>%
mutate(across(cipro:genta,
function(x) as.sir(x,
mo = "bacteria",
-
ab = "antibiotic",
guideline = "CLSI")))
# for veterinary breakpoints, add 'host':
@@ -642,18 +641,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
ab = "antibiotic",
host = "animal_species",
guideline = "CLSI")))
-
df_long %>%
-
# given certain columns, e.g. from AMP to TOB
+
df_wide %>%
mutate_at(vars(cipro:genta), as.sir,
mo = "bacteria",
ab = "antibiotic",
host = "animal_species",
guideline = "CLSI")
-
df_long %>%
+
df_wide %>%
mutate(across(cipro:genta,
function(x) as.sir(x,
mo = "bacteria",
-
ab = "antibiotic",
host = "animal_species",
guideline = "CLSI")))
@@ -673,66 +670,60 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
}
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#> all the details of the breakpoint interpretations.
-
#> Interpreting MIC values: 'AMP' (ampicillin) based on column
+
#> Interpreting MIC values: 'amoxicillin' (AMX) based on column
#> 'microorganism', EUCAST 2024...
#> OK
-
#> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column
+
#> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column
#> 'microorganism', EUCAST 2024...
-
#> NOTES
+
#> NOTE
#> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'.
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#> all the details of the breakpoint interpretations.
-
#> Interpreting MIC values: 'AMP' (ampicillin) based on column
+
#> Interpreting MIC values: 'amoxicillin' (AMX) based on column
#> 'microorganism', EUCAST 2024...
#> OK
-
#> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column
+
#> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column
#> 'microorganism', EUCAST 2024...
-
#> NOTES
+
#> NOTE
#> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'.
-
#> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column
-
#> 'microorganism', EUCAST 2024...
+
#> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on
+
#> column 'microorganism', EUCAST 2024...
#> OK
-
#> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column
-
#> 'microorganism', EUCAST 2024...
+
#> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin) based on
+
#> column 'microorganism', EUCAST 2024...
#> OK
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#> all the details of the breakpoint interpretations.
-
#> Interpreting MIC values: 'AMP' (ampicillin) based on column
+
#> Interpreting MIC values: 'amoxicillin' (AMX) based on column
#> 'microorganism', EUCAST 2024...
#> OK
-
#> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column
+
#> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column
#> 'microorganism', EUCAST 2024...
-
#> NOTES
+
#> NOTE
#> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'.
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#> all the details of the breakpoint interpretations.
-
#> Interpreting MIC values: 'AMP' (ampicillin) based on column
+
#> Interpreting MIC values: 'amoxicillin' (AMX) based on column
#> 'microorganism', EUCAST 2024...
#> OK
-
#> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column
+
#> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column
#> 'microorganism', EUCAST 2024...
-
#> NOTES
+
#> NOTE
#> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'.
-
#> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column
-
#> 'microorganism', EUCAST 2024...
-
#> OK
-
#> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column
-
#> 'microorganism', EUCAST 2024...
+
#> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on
+
#> column 'microorganism', EUCAST 2024...
#> OK
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#> all the details of the breakpoint interpretations.
-
#> Interpreting MIC values: 'AMP' (ampicillin) based on column
+
#> Interpreting MIC values: 'amoxicillin' (AMX) based on column
#> 'microorganism', EUCAST 2024...
#> OK
-
#> Interpreting MIC values: 'CIP' (ciprofloxacin) based on column
+
#> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column
#> 'microorganism', EUCAST 2024...
-
#> NOTES
+
#> NOTE
#> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'.
-
#> Interpreting disk diffusion zones: 'GEN' (gentamicin) based on column
-
#> 'microorganism', EUCAST 2024...
-
#> OK
-
#> Interpreting disk diffusion zones: 'TOB' (tobramycin) based on column
-
#> 'microorganism', EUCAST 2024...
+
#> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on
+
#> column 'microorganism', EUCAST 2024...
#> OK
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#> all the details of the breakpoint interpretations.
@@ -745,9 +736,104 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
#> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin),
#> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column
#> 'bacteria', CLSI 2024...
+
#> WARNING
+
#> • Returned an empty result, which is unexpected. Are all of mo, ab, and host set and available?
+
#> 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
-
#> Error in tbl_at_vars(tbl, vars, .include_group_vars = .include_group_vars, error_call = error_call): Can't select columns that don't exist.
-
#> ✖ Column `cipro` doesn't exist.
+
#> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024...
+
#> OK
+
#> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 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), CLSI 2024...
+
#> OK
+
#> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024...
+
#> OK
+
#> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024...
+
#> OK
+
#> 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.
+
#> ℹ Please note that in the absence of specific veterinary breakpoints for
+
#> certain animal hosts, breakpoints for dogs, cattle, swine, cats, horse,
+
#> aquatic, and poultry, in that order, are used as substitutes.
+
#> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin),
+
#> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column
+
#> 'bacteria', CLSI 2024...
+
#> NOTE
+
#> • Using dogs breakpoints since cattle for gentamicin (GEN) in Escherichia coli are not available.
+
#> 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.
+
#> ℹ Please note that in the absence of specific veterinary breakpoints for
+
#> certain animal hosts, breakpoints for dogs, cattle, swine, cats, horse,
+
#> aquatic, and poultry, in that order, are used as substitutes.
+
#> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin),
+
#> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column
+
#> 'bacteria', CLSI 2024...
+
#> NOTE
+
#> • Using dogs breakpoints since cattle for gentamicin (GEN) in Escherichia coli are not available.
+
#> 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.
+
#> ℹ Please note that in the absence of specific veterinary breakpoints for
+
#> certain animal hosts, breakpoints for dogs, cattle, swine, cats, horse,
+
#> aquatic, and poultry, in that order, are used as substitutes.
+
#> Interpreting MIC values: 'antibiotic' (TESTAB, test Antibiotic), CLSI
+
#> 2024...
+
#> WARNING
+
#> • No CLSI 2024 MIC breakpoints available for test Antibiotic (TESTAB).
+
#> Interpreting disk diffusion zones: 'antibiotic' (TESTAB, test Antibiotic),
+
#> CLSI 2024...
+
#> WARNING
+
#> • No CLSI 2024 DISK breakpoints available for test Antibiotic (TESTAB).
+
#> Interpreting disk diffusion zones: 'antibiotic' (TESTAB, test Antibiotic),
+
#> CLSI 2024...
+
#> WARNING
+
#> • No CLSI 2024 DISK breakpoints available for test Antibiotic (TESTAB).
+
#> 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.
+
#> ℹ Please note that in the absence of specific veterinary breakpoints for
+
#> certain animal hosts, breakpoints for dogs, cattle, swine, cats, horse,
+
#> aquatic, and poultry, in that order, are used as substitutes.
+
#> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024...
+
#> WARNING
+
#> • No CLSI 2024 MIC breakpoints available for ciprofloxacin (CIP).
+
#> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024...
+
#> OK
+
#> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024...
+
#> OK
+
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
+
#> all the details of the breakpoint interpretations.
+
#> Interpreting MIC values: column 'nitrofuratoin' (NIT, nitrofurantoin),
+
#> EUCAST 2024...
+
#> NOTE
+
#> • Breakpoints for nitrofurantoin (NIT) in Escherichia coli are only available for (uncomplicated) urinary tract infections (UTI); assuming uti = TRUE.
+
#> 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 MIC values: column 'nitrofuratoin' (NIT, nitrofurantoin),
+
#> EUCAST 2024...
+
#> NOTE
+
#> • Breakpoints for nitrofurantoin (NIT) in Escherichia coli are only available for (uncomplicated) urinary tract infections (UTI); assuming uti = TRUE.
+
#> 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
## Using base R ------------------------------------------------
@@ -755,38 +841,39 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
as.sir(df_wide)
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#> all the details of the breakpoint interpretations.
-
#> Interpreting MIC values: column 'AMP' (ampicillin), EUCAST 2024...
+
#> Interpreting MIC values: column 'amoxicillin' (AMX), EUCAST 2024...
#> OK
-
#> Interpreting MIC values: column 'CIP' (ciprofloxacin), EUCAST 2024...
-
#> NOTES
+
#> Interpreting MIC values: column 'cipro' (CIP, ciprofloxacin), EUCAST
+
#> 2024...
+
#> NOTE
#> • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'.
-
#> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST
+
#> Interpreting disk diffusion zones: column 'tobra' (TOB, tobramycin), EUCAST
#> 2024...
#> OK
-
#> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST
+
#> Interpreting disk diffusion zones: column 'genta' (GEN, gentamicin), EUCAST
#> 2024...
#> OK
#> Assigning class 'sir' to already clean column 'ERY' (erythromycin)...
#> OK
-
#> microorganism AMP CIP GEN TOB ERY
-
#> 1 Escherichia coli S I S S R
+
#> microorganism amoxicillin cipro tobra genta ERY
+
#> 1 Escherichia coli S I S S R
# return a 'logbook' about the results:
sir_interpretation_history()
-
#> # A tibble: 37 × 16
+
#> # A tibble: 55 × 16
#> datetime index ab_given mo_given host_given ab mo
#> * <dttm> <int> <chr> <chr> <chr> <ab> <mo>
-
#> 1 2024-06-17 12:56:05 4 AMX B_STRPT… human AMX B_STRPT_PNMN
-
#> 2 2024-06-17 12:56:11 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR
-
#> 3 2024-06-17 12:56:11 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR
-
#> 4 2024-06-17 12:56:05 3 AMX B_STRPT… human AMX B_STRPT_PNMN
-
#> 5 2024-06-17 12:56:11 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR
-
#> 6 2024-06-17 12:56:11 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR
-
#> 7 2024-06-17 12:56:05 2 AMX B_STRPT… human AMX B_STRPT_PNMN
-
#> 8 2024-06-17 12:56:11 2 cipro Escheri… human CIP B_[ORD]_ENTRBCTR
-
#> 9 2024-06-17 12:56:11 2 cipro Escheri… human CIP B_[ORD]_ENTRBCTR
-
#> 10 2024-06-17 12:56:04 1 ampicil… Strep p… human AMP B_STRPT_PNMN
-
#> # ℹ 27 more rows
+
#> 1 2024-06-17 14:56:02 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR
+
#> 2 2024-06-17 14:56:02 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR
+
#> 3 2024-06-17 14:56:02 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR
+
#> 4 2024-06-17 14:56:01 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR
+
#> 5 2024-06-17 14:56:02 3 tobra Escheri… horse TOB B_ESCHR_COLI
+
#> 6 2024-06-17 14:56:02 3 tobra Escheri… horse TOB B_ESCHR_COLI
+
#> 7 2024-06-17 14:56:01 2 cipro Escheri… human CIP B_[ORD]_ENTRBCTR
+
#> 8 2024-06-17 14:56:02 2 cipro Escheri… dogs CIP B_ESCHR_COLI
+
#> 9 2024-06-17 14:56:02 2 cipro Escheri… dogs CIP B_ESCHR_COLI
+
#> 10 2024-06-17 14:56:03 2 nitrofu… E. coli human NIT B_ESCHR_COLI
+
#> # ℹ 45 more rows
#> # ℹ 9 more variables: host <chr>, method <chr>, input <dbl>, outcome <sir>,
#> # notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
#> # breakpoint_S_R <chr>
@@ -801,7 +888,7 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with
#> all the details of the breakpoint interpretations.
#> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2024...
-
#> NOTES
+
#> NOTE
#> • Multiple breakpoints available for ampicillin (AMP) in Streptococcus pneumoniae - assuming body site 'Non-meningitis'.
#> Class 'sir'
#> [1] R
diff --git a/reference/atc_online.html b/reference/atc_online.html
index ef69c47f..0c143761 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -10,7 +10,7 @@
AMR (for R)
-
2.1.1.9056
+
2.1.1.9057
Response from CLSI and EUCAST
-
The CEO of CLSI and the chairman of EUCAST have endorsed the work and public use of this AMR
package (and consequently the use of their breakpoints) in June 2023, when future development of distributing clinical breakpoints was discussed in a meeting between CLSI, EUCAST, the WHO, and developers of WHONET and the AMR
package.
+
The CEO of CLSI and the chairman of EUCAST have endorsed the work and public use of this AMR
package (and consequently the use of their breakpoints) in June 2023, when future development of distributing clinical breakpoints was discussed in a meeting between CLSI, EUCAST, WHO, developers of WHONET software, and developers of this AMR
package.
diff --git a/reference/count.html b/reference/count.html
index 8ba50071..c60a65bd 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -12,7 +12,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
AMR (for R)
-
2.1.1.9056
+
2.1.1.9057
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index b0c29710..73b555bb 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/dosage.html b/reference/dosage.html
index 9d10d6e8..909472ac 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index e2a8f224..a865aba6 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index b23af175..a3a4f26e 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index dc5534d8..f4d7060d 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
index 936303d5..4a0e6217 100644
--- a/reference/export_ncbi_biosample.html
+++ b/reference/export_ncbi_biosample.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index cec8bdde..653dc0cb 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -12,7 +12,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/g.test.html b/reference/g.test.html
index 65932f17..c4fdc930 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/get_episode.html b/reference/get_episode.html
index 66c0d78b..290ff26d 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index ade84f66..c9a3830f 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 9dbed16e..3317bc97 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 9439d6f3..fd482ba7 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/index.html b/reference/index.html
index c1930e2a..0cfe9145 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
@@ -428,16 +428,26 @@
-
-
example_isolates
-
- - Data Set with 2 000 Example Isolates
-
-
-
microorganisms
- Data Set with 52 171 Microorganisms
-
+
antibiotics
antivirals
+
+ - Data Sets with 605 Antimicrobial Drugs
+
-
+
+
clinical_breakpoints
+
+ - Data Set with Clinical Breakpoints for SIR Interpretation
+
-
+
+
example_isolates
+
+ - Data Set with 2 000 Example Isolates
+
-
+
microorganisms.codes
- Data Set with 4 971 Common Microorganism Codes
@@ -448,11 +458,6 @@
- Data Set with 521 Microorganisms In Species Groups
-
-
antibiotics
antivirals
-
- - Data Sets with 605 Antimicrobial Drugs
-
-
-
intrinsic_resistant
- Data Set with Bacterial Intrinsic Resistance
@@ -473,11 +478,6 @@
- Data Set with Unclean Data
-
-
clinical_breakpoints
-
- - Data Set with Clinical Breakpoints for SIR Interpretation
-
-
-
WHONET
- Data Set with 500 Isolates - WHONET Example
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
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+ 2.1.1.9057
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
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diff --git a/reference/join.html b/reference/join.html
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- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
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--- a/reference/key_antimicrobials.html
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AMR (for R)
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diff --git a/reference/kurtosis.html b/reference/kurtosis.html
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AMR (for R)
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diff --git a/reference/like.html b/reference/like.html
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--- a/reference/like.html
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AMR (for R)
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diff --git a/reference/mdro.html b/reference/mdro.html
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AMR (for R)
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+ 2.1.1.9057
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 8db59f7e..286798fb 100644
--- a/reference/mean_amr_distance.html
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AMR (for R)
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diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
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diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
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+ 2.1.1.9057
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 257acc11..c953f004 100644
--- a/reference/microorganisms.html
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@@ -10,7 +10,7 @@
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+ 2.1.1.9057
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
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diff --git a/reference/mo_property.html b/reference/mo_property.html
index c51d273a..e5432a70 100644
--- a/reference/mo_property.html
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@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 1b4ea03a..b762beea 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/pca.html b/reference/pca.html
index 1a0a095f..135c4211 100644
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diff --git a/reference/plot.html b/reference/plot.html
index c7d841f8..32e74373 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -12,7 +12,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
AMR (for R)
- 2.1.1.9056
+ 2.1.1.9057
diff --git a/reference/proportion.html b/reference/proportion.html
index b374bfa6..ef096c00 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -12,7 +12,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
AMR (for R)
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+ 2.1.1.9057
diff --git a/reference/random.html b/reference/random.html
index f8f5759c..6597a3bf 100644
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@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9056
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diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
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diff --git a/reference/skewness.html b/reference/skewness.html
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@@ -12,7 +12,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
AMR (for R)
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+ 2.1.1.9057
diff --git a/reference/translate.html b/reference/translate.html
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diff --git a/search.json b/search.json
index 88e50c27..b4b9c3ea 100644
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@@ -1 +1 @@
-[{"path":"https://msberends.github.io/AMR/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://msberends.github.io/AMR/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://msberends.github.io/AMR/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://msberends.github.io/AMR/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 11 Dec 2022. 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 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. #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii #> (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi #> (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides #> (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae #> cloacae (0.571), Enterobacter cloacae complex (0.571), and Ehrlichia #> canis (0.567) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: 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), Kosakonia pseudosacchari (0.361), Kaistella #> palustris (0.333), Kocuria palustris (0.333), and Kocuria pelophila #> (0.333) #> -------------------------------------------------------------------------------- #> \"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), Salmonella #> Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness #> (0.587), Selenomonas artemidis (0.571), Salmonella choleraesuis #> arizonae (0.562), Streptococcus anginosus anginosus (0.561), and #> Salmonella Abaetetuba (0.548) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Serratia #> proteamaculans quinovora (0.545), Streptococcus pseudoporcinus (0.536), #> Staphylococcus pseudintermedius (0.532), Serratia proteamaculans #> proteamaculans (0.526), Salmonella Portanigra (0.524), Sphingomonas #> paucimobilis (0.520), Streptococcus pluranimalium (0.519), #> Streptococcus constellatus pharyngis (0.514), and Salmonella Pakistan #> (0.500) #> #> 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://msberends.github.io/AMR/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://msberends.github.io/AMR/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: 90% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 712 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 2,712 'phenotype-based' first isolates (90.4% 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: 2,712 × 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 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 4 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 5 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE #> 6 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 7 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 8 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 9 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> 10 Z1 A 2014-09-05 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,702 more rows"},{"path":"https://msberends.github.io/AMR/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:2712 Length:2712 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-05-03 #> Mode :character Mode :character Median :2015-06-16 #> Mean :2015-06-21 #> 3rd Qu.:2017-08-24 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :41.0% (n=1112) %S :52.0% (n=1409) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :16.1% (n=437) %I :12.0% (n=325) #> #2 :B_STPHY_AURS %R :42.9% (n=1163) %R :36.1% (n=978) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :51.5% (n=1396) %S :59.6% (n=1616) TRUE:2712 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.6% (n=178) %I : 3.1% (n=85) #> %R :42.0% (n=1138) %R :37.3% (n=1011) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,712 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\", \"Z1\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, S, S, R, R, S, S, S, S, R, S, S, S, R, R, R, R, S, R,… #> $ AMC I, I, I, S, S, S, S, S, S, S, S, S, I, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, R, S, 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, R, S, 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 1852 4 3 3 3 #> GEN first #> 3 1"},{"path":"https://msberends.github.io/AMR/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 1319 #> 2 Staphylococcus aureus 676 #> 3 Streptococcus pneumoniae 400 #> 4 Klebsiella pneumoniae 317"},{"path":"https://msberends.github.io/AMR/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: 2,712 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2015-12-10 S #> 4 2015-03-02 S #> 5 2018-03-31 S #> 6 2015-10-25 S #> 7 2019-06-19 S #> 8 2015-04-27 S #> 9 2011-06-21 S #> 10 2014-09-05 S #> # ℹ 2,702 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,712 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI S I #> 4 B_ESCHR_COLI S S #> 5 B_STPHY_AURS R S #> 6 B_ESCHR_COLI R S #> 7 B_ESCHR_COLI S S #> 8 B_STPHY_AURS S S #> 9 B_ESCHR_COLI S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,702 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,712 × 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 S I S S #> 4 B_ESCHR_COLI S S S S #> 5 B_STPHY_AURS R S R S #> 6 B_ESCHR_COLI R S S S #> 7 B_ESCHR_COLI S S S S #> 8 B_STPHY_AURS S S S S #> 9 B_ESCHR_COLI S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,702 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: 1,011 × 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 1,001 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 483 × 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 473 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: 483 × 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 473 more rows"},{"path":"https://msberends.github.io/AMR/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://msberends.github.io/AMR/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://msberends.github.io/AMR/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":"antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/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://msberends.github.io/AMR/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 WISCA, must state combination therapy antibiotics argument (similar Combination Antibiogram), define syndromic group syndromic_group argument (similar Syndromic Antibiogram) cases predefined based clinical demographic characteristics (e.g., endocarditis 75+ females). next example simplification without clinical characteristics, just gives idea WISCA can created:","code":"wisca <- antibiogram(example_isolates, antibiotics = c(\"AMC\", \"AMC+CIP\", \"TZP\", \"TZP+TOB\"), mo_transform = \"gramstain\", minimum = 10, # this should be >= 30, but now just as example syndromic_group = ifelse(example_isolates$age >= 65 & example_isolates$gender == \"M\", \"WISCA Group 1\", \"WISCA Group 2\")) wisca"},{"path":"https://msberends.github.io/AMR/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(wisca)"},{"path":"https://msberends.github.io/AMR/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: Author: Dr. Matthijs Berends, 26th Feb 2023","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4288348 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.342 #> 2 B 0.564 #> 3 C 0.372"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.1, 2016). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame( mo = c( \"Klebsiella\", \"Escherichia\" ), ampicillin = \"S\" ) oops #> mo ampicillin #> 1 Klebsiella S #> 2 Escherichia S eucast_rules(oops, info = FALSE) #> mo ampicillin #> 1 Klebsiella R #> 2 Escherichia S mo_is_intrinsic_resistant( c(\"Klebsiella\", \"Escherichia\"), \"ampicillin\" ) #> [1] TRUE FALSE mo_is_intrinsic_resistant( \"Klebsiella\", c(\"ampicillin\", \"kanamycin\") ) #> [1] TRUE FALSE data <- data.frame( mo = c( \"Staphylococcus aureus\", \"Enterococcus faecalis\", \"Escherichia coli\", \"Klebsiella pneumoniae\", \"Pseudomonas aeruginosa\" ), VAN = \"-\", # Vancomycin AMX = \"-\", # Amoxicillin COL = \"-\", # Colistin CAZ = \"-\", # Ceftazidime CXM = \"-\", # Cefuroxime PEN = \"S\", # Benzylenicillin FOX = \"S\", # Cefoxitin stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline( CIP == \"R\" & age > 60 ~ \"Elderly Type A\", ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #> 1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #> 2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #> 3. Otherwise: Negative #> #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #> Negative Elderly Type A Elderly Type B #> 1070 198 732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>% mdro() %>% freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame( rifampicin = random_sir(5000), isoniazid = random_sir(5000), gatifloxacin = random_sir(5000), ethambutol = random_sir(5000), pyrazinamide = random_sir(5000), moxifloxacin = random_sir(5000), kanamycin = random_sir(5000) ) my_TB_data <- data.frame( RIF = random_sir(5000), INH = random_sir(5000), GAT = random_sir(5000), ETH = random_sir(5000), PZA = random_sir(5000), MFX = random_sir(5000), KAN = random_sir(5000) ) head(my_TB_data) #> rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1 I R S S S S #> 2 S S I R R S #> 3 R I I I R I #> 4 I S S S S S #> 5 I I I S I S #> 6 R S R S I I #> kanamycin #> 1 R #> 2 I #> 3 S #> 4 I #> 5 I #> 6 I mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) #> ℹ No column found as input for col_mo, assuming all rows contain #> Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) #> Rows: 2,000 #> Columns: 46 #> $ date 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>% group_by( order = mo_order(mo), # group on anything, like order genus = mo_genus(mo) ) %>% # and genus as we do here summarise_if(is.sir, resistance) %>% # then get resistance of all drugs select( order, genus, AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT ) # and select only relevant columns head(resistance_data) #> # A tibble: 6 × 10 #> # Groups: order [5] #> order genus AMC CXM CTX CAZ GEN TOB TMP SXT #> #> 1 (unknown order) (unknown ge… NA NA NA NA NA NA NA NA #> 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA #> 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA #> 4 Campylobacterales Campylobact… NA NA NA NA NA NA NA NA #> 5 Caryophanales Gemella NA NA NA NA NA NA NA NA #> 6 Caryophanales Listeria NA NA NA NA NA NA NA NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #> \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\" #> Importance of components: #> PC1 PC2 PC3 PC4 PC5 PC6 PC7 #> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\" \"Enterobacterales\" \"Lactobacillales\" \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups: can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) + ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 5: S < SDD < < R < NI Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>% # get microbial ID based on given organism mutate(mo = as.mo(Organism)) %>% # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `sir` class mutate_at(vars(AMP_ND10:CIP_EE), as.sir) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_sir() function:","code":"data %>% group_by(Country) %>% select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>% ggplot_sir(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 52 171 rows 23 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, oxygen_tolerance, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence, snomed. data set R available microorganisms, load AMR package. last updated 14 July 2023 08:49:06 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.2 MB) Download tab-separated text file (11.7 MB) Download Microsoft Excel workbook (5.2 MB) Download Apache Feather file (5.5 MB) Download Apache Parquet file (2.6 MB) Download SAS transport (XPT) file (48.6 MB) Download IBM SPSS Statistics data file (17.8 MB) Download Stata DTA file (48.6 MB) NOTE: exported files SAS, SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. file structures compression techniques inefficient. Advice? Use R instead. ’s free much better many ways. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy five kingdoms List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de December 11th, 2022. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org January 8th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://phinvads.cdc.gov","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotics-antibiotic-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antibiotics: Antibiotic (+Antifungal) Drugs","title":"Data sets for download / own use","text":"data set 485 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antibiotics, load AMR package. last updated 14 June 2024 20:39:01 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (44 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (73 kB) Download Apache Feather file (0.2 MB) Download Apache Parquet file (0 kB) Download SAS transport (XPT) file (1.5 MB) Download IBM SPSS Statistics data file (0.4 MB) Download Stata DTA file (0.5 MB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antibiotics: Antibiotic (+Antifungal) Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antivirals, load AMR package. last updated 20 October 2023 12:51:48 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (17 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (13 kB) Download SAS transport (XPT) file (73 kB) Download IBM SPSS Statistics data file (32 kB) Download Stata DTA file (78 kB) tab-separated text file Microsoft Excel workbook, SAS, SPSS Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir","dir":"Articles","previous_headings":"","what":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","title":"Data sets for download / own use","text":"data set 34 085 rows 13 columns, containing following column names:guideline, type, host, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti. data set R available clinical_breakpoints, load AMR package. last updated 14 June 2024 20:39:01 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (71 kB) Download tab-separated text file (2.9 MB) Download Microsoft Excel workbook (1.6 MB) Download Apache Feather file (4.9 MB) Download Apache Parquet file (0 kB) Download SAS transport (XPT) file (9.3 MB) Download IBM SPSS Statistics data file (5.6 MB) Download Stata DTA file (9.2 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2011-2024) EUCAST (2011-2024). Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate development WHONET software. CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET AMR package. NOTE: AMR package (WHONET software well) contains internal methods apply guidelines, rather complex. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 134 634 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 16 December 2022 15:10:43 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (78 kB) Download tab-separated text file (5.1 MB) Download Microsoft Excel workbook (1.3 MB) Download Apache Feather file (1.2 MB) Download Apache Parquet file (0.2 MB) Download SAS transport (XPT) file (9.5 MB) Download IBM SPSS Statistics data file (7.4 MB) Download Stata DTA file (9.5 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 503 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt, eucast_version. data set R available dosage, load AMR package. last updated 22 June 2023 13:10:59 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (43 kB) Download Microsoft Excel workbook (25 kB) Download Apache Feather file (21 kB) Download Apache Parquet file (9 kB) Download SAS transport (XPT) file (0.1 MB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : (), ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021), ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2 000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, 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, RIF. data set R available example_isolates, load AMR package. last updated 15 June 2024 13:33:49 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3 000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP, GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-groups-species-groups-and-microbiological-complexes","dir":"Articles","previous_headings":"","what":"microorganisms.groups: Species Groups and Microbiological Complexes","title":"Data sets for download / own use","text":"data set 521 rows 4 columns, containing following column names:mo_group, mo, mo_group_name, mo_name. data set R available microorganisms.groups, load AMR package. last updated 14 July 2023 08:49:06 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (49 kB) Download Microsoft Excel workbook (20 kB) Download Apache Feather file (19 kB) Download Apache Parquet file (13 kB) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (81 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-8","dir":"Articles","previous_headings":"microorganisms.groups: Species Groups and Microbiological Complexes","what":"Source","title":"Data sets for download / own use","text":"data set contains species groups microbiological complexes, used clinical_breakpoints data set.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-codes-common-laboratory-codes","dir":"Articles","previous_headings":"","what":"microorganisms.codes: Common Laboratory Codes","title":"Data sets for download / own use","text":"data set 4 971 rows 2 columns, containing following column names:code mo. data set R available microorganisms.codes, load AMR package. last updated 16 June 2024 09:28:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (22 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (91 kB) Download Apache Feather file (0.1 MB) Download Apache Parquet file (0 kB) Download SAS transport (XPT) file (0 kB) Download IBM SPSS Statistics data file (0.1 MB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-9","dir":"Articles","previous_headings":"microorganisms.codes: Common Laboratory Codes","what":"Source","title":"Data sets for download / own use","text":"data set contains commonly used codes microorganisms, laboratory systems WHONET.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions: fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_sir_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\") # or: example_isolates %>% resistance_predict( col_ab = \"TZP\", model = \"binomial\" ) # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>% resistance_predict( col_ab = \"TZP\", model = \"binomial\" ) predict_TZP #> # A tibble: 33 × 7 #> year value se_min se_max observations observed estimated #> *