AMR is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with antibiotic properties by using evidence-based methods.
We created this package for academic research at the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). We released this under the GNU General Public Licence v2.0 (GPL-2) which makes it free for everybody to use and distribute, for personal and commercial use, but it may not be used for patent purposes. Read further about our GPL-2 licence here.
This package is ready-to-use for a professional environment by specialists in the following fields:
-
-
Medical Microbiology
+
Medical Microbiology:
Epidemiologists (both clinical microbiological and research)
Function age to calculate the (patients) age in years
Function age_groups to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
-
Functions filter_first_isolate and filter_first_weighted_isolate() to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
Added parameter combine_IR (TRUE/FALSE) to functions portion_df and count_df, to indicate that all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
Fix for portion_*(..., as_percent = TRUE) when minimal number of isolates would not be met
-
Added parameter also_single_tested for portion_* and count_* functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see ?portion
+
Added parameter also_single_tested for portion_* and count_* functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see ?portion
Using portion_* functions now throws a warning when total available isolate is below parameter minimum
Functions as.mo, as.rsi, as.mic, as.atc and freq will not set package name as attribute anymore
Functions as.mo and is.mo as replacements for as.bactid and is.bactid (since the microoganisms data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The as.mo function determines microbial IDs using Artificial Intelligence (AI):
For example: ratio(c(10, 500, 10), ratio = "1:2:1") would return 130, 260, 130
Support for Addins menu in RStudio to quickly insert %in% or %like% (and give them keyboard shortcuts), or to view the datasets that come with this package
@@ -576,13 +577,13 @@
A vignette to explain its usage
Support for rsi (antimicrobial resistance) to use as input
-as.mic("<=0.002; S") will return <=0.002
+as.mic("<=0.002; S") will return <=0.002
-
Now possible to coerce MIC values with a space between operator and value, i.e. as.mic("<= 0.002") now works
+
Now possible to coerce MIC values with a space between operator and value, i.e. as.mic("<= 0.002") now works
Classes rsi and mic do not add the attribute package.version anymore
-
Added "groups" option for atc_property(..., property). It will return a vector of the ATC hierarchy as defined by the WHO. The new function atc_groups is a convenient wrapper around this.
+
Added "groups" option for atc_property(..., property). It will return a vector of the ATC hierarchy as defined by the WHO. The new function atc_groups is a convenient wrapper around this.
Build-in host check for atc_property as it requires the host set by url to be responsive
Improved first_isolate algorithm to exclude isolates where bacteria ID or genus is unavailable
Fix for warning hybrid evaluation forced for row_number (924b62) from the dplyr package v0.7.5 and above
@@ -661,7 +662,7 @@
Full support for Windows, Linux and macOS
Full support for old R versions, only R-3.0.0 (April 2013) or later is needed (needed packages may have other dependencies)
-
Function n_rsi to count cases where antibiotic test results were available, to be used in conjunction with dplyr::summarise, see ?rsi
+
Function n_rsi to count cases where antibiotic test results were available, to be used in conjunction with dplyr::summarise, see ?rsi
Function guess_bactid to determine the ID of a microorganism based on genus/species or known abbreviations like MRSA
Function guess_atc to determine the ATC of an antibiotic based on name, trade name, or known abbreviations
Function freq to create frequency tables, with additional info in a header
@@ -674,7 +675,7 @@
Functions BRMO and MRGN are wrappers for Dutch and German guidelines, respectively
-
New algorithm to determine weighted isolates, can now be "points" or "keyantibiotics", see ?first_isolate
+
New algorithm to determine weighted isolates, can now be "points" or "keyantibiotics", see ?first_isolate
Use these functions to return a specific property of an antibiotic from the antibiotics data set, based on their ATC code. Get such a code with as.atc.
+
Use these functions to return a specific property of an antibiotic from the antibiotics data set, based on their ATC code. Get such a code with as.atc.
@@ -188,11 +188,11 @@
x
-
a (vector of a) valid atc code or any text that can be coerced to a valid atc with as.atc
+
a (vector of a) valid atc code or any text that can be coerced to a valid atc with as.atc
property
-
one of the column names of one of the antibiotics data set, like "atc" and "official"
+
one of the column names of one of the antibiotics data set, like "atc" and "official"
Convert antibiotic codes to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from antibiotics.
+
Convert antibiotic codes to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from antibiotics.
@@ -179,7 +179,7 @@
from, to
-
type to transform from and to. See antibiotics for its column names. WIth from = "guess" the from will be guessed from "atc", "certe" and "umcg". When using to = "atc", the ATC code will be searched using as.atc.
+
type to transform from and to. See antibiotics for its column names. WIth from = "guess" the from will be guessed from "atc", "certe" and "umcg". When using to = "atc", the ATC code will be searched using as.atc.
Use this function to determine the ATC code of one or more antibiotics. The data set antibiotics will be searched for abbreviations, official names and trade names.
+
Use this function to determine the ATC code of one or more antibiotics. The data set antibiotics will be searched for abbreviations, official names and trade names.
@@ -188,13 +188,13 @@
Details
-
Use the ab_property functions to get properties based on the returned ATC code, see Examples.
+
Use the ab_property functions to get properties based on the returned ATC code, see Examples.
In the ATC classification system, the active substances are classified in a hierarchy with five different levels. The system has fourteen main anatomical/pharmacological groups or 1st levels. Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups. The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance. The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
Source: https://www.whocc.no/atc/structure_and_principles/
See also
-
antibiotics for the dataframe that is being used to determine ATCs.
+
antibiotics for the dataframe that is being used to determine ATCs.
Examples
@@ -212,8 +212,8 @@
# Use ab_* functions to get a specific property based on an ATC codeCipro<-as.atc("cipro") # returns `J01MA02`
-ab_official(Cipro) # returns "Ciprofloxacin"
-ab_umcg(Cipro) # returns "CIPR", the code used in the UMCG
+ab_official(Cipro) # returns "Ciprofloxacin"
+ab_umcg(Cipro) # returns "CIPR", the code used in the UMCG# }
a data.frame to use for extra reference when translating x to a valid mo. The first column can be any microbial name, code or ID (used in your analysis or organisation), the second column must be a valid mo as found in the microorganisms data set.
+
a data.frame to use for extra reference when translating x to a valid mo. The first column can be any microbial name, code or ID (used in your analysis or organisation), the second column must be a valid mo as found in the microorganisms data set.
@@ -221,7 +221,7 @@
| ----> genus, a 5-7 letter acronym, mostly without vowels
----> taxonomic kingdom, either B (Bacteria), F (Fungi) or P (Protozoa)
-
Use the mo_property functions to get properties based on the returned code, see Examples.
+
Use the mo_property functions to get properties based on the returned code, see Examples.
This function uses Artificial Intelligence (AI) to help getting fast and logical results. It tries to find matches in this order:
Taxonomic kingdom: it first searches in bacteria, then fungi, then protozoa
Human pathogenic prevalence: it first searches in more prevalent microorganisms, then less prevalent ones
@@ -258,8 +258,8 @@ This package contains the complete microbial taxonomic data (wi
See also
-
microorganisms for the data.frame with ITIS content that is being used to determine ID's.
-The mo_property functions (like mo_genus, mo_gramstain) to get properties based on the returned code.
+
microorganisms for the data.frame with ITIS content that is being used to determine ID's.
+The mo_property functions (like mo_genus, mo_gramstain) to get properties based on the returned code.
Examples
@@ -291,10 +291,10 @@ The mo_property functions (like # Use mo_* functions to get a specific property based on `mo`
Ecoli<-as.mo("E. coli") # returns `B_ESCHR_COL`
-mo_genus(Ecoli) # returns "Escherichia"
-mo_gramstain(Ecoli) # returns "Gram negative"
+mo_genus(Ecoli) # returns "Escherichia"
+mo_gramstain(Ecoli) # returns "Gram negative"# but it uses as.mo internally too, so you could also just use:
-mo_genus("E. coli") # returns "Escherichia"
+mo_genus("E. coli") # returns "Escherichia"# }# NOT RUN {
@@ -303,16 +303,16 @@ The mo_property functions (like # the select function of tidyverse is also supported:
library(dplyr)
df$mo<-df%>%
- select(microorganism_name) %>%
+ select(microorganism_name) %>%as.mo()
# and can even contain 2 columns, which is convenient for genus/species combinations:df$mo<-df%>%
- select(genus, species) %>%
+ select(genus, species) %>%as.mo()
# although this works easier and does the same:df<-df%>%
- mutate(mo=as.mo(paste(genus, species)))
+ mutate(mo=as.mo(paste(genus, species)))
# }
@@ -206,16 +206,16 @@
plot(rsi_data) # for percentagesbarplot(rsi_data) # for frequencies
-freq(rsi_data) # frequency table with informative header
+freq(rsi_data) # frequency table with informative header# using dplyr's mutatelibrary(dplyr)
septic_patients%>%
- mutate_at(vars(peni:rifa), as.rsi)
+ mutate_at(vars(peni:rifa), as.rsi)
# fastest way to transform all columns with already valid AB results to class `rsi`:septic_patients%>%
- mutate_if(is.rsi.eligible,
+ mutate_if(is.rsi.eligible,
as.rsi)
# }
# NOT RUN {# What's the ATC of amoxicillin?
-guess_atc("Amoxicillin")
+guess_atc("Amoxicillin")
# [1] "J01CA04"# oral DDD (Defined Daily Dose) of amoxicillin
diff --git a/docs/reference/count.html b/docs/reference/count.html
index b2849e8de..9dc608324 100644
--- a/docs/reference/count.html
+++ b/docs/reference/count.html
@@ -191,7 +191,7 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
...
-
one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi if needed.
+
one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi if needed.
also_single_tested
@@ -199,11 +199,11 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
data
-
a data.frame containing columns with class rsi (see as.rsi)
+
a data.frame containing columns with class rsi (see as.rsi)
translate_ab
-
a column name of the antibiotics data set to translate the antibiotic abbreviations to, using abname. This can be set with getOption("get_antibiotic_names").
+
a column name of the antibiotics data set to translate the antibiotic abbreviations to, using abname. This can be set with getOption("get_antibiotic_names").
combine_IR
@@ -221,13 +221,13 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
Details
-
These functions are meant to count isolates. Use the portion_* functions to calculate microbial resistance.
-
n_rsi is an alias of count_all. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct. Their function is equal to count_S(...) + count_IR(...).
-
count_df takes any variable from data that has an "rsi" class (created with as.rsi) and counts the amounts of R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
+
These functions are meant to count isolates. Use the portion_* functions to calculate microbial resistance.
+
n_rsi is an alias of count_all. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct. Their function is equal to count_S(...) + count_IR(...).
+
count_df takes any variable from data that has an "rsi" class (created with as.rsi) and counts the amounts of R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
See also
-
portion_* to calculate microbial resistance and susceptibility.
+
portion_* to calculate microbial resistance and susceptibility.
Examples
@@ -251,17 +251,17 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
# calculate back to count e.g. non-susceptible isolates.# This results in the same:count_IR(septic_patients$amox)
-portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
+portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients%>%
- group_by(hospital_id) %>%
- summarise(R=count_R(cipr),
+ group_by(hospital_id) %>%
+ summarise(R=count_R(cipr),
I=count_I(cipr),
S=count_S(cipr),
n1=count_all(cipr), # the actual total; sum of all threen2=n_rsi(cipr), # same - analogous to n_distinct
- total=n()) # NOT the amount of tested isolates!
+ total=n()) # NOT the amount of tested isolates!# Count co-resistance between amoxicillin/clav acid and gentamicin,# so we can see that combination therapy does a lot more than mono therapy.
@@ -279,13 +279,13 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
# Get portions S/I/R immediately of all rsi columnsseptic_patients%>%
- select(amox, cipr) %>%
+ select(amox, cipr) %>%count_df(translate=FALSE)
# It also supports grouping variablesseptic_patients%>%
- select(hospital_id, amox, cipr) %>%
- group_by(hospital_id) %>%
+ select(hospital_id, amox, cipr) %>%
+ group_by(hospital_id) %>%count_df(translate=FALSE)
# }
diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html
index d9174890e..6ab019385 100644
--- a/docs/reference/eucast_rules.html
+++ b/docs/reference/eucast_rules.html
@@ -199,7 +199,7 @@
col_mo
-
column name of the unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.
+
column name of the unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.
column name of the unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.
+
column name of the unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.
col_testcode
@@ -214,7 +214,7 @@
col_keyantibiotics
-
column name of the key antibiotics to determine first weighted isolates, see key_antibiotics. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use col_keyantibiotics = FALSE to prevent this.
+
column name of the key antibiotics to determine first weighted isolates, see key_antibiotics. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use col_keyantibiotics = FALSE to prevent this.
episode_days
@@ -291,7 +291,7 @@ To conduct an analysis of antimicrobial resistance, you should only include the
@@ -302,11 +302,11 @@ To conduct an analysis of antimicrobial resistance, you should only include the
library(dplyr)
# Filter on first isolates:septic_patients%>%
- mutate(first_isolate=first_isolate(.,
+ mutate(first_isolate=first_isolate(.,
col_date="date",
col_patient_id="patient_id",
col_mo="mo")) %>%
- filter(first_isolate==TRUE)
+ filter(first_isolate==TRUE)
# Which can be shortened to:septic_patients%>%
@@ -317,15 +317,15 @@ To conduct an analysis of antimicrobial resistance, you should only include the
# Now let's see if first isolates matter:A<-septic_patients%>%
- group_by(hospital_id) %>%
- summarise(count=n_rsi(gent), # gentamicin availability
- resistance=portion_IR(gent)) # gentamicin resistance
+ group_by(hospital_id) %>%
+ summarise(count=n_rsi(gent), # gentamicin availability
+ resistance=portion_IR(gent)) # gentamicin resistanceB<-septic_patients%>%filter_first_weighted_isolate() %>%# the 1st isolate filter
- group_by(hospital_id) %>%
- summarise(count=n_rsi(gent), # gentamicin availability
- resistance=portion_IR(gent)) # gentamicin resistance
+ group_by(hospital_id) %>%
+ summarise(count=n_rsi(gent), # gentamicin availability
+ resistance=portion_IR(gent)) # gentamicin resistance# Have a look at A and B.# B is more reliable because every isolate is only counted once.
@@ -337,7 +337,7 @@ To conduct an analysis of antimicrobial resistance, you should only include the
# }# NOT RUN {# set key antibiotics to a new variable
-tbl$keyab<-key_antibiotics(tbl)
+tbl$keyab<-key_antibiotics(tbl)
tbl$first_isolate<-first_isolate(tbl)
diff --git a/docs/reference/freq.html b/docs/reference/freq.html
index 5f6bed31d..bbeabe993 100644
--- a/docs/reference/freq.html
+++ b/docs/reference/freq.html
@@ -317,34 +317,34 @@
# you could also use `select` or `pull` to get your variablesseptic_patients%>%
- filter(hospital_id=="A") %>%
- select(mo) %>%
+ filter(hospital_id=="A") %>%
+ select(mo) %>%freq()
# multiple selected variables will be pasted togetherseptic_patients%>%left_join_microorganisms%>%
- filter(hospital_id=="A") %>%
+ filter(hospital_id=="A") %>%freq(genus, species)
# group a variable and analyse anotherseptic_patients%>%
- group_by(hospital_id) %>%
+ group_by(hospital_id) %>%freq(gender)
# get top 10 bugs of hospital A as a vectorseptic_patients%>%
- filter(hospital_id=="A") %>%
+ filter(hospital_id=="A") %>%freq(mo) %>%top_freq(10)
# save frequency table to an objectyears<-septic_patients%>%
- mutate(year=format(date, "%Y")) %>%
+ mutate(year=format(date, "%Y")) %>%freq(year)
@@ -395,11 +395,11 @@
# only get selected columnsseptic_patients%>%freq(hospital_id) %>%
- select(item, percent)
+ select(item, percent)
septic_patients%>%freq(hospital_id) %>%
- select(-count, -cum_count)
+ select(-count, -cum_count)
# check differences between frequency tables
diff --git a/docs/reference/get_locale.html b/docs/reference/get_locale.html
index cd3df9605..b19d7c1f0 100644
--- a/docs/reference/get_locale.html
+++ b/docs/reference/get_locale.html
@@ -163,7 +163,7 @@
-
Determines the system language to be used for language-dependent output of AMR functions, like mo_gramstain and mo_type.
+
Determines the system language to be used for language-dependent output of AMR functions, like mo_gramstain and mo_type.
a data.frame with column(s) of class "rsi" (see as.rsi)
+
a data.frame with column(s) of class "rsi" (see as.rsi)
position
-
position adjustment of bars, either "fill" (default when fun is count_df), "stack" (default when fun is portion_df) or "dodge"
+
position adjustment of bars, either "fill" (default when fun is count_df), "stack" (default when fun is portion_df) or "dodge"
x
@@ -221,11 +221,11 @@
translate_ab
-
a column name of the antibiotics data set to translate the antibiotic abbreviations into, using abname. Default behaviour is to translate to official names according to the WHO. Use translate_ab = FALSE to disable translation.
+
a column name of the antibiotics data set to translate the antibiotic abbreviations into, using abname. Default behaviour is to translate to official names according to the WHO. Use translate_ab = FALSE to disable translation.
function to transform data, either count_df (default) or portion_df
nrow
@@ -251,9 +251,9 @@
Details
-
At default, the names of antibiotics will be shown on the plots using abname. This can be set with the option get_antibiotic_names (a logical value), so change it e.g. to FALSE with options(get_antibiotic_names = FALSE).
+
At default, the names of antibiotics will be shown on the plots using abname. This can be set with the option get_antibiotic_names (a logical value), so change it e.g. to FALSE with options(get_antibiotic_names = FALSE).
The functions
-geom_rsi will take any variable from the data that has an rsi class (created with as.rsi) using fun (count_df at default, can also be portion_df) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
+geom_rsi will take any variable from the data that has an rsi class (created with as.rsi) using fun (count_df at default, can also be portion_df) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
facet_rsi creates 2d plots (at default based on S/I/R) using facet_wrap.
scale_y_percent transforms the y axis to a 0 to 100% range using scale_continuous.
scale_rsi_colours sets colours to the bars: green for S, yellow for I and red for R, using scale_brewer.
@@ -268,7 +268,7 @@
library(ggplot2)
# get antimicrobial results for drugs against a UTI:
-ggplot(septic_patients%>%select(amox, nitr, fosf, trim, cipr)) +
+ggplot(septic_patients%>%select(amox, nitr, fosf, trim, cipr)) +
geom_rsi()
# prettify the plot using some additional functions:
@@ -282,17 +282,17 @@
# or better yet, simplify this using the wrapper function - a single command:septic_patients%>%
- select(amox, nitr, fosf, trim, cipr) %>%
+ select(amox, nitr, fosf, trim, cipr) %>%ggplot_rsi()
# get only portions and no counts:septic_patients%>%
- select(amox, nitr, fosf, trim, cipr) %>%
+ select(amox, nitr, fosf, trim, cipr) %>%ggplot_rsi(fun=portion_df)
# add other ggplot2 parameters as you like:septic_patients%>%
- select(amox, nitr, fosf, trim, cipr) %>%
+ select(amox, nitr, fosf, trim, cipr) %>%ggplot_rsi(width=0.5,
colour="black",
size=1,
@@ -301,25 +301,25 @@
# resistance of ciprofloxacine per age groupseptic_patients%>%
- mutate(first_isolate=first_isolate(.)) %>%
- filter(first_isolate==TRUE,
- mo==as.mo("E. coli")) %>%
+ mutate(first_isolate=first_isolate(.)) %>%
+ filter(first_isolate==TRUE,
+ mo==as.mo("E. coli")) %>%# `age_group` is also a function of this package:
- group_by(age_group=age_groups(age)) %>%
- select(age_group,
+ group_by(age_group=age_groups(age)) %>%
+ select(age_group,
cipr) %>%ggplot_rsi(x="age_group")
# }# NOT RUN {# for colourblind mode, use divergent colours from the viridis package:septic_patients%>%
- select(amox, nitr, fosf, trim, cipr) %>%
+ select(amox, nitr, fosf, trim, cipr) %>%ggplot_rsi() + scale_fill_viridis_d()
# it also supports groups (don't forget to use the group var on `x` or `facet`):septic_patients%>%
- select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
- group_by(hospital_id) %>%
+ select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
+ group_by(hospital_id) %>%ggplot_rsi(x=hospital_id,
facet=Antibiotic,
nrow=1) +
@@ -329,22 +329,22 @@
# genuine analysis: check 2 most prevalent microorganismsseptic_patients%>%# create new bacterial ID's, with all CoNS under the same group (Becker et al.)
- mutate(mo=as.mo(mo, Becker=TRUE)) %>%
+ mutate(mo=as.mo(mo, Becker=TRUE)) %>%# filter on top three bacterial ID's
- filter(mo%in%top_freq(freq(.$mo), 3)) %>%
+ filter(mo%in%top_freq(freq(.$mo), 3)) %>%# determine first isolates
- mutate(first_isolate=first_isolate(.,
+ mutate(first_isolate=first_isolate(.,
col_date="date",
col_patient_id="patient_id",
col_mo="mo")) %>%# filter on first isolates
- filter(first_isolate==TRUE) %>%
+ filter(first_isolate==TRUE) %>%# get short MO names (like "E. coli")
- mutate(mo=mo_shortname(mo, Becker=TRUE)) %>%
+ mutate(mo=mo_shortname(mo, Becker=TRUE)) %>%# select this short name and some antiseptic drugs
- select(mo, cfur, gent, cipr) %>%
+ select(mo, cfur, gent, cipr) %>%# group by MO
- group_by(mo) %>%
+ group_by(mo) %>%# plot the thing, putting MOs on the facetggplot_rsi(x=Antibiotic,
facet=mo,
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 0bf645849..6d9ee1ffa 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -286,7 +286,7 @@
Join the dataset microorganisms easily to an existing table or character vector.
+
Join the dataset microorganisms easily to an existing table or character vector.
@@ -188,7 +188,7 @@
by
-
a variable to join by - if left empty will search for a column with class mo (created with as.mo) or will be "mo" if that column name exists in x, could otherwise be a column name of x with values that exist in microorganisms$mo (like by = "bacteria_id"), or another column in microorganisms (but then it should be named, like by = c("my_genus_species" = "fullname"))
+
a variable to join by - if left empty will search for a column with class mo (created with as.mo) or will be "mo" if that column name exists in x, could otherwise be a column name of x with values that exist in microorganisms$mo (like by = "bacteria_id"), or another column in microorganisms (but then it should be named, like by = c("my_genus_species" = "fullname"))
These function can be used to determine first isolates (see first_isolate). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first weighted isolates.
+
These function can be used to determine first isolates (see first_isolate). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first weighted isolates.
@@ -187,7 +187,7 @@
col_mo
-
column name of the unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.
+
column name of the unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.
The function key_antibiotics returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using key_antibiotics_equal, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot ("."). The first_isolate function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible S. aureus (MSSA) found within the same episode (see episode parameter of first_isolate). Without key antibiotic comparison it wouldn't.
+
The function key_antibiotics returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using key_antibiotics_equal, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot ("."). The first_isolate function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible S. aureus (MSSA) found within the same episode (see episode parameter of first_isolate). Without key antibiotic comparison it wouldn't.
At default, the antibiotics that are used for Gram positive bacteria are (colum names): "amox", "amcl", "cfur", "pita", "cipr", "trsu" (until here is universal), "vanc", "teic", "tetr", "eryt", "oxac", "rifa".
At default, the antibiotics that are used for Gram negative bacteria are (colum names):
@@ -251,7 +251,7 @@
@@ -261,12 +261,12 @@
library(dplyr)
# set key antibiotics to a new variablemy_patients<-septic_patients%>%
- mutate(keyab=key_antibiotics(.)) %>%
- mutate(
+ mutate(keyab=key_antibiotics(.)) %>%
+ mutate(
# now calculate first isolates
- first_regular=first_isolate(., col_keyantibiotics=FALSE),
+ first_regular=first_isolate(., col_keyantibiotics=FALSE),
# and first WEIGHTED isolates
- first_weighted=first_isolate(., col_keyantibiotics="keyab")
+ first_weighted=first_isolate(., col_keyantibiotics="keyab")
)
# Check the difference, in this data set it results in 7% more isolates:
diff --git a/docs/reference/kurtosis.html b/docs/reference/kurtosis.html
index 7bcbecdb3..82f6e9e8c 100644
--- a/docs/reference/kurtosis.html
+++ b/docs/reference/kurtosis.html
@@ -193,7 +193,7 @@
A data set containing all bacteria codes of Certe MMB. These codes can be joined to data with an ID from microorganisms$mo (using left_join_microorganisms). GLIMS codes can also be translated to valid MOs with guess_mo.
+
A data set containing all bacteria codes of Certe MMB. These codes can be joined to data with an ID from microorganisms$mo (using left_join_microorganisms). GLIMS codes can also be translated to valid MOs with guess_mo.
@@ -173,12 +173,12 @@
A data.frame with 2,665 observations and 2 variables:
A data set containing the complete microbial taxonomy of the kingdoms Bacteria, Fungi and Protozoa. MO codes can be looked up using as.mo.
+
A data set containing the complete microbial taxonomy of the kingdoms Bacteria, Fungi and Protozoa. MO codes can be looked up using as.mo.
@@ -185,7 +185,7 @@
subkingdom
Taxonomic subkingdom of the microorganism as found in ITIS, see Source
kingdom
Taxonomic kingdom of the microorganism as found in ITIS, see Source
gramstain
Gram of microorganism, like "Gram negative"
-
prevalence
An integer based on estimated prevalence of the microorganism in humans. Used internally by as.mo, otherwise quite meaningless. It has a value of 25 for manually added items and a value of 1000 for all unprevalent microorganisms whose genus was somewhere in the top 250 (with another species).
+
prevalence
An integer based on estimated prevalence of the microorganism in humans. Used internally by as.mo, otherwise quite meaningless. It has a value of 25 for manually added items and a value of 1000 for all unprevalent microorganisms whose genus was somewhere in the top 250 (with another species).
ref
Author(s) and year of concerning publication as found in ITIS, see Source
@@ -203,7 +203,7 @@ This package contains the complete microbial taxonomic data (wi
A data set containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from microorganisms$mo (using left_join_microorganisms). GLIMS codes can also be translated to valid MOs with guess_mo.
+
A data set containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from microorganisms$mo (using left_join_microorganisms). GLIMS codes can also be translated to valid MOs with guess_mo.
Use these functions to return a specific property of a microorganism from the microorganisms data set. All input values will be evaluated internally with as.mo.
+
Use these functions to return a specific property of a microorganism from the microorganisms data set. All input values will be evaluated internally with as.mo.
any (vector of) text that can be coerced to a valid microorganism code with as.mo
+
any (vector of) text that can be coerced to a valid microorganism code with as.mo
language
-
language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
+
language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
diff --git a/docs/reference/portion.html b/docs/reference/portion.html
index 1e6fab8d8..a48d0f8ae 100644
--- a/docs/reference/portion.html
+++ b/docs/reference/portion.html
@@ -192,7 +192,7 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
...
-
one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
+
one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
minimum
@@ -208,11 +208,11 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
data
-
a data.frame containing columns with class rsi (see as.rsi)
+
a data.frame containing columns with class rsi (see as.rsi)
translate_ab
-
a column name of the antibiotics data set to translate the antibiotic abbreviations to, using abname. This can be set with getOption("get_antibiotic_names").
+
a column name of the antibiotics data set to translate the antibiotic abbreviations to, using abname. This can be set with getOption("get_antibiotic_names").
combine_IR
@@ -231,10 +231,10 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
Details
-
Remember that you should filter your table to let it contain only first isolates! Use first_isolate to determine them in your data set.
-
These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the count functions to count isolates. Low counts can infuence the outcome - these portion functions may camouflage this, since they only return the portion albeit being dependent on the minimum parameter.
-
portion_df takes any variable from data that has an "rsi" class (created with as.rsi) and calculates the portions R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
-
The old rsi function is still available for backwards compatibility but is deprecated.
+
Remember that you should filter your table to let it contain only first isolates! Use first_isolate to determine them in your data set.
+
These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the count functions to count isolates. Low counts can infuence the outcome - these portion functions may camouflage this, since they only return the portion albeit being dependent on the minimum parameter.
+
portion_df takes any variable from data that has an "rsi" class (created with as.rsi) and calculates the portions R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".
+
The old rsi function is still available for backwards compatibility but is deprecated.
To calculate the probability (p) of susceptibility of one antibiotic, we use this formula:
@@ -250,7 +250,7 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
See also
-
count_* to count resistant and susceptible isolates.
+
count_* to count resistant and susceptible isolates.
Examples
@@ -274,58 +274,58 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
septic_patients%>%portion_SI(amox)
septic_patients%>%
- group_by(hospital_id) %>%
- summarise(p=portion_S(cipr),
- n=n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
+ group_by(hospital_id) %>%
+ summarise(p=portion_S(cipr),
+ n=n_rsi(cipr)) # n_rsi works like n_distinct in dplyrseptic_patients%>%
- group_by(hospital_id) %>%
- summarise(R=portion_R(cipr, as_percent=TRUE),
+ group_by(hospital_id) %>%
+ summarise(R=portion_R(cipr, as_percent=TRUE),
I=portion_I(cipr, as_percent=TRUE),
S=portion_S(cipr, as_percent=TRUE),
- n=n_rsi(cipr), # works like n_distinct in dplyr
- total=n()) # NOT the amount of tested isolates!
+ n=n_rsi(cipr), # works like n_distinct in dplyr
+ total=n()) # NOT the amount of tested isolates!# Calculate co-resistance between amoxicillin/clav acid and gentamicin,# so we can see that combination therapy does a lot more than mono therapy:septic_patients%>%portion_S(amcl) # S = 67.1%
-septic_patients%>%count_all(amcl) # n = 1576
+septic_patients%>%count_all(amcl) # n = 1576septic_patients%>%portion_S(gent) # S = 74.0%
-septic_patients%>%count_all(gent) # n = 1855
+septic_patients%>%count_all(gent) # n = 1855septic_patients%>%portion_S(amcl, gent) # S = 92.0%
-septic_patients%>%count_all(amcl, gent) # n = 1517
+septic_patients%>%count_all(amcl, gent) # n = 1517septic_patients%>%
- group_by(hospital_id) %>%
- summarise(cipro_p=portion_S(cipr, as_percent=TRUE),
- cipro_n=count_all(cipr),
+ group_by(hospital_id) %>%
+ summarise(cipro_p=portion_S(cipr, as_percent=TRUE),
+ cipro_n=count_all(cipr),
genta_p=portion_S(gent, as_percent=TRUE),
- genta_n=count_all(gent),
+ genta_n=count_all(gent),
combination_p=portion_S(cipr, gent, as_percent=TRUE),
- combination_n=count_all(cipr, gent))
+ combination_n=count_all(cipr, gent))
# Get portions S/I/R immediately of all rsi columnsseptic_patients%>%
- select(amox, cipr) %>%
+ select(amox, cipr) %>%portion_df(translate=FALSE)
# It also supports grouping variablesseptic_patients%>%
- select(hospital_id, amox, cipr) %>%
- group_by(hospital_id) %>%
+ select(hospital_id, amox, cipr) %>%
+ group_by(hospital_id) %>%portion_df(translate=FALSE)
# }# NOT RUN {# calculate current empiric combination therapy of Helicobacter gastritis:my_table%>%
- filter(first_isolate==TRUE,
+ filter(first_isolate==TRUE,
genus=="Helicobacter") %>%
- summarise(p=portion_S(amox, metr), # amoxicillin with metronidazole
- n=count_all(amox, metr))
+ summarise(p=portion_S(amox, metr), # amoxicillin with metronidazole
+ n=count_all(amox, metr))
# }
This function is only useful for the MMB department of the UMCG. Use this function to import data by just defining the file parameter. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with as.mo and transform all antimicrobial columns with as.rsi.
+
This function is only useful for the MMB department of the UMCG. Use this function to import data by just defining the file parameter. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with as.mo and transform all antimicrobial columns with as.rsi.
The portion function to calculate resistance, lmglm
+
The portion function to calculate resistance, lmglm
Examples
@@ -252,7 +252,7 @@
# or use dplyr so you can actually read it:library(dplyr)
tbl%>%
- filter(first_isolate==TRUE,
+ filter(first_isolate==TRUE,
genus=="Haemophilus") %>%resistance_predict(amcl, date)
# }# NOT RUN {
@@ -261,11 +261,11 @@
library(dplyr)
septic_patients%>%# get bacteria properties like genus and species
- left_join_microorganisms("mo") %>%
+ left_join_microorganisms("mo") %>%# calculate first isolates
- mutate(first_isolate=first_isolate(.)) %>%
+ mutate(first_isolate=first_isolate(.)) %>%# filter on first E. coli isolates
- filter(genus=="Escherichia",
+ filter(genus=="Escherichia",
species=="coli",
first_isolate==TRUE) %>%# predict resistance of cefotaxime for next years
@@ -279,7 +279,7 @@
if (!require(ggplot2)) {
data<-septic_patients%>%
- filter(mo==as.mo("E. coli")) %>%
+ filter(mo==as.mo("E. coli")) %>%resistance_predict(col_ab="amox",
col_date="date",
info=FALSE,
diff --git a/docs/reference/rsi.html b/docs/reference/rsi.html
index 91ad0e01e..63bc0d04e 100644
--- a/docs/reference/rsi.html
+++ b/docs/reference/rsi.html
@@ -163,7 +163,7 @@
-
This function is deprecated. Use the portion functions instead.
+
This function is deprecated. Use the portion functions instead.
@@ -175,7 +175,7 @@
ab1, ab2
-
vector (or column) with antibiotic interpretations. It will be transformed internally with as.rsi if needed.
+
vector (or column) with antibiotic interpretations. It will be transformed internally with as.rsi if needed.
40 different antibiotics with class rsi (see as.rsi); these column names occur in antibiotics data set and can be translated with abname
+
mo
ID of microorganism created with as.mo, see also microorganisms
+
peni:rifa
40 different antibiotics with class rsi (see as.rsi); these column names occur in antibiotics data set and can be translated with abname
@@ -199,7 +199,7 @@
# Add first isolates to our data set:my_data<-my_data%>%
- mutate(first_isolates=first_isolate(my_data, "date", "patient_id", "mo"))
+ mutate(first_isolates=first_isolate(my_data, "date", "patient_id", "mo"))
# -------- ## ANALYSIS #
@@ -209,22 +209,22 @@
# and numbers (n) of E. coli, divided by hospital:my_data%>%
- filter(mo==guess_mo("E. coli"),
+ filter(mo==guess_mo("E. coli"),
first_isolates==TRUE) %>%
- group_by(hospital_id) %>%
- summarise(n=n_rsi(amox),
- p=portion_IR(amox))
+ group_by(hospital_id) %>%
+ summarise(n=n_rsi(amox),
+ p=portion_IR(amox))
# 2. Get the amoxicillin/clavulanic acid resistance# percentages of E. coli, trend over the years:my_data%>%
- filter(mo==guess_mo("E. coli"),
+ filter(mo==guess_mo("E. coli"),
first_isolates==TRUE) %>%
- group_by(year=format(date, "%Y")) %>%
- summarise(n=n_rsi(amcl),
- p=portion_IR(amcl, minimum=20))
+ group_by(year=format(date, "%Y")) %>%
+ summarise(n=n_rsi(amcl),
+ p=portion_IR(amcl, minimum=20))
# }
diff --git a/docs/reference/skewness.html b/docs/reference/skewness.html
index 937802849..8bccdcf4f 100644
--- a/docs/reference/skewness.html
+++ b/docs/reference/skewness.html
@@ -195,7 +195,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr
These data.tables are transformed from the microorganisms and microorganisms data sets to improve speed of as.mo. They are meant for internal use only, and are only mentioned here for reference.
+
These data.tables are transformed from the microorganisms and microorganisms data sets to improve speed of as.mo. They are meant for internal use only, and are only mentioned here for reference.
