These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in `summarise()`

from the `dplyr`

package and also support grouped variables, see *Examples*.

`resistance()`

should be used to calculate resistance, `susceptibility()`

should be used to calculate susceptibility.

```
resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
proportion_df(
data,
translate_ab = "name",
language = get_AMR_locale(),
minimum = 30,
as_percent = FALSE,
combine_SI = TRUE,
combine_IR = FALSE
)
rsi_df(
data,
translate_ab = "name",
language = get_AMR_locale(),
minimum = 30,
as_percent = FALSE,
combine_SI = TRUE,
combine_IR = FALSE
)
```

**M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. https://clsi.org/standards/products/microbiology/documents/m39/.

- ...
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
the minimum allowed number of available (tested) isolates. Any isolate count lower than

`minimum`

will return`NA`

with a warning. The default number of`30`

isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see*Source*.- as_percent
a logical to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of

`0.123456`

will then be returned as`"12.3%"`

.- only_all_tested
(for combination therapies, i.e. using more than one variable for

`...`

): a logical to indicate that isolates must be tested for all antibiotics, see section*Combination Therapy*below- data
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

`ab_property()`

- language
language of the returned text, defaults to system language (see

`get_AMR_locale()`

) and can also be set with`getOption("AMR_locale")`

. Use`language = NULL`

or`language = ""`

to prevent translation.- combine_SI
a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the argument

`combine_IR`

, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is`TRUE`

.- combine_IR
a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see argument

`combine_SI`

.

The function `resistance()`

is equal to the function `proportion_R()`

. The function `susceptibility()`

is equal to the function `proportion_SI()`

.

**Remember that you should filter your data to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use `first_isolate()`

to determine them in your data set.

These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the `count()`

functions to count isolates. The function `susceptibility()`

is essentially equal to `count_susceptible() / count_all()`

. *Low counts can influence the outcome - the proportion functions may camouflage this, since they only return the proportion (albeit being dependent on the minimum argument).*

The function `proportion_df()`

takes any variable from `data`

that has an `rsi`

class (created with `as.rsi()`

) and calculates the proportions R, I and S. It also supports grouped variables. The function `rsi_df()`

works exactly like `proportion_df()`

, but adds the number of isolates.

When using more than one variable for `...`

(= combination therapy), use `only_all_tested`

to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Drug A and Drug B, about how `susceptibility()`

works to calculate the %SI:

Please note that, in combination therapies, for `only_all_tested = TRUE`

applies that:

and that, in combination therapies, for `only_all_tested = FALSE`

applies that:

Using `only_all_tested`

has no impact when only using one antibiotic as input.

The lifecycle of this function is **stable**. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.

If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (https://www.eucast.org/newsiandr/).

**R = Resistant**

A microorganism is categorised as*Resistant*when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.**S = Susceptible**

A microorganism is categorised as*Susceptible, standard dosing regimen*, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.**I = Susceptible, Increased exposure**

A microorganism is categorised as*Susceptible, Increased exposure*when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.

This AMR package honours this (new) insight. Use `susceptibility()`

(equal to `proportion_SI()`

) to determine antimicrobial susceptibility and `count_susceptible()`

(equal to `count_SI()`

) to count susceptible isolates.

On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data.

`count()`

to count resistant and susceptible isolates.

```
# example_isolates is a data set available in the AMR package.
?example_isolates
resistance(example_isolates$AMX) # determines %R
susceptibility(example_isolates$AMX) # determines %S+I
# be more specific
proportion_S(example_isolates$AMX)
proportion_SI(example_isolates$AMX)
proportion_I(example_isolates$AMX)
proportion_IR(example_isolates$AMX)
proportion_R(example_isolates$AMX)
# \donttest{
if (require("dplyr")) {
example_isolates %>%
group_by(hospital_id) %>%
summarise(r = resistance(CIP),
n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi
example_isolates %>%
group_by(hospital_id) %>%
summarise(R = resistance(CIP, as_percent = TRUE),
SI = susceptibility(CIP, as_percent = TRUE),
n1 = count_all(CIP), # the actual total; sum of all three
n2 = n_rsi(CIP), # same - analogous to n_distinct
total = n()) # NOT the number 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:
example_isolates %>% susceptibility(AMC) # %SI = 76.3%
example_isolates %>% count_all(AMC) # n = 1879
example_isolates %>% susceptibility(GEN) # %SI = 75.4%
example_isolates %>% count_all(GEN) # n = 1855
example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%
example_isolates %>% count_all(AMC, GEN) # n = 1939
# See Details on how `only_all_tested` works. Example:
example_isolates %>%
summarise(numerator = count_susceptible(AMC, GEN),
denominator = count_all(AMC, GEN),
proportion = susceptibility(AMC, GEN))
example_isolates %>%
summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
denominator = count_all(AMC, GEN, only_all_tested = TRUE),
proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))
example_isolates %>%
group_by(hospital_id) %>%
summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),
cipro_n = count_all(CIP),
genta_p = susceptibility(GEN, as_percent = TRUE),
genta_n = count_all(GEN),
combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
combination_n = count_all(CIP, GEN))
# Get proportions S/I/R immediately of all rsi columns
example_isolates %>%
select(AMX, CIP) %>%
proportion_df(translate = FALSE)
# It also supports grouping variables
example_isolates %>%
select(hospital_id, AMX, CIP) %>%
group_by(hospital_id) %>%
proportion_df(translate = FALSE)
}
# }
```