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271 lines
9.5 KiB
Markdown
271 lines
9.5 KiB
Markdown
# Count Available Isolates
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These functions can be used to count resistant/susceptible microbial
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isolates. All functions support quasiquotation with pipes, can be used
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in [`summarise()`](https://dplyr.tidyverse.org/reference/summarise.html)
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from the `dplyr` package and also support grouped variables, see
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*Examples*.
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`count_resistant()` should be used to count resistant isolates,
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`count_susceptible()` should be used to count susceptible isolates.
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## Usage
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``` r
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count_resistant(..., only_all_tested = FALSE)
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count_susceptible(..., only_all_tested = FALSE)
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count_S(..., only_all_tested = FALSE)
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count_SI(..., only_all_tested = FALSE)
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count_I(..., only_all_tested = FALSE)
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count_IR(..., only_all_tested = FALSE)
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count_R(..., only_all_tested = FALSE)
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count_all(..., only_all_tested = FALSE)
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n_sir(..., only_all_tested = FALSE)
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count_df(data, translate_ab = "name", language = get_AMR_locale(),
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combine_SI = TRUE)
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```
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## Arguments
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- ...:
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One or more vectors (or columns) with antibiotic interpretations. They
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will be transformed internally with
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[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) if needed.
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- only_all_tested:
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(for combination therapies, i.e. using more than one variable for
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`...`): a [logical](https://rdrr.io/r/base/logical.html) to indicate
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that isolates must be tested for all antimicrobials, see section
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*Combination Therapy* below.
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- data:
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A [data.frame](https://rdrr.io/r/base/data.frame.html) containing
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columns with class [`sir`](https://amr-for-r.org/reference/as.sir.md)
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(see [`as.sir()`](https://amr-for-r.org/reference/as.sir.md)).
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- translate_ab:
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A column name of the
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[antimicrobials](https://amr-for-r.org/reference/antimicrobials.md)
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data set to translate the antibiotic abbreviations to, using
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[`ab_property()`](https://amr-for-r.org/reference/ab_property.md).
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- language:
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Language of the returned text - the default is the current system
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language (see
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[`get_AMR_locale()`](https://amr-for-r.org/reference/translate.md))
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and can also be set with the package option
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[`AMR_locale`](https://amr-for-r.org/reference/AMR-options.md). Use
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`language = NULL` or `language = ""` to prevent translation.
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- combine_SI:
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A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
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all values of S, SDD, and I must be merged into one, so the output
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only consists of S+SDD+I vs. R (susceptible vs. resistant) - the
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default is `TRUE`.
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## Value
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An [integer](https://rdrr.io/r/base/integer.html)
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## Details
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These functions are meant to count isolates. Use the
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[`resistance()`](https://amr-for-r.org/reference/proportion.md)/[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
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functions to calculate microbial resistance/susceptibility.
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The function `count_resistant()` is equal to the function `count_R()`.
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The function `count_susceptible()` is equal to the function
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`count_SI()`.
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The function `n_sir()` is an alias of `count_all()`. They can be used to
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count all available isolates, i.e. where all input antimicrobials have
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an available result (S, I or R). Their use is equal to
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[`n_distinct()`](https://dplyr.tidyverse.org/reference/n_distinct.html).
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Their function is equal to
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`count_susceptible(...) + count_resistant(...)`.
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The function `count_df()` takes any variable from `data` that has an
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[`sir`](https://amr-for-r.org/reference/as.sir.md) class (created with
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[`as.sir()`](https://amr-for-r.org/reference/as.sir.md)) and counts the
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number of S's, I's and R's. It also supports grouped variables. The
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function [`sir_df()`](https://amr-for-r.org/reference/proportion.md)
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works exactly like `count_df()`, but adds the percentage of S, I and R.
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## Interpretation of SIR
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In 2019, the European Committee on Antimicrobial Susceptibility Testing
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(EUCAST) has decided to change the definitions of susceptibility testing
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categories S, I, and R (<https://www.eucast.org/newsiandr>).
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This AMR package follows insight; use
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[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
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(equal to
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[`proportion_SI()`](https://amr-for-r.org/reference/proportion.md)) to
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determine antimicrobial susceptibility and `count_susceptible()` (equal
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to `count_SI()`) to count susceptible isolates.
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## Combination Therapy
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When using more than one variable for `...` (= combination therapy), use
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`only_all_tested` to only count isolates that are tested for all
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antimicrobials/variables that you test them for. See this example for
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two antimicrobials, Drug A and Drug B, about how
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[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
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works to calculate the %SI:
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--------------------------------------------------------------------
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only_all_tested = FALSE only_all_tested = TRUE
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----------------------- -----------------------
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Drug A Drug B considered considered considered considered
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susceptible tested susceptible tested
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-------- -------- ----------- ---------- ----------- ----------
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S or I S or I X X X X
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R S or I X X X X
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<NA> S or I X X - -
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S or I R X X X X
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R R - X - X
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<NA> R - - - -
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S or I <NA> X X - -
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R <NA> - - - -
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<NA> <NA> - - - -
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--------------------------------------------------------------------
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Please note that, in combination therapies, for `only_all_tested = TRUE`
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applies that:
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count_S() + count_I() + count_R() = count_all()
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proportion_S() + proportion_I() + proportion_R() = 1
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and that, in combination therapies, for `only_all_tested = FALSE`
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applies that:
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count_S() + count_I() + count_R() >= count_all()
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proportion_S() + proportion_I() + proportion_R() >= 1
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Using `only_all_tested` has no impact when only using one antibiotic as
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input.
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## See also
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[`proportion_*`](https://amr-for-r.org/reference/proportion.md) to
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calculate microbial resistance and susceptibility.
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## Examples
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``` r
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# example_isolates is a data set available in the AMR package.
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# run ?example_isolates for more info.
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# base R ------------------------------------------------------------
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count_resistant(example_isolates$AMX) # counts "R"
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#> [1] 804
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count_susceptible(example_isolates$AMX) # counts "S" and "I"
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#> [1] 546
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count_all(example_isolates$AMX) # counts "S", "I" and "R"
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#> [1] 1350
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# be more specific
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count_S(example_isolates$AMX)
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#> [1] 543
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count_SI(example_isolates$AMX)
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#> [1] 546
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count_I(example_isolates$AMX)
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#> [1] 3
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count_IR(example_isolates$AMX)
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#> [1] 807
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count_R(example_isolates$AMX)
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#> [1] 804
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# Count all available isolates
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count_all(example_isolates$AMX)
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#> [1] 1350
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n_sir(example_isolates$AMX)
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#> [1] 1350
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# n_sir() is an alias of count_all().
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# Since it counts all available isolates, you can
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# calculate back to count e.g. susceptible isolates.
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# These results are the same:
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count_susceptible(example_isolates$AMX)
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#> [1] 546
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susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX)
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#> [1] 546
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# dplyr -------------------------------------------------------------
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# \donttest{
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if (require("dplyr")) {
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example_isolates %>%
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group_by(ward) %>%
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summarise(
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R = count_R(CIP),
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I = count_I(CIP),
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S = count_S(CIP),
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n1 = count_all(CIP), # the actual total; sum of all three
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n2 = n_sir(CIP), # same - analogous to n_distinct
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total = n()
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) # NOT the number of tested isolates!
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# Number of available isolates for a whole antibiotic class
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# (i.e., in this data set columns GEN, TOB, AMK, KAN)
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example_isolates %>%
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group_by(ward) %>%
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summarise(across(aminoglycosides(), n_sir))
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# Count co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy.
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# Please mind that `susceptibility()` calculates percentages right away instead.
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example_isolates %>% count_susceptible(AMC) # 1433
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example_isolates %>% count_all(AMC) # 1879
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example_isolates %>% count_susceptible(GEN) # 1399
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example_isolates %>% count_all(GEN) # 1855
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example_isolates %>% count_susceptible(AMC, GEN) # 1764
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example_isolates %>% count_all(AMC, GEN) # 1936
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# Get number of S+I vs. R immediately of selected columns
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example_isolates %>%
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select(AMX, CIP) %>%
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count_df(translate = FALSE)
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# It also supports grouping variables
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example_isolates %>%
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select(ward, AMX, CIP) %>%
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group_by(ward) %>%
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count_df(translate = FALSE)
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}
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#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
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#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
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#> # A tibble: 12 × 4
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#> ward antibiotic interpretation value
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#> <chr> <chr> <ord> <int>
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#> 1 Clinical AMX SI 357
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#> 2 Clinical AMX R 487
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#> 3 Clinical CIP SI 741
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#> 4 Clinical CIP R 128
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#> 5 ICU AMX SI 158
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#> 6 ICU AMX R 270
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#> 7 ICU CIP SI 362
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#> 8 ICU CIP R 85
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#> 9 Outpatient AMX SI 31
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#> 10 Outpatient AMX R 47
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#> 11 Outpatient CIP SI 78
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#> 12 Outpatient CIP R 15
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# }
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```
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