AMR/reference/as.mic.md

# Transform Input to Minimum Inhibitory Concentrations (MIC)

This transforms vectors to a new class `mic`, which treats the input as
decimal numbers, while maintaining operators (such as "\>=") and only
allowing valid MIC values known to the field of (medical) microbiology.

## Usage

``` r
as.mic(x, na.rm = FALSE, keep_operators = "all",
  round_to_next_log2 = FALSE)

is.mic(x)

NA_mic_

rescale_mic(x, mic_range, keep_operators = "edges", as.mic = TRUE,
  round_to_next_log2 = FALSE)

mic_p50(x, na.rm = FALSE, ...)

mic_p90(x, na.rm = FALSE, ...)

# S3 method for class 'mic'
droplevels(x, as.mic = FALSE, ...)
```

## Arguments

- x:

  A [character](https://rdrr.io/r/base/character.html) or
  [numeric](https://rdrr.io/r/base/numeric.html) vector.

- na.rm:

  A [logical](https://rdrr.io/r/base/logical.html) indicating whether
  missing values should be removed.

- keep_operators:

  A [character](https://rdrr.io/r/base/character.html) specifying how to
  handle operators (such as `>` and `<=`) in the input. Accepts one of
  three values: `"all"` (or `TRUE`) to keep all operators, `"none"` (or
  `FALSE`) to remove all operators, or `"edges"` to keep operators only
  at both ends of the range.

- round_to_next_log2:

  A [logical](https://rdrr.io/r/base/logical.html) to round up all
  values to the next log2 level, that are not either 0.0001, 0.0002,
  0.0005, 0.001, 0.002, 0.004, 0.008, 0.016, 0.032, 0.064, 0.125, 0.25,
  0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, or 4096.
  Values that are already in this list (with or without operators), are
  left unchanged (including any operators).

- mic_range:

  A manual range to rescale the MIC values, e.g.,
  `mic_range = c(0.001, 32)`. Use `NA` to prevent rescaling on one side,
  e.g., `mic_range = c(NA, 32)`.

- as.mic:

  A \[logical\] to indicate whether the \`mic\` class should be kept -
  the default is \`TRUE\` for \[rescale_mic()\] and \`FALSE\` for
  \[droplevels()\]. When setting this to \`FALSE\` in \[rescale_mic()\],
  the output will have factor levels that acknowledge \`mic_range\`.

- ...:

  Arguments passed on to methods.

## Value

Ordered \[factor\] with additional class \[\`mic\`\], that in
mathematical operations acts as a \[numeric\] vector. Bear in mind that
the outcome of any mathematical operation on MICs will return a
\[numeric\] value.

## Details

To interpret MIC values as SIR values, use \[as.sir()\] on MIC values.
It supports guidelines from EUCAST (\`r min(as.integer(gsub("\[^0-9\]",
"", subset(clinical_breakpoints, guideline

This class for MIC values is a quite a special data type: formally it is
an ordered \[factor\] with valid MIC values as \[factor\] levels (to
make sure only valid MIC values are retained), but for any mathematical
operation it acts as decimal numbers:

“\` x \<- random_mic(10) x \#\> Class \<mic\> \#\> \[1\] 16 1 8 8 64
\>=128 0.0625 32 32 16

is.factor(x) \#\> \[1\] TRUE

x\[1\] \* 2 \#\> \[1\] 32

median(x) \#\> \[1\] 26 “\`

This makes it possible to maintain operators that often come with MIC
values, such "\>=" and "\<=", even when filtering using \[numeric\]
values in data analysis, e.g.:

“\` x\[x \> 4\] \#\> Class \<mic\> \#\> \[1\] 16 8 8 64 \>=128 32 32 16

df \<- data.frame(x, hospital = "A") subset(df, x \> 4) \# or with
dplyr: df \#\> x hospital \#\> 1 16 A \#\> 5 64 A \#\> 6 \>=128 A \#\> 8
32 A \#\> 9 32 A \#\> 10 16 A “\`

All so-called \[group generic functions\]\[groupGeneric()\] are
implemented for the MIC class (such as \`!\`, \`!=\`, \`\<\`, \`\>=\`,
\[exp()\], \[log2()\]). Some mathematical functions are also implemented
(such as \[quantile()\], \[median()\], \[fivenum()\]). Since \[sd()\]
and \[var()\] are non-generic functions, these could not be extended.
Use \[mad()\] as an alternative, or use e.g. \`sd(as.numeric(x))\` where
\`x\` is your vector of MIC values.

Using \[as.double()\] or \[as.numeric()\] on MIC values will remove the
operators and return a numeric vector. Do \*\*not\*\* use
\[as.integer()\] on MIC values as by the R convention on \[factor\]s, it
will return the index of the factor levels (which is often useless for
regular users).

The function \[is.mic()\] detects if the input contains class \`mic\`.
If the input is a \[data.frame\] or \[list\], it iterates over all
columns/items and returns a \[logical\] vector.

Use \[droplevels()\] to drop unused levels. At default, it will return a
plain factor. Use \`droplevels(..., as.mic = TRUE)\` to maintain the
\`mic\` class.

With \[rescale_mic()\], existing MIC ranges can be limited to a defined
range of MIC values. This can be useful to better compare MIC
distributions.

For \`ggplot2\`, use one of the
\[\`scale\_\*\_mic()\`\]\[scale_x_mic()\] functions to plot MIC values.
They allows custom MIC ranges and to plot intermediate log2 levels for
missing MIC values.

`NA_mic_` is a missing value of the new `mic` class, analogous to e.g.
base R's [`NA_character_`](https://rdrr.io/r/base/NA.html).

Use `mic_p50()` and `mic_p90()` to get the 50th and 90th percentile of
MIC values. They return 'normal'
[numeric](https://rdrr.io/r/base/numeric.html) values.

## See also

[`as.sir()`](https://amr-for-r.org/reference/as.sir.md)

## Examples

``` r
mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
mic_data
#> Class <mic>
#> [1] >=32    1       1       1       8       <=0.128 8       16      16     
is.mic(mic_data)
#> [1] TRUE

# this can also coerce combined MIC/SIR values:
as.mic("<=0.002; S")
#> Class <mic>
#> [1] <=0.002

# mathematical processing treats MICs as, and returns, numeric values
fivenum(mic_data)
#> [1]  0.128  1.000  8.000 16.000 32.000
quantile(mic_data)
#>     0%    25%    50%    75%   100% 
#>  0.128  1.000  8.000 16.000 32.000 
all(mic_data < 512)
#> [1] TRUE

# rescale MICs using rescale_mic()
rescale_mic(mic_data, mic_range = c(4, 16))
#> Class <mic>
#> [1] >=16 <=4  <=4  <=4  8    <=4  8    >=16 >=16

# round up to nearest log2 level, e.g. for CLSI breakpoint interpretation:
c(1:8)
#> [1] 1 2 3 4 5 6 7 8
as.mic(c(1:8), round_to_next_log2 = TRUE)
#> Class <mic>
#> [1] 1 2 4 4 8 8 8 8

# interpret MIC values
as.sir(
  x = as.mic(2),
  mo = as.mo("Streptococcus pneumoniae"),
  ab = "AMX",
  guideline = "EUCAST"
)
#> Class <sir>
#> [1] R
as.sir(
  x = as.mic(c(0.01, 2, 4, 8)),
  mo = as.mo("Streptococcus pneumoniae"),
  ab = "AMX",
  guideline = "EUCAST"
)
#> Class <sir>
#> [1] S R R R

# plot MIC values, see ?plot
plot(mic_data)

plot(mic_data, mo = "E. coli", ab = "cipro")


if (require("ggplot2")) {
  autoplot(mic_data, mo = "E. coli", ab = "cipro")
}

if (require("ggplot2")) {
  autoplot(mic_data, mo = "E. coli", ab = "cipro", language = "nl") # Dutch
}
```