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# Split Ages into Age Groups
Split ages into age groups defined by the `split` argument. This allows
for easier demographic (antimicrobial resistance) analysis. The function
returns an ordered [factor](https://rdrr.io/r/base/factor.html).
## Usage
``` r
age_groups(x, split_at = c(0, 12, 25, 55, 75), names = NULL,
na.rm = FALSE)
```
## Arguments
- x:
Age, e.g. calculated with
[`age()`](https://amr-for-r.org/reference/age.md).
- split_at:
Values to split `x` at - the default is age groups 0-11, 12-24, 25-54,
55-74 and 75+. See *Details*.
- names:
Optional names to be given to the various age groups.
- na.rm:
A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
missing values should be removed.
## Value
Ordered [factor](https://rdrr.io/r/base/factor.html)
## Details
To split ages, the input for the `split_at` argument can be:
- A [numeric](https://rdrr.io/r/base/numeric.html) vector. A value of
e.g. `c(10, 20)` will split `x` on 0-9, 10-19 and 20+. A value of only
`50` will split `x` on 0-49 and 50+. The default is to split on young
children (0-11), youth (12-24), young adults (25-54), middle-aged
adults (55-74) and elderly (75+).
- A character:
- `"children"` or `"kids"`, equivalent of: `c(0, 1, 2, 4, 6, 13, 18)`.
This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.
- `"elderly"` or `"seniors"`, equivalent of: `c(65, 75, 85)`. This
will split on 0-64, 65-74, 75-84, 85+.
- `"fives"`, equivalent of: `1:20 * 5`. This will split on 0-4, 5-9,
..., 95-99, 100+.
- `"tens"`, equivalent of: `1:10 * 10`. This will split on 0-9, 10-19,
..., 90-99, 100+.
## See also
To determine ages, based on one or more reference dates, use the
[`age()`](https://amr-for-r.org/reference/age.md) function.
## Examples
``` r
ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)
# split into 0-49 and 50+
age_groups(ages, 50)
#> [1] 0-49 0-49 0-49 50+ 0-49 50+ 50+ 0-49 0-49
#> Levels: 0-49 < 50+
# split into 0-19, 20-49 and 50+
age_groups(ages, c(20, 50))
#> [1] 0-19 0-19 0-19 50+ 20-49 50+ 50+ 20-49 20-49
#> Levels: 0-19 < 20-49 < 50+
age_groups(ages, c(20, 50), names = c("Under 20 years", "20 to 50 years", "Over 50 years"))
#> [1] Under 20 years Under 20 years Under 20 years Over 50 years 20 to 50 years
#> [6] Over 50 years Over 50 years 20 to 50 years 20 to 50 years
#> Levels: Under 20 years < 20 to 50 years < Over 50 years
# split into groups of ten years
age_groups(ages, 1:10 * 10)
#> [1] 0-9 0-9 10-19 50-59 30-39 70-79 100+ 40-49 20-29
#> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+
age_groups(ages, split_at = "tens")
#> [1] 0-9 0-9 10-19 50-59 30-39 70-79 100+ 40-49 20-29
#> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+
# split into groups of five years
age_groups(ages, 1:20 * 5)
#> [1] 0-4 5-9 15-19 50-54 30-34 75-79 100+ 40-44 20-24
#> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+
age_groups(ages, split_at = "fives")
#> [1] 0-4 5-9 15-19 50-54 30-34 75-79 100+ 40-44 20-24
#> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+
# split specifically for children
age_groups(ages, c(1, 2, 4, 6, 13, 18))
#> [1] 2-3 6-12 13-17 18+ 18+ 18+ 18+ 18+ 18+
#> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+
age_groups(ages, "children")
#> [1] 2-3 6-12 13-17 18+ 18+ 18+ 18+ 18+ 18+
#> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+
# \donttest{
# resistance of ciprofloxacin per age group
if (require("dplyr") && require("ggplot2")) {
example_isolates %>%
filter_first_isolate() %>%
filter(mo == as.mo("Escherichia coli")) %>%
group_by(age_group = age_groups(age)) %>%
select(age_group, CIP) %>%
ggplot_sir(
x = "age_group",
minimum = 0,
x.title = "Age Group",
title = "Ciprofloxacin resistance per age group"
)
}
#> Loading required package: ggplot2
# }
```