Split ages into age groups defined by the split parameter. This allows for easier demographic (antimicrobial resistance) analysis.

age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)

Arguments

x

age, e.g. calculated with age

split_at

values to split x at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See Details.

na.rm

a logical to indicate whether missing values should be removed

Value

Ordered factor

Details

To split ages, the input can be:

  • A numeric vector. A vector of e.g. c(10, 20) will split on 0-9, 10-19 and 20+. A value of only 50 will split 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, 10-14, ..., 90-94, 95-99, 100+.

    • "tens", equivalent of: 1:10 * 10. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.

Read more on our website!

On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.

See also

To determine ages, based on one or more reference dates, use the age function.

Examples

ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)

# split into 0-49 and 50+
age_groups(ages, 50)

# split into 0-19, 20-49 and 50+
age_groups(ages, c(20, 50))

# split into groups of ten years
age_groups(ages, 1:10 * 10)
age_groups(ages, split_at = "tens")

# split into groups of five years
age_groups(ages, 1:20 * 5)
age_groups(ages, split_at = "fives")

# split specifically for children
age_groups(ages, "children")
# same:
age_groups(ages, c(1, 2, 4, 6, 13, 17))

# resistance of ciprofloxacine per age group
library(dplyr)
example_isolates %>%
  filter_first_isolate() %>%
  filter(mo == as.mo("E. coli")) %>%
  group_by(age_group = age_groups(age)) %>%
  select(age_group, CIP) %>%
  ggplot_rsi(x = "age_group")