Split ages into age groups

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))

Arguments

x	age, e.g. calculated with age
split_at	values to split x at, defaults to age groups 0-11, 12-24, 26-54, 55-74 and 75+. See Details.

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 (26-54), middle-aged adults (55-74) and elderly (75+).

• A character:

  • "children", 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, 95). This will split on 0-64, 65-74, 75-84, 85-94 and 95+.

  • "fives", equivalent of: 1:20 * 5. This will split on 0-4, 5-9, 10-14, 15-19 and so forth.

  • "tens", equivalent of: 1:10 * 10. This will split on 0-9, 10-19, 20-29 and so forth.

Read more on our website!

On our website https://msberends.gitlab.io/AMR you can find a comprehensive 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

age to determine ages based on one or more reference dates

Examples

# NOT RUN {
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)
septic_patients %>%
  mutate(first_isolate = first_isolate(.)) %>%
  filter(first_isolate == TRUE,
         mo == as.mo("E. coli")) %>%
  group_by(age_group = age_groups(age)) %>%
  select(age_group,
         cipr) %>%
  ggplot_rsi(x = "age_group")
# }