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 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.

See also

age to determine ages based on one or more reference dates

Examples

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+

# 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, "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+
# same:
age_groups(ages, c(1, 2, 4, 6, 13, 17))
#> [1] 2-3   6-12  13-16 17+   17+   17+   17+   17+   17+  
#> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-16 < 17+

# resistance of ciprofloxacine per age group
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:testthat’:
#> 
#>     matches
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
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")
#> NOTE: Using column `mo` as input for `col_mo`.
#> NOTE: Using column `date` as input for `col_date`.
#> NOTE: Using column `patient_id` as input for `col_patient_id`.
#> => Found 1,317 first isolates (65.9% of total)
#> Warning: Removed 3 rows containing missing values (geom_bar).
#> Warning: Removed 3 rows containing missing values (geom_text).