age_groups.RdSplit 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)
| x | age, e.g. calculated with |
|---|---|
| split_at | values to split |
| na.rm | a logical to indicate whether missing values should be removed |
Ordered factor
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+.
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.
To determine ages, based on one or more reference dates, use the age function.
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)) if (FALSE) { # 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") }