conmat-population
conmat-population.Rmd
library(conmat)The main goal of conmat is to estimate contact rates between age groups. This means we require data describing the age population distribution. Effectively this is data that has a column describing age, and a column describing population, like this:
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
#> ✔ ggplot2 3.4.0 ✔ purrr 1.0.0
#> ✔ tibble 3.1.8 ✔ dplyr 1.0.10
#> ✔ tidyr 1.2.1 ✔ stringr 1.5.0
#> ✔ readr 2.1.3 ✔ forcats 0.5.2
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
dat_age <- tibble(
age = seq(0, 25, by = 5),
population = seq(1410, 1350, by = -12)
)
dat_age
#> # A tibble: 6 × 2
#> age population
#> <dbl> <dbl>
#> 1 0 1410
#> 2 5 1398
#> 3 10 1386
#> 4 15 1374
#> 5 20 1362
#> 6 25 1350We use this kind of data frequently in conmat, and it means that your code might sometimes have lots of repetition like this:
calculation(
data,
age_col = age,
population_col = population
)
estimation(
data,
age_col = age,
population_col = population
)While there isn’t anything particularly wrong about this, it does mean repetition in arguments, and the code could instead look like this:
calculation(data)
estimation(data)We can achieve this by creating a special object that is a dataframe
that knows which columns represent age, and population. This is a
conmat_population object.
We can create one with as_conmat_population:
dat_age_pop <- as_conmat_population(
data = dat_age,
age = age,
population = population
)
dat_age_pop
#> # A tibble: 6 × 2 (conmat_population)
#> - age: age
#> - population: population
#> age population
#> <dbl> <dbl>
#> 1 0 1410
#> 2 5 1398
#> 3 10 1386
#> 4 15 1374
#> 5 20 1362
#> 6 25 1350You can see when we print this out to the console that this class is
noted in parentheses (conmat_population), and the columns
are noted.
Accessing age and population information
If you want to access the age and population information, there are 2 main functions:
These return symbols, which can be used in programming.
age(dat_age_pop)
#> age
population(dat_age_pop)
#> populationalternatively there are functions that return character information:
age_label(dat_age_pop)
#> [1] "age"
population_label(dat_age_pop)
#> [1] "population"Brief example of using accessor functions
You could use this to extract out the values from the data and then summarise it, for example:
pop_var <- age_label(dat_age_pop)
dat_age_pop[[pop_var]]
#> [1] 0 5 10 15 20 25
mean(dat_age_pop[[pop_var]])
#> [1] 12.5
sd(dat_age_pop[[pop_var]])
#> [1] 9.354143
age_var <- population_label(dat_age_pop)
dat_age_pop[[age_var]]
#> [1] 1410 1398 1386 1374 1362 1350
mean(dat_age_pop[[age_var]])
#> [1] 1380
sd(dat_age_pop[[age_var]])
#> [1] 22.44994You could then wrap this in a function if you like:
summary_pop <- function(data){
dat_age_pop[[pop_var]]
mean_pop <-mean(dat_age_pop[[pop_var]])
sd_pop <-sd(dat_age_pop[[pop_var]])
age_var <- population_label(dat_age_pop)
dat_age_pop[[age_var]]
mean_age <- mean(dat_age_pop[[age_var]])
sd_age <- sd(dat_age_pop[[age_var]])
return(
tibble::tibble(
mean_pop,
sd_pop,
mean_age,
sd_age
)
)
}
summary_pop(dat_age_pop)
#> # A tibble: 1 × 4
#> mean_pop sd_pop mean_age sd_age
#> <dbl> <dbl> <dbl> <dbl>
#> 1 12.5 9.35 1380 22.4However if you would like to program with these variables, for
example write a function that uses functions like mutate
and arrange, from dplyr, you would need to get
the symbols and then evaluate them with !!, like so:
my_age_summary <- function(data){
age_col <- age(data)
data %>%
summarise(
mean_age = mean(!!age_col)
)
}
my_age_summary(dat_age_pop)
#> # A tibble: 1 × 1
#> mean_age
#> <dbl>
#> 1 12.5And for a slightly more complex use case
my_age_pop_summary <- function(data){
age_col <- age(data)
pop_col <- population(data)
data %>%
summarise(
across(c(!!age_col, !!pop_col),
c(mean = mean, sd = sd),
.names = "{.fn}_{.col}")
)
}
my_age_pop_summary(dat_age_pop)
#> # A tibble: 1 × 4
#> mean_age sd_age mean_population sd_population
#> <dbl> <dbl> <dbl> <dbl>
#> 1 12.5 9.35 1380 22.4An example use from the package
Internally within conmat we do some modelling work that requires us to know the midpoint of the ages, and a coupld of other bits - here’s an example of how we write that code now:
add_modelling_info <- function(data){
age_col <- age(data)
age_var <- age_label(data)
pop_col <- population(data)
diffs <- diff(data[[age_var]])
bin_widths <- c(diffs, diffs[length(diffs)])
data %>%
dplyr::arrange(
!!age_col
) %>%
dplyr::mutate(
# model based on bin midpoint
bin_width = bin_widths,
midpoint = !!age_col + bin_width / 2,
# scaling down the population appropriately
log_pop = log(!!pop_col / bin_width)
)
}
add_modelling_info(dat_age_pop)
#> # A tibble: 6 × 5 (conmat_population)
#> - age: age
#> - population: population
#> age population bin_width midpoint log_pop
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 1410 5 2.5 5.64
#> 2 5 1398 5 7.5 5.63
#> 3 10 1386 5 12.5 5.62
#> 4 15 1374 5 17.5 5.62
#> 5 20 1362 5 22.5 5.61
#> 6 25 1350 5 27.5 5.60