njtierney

---
title: example multi figures
format: html
---

We can also see that there is an interesting relationship between Ozone and temperature.

```{r}
#| label: fig-aq
#| fig-cap: 

njtierney

library(tidyverse)

group <- tribble(
  ~group,           ~golding, ~senior, ~hao,  ~windecker, ~tierney,  ~stretton, ~chikolwa, ~duncan, ~ryan, ~avenell, ~shearer, ~ohiolel, ~saraswati,
   "KRIA",           TRUE,     TRUE,   TRUE,  TRUE,       TRUE,      TRUE,      TRUE,      TRUE,    TRUE,  TRUE,     TRUE,     TRUE,    TRUE,
   "CHA",            TRUE,     TRUE,   TRUE,  TRUE,       TRUE,      TRUE,      TRUE,      TRUE,    TRUE,  FALSE,     TRUE,     TRUE,    TRUE,
   "IDEM",           TRUE,     TRUE,   TRUE,  TRUE,       TRUE,      TRUE,      TRUE,      TRUE,    TRUE,  FALSE,     TRUE,     TRUE,    TRUE,
   "IDDU",           TRUE,     TRUE,   TRUE,  TRUE,       TRUE,      TRUE,      TRUE,      TRUE,    TRUE,  FALSE,     TRUE,     TRUE,    TRUE,
   "IIDM",           TRUE,    FALSE,  FALSE, FALSE,      FALSE,     FALSE,     FALSE,     FALSE,   FALSE,  FALSE,    FALSE,    FALSE,   FALSE,

njtierney

This was code originally written by Nick Golding, in August 2022.

It was initially written to demonstrate using symmetrical terms and the benefit of them in conmat.

They handily demonstrate a nice plot of the different terms in a gam - effectively giving us a "contact matrix" of the effect of each term. The part where that happens is where predict is used inside mutate.

library(mgcv)
#> Loading required package: nlme
#> This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.

njtierney

library(greta)
#> 
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#> 
#>     binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#> 
#>     %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,

njtierney

# blog post?

my_mean <- function(x, ...) mean(x, na.rm = TRUE, ...)

vec <- c(1:5, NA, 5:1)

my_mean(vec)
#> [1] 3
my_mean(vec, na.rm = TRUE)

njtierney

library(tibble)
library(tidyverse)
ihr_car_ratio <- greta::uniform(0,1)
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#> 

ihr_car_ratio
#> greta array (variable following a uniform distribution)

njtierney

# something like this?
library(tidyverse)
library(lobstr)
existing_data_list <- list(
  data.frame(x = 1:5, y = 0),
  data.frame(z = 5:1),
  data.frame(y = 1:5, a = 2)
)

njtierney

library(conmat)
library(socialmixr)
library(tidyverse)

countries_of_interest <- c("Italy", "Spain")
list_countries <- map(countries_of_interest,
    \(x) wpp_age(x, years = 2015))

list_conmat_pop <- map(
  .x = list_countries,

njtierney

bee_trips <- 1:10

sample(bee_trips,
       size = 100,
       replace = TRUE)
#>   [1]  2  4  5  2  3  7  5  8  5  7  6  2  8  4 10  6  2  3 10  4  6  8  9  5  6
#>  [26]  6  8  7  6  8  5  4  7  5  6  3  5  3  7  1  9  4  8  7  8  3 10  3  5 10
#>  [51] 10  5  8  6  2  6  6  3  6  5  9  6  5  9  6  9  2  5  6  4  8  4  6  2  7
#&gt; [76] 8 9 10 7 1 3 4 5 5 4 6 8 6 2 4 5 9 10 5 9 4 5 2 5 3

njtierney

library(socialmixr)
library(conmat)

italy_2005 <- wpp_age("Italy", "2005")

head(italy_2005)

italy_2005_pop <- as_conmat_population(
  data = italy_2005,
  age = lower.age.limit,