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,
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--- | |
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: |
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")'.
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,
# 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)
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)
# 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)
)
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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, |
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
#> [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
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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, |
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