andrewheiss

---
title: "Fancy math"
output:
  html_document: default
  pdf_document: default
---

```{r setup, include=FALSE}
# Conditional tikz output types; use PDF if this is LaTeX, otherwise use a
# shrunken down high resolution PNG

andrewheiss

library(tidyverse)
library(brms)
library(tidybayes)
library(magrittr)

options(mc.cores = 4,
        brms.backend = "cmdstanr")

set.seed(7305)  # From random.org

andrewheiss

library(tidyverse)

# Make some data
data_to_plot <- mpg %>% 
  group_by(drv) %>% 
  summarize(avg_mpg = mean(hwy))
data_to_plot
#> # A tibble: 3 x 2
#>   drv   avg_mpg

andrewheiss

library(tidyverse)
library(broom)
library(brms)

data_401k <- haven::read_dta("https://www.stata-press.com/data/r17/401k.dta")
head(data_401k)
#> # A tibble: 6 x 10
#>   partic totemp employ mrate prate   age            sole ltotemp agesq ltotempsq
#>    <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>       <dbl+lbl>   <dbl> <dbl>     <dbl>
#> 1    188    353    296 0.581 0.635     2 0 [Not only pl…    5.87     4      34.4

andrewheiss

library(tidyverse)
library(brms)

panel_data <- read_csv("https://gist.githubusercontent.com/andrewheiss/6dfbebf552125a534fa95ca9d0ae1ddf/raw/5d3bbaf908217c4e751bc66eea98414053db84be/example_panel_data.csv", col_types = cols())

model <- brm(
  bf(prop_contentious_lead1 | weights(iptw) ~ 
       barriers_total + barriers_total_lag1_cumsum + 
       (1 | gwcode),
     zi ~ 1),

andrewheiss

andrewheiss

library(tidyverse)
library(sf)
library(albersusa)
library(tidygeocoder)

usa_map <- usa_sf("longlat") %>% 
  filter(!(name %in% c("Alaska", "Hawaii")))

some_cities <- tribble(

andrewheiss

library(tidyverse)
library(broom)

# Load data
world_happiness <- read_csv("https://raw.githubusercontent.com/andrewheiss/evalsp21.classes.andrewheiss.com/master/static/data/world_happiness.csv") %>% 
  mutate(latin_america = region == "Latin America & Caribbean") %>% 
  mutate(latin_america = factor(latin_america, labels = c("Not Latin America", "Latin America")))

# Basic model, no interaction

andrewheiss

library(tidyverse)
library(lme4)  # For mixed models
library(broom)
library(broom.mixed)  # Only necessary for models made with lmer()
library(knitr)  # For kable()

# Build some models
model1 <- lm(hwy ~ displ, data = mpg)
model2 <- lm(hwy ~ displ + drv, data = mpg)

andrewheiss

Haha so this should have been obvious to me all along, but the results for log_gdpPercap with gaussian() are identical to original gdpPercap with lognormal()

library(tidyverse)
library(gapminder)
library(brms)
library(broom)
library(broom.mixed)
library(modelsummary)