library(tidyverse)
library(marginaleffects)
library(gapminder)
gapminder_2007 <- gapminder |>
filter(year == 2007)
# Use log() in the model formula
model <- lm(lifeExp ~ log(gdpPercap), data = gapminder_2007)
library(tidyverse)
library(mlogit)
library(dfidx)
library(marginaleffects)
chocolate <- read_csv("https://www.andrewheiss.com/blog/2023/08/12/conjoint-multilevel-multinomial-guide/data/choco_candy.csv") %>%
mutate(
dark = case_match(dark, 0 ~ "Milk", 1 ~ "Dark"),
dark = factor(dark, levels = c("Milk", "Dark")),
library(tidyverse)
library(palmerpenguins)
penguins <- penguins |> drop_na()
# This splits the dataset into three smaller datasets behind the scenes, but
# then doesn't do anything with them. But secretly it's waiting to do things
# within the three groups (hence the "Groups: species [3]") note
penguins |>
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library(tidyverse) | |
library(tinytable) | |
inline_listify <- function(x) { | |
numbers <- seq_along(x) | |
prefixed <- paste0("(", numbers, ") ", x) | |
collapsed <- paste(prefixed, collapse = "; ") | |
return(collapsed) | |
} |
library(tidyverse)
library(broom)
model1 <- lm(hwy ~ displ + cyl, data = mpg)
model2 <- lm(hwy ~ displ + cyl + drv, data = mpg)
plot_data <- bind_rows(
tidy(model1, conf.int = TRUE) |> mutate(model = "Model 1"),
tidy(model2, conf.int = TRUE) |> mutate(model = "Model 2")
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library(tidyverse) | |
library(gapminder) | |
# ifelse() will happily work and coerce your numbers into text | |
gapminder1 <- gapminder |> | |
mutate(life_cat = ifelse(lifeExp > 75, "High", lifeExp)) | |
# if_else() will yell at you | |
gapminder1 <- gapminder |> | |
mutate(life_cat = if_else(lifeExp > 75, "High", lifeExp)) |
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--- | |
- hosts: ubuntu | |
become: yes | |
tasks: | |
- name: Add a new user named 'yourname' | |
user: | |
name: yourname | |
state: present | |
- name: Add 'yourname' to sudo group |
library(tidyverse)
example_draws <- tribble(
~category, ~shapes,
FALSE, c(10, 10, 13),
TRUE, c(7, 7, 8)
) |>
mutate(draws = map(shapes, ~{
withr::with_seed(1234, {
library(tidyverse)
library(broom)
library(marginaleffects)
library(palmerpenguins)
penguins <- penguins %>% drop_na(sex)
model1 <- lm(body_mass_g ~ flipper_length_mm + species, data = penguins)
library(tidyverse)
library(lme4)
library(marginaleffects)
# ?ChickWeight
# weight = body weight in grams
# Time = days since birth
# Chick = chick ID
# Diet = one of 4 possible diets
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