## ----setup-init, include=FALSE------------------------------------------------
library(knitr)
source("scripts/image_functions.R")






## ----multiple-regression-load-packages, eval=FALSE----------------------------
# library(tidyverse)
# library(moderndive)
# library(ISLR2)


## ----multiple-regression-load-packages-alt, echo=FALSE, message=FALSE, purl=TRUE----
library(tidyverse)
library(moderndive)
library(ISLR2)




## ----multiple-regression-create-UN_data_ch6-----------------------------------
UN_data_ch6 <- un_member_states_2024 |>
  select(country, 
         life_expectancy_2022, 
         fertility_rate_2022, 
         income_group_2024)|>
  na.omit()|>
  rename(life_exp = life_expectancy_2022, 
         fert_rate = fertility_rate_2022, 
         income = income_group_2024)|>
  mutate(income = factor(income, 
                         levels = c("Low income", "Lower middle income", 
                                    "Upper middle income", "High income")))


## ----multiple-regression-glimpse-UN_data_ch6----------------------------------
glimpse(UN_data_ch6)




## ----multiple-regression-alt, eval=FALSE--------------------------------------
# UN_data_ch6 |> sample_n(size = 10)




## ----multiple-regression-select-vars, eval=FALSE------------------------------
# UN_data_ch6 |>
#   select(life_exp, fert_rate, income) |>
#   tidy_summary()


## ----multiple-regression-select-vars-sized, echo=FALSE------------------------
UN_data_ch6 |> 
  select(life_exp, fert_rate, income) |> 
  tidy_summary() |> 
  kbl() |>
  kable_styling(
    font_size = ifelse(is_latex_output(), 8, 16),
    latex_options = c("HOLD_position")
  ) 


## ----multiple-regression-demo-code--------------------------------------------
UN_data_ch6 |>
  get_correlation(formula = fert_rate ~ life_exp)


## ----multiple-regression-scatter-lifeexp, eval=FALSE--------------------------
# ggplot(UN_data_ch6, aes(x = life_exp, y = fert_rate, color = income)) +
#   geom_point() +
#   labs(x = "Life Expectancy", y = "Fertility Rate", color = "Income group") +
#   geom_smooth(method = "lm", se = FALSE)




## ----multiple-regression-lm-fertility, eval=FALSE-----------------------------
# one_factor_model <- lm(fert_rate ~ income, data = UN_data_ch6)
# coef(one_factor_model)




## ----multiple-regression-assign-model_int, eval=FALSE-------------------------
# # Fit regression model and get the coefficients of the model
# model_int <- lm(fert_rate ~ life_exp * income, data = UN_data_ch6)
# coef(model_int)










## ----multiple-regression-scatter-lifeexp-colored, eval=FALSE------------------
# ggplot(UN_data_ch6, aes(x = life_exp, y = fert_rate, color = income)) +
#   geom_point() +
#   labs(x = "Life expectancy", y = "Fertility rate", color = "Income group") +
#   geom_parallel_slopes(se = FALSE)




## ----multiple-regression-lm-fertility-alt, eval=FALSE-------------------------
# # Fit regression model:
# model_no_int <- lm(fert_rate ~ life_exp + income, data = UN_data_ch6)
# 
# # Get the coefficients of the model
# coef(model_no_int)














## ----multiple-regression-reg-points, eval=FALSE-------------------------------
# regression_points <- get_regression_points(model_int)
# regression_points








## ----multiple-regression-load-ISLR2, message=FALSE----------------------------
library(ISLR2)
credit_ch6 <- Credit |> as_tibble() |> 
  select(debt = Balance, credit_limit = Limit, 
         income = Income, credit_rating = Rating, age = Age)


## ----multiple-regression-glimpse-credit_ch6-----------------------------------
glimpse(credit_ch6)




## ----multiple-regression-alt2, eval=FALSE-------------------------------------
# credit_ch6 |> sample_n(size = 5)




## ----multiple-regression-select-vars-alt, eval=FALSE--------------------------
# credit_ch6 |> select(debt, credit_limit, income) |> tidy_summary()


## ----tbl-credittable, echo=FALSE----------------------------------------------
credit_ch6 |> 
  select(debt, credit_limit, income) |> 
  tidy_summary() |> 
    kbl(
    digits = 3,
    caption = "Summary of credit data",
    booktabs = TRUE,
    linesep = ""
  ) |>
  kable_styling(
    font_size = ifelse(is_latex_output(), 8, 16),
    latex_options = c("HOLD_position")
  )


## ----multiple-regression-scatter, eval=FALSE----------------------------------
# ggplot(credit_ch6, aes(x = credit_limit, y = debt)) +
#   geom_point() +
#   labs(x = "Credit limit (in $)", y = "Credit card debt (in $)",
#        title = "Debt and credit limit") +
#   geom_smooth(method = "lm", se = FALSE)
# 
# ggplot(credit_ch6, aes(x = income, y = debt)) +
#   geom_point() +
#   labs(x = "Income (in $1000)", y = "Credit card debt (in $)",
#        title = "Debt and income") +
#   geom_smooth(method = "lm", se = FALSE)




## ----multiple-regression-alt2-dup1, eval=FALSE--------------------------------
# credit_ch6 |> get_correlation(debt ~ credit_limit)
# credit_ch6 |> get_correlation(debt ~ income)


## ----multiple-regression-select-vars-alt2, eval=FALSE-------------------------
# credit_ch6 |> select(debt, credit_limit, income) |> cor()




## ----multiple-regression-alt2-dup2--------------------------------------------
credit_ch6 |> get_correlation(debt ~ 1000 * income)










## ----multiple-regression-fit-lm, eval=FALSE-----------------------------------
# debt_model <- lm(debt ~ credit_limit + income, data = credit_ch6)
# coef(debt_model)




## ----multiple-regression-assign-simple_model, eval=FALSE----------------------
# # Fit regression model and get the coefficients of the model
# simple_model <- lm(debt ~ income, data = credit_ch6)
# coef(simple_model)














## ----multiple-regression-reg-points-alt, eval=FALSE---------------------------
# get_regression_points(debt_model)




## -----------------------------------------------------------------------------
#| label: ch6-exercises
#| results: asis
#| echo: false
#| message: false
source(if (file.exists("scripts/exercise_helpers.R")) "scripts/exercise_helpers.R" else "../scripts/exercise_helpers.R")
cat(render_chapter_exercises(6))