## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4
)

## ----setup, message = FALSE---------------------------------------------------
library(Applr)
library(ggplot2)

## -----------------------------------------------------------------------------
autoplot(lm(Petal.Length ~ Sepal.Length + I(Sepal.Length^2):Species, iris))

## ----results = "hide"---------------------------------------------------------
model <- lm(hwy ~ displ + drv + cyl, data = mpg)

autoplot(model, aes(color = factor(cyl))) + facet_wrap(~drv)

autoplot(model,
         aes(x = cyl, color = factor(drv)),
         interval = "none") +
  labs(title = "The Same Model From a Different Angle")

## ----message = TRUE, fig.show = "hold", out.width = "49%", fig.width = 4, fig.height = 3.2----
set.seed(123)
n <- 80
x <- runif(n, 0.1, 10)
x2 <- runif(n, 0.1, 10)
x3 <- sample(c(1, 20, 40), n, replace = TRUE)
x4 <- sample(c(1, 2), n, replace = TRUE)
y <- log(5 + x + x2 + x3 + 1/5 * x * x3 + 80 * x4) # no random noise
dat <- data.frame(x, x2, x3, x4, y)

model <- lm(exp(y) ~ x*x3 + x2 + x4, data = dat)   # exact model fit

ggplot(dat, aes(x, y)) +
  geom_point() +
  geom_slice(model) +
  labs(title = "A Perfectly Fit Model 'Slice'")

# The same model against its full linear predictor: an exact fit
ggplot(dat, aes(x = log(5 + x + x2 + x3 + 1/5 * x * x3 + 80 * x4), y)) +
  geom_point() +
  geom_slice(model) +
  labs(title = "Same Model, Different X-Axis")

## -----------------------------------------------------------------------------
ggplot(dat, aes(x, y, color = factor(x3))) +
  geom_point() +
  geom_slice(model, band = "x2", predict_vars = list(x2 = c(0, 10))) +
  facet_wrap(~x4) +
  geom_slice_text() +
  geom_slice_subtitle()

## -----------------------------------------------------------------------------
model <- lm(Sepal.Length ~ Sepal.Width + Petal.Length, iris)

ggplot(iris, aes(Petal.Length, Sepal.Length)) +
  geom_point() +
  geom_slice(
    model,
    predict_vars = list(Sepal.Width = c(2, 3, 4)),
    interval = "confidence"
  ) +
  geom_slice_text()

## ----fig.show = "hold", out.width = "49%", fig.width = 4, fig.height = 3.2----
model <- lm(hwy ~ displ + I(displ^2), data = mpg)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_slice(model, interval = "confidence") +
  labs(title = "Confidence Interval")

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_slice(model, interval = "prediction") +
  labs(title = "Prediction Interval")

## -----------------------------------------------------------------------------
model <- lm(hwy ~ displ * drv + cyl, data = mpg)

# Inferred: cyl is the only predictor not shown, so it spans 4 to 8
ggplot(mpg, aes(displ, hwy, color = drv)) +
  geom_point() +
  geom_slice(model, band = TRUE) +
  geom_slice_text()

# Specified: cyl from 5 to 7
ggplot(mpg, aes(displ, hwy, color = drv)) +
  geom_point() +
  geom_slice(model, band = "cyl", predict_vars = list(cyl = c(5, 7))) +
  geom_slice_text()

## ----fig.show = "hold", out.width = "33%", fig.width = 2.5, fig.height = 3.2----
model <- lm(log(mpg) ~ disp + hp, data = mtcars)

ggplot(mtcars, aes(disp, mpg)) +
  geom_point() +
  geom_slice(model) +
  labs(title = "normal y axis")

ggplot(mtcars, aes(disp, log(mpg))) +
  geom_point() +
  geom_slice(model) +
  labs(title = "log() y axis")

ggplot(mtcars, aes(disp, mpg)) +
  geom_point() +
  geom_slice(model) +
  scale_y_log10() +
  labs(title = "scale_y_log10() axis")

## ----fig.show = "hold", out.width = "49%", fig.width = 4, fig.height = 3.2----
model <- lm(hwy ~ displ + I(displ^2) + displ:drv, data = mpg)

ggplot(mpg, aes(displ, hwy, color = drv)) +
  geom_point() +
  geom_slice(model)

ggplot(mpg, aes(displ, hwy, color = drv)) +
  geom_point() +
  geom_slice(model, full_range = TRUE)

## ----fig.width = 7.5----------------------------------------------------------
set.seed(123)
n <- 80
x <- runif(n, -10, 10)
x2 <- runif(n, 0, 4)
g <- factor(sample(c("A", "B"), n, replace = TRUE))
y <- x * ifelse(g == "A", 0.5, 2) + ifelse(g == "A", 5, -5) + 4 * x2 + rnorm(n)
dat <- data.frame(x, x2, g, y)
model <- lm(y ~ x * g + x2, data = dat)

ggplot(dat, aes(x, y)) +
  geom_point(alpha = 0.5) +
  geom_slice(model, predict_vars = list(x2 = c(0, 2, 4), g = c("A", "B"))) +
  geom_slice_text(style = "legend")

## -----------------------------------------------------------------------------
model <- lm(Petal.Length ~ Sepal.Length + I(Sepal.Length^2):Species + Sepal.Width, iris)

ggplot(iris, aes(Sepal.Length, Petal.Length, color = Species)) +
  geom_point() +
  geom_slice(model, interval = "confidence") +
  geom_slice_subtitle()