# Setup and Data

  # Gapminder Data
  
#  We'll be working with data from Hans Rosling's [Gapminder](http://www.gapminder.org) project.
# An excerpt of these data can be accessed through an R package called `gapminder`, cleaned and assembled by Jenny Bryan at UBC.


  
#  In the console: 
install.packages("gapminder")

# Load the package and data:

library(gapminder)

  # Check Out Gapminder
  
#  The data frame we will work with is called `gapminder`, available once you have loaded the package. Let's see its structure:

str(gapminder)

# What's Interesting Here?
  
#   * **Factor** variables `country` and `continent`
# 
# + Factors are categorical data with an underlying numeric representation
# + We'll spend a lot of time on factors later!
# 
# * Many observations: $n=`r nrow(gapminder)`$ rows
# 
# * A nested/hierarchical structure: `year` in `country` in `continent`
# 
#    + These are panel data!

# Installing Tidyverse

# We'll want to be able to slice up this data frame into subsets (e.g. just the rows for Afghanistan, just the rows for 1997).

# We will use a package called `dplyr` to do this neatly. If you're unfamiliar, see my [previous workshop](https://clanfear.github.io/Intermediate_R_Workshop/)!

# `dplyr` is part of the [tidyverse](http://tidyverse.org/) family of R packages.


# If you have not already installed the tidyverse, type, in the console: `install.packages("tidyverse")`

# This will install a *large* number of R packages we will use throughout the term, including `dplyr` and `ggplot2`.
# 
# `dplyr` is a very useful and powerful package that we will talk more about soon, but today we're mostly going to use it for subsetting data.


  # Subsetting Example
  
#  For some examples to follow, I'd like to subset down only to data in China.

library(dplyr)
China <- gapminder %>%
  filter(country == "China")
head(China)

# `ggplot2`


## Base R Plots

plot(lifeExp ~ year, 
     data = China, 
     xlab = "Year", 
     ylab = "Life expectancy",
     main = "Life expectancy in China", 
     col = "red", 
     cex.lab = 1.5,
     cex.main= 1.5,
     pch = 16)


# `ggplot2`

# An alternative way of plotting many prefer (myself included)<sup>1</sup> uses the `ggplot2` package in R, which is part of the `tidyverse`.
# 
# .footnote[[1] [Though this is not without debate](http://simplystatistics.org/2016/02/11/why-i-dont-use-ggplot2/)]

library(ggplot2)

# The core idea underlying this package is the [**layered grammar of graphics**](https://doi.org/10.1198/jcgs.2009.07098): we can break up elements of a plot into pieces and combine them.

## Chinese Life Expectancy in `ggplot`

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy in China") +
  theme_bw(base_size=18) #<<

# Structure of a ggplot

# `ggplot2` graphics objects consist of two primary components:
# 
# 1. **Layers**, the components of a graph.
# 
#    * We *add* layers to a `ggplot2` object using `+`.
#    * This includes lines, shapes, and text.
# 
# 2. **Aesthetics**, which determine how the layers appear.
# 
#    * We *set* aesthetics using *arguments* (e.g. `color="red"`) inside layer functions.
#    * This includes locations, colors, and sizes.
#    * Aesthetics also determine how data *map* to appearances.

# Layers

# **Layers** are the components of the graph, such as:
# 
# * `ggplot()`: initializes `ggplot2` object, specifies input data
# * `geom_point()`: layer of scatterplot points
# * `geom_line()`: layer of lines
# * `ggtitle()`, `xlab()`, `ylab()`: layers of labels
# * `facet_wrap()`: layer creating separate panels stratified by some factor wrapping around
# * `facet_grid()`: same idea, but can split by two variables along rows and columns (e.g. `facet_grid(gender ~ age_group)`)
# * `theme_bw()`: replace default gray background with black-and-white
# 
# Layers are separated by a `+` sign. For clarity, I usually put each layer on a new line, unless it takes few or no arguments (e.g. `xlab()`, `ylab()`, `theme_bw()`).

# Aesthetics
# 
# **Aesthetics** control the appearance of the layers:
# 
# * `x`, `y`: $x$ and $y$ coordinate values to use
# * `color`: set color of elements based on some data value
# * `group`: describe which points are conceptually grouped together for the plot (often used with lines)
# * `size`: set size of points/lines based on some data value
# * `alpha`: set transparency based on some data value

## Aesthetics: Setting vs. mapping

# Layers take arguments to control their appearance, such as point/line colors or transparency (`alpha` between 0 and 1).
# 
# 
# * Arguments like `color`, `size`, `linetype`, `shape`, `fill`, and `alpha` can be used directly on the layers (**setting aesthetics**), e.g. `geom_point(color = "red")`. See the [`ggplot2` documentation](http://docs.ggplot2.org/current/vignettes/ggplot2-specs.html) for options. These *don't depend on the data*.
# 
#   * Arguments inside `aes()` (**mapping aesthetics**) will *depend on the data*, e.g. `geom_point(aes(color = continent))`.
#   
#   * `aes()` in the `ggplot()` layer gives overall aesthetics to use in other layers, but can be changed on individual layers (including switching `x` or `y` to different variables)
# 
#   This may seem pedantic, but precise language makes searching for help easier.
# 
#   Now let's see all this jargon in action.

## Axis Labels, Points, No Background

### 1: Base Plot

ggplot(data = China,  #<<
       aes(x = year, y = lifeExp)) #<<

## Axis Labels, Points, No Background

### 2: Scatterplot

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point() #<<

## Axis Labels, Points, No Background

### 3: Point Color and Size

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) #<<

## Axis Labels, Points, No Background

### 4: X-Axis Label

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") #<<

## Axis Labels, Points, No Background

### 5: Y-Axis Label

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy")

## Axis Labels, Points, No Background

### 6: Title

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy in China") #<<

## Axis Labels, Points, No Background

### 7: Theme

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy in China") +
  theme_bw() #<<

## Axis Labels, Points, No Background

### 8: Text Size

ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy in China") +
  theme_bw(base_size=18) #<<

# Plotting All Countries

# We have a plot we like for China... 

# ... but what if we want *all the countries*?


# Plotting All Countries

### 1: A Mess!

ggplot(data = gapminder,#<<
       aes(x = year, y = lifeExp)) +
  geom_point(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18)

# Plotting All Countries

### 2: Lines

  ggplot(data = gapminder, 
         aes(x = year, y = lifeExp)) +
    geom_line(color = "red", size = 3) + #<<
    xlab("Year") + 
    ylab("Life expectancy") +
    ggtitle("Life expectancy over time") +
    theme_bw(base_size=18)

# Plotting All Countries

### 3: Grouping

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country)) + #<<
  geom_line(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18)

# Plotting All Countries

### 4: Size

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country)) +
  geom_line(color = "red") + #<<
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18)

# Plotting All Countries

### 5: Color

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = continent)) + #<<
  geom_line() + #<<
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18) #<<

# Plotting All Countries

### 6: Facets

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = continent)) +
  geom_line() +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw(base_size=18) +
  facet_wrap(~ continent) #<<

# Plotting All Countries

### 7: Text Size

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, 
           group = country, 
           color = continent)) +
  geom_line() +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw() +  #<<
  facet_wrap(~ continent)

# Storing Plots

# We can assign a `ggplot` object to a name:

lifeExp_by_year <- 
  ggplot(data = gapminder, 
         aes(x = year, y = lifeExp, 
             group = country, color = continent)) +
  geom_line() +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy over time") +
  theme_bw() + 
  facet_wrap(~ continent)

# The graph won't be displayed when you do this. You can show the graph using a single line of code with just the object name, *or take the object and add more layers*.

  
  # Showing a Stored Graph

lifeExp_by_year
  
  ## Adding a Layer

lifeExp_by_year +
  theme(legend.position = "bottom")

  
  ## Common Problem: Overplotting
  
#  Often we want a scatterplot of things that have discrete units. All those dots plot over each other!
  
    ggplot(data = gapminder, aes(x = continent, y = year, color = continent)) +
      geom_point()

  
  ## Fixing Overplotting with Jitter
  
# Inside `geom_point()`, `position = position_jitter(width=a, height=b)` shifts points up to `a` units horizontally and `b` units vertically.

  ggplot(data = gapminder, aes(x = continent, y = year, color = continent)) +
    geom_point(position = position_jitter(width = 0.5, height = 2))

  # Histograms
  
#  `ggplot2` uses `geom_histogram()` to produce histograms. This geometry will automatically bin data, but you can adjust the number of bins with `bins =`.

gapminder %>% 
  ggplot(aes(x = lifeExp, fill = continent)) + 
  geom_histogram(bins = 30)


  # Changing the Axes
  
#   We can modify the axes in a variety of ways, such as:
#   
#   * Change the $x$ or $y$ range using `xlim()` or `ylim()` layers
# 
# * Change to a logarithmic or square-root scale on either axis: `scale_x_log10()`, `scale_y_sqrt()`
# 
# * Change where the major/minor breaks are: `scale_x_continuous(breaks =, minor_breaks = )`

  # Axis Changes
  
ggplot(data = China, aes(x = year, y = gdpPercap)) +
  geom_line() +
  scale_y_log10(breaks = c(1000, 2000, 3000, 4000, 5000), #<<
                labels = scales::dollar) + #<<
  xlim(1940, 2010) + ggtitle("Chinese GDP per capita")

  # Fonts Too Small?
  

ggplot(data = China, aes(x = year, y = lifeExp)) +
  geom_line() +
  ggtitle("Chinese life expectancy") +
  theme_gray(base_size = 20) #<<

  
  # Text and Tick Adjustments
  
#   Text size, labels, tick marks, etc. can be messed with more precisely using arguments to the `theme()` layer. 
# 
# Examples:
#   
#   * `plot.title = element_text(size = rel(2), hjust = 0)` makes the title twice as big as usual and left-aligns it
# * `axis.text.x = element_text(angle = 45)` rotates $x$ axis labels
# * `axis.text = element_text(colour = "blue")` makes the $x$ and $y$ axis labels blue
# * `axis.ticks.length = unit(.5, "cm")` makes the axis ticks longer
# 
# Note: `theme()` is a different layer than `theme_gray()` or `theme_bw()`, which you might also be using in a previous layer. See the [`ggplot2` documentation](http://docs.ggplot2.org/current/theme.html) for details. 
# 
# I recommend using `theme()` *after* `theme_bw()` or other *global themes*.

  
  # Scales for Color, Shape, etc.
  
#   **Scales** are layers that control how the mapped aesthetics appear. You can modify these with a `scale_[aesthetic]_[option]()` layer where `[aesthetic]` is `color`, `shape`, `linetype`, `alpha`, `size`, `fill`, etc. and `[option]` is something like `manual`, `continuous` or `discrete` (depending on nature of the variable).
# 
# Examples:
#   * `scale_linetype_manual()`: manually specify the linetype for each different value
# * `scale_alpha_continuous()`: varies transparency over a continuous range
# * `scale_color_brewer(palette = "Spectral")`: uses a palette from <http://colorbrewer2.org> (great site for picking nice plot colors!)
# 
# When confused... Google or StackOverflow it!
#   

  
  ## Legend Name and Manual Colors

    lifeExp_by_year + 
      scale_color_manual(
        name = "Which\ncontinent\nare we\nlooking at?", # \n adds a line break #<<
        values = c("Africa" = "seagreen", "Americas" = "turquoise1", 
                   "Asia" = "royalblue", "Europe" = "violetred1", "Oceania" = "yellow"))

  
  ## Fussy Manual Legend Example Code

    ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
      geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
      geom_line(stat = "smooth", method = "loess", #<<
                aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
      facet_wrap(~ continent, nrow = 2) + #<<
      scale_color_manual(name = "Life Exp. for:", #<<
                         values = c("Country" = "black", "Continent" = "blue")) + #<<
      scale_size_manual(name = "Life Exp. for:", 
                        values = c("Country" = 0.25, "Continent" = 3)) +
      theme_minimal(base_size = 14) + 
      ylab("Years") + xlab("") + 
      ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country") +
      theme(legend.position=c(0.75, 0.2), axis.text.x = element_text(angle = 45)) #<<

# Wow, there's a lot going on here!
# * Two different `geom_line()` calls
#   + One of them draws a [*loess* curve](https://en.wikipedia.org/wiki/Local_regression)
# * `facet_wrap()` to make a plot for each level of `continent`
# * Manual scales for size and color
# * Custom labels, titles, and rotated x axis text


## 1. Base Plot

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) #<<


## 2. Lines

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line() #<<

## 3. Continent Average

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line() +
  geom_line(stat = "smooth", method = "loess", #<<
            aes(group = continent)) #<<

## 4. Facets

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line() +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent)) +
  facet_wrap(~ continent, nrow = 2) #<<

## 5. Color Scale

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(aes(color = "Country")) + #<<
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent")) + #<<
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) #<<

## 6. Size Scale


ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(aes(color = "Country", size = "Country")) + #<<
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent")) + #<<
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) #<<

## 7. Alpha (Transparency)

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) + #<<
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) + #<<
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3))

## 8. Theme and Labels

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
  theme_minimal(base_size = 14) + ylab("Years") + xlab("") #<<

## 9. Title and Subtitle

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
  theme_minimal(base_size = 14) + ylab("Years") + xlab("") + 
  ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country") #<<


## 10. Angled Tick Values

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
  theme_minimal(base_size = 14) + ylab("Years") + xlab("") + 
  ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country") +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) #<<

## 11. Legend Position

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
  geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
  geom_line(stat = "smooth", method = "loess", 
            aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
  facet_wrap(~ continent, nrow = 2) +
  scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
  scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
  theme_minimal(base_size = 14) + ylab("Years") + xlab("") + 
  ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country") +
  theme(legend.position=c(0.85, 0.1), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) #<<

## Fussy Manual Legend

ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
    geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
    geom_line(stat = "smooth", method = "loess", aes(group = continent, color = "Continent", size = "Continent"), alpha = 0.5) +
    facet_wrap(~ continent, nrow = 2) +
    scale_color_manual(name = "Life Exp. for:", values = c("Country" = "black", "Continent" = "blue")) +
    scale_size_manual(name = "Life Exp. for:", values = c("Country" = 0.25, "Continent" = 3)) +
    theme_minimal(base_size = 14) + ylab("Years") + xlab("") + ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country") +
    theme(legend.position=c(0.85, 0.1), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

# Observation: One could use `filter()` to identify the countries with dips in life expectancy and investigate.

# Know Your History: What happened in Africa in the early 1990s and Asia in the mid-1970s that might reduce life expectancy suddenly *for one country*?


## More on Customizing Legends

# You can move the legends around, flip their orientation, remove them altogether, etc. The [Cookbook for R website](http://www.cookbook-r.com/Graphs/Legends_%28ggplot2%29) is a good resource for questions such as changing legend labels.

# Saving `ggplot` Plots

# When you knit an R Markdown file, any plots you make are automatically saved in the "figure" folder in `.png` format. If you want to save another copy (perhaps of a different file type for use in a manuscript), use `ggsave()`:

# ggsave("I_saved_a_file.pdf", plot = lifeExp_by_year,
#       height = 3, width = 5, units = "in")

# If you didn't manually set font sizes, these will usually come out at a reasonable size given the dimensions of your output file.

# **Bad/non-reproducible way**<sup>1</sup>: choose *Export* on the plot preview or take a screenshot / snip.


# Plotting Model Results

  # `geom_smooth()`
  
#  I have used `geom_smooth()` in multiple prior examples.

# `geom_smooth()` generates "smoothed conditional means" including loess curves and generalized additive models (GAMs).

#  Note, however, that most regression models are conditional mean models, such as ordinary least squares, generalized linear models. 
  
#  We can use `geom_smooth()` to add a layer depicting common bivariate models.

  # Default `geom_smooth()`
  
ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, color = continent)) +
  geom_point(position = position_jitter(1,0), size = 0.5) +
  geom_smooth()

# By default, `geom_smooth()` chooses either a loess smoother (N < 1000) or a GAM depending on the number of observations.

  # Linear `glm`
  
ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, color = continent)) +
  geom_point(position = position_jitter(1,0), size = 0.5) +
  geom_smooth(method = "glm", formula = y ~ x) #<<


# We could also fit a standard linear model using either `method = "glm"` or `method = "lm"` and a formula like `y ~ x`.

  # Polynomial `glm`

ggplot(data = gapminder, 
       aes(x = year, y = lifeExp, color = continent)) +
  geom_point(position = position_jitter(1,0), size = 0.5) +
  geom_smooth(method = "glm", formula = y ~ poly(x, 2)) #<<

# `poly(x, 2)` produces a quadratic model which contains a linear term (`x`) and a quadratic term (`x^2`).

  # `ggeffects`
  
#  If we want to look at more complex models, we can use `ggeffects` to create and plot tidy 
# *marginal effects*.

# That is, tidy dataframes of *ranges* of predicted values that can be
# fed straight into `ggplot2` for plotting model results.

# We will focus on two `ggeffects` functions:
  
#  * `ggpredict()` - Computes predicted values for the outcome variable at margins of specific variables.
# * `plot.ggeffects()` - A plot method for `ggeffects` objects (like `ggredict()` output)

library(ggeffects)

  # Quick Simulated Data
  
#  To show off `ggeffects`, I need a data frame with a variety of numeric and categorical variables with strong relationships. It is easiest to just simulate it:
  
ex_dat <- data.frame(num1 = rnorm(200, 1, 2), 
                     fac1 = sample(c(1, 2, 3), 200, TRUE),
                     num2 = rnorm(200, 0, 3),
                     fac2 = sample(c(1, 2))) %>%
  mutate(yn = num1 * 0.5 + fac1 * 1.1 + num2 * 0.7 +
           fac2 - 1.5  + rnorm(200, 0, 2)) %>% 
  mutate(yb = as.numeric(yn > mean(yn))) %>%
  mutate(fac1 = factor(fac1, labels = c("A", "B", "C")),
         fac2 = factor(fac2, labels = c("Yes", "No")))

  # `ggpredict()`
  
#  When you run  `ggpredict()`, it produces a dataframe with a row for every unique 
# value of a supplied predictor ("independent") variable (`term`). 

# Each row contains an expected (estimated) value for the outcome ("dependent") variable, plus confidence intervals. 

lm_1 <- lm(yn ~ num1 + fac1, data = ex_dat)
lm_1_est <- ggpredict(lm_1, terms = "num1")

# If desired, the argument `interval="prediction"` will give predicted intervals instead.

#  #`ggpredict()` output
  
lm_1_est

  # `plot()` for `ggpredict()`
  
#  `ggeffects` features a `plot()` *method*, `plot.ggeffects()`, which produces
#a ggplot when you give `plot()` output from `ggpredict()`.


  plot(lm_1_est)



  # Grouping with `ggpredict()`
  
#  When using a vector of `terms`, `ggeffects` will plot the first along the x-axis and use
# others for *grouping*. Note we can pipe a model into `ggpredict()`!
  
    glm(yb ~ num1 + fac1 + num2 + fac2, data = ex_dat, family=binomial(link = "logit")) %>%
      ggpredict(terms = c("num1", "fac1")) %>% plot()


# Faceting with `ggpredict()`
  
#  You can add `facet=TRUE` to the `plot()` call to facet over *grouping terms*.


  glm(yb ~ num1 + fac1 + num2 + fac2, data = ex_dat, family = binomial(link = "logit")) %>%
    ggpredict(terms = c("num1", "fac1")) %>% plot(facet=TRUE)

#  You can add values in square brackets in the `terms=` argument to specify counterfactual values.

  glm(yb ~ num1 + fac1 + num2 + fac2, data=ex_dat, family=binomial(link="logit")) %>%
    ggpredict(terms = c("num1 [-1,0,1]", "fac1 [A,B]")) %>% plot(facet=TRUE)

 # Representative Values
  
#  You can also use `[meansd]` or `[minmax]` to set representative values.

  glm(yb ~ num1 + fac1 + num2 + fac2, data = ex_dat, family = binomial(link = "logit")) %>%
    ggpredict(terms = c("num1 [meansd]", "num2 [minmax]")) %>% plot(facet=TRUE)

# Dot plots with `ggpredict()`
  
#  `ggpredict` will produce dot plots with error bars for categorical predictors.

  lm(yn ~ fac1 + fac2, data = ex_dat) %>% 
    ggpredict(terms=c("fac1", "fac2")) %>% plot()