ggplot Example 1

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
    mapping = aes(<MAPPINGS>),
    stat = <STAT>, 
    position = <POSITION>
  ) +
  <COORDINATE_FUNCTION> +
  <FACET_FUNCTION>

library(ggplot2)
data(mpg)

p <- ggplot(data = mpg) +
  
  # Use geom_jitter instead of geom_point to add a small
  # amount to x coordinates to avoid overplotting.
  # 
  # Wondering why I chose the shape 16 ?
  # Please read this ...
  # https://r4ds.had.co.nz/data-visualisation.html#fig:shapes
  #
  geom_jitter(mapping = aes(
    x = displ, y = hwy, color = as.factor(year),
    alpha = 0.1), shape = 16, size = 1.5, height = 0) +
  
  # Define the scale of x and y
  scale_x_continuous(breaks = seq(1, 7.5, by = 0.5)) +
  scale_y_continuous(breaks = seq(10, 45, by = 4)) +
  
  # Define a different color scheme
  scale_color_brewer(palette = "Set1") +
  # scale_color_manual(values = c('purple', 'red')) +
  
  # Define x and y axis titles
  labs(x = 'Engine displacement (L)',
       y = quote(miles / gallon)) +
  
  # Define each element in the legend
  guides(color = guide_legend(reverse = F, title = 'Year'),
         alpha = 'none') +
  
  # Define the theme first
  theme_bw() +
  
  # Define the parameters for the theme
  theme(legend.background = element_rect(fill = 'transparent'),
        legend.position = c(0.5, 1),
        legend.justification = 'top',
        legend.direction = 'horizontal')

print(p)
# ggsave('ggnotes-01.png', device = 'png', width = 3.5, height = 3.5)

p + facet_wrap(~manufacturer, scales = 'free') +
  expand_limits(x = c(1, 10))

plot_grid(ggplot1, ggplot2, labels = c("A", "B"), align = "h")

library(ggplot2)
library(reshape2)
library(cowplot)

#########################
# Create synthetic data #
#########################

# Define x
x <- seq(-5, 8, 0.04)

# Generate from a Gaussian distribution
u <- dnorm(x, mean = 1, sd = 2)

# Generate from a Gamma distribution
v <- dgamma(x, 3)

# Plot to see what they look like
plot(x, u, type = 'l')
lines(x, v, col = 'red')

# The Farlie-Gumbel-Morgenstern family of copula functions
# Referenced from Genest, Christian, and Anne-Catherine Favre.
# "Everything you always wanted to know about copula modeling 
# but were afraid to ask." Journal of hydrologic engineering
# 12.4 (2007): 347-368.
#
C <- function (u, v, theta = 0.5) {
  
  # Value should be a probability
  stopifnot(min(u) >= 0 & max(u) <= 1 &
              min(v) >= 0 & max(v) <= 1)
  
  # The copula function
  return(u * v + theta * u * v * (1 - u) * (1 - v))
}

# Compute the joint distribution
c <- sapply(u, C, v = v)

# Sanity check
stopifnot(is.matrix(c))

# Create data frames for ggplot
df.univariate <- data.frame(x = x, Gaussian = u, Gamma = v)

df.copula <- expand.grid(x, x)
names(df.copula) <- c('x' , 'y')
df.copula$Copula <- as.vector(c)

# Remove extra variables
rm(C, c, u, v, x)

##################
# Generate plots #
##################

# Generate basic raster plot
p.copula <- ggplot(data = df.copula) +
  
  # All raster tiles should have the same size
  geom_raster(mapping = aes(x = x, y = y, fill = Copula)) +
  
  # Change the color scheme by name
  # scale_fill_distiller(palette = 'YlOrBr', direction = 1) +
  
  # Manually create a new color scheme and set limit
  scale_fill_gradientn(colors = c('blue', 'orange', 'green'),
                       values = c(.1, .5, 1), na.value = 'white') +
  
  # Change the legend theme
  theme(legend.position = 'bottom', legend.justification="center") +
  
  # Change the color bar attributes
  guides(fill = guide_colorbar(
    barwidth = grid::unit(0.7, 'npc'),
    title.position = 'right',
    title.vjust = 0.90))

# Define the shared theme for marginal plots
theme.marginal <- theme(
  legend.position="none",
  
  # Remove extra texts
  axis.line = element_blank(), axis.ticks = element_blank(),
  axis.title.x = element_blank(), axis.title.y = element_blank(),
  axis.text.x = element_blank(), axis.text.y = element_blank())

# Define marginal plots
p.gaussian <- ggplot(data = df.univariate) +
  geom_line(mapping = aes(x = x, y = Gaussian)) +
  coord_flip() +
  annotate("text", x = Inf, y = Inf, label = "Gaussian",
           vjust = 1, hjust = 0, angle = -90) +
  theme.marginal

p.gamma <- ggplot(data = df.univariate) +
  geom_line(mapping = aes(x = x, y = Gamma)) +
  annotate("text", x = min(df.univariate$x),
           y = Inf, label = "Gamma", vjust = 1, hjust = 0) +
  theme.marginal

# You can get legend to manually change it.
# It might be usefull in some cases.
#
# p.copula.legend <- get_legend(p.copula)
# p.copula <- p.copula + theme(legend.position = 'none')

p.combine <- insert_xaxis_grob(p.copula, p.gamma, grid::unit(.15, "null"))
p.combine <- insert_yaxis_grob(p.combine, p.gaussian, grid::unit(.15, "null"))
ggdraw(p.combine)

ggsave('ggnotes-02.png', device = 'png', width = 8, height = 8)