# Install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("R-CoderDotCom/econocharts")

supply() # Default plot

supply(ncurves = 1,          # Number of supply curves to be plotted
      type = "line",         # Type of the curve
      x = c(2, 4, 5),        # Y-axis values where to create intersections
      linecol = 2,           # Color of the curves
      geom = "label",        # Label type of the intersection points
      geomfill = "pink",     # If geom = "label", is the background color of the label
      main = "Supply curve") # Title of the plot

supply(ncurves = 3, # Three supply curves
       xlab = "X",  # X-axis label
       ylab = "Y",  # Y-axis label
       bg.col = "lightblue") # Background color

demand(x = 3:6,  # Intersections
      generic = FALSE) # Axis values with the actual numbers

demand(main = "Demand", # Title
       sub = "curve",   # Subtitle
       xlab = "X",      # X-axis label
       ylab = "Y",      # Y-axis label
       names = "D[1]",  # Custom name for the curve
       geomcol = 2)     # Color of the custom name of the curve

sdcurve() # Default supply and demand plot

# Custom data
supply1 <- data.frame(x = c(1, 9), y = c(1, 9))
supply1

demand1 <- data.frame(x = c(7, 2), y = c(2, 7))
demand1

supply2 <- data.frame(x = c(2, 10), y = c(1, 9))
supply2

demand2 <- data.frame(x = c(8, 2), y = c(2, 8))
demand2

p <- sdcurve(supply1,   # Custom data
             demand1,
             supply2, 
             demand2,
             equilibrium = TRUE, # Calculate the equilibrium
             bg.col = "#fff3cd") # Background color
p + annotate("segment", x = 2.5, xend = 3, y = 6.5, yend = 7,                # Add more layers
             arrow = arrow(length = unit(0.3, "lines")), colour = "grey50")

neolabsup(x = c(2, 3, 5, 7), xlab = "Quantity of\n labor supplied", ylab = "Wage rate")

indifference() # Default indifference curve

indifference(ncurves = 2,  # Two curves
             x = c(2, 4),  # Intersections
             main = "Indifference curves",
             xlab = "Good X",
             ylab = "Good Y",
             linecol = 2,  # Color of the curves
             pointcol = 2) # Color of the intersection points

p <- indifference(ncurves = 2, x = c(2, 4), main = "Indifference curves", xlab = "Good X", ylab = "Good Y")

int <- bind_rows(curve_intersect(data.frame(x = 1:1000, y = rep(3, nrow(p$curve))), p$curve + 1))

p$p + geom_segment(data = int, aes(x = 0, y = y, xend = x, yend = y), lty = "dotted")  +
      geom_segment(data = int, aes(x = x, y = 0, xend = x, yend = y), lty = "dotted") +
      geom_point(data = int, size = 3)

indifference(ncurves = 2,    # Two curves
             type = "pcom",  # Perfect complements
             main = "Indifference curves",
             sub = "Perfect complements",
             xlab = "Good X",
             ylab = "Good Y",
             bg.col = "#fff3cd", # Background color
             linecol = 1)  # Color of the curve

indifference(ncurves = 5,     # Five curves
             type = "psubs",  # Perfect substitutes
             main = "Indifference curves",
             sub = "Perfect substitutes",
             xlab = "Good X",
             ylab = "Good Y",
             bg.col = "#fff3cd", # Background color
             linecol = 1) # Color of the curve

ppf(x = 1:6, # Intersections
   main = "PPF",
   geom = "text",
   generic = TRUE, # Generic axis labels
   xlab = "X",
   ylab = "Y",
   labels = 1:6,
   acol = 3)$p

p <- ppf(x = 4:6, # Intersections
        main = "PPF",
        geom = "text",
        generic = TRUE, # Generic labels
        labels = c("A", "B", "C"), # Custom labels
        xlab = "BIKES",
        ylab = "CARS",
        acol = 3)      # Color of the area

p$p + geom_point(data = data.frame(x = 5, y = 5), size = 3) +
  geom_point(data = data.frame(x = 2, y = 2), size = 3) +
  annotate("segment", x = 3.1, xend = 4.25, y = 5, yend = 5,
           arrow = arrow(length = unit(0.5, "lines")), colour = 3, lwd = 1) +
  annotate("segment", x = 4.25, xend = 4.25, y = 5, yend = 4,
           arrow = arrow(length = unit(0.5, "lines")), colour = 3, lwd = 1)

# Data
demand <- function(Q) 20 - 0.5 * Q
supply <- function(Q) 2 + 0.25 * Q
supply_tax <- function(Q) supply(Q) + 5

# Chart
tax_graph(demand, supply, supply_tax, NULL)

# Chart with shaded areas
tax_graph(demand, supply, supply_tax, shaded = TRUE)

ptvalue(sigma = 0.3,             
        lambda = -2.25,          
        col = 2,                  # Color of the curve
        xint = seq(0, 75, 25),    # Intersections
        xintcol = 4,              # Color of the intersection segments
        ticks = TRUE,             # Display ticks on the axes
        xlabels = TRUE,           # Display the X-axis tick labels
        ylabels = TRUE,           # Display the Y-axis tick labels
        by_x = 25, by_y = 50,     # Axis steps
        main = "Prospect Theory Value Function")

laffer(ylab = "T", xlab = "t",
       acol = "lightblue", # Color of the area
       pointcol = 4)       # Color of the maximum point

laffer(xmax = 20, # Modify the curve
       t = c(3, 6, 9), # Intersections
       generic = FALSE,
       ylab = "T",
       xlab = "t",
       acol = "lightblue", # Color of the area
       alpha = 0.6,        # Transparency of the area
       pointcol = 4)       # Color of the maximum point

# Curves
curve1 <- data.frame(Hmisc::bezier(c(1, 8, 9), c(1, 5, 9)))
curve2 <- data.frame(Hmisc::bezier(c(1, 3, 9), c(9, 3, 1)))

# Calculate the intersections
curve_intersection <- curve_intersect(curve1, curve2)

# Create the chart
ggplot(mapping = aes(x = x, y = y)) +
  geom_line(data = curve1, color = "red", size = 1) +
  geom_line(data = curve2, color = "blue", size = 1) +
  geom_vline(xintercept = curve_intersection$x, linetype = "dotted") +
  geom_hline(yintercept = curve_intersection$y, linetype = "dotted") +
  theme_classic()

# Define curves with functions
curve1 <- function(q) (q - 10)^2
curve2 <- function(q) q^2 + 2*q + 8

# X-axis range
x_range <- 0:5

# Calculate the intersections between the two curves
curve_intersection <- curve_intersect(curve1, curve2, empirical = FALSE, 
                                      domain = c(min(x_range), max(x_range)))

# Create your custom plot
ggplot(data.frame(x = x_range)) +
  stat_function(aes(x = x), color = "blue", size = 1, fun = curve1) +
  stat_function(aes(x = x), color = "red", size = 1, fun = curve2) +
  geom_vline(xintercept = curve_intersection$x, linetype = "dotted") +
  geom_hline(yintercept = curve_intersection$y, linetype = "dotted") +
  theme_classic()

To cite package ‘econocharts’ in publications use:

  José Carlos Soage González and Andrew Heiss (2020). econocharts: Microeconomics and Macroeconomics Charts Made with 'ggplot2'. R package version 1.0.
  https://r-coder.com/, https://r-coder.com/economics-charts-r/.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {econocharts: Microeconomics and Macroeconomics Charts Made with 'ggplot2'},
    author = {José Carlos {Soage González} and Andrew Heiss},
    year = {2020},
    note = {R package version 1.0},
    url = {https://r-coder.com/, https://r-coder.com/economics-charts-r/},
  }