econocharts package

Microeconomics/macroeconomics graphs made with ggplot2

This package allows creating microeconomics or macroeconomics charts in R with simple functions. This package inspiration is reconPlots by Andrew Heiss.

THE PACKAGE IS UNDER HEAVY DEVELOPMENT. WORK IN PROGRESS. You can suggest ideas by submitting an Issue or contributing submitting Pull Requests.

TODO

• [ ] Finish documentation
• [x] Price control (in sdcurve function)
• [ ] Allow drawing custom functions
• [ ] Add graph for budget constraints
• [ ] Fix linecol argument
• [x] Tax graph
• [ ] Shade producer and consumer surplus
• [ ] Add Edgeworth box
• [ ] General equilibrium (suggested by Ilya)
• [x] Prospect theory value function (suggested by @brshallo)
• [x] Neoclassical labor supply (suggested by @hilton1)

Index

• Installation
• Supply curve
• Demand curve
• Supply and demand
• Neoclassical labor supply
• Indifference curves
• Production–possibility frontier
• Tax graph
• Prospect Theory value function
• Laffer curve
• Calculating the intersections
• Citation

Installation

GitHub

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

CRAN

The package will be on CRAN as soon as possible

Supply

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

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

Supply and demand

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")

Neoclassical labor supply

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

Indifference curves

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

Production–possibility frontier

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)

Tax graph

Original function by Andrew Heiss.

# 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)

Prospect theory value function

ptvalue(sigma = 0.88,             # From Tversky & Kahneman, 1992
        lambda = -2.25,           # From Tversky & Kahneman, 1992
        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 curve

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

Intersections

The functions above can have a limited functionality if you want a fully customized plot. The curve_intersection function allows you to calculate the intersection points between two curves. You can use this function to create your custom charts.

Credits to Andrew Heiss for this function and examples.

Curved Bézier lines with empirical data

# 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()

Curved lines defined with functions

Specify a X-axis range and set empirical = FALSE.

# 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()

Citation

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/},
  }

Social Media

• Facebook: https://www.facebook.com/RCODERweb
• Twitter: https://twitter.com/RCoderWeb