In AlexB4891/phenomenology: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

First load phenomenology package. It provides some function to learn microeconomics using R.

library(phenomenology)

After loading it, we must understand how it works.

1. Consider the next equation:

   $$P = \alpha + \beta Q$$ In economics there's this convention to plot prices in the y axis. But we are interested in how quantities change as the prices changes. Like in this expression:

   $$Q = \frac{\alpha}{\beta } - \frac{1}{\beta }P$$ The sign of $\beta$ is the key. It determine if you are talking about supply (positive) or demand (negative).

2. Functions inside phenomenology package work using the first definition. Then:

   • $\beta$ becomes slope argument and it's the rate at which prices fall or increase due to changes in quantity.

   • $\alpha$ turns in price_q0 and it's the price that leads to a complete fall on quantity, also known as the intercept with the y axis.

   Having this definitions in mind let's create our first market example:

demand <- create_market(price_q0 = 100,
              slope = -2)

str(demand)

The create_market function is our first step. Above I defined a demand curve following the next definition:

$$ P = 100 - 2Q_D $$

But markets are composed by at least two parts, so we must add our supply curve:

$$ P = 30 + 3.5Q_s $$

This is easily done with the create_market function:

market <- create_market(
  price_q0 = c(100,30), #
  slope = c(-2,3.5))

str(market)

Now we have a little more complex structure. As you can see when I declared just the demand curve, I got a list of 5 elements that defines the curve (see package documentation). Now I got a nested list with that same structure but defined for each pair of price_q0 and slope. And so easy you have your market definition.

Until now we build our materials to make the analysis. Now let me introduce the linear_curve package.

analisis <- linear_curve(market = market,
             market_name = "Chocolate market in black market")

names(analisis)

This new element has some interesting outputs (check package documentation). Here we will focus in market:

plot <- analisis$market

print(plot)

Interpretation

Interesting result. Let's apply some concepts. The point E1 is the equilibrium in the market. In this point consumers and suppliers have agreed in a price of r round(dplyr::pull(analisis$cross_points,optim_p)) and the quantity demanded or supplied in that point is r round(dplyr::pull(analisis$cross_points,optim_q))

AlexB4891/phenomenology documentation built on March 29, 2022, 8:56 p.m.