Numbers, quantities, and names

The complexity mentioned in the previous section stems the uses and real-world situations to which we want to be able to apply the mathematical idea of functions. The inputs taken by functions and the outputs produced by them are not necessarily numbers. Often, they are quantities.

x <- 7

R-Language functions

R, like most computer languages, has a programming construct to represent operations that take one or more inputs and produce an output. In R, these are called “functions.” In R, everything you do involves a function, either explicitly or implicitly.

as_daily_income <- function(yearly_income, duration = 365) {
  yearly_income / duration
}
as_daily_income(61362)
## [1] 168.1151
as_daily_income(61362, duration = 366)
## [1] 167.6557

Literate use of arguments

There will be three quantities involved in even a simple calculation of this: the dosage, the amount of time since the dose was taken, and what’s called the “time constant”

dose <- 100 # mg
duration <- 10 # days
time_constant <- 4 # days
dose * exp(- duration / time_constant)
## [1] 8.2085
library(mosaic)
## Registered S3 method overwritten by 'mosaic':
##   method                           from   
##   fortify.SpatialPolygonsDataFrame ggplot2
## 
## The 'mosaic' package masks several functions from core packages in order to add 
## additional features.  The original behavior of these functions should not be affected by this.
## 
## Attaching package: 'mosaic'
## The following objects are masked from 'package:dplyr':
## 
##     count, do, tally
## The following object is masked from 'package:Matrix':
## 
##     mean
## The following object is masked from 'package:ggplot2':
## 
##     stat
## The following objects are masked from 'package:stats':
## 
##     binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
##     quantile, sd, t.test, var
## The following objects are masked from 'package:base':
## 
##     max, mean, min, prod, range, sample, sum
drug_remaining <- function(dose, duration, time_constant) {
  dose * exp(- duration / time_constant)
}
t = 0:20
plotFun(drug_remaining(dose = 100, time_constant = 4, duration = t) ~ t, t.lim = range(0:20))