02-10-2026                       >eR-BioStat and STAEM-3D

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Modeling infectious diseases using R - basic programming in R

Ziv Shkedy, Rashider Aloni and Leyla Kodalci

## load/install libraries
.libPaths(c("./Rpackages",.libPaths()))
library(knitr)
library(tidyverse)
library(deSolve)
library(minpack.lm)
library(ggpubr)
library(readxl)
library(gamlss)
library(data.table)
library(grid)
library(png)
library(nlme)
library(gridExtra)
library(mvtnorm)
library(e1071)
library(lattice)
library(ggplot2)
library(dslabs)
library(NHANES)
library(plyr)
library(dplyr)
library(nasaweather)
library(ggplot2)
library(gganimate)
library(av)
library(gifski)
library(foreach)
library("DAAG")
library(DT)
library(TeachingDemos)
library(gridExtra)

1. Defining objects in R

We consider two vectors, \(x\) and \(y\):

\(x=(1.9,1.2,0.7,2.7,1.2,3.1,2.3,2.1,2.1,1.4)\) and \(y=(1.8,1.1,0.6,2.7,1.4,3.0,2.5,1.9,1.8,1.4)\).

We define the two vectors in R in the following way. For \(x\):

x<-c(1.9,1.2,0.7,2.7,1.2,3.1,2.3,2.1,2.1,1.4)
x

##  [1] 1.9 1.2 0.7 2.7 1.2 3.1 2.3 2.1 2.1 1.4

and for \(y\):

y<-c(1.8,1.1,0.6,2.7,1.4,3.0,2.5,1.9,1.8,1.4)
y

##  [1] 1.8 1.1 0.6 2.7 1.4 3.0 2.5 1.9 1.8 1.4

2. Calculate the mean of \(x\) and \(y\) in R

Our first data analysis task is to calculate the mean of \(x\) and \(y\). In R, the function mean() can be used to calculate the sample mean. In our example we use mean(x) and mean(y):

mean(x)

## [1] 1.87

mean(y)

## [1] 1.82

3. Correlation between \(x\) and \(y\)

Our second data analysis task is to calculate and visualize the correlation between \(x\) and \(y\). To calculate the correlation we use the function cor().

cor(x,y)

## [1] 0.9774492

We use the function plot to produce the scatterplot.

plot(x,y)

4. User functions

A function is R. is a code that can be used to produce output. For example, suppose that we want to calculate (1) the mean of two vectors, (2) the correlation between two vectors and (3) to plot a scatterplot. We can write a R function, discript that can be used to produce the output.

discript<-function(x,y)
{
print(mean(x))
print(mean(y))
print(cor(x,y))
plot(x,y)
}

To apply the function to the the vectors \(x\) and \(y\) we use discript(x,y).

discript(x,y)

## [1] 1.87
## [1] 1.82
## [1] 0.9774492

5. The cars dataset

The cars dataset contains information about cars’ speed and cars’ stoping distance.

head(cars)

##   speed dist
## 1     4    2
## 2     4   10
## 3     7    4
## 4     7   22
## 5     8   16
## 6     9   10

The first column is the cars’ speed and the second column is the cars’ stoping distance. Our data analysis task is

• Calculate the mean of the cars’ speed.
• Calculate the mean of the cars’ stopping distance.
• Calculate the corelation between cars’ speed and stopping distance and produce a scaterplot.

discript(cars[,1],cars[,2])

## [1] 15.4
## [1] 42.98
## [1] 0.8068949