ENV221 L03

1 Overview of the module and R

2 R Basic Operations

3 R Programming

3.1 Learning objectives

In this lecture, you will learn

1. the basic flow works of programming,
2. the R functions for conditional statements,
3. the R functions for loop,
4. the fundamental knowledge of R packages, and
5. how to find useful R packages for researches on environmental science.

3.2 Conditional statements

3.2.1 Logical operations

# <, >, ==, <=, >=, != 
1 > 2
1 <= 2
x <- 1:9
x > 5
iris$Sepal.Length > 4
iris$Species == "versicolor"
iris$Sepal.Length[iris$Species == "versicolor"]

# & ! |
1 < 2 & 3 > 4
1 < 2 | 3 > 4
! 1 < 2


# maths
TRUE + TRUE
TRUE + FALSE
TRUE * FALSE
TRUE / FALSE
FALSE / TRUE

sum(c(TRUE, FALSE, TRUE))
sum(iris$Species == "versicolor")

Action: What does && and || mean?

3.2.2 if () {}

hour <- 12
if (hour == 12) {
  print('Time for lunch. Go!')
}

3.2.3 if () {} else {}

hour <- 12
if(hour < 12) {
  print('Good morning.')
} else {
  print('Good afternoon.')
}

3.2.4 ifelse()

iris$long_sepal <- ifelse(
  iris$Sepal.Length > mean(iris$Sepal.Length), 
  'long', 
  'short')
iris$big <- ifelse(
  iris$Sepal.Length > mean(iris$Sepal.Length) & iris$Sepal.Width > mean(iris$Sepal.Width), 
  'big', 
  'small')

Demonstration: Add a new column to the Air Quality dataset, showing the months in English.

3.3 Loop

3.3.1 for () {}

x <- 1:10

# repeat the same work
for(i in x){
  print('Hello World!')
}

# different work
for(i in x){
  print(i)
}

Demonstration:

Suppose the concentration of ambient CO₂ is 400 ppm this year. It increases by 1% every year. What will it be after 50 years?

x <- 400
for (i in 1:50){
  x <- x + 0.01 * x
}
x

400 * (1.01 ^ 50)

Demonstration: Create multiple folders.

dir.create("test")
x <- letters[1:7]
for(i in x){
  dir.create(i)
}

3.3.2 while () {}

Demonstration:

Suppose the concentration of ambient CO₂ is 400 ppm this year. It increases by 1% every year. After how many years will it reach 1000 ppm?

x <- 400
r <- 0.01
year <- 0
while(x < 1000){
  year <- year + 1
  x <- x + x * r
}
year

3.3.3 repeat()

repeat print(1)

Demonstration:

\(x = 1 + 2 + 3 + ... + n\). What it the smallest value of \(n\) when \(x\) > 10000?

sum <- 0
n <- 1
repeat{
 sum <- sum + n
  n <- n + 1
  if (sum > 10000){
  break
 }
}
n

Action: Rewrite these codes using the while () {} function.

3.4 Functions

3.4.1 Structure

A function is a cooking machine:

# Common form:
mean(1:10)
plot(iris$Sepal.Length, iris$Sepal.Width)

# Complete form:
mean(x = 1:10)
plot(x = iris$Sepal.Length, y = iris$Sepal.Width)
plot(y = iris$Sepal.Width, x = iris$Sepal.Length)

3.4.2 Help

help(mean)
?mean
# F1
mean(x, trim = 0, na.rm = FALSE, ...)

Action: What is the complete form for the function read.csv() and write.csv()? Explain each argument.

3.4.3 User-defined functions

function_name <- function(argument_1, argument2, ...)

Convert Wind from mph (mile per hour) to m/s (meter per second):

\[v_\text {ms} = v_\text{mph} \times 1.609344 \times 1000 / 3600\]

# Define the function:
convert_windspeed <- function(x){
  y <- x * 1.609344 * 1000 / 3600
  return(y)
}
# Use the function:
convert_windspeed(x = 1)

# A short one:
convert_windspeed <- function(x){
  x * 1.609344 * 1000 / 3600
}


# A little more complicated:
convert_windspeed2 <- function(x, unit = "m/s"){
  if (unit == "m/s"){
    y <- x * 1.609344 * 1000 / 3600
  }
  if (unit == "km/hour"){
    y <- x * 1.609344
  }
  return(y)
}

convert_windspeed2(1, "km/hour")

Question: What if the user misspell the unit, such as unit = "ms"?

3.5 R packages

A function is a cooking machine.

An R package is a kitchen with one or more cooking machines.

• MatLab: toolboxes
• python: libraries
• MS Office: modules
• Chrome: add-ons/plugins
• R: packages. Over 16,000 packages on CRAN + countless packages on GitHub + …

Number of R packages published on CRAN (Image source)

3.6 Install packages

3.6.1 From CRAN

Something like peer reviewed:

install.packages(ggplot2)
install.packages(remotes)

3.6.2 From github

Something like grey papers or self-published:

library(remotes)
install_github("pzhaonet/beginr")

3.6.3 From local files

1. Download a package archive file
2. RStudio -> Tools -> Install packages –> Package Archive File

3.6.4 Where are they installed

Check the installation path:

.libPaths()

3.7 Load a package

library()
require()
beginr::plotpch()

Question: What is the difference between library() and require()?

3.8 Overview a package

3.8.1 What is it used for

help()
help(package = "beginr")
help(package = "plotrix")

demo()
demo(plotrix)

vignette()
vignette(datatable-intro)

3.8.2 How popular is it

beginr::plotpkg("plotrix")
beginr::plotpkg("ggplot2")

3.8.3 What is the usage of a function in it

help(topic = plotpkg, package = "beginr")

library(beginr)
help("plotpkg")

library(ggplot2)
example(qplot)

3.8.4 What data does it have

data()

3.8.5 Who develop(s) it

citation() # search the authors

3.9 Find and choose packages

1. Task view: https://cran.r-project.org/web/views/
2. Search engine
3. Reputation
4. References in literature
5. CRAN Explorer: https://nz-stefan.shinyapps.io/cran-explorer/

3.10 Further readings

• (Optional) R packages