Software Tools for Earth and Environmental Science

–6th WEEK–

Emir Toker

27/11/2020

R Language - Part 1 & 2

  • Syllabus, Last Week and Book

  • This Week

    • R Language Part-1 : Data Types (Repeat)
    • R Language Part-2 : Data Structures
    • Take Home
      • HMW-1 : Create a New R-Notebook
      • HMW-2 : Practice for Data Types and Data Structures
    • QUIZ

  • Next Week

    • R Language Part-3 : Read, Write and Plot Data
    • Midterm Project

Syllabus, Last Week and Book

Syllabus

Extended Syllabus PDF

Last Week

LINK

Book

PDF - (Pg. 22-36 and 89-101)

R Language

R Language - Part 1

  • Getting Started
    • Assignment
    • Comment
    • Basic and Scientific Math
  • Data Types - Classes
    • Numeric
    • Integer
    • Logical
    • Character
    • Special

R Language - Part 2

  • Data Structures - Objects
    • Vector
    • Matrice
    • Array
    • Data Frame
    • List

R Language - Part 3

  • Attributes
  • Read
  • Write
  • Plot
  • ggplot2 Package

R Language - Part 1

Summary

  • Assignment; <-
  • Comment; #
  • Help; ?func .or. help(func)
  • Install Packages; install.packages()
  • Call from Library; library()
  • Basic Math;
    • addition; +
    • subtraction; -
    • multiplication; *
    • division; /
    • exponentiation; ^
    • the square root; sqrt

Questions

  1. What is Function ?

  2. What is Package ?

  3. What is R Library ?

Answer

R packages are a collection of R functions, complied code and sample data. They are stored under a directory called “library” in the R environment.

Codes –> Functions –> Packages –> Library

  • R Library
    • Packages
      • Functions
        • Codes

Question

How can i download a package in my library ?

Install Package - 1st Solution

Install Package - 1st Solution - ggplot2

Install Package - 1st Solution - ggplot2

Question

How can i use or call my package in R ?

Call Package - 1st Solution - ggplot2

Call Package - 1st Solution - ggplot2

Install and Call Package - 2nd Solution - ggplot2

Console

  • install.packages(“ggplot2”)
  • library(ggplot2)

Basic Math

Basic Math

a <- 2.3
(6*a+42)/(3^(4.2-3.62))
## [1] 29.50556

Question

a <- 2.3
(6*a+42)/(3^(4.2-3.62))
## [1] 29.50556
isTRUE((6*a+42)/(3^(4.2-3.62))==29.50556)
## [1] FALSE

Question

a <- 2.3
(6*a+42)/(3^(4.2-3.62))
## [1] 29.50556
isTRUE((6*a+42)/(3^(4.2-3.62))==29.50556)
## [1] FALSE
  • hint : print(x, digits=16)

Data Types - Classes

  • Numeric
# Any number with (or without) a decimal point.
a <- 3
  • Integer
# Sub-class of the numeric class. The suffix L tells R to store.
a <- 3L
  • Logical
# TRUE or FALSE - Logical Operators. < , > , == , >= , <= , != ... 
a <- 3<2
  • Character
# Data type consists of letters or words. String. with quotes: " … "
a <- "3"

is.XXX() and class()

Data Types - Classes

name1 <- emir
name1 <- "emir"
name2 <- name1
name3 <- "name1"

number1 <- 32
number2 <- "32"
number3 <- 1:10
number4 <- seq(1,10)
number5 <- seq(1,10,2)

var1 <- TRUE
var2 <- "TRUE"

answer1 <- is.logical(var1)
answer2 <- var1 + answer1 / 3
surname1 <- "toker"
print(name1)
print(surname1)

print(name1,surname1)

is.XXX() and class()

BONUS - Data Structures - Factor

  • Factors are a special variable type for storing categorical variables.

  • They sometimes behave like strings, and sometimes like integers.

gender = c("male", "female", "male", "male", "female")
gender
class(gender)
str(gender)
gender[2]


gender_factor <- factor(c("male", "female", "male", "male", "female"))
gender_factor
class(gender_factor)
str(gender_factor)
gender_factor[2]

R Language - Part 2

R Language - Part 2

  • Data Structures - Objects
    • Vector
    • Matrice
    • Array
    • Data Frame
    • List

Data Structures (R-Objects)

(Atomic) Vector

The simplest data structure in R

Vectors are a list-like structure that contain items of the same data type.

spring_month <- "April"
spring_month
spring_months <- c("March", "April","May","June")
spring_months
class(spring_months)

c means “combine”

Data Structures - (Atomic) Vector

Vector : The simplest data structure in R

name <- "emir"
surname <- "toker"
name_surname <- c(name,surname)
class(name_surname)
length(name_surname)

vector1 <- c("21","21")  
vector2 <- c("21",21)  
vector3 <- c(21,21)

print(), class(), length()

Data Structures - (Atomic) Vector

spring_months <- c("March", "April","May","June")

spring_months

length(spring_months)

dim(spring_months)     # dimension

[] for indexing

spring_months[1]

spring_months[3:4]

str(spring_months)     # structure

print(), class(), length(), dim(), str()

Data Structures - (Atomic) Vector

Basic string manipulations

substr(spring_months, start = 1, stop = 2)     # substrings

strsplit(spring_months,"")          # string split

gsub("a", "A", spring_months)       # matching and eeplacement

?substr , ?strsplit , ?gsub

(Atomic) Vector

myvec <- c(1, 3, 1, 42)
a <- 35
myvec2 <- c(3L, 3.45, 1e+03, 64^0.5, 2+(3-1.1)/9.44, a)
myvec3 <- c(myvec, myvec2)
class(myvec3)
x <- c("all", "b", "olive")

Length of a vector, length(vector_name)

length(x)

Indexing element, vector_name[element_position]

x[2]

Manipulating element of vector, assigning arrow

x[2] <- "b_new"
x

Note: In R, counting elements start position 1, not 0.

(Atomic) Vector

y <- c( 1.2, 5, "Rt", "2000", 20, 4905)
y [0]
class(y)
y

Sequences

7:16.4
a <- 7:16
a
seq(from=7,to=16,by=3)
seq(50,150,25)
seq(50,149,25)
seq(from=3,to=27,length.out=40)

Round

3/2
round(3/2)
round(5.1)
round(pi)
round(pi, digits = 6)

(Atomic) Vector

Repetition

rep(x=1, times=4)
rep(x=c(3, 62, 8),times=3)
rep(x=c(3, 62, 8),each=2,times=3)

Sorting

sort(x=c(2.5, -1, -10, 3.44))             # decreasing=FALSE (default)
sort(x=c(2.5, -1,- 10, 3.44), decreasing=TRUE)

Random - Uniform Distribution

runif(15, min = 20, max = 45)
runif(15, 20, 45)
runif(25, 60, 50)

Random variable can be saved

set.seed(1)
runif(15, 20, 45)

Data Structures - Matrice

Vectors indexed using two indices instead of one.

[ row, col ]

a <- c(1:3)
# str(a), dim(a), length(a)
b <- matrix(1:3, nrow = 1, ncol = 3)
# str(b), dim(b), length(b)

Data Structures - Matrix

a <- c(1:3)
b <- matrix(1:3, nrow = 1, ncol = 3)
## [1] 1 2 3
##      [,1] [,2] [,3]
## [1,]    1    2    3
c <- matrix(1:9, nrow = 3, ncol = 3)
##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9
d <- matrix(1:9, nrow = 3, ncol = 3, byrow = TRUE)
##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6
## [3,]    7    8    9

Matrices

m_mycol <- matrix(c(1, 2, 3, 4, 5, 6),
                  nrow = 2,
                  ncol = 3,
                  byrow = FALSE)       # Default
m_mycol 

m_byrow <- matrix(c(1, 2, 3, 4, 5, 6),
                  nrow = 2,
                  ncol = 3,
                  byrow = TRUE)     
m_byrow      
t(m_byrow)
length(m_byrow)
dim(m_byrow)

Matrices

n <- runif(9,1,100)    
n
matrix(n, nrow = 3, ncol = 3)

n2 <- runif(10,1,100)    
matrix(n2, nrow = 3, ncol = 3)

Matrices

x <- as.numeric(seq(from=10,to=120,by=10))
length(x)
mx <- matrix(x,3,4)                  # n, nrow, ncol
mx
class(mx)
class(mx[1])
typeof(mx)
mx[1,]
mx[,2]
mx[,2:4]
mx_new <- mx[,2:4]
mx
mx[2,3] <- "rose"
mx
class(mx)
typeof(mx)

mx_new <- as.numeric(mx)
mx_new
class(mx_new)
typeof(mx_new)

Data Structures - Matrix

my_mat <- matrix(runif(n=20, min=0, max=100), nrow = 4,  ncol = 5)
##          [,1]     [,2]     [,3]     [,4]     [,5]
## [1,] 62.97103 63.32477 66.15347 36.81336 94.89551
## [2,] 80.45485 75.46393 11.02355 18.83055 95.70202
## [3,] 89.69659  3.65689 75.38308 62.67887 15.56819
## [4,] 16.83077 27.61296 97.97371 92.28101 78.88344
add <-  matrix(seq(from=10, to=60, by=10), nrow = 2, ncol = 3)
##      [,1] [,2] [,3]
## [1,]   10   30   50
## [2,]   20   40   60
my_mat[2:3,2:4] <- add
##          [,1]     [,2]     [,3]     [,4]     [,5]
## [1,] 62.97103 63.32477 66.15347 36.81336 94.89551
## [2,] 80.45485 10.00000 30.00000 50.00000 95.70202
## [3,] 89.69659 20.00000 40.00000 60.00000 15.56819
## [4,] 16.83077 27.61296 97.97371 92.28101 78.88344

Data Structures - Array

lots of matrices…

array(x, dim = c(raw,col,level))

Data Structures - Array

[ row, col, level ]

Data Structures - Array

str(arr)
##  int [1:3, 1:4, 1:2] 1 2 3 4 5 6 7 8 9 10 ...
# dim(), class()
# length(), typeof()
# QUESTION : where is 17
# x[ ? , ? , ? ]
x <- 1:24
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
arr <- array(x, dim = c(3,4,2)) #raw,col,level
## , , 1
## 
##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4]
## [1,]   13   16   19   22
## [2,]   14   17   20   23
## [3,]   15   18   21   24

Data Structures - Array

[ row, col, level ]

arr <- array(data=10:30,dim=c(2,5,2))
## , , 1
## 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]   10   12   14   16   18
## [2,]   11   13   15   17   19
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]   20   22   24   26   28
## [2,]   21   23   25   27   29
arr[2,2:4,]
##      [,1] [,2]
## [1,]   13   23
## [2,]   15   25
## [3,]   17   27
arr[1,1:5,2]
## [1] 20 22 24 26 28

Data Structures - Array

array <- array(data=seq(2,144,2),dim=c(3,6,4))
## , , 1
## 
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]    2    8   14   20   26   32
## [2,]    4   10   16   22   28   34
## [3,]    6   12   18   24   30   36
## 
## , , 2
## 
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]   38   44   50   56   62   68
## [2,]   40   46   52   58   64   70
## [3,]   42   48   54   60   66   72
## 
## , , 3
## 
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]   74   80   86   92   98  104
## [2,]   76   82   88   94  100  106
## [3,]   78   84   90   96  102  108
## 
## , , 4
## 
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]  110  116  122  128  134  140
## [2,]  112  118  124  130  136  142
## [3,]  114  120  126  132  138  144
array[1,1,1:4]
array[1,1,]
array[1,1,4:1]
array[1,1,c(1:4!=2)]

array[1,1,2]
array[1,1,which(x==2)]
array[1,1,which(x<=2)]

array[1,1,2:4]
array[1,1,-1]
array[1,1,c(-1,-2)]

array[1,2:5,2]
array[1,c(2,3,4,5),2]
array[1,c(2,5),2]
array[1,c(2,5),2:3]
array[,c(2,5),2:3]

[ row, col, level ]

Data Structures (R-Objects)

Data Structures (R-Objects)

Data Structures - Data Frame

  • Each element is of the same length, like a matrix.
  • A column can have different types.
  • BUT, all the elements within a column are the same type.

Data Structures - Data Frame

person  <- c("Peter","Lois","Meg","Chris","Stewie")
age     <- c(42,40,17,14,1)
gender  <- factor(c("M","F","F","M","M"))
married <-c(TRUE,TRUE,FALSE,FALSE,FALSE)

m1 <- matrix(c(person,married),5,2)
m2 <- matrix(c(age,married),5,2)
m3 <- matrix(c(person,age),5,2)
m4 <- matrix(c(person,gender),5,2)

no need to Combine

df <- data.frame(person,married)
df

class(df)
dim(df)
length(df)
str(df)
df$
df$person
as.character(df$person)

df <- data.frame(person,age,gender,married)
df
str(df)

Data Structures - Data Frame

new_person  <- "Brian"
new_age     <- 7
new_gender  <- factor("M")
new_married <- FALSE

new_record_row <- data.frame(new_person,new_age,new_gender,new_married)
new_df1 <- rbind(df, new_record_row)      # Combine R Objects by Rows
new_df1

surname <- c("Yilmaz","Zeki","Fena","Caliskan","Uslu","Guzel")
new_record_col <- data.frame(surname)
new_df2 <- cbind(new_df1, new_record_col) # Combine R Objects by Columns

new_df2
new_df2[c(5,6),]
new_df2[c(5,6),] <- new_df2[c(6,5),]
new_df2

new_df2[5]

rbind(), cbind()

Data Structures - Data Frame

new_df2
new_df2[1]   # 1st column
new_df2[1,]    # 1st row, all columns
length(new_df2[1])
dim(new_df2[1])


new_df2[[1]]
length(new_df2[[1]])
dim(new_df2[[1]])

new_df2[[1]][2]
new_df2[[1]][2:5]

new_df2$person[2]
new_df2$person[2:5]
new_df2
new_df2[2:3,1:5]
new_df2[2:3,]

new_df2[2,1]
new_df2[2:2,1:1]

attributes(new_df2)

Data Structures - List

  • Lists are like atomic vectors because they group data into a one-dimensional set.
  • Lists are like data frame because they can group different types of data.
  • BUT, the length of elements is NOT important.

Data Structures - List

my_matrix <- matrix(data=1:4,nrow=2,ncol=2)
my_vector <- c(T,F,T,T)
my_var <- "hello"
my_data_frame <- new_df2     # I've created already

my_list  <- list(my_matrix,my_vector,my_var,my_data_frame)
my_list

Data Structures - List

my_list
class(my_list)
str(my_list)
dim(my_list)
length(my_list)

Data Structures - List

How can i choose first member/component of my_list ?

Data Structures - List

my_list

my_list[1]

my_list[1][1]

my_list[1][1][1]

my_list[[1]]

Data Structures - List

How can i choose the first row and the first column of the first member/component of my_list ?

Data Structures - List

my_list

my_list[[1]][1,1]

QUIZ

R Language - Part - I & II

kahoot.it

Next Week

Next Week

R Language - Part 3

  • Read

  • Write

  • Plot

  • Practice with the data

  • Create a function

  • Midterm Project

Read

library(help="datasets")
list.files("/Users/emirtoker/Desktop/Dersler/Memurluk/Software_Tools_for_Earth_&_Environmental_Science/Software_Tools_R_Github/Presentation")
file.choose()
read.table(file = "18397_Cekmekoy_Omerli_15dk.txt")

read.table(file = "18397_Cekmekoy_Omerli_15dk.txt", 
          header=TRUE, sep=";")
          
read.table(file = "18397_Cekmekoy_Omerli_15dk.txt", 
          header=TRUE, sep=";", na.strings="-9999")

mydata_txt <- read.table(file = "18397_Cekmekoy_Omerli_15dk.txt",
                        header=TRUE, 
                        sep=";",
                        na.strings="-9999")
              
str(mydata_txt)
mydata_csv <- read.csv(file="18397_Cekmekoy_Omerli.csv",
                      header=TRUE,
                      na.strings="-9999")
                      
str(mydata_csv)

Read and Write

url <- "https://web.itu.edu.tr/tokerem/18397_Cekmekoy_Omerli_15dk.txt"
urldata_txt <- read.table(url,
                          header=TRUE, 
                          sep=";",
                          na.strings="-9999")

Write .TXT and .CSV

write.table(x=urldata_txt,file="somenewfile.txt")

write.table(x=urldata_txt,file="somenewfile.txt",
           sep=";",na="-9999",quote=FALSE,row.names=FALSE)

new_df2
write.table(x=new_df2,file="dffile.txt",
            sep=";",na="-9999",quote=FALSE,row.names=FALSE)
            
write.table(x=new_df2,file="dffile.csv",
            sep=";",na="-9999",quote=FALSE,row.names=FALSE)

Plot

foo <- c(1.1,2,3.5,3.9,4.2)
bar <- c(2,2.2,-1.3,0,0.2)
plot(foo,bar)
  • type the supplied coordinates (for example, as stand-alone points or joined by lines or both dots and lines).
  • main, xlab, ylab Options to include plot title, the horizontal axis label, and the vertical axis label, respectively.
  • col Color (or colors) to use for plotting points and lines.
  • lty Stands for line type. (for example, solid, dotted, or dashed).
  • lwd This controls the thickness of plotted lines.
  • xlim, ylim limits for the horizontal range and vertical range (respectively)

Plot

plot(foo,bar)
plot(foo,bar,type="l")
plot(foo,bar,type="b",main="My lovely plot",xlab="x axis label", ylab="location y")
plot(foo,bar,type="b",main="My lovely plot",xlab="",ylab="",col="red")

x <- 1:20
y <- c(-1.49,3.37,2.59,-2.78,-3.94,-0.92,6.43,8.51,3.41,-8.23,
-12.01,-6.58,2.87,14.12,9.63,-4.58,-14.78,-11.67,1.17,15.62)
plot(x,y,type="n",main="")
abline(h=c(-5,5),col="red",lty=2,lwd=2)
segments(x0=c(5,15),y0=c(-5,-5),x1=c(5,15),y1=c(5,5),col="red",lty=3,
lwd=2)
points(x[y>=5],y[y>=5],pch=4,col="darkmagenta",cex=2)
points(x[y<=-5],y[y<=-5],pch=3,col="darkgreen",cex=2)
points(x[(x>=5&x<=15)&(y>-5&y<5)],y[(x>=5&x<=15)&(y>-5&y<5)],pch=19,
col="blue")
points(x[(x<5|x>15)&(y>-5&y<5)],y[(x<5|x>15)&(y>-5&y<5)])
lines(x,y,lty=4)
arrows(x0=8,y0=14,x1=11,y1=2.5)
text(x=8,y=15,labels="sweet spot")
legend("bottomleft",
legend=c("overall process","sweet","standard",
"too big","too small","sweet y range","sweet x range"),
pch=c(NA,19,1,4,3,NA,NA),lty=c(4,NA,NA,NA,NA,2,3),
col=c("black","blue","black","darkmagenta","darkgreen","red","red"),
lwd=c(1,NA,NA,NA,NA,2,2),pt.cex=c(NA,1,1,2,2,NA,NA))

Plot

mydata_txt <- read.table(file = "18397_Cekmekoy_Omerli_15dk.txt",
                        header=TRUE, 
                        sep=";",
                        na.strings="-9999")

mydata_txt

plot(mydata_txt$temp, type="l" )

Create a Function

Take Home - HMW

  • HMW-1 : Create a New R-Notebook
  • HMW-2 : Practice for Data Types and Data Structures

I will upload Homework-1 and Homework-2 to Ninova

Additional Course (is Turkish)

We can do it again in the next week

After 7th Course ?

  • Data Types
  • Data Structure
  • Read, Write and Plot Data