R fundamentals 3:data frame, matrix and array

文章目錄

• dataframe
• • data.frame() to create a dataframe
  • • data frame is like a spreadsheet with row numbers and column names
  • common operation on data frame
  • • subsetting: extract element(s)
    • • use index numbers to extract elements
      • • single bracket to return elements of the same type
        • double bracket to return elements of its own type
      • use column names to extract elements
      • use dollar sign "$" to extract elements
      • use logical value to extract elements
    • get elements matching specific conditions
• matrix
• • 2 ways to construct a matrix
  • • use cbind() or rbind() to create a matrix
  • use matrix to construct a matrix
  • common operations on matrice
  • • subsetting:extract elements
  • summary operations about matrice in DA
  • • get the row wise sum via rowSums() not rowsum()
    • also get the column wise sum via colSums() not colsum()
    • get the column wise mean via colMeans() not colmean()
• array
• • use array() to create an array
  • subsetting
  • • use index number to extract elements

dataframe

• heterogeneous data structure
• contains elements of different classes
• 2 dimensional arrangement

  players.name=c("KD","Curry","Klay","Green")
    players.number=c(35,30,11,23)
    players.2K=c(87,96,91,85)
    players.gender=factor(c("male","male","male","male"),levels = c("male","female"))
             

data.frame() to create a dataframe

players=data.frame(players.name,players.number,players.gender,players.2K)
str(players)
           

notice the string type players.names are converted to factor

However we can fix it by adding parameter stringsAsFactors=False

make sure not typing as stringAsFactors

players=data.frame(players.name,players.number,players.gender,players.2K,stringsAsFactors=FALSE)
str(players)
players
           

data frame is like a spreadsheet with row numbers and column names

common operation on data frame

subsetting: extract element(s)

4 ways

use index numbers to extract elements

single bracket to return elements of the same type

players[2]
#single number in the brackets means column number
typeof(players[2])
           

data frame is basically a list

double bracket to return elements of its own type

players[[2]]
typeof(players[[2]])
           

2 numbers in the bracket means row and column number

#for example:below means extract the element at row1 and column2

players[1,2]

some other examples

players[1:3,2:3]
players[c(1,3),c(2,4)]
players[1,]
players[,1]
           

use column names to extract elements

players["players.name"]
typeof(players["players.name"])
players[["players.name"]]
typeof(players[["players.name"]])
           

single bracket to return elements of the same type

double bracket to return elements of its own type

players[c("players.name","players.gender")]
           

use dollar sign “$” to extract elements

players$players.name
           

use logical value to extract elements

players[c(T,F,F,F)]
           

get elements matching specific conditions

players[players.2K>90,]
players[players.gender="male"]
players[players.gender=="male"]
           

be sure to use “==” instead of “=” to do logical judgement

vector, list and data frame are the most common data structures in DA

matrix

• homogeneous data structure to store elements of the same type
• 2D data arrangement

• usually the matrix is used to store numerical elements for computing

  student.English.score=c(100L,98L,99L,96L)

  student.math.score=c(10L,11L,9L,15L)

2 ways to construct a matrix

use cbind() or rbind() to create a matrix

• rbind() means row bind

• row names are given by vector names and column names are autogenerated

  student.score1=rbind(student.math.score,student.English.score)

  student.score1

  str(student.score1)

column names can be given afterwards by colnames()

colnames(student.score1)=c("KD","Curry","Klay","Green")

student.score1
           

• cbind means column bind
• column names are given

codes:

student.score2=cbind(student.math.score,student.English.score)
student.score2
str(student.score2)
rownames(student.score2)=c("KD","Curry","Klay","Green")
student.score2
           

use matrix to construct a matrix

student.score3=matrix(c(10L,11L,9L,15L,100L,98L,99L,96L),ncol=2,nrow=4)
student.score3
           

• generate the matrix by listing elements and give the row and column numbers
• in default, the matrix is generated vertically

• we can generate the matrix horizonally by adding "byrow=true"

  codes:

  student.score4=matrix(c(10L,11L,9L,15L,100L,98L,99L,96L),ncol=4,nrow=2,byrow=TRUE)

  student.score4

  

common operations on matrice

student.score2
           

subsetting:extract elements

• subsetting of matrice is simliar to that of dataframe
• matrice use index number or logical value to extract elements

codes:

student.score2[4,2]
student.score2[1,]
student.score2[c(T,T,F,F),]
student.score2[student.math.score>10,]
           

summary operations about matrice in DA

student.score2
           

get the row wise sum via rowSums() not rowsum()

rowSums(student.score2)
           

also get the column wise sum via colSums() not colsum()

colSums(student.score2)
           

get the column wise mean via colMeans() not colmean()

colMeans(student.score2)
           

array

-homogeneous data structure

-n- dimensional

-array can be regarded as multiple sheets or multiple sheets stacj over each other

-a matrix is like a sheet as we discuss above

#create first matrix
class1.student.math=c(100,99,87,96)
class1.student.English=c(90,98,90,87)
class1.student.marks=cbind(class1.student.math,class2.student.English)
class1.student.marks
#create the second matrix
class2.student.math=c(91,93,91,92)
class2.student.English=c(98,92,80,78)
class2.student.marks=cbind(class2.student.math,class2.student.English)
class2.student.marks
           

use array() to create an array

student.marks=array(c(class1.student.marks,class2.student.marks),dim=c(4,2,2))
student.marks
           

subsetting

use index number to extract elements

student.marks[1,,1]