List

 Lst <- list(name="Fred", wife="Mary", no.children=3,
child.ages=c(4,7,9))

Lst$name

## [1] "Fred"

Lst$wife

## [1] "Mary"

x <- "name"; Lst[[x]]

## [1] "Fred"

#Constructing and modifying

Lst$name

## [1] "Fred"

Lst$wife

## [1] "Mary"

Lst$no.children

## [1] 3

Lst$child.ages

## [1] 4 7 9

Mat <- matrix(1:4, nrow = 2, ncol = 2)
Lst[5] <- list(matrix=Mat)
Lst

## $name
## [1] "Fred"
## 
## $wife
## [1] "Mary"
## 
## $no.children
## [1] 3
## 
## $child.ages
## [1] 4 7 9
## 
## [[5]]
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4

Concatenating lists

list.A <- list(angka = 1:3)
list.B <- list(huruf = c("a", "b", "c"))
list.C <- list(status = TRUE)

list.ABC <- c(list.A, list.B, list.C)
list.ABC

## $angka
## [1] 1 2 3
## 
## $huruf
## [1] "a" "b" "c"
## 
## $status
## [1] TRUE

data frames

Making data frames

# Buat data negara bagian dan ubah jadi faktor
state <- c("tas", "sa", "qld", "nsw", "nsw", "nt", "wa", "wa",
           "qld", "vic", "nsw", "vic", "qld", "qld", "sa", "tas",
           "sa", "nt", "wa", "vic", "qld", "nsw", "nsw", "wa",
           "sa", "act", "nsw", "vic", "vic", "act")
statef <- factor(state)

# Buat data pendapatan (incomes)
incomes <- c(60, 49, 40, 61, 64, 60, 59, 54, 62, 69, 70, 42, 56,
             61, 61, 61, 58, 51, 48, 65, 49, 49, 41, 48, 52, 46,
             59, 46, 58, 43)
# Potong pendapatan jd bbrp kategori
incomef <- factor(cut(incomes, breaks = 3))

accountants <- data.frame(home = statef, loot = incomes, shot = incomef)
accountants

##    home loot    shot
## 1   tas   60 (50,60]
## 2    sa   49 (40,50]
## 3   qld   40 (40,50]
## 4   nsw   61 (60,70]
## 5   nsw   64 (60,70]
## 6    nt   60 (50,60]
## 7    wa   59 (50,60]
## 8    wa   54 (50,60]
## 9   qld   62 (60,70]
## 10  vic   69 (60,70]
## 11  nsw   70 (60,70]
## 12  vic   42 (40,50]
## 13  qld   56 (50,60]
## 14  qld   61 (60,70]
## 15   sa   61 (60,70]
## 16  tas   61 (60,70]
## 17   sa   58 (50,60]
## 18   nt   51 (50,60]
## 19   wa   48 (40,50]
## 20  vic   65 (60,70]
## 21  qld   49 (40,50]
## 22  nsw   49 (40,50]
## 23  nsw   41 (40,50]
## 24   wa   48 (40,50]
## 25   sa   52 (50,60]
## 26  act   46 (40,50]
## 27  nsw   59 (50,60]
## 28  vic   46 (40,50]
## 29  vic   58 (50,60]
## 30  act   43 (40,50]

Attach () and detach()

attach(accountants)

home

##  [1] tas sa  qld nsw nsw nt  wa  wa  qld vic nsw vic qld qld sa  tas sa  nt  wa 
## [20] vic qld nsw nsw wa  sa  act nsw vic vic act
## Levels: act nsw nt qld sa tas vic wa

loot

##  [1] 60 49 40 61 64 60 59 54 62 69 70 42 56 61 61 61 58 51 48 65 49 49 41 48 52
## [26] 46 59 46 58 43

shot

##  [1] (50,60] (40,50] (40,50] (60,70] (60,70] (50,60] (50,60] (50,60] (60,70]
## [10] (60,70] (60,70] (40,50] (50,60] (60,70] (60,70] (60,70] (50,60] (50,60]
## [19] (40,50] (60,70] (40,50] (40,50] (40,50] (40,50] (50,60] (40,50] (50,60]
## [28] (40,50] (50,60] (40,50]
## Levels: (40,50] (50,60] (60,70]

detach(accountants)

data frame baru

lentils <- data.frame(
  u = c(1, 2, 3),
  v = c(4, 5, 6),
  w = c(7, 8, 9)
)

attach(lentils)

u <- v+w
lentils$u <- v+w

detach()

Managing the search path

search()

## [1] ".GlobalEnv"        "package:stats"     "package:graphics" 
## [4] "package:grDevices" "package:utils"     "package:datasets" 
## [7] "package:methods"   "Autoloads"         "package:base"

attach(lentils)

## The following object is masked _by_ .GlobalEnv:
## 
##     u

search()

##  [1] ".GlobalEnv"        "lentils"           "package:stats"    
##  [4] "package:graphics"  "package:grDevices" "package:utils"    
##  [7] "package:datasets"  "package:methods"   "Autoloads"        
## [10] "package:base"

detach("lentils")

search()

## [1] ".GlobalEnv"        "package:stats"     "package:graphics" 
## [4] "package:grDevices" "package:utils"     "package:datasets" 
## [7] "package:methods"   "Autoloads"         "package:base"

BAB 7

the read table () function

file_text_1 <- "Price Floor Area Rooms Age Cent.heat
01 52.00 111.0 830 5 6.2 no
02 54.75 128.0 710 5 7.5 no
03 57.50 101.0 1000 5 4.2 no
04 57.50 131.0 690 6 8.8 no
05 59.75 93.0 900 5 1.9 yes"
writeLines(file_text_1, "houses.data")

HousePrice <- read.table("houses.data")
HousePrice

##    Price Floor Area Rooms Age Cent.heat
## 01 52.00   111  830     5 6.2        no
## 02 54.75   128  710     5 7.5        no
## 03 57.50   101 1000     5 4.2        no
## 04 57.50   131  690     6 8.8        no
## 05 59.75    93  900     5 1.9       yes

the scan () function

input_text <- "A 10 20
B 30 40
C 50 60"
writeLines(input_text, "input.dat")

membaca data dgn tipe dummy list

inp <- scan("input.dat", list("", 0, 0))

label <- inp[[1]]
x     <- inp[[2]]
y     <- inp[[3]]

label

## [1] "A" "B" "C"

## [1] 10 30 50

## [1] 20 40 60

membaca dummy list yg sdh diberi nama elemen

inp <- scan("input.dat", list(id = "", x = 0, y = 0))

label <- inp$id
x     <- inp$x
y     <- inp$y

inp$id

## [1] "A" "B" "C"

inp$x

## [1] 10 30 50

inp$y

## [1] 20 40 60

membaca data angka tunggal dan diubah jd matriks

writeLines("1 2 3 4 5 6 7 8 9 10", "light.dat")

X <- matrix(scan("light.dat", 0), ncol = 5, byrow = TRUE)
X

##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    2    3    4    5
## [2,]    6    7    8    9   10

Accessing Builtin datasets

data()

data(infert)

head(infert)

##   education age parity induced case spontaneous stratum pooled.stratum
## 1    0-5yrs  26      6       1    1           2       1              3
## 2    0-5yrs  42      1       1    1           0       2              1
## 3    0-5yrs  39      6       2    1           0       3              4
## 4    0-5yrs  34      4       2    1           0       4              2
## 5   6-11yrs  35      3       1    1           1       5             32
## 6   6-11yrs  36      4       2    1           1       6             36

Loading data from other R packages

data(package = "datasets")

data(Puromycin, package = "datasets")
head(Puromycin)

##   conc rate   state
## 1 0.02   76 treated
## 2 0.02   47 treated
## 3 0.06   97 treated
## 4 0.06  107 treated
## 5 0.11  123 treated
## 6 0.11  139 treated

Editing Data

xold <- data.frame(
  Nama = c("Ed", "Arra", "Martin"),
  Nilai = c(80, 90, 75)
)

xnew <- edit(xold)

View(xold)

x_baru <- edit(data.frame())

Bab 6-7

Ar Rahmah Khullatul L

2026-09-14

List

Concatenating lists

data frames

Making data frames

Attach () and detach()

data frame baru

Managing the search path

BAB 7

the read table () function

the scan () function

membaca data dgn tipe dummy list

membaca dummy list yg sdh diberi nama elemen

membaca data angka tunggal dan diubah jd matriks

Accessing Builtin datasets

Loading data from other R packages

Editing Data