Email : dhelaagatha@gmail.com
RPubs : https://rpubs.com/dhelaasafiani
Jurusan : Statistika Bisnis
Address : ARA Center, Matana University Tower
Jl. CBD Barat Kav, RT.1, Curug Sangereng, Kelapa Dua, Tangerang, Banten 15810.
list0 = list() # Membuat List kosong
list5 = list("Nanda", "Felicia", "Diva", "Shifa", "Abed") # Membuat List berisi 5 item
print(list5)## [[1]]
## [1] "Nanda"
##
## [[2]]
## [1] "Felicia"
##
## [[3]]
## [1] "Diva"
##
## [[4]]
## [1] "Shifa"
##
## [[5]]
## [1] "Abed"
list5 = list("Nanda", "Felicia", "Diva", "Shifa", "Abed")
print(list5[1]) # Print nilai pada index 2## [[1]]
## [1] "Nanda"
list5 = list("Nanda", "Felicia", "Diva", "Shifa", "Abed")
list5[5] = "Alicia" # Mengganti item Abed menjadi Alicia
print(list5)## [[1]]
## [1] "Nanda"
##
## [[2]]
## [1] "Felicia"
##
## [[3]]
## [1] "Diva"
##
## [[4]]
## [1] "Shifa"
##
## [[5]]
## [1] "Alicia"
length(list5) # Menghitung banyak teman yang berada di dalam List## [1] 5
library(sets) # Panggil library sets terlebih dulu
tuple0 = tuple() # Membuat isi tuple (kosong)
tuple1 = tuple("Warna") # Membuat isi tuple 1 item
tuple3 = tuple("Warna","Kuning") # Membuat isi tuple 2 item
tuple5 = tuple("Kamu", "Lebih", "Suka", "Warna", "Apa?") # Membuat isi tuple 5 itemtuple5 = tuple("Kamu", "Lebih", "Suka", "Warna", "Apa?")
print(tuple5[4]) # Mengakses nilai 4 pada tuple5## ("Warna")
print(tuple5[5]) # Mengakses nilai 5 pada tuple5## ("Apa?")
print(tuple5[4:5]) # Memotong tuple 5 berdasarkan order## ("Warna", "Apa?")
tuple7 = rep(tuple5, 3) # Mengisi tuple7 dengan tuple5 dan diulang 3 kali
tuple8 = c(tuple5, tuple3) # Mengisi tuple8 dengan tuple5 dan tuple3
print(tuple7)## ("Kamu", "Lebih", "Suka", "Warna", "Apa?", "Kamu", "Lebih", "Suka",
## "Warna", "Apa?", "Kamu", "Lebih", "Suka", "Warna", "Apa?")
print(tuple8)## ("Kamu", "Lebih", "Suka", "Warna", "Apa?", "Warna", "Kuning")
library(Dict) # Mengaktifkan library Dict##
## Attaching package: 'Dict'
## The following object is masked from 'package:sets':
##
## %>%
agathasxx = dict(
nama = "Dhela Asafiani", # Memasukkan nama pada dict
umur = as.integer(19), # MEmasukkan umur pada dict
hobi = list("membaca", "olahraga", "menulis"), # Memasukkan List hobi
menikah = FALSE,
sosmed = tuple(instagram= "dhelaasf",
twitter = "agathasxx"
)
)cat("Nama saya adalah:", agathasxx$get('nama')) # Mengakses nama pada dict agathasxx## Nama saya adalah: Dhela Asafiani
print(agathasxx$get('sosmed')['twitter']) # Mengakses sosmed twitter## (twitter = "agathasxx")
agathasxx["nama"] = "Dhela Asafiani Agatha" # Mengubah nilai item dictionary
print(agathasxx$get('nama'))## [1] "Dhela Asafiani Agatha"
agathasxx$remove("sosmed") # Mengapus item sosmed pada dictionary
print(agathasxx)## # A tibble: 4 x 2
## key value
## <chr> <list>
## 1 hobi <list [3]>
## 2 menikah <lgl [1]>
## 3 nama <chr [1]>
## 4 umur <int [1]>
df1_R <- data.frame(kode = c (1:5), # Memberi kode pada data frame 1-5
nama = c("Nanda","Felicia","Diva","Shifa","Alicia"), # Memberi List berisi item nama
gaji = c("400","600","680","990","350"), # Memberi List berisi item gaji
mulai_kerja = as.Date(c("2022-08-11","2022-04-18","2022-11-30","2022-03-02","2022-09-25")), # Memberi List berisi item tanggal
divisi = c("DS","BA","BA","DA","DA"), stringsAsFactors = F) # Memberi List berisi item divisi
print(df1_R)## kode nama gaji mulai_kerja divisi
## 1 1 Nanda 400 2022-08-11 DS
## 2 2 Felicia 600 2022-04-18 BA
## 3 3 Diva 680 2022-11-30 BA
## 4 4 Shifa 990 2022-03-02 DA
## 5 5 Alicia 350 2022-09-25 DA
df2_R <- data.frame(kode = c (6:10), # Memberi kode pada data frame 1-5
nama = c("Ibeth","Sofie","Abed","Gaby","Kevin"), # Memberi List berisi item nama
gaji = c("530","650","580","539","780"), # Memberi List berisi item gaji
mulai_kerja = as.Date(c("2022-03-21","2022-01-12","2022-12-20","2022-09-02","2022-06-21")), # Memberi List berisi item tanggal
divisi = c("Lawyer","Lawyer","BA","Actuaries","Actuaries"), stringsAsFactors = F) # Memberi List berisi item divisi
print(df2_R)## kode nama gaji mulai_kerja divisi
## 1 6 Ibeth 530 2022-03-21 Lawyer
## 2 7 Sofie 650 2022-01-12 Lawyer
## 3 8 Abed 580 2022-12-20 BA
## 4 9 Gaby 539 2022-09-02 Actuaries
## 5 10 Kevin 780 2022-06-21 Actuaries
typeof(df1_R) # Cek tipe data## [1] "list"
class(df1_R) # Cek tipe data## [1] "data.frame"
df1_R[2,4] # Ekstrak elemen di baris ke-2 dan kolom ke-4## [1] "2022-04-18"
df1_R$nama # Ekstrak spesifik kolom ('nama')## [1] "Nanda" "Felicia" "Diva" "Shifa" "Alicia"
df1_R[,c('gaji','divisi')] # Ekstrak spesifik kolom ('gaji','divisi')## gaji divisi
## 1 400 DS
## 2 600 BA
## 3 680 BA
## 4 990 DA
## 5 350 DA
df1_R[ ,1:3] # Ekstrak 3 baris pertama df1_R## kode nama gaji
## 1 1 Nanda 400
## 2 2 Felicia 600
## 3 3 Diva 680
## 4 4 Shifa 990
## 5 5 Alicia 350
df1_R[1:2, ] # Ekstrak 2 kolom pertama df1_R## kode nama gaji mulai_kerja divisi
## 1 1 Nanda 400 2022-08-11 DS
## 2 2 Felicia 600 2022-04-18 BA
subset(df1_R, select=c(1:5)) # Ekstrak kolom 1 sampai kolom 5## kode nama gaji mulai_kerja divisi
## 1 1 Nanda 400 2022-08-11 DS
## 2 2 Felicia 600 2022-04-18 BA
## 3 3 Diva 680 2022-11-30 BA
## 4 4 Shifa 990 2022-03-02 DA
## 5 5 Alicia 350 2022-09-25 DA
subset(df1_R, select=c(2,3)) # Ekstrak kolom tertentu## nama gaji
## 1 Nanda 400
## 2 Felicia 600
## 3 Diva 680
## 4 Shifa 990
## 5 Alicia 350
subset(df1_R, select = divisi) # Ekstrak spesifik kolom divisi## divisi
## 1 DS
## 2 BA
## 3 BA
## 4 DA
## 5 DA
subset(df1_R, select = 4) # Ekstrak spesifik kolom 4## mulai_kerja
## 1 2022-08-11
## 2 2022-04-18
## 3 2022-11-30
## 4 2022-03-02
## 5 2022-09-25
summary(df1_R)## kode nama gaji mulai_kerja
## Min. :1 Length:5 Length:5 Min. :2022-03-02
## 1st Qu.:2 Class :character Class :character 1st Qu.:2022-04-18
## Median :3 Mode :character Mode :character Median :2022-08-11
## Mean :3 Mean :2022-07-17
## 3rd Qu.:4 3rd Qu.:2022-09-25
## Max. :5 Max. :2022-11-30
## divisi
## Length:5
## Class :character
## Mode :character
##
##
##
df2_R<-df1_R # Merubah nama data frame df1_R menjadi df2_R
names(df2_R)<-c("Kode", # Merubah nama variable pada data frame df2_R
"Nama",
"Gaji",
"Mulai Bekerja",
"Divisi")
print(df2_R)## Kode Nama Gaji Mulai Bekerja Divisi
## 1 1 Nanda 400 2022-08-11 DS
## 2 2 Felicia 600 2022-04-18 BA
## 3 3 Diva 680 2022-11-30 BA
## 4 4 Shifa 990 2022-03-02 DA
## 5 5 Alicia 350 2022-09-25 DA